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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 < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 67.3% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.

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Aims/Introduction To investigate the relationship between different body mass index (BMI) levels and vascular complications in type 2 diabetes mellitus patients. Materials and Methods Data were collected from 3,224 individuals with type 2 diabetes mellitus (male/female: 1,635/1,589; age 61.31 ± 11.45 years), using a retrospective case study design. The association of BMI quintiles and diabetes mellitus vascular complications was assessed using multiple logistic regression models adjusting for age, sex, diabetes duration, smoking status, drinking and other confounders, using those with the lowest quintile of BMI as the reference group. Results With increasing BMI, the detection rate of diabetic peripheral neuropathy and peripheral arterial disease initially decreased and then it increased, whereas the detection rate of diabetic kidney disease and carotid atherosclerotic plaques showed an upward trend; however, diabetic retinopathy was irregular. The odds ratios of diabetic peripheral neuropathy decreased as BMI increased from the 21st percentile to the 80th percentile initially, and increased when BMI was in >80th percentile. The same result was shown in peripheral arterial disease. BMI >80th percentile showed a 1.426‐fold risk of diabetic kidney disease and a 1.336 ‐fold risk of carotid atherosclerotic plaque. Conclusions In patients with type 2 diabetes mellitus, the relationship between different BMIs and vascular complications varies. A U‐shaped relationship was observed between BMI and diabetic peripheral neuropathy, as well as BMI and peripheral arterial disease. BMI is positively correlated with diabetic kidney disease and carotid atherosclerotic plaque; however, it is not correlated with diabetic retinopathy.

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Recent studies have demonstrated the benefits of osteocalcin (OCN) on glucose homeostasis and metabolic dysregulation. However, its role in body composition and vascular function remains unknown. This study was designed to examine changes in metabolic parameters and body composition as well as arterial stiffness after OCN treatment in type 2 diabetic rats. Adult male Sprague Dawley (SD) rats were fed chow or high fat diet (HFD) for 8 weeks, and then diabetes was induced with an injection of low-dose streptozotocin (STZ) and treated daily with intraperitoneal injections of OCN for 12 weeks. Our data showed that OCN treatment improved glucose homeostasis and lipid metabolism. Further analysis revealed that OCN treatment resulted in increased insulin sensitivity. In addition, untreated diabetic rats experienced significant weight loss, whereas OCN-treated rats better maintained body weight (300.75±38.14 g vs. 335.50±23.70, =0.005). OCN also changed body composition, as evidenced by reduced body fat mass, specifically abdominal fat mass. OCN-treated diabetic rats also demonstrated decreased pulse-wave velocity, indicating of improved arterial stiffness. Taken together, our findings in the current study revealed that OCN therapy prevents arteriosclerosis in an induced diabetic rat model by exerting beneficial effects on glucose levels, insulin sensitivity, lipid metabolites, and body composition changes.

We aimed to investigate the potential association between urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) and diabetic peripheral neuropathy (DPN). We were especially interested in the relationship between normal or mildly abnormal UACR and eGFR with DPN. A retrospective study was performed in 1059 patients with type 2 diabetes patients from Fuzhou, China, who were seen between 2010 and 2015. The DPN population demonstrated higher UACR and lower eGFR than the non-DPN population. Nerve conduction velocities (NCVs) were negatively correlated with UACR and were positively correlated with eGFR. UACR and eGFR were associated with the risk of DPN. Even in the UACR < 30 mg/g and eGFR ≥ 60 ml/min/1.73 m groups, the relationship above still existed and patients in the highest tertiles of UACR and lowest tertiles of eGFR demonstrated a greater risk of DPN (OR = 2.456, 95% CI 1.461-4.127; OR = 2.021, 95% CI 1.276-3.203). Receiver operating characteristic (ROC) analysis revealed that the area under curve (AUC) of UACR, eGFR, and joints indicates that DPN was 0.749, 0.662, and 0.731, respectively. Lower eGFR and higher UACR may be associated with the risk of DPN, even though normal or mildly abnormal UACR and eGFR have already been found to be predictive factors of DPN. Further, UACR is more sensitive than eGFR. Separately, UACR was a moderate indication of DPN, and combining it with eGFR did not increase its effect of indication to DPN.