Associations between CD36 gene polymorphisms and metabolic response to a short-term endurance-training program in a young-adult population
Bibliographic record
Abstract
Recent studies have shown that CD36 gene variants are associated with an increased prevalence of chronic disease. Although a genetic component to trainability has been proven, no data are available specifically on the influence of CD36 on training response. Two single nucleotide polymorphisms (SNPs) (rs1527479 and rs1984112) were assessed for associations with whole-body substrate oxidation, response to a 75-g dextrose oral glucose tolerance test, fasting plasma lipids, and cardiovascular disease risk factors in a young healthy cohort, both using cross-sectional analysis and following a 4-week endurance-exercise training program. Genotyping was performed using real-time polymerase chain reaction. Cross-sectional data were collected in 34 individuals (age, 22.7 ± 3.5 years), with 17 completing the training program. At baseline, TT SNP carriers at rs1527479 and wild-type GG carriers at rs1984112 were associated with significantly greater whole-body rate of fat oxidation (Fatox) during submaximal exercise (P < 0.05), whilst AA carriers at the same position were associated with elevated triglyceride (TG) levels. A significant genotype × time interaction in Fatox at SNP rs1984112 was identified at rest. Significant genotype × time interactions were present at rs1527479, with TT carriers exhibiting a favourable response to training when compared with C-allele carriers for fasting TG, diastolic blood pressure (DBP), and mean arterial pressure (MAP). In conclusion, cross-sectional assessment identified associations with Fatox and TG. Training response at both SNPs identified "at-risk" genotypes responding favourably to the training stimulus in Fatox, TG, DBP, and MAP. Although these data show potential pleiotropic influence of CD36 SNPs, assessment in a larger cohort is warranted.
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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 | 0.000 | 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) | 0.001 | 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".