scholarly journals Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Yikang Wang ◽  
Liying Zhang ◽  
Miaomiao Niu ◽  
Ruiying Li ◽  
Runqi Tu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Type 2 Diabetes Mellitus ◽  
Cohort Study ◽  
Prediction Model ◽  
Genetic Risk ◽  
Prediction Models

Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults.Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models.Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (Ptrend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS.Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations.Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach

2019 ◽  
pp. 103-116 ◽  
Author(s):  
Muhammad Younus ◽  
Md Tahsir Ahmed Munna ◽  
Mirza Mohtashim Alam ◽  
Shaikh Muhammad Allayear ◽  
Sheikh Joly Ferdous Ara
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals 184Are genetic risk factors clinically useful in prognostic models for type 2 diabetes mellitus?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Vera Helen Buss ◽  
Margo Barr ◽  
Marlien Varnfield ◽  
Mark Harris
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Genetic Risk ◽  
Prediction Models ◽  
Genetic Risk Factors ◽  
Genetic Models ◽  
Prognostic Models

Abstract Background Many prognostic models exist for the prediction of type 2 diabetes mellitus. Some of these include genetic risk factors. The objective of this review was to investigate whether including genetic variables in prediction models for type 2 diabetes mellitus is clinically useful. Methods Studies were included if they reported on prognostic prediction models for type 2 diabetes mellitus. Seven medical and bioengineering databases were searched using terms regarding risk prediction and diabetes combined via Boolean operators. In studies including genetic variables, the c-statistics of genetic and non-genetic models were compared. Results Seventy-six studies published between 2002 and 2019 were included in the review. Twenty of these (published 2008 to 2019) included genetic variables, namely deleterious alleles and specific small nuclear polymorphisms. Study samples represented the general population. When comparing genetic to non-genetic models, one study reported a statistically significantly greater c-statistic for the genetic and four for the non-genetic models. Adding genetic risk factors to a clinical model did not substantially increase the predictive accuracy of any study. Conclusions The use of genetic data did not show any meaningful improvements in the predictive performance for the general population compared to clinical models. Studies using genetic data were often based on small sample sizes and not externally validated, raising concerns regarding potential overfitting and lack of generalisability. Key messages Currently, the clinical usefulness of genetic risk scores for type 2 diabetes mellitus seems quite limited.

scholarly journals Metabolic Syndrome and Its Components Predict the Risk of Type 2 Diabetes Mellitus in the Mainland Chinese: A 3-Year Cohort Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Wang ◽  
Qun-Fang Yang ◽  
Xing-Lin Chen ◽  
Yu-Wei Liu ◽  
Sheng-Shuai Shan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Type 2 Diabetes Mellitus ◽  
Cohort Study ◽  
Prediction Model ◽  
Predictive Value ◽  
Mainland Chinese ◽  
Increased Risk

Introduction. It has well established that metabolic syndrome (MetS) can predict the risk of type 2 diabetes mellitus (T2DM) in some population groups. However, limited evidence is available regarding the predictive effect of MetS for incident T2DM in mainland Chinese population. Methods. A 3-year cohort study was performed for 9735 Chinese without diabetes at baseline. MetS and its components were assessed by multivariable analysis using Cox regression. Prediction models were developed. Discrimination was assessed with area under the receiver operating characteristic curves (AUCs), and performance was assessed by a calibration curve. Results. The 3-year cumulative incidence of T2DM was 11.29%. Baseline MetS was associated with an increased risk of T2DM after adjusting for age (HR = 2.68, 95% CI, 2.27–3.17 in males; HR = 2.59, 95% CI, 1.83–3.65 in females). Baseline MetS exhibited relatively high specificity (88% in males, 94% in females) and high negative predictive value (90% in males, 94% in females) but low sensitivity (36% in males, 23% in females) and low positive predictive value (31% in males and females) for predicting the 3-year risk of T2DM. AUCs, including age and components of MetS, for the prediction model were 0.779 (95% CI: 0.759–0.799) in males and 0.860 (95% CI: 0.836–0.883) in females. Calibration curves revealed good agreement between prediction and observation results in males; however, the model could overestimate the risk when the predicted probability is >40% in females. Conclusions. MetS predicts the risk of T2DM. The quantitative MetS-based prediction model for T2DM risk may improve preventive strategies for T2DM and present considerable public health benefits for the people in mainland China.

scholarly journals Early detection of type 2 diabetes mellitus using machine learning-based prediction models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Leon Kopitar ◽  
Primoz Kocbek ◽  
Leona Cilar ◽  
Aziz Sheikh ◽  
Gregor Stiglic
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Early Detection ◽  
Prediction Models

scholarly journals Nomogram Prediction Model for Diabetic Retinopathy Development in Type 2 Diabetes Mellitus Patients: A Retrospective Cohort Study

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaomei Chen ◽  
Qiying Xie ◽  
Xiaoxue Zhang ◽  
Qi Lv ◽  
Xin Liu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Cohort Study ◽  
Prediction Model ◽  
Retrospective Cohort ◽  
Cox Regression

Background. This study is aimed at investigating the systemic risk factors of diabetic retinopathy and further establishing a risk prediction model for DR development in T2DM patients. Methods. This is a retrospective cohort study including 330 type 2 diabetes mellitus (T2DM) patients who were followed up from December 2012 to November 2020. Multivariable cox regression analysis identifying factors associated with the hazard of developing diabetic retinopathy (DR) was used to construct the DR risk prediction model in the form of nomogram. Results. 50.6% of participants (mean age: 58.60 ± 10.55 ) were female, and mean duration of diabetes was 7.09 ± 5.36   years . After multivariate cox regression, the risk factors for developing DR were age (HR 1.068, 95%Cl 1.021-1.118, P = 0.005 ), diabetes duration (HR 1.094, 95%Cl 1.018-1.177, P = 0.015 ), HbA1c (HR 1.411, 95%Cl 1.113-1.788, P = 0.004 ), albuminuria (HR 6.908, 95%Cl 1.794-26.599, P = 0.005 ), and triglyceride (HR 1.554, 95%Cl 1.037-2.330, P = 0.033 ). The AUC values of the nomogram for predicting developing DR at 3-, 4-, and 5-year were 0.854, 0.845, and 0.798. Conclusion. Combining age, diabetes duration, HbA1c, albuminuria, and triglyceride, the nomogram model is effective for early recognition and intervention of individuals at high risk of DR development.

Sign in / Sign up

Export Citation Format

Share Document