Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes
Open accessAbstract
Background: Patients with diabetes have a two-to four-fold increased incidence of cardiovascular diseases compared with non-diabetics. Currently, there is no recognized model to predict the occurrence and progression of CVDs in diabetics. Objective: This work aimed to develop a metabolic biomarker-assisted model, a combination of metabolic markers with clinical variables, for risk prediction of CVDs in diabetics. Methods: A total of 475 patients with diabetes were studied. Each patient underwent coronary angiography. Plasma samples were analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry. Ordinal logistic regression and random forest were used to screen metabolites. Receiver operating characteristic (ROC) curve, nomogram, and decision curve analysis (DCA) were employed to evaluate their prediction performances. Results: Ordinal logistic regression screened out 34 differential metabolites (adjusted-false discovery rate p < 0.05) from 2059 ion features by comparisons of diabetics with and without CVDs. Random forest identified methylglutarylcarnitine and lysoPC (18:0) as the metabolic markers (mean decrease gini >1.0) for non-significant CVDs (nos-CVDs) versus normal coronary artery (NCA), 1,3-Octadiene and 3-Octanone for acute coronary syndrome (ACS) versus nos-CVDs, and lysoPC (18:0) for acute coronary syndrome versus normal coronary artery. For risk prediction, the metabolic marker-assisted models provided areas under the curve of 0.962–0.979 by ROC (0.576–0.779 for the base models), and c-indices of 0.8477–0.9537 by nomogram analysis (0.1514–0.5196 for the base models). Decision curve analysis (DCA) showed that the models produced greater benefits throughout a wide range of risk probabilities compared with the base model. Conclusion: Metabolic biomarker-assisted model remarkably improved risk prediction of cardiovascular disease in diabetics (>90%).
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None of the included multimodal studies performed comprehensive clinical cost-benefit analysis, and only one reported clinical utility evaluations using decision-curve analysis (DCA).
“None of the included studies performed comprehensive clinical cost–benefit analysis, and only one reported clinical utility evaluations using decision-curve analysis (DCA) [104].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [104]
The Methods and Results report decision-curve analysis (DCA) to evaluate the models’ clinical net benefit. This supports identifying the cited study as one that performed a clinical utility evaluation. The paper does not establish that it was the only included study to do so, or that none of the included studies performed a comprehensive clinical cost–benefit analysis. (LLM, conf=0.88)
Experimental — accuracy not yet measuredShen et al. used a RF-based model to evaluate CVD risk in diabetic patients assisted by metabolic features, selecting metabolic biomarkers with a mean decrease in Gini >1.0 for model construction and training.
“Shen et al. [104] used a RF-based model to evaluate CVD risk in diabetic patients assisted by metabolic features, selecting metabolic biomarkers with a mean decrease in Gini >1.0 for model construction and training.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [104]
The Results (page not provided) say random forest selected metabolites with mean decrease Gini >1.0 in both training and test sets for further model development. But the Methods and Results describe logistic regression for the CVD risk-prediction models; random forest was used for biomarker selection, not as the prediction model. (LLM, conf=0.87)
Experimental — accuracy not yet measuredShen et al. showed that integrating metabolomic information with clinical variables substantially enhanced predictive performance of cardiovascular disease.
“For example, Shen et al. [104] showed that integrating metabolomic information with clinical variables substantially enhanced predictive performance of CVD, while Looker et al. [105] demonstrated that combining protein biomarkers with clinical covariates improved cardiovascular risk stratification in individuals with T2D.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [104]
The Results report substantially higher AUCs when metabolic markers were added to clinical variables, supporting the claimed improvement. The study evaluated patients with diabetes, however; the citing sentence does not retain that population qualifier for Shen et al.’s finding. (LLM, conf=0.84)
Experimental — accuracy not yet measuredShen et al. reported that a metabolite-assisted model incorporating four key metabolites and nine clinical factors achieved an AUC of 0.962-0.979, significantly outperforming clinical-only models (AUC 0.576-0.745).
“Shen et al. [104] reported that a metabolite-assisted model incorporating four key metabolites and nine clinical factors achieved an AUC of 0.962–0.979, significantly outperforming clinical-only models (AUC 0.576–0.745).”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [104]
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- Authors
- Shen, M., Xie, Q., Zhang, R., Yu, C., & Xiao, P.
- Affiliations
- Jiangsu Province Hospital (CN)
- Nanjing Medical University (CN)
- Wuxi Fourth People's Hospital (CN)
- Corresponding author
- Yu, Chunjing
- Xiao, Pingxi
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2023-03-24
- Volume
- 14
- Pages
- 1175021-1175021
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC10081143
- OpenAlex
- W4360850257
- Cited by
- 11
- References
- 30
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- coronary artery disease, diabetes mellitus, cardiovascular risk prediction, metabolic biomarkers, plasma metabolites, acute coronary syndrome, nomogram, decision curve analysis, coronary angiography
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