Life-Course Fertility and Cardio-Kidney-Metabolic Health in Chinese Middle-Aged and Older Women: A Cross-Sectional CHARLS Study

Technical Summary: Life-Course Fertility and Cardio-Kidney-Metabolic Health in Chinese Middle-Aged and Older Women

Problem Statement
The American Heart Association (AHA) recently conceptualized Cardio-Kidney-Metabolic (CKM) syndrome as a unified staging framework integrating cardiovascular, renal, and metabolic dysfunction. Despite the profound impact of pregnancy on all three physiological domains, no prior study has examined the association between parity (fertility history) and CKM syndrome stages. Furthermore, traditional assessments of kidney function in women often rely on creatinine-based equations, which can be biased by age-related muscle mass loss and hormonal changes post-menopause. This study addresses the gap in understanding how life-course fertility influences CKM risk in Chinese women, utilizing a more robust biomarker (cystatin C) and the 2023 AHA staging framework.

Methodology
This cross-sectional study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS), covering 28 provinces.

• Population: The primary analysis included 4,814 women aged 45 and older with complete blood biomarker data. A sensitivity analysis using Multiple Imputation by Chained Equations (MICE) was conducted on the full eligible sample (n=7,236) to address missing data (33.5% in biomarkers).
• Exposure: Parity was categorized as 0, 1, 2 (reference), 3, 4–5, and 6 or more live births.
• Outcome: CKM syndrome stages were classified per the 2023 AHA framework, adapted for Asian-specific cutoffs. The primary outcome was CKM Stage 2 or higher. Kidney function was assessed using cystatin C-based estimated glomerular filtration rate (eGFR) via the 2021 CKD-EPI equation to avoid muscle-mass bias.
• Statistical Analysis:
  • Poisson regression with robust standard errors was used to estimate adjusted prevalence ratios (aPR), chosen due to the high outcome prevalence (>10%).
  • Restricted Cubic Splines (RCS) with four knots assessed nonlinear dose-response relationships.
  • Exploratory Factor Analysis (EFA) with Oblimin rotation identified multimorbidity patterns among 13 self-reported chronic conditions.
  • Models were adjusted for age, education, residence, marital status, smoking, and alcohol consumption. BMI was excluded from main models to avoid overadjustment bias, as it may lie on the causal pathway.

Key Results

• Prevalence: The prevalence of CKM Stage 2 or higher was 53.2%.
• Parity-CKM Association: A U-shaped association was observed between parity and CKM Stage 2+. Compared to parity=2 (the reference group), both lower parity (parity=1: aPR 0.88, 95% CI: 0.75–1.02) and higher parity (parity≥6: aPR 0.87, 95% CI: 0.74–1.03) showed borderline lower prevalence.
• Dose-Response: RCS analysis confirmed a nonlinear U-shaped dose-response relationship, with the nadir (lowest risk) occurring at parity=2–3 (Wald P=0.042). The curve descended steeply from parity=0 to 2–3, remained flat through parity=4–5, and showed a modest upturn at parity≥6.
• Component Analysis: Hypertension and CKD prevalence showed steep gradients increasing with parity (CKD rising from 2.1% to 9.6%), while dyslipidemia remained stable.
• Multimorbidity Patterns: Factor analysis identified three distinct patterns:
  1. Respiratory-Cardio-Arthritic: Positively associated with parity (beta=+0.026, P=0.001).
  2. Digestive-Arthritic-Renal: Positively associated with parity (beta=+0.014, P=0.020).
  3. Cardio-Metabolic: No significant association with parity (beta≈0, P=0.98).
• Robustness: The U-shaped pattern persisted across sensitivity analyses (varying BMI/waist cutoffs, age groups, and reference parity) and was confirmed in the MICE-imputed dataset, suggesting the findings were not driven by selection bias or specific model specifications.

Significance and Claims
The authors claim this is the first study to investigate the parity-CKM association using the 2023 AHA framework and cystatin C-based kidney function assessment. The study posits a "healthy mother effect" to explain the U-shaped pattern, suggesting two protective pathways:

1. Low Parity ("Privileged Singleton"): May reflect higher socioeconomic status, delayed childbearing with extended endogenous estrogen exposure, and better healthcare access.
2. High Parity ("Robust Fertility"): May reflect a biologically selected group with superior innate resilience capable of surviving repeated pregnancies.

The findings suggest that reproductive history influences distinct disease domains through unique mechanisms (e.g., immune modulation for respiratory/cardio-arthritic clusters and renal hemodynamics for digestive/renal clusters), rather than a uniform effect on the core cardio-metabolic axis. The authors argue that integrating fertility history into sex-specific CKM risk assessment could enhance clinical screening, particularly for renal function in high-parity women and cardiovascular monitoring in low-parity women.

Limitations
The authors acknowledge that the cross-sectional design precludes causal inference. The study lacks data on pregnancy complications (e.g., preeclampsia), miscarriage, and breastfeeding, which may attenuate observed associations. Additionally, high-parity survivors may represent a biologically selected subgroup, and the use of Asian-specific cutoffs requires further validation. However, the consistency of results across complete-case and imputed analyses supports the robustness of the observed associations.