Photobiomodulation therapy as an adjunct to resistance exercises on muscle metrics, functional balance, functional capacity, and physical performance among older adults: A systematic scoping review.

This scoping review aims to summarize the literature on photobiomodulation (PBM) therapy as an adjunct to resistance exercise among older adults, focusing on its effects on muscle metrics, functional balance, capacity, and physical performance. The participants included were older adults aged ≥ 60 years and either gender. The concept was the application of PBM and resistance exercises with no limits on the context parameters. The databases Medline, Embase, Scopus and Web of Science were searched from inception till February 2024. Methodological quality was assessed using the Cochrane risk of bias 2.0. A total of 10 studies were included in the review. PBM, along with resistance training, was reported in six studies, whereas four reported PBM with isometric fatigue protocol. PBM with a wavelength of 808 nm was given on the belly of the rectus femoris muscle in most of the studies. The common dosimetry parameters used were- power density 35.7 W/cm2, energy density 250 J/cm2, 8 sites, energy per site 7 J, duration of 70 s per site, spot size per diode of 0.028 cm2, and stationary contact of application. The application of PBM therapy alongside resistance exercise has been found to improve muscle metrics, functional capacity, and functional performance and reduce fatigability when compared with the control group in most of the studies. When incorporating PBM therapy as an adjunct to resistance training, it is crucial to consider the dosimetry parameters involved-to achieve the intended therapeutic effect. Adjusting treatment parameters with this information can optimize the effectiveness of PBM therapy and improve treatment outcomes for patients.

DNA methyltransferase isoforms regulate endothelial cell exosome proteome composition.

Extrinsic and intrinsic pathological stimuli in vascular disorders induce DNA methylation based epigenetic reprogramming in endothelial cells, which leads to perturbed gene expression and subsequently results in endothelial dysfunction (ED). ED is also characterized by release of exosomes with altered proteome leading to paracrine interactions in vasculature and subsequently contributing to manifestation, progression and severity of vascular complications. However, epigenetic regulation of exosome proteome is not known. Hence, our present study aimed to understand influence of DNA methylation on exosome proteome composition and their influence on endothelial cell (EC) function. DNMT isoforms (DNMT1, DNMT3A, and DNMT3B) were overexpressed using lentivirus in ECs. Exosomes were isolated and characterized from ECs overexpressing DNMT isoforms and C57BL/6 mice plasma treated with 5-aza-2'-deoxycytidine. 3D spheroid assay was performed to understand the influence of exosomes derived from cells overexpressing DNMTs on EC functions. Further, the exosomes were subjected to TMT labelled proteomics analysis followed by validation. 3D spheroid assay showed increase in the pro-angiogenic activity in response to exosomes derived from DNMT overexpressing cells which was impeded by inclusion of 5-aza-2'-deoxycytidine. Our results showed that exosome proteome and PTMs were significantly modulated and were associated with dysregulation of vascular homeostasis, metabolism, inflammation and endothelial cell functions. In vitro and in vivo validation showed elevated DNMT1 and TGF-ß1 exosome proteins due to DNMT1 and DNMT3A overexpression, but not DNMT3B which was mitigated by 5-aza-2'-deoxycytidine indicating epigenetic regulation. Further, exosomes induced ED as evidenced by reduced expression of phospho-eNOSser1177. Our study unveils epigenetically regulated exosome proteins, aiding management of vascular complications.

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.

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.

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.

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.

Effect of family-centered care interventions on well-being of caregivers of children with cerebral palsy: a systematic review.

Background: Caring for a child with long-term functional limitations can have a negative impact on the physical and psychological well-being of the caregiver. Family-centered care (FCC) interventions have the potential to empower caregivers and contribute to their well-being. This systematic review aimed to synthesize existing evidence on the effectiveness of FCC interventions in improving the well-being of caregivers of children with cerebral palsy (CP), and identify the key components of such interventions that are most commonly practiced and deemed effective. Methods: This review systematically searched seven databases for randomized controlled trials that evaluated the effectiveness of any FCC intervention on the well-being of caregivers of children with or at risk of CP. We used the Cochrane RoB 2.0 tool to assess risk of bias and Critical Appraisal Skills Programme (CASP) checklist for critical appraisal. Due to high heterogeneity of studies, narrative synthesis was used to summarize the data. Results: The review consists of 11 studies which were categorized into five sections based on the components of FCC intervention provided in each individual study: 1. Information provision, and Enabling and partnership (n= 5); 2. Information provision, and Respectful and supportive care (n= 1); 3. Enabling and partnership (n= 2); 4. Enabling and partnership, and Respectful and supportive care (n= 2); 5. Information provision, Enabling and partnership and Respectful and supportive care (n= 1). Risk of bias was low in four studies, unclear in two studies, and high in five studies. Conclusion: FCC interventions were found to be effective in improving caregivers' satisfaction with attainment of child and caregiver goals. Evidence from multiple studies does not strongly support the effectiveness of FCC interventions on caregiver's mental health, parenting and personal outcomes. Limited evidence precludes a conclusion on the effectiveness of the components of FCC on well-being of caregivers of children with CP.

Oxygen Scarcity and COVID-19: Lessons Learned During the May-June 2021 Peak, Karnataka, India.

SUMMARY: Oxygen is a most essential medical gas. This report shows the observations during the second wave of oxygen management in Karnataka, India, where there was an unprecedented rise in COVID-19 cases during May-June 2021. The state was allocated 731 metric tons against the actual requirement 1200 (63%). While there were several bottlenecks in the production, filling, transportation, and refilling, there were issues with supply-chain management, which led to the death of 24 patients on life-support in a district. The pandemic showed fragility in existing supply chain management and warrants further streamlining to address future crises.

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.