ObjectiveTo develop and validate a nomogram for predicting the risk of peripheral artery disease (PAD) in patients with type 2 diabetes mellitus (T2DM) and assess its clinical application value.MethodsClinical data were retrospectively collected from 474 patients with T2DM at the Air Force Medical Center between January 2019 and April 2022. The patients were divided into training and validation sets using the random number table method in a ratio of 7:3. Multivariate logistic regression analysis was performed to identify the independent risk factors for PAD in patients with T2DM. A nomogram prediction model was developed based on the independent risk factors. The predictive efficacy of the prediction model was evaluated using the consistency index (C-index), area under the curve (AUC), receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (HL) test, and calibration curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the prediction model’s performance during clinical application.ResultsAge, disease duration, blood urea nitrogen (BUN), and hemoglobin (P<0.05) were observed as independent risk factors for PAD in patients with T2DM. The C-index and the AUC were 0.765 (95% CI: 0.711-0.819) and 0.716 (95% CI: 0.619-0.813) for the training and validation sets, respectively, indicating that the model had good discriminatory power. The calibration curves showed good agreement between the predicted and actual probabilities for both the training and validation sets. In addition, the P-values of the HL test for the training and validation sets were 0.205 and 0.414, respectively, indicating that the model was well-calibrated. Finally, the DCA curve indicated that the model had good clinical utility.ConclusionA simple nomogram based on three independent factors–duration of diabetes, BUN, and hemoglobin levels–may help clinicians predict the risk of developing PAD in patients with T2DM.

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

Objective: To investigate the factors that influence diabetic foot (DF) minor amputation. Methods: In this case-control study, the clinical data of 955 hospitalized patients with DF were retrospectively analyzed, according to whether hospitalization amputation was divided into minor amputation and the non-amputation group, compared two groups of general data, laboratory examination, diabetes complications and complications, such as differences, multiple factors regression analysis DF Risk factors associated with minor amputation in patients. Results: There were statistically significant differences between the two groups in DPN, DR, PAD, ABI, TBI, and Wagner grades, as well as age, sex, HbA1c, FPG, Scr, SUA, TC, ALB, HDL-C, WBC, and Hb (P<0.05). The logistic regression analysis that HbA1c (odds ratio [OR] 1.082 [95% CI 1.011–1.158], p= 0.023), ABI＜0.9 (odds ratio [OR] 1.793 [95% CI 1.316–2.443],p=0.000), TBI＜0.7(odds ratio [OR] 2.569 [95% CI 1.889–3.495], p=0.000), Wagner classification (odds ratio [OR] 2.792 [95% CI 2.303–3.384], p=0.000) and PAD (odds ratio [OR] 2.343 [95% CI 1.731–3.170], p=0.000) were significant risk factors for DF minor amputation (P＜0.05). Higher Hb (odds ratio [OR] 0.981 [95% CI 0.973–0.988], p=0.000) was an independent protective factor for minor amputation. Conclusion:HbA1c, lower ankle brachial index level, and lower toe-brachial index level were all related with minor amputation. Wagner classification and diabetic peripheral angiopathy may represent a novel independent factor. In light of these concerns, early preventive and timely multidisciplinary assistance is critical to prevent diabetic foot minor amputation.