title: Model of complications of NIDDM. I. Model construction and assumptions.
abstract: OBJECTIVE
  To develop a model of NIDDM for analyzing prevention strategies for NIDDM.
  
  
  METHODS
  A Markov type model with Monte Carlo techniques was used. Age, sex, and ethnicity of cohort was based on U.S. data. Incidence rates of complications were also based on community and population studies.
  
  
  RESULTS
  Nonproliferative retinopathy, proliferative retinopathy, and macular edema are predicted in 79, 19, and 52%, respectively, of people with NIDDM; 19% are predicted to develop legal blindness. Microalbuminuria, gross proteinuria, and end-stage renal disease related to diabetes are predicted in 53, 40, and 17%, respectively. Symptomatic sensorimotor neuropathy and lower-extremity amputation are predicted in 31 and 17%, respectively. Cardiovascular disease is predicted in 39%. Higher rates of complications (1.1-3.0x) are predicted in minority populations. Predicted average life expectancy is 17 years after diagnosis.
  
  
  CONCLUSIONS
  A probabilistic model of NIDDM predicts the vascular complications of NIDDM in a cohort representative of the incident cases of diabetes in the U.S. before age 75 years. Predictions of complications and mortality are consistent with the known epidemiology of NIDDM. The model is suitable for evaluating the effect of preventive interventions on the natural history of NIDDM.
authors.affiliation: Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland 20892-2560, USA.
authors.forename: R C
authors.initials: RC
authors.lastname: Eastman
authors.forename: J C
authors.initials: JC
authors.lastname: Javitt
authors.forename: W H
authors.initials: WH
authors.lastname: Herman
authors.forename: E J
authors.initials: EJ
authors.lastname: Dasbach
authors.forename: A S
authors.initials: AS
authors.lastname: Zbrozek
authors.forename: F
authors.initials: F
authors.lastname: Dong
authors.forename: D
authors.initials: D
authors.lastname: Manninen
authors.forename: S A
authors.initials: SA
authors.lastname: Garfield
authors.forename: C
authors.initials: C
authors.lastname: Copley-Merriman
authors.forename: W
authors.initials: W
authors.lastname: Maier
authors.forename: J F
authors.initials: JF
authors.lastname: Eastman
authors.forename: J
authors.initials: J
authors.lastname: Kotsanos
authors.forename: C C
authors.initials: CC
authors.lastname: Cowie
authors.forename: M
authors.initials: M
authors.lastname: Harris
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000419:Albuminuria; D000671:Amputation, Surgical / Q000706:statistics & numerical data; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D015331:Cohort Studies; D003198:Computer Simulation; D003710:Demography; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000453:epidemiology / Q000503:physiopathology* / Q000517:prevention & control; D003925:Diabetic Angiopathies / Q000453:epidemiology; D003928:Diabetic Nephropathies / Q000453:epidemiology; D003929:Diabetic Neuropathies / Q000453:epidemiology; D003930:Diabetic Retinopathy / Q000453:epidemiology; D005006:Ethnicity; D005260:Female; D006801:Humans; D007676:Kidney Failure, Chronic / Q000453:epidemiology; D008017:Life Expectancy; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008913:Minority Groups; D008962:Models, Theoretical; D009010:Monte Carlo Method; D015995:Prevalence; D011507:Proteinuria; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000453:epidemiology; D012984:Software; D014481:United States
chemical_list: D002784:Cholesterol