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Dietary Patterns and Gestational Weight Gain in the Alberta Pregnancy Outcomes and Nutrition Study

2016· article· en· W2521448435 on OpenAlexaffabout
Megan Jarman, Rhonda C. Bell, Paula J. Robson

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsObstetricsWeight gainPregnancyMedicineBody weightBiologyEndocrinology

Abstract

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Inappropriate weight gain during pregnancy is associated with adverse health outcomes for both mother and baby. In 2010, Health Canada adopted the Institute of Medicine's guidelines on gestational weight gain (GWG). This study explored the extent to which dietary patterns reported in the 2 nd trimester of pregnancy were associated with adherence to Health Canada's (2010) guidelines for total GWG. The Alberta Pregnancy Outcomes and Nutrition study (APrON) is a prospective cohort of women recruited and followed through pregnancy and the post‐partum period. Total GWG for each woman (n=2067) was calculated by subtracting pre‐pregnancy weight from highest weight in pregnancy. Women were categorized by pre‐pregnancy BMI (self‐reported pre‐pregnancy weight (kg)/measured height (m) 2 ) and whether total GWG was below, met or exceeded the guidelines. Diet was assessed by 24‐hour recall in the 2 nd trimester and foods/beverages were coded into 51 food groups based on nutritional similarity. Principal components analysis was run on the energy adjusted food groups. For components which accounted for the greatest variance, a dietary pattern score was calculated by multiplying each woman's standardized consumption (g/day) of each food group by the corresponding coefficient for the component and summing. Scores were expressed as a z‐score, with a mean of zero and standard deviation of one. Associations between dietary pattern scores and adherence to GWG guidelines were assessed using multinomial logistic regression. shows proportions of women who were below, met or exceeded the guidelines by pre‐pregnancy BMI group. Three main dietary patterns were identified in this cohort: (i) ‘Healthy’, (3.8% of the variance) was characterized by higher intakes of fruits, vegetables, wholemeal bread, and lower intakes of high‐energy soft drinks, processed meat and white‐bread; (ii) ‘Refined Carbs’ (3.3% of the variance) was characterized by higher intakes of rice and pasta, added sugar, breakfast cereal and white bread, and lower intakes of wholemeal bread; and (iii) ‘Tea and Toast’ (3.1% of the variance), was characterized by higher intakes of tea and coffee, added sugar, full‐fat milk and white bread. In the unadjusted models, relative to women who met the guidelines, the odds of exceeding the guidelines were lower in women with higher ‘healthy’ diet scores (OR 0.71, 95% CI: 0.52, 0.96). This association was observed only in women with a pre‐pregnancy BMI in the overweight group, and it became non‐significant when adjusted for educational attainment. Neither the ‘Refined Carbs’ nor ‘Tea and Toast’ patterns were associated with GWG in any of the pre‐pregnancy BMI categories. Dietary patterns analysis can be a useful tool in observational studies. However, assessing the impact of diet on GWG is challenging and further research is needed to account for measurement error when using single 24 hour recalls for dietary assessment. Support or Funding Information This project is funded by the Interdisciplinary Team Grants Program of Alberta Innovates‐Health Solutions (AI‐HS) Proportions of women who were below, met or exceeded the GWG guidelines according to their pre‐pregnancy BMI category Pre‐pregnancy BMI category Underweight (n(%)) Normal weight (n(%)) Overweight (n(%)) Obese (n(%)) Below guidelines 16 (26) 257 (23) 25 (7) 28 (15) Met guidelines 30 (48) 403 (37) 82 (23) 35 (19) Exceeded guidelines 16 (26) 443 (40) 256 (70) 125 (66)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.297
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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