Abstract
Background:The metabolic syndrome (MetS) was first proposed to predict the occurrence of cardiovascular disease and type 2 diabetes. However, it is difficult to identify subjects with MetS early. No previous studies designed to develop a predictive model for MetS in the Chinese population exists; this study was designed to fill that gap.Methods:A middle-aged Chinese cohort of 198 men and 154 women were followed for two years. The binary logistic regression and receiver operation characteristic (ROC) curve were used to develop a predictive model for the future development of MetS.Results:Over two years of follow up, 30 of the 352 subjects (8.52%) without MetS at baseline subsequently developed MetS. Triglycerides (TG) had the highest area under the curve (AUC), while diastolic blood pressure had the lowest. In order to increase the prediction power, MetS components were arranged in the ROC model according to their AUC. After adding waist circumference (WC) to TG (model 1), the AUC was significantly higher than for TG alone. Adding other components into the model did not increase the AUC significantly. A risk score cutoff (0.078) was selected for the best predictive power of model 1 (sensitivity of 76.7%, specificity of 63.4%, with AUC of 76.8%).Conclusions:These results imply that WC and TG are related to the pathophysiologies of MetS, and model 1 could also be used clinically for screening subjects at high risks for MetS.
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References
References1. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735–2752.GrundySM]]CleemanJI]]DanielsSR&etal;Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.Circulation20051122735-27522. Orchard TJ, Temprosa M, Goldberg R, et al. The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the Diabetes Prevention Program randomized trial. Ann Intern Med 2005;142:611–619.OrchardTJ]]TemprosaM]]GoldbergR&etal;The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the Diabetes Prevention Program randomized trial.Ann Intern Med2005142611-6193. Ratner R, Goldberg R, Haffner S, et al. Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program. Diabetes Care 2005;28:888–894.RatnerR]]GoldbergR]]HaffnerS&etal;Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program.Diabetes Care200528888-8944. Reaven GM, Chen YD. Role of insulin in regulation of lipoprotein metabolism in diabetes. Diabetes Metab Rev 1988;4:639–652.ReavenGM]]ChenYDRole of insulin in regulation of lipoprotein metabolism in diabetes.Diabetes Metab Rev19884639-6525. Ferrannini E, Haffner SM, Mitchell BD, et al. Hyperinsulinaemia: the key feature of a cardiovascular and metabolic syndrome. Diabetologia 1991;34:416–422.FerranniniE]]HaffnerSM]]MitchellBD&etal;Hyperinsulinaemia: the key feature of a cardiovascular and metabolic syndrome.Diabetologia199134416-4226. Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 2001;285:2486–2497.&NA;Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III).JAMA20012852486-24977. Saad MF, Rewers M, Selby J, et al. Insulin resistance and hypertension: the Insulin Resistance Atherosclerosis study. Hypertension 2004;43:1324–1331.SaadMF]]RewersM]]SelbyJ&etal;Insulin resistance and hypertension: the Insulin Resistance Atherosclerosis study.Hypertension2004431324-13318. Toft I, Bonaa KH, Jenssen T. Insulin resistance in hypertension is associated with body fat rather than blood pressure. Hypertension 1998;32:115–122.ToftI]]BonaaKH]]JenssenTInsulin resistance in hypertension is associated with body fat rather than blood pressure.Hypertension199832115-1229. Howard BV, Ruotolo G, Robbins DC. Obesity and dyslipidemia. Endocrinol Metab Clin North Am 2003;32:855–867.HowardBV]]RuotoloG]]RobbinsDCObesity and dyslipidemia.Endocrinol Metab Clin North Am200332855-86710. Yuan G, Al-Shali KZ, Hegele RA. Hypertriglyceridemia: its etiology, effects and treatment. CMAJ 2007;176:1113–1120.YuanG]]Al-ShaliKZ]]HegeleRAHypertriglyceridemia: its etiology, effects and treatment.CMAJ20071761113-112011. Cheal KL, Abbasi F, Lamendola C, et al. Relationship to insulin resistance of the adult treatment panel III diagnostic criteria for identification of the metabolic syndrome. Diabetes 2004;53:1195–1200.ChealKL]]AbbasiF]]LamendolaC&etal;Relationship to insulin resistance of the adult treatment panel III diagnostic criteria for identification of the metabolic syndrome.Diabetes2004531195-120012. Hsieh CH, Hung YJ, Wu DA, et al. Impact of clinical characteristics of individual metabolic syndrome on the severity of insulin resistance in Chinese adults. J Korean Med Sci 2007;22:74–80.HsiehCH]]HungYJ]]WuDA&etal;Impact of clinical characteristics of individual metabolic syndrome on the severity of insulin resistance in Chinese adults.J Korean Med Sci20072274-8013. Kadowaki T, Yamauchi T. Adiponectin and adiponectin receptors. Endocr Rev 2005;26:439–451.KadowakiT]]YamauchiTAdiponectin and adiponectin receptors.Endocr Rev200526439-45114. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Invest 2006;116:1793–1801.ShoelsonSE]]LeeJ]]GoldfineABInflammation and insulin resistance.J Clin Invest20061161793-180115. Despres JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature 2006;444:881–887.DespresJP]]LemieuxIAbdominal obesity and metabolic syndrome.Nature2006444881-88716. Laaksonen DE, Lakka HM, Salonen JT, et al. Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome. Diabetes Care 2002;25:1612–1618.LaaksonenDE]]LakkaHM]]SalonenJT&etal;Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome.Diabetes Care2002251612-161817. Raikkonen K, Matthews KA, Kuller LH. Depressive symptoms and stressful life events predict metabolic syndrome among middle-aged women: a comparison of World Health Organization, Adult Treatment Panel III, and International Diabetes Foundation definitions [Erratum in Diabetes Care 2007;30:2761]. Diabetes Care 2007;30:872–877.18. Mohan V, Sandeep S, Deepa M, et al. A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians—the Chennai Urban Rural Epidemiology Study (CURES-38). Diabetes Obes Metab 2007;9:337–343.MohanV]]SandeepS]]DeepaM&etal;A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians—the Chennai Urban Rural Epidemiology Study (CURES-38).Diabetes Obes Metab20079337-34319. Saaristo T, Peltonen M, Lindstrom J, et al. Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res 2005;2:67–72.SaaristoT]]PeltonenM]]LindstromJ&etal;Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome.Diab Vasc Dis Res2005267-7220. Heldgaard PE, Griffin SJ. Routinely collected general practice data aids identification of people with hyperglycaemia and metabolic syndrome. Diabet Med 2006;23:996–1002.HeldgaardPE]]GriffinSJRoutinely collected general practice data aids identification of people with hyperglycaemia and metabolic syndrome.Diabet Med200623996-100221. Lamarche B, Tchernof A, Mauriege P, et al. Fasting insulin and apolipoprotein B levels and low-density lipoprotein particle size as risk factors for ischemic heart disease. JAMA 1998;279:1955–1961.LamarcheB]]TchernofA]]MauriegeP&etal;Fasting insulin and apolipoprotein B levels and low-density lipoprotein particle size as risk factors for ischemic heart disease.JAMA19982791955-196122. Lemieux I, Pascot A, Couillard C, et al. Hypertriglyceridemic waist: a marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men? Circulation 2000;102:179–184.LemieuxI]]PascotA]]CouillardC&etal;Hypertriglyceridemic waist: a marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men?Circulation2000102179-18423. Despres JP, Lemieux I, Prud’homme D. Treatment of obesity: need to focus on high risk abdominally obese patients. BMJ 2001;322:716–720.DespresJP]]LemieuxI]]Prud’hommeDTreatment of obesity: need to focus on high risk abdominally obese patients.BMJ2001322716-720
