Blood pressure phenotypes and kidney impairment in adults with diabetes: a machine learning study
Year: 2026
Type: article
Abstract: Background Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease, with hypertension being a key modifiable risk factor. However, associations between specific blood pressure (BP) phenotypes and concurrent DKD risk remain poorly understood. Methods This cross-sectional study analyzed 1,240 adults with diabetes from community health records in Shanghai. Concurrent DKD was defined by UACR ≥30 mg/g or eGFR <60 mL/min/1.73 m 2 . BP phenotypes (140/90 mmHg threshold) were classified as normal (NBP), isolated systolic (ISH), isolated diastolic (IDH), and systolic-diastolic hypertension (SDH). Multivariable logistic regression (LR) estimated adjusted odds ratios (aORs). The cohort was split 80:20 into training (n = 992) and test (n = 248) sets. Five machine learning algorithms were evaluated using strict within-training-fold preprocessing. Results Concurrent DKD was present in 331/1,240 participants (26.69%). After adjustment, ISH and SDH were significantly associated with higher DKD odds versus NBP. On the independent test set, ROC-AUCs were 0.776 for XGBoost and 0.771 for LR, with no statistically significant discrimination difference. XGBoost showed a slightly lower Brier score, while LR offered superior calibration stability and nomogram interpretability. Conclusion ISH and SDH are associated with a higher prevalence of concurrent DKD in adults with diabetes. Routine clinical features and BP phenotypes provide moderate discrimination. These models function for opportunistic concurrent risk pattern recognition rather than predictive screening, supporting further external validation prior to clinical implementation.
Source: Frontiers in Aging
Authors: Jialin Song, Juexuan Li, Huating Zhang, Junqiang Ding, Kai Wei, Xiangyi Ma, Bishan He, Rongyang Zhou, Hengbing Zhou, Qingyu Zhou, Tian Cao, Peiyan Dong, Biying Wu, Wencai Ke, Ding Yang, Jianming Zhou, Qinyu Deng, Yiming Sun, Yao Yao, Hongmei Zi, Lu Zheng, Jianhua Zhang, Qiudan Chen, Xiaotong Chen, Yong Lin, Shuying Chen
Institutions: Fudan University, Huashan Hospital, Guizhou Provincial People's Hospital, Jinshan Hospital of Fudan University, Obstetrics and Gynecology Hospital of Fudan University, Massachusetts Eye and Ear Infirmary, Massachusetts Institute of Technology, Shanghai Medical College of Fudan University
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,077
OpenAlex ID: W7225652629