Postpartum metabolic syndrome and high‐sensitivity C‐reactive protein after gestational hypertension and pre‐eclampsia
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between metabolic syndrome (MetS) and high-sensitivity C-reactive protein (hsCRP), a biomarker of chronic inflammation and an independent predictor for cardiovascular disease overall and in subgroups of women with/without pre-eclampsia and gestational hypertension (GHT). METHODS: A prospective cohort study was conducted in Nairobi, Kenya. Women with pre-eclampsia or GHT and normotensive women within 12 weeks postpartum underwent physical, anthropometric, fasting lipid profile, plasma glucose, and hsCRP measurements at 6 months postpartum. A generalized linear regression model with Poisson distribution adjusted for body mass index and age was used to estimate the association between elevated hsCRP and MetS overall and stratified by pre-eclampsia or GHT. RESULTS: In the 171 women included in the study, risk of elevated hsCRP (>3 mg/L) was greater among women with compared to those without MetS (adjusted relative risk [ARR] 1.70, 95% confidence interval [CI] 1.05-2.73, P=0.03) and was statistically significantly higher in the hypertensive (ARR 2.16 95% CI 1.01-4.62, P=0.04) but not in the normotensive (ARR 1.46, 95% CI 0.93-2.28) group. CONCLUSION: Increased risk of elevated hsCRP postpartum can guide longitudinal mechanistic and intervention studies to reduce postpartum cardiovascular morbidity in women with MetS, especially after pre-eclampsia or GHT.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".