Ethnic Variation in Fat and Lean Body Mass and the Association with Insulin Resistance
Bibliographic record
Abstract
CONTEXT: Body fat distribution varies among different ethnic groups, yet less is known regarding differences in lean mass and how this may affect insulin resistance. OBJECTIVE: Our objective was to compare total body fat to lean mass ratio (F:LM) in Aboriginal, Chinese, European, and South Asian individuals with differences in insulin resistance. PARTICIPANTS, DESIGN, AND SETTING: Aboriginal (196), Chinese (222), European (202), and South Asian (208) individuals were recruited across a range of body mass index to participate in this cross-sectional community study. MAIN OUTCOME MEASURES: Total body fat, lean mass, and insulin resistance were assessed using homeostasis model assessment (HOMA). RESULTS: After adjustment for confounders and at a given body fat, South Asian men had less lean mass than Aboriginal [3.42 kg less; 95% confidence interval (CI) = 1.55-5.29], Chinese (3.01 kg less; 95% CI = 1.33-4.70), and European (3.57 kg less; 95% CI = 1.82-5.33) men, whereas South Asian women had less lean mass than Aboriginal (1.98 kg less; 95% CI = 0.45-3.50), Chinese (2.24 kg less; 95% CI = 0.81-3.68), and European (2.97 kg less; 95% CI = 1.67-4.27) women. In adjusted models, F:LM was higher in South Asian compared with Chinese and European men and higher in South Asian compared with Aboriginal, Chinese, and European women (P < 0.01 for all). Insulin and HOMA were greatest in South Asians after adjustment; however, these differences were no longer apparent when F:LM was considered. CONCLUSIONS: South Asians have a phenotype of high fat mass and low lean mass, which may account for greater levels of insulin and HOMA compared with other ethnic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".