Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.

Autor:	Hu X; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Li XK; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Wen S; Centre for Artificial Intelligence, Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia., Li X; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China., Zeng TS; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Zhang JY; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Wang W; Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China., Bi Y; Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China., Zhang Q; Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China., Tian SH; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Min J; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Wang Y; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Liu G; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Huang H; Yiling Hospital, Yichang, China., Peng M; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Zhang J; Yiling Hospital, Yichang, China., Wu C; Department of Nutrition and Food Science, Texas A&M University, College Station, TX, USA., Li YM; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Sun H; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China., Ning G; Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China., Chen LL; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2022 Dec 10; Vol. 8 (12), pp. e12343. Date of Electronic Publication: 2022 Dec 10 (Print Publication: 2022).
DOI:	10.1016/j.heliyon.2022.e12343
Abstrakt:	Background: There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice. Objective: To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population. Methods: The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three prediction models for different scenarios in hospital (Model 1, 2) or at home (Model 3) based on LightGBM (LGBM) technique and corresponding logistic regression (LR) models were also constructed for comparison. Model 1 included variables of laboratory tests, lifestyles and anthropometric measurements while model 2 was built with components of MetS excluded based on model 1, and model 3 was constructed with blood biochemical indexes removed based on model 2. Additionally, we also investigated the strength of association between the predictive factors and MetS, as well as that between the predictors and each component of MetS. Results: In this study, 2714 (30.3%) participants suffer from MetS accordingly. The performances of the LGBM models in predicting the probability of suffering from MetS produced good results and were presented as follows: model 1 had an area under the curve (AUC) value of 0.993 while model 2 indicated an AUC value of 0.885. Model 3 had an AUC value of 0.859, which is close to that of model 2. The AUC values of LR model 1 and 2 for the scenario in hospital and model 3 at home were 0.938, 0.839 and 0.820 respectively, which seemed lower than that of their corresponding machine learning models, respectively. In both LGBM and logistic models, gender, height and resting pulse rate (RPR) were predictors for MetS. Women had higher risk of MetS than men (OR 8.84, CI: 6.70-11.66), and each 1-cm increase in height indicated 3.8% higher risk of suffering from MetS in people over 58 years, whereas each 1- Beat Per Minute (bpm) increase in RPR showed 1.0% higher risk in individuals younger than 62 years. Conclusion: The present study showed that the prediction models developed by machine learning demonstrated effective in evaluating the probability of suffering from MetS, and presented prominent predicting efficacies and accuracies. Additionally, we found that women showed a higher risk of MetS than men, and height in individuals over 58 years was important factor in predicting the probability of suffering from MetS while RPR was of vital importance in people aged 40-62 years. Competing Interests: The authors declare no conflict of interest. (© 2022 Published by Elsevier Ltd.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu