ArticleCardiovascular diabetology. Endocrinology reports2025
From deep learning discovery to clinical validation: a new composite marker predicts mortality in type 2 diabetes.
Article in Cardiovascular diabetology. Endocrinology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Article
- Vitamin D in Cardiovascular Medicine: From Molecular Mechanisms to Clinical Translation.Nutrients · 2026Review
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7 authors.
Funding
Abstract
objectiveBoth alkaline phosphatase (ALP) and serum creatinine (sCr) have been individually associated with adverse outcomes in patients with diabetes. This study investigates a novel composite indicator-ln[ALP × sCr]-to predict all-cause and CVD mortality risk among U.S. adults with diabetes. RESEARCH DESIGN AND
methodsWe analyzed data from 82,091 U.S. adults enrolled in NHANES (1999-2014), with mortality follow-up through December 31, 2019. A deep learning model identified ALP, sCr, and vitamin D as top mortality-related biomarkers. Based on these results, we derived a composite index, ln[ALP × sCr], to reflect integrated cardiac-renal dysfunction. Restricted cubic spline analysis was used to define risk thresholds. Cox proportional hazards models assessed the association between ln[ALP × sCr] and all-cause, cardiovascular, and diabetes-related mortality.
resultsOver a median follow-up of 11.4 years, 4,839 T2DM patients in the highest quartile of ln[ALP × Scr] exhibited significantly elevated risks of all-cause (HR 1.47, 95% CI 1.18-1.82), cardiovascular (HR 1.44, 95% CI 1.01-2.04), and diabetes-related mortality (HR 2.50, 95% CI 1.58-3.96), compared to those in the lowest quartile. Mediation analysis indicated that serum vitamin D accounted for 24.3% of the association between the composite biomarker and all-cause mortality (P < 0.001).
conclusionsIn this nationally representative cohort, ln[ALP × Scr] showed a J-shaped association with all-cause mortality. These findings highlight the potential utility of ln[ALP × Scr] as a simple, noninvasive biomarker for mortality risk assessment in individuals with diabetes. This study also illustrates the value of integrating AI-based feature selection with traditional epidemiologic modeling to enhance long-term mortality risk stratification and inform public health strategies.
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