No strong evidence of the protein leverage hypothesis in pregnant women with obesity and their infants
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to investigate the role of dietary protein on macronutrient and energy intake, maternal adiposity during pregnancy, and infant adiposity at birth. METHODS: In 41 women with obesity, early-pregnancy (13-16 weeks) protein intake was assessed with food photography and expressed as a ratio of Estimated Average Requirements (EAR) in pregnancy for protein (0.88 g/kg/d), herein "protein balance." Energy intake was measured by the intake-balance method, gestational weight gain as grams per week, and fat mass by a three-compartment model. Spearman correlations and linear models were computed using R version 4.1.1 (p < 0.05 considered significant). RESULTS: , and the majority were non-White (n = 23, 56.1%). Protein balance in early pregnancy was not significantly associated with energy intake across mid and mid/late pregnancy (β = 328.7, p = 0.30 and β = 286.2, p = 0.26, respectively) or gestational weight gain (β = 117.0, p = 0.41). Protein balance was inversely associated with fat mass in early, mid, and late pregnancy (β = -10.6, p = 0.01, β = -10.4, p = 0.03, β = -10.3, p = 0.03, respectively). Protein balance did not predict infant adiposity at birth (p > 0.05). CONCLUSIONS: Low protein intake may have been present before pregnancy, explaining early relationships with adiposity in this cohort. The protein leverage hypothesis is likely not implicated in the intergenerational transmission of obesity.
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 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".