Objective:To investigate the risk factors for concomitant cardiac autonomic neuropathy in diabetic patients and to develop a Nomogram prediction model.Methods:One hundred and fifty-eight diabetic patients admitted to in Southern Hospital Zengcheng Branch from March 2019 to March 2021 were selected. Patients with normal heart rate variability were the diabetic group, and patients with abnormal heart rate variability were the group with diabetes mellitus complicated by cardiac autonomic neuropathy. Logistic regression analysis was used to analyze the risk factors of cardiac autonomic neuropathy. Nomogram models were developed and model performance was evaluated. Decision curve analysis (DCA) was used to assess the net clinical benefit of the Nomogram model.Results:Comparison of general data showed that fasting blood glucose, tumour necrosis factor-α (TNF-α), glomerular filtration rate (eGER), uric acid, C-reactive protein (CRP), interleukin-6 (IL-6), free fatty acids (FFA), standard deviation of sinus heart beat RR interval (SDNN), and duration of diabetes compared to the diabetic group had statistically significant ( P<0.05); the results of the subject work characteristics (ROC) curve analysis showed that the best cut-off values for fasting glucose, TNF-α, eGFR, uric acid, CRP, IL-6, FFA, SDNN and duration of diabetes were >7.53 mmol/L, >98.45 ng/L, ≤94.79 ml/(min·1.73 m 2), > 87.3 μmol/L, >6.22 μmol/L, >37.84 ng/L, >839.19 μmol/L, ≤ 95.88 ms, >9 years; multi-factorial Logistic regression analysis showed that fasting glucose (>7.53 mmol/L), TNF-α (>98.45 ng/L), CRP (>6.22 μmol/L), IL-6 (>37.84 ng/L), FFA (>839.19 μmol/L), SDNN (≤95.88 ms), and duration of diabetes (>9 years) were risk factors for the development of cardiac autonomic neuropathy in diabetic patients; internal validation showed that the Nomogram model predicted a C-index of 0.706 (95% CI 0.668 - 0.751) for the risk of cardiac autonomic neuropathy. The DCA results showed that the Nomogram model predicted a risk threshold of >0.25 for the development of cardiac autonomic neuropathy and that the Nomogram model provided a net clinical benefit. Conclusions:There are many risk factors for cardiac autonomic neuropathy, and the nomogram model based on risk factors in this study has good predictive power and may provide a reference for clinical screening of high-risk patients and further improvement of treatment planning.

Objective: To study the effects of long non-coding RNA linc-01135 on the osteogenic differentiation of inflammatory PDLSCs(P-PDLSCs) under 12％ static mechanical strain loading. Methods: Cells were isolated and cultured from the healthy and periodontitis samples respectively to obtain healthy PDLSCs(H-PDLSCs) and P-PDLSCs. RT-PCR were used to identify the expression level of linc-01135. Lentiviruses were used to upregulate and downregulate the expression of linc-01135, and the osteogenesis gene expression were analyzed by RT-PCR and the osteogenesis differentiation capability were evaluated by alizarin red staining. Results: linc-01135 expression in P-PDLSCs was lower than that in H-PDLSCs. When the expression of linc-01135 was upregulated in P-PDLSCs before 12％ SMS loading, the expression level of RUNX2, ALP, OPG was significantly increased. In contrast, the expression of RUNX2, ALP, OPG was significantly decreased when the expression of linc-01135 was suppressed. Alizarin red staining proved the same trend. Conclusion: linc-01135 can promote the osteogenic differentiation capability of P-PDLSCs under 12％SMS loading.