The Effect of High Added Sugar Intake on Micronutrient Intakes During Pregnancy
Bibliographic record
Abstract
High added sugar intake in pregnancy increases risk of excess gestational weight gain, gestational diabetes and preeclampsia. These effects may be due to low diet quality observed with higher sugar intakes. This study described micronutrient intakes in pregnant women with varying intakes of added sugar. Pregnant women (n = 489; < 27 wks gestation) completed a validated FFQ to assess dietary intake in the 12 months prior to pregnancy. Percent energy from added sugars was assessed using an expanded sugar database. Women were classified as having Low (0–14.9 % kcal; n=423), Medium (15.0–24.9 % kcal; n=60), or High (>;25.0 % kcal; n=6 ) added sugar intakes. Linear regression was used to assess the effect added sugar intake on micronutrients. Women with higher added sugar intakes consumed fewer total kcal (Low: 1860 ± 560 kcal/d; Medium: 1740 ± 550 kcal/d; High: 1290 ± 480 kcal/d; p= 0.02) but were similar in demographic characteristics. Women in Medium and High groups had lower vitamin E, potassium and sodium intakes compared to Low (p<0.05). After adjusting for total kcal, vitamin E intake remained significantly lower in Medium and High groups (p<0.05 Median ± SE: Low: 8.5 ± 0.11mg/d; Medium: 6.7 ± 0.24mg/d; High: 4.9 ± 0.55mg/d). High intake of added sugars during pregnancy may be associated with lower micronutrient intakes, however, further studies of women with a wider variety of sugar intakes is warranted.
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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 | 0.000 |
| 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) | 0.001 | 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".