ArticleJournal of diabetes investigation2022
Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care.
Article in Journal of diabetes investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- A dual domain systematic review and meta-analysis of risk tool accuracy to predict cardiovascular morbidity in prehypertension and diabetic morbidity in prediabetes.Frontiers in endocrinology · 2025Pooled it
- Impacts of maternal lipid and homocysteine levels on the risks of pregnancy complications and adverse outcomes in both singleton and twin pregnancies: a population-based observational cohort study.BMC pregnancy and childbirth · 2026Observational
- Development of a biochemical model for identifying prediabetes in Chinese adults: a case-control study.BMC endocrine disorders · 2026Article
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Developing a Simple Non-Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study.Journal of diabetes science and technology · 2025Article
- Type 2 Diabetes Prediction Model in China: A Five-Year Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study.JMIR bioinformatics and biotechnology · 2025Article
- Cardiovascular Risk across Glycemic Categories: Insights from a Nationwide Screening in Mongolia, 2022-2023.Journal of clinical medicine · 2024Article
- External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care.Journal of diabetes investigation · 2024Article
- Review
- Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care.Journal of diabetes investigation · 2022Article
- Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.Computational and mathematical methods in medicine · 2022Article
- Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
introductionMore than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults.
methodsBased on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison.
resultsThe prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models.
conclusionsSleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.