Effects of the <i>FABP2</i> A54T Mutation on Triglyceride Metabolism of Viscerally Obese Men
Bibliographic record
Abstract
OBJECTIVE: Viscerally obese individuals are frequently characterized by a proatherogenic condition. A missense mutation (A54T) in the fatty acid binding protein type 2 (FABP2) gene has been associated with insulin resistance and obesity. This study examined the effect of this mutation on lipoprotein levels in viscerally obese hyperinsulinemic condition. RESEARCH METHODS AND PROCEDURES: A total of 217 men were assigned to one of two groups based on their FABP2 A54T polymorphism. RESULTS: The two genotypic groups showed no difference in either physiological characteristics or lipoprotein/lipid profile, before or after statistical adjustment for age. From this initial sample, 50 men accepted to have their postprandial lipid response assessed and 10 T54/A54 heterozygotes were then individually matched for visceral adipose tissue accumulation and fasting plasma triglyceride (TG) levels with 10 A54/A54 homozygotes. High-density lipoprotein (HDL)-TG levels were significantly increased in the fasting state as well as 4 hours after the test meal (p = 0.04 and p = 0.0008, respectively) in men bearing the A54T mutation. In addition, the area under the curve of postprandial HDL-TG levels was also significantly higher among T54/A54 heterozygotes than among A54/A54 homozygotes (p = 0.04). Interestingly, fasting TG concentrations in large TG-rich lipoproteins (large-TRL; S(f) > 400) were correlated with HDL-TG levels at 4 (r = 0.74, p = 0.01) and 8 hours (r = 0.73, p = 0.01) after the test meal in T54/A54 heterozygotes only. DISCUSSION: The FABP2 A54T missense mutation may contribute to the TG enrichment of HDL in the postprandial state that, in turn, may alter the risk of atherosclerotic vascular disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".