title: One risk assessment tool for cardiovascular disease, type 2 diabetes, and chronic kidney disease.
abstract: OBJECTIVE
  Individuals at high risk for chronic cardiometabolic disease (cardiovascular disease [CVD], type 2 diabetes, and chronic kidney disease [CKD]) share many risk factors and would benefit from early intervention. We developed a nonlaboratory-based risk-assessment tool for identification of people at high cardiometabolic disease risk.
  
  
  METHODS
  Data of three population-based cohorts from different regions of the Netherlands were merged. Participants were 2,840 men and 3,940 women, white, aged 28-85 years, free from CVD, type 2 diabetes, and CKD diagnosis at baseline. The outcome was developing cardiometabolic disease during 7 years follow-up.
  
  
  RESULTS
  Age, BMI, waist circumference, antihypertensive treatment, smoking, family history of myocardial infarction or stroke, and family history of diabetes were significant predictors, whereas former smoking, history of gestational diabetes, and use of lipid-lowering medication were not. The models showed acceptable calibration (Hosmer and Lemeshow statistics, P > 0.05) and discrimination (area under the receiver operating characteristic [ROC] curve 0.82 [95% CI 0.81-0.83] for women and 0.80 [0.78-0.82] for men). Discrimination of individual outcomes was lowest for diabetes (area under the ROC curve 0.70 for men and 0.73 for women) and highest for CVD mortality (0.83 for men and 0.85 for women).
  
  
  CONCLUSIONS
  We demonstrate that a single risk stratification tool can identify people at high risk for future CVD, type 2 diabetes, and/or CKD. The present risk-assessment tool can be used for referring the highest risk individuals to health care for further (multivariable) risk assessment and may as such serve as an important part of prevention programs targeting chronic cardiometabolic disease.
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. marjanalssema@hotmail.com
authors.forename: Marjan
authors.initials: M
authors.lastname: Alssema
authors.forename: Rachel S
authors.initials: RS
authors.lastname: Newson
authors.forename: Stephan J L
authors.initials: SJ
authors.lastname: Bakker
authors.forename: Coen D A
authors.initials: CD
authors.lastname: Stehouwer
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
authors.forename: Giel
authors.initials: G
authors.lastname: Nijpels
authors.forename: Hans L
authors.initials: HL
authors.lastname: Hillege
authors.forename: Albert
authors.initials: A
authors.lastname: Hofman
authors.forename: Jacqueline C M
authors.initials: JC
authors.lastname: Witteman
authors.forename: Ron T
authors.initials: RT
authors.lastname: Gansevoort
authors.forename: Jacqueline M
authors.initials: JM
authors.lastname: Dekker
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007676:Kidney Failure, Chronic / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009426:Netherlands / Q000453:epidemiology; D012107:Research Design; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors