Murugappan · American journal of obstetrics & gynecology MFM 2021 · Retrospective cohort study · n=669,256 births

Association of preconception paternal health and adverse maternal outcomes among healthy mothers.

Cited 20 times in the scientific literature.

Level 3 - non-randomized controlled study

Retrospective cohort analysis of an administrative claims database.

PubMed 33895399 · doi:10.1016/j.ajogmf.2021.100384 · record verified 2026-08-29

What was done

A retrospective cohort study analyzed 669,256 live births (2009–2016) to healthy women aged 20–45 years linked to fathers via family IDs in the IBM MarketScan database. Women with metabolic syndrome components were excluded. Preconception paternal health was categorized by the number of metabolic syndrome diagnoses (hypertension, diabetes, obesity, hyperlipidemia) and other chronic conditions. Outcomes included abnormal placentation, preeclampsia with or without severe features/eclampsia, and severe maternal morbidity (CDC index up to 6 weeks postpartum). Associations were evaluated with Cochran-Armitage trend tests and generalized estimating equations adjusting for maternal/paternal age, birth region/year, maternal smoking, and outpatient visit frequency.

What was found

Across 669,256 births, adverse maternal outcomes trended higher with worsening paternal metabolic health (P < .001). Comparing fathers with ≥2 metabolic syndrome diagnoses against those with 0: - Preeclampsia without severe features was increased by 21% (aOR 1.21, 95% CI 1.17–1.26). - Preeclampsia with severe features and eclampsia was increased by 19% (aOR 1.19, 95% CI 1.09–1.30). - Severe maternal morbidity was increased by 9% (aOR 1.09, 95% CI 1.002–1.19). - Abnormal placentation showed no significant difference (aOR 0.96, 95% CI 0.89–1.03).

Why it matters

This study indicates that paternal preconception cardiometabolic health is associated with maternal obstetrical complications, suggesting preconception risk assessment should extend beyond maternal factors alone.

Limits

The analysis relies on administrative claims data, which are vulnerable to diagnostic miscoding and underreporting of conditions like obesity. Important confounders—such as race, ethnicity, socioeconomic status, dietary patterns, and direct clinical biomarkers—were not captured in the abstract. Residual confounding cannot be ruled out.

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