title: Metabolic syndrome and risk of age-related cataract over time: an analysis of interval-censored data using a random-effects model.
abstract: OBJECTIVE
  To investigate whether the effect of metabolic syndrome (MetS) and its components on the incidence of different cataract subtypes (cortical, nuclear, and posterior subcapsular cataract [PSC]) change with time.
  
  
  METHODS
  A prospective cohort of persons 49 years of age and older were followed over 10 years in the Blue Mountains Eye Study, west of Sydney, Australia. MetS components as defined by the International Diabetes Federation criteria were measured at baseline (1992-1994), after 5 years (1997-1999), and after 10 years (2002-2004). The incidence of different cataract subtypes was obtained from standard photographic grading at these intervals (n = 1997). Using a random-effects complementary log-log regression model with time to cataract development in discrete time interval, we estimated the effect of MetS and its components on the incidence of different cataract subtypes at different time intervals.
  
  
  RESULTS
  After accounting for changes in MetS components over time and controlling for possible confounders, MetS was found to be associated with an increased 5-year incidence of cortical cataract (hazard ratio [HR] 1.48; 95% confidence interval [CI], 1.05-2.09) and PSC cataract (HR 1.75; 95% CI, 1.01-3.04). Among the five MetS components, high glucose and obesity predicted an increased 5-year incidence of cortical cataract. In addition, low high-density lipoprotein and high glucose were associated with an increased 10-year incidence of cortical and PSC cataracts, respectively.
  
  
  CONCLUSIONS
  Changes in MetS predicted the 5-year incidence of cortical and PSC cataracts. Different MetS components predicted the incidence of cortical and PSC cataracts at varying time intervals.
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
authors.forename: Haleh
authors.initials: H
authors.lastname: Ghaem Maralani
authors.forename: Bee Choo
authors.initials: BC
authors.lastname: Tai
authors.forename: Tien Y
authors.initials: TY
authors.lastname: Wong
authors.forename: E Shyong
authors.initials: ES
authors.lastname: Tai
authors.forename: Jialiang
authors.initials: J
authors.lastname: Li
authors.forename: Jie Jin
authors.initials: JJ
authors.lastname: Wang
authors.forename: Paul
authors.initials: P
authors.lastname: Mitchell
mesh_terms: D000375:Aging; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure; D015992:Body Mass Index; D002386:Cataract / Q000145:classification / Q000453:epidemiology*; D008076:Cholesterol, HDL / Q000097:blood; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology*; D008875:Middle Aged; D009517:New South Wales / Q000453:epidemiology; D009765:Obesity / Q000453:epidemiology; D011446:Prospective Studies; D012307:Risk Factors; D013997:Time Factors; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL; D014280:Triglycerides