Four-Step Co-Designing of the Reablement Strategies Targeting Sarcopenia (ReStart-S): An Exercise-Based Multicomponent Program for Older Adults Residing in Long-Term Care Settings.

Background: The prevalence of sarcopenia is concerningly high in long-term care settings (LTCS); yet, no exercise programs specifically targeting older adults living in residential care are available. Objective: The goal of the present study was to co-design and validate a program named Reablement Strategies targeting Sarcopenia (ReStart-S) for older long-term care residents. Design: Cross-sectional study with an exploratory phase. Settings: LTCS in Udupi, Karnataka, India. Participants: Sarcopenic older adults diagnosed using Asian Working Group for Sarcopenia 2019 criteria. Material and Methods: The program was designed using a four-step intervention mapping technique involving systematic progression after completing each step. The steps included 1) identifying the appropriate exercise-based intervention for sarcopenia, 2) determining objectives and expected outcomes, 3) seeking expert views through a Delphi consensus approach, and 4) assessing the feasibility of ReStart-S program among older adults living in LTCS. Results: A comprehensive literature review appraised existing exercise programs for managing sarcopenia. A workshop held with six older adults and one caretaker, decided on morning exercise sessions, recommended 2-7 days/week. The results of the review and workshop were compiled for the Delphi process that had seven experts from 5 countries, achieving a 71% response rate after four rounds. In the last step, a pilot study on eight LTCS residents, two males and six females with a mean age of 78.3 ± 8.3 years, was conducted and the program was found to be feasible. Conclusion: The ReStart-S program for managing sarcopenia among older adults residing in LTCS incorporates evidence from the literature and the engagement of older adults, caregivers, and experts, making it a contextually appropriate intervention. Our study also provides researchers and healthcare professionals insight into co-designing an intervention program for vulnerable older adults. Finally, the program evaluation indicates that a full-scale trial testing the efficacy of the ReStart-S program is feasible.

Barriers and Enablers for Physical Activity Engagement Among Individuals From India With Type 2 Diabetes Mellitus: A Mixed-Method Study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex, chronic condition that can cause multiple complications due to poor glycemic control. Self-management plays a crucial role in the management of T2DM. Lifestyle modifications, including physical activity (PA), are fundamental for self-management. This study explored the knowledge, perception, practice, enablers, and barriers of PA among individuals with T2DM. METHODS: A mixed-method study was conducted among individuals with T2DM in Udupi taluk, India. A cross-sectional survey (n = 467) followed by an in-depth interview (n = 35) was performed. The data were analyzed using descriptive statistics and thematic analysis, respectively. RESULTS: About half (48.8%) of the participants engaged in PA of which 28.3% had an adequate score in the practice of PA. Walking was the most preferred mode. Self-realization, Comprehension, perception, and source of information, PA training, Current PA practices, enablers and barriers for PA were 6 themes derived under knowledge, perception, and practice of PA. CONCLUSION: Despite knowing the importance of PA, compliance with PA was poor. The personal/internal, societal, and external factors constituted the trinity of barriers and enablers in compliance with PA. Behavioral changes, societal changes, policy initiatives, and PA training in health care settings may enhance PA practice among individuals with T2DM.

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.

The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.

Effect of a Customized Physical Activity Promotion Program on Visceral Fat and Glycemic Parameters in Individuals With Prediabetes: A Randomized Controlled Trial.

BACKGROUND: Physical activity of any amount results in substantial health benefits. However, public awareness of physical activity benefits in chronic diseases is inadequate in India. Prediabetes is a significant health issue on a global scale. Visceral fat (VF) is considered as an early predictor of prediabetes. Ethnicity and race have a substantial impact on VF. Hence, this study intended to evaluate the effect of a customized physical activity promotion program on VF and glycemic parameters in individuals with prediabetes. METHODS: In the current, parallel group randomized controlled trial, a total of 158 participants were recruited: 79 in intervention and 79 in control group. The study included the prediabetes individuals based on American Diabetes Association criteria. Participants from the intervention group received the customized physical activity promotion program for 24 weeks. The primary outcome measures of the study were VF level and glycemic parameters that included fasting blood sugar and glycosylated hemoglobin. Two-way mixed analysis of variance was used to study the mean difference of an outcome between 2 groups over time. RESULTS: The study found a statistically significant interaction between the intervention and times on VF level, F1,136 = 23.564, fasting blood sugar levels, F1,136 = 8.762, and glycosylated hemoglobin levels, F1,136 = 64.582 at the end of 24 weeks (P < .05). CONCLUSIONS: This study concluded that a customized physical activity promotion program was effective in reducing VF in individuals with prediabetes as compared with controls. It improved glycemic control by reducing fasting blood sugar and glycosylated hemoglobin levels.

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers.

OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. METHODS: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. RESULTS: After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. CONCLUSION: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.

Deep learning based detection of monkeypox virus using skin lesion images.

As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.

Psychometric performance of the Kannada version of sarcopenia quality of life questionnaire (SarQoL®).

BACKGROUND: The Sarcopenia Quality of Life (SarQoL®) is a patient reported quality-of-life questionnaire specific to sarcopenia. In the Indian context, its availability is limited to Hindi, Marathi and Bengali vernacular languages. AIMS: This study aimed to translate, cross-culturally adapt the SarQoL® questionnaire into Kannada and investigate its psychometric properties. METHODS: The SarQoL®-English version was translated into Kannada with the developer's permission and in accordance with their requirements. To validate the discriminative power, internal consistency and floor and ceiling effect of the SarQoL®-Kannada questionnaire were assessed in the first step. In the second step, the construct validity and the test-retest reliability of the SarQoL®-Kannada was determined. RESULT: There was no difficulty in the translation process. A total of n = 114 participants (sarcopenic participants n = 45 and n = 69 non-sarcopenic participants) were included. The good discriminative power of the SarQoL®-Kannada questionnaire {quality of life for sarcopenic subjects [56.43 ± 11.32] vs. non-sarcopenic ones [79.38 ± 8.16], p < 0.001}. High internal consistency (Cronbach's alpha coefficient was 0.904) and no ceiling/ floor effect were reflected. Excellent test-retest reliability (intraclass correlation coefficient was 0.97, 95% CI 0.92-0.98) were found. A good convergent and divergent validity with similar and different domains of WHOQOL-BREF was observed, while EQ-5D-3L had good convergent and weak divergent validity. CONCLUSION: The SarQoL®-Kannada questionnaire is valid, consistent and reliable for the measurement of quality of life of sarcopenic participants. SarQoL®-Kannada questionnaire is now available to be used in clinical practice and as a treatment outcome indicator in research.

Impact of anemia on sputum smear conversion and treatment outcomes among newly diagnosed tuberculosis patients in Southern India.

Background: Tuberculosis (TB) is a leading cause of mortality worldwide. The higher prevalence of anemia among TB patients is concerning due to its association with delayed sputum conversion and poor treatment outcomes. The present study aimed to evaluate the association of anemia with sputum smear conversion and treatment outcomes among TB patients. Methods: In a prospective community-based cohort study, TB patients were recruited from 63 primary health centers in the district. Blood samples were collected at baseline, at 2 months, and at the end of 6 months. Data were analyzed using SPSS software version 15. Results: Out of 661 patients recruited, anemia was observed among 503 (76.1%) participants. Prevalence of anemia was more among males 387 (76.9%) than 116 (23.1%) females. Out of 503 anemic patients, 334 (66.4%) had mild, 166 (33.0%) had moderate, and 3 (0.6%) had severe anemia at baseline. At 6-month treatment completion, 16 (6.3%) were still anemic. Among 503 anemic patients, 445 (88.4%) were given iron supplements and remaining 58 (11.6%) were managed with diet modifications. After completion of TB treatment, 495 (98.4%) patients had favorable treatment outcomes, whereas 8 (1.6%) patients had died. Severe anemia was not associated with poor outcomes. Conclusions: The presence of anemia among newly diagnosed TB patients, especially pulmonary TB was high. Increased risk of anemia was noted among males who were alcohol and tobacco consumers. There was no significant association between the presence of anemia and sputum conversion from baseline to 6 months of treatment completion.

A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.

The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure.

Erythrocyte miRNA-92a-3p interactions with PfEMP1 as determinants of clinical malaria.

Based on the recently added high throughput analysis data on small noncoding RNAs in modulating disease pathophysiology of malaria, we performed an integrative computational analysis for exploring the role of human-host erythrocytic microRNAs (miRNAs) and their influence on parasite survival and host homeostasis. An in silico analysis was performed on transcriptomic datasets accessed from PlasmoDB and Gene Expression Omnibus (GEO) repositories analyzed using miRanda, miRTarBase, mirDIP, and miRDB to identify the candidate miRNAs that were further subjected to network analysis using MCODE and DAVID. This was followed by immune infiltration analysis and screening for RNA degradation mechanisms. Seven erythrocytic miRNAs, miR-451a, miR-92a-3p, miR-16-5p, miR-142-3p, miR-15b-5p, miR-19b-3p, and miR-223-3p showed favourable interactions with parasite genes expressed during blood stage infection. The miR-92a-3p that targeted the virulence gene PfEMP1 showed drastic reduction during infection. Performing pathway analysis for the human-host gene targets for the miRNA identified TOB1, TOB2, CNOT4, and XRN1 genes that are associated to RNA degradation processes, with the exoribonuclease XRN1, highly enriched in the malarial samples. On evaluating the role of exoribonucleases in miRNA degradation further, the pattern of Plasmodium falciparum_XRN1 showed increased levels during infection thus suggesting a defensive role for parasite survival. This study identifies miR-92a-3p, a member of C13orf25/ miR-17-92 cluster, as a novel miRNA inhibitor of the crucial parasite genes responsible for symptomatic malaria. Evidence for a plausible link to chromosome 13q31.3 loci controlling the epigenetic disease regulation is also suggested.

Effect of targeted intervention on C-terminal agrin fragment and its association with the components of sarcopenia: a scoping review.

BACKGROUND: C-terminal Agrin Fragment (CAF) has emerged as a potent biomarker for identifying sarcopenia. However, the effect of interventions on CAF concentration and the association of CAF with sarcopenia components are unclear. OBJECTIVE: To review the association between CAF concentration and muscle mass, muscle strength, and physical performance among individuals with primary and secondary sarcopenia and to synthesize the effect of interventions on the change in the level of CAF concentration. METHODS: A systematic literature search was conducted in six electronic databases, and studies were included if they met the selection criteria decided a priori. The data extraction sheet was prepared, validated, and extracted relevant data. RESULTS: A total of 5,158 records were found, of which 16 were included. Among studies conducted on individuals with primary sarcopenia, muscle mass was significantly associated with CAF levels, followed by hand grip strength (HGS) and physical performance, with more consistent findings in males. While in secondary sarcopenics, the strongest association was found for HGS and CAF levels, followed by physical performance and muscle mass. CAF concentration was reduced in trials that used functional, dual task, and power training, whereas resistance training and physical activity raised CAF levels. Hormonal therapy did not affect serum CAF concentration. CONCLUSION(S): The association between CAF and sarcopenic assessment parameters varies in primary and secondary sarcopenics. The findings would help practitioners and researchers choose the best training mode/parameters/exercises to reduce CAF levels and, eventually, manage sarcopenia.

Disseminated tuberculosis masquerading as depression.

We report the case of a 23-year-old man without any significant premorbid conditions initially presenting to a psychiatrist with suspected depression but later referred to our hospital owing to the possibility of systemic disease and subsequently diagnosed as having disseminated tuberculosis.

Two viruses in a pod: a case series of coinfection of COVID-19 with dengue.

In regions endemic to both COVID-19 and dengue, cases of coinfections are possible. Since they have similar clinical presentations, but management might be different, it is crucial to identify these cases of coinfections. We diagnosed seven cases of dengue-COVID-19 coinfections. Fever, myalgia, rash and thrombocytopenia were found to be the common features. All patients recovered well with supportive treatment. We report this series to highlight the possibility of rare coinfections in endemic areas and the importance of a high index of suspicion, early diagnosis and prompt management.

Measurement of visceral fat for early prediction of prediabetes-Cross-sectional study from Southern India.

Objective: Prediabetes is a precursor to type 2 diabetes mellitus and routine screening of prediabetes is crucial. Visceral fat (VF) is associated with prediabetes and insulin resistance. Ethnic and racial differences resulting in different levels of VF in the Indian population necessitates an India-specific study. There is a dearth of literature on the cut-off values of VF measured using a bioelectrical impedance analyzer (BIA) to predict prediabetes in the Indian population. Hence, the main objective of this study was to determine the sex-specific cut-off value of VF on BIA to predict prediabetes in the Indian population. Methods: Three hundred individuals aged 18-55 years of both sexes were selected for this cross-sectional study. VF was evaluated as a part of body composition analysis using BIA. The body composition variables for the prediction of prediabetes were examined using backward logistic regression. Optimal cut-off levels of VF to predict prediabetes were identified using receiver operator characteristic curve (ROC) analysis. Results: VF, total fat, and age were found to be associated with prediabetes (p ≤ 0.05). In females, the cut-off value of VF for predicting prediabetes was identified as 8 with 77.8% sensitivity and 69.3% specificity; in males, it was 11 with 84% sensitivity and 62.9% specificity. Conclusion: This study contributes to the sex-specific cut-off values of VF level on BIA that can be used for predicting prediabetes in the Indian population.

A review of the components of exercise prescription for sarcopenic older adults.

PURPOSE: To synthesize the details of the exercises/exercise program prescribed for the improvement of muscle mass/muscle strength/physical performance among sarcopenic older adults. METHODS: A systematic literature search was conducted in five electronic databases and the details of exercises such as single component or multicomponent exercise program, frequency/week, intensity, duration of the exercise program, type of exercises, progression, adverse events reported, outcome measures used, and whether technology or other educational aids were used to deliver the program were extracted. RESULTS: A total of 10,045 records were identified and 27 records were included. Resistance exercises were included in all the studies, with the frequency ranging from 1 to 5/week, intensity ranging from 20 to 80% of 1 repetition maximum (RM), or 6-14 points on ratings of perceived exertion (RPE), and duration per session ranging from 20 to 75 min. The intensity of aerobic exercises ranged from 50 to 70% of heart rate max or a level of 7-17 in RPE with a duration ranging from 6 to 30 min per session for 2-5 days/week. For balance exercises, the intensity was mentioned as the level of effort 3 on a scale of 10, and the time duration per session ranged from 5 to 30 min for a frequency of 2/3 per week. CONCLUSION: This review synthesized the components of exercise prescription for sarcopenic older adults which would help practitioners and researchers in selecting the frequency, intensity, duration, type, mode, and progression while prescribing exercises.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics.

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.

Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients.

Extrinsic and intrinsic factors influencing metabolic memory in type 2 diabetes.

Direct and indirect influence of pathological conditions in Type 2 Diabetes (T2D) on vasculature manifests in micro and/or macro vascular complications that act as a major source of morbidity and mortality. Although preventive therapies exist to control hyperglycemia, diabetic subjects are always at risk to accrue vascular complications. One of the hypotheses explained is 'glycemic' or 'metabolic' memory, a process of permanent epigenetic change in different cell types whereby diabetes associated vascular complications continue despite glycemic control by antidiabetic drugs. Epigenetic mechanisms including DNA methylation possess a strong influence on the association between environment and gene expression, thus indicating its importance in the pathogenesis of a complex disease such as T2D. The vascular system is more prone to environmental influences and present high flexibility in response to physiological and pathological challenges. DNA methylation based epigenetic changes during metabolic memory are influenced by sustained hyperglycemia, inflammatory mediators, gut microbiome composition, lifestyle modifications and gene-nutrient interactions. Hence, understanding underlying mechanisms in manifesting vascular complications regulated by DNA methylation is of high clinical importance. The review provides an insight into various extrinsic and intrinsic factors influencing the regulation of DNA methyltransferases contributing to the pathogenesis of vascular complications during T2D.