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Objective To identify independent risk factors for diabetic neuropathy (DN) in patients with type 2 diabetes mellitus (T2DM). Methods We retrospectively analyzed 376 patients with T2DM at the First Affiliated Hospital of Fujian Medical University, China between January 2013 and October 2016. Multivariate logistic regression was used to explore potential risk factors for progression of DN in patients with T2DM. Effect sizes were estimated using odds ratios (ORs) and 95% confidence intervals (CIs). Results The prevalence of DN in patients with T2DM was 43.1%. Multivariate logistic regression indicated that retinopathy (OR: 2.755, 95% CI: 1.599–4.746); diabetic nephropathy (OR: 2.196, 95% CI: 1.279–3.772); longer duration of T2DM (OR: 1.081, 95% CI: 1.045–1.120); use of insulin (OR: 1.091, 95% CI: 1.018–1.170); longer history of alcohol consumption (OR: 1.034, 95% CI: 1.010–1.059); and higher blood urea nitrogen (OR: 1.081, 95% CI: 1.009–1.159) were associated with increased risk of DN in patients with T2DM. Conclusions Retinopathy, diabetic nephropathy, longer duration of T2DM, use of insulin, longer history of alcohol consumption, and higher blood urea nitrogen were independent risk factors for DN. These findings should be verified in large-scale prospective studies.

Background: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20–70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P