Background: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes. Seen another way, osteoporosis can be ascribed to the imbalance of bone metabolism, which is closely related to diabetes as well. Methods: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM (support vector machine) model was established based on different combinations of biochemical indexes, which were collected from patients who did the test of bone turn-over markers (BTMs) from January 2016 to March 2018 in the department of endocrine, Zhongda Hospital affiliated to Southeast University. The classification was done based on a software package of machine learning in Python. The classification performance was measured by SKLearn program incorporated in the Python software package and compared with the clinical diagnostic results. Results: The predicting accuracy rate of final model was above 88%, with feature combination of sex, age, BMI (body mass index), TP1NP (total procollagen I N-terminal propeptide) and OSTEOC (osteocalcin). Conclusions: Experimental results show that the model showed an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.

Background: The purpose of this analysis was to investigate the associations between serum uric acid and phase angle in patients with type 2 diabetes mellitus.Methods: In this retrospective cross-sectional study, we included 200 type 2 diabetes mellitus (T2DM) patients treated during 2018–2019 at Zhongda Hospital Southeast University. Phase angle (PhA) and other body composition indicators were measured by bioelectrical impedance analysis (BIA). All patients underwent routine clinical examinations on the day of hospitalization, and the basic information and clinical symptoms of these patients were recorded.Results: Serum uric acid (UA) was significantly associated with PhA (p <0.001). Overall, in the crude model and minor, all adjusted models (crude model, Models I–II), the phase angle increased as the tertiles of serum uric acid increased. In the minor adjusted model (Model I, adjustment for age and duration) fully adjusted model (Model II, adjustment for age, duration, Lpa, BMI, and WHR), the adjusted β for participants in tertiles of serum uric acid were 0.26 (95% CI: 0.05–0.46) and 0.32 (95% CI: 0.11–0.54), respectively, compared with those in the lowest tertile 1.Conclusion: There was a nonlinear relationship between serum uric acid and PhA in T2DM patients, and the phase angle increased as uric acid increased within a certain range, and this effect disappeared when uric acid exceeded a certain value.

Background: The purpose of this analysis was to investigate the associations between phase angle (PhA), body mass index (BMI) and insulin resistance (IR) in patients with type 2 diabetes mellitus (T2DM). Methods: The retrospective cross-sectional study included 200 T2DM patients treated during 2018 to 2019 in Zhongda Hospital Southeast University. PhA and other body composition indicators were measured by bioelectrical impedance analysis (BIA). Subjects were classified into four groups based on body composition: low phase angle and low body mass index (LPLB), low phase angle and high body mass index (LPHB), high phase angle and low body mass index (HPLB) and high phase angle and high body mass index (HPHB). Results: Overall, in the unadjusted model and minor, all adjusted models (unadjusted model, models 1–4), homeostasis model assessment of insulin resistance (HOMA-IR) was higher in the LPHB group than in the LPLB group (P = 0.034). In the unadjusted model, Model 1 (adjustment for age), Model 2 (adjust for age+duration), Model 3 (adjust for age+duration+sex+UA+TG+TC) and Model 4 (adjust for age+duration+sex+UA+TG+TC+HDL+HbA1c), the adjusted ORs for participants were 4.4 (95% CI [1.72–11.24]), 4.41 (95% CI [1.73–11.27]), 4.75 (95% CI [1.83–12.32]), 2.93 (95% CI [1.04–8.23]) and 3.1 (95% CI [1.09–8.86]) respectively, compared to LPHB group. Conclusions: T2DM patients with the body composition of low phase angle and high body mass index exhibited the most severe degree and the highest risk of insulin resistance.

Background: The purpose of this analysis was to investigate the association between triglyceride-glucose body mass index (TyG-BMI) and gestational diabetes mellitus (GDM) in Chinese pregnant women. Methods: This observational study utilized a cohort of pregnant women who were screened for GDM using the oral glucose tolerance test (OGTT). The TyG-BMI was calculated using the formula: TyG index = ln (fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2). TyG-BMI = TyG × BMI. Statistical analyses were performed to assess the correlation between TyG-BMI and gestational diabetes mellitus incidence. Results: A total of 2,111 pregnant women were enrolled, out of which 281 (13.3%) women were diagnosed with gestational diabetes mellitus. Some differences existed between the TyG-BMI level groups with respect to various covariates (TG, BMI, age, TC, T-Bil, D-Bil, postpartum blood loss, vaginal birth, and GDM, p < 0.05). Compared to the lowest quartile of TyG-BMI, the third quartile of TyG-BMI is associated with the highest risk of developing gestational diabetes mellitus (OR: 1.8; 95%CI: 1.28 ~ 2.52). All models showed similar results. The stratified analyses were performed to examine whether the association between TyG-BMI and GDM was stable among different subgroups. None of the variables, including age (< 35 years and ≥35 years), premature delivery (yes or no), and vaginal birth (yes or no), significantly affected the association between TyG-BMI and GDM (all P for interaction > 0.05). Conclusion: A cohort study of Chinese pregnant women concluded that TyG-BMI might be a valuable index for identifying IR in patients at high risk of gestational diabetes mellitus. TyG-BMI could be recommended as part of routine surveillance during early pregnancy.

Background: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes. Seen another way, osteoporosis can be ascribed to the imbalance of bone metabolism, which is closely related to diabetes as well. Method: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM model was established based on different combinations of biochemical indices, including bone turnover makers, calcium and phosphorus, etc. The classification performance was measured using several evaluations. Results: The predicting accuracy rate of final model is above 88%, with feature combination of Sex, Age, BMI, TP1NP and OSTEOC. Conclusion: Experimental results show that the model has come to an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.