Associations between diet quality, blood pressure, and glucose levels among pregnant women in the Asian megacity of Jakarta
Bibliographic record
Abstract
BACKGROUND: The prevalence of gestational hypertension and diabetes in pregnancy is increasing worldwide. Diet is a modifiable factor that may influence these conditions, but few studies have examined the association between diet quality and blood pressure and glucose profiles among pregnant women. Data are especially scarce for women in low- and middle-income countries (LMICs), where 90% of global pregnancies occur, and in urban settings. We, therefore, assessed these associations among 174 pregnant women in the Asian megacity of Jakarta in a cross-sectional study of the Brain Probiotic and LC-PUFA Intervention for Optimum Early Life (BRAVE) project. METHODS: Trained field-enumerators collected socio-demographic characteristics, measured Mid-Upper Arm Circumference (MUAC), and assessed diet by two 24-hour recalls, which were used to calculate the Alternate Healthy Eating Index for Pregnancy (AHEI-P). Blood pressure was measured by automated sphygmomanometer, and fasting blood glucose by capillary glucometer. General linear models were used to identify associations. RESULTS: The median AHEI-P score was 47.4 (IQR 19.1-76.6). The middle tertile of the AHEI-P score (39.59-56.58) was associated with a 0.4 SD (standardized effect size, 95% CI -0.7 to -0.06; p = 0.02) lower diastolic blood pressure compared with the lowest tertile (<39.59), after adjustment for level of education, smoking status, MUAC, gestational age, history of hypertension, and family history of hypertension. However, no associations were found between the AHEI-P score and systolic blood pressure and blood glucose. CONCLUSION: Higher diet quality was associated with lower diastolic blood pressure among pregnant women in an urban LMIC community, but not with systolic blood pressure and blood glucose. A behavioral change intervention trial would be warranted to confirm the influence of diet quality on blood pressure and glucose levels and among pregnant women, and even before pregnancy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".