Elevation in Cardiovascular Disease Risk in South Asians Is Mediated by Differences in Visceral Adipose Tissue
Bibliographic record
Abstract
South Asians have a higher risk for cardiovascular disease (CVD) that remains largely unexplained. We hypothesized that the increased CVD risk in South Asians compared to Europeans is mediated through higher levels of visceral adipose tissue (VAT) in South Asians compared to total body fat and subcutaneous abdominal adipose tissue (SAT). South Asians (207) and Europeans (201) underwent assessment for demographics, body fat, and risk factors. Linear regression models were created by sex for each risk factor to explore mediation effects of total body fat, SAT, and VAT adjusted for age, income, smoking, and BMI (menopausal status for women). Mediation was based on changes in the ethnicity β coefficient due to additional adjustment for our adipose variable of interest and the Sobel test for mediation. South Asians had worse lipid, glucose, insulin, and C-reactive protein (CRP) levels than Europeans after adjusting for confounders. Most of these differences remained even after further adjustment by either total body fat or SAT. In contrast, VAT attenuated the ethnic differences in risk factors by 16%-52%. After adjusting for VAT, there were no longer ethnic differences in total cholesterol (TC), LDL-C, TC/HDL-C, glucose, and diastolic blood pressure (BP) in men, and in HDL-C, triglycerides (TG), TC/HDL-C, and homeostasis model (HOMA) in women, and VAT was a significant mediator for these risk factors. Higher levels of risk factors for CVD in South Asians are predominantly because of the unique phenotype of South Asians having greater VAT than Europeans even at the same BMI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".