Recent Choline Intake Is Inversely Associated with Inflammation in Pregnancy – Evidence from the Canadian APrON Cohort
Bibliographic record
Abstract
BACKGROUND: Maternal nutrition plays a role in regulating inflammation during pregnancy, which can impact maternal and fetal health. OBJECTIVES: This study explored the association between recent maternal dietary choline intake and high-sensitivity C-reactive protein (hs-CRP) in the third trimester of pregnancy, leveraging data from the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort. METHODS: Dietary choline intake was assessed using a validated 24-h dietary recall and hs-CRP was measured from nonfasting maternal blood samples. Statistical analyses included natural spline regression models to assess the relationship between recent choline intake and hs-CRP, with interaction terms for consumption of other methyl donor nutrients. We also assessed the likelihood of clinically elevated hs-CRP based on choline intake categories. RESULTS: Our analyses of 1300 pregnant people revealed a significant nonlinear inverse association between maternal choline intake and hs-CRP concentrations. Additionally, participants with higher choline intakes had reduced odds of having had hs-CRP above the clinical cutoff of 5 mg/L when compared with those with lower choline intakes (e.g. intake >700 compared with 200 mg/d, odds ratio = 0.07, 95% confidence interval: 0.02, 0.15). CONCLUSIONS: These findings suggest that higher dietary choline intake may be associated with lower inflammation during pregnancy, highlighting the importance of adequate choline consumption for maternal health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".