Familial Resemblance for Plasma Leptin: Sample Homogeneity across Adiposity and Ethnic Groups
Bibliographic record
Abstract
OBJECTIVE: Previous studies show a wide range in the percentage of variance in leptin levels attributable to genetic factors. These studies differ markedly with respect to ethnicity, study design, and statistical methodology. Therefore, the purpose of this study was to investigate heterogeneity hypotheses across ethnic groups and by adiposity level, using the same statistical methods. RESEARCH METHODS AND PROCEDURES: Samples included black vs. white (HERITAGE Family Study) and random vs. obese (Québec Family Study) individuals from 432 families (1432 individuals). Heritability for leptin, alternatively adjusted for age and sex and then for age, sex, and adiposity was estimated with the use of familial correlations. Heterogeneity in the magnitude of the familial resemblance between samples and the effect of adjusting for adiposity was explored. RESULTS: Heritability did not vary across samples stratified by adiposity level or ethnic group or across adjustment schemes. Maximal heritability, the percentage of additive phenotypic variability due to all familial sources, was 32%. DISCUSSION: Whereas leptin and adiposity were highly correlated within individuals, removing the effects of adiposity did not significantly alter the magnitude of the familial component for leptin. Moreover, this effect did not vary as a function of ethnicity (black vs. white) or adiposity level. Thus, no evidence for heterogeneity was detected. However, a comparison among previous studies raises questions concerning possible genetic heterogeneity in other ethnic groups in which complex interactions among leptin, adiposity, and diabetes status may be important.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".