Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models

2021 ◽  
Author(s):  
Samaneh Asgari ◽  
Davood Khalili ◽  
Farid Zayeri ◽  
Fereidoun Azizi ◽  
Farzad Hadaegh
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Risk Classification ◽  
Dynamic Prediction ◽  
Static Models

scholarly journals Arrhythmic gut microbiome signatures for risk profiling of Type-2 Diabetes

2019 ◽  
Author(s):  
Sandra Reitmeier ◽  
Silke Kießling ◽  
Thomas Clavel ◽  
Markus List ◽  
Eduardo L. Almeida ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Factors Affecting ◽  
Risk Profiling ◽  
Initial Sampling ◽  
Risk Pattern

SummaryTo combat the epidemic increase in Type-2-Diabetes (T2D), risk factors need to be identified. Diet, lifestyle and the gut microbiome are among the most important factors affecting metabolic health. We demonstrate in 1,976 subjects of a prospective population cohort that specific gut microbiota members show diurnal oscillations in their relative abundance and we identified 13 taxa with disrupted rhythmicity in T2D. Prediction models based on this signature classified T2D with an area under the curve of 73%. BMI as microbiota-independent risk marker further improved diagnostic classification of T2D. The validity of this arrhythmic risk signature to predict T2D was confirmed in 699 KORA subjects five years after initial sampling. Shotgun metagenomic analysis linked 26 pathways associated with xenobiotic, amino acid, fatty acid, and taurine metabolism to the diurnal oscillation of gut bacteria. In summary, we determined a cohort-specific risk pattern of arrhythmic taxa which significantly contributes to the classification and prediction of T2D, highlighting the importance of circadian rhythmicity of the microbiome in targeting metabolic human diseases.

scholarly journals Predictive Supervised Learning Model for Classification of Type 2 Diabetes Mellitus

2021 ◽  
Author(s):  
M.S Roobini ◽  
M Lakshmi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Performance Measures ◽  
Prediction Models ◽  
Random Forest Algorithm ◽  
Ensemble Approach ◽  
To Receive

Abstract There is a tremendous increase in severe cases of type 2 diabetes in the day today's life. Therefore, proper assessment of the disease is critical to saving society. Many prediction models help identify type 2 diabetes. At the same time, every model varies based on the performance measures. Various kinds of algorithms such as Decision Tree, Logistic Regression, KNN, Random Forest algorithm are applied to identify type 2 diabetes. At this juncture, used the implementation of type 2 Classification by AdaBoost algorithms, an ensemble approach. Here, the proposed methodology of the paper is to implement an ensemble approach of machine learning to receive a better efficiency compared to other existing algorithms for the classification of type 2 diabetes. When compared to all different algorithms, this ensemble approach shows an efficiency of 83%. The accuracy is calculated based on various performance measures.

Classification of Type-2 Diabetes using Dual-Stage Cascade Network

2020 ◽  
pp. 99-110
Author(s):  
Ramalingaswamy Cheruku ◽  
Damodar Reddy Edla ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Type 2 Diabetes ◽  
Dual Stage

Classification of Type-2 Diabetes using Bat-based Fuzzy Rule Miner

2020 ◽  
pp. 79-97
Author(s):  
Ramalingaswamy Cheruku ◽  
Damodar Reddy Edla ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Type 2 Diabetes ◽  
Fuzzy Rule

Classification of Type-2 Diabetes using Spider Monkey Crisp Rule Miner

2020 ◽  
pp. 53-77
Author(s):  
Ramalingaswamy Cheruku ◽  
Damodar Reddy Edla ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Type 2 Diabetes ◽  
Spider Monkey

Development and validation of prediction models for the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes

Metabolism ◽  
2018 ◽  
Vol 85 ◽  
pp. 38-47 ◽  
Author(s):  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
Chih-Hsueh Lin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Hospital Mortality ◽  
Prediction Models ◽  
Development And Validation

Abstract P122: P-wave Axis is Associated With All-Cause Mortality in Diabetes: The ACCORD Trial

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Lauren Ehrhardt-Humbert ◽  
Matthew J Singleton ◽  
Bharathi Upadhya ◽  
Muhammad Imtiaz-Ahmad ◽  
Elsayed Z SOLIMAN ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
P Wave ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Increased Risk ◽  
All Cause Mortality

Introduction: Abnormal P-wave axis (PWA) has emerged as a novel marker of risk for both cardiovascular disease and all-cause mortality in the general population, though this relationship has not been adequately explored among those with type 2 diabetes. Hypothesis: We hypothesized that abnormal PWA is associated with all-cause mortality in a large, well-phenotyped group of participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Methods: This analysis included 8,899 ACCORD participants with available PWA data on baseline electrocardiogram. Cox proportional hazards models were used to examine the association between PWA and ACM in models adjusted for demographics, ACCORD trial treatment assignment, and potential confounders. PWA was modeled as either normal (0° - 75°) or abnormal (<0° or >75°). We evaluated the predictive value of PWA by comparing area under the receiver operating characteristic curves in models with and without PWA. Results: Over 44,000 person-years, there were 609 deaths. Participants with abnormal PWA had increased risk of all-cause mortality (HR 1.61, 95% CI 1.25 – 2.08). After multivariable adjustment, the association remained significant (HR 1.32, 95% CI 1.02 – 1.71; see TABLE). Inclusion of abnormal PWA in prediction models afforded a small increase in area under the receiver operating characteristic curves (AUC 0.653 vs. 0.643, p-value for difference of 0.002). Conclusions: In conclusion, among ACCORD trial participants, abnormal PWA was associated with an increased risk of mortality. Abnormal PWA may have added value beyond traditional risk factors in prediction models.

scholarly journals Insomnia is associated with an increased risk of type 2 diabetes in the clinical setting

2018 ◽  
Vol 6 (1) ◽  
pp. e000604 ◽  
Author(s):  
Erin S LeBlanc ◽  
Ning X Smith ◽  
Gregory A Nichols ◽  
Michael J Allison ◽  
Gregory N Clarke
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Clinical Setting ◽  
Clinical Importance ◽  
International Classification Of Diseases ◽  
Hospital Inpatient ◽  
Increased Risk ◽  
Traditional Risk Factors

ObjectiveTo determine the possible association between insomnia and risk of type 2 diabetes mellitus (T2DM) in the naturalistic clinical setting.Research design and methodsWe conducted a retrospective cohort study to examine the risk of developing T2DM among patients with pre-diabetes with and without insomnia. Participants with pre-diabetes (identified by a physician or via two laboratory tests) between January 1, 2007 and December 31, 2015 and without sleep apnea were followed until December 31, 2016. Patients were determined to have T2DM when two of the following occurred within a 2-year window: physician-entered outpatient T2DM diagnosis (International Classification of Diseases [ICD]-9 250.00; ICD-10 E11), dispensing of an antihyperglycemia agent, and hemoglobin A1c (A1c) >6.5% (48 mmol/mol) or fasting plasma glucose (FPG) >125 mg/dL. One hospital inpatient stay with an associated T2DM diagnosis was also sufficient for classification of T2DM.ResultsOur cohort consisted of 81 233 persons with pre-diabetes, 24 146 (29.7%) of whom had insomnia at some point during the 4.3-year average observation period. After adjustment for traditional risk factors, those with insomnia were 28% more likely to develop T2DM than those without insomnia (HR 1.28; 95% CI 1.24 to 1.33). The estimate was essentially unchanged after adjusting for baseline A1c level (HR 1.32; 95% CI 1.25 to 1.40) or FPG (HR 1.28; 95% CI 1.23 to 1.33).ConclusionsInsomnia imparts an increased risk of T2DM comparable with that conferred by traditional risk factors (eg, overweight, non-white race, cardiovascular risk factors). This association could have clinical importance because it suggests a new potentially modifiable risk factor that could be targeted to prevent diabetes.

GAD antibodies in classification of Asian type 2 diabetes

Diabetes Care ◽  
1999 ◽  
Vol 22 (6) ◽  
pp. 1011-1012 ◽  
Author(s):  
M. Fukui ◽  
N. Nakamura ◽  
M. Kondo
Keyword(s):  
Type 2 Diabetes ◽  
Gad Antibodies

scholarly journals Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

2015 ◽  
Vol 22 (4) ◽  
pp. 545-559 ◽  
Author(s):  
Rafael Ríos ◽  
Carmen Belén Lupiañez ◽  
Daniele Campa ◽  
Alessandro Martino ◽  
Joaquin Martínez-López ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Multiple Myeloma ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Area Under The Curve ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Stratified Analysis

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

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