Obesity Affects Maternal and Neonatal HDL Metabolism and Function
Bibliographic record
Abstract
Pregravid obesity is one of the major risk factors for pregnancy complications such as gestational diabetes mellitus (GDM) and an increased risk of cardiovascular events in children of affected mothers. However, the biological mechanisms that underpin these adverse outcomes are not well understood. High-density lipoproteins (HDLs) are antiatherogenic by promoting the efflux of cholesterol from macrophages and by suppression of inflammation. Functional impairment of HDLs in obese and GDM-complicated pregnancies may have long-term effects on maternal and offspring health. In the present study, we assessed metrics of HDL function in sera of pregnant women with overweight/obesity of the DALI lifestyle trial (prepregnancy BMI ≥ 29 kg/m2) and women with normal weight (prepregnancy BMI < 25 kg/m2), as well as HDL functionalities in cord blood at delivery. We observed that pregravid obesity was associated with impaired serum antioxidative capacity and lecithin−cholesterol acyltransferase activity in both mothers and offspring, whereas maternal HDL cholesterol efflux capacity was increased. Interestingly, functionalities of maternal and fetal HDL correlated robustly. GDM did not significantly further alter the parameters of HDL function and metabolism in women with obesity, so obesity itself appears to have a major impact on HDL functionality in mothers and their offspring.
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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 | 0.000 | 0.000 |
| 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".