Plasma metabolomic signature of breastfeeding and risk of cardiometabolic diseases
Bibliographic record
Abstract
Breastfeeding is inversely associated with cardiometabolic disease incidence in prospective studies; however, the metabolic pathways underlying these associations remain largely unknown. Here, we derive a plasma metabolomic score of lifetime total duration of breastfeeding using elastic net regularized regression in Nurses’ Health Studies (n = 4349) and replicate in the Women’s Health Initiative (n = 2088). Data include 181 untargeted plasma metabolites profiled by liquid chromatography mass spectrometry using blood samples collected in mid-life, and self-reported lifetime total duration of breastfeeding. We then examine the associations between the metabolite-based breastfeeding score and risk of T2D and CVD using multivariable Cox regression models and replicated in two external cohorts. The metabolite-based breastfeeding score comprised of 5 metabolites (i.e., C54:2 triglyceride, C56:2 triglyceride, C56:3 triglyceride, cotinine, indole-3-propionate), which show a modest but statistically significant correlation with lifetime total duration of breastfeeding. The metabolite-based breastfeeding score significantly inversely associate with T2D incidence (HR = 0.76, 95%CI = 0.71-0.82) and with CVD incidence (HR = 0.88, 95%CI = 0.84-0.93) independent of T2D and CVD risk factors. We identify plasma metabolite profiles in mid-life associated with breastfeeding duration, which is also linked to CVD and T2D risk. Breastfeeding is inversely associated with cardiometabolic disease; however, the metabolic pathways underlying these associations remain largely unknown. Here, the authors show plasma metabolite profiles in mid-life associated with breastfeeding duration, which is also linked to CVD and T2D risk.
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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 | 0.002 |
| 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 | 0.000 | 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".