title: Optimal uric acid threshold to identify insulin resistance in healthy women.
abstract: BACKGROUND
  Identifying insulin-resistant (IR) individuals is an issue of particular interest in the assessment of cardiometabolic risk, but there is no simple way to accomplish this task. Our aims were to determine the relationship between uric acid and insulin resistance and to define the optimal uric acid cutoff to identify insulin resistance in women.
  
  
  METHODS
  A population-based sample of 588 women without cardiovascular diseases, diabetes, or low glomerular filtration rate (GFR) was divided according to uric acid tertiles. Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting glucose, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), TG/HDL-C ratio, insulin [fasting plasma insulin (FPI)], and homeostasis model assessment of insulin resistance (HOMA-IR) were compared between the mentioned tertiles. Receiver operating characteristic (ROC) curves were constructed to determinate the optimal cutoff of uric acid and identify IR individuals defined by the top tertile of FPI and HOMA-IR. The same variables were compared using the top tertile of uric acid and the maximum Youden index as cutoffs. The P values were adjusted by age, GFR, and body mass index (BMI) using analysis of covariance (ANCOVA).
  
  
  RESULTS
  FPI, HOMA-IR, and all of the cardiometabolic risk factors increased through uric acid tertiles. Both cutoffs of uric acid, estimated by the top tertile and by ROC, were identical (4.0 mg/dL). FPI, HOMA-IR, SBP, DBP, TG, TG/HDL-C ratio, and fasting glucose were higher, whereas HDL-C was lower in women who had uric acid levels >4.0 mg/dL. All of the differences remained significant after the adjustment by age and GFR.
  
  
  CONCLUSIONS
  In healthy women, uric acid levels >4.0 mg/dL were associated with all the markers of IR.
authors.affiliation: Hospital Universitario Gral. San Martín, La Plata, Buenos Aires, Argentina. salazarlandea@gmail.com
authors.forename: Martin R
authors.initials: MR
authors.lastname: Salazar
authors.forename: Walter G
authors.initials: WG
authors.lastname: Espeche
authors.forename: Carlos E
authors.initials: CE
authors.lastname: March
authors.forename: Alberto G
authors.initials: AG
authors.lastname: Marillet
authors.forename: Eduardo
authors.initials: E
authors.lastname: Balbín
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Dulbecco
authors.forename: Horacio A
authors.initials: HA
authors.lastname: Carbajal
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001118:Argentina; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D015992:Body Mass Index; D005260:Female; D005919:Glomerular Filtration Rate; D044882:Glucose Metabolism Disorders / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D016014:Linear Models; D008055:Lipids / Q000097:blood; D016015:Logistic Models; D016017:Odds Ratio; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D014527:Uric Acid / Q000097:blood*
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D007328:Insulin; D008055:Lipids; D014527:Uric Acid