Early-pregnancy maternal metabolite signatures, genetic predisposition, and perinatal depressive symptom trajectories: A prospective birth cohort study

Na Zhang; Xinyu Liu; Hui Wang; Wen Jiang; Xiaojing Zeng; Xiaoqing He; Qi Li; Xiaolin Wang; Yaorui Ye; Qianlong Zhang; Guowang Xu; Jun Zhang; Shanghai Birth Cohort

doi:10.1016/j.jad.2025.119768

Early-pregnancy maternal metabolite signatures, genetic predisposition, and perinatal depressive symptom trajectories: A prospective birth cohort study

J Affect Disord. 2025 Jun 25:119768. doi: 10.1016/j.jad.2025.119768. Online ahead of print.

Authors

Na Zhang¹, Xinyu Liu², Hui Wang³, Wen Jiang³, Xiaojing Zeng³, Xiaoqing He³, Qi Li², Xiaolin Wang², Yaorui Ye², Qianlong Zhang³, Guowang Xu⁴, Jun Zhang⁵; Shanghai Birth Cohort

Affiliations

¹ Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China; Liaoning Province Key Laboratory of Metabolomics, Dalian, China.
³ Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China; Liaoning Province Key Laboratory of Metabolomics, Dalian, China. Electronic address: xugw@dicp.ac.cn.
⁵ Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: junjimzhang@sjtu.edu.cn.

PMID: 40578692
DOI: 10.1016/j.jad.2025.119768

Abstract

Objective: To identify early-pregnancy maternal circulating metabolites linked to perinatal depressive symptom trajectories, and to assess the potential role of genetic predisposition in these associations.

Methods: We included 990 pregnant women from the Shanghai Birth Cohort, enrolled during early pregnancy. Maternal depressive symptoms were assessed during mid-pregnancy and at 42 days and 6 months postpartum using the Center for Epidemiologic Studies Depression Scale and Edinburgh Postnatal Depression Scale. A liquid chromatography-mass spectrometry metabolomics approach was employed to profile maternal serum metabolites. Perinatal depressive symptom trajectories were determined through group-based trajectory modelling, and metabolites were identified using elastic net and multinomial logistic regression.

Results: Five perinatal depressive symptom trajectories were established: no symptoms (52.1 %, n = 516), low-stable (29.4 %, n = 291), resilient (10.9 %, n = 108), and recurrent (4.0 %, n = 40), and emergent (3.5 %, n = 35). Each unit increase in natural log-transformed FA 24: 0 (odds ratio (OR): 1.26, 95 % confidence interval (CI): 1.07-1.48) and 16,17-didehydropregnenolone (OR: 1.35, 95 % CI: 1.16-1.58) was associated with an elevated risk of a low-stable trajectory, while 16,17-didehydropregnenolone also linked to a higher risk of the recurrent trajectory (OR: 1.73, 95 % CI: 1.22-2.47). Conversely, metabolites such as 3beta,7alpha-Dihydroxy-5-cholestenoate and FA 6:0 were correlated with a reduced risk. No significant interactions were found between the identified metabolites and polygenetic risk.

Conclusions: Early-pregnancy maternal metabolites were associated with perinatal depressive symptom trajectories, independent of genetic predisposition. These findings underscore the potential role of metabolic biomarkers in predicting perinatal depression. Future studies are warranted to replicate these findings in diverse cohorts and explore causality.

Keywords: Maternal metabolites; Perinatal depressive symptoms; Polygenic risk; Trajectory.