title: Evaluation of risk prediction updates from commercial genome-wide scans.
abstract: OBJECTIVE
  Commercial internet-based companies offer genome-wide scans to predict the risk of common diseases and personalize nutrition and lifestyle recommendations. These risk estimates are updated with every new gene discovery.
  
  
  METHODS
  To assess the benefits of updating risk information in commercial genome-wide scans, we compared type 2 diabetes risk predictions based on TCF7L2 alone, 18 polymorphisms alone, and 18 polymorphisms plus age, sex, and body mass index. Analyses were performed using data from the Rotterdam study, a prospective, population-based study among individuals aged 55 years and older. Data were available from 5297 participants.
  
  
  RESULTS
  The actual prevalence of type 2 diabetes in the study population was 20%. Predicted risks were below average for carriers of the TCF7L2 CC genotype (predicted risk 17.6%) and above average for the CT and TT genotypes (20.8% and 28.0%). Adding the other 17 polymorphisms caused 34% of participants to be reclassified (i.e., switched between below and above average): 24% of the CC carriers changed to increased risk, 52% and 6% of the CT and TT carriers changed to decreased risk. Including information on age, sex, and body mass index caused 29% to change categories (27%, 31%, and 19% for CC, CT, and TT carriers, respectively). In total, 39% of participants changed categories once when risk factors were updated, and 11% changed twice, i.e., back to their initial risk category.
  
  
  CONCLUSIONS
  Updating risk factors may produce contradictory information about an individual's risk status over time, which is undesirable if lifestyle and nutritional recommendations vary accordingly.
authors.affiliation: Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
authors.forename: Raluca
authors.initials: R
authors.lastname: Mihaescu
authors.forename: Mandy
authors.initials: M
authors.lastname: van Hoek
authors.forename: Eric J G
authors.initials: EJ
authors.lastname: Sijbrands
authors.forename: André G
authors.initials: AG
authors.lastname: Uitterlinden
authors.forename: Jacqueline C M
authors.initials: JC
authors.lastname: Witteman
authors.forename: Albert
authors.initials: A
authors.lastname: Hofman
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: van Duijn
authors.forename: A Cecile J W
authors.initials: AC
authors.lastname: Janssens
mesh_terms: D000368:Aged; D015992:Body Mass Index; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D005260:Female; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000379:methods; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods; D005838:Genotype; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011110:Polymorphism, Genetic; D015995:Prevalence; D012307:Risk Factors; D051820:TCF Transcription Factors / Q000235:genetics*; D058118:Transcription Factor 7-Like 2 Protein
chemical_list: D051820:TCF Transcription Factors; C544894:TCF7L2 protein, human; D058118:Transcription Factor 7-Like 2 Protein