AIMC Topic: Prediabetic State

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Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes.

Diabetes Oct 6, 2020
Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolit...
Humans Middle Aged Blood Glucose Diabetes Mellitus, Type 2 Renal Insufficiency, Chronic Machine Learning Prediabetic State Biomarkers Aged Aged, 80 and over Adult
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Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

Scientific reports May 20, 2020
To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjust...
Gray Matter Prediabetic State Life Style Female Confounding Factors, Epidemiologic Cross-Sectional Studies Humans Case-Control Studies Models, Neurological Male Machine Learning Diabetes Mellitus, Type 2 Cardiovascular Diseases Organ Size Prospective Studies Glomerular Filtration Rate Middle Aged Linear Models Follow-Up Studies Germany Magnetic Resonance Imaging
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IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms.

Sensors (Basel, Switzerland) Apr 30, 2020
The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model-referred to as IGRNet-is developed to effectively detect and di...
Electrocardiography Humans Prediabetic State Neural Networks, Computer Deep Learning ROC Curve
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Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews Jan 14, 2020
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...
Aged, 80 and over Adult Male Middle Aged Risk Assessment Electronic Health Records Female Risk Factors Aged Follow-Up Studies Patient Selection Israel United Kingdom Prediabetic State Disease Progression Cohort Studies Time Factors Canada Diabetes Mellitus Humans Machine Learning Databases, Factual Prognosis
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Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Journal of medical systems Dec 9, 2019
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney funct...
Cardiovascular Diseases Middle Aged Diabetes Mellitus Regression Analysis Humans Pulse Wave Analysis Machine Learning Prediabetic State Female Male
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Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Journal of the American College of Nutrition Apr 25, 2019
Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be ad...
Prediabetic State Overweight Humans Vitamin D Neural Networks, Computer Vitamin D Deficiency Retrospective Studies Data Mining Female Aged Adult Male Middle Aged Cohort Studies
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Relationship Between Serum Macrophage Migration Inhibitory Factor Level and Insulin Resistance, High-Sensitivity C-Reactive Protein and Visceral Fat Mass in Prediabetes.

The American journal of the medical sciences Oct 26, 2017
BACKGROUND: Growing evidence suggest that macrophage migration inhibitory factor (MIF) plays a vital role in glucose metabolism. We aimed to ascertain whether MIF levels are altered in subjects with prediabetes and also to determine the relationship ...
Body Mass Index Prediabetic State Humans Cross-Sectional Studies Intra-Abdominal Fat Biomarkers C-Reactive Protein Male Female Intramolecular Oxidoreductases Middle Aged Adult Macrophage Migration-Inhibitory Factors Blood Glucose Insulin Resistance
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Defining and characterizing the critical transition state prior to the type 2 diabetes disease.

PloS one Jul 7, 2017
BACKGROUND: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed throu...
Humans Electronic Health Records Female Adult Male Health Information Exchange Markov Chains Gene Expression Profiling Datasets as Topic Diabetes Mellitus, Type 2 Gene Expression Regulation Insulin Resistance Prediabetic State Support Vector Machine Maine
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Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

Journal of diabetes science and technology Dec 20, 2015
BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2...
ROC Curve Diabetes Mellitus, Type 2 Area Under Curve Medical Informatics Prediabetic State Female Risk Factors Adult Male Disease Progression Humans Machine Learning Retrospective Studies Electronic Health Records
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Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.

Cardiovascular diabetology May 9, 2025
BACKGROUND: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognosti...
Female Risk Factors Predictive Value of Tests Biomarkers Aged Cause of Death Humans Risk Assessment Male Atherosclerosis Machine Learning Prognosis Blood Glucose Middle Aged Stress, Physiological Databases, Factual Retrospective Studies Time Factors Prediabetic State Critical Illness Hyperglycemia Diabetes Mellitus
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