title: Statistical models to predict type 2 diabetes remission after bariatric surgery.
abstract: BACKGROUND
  Type 2 diabetes (T2D) remission may be achieved after bariatric surgery (BS), but rates vary according to patients' baseline characteristics. The present study evaluates the relevance of several preoperative factors and develops statistical models to predict T2D remission 1 year after BS.
  
  
  METHODS
  We retrospectively studied 141 patients (57.4% women), with a preoperative diagnosis of T2D, who underwent BS in a single center (2006-2011). Anthropometric and glucose metabolism parameters before surgery and at 1-year follow-up were recorded. Remission of T2D was defined according to consensus criteria: HbA1c <6%, fasting glucose (FG) <100 mg/dL, absence of pharmacologic treatment. The influence of several preoperative factors was explored and different statistical models to predict T2D remission were elaborated using logistic regression analysis.
  
  
  RESULTS
  Three preoperative characteristics considered individually were identified as the most powerful predictors of T2D remission: C-peptide (R2  = 0.249; odds ratio [OR] 1.652, 95% confidence interval [CI] 1.181-2.309; P = 0.003), T2D duration (R2  = 0.197; OR 0.869, 95% CI 0.808-0.935; P < 0.001), and previous insulin therapy (R2  = 0.165; OR 4.670, 95% CI 2.257-9.665; P < 0.001). High C-peptide levels, a shorter duration of T2D, and the absence of insulin therapy favored remission. Different multivariate logistic regression models were designed. When considering sex, T2D duration, and insulin treatment, remission was correctly predicted in 72.4% of cases. The model that included age, FG and C-peptide levels resulted in 83.7% correct classifications. When sex, FG, C-peptide, insulin treatment, and percentage weight loss were considered, correct classification of T2D remission was achieved in 95.9% of cases.
  
  
  CONCLUSIONS
  Preoperative characteristics determine T2D remission rates after BS to different extents. The use of statistical models may help clinicians reliably predict T2D remission rates after BS.
authors.affiliation: Department of Endocrinology and Nutrition, La Princesa University Hospital, La Princesa Investigation Institute, Madrid, Spain.
authors.forename: Ana M
authors.initials: AM
authors.lastname: Ramos-Levi
authors.forename: Pilar
authors.initials: P
authors.lastname: Matia
authors.forename: Lucio
authors.initials: L
authors.lastname: Cabrerizo
authors.forename: Ana
authors.initials: A
authors.lastname: Barabash
authors.forename: Andres
authors.initials: A
authors.lastname: Sanchez-Pernaute
authors.forename: Alfonso L
authors.initials: AL
authors.lastname: Calle-Pascual
authors.forename: Antonio J
authors.initials: AJ
authors.lastname: Torres
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Rubio
mesh_terms: D000328:Adult; D000368:Aged; D050110:Bariatric Surgery; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis / Q000209:etiology / Q000601:surgery*; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D009765:Obesity / Q000150:complications / Q000175:diagnosis / Q000601:surgery*; D016017:Odds Ratio; D012074:Remission Induction; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D015431:Weight Loss; D055815:Young Adult
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: bariatric surgery; logistic regression analysis; metabolic surgery; prediction models; type 2 diabetes mellitus; 关键词：减肥手术，对数回归分析，代谢手术，预测模型，2型糖尿病