Glycemic Load, Dietary Fiber, Added Sugar, and Spontaneous Abortion in Two Preconception Cohorts
Bibliographic record
Abstract
BACKGROUND: Spontaneous abortion (SAB)-pregnancy loss before the 20th week of gestation-has adverse psychological and physical sequelae. Some medical conditions known to affect insulin sensitivity, including polycystic ovary syndrome and diabetes, can affect the risk of SAB. No prior studies have examined glycemic load and incidence of SAB in populations without conditions known to affect insulin sensitivity. OBJECTIVES: We prospectively evaluated the association between preconception glycemic load and intake of carbohydrates, dietary fiber, and added sugar and risk of SAB. METHODS: During 2013-2020, we recruited pregnancy planners from Denmark (SnartForaeldre.dk; SF) and the United States and Canada (Pregnancy Study Online; PRESTO). Participants completed a baseline questionnaire and a cohort-specific FFQ evaluated for validity. We estimated preconception glycemic load and intake of carbohydrates, dietary fiber, and added sugar from individual foods and mixed recipes. We included 2238 SF and 4246 PRESTO participants who reported a pregnancy during the course of the study. SAB data were derived from questionnaires and population registries. We used Cox proportional hazards regression to estimate HRs and 95% CIs. RESULTS: In the study population, 15% of SF participants and 22% of PRESTO participants experienced SAB. Across both cohorts, there was no appreciable association between glycemic load, carbohydrate quality, dietary fiber, or added sugar intake and SAB. Compared with daily mean glycemic load <110, the HR for women with daily mean glycemic load ≥130 was 0.76 (95% CI: 0.52, 1.10) in SF and 1.01 (95% CI: 0.86, 1.19) in PRESTO. CONCLUSIONS: Diets with high glycemic load, carbohydrates, and added sugars were not consistently associated with risk of SAB in parallel analyses of 2 preconception cohort studies of women in North America and Denmark.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".