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 \ub1 8.4 years) and 151 age- and BMI-matched control subjects (age 32.6 \ub1 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 (20.11 \ub1 1.2 and 20.32 \ub1 1.4, respectively) than in control subjects (0.59 \ub1 1, P 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 HbA1c 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