Adipocyte Hypertrophy, Fatty Liver and Metabolic Risk Factors in South Asians: The Molecular Study of Health and Risk in Ethnic Groups (mol-SHARE)
Bibliographic record
Abstract
OBJECTIVE: We sought to determine if differences in the distribution and characteristics of adipose tissue between South Asians and white Caucasians account for differences in risk factors for cardiovascular disease. RESEARCH DESIGN AND METHODS: We recruited 108 healthy South Asians (36.8 years) and white Caucasians (34.2 years) within three BMI strata. Body composition, adipocyte size, abdominal fat area, and hepatic adiposity were assessed and related to fasting glucose, insulin, lipids and adiponectin. RESULTS: After adjustment for age, sex, and BMI, South Asians compared to white Caucasians had higher ln fasting insulin (mean difference (MD): 0.44; 95% CI: 0.20-0.69), lower HDL cholesterol (md: -0.13; 95% CI:-0.26 to -0.01), and lower adiponectin (md: -2.38; 95% CI: -3.59 to -1.17). South Asians also had more body fat (md: 2.69; 95% CI: 0.70 to 4.69), lower lean muscle mass (md: -3.25; 95%CI: -5.35 to -1.14), increased waist to hip ratio (md: 0.03; 95% CI: 0.01-0.05), less superficial subcutaneous abdominal adipose tissue (md: -2.94; 95% CI: -5.56 to-0.32), more deep/visceral to superficial adipose tissue ratio (md 0.34; 95% CI: 0.02 to 0.65), and more liver fat (md: 7.43%; 95% CI: 2.30 to 12.55%). Adipocyte area was increased in South Asians compared to white Caucasians (md: 64.26; 95% CI: 24.3 to 104.1) units(2). Adjustment for adipocyte area attenuated the ethnic differences in insulin (md: 0.22; 95% CI: -0.07 to 0.51), HDL (md: -0.01; 95% CI: -0.16 to 0.13) and adiponectin (md: -1.11; 95% CI: -2.61 to 0.39). Adjustment for differences in adipocyte area and fat distribution attenuated the ethnic difference in liver fat (md: 5.19; 95% CI: 0.31 to 10.06). CONCLUSION: South Asians have an increased adipocyte area compared to white Caucasians. This difference accounts for the ethnic differences in insulin, HDL cholesterol, adiponectin, and ectopic fat deposition in the liver.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".