Genetic Determinants of Micronutrient Metabolism and Premenstrual Symptoms
Bibliographic record
Abstract
Background: Premenstrual symptoms are a set of psychological and somatic symptoms experienced during the luteal phase of the menstrual cycle by up to 90% of females of reproductive age. The evidence on the efficacy of dietary supplements for premenstrual symptoms has been inconsistent. These inconsistencies may be due to unaccounted genetic differences in micronutrient metabolism. Objective: To determine the association between iron, vitamin C, folate and magnesium and premenstrual symptoms by assessing dietary intakes, genetic determinants of metabolism, and diet-gene interactions for each micronutrient. Methods: Subjects were around 678 young females from the Toronto Nutrigenomics and Health Study. HFE (rs1800562 and rs1799945), TMPRSS6 (rs482026), TFR2 (rs3811647), TF (rs738584), GSTT1 (Ins/Del), MTHFR (rs1801133), and TRPM6 (rs11144134) were genotyped. Fifteen premenstrual symptoms were self-reported by all female participants. Serum ascorbic acid was assessed from fasting blood. Dietary intake for iron, vitamin C, folate and magnesium was captured using a 196-item validated Toronto modified Harvard food frequency questionnaire. Logistic regression was used to assess all associations. Results: Women with an elevated risk of iron overload were less likely to experience premenstrual confusion, headaches, and nausea. Increased vitamin C intake was associated with premenstrual appetite changes. Compared to deficient ascorbic acid levels, suboptimal levels were associated with premenstrual appetite changes and bloating/swelling. Women with the GSTT1 functional variant (Ins*Ins) had an increased risk of premenstrual bloating/swelling. We also found that among women with lower folate intake and those with the TC genotype of MTHFR had increased odds of reporting premenstrual confusion/difficulty concentrating/forgetfulness and depression, compared to those with the CC genotype. Those with the TT genotype of MTHFR also had a higher risk of reporting premenstrual depression. Magnesium intake was associated with lower premenstrual depression and confusion. Conclusions: We found that iron overload was inversely associated with premenstrual nausea, headaches, and confusion and vitamin C biomarkers were positively associated with increased appetite. We also found that genetic markers of low folate by MTHFR genotype and magnesium intake was associated with increased premenstrual depression and confusion, and magnesium intake was inversely associated with premenstrual depression and confusion.
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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 | 0.001 |
| 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) | 0.003 | 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".