Abstract
Coronary artery disease poses a significant public health threat, and coronary computed tomography angiography is the preferred imaging modality for diagnosis and risk assessment of coronary artery disease through plaque evaluation. However, understandings of how atherosclerotic characteristics vary by age and sex remains limited due to challenges in manual quantitative plaque assessment. Here, we conducted a retrospective, consecutive, multi-center Chinese cohort study of 16,300 patients undergoing clinically indicated coronary computed tomography angiography that revealed multi-level quantitative patterns of atherosclerosis stratified by age and sex. We found that females experienced a delayed atherosclerosis onset by approximately 20 years compared to males, with plaque …
Abstract
Coronary artery disease poses a significant public health threat, and coronary computed tomography angiography is the preferred imaging modality for diagnosis and risk assessment of coronary artery disease through plaque evaluation. However, understandings of how atherosclerotic characteristics vary by age and sex remains limited due to challenges in manual quantitative plaque assessment. Here, we conducted a retrospective, consecutive, multi-center Chinese cohort study of 16,300 patients undergoing clinically indicated coronary computed tomography angiography that revealed multi-level quantitative patterns of atherosclerosis stratified by age and sex. We found that females experienced a delayed atherosclerosis onset by approximately 20 years compared to males, with plaque burden increasing nonlinearly with age and accelerating more evidently after menopause. The built coronary atlas identified plaque clusters, primarily within proximal segments of major coronary arteries, slightly upstream side branch bifurcations. Our findings provide deeper insights into coronary atherosclerosis in the Chinese population, supporting more tailored prevention strategies.
Data availability
A sample dataset and an interactive demo of PLASMA, which features automated coronary artery reconstruction and plaque analysis, are available at https://aidemo.united-imaging.com/ (username: demo; password: Uai@434254!). Statistical atlases of plaque characteristics derived from the cohort are shared in GitHub (https://github.com/MEI243/PS-CTA-Plaque-Atlas) and archived on Zenodo (https://zenodo.org/records/16741227)70. All shared data is fully anonymized to protect patient privacy in accordance with relevant legal requirements. Due to restrictions imposed by institutional review boards, the raw datasets used in this study are not publicly accessible. However, requests for access to aggregated data and supporting clinical documentation will be evaluated by an independent review panel based on the scientific merit of the request. For data access inquiries related to this study, please contact the corresponding author, Dinggang Shen (dinggang.shen@gmail.com). Requests will be reviewed and responded to within two months. Source data are provided with this paper.
Code availability
The implementation of PLASMA relies on proprietary internal tools and infrastructure and is covered by patents (application numbers: CN111815599B71, CN113902693A72), which prevent public release of the code. Nevertheless, a demo for plaque atlas calculation, along with example data, is openly available on GitHub (https://github.com/MEI243/PS-CTA-Plaque-Atlas) and archived on Zenodo (https://zenodo.org/records/16741227)70. Detailed descriptions of all experiments and implementation procedures are provided in the Methods section, enabling replication using freely available libraries. Additionally, core components relevant to our work can be accessed through open-source platforms, such as PyTorch (https://pytorch.org/) and ResNet (https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).
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Acknowledgements
We gratefully acknowledge the support of Beijing Natural Science Foundation (No. Z210013) (Y.W., D.W.), National Key Research and Development Program of China (No. 2022YFE0209800) (D.W.), National Natural Science Foundation of China (Nos. U23A20295 (D.S.), 82441023 (D.S.), 62131015 (D.S.), 22322816 (Y.D.), 82471982 (J.Z.), 62471418 (Q.H.)), the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, Shanghai Municipal Central Guided Local Science and Technology Development Fund (No. YDZX20233100001001) (D.S.), Key Project of Shanghai Municipal Education Commission (No. 2024AIZD017) (J.Z.), City University of Hong Kong Project (No. 9610640) (Y.D.), and HPC Platform of ShanghaiTech University (D.S.).
Author information
Author notes
These authors contributed equally: Xinnian Yang, Jiayin Zhang.
Authors and Affiliations
School of Data Science, City University of Hong Kong, Hong Kong, China
Xinnian Yang & Yining Dong 1.
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
Xinnian Yang & Dinggang Shen 1.
Hong Kong Centre for Cerebro-cardiovascular Health Engineering, Hong Kong, China
Xinnian Yang & Dengqiang Jia 1.
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Jiayin Zhang 1.
Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
Yanli Song, Yiqiang Zhan, Xiang Sean Zhou, Dijia Wu & Dinggang Shen 1.
Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
Dengqiang Jia 1.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
Qingqi Hong 1.
Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
Yining Wang 1.
Shanghai Clinical Research and Trial Center, Shanghai, China
Dinggang Shen
Authors
- Xinnian Yang
- Jiayin Zhang
- Yanli Song
- Dengqiang Jia
- Qingqi Hong
- Yiqiang Zhan
- Xiang Sean Zhou
- Yining Dong
- Yining Wang
- Dijia Wu
- Dinggang Shen
Contributions
D.S. conceived the idea. X.Y., Y.S., Y.W., D.W. and D.S. designed the study. J.Z., Y.S. and D.W. collected and curated the data. X.Y. and Y.S. implemented and performed the experiments. X.Y., J.Z., Y.S., D.J., Q.H., Y.Z., X.S.Z., Y.D. and D.W. analyzed the data and interpreted the results. X.Y. and D.W. wrote the paper. All the authors reviewed, edited and approved the paper.
Corresponding authors
Correspondence to Yining Wang, Dijia Wu or Dinggang Shen.
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Yang, X., Zhang, J., Song, Y. et al. Deciphering age- and sex-specific patterns of coronary artery atherosclerosis from a large Chinese cohort. Nat Commun (2025). https://doi.org/10.1038/s41467-025-64940-8
Received: 24 January 2025
Accepted: 01 October 2025
Published: 22 November 2025
DOI: https://doi.org/10.1038/s41467-025-64940-8