Ethnic and Regional Variability in Cardiometabolic Risk Among Urban South Asians: A Systematic Review
Bibliographic record
Abstract
Background: Cardiometabolic diseases (CMDs), including diabetes, hypertension, and metabolic syndrome, are rising sharply in South Asia’s urban populations. However, the influence of ethnic and regional variation within these urban settings remains poorly understood. Objective: To systematically review the literature on ethnic and regional disparities in cardiometabolic risk among urban South Asian adults, with emphasis on subnational variability in India, particularly Odisha and Kalahandi district. Methods: This review followed PRISMA 2020 guidelines. A comprehensive search was conducted across PubMed, Scopus, Cochrane, Ovid MEDLINE, Web of Science, Google Scholar, and grey literature (WHO, ICMR) for studies published between 2015 and 2025. Inclusion criteria focused on urban South Asian adults (≥18 years) with data stratified by ethnicity or region. Quality was assessed using the Newcastle–Ottawa Scale and AMSTAR-2. A narrative synthesis was conducted. Results: Thirteen studies were included. CMD prevalence varied by country, state, and district. Northern and western India showed higher CMD rates than eastern regions. In Odisha, metabolic syndrome prevalence ranged from 24% to 33.5%, with a pronounced gender gap in central obesity and pre-metabolic syndrome. Findings from Kalahandi district revealed a substantial burden of early-stage CMD risk, especially among young urban adults. Conclusion: Cardiometabolic risk among urban South Asians is shaped by complex regional and ethnic factors. Public health strategies must move beyond generalized models to adopt localized, culturally tailored interventions that address the specific needs of diverse urban populations. Keywords: Cardiometabolic risk, Urban South Asia, Ethnic disparities, Regional variation, India, Odisha, Systematic review
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".