Phenotyping obesity through a two-dimensional tree structure reveals cardiometabolic heterogeneity.

IF 10.6 1区医学 Q1 CELL BIOLOGY

Cell Reports Medicine Pub Date : 2025-09-25 DOI:10.1016/j.xcrm.2025.102372

Xiaojing Jia, Hong Lin, Yilan Ding, Chunyan Hu, Shuangyuan Wang, Mian Li, Yu Xu, Min Xu, Feiyue Huang, Feixia Shen, Xuejiang Gu, Yiming Mu, Lulu Chen, Tianshu Zeng, Lixin Shi, Qing Su, Xuefeng Yu, Li Yan, Guijun Qin, Qin Wan, Gang Chen, Xulei Tang, Zhengnan Gao, Ruying Hu, Zuojie Luo, Yingfen Qin, Li Chen, Xinguo Hou, Yanan Huo, Qiang Li, Guixia Wang, Yinfei Zhang, Chao Liu, Youmin Wang, Shengli Wu, Yujin Zhu, Tao Yang, Huacong Deng, Jiajun Zhao, Yifang Zhang, Xingkun Xu, Huapeng Wei, Jie Zheng, Tiange Wang, Zhiyun Zhao, Guang Ning, Yuhong Chen, Weiqing Wang, Yufang Bi, Jieli Lu

{"title":"Phenotyping obesity through a two-dimensional tree structure reveals cardiometabolic heterogeneity.","authors":"Xiaojing Jia, Hong Lin, Yilan Ding, Chunyan Hu, Shuangyuan Wang, Mian Li, Yu Xu, Min Xu, Feiyue Huang, Feixia Shen, Xuejiang Gu, Yiming Mu, Lulu Chen, Tianshu Zeng, Lixin Shi, Qing Su, Xuefeng Yu, Li Yan, Guijun Qin, Qin Wan, Gang Chen, Xulei Tang, Zhengnan Gao, Ruying Hu, Zuojie Luo, Yingfen Qin, Li Chen, Xinguo Hou, Yanan Huo, Qiang Li, Guixia Wang, Yinfei Zhang, Chao Liu, Youmin Wang, Shengli Wu, Yujin Zhu, Tao Yang, Huacong Deng, Jiajun Zhao, Yifang Zhang, Xingkun Xu, Huapeng Wei, Jie Zheng, Tiange Wang, Zhiyun Zhao, Guang Ning, Yuhong Chen, Weiqing Wang, Yufang Bi, Jieli Lu","doi":"10.1016/j.xcrm.2025.102372","DOIUrl":null,"url":null,"abstract":"<p><p>Obesity, a major public health challenge, is characterized by substantial phenotypic heterogeneity. Here, we employ the discriminative dimensionality reduction tree (DDRTree) method to routine clinical data from 18,733 Chinese individuals with obesity enrolled in the nationwide China Cardiometabolic Disease and Cancer Cohort (4C) study. We identify five distinct metabolic phenotypes, among which the phenotype characterized by hyperglycemia and insulin resistance exhibits a higher risk of glycemic deterioration, while the phenotype characterized by hypertension and dyslipidemia demonstrates an elevated risk of microvascular and macrovascular diseases. These findings are validated in an independent prospective cohort. Additionally, we reveal distinctive metabolomic features that contribute to the heterogeneity of obesity in the 4C study. To translate our findings into practice, we develop a user-friendly online tool to assess event risks in the obese population. Overall, our analysis illustrates the underlying phenotypic variations influencing subsequent obesity-related outcomes, emphasizing the importance of precision medicine in obesity management.</p>","PeriodicalId":9822,"journal":{"name":"Cell Reports Medicine","volume":" ","pages":"102372"},"PeriodicalIF":10.6000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.xcrm.2025.102372","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Obesity, a major public health challenge, is characterized by substantial phenotypic heterogeneity. Here, we employ the discriminative dimensionality reduction tree (DDRTree) method to routine clinical data from 18,733 Chinese individuals with obesity enrolled in the nationwide China Cardiometabolic Disease and Cancer Cohort (4C) study. We identify five distinct metabolic phenotypes, among which the phenotype characterized by hyperglycemia and insulin resistance exhibits a higher risk of glycemic deterioration, while the phenotype characterized by hypertension and dyslipidemia demonstrates an elevated risk of microvascular and macrovascular diseases. These findings are validated in an independent prospective cohort. Additionally, we reveal distinctive metabolomic features that contribute to the heterogeneity of obesity in the 4C study. To translate our findings into practice, we develop a user-friendly online tool to assess event risks in the obese population. Overall, our analysis illustrates the underlying phenotypic variations influencing subsequent obesity-related outcomes, emphasizing the importance of precision medicine in obesity management.

查看原文本刊更多论文

通过二维树结构对肥胖进行表型分析，揭示了心脏代谢的异质性。

肥胖是一项重大的公共卫生挑战，其特点是存在显著的表型异质性。在这里，我们采用判别降维树（DDRTree）方法对参加中国心脏代谢疾病和癌症队列（4C）研究的18,733名中国肥胖患者的常规临床数据进行分析。我们确定了五种不同的代谢表型，其中以高血糖和胰岛素抵抗为特征的表型表现出更高的血糖恶化风险，而以高血压和血脂异常为特征的表型则表现出微血管和大血管疾病的高风险。这些发现在一个独立的前瞻性队列中得到了验证。此外，我们揭示了在4C研究中导致肥胖异质性的独特代谢组学特征。为了将我们的发现转化为实践，我们开发了一个用户友好的在线工具来评估肥胖人群的事件风险。总的来说，我们的分析说明了影响随后肥胖相关结果的潜在表型变异，强调了精准医学在肥胖管理中的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Cell Reports Medicine Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)

CiteScore

15.00

自引率

1.40%

发文量

231

审稿时长

40 days

期刊介绍： Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine. Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.