Genetic Polymorphisms of TNF‐α Modify the Effect of Dietary Polyunsaturated Fatty Acids on Fasting Plasma Levels of HDL‐C and ApoA in Individuals with Type 2 Diabetes
Bibliographic record
Abstract
Dietary fatty acids are known to alter the levels of plasma lipids, which play an important role in the development of cardiovascular disease (CVD). Tumor necrosis factor (TNF)‐α is a proinflammatory cytokine that has been associated with an altered lipid profile and increased risk of CVD. Heterogeneity in plasma lipid levels in response to dietary fatty acids might be due to genetic differences, however, the role of TNF‐α genotypes is not known. Subjects (n=123) recruited were type 2 diabetic men (n=56) and women (n=67) aged 36–75 years. Blood samples were collected to determine fasting plasma lipid levels and genotyping was performed by PCR‐RFLP for the TNF‐α‐238G>A and ‐308G>A polymorphisms. When comparing the highest to the lowest tertile of polyunsaturated fatty acid (PUFA) intake (% energy), high‐density lipoprotein cholesterol (HDL‐C) (mmol/L) was higher among carriers of the −238A allele (1.22±0.12 vs 0.99±0.08), but lower among those with the GG genotype (1.13±0.05 vs 1.26±0.05) (p=0.03 for interaction). Among carriers of the −308A allele, subjects in the highest tertile of PUFA intake had lower HDL‐C compared to those in the lowest tertile (0.99±0.06 vs 1.31±0.08). However, for those with the −308GG genotype, HDL‐C was greater in the upper compared to the lower tertile of PUFA intake (1.20±0.05 vs 1.09±0.04) (p =0.01 for interaction). Adjusting for age, sex, BMI and total energy intake did not alter these results. Similar effects were observed for plasma ApoA, but there was no association with other plasma lipids. In summary, TNF‐α genotypes modify the effect of dietary PUFA on HDL‐C and ApoA concentrations in individuals with type 2 diabetes. Supported by CIHR (MCT‐44205).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".