Folate intake, <i>MTHFR</i> genotype and premenstrual symptoms
Bibliographic record
Abstract
Abstract Premenstrual symptoms are a cyclic set of symptoms that affect women’s psychological and physical well-being. Growing evidence suggests that micronutrients may contribute to the risk and severity of premenstrual symptoms such as depression. Yet the relationship between folate and premenstrual symptoms remains inconclusive. The objective of this study was to determine the association between folate intake and MTHFR genotype with premenstrual symptoms. Females ( n 678) aged 20–29 years from the Toronto Nutrigenomics and Health Study self-reported fifteen premenstrual symptoms. Dietary intake was measured using a validated 196-item Toronto-modified Harvard food frequency questionnaire. DNA was isolated from peripheral white blood cells and genotyped for the C677T MTHFR (rs1801133) polymorphism. Using logistic regression, the odds of experiencing premenstrual symptoms were compared between total folate intake below and above the median (647 mcg/d) and between MTHFR genotypes. We found associations between MTHFR genotype and some premenstrual symptoms. Among women with low folate intake, an additive association was observed between the Tallele of MTHFR and premenstrual depression. Compared with those with the CC genotype, the OR (95 % CI) for depression was 1·66 (0·98, 2·87) for those with the CT genotype and 2·41 (1·08, 5·38) for those with the TT genotype. No associations were observed between MTHFR genotype and premenstrual depression among those with higher habitual intakes of folate. Since the MTHFR genotype is involved in the folate metabolic pathway, these findings suggest that folate or its metabolites may be related to the risk of premenstrual depression.
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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 | 0.002 |
| 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) | 0.004 | 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".