title: Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature.
abstract: OBJECTIVE
  Clinicians predominantly use clinical features to differentiate type 1 from type 2 diabetes yet there are no evidence-based clinical criteria to aid classification of patients. Misclassification of diabetes is widespread (7-15% of cases), resulting in patients receiving inappropriate treatment. We sought to identify which clinical criteria could be used to discriminate type 1 and type 2 diabetes.
  
  
  METHODS
  Systematic review of all diagnostic accuracy studies published since 1979 using clinical criteria to predict insulin deficiency (measured by C-peptide).
  
  
  METHODS
  14 databases including: MEDLINE, MEDLINE in Process and EMBASE. The search strategy took the form of: (terms for diabetes) AND (terms for C-Peptide).
  
  
  METHODS
  Diagnostic accuracy studies of any routinely available clinical predictors against a reference standard of insulin deficiency defined by cut-offs of C-peptide concentrations. No restrictions on race, age, language or country of origin.
  
  
  RESULTS
  10,917 abstracts were screened, and 231 full texts reviewed. 11 studies met inclusion criteria, but varied by age, race, year and proportion of participants who were C-peptide negative. Age at diagnosis was the most discriminatory feature in 7/9 studies where it was assessed, with optimal cut-offs (>70% mean sensitivity and specificity) across studies being <30 years or <40 years. Use of/time to insulin treatment and body mass index (BMI) were also discriminatory. When combining features, BMI added little over age at diagnosis and/or time to insulin (<1% improvement in classification).
  
  
  CONCLUSIONS
  Despite finding only 11 studies, and considerable heterogeneity between studies, age at diagnosis and time to insulin were consistently the most discriminatory criteria. BMI, despite being widely used in clinical practice, adds little to these two criteria. The criteria identified are similar to the Royal College of General Practitioners National Health Service (RCGP/NHS) Diabetes classification guidelines, which use age at diagnosis <35 years and time to insulin <6 m. Until further studies are carried out, these guidelines represent a suitable classification scheme.
  
  
  BACKGROUND
  PROSPERO reference CRD42012001736.
authors.affiliation: Department of NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Beverley M
authors.initials: BM
authors.lastname: Shields
authors.affiliation: Department of Evidence Synthesis & Modelling for Health Improvement (ESMI), University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Jaime L
authors.initials: JL
authors.lastname: Peters
authors.affiliation: Department of Evidence Synthesis & Modelling for Health Improvement (ESMI), University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Chris
authors.initials: C
authors.lastname: Cooper
authors.affiliation: Department of Evidence Synthesis & Modelling for Health Improvement (ESMI), University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Jenny
authors.initials: J
authors.lastname: Lowe
authors.affiliation: Department of NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, UK Department of Research and Development, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
authors.forename: Bridget A
authors.initials: BA
authors.lastname: Knight
authors.affiliation: Department of Research and Development, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
authors.forename: Roy J
authors.initials: RJ
authors.lastname: Powell
authors.affiliation: Department of NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Angus
authors.initials: A
authors.lastname: Jones
authors.affiliation: Department of Evidence Synthesis & Modelling for Health Improvement (ESMI), University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Hyde
authors.affiliation: Department of NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter, Exeter, UK.
authors.forename: Andrew T
authors.initials: AT
authors.lastname: Hattersley
mesh_terms: D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis*; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D003937:Diagnosis, Differential; D006801:Humans
keywords: STATISTICS & RESEARCH METHODS