Observational studyScientific reports2020
A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants.
Observational study in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants.The EPMA journal · 2022Article
- A Qualitative Exploration of Self-Management Behaviors and Influencing Factors in Patients With Type 2 Diabetes.Frontiers in endocrinology · 2022Article
- New risk score model for identifying individuals at risk for diabetes in southwest China.Preventive medicine reports · 2021Article
- A predictive nomogram: a cross-sectional study on a simple-to-use model for screening 12-year-old children for severe caries in middle schools.BMC oral health · 2021Article
- Uncurtaining the pivotal role of ABC transporters in diabetes mellitus.Environmental science and pollution research international · 2021Review
- Nomogram Model for Screening the Risk of Type II Diabetes in Western Xinjiang, China.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
- A Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
- Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.Journal of diabetes research · 2020Article
- Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.
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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 OpenQuestion graph.