[논문]Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

Kim, Sang Yeob; Nam, Gyeong Hee; Heo, Byeong Mun

doi:10.3390/app10217741

[해외논문] Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning 원문보기

Applied sciences, v.10 no.21, 2020년, pp.7741 -

Kim, Sang Yeob (National Center for Standard Reference Data, Korea Research Institute of Standards and Science, Daejeon 34113, Korea) , Nam, Gyeong Hee (National Center for Standard Reference Data, Korea Research Institute of Standards and Science, Daejeon 34113, Korea) , Heo, Byeong Mun (Division of Epidemiology and Health Index, Center for Genome Science, Korea National Institute of Health, Cheongju 28159, Korea)

Abstract ▼ AI-Helper

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328-3.158]) and women (OR = 3.515 [3.042-4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311-1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447-3.313]). Forced expiratory volume in 6s and forced expiratory flow 25-75% strongly associated with MS in both men (OR = 0.822 [0.749-0.903]) and women (OR = 1.150 [1.060-1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

참고문헌 (47)

Cameron The metabolic syndrome: Prevalence in worldwide populations Endocrinol. Metab. Clin. N. Am. 2004 10.1016/j.ecl.2004.03.005 33 351

상세보기
Wilson Cardiometabolic risk: A Framingham perspective Int. J. Obes. 2008 10.1038/ijo.2008.30 32 S17

상세보기
Stocks Blood pressure and risk of cancer incidence and mortality in the Metabolic Syndrome and Cancer Project Hypertension 2012 10.1161/HYPERTENSIONAHA.111.189258 59 802

상세보기
Sacco Metabolic syndrome and ischemic stroke risk Stroke 2008 10.1161/STROKEAHA.107.496588 39 30

상세보기
(2020, August 21). Korea Center for Disease Control and Prevention. Available online: http://www.cdc.go.kr/.
(2020, August 21). Statistics Research Institute. Available online: http://kostat.go.kr/sri/srikor/index.action.
Harhay Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010 J. Am. Coll. Cardiol. 2013 10.1016/j.jacc.2013.05.064 62 697

상세보기
10.1186/s12889-017-4041-1 Ranasinghe, P., Mathangasinghe, Y., Jayawardena, R., Hills, A.P., and Misra, A. (2017). Prevalence and trends of metabolic syndrome among adults in the Asia-Pacific region: A systematic review. BMC Public Health, 17.

상세보기
Mannino Prevalence and outcomes of diabetes, hypertension and cardiovascular disease in COPD Eur. Respir. J. 2008 10.1183/09031936.00012408 32 962

상세보기
Schroeder Lung function and incident coronary heart disease: The Atherosclerosis Risk in Communities Study Am. J. Epidemiol. 2003 10.1093/aje/kwg276 158 1171

상세보기
Hedblad Increased incidence of myocardial infarction and stroke in hypertensive men with reduced lung function J. Hypertens. 2001 10.1097/00004872-200102000-00017 19 295

상세보기
Sagun Application of alternative anthropometric measurements to predict metabolic syndrome Clinics 2014 10.6061/clinics/2014(05)09 69 347

상세보기
Mooney Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting Obes. Res. Clin. Pract. 2013 10.1016/j.orcp.2012.10.004 7 e55

상세보기
Hsieh Metabolic syndrome in Japanese men and women with special reference to the anthropometric criteria for the assessment of obesity: Proposal to use the waist-to-height ratio Prev. Med. 2006 10.1016/j.ypmed.2005.08.007 42 135

상세보기
Shen Waist circumference correlates with metabolic syndrome indicators better than percentage fat Obesity 2006 10.1038/oby.2006.83 14 727

상세보기
Waist-to-height ratio is a better anthropometric index than waist circumference and BMI in predicting metabolic syndrome among obese Mexican adolescents Int. J. Endocrinol. 2014 10.1155/2014/195407 2014 1

상세보기
Katzmarzyk Metabolic syndrome, obesity, and mortality: Impact of cardiorespiratory fitness Diabetes Care 2005 10.2337/diacare.28.2.391 28 391

상세보기
Williams A cross-sectional study of dietary patterns with glucose intolerance and other features of the metabolic syndrome Br. J. Nutr. 2000 10.1017/S0007114500000337 83 257

상세보기
Lao White blood cell count and the metabolic syndrome in older Chinese: The Guangzhou Biobank Cohort Study Atherosclerosis 2008 10.1016/j.atherosclerosis.2007.12.053 201 418

상세보기
Funakoshi Association between airflow obstruction and the metabolic syndrome or its components in Japanese men Intern. Med. 2010 10.2169/internalmedicine.49.3882 49 2093

상세보기
McDevitt Macronutrient disposal during controlled overfeeding with glucose, fructose, sucrose, or fat in lean and obese women Am. J. Clin. Nutr. 2000 10.1093/ajcn/72.2.369 72 369

상세보기
Paek Association between low pulmonary function and metabolic risk factors in Korean adults: The Korean National Health and Nutrition Survey Metabolism 2010 10.1016/j.metabol.2009.12.005 59 1300

상세보기
Park Chronic obstructive pulmonary disease and metabolic syndrome: A nationwide survey in Korea Int. J. Tuberc. Lung Dis. 2012 10.5588/ijtld.11.0180 16 694

상세보기
Lin Impaired lung function is associated with obesity and metabolic syndrome in adults Obesity 2006 10.1038/oby.2006.190 14 1654

상세보기
10.1371/journal.pone.0233678 Wulczyn, E., Steiner, D.F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C.H., Chen, P.-H.C., Liu, Y., and Stumpe, M.C. (2020). Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS ONE, 15.

상세보기
Kopp Deep learning for genomics using Janggu Nat. Commun. 2020 10.1038/s41467-020-17155-y 11 1

상세보기
10.1371/journal.pone.0235487 Sonogashira, M., Shonai, M., and Iiyama, M. (2020). High-resolution bathymetry by deep-learning-based image superresolution. PLoS ONE, 15.

상세보기
10.3390/ijerph17186513 Davagdorj, K., Pham, V.H., Theera-Umpon, N., and Ryu, K.H. (2020). XGBoost-based framework for smoking-induced noncommunicable disease prediction. Int. J. Environ. Res. Public Health, 17.

상세보기
10.1016/j.knosys.2020.105534 Munkhdalai, L., Munkhdalai, T., and Ryu, K.H. (2020). GEV-NN: A deep neural network architecture for class imbalance problem in binary classification. Knowl. Based Syst., 105534.

상세보기
10.3390/app10093307 Davagdorj, K., Lee, J.S., Pham, V.H., and Ryu, K.H. (2020). A comparative analysis of machine learning methods for class imbalance in a smoking cessation intervention. Appl. Sci., 10.

상세보기
10.1371/journal.pone.0225991 Amarbayasgalan, T., Park, K.H., Lee, J.Y., and Ryu, K.H. (2019). Reconstruction error based deep neural networks for coronary heart disease risk prediction. PLoS ONE, 14.

상세보기
10.3390/ijerph16193628 Batbaatar, E., and Ryu, K.H. (2019). Ontology-based healthcare named entity recognition from Twitter messages using a recurrent neural network approach. Int. J. Environ. Res. Public Health, 16.

상세보기
Ryu Risk scoring system for prognosis estimation of multivessel disease among patients with ST-segment elevation myocardial infarction Int. Hear. J. 2019 10.1536/ihj.17-337 60 708

상세보기
10.3390/ijerph15112571 Heo, B.M., and Ryu, K.H. (2018). Prediction of prehypertension and hypertension based on anthropometry, blood parameters, and spirometry. Int. J. Environ. Res. Public Health, 15.

상세보기
Piao Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles Comput. Biol. Med. 2017 10.1016/j.compbiomed.2016.11.008 80 39

상세보기
10.3390/sym8060047 Kim, H., Ishag, M.I.M., Piao, M., Kwon, T., and Ryu, K.H. (2016). A data mining approach for cardiovascular disease diagnosis using heart rate variability and images of carotid arteries. Symmetry, 8.

상세보기
Ryu Comparison of clinical outcomes between culprit vessel only and multivessel percutaneous coronary intervention for ST-segment elevation myocardial infarction patients with multivessel coronary diseases J. Geriatr. Cardiol. 2015 12 208
Choe Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population Genom. Inform. 2018 10.5808/GI.2018.16.4.e31 16 e31

원문보기 상세보기
(2020, January 20). Korea Centers for Disease Control and Prevention: Korea National Health and Nutrition Examination Survey. Available online: https://knhanes.cdc.go.kr/knhanes/eng/index.do.
Ministry of Health and Welfare of Korea, and Korea Centers for Disease Control and Prevention (2012). The Fifth Korea National Health and Nutrition Examination Survey Data User Guide (KNHANES V) 2010-2012, Korea Centers for Disease Control and Prevention Press.
Ministry of Health and Welfare of Korea, and Korea Centers for Disease Control and Prevention (2015). The Sixth Korea National Health and Nutrition Examination Survey Data User Guide (KNHANES VI) 2013-2015, Korea Centers for Disease Control and Prevention Press.
Grundy Diagnosis and Management of the Metabolic Syndrome Circulation 2005 10.1161/CIRCULATIONAHA.105.169404 112 2735

상세보기
Saeys A review of feature selection techniques in bioinformatics Bioinformatics 2007 10.1093/bioinformatics/btm344 23 2507

상세보기
(2020, September 01). Statistics Korea Web Sites. Available online: http://kostat.go.kr.
10.3390/app10093046 Kim, S.Y., and Nam, G.H. (2020). Assessment of Anthropometric and Body Composition Risk Factors in Patients with both Hypertension and Stroke in the Korean Population. Appl. Sci., 10.

상세보기
Kohavi, R. (1995, January 20-25). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, QC, Canada.
Kopitar Early detection of type 2 diabetes mellitus using machine learning-based prediction models Sci. Rep. 2020 10.1038/s41598-020-68771-z 10 11981

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