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Kawakita T, Greenland P, Pemberton VL, Grobman WA, Silver RM, Bairey Merz CN, McNeil RB, Haas DM, Reddy UM, Simhan H, Saade GR. Prediction of metabolic syndrome following a first pregnancy. Am J Obstet Gynecol 2024:S0002-9378(24)00460-5. [PMID: 38527600 DOI: 10.1016/j.ajog.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/09/2024] [Accepted: 03/21/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome. OBJECTIVE This study aimed to develop machine learning models to predict the development of metabolic syndrome using factors ascertained in nulliparous pregnant individuals. STUDY DESIGN This was a secondary analysis of a prospective cohort study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be Heart Health Study [nuMoM2b-HHS]). Data were collected from October 2010 to October 2020, and analyzed from July 2023 to October 2023. Participants had in-person visits 2 to 7 years after their first delivery. The primary outcome was metabolic syndrome, defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, which was measured within 2 to 7 years after delivery. A total of 127 variables that were obtained during pregnancy were evaluated. The data set was randomly split into a training set (70%) and a test set (30%). We developed a random forest model and a lasso regression model using variables obtained during pregnancy. We compared the area under the receiver operating characteristic curve for both models. Using the model with the better area under the receiver operating characteristic curve, we developed models that included fewer variables based on SHAP (SHapley Additive exPlanations) values and compared them with the original model. The final model chosen would have fewer variables and noninferior areas under the receiver operating characteristic curve. RESULTS A total of 4225 individuals met the inclusion criteria; the mean (standard deviation) age was 27.0 (5.6) years. Of these, 754 (17.8%) developed metabolic syndrome. The area under the receiver operating characteristic curve of the random forest model was 0.878 (95% confidence interval, 0.846-0.909), which was higher than the 0.850 of the lasso model (95% confidence interval, 0.811-0.888; P<.001). Therefore, random forest models using fewer variables were developed. The random forest model with the top 3 variables (high-density lipoprotein, insulin, and high-sensitivity C-reactive protein) was chosen as the final model because it had the area under the receiver operating characteristic curve of 0.867 (95% confidence interval, 0.839-0.895), which was not inferior to the original model (P=.08). The area under the receiver operating characteristic curve of the final model in the test set was 0.847 (95% confidence interval, 0.821-0.873). An online application of the final model was developed (https://kawakita.shinyapps.io/metabolic/). CONCLUSION We developed a model that can accurately predict the development of metabolic syndrome in 2 to 7 years after delivery.
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Affiliation(s)
- Tetsuya Kawakita
- Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, VA.
| | - Philip Greenland
- Departments of Preventive Medicine and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Victoria L Pemberton
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - William A Grobman
- Department of Obstetrics and Gynecology, The Ohio State University, Columbus, OH
| | - Robert M Silver
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT
| | - C Noel Bairey Merz
- Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - David M Haas
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN
| | - Uma M Reddy
- Department of Obstetrics and Gynecology, Columbia University, New York, NY
| | - Hyagriv Simhan
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA
| | - George R Saade
- Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, VA
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Boitor O, Stoica F, Mihăilă R, Stoica LF, Stef L. Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease. Diagnostics (Basel) 2023; 13:3631. [PMID: 38132215 PMCID: PMC10743072 DOI: 10.3390/diagnostics13243631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.
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Affiliation(s)
- Ovidiu Boitor
- Dental Medicine Research Center, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Florin Stoica
- Department of Mathematics and Informatics, Research Center in Informatics and Information Technology, Faculty of Sciences, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Romeo Mihăilă
- Department of Internal Medicine, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Laura Florentina Stoica
- Department of Mathematics and Informatics, Research Center in Informatics and Information Technology, Faculty of Sciences, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Laura Stef
- Department of Oral Health, Dental Medicine Research Center, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
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Zou G, Zhong Q, OUYang P, Li X, Lai X, Zhang H. Predictive analysis of metabolic syndrome based on 5-years continuous physical examination data. Sci Rep 2023; 13:9132. [PMID: 37277414 DOI: 10.1038/s41598-023-35604-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 05/20/2023] [Indexed: 06/07/2023] Open
Abstract
Metabolic syndrome (MetS) represents a complex group of metabolic disorders. As MetS poses a significant challenge to global public health, predicting the occurrence of MetS and the development of related risk factors is important. In this study, we conducted a predictive analysis of MetS based on machine learning algorithms using datasets of 15,661 individuals. Five consecutive years of medical examination records were provided by Nanfang Hospital, Southern Medical University, China. The specific risk factors used included WC, WHR, TG, HDL-C, BMI, FGLU, etc. We proposed a feature construction method using the examination records over the past four consecutive years, combining the differences between the annual value and the normal limits of each risk factor and the year-to-year variation. The results showed that the feature set, which contained the original features of the inspection record and new features proposed in this study yielded the highest AUC of 0.944, implying that the new features could help identify risk factors for MetS and provide more targeted diagnostic advice for physicians.
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Affiliation(s)
- Guohan Zou
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, 510000, China
- Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine & Big Data, SCNU, Guangzhou, 510006, China
| | - Qinghua Zhong
- School of Electronics and Information Engineering, SCNU, Foshan, 528225, China.
- Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine & Big Data, SCNU, Guangzhou, 510006, China.
| | - Ping OUYang
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiaoxi Li
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiaoying Lai
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Han Zhang
- School of Electronics and Information Engineering, SCNU, Foshan, 528225, China.
- Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine & Big Data, SCNU, Guangzhou, 510006, China.
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Hu X, Li XK, Wen S, Li X, Zeng TS, Zhang JY, Wang W, Bi Y, Zhang Q, Tian SH, Min J, Wang Y, Liu G, Huang H, Peng M, Zhang J, Wu C, Li YM, Sun H, Ning G, Chen LL. Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study. Heliyon 2022; 8:e12343. [PMID: 36643319 PMCID: PMC9834713 DOI: 10.1016/j.heliyon.2022.e12343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 06/16/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Xiang Hu
- 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
| | - Xue-Ke Li
- 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
| | - Shiping Wen
- Centre for Artificial Intelligence, Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Xingyu Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Tian-Shu Zeng
- 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
| | - Jiao-Yue Zhang
- 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
| | - Weiqing Wang
- 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
| | - Yufang Bi
- 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
| | - Qiao Zhang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-Hua Tian
- 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
| | - Jie Min
- 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
| | - Ying Wang
- 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
| | - Geng Liu
- 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
| | | | - Miaomiao Peng
- 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
| | | | - Chaodong Wu
- Department of Nutrition and Food Science, Texas A&M University, College Station, TX, USA
| | - Yu-Ming Li
- 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
| | - Hui Sun
- 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
| | - Guang Ning
- 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
| | - Lu-Lu Chen
- 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,Corresponding author.
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