title: Genetic, physiological, and lifestyle predictors of mortality in the general population.
abstract: OBJECTIVE
  We investigated the quality of 162 variables, focusing on the contribution of genetic markers, used solely or in combination with other characteristics, when predicting mortality.
  
  
  METHODS
  In 5974 participants from the Rotterdam Study, followed for a median of 15.1 years, 7 groups of factors including age and gender, genetics, socioeconomics, lifestyle, physiological characteristics, prevalent diseases, and indicators of general health were related to all-cause mortality. Genetic variables were identified from 8 genome-wide association scans (n = 19,033) and literature review.
  
  
  RESULTS
  We observed 3174 deaths during follow-up. The fully adjusted model (C-statistic for 15-year follow-up [C15y] = 0.80; 95% confidence interval [CI] = 0.79, 0.81) predicted mortality well [corrected]. Most of the additional information apart from age and sex stemmed from physiological markers, prevalent diseases, and general health. Socioeconomic factors and lifestyle contributed meaningfully to mortality risk prediction with longer prediction horizon. Although specific genetic factors were independently associated with mortality, jointly they contributed little to mortality prediction (C(15y) = 0.56; 95% CI = 0.55, 0.57).
  
  
  CONCLUSIONS
  Mortality can be predicted reasonably well over a long period. Genetic factors independently predict mortality, but only modestly more than other risk indicators.
authors.affiliation: Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
authors.forename: Stefan
authors.initials: S
authors.lastname: Walter
authors.forename: Johan
authors.initials: J
authors.lastname: Mackenbach
authors.forename: Zoltán
authors.initials: Z
authors.lastname: Vokó
authors.forename: Stefan
authors.initials: S
authors.lastname: Lhachimi
authors.forename: M Arfan
authors.initials: MA
authors.lastname: Ikram
authors.forename: André G
authors.initials: AG
authors.lastname: Uitterlinden
authors.forename: Anne B
authors.initials: AB
authors.lastname: Newman
authors.forename: Joanne M
authors.initials: JM
authors.lastname: Murabito
authors.forename: Melissa E
authors.initials: ME
authors.lastname: Garcia
authors.forename: Vilmundur
authors.initials: V
authors.lastname: Gudnason
authors.forename: Toshiko
authors.initials: T
authors.lastname: Tanaka
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Tranah
authors.forename: Henri
authors.initials: H
authors.lastname: Wallaschofski
authors.forename: Thomas
authors.initials: T
authors.lastname: Kocher
authors.forename: Lenore J
authors.initials: LJ
authors.lastname: Launer
authors.forename: Nora
authors.initials: N
authors.lastname: Franceschini
authors.forename: Maarten
authors.initials: M
authors.lastname: Schipper
authors.forename: Albert
authors.initials: A
authors.lastname: Hofman
authors.forename: Henning
authors.initials: H
authors.lastname: Tiemeier
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D005819:Genetic Markers / Q000235:genetics*; D006304:Health Status; D006801:Humans; D008019:Life Style; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D009426:Netherlands; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015995:Prevalence; D011446:Prospective Studies; D012372:ROC Curve; D012307:Risk Factors
chemical_list: D005819:Genetic Markers