ArticleJournal of Taibah University Medical Sciences2022
Measurement of visceral fat for early prediction of prediabetes-Cross-sectional study from Southern India.
Article in Journal of Taibah University Medical Sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Quantitative metabolomics and machine learning identify differential metabolomic biomarkers across normoglycaemia, pre-diabetes and diabetes in a Qinghai Plateau population.BMJ public health · 2026Article
- A Systematic Review and Meta-Analysis of Efficacy and Safety of Liraglutide in Patients with Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Relationship between skeletal muscle mass and glycemic parameters in individuals with young-onset type 2 diabetes mellitus.Irish journal of medical science · 2025Article
- Epigenetics in Obesity and Diabetes Mellitus: New Insights.Nutrients · 2023Review
- Serum Uric Acid to High‑density Lipoprotein Cholesterol Ratio is Associated with Visceral Fat in Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Anthropometric, Bioelectric, and Visceral Fat Indices to Predict Hypertension in Rural Rohtak, Haryana.Indian journal of community medicine : official publication of Indian Association of Preventive & Social MedicineArticle
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Authors and funding
4 authors.
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Abstract
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 ( 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.
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