title: Do apolipoproteins improve coronary risk prediction in subjects with metabolic syndrome? Insights from the North Italian Brianza cohort study.
abstract: OBJECTIVE
  We assessed predictive abilities and clinical utility of CVD risk algorithms including ApoB and ApoAI among non-diabetic subjects with metabolic syndrome (MetS).
  
  
  METHODS
  Three independent population-based cohorts (3677 35-74 years old) were enrolled in Northern Italy, adopting standardized MONICA procedures. Through Cox models, we assessed the associations between lipid measures and first coronary events, as well as the changes in discrimination and reclassification (NRI) when standard lipids or apolipoproteins were added to the CVD risk algorithm including non-lipids risk factors. Finally, the best models including lipids or apolipoproteins were compared.
  
  
  RESULTS
  During the 14.5 years median follow-up time, 164 coronary events were validated. All measures showed statistically significant associations with the endpoint, while in the MetS subgroup HDL-C and ApoAI (men, HR = 1.59; 95%CI: 0.96-2.65) were not associated. Models including HDL-C plus TC and ApoB plus ApoAI for lipids and apolipoproteins, respectively, showed the best predictive values. When ApoB plus ApoAI replaced TC plus HDL-C, NRI values improved in subjects with MetS (13.8; CI95%: -5.1,53.1), significantly in those previously classified at intermediate risk (44.5; CI95% 13.8,129.6). In this subgroup, 5.5% of subjects was moved in the high (40.0% of expected events) and 17.0% in the low risk class (none had an event at 10 years).
  
  
  CONCLUSIONS
  ApoB and ApoAI could improve coronary risk prediction when used as second level biomarkers in non-diabetic subjects with MetS classified at intermediate risk. The absence of cases moved downward suggests the gain in avoiding treatments in non-cases and favor the use of apolipoproteins for risk assessment.
authors: Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy.
authors: Francesco
authors: F
authors: Gianfagna
authors: Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy.
authors: Giovanni
authors: G
authors: Veronesi
authors: Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy; Research Centre on Dyslipidemia, Department of Clinical and Experimental Medicine, University of Insubria, Viale Borri 57, 21100 Varese, Italy.
authors: Luigina
authors: L
authors: Guasti
authors: Department of Biostatistics, University of North Carolina at Chapel Hill, 137 E Franklin Street, Chapel Hill, NC 27514, USA.
authors: Lloyd E
authors: LE
authors: Chambless
authors: Department of Health Sciences, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy.
authors: Paolo
authors: P
authors: Brambilla
authors: Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
authors: Giovanni
authors: G
authors: Corrao
authors: IRCCS Istituto Auxologico Italiano, University of Milano-Bicocca, Milano, Italy.
authors: Giuseppe
authors: G
authors: Mancia
authors: Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
authors: Giancarlo
authors: G
authors: Cesana
authors: Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy. Electronic address: marco.ferrario@uninsubria.it.
authors: Marco M
authors: MM
authors: Ferrario
mesh_terms: D054058:Acute Coronary Syndrome / Q000453:epidemiology*; D000328:Adult; D000368:Aged; D016632:Apolipoprotein A-I / Q000097:blood*; D001055:Apolipoproteins B / Q000097:blood*; D019540:Area Under Curve; D015415:Biomarkers; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008055:Lipids / Q000097:blood; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood* / Q000453:epidemiology; D008875:Middle Aged; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000453:epidemiology*; D009204:Myocardial Revascularization / Q000706:statistics & numerical data*; D011379:Prognosis; D016016:Proportional Hazards Models; D012306:Risk; D012737:Sex Factors
chemical_list: D016632:Apolipoprotein A-I; D001055:Apolipoproteins B; D015415:Biomarkers; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D008055:Lipids
keywords: Apolipoprotein A-I; Apolipoprotein B-100; Dysfunctional HDL; Mathematical modeling; Metabolic syndrome; Primary prevention