Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes
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-as...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1175021/full |
| _version_ | 1797860810155884544 |
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| author | Min Shen Qingya Xie Ruizhe Zhang Chunjing Yu Pingxi Xiao |
| author_facet | Min Shen Qingya Xie Ruizhe Zhang Chunjing Yu Pingxi Xiao |
| author_sort | Min Shen |
| collection | DOAJ |
| description | 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%). |
| first_indexed | 2024-04-09T21:51:52Z |
| format | Article |
| id | doaj.art-46318f04aeca4ab398cb23aaee562d10 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-09T21:51:52Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-46318f04aeca4ab398cb23aaee562d102023-03-24T13:13:50ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-03-011410.3389/fphar.2023.11750211175021Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetesMin Shen0Qingya Xie1Ruizhe Zhang2Chunjing Yu3Pingxi Xiao4Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Cardiology, The Fourth Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Cardiology, The Fourth Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, ChinaDepartment of Cardiology, The Fourth Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaBackground: 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%).https://www.frontiersin.org/articles/10.3389/fphar.2023.1175021/fullmetabolic marker-assisted modelrisk predictiondiabeticscardiovascular diseasesnomograms |
| spellingShingle | Min Shen Qingya Xie Ruizhe Zhang Chunjing Yu Pingxi Xiao Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes Frontiers in Pharmacology metabolic marker-assisted model risk prediction diabetics cardiovascular diseases nomograms |
| title | Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| title_full | Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| title_fullStr | Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| title_full_unstemmed | Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| title_short | Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| title_sort | metabolite assisted models improve risk prediction of coronary heart disease in patients with diabetes |
| topic | metabolic marker-assisted model risk prediction diabetics cardiovascular diseases nomograms |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1175021/full |
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