Low Bone Mineral Density and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic Statistics and Artificial Neural Network Analysis

OBJECTIVE-To investigate factors associated with bone mineral density (BMD) in type 1 diabetes by classic statistic and artificial neural networks. RESEARCH DESIGN AND 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, BM I 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 (CICr), 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 CICr. The cutoffs for predicting low BMD were as follows: BMI <23.5 kg/m(2), DID >0.67 units/kg, and CICr <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 CICr and high DID. Chronic complications negatively influence BMD.