Leptin and adiponectin in relation to body fat percentage, waist to hip ratio and the apoB/apoA1 ratio in Asian Indian and Caucasian men and women
Bibliographic record
Abstract
BACKGROUND: Asian Indian immigrants have an increased risk for developing cardiovascular disease (CVD); however, there is very little data examining how the adipokines leptin and adiponectin relate to CVD risk factors such as body fat percentage (BF%), waist to hip ratio (WHR) and the apoB/apoA1 ratio in Asian Indian men and women living in Canada. SUBJECTS AND METHODS: A cross-sectional study comparing leptin, adiponectin, lipoproteins and anthropometric parameters in Asian Indian men and women to Caucasian men and women (4 groups). Anthropometric data (BMI, BF%, WHR), circulating lipids (apoA1, apoB, total cholesterol, and HDL-cholesterol), leptin and adiponectin were measured. RESULTS: Asian Indian men and women had higher leptin and lower adiponectin concentrations then Caucasian men and women, respectively. Leptin (positively) and adiponectin (negatively) correlated with anthropometric parameters and lipoproteins in all four groups. Using stepwise forward multiple regression, a model including TC/HDL-C ratio, WHR, BF%, hip circumference and waist circumference predicted 74.2% of leptin concentration in men. In women, apoB, BF%, waist circumference and age predicted 77.5% of leptin concentration. Adiponectin concentrations in men were predicted (30.2%) by HDL-C, total cholesterol, hip circumference and BF% while in women 41.2% of adiponectin concentration was predicted by the apoB/apoA1 ratio, WHR and age. CONCLUSION: As is evident from our data, there is a strong relationship between leptin, adiponectin, and abdominal obesity with increased CVD risk, as assessed by the apoB/apoA1 ratio. Dysregulation of these parameters may account for the increased risk of CVD in Asian Indians.
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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 | 0.001 | 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".