ArticleJournal of endocrinological investigation2017
Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study.
Article in Journal of endocrinological investigation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study.Cardiovascular diabetology · 2022Pooled it
- Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies.International journal of obesity (2005) · 2022Pooled it
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Sex and age differences in the association between obesity and long-term changes with the risk of incident type 2 diabetes mellitus: the Rural Deqing Cohort Study.Hepatobiliary surgery and nutrition · 2026Article
- [Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- Associations between eating speed and food temperature and type 2 diabetes mellitus: a cross-sectional study.Frontiers in nutrition · 2023Article
- Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.BMC public health · 2022Article
- Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.Frontiers in public health · 2022Article
- Development of a Validated Diabetes Risk Chart as a Simple Tool to Predict the Onset of Diabetes in Bogor, Indonesia.Journal of the ASEAN Federation of Endocrine Societies · 2022Article
- Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.International journal of endocrinology and metabolism · 2021Article
- Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents.The EPMA journal · 2019Article
- Clinical inertia, reverse clinical inertia, and medication non-adherence in type 2 diabetes.Journal of endocrinological investigation · 2019Review
- Importance of family history of diabetes in computing a diabetes risk score in Korean prediabetic population.Scientific reports · 2018Article
- Using Neighborhood-Level Census Data to Predict Diabetes Progression in Patients with Laboratory-Defined Prediabetes.The Permanente journal · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveRisk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults.
methodsA prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model.
resultsBy 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population.
conclusionsOur non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.
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