title: Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.
abstract: BACKGROUND
  Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance.
  
  
  OBJECTIVE
  To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories.
  
  
  METHODS
  We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013.
  
  
  RESULTS
  HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively).
  
  
  CONCLUSIONS
  This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention.
authors.affiliation: Department of Family Medicine, Department of Public Health and Preventive Medicine, Division of Biostatistics, Oregon Health Science University, Portland, Oregon. Electronic address: marinom@ohsu.edu.
authors.forename: Miguel
authors.initials: M
authors.lastname: Marino
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Duke Clinical Research Institute and Department of Biostatistics and Bioinformatics and Framingham Heart Study, Duke University, Durham, North Carolina.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Pencina
authors.affiliation: Department of Mathematics/Statistics, Biostatistics and Epidemiology, Boston University.
authors.forename: Ralph B
authors.initials: RB
authors.lastname: D'Agostino
authors.affiliation: Harvard Center for Population and Development Studies, Cambridge, Massachusetts.
authors.forename: Lisa F
authors.initials: LF
authors.lastname: Berkman
authors.affiliation: Department of Medicine, Brigham and Women's Hospital, Department of Social and Behavioral Sciences, Harvard School of Public Health, Division of Sleep Medicine, Harvard Medical School Boston; Department of Biobehavioral Health, Pennsylvania State University, University Park, Pennsylvania.
authors.forename: Orfeu M
authors.initials: OM
authors.lastname: Buxton
mesh_terms: D000328:Adult; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D008297:Male; D008659:Metabolic Diseases / Q000453:epidemiology* / Q000209:etiology; D008875:Middle Aged; D011237:Predictive Value of Tests; D011322:Primary Prevention / Q000379:methods*; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors
chemical_list: D006442:Glycated Hemoglobin