ADIPOSITY SIGNIFICANTLY MODIFIES GENETIC RISK FOR DYSLIPIDEMIA
Bibliographic record
Abstract
Background The Global Lipids Genetics Consortium identified 95 common loci that explained 12.2% (LDL-C), 12.1% (HDL-C) and 9.6% (triglycerides [TG]) of total variance in plasma lipid traits in the Framingham Heart Study. Since adiposity is associated with plasma levels of TG and HDL-C, we hypothesized that the predictive value of common risk variants for these lipid traits would differ for obese versus lean subjects. Methods The study population consisted of two independent cohorts of subjects of European de-scent, genotyped on the Affymetrix 6.0 array with 1000G imputation. 1) OBLE: 959 obese/869 lean. 2) CC (healthy elderly subjects recruited as con-trols for a CAD study) 830 obese/1,044 lean. A genetic predisposition score was calculated for each individual as a sum across SNPs of the number of risk alleles at that SNP multiplied by the effect size of the SNP. Results In the OBLE cohort, a genetic risk score explained a greater percentage of the total TG variance in obese vs lean (beta GRS=0.154 mmol/L (p<2e-16) vs 0.094 mmol/L (p=5.8e-10); adjusted R2=0.090 vs 0.042), a finding that was replicated in the CC cohort for obese vs lean (beta GRS=0.153 mmol/L (p=1.5e-7) vs 0.102 mmol/L (p=4.2e-6); adjusted R2=0.044 vs 0.024). A similar but less significant trend was noted for LDL-C. In addition, the genetic risk score predicted variability in TG better than sex/age for obese subjects. In contrast a genetic risk score for HDL-C was a better predictor of this trait in lean versus obese subjects in both the OBLE and CC cohorts. Genetic risk scores for each lipid trait showed no association with BMI (P>0.2) indicating that the above findings are not due to possible overlap between genetic loci for BMI and lipid traits. Conclusions Genetic predisposition to elevated plasma triglycerides and LDL-C and low HDL-C is highly sensitive to extremes of BMI.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".