ArticleFrontiers in endocrinology2023
From normal population to prediabetes and diabetes: study of influencing factors and prediction models.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- An Evaluation of the Effects of the Ruthenium (II)-Uracil Schiff Base Complex on Selected Markers of Glucose Homeostasis in Diet-Induced Prediabetic Male Rats.Pharmaceutics · 2026Article
- Evaluation of Capillary Blood Glucose and Chinese Diabetes Risk Score in Screening for Diabetes and Prediabetes Among Elderly Chinese Population: A Cross-Sectional Study.Clinical interventions in aging · 2026Article
- Article
- Could loneliness accelerate the progression of diabetes among adults with prediabetes: national longitudinal cohort.BMC public health · 2025Article
- Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.Nutrients · 2025Article
- Comparative analysis of retinal vascular structural parameters in populations with different glucose metabolism status based on color fundus photography and artificial intelligence.Frontiers in cell and developmental biology · 2025Article
- Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning.International journal of ophthalmology · 2024Article
- Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study.Frontiers in public health · 2024Article
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10 authors.
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Abstract
Objective: The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes. Materials and methods: The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman's correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model). Results: This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set. Conclusion: Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.
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