Format

Send to

Choose Destination
EBioMedicine. 2018 Sep;35:307-316. doi: 10.1016/j.ebiom.2018.08.009. Epub 2018 Aug 13.

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

Author information

1
Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China.
2
School of Public Health, Southeast University, Nanjing, China.
3
Department of Endocrinology, People's Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, China.
4
Department of Endocrinology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
5
Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
6
Department of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, China.
7
Department of Endocrinology, The Third Hospital of Nanchang, Nanchang, China.
8
Department of Endocrinology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
9
Department of Endocrinology, The First Affiliated Hospital of Fujian Medical University, Diabetes Research Institute of Fujian Province, Fuzhou, China.
10
Department of Endocrinology, Xinjiang Uygur Autonomous Region Hospital of traditional Chinese Medicine, Urumqi, China.
11
Suzhou MetroHealth Medical Technology, Co., LTD, Suzhou, China.
12
Department of Medical Cell Biology, Uppsala University, SE-75123 Uppsala, Sweden.
13
Department of Medical Cell Biology, Uppsala University, SE-75123 Uppsala, Sweden. Electronic address: per-ola.carlsson@mcb.uu.se.
14
Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China. Electronic address: sunzilin1963@126.com.

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.

KEYWORDS:

Decision curve; Diabetes; Nomogram; Risk algorithm

PMID:
30115607
PMCID:
PMC6154869
DOI:
10.1016/j.ebiom.2018.08.009
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center