A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease

Q: What problem does the study address in diabetic kidney disease?

The study targets the need for more accurate stratification of cardio-kidney risk, since patients often experience both cardiac and kidney events and current prediction tools have limited discriminatory ability.

Q: How was the risk model developed and what outcome did it predict?

Clinical variables and biomarkers were evaluated to predict the adjudicated primary composite endpoint of CREDENCE by 3 years, and a parsimonious machine-learning risk algorithm was created.

Aug 5, 2026

Upload by Riley

Diabetic kidney disease patients face high rates of both cardiac and kidney events, motivating a need for more precise risk stratification. Clinical variables and biomarkers were tested for their ability to predict a trial-adjudicated composite endpoint over 3 years. A parsimonious machine-learning risk algorithm was derived and evaluated, identifying key predictors including demographic measures, hemodynamic parameters, and circulating cardiac and renal biomarkers. The model showed strong discrimination, risk reclassification into low, medium, and high strata, and prognostic use of 1-year score changes, with validated accuracy in independent cohorts.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Methods

Results

Conclusions

Graphical abstract

Research insights

What is currently known

What is the research question

What is new

How might this study influence clinical practice

Januzzi etal. Cardiovascular Diabetology (2025) 24:213 [https://doi.org/10.1186/s12933-025-02779-5](https://doi.org/10.1186/s12933-025-02779-5) Cardiovascular Diabetology RESEARCH Open Access A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease James L. Jr. Januzzi 1,2*, Naveed Sattar3, Muthiah Vaduganathan4, Craig A. Magaret5, Rhonda F. Rhyne5, Yuxi Liu2, Serge Masson6, Javed Butler7,8 and Michael K. Hansen9 Abstract Background Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD. Methods Clinical variables and biomarkers were evaluated for their ability to predict the adjudicated primary composite endpoint of CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) by 3 years. Using machine learning techniques, a parsimonious risk algorithm was developed. Results The final model included age, body-mass index, systolic blood pressure, and concentrations of N-terminal pro-B type natriuretic peptide, high sensitivity cardiac troponinT, insulin-like growth factor binding protein-7 and growth differentiation factor-15 . The model had an in-sample C-statistic of 0.80 (95% CI = 0 .77–0. 83; P < 0. 001) . Dividing results into low, medium and high risk categories, for each increase in level the hazard ratio increased by 3.43 (95% CI = 2 .72–4. 32; P < 0. 001) . Low risk scores had negative predictive value of 94%, while high risk scores had positive predictive value of 58% . Higher values were associated with shorter time to event (log rank P < 0. 001) . Rising values at 1 year predicted higher risk for subsequent DKD events. Canagliflozin treatment reduced score results by 1 year with consistent event reduction across risk levels. Accuracy of the risk model was validated in separate cohorts from CREDENCE and the generally lower risk Canagliflozin Cardiovascular Assessment Study. Conclusions We describe a validated risk algorithm that accurately predicts cardio-kidney outcomes across a broad range of baseline risk. Trial registration CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation; NCT02065791) and CANVAS (Canagliflozin Cardiovascular Assessment Study; NCT01032629/ NCT01989754) . Keywords Diabetic kidney disease, Canagliflozin, Diabetes mellitus, Risk prediction, Prognosis *Correspondence: James L. Jr. Januzzi[jjanuzzi@mgb.org](jjanuzzi@mgb.org) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Januzzi et al. Cardiovascular Diabetology (2025) 24:213 Page 2 of 13 Graphical abstract Persons with diabetic kidney disease (DKD) are at riskfor progressive kidney failure and cardiovascular (CV) events. Using datafrom the CREDENCE trial of patients with type 2 diabe

FAQs

What problem does the study address in diabetic kidney disease?

The study targets the need for more accurate stratification of cardio-kidney risk, since patients often experience both cardiac and kidney events and current prediction tools have limited discriminatory ability.

How was the risk model developed and what outcome did it predict?

Clinical variables and biomarkers were evaluated to predict the adjudicated primary composite endpoint of CREDENCE by 3 years, and a parsimonious machine-learning risk algorithm was created.

Which variables were included in the final model?

The final model included age, body-mass index, systolic blood pressure, and biomarker concentrations including N-terminal pro-B type natriuretic peptide, high-sensitivity cardiac troponin T, insulin-like growth factor binding protein-7, and growth differentiation factor-15.

Aug 5, 2026

Upload by Riley