title: Low bone mineral density and its predictors in type 1 diabetic patients evaluated by the classic statistics and artificial neural network analysis.
abstract: OBJECTIVE
  To investigate factors associated with bone mineral density (BMD) in type 1 diabetes by classic statistic and artificial neural networks.
  
  
  METHODS
  A total of 175 eugonadal type 1 diabetic patients (age 32.8 ± 8.4 years) and 151 age- and BMI-matched control subjects (age 32.6 ± 4.5 years) were studied. In all subjects, BMI and BMD (as Z score) at the lumbar spine (LS-BMD) and femur (F-BMD) were measured. Daily insulin dose (DID), age at diagnosis, presence of complications, creatinine clearance (ClCr), and HbA(1c) were determined.
  
  
  RESULTS
  LS- and F-BMD levels were lower in patients (-0.11 ± 1.2 and -0.32 ± 1.4, respectively) than in control subjects (0.59 ± 1, P < 0.0001, and 0.63 ± 1, P < 0.0001, respectively). LS-BMD was independently associated with BMI and DID, whereas F-BMD was associated with BMI and ClCr. The cutoffs for predicting low BMD were as follows: BMI <23.5 kg/m(2), DID >0.67 units/kg, and ClCr <88.8 mL/min. The presence of all of these risk factors had a positive predictive value, and their absence had a negative predictive value for low BMD of 62.9 and 84.2%, respectively. Data were also analyzed using the TWIST system in combination with supervised artificial neural networks and a semantic connectivity map. The TWIST system selected 11 and 12 variables for F-BMD and LS-BMD prediction, which discriminated between high and low BMD with 67 and 66% accuracy, respectively. The connectivity map showed that low BMD at both sites was indirectly connected with HbA(1c) through chronic diabetes complications.
  
  
  CONCLUSIONS
  In type 1 diabetes, low BMD is associated with low BMI and low ClCr and high DID. Chronic complications negatively influence BMD.
authors.affiliation: Department of Medical Sciences, University of Milan, Endocrinology and Diabetology Unit, Fondazione IRRCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy.
authors.forename: Cristina
authors.initials: C
authors.lastname: Eller-Vainicher
authors.forename: Volha V
authors.initials: VV
authors.lastname: Zhukouskaya
authors.forename: Yury V
authors.initials: YV
authors.lastname: Tolkachev
authors.forename: Sergei S
authors.initials: SS
authors.lastname: Koritko
authors.forename: Elisa
authors.initials: E
authors.lastname: Cairoli
authors.forename: Enzo
authors.initials: E
authors.lastname: Grossi
authors.forename: Paolo
authors.initials: P
authors.lastname: Beck-Peccoz
authors.forename: Iacopo
authors.initials: I
authors.lastname: Chiodini
authors.forename: Alla P
authors.initials: AP
authors.lastname: Shepelkevich
mesh_terms: D000328:Adult; D015992:Body Mass Index; D015519:Bone Density / Q000187:drug effects*; D016022:Case-Control Studies; D003404:Creatinine / Q000378:metabolism; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy / Q000378:metabolism* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use; D008159:Lumbar Vertebrae / Q000187:drug effects / Q000378:metabolism; D008297:Male; D016571:Neural Networks, Computer; D012307:Risk Factors; D055815:Young Adult
chemical_list: D007328:Insulin; D003404:Creatinine