Bivariate Linkage between Acylation‐Stimulating Protein and BMI and High‐Density Lipoproteins
Bibliographic record
Abstract
OBJECTIVE: Given the importance of visceral adiposity in the metabolic syndrome, whether levels of adipokines have shared genetic effects (pleiotropy) with aspects of the metabolic syndrome should be addressed. Acylation-stimulating protein (ASP), an adipose-derived protein, influences lipid metabolism, obesity, and glucose use. Therefore, our objective was to examine the genetic regulation of ASP and associated pleiotropic effects. RESEARCH METHODS AND PROCEDURES: We assayed serum ASP levels in 435 Mexican Americans participating in the San Antonio Family Heart Study and performed univariate and bivariate variance components analysis. RESULTS: Additive genetic heritability of ASP was 26% (p = 0.0004). Bivariate genetic analysis detected significant genetic correlations between ASP and several lipid measures but not between ASP and adiposity or diabetes measures. We detected two potential quantitative trait loci influencing ASP levels. The strongest signal was on chromosome 17 near marker D17S1303 [log of the odds ratio (LOD) = 2.7]. The signal on chromosome 15 reached its peak near marker D15S641 (LOD = 2.1). Both signals localize in regions reported to harbor quantitative trait loci influencing obesity and lipid phenotypes in this population. Bivariate linkage analysis yielded LODs of 4.7 for ASP and BMI on chromosome 17 and 3.2 for ASP and high-density lipoprotein2a on chromosome 15. DISCUSSION: Given these findings, there seems to be a significant genetic contribution to variation in circulating levels of ASP and an interesting pattern of genetic correlation (i.e., pleiotropy) with other risk factors associated with the metabolic syndrome.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".