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ArticleFrontiers in cardiovascular medicine2026

Cardio-Oncology Risk Phenotypes in Cancer Patients Treated with Chemotherapy: A Data-Driven Analysis.

Xiaolin Gao, Kexin Chen, Haowen Liang, Andong Huang, Qiexinhao Li, Jianpeng Liang, Qiutong Pan, Ruilang Zhong, Jiayao Zhang, Ruirui Liang and 4 more

Abstract read
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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Xiaolin Gao *Department of Anesthesiology, The Second Clinical College of Guangzhou Medical University, Guangzhou, China.
Kexin Chen *Department of Clinical Medicine, The Nanshan Clinical College of Guangzhou Medical University, Guangzhou, China.
Haowen Liang *Department of Clinical Medicine, The Third Clinical College of Guangzhou Medical University, Guangzhou, China.
Andong HuangDepartment of Anesthesiology, The Second Clinical College of Guangzhou Medical University, Guangzhou, China.
Qiexinhao LiDepartment of Clinical Medicine, The Nanshan Clinical College of Guangzhou Medical University, Guangzhou, China.
Jianpeng LiangDepartment of Clinical Medicine, The Second Clinical Medical College, Southern Medical University, Guangzhou, China.
Qiutong PanDepartment of Clinical Medicine, The Sixth Clinical College of Guangzhou Medical University, Guangzhou, China.
Ruilang ZhongDepartment of Clinical Medicine, The First Clinical College of Guangzhou Medical University, Guangzhou, China.
Jiayao ZhangDepartment of Clinical Medicine, The Third Clinical College of Guangzhou Medical University, Guangzhou, China.
Ruirui LiangDepartment of Clinical Medicine, The Second Clinical Medical College, Southern Medical University, Guangzhou, China.
Weipeng ZhengDepartment of Orthopaedics, School of Medicine, The Second Affiliated Hospital of South China University of Technology (Guangzhou First People's Hospital), Guangzhou, China.
Kenie WangShengli Clinical Medical College of Fujian Medical University, Department of Breast Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Jun PengDepartment of Cardiovascular Medicine, The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Guangdong, China.
Tao YangDepartment of Cardiovascular Medicine, The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease (CVD) is a major long-term complication in cancer survivors treated with chemotherapy, yet risk varies markedly across individuals. Using the UK Biobank, we studied chemotherapy-treated cancer patients without baseline CVD and identified data-driven cardio-oncology risk phenotypes with unsupervised clustering (UMAP embedding and HDBSCAN). We evaluated associations between phenotypes and incident CVD using Cox proportional hazards models and assessed 5-year risk discrimination with and without phenotype labels. Among 18,693 patients, 9 stable phenotypes were identified, characterized by distinct patterns of cancer treatment exposure, baseline cardiovascular medication use, cardiometabolic burden, renal function, and lifestyle factors. Compared with the reference phenotype, all phenotypes had higher risks of composite CVD, and the highest-risk phenotype demonstrated a hazard ratio of 4.06 (95% CI, 3.44-4.78). Phenotype-defining features were interpreted using SHAP values. In subtype analyses, heterogeneity was most apparent for ischemic heart disease, angina pectoris, and acute myocardial infarction. Adding phenotype labels modestly improved 10-year discrimination, with AUC values increasing from 0.648 to 0.692. These findings suggest that phenotype-based profiling may provide an exploratory framework for risk stratification in cardio-oncology and may help characterize risk heterogeneity across cancer survivors treated with chemotherapy.

Indexed as

cancer survivorscardio-oncologycardiovascular diseasechemotherapymachine learningrisk phenotyping

Identifiers

PMID42609512
PMCPMC13478683

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.