Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional study

Abstract

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.

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

“…26 An article published in EBioMedicine applied the similar methods were used to screen the undiagnosed diabetes. 27 Besides, these methods were also used to predict the risk of drug noncompliance in Chinese patients with inflammatory rheumatism. 2,2 To further verify the stability of this prediction model, the whole data set was used for internal verification, and the C index was obtained.…”

Tea consumption and risk of diabetes in the Chinese population: a multi-centre, cross-sectional study

“…This multi-centre, cross-sectional study, based on the 'Non-lab and semi-lab algorithms for screening undiagnosed diabetes' study (16) , was conducted in eight centres including seven provinces in China from November 2016 to June 2017. A multi-stage cluster and simple randomisation method was applied to recruit subjects aged 20-70 years in 1-2 provinces that were randomly selected in east, south, west, north and central regions across China.…”

Exploration and Validation of the Performance of Hemoglobin A1c in Detecting Diabetes in Community-Dwellers With Hypertension

“…All study individuals were from the Study on Evaluation of iNnovated Screening tools and determInation of optimal diagnostic cut-off points for type 2 diaBetes in Chinese muLti-Ethnic (SENSIBLE) study [ 15 ] and the SENSIBLE-Addition study (see below). In the present study, which had a prospective cross-sectional design, individuals who were 20–70 years old and diagnosed as having hypertension, but free of known diabetes, were selected [ 15 ].…”

“…All study individuals were from the Study on Evaluation of iNnovated Screening tools and determInation of optimal diagnostic cut-off points for type 2 diaBetes in Chinese muLti-Ethnic (SENSIBLE) study [ 15 ] and the SENSIBLE-Addition study (see below). In the present study, which had a prospective cross-sectional design, individuals who were 20–70 years old and diagnosed as having hypertension, but free of known diabetes, were selected [ 15 ]. Eligible individuals from the SENSIBLE study were split randomly into two groups: one (70% of the sample) to assess the performance of HbA 1c in detecting diabetes (the exploration dataset) and another (the remaining 30%) for internal validation (the internal validation dataset).…”