Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study leveraged 8,804 women with delivery records in the All of Us cohort, including 438 with clinically diagnosed postpartum depression (PPD), to characterize multimorbidity trajectories and develop integrated prediction models. Comorbidities were grouped into 38 conditions across psychiatric, autoimmune, metabolic, neurological/pain, and reproductive/gynecological categories and examined both cross-sectionally and in monthly time bins from 250 months before to 500 months after delivery. Latent class analysis identified three pre- and post-delivery multimorbidity profiles and transitions between classes, while polygenic risk scores for depression and obstetric, clinical and socioeconomic variables were combined in machine learning models to predict PPD, post-delivery class membership, and symptom worsening among initially low-burden women. PPD cases showed higher odds of several psychiatric, autoimmune, and metabolic conditions and a tendency toward greater post-delivery comorbidity accumulation, particularly among women who were healthy pre-pregnancy. Multimorbidity profiles based on latent classes captured clinically meaningful risk gradients, and transition analyses revealed that incident PPD in previously healthy women marked a shift toward more symptomatic post-delivery profiles. Machine learning models achieved moderate discrimination for PPD and comorbidity outcomes and highlighted the importance of genetic liability, obstetric complications, and socioeconomic disadvantage, but low positive predictive values limit clinical implementation. These findings position PPD as a critical event in women's psychiatric, cardiometabolic, and pain-related health trajectories and support life-course, multimorbidity-informed screening and prevention strategies that extend beyond the traditional postpartum period.

Authors

Institutions

Publication Details

Journal
medRxiv
Published
2026-02-18
DOI
https://doi.org/10.64898/2026.02.18.26346535
Primary Topic
Maternal Mental Health During Pregnancy and Postpartum
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Marina Mitjans, Selena Aranda, Ariadna Bada-Navarro, Elisa Llurba et al.
medRxiv
Maternal Mental Health During Pregnancy and Postpartum
article

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Marina Mitjans, Selena Aranda, Ariadna Bada-Navarro, Elisa Llurba, M. Cano, Bru Cormand, Narcis Cardoner, Dora Koller
article en

Abstract

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study leveraged 8,804 women with delivery records in the All of Us cohort, including 438 with clinically diagnosed postpartum depression (PPD), to characterize multimorbidity trajectories and develop integrated prediction models. Comorbidities were grouped into 38 conditions across psychiatric, autoimmune, metabolic, neurological/pain, and reproductive/gynecological categories and examined both cross-sectionally and in monthly time bins from 250 months before to 500 months after delivery. Latent class analysis identified three pre- and post-delivery multimorbidity profiles and transitions between classes, while polygenic risk scores for depression and obstetric, clinical and socioeconomic variables were combined in machine learning models to predict PPD, post-delivery class membership, and symptom worsening among initially low-burden women. PPD cases showed higher odds of several psychiatric, autoimmune, and metabolic conditions and a tendency toward greater post-delivery comorbidity accumulation, particularly among women who were healthy pre-pregnancy. Multimorbidity profiles based on latent classes captured clinically meaningful risk gradients, and transition analyses revealed that incident PPD in previously healthy women marked a shift toward more symptomatic post-delivery profiles. Machine learning models achieved moderate discrimination for PPD and comorbidity outcomes and highlighted the importance of genetic liability, obstetric complications, and socioeconomic disadvantage, but low positive predictive values limit clinical implementation. These findings position PPD as a critical event in women's psychiatric, cardiometabolic, and pain-related health trajectories and support life-course, multimorbidity-informed screening and prevention strategies that extend beyond the traditional postpartum period.

medRxiv
Universitat Autònoma de Barcelona (ES), Instituto de Salud Carlos III (ES), Hospital de Sant Pau (ES), Yale University (US), Centro de Investigación Biomédica en Red de Salud Mental (ES), Centre for Biomedical Network Research on Rare Diseases (ES), Sant Joan de Déu Research Foundation (ES), Institut de Recerca Sant Pau (ES), Universitat de Barcelona (ES)
Fundació la Marató de TV3, Ministerio de Ciencia, Innovación y Universidades, European Commission, National Institutes of Health, Agència de Gestió d'Ajuts Universitaris i de Recerca, Agencia Estatal de Investigación
Gender equality
Openalex Percentile: Top 54%
Maternal Mental Health During Pregnancy and Postpartum
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.