Nature outlook envejecimiento by Javier Ortega

OUTLOOK

AGEING

6 December 2012 / Vol 492 / Issue No 7427

outlook AGEING

Produced with support from:

How time takes its toll

Cover art: Claudia Bentley

Editorial Herb Brody, Michelle Grayson, Tony Scully, Nick Haines, Afsaneh Gray, Rebecca Dargie Art & Design Wes Fernandes, Alisdair Macdonald, Andrea Duffy Production Donald McDonald, Yvonne Strong, Kelly Hopkins, Leonora Dawson-Bowling Sponsorship Reya Silao, Yvette Smith Marketing Elena Woodstock, Hannah Phipps Project Managers Claudia Deasy, Christian Manco Art Director Kelly Buckheit Krause Chief Magazine Editor Tim Appenzeller Editor-in-Chief Phil Campbell

geing is inevitable. Yet for centuries people have tried to slow or stop it, from bathing in the blood of virgin girls to concocting an elixir of life. These days, anti-ageing research is on a more scientific footing. And while we are no closer to finding the fountain of youth, humans — for a variety of reasons — are living longer than ever before (page S2). Hitting the biologically arbitrary 100-year milestone used to be the preserve of the lucky few, who would often reach it in rude health. In theory, studying these centenarians might reveal the secrets of healthy ageing. But as life expectancy increases, more people are reaching their eleventh decade, muddying the gene pool. Might more valuable data be gleaned from the supercentenarians who reach 110 (S6)? Scientific efforts to extend lifespan are progressing on several fronts. A short-lived species can evolve into a long-lived one, and researchers are keen to find out how (S10). Studies in other species have already shown that a severely restricted diet can add years of healthy living (S18). Diet affects ageing in humans too — how our food influences our gut microbes, and how they in turn affect our health and longevity, is under investigation (S14). Another line of enquiry focuses on harnessing the regenerative powers of stem cells (S12). But what does healthy ageing mean? Sociologist Eva Kahana talks about this “slippery concept”, which she says is different for each individual (S9). With the threat of Alzheimer’s disease looming large, there is a lack of data on how the brain changes over time — a deficit that a new long-term study aims to correct (S4). In the meantime, for those of us who need a little help in our later years, new technologies can support, predict and possibly prevent some of the worst health problems associated with ageing (S16). We are pleased to acknowledge the financial support of Nestlé Research Center in producing this Outlook. As always, Nature retains sole responsibility for all editorial content. Michelle Grayson Senior editor, supplements

Nature Outlooks are sponsored supplements that aim to stimulate interest and debate around a subject of interest to the sponsor, while satisfying the editorial values of Nature and our readers’ expectations. The boundaries of sponsor involvement are clearly delineated in the Nature Outlook Editorial guidelines available at http://www. nature.com/advertising/resources/pdf/outlook_guidelines.pdf

CONTENTS S2 DEMOGRAPHY

To the limit The drive to increase life expectancy

S4 COGNITION

The brain’s decline What happens to our cognitive functions when we get old?

S6 CENTENARIANS

Great expectations The genetic link to a long, healthy life

S9 Q&A

Ageing proactively Eva Kahana explores healthy ageing

S10 COMPARATIVE BIOLOGY

Looking for a master switch Species with long lifespans might hold the key to extending our own

S12 STEM CELLS

Repeat to fade The regeneration game

S14 MICROBIOME

Cultural differences A gut reaction to our diet

S16 TECHNOLOGY

Dancing with robots High-tech help for later life

S18 INTERVENTIONS

Live long and prosper Eating less and the pathway to old age

COLLECTION S21 Recruiting adaptive cellular stress

responses for successful brain ageing Alexis M. Stranahan and Mark P. Mattson

S29 Gut microbiota composition correlates

All featured articles will be freely available for 6 months.

CITING THE OUTLOOK Cite as a supplement to Nature, for example, Nature Vol XXX, No. XXXX Suppl, Sxx–Sxx (2012). To cite previously published articles from the collection, please use the original citation, which can be found at the start of each article.

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VISIT THE OUTLOOK ONLINE The Nature Outlook Ageing supplement can be found at http://www. nature.com/nature/outlook/ageing

with diet and health in the elderly Marcus J. Claesson et al.

S36 Impact of caloric restriction on health

and survival in rhesus monkeys from the NIA study Julie A. Mattison et al.

S40 Shorter telomeres are associated with obesity and weight gain in the elderly OT Njajou et al.

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OUTLOOK AGEING

TO THE LIMIT

More people are surviving to older ages than ever before, pushing life expectancy from birth to unprecedented highs. Further gains will require tackling age-related conditions, across the world,with ramifications for society as a whole. By Tony Scully. 100

BORN SURVIVORS

For humanity to continue to extend life expectancy, the rates of mortality in the oldest age groups must decline the most. This will only be achieved if the biggest killers — cancer, heart disease, stroke and Alzheimer’s disease — are consigned to the past. But this will be tough given the rising levels of obesity and diabetes. Another strategy to prolong life might be to slow the ageing process itself (see ‘Live long and prosper’, page S18).

CONTRIBUTION TO RISE IN LIFE EXPECTANCY AT BIRTH

95 90

Contribution of reduced mortality to rise in life expectancy (%)

Over the past 150 years, modern medicine and improved standards of living have increased life expectancy at birth by roughly 3 months per year. This graph plots women in several industrialized countries, but this trend is also true for men, and most of the world is following behind.

80+ years

85 80

LIFE EXPECTANCY AT BIRTH (YEARS)

70 65

OLDER ADULTS

From around the 1990s, increased survival in people older than 64 years of age contributed the most to rising human life expectancy

55 50

Future gains will depend on beating the biggest age-related killers

OLDEST ADULTS

CHILD MORTALITY

ADULT MORTALITY

From 1900 to 1925, the rise in life expectancy at birth in industrialized countries was mainly attributable to reductions in child mortality

65–79 years

From 1925 to 1975, the age range 14–65 years accounted for the greatest contribution to increased expectancy

15–64 years 0

0–14 years

1850

years is average life expectancy for a hunter- gatherer in the world

Prevalence (%)

1900

1925

1950

1975

2000

THE COMING OF AGE A survey in England asked over 10,000 people aged 50 years and older to report their health status and any limitations with independent living. The survey found that the prevalence of health problems increases with age while people become increasingly dependent on healthcare and community support to survive.

1875

2025

Arthritis

ACTIVITIES OF DAILY LIVING

Impairment to one or more activities of daily living

A measure of a person's ability to live independently and care for oneself

FALL IN INCIDENCE

Coronary heart disease

Fewer people with cancer and diabetes survive into their 80s 20

Diabetes

Cancer Depression 0 52–54

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55–59

60–64

65–69

Age group (2011/2012)

70–74

75–79

80+

2050

25.2

LIFE EXPECTANCY AT 60 YEARS OF AGE

years added to life expectancy for people living in less developed countries since 1950

A better indicator of the prospects of elderly people than life expectancy from birth, a 60-year-old person living in one of the least developed regions is still expected to reach 70 years of age and a person living in a developed country has a fair chance of reaching 90 years. The global population of people aged 60 years and older will more than double, from 542 million in 1995 to about 1.2 billion in 2025.

FRANCE

CHINA

Men enjoy 24 years of retirement, compared with 19.8 in Germany, 13.6 in Japan and 9.1 years in Mexico

8% of the population is aged over 65, estimated to grow to 25% by 2050

JAPAN

Since the 1960s, the proportion of people 65 and over living with children has halved

UNITED STATES

Since the 1980s, severe disability among 65s and older fell by 25%, a trend threatened by rising obesity and a growing wealth gap

MAXIMUM LIFE EXPECTANCY AT 60 YEARS 2010–2015

HONG KONG Women live an average of 86.7 years, the longest in the world

LIFE EXPECTANCY AT 60 YEARS TANZANIA

2 million elderly people live in poverty, many of whom are left to care for grandchildren orphaned by HIV

Years

28–30 26–27 24–25 21–23 18–20 14–17 10–13

GREEN JAPAN

10 5 0

More developed regions

Less developed regions

Male

CHANGING SHAPE OF GLOBAL SOCIETY Throughout history, most human populations have formed a sort of pyramid structure: a wide base of children and economically productive adults supporting relatively few and more socially dependent elders. As the world develops, people have fewer children and live longer. The traditional pyramid is morphing into a rectangle; society’s structure is changing and people will need to adapt.

YEAR 2100 projection 2050 projection 2010 1950

Least developed countries

Female

People (millions)

AGE

300

200

100

200

300

100+ 95–99 90–94 85–89 80–84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4

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BORN SURVIVORS: CHRISTENSEN, K. ET AL. LANCET 374: 1196–208 (2009)/THE COMING OF AGE: ELSA (THE INSTITUTE FOR FISCAL STUDIES,2012)/ MAP: POPULATION AGEING AND DEVELOPMENT (UNITED NATIONS, 2012), WHO, NEW YORK TIMES/BBC/CHANGING SHAPE OF SOCIETY: THE ECONOMIST 2011

AGEING OUTLOOK

ambitious study is unusual in providing a wide range of tests on the same subjects over the coming years, with the aim of understanding how cognition declines as the brain ages — and whether we can do anything about it. Bäckman’s goal is to understand ordinary, non-disease-influenced ageing. “We all want to know what’s normal,” says Cheryl Grady, who studies ageing brains at the Rotman Research Institute in Toronto, Canada, “both as a reference for dementia research, and as a guide for any future interventions.” Given the concern in most developed countries about their ageing populations and the problem of dementia, it is surprising how little we know about healthy brain ageing. There are only a few things that researchers can agree on. First, after the age of 60, nearly everyone will start to experience some decline in cognitive skills, most noticeably in memory, and this decline will be accompanied by a change in brain structure. Second, aerobic exercise slows or delays this mental slippage. The realm of uncertainty is much larger. Researchers still don’t understand the mechanisms underlying the decline or the order of events. They can’t explain why some people manage to stay cogent and alert well into their 80s, whereas others become slow-witted and forgetful in their 60s. They don’t even know whether Alzheimer’s disease is an abnormal pathological condition or simply an acceleration of normal ageing. And no one knows of any drugs that can help those who lose cognitive function as they age, or whether brain training programmes really help.

MEMORY FAILURE

Elderly people often retain existing memories but may need help remembering new things.

CO GNITIO N

The brain’s decline Treating cognitive problems common in elderly people requires a deeper understanding of how a healthy brain ages. BY ALISON ABBOTT

wedish brothers Jonas and Robert af Jochnick became wealthy philanthropists after their direct-selling company, Oriflame Cosmetics, found success peddling dreams of ever-youthful complexions to post-communist Eastern Europe. The brothers are now in their 70s, an age where dreams of youthful skin have morphed

nervously into hopes of a youthful brain. They recently donated SKr50 million (US$7.6 million) to the Karolinska Institute in Stockholm to support its research into the healthy ageing of the brain. Neuroscientist Lars Bäckman won the entire endowment for his proposal to launch a largescale, multimodal imaging study called COBRA (for Cognition, Brain and Ageing), which this year began recruiting its 180 participants. This

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The COBRA study is recruiting healthy subjects aged 63–65 and aims to follow them for at least a decade. It will carry out various tests, including structural and functional imaging to see which parts of the brain are active on any given task, and how its volume changes over time. Diffusion tensor imaging will visualize the integrity of white matter — the long bands of neuronal axons that transmit signals between different brain regions. And positron-emission tomography (PET) will assess dopamine levels — this is rarely used, says Bäckman, because a single PET scan can cost several thousand dollars. In addition, subjects will carry out a wide range of cognitive tests to measure attributes such as working memory. Participants will also provide information about their health and lifestyle, and provide blood samples for extensive analyses, including genetic testing. There have been many studies into these topics but, unlike COBRA, they have typically compared young adults with older ones, and study cohorts have been small. Probably for these reasons, the results have been contradictory. “These cross-sectional studies have led to discrepancies in the literature,” says Bäckman. “Individuals vary massively in how their brains function and the speed of their age-related

ANNE DE HAAS/GETTY IMAGES

OUTLOOK AGEING

S. W. S. MACDONALD ET AL. J. NEUROSCI. 32, 8188 (2012), SOCIETY FOR NEUROSCIENCE

AGEING OUTLOOK decline.” In addition, he points out, comparing a cohort born in the 1930s with one born in the 1980s introduces confounding factors: variables other than age, such as early experiences, environment and education, will be different between the two groups. Even so, cross-sectional studies have provided some reliable pointers. They have shown that as the brain ages, the grey matter shrinks and the white matter starts to break up. Shrinkage is particularly evident in the prefrontal cortex, where high-level thinking and reasoning takes place, and the hippocampus, which plays a central role in memory. There are fewer receptors and other proteins too, but the most dramatic fall is in the amount of dopamine (see ‘Ages of dopamine’) — a neurotransmitter whose many functions include movement control, general motivation and learning. From early adulthood onwards, dopamine levels drop by about 10% per decade, making it a powerful marker for brain ageing. Some cognitive functions seem to be more sensitive to the ageing process than others. Semantic long-term memory — general knowledge not linked to personal experience — is typically well preserved as we age, but many other attributes start to decline. Reaction speed deteriorates rapidly, for example. Episodic memory, which allows us to reflect on past events, and working memory, which allows distinct pieces of information to be held transiently in the mind, both deteriorate with the advancing years. As a result, as people age they are more easily distracted and find it more difficult to switch quickly between cognitive tasks. They also tend to forget names. And they complain that by the time they open the fridge door they can no longer remember what they wanted — but they still know what a fridge is for. Not everyone is convinced that some cognitive functions fade faster than others, however. Ulman Lindenberger, a COBRA collaborator and director of the Centre for Lifespan Psychology at the Max Planck Institute for Human Development in Berlin, suspects that all functions fade together within an individual, but that previous studies have been too simple to capture this effect. He hopes that COBRA’s approach of using different cognitive tests to measure the same function will help clarify such questions.

A HEALTHY INTEREST

Long-term, prospective studies such as COBRA tend to be expensive to run and take many years to gather data. Yet they provide the best opportunity to address many important questions about ageing. For example, which structural changes in an individual’s brain affect the different cognitive skills? What genetic or environmental factors will be protective? What is the best way to develop treatment strategies? Most longitudinal studies into ageing focus on dementias such as Alzheimer’s disease, which is associated with deposits of amyloid

plaque and tangles of tau protein in the brain. Politicians find it easier to support research on such burdensome diseases than on healthy cognitive ageing, says Bäckman. One of the largest studies of this sort is the Alzheimer’s Disease Neuroimaging Initiative (ADNI), based at the University of California, San Francisco (UCSF), and funded by the US National Institutes of Health Some cognitive and several pharmaceutical companies. functions seem It studies 200 people to be more with Alzheimer’s sensitive to the disease, 400 subjects ageing process with mild cognitive than others. impairment, and 200 elderly controls with no diagnosed mental deterioration. Its primary aim is to find markers that can predict the onset or progression of Alzheimer’s, although data from subjects with mild cognitive impairment and controls without dementia will provide leads for understanding non-pathologic ageing.

AGES OF DOPAMINE Levels of the neurotransmitter dopamine are higher in some cortical regions in young brains (left) than in old brains (right).

Low

High

Dopamine concentration

Understanding how Alzheimer’s disease begins will also help studies on healthy ageing screen out those with pathologies to ensure that researchers are not detecting the wrong thing. “We are never sure if the cognitive declines we are measuring are actually the otherwiseinvisible beginnings of a dementia that does not become overt during the study,” says behavioural neuroscientist Susan Resnick of the US National Institute on Aging in Baltimore, Maryland. Detection of abnormal levels of protein is not a specific enough test. “Nearly a third of old people who are cognitively normal have enough amyloid and tau in their brains to meet the criteria for Alzheimer’s disease.” Another problem with longitudinal studies is that it can be hard to convince people to keep returning — a particular problem for ageing studies. “As subjects decline, their families become overstressed and the study seems lower priority,” says ADNI director Michael

Weiner at UCSF. In an attempt to avoid this issue, COBRA is recruiting subjects in the northern Swedish town of Umeå, where the population is settled and has strong trust in health authorities.

EXERCISE FOR THE BRAIN

Describing the process of normal cognitive ageing is a necessary prelude to helping people achieve it. Bäckman believes that successful cognitive ageing depends on maintaining brain integrity — the fewer signs of pathology or brain shrinkage, the better the cognitive function. This corresponds with the widely held view that if we lived long enough, we would all eventually accumulate enough physical damage in the brain to develop dementia. Damage is caused by the accumulation of amyloid plaques or tau tangles, or leakages from small blood vessels in the brain, which occur at different speeds in different individuals. The brain maintenance theory is consistent with evidence that aerobic exercise can slow cognitive decline by stimulating production of brain-derived neurotrophic factor, which helps the growth of new blood vessels, not only in the muscles being exercised but in the brain as well. But it’s a rare octogenarian who can take a long run or play a heart-pounding game of squash, so scientists hope that studies such as COBRA will lead to alternative strategies. One idea is to develop drugs that improve particular types of neurotransmission, but this might have limited value against a background of brain tissue shrinkage. Another strategy could be to use computer-based cognitive training programmes designed to sharpen mental processing speed or working memory. Several studies have shown that people of all ages can improve their performance on specific computer tasks in which they train. Neuroscientist Adam Gazzaley at UCSF is so confident in the potential of this approach that he is developing commercial software for use at home. But even he admits there is no evidence that the improvements seen in computer-based tasks can help with life-related activities — such as helping you remember why you went to the fridge. As is perhaps appropriate for ageing research, answers will come only with time. “Once we have a tighter link between neurochemistry and structural changes in the brain in relation to cognitive outcome, we’ll know what are the main pacemakers of change at this level,” says Lindenberger. “Then we’ll know how to think about interventions.” The af Jochnick brothers, meanwhile, are learning from the science they support. Both work out in the gym and play tennis. “Our cosmetics helped keep skin young,” says 72-yearold Robert. “Hopefully this research will eventually help keep brains young.” ■ Alison Abbott is Nature’s senior European correspondent based in Munich.

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CHRISTOPHER LANE/CONTOUR BY GETTY IMAGES

OUTLOOK AGEING

Irving Kahn, the oldest trader on Wall Street, is remarkably active despite being over 100 years of age — and scientists hope many more will match him.

CENT ENARIANS

Great expectations Scientists are searching for a genetic blueprint that will enable humans to stay healthy and vital well into their old age. BY MICHAEL EISENSTEIN

n any weekday morning, you might catch Irving Kahn heading into his office in Manhattan, where he works as an investor and financial analyst — seemingly unremarkable, except for the fact that he has been in the business more or less continuously since 1928. The 106-year-old Kahn is one of many who have managed to live well into their eleventh decade with mental faculties intact and in surprisingly good health — and researchers into ageing have taken notice. Thomas Perls, a gerontologist at Boston University in Massachusetts and director of the New England Centenarian study, recalls an early encounter with two centenarians that challenged his expectation that the remarkably old would be remarkably unhealthy. While he was training at a rehabilitation centre, Perls saw one centenarian “out and about playing piano for everybody”, while another — a retired tailor — “was in occupational therapy mending people’s

clothes and teaching other people how to sew”. But the data increasingly suggest that people who reach such ripe old ages are getting a biological helping hand (see ‘Disease delay and genetics’). For example, recent research from Perls supports a hypothesis known as ‘compression of morbidity’, in which individuals whose lifespan is considerably longer than average (at least 100 years old) tend to stay healthy for longer, with delayed onset of age-associated diseases such as cancer and cardiovascular disease1. “These diseases don’t appear until roughly the last 5% of their lives,” says Perls. If this is the case, exploring extreme longevity could provide insights into the foundations of many common diseases — and into new weapons with which to fight them.

ARMOUR AGAINST AGEING

Much of the seminal work in assessing genetic contributions to healthy ageing in the general population has been done in Scandinavia, where political peace and a strong societal infrastructure have minimized the external

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forces that prematurely shorten life elsewhere. “Over the past 100 years, we’ve basically had ‘laboratory conditions’ for humans,” jokes Kaare Christensen, a genetic epidemiologist specializing in human ageing at the University of Southern Denmark in Odense. From studies of fraternal and identical twins, Christensen has found that roughly 25% of longevity is attributable to hereditary factors2. Furthermore, he suspects there is a clear age dependency for this genetic contribution. “Before age 60, genetic factors are not that important in the cohorts that we have studied,” says Christensen, “but after age 60 their impact increases, and seems to get strongest at the very highest ages.” In other words, a healthy lifestyle and environment are the key determinants of whether most people will reach their seventh decade, but after that it’s increasingly down to their genes. However, a healthy lifestyle might not be mandatory for everybody. Many specialists in ageing now believe that the extremely old possess beneficial genetic variants that protect

THE HUNT IS ON

For most diseases with a heritable component, the search for contributory genes takes the form of a genome-wide association study (GWAS). This is essentially a fishing expedition for individual variations — single nucleotide polymorphisms (SNPs) — that are statistically more or less common in individuals with a trait of interest than in a control population. However, to avoid overloading researchers with false-positive results, a high bar is set for designating hits. Furthermore, it can be nearly impossible to discover factors that are rare or exert only a modest effect on their own, as these might appear as noise in a large study population. “It’s highly unlikely that there’s a single genetic variant or even a handful of genetic variants that have a powerful enough influence to pop up in a GWAS and be independently associated with longevity,” says Perls. To sidestep this problem, Perls and his

DISEASE DELAY AND GENETICS Long-lived individuals show apparent compression of morbidity, with delayed onset of age-related diseases. Control

Age<100

100–104

105–109

1.0

Disease-free survival

them against the vicissitudes of ageing throughout life. It is only beyond a certain age — as the health of less-fortunate people begins to decline — that these variants become apparent. Gerontologist Nir Barzilai of the Albert Einstein College of Medicine in New York is among the leading researchers in this field. He has been tracking a large cohort of Ashkenazi Jews for many years in an effort to understand what sets the extremely old apart from their peers. “We have 2,500 people between the ages of 60 and 112, including nearly 600 people over 95,” says Barzilai. His aim is to identify genomic variants that are more common in the oldest cohort than in those who achieve only an average lifespan. “Most genotypes do not change in frequency because they’re not involved in lifespan,” he says. “Therefore, those genotypes that do change are either going down in frequency because they’re killing people or going up because they are promoting longevity.” One might expect these ‘longevity genotypes’ to be perfectly attuned for health — devoid of variants associated with increased disease risk. But several studies have shown that this is not the case. For example, the Leiden Longevity Study found that the genomes of nonagenarians were as likely to contain common risk factors for cancer, diabetes and other diseases as were the genomes of a young, control population3. This suggests that other variants in the longevity genome are somehow insulating their possessors against the effects of these potentially harmful genes — an effect that Christensen has also observed in family studies. “In Denmark, we have seen that children of the long-lived have about 25% lower cancer risk compared with other people.” For Barzilai, patterns like this suggest that the genomes of the extremely old might provide clinical researchers with a guide for understanding how health deteriorates over time. “What really controls our ageing rate,” he says, “are protective mechanisms and protective genes.”

0.9 0.8 0.7 0.6 0.5 0.4 0

100

120

Age of onset of cancer 8

FOXO3A

CETP

110+ 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0

100

120

Age of onset of cardiovascular disease 8

Frequency trends of favourable genotypes

HeLa cells showing FOXO3A protein (red)

APOC3

60 Genotype frequency (%)

PHILIP ROSENSTIEL (KIEL)

AGEING OUTLOOK

50 40 30 20 10 0

55–64 65–74 75–84 85–94 95–104 Age

colleague at Boston University, Paola Sebastiani, performed a different kind of GWAS. They focused not on individual SNPs but on groups of SNPs, seeking variants with weak individual effects that seem to act synergistically in long-lived individuals. These SNP groups might then reveal dependencies in the genome — clusters of variants that must occur together — that establish a protective biological environment that favours an extended lifespan. Perls and Sebastiani uncovered a number of such ‘fingerprints’, but their 2010 paper was retracted from Science a year later owing to technical mistakes that called their analysis into question. The team partnered with genetic epidemiologists at Yale University in New Haven, Connecticut, to address these issues, and re-published their research in PLoS ONE4 in 2012. Perls concedes that the retraction “Genotypes cast an unfortunate that change in cloud over the study, frequency are but he stands by his either going team’s discovery: a down because collection of 281 SNPs they kill people linked to at least 130 or up because genes that seem to be they promote notably enriched in longevity.” centenarians. “Here’s a bunch of variants that together are probably influencing one another and interacting with the environment to have an important impact on living to these most extreme ages,” says Perls, adding that “the accuracy of the model became greater and greater with subjects of older and older ages”.

Several genes identified in this study have also cropped up in research in animal models. Indeed, data from animal studies have generally been more effective in uncovering human genetic variations associated with healthy ageing than GWAS. One of the associations that has been most heavily replicated in humans is a variant of the apolipoprotein E (APOE) gene known as E4, which is linked not with longevity but with frailty5 — understandable given that this variant greatly increases the risk of both Alzheimer’s disease and cardiovascular problems. However, some see it purely as a risk factor for disease and are reluctant to call it a true ‘ageing gene’. With regards to the latter, variations in a gene encoding a regulatory factor called forkhead box O3A (FOXO3A) — the human counterpart of daf-16, a gene that modulates lifespan in worms — have been repeatedly linked to longevity in diverse populations of humans6. “It has been replicated in Han Chinese, Japanese, Ashkenazi Jews, southern Europeans and Germans,” says Stefan Schreiber, director of the Research Group for Healthy Ageing at Christian Albrechts University in Kiel, Germany. “This means that the origin of the genetic variant must be very old.” FOXO3A is part of a set of signalling pathways that govern growth and metabolic activity. In research that further supports the importance of metabolic pathways in ageing, Barzilai has found similar results in his Ashkenazi cohort. In particular, he has identified variants in genes encoding two proteins involved in lipid metabolism that reduce the levels of functional cholesterol esterase transport protein (CETP) and apolipoprotein C3

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OUTLOOK AGEING (APOC3)7. “They seem to behave like longevity genes — these variants go from 8–10% frequency in a population of 60-year-olds to about 20% in centenarians,” says Barzilai. Studies of centenarians are fraught with difficulties, however. For example, there is the issue of control groups: centenarians have experienced environmental and lifestyle changes that will not necessarily be matched in modern-day populations. “If you’re studying centenarians born in 1910, ideally you’d want a cohort of individuals who were also born in 1910 and died at age 50, and there’s little or no DNA available to do those studies,” says Nicholas Schork, a bioinformatician at the Scripps Research Institute in San Diego, California. On the other hand, dramatic improvements in contemporary medical care and diet mean that would-be control cohorts might harbour secret centenarians — lucky individuals with ‘normal’ genotypes who reach a ripe old age today, but who would have probably died younger in harder times. “The bet is that only a small number of control individuals will live to a very old age,” says Perls, “but there may be more people than were once thought who can live to 100.”

SUPER-OLD, SUPER-HEALTHY

Researchers are devising craftier strategies for tracking down biological factors that support very long life. Schreiber’s team is among those beginning to focus on ‘supercentenarians’ — those rare individuals who reach 110 years of age. “We’re starting with the most extreme and deploying all of our genomic and genetic tools to really dive deep,” says Schreiber. He notes that he has successfully used this type of approach for Crohn’s disease: by focusing on children who developed the disease at an unusually early age he has uncovered several causative genetic factors. Another approach is to bank on the compression of morbidity model and focus on individuals in their 80s or 90s who are ‘biologically young’. Schork is involved with the Wellderly study at Scripps, which works along these lines. “If somebody is 80 years old and as fit as a 50-year-old, studying them could shed light on what allows people to live to old age,” he says. As with GWAS, the difficulty in finding longevity gene candidates across populations might in part be a result of scientists casting their nets too wide. Given that most of the benefits of longevity genes are likely to kick in well after our child-rearing days, these variants probably lack the evolutionary momentum to spread, existing only as ‘family heirlooms’ that are passed from parent to child. There is certainly anecdotal evidence of this — all three of Kahn’s siblings, for example, also lived past the age of 100. Accordingly, several research groups and collaborative efforts such as the multinational Long Life Family Study, backed by the US National Institute on Aging (NIA) in Bethesda, Maryland, are attempting to get a better handle on this relationship. “We’re

People who enjoy good health in old age may have a genetic advantage.

looking at longevity-enriched families; for control persons we are using their spouses,” says Christensen, one of the study’s investigators. “We’ve performed GWAS on these individuals, and now we are moving on to sequencing.” As sequencing technology becomes cheaper and more powerful, it is likely to become an essential tool in the field. “I don’t think there will be major progress until we can analyse and interpret the whole-genome sequences of our centenarians,” says “If somebody is Schork. Barzilai has 80 and as fit as long been interested a 50-year-old, in this approach: a studying them grant proposal from could shed light his group to perform whole-genome on what allows sequencing on cenpeople to live to tenarians didn’t find old age.” traction with the US National Institutes of Health (NIH) but ultimately became the foundation for the Archon Genomics X PRIZE. This competition will award US$10 million to the genome-sequencing team that can deliver the fastest, best and cheapest sequences for Archon’s cohort of centenarian volunteers, termed the ‘100 over 100’. This effort has continued to benefit from input from both Barzilai and Perls, and both scientists see it as a good start. “One hundred people is not a large enough sample size,” says Perls, “but it is a fantastic step in the right direction.” Still, Barzilai cautions against thinking of genetic analysis as an end in itself. He would

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like to see how genetic variants translate into physiological effects, such as shifts in a person’s metabolic profile that directly reflect the state of their health. “Measure something in the blood, and then tell us if it’s relevant,” he says. He notes that although there might be numerous variations in different genes, they could all lead to the same life-extending result. “All our findings have had a relationship to a phenotype,” says Barzilai. “With CETP, the CETP levels were low. With APOC3, the APOC3 levels were low. And with both of them there were changes in cholesterol levels.” He further notes that although FOXO3 variations have been linked to longevity in several genetic studies, their physiological impact has yet to be demonstrated. Fortunately, there are a handful of other prospective, longitudinal studies of ageing-related health that can provide phenotypic data with which to compare genotypic findings. These include two osteoporosis studies that have been running at the University of California, San Francisco, since 2000, one focusing on men and the other on women. “Large numbers of those cohorts have passed away because they’ve reached their 70s and 80s,” says Perls. “But they happen to be about the same birth cohort as the children of our centenarians, the vast majority of whom are still alive.” Despite all these leads, researchers in both Europe and the United States are hampered by a lack of funding for longevity research. For example, the NIA-backed Longevity Consortium, which has supported many genetic studies of human ageing, is running on a limited and dwindling budget, says Schork. On the other hand, the field has strong support from NIH director Francis Collins. Collins is a driving force behind the NIH’s new Geroscience Interest Group, which envisages ageing as a primary link between many diseases. According to this perspective, understanding ageing might indicate a point of attack for treating or preventing conditions that have otherwise proven difficult to conquer, such as Alzheimer’s and cardiovascular disease (see ‘Live long and prosper’, page S18). “You have many more genetic susceptibilities within you than you will have diseases — and the mechanisms that either make a disease manifest or protect you are therefore of extreme importance,” says Schreiber. “Studying longevity is one way into this.” ■ Michael Eisenstein is a freelance science writer based in Philadelphia, Pennsylvania. 1. Andersen, S. L. et al. J. Gerontol. A Biol. Sci. Med. Sci. 67, 395–405 (2012). 2. Herskind, A. M. et al. Hum. Genet. 97, 319–323 (1996). 3. Beekman, M. et al. Proc. Natl Acad. Sci. USA 107, 18046–18049 (2010). 4. Sebastiani, P. et al. PLoS ONE 7, e29848 (2012). 5. Nebel, A. et al. Mech. Ageing. Dev. 132, 324–330 (2011). 6. Kenyon, C. J. Nature 464, 504–512 (2010). 7. Bergman, A. PLoS Comp. Biol. 3, e170 (2007).

AGEING OUTLOOK JOE POLLACK

for a long time. We also found that they enjoyed living among older people, which many gerontologists thought was not beneficial. On average they scored high2 on measures of happiness and life satisfaction, despite being far from their families. Many of them said: “Our grandchildren visit at Christmas. We’re happy to see them come, and we’re even happier to see them go.” Did that surprise you?

I wouldn’t want to live in an age-segregated retirement community, far away from my children and grandchildren, so I presumed these people would not be very happy or healthy. But, much to our surprise, they were flourishing. That’s the wonderful thing about science — you don’t always find what you expected. There are great differences among individuals, and people who choose to enter agesegregated communities or move away from the younger generation can do just fine. Eva Kahana with her husband and collaborator Boaz.

Q&A Eva Kahana

Has your own ageing changed your approach to this research?

Ageing proactively Why do some people cope better than others with getting old? Sociologist Eva Kahana, director of the Elderly Care Research Center at Case Western Reserve University, offers some clues. Is there a secret to ageing well?

There are a few. I’ve been studying how older people cope with the stressors of later life — the physical decline and loss of friends and relatives that often result in adverse psychological and social outcomes. It turns out that the people who age best engage in two kinds of adaptation. The first is preventive behaviours, such as physical exercise and healthy diet. Helping others and holding altruistic attitudes also seem to improve happiness in late life — that’s a phenomenon I’m investigating now with my collaborator on all this work — my husband Boaz Kahana, a psychologist at Cleveland State University. The other type of adaptation comes into play after the stressors have arisen. In a study published in May 2012, we found that actively asking for help and planning ahead for purchases or trips are important predictors of maintaining psychological well-being and social connectedness1. In other words, the people who do best still try to make the most of their future. Your observations have led to your theory of ‘successful ageing’. Isn’t that a loaded term?

Successful ageing is a slippery concept, but most gerontologists use it. A lot of early models assumed that you have to be healthy, wealthy and wise to age successfully. Today, the term

is in flux. Some people say it’s subjective — if you think you’re ageing successfully, then you are. Others say that if you’re sick and depressed with no social support then you are not ageing very successfully — and self-perception is neither here nor there. Our view is that people are dealt different hands in life. As long as they do the best they can to make their lives better they can have a place at the table of successful ageing. On what research do you base these ideas?

Much of it comes from a long-term project, the Florida Retirement Study. It started in 1989 with 1,000 people who had relocated to a large retirement community in Florida. We hypothesized that if you’re far from your previous social supports, such as your children, you will have more problems facing the stressors of old age. Every year, we interviewed each resident about their lives, leisure pursuits, aspirations, health, well-being and social relationships. We followed the same group for 20 years, by which time we were down to fewer than 100 people. It’s one of the longest studies of its kind. We found that the progression of frailty identified by many studies did occur, but slowly. These people led active, leisure-oriented lives, and maintained good health and functioning

I’m 71. Studying ageing while ageing is wonderful because I find that many of my ideas come from my experience. For instance, as my husband and I have encountered our own health challenges, I have become acutely aware of the importance of advocacy in obtaining good healthcare. We now have a study teaching disadvantaged older people at senior centres to communicate with their doctors, for example, to prepare questions in advance of an appointment, or to take somebody along who can listen in case they don’t catch something. That way, they can advocate for themselves and not just be passive healthcare consumers. Has life informed your research in other ways?

When my late mother, who was a Holocaust survivor, was admitted to the hospital, I fought unsuccessfully to get her a private room. But then she told me she didn’t want a private room. She said: “If there’s another person in the room, then nobody will mistreat me.” I was shocked, and I began to think about it. Why do we assume that everybody wants the same thing? That led to my theory of person–environment fit3, a model for understanding how living environments affect older people’s well-being. Gerontologists tend to look for universal solutions and say something is good or harmful for everybody. But as we found with the Florida study, that’s not always the case. Even though it’s been 30 years, this theory of person–environment fit remains my most cited work. ■ Interview by Rebecca Kessler, a freelance science journalist in Providence, Rhode Island. 1. Kahana, E. et al. Aging Mental Health 16, 438–451 (2012). 2. Kahana, E. et al. Psychosomat. Med. 64, 382–394 (2002). 3. Kahana, E. A. in Theory Development in Environment and Aging (eds Windley, P. G., Byerts, T. O. & F.G. Ernst, F. G.) 181-217 (Wiley, New York, 1975).

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OUTLOOK AGEING CO MPARATIVE B IO LO GY

Looking for a master switch BY SARAH DEWEERDT

onesome George, the Galapagos tortoise famous for being the last of his subspecies, was thought to be about 100 years old when he died on 24 June 2012. That’s a lifespan that fewer than 1 in 10,000 humans attain. But for his species, it was nothing special — giant tortoises can live for about 180 years, proving that slow and steady really can win the race. To humans, contemplating a mortality that rushes up all too quickly, such long-lived creatures are fascinating. How do they last for so long — and could we learn to do the same? So far, most research on the mechanisms of ageing has involved model organisms such as mice, roundworms and yeast. These studies have helped scientists uncover various ageingrelated genes and biochemical pathways (see ‘Live long and prosper’, page S18). But these species have become model organisms precisely because they don’t live very long. They mature quickly, reproduce prolifically and soon die — all qualities that make scientific studies feasible. “We may be overlooking a whole category of tricks for long life that you’re never going to see in short-lived animals,” says Steven Austad, interim director of the Barshop Center for Longevity and Aging Studies at the University of Texas Health Science Center in San Antonio. So researchers are looking for clues in other animals, including some with impressive lifespans in absolute terms and others that outlive related species. The first strategy is to study animals that exhibit what Caleb Finch, director of the Gerontology Research Institute at the University of Southern California in Los Angeles, has dubbed ‘negligible senescence’. Examples include deepsea rockfish, which still produce a normal number of eggs at the age of 100; long-lived species of turtles and tortoises; and certain clams and oysters that live for nearly half a millennium (see ‘Maximum lifespans’).

MAXIMUM LIFESPANS Roundworms 58 days Fruitfly 109 days

LEARNING TO LIVE LONGER

One result of surveying long-lived species is the realization that we are already members of that select group. “Humans are really sort of outliers,” says Vera Gorbunova, professor of biology at the University of Rochester in New York. We have the longest lifespan of any primate, and live four times longer than similar-sized animals such as deer and cougars. Even so, some researchers think we can still glean useful insights from species with even greater feats of longevity. Austad is studying hydras, small freshwater polyps related to jellyfish. “So far as we can tell, those things never age,” Austad says. But hydras only achieve an indefinite lifespan if they reproduce asexually, budding off daughters from the mother’s body wall. If environmental conditions turn harsh, the change can trigger each hydra to begin

Naked-mole rat 31 years

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Chimpanzee 59 years

African grey parrot 49 years

White-tailed deer / Eastern grey squirrel 23 years

Marmoset 15 years

producing either sperm or eggs — and then it lives only as long as a fruitfly. It’s not clear how the hydra’s genome is tuned to produce either extremely slow or extremely rapid ageing. But investigating this creature has great benefits: Austad points out that the hydra shares many genes with humans that have been lost in worms and fruitflies. “There’s a universe of genes that we haven’t been able to investigate in the traditional invertebrate models that may lead us to some new genetic pathways” involved in human ageing, he says. Others are more cautious about extrapolating from long-lived species to humans. Many of these species are only distantly related to us and have very different lifestyles. Turtles, for example, have a low metabolic rate and live a slow, sluggish existence. They are “close to being dead most of the time, physiologically”, says George Zug, curator emeritus of amphibians and reptiles at the Smithsonian Institution’s National Museum of Natural History in Washington DC. Zug adds that evolution doesn’t select for longevity directly, but for how long an organism takes to become reproductively mature, and for the length of its reproductive lifespan. So looking to evolution for insights on how to extend our post-reproductive lifespan might be a non-starter. It might be better to compare species in the same taxonomic group with similar genetic material. For example, laboratory mice live for 4 years at most, but the longest-lived rodent, the naked mole-rat — a buck-toothed, wrinkly creature that dwells in colonies underground — can survive for nearly 30 years. Moreover, longevity has evolved in four different rodent lineages: porcupines, beavers and squirrels can also live for more than 20 years. Even though none of these species lives as long as humans, we can learn a lot from them. Gorbunova points out that by manipulating single genes in a mouse, scientists have been able to extend their lives by 10–20%. “But compared to a mouse, a naked mole-rat lives ten

Oldest confirmed ages for a range of species

Broad-tailed hummingbird 14 years

Mice 4 years

Long-lived animals tend have one attribute in common: protection. Oysters and clams have tough outer shells, whereas bowhead whales, which can reach 200 years old, are protected by their size — larger species tend to live longer than smaller species. “There’s no point building a mouse that can live 40 or 50 years, because they’re all going to get eaten in the first year of life,” says Richard Miller, who studies the biology of ageing in mammals at the University of Michigan in Ann Arbor. But that’s not such a problem for a bat that can fly out of harm’s way, a sea urchin that can deter enemies with its spines, or an elephant that is too powerful to be taken down by predators. “Nature makes long-lived species whenever there’s an opportunity in the form of a low-hazard niche,” Miller says. And that opportunity has arisen over and over again. “Every kind of animal in every phylum has species that are short lived and species that are long lived,” says Finch. “So lifespans can go in either direction, depending on the evolutionary pressures.” In other words, evolution can extend the lifespan while keeping the same basic body plan and genetic heritage.

Horse 57 years

Brandt’s bat 41 years

SOURCE: THE ANAGE DATABASE

Evolution can extend a species’ lifespan by an order of magnitude. Can we learn the same tricks?

TUI DE ROY/GETTY IMAGES

AGEING OUTLOOK

Lonesome George lived to about 100, which is merely middle-aged for a Galapagos tortoise.

times longer,” she says. Clearly evolution is the superior experimentalist here.

MECHANICS OF AGEING

Comparative studies are beginning to give clues to the cellular and molecular mechanisms that enable some species to live longer than related species. Miller’s team, for example, cultured skin cells from nine rodent species and exposed them to various stresses, including cadmium, hydrogen peroxide and heat1. Similar experiments2 involved skin cells from 35 different bird species. Both studies showed that cells from long-lived animals are more resistant to stresses than those of short-lived species, says Miller. Similar research also suggests one possible reason why birds tend to live longer than mammals of similar size, Miller adds. “Bird cells tend to be three- to ten-fold more resistant to many

100 years

Elephant 65 years Andean condor 79 years

of these stresses than cells from rodents of the same size. We can’t prove that’s why birds live a long time, but it’s a good guess.” Another possible mechanism of longevity comes from Austad’s studies of protein stability3 — the ability of proteins to remain properly folded when researchers try to disrupt them with chemicals or heat. “We’ve looked at protein stability in a number of long-lived organisms, and it seems to be the one thing that reliably associates with long life” in creatures as diverse as bats, naked mole-rats and clams, he reports. Miller’s and Austad’s results don’t necessarily contradict each other. “When an organism ages, so many things go wrong,” Gorbunova says. To build an organism that lives substantially longer than related species, “you need to improve multiple maintenance mechanisms”. Long-lived species might have better mechanisms of DNA

repair, for example, something Gorbunova is currently investigating in cells from 20 different rodent species. Her team has shown4 that naked mole-rat cells are hypersensitive to contact inhibition, the tendency to stop growing and dividing when they touch other cells. This characteristic makes the species extraordinarily resistant to cancer. The naked mole-rat seems to have several potential protective mechanisms at its disposal. For example, a team of Israeli and US researchers recently reported5 that the animals also have an unusually high level of NRG-1, a protein that protects nerve cells in the brain. A more comprehensive approach to investigating the mechanics of ageing is provided by metabolomics, which attempts to identify the small molecules that comprise the metabolic profiles of cells. Daniel Promislow, a geneticist at the University of Georgia in Athens, investigated6 the levels of about 2,500 different molecules in the bloodstream of young and old marmosets. This small monkey, native to South America, is becoming a popular model for studying ageing in primates because it is relatively short-lived and easy to keep in captivity. “All these metabolites and their interactions paint a portrait of the state of that individual,” explains Promislow. “And that portrait changes with age.” Promislow’s group is carrying out an even larger metabolomics study that will track the levels of more than 20,000 molecules over five years, charting differences between young and old marmosets, and in individual monkeys over time. He has also just finished collecting a similar data set in fruitflies. So researchers are not short of anti-ageing mechanisms to investigate. For Miller the bigger question is whether these mechanisms are all separate or derived from a common ‘master switch’ for longevity. As he puts it: “When Nature wants to build a long-lived species, does she have more than one trick to do it?” ■ Sarah Deweerdt is a freelance science writer based in Seattle, Washington. 1. Harper, J. M. et al. Aging Cell 6, 1–13 (2007). 2. Harper, J. M. et al. J. Exp. Biol. 214, 1902–1910 (2011). 3. Austad, S. N. J. Comp. Pathol. 142, S10–S21 (2010). 4. Seluanov, A. et al. Proc. Natl Acad. Sci. USA 106, 19352–19357 (2009). 5. Edrey, Y. H. et al. Aging Cell 11, 213–222 (2012). 6. Soltow, Q. A., Jones, D. P. & Promislow, D. E. L. Integr. Comp. Biol. 50, 844–854 (2010).

Bowhead whale 211 years

In lobsters, egg production seems to increase with age, indicating potentially negligible Human senescence 122 years

100

Giant tortoise 177 years

Clam 400 years Deep sea rockfish 205 years

400

Hydra Immortal

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tem cells are the cells that keep on giving. They resupply the body with new cells as the old ones wear out from DNA damage, the accumulation of malformed proteins, or shortening of the telomeres (DNA caps on the tips of the chromosomes). They also make copies of themselves, replenishing their own ranks in the process. Little wonder that harnessing stem cells is seen as a possible way to fix or maintain failing organs and tissues, and maybe slow the general physical decline of old age. With a ready supply of active stem cells, older muscles might be made stronger, failing brains could become less prone to cognitive lapses, and aged bone marrow could be better able to produce the infection-fighting T and B cells. If only it were that simple. As with so much else, stem cells in an older person are not the same as those in someone younger. They tend to be less productive and less reliable, and become slower and less predictable when it comes to replenishing cells affected by injury, illness or senescence — and the tissues they serve become less healthy and vital. In other words, stem cells are prominent in the fundamental biology of ageing. If stem cells in older people could be made to retain their effectiveness, perhaps broken bones and skin wounds could be made to heal faster and, with time, we might be able to treat the conditions of old age, such as dementia and heart disease. Thomas Rando, a stem-cell researcher at Stanford University in California, points out that we already transplant bone marrow and perform skin grafts. Stem-cell transplantation of certain types of cells — those that mature into pancreatic cells, for example, to treat diabetes — could become a reality in five years, he says. “It’s not so futuristic.”

STEM-CELL HIERARCHY

Helping stem cells to replicate over and over again could hold back the signs of ageing.

ST EM CELLS

Repeat to fade Stem cells rejuvenate our tissues but are not resistant to ageing themselves. How can they retain their effectiveness? BY PETER WEHRWEIN S 1 2 | NAT U R E | VO L 4 9 2 | 6 D E C E M B E R 2 0 1 2

There are different categories of stem cells with varying degrees of potency — the potential to differentiate into other cell types. Totipotent stem cells — found only in embryos — can become any type of cell in the body. As these stem cells differentiate, they become more specific to certain tissue types. Examples of these multipotent stem cells include neural stem cells, which develop only into neurons, astrocytes and oligodendrocytes. Muscle stem cells are even more specialized — these unipotent cells produce only muscle cells. Ageing affects various stem-cell types in different ways. Blood-forming (haematopoietic) stem cells in the bone marrow, for example, shift towards making more myeloid cells and fewer of the lymphoid cells that generate T and B cells. This change might help explain why older people are more NATURE.COM likely to develop myeloidrelated cancers and are For some of the more vulnerable to infec- latest research on tions. Similarly, accord- stem cells: ing to a study1 by Mark go.nature.com/bLxt1n

ERIK DREYER/GETTY IMAGES

OUTLOOK AGEING

AGEING OUTLOOK LaBarge, a cell biologist at Lawrence Berkeley National Laboratory in California, older stem cells in human breast tissue tend to produce fewer tumour-suppressing myoepithelial cells than younger stem cells in the breast. In both cases, the number of stem cells may remain the same, or even increase, with age. With the brain and hair, it seems to be a different story. The pool of available stem cells starts to deplete. Indeed, the cognitive and sensory decline of old age has been associated with a reduction in the number of neural stem cells and hence the production of new brain cells. And fewer melanin-producing melanocyte stem cells leads to greying hair, one of the most obvious signs of senior status.

MIODRAG STOJKOVIC/SCIENCE PHOTO LIBRARY

NICHE EFFECTS

Stem cells don’t live in splendid isolation. Their behaviour is heavily influenced by their surroundings, and it has become apparent that the ageing stem cell is as much a product of its environment as of its intrinsic make-up. In 2005, Rando and Irina Conboy, a bioengineer who was then working in Rando’s Stanford lab, conducted a landmark experiment2 showing the dramatic effect that external factors can have on stem cells. Working from what Rando says was a hunch, the researchers surgically attached the circulatory systems of pairs of mice — one young, one old — so that the two shared the same blood. They found that a minor muscle injury inflicted on the older mouse healed much better when the animal was attached to a younger mouse. Furthermore, tests showed that this improved healing was the result of the activity of the older mouse’s stem cells, not those of its younger, conjoined companion. Clearly, something in the blood of the younger animal was rejuvenating the stem cells in the older one. This finding opened up the possibility of arresting, or even reversing, the decline of older stem cells by manipulating their environment. Or, as Rando puts it, “enhancing the niche may be just as important as finding the best stem cells”. Using similar studies, other researchers have extended Rando and Conboy’s findings to different types of stem cell. Conboy, now at the University of California, Berkeley, offers a hypothesis to explain this effect. She notes that stem cells are typically quiescent. “They have a talent for sitting quietly and waiting,” she says. In old tissue, signals to stem cells might not get through “so they continue to sit quietly and do nothing”. But enliven their environment and the signal can carry. Now comes the hard work: working out precisely what it is that affects the stem-cell niche. As Conboy points out, it’s likely to involve many factors, including some that reduce stem-cell activity as well as those that rev it up. As an example of a debilitating factor, Rando’s team reported3 that eotaxin — a chemokine, or immune-system chemical messenger — seems to contribute to

age-related cognitive impairment by inhibiting adult neural stem cells. Another aspect of the stem-cell environment is the ageing of normal cells. Senescent cells secrete a variety of signalling molecules such as cytokines and chemokines; proteins such as growth factors; and enzymes such as proteases. According to Judith Campisi, who studies senescence at the Buck Institute for Research on Aging in Novato, California, cytokines can act directly on stem cells to restrict proliferation and versatility, and proteases can degrade the extracellular environment. Transplanting young stem cells into a neighbourhood full of older cells, some of which are senescent, “Stem cells have a talent for is probably not going to work very well, she sitting quietly says. “There’s mountand waiting.” ing evidence that senescent cells in the niche are going to be an important part of the stem-cell transplant story.” How does a change in the niche alter a stem cell’s behaviour? The cell’s genes don’t change, but epigenetic factors might cause them to be expressed in different quantities. So, both genes and environment play a role. “A cell’s behaviour is always dictated by the microenvironment it is in,” says LaBarge. “But the array of potential responses within those contexts is probably dictated by the genetic state of the cell.”

Embryonic stem cells can replenish any type of cell, but that potency is lost during ageing.

One way stem cells are connected to their niches is through signalling pathways, whose complexities are slowly coming into focus. Research has shown4,5, for example, that an active Notch signalling pathway turns on the regenerative power of muscle stem cells, whereas firing up the Wnt signalling pathway leads to fibrosis. These pathways aren’t isolated from one another. The strength of the Notch pathway depends in part on another pathway,

known as MAP/ERK6. The Wnt pathway has a role in regulating telomerase, an enzyme that restores the chromosomal DNA caps, or telomeres, that otherwise shorten each time the chromosome is copied.

THE FLIP-SIDE OF THE COIN

Researchers studying stem-cell rejuvenation see an array of potential clinical applications. Bioengineered polymers could deliver drug packages that ramp up signalling pathways such as Notch. Agents that tweak DNAreading RNA could alter which genes are expressed in an effort to return youthful vigour to decrepit stem cells. Investigation of agents already known to have anti-ageing effects, such as rapamycin (see ‘Live long and prosper’, page S18), might reveal pathways that alter older stem cells or their niches, or both. However, says Rando, any treatment needs to be targeted, both in duration and specific location, to avoid the potential harm of systemically and chronically stimulating stem-cell function. The most worrying of the potential side effects is cancer. Many of the factors and mechanisms that reduce the efficacy of stem cells also keep cancer in check. For example, when dividing cells are damaged or stressed, cell senescence keeps them from becoming cancerous. But once cells are senescent, they secrete cytokines and other molecules that might tip their neighbours — and possibly more distal cells — into a proliferative, cancerous state. Campisi says that some adult stem cells — for example, mesenchymal stem cells in connective tissue, and blood-forming haematopoietic stem cells — also undergo senescence. These cells are particularly disruptive and can activate nearby dormant cancer stem cells. The p16 tumour-suppressor gene is another good example of the ageing–cancer trade-off 7. Increased expression of p16 has been observed in a number of older tissues — so much so that active p16 seems to be an overall marker for ageing. But p16 is a tumour-suppressor gene that might limit age-related diseases, so targeting it is a high-risk strategy. “Tumour suppression and ageing are two sides of the same coin,” notes Rando. So, as enthusiastic as researchers are about making old stem cells young again, they are well aware that this is new territory where the best of intentions could easily have unintended consequences. ■ Peter Wehrwein is a freelance science writer based in Newton, Massachusetts. 1. Garbe, J. C. et al. Cancer Res. 72, 3687–3701 (2012). 2. Conboy, I. M. et al. Nature 433, 760–764 (2005). 3. Villeda, S. A. et al. Nature 477, 90–94 (2011). 4. Brack, A. S. et al. Science 317, 807–810 (2007). 5. Naito, A. T. et al. Cell 149, 1298–1313 (2012). 6. Conboy, I. M. et al. Aging 3, 555–563 (2011). 7. Jones, D. L. & Rando, T. A. Nature Cell Biol. 13, 506–512 (2011).

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OUTLOOK AGEING

Could a Mediterranean diet, rich in olive oil, fish and fresh fruit, lead to a healthy microbiome in old age?

MICRO B IO ME

Cultural differences Studies of gut bacteria are beginning to untangle how diet affects health in old age — but determining cause and effect is tricky. BY VIRGINIA HUGHES

lmost everything about eating gets more difficult with age. Elderly people typically cannot taste or smell as well as they used to, decreasing the appeal of some foods. Dental issues or a dry mouth can impede chewing; loss of muscle tone in the pharynx can make swallowing difficult; constipation and the side effects of medication can make digestion uncomfortable; and decreased mobility makes a chore of grocery shopping or cooking complex meals. Little wonder that older people eat an increasingly narrow range of foods. But can this, in itself, adversely affect health? Recent research shows that diet influences the composition of the gut microbiome — the bacterial community in our intestines — in the elderly. In July, a group of researchers, mostly based in Ireland, published1 the largest study so far of the microbiome in an elderly population. The data indicate that the frailest older people tend to harbour similar intestinal microbial communities. More provocatively, the study also suggests that this

microbial make-up is driven by a diet high in fat and lacking in fibre, and that a decline in our microbial community underlies ill health as we grow old. The conclusion is controversial, as many scientists say these associations can go the other way. An individual’s health, and thus the state of his or her immune system, can also affect the gut microbiota and drive eating habits. One thing on which everyone agrees, however, is the value of finding out how to alter the microbiome in our favour. “The potential is enormous, especially the idea of figuring out what diet is right for individuals,” says Rob Knight, a microbiome expert at the University of Colorado in Boulder, who was not involved in the new study. “We just don’t have a very good idea yet of the specific parameters that could set the microbiota in a good direction versus a bad direction.”

THOUSANDS OF HITCHHIKERS

The microbiome has received a lot of scientific attention of late. By sequencing the DNA of our microscopic stowaways, researchers have

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discovered2, for instance, that more than 1,000 bacterial species can live in the human gut, helping us break down food and boosting the immune system. Microbial profiles vary among individuals, with the average person harbouring about 160 different species. The intestinal microbiome is stable for most of our lives. But “at the extremes of life, both in babies and old people, it’s chaotic”, notes Paul O’Toole, a geneticist at University College Cork in Ireland, and leader of the new study. There are no microorganisms in the womb; infants get their first exposure in the birth canal. Over the next few months, as babies drink milk and interact with the environment, additional species move in. The microscopic community does not settle down until about 12 months of age. But the changes that take place in the microbiome towards the end of life are less well NATURE.COM understood. O’Toole’s interest in the For some of the subject started in 2007, latest research on when Ireland’s Depart- the microbiome: ment of Agriculture, Food go.nature.com/zrvrut

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AGEING OUTLOOK

A nursing-home diet has a marked effect on an individual’s gut microbiome.

and the Marine in Dublin started an initiative to fund research to promote the food industry. The government was particularly interested in the diets of older consumers, a group whose numbers will rise dramatically in the next couple of decades. O’Toole received a €5-million (US$7-million), five-year grant to study the gut microbiota of the elderly. Results from research since the 1970s suggested that he would find certain patterns. For example, several studies showed that stool samples from older people contain fewer species in the genus Bifidobacterium — which are thought to have beneficial health effects — than samples from middle-aged controls. These earlier studies, however, analysed only those microorganisms that could be cultured in the laboratory, which make up about one-third of the total number of species in the gut, says O’Toole. So he set out to sequence the genes of all the organisms found in faecal samples from hundreds of people aged 65 or older, and to mine this massive data set for links between microbes and health.

CAUSE OR EFFECT?

In 2011, O’Toole’s team published3 the first significant batch of data from the project, dubbed ELDERMET. Echoing previous studies, the scientists found that the diversity of species living in an individual declines with age. They also showed that the type of species lost varies greatly from person to person, meaning that the gut microbiota of two older people look more different from each other than do those

of two middle-aged people. The latest ELDERMET study1 aimed to find out what was driving this variability. O’Toole categorized 178 participants into four groups based on where they lived: in the general community, day hospitals, short-term hospital care or long-term nursing homes. He found that the microbial profiles of the first two groups were similar to those of 13 younger adult controls. But the profiles of the older people in institutional care were notably different: they carried a higher proportion of bacteria from the phylum Bacteroidetes, and a lower proportion from the phylum Firmicutes. Importantly, these links correlated with diet. Residents of nursing homes often eat high-fat, low-fibre diets, heavy with starchy foods such as porridge and potatoes, fried meats, puddings and sugary juices. Outside nursing homes, older people tend to have a much more balanced diet, with more fibre, less red meat and more oily fish. The study also found that certain microbial profiles were associated with specific health measures. For example, a gut high in Bacteroidetes correlated with several markers of inflammation, high blood pressure and small calf circumference (a measure of frailty). The researchers also looked at the timing of these dietary and health changes. When individuals move into a nursing home, their diets change within a couple of weeks. Their microbial profiles took up to one year to change completely, whereas their health took several years to deteriorate. “The microbiota appear to be driven by what people eat,” O’Toole says. And this microbial profile, in turn, “correlates with whether or not the subject is healthy or frail, inflamed or not inflamed, has lots of muscle tone or poor muscle tone.” Tracking the nursing-home residents over time adds weight to O’Toole’s argument, “The microbiota “but there probably appear to be were other things hapdriven by what pening to those people people eat.” over the course of the year,” notes James Lewis, a specialist in epidemiology and gastroenterology at the University of Pennsylvania in Philadelphia. “We have to be cautious about trying to extrapolate too far in terms of what came first.” Lewis and several other scientists argue that there are probably many non-dietary factors influencing the microbiota of the elderly in O’Toole’s study. After all, they say, some amount of weakness or frailty is generally what puts someone in a nursing home in the first place. And studies of younger adults who have acute gastroenteritis or Crohn’s disease, for example, show a similar loss of microbial diversity to that seen in the elderly. “An already compromised health status could be among the major driving forces that differentiates the microbiomes of the free-living elderly from those of the long-term-care residents,” says

Elena Biagi, a molecular microbiologist at the University of Bologna in Italy, who has studied the gut microbiota of centenarians. Other factors, such as constipation and dental hygiene, could also explain part of the association. As Knight notes, when it comes to microbiome studies, “there are very few cases where cause and effect are known”.

MEDITERRANEAN MODELS

There have been some short-term studies of how dietary patterns influence the microbiome. Last year, Lewis and colleagues showed4 that changing an individual’s diet for ten days has little effect on the gut microbiome. Only long-term dietary patterns were associated with specific and stable microbial profiles. Investigating the latter in more detail requires a more rigorous — and time-consuming — approach. That is what O’Toole and Claudio Franceschi, an immunologist at the University of Bologna, plan to use to investigate whether the so-called Mediterranean diet can help people age well. Franceschi has been studying the elderly for more than 25 years. Several of his studies centre on the Italian island of Sardinia, which has an unusually high number of male centenarians5. He attributes this preponderance at least in part to the men’s regular physical exercise and simple Mediterranean diet — rich in olive oil, fish, fresh vegetables and fruits. Intriguingly, this diet is also broadly similar — low in fat, high in fibre — to the diets of the healthiest elderly people in O’Toole’s recent study. Franceschi, O’Toole and two dozen other academic and industry groups are now part of a €9 million project called NU-AGE, which includes 1,250 older individuals from France, Italy, Poland, the Netherlands and the United Kingdom. For one year, half will be given the Mediterranean diet, half will remain on their normal diet, and the NU-AGE researchers will measure how their health changes. O’Toole’s team will sequence the participants’ gut microbiota before and after the dietary intervention, while other researchers will look at genetic, epigenetic and metabolic signatures in their blood. Each of these biological levels might give insight on how the diet changes the microbiome. NU-AGE is exactly the kind of large, longitudinal study that scientists the world over are clamouring for. The hope is that interrogating the link between diet and the microbiome will show how some of our trillions of microbial hitchhikers can steer us to long and healthy lives — and how we can entice them to stay. ■ Virginia Hughes is a freelance science journalist based in Brooklyn, New York. 1. Claesson, M. J. et al. Nature 488, 178–184 (2012). 2. Qin, J. et al. Nature 464, 59–65 (2010). 3. Claesson, M. J. et al. Proc. Natl. Acad. Sci. USA 108, 4586–4591 (2011). 4. Wu, G. D. et al. Science 334, 105–108 (2011). 5. Poulain, M. et al. Exp. Gerontol. 39, 1423–1429 (2004).

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OUTLOOK AGEING T ECH NO LO GY

Dancing with robots High-tech gadgets such as sensors that detect falls and robots that can fetch items are helping people stay independent and safe into their later years.

arilyn Rantz knows only too well how vulnerable old age can make us. Her mother fell one day, badly fracturing her shoulder. She owned a wearable device that would summon help at the press of a button, but had left it just out of reach from where she fell. After eight hours on the floor, she was able to crawl to the device and call for help, but the stress of those hours bearing untreated injuries took its toll — within six months she was dead. To circumvent the limitations of such devices, Rantz, a gerontologist and associate director of the Interdisciplinary Center on Aging at the University of Missouri in Columbia, prefers systems that people don’t have to carry with them. “It’s one of the reasons we’ve moved to environment-based sensors instead of wearable sensors,” she says, “because people don’t wear them.” Rantz and Marjorie Skubic, an electrical and computer engineer and director of the university’s Center for Eldercare and Rehabilitation Technology, are developing systems to monitor the health and well-being of older adults. Research into this type of technology is burgeoning in line with the growing proportion of elderly people. Sensors are being developed that can not only detect falls, but also monitor changes in gait or daily routine that could flag concerns and alert caregivers before physical problems become acute. Other technologies, such as robotics, could prolong independence and help people stay active.

community. The aim is to allow people to stay in their apartments until the end of their lives without moving into nursing homes — a trend known as ageing in place. Rantz is executive director of the Aging in Place programme at Tiger Place, and she says that most people prefer not to move frequently; the stress of transition can contribute to health problems and earlier death. “Just the process of moving is enough to kick some people over — people who might have had many years ahead of them,” she says. And for most people, going into a nursing home lessens the incentive to help themselves, leading to reduced activity and a loss of physical capability. Tiger Place provides an environment for researchers to test their sensors. Some measures installed in its apartments are simply infrared motion detectors, the kind developed for security systems. The detectors help researchers discern individuals’ personal patterns of activity: how much they move around, when and how often they leave their home, and how long they’re gone for. Changed activity levels can be early warnings of problems — they can be more telling than a questionnaire in a doctor’s office, and can highlight problems a lot sooner, Rantz says. A onebedroom apartment in Tiger Place might be discreetly equipped with 10 motion sensors, a

NOT SO TECHNOPHOBIC

Although elderly people can be reluctant to embrace new gadgets, they are happy to adopt technology if it’s easy to use and will help them retain their independence, says Wendy Rogers, an engineering psychologist at the Georgia Institute of Technology in Atlanta. Over-65s have a wide spectrum of abilities and conditions, says Rogers, and range from people who are still working to those in the late stages of dementia. Their needs require a wide variety of technologies from smartphone fitness apps for the active to vital-sign monitors for the bed-ridden. To study how technology can best help older adults, the University of Missouri, in collaboration with private company Americare, developed Tiger Place — an active retirement S 1 6 | NAT U R E | VO L 4 9 2 | 6 D E C E M B E R 2 0 1 2

gait-monitoring system, and bed sensors that can measure restlessness, heart rate and breathing. One of the main hazards being monitored is falling. According to the US Centers for Disease Control and Prevention, a third of Americans aged over 65 fall each year; in 2009, this resulted in 20,000 fatalities. And the longer people lie injured, the poorer their prospects for healing. One Tiger Place project uses an array of microphones to detect the sound of a fall. Computers using statistical techniques can differentiate the distinctive acoustic characteristics of a body falling from, for example, a book being dropped or noise from a television. Preliminary work used stuntmen simulating falls, and microphones have now been installed in apartments to get more real-world data and make the system more accurate. It is, of course, better to prevent falls occurring in the first place. Skubic is monitoring how people walk, gathering information about speed, stride length and sway to discover whether they have balance or other problems that increase the risk of falling. One of the sensors she’s investigating is the Microsoft Kinect, a three-dimensional camera system designed as a hands-free controller for video games. Off-the-shelf sensors would hasten the adoption of these systems — the Kinect provides

WILLOW GARAGE

B Y N E I L S AVA G E

GEORGIA INSTITUTE OF TECHNOLOGY

AGEING OUTLOOK

Robots such as the PR2 can help elderly people with limited mobility by fetching items and reminding them to take medication.

sophisticated 3D imaging for less than US$150. The challenge lies in developing software that can make sense of the data. Skubic says that although she gets near-perfect results for gait measurement in a laboratory setting, it’s more difficult to sort out relevant information from noise in a person’s home, where lighting conditions vary, different people walk around, furniture gets moved, and so on. More personal types of sensor system are under development. “When someone gets depressed, they physically slow down — and we would be able to detect that,” says Tanzeem Choudhury, a computer scientist at Cornell University in Ithaca, New York. She adds that people under stress speak more loudly, with less tonal variation and more jitter in their voice; stress is a risk factor for poor health. To test these ideas, Choudhury has developed a smartphone app, BeWell. The app listens to the sound of conversation — without keeping a recording of actual words spoken — to estimate a person’s level of social interaction. The phone’s accelerometer measures how much the person moves, and its GPS can tell if they’re leaving the house, indicating physical activity and a higher chance of social contact. The app even attempts to monitor sleep by noting a lack of activity, although Choudhury says that’s mostly a best-guess approximation. Among the unresolved issues are the willingness of older people to adopt monitoring software (or even the phones themselves) and technical considerations such as the effect on battery life.

THE RISE OF THE ROBOTS

Sensors can provide important health information, but older people often need physical assistance — and here robotic technology

could fill the gap. Toyota, for instance, plans to launch four devices next year it calls Nursing and Healthcare Partner Robots, designed to assist people who have trouble walking. They range in size: the smallest is a sort of knee brace attached to a footpad, worn on a paralysed leg. An accelerometer and gyroscope attached to the thigh and a load sensor in the footpad determine when a person tries to walk and how fast, and bend the knee joint accordingly. The largest of Toyota’s robots assists in actually moving an immobile person to walk between, say, the bed and the toilet. For intensive support with independence, Yoshiyuki Sankai, an engineer at the University of Tsukuba in Japan, has designed Machines that the Hybrid Assisbuzz around tive Limb (HAL) suit the house doing — a wearable robot laundry and that provides joint preparing strength and limb dinner are still support for people in the realm of w it h d i m i n i she d science fiction. function. Sensors on the skin read weak electrical signals involved in muscle movement and trigger the suit to move appropriately. A spin-off company from the Tsukuba lab, Cyberdyne, produces HAL suits that healthcare facilities can hire. Charles Kemp, director of the Healthcare Robotics Lab at Georgia Tech, thinks mobile robots for the home could become a reality within the decade. Kemp has been conducting research on the PR2, a robot built by Willow Garage in Menlo Park, California, that has helped a quadriplegic man shave himself. Kemp says that older adults he has worked with are surprisingly willing to have a robot

help out: telling a machine to perform tasks rather than asking a relative or hiring an aide helps preserve privacy and a sense of control, he says. Kemp sees other benefits too. “Robots may enhance people’s lives in surprising ways,” he says. “Dancing with robots might be fun, healthy and even therapeutic.” Unlike industrial robots that perform tasks such as moving objects from one fixed location to another, personal robots will have to deal with the more variable and unstructured environment of a home. Such variation presents a challenge for their designers. “You can’t completely predict where the coffee mug is going to be in the kitchen,” say Kemp. The other major hurdle, he says, is the cost — a robot arm alone can cost $100,000. Kemp is optimistic that robotics will follow the same path as computers, which went from rare and expensive to ubiquitous and cheap. There are signs this is already happening: Rethink Robotics, a Boston company, recently released a robotic arm costing $22,000. “It’s going to be a while before we have robots that are fully intelligent and have human movement capabilities,” Kemp says. Machines that buzz around the house doing laundry and preparing dinner are still in the realm of science fiction, but smaller devices that can pick up a dropped remote control, remind people to take medication or help with personal hygiene are possible in the next five years. Older people may not be the digital natives of the young generation, says Rogers, but they are open to new technology if the benefit to them is clear. “They don’t want novelty for novelty’s sake.” ■ Neil Savage is a freelance science and technology writer based in Lowell, Massachusetts.

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he Ames mice in Andrzej Bartke’s lab look alike at birth. In every litter, however, some mice will have a genetic mutation that inhibits or prevents the production of growth hormone or insulin-like growth factor (IGF). These hormone-free mice will grow to a third of the size of their siblings without the mutation. But around middle age, their fortunes change: the tiny mice age differently. As Bartke first observed in the early 1990s, when the normal mice started to appear hunched and grey, the dwarf mice stood apart. “They stay healthy and they look young,” says Bartke, a gerontologist at the Southern Illinois School of Medicine in Springfield. To test whether they really did live longer, Bartke started a lifespan study. In 1996 he reported his results1: normal Ames mice typically live about 720 days; male dwarf mice got an extra 350 days, and females lived another 470. Two of the dwarf females in the study lived for four years. This was the first solid evidence that a single genetic mutation could extend lifespan in a mammal. By studying models such as the Ames mice, researchers like Bartke are learning that ageing is not an uncontrollable, entropic process. Using clues from studies of diets and other interventions, researchers are delving into underlying molecular mechanisms in the hope of developing drugs that thwart the process of ageing. Such therapies will protect against “potentially every age-related disease”, says Felipe Sierra, a gerontologist at the US National Institute on Aging (NIA) in Bethesda, Maryland. “If we can get it to work in humans we will make a big impact on quality of life.”

EAT LESS, LIVE LONGER

Will it be possible to stop the clock and prevent the effects of ageing?

ANT I-AGEING

Live long and prosper Researchers are learning about the molecular basis of ageing — and finding clues about how to treat diseases in the process. B Y K AT H E R I N E B O U R Z A C S 1 8 | NAT U R E | VO L 4 9 2 | 6 D E C E M B E R 2 0 1 2

Much of the research into the mechanisms of ageing can be traced back to work in the 1930s by nutritionist and gerontologist Clive McCay at Columbia University in New York. McCay devised the caloric restriction diet, which involves reducing calories by about 30% without causing malnutrition. He pioneered the technique in mice and rats. Since then, caloric restriction has been found to extend lifespan in every species studied, including yeast, worms, flies and dogs. In mice, caloric restriction extends life by 30–40%. It also broadly protects against agerelated diseases, including cancer, diabetes and autoimmune disease. “Caloric restriction is the most powerful known intervention in ageing,” says Luigi Fontana, a gerontologist at the Washington University School of Medicine in St Louis, Missouri. For this reason, researchers are using the diet to explore the mechanisms of ageing with a view to extending the work to humans. NATURE.COM Two major long-term studies are testing caloric For some of the restriction in non-human latest research on primates. Although both anti-ageing: have demonstrated major go.nature.com/ixmhin

ASIASELECTS/GETTY IMAGES

OUTLOOK AGEING

JEFF MILLER

AGEING OUTLOOK health benefits, they are far less conclusive about the effect on lifespan. At the Wisconsin National Primate Research Center, the average lifespan of a male rhesus monkey is about 27 years. By this age, a normal monkey is stooped, with grey fur on its face and sagging skin on its torso. In contrast, a monkey the same age on a calorie-restricted diet has a lively look in its eyes, a full brown coat, and holds its tail up. These monkeys also have a threefold increase in resistance to ageassociated diseases, says geriatrics researcher Richard Weindruch, who heads the Wisconsin study. Rates of cancer, diabetes, brain atrophy and cardiovascular disease are all much lower in these animals. The second study, also in rhesus monkeys, is being conducted by the NIA at its Poolesville site in Maryland. In August 2012, the NIA group reported2 that monkeys on a calorierestricted diet not only had lower cancer rates, but also delayed onset of age-related diseases. Calorie restriction did not, however, seem to provide cardiovascular benefits or reduce the incidence of diabetes. The biggest divergence in the two studies relates to lifespan extension. In 2009, the Wisconsin group published3 preliminary evidence that fewer calorie-restricted monkeys died of age-related diseases than the control group. Equivalent results from the NIA don’t show this effect. Researchers on both teams say this difference is probably the result of their study designs — the control diet, in particular. Julie Mattison, an experimental gerontologist and one of the leaders of the NIA study, says that their control monkeys have an especially healthy diet. “We didn’t want to stack the deck with the diet,” she explains. In contrast, the control monkeys in the Wisconsin group are able to eat as much as they want. “This is more like what you see in the human population,” says Ricki Colman, a senior scientist at the Wisconsin Center. It makes sense that a diet providing minimal yet balanced nutrition will appear to be better in comparison with an unhealthy diet than with a healthy one. The Wisconsin and NIA teams plan to pool their data to come up with more definitive results, say Mattison and Colman. The two groups have been collecting microarray data about genes that are up- or downregulated in monkeys on the diet. This should help them to understand the mechanisms underlying ageing, and the beneficial health effects of the diet. However, conclusive results will not be available until after all the monkeys have died, which won’t be for another decade at least. “As a guy who’s done lots of mouse lifespan studies, I can say this requires patience,” says Weindruch.

HUMAN GUINEA-PIGS

While researchers wait for statistical proof of the diet’s effects in primates, some people have elected to go on the diet anyway. CRONies — the label adopted by those on a diet of

A hunger for life: an elderly rhesus monkey fed a calorie-restricted diet (left) appears younger and has better health than normal monkeys of a similar age (right).

Caloric Restriction with Optimal Nutrition — voluntarily eat 30% fewer calories than recommended by the US Department of Agriculture. That can be as low as 1,400 calories a day for men, and 1,120 for women. Fontana, who studies the CRONies, says most of the health benefits seen in animals on the caloric restriction diet also appear in humans. He says that people who started caloric restriction in middle age and stayed with the regimen for eight years have “Caloric a “fantastic” cardiorestriction metabolic profile. He is the most adds that he has seen powerful known subjects in their late intervention in 70s with the blood ageing.” pressure of teenagers. Fontana’s group has published data showing that caloric restriction protects against atherosclerosis4 and leads to greater heart elasticity and heart-rate variability5 — a marker of cardiac health. “These studies are proving, in humans, that it’s possible to completely prevent obesity, diabetes and cardiovascular diseases,” Fontana says. “This is the most powerful thing I’ve seen in my life as a physician.” Fontana is compiling some of the first molecular data from humans on the diet. Using biopsies from fat and muscle tissue, he will examine patterns of gene expression and hormone levels to see if they correlate with those found in animals on the diet. It’s unlikely, however, that large numbers of people will ever sign up to such a curtailed diet. After all, it’s hard enough getting people to limit themselves to only the recommended amount of calories. Fontana says in his experience, US men who are not dieting tend to eat more than

the recommended upper limit of 3,000 calories a day. Even researchers who study caloric restriction rarely practise it — none of those interviewed for this report do. Instead, the value of caloric restriction is that it demonstrates that in principle it is possible to prevent many age-related diseases with one intervention. “We’ve spent the last 80 years trying to treat one disease at a time,” says Rafael de Cabo, an experimental gerontologist at the NIA and the principle investigator of its monkey calorie-restriction study. Learning about the processes underlying ageing, he says, may make it possible to tackle multiple age-related diseases at once. Caloric restriction also provides a useful tool to study the genetics of the ageing process in animal models. Previous studies into the genetics of longevity have focused on breeding short-lived invertebrates such as worms or flies that lack or can’t express certain genes. Comparing the life-spans of these model animals with normal ones reveals whether the missing genes are important in ageing. To figure out which genes are important in caloric restriction researchers put established animal models on the diet; if the diet doesn’t extend lifespan, the missing gene is probably vital to the process. There is one fundamental unknown factor about caloric restriction: how does eating less lead to such drastic longevity and health benefits? Most researchers believe that two pathways are central to the process. One is IGF — the same pathway Bartke found was key to the long life of the Ames dwarf mice. The second is TOR (target of rapamycin), which is involved in both protein translation and intracellular clean-up. A third possible pathway involves the sirtuins — a group of seven

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RISKY RAPAMYCIN Rapamycin has been shown to increase lifespan in mice, perhaps by slowing the progression of cancer. But potential immune-related side effects have driven the search for safer alternatives with a similar mechanism of action.

A slice of uterus from a mouse given rapamycin appears perfectly healthy.

related proteins that caused a lot of excitement after their discovery in 1999. In mice that can’t make the proteins SIRT1 or SIRT3, the effects of caloric restriction are blocked. Some researchers, including Harvard University’s David Sinclair, say this means that these proteins are critical to the diet’s effects, and play an important role in ageing. But the proposed connection between sirtuins and ageing is contentious. The disagreement came to a head in 2010, when a review of the field in Science by Fontana, Valter Longo at the University of Southern California, and Linda Partridge at University College London excluded sirtuins from the molecular mechanisms behind caloric restriction. The review prompted a flood of correspondence to the journal. Researchers including Sinclair and biologist Leonard Guarente at the Massachusetts Institute of Technology in Cambridge were adamant that sirtuins are connected with lifespan extension in mice on caloric restriction — only to have the review’s authors reply that they were not convinced by the data for mammals. Sirtuins are likely to be connected to health and disease, but not ageing, they wrote. Sirtuins are still controversial. “The field is polarized, and that’s OK,” says Brian Kennedy, president of the Buck Institute for Research on Aging in Novato, California. While at MIT, working under Guarente, Kennedy helped to establish the role of sirtuins in ageing. Now, however, he focuses mostly on TOR.

AN ANTI-AGEING PILL?

To mimic the beneficial effects of caloric restriction in a drug, it’s not necessary to know exactly how it works. Large-scale studies can test promising compounds in mammals, looking for any evidence of lifespan extension. Several are already underway, including the NIA’s rigorous Interventions Testing Program (ITP).

A similar slice from a control mouse shows signs of cystic endometrial hyperplasia, a risk factor for cancer.

Started in 2004, the ITP encompasses three study sites where five compounds are screened each year in genetically heterogeneous mice eating food from the same supplier and sleeping on the same bedding. These studies are designed to be able to detect a 10% change in average lifespan with high confidence, even if data from one of the sites proves to be unusable. The greatest recent success is rapamycin, which targets TOR. (The ‘target of rapamycin’ pathway is named after the drug that shuts it down.) A 2009 study6 showed that adding rapamycin to a mouse’s food starting at 600 days old — roughly equivalent to a human age of 60 years — increases lifespan by “I never thought 14% in females and we’d have the 9% in males. potential for These numbers an anti-ageing have convinced many pill.” researchers who study ageing. Arlan Richardson, a biologist at the University of Texas Health Sciences Center in San Antonio, has been doing ageing research for 40 years. He has seen many supposed anti-ageing drugs come and go, including vitamins E and C and melatonin. “I never thought we’d have the potential for an anti-ageing pill,” he says. “Rapamycin is a breakthrough.” His group is testing rapamycin in marmosets. It isn’t clear whether rapamycin extends lifespan by slowing cancer growth, by intervening in the mechanisms of ageing, or both, says Richard Miller, a pathologist at the University of Michigan in Ann Arbor who heads the ITP. He says his Michigan group has been looking into this question, and has unpublished data showing that the drug slows down ageing in multiple normal cell types, suggesting a broad anti-ageing effect. However, rapamycin has several negative side effects that are likely to keep it out of the

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running as a potential anti-ageing drug. It is known to cause cataracts in mice, and in humans it is used as an immunosuppressant to prevent organ-transplant rejection. This suggests that it might have serious immunerelated consequences. “I don’t view the studies we’re doing as a precursor to clinical trials,” says Miller. The next step is to figure out which mechanisms in the TOR pathway are responsible for the positive effects of rapamycin, and then develop a more targeted drug. Understanding mechanisms “helps in terms of scoping out potential side effects”, says David Glass, a specialist in muscle diseases at the Novartis Institutes for Biomedical Research in Cambridge, Massachusetts. Miller’s group is trying to discern these effects by studying rapamycin in various different tissue types in the mice. Other researchers are testing derivatives that may have more targeted effects. Kennedy’s team, for example, is currently working with Biotica Technology, a drug development company in Cambridge, UK, that makes rapamycin derivatives, in the hope of finding a version that works without the side effects. Not all compounds screened by the ITP show as much promise as rapamycin. Resveratrol, a compound found in red wine that targets sirtuins, had been shown to extend lifespan in obese mice fed a high-fat diet — but it failed to produce results in the ITP studies at any dose, says Miller. Many researchers still hope that resveratrol has positive effects. Sirtris Pharmaceuticals, a Cambridge, Massachusettsbased biotechnology company owned by GlaxoSmithKline, is testing resveratrol derivatives in ulcerative colitis and psoriasis. Meanwhile, the ITP will soon publish data on two new life-extending compounds, says Miller. Ageing isn’t a disease, and lifespan extension will be almost impossible to prove in humans. Instead, Glass, Sierra and others hope that research on ageing interventions will change the way we think about disease and drug development, and lead to treatments that tackle multiple age-related diseases at once. Major causes of death worldwide, including cancer and cardiovascular disease, share a common risk factor: age. Tackling one disease at a time isn’t working, says the NIA’s de Cabo. “Ageing is the leading risk factor for all chronic diseases,” he says. “Postpone ageing, and you postpone these diseases.” ■ Katherine Bourzac is a freelance journalist based in San Francisco. 1. Brown-Borg, H. M. et al. Nature 384, 33 (1996). 2. Mattison, J. A. et al. Nature 489, 318–321 (2012). 3. Colman, R. J. et al. Science 325, 201–204 (2009). 4. Fontana, L. et al. Proc. Natl Acad. Sci. USA 101, 6659–6663 (2004). 5. Stein, P. K et al. Aging Cell 11, 644–650 (2012). 6. Harrison, D. E. et al. Nature 460, 392–395 (2009).

JOHN E. WILKINSON/UNIVERSITY OF MICHIGAN MEDICAL SCHOOL

OUTLOOK AGEING

GERIATRIC ORIGINAL ARTICLE

Shorter telomeres are associated with obesity and weight gain in the elderly OT Njajou1, RM Cawthon2, EH Blackburn3, TB Harris4, R Li5, JL Sanders6, AB Newman6, M Nalls7, SR Cummings8 and W-C Hsueh1 for the Health ABC study OBJECTIVE: Obesity and shorter telomeres are commonly associated with elevated risk for age-related diseases and mortality. Whether telomere length (TL) may be associated with obesity or variations in adiposity is not well established. Therefore, we set out to test the hypothesis that TL may be a risk factor for increased adiposity using data from a large population-based cohort study. DESIGN: Levels of adiposity were assessed in six ways (obesity status, body mass index (BMI), the percentage of body fat or % body fat, leptin, visceral and subcutaneous fat mass) in 2721 elderly subjects (42% black and 58% white). Associations between TL measured in leukocytes at baseline and adiposity traits measured at baseline, and three of these traits after 7 years of follow-up were tested using regression models adjusting for important covariates. Additionally, we look at weight changes and relative changes in BMI and % body fat between baseline and follow-up. RESULTS: At baseline, TL was negatively associated with % body fat (ß ¼ 0.35±0.09, P ¼ 0.001) and subcutaneous fat (ß ¼ 2.66±1.07, P ¼ 0.01), and positively associated with leptin after adjusting for % body fat (ß ¼ 0.32±0.14, P ¼ 0.001), but not with obesity, BMI or visceral fat. Prospective analyses showed that longer TL was associated with positive percent change between baseline and 7-year follow-up for both BMI (ß ¼ 0.48±0.20, P ¼ 0.01) and % body fat (ß ¼ 0.42±0.23, P ¼ 0.05). CONCLUSION: Our study suggests that shorter TL may be a risk factor for increased adiposity. Coupling with previous reports on their reversed roles, the relationship between adiposity and TL may be complicated and may warrant more prospective studies. International Journal of Obesity (2012) 36, 1176 -- 1179; doi:10.1038/ijo.2011.196; published online 18 October 2011 Keywords: telomere length; adiposity; telomeres; aging

INTRODUCTION Obesity is a common risk factor for increased morbidity and mortality, including many aging-related pathologies.1 - 3 Obesity has been consistently associated with increased systemic inflammation and oxidative stress,4 - 6 which are also known to lead to shorter telomere length (TL) in cells.7 - 9 Telomeres are DNAprotein complexes that cap the ends of eukaryotic chromosomes. In humans TL varies with age, but, in general, is progressively shortened with age.10 - 12 Like obesity, TL has been associated with oxidative stress, inflammation and many age-related diseases.13 - 15 Whether TL may be associated with obesity as well as the direction of their relationship is unclear. In addition, the biological mechanism underlying these associations may be complicated and even involves feedback loops. A small number of studies have investigated the relationship between TL and obesity. Some crosssectional studies have reported significant associations between TL and obesity16 - 18 but some did not.11,19,20 The only prospective study reported an increased TL in rectal mucosa of people who lost weight.21 All of these studies considered obesity as a proxy of oxidative stress levels, being a risk factor for telomere shortening. Whereas this may be plausible, the reverse, that is, telomere erosion contributes to an increased risk of disorders related to fat metabolism is also possible but has been rarely investigated. 1

We hypothesize that short telomeres are a risk factor for elevated levels of adiposity. The aim of this study was to evaluate the relation between TL, obesity and its related traits in a large bi-racial cohort of elderly individuals. We tested our hypothesis by using more extensive measures of regional and global adiposity in both cross-sectional and prospective analysis. Our ﬁndings provide new insight into the complex relationship between adiposity and TL.

PATIENTS AND METHODS Study participants The Health, Aging, and Body Composition (Health ABC) Study is a prospective cohort study aimed at studying the relation of age-related changes in health and body composition with incident functional limitations in initially well-functioning elderly black and white individuals. At baseline, the cohort included 3075 persons aged 70 - 79 years; 41.6% were black and 51.6% were female. Participants were recruited from Medicare listings in Pittsburgh, Pennsylvania and Memphis, Tennessee between April 1997 and June 1998. Eligibility criteria included: (1) reported ability to walk one quarter mile (0.4 km), climb 10 steps and perform basic activities of daily living without difﬁculty; (2) absence of life-threatening illness; and (3) intention to remain in the current geographic area for at least 3 years. All participants gave informed written consent and the study

Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; 2Department of Human Genetics, University of Utah, Salt Lake City, UT, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA; 4Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, MD, USA; 5Department of Preventive Medicine, University of Tennessee, Memphis, TN, USA; 6Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; 7Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA and 8San Francisco Coordinating Center, California Paciﬁc Medical Center Research Institute, San Francisco, CA, USA. Correspondence: Dr W-C Hsueh, Department of Medicine, School of Medicine, University of California, San Francisco, 513 Parnassus Avenue, HSE 672C, San Francisco, CA 94143-0794, USA. E-mail: wen-chi.hsueh@ucsf.edu Received 18 February 2011; revised 28 June 2011; accepted 29 July 2011; published online 18 October 2011 3

Telomere length and adiposity OT Njajou et al

1177 protocol was approved by the institutional review boards of the clinical sites and the Data Coordinating Center (University of California, San Francisco).

Phenotypic measurements Race, sex, age and other socio-demographic variables were self-reported during the initial clinic visit. Body weight was measured with a standard balance beam scale to the nearest 0.1 kg. Height was measured barefoot using a Harpenden stadiometer (Holtain, UK) to the nearest 0.1 cm. Body mass index (BMI) was calculated as weight divided by height squared (kg m 2). Obesity was defined as having a BMI of X30 kg m 2. A total-body DXA scan was performed to measure the percentage of total body fat (% body fat) using fan-beam technology (Hologic QDR4500A, Waltham, NY, USA). Abdominal computed tomography scans were performed to determine abdominal and subcutaneous fat masses at the L4 -- L5 level. Fat areas were calculated by multiplying the number of pixels of a given tissue type by the pixel area using Interactive Data Language software (ITT Visualization Solutions, Boulder, CO, USA). Visceral fat tissue was manually distinguished from subcutaneous fat tissue by tracing along the facial plane defining the internal abdominal wall and measured in cm2. Total circulating level of serum leptin was measured in duplicate by radioimmuno-assay (Linco Research, St Charles, MO, USA). BMI and % body fat were re-assessed after 7 years of follow-up and the obesity status was also determined. We calculated the percentage of change in these two traits between baseline and follow-up as follows: 100 (baseline value follow-up value)/baseline value. The weightchange status was classified into three categories: weight gain, stable weight and weight loss, if the individuals gained X3 kg, gained or lost within 3 kg, or lost X3 kg, respectively. Average TL in leukocytes (peripheral blood mononucleocytes) was measured using a validated quantitative PCR method.22 This method measures the relative average TLs in genomic DNA by determining the ratio of telomere repeat copy number to single copy gene copy number (T/S ratio) in experimental samples relative to a reference sample. All samples were measured in triplicate, and their mean was used. Results obtained using this method correlate well with those obtained with the traditional terminal restriction fragment length by Southern blot technique (r ¼ 0.7).22 For this study the T/S ratio was converted to a TL in kilo basepairs (kbp) by multiplying the T/S value by the known TL of the reference DNA, 4270 bp. To obtain the TL for the reference DNA, we used the T/S ratios of 64 DNA samples from Utah Caucasians with known mean terminal restriction fragment lengths. The slope of the linear regression line through a plot of T/S ratio (x axis) vs mean terminal restriction fragment length (y axis) is the number of basepairs of telomeric DNA corresponding to a single T/S unit. As the reference DNA has a T/S of 1.0, by definition, this slope is also the average TL of the reference DNA sample, 4270 bp in our case.

Statistical analysis The association between TL and quantitative traits was examined by ﬁtting multiple linear regression models adjusting for age, sex, race, type-2 diabetes status, TL assay plates (modeled as a random factor) and subjects’ recruitment site. In addition, we adjusted for factors commonly known to inﬂuence adiposity, including physical activity and smoking habits. Furthermore, in the analysis of abdominal subcutaneous and visceral fat

Table 1a.

mass, we further adjusted for body height and weight. In the analysis of leptin, the effect of % body fat was also adjusted. Similar modeling approaches were used for analyses of discrete traits (obesity status and weight gain/loss) in logistic regression models. Effect modification by sex and race was evaluated by adding interaction terms to the regression models. Co-linearity of the covariates was checked and determined not to affect inclusion in the models. As race and sex were not found to be effect modifiers, no stratified analysis by these factors was performed. A nominal P-value o0.05 was considered significant. All analyses were performed using SPSS for Windows version 14 (SPSS, Chicago, IL, USA).

RESULTS Measurements of TL at baseline were available in 2721 individuals. Information on adiposity measures was available in all 2721 subjects at baseline and in 1958 subjects after 7 years of follow-up. Tables 1a and b summarize the baseline characteristics of the study population. The mean age at recruitment was similar for men and women. On average, TL was significantly longer in women compared with men, whereas no apparent difference in TL between blacks and whites within the age range of our study population (70 -- 80 years old) was observed. Approximately one quarter (25.8%) and 24.2% of individuals were identified as obese at baseline and after 7 years of follow-up, respectively. The prevalence of obesity was significantly higher in blacks compared with whites. Among blacks, there were significantly more obese women than men whereas among whites, there were more obese men than women. Mean levels of % body fat, leptin and subcutaneous fat were significantly higher in women compared with men in both races. As expected in an elderly population, more people lost rather than gained weight during the 7 years of follow-up. The relative change in BMI and % body fat did not follow any pattern and no significant difference was observed, but in general women had less change in % body fat. As race and sex were not found to be effect modifiers, no stratified analysis by these factors was performed. Using adiposity measures obtained at baseline, TL was not associated with obesity status (odds ratio (OR) ¼ 1.0, 95% confidence interval (CI) ¼ 0.9--1.1) (Table 2). Longer TL was significantly associated with less % body fat (b ¼ 0.35±0.09, P ¼ 0.001), less abdominal subcutaneous fat (b ¼ 2.66±1.07, P ¼ 0.01) and higher levels of fasting leptin after adjusting for the effect of % body fat (b ¼ 0.32±0.14, P ¼ 0.02). At follow-up, TL was also not significantly associated with obesity (OR ¼ 1.0, 95% CI ¼ 0.9 -- 1.1), BMI (b ¼ 0.06±0.09, P ¼ 0.5). The relationship between TL and % body fat became weaker, albeit remained in the same direction (b ¼ 0.14±0.11, P ¼ 0.4) using % body fat measured at follow-up. We further examined whether TL measured at baseline was associated with the change in adiposity traits between baseline and follow-up. TL was significantly associated with the percentage of

Characteristics of the study population by race and sex at baseline

Traits (mean±s.d. or number, %)

Whites

Age, years Telomere length, kbp Obese (n, %) BMI, kg m 2 Percentage total body fat, % Leptin, ng ml 1 Visceral fat, cm2 Subcutaneous fat, cm2 Abbreviation: BMI, body mass index. Men vs women, Po0.001.

& 2012 Macmillan Publishers Limited

Blacks

Male (n ¼ 933)

Female (n ¼ 844)

Male (n ¼ 543)

Female (n ¼ 717)

73.9±2.9 4.6±1.2 180 (19.3%) 27.0±3.7 28.7±4.8 7.6±6.3 170±71 229±84

73.6±2.8 5.0±1.3a 143 (16.9%)a 26.0±4.5 39.0±5.6a 16.1±10.5a 132±63a 308±109a

73.5±2.8 4.6±1.1 138 (25.4%) 27.2±4.4 26.8±5.3 8.0±6.4 130±67 237±100

73.4±3.0 5.0±1.2a 317 (44.2%)a 29.7±5.9a 40.0±6.1a 21.3±11.8a 130±59 372±138a

International Journal of Obesity (2012) 1176 - 1179

Telomere length and adiposity OT Njajou et al

1178 Table 1b.

Characteristics of the study population by race and sex at follow-up

Traits (mean±s.d. or number, %)

Whites

Obese (n, %) BMI, kg m 2 Percentage total body fat, % Weight gain/weight loss (n, %) % Change in BMI % Change in the percentage of body fat

Blacks

Male (n ¼ 933)

Female (n ¼ 844)

Male (n ¼ 543)

Female (n ¼ 717)

137 (19.4%) 27.0±3.7 29.6±5.0 95 (13.8%)/226 (32.9%) 0.2±6.5 3.1±8.9

128 (18.8%)a 26.1±4.7 39.2±5.4a 91 (14%)/189 (29.1%) 0.8±9.1 0.5±7.2a

81 (24.7%) 27.1±4.7 28.0±5.7 50 (16.2%)/108 (35.1%) 0.5±7.7 4.2±12

216 (43.4%)a 29.4±6.2a 39.8±6.2a 72 (15.7%)/167 (36.3%) 1.3±8.7 0.6±8.9a

Abbreviation: BMI, body mass index. a Men vs women, Po0.001.

Table 2.

Association between TL (kbp) and adiposity at baseline and after 7 years of follow-up

Trait BMI % Total body fat Leptin Visceral fat Subcutaneous fat

Baseline

Follow-up

% changea

2715 2614 2459 2669 2648

0.12±0.08 0.35±0.09** 0.32±0.14* 1.05±0.82 2.66±1.07*

1958 1755

0.06±0.09 0.14±0.11 NA NA NA

1958 1755

0.48±0.20* 0.42±0.23* NA NA NA

change in BMI and % total body fat between baseline and followup. In other words, the longer the TL at baseline, the greater increase in BMI (b ¼ 0.48±0.20, P ¼ 0.01) and % body fat (b ¼ 0.42±0.23, P ¼ 0.05) during the 7-year follow-up (Table 2). Figure 1 shows the mean TL in people who gained weight compared with people who lost weight during the follow-up period. We observed that the mean TL was signiﬁcantly longer in individuals who gained weight compared with individuals who lost weight (5.01 vs 4.75 kbp; P ¼ 0.01). In other words, for individuals who lost weight during the follow-up period, their TL was shorter by an average of 260 basepairs. This observation is in concordance with results from analyses of changes in BMI and % body fat.

DISCUSSION AND CONCLUSION In this study, we investigated the relationship between TL and adiposity using both cross-sectional and follow-up data. Our findings suggest that shorter TL may be a risk factor for increased adiposity. TL measured at baseline was significantly associated with several adiposity measures, including % body fat, subcutaneous fat and leptin, but not with obesity, BMI or visceral fat. In our prospective analyses of three available adiposity measures, the direction of their relationship with TL remained the same but the significance was weaker. Furthermore, we also found TL to be associated with positive change in BMI and % body fat after 7 years of follow-up. It might appear somewhat puzzling as to why only certain adiposity measures were associated with TL whereas some others were not. In our cohort composed of elderly individuals, quantitative traits showing stronger association with TL were also in higher correlations with each other (r ¼ 0.66 -- 0.80), whereas all other pair-wise correlation estimates were weaker (p0.56) except for that between BMI and subcutaneous fat (0.76). There were also varying degrees of statistical power in analyses of different traits. Although our sample size was large, we still did not have sufficient power to detect the observed effects for visceral fat and BMI International Journal of Obesity (2012) 1176 - 1179

Mean TL (kbp)

Abbreviations: BMI, body mass index; TL, telomere length. Adiposity related trait ¼ age+sex+race+site+type 2 diabetes+smoking+physical activity+TL. a % change ¼ 100 (follow-up baseline)/baseline. *Po0.05, **Po0.001.

5.1 5 4.9 4.8 4.7 4.6 4.5

P = 0.012

Weight loss (n = 612)

Weight gain (n = 264)

Figure 1. Adjusted mean TL by weight gain or loss at follow-up. Error bars represent one standard error.

(powerE0.6) whereas the power was sufficient for the other three adiposity measures. In addition, one question that comes to mind is whether BMI is a good measure of adiposity in the elderly. Some previous studies have suggested that BMI may not be as informative as a proxy for adiposity or an appropriate measure for defining obesity in older individuals.23,24 This may help explain the lack of association between BMI/obesity and TL. There are a handful of previous investigations on the association between TL and obesity-related traits, all of which considered obesity being a risk factor for shorter TL. Some studies found significant associations between shorter TL and obesity or higher levels of its related quantitative measures,16,17,25,26 whereas others did not.19,20 Yet no study has been conducted in the elderly. Some studies only provided correlation coefficients,16,21,25 which tends to be less informative compared with association testing as it does not evaluate the magnitude of the effects. Weight, BMI, leptin and waist-to-hip ratio were most commonly investigated in earlier studies. BMI was inversely associated with TL in middle-aged individuals.16,18,25,27 We also observed a weak but negative relationship between TL and BMI, and a significantly negative association with % body fat in our cohort of elderly. Using BMIX30 kg m 2 to define obesity, MacEneaney et al.19 reported no difference in TL between normal and overweight/ obese individuals. One study by Valdes et al.16 reported a negative correlation (r ¼ 0.124, Po0.0001) between TL and leptin, whereas another smaller study did not.27 In our analysis of leptin, & 2012 Macmillan Publishers Limited

Telomere length and adiposity OT Njajou et al

1179 when unadjusted for % body fat, we observed a similar result as that reported by Valdes et al.16 However, we considered it informative to evaluate such a relationship independent of body fat, as leptin is produced by fat cells. As expected, we observed that shorter TL was associated with lower leptin levels after adjusting for the effect of % body fat. Leptin plays a role in appetite suppression and one would expect that higher leptin level be associated with better health, thus with longer TL. Two studies examined the effect of waist-to-hip ratio in TL and reported significantly negative association with TL.27,28 We used measurements from computed tomography scans to obtain more precise measures of regional adiposity and observed a significantly negative association of TL with subcutaneous fat, but not with visceral fat. Our study is novel in that we treated TL as a risk factor. It is interesting to see that whereas our study treated TL as a risk factor contrary to previous studies, we could still observe comparable findings mainly on the direction of the associations. There are several advantages of our study. One of the main strengths is the prospective nature of our study with several repeated adiposity measures ascertained 7 years apart. This enabled us to test our hypothesis that TL may be a risk factor for increased adiposity in a prospective fashion. In addition, our study is the first large study to examine the association between TL and adiposity in a non-Caucasian sample, and our findings suggest that race does not appear to affect the association between adiposity and TL. The ages of our study subjects were notably older than previous studies, which provided evidence that the association between TL and adiposity observed in younger populations was also present in the elderly. However, as we did not have TL measured at follow-up, our study could not examine whether adiposity could be a risk factor for telomere shortening in a prospective fashion. Nevertheless, our observations suggest that TL may be considered a risk factor for adiposity. One possible limitation to the current study is that we measured TL in leukocytes as a proxy for TL in other tissues. However, it is well documented that TL in easily accessible tissues such as blood could serve as a surrogate parameter for the relative TL in other tissues.29,30 In summary, we found that shortening of TL was associated with adiposity. However, whether telomere shortening is a cause or consequence of increased adiposity could not be determined based on our data alone. Although it may be possible that TL shortening is a consequence of increased adiposity due to elevated levels of oxidative stress9,29 and other factors, our study suggests their relationship may be more complicated. The possible biological mechanism(s) for these associations deserves further investigations. A better understanding of their relationship may have implication on our effort to reduce obesity burden and to promote healthy aging. CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS This study was supported in part by the Intramural Research Program of the National Institute on Aging, NIH contracts (N01-AG-6-2101, N01-AG-6-2103 and N01-AG-62106) and grants (K01 AG022782 and R01 AG023692). Dr OT Njajou is supported by the Training in Molecular and Genetic Epidemiology of Cancer, National Institutes of Health, National Cancer Institute grant (R25 CA112355).

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International Journal of Obesity (2012) 1176 - 1179

ARTICLE

doi:10.1038/nature11319

Gut microbiota composition correlates with diet and health in the elderly Marcus J. Claesson1,2*, Ian B. Jeffery1,2*, Susana Conde3, Susan E. Power1, Eibhlı´s M. O’Connor1,2, Siobha´n Cusack1, Hugh M. B. Harris1, Mairead Coakley4, Bhuvaneswari Lakshminarayanan4, Orla O’Sullivan4, Gerald F. Fitzgerald1,2, Jennifer Deane1, Michael O’Connor5,6, Norma Harnedy5,6, Kieran O’Connor6,7,8, Denis O’Mahony5,6,8, Douwe van Sinderen1,2, Martina Wallace9, Lorraine Brennan9, Catherine Stanton2,4, Julian R. Marchesi10, Anthony P. Fitzgerald3,11, Fergus Shanahan2,12, Colin Hill1,2, R. Paul Ross2,4 & Paul W. O’Toole1,2

Alterations in intestinal microbiota composition are associated with several chronic conditions, including obesity and inflammatory diseases. The microbiota of older people displays greater inter-individual variation than that of younger adults. Here we show that the faecal microbiota composition from 178 elderly subjects formed groups, correlating with residence location in the community, day-hospital, rehabilitation or in long-term residential care. However, clustering of subjects by diet separated them by the same residence location and microbiota groupings. The separation of microbiota composition significantly correlated with measures of frailty, co-morbidity, nutritional status, markers of inflammation and with metabolites in faecal water. The individual microbiota of people in long-stay care was significantly less diverse than that of community dwellers. Loss of community-associated microbiota correlated with increased frailty. Collectively, the data support a relationship between diet, microbiota and health status, and indicate a role for diet-driven microbiota alterations in varying rates of health decline upon ageing.

The gut microbiota is required for development and for homeostasis in adult life. Compositional changes have been linked with inflammatory and metabolic disorders1, including inflammatory bowel disease2,3, irritable bowel syndrome4,5 and obesity6 in adults. The composition of the human intestinal microbiota is individual-specific at the level of operational taxonomic units (OTUs) and stable over time in healthy adults7. The composition of the intestinal microbiota in older people (.65 years) is extremely variable between individuals8, and differs from the core microbiota and diversity levels of younger adults8,9. A feature of the ageing process is immunosenescence, evidenced by persistent NF-kB-mediated inflammation and loss of naive CD41 T cells10. The microbiota is pivotal for homeostasis in the intestine11, and chronic activation of the innate and adaptive immune system is linked to immunosenescence12. Correlations have previously been made between specific components of the microbiota and proinflammatory cytokine levels, but these did not separate young adults from older people9. Alterations in the microbiota composition have also been associated with frailty13, albeit in a small cohort from a single residence location. Deterioration in dentition, salivary function, digestion and intestinal transit time14 may affect the intestinal microbiota upon ageing. A controllable environmental factor is diet, which has been shown to influence microbiota composition in animal models, in small-scale human studies15–20 and over the longer term21. However, links between diet, microbiota composition and health in large human cohorts are unclear. To test the hypothesis that variation in the intestinal microbiota of older subjects has an impact on immunosenescence and frailty across the community, we determined the faecal microbiota composition in 178 older people. We also collected dietary intake information, and measured a range of physiological, psychological

and immunological parameters. Dietary groupings were associated with separations in the microbiota and health data sets; the healthiest people live in a community setting, eat differently and have a distinct microbiota from those in long-term residential care. Measures of increased inflammation and increased frailty support a diet– microbiota link to these indicators of accelerated ageing, and suggest how dietary adjustments could promote healthier ageing by modulating the gut microbiota.

Microbiota and residence location We previously identified considerable inter-individual variability in the faecal microbiota composition of 161 older people ($65 years), including 43 receiving antibiotics8. To investigate links between diet, environment, health and microbiota, we analysed 178 subjects, nonantibiotic-treated, for whom we also had dietary information, and stratified by community residence setting: (1) community-dwelling, n 5 83; (2) attending an out-patient day hospital, n 5 20; (3) in shortterm (,6 weeks) rehabilitation hospital care, n 5 15; (4) in long-term residential care (long-stay), n 5 60. The mean subject age was 78 ( 6 8 s.d.) years, with a range of 64 to 102 years, and all were of Caucasian (Irish) ethnicity. We included 13 young adults with a mean age of 36 ( 6 6 s.d.) years. We generated 5.4 million sequence reads from 16S rRNA gene V4 amplicons, with an average of 28,099 ( 6 10,891 s.d.) reads per subject. UniFrac b-diversity analysis indicates the extent of similarity between microbial communities22. UniFrac PCoA (principal co-ordinate) analysis of 47,563 OTUs (grouped at 97% sequence identity) indicated a clear separation between community-dwelling and long-stay subjects using both weighted and un-weighted analysis (Fig. 1a, b). Microbiota from the 13 younger controls clustered with

Department of Microbiology, University College Cork, Ireland. 2Alimentary Pharmabiotic Centre, University College Cork, Ireland. 3Department of Statistics, University College Cork, Ireland. 4Teagasc, Moorepark Food Research Centre, Moorepark, Fermoy, Co, Cork, Ireland. 5Cork University Hospital, Wilton, Cork, Ireland. 6St. Finbarr’s Hospital, Douglas Road, Cork, Ireland. 7Mercy University Hospital, Grenville Place, Cork, Ireland. 8South Infirmary, Victoria University Hospital, Cork, Ireland. 9Institute of Food and Health, University College Dublin, Ireland. 10School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AT, UK. 11Department of Epidemiology and Public Health, University College Cork, Ireland. 12Department of Medicine, University College Cork, Ireland. *These authors contributed equally to this work.

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ARTICLE RESEARCH c

PC2 (3.6%)

Colour key

4A 3A

3B 4B

–3 –1 1 3

Bacteroidaceae Rikenellaceae Porphyromonadaceae Prevotellaceae Marinilabiaceae Ruminococcaceae Lachnospiraceae

PC1 (6.7%)

Incertae Sedis XIV

PC3 (5.3%)

Eubacteriaceae Clostridiaceae Erysipelotrichaceae

PC1 (23%)

Relative abundance (%)

100

Alcaligenaceae

Bifidobacteriaceae

Other Unclassified

Figure 1 | Microbiota analysis separates elderly subjects based upon where they live in the community. a, Unweighted and b, weighted UniFrac PCoA of faecal microbiota from 191 subjects. Subject colour coding: green, community; yellow, day hospital; orange, rehabilitation; red, long-stay; and purple, young healthy control subjects. c, Hierarchical Ward-linkage clustering based on the Spearman correlation coefficients of the proportion of OTUs, filtered for OTU subject prevalence of at least 20%. Subjects colour coding as in a. Labelled

clusters in top of panel c (basis for the eight groups in Fig. 4) are highlighted by black squares. OTUs are clustered by the vertical tree, colour-coded by family assignments. Bacteroidetes phylum, blue gradient; Firmicutes, red; Proteobacteria, green; and Actinobacteria, yellow. Only 774 OTUs confidently classified to family level are visualized. The bottom panel shows relative abundance of family-classified microbiota.

community-dwelling subjects. Eighteen other non-UniFrac b-diversity metrics supported microbiota separation by residence location (Supplementary Fig. 1). When we examined OTU abundance, we identified a cluster comprised of the majority of the long-stay subjects, separated from the majority of the community-dwelling and young healthy subjects (Fig. 1c). Family-level microbiota assignments showed that long-stay microbiota had a higher proportion of phylum Bacteroidetes, compared to a higher proportion of phylum Firmicutes and unclassified reads in community-dwelling subjects (Fig. 1c). At genus level, Coprococcus and Roseburia (of the Lachnospiraceae family) were more abundant in the faecal microbiota of community-dwelling subjects (Supplementary Table 1 shows complete list of genera differentially abundant by community location). Genera associated with long-stay subjects included

Parabacteroides, Eubacterium, Anaerotruncus, Lactonifactor and Coprobacillus (Supplementary Table 2). The genera associated with community belonged to fewer families, Lachnospiraceae were the most dominant. Thus, the microbiota composition of an individual segregated depending on where they lived within a single ethnogeographic region, in a homogeneous cohort where confounding effects of climate, culture, nationality and extreme environment were not a factor.

Dietary data (for 168 of the 178 subjects, plus five percutaneous endoscopic gastrostomy (PEG)-fed subjects) was collected through a semiquantitative, 147-item, food frequency questionnaire (FFQ), weighted by 10 consumption frequencies. The data were visualized with correspondence analysis (CoA; Fig. 2a). The first CoA axis Colour key

Unweighted UniFrac

PC2

Concordance of diet and microbiota

–3 –2 –1 0 1 2 3

PC2

Non-mineral/vitamin supp.

PC1

Weighted UniFrac

Vitamin supp. Dairy desserts Herbal tea Porridge Milk Probiotic yoghurt Mineral supp. Spinach Brown rice Wheat-free bread Chicken Dried fruit Mashed potatoes Sweet peppers Oily fish Milk pudding Garlic Wine White fish Fried fish Low-fat milk Boiled potatoes Citrus fruit Onions Processed meat Sugar Cheese Sweets Coffee Tomatoes Jam Plain buiscuits Butter Choc. biscuits White bread

PC1

Figure 2 | Dietary patterns in community location correlate with separations based on microbiota composition. a, Food correspondence analysis. Top panel, FFQ PCA; bottom panel, driving food types. b, Procrustes analysis combining unweighted and weighted UniFrac PCoA of microbiota (non-circle end of lines) with food type PCA (circle-end of lines). c, Four dietary groups (DG1, DG2, DG3 and DG4) revealed through complete linkage clustering using Euclidean distances applied to first eigenvector in

DG1

DG2

DG3

DG4

correspondence analysis. Colour codes in a, and horizontal clustering in b and c, are community location, as per Fig. 1. Food labelling in lower panel in a, and vertical clustering in c: green, fruit and vegetables; orange, grains such as potatoes, cereals and bread; brown, meat; cyan, fish; yellow, dairy products; blue, sweets, cakes and alcohol; grey vitamins, minerals and tea. Only peripheral and most driving foods are labelled; for a complete list see Supplementary Table 2.

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RESEARCH ARTICLE described over 11% of the data set variance and most differences in food consumption between community-dwelling and long-stay subjects. The most discriminating food types were vegetables, fruit and meat, whose consumption changed in a gradual manner along the first eigenvector. Procrustes analysis of the FFQ and the microbiota b-diversity was used to co-visualize the data (Fig. 2b). Separations based on either diet or microbiota co-segregated along the first axis of both data sets (unweighted and weighted UniFrac, Fig. 2b; Monte-Carlo P value , 0.0001). Application of complete linkage clustering and Euclidean distances to the first eigenvector (Fig. 2c) revealed four dietary groups (DGs). DG1 (‘low fat/high fibre’) and DG2 (‘moderate fat/high fibre’) included 98% of the community and day hospital subjects, and DG3 (‘moderate fat/low fibre’) and DG4 (‘high fat/low fibre’) included 83% of the long-stay subjects. For a complete description of dietary groups, see Supplementary Notes and Supplementary Table 3. The healthy food diversity index (HFD23) positively correlated with three microbiota diversity indices (Supplementary Fig. 2a), and all four indices showed significant differences between community and long-stay subjects (Supplementary Fig. 2b), indicating that a healthy, diverse diet promotes a more diverse gut microbiota. Analysing by dietary groups rather than residence location confirmed that both microbiota and diet were most diverse in DG1, and least diverse in DG3 and DG4 (Supplementary Fig. 3). Procrustes analysis similarly showed that the dietary groups were associated with separations in microbiota composition (Supplementary Fig. 3). Furthermore, the microbiota was associated with the duration in long-stay, with residents of more than a year having a microbiota that was furthest separated from community-dwelling subjects (Supplementary Fig. 4). For the majority of these longer-term residents, the diet was different from that in more recently admitted subjects (Supplementary Fig. 4). Examination of duration of care (Supplementary Fig. 4c) showed that diet changed more quickly than the microbiota did; both diet and microbiota moved in the direction away from the community types. After 1 month in long stay, all subjects had a long-stay diet, but it took a year for the microbiota to be clearly the long-stay type. Collectively the data indicate that the composition of the microbiota is determined by the composition and diversity of the diet.

Community setting and faecal metabolome Faecal metabolites correlate with microbiota composition and inflammatory scores in Crohn’s disease24. We therefore performed metabolomic analysis (NMR spectroscopy) of faecal water from 29

subjects, representative (by UniFrac) of three community settings. (Day-hospital subjects grouped closely to community dwellers by microbiota and dietary analysis, and were not included.) A representative NMR profile is presented in Supplementary Fig. 5. Initial PCA (principal component analysis) analysis showed a trend for separation according to community setting (data not shown). Pair-wise statistical models were therefore constructed according to the cluster groups. Valid and robust models were obtained for comparison of NMR spectra from community and long-stay subjects, and community and rehabilitation subjects (Fig. 3). The major metabolites separating community from long-stay subjects were glucose, glycine and lipids (higher levels in long-stay than community subjects), and glutarate and butyrate (higher levels in community subjects). Co-inertia analysis of the genus-level microbiota and metabolome data revealed a significant relationship (P value , 0.01) between the two data sets (Supplementary Fig. 6 and Supplementary Notes). Notwithstanding three longstay subjects, a diagonal separated community from long-stay in both microbiota and metabolome data sets. Other metabolites of interest were acetate, propionate and valerate, which were more abundant in community dwellers (Supplementary Fig. 6). To investigate microbial short-chain fatty acid (SCFA) production further, the frequency of microbial genes for SCFA production was investigated by shotgun metagenomic sequencing. We sequenced 125.9 gigabases (Gb) of bacterial DNA from 27 of the 29 subjects, and assembled contigs with a total length of 2.20 Gb, containing 2.51 million predicted genes (Supplementary Table 4). Consistent with reduced microbiota diversity (Supplementary Fig. 3), there were significantly fewer total genes predicted, and higher N50 values (N50 is the length of the smallest contig that contains the fewest (largest) contigs whose combined length represents at least 50% of the assembly), in the assembled metagenomic data of long-stay subjects compared to rehabilitation or community subjects (Supplementary Fig. 7). The metagenomes were then searched for key microbial genes in butyrate, acetate and propionate production, revealing significantly higher gene counts and coverage for butyrate- and acetate-producing enzymes (BCoAt and ACS, respectively) in community and rehabilitation compared to long-stay subjects (Supplementary Fig. 8 and Supplementary Table 5). There was also significantly higher coverage of the propionaterelated genes (PCoAt) in community compared to long-stay subjects, but the higher gene count was not significant (Supplementary Table 5). These observations are consistent with the association of butyrate, acetate and propionate and the direction of the main split between long-stay and community subjects in the metabolome; candidate b 0.8

a 0.6

0.6

0.4

0.4 0.2

0.0

0.2

0.0

–0.2

–0.4

–0.6 –0.6 –1.2

–0.8 –0.8

–0.4

–0.0 t1

0.4

0.8

1.2

Figure 3 | PLS-DA plots of 1H NMR spectra of faecal water from community, long-stay and rehabilitation subjects. a, Community subjects (green) versus long-stay subjects (red); R2 5 0.517, Q2 5 0.409, twocomponent model. b, Community subjects (green) versus rehabilitation subjects (orange); R2 5 0.427, Q2 5 0.163, two-component model. The ellipses represent the Hotellings T2 with 95% confidence. To confirm the validation of the model, permutation tests (n 5 1,000) were performed. For model a, the 95%

–0.6

–0.4

–0.2

0.0 t1

0.2

0.4

0.6

confidence interval for the misclassification error rate (MER) was (0.43, 0.57). Using the PLS-DA model on the data resulted in an MER of 0.2 which is outside the 95% confidence interval obtained for random permutation tests, thus validating the model. For model b, using permutation testing the 95% confidence interval for the MER was (0.45, 0.55). Using the PLS-DA model on the data resulted in an MER of 0.16 which is outside the 95% confidence interval obtained for random permutation tests.

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ARTICLE RESEARCH genera associated include Ruminococcus and Butyricicoccus for butyrate production (Supplementary Fig. 6), but require validation in larger cohorts. Microbiota function deduced from the metagenome thus corresponded to the measured metabolome for at least one key metabolite that can affect health25.

Microbiota–health correlations Markers of inflammation (serum TNF-a, IL-6 and IL-8 and C-reactive protein (CRP)) had significantly higher levels in long-stay and rehabilitation subjects than in community dwellers (Supplementary Fig. 9). Long-stay subjects also scored poorly for diverse health parameters (Supplementary Tables 6 and 7), including the Charlson comorbidity index (CCI, a robust predictor of survival encompassing 19 medical conditions26), the geriatric depression test (GDT), the Barthel index27, functional independence measure (FIM28), mini-mental state exam (MMSE29) and mini nutritional assessment (MNA30). Correlations between health parameters and microbiota composition were examined using quantile (median) regression tests, adjusted for gender, age and community setting with an additive model (Supplementary Methods). Median regression gives less weight to extreme values than the linear regression based on ordinary least squares and consequently, is less influenced by outliers. The model was adjusted for medications that might influence the tested parameters (Supplementary Table 8). The effect of medication was generally small (Supplementary Table 8). Because ethnicity was exclusively Irish Caucasian it did not require model adjustment. The microbiota composition did not differ for males and females after adjusting for age and location. Significant associations between several health/frailty measurements and the major separations from microbiota UniFrac analysis (Fig. 1) are shown in Table 1. For example, a positive change in

microbiota along the full range of the PC1 axis in the un-weighted UniFrac PCoA for long-stay-only subjects was associated with inflammation (CRP increase of 13.9 mg l21), and other inflammatory markers significantly correlated with microbiota (IL-6 and IL-8, whole cohort). As expected, there was minimal variability amongst community-dwelling subjects, but within the long-stay subjects the most significant associations were related to functional independence (FIM), Barthel index and nutrition (MNA), followed by blood pressure and calf circumference. The latter may be attributable to the influence of diet and/or the microbiota on muscle mass, sarcopaenia31 and thereby on frailty. This was supported by investigation of linkage between frailty and faecal metabolites (probabilistic principal components and covariates analysis; PPCCA32). Thus, the FIM and Barthel indices were significant covariates with the faecal water metabolome (Supplementary Fig. 10) and levels of acetate, butyrate and propionate increased with higher values of both indices (that is, less frail subjects). Among community-dwelling subjects, there was also a strong association between microbial composition and nutrition (MNA) and a weaker link with blood pressure, for which a relationship with the microbiota has previously been established33. There was no correlation between the Bacteroidetes:Firmicutes ratio and body mass index (BMI), although there was a correlation with overall microbiota in long-stay subjects. Measures for the geriatric depression test (GDT) showed significant microbiota association with PCoA axis 2 (Table 1). We detected no significant confounding of microbiota–health correlations due to medications, antibiotic treatment (before the 1-month exclusion window), and diet–health correlations separate from dietary impact on microbiota (Supplementary Notes). Taken together, the major trends in the microbiota that separated healthy community subjects from less healthy long-stay subjects were

Table 1 | Regression tests of associations between clinical measurements and microbiota composition. a Unweighted UniFrac PCoA for all four residence locations Parameter

GDT Diastolic blood pressure Weight CC IL-6 IL-8 TNF-a

PC1

PC2

PC3

RC range

RC s.d.

RC range

RC s.d.

RC range

RC s.d.

–0.42 0.97 –14.6 –3.9 6.71 4.23 1.1

–0.11 0.25 –3.8 –1.01 1.7 1.1 0.28

0.6 0.81 0.033 0.022 0.006 0.43 0.31

–2.7 –10.1 –7.16 –2.9 6.1 13.6 0.62

–0.54 –2.02 –1.43 –0.58 1.22 2.7 0.13

0.037 0.033 0.27 0.19 0.007 0.03 0.72

0.18 –14.2 –1.57 –3.2 2.08 4.06 3.9

0.04 –3.1 –7.2 –0.7 0.45 0.89 0.9

0.84 0.001 0.18 0.047 0.2 0.47376716 0.0005

b Unweighted UniFrac PCoA for community-only subjects Parameter

MNA Diastolic blood pressure GDT

PC1

PC2

PC3

RC range

RC s.d.

RC range

RC s.d.

RC range

RC s.d.

–1.1 –8.4 –0.13

–0.26 –1.98 –0.03

0.29 0.08 0.8

1.9 14.3 –1.5

0.5 3.4 –0.35

0.006 0.035 0.02

0.7 –15.72 –0.8

0.14 –3.26 –0.16

0.59 0.13 0.4

c Unweighted UniFrac PCoA for long-stay-only subjects Parameter

Barthel FIM MMSE MNA BMI CC Diastolic blood pressure Systolic blood pressure Weight IL-8 CRP

PC1

PC2

PC3

RC range

RC s.d.

RC range

RC s.d.

RC range

RC s.d.

–6 –30.8 –12.15 –3.87 –1.2 0.2 19.3 36.5 –3.2 –2.56 13.9

–1.5 –7.8 –3.08 –0.98 –0.31 0.05 4.9 9.3 –0.81 –0.65 3.53

0.004 0.046 0.14 0.23 0.69 0.93 0.015 0.007 0.69 0.78 0.02

–4.8 –33.3 –18.4 –11.2 –5 –6.8 –12.4 –1.57 –12.7 22.31 –3.01

–1.3 –4.7 –4.8 –3 –1.3 –1.77 –3.24 –0.41 –3.3 5.84 –0.8

0.036 0.024 0.009 0.004 0.047 0.0016 0.034 0.83 0.024 0.006 0.27

–0.6 –2.42 3.22 –0.02 –0.24 0.45 –15.4 –2.05 –2.48 1.14 –2.54

–0.15 –0.6 0.8 –0.005 –0.06 0.11 –3.81 –0.51 –0.61 0.28 –0.63

0.71 0.86 0.63 0.99 0.92 0.82 0.007 0.87 0.72 0.93 0.61

Quantile (median) regression tests of associations between clinical measurements and microbiota composition as measured by unweighted UniFrac PCoA across all four residence locations (that is, all subjects (a), community-only subjects (b) and long-stay-only subjects (c)). Column headings are: RC range, regression coefficients scaled to the full variation along each PCoA axis, thus indicating relative magnitude and direction of the health association; RC s.d., regression coefficients scaled to one standard deviation; P, quantile regression P values generated by boot-strap analysis. Significant associations are in bold. An additive model was used to adjust for the effects of age, sex, residence location, relevant medication and the two other principal coordinates. CC, calf circumference; IL, interleukin; MMSE, mini-mental state examination.

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RESEARCH ARTICLE Ruminococcus, Oscillibacter, Alistipes and the central Odoribacter CAG. These CAG relationships are termed Wiggum plots, in which genus abundance can be represented as discs proportional to abundance (Supplementary Fig. 12), to normalized over-abundance (Fig. 4), or to differential over-abundance (Supplementary Fig. 13). In the Wiggum plot corresponding to the whole cohort (Supplementary Fig. 12), the path away from the Ruminococcus CAG towards the Oscillibacter CAG shows a reduced number of genera that make butyrate, and an increased number able to metabolize fermentation products. To simplify the microbiota data for health correlation, we used the eight subject divisions identified by OTU clustering (Fig. 1c). These eight divisions were superimposed on a UniFrac PCoA analysis of the data in Fig. 1a, defining 8 subject groups (Fig. 4, Groups 1A through 4B). These are separation points within a microbiota composition spectrum that represent groups of individuals who have significantly different microbiota as defined by the permutation multivariate analysis of variance (MANOVA) test on unweighted UniFrac data. We then constructed individual Wiggum plots for the microbiota in these 8 groups (Fig. 4). The transition from healthy community-dwelling subjects, to frail long-term care residents, is accompanied by distinctive CAG dominance, most significantly in abundances of Prevotella and Ruminococcus CAGs (community associated CAGs) and Alistipes and Oscillibacter CAGs (long-stay-associated CAGs). Our analysis of Fig. 4 suggested two paths from communityassociated health to long-stay-associated frailty (plot 1A–4A, and 1B–4B), which were examined with reference to health correlations

associated with markers for increased frailty and poorer health, having adjusted for gender, age and location. Because location largely determines diet (Fig. 2), adjusting for location reduces the effect of diet, and as there was also clear evidence for microbiota–health associations within the long-stay setting, we infer that the causal relationship is in a diet–microbiota–health direction.

Microbiota structure and healthy ageing Gut microbiota can be assigned to one of three enterotypes34, driven by Bacteroides, Prevotella and Ruminococcus species. A recent study detected only the Bacteroides and Prevotella enterotypes, which were associated with diets rich in protein and carbohydrate, respectively21. Using those methods, we predicted an optimal number of two clusters using five out of six methodologies, albeit with weaker support than previous studies (Supplementary Fig. 11). In line with a previous study21, the two clusters associated with Bacteroides and Prevotella, but not with Ruminococcus. Although enterotype assignments from the three approaches were very different (Supplementary Fig. 11), community subjects were more frequently of the Prevotella enterotype. To identify patterns in the microbiota, we established co-abundance associations of genera (Supplementary Fig. 12a), and then clustered correlated genera into six co-abundance groups (CAGs) (Supplementary Fig. 12b). These are not alternatives to enterotypes, which are subject-driven and poorly supported in this elderly cohort, but they describe the microbiota structures found across the subject groups in statistically significant co-abundance groups (Supplementary Notes). The dominant genera in these CAGs were Bacteroides, Prevotella,

46 Sporobacter Acidaminobacter Rikenella Oscillibacter Acetivibrio Methanobrevibacter Robinsoniella Anaerophaga Acetitomaculum Desulfovibrio Eubacterium Akkermansia Prevotella Bulleidia Howardella Acidaminococcus Natronincola Cerasicoccus Victivallis Clostridium Alkaliphilus Escherichia/Shigella Hydrogenoanaerobacterium Sporacetigenium Sarcina Butyricimonas Herbaspirillum Peptococcus Lachnobacterium Coprococcus Anaerosporobacter

Catenibacterium Parasporobacterium Oribacterium Paraprevotella Syntrophococcus Acidaminobacter Barnesiella Parasutterella Sporobacter Ethanoligenens Oscillibacter Acetivibrio Robinsoniella Anaerovorax Methanobrevibacter Anaerophaga Mogibacterium Bulleidia Howardella Acidaminococcus Prevotella Natronincola Cerasicoccus Clostridium Rothia Victivallis HydrogenoanaerobacteriumAlkaliphilus Bifidobacterium Sporacetigenium BP Herbaspirillum Lachnobacterium Phascolarctobacterium Peptococcus Coprococcus Pseudobutyrivibrio Streptophyta Butyricicoccus Veillonella Catenibacterium Dialister Parasporobacterium 1A Ruminococcus 4B Oribacterium CC Sutterella Paraprevotella Anaerostipes Weight Actinobacillus

1A 2A 1B

2B 3B

4A Anaerostipes

IL-6

Barnesiella

FIM Barthel

CRP Diastolic BP Systolic BP

4B red

Ethanoligenens

Oscillibacter

GDT

IL-8

4A 3A

Sporobacter

Ethanoligenens Acetivibrio Rikenella Parabacteroides Anaerovorax Methanobrevibacter Papillibacter Akkermansia Cloacibacillus Eubacterium Desulfovibrio Anaerofilum Subdoligranulum Mogibacterium Sedimentibacter Eggerthella Lutispora Tepidibacter Lactonifactor Coprobacillus Anaerotruncus Holdemania Phascolarctobacterium Leuconostoc Weissella

IL-8 IL-6

MNA Diastolic

Acidaminobacter Sporobacter Ethanoligenens Parabacteroides Acetivibrio Rikenella Robinsoniella Anaerovorax Alistipes Methanobrevibacter Papillibacter Cloacibacillus Anaerofilum Akkermansia Acetanaerobacterium Eggerthella Lutispora Tepidibacter Sedimentibacter Clostridium Coprobacillus Victivallis Hespellia Bifidobacterium Anaerotruncus Lactonifactor Butyricimonas Holdemania Peptococcus Odoribacter Weissella

Anaerophaga Anaerofilum Prevotella Howardella Rothia Subdoligranulum Bulleidia Natronincola Clostridium Hydrogenoanaerobacterium Hespellia Actinomyces Sporacetigenium Butyricimonas Odoribacter LeuconostocWeissella Lachnobacterium Coprococcus Streptophyta Pseudobutyrivibrio Dialister Butyricicoccus Blautia Asaccharobacter Catenibacterium Roseburia Parasporobacterium Ruminococcus Veillonella Streptococcus Oribacterium Faecalibacterium Actinobacillus Syntrophococcus Anaerostipes Parasutterella Moryella Barnesiella

Diastolic BP GDT

Bacteroides Sharpea Veillonella Streptococcus

Blautia

Subdoligranulum Sedimentibacter

Bifidobacterium Sarcina Odoribacter Phascolarctobacterium Coprococcus Bacteroides Butyricicoccus Dialister Anaerosporobacter Roseburia Blautia Catenibacterium Ruminococcus Sutterella Faecalibacterium Oribacterium Anaerostipes Actinobacillus Moryella Barnesiella

Akkermansia Eubacterium Anaerofilum Acetanaerobacterium 1B Eggerthella Lutispora Dorea Oxobacter Lactonifactor Lactococcus Coprobacillus Anaerotruncus Escherichia/Shigella Actinomyces Leuconostoc Lactobacillus

Parabacteroides

Oscillibacter Rikenella Parabacteroides Alistipes Eubacterium Mogibacterium Anaerofilum Acetanaerobacterium Rothia Dorea Oxobacter Clostridium Lactococcus Bifidobacterium Coprobacillus Lactonifactor Odoribacter Escherichia/Shigella Lactobacillus Actinomyces Leuconostoc Weissella

Barthel FIM MNA CC MMSE Weight BMI Diastolic BP

Bacteroides Pseudobutyrivibrio Butyricicoccus Sharpea Asaccharobacter Roseburia Blautia Dialister Veillonella Sutterella Streptococcus Oribacterium Actinobacillus Moryella Parasutterella Barnesiella

Figure 4 | Transition in microbiota composition across residence location is mirrored by changes in health indices. The PCoA plots show 8 groups of subjects defined by unweighted UniFrac microbiota analysis of community subjects (left), the whole cohort (centre), and long-stay subjects (right). The main circle shows the Wiggum plots corresponding to the 8 groups from whole-cohort analysis, in which disc sizes indicate genus over-abundance

relative to background. The pie charts show residence location proportions (colour coded as in Fig. 1c) and number of subjects per subject group. Curved arrows indicate transition from health (green) to frailty (red). FIM, functional independence measure; MNA, mini nutritional assessment; GDT, geriatric depression test; CC, calf circumference; CRP, C-reactive protein; IL, interleukin; BP, blood pressure; MMSE, mini-mental state examination.

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ARTICLE RESEARCH in Table 1, plus separate PCoAs for the community-only, and longstay-only subjects. The community and whole-cohort analyses identified an association of depression with axis 2—subjects in the lower path had higher GDT scores. IL-6 and IL-8 levels were higher in the upper path by whole-cohort analysis (Fig. 4 and Supplementary Fig. 14), whereas CRP levels were higher in the lower path in longstay-only analysis. Furthermore, subjects in the lower path had higher systolic and diastolic blood pressure, except in the community-only analysis. This apparent inconsistency is explained by a highly significant change in diastolic blood pressure along the primary PCoA axis in the long-stay subjects, emphasizing the value of a stratified cohort. The subjects in the upper path were older but had higher Barthel and FIM scores than subjects of a similar age in the lower path (Supplementary Fig. 14), consistent with healthier ageing. Movement along PCoA axis 1 of the whole cohort (that is, from community to long-stay, left to right, Fig. 4) is associated with a reduction in abundance of Ruminococcus and Prevotella, and increased abundance of the Oscillibacter CAG, accompanied by calf circumference decrease and weight decrease (Table 1), and increase in IL-6 levels. Moving along axis 1 of the longstay PCA (that is, between the two right-ward arms), there is a reduction in the Oscillibacter CAG, increase in abundance of the Bacteroides CAG, reduced FIM and Barthel indices, and increased levels of CRP (Fig. 4). Consideration of the microbiota–health correlations in the long-stay cohort (Fig. 4), upwards along axis 2, highlights the association with increased frailty, reduced muscle mass, and poorer mental activity moving away from community-type microbiota. Health–microbiota associations were statistically significant, even when regression models were adjusted for location. Although other factors undoubtedly contribute to health decline, and are difficult to completely adjust for in retrospective studies, the most plausible interpretation of our data is that diet shapes the microbiota, which then affects health in older people. Diet-determined differences in microbiota composition may have subtle impacts in young adults in developed countries. These would be difficult to correlate with health parameters, but become far more evident in the elderly who are immunophysiologically compromised. This is supported by the stronger microbiota–health associations evident in the long-stay cohort, and there is now a reasonable case for microbiota-related acceleration of ageing-related health deterioration. An ageing population is now a general feature of western countries35,36 and an emerging phenomenon even among developing countries. The association of the intestinal microbiota of older people with inflammation12 and the clear association between diet and microbiota outlined in this and previous studies20,21,37,38 argue in favour of an approach of modulating the microbiota with dietary interventions designed to promote healthier ageing. Dietary supplements with defined food ingredients that promote particular components of the microbiota may prove useful for maintaining health in older people. On a community basis, microbiota profiling, potentially coupled with metabolomics, offers the potential for biomarker-based identification of individuals at risk for, or undergoing, less-healthy ageing.

Full Methods and any associated references are available in the online version of the paper. Received 10 January; accepted 14 June 2012. Published online 13 July 2012. 1. 2.

3. 4. 5. 6. 7.

9. 10. 11. 12.

13. 14. 15. 16.

17. 18.

19.

20. 21. 22. 23.

24. 25.

26.

METHODS SUMMARY Amplicons of the 16S rRNA gene V4 region were sequenced on a 454 Genome Sequencer FLX Titanium platform. Sequencing reads were quality filtered, OTU clustered, ChimeraSlayer filtered and further analysed using the QIIME pipeline39 and RDP-classifier40. Statistical analysis was performed using Stata and R software packages. Nuclear magnetic resonance (NMR) spectroscopy was performed on a 600 MHz Varian NMR Spectrometer as previously described41. Habitual dietary intake was assessed using a validated, semiquantitative, FFQ, administered by personnel who received standardized training in dietary assessment. FFQ coding, data cleaning and data checks were conducted by a single, trained individual to ensure consistency of data.

27. 28. 29.

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O’Toole, P. W. & Claesson, M. J. Gut microbiota: changes throughout the lifespan from infancy to elderly. Int. Dairy J. 20, 281–291 (2010). Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007). Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010). Kassinen, A. et al. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology 133, 24–33 (2007). Jeffery, I. B. et al. An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut 61, 997–1006 (2012). Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006). Rajilic´-Stojanovic´, M. et al. Development and application of the human intestinal tract chip, a phylogenetic microarray: analysis of universally conserved phylotypes in the abundant microbiota of young and elderly adults. Environ. Microbiol. 11, 1736–1751 (2009). Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108 (Suppl 1), 4586–4591 (2011). Biagi, E. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010). Franceschi, C. et al. Inflamm-aging: an evolutionary perspective on immunosenescence. Ann. NY Acad. Sci. 908, 244–254 (2000). Garrett, W. S., Gordon, J. I. & Glimcher, L. H. Homeostasis and inflammation in the intestine. Cell 140, 859–870 (2010). Guigoz, Y., Dore, J. & Schiffrin, E. J. The inflammatory status of old age can be nurtured from the intestinal environment. Curr. Opin. Clin. Nutr. Metab. Care 11, 13–20 (2008). van Tongeren, S. P., Slaets, J. P., Harmsen, H. J. & Welling, G. W. Fecal microbiota composition and frailty. Appl. Environ. Microbiol. 71, 6438–6442 (2005). Lovat, L. B. Age related changes in gut physiology and nutritional status. Gut 38, 306–309 (1996). Hildebrandt, M. A. et al. High-fat diet determines the composition of the murine gut microbiome independently of obesity. Gastroenterology 137, 1716–1724 (2009). Mai, V., McCrary, Q. M., Sinha, R. & Glei, M. Associations between dietary habits and body mass index with gut microbiota composition and fecal water genotoxicity: an observational study in African American and Caucasian American volunteers. Nutr. J. 8, 49 (2009). Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011). De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010). Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009). Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011). Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011). Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005). Drescher, L. S., Thiele, S. & Mensink, G. B. A new index to measure healthy food diversity better reflects a healthy diet than traditional measures. J. Nutr. 137, 647–651 (2007). Jansson, J. et al. Metabolomics reveals metabolic biomarkers of Crohn’s disease. PLoS ONE 4, e6386 (2009). Pryde, S. E., Duncan, S. H., Hold, G. L., Stewart, C. S. & Flint, H. J. The microbiology of butyrate formation in the human colon. FEMS Microbiol. Lett. 217, 133–139 (2002). de Groot, V., Beckerman, H., Lankhorst, G. J. & Bouter, L. M. How to measure comorbidity. a critical review of available methods. J. Clin. Epidemiol. 56, 221–229 (2003). Mahoney, F. I. & Barthel, D. W. Functional evaluation: the Barthel index. Md. State Med. J. 14, 61–65 (1965). Kidd, D. et al. The functional independence measure: a comparative validity and reliability study. Disabil. Rehabil. 17, 10–14 (1995). Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘‘Mini-mental state’’: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975). Bauer, J. M., Kaiser, M. J., Anthony, P., Guigoz, Y. & Sieber, C. C. The mini nutritional assessment–its history, today’s practice, and future perspectives. Nutr. Clin. Pract. 23, 388–396 (2008). Cruz-Jentoft, A. J. et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 39, 412–423 (2010). Nyamundanda, G., Brennan, L. & Gormley, I. C. Probabilistic principal component analysis for metabolomic data. BMC Bioinformatics 11, 571 (2010). 9 AU G U S T 2 0 1 2 | VO L 4 8 8 | N AT U R E | 1 8 3

RESEARCH ARTICLE 33. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011). 34. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011). 35. European Commission. Population structure and ageing http:// epp.eurostat.ec.europa.eu/statistics_explained/index.php/ Population_structure_and_ageing (2011). 36. Kinsella, K. & He, W. An Aging World: 2008 (US Government Printing Office, 2009). 37. Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011). 38. Walker, A. W. et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 5, 220–230 (2011). 39. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010). 40. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007). 41. O’Sullivan, A., Gibney, M. J. & Brennan, L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am. J. Clin. Nutr. 93, 314–321 (2011). Supplementary Information is linked to the online version of the paper at www.nature.com/nature.

Acknowledgements This work was supported by the Government of Ireland National Development Plan by way of a Department of Agriculture Food and Marine, and Health Research Board FHRI award to the ELDERMET project, as well as by a Science Foundation Ireland award to the Alimentary Pharmabiotic Centre. M.J.C. is funded by a fellowship from the Health Research Board of Ireland. We thank K. O’Donovan and P. Egan for clinical assistance, staff in Cork City and County hospitals for facilitating subject recruitment, S. Wong and B. Clayton for supercomputer access. Author Contributions All authors are members of the ELDERMET consortium (http:// eldermet.ucc.ie). P.W.O.T., E.M.O.C., S.Cu.1 and R.P.R. managed the project; D.v.S., G.F.F., C.S., J.R.M., F.S., C.H., R.P.R. and PWOT designed the analyses; M.J.C., I.B.J., S.Co.3, E.M.O.C., H.M.B.H., M.C., B.L., O.O.S., A.P.F., S.E.P., M.W. and L.B. performed the analyses; J.D. performed DNA extraction and PCR; M.W. and L.B. performed NMR metabolomics; M.O.C., N.H., K.O.C. and D.O.M. performed clinical analyses; M.J.C., I.B.J., S.Co.3, E.M.O.C., L.B., J.R.M., A.P.F., R.P.R., C.H., F.S. and P.W.O.T. wrote the manuscript. Author Information Ampliconsequencedata,andshotgunsequencedata,contigs,genes and annotations, have been deposited in MG-RAST under the Project ID 154 (http:// metagenomics.anl.gov/linkin.cgi?project154). Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Readers are welcome to comment on the online version of this article at www.nature.com/nature. Correspondence and requests for materials should be addressed to P.W.O.T. (pwotoole@ucc.ie).

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ARTICLE RESEARCH METHODS Subject recruitment and sample collection. This study was approved by the Cork Clinical Research Ethics Committee. Subjects older than 64 years were recruited and clinically investigated in two local hospitals, which serve a population base of ,481,000 in the Cork city and county region. They were defined as (1) community-dwelling (community); (2) attending an out-patient day hospital (out-patient); (3) in short-term rehabilitation hospital care (rehabilitation; under 6 weeks stay) or (4) in long-term institutionalized care (long stay; more than 6 weeks). The mean age of the subjects was 78 (6 8) years, with a range of 64 to 102 years. The subjects were all of Irish ethnicity. None of the faecal samples from elderly subjects from our previous study8 were analysed in the current analysis, because we did not have food frequency data for all that cohort. Exclusion criteria were a history of alcohol abuse, participation in an investigational drug evaluation or antibiotic treatment within the previous 30 days, or advanced organic disease. Informed consent was obtained from all subjects or, in cases of cognitive impairment, by next-of-kin in accordance with the local research ethics committee guidelines. Data collected included anthropometric measurements, clinical history and status and medication history. Antibiotic use before the one-month exclusion period was also recorded for each subject. Thirteen younger adult subjects of age ranging 28–46 years, which had not been treated with antibiotics within 30 days, were also recruited by informed consent. Clinical and nutritional data collection. Habitual dietary intake was assessed using a validated, semiquantitative, food frequency questionnaire (FFQ) based upon the SLAN study42. Food properties were determined using the UK Food Standards Agency Nutrient databank43. The mini nutritional assessment (MNA) was used as a screening and assessment tool to identify subjects at risk of malnutrition. Non-fasted blood samples were collected and analysed at Cork University Hospital clinical laboratories. Cytokines were measured using validated, commercial multi-spot microplates (Meso Scale Diagnostics). Anthropometric measures included height, weight, calf and mid-arm circumference. Charlson comorbidity index, mini mental state exam, geriatric depression test, Barthel score and functional independence measures were carried out on all participants. For long-term care, dayhospital and rehabilitation subjects, a research nurse reviewed the medical records for information on disease and current medication usage. Molecular methods and bioinformatics. DNA was extracted from faecal samples, and the V4 region of the 16S rRNA gene was amplified, sequenced and analysed, as described previously44. Briefly, V4 amplicons were sequenced on a 454 Genome Sequencer FLX Titanium platform (Roche Diagnostics and Beckman Coulter Genomics). Raw sequencing reads were quality trimmed using the QIIME pipeline39 according to the following criteria: (1) exact matches to primer sequences and barcode tags, (2) no ambiguous bases (Ns); (3) read-lengths not shorter than 150 base pairs (bp) or longer than 350 bp; (4) the average quality score in a sliding window of 50 bp not to fall below 25. For large-scale assignments into the new Bergey’s bacterial taxonomy45 we used the RDP-classifier version 2.2 with 50% as confidence value threshold. This was based on what was found suitable for V4 amplicons from the human gut environment44. RDP classifications were imported into a MySQL database for efficient storage and advanced querying. The amplicon reads were clustered into OTUs at 97% identity level, and filtered for chimaeric sequences using ChimeraSlayer (http://microbiomeutil.sourceforge. net/#A_CS). Representative sequences (the most abundant) for each OTU were aligned using PyNAST46 before tree building using FastTree47. These phylogenies were combined with absence/presence or abundance information for each OTU to calculate unweighted or weighted UniFrac distances, respectively48. Principal coordinate analysis and Procrustes superimposition were then performed from the UniFrac distances and Food Frequency data. The amplicon sequences were deposited in MG-RAST under the Project ID 154. Metagenomes were sequenced from libraries with 91 bp paired-end Illumina reads and 350 bp insert size and assembled using MetaVelvet49. Samples EM039 and EM173 were sequenced from libraries of 101 bp paired-end Illumina reads with a 500 bp insert size, and subsequently assembled using MIRA50 in hybrid with 551,726 and 665,164 454 Titanium reads, respectively. Protein sequences from enzymes were screened against the assembled metagenomes using TBLASTN with an amino acid identity cut-off of 30% and an alignment length cut-off of 200 bp. We screened the metagenome data for enzymes associated with production of butyrate (butyryl-CoA transferase/acetyl-CoA hydrolase), acetate (acetate-formyltetrahydrofolate synthetase/formate-tetrahydrofolate ligase), and propionate (propionyl-CoA:succinate-CoA transferase/propionate CoAtransferase). Genes were predicted using MetaGene51. NMR analysis of the faecal water metabolome. Faecal water samples were prepared by the addition of 60 ml D2O and 10 ml tri-methylsilyl-2,2,3,3-tetradeuteriopropionate to 540 ml faecal water. Spectra of samples were acquired by using a Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence with 32k data points and 256 scans. Spectra were referenced to TSP at 0.0 p.p.m., phase and baseline

corrected with a line broadening of 0.3 Hz using the processor on Chenomx NMR suite 7 (Chenomx). The spectra were integrated at full resolution for data analysis (PCA, PLS-DA, CIA) with the water region (4–6 p.p.m.) excluded and the data was normalized to the sum of the spectral integral. For PPCCA data analysis, the spectra were integrated into spectral regions (0.01 p.p.m.). Two-dimensional 1 H–1H correlation spectroscopy (COSY) and total correlation spectroscopy (TOCSY) were acquired on a 600 MHz NMR spectrometer. TOCSY spectra were acquired with a spin lock of 65 ms. All two-dimensional data were recorded with standard Varian pulse sequences collecting 1,024 3 128 data points with a sweep width of 9.6 kHz and 32 scans per increment. Statistical methods and metabolome data analysis. Statistical analysis was carried out using R (version 2.13.2) or Stata (version 11) software packages. Kruskal–Wallis and Mann–Whitney tests were used to find significant differences in microbial taxa, clinical and biochemical measures, alpha diversity, and Healthy Food Diversity (HFD). Data were visualized by boxplots. Unless stated otherwise, box plots represented the median and interquartile ranges, with the error bars showing the last datum within 1.5 of the interquartile range of the upper and lower quartiles. We used least square linear regression for comparing alpha diversity and HFD. Median regression52 was used to compare clinical measures and microbiota, while adjusting for age, gender, medications, and when appropriate residence location. For median regression, the median was modelled as a linear function of independent variables. Model parameters are estimated such that they minimised the sum of the absolute differences between observed and predicted values. P values were generated using the wild bootstrap method53 to estimate variance. A linear quantile (median) regression for two variables—a response variable (y) and a predictor variable (x)—is the following: median (y) 5 b0 1 b1x where b0 is the intercept (value when y 5 0) and b1 is the slope (change in median of y for a unit change in x). Together, these parameters describe the association between y and x, where x is a predictor of y. In the case of multiple predictor variables, each one is added to the regression equation and so the equation becomes median (y) 5 b0 1 b1x1 1 b2x2 and now the slope b1 is interpreted as the median change in x1 after adjusting for x2. This can be likened to a laboratory experiment where the specific effect of one variable on another is isolated by holding all other relevant variables constant. Following statistical analysis of the taxonomic classifications, we estimated FDR values using the Benjamini–Hochberg method54 to control for multiple testing. The exception to this were analyses at the genus level where we estimated the proportion of true null hypotheses with the Q-value function unless the estimated p0 was less than or equal to zero55. Statistical analysis of the NMR data was performed using diverse software packages: PCA and PLS-DA analysis was performed in SIMCA-P1 (Umetrics); permutation testing was performed in R and PPCCA was performed in R using the MetabolAnalyze package. The NMR data was Pareto-scaled before data analysis. Assignment of the spectral peaks (Supplementary Table 9) was performed using in-house libraries, statistical correlation analysis and twodimensional NMR spectra (TOCSY and COSY). 42. Harrington, J. et al. Sociodemographic, health and lifestyle predictors of poor diets. Public Health Nutr. 14, 2166–2175 (2011). 43. McCance, R. A. & Widdowson, E. M. The composition of foods 6th edn (Royal Soc. Chemistry, 2002). 44. Claesson, M. J. et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS ONE 4, e6669 (2009). 45. Lilburn, T. G. & Garrity, G. M. Exploring prokaryotic taxonomy. Int. J. Syst. Evol. Microbiol. 54, 7–13 (2004). 46. Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010). 47. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximumlikelihood trees for large alignments. PLoS ONE 5, e9490 (2010). 48. Hamady, M., Lozupone, C. & Knight, R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17–27 (2010). 49. Namiki, T., Hachiya, T., Tanaka, H. & Sakakibara, Y. in ACM Conference on Bioinformatics Computational Biology and Biomedicine (Association for Computing Machinery, 2011). 50. Chevreux, B. et al. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 14, 1147–1159 (2004). 51. Noguchi, H., Park, J. & Takagi, T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34, 5623–5630 (2006). 52. Koenker, R. & Basset, G. Regression quantiles. Econometrica 46, 33–50 (1978). 53. Feng, X. D., He, X. M. & Hu, J. H. Wild bootstrap for quantile regression. Biometrika 98, 995–999 (2011). 54. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995). 55. Dabney, A., Storey, J. D. & Warnes, G. R. qvalue: Q-value estimation for false discovery rate control; R package version 1.24.20 (2010).

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doi:10.1038/nature11432

Impact of caloric restriction on health and survival in rhesus monkeys from the NIA study Julie A. Mattison1, George S. Roth2, T. Mark Beasley3, Edward M. Tilmont1, April M. Handy1,4, Richard L. Herbert5, Dan L. Longo6, David B. Allison7, Jennifer E. Young1, Mark Bryant8, Dennis Barnard9, Walter F. Ward10, Wenbo Qi11, Donald K. Ingram12 & Rafael de Cabo13

Calorie restriction (CR), a reduction of 10–40% in intake of a nutritious diet, is often reported as the most robust non-genetic mechanism to extend lifespan and healthspan. CR is frequently used as a tool to understand mechanisms behind ageing and ageassociated diseases. In addition to and independently of increasing lifespan, CR has been reported to delay or prevent the occurrence of many chronic diseases in a variety of animals. Beneficial effects of CR on outcomes such as immune function1,2, motor coordination3 and resistance to sarcopenia4 in rhesus monkeys have recently been reported. We report here that a CR regimen implemented in young and older age rhesus monkeys at the National Institute on Aging (NIA) has not improved survival outcomes. Our findings contrast with an ongoing study at the Wisconsin National Primate Research Center (WNPRC), which reported improved survival associated with 30% CR initiated in adult rhesus monkeys (7–14 years)5 and a preliminary report with a small number of CR monkeys6. Over the years, both NIA and WNPRC have extensively documented beneficial health effects of CR in these two apparently parallel studies. The implications of the WNPRC findings were important as they extended CR findings beyond the laboratory rodent and to a long-lived primate. Our study suggests a separation between health effects, morbidity and mortality, and similar to what has been shown in rodents7–9, study design, husbandry and diet composition may strongly affect the life-prolonging effect of CR in a long-lived nonhuman primate. For over 20 years, the NIA has studied the effects of CR in long-lived nonhuman primates (NHPs) (Macaca mulatta, average lifespan in captivity is ,27 years and maximum reported lifespan is ,40 years), to verify whether the life-prolonging effects observed in lower organisms also occur in monkeys and thus, might plausibly translate to human ageing10,11. The NIA CR study began in 1987 at the NIH Animal Center12. CR was initiated in monkeys of varying ages to evaluate the impact of age of onset of CR on its biological effects. Study design has been reported elsewhere12,13. Male and female monkeys were enrolled into the study at young, middle and older ages12. Data reported here are grouped as either young-onset (includes juvenile, adolescent and adult) or old-onset monkeys. Supplementary Table 1 reports the current census. Any animal that died underwent a complete necropsy by a boardcertified pathologist. A gross description of the pathology related to each organ was provided along with the probable cause of death and any contributing factors. Survival data were analysed in two ways:

all-cause mortality and age-related deaths; a distinction also reported previously5. In both studies (NIA and WNPRC), age-related survival excluded deaths due to acute conditions that do not have an agerelated increase in risk such as gastrointestinal bloat, anaesthesia, injury or endometriosis. Pathology details are in the Supplementary Information. Old-onset CR monkeys (16–23 years) did not live longer than controls in either the all-cause (Fig. 1a) or age-related survival analysis (there were three cases of non-age related deaths in the CR group and 2 in the control group, graph not shown). In this group, males had significantly longer survival compared to females (P 5 0.0003) and neither sex benefitted from CR. To date, four CR monkeys and one control from the old-onset group have lived beyond 40 years. Although CR has not increased mean or maximum lifespan relative to control, 50% survival for the females is 27.8 years and 35.4 years for the males, exceeding the ,27 year median lifespan previously reported for monkeys in captivity14. These monkeys may have benefitted from excellent husbandry conditions and thus CR started at older ages provided no additional increase in survival. Furthermore, there were no apparent differences in causes of death between the two diet groups. Neoplasia, cardiovascular disease, amyloidosis and general organism deterioration in the oldest animals were equally represented in both diet groups. Old-onset CR was beneficial on several measures of metabolic health and overall function. Both male and female CR monkeys weighed less than the control counterparts, although the diet effect was greater in the males. In longitudinal measures from serum of fasted monkeys, triglycerides, cholesterol and glucose levels increased with age for both male and female controls. However, triglycerides were significantly lower in the CR monkeys (F(1,21) 5 5.76, P 5 0.026) (Fig. 1b), and cholesterol remained significantly lower in the CR males (Fig. 1c) (F(40,774) 5 1.53, P 5 0.02). At the oldest ages, fasting glucose was numerically lower in the CR monkeys (Fig. 1d) and significantly lower in CR males compared to controls (P 5 0.04). On a single measure of plasma-free isoprostane, an indicator of oxidative stress, control males had significantly higher levels than the CR monkeys (23.24 6 1.25 versus 15.93 6 1.97 pg ml21; P 5 0.009). In contrast, we previously reported that old-onset CR may negatively affect immune function15. Current survival curves for the young-onset male and females are shown in Fig. 2a (all-cause mortality) and Fig. 2b (age-related mortality). No significant diet effects are noted in survival between control and CR

Laboratory of Experimental Gerontology, National Institute on Aging, NIH Animal Center, 16701 Elmer School Road Building 103, Dickerson, Maryland 20842, USA. 2GeroScience, 1124 Ridge Road Pylesville, Maryland 21132, USA. 3Department of Biostatistics, Ryals Public Health Bldg 343C University of Alabama at Birmingham, 1530 3rd Avenue S, Birmingham, Alabama 35294, USA. 4SoBran, Inc., 4000 Blackburn Lane, Suite 100, Burtonsville, Maryland 20866, USA. 5National Institute of Allergy and Infectious Disease, NIH Animal Center, 16701 Elmer School Road, Building 102, Dickerson, Maryland 20842, USA. 6Laboratory of Molecular Biology and Immunology, National Institute on Aging, NIH, 251 Bayview Boulevard Room 08C228, Baltimore, Maryland 21224, USA. 7Office of Energetics, University of Alabama at Birmingham, 1665 University Boulevard, RPH 140J Birmingham, Alabama 35294, USA. 8Office of the Director, Diagnostic and Research Services Branch, NIH, Bldg 28A, Room 114, 28 Service Road West, Bethesda, Maryland 20814, USA. 9Office of the Director, Diagnostic and Research Services Branch, NIH, Building 14A, Room 119A, 14 Service Road West, Bethesda, Maryland 20814, USA. 10Department of Physiology/Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, USA. 11 Department of Cellular and Structural Biology/ Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, USA. 12Nutritional Neuroscience and Aging Laboratory, Pennington Biomedical Research Center, Louisiana State University, 6400 Perkins Road, Baton Rouge, Louisiana 70808, USA. 13Laboratory of Experimental Gerontology, National Institute on Aging, NIH, 251 Bayview Boulevard Suite 100, Baltimore, Maryland 21224, USA. 3 1 8 | N AT U R E | VO L 4 8 9 | 1 3 S E P T E M B E R 2 0 1 2

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Figure 1 | Survival curve and triglycerides, cholesterol and glucose levels for old-onset monkeys. a, Kaplan–Meier survival curve for all-cause mortality for old-onset monkeys. All-cause mortality was analysed using Cox regression with age of onset, sex and diet as predictors. The effect of diet was not significant (P 5 0.934) and sex was the only significant predictor (P 5 0.003). Open circles represent alive monkeys. b, Fasting serum triglycerides (mg dl21) predicted from age-dependent individual-specific trajectories for old-onset monkeys. Triglyceride levels increased with age (F(16,162) 5 2.12, P 5 0.0096) and CR monkeys had significantly lower levels than control (F(1,21) 5 5.76, P 5 0.026). F, female; M, male. Overall triglyceride trajectories were based on 243 observations for 34 monkeys (50 observations for 8 control-F; 81 for 10 controlM; 32 for 7 CR-F; 80 for 9 CR-M). Age breakdowns for all figures are in the supplementary material. c, Cholesterol predicted from age-dependent individual-specific trajectories for old-onset monkeys. Cholesterol levels increased with age (F(53,774) 5 1.54, P 5 0.009), and male monkeys had significantly lower levels than females (F(1,24) 5 23.60, P , 0.0001). A significant three-way diet–sex–age interaction (F(40,774) 5 1.53, P 5 0.02) indicated that cholesterol levels increased with age for control males whereas CR males tended to have a slight reduction in cholesterol. Thus, at older ages (.30 years), CR male monkeys have significantly lower cholesterol levels compared to controls. Overall cholesterol trajectories were based on 994 observations for 28 animals (204 for 7 control-F; 301 for 7 control-M; 134 for 5 CR-F; and 355 for 9 CR-Male). d, Fasting serum glucose (mg dl21) levels predicted from age-dependent individual-specific trajectories for old-onset monkeys. Five glucose measurements above 100 mg dl21 for one diabetic control-M were omitted to remove the influence of these outliers on the analyses and graphs. There were significant changes in glucose over time (F(20,285) 5 10.48, P , 0.0001) and males and females were significantly different in the trends over time (F(18,285) 5 3.58, P , 0.0001) with males having increases in glucose levels over time, whereas the glucose levels of the females slightly decreased. The overall CR difference was not significant, F(1,22) 5 1.18, P 5 0.288, and the CR differences in trend over time were not significant, F(20,285) 5 1.23, 5 0.2259. Additional analyses stratified by sex conditions showed that control males had significantly higher glucose levels compared to CR males, F(1, 14) 5 5.27, P 5 0.04. Overall glucose trajectories were based on 387 observations for 34 monkeys (79 observations for 8 control-F; 131 for 10 control-M; 48 for 7 CR-F; 129 for 9 CR-M).

monkeys for either analysis. Statistical controls are described in Methods. Of the original 86 monkeys in the young-onset cohorts, 24% (11/46) of the control animals and 20% (8/40) of the CR group died of age-related causes. The NIA findings contrast with the adultonset study at WNPRC that demonstrated a beneficial CR effect in which 37% of the control monkeys had died from age-related causes compared to only 13% in the CR group. When accounting for all deaths, the trend persisted with 9 control and 13 CR animals dying of non-age related causes. Survival probabilities for all NIA age groups combined are shown in Supplementary Fig. 1a, b. Considering that just less than 50% of young monkeys are still alive, these data do not represent final lifespan curves in this study. On

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Figure 2 | Survival curves and glucose and triglycerides levels for youngonset monkeys. a, b, Kaplan–Meier survival curves for all-cause (a) and agerelated mortality (b) for young-onset monkeys. Both were analysed using Cox regression with age of onset, origin, sex and diet (P 5 0.255 and P 5 0.975, respectively) as predictors with none of these factors being statistically significant. Open circles represent monkeys that are still alive and non-age related deaths in b. c, Fasting serum glucose (mg dl21) levels predicted from age-dependent individual-specific trajectories for young-onset monkeys. 14 glucose measurements above 100 mg dl21 in diabetic monkeys (7 observations for 3 control-M; 5 for 2 CR-M; 2 for 1 control-F) were omitted to remove the influence of these outliers on the analyses and graphs. There were significant changes in glucose over time (F(18,1,112) 5 11.24, P , 0.0001), and males and females were significantly different in the trends over time (F(18,1,112) 5 1.98, P 5 0.0088) with males having a larger increase in glucose levels over time. There was no significant difference due to diet group. Overall glucose trajectories were based on 1,260 observations for 81 monkeys (346 observations for 23 control-F; 350 for 20 control-M; 281 for 20 CR-F; 283 for 18 CR-M). d, Fasting serum triglycerides (mg dl21) predicted from age-dependent individual-specific trajectories for young-onset monkeys. There were significant changes in triglycerides over time (F(14,843) 5 17.59, P , 0.0001) and males and females were significantly different in the trends over time (F(14,843) 5 5.36, P , 0.0001). Furthermore, there was a diet–sex interaction indicating that the overall effect of CR on triglycerides was significantly different for male and female monkeys (F(1,68) 5 5.07, P 5 0.0276). Specifically, CR males had lower triglycerides than control males. By contrast, CR females had higher triglyceride levels than control females. Overall triglyceride trajectories were based on 973 observations for 81 monkeys (266 observations for 23 control-F; 280 for 20 control-M; 213 for 20 CR-F; 214 for 18 CR-M).

the basis of lifespan projections using the hazard function16, most animals are projected to be dead 10 years from now and the estimated probability statistics indicates a likelihood of less than 0.1% chance that the overall survival outcome would favour the CR group. The probability that a significantly different effect on mean survival will emerge in the next 5–10 years of the study is very low; however, a potential effect on maximum lifespan cannot be ruled out. As there is a clear difference in CR effect on mortality between the colonies at NIA and WNPRC, further comparisons of these two longitudinal studies are warranted and planned. In an estimate of NIA’s current data (as of 1 December 2011) to the published WNPRC data summarized as of 22 February 2008 and reported in ref. 5, NIA monkeys, both control and CR, may have a lifespan advantage comparable to the WNPRC CR monkeys. Although they eat less (Supplementary Table 2) and weigh less13, young-onset CR monkeys lack many of the expected CR benefits. Fasting serum glucose levels were not significantly lower in the CR monkeys compared to control (Fig. 2c), and only the CR males had somewhat lower triglycerides compared to respective controls (P 5 0.051) (Fig. 2d). However, in a ligature-induced model of 1 3 S E P T E M B E R 2 0 1 2 | VO L 4 8 9 | N AT U R E | 3 1 9

RESEARCH LETTER inflammation in the oral cavity1, we have shown an improved immune response in young-onset CR monkeys and beneficial effects in T cells isolated from adolescent-onset males2. The incidence of cancer was markedly improved in young-onset CR monkeys (P 5 0.028 compared to controls); in fact, neoplasia has not been identified in any monkey from this group (Fig. 3a). In contrast, five of the six cases in young-onset control monkeys were considered the cause of death with a mean age at diagnosis of 22.8 6 1.7 years. Glucoregulatory function was also improved in CR monkeys (Fig. 3a). However, two cases of diabetes have been diagnosed in CR monkeys; thus, the prevention of obesity did not prevent the occurrence of insulin-dependent diabetes and further investigation of the aetiology of such cases is of interest. Interestingly, CR did not reduce the incidence of cardiovascular disease as was reported in the WNPRC colony. Our findings are based on tissue pathology because these diagnoses were identified after death. An analysis of first occurrence of age-related disease was done on the NIA monkeys using the same disease criteria as defined by the WNPRC study. These conditions included: cancer, diabetes, arthritis, diverticulosis and cardiovascular disease. Although age-related diseases a

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Figure 3 | Incidence and estimated proportions of age-related diseases. a, Incidence of three major age-related conditions. Age at diagnosis is represented with a ‘Y’ for young-onset monkeys and ‘O’ for old-onset monkeys. Animals may be represented more than once if multiple conditions existed. b, Estimated proportions for the first occurrence of any age-related disease in each monkey from the young-onset age group (males and females combined) statistically controlling for sex and sex–CR interaction. These conditions included: cancer, diabetes, arthritis, diverticulosis and cardiovascular disease. The difference between control and CR is not statistically significant, P 5 0.06. Old-onset monkeys are not represented.

were detected in control monkeys at an earlier age than in CR monkeys, the incident curves were not significantly different (P 5 0.06) (Fig. 3b). Considering that these two projects maintain high quality veterinary support in comparable experimental settings, study the same species of primates, and test the same intervention, what could account for the differences in survival outcome? A notable difference between the two studies is diet composition. The NIA-1-87 formulation (Labdiet, PMI Nutrition International) has a natural ingredient base whereas WNPRC diet is purified (Harlan Teklad). Although natural ingredient diets risk having some variation between batches, they contain components that may have an impact on health such as phytochemicals, ultra-trace minerals and other unidentified elements17. In purified diets, each ingredient supplies a specific nutrient and each required mineral and vitamin is added as a separate component. Nutrient sources were also different. Protein was derived from wheat, corn, soybean, fish and alfalfa meal for the NIA diet, whereas the WNPRC diet protein source was lactalbumin. The NIA diet also contained flavonoids, known for their antioxidant activity, and fat from soy oil and the oils from the other natural ingredients (that is, corn, wheat and fish). Fish meal contains approximately 8–12% fat and is rich in omega-3 fatty acids. The WNPRC study dietary fat was derived from corn oil. Carbohydrate content was also notably different; although both diets had 57–61% carbohydrate by weight, the NIA study diet was comprised primarily of ground wheat and corn, whereas the WNPRC study diet contained corn starch and sucrose. Indeed, the WNPRC diet was 28.5% sucrose, whereas the NIA study diet was only 3.9% sucrose. This latter point may be particularly important as a diet high in sucrose may contribute to the incidence of type II diabetes18,19. The NIA and WNPRC studies also approached vitamin and mineral supplementation differently. The NIA study used one diet for both CR and control monkeys, which was supplemented with an additional 40% of the daily-recommended allowance to insure adequate nutrition for the CR monkeys. Thus, the NIA diet formulation supersupplemented the control monkeys. The WNPRC study fed two different diets and only the CR monkeys were supplemented. Another important difference in study design was that the NIA study control monkeys were not truly fed ad libitum, unlike the WNPRC study. The regulated portioning of food for the NIA control monkeys may be a slight restriction and thus, largely prevented obesity. It has been reported that 10% CR increased lifespan in rats compared to ad libitum, even more than 25 and 40% CR20. The NIA control monkeys may experience survival benefits from this slight restriction. Calorie restriction effectively lowered body weight in the NIA and WNPRC monkeys (Supplementary Table 2)13,21. However, WNPRC monkeys generally weighed more than corresponding NIA monkeys. For example, at 17 years of age, WNPRC males weighed approximately 12% more than corresponding NIA males and the difference was approximately 18% for the females. Thus, the NIA monkeys may be in an optimal weight range. NIA monkeys originated from both China and India, and have greater genetic diversity compared to the strictly Indian colony at WNPRC. In rodent studies, genetic differences have affected the survival outcome in CR studies, even shortening it in some recombinant inbred mouse strains22. In genetically heterogeneous wild-caught mice, although hormonal and weight loss effects were consistent with CR, there was no overall mean longevity effect23. It is apparent that the effect of CR is not straightforward, and genetic differences may have a larger role than has been considered to date. A final analysis which includes all monkeys and controls for genetic origin can address this confounding variable. Lastly, as in rodent studies24, the age of onset of the CR regimen for the two studies could certainly impact survival outcome as it has other measures. CR initiated in the youngest male monkeys delayed maturation25 and slowed skeletal growth26. Additionally, only the immune response of the adolescent males was improved by CR15.

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LETTER RESEARCH In the first randomized trial in humans, 6 months of CR improved several biomarkers of ageing and improved cardiovascular health, suggesting a reduction in risk of age-related disease. However, a lifespan study in humans is improbable27. Even a self-imposed CR regimen in lean individuals improved several metabolic, inflammatory and cardiovascular measures28. Current findings show that in nonhuman and human primates, CR evokes very similar metabolic, hormonal and physiological changes that are linked to longevity in CR rodents28. It will be valuable to continue to compare findings from ongoing monkey CR studies to dissect the mechanisms behind the improvement in health that occurred with and without significant effects on survival.

METHODS SUMMARY Approval. This study was approved by the Animal Care and Use Committee of the NIA, NIH and was conducted in an AALAC-accredited facility. Diagnostics. In live animals, diagnostic evaluations were made on the basis of clinical presentation. Radiographs confirmed conditions of osteoarthritis; endoscopic evaluation of diverticulosis revealed hernia-like outpouching in the mucosa of the descending colon with trapped faecal material in the diverticula; diabetes was confirmed by consistent elevated fasting glucose and glucose response during an intravenous glucose tolerance test; surgical biopsy or removal of tumours confirmed neoplasia. Cardiovascular abnormalities such as myofibre loss and fibrosis were diagnosed at necropsy as well as death due to acute congestive heart failure. Blood sampling. For longitudinal measures, blood samples were obtained under ketamine (7–10 mg kg21, intramuscular) or Telazol (3.5 mg kg21, intramuscular) anaesthesia following an overnight fast. Serum samples were stored at 280 uC until analysed. Plasma free isoprostane samples were collected in 2005 and measured according to the description in ref. 29. Full Methods and any associated references are available in the online version of the paper. Received 27 October 2011; accepted 23 July 2012. Published online 29 August 2012. 1. 2. 3. 4. 5. 6. 7. 8. 9.

Branch-Mays, G. L. et al. The effects of a calorie-reduced diet on periodontal inflammation and disease in a non-human primate model. J. Periodontol. 79, 1184–1191 (2008). Messaoudi, I. et al. Delay of T cell senescence by caloric restriction in aged longlived nonhuman primates. Proc. Natl Acad. Sci. USA 103, 19448–19453 (2006). Kastman, E. K. et al. A calorie-restricted diet decreases brain iron accumulation and preserves motor performance in old rhesus monkeys. J. Neurosci. 30, 7940–7947 (2010). Colman, R. J., Beasley, T. M., Allison, D. B. & Weindruch, R. Attenuation of sarcopenia by dietary restriction in rhesus monkeys. J. Gerontol. A 63, 556–559 (2008). Colman, R. J. et al. Caloric restriction delays disease onset and mortality in rhesus monkeys. Science 325, 201–204 (2009). Bodkin, N. L., Alexander, T. M., Ortmeyer, H. K., Johnson, E. & Hansen, B. C. Mortality and morbidity in laboratory-maintained Rhesus monkeys and effects of long-term dietary restriction. J. Gerontol. A 58, B212–B219 (2003). Forster, M. J., Morris, P. & Sohal, R. S. Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J. 17, 690–692 (2003). Murtagh-Mark, C. M., Reiser, K. M., Harris, R. Jr & McDonald, R. B. Source of dietary carbohydrate affects life span of Fischer 344 rats independent of caloric restriction. J. Gerontol. A 50A, B148–B154 (1995). Swindell, W. R. Dietary restriction in rats and mice: a meta-analysis and review of the evidence for genotype-dependent effects on lifespan. Ageing Res. Rev. 11, 254–270 (2012).

10. Messaoudi, I. et al. in Calorie Restriction, Aging, and Longevity (eds Everitt, A. V., Rattan, S., Le Couteur, D. & de Cabo, R.) 55–78 (Springer, 2010). 11. Roth, G. S. et al. Aging in rhesus monkeys: relevance to human health interventions. Science 305, 1423–1426 (2004). 12. Ingram, D. K. et al. Dietary restriction and aging: the initiation of a primate study. J. Gerontol. 45, B148–B163 (1990). 13. Mattison, J. A. et al. Age-related decline in caloric intake and motivation for food in rhesus monkeys. Neurobiol. Aging 26, 1117–1127 (2005). 14. Colman, R. J. & Kemnitz, J. W. in Methods in Aging Research (ed. Yu, B. P.) 249–267 (CRC, 1998). 15. Messaoudi, I. et al. Optimal window of caloric restriction onset limits its beneficial impact on T-cell senescence in primates. Aging Cell 7, 908–919 (2008). 16. Allison, P. D. Survival Analysis Using SAS: A Practical Guide (SAS Institute, 1995). 17. Nadon, N. L. Exploiting the rodent model for studies on the pharmacology of lifespan extension. Aging Cell 5, 9–15 (2006). 18. Lomba, A. et al. A high-sucrose isocaloric pair-fed model induces obesity and impairs NDUFB6 gene function in rat adipose tissue. J. Nutrigenet. Nutrigenomics 2, 267–272 (2009). 19. Roncal-Jimenez, C. A. et al. Sucrose induces fatty liver and pancreatic inflammation in male breeder rats independent of excess energy intake. Metabolism 60, 1259–1270 (2011). 20. Duffy, P. H. et al. The effects of different levels of dietary restriction on aging and survival in the Sprague-Dawley rat: implications for chronic studies. Aging (Milano) 13, 263–272 (2001). 21. Raman, A. et al. Influences of calorie restriction and age on energy expenditure in the rhesus monkey. Am. J. Physiol. Endocrinol. Metab. 292, E101–E106 (2007). 22. Liao, C. Y., Rikke, B. A., Johnson, T. E., Diaz, V. & Nelson, J. F. Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010). 23. Harper, J. M., Leathers, C. W. & Austad, S. N. Does caloric restriction extend life in wild mice? Aging Cell 5, 441–449 (2006). 24. Speakman, J. R. & Hambly, C. Starving for life: what animal studies can and cannot tell us about the use of caloric restriction to prolong human lifespan. J. Nutr. 137, 1078–1086 (2007). 25. Roth, G. S. et al. Age-related changes in androgen levels of rhesus monkeys subjected to diet restriction. Endocr. J. 1, 227–234 (1993). 26. Lane, M. A. et al. Aging and food restriction alter some indices of bone metabolism in male rhesus monkeys (Macaca mulatta). J. Nutr. 125, 1600–1610 (1995). 27. Redman, L. M. & Ravussin, E. Caloric restriction in humans: impact on physiological, psychological, and behavioral outcomes. Antioxid. Redox Signal. 14, 275–287 (2011). 28. Omodei, D. & Fontana, L. Calorie restriction and prevention of age-associated chronic disease. FEBS Lett. 585, 1537–1542 (2011). 29. Ward, W. F. et al. Effects of age and caloric restriction on lipid peroxidation: measurement of oxidative stress by F2-isoprostane levels. J. Gerontol. A 60, 847–851 (2005). Supplementary Information is available in the online version of the paper. Acknowledgements We thank the animal care staff and technicians, both past and present, especially J. Travis and M. Szarowicz; K. Vaughan for her editorial help; and the many collaborators that have contributed to this project. This research was supported by the Intramural Research Program of the NIH, National Institute on Aging. Author Contributions G.S.R. and D.K.I. jointly conceived the original study and implemented it. J.A.M., R.d.C., D.K.I. and G.S.R. designed experiments, analysed and discussed data. J.A.M., R.d.C. and D.K.I. wrote the paper. T.M.B. and D.B.A. conducted statistical analyses and consultation. E.M.T., A.M.H. and J.E.Y. provided many years of technical support, data collection and supervision. R.L.H. provided veterinary support. D.L.L. assisted with data interpretation, discussion and paper edits. M.B. performed pathology assessments. D.B. assisted with initial diet formulation and all diet analyses and comparisons. W.F.W. and W.Q. designed and performed the isoprostane assays. Author Information Reprints and permissions information is available at www.nature.com/reprints. Readers are welcome to comment on the online version of the paper. The authors declare competing financial interests: details accompany the full-text HTML version of the paper at www.nature.com. Correspondence and requests for materials should be addressed to J.A.M. (mattisonj@mail.nih.gov), R.d.C. (deCaboRa@grc.nia.nih.gov) or D.K.I. (Donald.Ingram@pbrc.edu).

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RESEARCH LETTER METHODS Animals. With the exception of six old-onset males, all monkeys had known birthdates. Estimated ages were assigned to these six based on dental archives and historical records. No monkey had been used in invasive experiments before procurement. After procurement, monkeys were initiated on the study after required quarantine. Food intake was considered ad libitum during this time. Husbandry has been described previously13. NIA monkeys were fed a natural ingredient diet containing 56.9% carbohydrate, 17.3% protein and 5% fat. Statistical methods. A Fisher’s exact test was used to compare the incidence of neoplasia in the young-onset cohort. Analyses of age-associated diseases and mortality included all animals with known diagnoses or cause of death before 1 December 2011. Twenty of the 26 adult-onset females were obtained from a military research facility, and 19 of these monkeys developed severe and rapidly progressing endometriosis. The twentieth monkey of this group died at the age of 12 years from renal necrosis. It seemed apparent that this cohort was differentially affected in terms of long-term health, and thus, an indicator variable that designated the source of this monkey group as ‘Aberdeen’ was created and was included in most analyses to control statistically for the effects of these animals on the outcomes of interest. To determine the effect of CR on the onset of age-associated diseases (morbidity) and mortality, a Cox proportional hazard16 regressions with sex and caloric restriction (CR), a sex–CR interaction term, and a covariate to adjust for whether the animal was obtained from the Aberdeen site as predictors were used to estimate the survival and hazard functions. The proportional hazards (PH) assumption was tested by fitting a non-PH Cox regression with a CR–time interaction, which was not significant for either analysis, and thus, PH models were considered valid. Animals that died of non-age-related causes (for example, death from anaesthesia, gastrointestinal bloat) were censored in both the mortality and morbidity analyses. Their age at death was used as the time variable in the Cox regressions. For the morbidity analysis, the age at which the animal experienced its first age-related diagnosis was used as the time variable in the Cox regression. Animals that received a non-age-related diagnosis were censored and their current age was used as the time variable. Animals that died of an age-related cause without ever receiving an age-related diagnosis were not censored. The designation of ‘age-related’ was based on the same rationale and list of conditions as reported by WNPRC. Death was considered as their first age-related diagnosis and their age at death was used as the time variable. All analyses were performed in SAS PROC PHREG and likelihood ratio tests were computed to assess statistical significance. A linear mixed model30 approach was used to estimate longitudinal trends in the data while accounting for the dependency in the data due to multiple observations per subject. SAS PROC MIXED was used to estimate the trends and group differences among the repeatedly measured outcomes (for example, body weight, glucose, cholesterol and triglycerides across the years of measurement. The effects of CR on overall outcome levels and differences in longitudinal trends were tested by including diet main effect and diet–year interaction terms in the model. Male and female monkeys were analysed together and sex main effects and sex–diet and

–year interactions were also included in the models. The young- and old-onset groups were analysed separately. Age at the first measurement (that is, starting age) was used as a covariate to control for differences in age among the animals within a given year of measurement and a lag-1 autoregressive process over time was assumed. For the young-onset group a covariate to adjust for whether they were obtained from the Aberdeen site was added. Outliers were screened and removed. Specifically, a few young animals had glucose levels substantially above 200 mg dl21. Also one old control male that was eventually diagnosed with diabetes had extremely high triglyceride levels ranging from 342 to 1,314 mg dl21 and these values influenced the significance of some effects. Briefly, the linear mixed model approach estimates a growth trajectory for each individual animal (for example, individual change in weight over time), adjusting for covariates. Then a weighted composite of these individual trajectories is computed to show the average trend over the age of the animals in a particular group (for example, average weight of animals at varying ages for CR-M). The weights for these composites are based on the number of observations each animal contributes to the data. For example, animals that live longer will contribute more data, and therefore will get larger weights. To smooth the trends for plotting graphs, the predicted values from each individual trajectory was averaged, and loess trend lines were constructed. Competing risk. The analyses in this paper as well as in ref. 5 distinguished between age-related and all-cause mortality. To address the issue that the nonage-related deaths are associated to CR, a competing risks Cox proportional hazard regression models16 were conducted separately for the young-onset group (9 control and 13 CR non-age-related deaths) and old-onset (2 control and 3 CR non-age-related deaths). Briefly, a competing risks model treats the events as if age-related and non-age-related deaths are mutually exclusive and compared to neither event occurring (that is, animals still alive are censored). These events have competing risks in that if an animal dies from a non-age-related cause they are no longer at-risk for an age-related death (and vice versa). For the old-onset animals, age at start of the experiment was not significantly related to non-age- (P 5 0.188) or age-related deaths (P 5 0.269). CR was not significantly related to non-age(P 5 0.260) or age-related deaths (P 5 0.490). Also, sex was not significantly related to non-age- (P 5 0.991) or age-related deaths (P 5 0.053); however, this association of sex with age-related mortality is of marginal significance, which is consistent with the trend for males for have higher survival curves (see Figs 1a and 2a, b). For the young-onset animals, age at start of the experiment was not significantly related to non-age- (P 5 0.604) or age-related deaths (P 5 0.653). Sex was not significantly related to non-age- (P 5 0.790) or age-related deaths (P 5 0.480). CR was not significantly related to non-age- (P 5 0.147) or agerelated deaths (P 5 0.975). Also, the origin (Aberdeen) of the animal was not significantly related to age-related deaths (P 5 0.513), and the relationship to non-age-related deaths was not statistically significant. This marginal P value (P 5 0.0889) could suggest that origin may be a confounding factor. 30.

Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D. & Schabenberger, O. SAS for Mixed Models (SAS Institute Inc., 2006).

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Recruiting adaptive cellular stress responses for successful brain ageing Alexis M. Stranahan and Mark P. Mattson

Abstract | Successful ageing is determined in part by genetic background, but also by experiential factors associated with lifestyle and culture. Dietary, behavioural and pharmacological interventions have been identified as potential means to slow brain ageing and forestall neurodegenerative disease. Many of these interventions recruit adaptive cellular stress responses to strengthen neuronal networks and enhance plasticity. In this Science and Society article, we describe several determinants of healthy and pathological brain ageing, with insights into how these processes are accelerated or prevented. We also describe the mechanisms underlying the neuroprotective actions of exercise and nutritional interventions, with the goal of recruiting these molecular targets for the treatment and prevention of neurodegenerative disease. Ageing is associated with numerous physiological alterations across multiple organ systems, including the brain. In the CNS, the effects of ageing can be partly attributed to intrinsic responses to accumulated ‘wear and tear’, but may also be a result of ageing in other organ systems, including ageing of the reproductive system1 and muscle loss2. Some changes that occur in the context of ageing are adaptive. For example, despite increased physical ailments, the elderly self-report higher levels of happiness relative to middle-aged and younger populations3 and, presumably, the increased rate of contentment in older adults has some neuronal correlate. Other age-related alterations, such as declining executive function and motor impairment, are more pernicious; ideally, these changes could be delayed or prevented by targeted CNS interventions. Many of these interventions, including exercise and dietary moderation, resemble the dictates of common sense. In this article we describe mechanisms underlying neuroprotective ‘common sense’ manipulations, with the goal of harnessing these mechanisms to protect against the deleterious consequences of brain ageing.

Salient features of brain ageing The ageing process leads to an increase in the variability associated with cognitive and motor capabilities in humans4 and rodent models5. Just as some humans maintain cognitive function into their eighth decade and beyond, a subset of aged rodents retains the capacity for performance across a range of cognitive tasks5–7. In this regard, it is possible to distinguish among healthy ageing with preserved cognition, age-related cognitive impairment and neuropathology (FIG. 1). As described in the remaining sections of this article, these general functional features of brain ageing are caused by cells becoming less able to respond to stress. In other words, neurons become impaired in their ability to adapt to and rebound from potentially damaging alterations to the local environment and the endocrine milieu. Regardless of genetic background, there are several major cellular and molecular changes that occur in the CNS during normal ageing that are shared, to a certain extent, with age-related alterations in other organ systems. These fundamental aspects of brain ageing, which are common across the spectrum of cognitive performance,

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include increased oxidative stress, impaired cellular energy metabolism, perturbed cellular calcium signalling and the abnormal accumulation of damaged proteins and organelles8. Superoxide anion radicals, hydroxyl radicals, nitric oxide and peroxynitrite are major oxygen free radicals that have been implicated in normal brain ageing and cognitive impairment 9. Protein oxidation and modification by lipid peroxidation products and peroxynitrite increases progressively during ageing, and is particularly prominent in the neurons that degenerate in Alzheimer’s disease. Likewise, normal ageing is associated with increased immunoreactivity for indices of oxidative stress in humans10, and pathological ageing further exacerbated this effect 11. Oxidative damage to membrane lipids occurs more severely in aged, cognitively impaired animals12 and to an even greater extent in experimental models of neurodegenerative diseases13,14. For example, membrane-associated oxidative stress results in neuronal dysfunction and degeneration by a mechanism involving the lipid peroxidation product 4‑hydroxynonenal, which covalently modifies proteins that are crucial for cellular ion homeostasis, including Na+ and Ca2+ motive ATPases and glucose and glutamate transport proteins15. Such aberrant oxidative damage is likely to contribute to an excitatory imbalance that may presage the onset of age-related cognitive impairment and Alzheimer’s disease16. With advancing age, neurons may suffer from reduced production of the molecular energy couriers ATP and NAD+, with impaired mitochondrial function being a principal reason for such a cellular energy deficit 17. Changes that lead to cellular energy deficits include oxidative damage to mitochondrial DNA, electron transport chain proteins18 and the plasma membrane redox system19. Disease-specific factors may also compromise energy metabolism; such factors include amyloid-β (Aβ) in Alzheimer’s disease, α‑synuclein in Parkinson’s disease, huntingtin in Huntington’s disease and Cu/Zn-superoxide dismutase (SOD) in amyotrophic lateral sclerosis (ALS)17. Ageing is also accompanied by the accumulation of damaged proteins, nucleic acids and organelles20,21. Deficits in proteasomal VOLUME 13 | MARCH 2012 | 209

PERSPECTIVES degradation and molecular repair processes are increasingly recognized as central to age-related CNS dysfunction, particularly following physiological challenges to homeostasis22,23. As with oxidative stress, cellular energy deficits and accumulation of damaged molecules and organelles tip the balance of activity in neuronal circuits towards uncontrolled excitation24. The impact of energy on brain ageing Studies of human subjects and animal models support a bidirectional relationship between energy intake and brain function; excessive intake impairs function, whereas dietary energy restriction enhances function25. The underlying mechanisms for this relationship converge on changes in synaptic plasticity and neurogenesis. Diabetes

and obesity are common consequences of excessive energy intake that accelerate brain ageing by compromising hippocampal neurogenesis and long-term potentiation (LTP), which is a candidate cellular model for the synaptic changes underlying learning and memory 26. Excessive caloric intake may also increase the risk for neurodegenerative diseases and stroke, possibly by compromising the integrity of the blood–brain barrier 27, resulting in deficient neurovascular coupling. Through these mechanisms and others, conditions arising from excessive energy intake have an impact on brain regions implicated in various facets of cognition. Perturbation of the levels and rhythmi city of a number of hormones in the context of obesity and the metabolic syndrome (BOX 1) leads to maladaptive alterations in

Mutant huntingtin

Mitochondrial oxidative stress

circuit function that are not restricted to regions that are traditionally associated with feeding and metabolism. Ghrelin, a hormone produced in the stomach, is one example of a metabolically relevant peptide that influences cognition and neuroplasticity. Ghrelin acts on the hypothalamic arcuate nucleus to stimulate feeding and promote the deposition of adipose tissue28. Ghrelin administration enhances hippoc ampal synaptic density, whereas loss-offunction results in decreased synapse numbers and impaired Schaffer collateral LTP29. Targeted disruption of the gene encoding ghrelin compromises synaptic function and is associated with behavioural deficits in learning and memory29. In addition to regulating synaptic density, LTP and learning, ghrelin promotes adult neurogen esis in rats30. These findings support a role for Synapse loss

Normal ageing Hippocampus

Lipid peroxidation

Ca2+

Caspases

Mitotoxicity

REST

ROS Reduced transcription

Nucleus

Substantia nigra

Neurogenesis Cerebral cortex

Cerebral cortex Huntington’s disease

Substantia nigra

Parkinson’s disease

Striatum Brain stem

Alzheimer’s disease Spine loss

α-synuclein

Hippocampus

Aβ

Mitochondrial dysfunction

Ca2+

ROS

Temporal lobe

Mitotoxicity

Lewy bodies

ROS ↓BDNF

Frontal lobe

P P P Tau P P P

Mutant DJ1 Nucleus

Figure 1 | Intrinsic features of normal and pathological ageing. Normal ageing is accompanied by alterations in neuronal calcium handling and changes in lipid peroxidation, leading to increased generation of reactive oxygen species (ROS) and damage to mitochondria. These changes are permissive or instructive for the suppression of adult neurogenesis beginning in middle age. Successful ageing is characterized by the implementation of alternative plasticity mechanisms to compensate for changes in the local

microenvironment. Age-related pathologies such as Alzheimer’s disease, Parkinson’s disease and Huntington’s disease arise from a |combination Nature Reviews Neuroscienceof genetic and environmental factors, but each disease shares a common feature in that age is a risk factor for disease onset. In this respect, ageing sets the stage for the onset of pathology. Aβ, amyloid-β; BDNF, brain-derived neurotrophic factor; DJ1, Parkinson’s disease protein 7; P, phosphorylation site; REST, repressor element 1‑silencing transcription factor.

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PERSPECTIVES Box 1 | The metabolic syndrome The metabolic syndrome is a constellation of risk factors that increase the probability of developing diabetes, stroke and cardiovascular diseases. Individuals with the metabolic syndrome have: a high waist-to-hip ratio that is indicative of central adiposity; elevated fasting glucose levels that, although they may not meet the criteria for type 2 diabetes, indicate that insulin sensitivity may be compromised; elevated triglycerides; and increased blood pressure. The most effective treatment for the metabolic syndrome is weight loss. The endocrine consequences of the metabolic syndrome are not restricted to insulin signalling, but also involve perturbation of leptin levels and sensitivity, changes in corticosteroid synthesis and rhythmicity, and alterations in ghrelin secretion. Because these endocrine factors have each been shown to influence neuronal function26,75,76, particularly in the hippocampus, it is likely that the metabolic syndrome could accelerate age-related cognitive deficits. Indeed, recent studies in animal models and human subjects have demonstrated improvements in cognitive performance in response to interventions that increase insulin sensitivity, including dietary energy restriction, exercise and pharmacological agents such as glucagon-like peptide 1 analogues77–80.

ghrelin in hippocampal plasticity and raise the possibility that disruption of ghrelin signalling in the context of obesity could lead to impairment of hippocampal function. Owing to pre-existing societal prejudice against obese individuals, studies on the mechanisms related to cognition in obesity should be interpreted cautiously. The perception of obesity as a condition arising from lack of discipline, rather than a disease that, like certain psychiatric conditions, arises from a mixture of environmental pressures and genetic predisposition, often facilitates negative bias among the general public, and could potentially influence opinions among the scientific and health-care providers. It is also important to recognize that experimental observations in genetically homogenous rodent populations may not always translate to humans. Given these precautions, it should be noted that, for both humans and animal models, there is evidence for and against cognitive dysfunction in the context of obesity (TABLE 1). The results of human studies reveal a critical time window in which obesity, and the metabolic syndrome more generally, has the potential to affect cognitive ageing (TABLE 1). Exposure to the direct oxidative and indirect endocrine consequences of excessive energy intake during the fourth and fifth decade of life increases the likelihood of cognitive decline during later years31, whereas higher body mass indices may not cause cognitive impairment later in life32. One interpretation is that the middleaged brain is most susceptible to the endocrine adversities of the metabolic syndrome, whereas another hypothesis would suggest that other factors that are correlated with the prevalence of obesity, such as socioeconomic status, exert their effects on cognition during this same window. External variables, such as socioeconomic status and level of

education, are controlled for by some, but not all, studies examining cognitive changes in relationship to obesity, so the degree to which obesity has an impact on cognition remains an open question. Research in animal models demonstrates more consistent impairments on various measures associated with learning and memory in rodents exposed to excessive caloric intake. Diets high in fats and simple sugars impair spatial learning in the water maze33,34, spatial recognition in the Y‑maze35, complex maze learning 36 and operant discrimination37,38. However, the time course for memory impairment in rats fed Western diets varies. Some studies report memory impairment after 2 to 3 months of diet exposure33,35,37 using diets that range from 20% fat to 45% fat; by contrast, another study

did not detect cognitive impairment using a diet that was 45% fat until after 8 months of diet exposure34. Other researchers have failed to detect any impairment at all when feeding mice diets comparably enriched with fats and simple sugars39. Understanding the time course, and the underlying cellular and molecular mechanisms, of memory impairment caused by diets that elicit features of the metabolic syndrome is crucial in light of evidence for a critical time window in humans. However, the question of when, and how, cognitive impairment develops in the context of the metabolic syndrome remains unanswered. Dietary energy restriction can protect the brain against ageing. Early studies in mice showed that lifelong energy restriction through an alternate-day fasting regimen improves learning in a complex maze40. Intermittent energy restriction also improves memory and Schaffer collateral LTP in young mice41 and enhances neurogenesis through a brain-derived neurotrophic factor (BDNF)-dependent mechanism42. Dietary moderation is also positively associated with successful ageing (healthy rather than pathological ageing) in human populations25. Interestingly, age-related impairments in cellular energy metabolism can be reversed by dietary energy restriction19, as can pathological deficits in learning and memory in Alzheimer’s disease model mice43,44. Dietary energy restriction was also effective in protecting dopaminergic neurons and improving motor function in mouse45 and monkey 46

Table 1 | Recent studies of cognitive function in relationship to obesity Study

Age group

Cognitive outcome

Refs

Roriz-Cruz et al. (2007)

Aged

↓

Gunstad et al. (2007)

Adult

↓

van den Berg et al. (2007)

Aged

↑

Sturman et al. (2008)

Aged

No change

Li et al. (2008)

Young

↓

Huizinga et al. (2008)

Adult

↓

Sabia et al. (2009)

Adult

↓

Volkow et al. (2009)

Adult

↓

Fergenbaum et al. (2009)

Adult

↓

Morrison et al. (2010)

Aged

↓

McNay et al. (2010)

Adult

↓

Granholm et al. (2008)

Adult

↓

Kanoski et al. (2007)

Adult

↓

Mielke et al. (2006)

Aged

No change

Human studies

Animal models

NATURE REVIEWS | NEUROSCIENCE

PERSPECTIVES models of Parkinson’s disease, protecting striatal and cortical neurons, and extending survival in a mouse model of Huntington’s disease47. A major mechanism by which energy restriction protects neurons against ageing and disease is by engaging adaptive cellular stress response pathways that result in the production of a range of cytoprotective proteins, including neurotrophic factors, protein chaperones, DNA repair enzymes and mitochondrial uncoupling proteins, among others48–50 (FIG. 2).

against impairment and promoting adaptive plasticity, or by acting in a permissive or instructive fashion for deficits in neuronal function. For example, regular consumption of refined sugars and saturated fats accelerates age-related deficits in spatial learning 34, whereas consuming relatively high amounts of vegetables and fruits may counteract the ageing process53. Emerging evidence suggests that some neuroprotective phytochemicals engage adaptive cellular stress response pathways in neurons to upregulate the expression of neurotrophic factors, antioxidant enzymes and proteins involved in cellular energy metabolism51. In this regard, the biological framework for nutritional neuroprotection conforms to the theory of hormesis (BOX 2). Phytochemicals that have been shown to protect neurons against dysfunction and degeneration in animal models of

Influence of specific dietary components Epidemiological data from human populations and mechanistic data from animal models support a protective role for certain dietary phytochemicals51 and a deleterious effect of saturated fats and refined sugars52. Specific macro- and micro-nutrients counteract brain ageing by protecting

neurodegenerative disorders include folic acid54, thiamine55, curcumin56, epigallocatechin-3‑gallate57, plumbagin58 and resveratrol59. Signalling pathways activated by these phytochemicals include the nuclear regulatory factor 2 (NRF2)–antioxidant response element (ARE) pathway, the Ca2+–cyclic AMP response element-binding protein (CREB) pathway, and the sirtuin– forkhead box O (FOXO) pathway (FIG. 2). Neuroprotective genes induced by the Ca2+–CREB and sirtuin–FOXO pathways include those encoding neurotrophic factors (for example, BDNF), protein chaperones (for example, heat shock protein 70 and glucose-regulated protein 78), antioxidant enzymes (for example, MnSOD, haem oxygenase 1 and NADH quinone oxidoreductase 1), energy-regulating proteins (for example, mitochondrial uncoupling proteins) and DNA repair proteins (for g GABA

hyperpolarization ↓Calcium

Glutamate Exercise Glutamate Calcium CaMKII CREB

Mitochondrion

Cognitive challenges

Hippocampal neuron

Nucleus

BDNF DNA repair

Endoplasmic reticulum

e Glutamate Calcium Energetic shift

Protection against excitotoxicity

GABA

d Acetylcholine Dietary energy restriction

Trophic factors UCPs Protein chaperones PMRS enzymes

Cholinergic aﬀerent Serotonin Cyclic AMP CREB BDNF

DAG, IP3 PKC, calcium BCL-2 Chaperones

↓ATP SIRT HSF1 FOXO3 AOEs Chaperones

ROS NRF2 NF-κB HO1 NQO1 MnSOD Ph2E

Serotonergic aﬀerent

mTOR

Figure 2 | Adaptive cellular stress response signalling mediates the beneficial effects of environmental challenges on neuroplasticity and vulnerability to degeneration. A typical glutamatergic neuron in the hippocampus is depicted receiving excitatory inputs (red) from neurons activated in response to exercise, cognitive challenges and dietary energy restriction. Examples of seven different adaptive stress response signalling pathways that protect neurons against degeneration and promote synaptic plasticity are shown. During exercise and cognitive challenges, postsynaptic receptors for glutamate (a,b), serotonin (c) and acetylcholine (d) are activated to engage intracellular signalling cascades and transcription factors that induce the expression of neuroprotective proteins including brain-derived neurotrophic factor (BDNF), mitochondrial uncoupling proteins (UCPs) and anti-apoptotic proteins (for example, BCL‑2). BDNF promotes neuronal growth, in part, by activating mammalian target

of rapamycin (mTOR). Mild cellular stress resulting from reduced energy substrates (e) and reactive oxygen species (ROS) (f) engages adaptive stress Nature Reviews | Neuroscience response pathways, including those that upregulate antioxidant enzymes (AOEs) and protein chaperones. Release of GABA from interneurons (g) in response to activity in excitatory circuits (as occurs during exercise and cognitive challenges) hyperpolarizes excitatory neurons protecting them from Ca2+ overload and excitotoxicity. CaMKII, calcium/calmodulin kinase II; CREB, cyclic AMP response element-binding protein; DAG, diacylglycerol; FOXO3, forkhead box protein O3; HO1, haem oxygenase 1; HSF1, heat shock factor 1; IP3 PKC, inositol-trisphosphate 3 protein kinase C; MnSOD, manganese superoxide dismutase; NF‑κB, nuclear factor-κB; NQO1, NAD(P)H-quinone oxidoreductase 1; NRF2, nuclear regulatory factor 2; Ph2E, phase 2 enzyme; PMRS, plasma membrane redox system; SIRT, sirtuin.

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PERSPECTIVES example, AP endonuclease 1). Based on these natural products, medicinal chemists may be able to develop therapeutic agents with greater specificity for a stress response pathway and reduced toxicity. Physical and cognitive exercise Evidence in favour of experiential neuroprotection is compelling, and emerging data suggest that lifestyle factors can be harnessed to prevent or treat neurological disease. Physical and mental activities can protect neurons against age-related dysfunction and disease through a variety of mechanisms, including upregulation of neurotrophic factors, enhanced synaptic plasticity and stimulation of neurogenesis. Lifestyle factors such as cognitive stimulation60 and aerobic exercise61 engage a variety of signalling cascades to maintain transcriptional profiles that are more commonly observed in the structurally dynamic developing brain. Such lifestyle factors could, in principle, enhance the efficacy of pharmacological treatments designed to forestall neurodegenerative diseases. One example of a synergistic interaction between exercise and pharmacological neuroprotection involves the neurotransmitter noradrenaline (FIG. 3). Noradrenergic innervation of the hippocampus is necessary for exercise-induced upregulation of BDNF expression62. The locus coeruleus is the origin of noradrenergic fibres, and depletion of locus coeruleus noradrenaline exacerbates amyloid plaque deposition and cognitive impairment in Alzheimer’s disease model mice63. Both of these studies used loss-of-function pharmacological approaches, but it remains to be seen whether synergistic gain-of-function could be detected through the concomitant treatment of Alzheimer’s disease model mice with exercise and selective noradrenaline reuptake inhibitors (SNRIs) (FIG. 3). Whereas healthy ageing is accompanied by increased variability in cognitive performance5–7, aged rats are uniformly more susceptible to memory deficits following an immune challenge64. Increased vulnerability to infection-induced learning deficits is accompanied by exacerbation of hippocampal inflammatory responses. Although aged rats run far less than their younger counterparts, even a limited amount of voluntary wheel running was sufficient to prevent impaired learning following Escherichia coli infection and attenuate neuroinflammation in the hippocampus64. Although this study demonstrates that exercise is indeed neuroprotective, no studies to date have addressed whether late-onset physical activity can ameliorate cognitive deficits in a naturally

Box 2 | Successful brain ageing through hormesis Intermittent exposure to mild cognitive or physiological stress can increase neuronal resistance to the degenerative disorders of ageing48. Hormesis — the process through which exposure to low levels of a stressor activates biological repair mechanisms81 — occurs during exercise, in response to dietary energy restriction and when neurons are stimulated with certain phytochemicals. Hormetic stress stimulates signalling pathways that enhance the abilities of neurons to resist oxidative stress, DNA damage, mitochondrial impairment, and protein misfolding and aggregation. Some of the major proteins involved in such adaptive stress responses include neurotrophic factors, protein chaperones and proteins involved in mitochondrial biogenesis, protein degradation and autophagy (FIG. 2). The cellular signalling pathways that mediate hormetic responses in neurons are being identified. For example, cognitive stimulation and exercise involve the activation of glutamate receptors (resulting in Ca2+ influx), Ca2+/calmodulin-dependent kinases and the transcription factor cyclic AMP response element-binding protein (CREB), as well as the induction of genes encoding brain-derived neurotrophic factor (BDNF) and the DNA repair enzyme apurinic-apyrimidinic endonuclease 1 (APE1), among others60,61. Dietary energy restriction has been reported to activate genes encoding proteins involved in synaptic plasticity, cellular energy metabolism and cell survival82. It has been proposed that there is an evolutionarily conserved network of genes that orchestrate hormetic responses of cells and organisms to various stressful challenges. These so-called ‘vitagenes’ encode proteins that are crucial for the control of protein quality, redox balance, ion homeostasis, membrane integrity and energy metabolism83. Examples of phytochemicals that act via hormesis are: sulphoraphane (which is present in relatively high amounts in broccoli) and plumbagin, which activate the nuclear regulatory factor 2 (NRF2) –antioxidant response element (ARE) pathway, resulting in the expression of the antioxidant enzymes haem oxygenase 1 and NAD(P)H-quinone oxidoreductase 1 (also known as NAD(P)H dehydrogenase [quinone] 1 (REF. 84); curcumin (curry spice), which stimulates adult hippocampal neurogenesis by inducing a mild cellular stress response involving the extracellular signal-regulated kinases (ERKs)85; and flavonoids from fruits such as grapes and blueberries, which enhance learning and memory, possibly by activating sirtuins and CREB86. A better understanding of the mechanisms by which phytochemicals stimulate or stress neurons in ways that enhance CNS function and resilience may lead to the development of novel interventions for neurodegenerative disorders.

occurring model of age-related cognitive impairment, such as the aged-impaired rat model5. Possible interactions between exercise and ageing of the noradrenergic system in healthy individuals also remain uncharacterized. Late-life resilience During the past few decades, there has been increased interest in regenerative medicine, largely as the result of major advances in research on stem cells and neurotrophic factors. Here, we briefly describe the mechanisms that regulate neurogenesis, as well as advances in the development of therapeutic interventions that either stimulate endogenous neural stem cells or involve transplantation of neural progenitors derived from embryonic stem cells or autologous induced pluripotent stem (iPS) cells. Several environmental factors and chemical agents have been shown to stimulate neurogenesis in one or both of the two major populations of neural progenitors in the adult brain, which are located in the hippocampal dentate gyrus and the subventricular zone. Exercise, dietary energy restriction and enriched environments enhance neurogenesis by a mechanism involving the upregulation of BDNF expression65. The ability of these

NATURE REVIEWS | NEUROSCIENCE

lifestyle-related factors to enhance neurogenesis is retained during ageing, although their efficacy may be attenuated. Conversely, chronic adverse stressors (for example, psychosocial stress and depression) and metabolic derangements (for example, diabetes and insulin resistance) impair neurogenesis by elevating glucocorticoid levels and reducing BDNF expression66–68. Unfortunately, because there may not be neural progenitors that are capable of supplying new neurons to the vast majority of neuronal circuits in the brain, it is unlikely that stimulating endogenous progenitors will be sufficient to repair all damaged circuits in neurodegenerative disorders. Neuronal replacement by transplantation of neural progenitors, embryonic stem cells or autologous iPS cells is being pursued in preclinical and translational research projects. Several studies have documented the restorative effects of transplanted stem or progenitor cells in animal models of brain injury or neurodegenerative disorders69. Human studies in which human fetal neural progenitors have been transplanted into patients with Parkinson’s disease have yielded mixed results70. Nevertheless, emerging findings with iPS cells generated from fibroblasts or other cell types suggest that VOLUME 13 | MARCH 2012 | 213

PERSPECTIVES BDNF Glutamate

AMPA

Noradrenaline reuptake inhibitor

Ca2+

+ BDNF

Noradrenaline

NMDA

TRKB β-receptor Synaptic plasticity Neurogenesis

GTP

G protein

P CaMKII

Adenyl cyclase

P MEK1/2 CREB

Locus coeruleus

cAMP

ERK

Nucleus

Figure 3 | Potential for mechanistic synergy between exercise and pharmacological treatments designed to maintain cognition in an ageing population. The locus coeruleus projection to the hippocampus is essential for exercise-induced brain-derived neurotrophic factor (BDNF) expression. Exercise enhances hippocampal BDNF synthesis, leading to activation of the extracellular signal-related kinase (ERK) pathway, which converges on the transcription factor cyclic AMP response element-binding protein (CREB). Treatment with drugs that block noradrenaline reuptake

the transplantation of these cells has beneficial effects in a rodent model of Parkinson’s disease71. Some of the hurdles encountered in transplantation studies (such as immune rejection, insufficient trophic support or an ‘unfriendly’ cellular niche) might be circumvented by inducing endogenous glial cells to become multipotent neural progenitors, which could then be coaxed towards neuronal phenotypes using one or more of the environmental factors described above or by co-treatment with a neurotrophic factor (see below). For example, glial cells could be infected with an innocuous virus expressing two or three genes that encode proteins that change the phenotype of the cells to that of iPS cells or neurons. Neurotrophic factor replacement via infusion or gene therapy is another promising approach for halting and reversing age-related CNS dysfunction72. A long and rich history of preclinical research in animal models, together with considerable clinical experience in Alzheimer’s disease and Parkinson’s disease, suggests that neurotrophic factor-based therapies are likely to counteract disease processes in patients. However, to be successful, it is likely that the treatment must be initiated early in the disease process. In addition, such neurotrophic factor therapy assumes that neurons will be responsive (that is, express functional neurotrophic factor receptors), which in some cases may not be so73 (for example,

enhance noradrenergic neurotransmission, leading to activation of adenyl Nature Reviews | Neuroscience cyclase and cAMP, which also activates CREB, leading to transcription of a number of different genes associated with synaptic plasticity and neurogenesis. Concurrent exercise and noradrenaline reuptake inhibitor treatment could additively enhance hippocampal BDNF production and be neuroprotective. CaMKII, calcium/calmodulin kinase II; MEK, mitogenactivated protein kinase; P, phosphorylation site; TRKB, high-affinity BDNF receptor.

defects were found in insulin-like growth factor 1 and insulin receptors in Alzheimer’s disease). Because neurotrophic factors may not readily cross the blood–brain barrier, there are efforts to develop low-molecularweight agonists of neurotrophic factors with high specificity and potency 74, although the potential for adverse effects of such agents on peripheral neurons may hinder their development. Lifestyle-based neuroprotection A major implication of the research described above is that successful brain ageing is possible for most individuals if they maintain healthy diets and lifestyles throughout their adult life. Unfortunately, this is currently one of the major conundrums in modern societies, where high-energy food is readily available, exercise is unnecessary in daily routines and preventive medicine is being suppressed by the actions of the food and pharmaceutical industries. Healthy lifestyle choices should be implemented and incentivized through the coordinated efforts of government, medical schools and health-care providers. Moreover, additional research into the mechanisms underlying nutritional and experiential neuroprotection is warranted in order to harness these mechanisms through pharmacomimetic strategies. Greater awareness of the extent to which physiological ageing conforms to a hormetic framework will aid in this effort.

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Acknowledgements

This work was supported by the Intramural Research Program of the National Institute on Aging.

Competing interests statement

The authors declare no competing financial interests.