1055-P: The Machine Learning Prediction Model NASHmap Identifies Higher Insulin Resistance in Type 2 Diabetes Mellitus (T2DM) Patients at Risk for Nonalcoholic Steatohepatitis (NASH)

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1055-P
Author(s):  
ANDREAS TIETZ ◽  
GIOVANNI BADER ◽  
MATT DOCHERTY ◽  
BRENDA REINHART ◽  
MARIA-MAGDALENA BALP ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
At Risk ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Nonalcoholic Steatohepatitis ◽  
Patients At Risk

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 A Follow-up Study on BMI-SDS and Insulin Resistance in Overweight and Obese Children at Risk for Type 2 Diabetes Mellitus

2015 ◽  
Vol 2 ◽  
pp. 2333794X1456845 ◽  
Author(s):  
Soulmaz Fazeli Farsani ◽  
Marloes P. van der Aa ◽  
Catherijne A. J. Knibbe ◽  
Anthonius de Boer ◽  
Marja M. J. van der Vorst
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
At Risk ◽  
Type 2 Diabetes Mellitus ◽  
Model Assessment ◽  
Children At Risk ◽  
The Mean

Objectives. To evaluate body mass index standard deviation score (BMI-SDS), insulin sensitivity, and progression to type 2 diabetes mellitus (T2DM) in children at risk for T2DM approximately 3 years after being diagnosed with overweight/obesity and insulin resistance (measured by Homeostasis Model Assessment of Insulin Resistance [HOMA-IR]). Methods. Out of 86 invited children, 44 (mean age 15.4 ± 3.6 years) participated. Medical history, physical examination, and laboratory workup were performed. Results. While the mean BMI-SDS significantly increased from 2.9 to 3.4, the mean HOMA-IR significantly decreased from 5.5 to 4.6 (baseline vs follow-up visit). Change in HOMA-IR was only due to a decrease in mean fasting plasma insulin (24.1 vs 21.1, P = .073). Conclusions. Although increase in BMI-SDS in these children is worrisome, the American Diabetes Association recommended screening interval of 3 years for children at risk for T2DM is not too long based on the fact that none of our study participants developed T2DM.

scholarly journals Soluble urokinase receptor (suPAR) predicts microalbuminuria in patients at risk for type 2 diabetes mellitus

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Martina Guthoff ◽  
Robert Wagner ◽  
Elko Randrianarisoa ◽  
Erifili Hatziagelaki ◽  
Andreas Peter ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
At Risk ◽  
Type 2 Diabetes Mellitus ◽  
Urokinase Receptor ◽  
Patients At Risk

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.

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