Allele Frequencies for Candidate Genes in Atherosclerosis and Diabetes among Trinidadian Neonates
Bibliographic record
Abstract
Trinidadians of South Asian origin have a high prevalence of cardiovascular disease and diabetes compared to Trinidadians of African origin. The degree to which these differences are related to genetic and/or environmental factors is unclear. To determine whether there might be a genetic basis for this difference in prevalence of deleterious phenotypes we examined allele frequencies for candidate genes in atherosclerosis and diabetes. We genotyped 81 consecutive neonates of African origin and 103 consecutive neonates of South Asian origin. We evaluated common polymorphisms in 11 candidate genes for atherosclerosis and diabetes. We found differences between the two subpopulations in the allele frequencies of several candidate genes, including APOE, LIPC, APOC3, PON1, PON2, and PPP1R3. However, the differences in the allele frequencies were not all consistent with the pattern of CHD expression between these two ethnic groups in adulthood. Thus, differences in genetic architecture alone may not explain the wide disparities in disease prevalence between these two subpopulations. It is very likely that environmental factors, or unmeasured genetic factors, influence the genetic susceptibility to disease in these subpopulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".