title: Identifying an Optimal Cutpoint for the Diagnosis of Hypertriglyceridemia in the Nonfasting State.
abstract: BACKGROUND
  Nonfasting triglycerides are similar or superior to fasting triglycerides at predicting cardiovascular events. However, diagnostic cutpoints are based on fasting triglycerides. We examined the optimal cutpoint for increased nonfasting triglycerides.
  
  
  METHODS
  We obtained baseline nonfasting (<8 h since last meal) samples from 6391 participants in the Women's Health Study who were followed prospectively for ≤17 years. The optimal diagnostic threshold for nonfasting triglycerides, determined by logistic regression models by use of c-statistics and the Youden index (sum of sensitivity and specificity minus 1), was used to calculate hazard ratios (HRs) for incident cardiovascular events. Performance was compared to thresholds recommended by the American Heart Association (AHA) and European guidelines.
  
  
  RESULTS
  The optimal threshold was 175 mg/dL (1.98 mmol/L), with a c-statistic of 0.656, statistically better than the AHA cutpoint of 200 mg/dL (c-statistic 0.628). For nonfasting triglycerides above and below 175 mg/dL, after adjusting for age, hypertension, smoking, hormone use, and menopausal status, the HR for cardiovascular events was 1.88 (95% CI 1.52-2.33, P < 0.001), and for triglycerides measured at 0-4 and 4-8 h since the last meal, 2.05 (1.54- 2.74) and 1.68 (1.21-2.32), respectively. We validated performance of this optimal cutpoint by use of 10-fold cross-validation and bootstrapping of multivariable models that included standard risk factors plus total and HDL cholesterol, diabetes, body mass index, and C-reactive protein.
  
  
  CONCLUSIONS
  In this study of middle-aged and older apparently healthy women, we identified a diagnostic threshold for nonfasting hypertriglyceridemia of 175 mg/dL (1.98 mmol/L), with the potential to more accurately identify cases than the currently recommended AHA cutpoint.
authors.affiliation: Division of Preventive Medicine, Division of Internal Medicine, and.
authors.forename: Khendi T
authors.initials: KT
authors.lastname: White
authors.affiliation: Division of Preventive Medicine.
authors.forename: M V
authors.initials: MV
authors.lastname: Moorthy
authors.affiliation: Division of Preventive Medicine.
authors.forename: Akintunde O
authors.initials: AO
authors.lastname: Akinkuolie
authors.affiliation: Division of Preventive Medicine.
authors.forename: Olga
authors.initials: O
authors.lastname: Demler
authors.affiliation: Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Epidemiology, Harvard School of Public Health, Boston, MA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Ridker
authors.affiliation: Division of Preventive Medicine, Department of Epidemiology, Harvard School of Public Health, Boston, MA.
authors.forename: Nancy R
authors.initials: NR
authors.lastname: Cook
authors.affiliation: Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; smora@partners.org.
authors.forename: Samia
authors.initials: S
authors.lastname: Mora
mesh_terms: D002318:Cardiovascular Diseases / Q000209:etiology; D005215:Fasting; D005260:Female; D006801:Humans; D015228:Hypertriglyceridemia / Q000097:blood* / Q000150:complications / Q000175:diagnosis*; D008875:Middle Aged; D019518:Postprandial Period; D012372:ROC Curve; D014280:Triglycerides / Q000097:blood*
chemical_list: D014280:Triglycerides