The Association Between Pregnancy Complications and Long-term Maternal Cardiometabolic Health in the MIREC Cohort Study
Bibliographic record
Abstract
CONTEXT: During pregnancy, women who experience certain pregnancy complications show elevations in biomarkers of inflammation and insulin resistance; however, few studies have examined these cardiometabolic biomarkers in the decade following pregnancy. OBJECTIVE: To examine the association between pregnancy complications and cardiometabolic biomarkers 9 years postpartum including blood pressure, blood lipids, body fat percentage, insulin resistance [glucose, insulin, proinsulin, C-peptide, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), glycated hemoglobin (HbA1c), leptin, adiponectin], and inflammation (high-sensitivity-C-reactive protein). METHODS: Using data from the Maternal-Infant Research on Environmental Chemicals cohort study (2008-2021), we determined 3 groups of pregnancy complications: (1) hypertensive disorders of pregnancy (HDP) (n = 35); (2) any pregnancy complication in the index pregnancy, defined as preterm birth, HDP, impaired glucose tolerance or gestational diabetes mellitus (n = 55); and (3) self-reported recurrence of 1 of these pregnancy complications (n = 19). Our comparison group included 186 women with uncomplicated pregnancies. RESULTS: In our adjusted linear regression results, all pregnancy complication groups showed significantly higher systolic and diastolic blood pressure 9 years later. HOMA-IR was 23% [95% confidence interval (CI): -4.4%, 57%], 26% (95% CI: 2.0%, 55%), and 51% (95% CI: 12%, 104%) higher at follow-up in participants who had experienced a prior HDP, an index pregnancy complication, or a recurrent pregnancy complication, respectively. Elevations were also seen with HbA1c, insulin, C-peptide, and leptin, especially among those with recurrent complications. CONCLUSION: This study contributes to the body of evidence that women with a history of certain pregnancy complications merit special attention in the prevention of cardiovascular disease. We recommend further exploration into these associations in larger cohorts.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".