Frequency and allelic association of common variants in the lipoprotein lipase gene in different ethnic groups: The Wandsworth Heart and Stroke Study
Bibliographic record
Abstract
The lower serum triglyceride (Tg), higher high density cholesterol (HDL-C) levels and low coronary heart disease (CHD) mortality in black populations, contrast with that in whites. By comparison, South Asian populations display a higher mortality from CHD associated with increased Tg and low HDL-C levels. Lipoprotein lipase (LPL) plays a major role in Tg metabolism. To determine if variation in the LPL gene contributes to the differences in lipid levels, we studied the frequencies and allelic associations of five common variants in the lipoprotein lipase (LPL) gene (-93T/G, D9N, N291S, S447X, and the HinddIII RFLP in intron 8) with serum Tg and HDL-cholesterol concentrations in population samples of middle-aged men and women of whites, South Asians, and blacks of African origin co-resident in South London. Significantly higher frequencies of the H(-) (P < 0.00001), N9 (P < 0.001), and -93G (P < 10(-10)) alleles were seen in blacks compared to the other two groups. Allelic association between -93G and N9, and H(+) and X447 was strong in all three groups. However, no association was observed between serum Tg and HDL-cholesterol concentrations and these variants in the three ethnic groups. A single common polymorphism in the LPL gene is unlikely to account for the differences in fasting serum Tg in populations of different ethnic background. The importance of the differences in frequencies and the mechanism(s) whereby these may contribute towards a beneficial LPL genotype in black populations remain to be determined.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".