title: Association and predictive value analysis for metabolic syndrome on systolic and diastolic heart failure in high-risk patients.
abstract: BACKGROUND
  The purpose of this study was, in high-risk patients, to simultaneously estimate the effect of metabolic syndrome (MetS) on diastolic or systolic heart failure (DHF or SHF), to evaluate MetS predictive value for both outcomes.
  
  
  METHODS
  We retrospective enrolled 347 high-risk patients who were scheduled to undergo coronary angiography. They were categorized into DHF cases, SHF cases and reference group. The association of MetS with DHF or SHF was assessed by multinomial logistic regression model. The shared contributor to both outcomes was estimated by bivariate association analysis. The predictive performance of MetS severity score was evaluated using the area under the receiver-operating characteristic curve (AUC).
  
  
  RESULTS
  Hypertension (HT) and triglycerides (TG) were detected to independently associate with DHF (P = 0.044 and 0.049, respectively), while HT and fasting plasma glucose (FPG) independently associate with SHF (P = 0.036 and 0.016, respectively). Bivariate association analysis showed that HT as a shared predictor to both outcomes (P = 0.028). MetS severity score significantly associated with DHF or SHF independently (P = 0.004 and 0.043, respectively), and was a shared predictor to both outcomes (P = 0.049), and showed a high value in predicting DHF and SHF (AUC = 0.701 and 0.722, respectively).
  
  
  CONCLUSIONS
  Our findings signify that MetS is an independently shared predictor of DHF and SHF, and HT is also independently associated with both outcomes in high-risk patients. Prevalence of DHF or SHF trends to increase with increasing MetS severity showing high predictive value for both outcomes.
authors.affiliation: Department of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine, Building 2nd, NO,389 Xincun Road, Shanghai 200063, China. albert.tang@163.com.
authors.forename: Zi-Hui
authors.initials: ZH
authors.lastname: Tang
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
authors.forename: Fangfang
authors.initials: F
authors.lastname: Zeng
authors.forename: Keqin
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D016009:Chi-Square Distribution; D002681:China / Q000453:epidemiology; D050171:Dyslipidemias / Q000097:blood / Q000453:epidemiology; D005260:Female; D044882:Glucose Metabolism Disorders / Q000097:blood / Q000453:epidemiology; D054144:Heart Failure, Diastolic / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D054143:Heart Failure, Systolic / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D006801:Humans; D006973:Hypertension / Q000453:epidemiology / Q000503:physiopathology; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D011237:Predictive Value of Tests; D015995:Prevalence; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D014280:Triglycerides / Q000097:blood
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D014280:Triglycerides