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Mapping burdens and inequalities of polycystic ovary syndrome in young females across 953 locations 1990–2040 with deep learning forecasts

  • 60 authors, including
  • , Yaling Wu (Co-first Author)
  • , Wenxiang Cai (Co-first Author)
  • , Mao Chen (Co-first Author)
  • , Susu Luo (Co-first Author)
  • , Baozhen Huang
  • , Yihang Chu
  • , Pengpeng Ye*
  • , Dongzi Yang*
  • , Shixuan Wang*
  • , Azeem Majeed*
  • , Helena Teede*
  • , Wenyi Jin*
  • , Queran Lin*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Downloads (CityUHK Scholars)

Abstract

Polycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder impacting the health of young female populations. Using harmonized estimates from 953 locations, we assessed temporal trends, geographic inequalities, and forecasts of PCOS among individuals aged 10–24 years from 1990 to 2040. Between 1990 and 2021, global prevalence and disability burden increased by more than 30%, with the highest burden observed among those aged 20–24 years. Substantial geographic heterogeneity was identified, with particularly high prevalence in Europe and East Asia. While high-income regions carried the greatest absolute burden, low- and middle-income regions showed the fastest growth, accompanied by widening socio-economic inequalities. Forecasting analyses suggest that PCOS burden in young populations will continue to rise through 2040. These findings characterize the evolving global landscape of PCOS, providing evidence to support targeted public health surveillance and prevention strategies for adolescent and young adult populations. © 2026 The Author(s)
Original languageEnglish
Article number115116
JournaliScience
Volume29
Issue number4
Online published25 Feb 2026
DOIs
Publication statusPublished - 17 Apr 2026

Funding

We sincerely thank the global staff who collected and compiled the exceptional data for the GBD study. We also gratefully acknowledge the computational support provided by the National Supercomputer Centre in Guangzhou, which enabled us to train our iTransformer models.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Research Keywords

  • health sciences
  • medicine
  • public health
  • reproductive medicine

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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