Does polymorphisms in <i>PPAR</i> and <i>APOE</i> genes modify associations between fatty acid desaturase (<i>FADS</i>), <i>n</i>-3 long-chain PUFA and cardiometabolic markers in 8–11-year-old Danish children?
Bibliographic record
Abstract
n-3 Long-chain PUFA (LCPUFA) can improve cardiometabolic blood markers, but studies in children are limited. SNP in the FADS genes, which encode fatty acid desaturases, influence endogenous LCPUFA production. Moreover, SNP in genes that encode PPAR and apoE may modulate the effects of n-3 LCPUFA. We explored whether FADS polymorphisms were associated with blood cholesterol and TAG, insulin and glucose and whether polymorphisms in PPAR and APOE modified associations between FADS or n-3 LCPUFA status and the cardiometabolic blood markers. We measured fasting cholesterol and TAG, insulin, glucose and n-3 LCPUFA in 757 Danish 8-11-year-old children and genotyped SNP in FADS (rs1535 and rs174448), PPARG2 (rs1801282), PPARA (rs1800206) and APOE (rs7412+rs429358). Carriage of two FADS rs174448 major alleles was associated with lower TAG (P = 0·027) and higher HDL-cholesterol (P = 0·047). Blood n-3 LCPUFA was inversely associated with TAG and insulin in PPARG2 minor allele carriers and positively with LDL-cholesterol in major allele homozygotes (Pn-3 LCPUFA × rs180182 < 0·01). Associations between n-3 LCPUFA and cardiometabolic markers were not modified by APOE genotype (Pn-3 LCPUFA × APOE > 0·11), but interaction between FADS rs1535 and APOE showed that rs1535 major allele homozygotes who also carried APOE2 had higher HDL-cholesterol than all other genotype combinations (Prs1535 × APOE = 0·019, pairwise-P < 0·05). This indicates that FADS genotypes, which increase endogenous LCPUFA production, may beneficially affect children's cardiometabolic profile in a partly APOE-dependent manner. Also, the degree to which children benefit from higher n-3 LCPUFA intake may depend on their PPARG2 genotype.
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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 | 0.000 |
| 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".