Lower levels of leptin and adiponectin independent of body mass index in Japanese American women: The Multiethnic Cohort
Bibliographic record
Abstract
Ethnic differences in obesity and body fat distribution may contribute to varying chronic disease risks, e.g., for breast cancer and diabetes. To explore if adipokines mediate this connection, we evaluated the relation of body mass index (BMI as defined by WHO) and serum levels of leptin and adiponectin measured by ELISA among 936 white, Japanese American (JA), African American (AA), Latino (LA), and Native Hawaiian (NH) female control participants within the Multiethnic Cohort. BMI differed significantly by ethnicity (p<0.0001). As compared to whites (25.3±5.2 kg/m2), BMI was lower in JA (23.7±3.8 kg/m2) and higher among AA, LA, and NH (28.9±5.8, 28.0±5.2, 28.3±5.9 kg/m2, respectively). Linear models were applied to compare means of log‐transformed biomarkers by adiposity status and ethnicity. In all ethnic groups, leptin was higher in overweight and obese than in normal weight women, whereas adiponectin showed an inverse trend (p<0.0001 for all). JA women had significantly lower leptin and adiponectin levels than whites across the 3 BMI categories; the respective differences between the two ethnic groups were 4.0, 8.7, and 18.9 ng/mL for leptin (p=0.0004) and 5.9, 4.4, and 3.6 μg/mL for adiponectin (p<0.0001). The higher obesity‐related disease risk in JA and other ethnic groups may be in part mediated by differences in adipokine either stemming from endocrine genetics or fat distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".