Application of Artificial Intelligence In Identifying Antioxidants for Aging Part-I
Woolly Hair Syndrome: A Case Report
Management of Vitiligo with Combination of Treatment: A Case Study
A Clinico-Epidemiological Study of Geriatric Dermatoses
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Published for the period of June -2023
The burden of skin disease refers to the impact and consequences of skin conditions on individuals, communities, and healthcare systems. Skin diseases can encompass a wide range of conditions, including dermatitis, psoriasis, acne, eczema, skin infections, skin cancer, Hair related conditions and many others. Skin diseases have a multidimensional impact on patients, including psychological, social and financial consequences. Chronic and incurable skin diseases, such as psoriasis and eczema, can significantly impair patients' quality of life, while malignant diseases, such as malignant melanoma, can be life-threatening. The burden of skin diseases can be measured using various health status and quality-of-life measures. Skin conditions contribute to a considerable workload for healthcare professionals. Dermatologists and other healthcare providers who manage skin diseases face challenges in terms of timely access to care, long waiting times and limited resources. Addressing the burden of skin disease requires a multifaceted approach that includes prevention, early detection, effective treatment options, patient education and improved access to dermatological care. Skin diseases require ongoing management, and the demand for dermatological services often exceeds the available capacity. Public health initiatives, awareness campaigns, research and collaboration between healthcare providers and policymakers are essential to reduce the burden of skin disease and improve the overall well-being of individuals affected by these conditions.
In this issue we have articles on Vitiligo, Geriatric Dermatoses, Application of Artificial Intelligence In Identifying Antioxidants for Aging and Woolly Hair Syndrome.
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- Dom Daniel Executive Editor & PublisherApplication of Artificial Intelligence In Identifying Antioxidants for Aging Part-I
Dr. Saad Sami AlSogair, MD
A Clinico-Epidemiological Study of Geriatric Dermatoses
Dr. Awani Kansagra, 3rd Year Postgraduate Resident
Dr. Vishal S. Patel, MD
Dr. Sanjay Kanodia, MD
Management of Vitiligo with Combination of Treatment: A Case Study
Dr. Vandana Dave, MD (Skin) & DVD
Woolly Hair Syndrome: A Case Report
Dr. Dhiral Shah, MBBS, MD (D.V.L)
Dr. Yogesh S. Marfatia, Professor (Skin & VD)
Dr. Saad Sami AlSogair
MD
Dermatologist & Anti-aging Consultant
Vice President of the Middle East International Dermatology & Aesthetic Medicine Conference & Exhibition - MEIDAM
Dr. Vishal S. Patel
MD (Skin), Fellowship in Hair transplant
Consultant at AV Skin, Laser & Hair Transplant Clinic
Rajkot, Gujarat, India
Dr. Sanjay Kanodia
MD (Dermatology)
Professor
Department of Dermatology
National Institute of Medical Sciences and Research, Jaipur, Rajasthan, India
Dr. Yogesh S. Marfatia
Professor (Skin & VD)
SBKS Medical Institute and Research Centre
Vadodara, Gujarat, India
Dr. Dhiral Shah
MBBS, MD (D.V.L)
Fellowship in Aesthetic Medicine
Consultant Dermatologist & Cosmetologist
Ashirwad Hospital, Gota, Ahmedabad
Dr. Awani Kansagra
3rd Year Postgraduate Resident
Department of Dermatology
National Institute of Medical Sciences and Research, Jaipur, Rajasthan, India
Dr. Vandana Dave
MD (Skin) & DVD
Consulting Dermatologist and Cosmetologist
Surat, Gujarat
Dermatologist & Anti-aging Consultant
Vice President of the Middle East International Dermatology & Aesthetic Medicine Conference & Exhibition - MEIDAM
Abstract
Scientific research continuously illustrates that aging can be delayed, treated, or even avoided and, as a result, health can be maintained by consuming health-promoting antioxidants. Food, nutrition, nutrients, and the health advantages linked with them are becoming more important to consumers. As a result, a nutrition industry based on natural foods and antioxidants is emerging, with consumer decisions driving economic growth. Information technology, especially artificial intelligence (AI), is set to dramatically extend the field of skin aging therapies accessible to consumers by systematically finding and characterizing natural, effective, and safe bioactive compounds (bioactives) that address aging. A literature search was conducted using the keywords in PubMed: "deep learning antioxidant", "algorithms antioxidant", "neural networks antioxidant", "support vector machine antioxidant", "natural language processing antioxidant", "computer vision antioxidant", "artificial intelligence antioxidant", and "machine learning antioxidant". There were a total of 146 papers chosen
for evaluation. This research only looked at full-text papers that were available for free. The relevance of the abstracts was determined, and 29 publications were judged to be relevant to the current study. To summarize, AI has the potential to help in a variety of antioxidant identification discovery fields. As with any concept, it is unlikely to be a panacea, but its use should be expanded to help scientists in their various roles and specialties throughout the process.
Introduction
Scientific research continuously illustrates that aging can be delayed, treated, or even avoided and, as a result, health can be maintained by consuming healthpromoting antioxidants. Food, nutrition, nutrients, and the health advantages linked with them are becoming more important to consumers. As a result, a nutrition industry based on natural foods and antioxidants is emerging, with consumer decisions driving economic growth. Information technology, especially artificial intelligence (AI), is set to dramatically extend the field of aging therapies accessible to consumers by
systematically finding and characterizing natural, effective, and safe bioactive compounds (bioactives) that address aging. 1
For a variety of reasons, the beauty and skin care sectors have been slow to embrace AI technology for their substance development processes, resulting in a lack of viable methodologies for large-scale and increased molecular and biochemical component identification. The emergence of the AI-driven technological revolution enables thorough characterization and knowledge of the universe of antioxidant and therapeutic compounds, enabling unparalleled mining of the food and natural product space. As a consequence of this increase in bioactives, the consumer's antioxidant repertoire expands dramatically, ultimately leading to bioactives being produced particularly to meet unmet health requirements.1
The application of artificial intelligence (AI) in the discovery of anti-aging antioxidants is the subject of this article. It provides a brief (re)introduction to the role of antioxidants in aging, the history of artificial intelligence in drug discovery, and the types of artificial Intelligence, machine learning and deep learning. It then attempts to review various studies published on the applications of AI techniques in antioxidant discovery.
The biggest contact area between the human body and the external environment is the skin, which serves as a barrier between the human body and the environment. It offers an aesthetic impact in addition
to protecting the body from external environmental harm and preventing water loss. Organ aging happens throughout our lives. The skin, being the biggest organ of the human body, exhibits apparent indications of aging as a result of age, UVR exposure, and chemical pollution. People are paying greater attention to skin aging and attempting to have a better knowledge of it as science and technology advance and human living standards rise. Cosmetics and pharmaceuticals for the treatment and prevention of skin aging account up a major chunk of many people's daily spending, particularly women. This enormous demand continues to fuel research into skin aging prevention and therapy.2
The two types of skin aging are chronological and photo-aging (or internal aging and external aging). The chronological aging of the skin happens throughout the body, whereas photoaging occurs on the body's lightexposed regions, as the term implies. Internal causes cause chronological aging, which is difficult to modify, however it is feasible to postpone photo-aging by adjusting external influences. A healthy diet and well-balanced nutrition are essential factors in delaying aging and extending life. 2
Lipid peroxidation, DNA damage, and inflammation are the fundamental causes of skin aging, illness, and malfunction, according to the free radical hypothesis. As a result, a medical revolution centered on antioxidants and free radical scavengers for skin aging prevention and therapy. Oxygen-free radicals are present throughout the cell
metabolism process and can interact with DNA, proteins, and polyunsaturated fatty acids in the body, resulting in DNA breaks, oxidative damage, protein–protein cross-linking, protein–DNA cross-linking, and lipid metabolism oxidation, among other things. ROS has been linked to a variety of cardiovascular illnesses, malignancies, and the aging process. Exogenous antioxidant supplements, with food as a major source, have become a study issue since in vivo oxidation ultimately leads to organism aging. 2
The R&D cycle for novel therapies confronts a number of obstacles, including high costtomarket, low clinical trial success, and protracted cycle lengths. Despite record expenditures, the pharmaceutical industry's productivity of drug R&D remains on the decline.
This tendency may be attributed to a variety of factors, including present market saturation, challenges in getting innovative chemical substances through a rigorous approvals procedure, and willingness-to-pay in established and emerging markets, to name a few. We'll focus on a key obstacle in medicinal chemistry in this section: the intrinsic difficulty of transitioning the drug development process from fundamental research to early clinical trials.3
Scientists now have more information than ever before on a broad variety of important issues, considerably surpassing the capabilities of most individuals to appropriately interpret and incorporate it into their own workflows and research aims. One solution is
to "delegate" our thinking to ai technology when it comes to data integration and diverse data processing. Machine learning and domain-specific ("weak") artificial intelligence (AI) present novel opportunities for smallmolecule drug discovery in this context.
Machine learning systems that may be classified as weak AI have achieved significant improvements in both their underlying algorithms and applications. As a consequence, while there is presently no "strong" (universal) AI, we shall use the word "AI" as a synonym for certain machine learning approaches. As a consequence, the focus of this study will be on components of this potential sector that have already shown their utility and application, as well as the technologies that look to be the most promising for the next phase of AI in drug development.3
In 1956, during the Dartmouth Conference, John McCarthy invented the phrase "artificial intelligence" to characterize "the science and engineering of creating intelligent machines." AI is a multidisciplinary discipline that combines information from several domains such as computer programming, math, biology, languages, philosophy, neuro-science, artificial cognitive science, and others. Recent intellectual and technical advancements have facilitated the field's growth from solely theoretical research to the deployment of intelligent systems that address issues in a range of areas. Despite the wide variety of issues that AI can solve, several basic approaches,
such as information acquisition and maintenance, knowledge representation, solution search, logic reasoning, and machine learning, are essential in all circumstances.4
Alan Turing proposed the concept of a Turing Machine in the 1930s, which could simulate computers. Optimisation was at the heart of the early heydey, in the 1950s and 1960s. Symbolic techniques for semantic processing were introduced at this period, as were the notions of logical reasoning and heuristic searching, and man-machine interaction. STUDENT, a 1964 machine that could execute machine proofs of some mathematical concepts and logical reasoning of propositions, was one of the first computers with rudimentary intelligence. ELIZA (1966), a computer that could, although in a limited fashion, replicate human communication, was another early example. The quick creation of these and other AI examples generated a frenzy, culminating in a cycle of unreasonable enthusiasm followed by disillusionment in AI's capabilities. The first "AI winter" was named after the subsequent cooling of such views. Importantly, and AI advocates past and present should be aware of this, AI is based on probability and statistics, whose correct applications are reliant on mathematics, the availability of relevant data, and the capabilities of our technology.5
Artificial intelligence achieved its second peak in the early 1980s.Two AI-related scientific models, the multilayered feedforward neural net and the back - propagation algorithm algorithm, have made great progress. These tools enable
the creation of an abstract model of the world as well as the updating of the model based on input (learning from feedback). This combination was the first to defeat a human chess player, and it paved the way for a lot of later research in the topic.6
The forecasting of conformations from protein sequence information was the first use of such methods in chemistry and molecular genetics. Simultaneously, a slew of expert systems hit the market. Carnegie Mellon University, for example, had developed an expert system for the DEC Company. DEC is said to have saved $40 million each year because to this expert system's automatic decision-making. This accomplishment spurred several nations, particularly Japan and the United States, to spend extensively in the creation of 5th computers, sometimes known as "AI computers." Their inability to learn computer algorithms from data and resolve confusion in thinking, on the other hand, was a clear disadvantage. Moreover, the rising cost of maintenance of intelligent systems, blended with the introduction of less costly and speedier computers originally developed by Apple and IBM, ended up causing the market for such systems to come crashing down, heralding AI into a cold season with hardly any hope of re-emerging into the mass market.7
Despite its removal from public view and a corresponding reduction in funding, work on such issues did not come to a halt. Improvements focused on increasing the statistical validity of AI models' reasoning. A new paradigm, machine learning, placed a strong emphasis on extracting actionable insights
from complex data, which sparked interest in the scientific community as a whole.
Assumption, Bayesian networks, support vector machines (SVM), and decision trees were among the innovative algorithms and approaches described. Expert systems were overtly "conditioned," whereas machine learning models are "trained" to discover patterns in data. Predicting whether molecular properties of a collection of chemical structures are associated to a given set of biological consequences and extrapolating new territory from such descriptions, for example, if a molecule is likely to produce severe toxicity, is one instance. In certain aspects, this idea allows for the automating of much of the quantitative structure–activity relationship (QSAR) modeling work done in the area of cheminformatics.8
The present AI boom started in the half of the 20th centuries, pushed by the fast development of stored data ("big data"), a simultaneous surge in computer capacity (GPUs, Google's tensor processing units (TPUs), and continual algorithms improvement, such as deeplearning. For the first time, we can train nontrivial combinations of network parts on enormous quantities of data in a reasonable amount of time, boosting the applicability of such models. In sectors as varied as e-commerce, games (e.g., AlphaGo, Poker, and DOTA2), medical image analysis, and self-driving cars, the ability to train deep hierarchical network models in a decent length of time has proved the extraordinary possibilities of such techniques.9
While AI is not new, its application
to drug discovery, particularly in modeling generalized structure–activity relationships, is. Actually, the use of experiment data and a "descriptor set" for correlation goes all the way back to (and conceivably even before) Hammett's innovative equation linking reaction rates and equilibrium constants for interactions of benzene derivatives and Hansch's machine detection and characterization of physicochemical characteristics of biologically active compounds, who is generally regarded as the "father of QSAR" in the drug sector.10
Ever since, a growing number of medicinal chemists have turned to artificial intelligence (AI) to help them evaluate and anticipate chemical biological functions. For instance, pattern recognition focuses on clarifying and analysing patterns shared by chemical entities, based on general premise that substances with similar structural arrangements should have comparable physical qualities and in vitro biological actions. Early neural network designs and applications (for example, the Perceptron and its enhanced versions) appeared, promising to overcome such difficulties. Around 1990, neural networks started to have an influence on the pharmaceutical business due to their usability as pattern matching engines.11
Weinstein et al. published a paper in 1992 in which they created neural networks to anticipate the mechanisms of action in a cancer medication screening program. The first automation molecular design approach based on neural networks and algorithms was reported in 1994. These
integrated learning and decisionmaking models are the first functional machine learning models, and they possess all features of AI, such as the capacity to (i) solve problems, (ii) learn and adapt, and (iii) deal with new circumstances.12
A variety of machine learning techniques have been created and deployed to drug design to assist bridge the gap between serendipity-based and rational drug design, in addition to the aspects mentioned in the preceding section. They all confront the same difficulty in some way: choosing which biological traits should be combined to collect the information that may lead to the most accurate estimations.13 More competent ways were needed, which supported the creation of contemporary deep learning, which became a more solid idea about 2010 after individual advancements in the 1990s. The capacity of certain deep learning approaches to investigate and anticipate intricate correlations between molecular representations and observations (bioactivity, toxicity, and so on) gives reason to be optimistic that these methods will provide more meaningful, generalizable insights.14
These techniques won the Kaggle competition for chemical activity forecasting in 2012 and the NIH Tox21 challenge for toxicity prediction in 2014, both of which were regarded as difficult, and they performed as good as or better than the best methods available at the time. Several large-scale agreements between top pharma and AI businesses have been revealed in recent years, indicating that pharmaceutical corporations
that traditionally stood on the sidelines of contemporary AI are suddenly joining in.15
Algorithms for artificial intelligence are a vast area that encompasses a wide range of techniques. To help readers better grasp AI-assisted drug development, we will present a quick review of the main concepts of those algorithms that are extensively utilized in drug discovery.
The seven kinds of learning functions and methodologies utilized in drug discovery projects include supervised, unsupervised, semisupervised, active, reinforcement, transfer, and multitask learning. Every class has its own set of benefits and drawbacks. The job at hand should dictate the technique of choosing.3
A supervised learning process is used by many AI learning algorithms, in which a set of input data and known responses to the data are required, and the aim of this new technique is to understand a mapping function from the input to the output:, such that the class labels Y or target values Y for unseen supervised learning approaches include all categorization and regression algorithms.16
When just input data (X) and no matching output variables are available, unsupervised learning techniques are utilized. In other words, the data is unlabeled in an unsupervised model. It's tough to come up with a relevant performance statistic for the algorithm in this situation. Instead, the
algorithm will extract structures (= features) from the data (= patterns) that may be utilized to categorize the input samples into groups. Unsupervised learning algorithms are used in tasks like computation and dimension reduction, for example. Unsupervised learning contrasts with supervised learning in that it does not employ a feedback signal to assess the quality of feasible alternatives. Unsupervised learning methods such as clustering and projection are two common examples.17
Semisupervised learning lies at the intersection of supervised and unsupervised learning methodologies, and may be effective when there are a lot of input data (X) but just a few labeled examples (Y). This category encompasses a large number of realworld drug discovery issues. Semisupervised learning may make the most of unlabeled data by modifying or reprioritizing predictions derived just from limited labeled data. This is commonly achieved by training a model with the available labeled data using a supervised learning method, using the trained model to predict labels for the unlabeled data using the trained model; and, retraining the model with the quasi samples and the labeled data. In this method, the original labeled sample distributions are utilized to create the model and possibly improve its prediction capacity with minimal extra realworld, e.g., practical experimental, cost.18
Active learning is a particular variant of semisupervised learning that takes a different
approach to the problem of inadequate labeled training data. In this situation, the user (or another source of data) may be asked to provide labels for data sets in the input space areas where the algorithm is least confident. Active learning seeks to reduce the number of labeled examples necessary for learning at the same time, rather than seeking to utilize the underlying structure of data sets with the explicit objective of improving label predictions.19
Reinforcement learning aspires to be similar to incentive learning in certain ways. In its most basic form, an agent tries to identify the best set of actions to achieve a certain goal. This aim is achieved by assessing the environment, taking actions to change the environment (using policies to convert the agent's internal state into actions), and rating the results of those activities (reward). This pattern of effective penalties and rewards enables us to create and develop systems without necessarily understanding the "right" or "ideal" strategy, as long as reward function success corresponds pretty well with objective success.20
The phrase "transfer learning" refers to a set of techniques that do away with the constraint that training and validation data be in the same feature set and have the same distribution. As the name indicates, transfer learning approaches learn and transfer useful knowledge from old information domains to a new data domain, with the purpose of enhancing anticipated performance on the target domain if accomplished.21
In past few years, another learning technique, multitask learning, has gained increasing popularity of drug discovery. Rather of learning just one task at a time, as is the case with singletask learning, multiple separate but significantly associated tasks are acquired in parallel and are attempted to share an underlying structure. When it comes to presuming multitarget prediction models, this notion is extremely intriguing. Although multitasking and transfer learning are similar, they vary in how they approach the tasks at hand, with multitasking assuming task equality and learning techniques assuming a well-defined imbalance.22
A literature search was conducted using the keywords in PubMed: "deep learning antioxidant", "algorithms antioxidant", "neural networks antioxidant", "support vector machine antioxidant", "natural language processing antioxidant", "computer vision antioxidant", "artificial intelligence antioxidant", and "machine learning antioxidant". There were a total of 146 papers chosen for evaluation. This research only looked at fulltext papers that were available for free. The relevance of the abstracts was determined, and 29 publications were judged to be relevant to the current study. Relevant references were examined in bibliographies, as well as headlines relating to the issue.
1. Doherty, A., Wall, A., Khaldi, N., & Kussmann, M. (2021). Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides.
Frontiers in genetics, 12, 768979. https:// doi.org/10.3389 fgene.2021.768979
2. Cao, C., Xiao, Z., Wu, Y., & Ge, C. (2020). Diet and Skin Aging-From the Perspective of Food Nutrition. Nutrients, 12(3), 870. https://doi.org/10.3390/ nu12030870
3. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev. 2019 Sep 25;119(18):10520-10594. doi: 10.1021/ acs.chemrev.8b00728. Epub 2019 Jul 11. PMID: 31294972.
4. Mccarthy, J.; Hayes, P. Some Philosophical Problems From the Standpoint of Artificial Intelligence. In Machine Intelligence; Edinburgh University Press: Edinburgh, 1969.
5. Bobrow, D. G. Natural Language Input for a Computer Problem Solving System. In Semantic Information Processing; MIT Press: Cambridge, 1968.
6. Rumelhart, D.; Hinton, G.; Williams, R. Learning Internal Representations by Error Propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition; MIT Press: Cambridge, 1986.
7. Schwartz, T. Expert Systems Prove Adept at Physics: Recent Successes Demonstrate the Potential to Operate Instrumentation and Distribute Knowledge in the Laboratory. Comput. Phys. 1988, 2, 40– 45, DOI: 10.1063/1.4822649
8. Quinlan, J. R. Simplifying Decision Trees. Int. J. Man-Mach. Stud. 1987, 27, 221– 234, DOI:10.1016/S00207373(87)80053-6
9. Gawehn, E.; Hiss, J.; Brown, J.; Schneider, G. Advancing Drug Discovery via GPU-Based Deep Learning. Expert Opin. Drug Discovery 2018, 13, 579– 582, DOI: 10.1080/17460441.2018.1465407
10. Hansch, C.; Fujita, T. p-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. J. Am. Chem. Soc. 1964, 86, 1616– 1626, DOI: 10.1021/ja01062a035
11. Demeler, B.; Zhou, G. Neural Network Optimization for E. Coli Promoter Prediction. Nucleic Acids Res. 1991, 19, 1593– 1599, DOI: 10.1093/ nar/19.7.1593
12. Weinstein, J. N.; Kohn, K. W.; Grever, M. R.; Viswanadhan, V. N.; Rubinstein, L. V.; Monks, A. P.; Scudiero, D. A.; Welch, L.; Koutsoukos, A. D.; Chiausa, A. J.Neural Computing in Cancer Drug Development: Pedicting Mchanism of Ation. Science 1992, 258, 447– 451, DOI: 10.1126/science.1411538
13. Hearst, M. A.; Dumais, S. T.; Osuna, E.; Platt, J.; Scholkopf, B. Support Vector Machines. IEEE Intelligent Systems and Their Applications 1998, 13, 18– 28, DOI: 10.1109/5254.708428
14. Dahl, G. E.; Jaitly, N.; Salakhutdinov, R. Multi-Task Neural Networks for QSAR Predictions. arXiv:1406.1231 2014.
15. Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: Toxicity Prediction Using Deep Learning. Frontiers in Environmental Science 2016, 3, 80, DOI: 10.3389/fenvs.2015.00080
16. Raymond, J. L.; Medina, J. F. Computational Principles of Supervised Learning in the Cerebellum. Annu. Rev. Neurosci. 2018, 41, 233– 253, DOI: 10.1146/annurev-neuro-080317061948
17. Hartigan, J. A.; Wong, M. A. Algorithm AS 136: A k-Means Clustering Algorithm. J. R. Stat. Soc. C-Appl. 1979, 28, 100–108, DOI: 10.2307/2346830
18. Chapelle, O.; Schölkopf, B.; Zien, A. Semi-Supervised Learning, Ser. Adaptive Computation and Machine Learning; MIT Press: Cambridge, 2006.
19. Reker, D.; Schneider, G. ActiveLearning Strategies in ComputerAssisted Drug Discovery. Drug Discovery Today 2015, 20, 458– 465, DOI: 10.1016/j. drudis.2014.12.004
20. Sutton, R. S.; Barto, A. G. Reinforcement Learning: An Introduction; MIT Press: Cambridge, MA, USA, 1998.
21. Pan, S. J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345– 1359, DOI: 10.1109/TKDE.2009.191
22. Kovac, K. Multitask Learning for Bayesian Neural Networks. Master’s Thesis, University of Toronto, 2005.
3rd Year Postgraduate Resident
Department of Dermatology
National Institute of Medical Sciences and Research, Jaipur, Rajasthan, India
MD (Skin), Fellowship in Hair transplant
Consultant at AV Skin, Laser & Hair Transplant Clinic
Rajkot, Gujarat, India
MD (Dermatology)
Professor
Department of Dermatology
National Institute of Medical Sciences and Research, Jaipur, Rajasthan, India
Abstract
Background: Increasing number of geriatric population is noted to have characteristic group of dermatoses called geriatric dermatoses. Ageing population is dramatically increasing with increase in average life and is faced with different health problems including skin. The aim was to study the clinical pattern of geriatric dermatoses and associated systemic diseases.
Materials and Methods:
The present study was hospital-based cross-sectional observational study, conducted
in Department of Dermatology over a period of one and a half year from January 2021 to June 2022 at a tertiary care hospital. A total of 350 cases with age 60 years and above from OPD/IPD were chosen as study group.
Results: Out of 350 patients; 251(71.71%) were males and 99 (28.29%) were females, male:female ratio was 2.5:1.
Wrinkling (100%) and graying of hair (98.57%) were the commonest physiological changes observed. Among pathological changes infections and infestations was the
commonest seen in 22.57%.
Hypertension (28.29%) was the commonest systemic disease followed by Diabetes Mellitus (17.14%) in study population.
Conclusion: This study concluded that, through knowledge regarding spectrum of geriatric dermatoses is important as it is rapidly changing with wide variety of physiological and pathological skin changes. Thus early detection and proper management is very important and improving QOL in this age group of patients.
Keywords: Geriatric dermatoses, Ageing, Xerosis.
“Geriatric” is the term used for population aging 60 years and above.1 Aging is the term used for progressive reduction and decline capacity in function of various organs and systems of the body including skin.
Aging is a process of progressive reduction in maximal functioning and reserve capacity of all organs in body including skin.2 Skin undergoes intrinsic and extrinsic aging.3 Intrinsic skin aging is inevitable physiological changes affecting all persons. It is characterized by thinning of upper layer of skin, reduced amount of lipids, decrease blood flow and collagen fragmentation leading to dry, pale skin with fine wrinkles.4 Extrinsic skin aging also known as photoaging is related to exposure of UV radiation resulting in accumulation of abnormal elastin and disintegration of collagen fibrils presenting with coarse wrinkles, skin laxity, hyperpigmentation, senile lentigines, freckles and leathery skin appearance.
Common dermatological manifestation in elderly are xerosis, wrinkles, senile pruritus, infections like herpes zoster, superficial fungal infections, scabies and other infestations, eczematous conditions like asteatotic eczema, stasis eczema, papulosquamous disorders, photoaging (dermatoheliosis), benign tumors like acrochordons, seborrheic keratosis and cherry angioma. Skin cancers which includes: basal cell carcinoma, squamous cell carcinoma, age spots and bed sores are common in the geriatric population as compared to young population.5
To study the clinical profiles of various geriatric dermatoses.
• To identify clinical patterns of various dermatological disorders in elderly.
• To determine factors contributing to these dermatoses and their association with systemic disease (if any).
The present study was a Hospital-based Cross-sectional observational study, conducted at a Department of Dermatology, Venereology and Leprology, tertiary care hospital from January 2021 to June 2022. A total of 350 patients with the age ≥60 years and above are recruited in our study after written informed consent.
A detailed history was taken, complete general physical and systemic examination was done. Relevant investigations were performed wherever required.
Patients of age ≥60 years attending outpatient and indoor department willing to participate were included in the study.
Patients unwilling to give consent were excluded from the study.
• After collecting the data, statistical analysis was done by using Microsoft Excel 2010 / SPSS software v27.
In this study out of 350 patients, 251 (71.71%) were males and 99 (28.29%) were females. Male:female ratio was 2.5:1.
In this study, 350 patients were categorized into four age groups. Among the age groups, maximum number of patients 260 (74.28%) belonged to age group of 60-70 years followed by 80 (22.85%) in age group of 71-80 years, 9 (2.57%) in age group from 81-90 years, only 1 case lies in the age group of 91100 years.
Majority of the male patients were agricultural worker 136 (38.85%) and most of the female patients were housewives 77 (22%). Maximum numbers of patients were from the rural background and lower socioeconomic status.
All the patients showed signs of aging. Among them, commonest was skin wrinkling (350, 100%) and greying of hair (345, 98.57%) followed by skin xerosis (153, 43.71%), idiopathic guttate hypomelanosis (43,12.28%), senile purpura (42, 12%), senile comedo (33, 9.42%), seborrheic keratosis (29, 8.28%), acrochordon and fissured soles
(23, 6.57%), Favre - Racouchot syndrome (19, 5.42), solar lentigens (17, 4.85%) and cherry angioma (13, 3.71%). In some of these cases combination of above findings were seen. (Figure 1)
Infections and infestations were seen in 79 cases among which fungal infection was the commonest seen in 38 cases (48.10%) followed by bacterial infection seen in 16 cases (20.25%), viral infection in 10 cases (12.66%). Infestations were seen in 15 cases (18.99%).
Out of 350 patients enrolled for the study, most common dermatoses were infections and infestations (79, 22.57%), followed by cutaneous neoplasia (74, 21.14%), miscellaneous disorders (37, 10.57%), eczemas (32, 9.14%), pigmentary disorders (31, 8.86%), papulosquamous skin disorders (30, 8.57%), photodermatoses and psychodermatoses (16, 4.57%), vascular disorders (13, 3.71%), vesicobullous disorders and cutaneous drug reactions (11, 3.15%). (Table 1)
Cutaneous neoplasm were seen in 74 cases out of which benign neoplasm was seen in 70 cases (94.60%) which includes seborrheic keratosis, lentiges, cherry angioma, sebaceous hyperplasia, actinic keratosis, porokeratosis, syringoma etc and malignant neoplasm was seen in 4 cases (5.40%) that includes basal cell carcinoma seen in 3 cases and squamous cell carcinoma in 1 case.
Among miscellaneous dermatoses, keloid, palmoplantar keratoderma and post herpetic neuralgia was seen in 5 cases (13.51%), leg ulcer in 4 cases (10.81%), acquired perforating disorder in 3 cases (8.10%), granuloma annulare, miliaria, neurofibromatosis, xanthelasma, pyoderma gangrenosum and Alopecia areata in 2 cases (5.41%) each, acrokerataloelastoidosis marginalis, amyloidosis and hypertrichosis ectodermal dysplasia in 1 case each (2.70%). Eczematous conditions were seen in 32 cases. Different types of eczema were noted in the study. Among various types of Eczemas, Allergic contact dermatitis was the commonest noted in 6 cases (18.74%), followed by airborne contact dermatitis seen in 5 cases (15.64%), lichenified and endogenous dermatitis in 4 cases each (12.48%), Stasis
eczema in 3 cases (9.37%), Irritant, Palmoplantar and Atopic eczema in 2 cases each (6.24%), Asteatotic, Seborrheic and Infectious eczema in 1 case each (3.12%).
Pigmentary disorders were observed in 31 cases out of which Melasma was seen in 18 cases (58.06%), Vitiligo in 6 cases (19.36%), Post inflammatory hypo or hyperpigmentation in 4 cases (12.90%), Lichen planus pigmentosus in 2 cases each (6.45%) and Cushing Syndrome related pigmentation in 1 case (3 .23%).
Total 30 cases of papulosquamous disorders were seen in the study, of which Psoriasis was the commonest seen in 16 cases (53.33%), connective tissue disorder in 5 cases (16.66%), lichen planus in 4 cases (13.32%), erythroderma in 2 cases (6.67%), ichtyosis, P. rosea and lichen scleroses et atrophicus in 1 case each (3.34%).
There were 16 cases of photodermatoses. Among which Chronic Actinic dermatitis was the commonest seen in 12 cases (75%), followed by polymorphic light eruption seen in 3 cases (18.75%) and Solar urticaria in 1 case (6.25%).
In Psychodermatoses; total 16 cases were seen, of which Lichen Simplex Chronicus was seen in 12 cases (75%), followed by delusion of parasitosis in 2 cases (12.50%), prurigo nodularis and neurodermatitis in 1 case each (6.25%).
13 cases of Vascular disorder were found in the study, of which Vasculitis and thrombophebitis was seen in 5 cases (38.46%),
followed by Raynoulds phenomena in 2 cases (15.38%) and angiokeratoma of Fordyce in 1 case (7.70).
Vesiculobullous disorder were seen in 11 cases, of which 5(45.45%) cases had Pemphigus vulgaris, 3 (27.27%) cases of Bullous pemphigoid, 2(18.18%) cases of Pemphigus foliaceous and only one case of Paraneoplastic pemphigus (9.10%) was seen.
11 cases of adverse cutaneous drug reactions were seen in the study; of which most common was fixed drug eruption seen in 4 cases (36.36%), followed by erythema multiforme in 3 cases (27.27%), toxic epidermal necrolysis in 2 cases (18.19%), maculopapular drug reaction (MPDR) and Steven-Johnson Syndrome in 1 case (9.09%).
Out of 350 patients enrolled for the study, Hypertension was the commonest systemic disease seen in geriatric population shown in Figure 2.
In our study, a total of 350 patients with age ≥60 years and above were examined. The eldest patient was of 92 years. Of these, 251(71.71%) patients were males and 99 (28.29%) were females. In the present study, the number of males outnumbered the females which coincide with most of the other studies.7, 8, 9, 10, 11 Male:female ratio of the study population was 2.5:1.
In this study 51.42% were literate and remaining 48.57% were illiterate. 38.85% of study subjects were agricultural worker, 38.71% were retired, 22% were housewife and 0.57% were construction worker. Sociodemographic profile of our study subjects was different from other studies. This might be due to variation in socioeconomic, geographical and environmental differences in study population.
Various physiological changes noted in this study were wrinkling, graying of hairs, xerosis, idiopathic guttate hypomelanosis, senile purpura, senile comedones, seborrheic keratosis, acrochordon, fissured soles, Favre-Racouchot syndrome, Senile lentigens, Cherry angioma and Callosity. Wrinkling was the commonest physiological change seen in all cases. This was in accordance with study done by Pavithra S, Shukla P et al7 (2010), who reported 99.3% cases of skin wrinkling.
Greying of hairs was the second most common change observed and it was prevalent in 98.57% of the study population. This was almost similar with study by Pavithra S, Shukla P et al7 (2010) who recorded 96.8% incidence of graying of hairs.
Idiopathic guttate hypomelanosis was present in 12.28% cases. Our study had lower incidence as compared to study done by Grover, Narasimhalu et al12 (2009). The incidence of idiopathic guttate hypomelanosis reported in their studies is 76.5%, 51.8% and 45.35% respectively.
Senile Purpura was seen in 12% of the study population. This might be due to thin fragile skin in the elderly people. A study done by Priya Cinna Durai et al15 (2012) and Reetu Agarwal et al 13 (2019) reported 1% and 3% cases with senile purpura.
Senile comedones were observed in 33 cases (9.42%) was higher when compared to a studies done by Pavithra et al7 (2010) 8.5% and Grover, Narasimhalu et al12 (2009) 6.5%. This might be due to environmental and various such factors.
Seborrheic Keratosis occurred in 8.28% of the study population whereas Pavithra et al7 (2010) reported an incidence of 27.5% in their study. This might be due to various in genetic, epidermological and environmental factors.
Acrochordon was found in 6.57% of the study population which was lower as compared to a study done by Priya Cinna Durai et al15 (2012) and Grover, Narasimhalu et al12 (2009). They reported 49% and 24.5% cases
with acrochordon.
Fissured soles were seen in 6.57% of the study population. Similiar results was also observed in a study by Pavithra et al7 (2010) reported 6.8%.
Favre – Racouchot Syndrome was found in 5.42% of the study population and all patients were males. Various other study by Leena Raveendra et al10 (2014) 2%, Patange VS, Fernandez RJ et al9 (1995) reported incidence as 2 % and 3% respectively in their studies.
Senile Lentigens was found in 4.85% of the study population and its presence was attributed by prolonged sun exposure. The incidence of senile lentigens in various studies done by Grover, Narasimhalu et al12 (2009), Patange VS, Fernandez RJ et al9 (1995) and Sheethal MP et al14 (2014) were 10% 12% and 30.3% respectively.
Cherry Angioma was the physiological dermatoses seen in 3.71% of our study population. This was much less as compared to the incidence quoted in the study by Priya Cinna Durai et al15 (2012) reported 7.2%.
A wide variety of pathological changes were seen in this study, they were classified into infections and infestations, neoplasia, eczema, pigmentory disorders, papulosquamous disorders, photodermatoses, psychodermatoses, vascular disorders, vesiculobullous disorders, cutaneous drug reactions and Miscellaneous skin changes.
In this present study, Infections and infestations were seen in (22.57%). Of which fungal infection in (48.10%), bacterial
infections in (20.25%), viral infections in (12.66%) and (18.99%) of infestations were seen. The prevalence of infective conditions in our study is less compared to study done by Patange VS, Fernandez RJ et al9 (1995) and Grover, Narasimhalu et al12 (2009) reported 34.5% and 43.5% respectively.
Neoplasia was the second most common pathological dermatoses seen in our study population i.e 74 cases (21.14%). Benign cutaneous tumors were present in 70 (94.60%) cases and 4 (5.40%) cases were suffering from malignant cutaneous tumors. A study by Pavithra S, Shukla P et al7 (2010) noted benign neoplasms as the commonest pathological dermatoses and their incidence was 80.5% which is marginally less as compared to our study.
Among Malignant neoplasm, numbers of cases suffered from basal cell carcinoma were 75% while 25% had squamous cell carcinoma. A study by Sandhyarani Kshetrimayum et al6 (2017) reported 0.8% cases of squamous cell carcinoma and 0.4% of Basal cell carcinoma a lesser incidence as compared to our study.
In the present study eczema was noted in 9.14% of the study subject. The total percentage of eczema in study done by Leena Raveendra et al10 (2014) and DP Thapa, AK Jha et al16 (2012) were 31% and 35.8%. A higher incidence compared to our study.
Pigmentary disorders were seen in 8.86% of the study subjects and Melasma was the commonest pigmentary disorder
as per our study (58.06%) followed by Vitiligo (19.36%). A Study by Priya Cinna Durai et al15 (2012) also reported melasma as the commonest pigmentary disorder seen in 8.3% cases with photoaging.
Papulosquamous Disorders were noted in 8.57% of the study population and Psoriasis was the commonest papulosquamous disorder noted in 53.33%. The incidence of papulosquamous disorders in our study was less in comparison with studies by Pavithra S, Shukla P et al7 (2010) 12.4% and Leena Raveendra et al10 (2014) 12%.
Photodermatoses was seen in 4.57% of the study subjects. Chronic actinic dermatitis (75%) was the most common followed by Polymorphic light eruption seen in (18.75%) cases and solar urticaria in 1 case. A study by Shashikant B. Dhumale et al17 (2016) also reported almost similiar incidence of photodermatoses 4%.
Psychodermatoses was seen in 4.57% of the study subjects. Lichen Simplex Chronicus was the commonest among psychodermatoses in 75% followed by Delusion of parasitosis in 12.50%. Prurigo nodularis and neurodermatitis was seen in (6.25%) each. The incidence of lichen simplex chronicus was 12% in the studies done by Leena Raveendra10 (2014) and Patange VS, Fernandez R J et al9 (1995) much less as compared to our study.
Vascular Disorders were seen in 3.71% of the study subjects.
Vasculitis and thrombophebitis were the commonest seen in 38.46% cases followed by Raynoulds Phenomena in
15.38% and Angiokeratoma of Fordyce was seen in (7.70%). A study done by Leena Raveendra10 (2014) reported 11% incidence of vascular disorder higher as compared to our study.
Vesiculobullous disorders were seen in 3.15% of the study population. Pemphigus Vulgaris was the commonest seen in 45.45% cases followed by Bullous Pemphigiod in 27.27% , Pemphigus foliaceus seen in 18.18% cases and Paraneoplastic Pemphigus in 1 case (9.10%). A study done by Muhammed Kutty Simin et al18 (2020) also reported similar incidence of vesiculo-bullous disorders in 3.5%.
Cutaneous drug reactions were seen in 3.15% of the study population. Fixed drug eruption was the commonest seen in 36.36%. A study by Sandhyarani Kshetrimayum et al6 (2017) reported 2.4% a lesser incidence as compared to our study.
Other miscellaneous geriatric dermatoses were seen in 10.57% of the study group. Among which Palmoplantar Keratoderma, Keloid and Post Herpetic Neuralgia were the highest and seen in 13.51%. A study by Sandhyarani Kshetrimayum et al6 (2017) reported 7.6% as other (miscellaneous) geriatric dermatoses and reported Post Herpetic Neuralgia as highest seen in 4.4%.
On analyzing the associated disease, it was found that Hypertension was the commonest (28.29%) affecting both male and female study subjects. The next was Diabetes Mellitus affecting (17.14%) of the study population. The
study done by Sandhyarani Kshetrimayum et al6 (2017) also reported Hypertension as the commonest associated disease seen in 16.4% followed by Diabetes Mellitus in 6.8% cases.
Spectrum of geriatric dermatoses is rapidly changing causing wide variety of physiological and pathological skin changes. Thus early detection and proper management is crucial to prevent local tissue destruction. Hence this study was undertaken to evaluate the spectrum of cutaneous manifestations to find out pattern as well as frequency of skin diseases in geriatric population in our part of the country and can help in better management.
1. World Health Organization. Definition of an older person. Available at http://www.who.int/healthinfo/ survey/ageingdefolder/en/index.html. Accessed on 18 September 2015.
2. Liebig PS, Rajan SI. An aging India: Perspectives,Prospects and Policies. 2014: 144-146.
3. Jafferany M, Huynh TV, Silverman MA, Zaidi Z. Geriatric dermatosis: a clinical review of Skin diseases in an aging population, Int J Dermatol; 51:509-522.
4. Hashizume H. Skin aging and dry skin. J Dermatol 2004; 31:603-609.
5. Fisher GJ, Wang ZQ, Datta SC, Varani J, Kang S, Voorhees JJ. Pathophysiology of premature skin aging induced by ultraviolet light. N Engl J Med 1997;337:1419- 1428.
6. Kshetrimayum S, Thokchom NS, Vanlalhriatpuii, Hafi NAB. Pattern of geriatric dermatosis at a tertiary care center in North-East India. Int Res Dermatol 2017;3:527-34.
7. Pavithra S, Shukla P, Pai G S. Cutaneous manifestations in senile skin in coastal Goa. Nepal Journal of Dermatology, Venereology & Leprology. 2010;9(1):1-6.
8. Darjani A, Mohtasham-Amiri Z, Mohammad Amini K, Golchai J, Sadre-Eshkevari S, Alizade N. Skin Disorders among Elder Patients in a Referral Center in Northern Iran (2011). Dermatology Research and Practice. 2011:1-5.
9. Patange S V, Fernandez R J.A study of geriatric dermatoses. Indian J Dermatol Venereol Leprol 1995;61: 206-8.
10. Raveendra L. A clinical study of geriatric dermatoses. Our Dermatol Online. 2014;5(3):235-239
11. Varma K, Shesha H, Kumar U, A Clinico- Epidemiological Study of Geriatric Dermatosis in Tertiary Care Centre, Ujjain. IP Indian J Clin Exp Dermatol 2017;3(4):142-147
12. Grover, Sanjiv & Crv, Narasimhalu. (2009). A clinical study of skin changes in geriatric population. Indian journal of dermatology, venereology and leprology. 75. 305-6. 10.4103/03786323.51266.
13. Agarwal R, Sharma L, Chopra A, Mitra D, Saraswat N. A Cross-Sectional Observational Study of Geriatric Dermatoses in a Tertiary Care Hospital of Northern India. Indian Dermatol Online J. 2019 Aug 28;10(5):524-529.
14. Sheethal MP, Shashikumar BM (2015). A cross-sectional study on the dermatological conditions among the elderly population in Mandya city. International Journal of Medical Science and Public Health, 4(4), 467-470.
15. Durai PC, Thappa DM, Kumari R, Malathi M. Aging in elderly: chronological versus photoaging. Indian J Dermatol. 2012 Sep;57(5):343-52. doi: 10.4103/0019-5154.100473. PMID: 23112352; PMCID: PMC3482795.
16. DP, Jha AK, Kharel C, Shreshta S. Dermatological problems in geriatric patients: a hospital based study. Nepal Med Coll J. 2012;14:193-195.
17. Dhumale SB, Khyalappa R. Study of cutaneous manifestations in geriatrics. Int J Res Med Sci 2016;4:1343-6.
18. Simin MK, Sasidharanpillai S, Rajan U, Riyaz N. Dermatoses among patients aged 60 years and above attending a tertiary referral center: A cross-sectional study from North Kerala. J Skin Sex Transm Dis 2021;3:166-72.
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Introduction
Vitiligo is a chronic skin condition characterized by the loss of pigmentation in the skin, resulting in the development of white patches. It can affect anyone, but is more noticeable in people with darker skin tones. The cause of vitiligo is not fully understood, but it is believed to be related to a problem with the immune system. Vitiligo can affect people of any age, gender or ethnicity, but it is more noticeable in people with darker skin tones. The condition is diagnosed by the presence of white patches of skin on the body, which can be accompanied by other symptoms such as itching or burning.1 Symptoms of vitiligo include loss of colour in the skin and the development of white patches or spots on the skin, hair or mucous membranes. The loss of pigmentation can be symmetric or asymmetric and can be found in any part of the body, but it is common on the face, neck, underarms, hands, feet and genitalia. It can also affect scalp hair, beard hair and eyelashes. The affected areas can be small and restricted or
disseminated throughout the body. These patches usually start out as small areas, but can become larger over time. Physically it appears as chalkywhite macules with clear edges dispersed throughout the body parts.2
It is classified into several types, based on the pattern of the depigmented areas, such as: 3
• Acrofacial (affecting fingers and face)
• Mucosal (affecting mucous membrane)
• Universal (affecting more than 80% of the body)
• Segmental (affecting one segment of the body)
The exact cause of vitiligo is not known, but several theories have been proposed. Some experts believe that it is an autoimmune disorder, in which the immune system mistakenly attacks the melanocytes (cells that produce pigment) in the skin are destroyed. Some associated autoimmune diseases such as Hashimoto's Thyroiditis, Addison's disease and
pernicious anaemia may also contribute in causing vitiligo.1
Others believe that it is caused by a combination of genetic (as it tends to run in families) and environmental factors. Certain triggers like sunburn, stress or exposure to certain chemicals can worsen the condition. The condition is not contagious, painful or life-threatening, but it may lead to significant psychological distress and social isolation.1
The diagnosis is made primarily on the clinical examination by a dermatologist and there is no laboratory test to confirm it. There is no cure for vitiligo, so treatment focuses on restoring colour to the affected skin, improving the appearance of the skin and minimizing the spread of depigmentation.2 The best approach will vary depending on the severity of the condition and individual patient circumstances. The absence of melanocytes in an area is usually done using confocal microscopy.1
While there is no cure for vitiligo, treatment can help to manage the symptoms and improve the appearance of affected skin. Treatment options include topical medications, light therapy, surgery and depigmentation. Other treatment options include topical corticosteroids, topical immunomodulators, phototherapy, depigmenting agents and surgical options.3
Lifestyle modifications such as protecting the skin from sunburn and avoiding certain triggers can also be helpful in managing the symptoms. Vitiligo can be a difficult condition to manage and treatment options vary
depending on the size, location and progression of the condition.1 It is important to work with a healthcare provider to develop an appropriate treatment plan as the best approach will vary depending on the severity of the condition and individual patient circumstances. Additionally, psychological support is also important for many people living with vitiligo, as it can have a significant impact on one's selfesteem and self-confidence.
A 65 years old female patient was presented to the Dermatology clinic for having whitish patches over forearm, legs, fingers etc. She was suffering this condition since last 5 years. She is diabetic patient, obese and having sedentary life style. She asked me to give her some treatment. On physical examination, she had large areas of unpigmented skin with well-demarcated white macules which are encircled with normal pigmented skin. Further I investigated the case by performing some pathology reports. Looking at her reports and her present condition I started her on some medications. I had started her oral tofacitinib 5mg 2 times a day for 1 year and then gradually to 5mg at night for 8 months and still continuing her medications. No side effects were seen. Tofacitinib, was found to be a janus kinase (JAK) inhibitor, recent advances have resulted in significant regimentation. Along with oral medication, she was also applying bakuchi oil at morning and tacrolimus at night for long term base. After the application the patient was advised to expose it to morning sunlight daily for growth of melanocyte migration and its proliferation. Topical tacrolimus also cause repigmentation of the skin. She was my regular follow up patient and I am very happy to see her results.
Treatment for vitiligo can be challenging and depends on the extent, location and progression of the condition. The goal of treatment is to restore the colour to the affected skin and to prevent the spread of white patches. There is no single treatment that is effective for everyone and treatment plans may involve a combination of different therapies. Some of the treatment options for vitiligo include:
Topical Corticosteroids: These include corticosteroids, calcineurin inhibitors, and vitamin D analogs. These are first line treatment being very cost effective and having patient compliance. These medications are applied directly to the skin and can help to reduce inflammation and slow down the progression of the vitiligo. These are a type of cream or ointment that can help to restore colour to the affected skin. They are typically used for small areas of vitiligo and are most effective when used in the early stages of the condition. These drugs inhibit collagen synthesis and can cause skin atrophy on chronic use.3,4
Topical Immunomodulators: These are a type of cream or ointment that can help to reduce inflammation and restore colour to the affected skin. They are typically used for small areas of vitiligo and are most effective when used in the early stages of the condition. These drugs can be used to target the immune system and slow down or stop the progression of the vitiligo, but these are usually prescribed for more severe cases. Calcineurin inhibitors like
tacrolimus and pimecrolimus are immunosuppressant’s having good absorption when applied topically. These inhibit calcineurin function by hampering activation and maturation of T cell.
Cyclosporine having poor skin penetration is used systemically.5
Janus kinase (JAK) inhibitors: Tofacitinib, ruxolitinib are commonly used. These are known to cause down regulation of the JAK-STAT pathway which reduces the IFN-y count, which is responsible for cell mediated immunity in vitiligo. These are associated with side effects like arthralgia, increased lipid level, upper respiratory infections.6
Phototherapy: This treatment involves exposing the skin to controlled amounts of natural or artificial light, especially narrowband UVB, is a common treatment option. It is the oldest forms of treatment for vitiligo. Phototherapy can be done in a clinic or at home using a special UV lamp. Phototherapy can help to restore colour to the affected skin and can be used alone or in combination with other treatments. Topical psoralen photo chemotherapy (PUVA) is treatment of choice in patients having being affected to less than 20% of the body. Sunburn and blister formation are some of its side effect. In current times, narrow-band UVB are more preferred for vitiligo.4 Monochromatic excimer light which is a combination of excimer light with xenon chloride gas producing wavelength of 308nm can also be used.3
Surgical options: These options are used in more advanced or stable cases, such as skin grafting, blister grafting
and melanocyte transplantation, these options usually have a good result but are not commonly used because of its invasive nature and potential risks. Such therapies are used in patients who failed to react to classical therapies.3 Skin grafting involves transplanting healthy skin from one part of the body onto the affected area and micro pigmentation, where a tattooing technique is used to restore the colour to the affected area. This treatment often provides cobblestone appearance.3 Suction Blister Epidermal Grafting (SBEG) is achieved dermoepidermal separation by prolonged vacuum suction in the donor area.3
Depigmenting agents: These agents are topical creams or pills that are used to reduce the colour of normal skin surrounding the white patches. By making the surrounding skin colour lighter, the contrast between the white patches and the surrounding skin is reduced, making the vitiligo less noticeable. This option is more commonly used in larger vitiligo patches. Melanocytotoxic agents are commonly used agents like monobenzyl ether, 4-methoxyphenol etc.4 Depigmentation is usually observed after 3-6 months of application to the affected areas.6
New Drug Delivery Systems: The failure of topical medications due to poor penetration and inability of drug to reach the site of action and sometimes failure of systemic absorption, hence a tailored system is needed to for carrying the drug to desired targets which has lead to development of new drug delivery systems like drug-
carrier systems which uses phospholipid-structured carriers for its topical drug delivery. This system has provided enhanced drug penetration, prolonged action and dose reduction.
Vesicular approaches by using Transferosomes, Ethosomes, Liposome’s and Non-vesicular approaches like lipid emulsion, solid lipid nanoparticles, and lecithin organogels. These approaches have improved the pharmacokinetic and pharmacodynamic properties of drug molecules.4
Cosmetics: Used for camouflage the affected area.4
It is important to work with a healthcare provider to develop an appropriate treatment plan, as the best approach will vary depending on the severity of the condition, location, progression and individual patient circumstances. It's also worth noting that, even with treatment, repigmentation may not be complete, and sometimes the therapy can't guarantee that white patches won't come back.
Vitiligo is a depigmenting skin disorder, commonly acquired pigmentary skin disorder that causes demolition of melanocytes. The etiology is unknown but recent progress explain its pathogenesis as autoimmune disease, oxidative stress, genetic (as it tends to run in families) and environmental factors.7,8 In some cases, there may be a family history of vitiligo and certain triggers such as sunburn, stress or injury to the skin may lead to the development of white patches. Some researchers think that vitiligo occurs when the immune
system mistakenly attacks and damages certain cells in the skin called melanocytes, which are responsible for producing the pigment that gives colour to the skin.1
Many mechanisms are involved in subsequent loss of melanocyte most of them are explained either by immune attack or cell degeneration and detachment. Some authors have suggests neural system playing some role in its pathogenesis.1 The colour and size of white patches may change over time and new patches may appear. Diagnosis of vitiligo is usually made by visual examination; a physician will also perform some tests such as wood's lamp examination, skin biopsy and blood tests to rule out other pigmentation disorders.8
Treatment for vitiligo can be challenging and it depends on the size, location and progression of the condition. It can include topical corticosteroids, topical immunomodulators, phototherapy (such as narrowband ultraviolet B) and depigmenting agents, newer drug delivery. Sometimes, a combination of these methods is used and sometimes surgical options can be considered.3 The best approach will vary depending on the severity of the condition and individual patient circumstances.
Moreover, while some people experience little to no psychological impact, others may experience a significant impact on their self-esteem and quality of life. Support and counselling can be helpful to many people dealing with vitiligo.
Vitiligo is an acquired cutaneous disorder of depigmentation with no underline cause and with no definitive cure having complex pathogenesis. Constant efforts have been put to reconstruct its treatment and are still under studies and observation. The diagnosis of vitiligo is usually made by a dermatologist based on the appearance of the white patches on the skin and a physical examination. Treatment for vitiligo can be challenging as it is a chronic condition with no definite cure. The treatment options available are aimed at restoring the skin colour and slowing down or stopping the progression of the condition.
Treatment plans should be tailored to each individual patient and may involve a combination of different treatments. The best approach will depend on the individual case, taking into account the severity, location, progression of the condition, subtype of the disease, as well as the preferences of the patient. Advice on cosmetic camouflage should be offered by specialized nurse or practitioner on the affected areas. These may include foundation based sunscreens which are self tanning products containing dihydroxyacetone are used.
Topical therapy is preferred for mild-to-moderate vitiligo conditions. This therapy mainly targets the immune system and arrest the spread of white patches. Topical corticosteroids having anti-inflammatory and immunomodulating capacity are primarily used. On chronic use of this corticosteroid is known to
cause skin atrophy hence care must be taken for application on skin, genitals or for that matter on children. Treatment response of topical corticosteroid with phototherapy is providing promising synergistic effect. Surgical methods can be offered for patients who failed to respond to above mentioned therapies. Living with vitiligo can be challenging, as the condition can have a significant impact on a person's self-esteem and quality of life. Many people with vitiligo find it helpful to connect with support groups and talk to a counsellor or therapist who can help them cope with the emotional aspects of the condition. Regular follow-ups, monitoring and adjusting the treatment plan as per the need will help to achieve better control of symptoms and improve the quality of life.
1. Bergqvist C, Ezzedine K. Vitiligo: A Review. Dermatology. 2020;236(6):571592. doi: 10.1159/000506103. Epub 2020 Mar 10. PMID: 32155629.
2. Joge R R, Kathane P U, Joshi S H (September 18, 2022) Vitiligo: A Narrative Review. Cureus 14(9): e29307. doi:10.7759/cureus.29307
3. Faria AR, Tarlé RG, Dellatorre G, Mira MT, Castro CC. Vitiligo--Part 2--classification, histopathology and treatment. An Bras Dermatol. 2014 Sep-Oct;89(5):784-90. doi: 10.1590/ abd1806-4841.20142717. PMID: 25184918; PMCID: PMC4155957.
4. Garg BJ, Saraswat A, Bhatia A, Katare OP. Topical treatment in vitiligo and the potential uses of new drug delivery systems. Indian J Dermatol Venereol Leprol 2010;76:231-238
5. Dr Kresimir Kostovic, Aida Pasic (2005). New Treatment Modalities for Vitiligo. , 65(4), 447–459. doi:10.2165/00003495-20056504000002
6. Kubelis-López DE, Zapata-Salazar NA, Said-Fernández SL, SánchezDomínguez CN, Salinas-Santander MA, Martínez- Rodríguez HG, VázquezMartínez OT, Wollina U, Lotti T, Ocampo - Candiani J. Updates and new medical treatments for vitiligo (Review). Exp Ther Med. 2021 Aug;22(2):797. doi: 10.3892/etm.2021.10229. Epub 2021 May 25. PMID: 34093753; PMCID: PMC8170669.
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MBBS, MD (D.V.L)
Fellowship in Aesthetic Medicine
Consultant Dermatologist & Cosmetologist
Ashirwad Hospital, Gota, Ahmedabad
Professor (Skin & VD)
SBKS Medical Institute and Research Centre
Vadodara, Gujarat
Abstract
Woolly hair can be due to autosomal dominant or recessive inheritance or as a part of complex syndromes like Naxos disease and Carvajal syndrome. A 13 year old girl born of consanguineous marriage presented to Dermatology clinic with woolly hair and multiple hyperkeratotic crusted plaques with few bullae over back, buttocks, bilateral knees and legs. X-ray Chest, ECG and 2-D ECHO were not indicative of arrhythmia or cardiomyopathy. Naxos disease and Carvajal syndrome show similar cutaneous manifestations whereas Cardiac manifestation of arrythmogenic right ventricular dilated cardiomyopathy (ARVC) is seen in Naxos disease and left ventricular dilated cardiomyopathy in Carvajal syndrome. Though skin and hair changes are present since birth, ARVC starts at or
after adolescence. Our case has entered into adolescence and hence it is prudent to periodically evaluate her for cardiac manifestations so as to offer suitable interventions and thereby decrease morbidity.
Key-words: Woolly hair, Plantar keratoderma, Naxos disease, Carvajal syndrome
Introduction
Woolly hair can occur as an autosomal dominant trait or autosomal recessive inheritance or as woolly hair naevus. Whenever woolly hair is associated with any kind of palmo-plantar keratoderma, a search for possible cardiac abnormalities is recommended. It may be a forerunner of Naxos disease [Arrythmogenic right ventricular dilated cardiomyopathy (ARVC)] or
Carvajal syndrome [Left ventricular dilated cardiomyopathy]. It is thus essential to screen such cases for relevant systemic involvement. Affected families have been detected in Greek islands with a prevalence of 1:1,000. Cases have also been reported in Turkey, Israel and Saudi Arabia and two cases have been reported from India.
A 13 year old girl born of consanguineous marriage presented to Dermatology clinic with woolly hair and skin lesions over abdomen, back, buttocks, thighs and bilateral lower legs since birth. No family history of similar complaints. Her birth and developmental history had been normal. Height was 138 cm and weight 32 kg. Her vitals were normal. On cutaneous examination, multiple hyperkeratotic psoriasiform crusted plaques with few bullae were present on back, buttocks, bilateral knees and legs. Other significant findings were plantar keratoderma, follicular keratosis. Bilateral finger and toe nails showed onychogryphosis. Other systemic examination did not reveal any abnormality. X-ray Chest showed no cardiomegaly. ECG was not indicative of arrhythmia. 2D- ECHO showed good biventricular function with no dilatation. Genetic screening could not be done. Patient was provisionally diagnosed to have Naxos disease or Carvajal syndrome. At present, as our case has no cardiac symptom, counselling has been done regarding periodic follow up and regular cardiac screening. Tablet Acitretin 10 mg orally daily and Salicylic acid 12% ointment application on soles has been started.
• Naxos disease and Carvajal syndrome are disorders with mutations in JUP and DSP gene respectively. These genes encode for desmosomal proteins, plakoglobin and desmoplakin, so mutation results in weakening and disruption of desmosomes and adherens junctions of myocardium and epidermis predominantly.
Though skin and hair changes are present since birth, ARVC starts at or after adolescence and the penetrance of the disease in individuals with a plakoglobin gene mutation is shown to be 100%.1
• If the cause is a plakoglobin gene mutation, patients can present with syncope and palpitation (caused by ventricular tachycardia). If the cause is a desmoplakin gene mutation, the predominant findings are related with heart failure.
• Ali Baykan, Seref Olgar et al. reported 6 paediatric patients with woolly hair and palmoplantar keratoderma; 2 cases presented with ventricular tachycardia attack and 2 cases with severe heart failure while 2 cases having only cutaneous findings without cardiac involvement at time of diagnosis.2
• Since our case had no cardiac symptom, parents were counseled regarding significance of regular follow-up of this patient.
• Primary objective of treatment is the prevention of prolonged arrhythmia attacks and sudden cardiac deaths. Thrombus may develop as a result of severe heart failure or recurrent and prolonged arrhythmias or may be due to myocardial hypokinesia related to advanced cardiomyopathy.3 So, Periodic monitoring and initiation of treatment at the time of cardiac symptoms will prevent sudden death.
• On Conclusion, for cases with woolly hair and palmoplantar keratoderma, cardiac assessment is must considering Naxos or Carvajal disease along
with gene mutation study if feasible.
• Early diagnosis, periodic cardiac assessment and treatment of arrhythmias and heart failure may increase life expectancy of children. Referral to cardiac physician at the time of presentation is essential.
References
1. Protonotarios N, Tsatsopoulou A. Naxos disease: cardiocutaneous syndrome due to cell adhesion defect. Orphanet J Rare Dis 2006; 1: 4.
2. Ali Baykan, Seref Olgar & Mustafa Argun : Different clinical presentations of Naxos disease and Carvajal syndrome:Case series from a single tertiary center and review of the literature. Anatol J Cardiol 2015; 15:404-8
3. Narin N, Akcakus M, Gunes T, Celiker A, Baykan A, Uzum K, et al.Arrhythmogenic right ventricular cardiomyopathy (Naxos disease): reportof Turkish boy. Pacing Clin Electrophysiol 2003; 26: 2326-9
