Genetic and environmental determinants of lipid profile in black and white youth: a study of four candidate genes.
Bibliographic record
Abstract
OBJECTIVE: To identify genotypes and gene-environment interactions, which may explain ethnic differences on lipid profile in Black and White youth. DESIGN, SETTING, PARTICIPANTS: Healthy adolescents and young adults (N=413, 18.6 +/-2.8 yrs, 44% Black, 53% Male) drawn from a cardiovascular study. MAIN OUTCOME MEASURES: Total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), and triglyceride (TG) concentrations were obtained from frozen plasma. The ApoB Glu4154Lys, LDL receptor (LDLR) T1773C, PPARgamma Pro12Ala, and TNFalpha -308G/A polymorphisms were genotyped. Analyses adjusted for age, sex, ethnicity, body mass index (BMI), socioeconomic status (SES), and interactions. RESULTS: The ApoB Glu4154Lys polymorphism interacted with obesity and age to predict TC levels. As BMI increased, 4154Lys ApoB allele carriers had higher TC levels than 4154Glu homozygotes (difference=0.23 mmol/L at BMI=30 kg/m2, 0.54 at BMI=40, P<.05). Juvenile, but not adult, ApoB 4154Lys allele carriers had higher TC (0.34 mmol/L, P<.01). Male -308A TNFalpha allele carriers had lower HDLC (0.10 mmol/L, P<.01). Carriers of the T1 773 LDLR allele had higher TG (0.26 mmol/ L, P<.01). No effect of the PPARgamma Pro12Ala polymorphism was found; the 12Ala PPARgamma allele was rare among Blacks (2%). CONCLUSIONS: The ApoB, TNFalpha, and LDLR candidate genes influenced lipid profiles in youth independent of environmental factors. The T1773 LDLR allele, which is rare among Blacks (7%), may contribute to lower TG in Blacks. The -308A TNFalpha allele may contribute to lower HDLC in males. These gene effects and gene-environment interactions may inform prevention and treatment of atherosclerosis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".