The Role of Diet in Maternal Blood Glucose Levels and Gestational Diabetes Mellitus
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a common metabolic disorder of pregnancy that affects both mother and offspring. Diet is a modifiable risk factor for GDM, but no single dietary component stands out as most important. This thesis presents a series of papers that estimates the association of three components of diet (red meat, processed meat, and dietary fats) with maternal blood glucose levels and GDM and proposes two studies to: 1) assess the efficacy of health coaching to improve diet on maternal glucose levels during pregnancy; and 2) describe the experience of expectant women exposed to the dietary intervention. The first paper is presented in Chapter 2 and provides a cross-sectional analysis of the association of red and processed meat with odds of GDM among expectant mothers from two birth cohorts: the SouTh Asian biRth cohorT (START; n = 976 – South Asian) study and the Family Atherosclerosis Monitoring In earLY life (FAMILY; n =581 – White European) study. Chapter 3 examines the impact of the statistical approach to controlling confounding by total energy intake on the association between dietary fat intake and maternal blood glucose levels, gestational weight gain, and birth outcomes (length and weight). This cross-sectional study includes 1,357 mother-child dyads (569 in FAMILY and 788 in START). Chapter 4 presents the protocol of a randomized controlled trial titled “A culturally tailored personaliseD nutrition intErvention in South ASIan women at risk of Gestational Diabetes Mellitus (DESI GDM)”. This study aims to address diet as a modifiable risk factor for glycemia through a health coaching intervention and understand women’s perceptions of the intervention. Chapter 5 presents a qualitative study protocol that seeks to describe the experiences of women participating in the intervention arm of the DESI-GDM intervention study. Collectively, no clear association was found between red and processed meat, and types of fat and the odds of GDM; but there were differences in GDM prevalence among South Asian and White Europeans. Using different approaches to adjusting for energy intake did not substantively impact the relationship between dietary fat intake and maternal and birth outcomes. A culturally tailored dietary intervention and qualitative study protocol allows further investigation into the role of overall dietary intake in developing GDM in high-risk populations.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".