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A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Shusheng Wang1, Ru Xu1, Gang Li2

  • 1Department of Traditional Chinese Medicine, Jinshan Hospital, Fudan University, Shanghai 201508, China.

Journal of Proteome Research
|January 8, 2025
PubMed
Summary

Researchers identified key proteins in plasma to predict postpartum depression (PPD) risk. This proteomic analysis offers potential for early PPD risk assessment and personalized prediction strategies.

Keywords:
machine learningpostpartum depression riskproteomics

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Maternal Health

Background:

  • Postpartum depression (PPD) presents significant risks to maternal and infant well-being.
  • Limited proteomic studies exist for women at risk of PPD.

Purpose of the Study:

  • To identify differentially expressed proteins in plasma of women at risk for PPD.
  • To develop a predictive model for early PPD risk assessment.

Main Methods:

  • Plasma samples from 30 healthy and 30 PPD-risk postpartum women were analyzed using mass spectrometry.
  • Machine learning models (XGBoost, LASSO, logistic regression) were employed to identify key proteins.
  • Principal component analysis, functional enrichment, and protein-protein interaction analyses were performed.

Main Results:

  • 98 differentially expressed proteins were identified (29 upregulated, 69 downregulated).
  • Distinct protein expression profiles differentiated PPD-risk from healthy women.
  • A 5-protein signature (PLS3, CLDN17, ST8SIA3, IGKV1D-33, MATR3) showed excellent predictive performance.

Conclusions:

  • The identified protein signature shows promise as a biomarker for early PPD risk assessment.
  • These findings could facilitate personalized prediction and intervention strategies for PPD.
  • Further validation in larger, diverse cohorts is needed to confirm clinical applicability.