3.2 Associations of ED and osteoporosis and comparison of
different models for assessing the prevalence of osteoporosis
As shown in Table 2, we performed logistic regression analysis
to explore the associated of ED and osteoporosis. ED can be used as risk
factor for prevalence of osteoporosis, the prevalence of osteoporosis
was 1.70-fold higher in the ED group compared with the non-ED group (OR:
1.70, 95% CI: 0.99-2.87, P =0.051) after adjustment in total
population.
The LASSO regression accurately identified the important candidates for
assessing osteoporosis. As shown in Figure 3A , after the LASSO
regression analysis, ten features with nonzero coefficients out of 20
clinical parameters (age, education, smoking, drinking, SBP, DBP, TC,
LDL-C, HDL-C, TG, BMI, waistline, family history of diabetes, neck
circumference, P, WHR, heart rate, PFG, OGTT2h and HbA1c) were selected
as important candidates for assessing prevalence of osteoporosis. Of the
10 indices selected by LASSO regression by λ=0.01 included age,
education, smoking, drinking, SBP, LDL-C, BMI, neck circumference, heart
rate and FPG. Different models were established to assess the ability of
diagnostic for prevalence of osteoporosis. Model 1 included age,
education, smoking, drinking, SBP, neck circumference, BMI and heart
rates, which all of those were non-traumatic parameters. Model 2 further
included LDL-C and FPG based on model 1. Model 3 were further included
ED based on model 2. We constructed ROC curve to assess the ability of
different models to assess the prevalence of osteoporosis
(Figure 3B ). The AUC for the ROC curves were 0.70 (95% CI:
0.65-0.80), 0.72 (95% CI: 0.67-0.78) and 0.73 (0.68-0.79) for model 1,
model 2 and model 3, respectively. Compared to model 1, model 3 plus
LDL-C, FPG and ED were significantly improved the diagnostic
capabilities (P=0.017).