Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

Article Endocrinology & Metabolism

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

Bobbie-Jo M. Webb-Robertson et al.

Summary: This study aimed to investigate whether a combination of genetic, immunologic, and metabolic features measured in infancy can predict the likelihood of developing T1D in children by age 6.

JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM (2022)

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Article Environmental Sciences

An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data

Rosy Oh et al.

Summary: This study developed an interactive online application that uses a Bayesian network classifier to predict the probabilities of prediabetes and diabetes. It found that biomarkers of exposure to environment-polluting chemicals have interactive effects on diabetes progression. The prediction system allows for customized diabetes prediction and simulation of the effects of controlling risk factors.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2022)

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Article Health Care Sciences & Services

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Qing Liu et al.

Summary: This study developed effective prediction models based on machine learning to predict the risk of type 2 diabetes in Chinese elderly individuals. The XGBoost model achieved the best performance and identified fasting plasma glucose, education, exercise, gender, and waist circumference as the most important predictors.

JOURNAL OF PERSONALIZED MEDICINE (2022)

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Article Endocrinology & Metabolism

Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches

Shahid Mohammad Ganie et al.

Summary: This research aims to develop a framework based on machine learning techniques using only lifestyle indicators for the prediction of Type 2 Diabetes Mellitus (T2DM) disease. The study found that the Gradient Boosting classifier performed the best, with an accuracy rate of 97.24% for training and 96.90% for testing. The research also demonstrated that Gradient Boosting had superior precision, recall, and f1-score compared to other classifiers. Additionally, the study found that both Gradient Boosting and Naive Bayes had better area under the curve in terms of ROC analysis.

JOURNAL OF DIABETES AND METABOLIC DISORDERS (2022)

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Article Immunology

Child type 1 diabetes associated with mother vaginal bacteriome and mycobiome

A. L. Ruotsalainen et al.

Summary: This study found that mother vaginal microbiome contributes to the microbiome of vaginally delivered neonates. Mothers with a diabetic child had increased alpha-diversity of bacteriome and decreased alpha-diversity of mycobiome compared to control mothers. The study also suggests an association between certain fungal genera and bacterial metabolic pathways of the mother vaginal microbiome and child T1D.

MEDICAL MICROBIOLOGY AND IMMUNOLOGY (2022)

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Article Chemistry, Analytical

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods

Hansel Hu et al.

Summary: This research proposes a machine learning-based predictive model to detect adolescent diabetes. The best model achieved an AUC score of 71%. The study identifies key predictors for youth diabetes, including physical characteristics, dietary information, and demographics.

SENSORS (2022)

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Article Endocrinology & Metabolism

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

Bobbie-Jo M. Webb-Robertson et al.

Summary: The TEDDY study employed a machine learning model to predict the development of persistent islet autoantibodies in children, identifying key markers and metabolites that may play a role in triggering islet autoimmunity.

JOURNAL OF DIABETES (2021)

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Article Endocrinology & Metabolism

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021

Summary: The American Diabetes Association's Standards of Medical Care in Diabetes provides clinical practice recommendations, treatment goals, guidelines, and tools for evaluating care quality. It is updated annually by a multidisciplinary expert committee, and readers are invited to comment on the standards at professional.diabetes.org/SOC.

DIABETES CARE (2021)

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Article Endocrinology & Metabolism

Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

Yixuan He et al.

Summary: The study established a polyexposure score (PXS) for type 2 diabetes and found that it provides modest incremental predictive value over traditional clinical risk factors. Adding PXS and polygenic risk score (PGS) improved T2D classification accuracy, indicating their potential usefulness in chronic disease risk prediction models.

DIABETES CARE (2021)

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Review Endocrinology & Metabolism

A review on current advances in machine learning based diabetes prediction

Varun Jaiswal et al.

Summary: Diabetes is a major global health issue with severe complications such as cardiovascular disease and stroke. Machine learning and data mining techniques can be used for diabetes diagnosis, but current methods have limitations and need improvement.

PRIMARY CARE DIABETES (2021)

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Review Medicine, General & Internal

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

Sigit Ari Saputro et al.

Summary: A systematic review and meta-analysis were conducted to summarize the performances of prognostic models for diabetic microvascular complications. Most models were developed for DR, CKD, and ESRD, using traditional statistical models or machine learning. Predictors such as age, sex, HbA1c were common, and the discriminative performance varied among different complications, highlighting the need for more external validations and impact analyses.

SYSTEMATIC REVIEWS (2021)

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Article Environmental Sciences

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Henock M. Deberneh et al.

Summary: This study developed a machine learning model to predict the occurrence of type 2 diabetes using key features selected from health records data, and found that ensemble models outperformed single models in predicting diabetes occurrence.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

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Article Endocrinology & Metabolism

Plasma Metabolome and Circulating Vitamins Stratified Onset Age of an Initial Islet Autoantibody and Progression to Type 1 Diabetes: The TEDDY Study

Qian Li et al.

Summary: The study found that children's plasma lipidome can reflect gene regulation and dietary exposure, predicting the development of islet autoantibodies and type 1 diabetes. Infants with reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while those with early onset of GADA-first had reduced sphingomyelins in infancy. Plasma ascorbic acid and 25(OH)D were lower in HLA-DR3/DR4 children among IA case subjects, suggesting dysregulation of circulating vitamins as potential signals for IA or T1D progression.

DIABETES (2021)

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Article Endocrinology & Metabolism

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

Brigitte I. Frohnert et al.

DIABETES (2020)

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Article Endocrinology & Metabolism

Longitudinal Metabolome-Wide Signals Prior to the Appearance of a First Islet Autoantibody in Children Participating in the TEDDY Study

Qian Li et al.

DIABETES (2020)

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Article Microbiology

Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes

Sandra Reitmeier et al.

CELL HOST & MICROBE (2020)

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Article Endocrinology & Metabolism

Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked

Roberto Biassoni et al.

JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM (2020)

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Article Endocrinology & Metabolism

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Mingyue Xue et al.

JOURNAL OF DIABETES RESEARCH (2020)

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Article Endocrinology & Metabolism

Estimation of Diabetes in a High -Risk Adult Chinese Population Using J48 Decision Tree Model

Dongmei Pei et al.

DIABETES METABOLIC SYNDROME AND OBESITY-TARGETS AND THERAPY (2020)

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Article Computer Science, Information Systems

Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study

Asif Hassan Syed et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

A combined strategy of feature selection and machine learning to identify predictors of prediabetes

Kushan De Silva et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)

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Article Public, Environmental & Occupational Health

Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends

Moien Abdul Basith Khan et al.

JOURNAL OF EPIDEMIOLOGY AND GLOBAL HEALTH (2020)

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Review Public, Environmental & Occupational Health

Prevalence and incidence of type 1 diabetes in the world: a systematic review and meta-analysis

Majid Mobasseri et al.

HEALTH PROMOTION PERSPECTIVES (2020)

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Article Medical Informatics

Accurate and rapid screening model for potential diabetes mellitus

Dongmei Pei et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2019)

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Review Medicine, General & Internal

Machine Learning in Medicine

Alvin Rajkomar et al.

NEW ENGLAND JOURNAL OF MEDICINE (2019)

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Review Computer Science, Artificial Intelligence

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

Ashenafi Zebene Woldaregay et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2019)

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Article Public, Environmental & Occupational Health

Using decision trees to understand the influence of individual- and neighborhood-level factors on urban diabetes and asthma

Hillary A. Cuesta et al.

HEALTH & PLACE (2019)

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Article Public, Environmental & Occupational Health

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques

Zidian Xie et al.

PREVENTING CHRONIC DISEASE (2019)

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Article Medical Informatics

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning

An Dinh et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2019)

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Article Endocrinology & Metabolism

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

Dongmei Pei et al.

JOURNAL OF DIABETES RESEARCH (2019)

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Article Endocrinology & Metabolism

Economic Costs of Diabetes in the US in 2017

Wenya Yang et al.

DIABETES CARE (2018)

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Review Environmental Sciences

Environmental Risk Factors for Developing Type 2 Diabetes Mellitus: A Systematic Review

Tashi Dendup et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2018)

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Article Medicine, General & Internal

PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Andrea C. Tricco et al.

ANNALS OF INTERNAL MEDICINE (2018)

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Article Medicine, General & Internal

Human Early Life Exposome (HELIX) study: a European population-based exposome cohort

Lea Maitre et al.

BMJ OPEN (2018)

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Article Medicine, General & Internal

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes ELSA-Brasil: accuracy study

Andre Rodrigues Olivera et al.

SAO PAULO MEDICAL JOURNAL (2017)

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Article Biochemistry & Molecular Biology

Machine Learning and Data Mining Methods in Diabetes Research

Ioannis Kavakiotis et al.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2017)

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Article Endocrinology & Metabolism

Early metabolic markers identify potential targets for the prevention of type 2 diabetes

Gopal Peddinti et al.

DIABETOLOGIA (2017)

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Review Medicine, General & Internal

Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes

Hubert Kolb et al.

BMC MEDICINE (2017)

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Article Health Care Sciences & Services

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Wei Luo et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2016)

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Article Medicine, General & Internal

Environmental risk factors for type 1 diabetes

Marian Rewers et al.

LANCET (2016)

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Article Medicine, General & Internal

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study

Azra Ramezankhani et al.

BMJ OPEN (2016)

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Article Medicine, General & Internal

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

Karel G. M. Moons et al.

ANNALS OF INTERNAL MEDICINE (2015)

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Article Cardiac & Cardiovascular Systems

Machine Learning in Medicine

Rahul C. Deo

CIRCULATION (2015)

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Editorial Material Biochemical Research Methods

Knowledge Discovery Using Big Data in Biomedical Systems

Sarath Chandra Janga et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2015)

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Editorial Material Medicine, General & Internal

UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

Cathie Sudlow et al.

PLOS MEDICINE (2015)

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Article Endocrinology & Metabolism

An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database

Azra Ramezankhani et al.

INTERNATIONAL JOURNAL OF ENDOCRINOLOGY AND METABOLISM (2015)

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Article Mathematical & Computational Biology

Screening for Prediabetes Using Machine Learning Models

Soo Beom Choi et al.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2014)

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Article Biology

The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis

Hui Chen et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2014)

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Article Endocrinology & Metabolism

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study

Azra Ramezankhani et al.

DIABETES RESEARCH AND CLINICAL PRACTICE (2014)

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Article Respiratory System

The exposome: a new paradigm to study the impact of environment on health

Martine Vrijheid

THORAX (2014)

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Article Computer Science, Interdisciplinary Applications

KmL3D: A non-parametric algorithm for clustering joint trajectories

C. Genolini et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2013)

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Article Endocrinology & Metabolism

Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach

Chongjian Wang et al.

DIABETES RESEARCH AND CLINICAL PRACTICE (2013)

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Article Medicine, Research & Experimental

Comparison of three data mining models for predicting diabetes or prediabetes by risk factors

Xue-Hui Meng et al.

KAOHSIUNG JOURNAL OF MEDICAL SCIENCES (2013)

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Article Engineering, Environmental

Providing the Missing Link: the Exposure Science Ontology ExO

Carolyn J. Mattingly et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2012)

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Review Public, Environmental & Occupational Health

The exposome: from concept to utility

Christopher Paul Wild

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2012)

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Article Multidisciplinary Sciences

Gut Microbiome Metagenomics Analysis Suggests a Functional Model for the Development of Autoimmunity for Type 1 Diabetes

Christopher T. Brown et al.

PLOS ONE (2011)

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Article Medical Informatics

Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

Wei Yu et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2010)

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Article Statistics & Probability

KmL: k-means for longitudinal data

Christophe Genolini et al.

COMPUTATIONAL STATISTICS (2010)

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Review Endocrinology & Metabolism

Built environment and diabetes

Sudhir Kumar Pasala et al.

INTERNATIONAL JOURNAL OF DIABETES IN DEVELOPING COUNTRIES (2010)

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Article Chemistry, Analytical

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus

Xiang Li et al.

ANALYTICA CHIMICA ACTA (2009)

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Article Endocrinology & Metabolism

The Environmental Determinants of Diabetes in the Young (TEDDY) Study TEDDY Study Group

Marian Rewers

IMMUNOLOGY OF DIABETES V: FROM BENCH TO BEDSIDE (2008)

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Article Endocrinology & Metabolism

Environmental triggers and determinants of type 1 diabetes

M Knip et al.

DIABETES (2005)

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Article Psychiatry

Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes

SA Everson et al.

JOURNAL OF PSYCHOSOMATIC RESEARCH (2002)

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Article Endocrinology & Metabolism

Diabetes-related morbidity and mortality in a national sample of US elders

AG Bertoni et al.

DIABETES CARE (2002)

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Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

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