Maternal pre-pregnancy diet and prenatal depression: the mediating role of pre-pregnancy weight status and prenatal inflammation
Bibliographic record
Abstract
Abstract Depression is a common prenatal psychological complication. We aimed to investigate if maternal pre-pregnancy diet can impact prenatal depressive symptoms and the mediating role of pre-pregnancy BMI and inflammation. We used data ( N 1141) from the Alberta Pregnancy Outcomes and Nutrition cohort study. We calculated Mediterranean diet adherence (MED) and dietary inflammatory index (DII) scores using data from pre-pregnancy FFQ. In the third-trimester, we assessed depressive symptoms using Edinburgh Postpartum Depression Scale (EPDS) and inflammation through serum C-reactive protein (CRP) levels. BMI was calculated from self-reported pre-pregnancy weight. Race-stratified analyses (white and people of colour) were run. We observed no association between MED or DII tertiles and depressive symptoms. However, white participants in the MED tertile-3 had lower risk of depression (EPDS < 10) compared with tertile-1 (OR = 0·56, 95 % CI, 0·33, 0·95). White individuals in MED tertile-3 had lower BMI (MD = –1·08; 95 % CI, −1·77, −0·39) and CRP (MD = –0·53; 95 % CI, −0·95, −0·11) than tertile-1, and those in DII tertile-2 (MD = 0·44; 95 % CI, 0·03, 0·84) and tertile-3 (MD = 0·42; 95 % CI, 0·01, 0·83) had higher CRP than tertile-1. Among people of colour, neither MED nor DII was associated with BMI or CRP, but BMI was negatively associated with depressive symptoms ( β = –0·25, 95 % CI, −0·43, −0·06). We found no association between diet and depressive symptoms through BMI or CRP, in either race. Pre-pregnancy diet might affect the risk of prenatal depression in a race-specific way. Further research is required to explore the racial differences in the association between maternal diet and prenatal depressive symptoms/depression risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".