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Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional study
Wei Li1, Bo Xie1, Shanhu Qiu1
1Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China.
Ebiomedicine
|August 18, 2018
Summary
A new semi-lab nomogram effectively screens for diabetes in China, outperforming existing methods. This tool uses non-lab data and a simple urine test for early diabetes detection, improving public health outcomes.
Area of Science:
- Endocrinology
- Public Health
- Medical Informatics
Background:
- High prevalence and undiagnosed rates of diabetes pose a significant public health challenge.
- There is an urgent need for efficient, cost-effective early diabetes recognition methods.
- This study focuses on developing user-friendly nomograms for diabetes screening in diverse Chinese ethnic groups using non-lab or semi-lab data.
Purpose of the Study:
- To develop and validate innovative nomograms for diabetes screening in China.
- To utilize non-lab and semi-lab data for accessible diabetes risk assessment.
- To compare the performance of developed nomograms against existing risk scores.
Main Methods:
- A multicenter, multi-ethnic, cross-sectional study involving 10,794 participants aged 20-70 in China.
- Data collection included sociodemographic, anthropometric characteristics, and blood/urine samples post-glucose load.
- Nomograms were developed using stepwise binary logistic regression and validated internally and externally, with performance assessed by AUC and decision curve analysis.
Main Results:
- The prevalence of undiagnosed diabetes was 9.8%. Key risk factors identified included gender, age, BMI, waist circumference, hypertension, ethnicity, vegetable intake, and family history.
- The semi-lab nomogram, incorporating 2-hour post-meal glycosuria, significantly improved performance (AUC 0.868) compared to the non-lab model (AUC 0.763).
- The semi-lab model demonstrated superior sensitivity (76.3%) and specificity (81.6%) and reduced avoidable Oral Glucose Tolerance Tests (OGTTs) more effectively than the non-lab model and NCDRS.
Conclusions:
- Non-lab and semi-lab nomograms are reliable tools for diabetes screening, particularly in developing countries.
- The semi-lab nomogram shows superior predictive performance compared to the non-lab model and the New Chinese Diabetes Risk Score (NCDRS).
- The semi-lab nomogram is recommended as a decision support tool for diabetes screening in China.
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