What's New

Last Posted: Jun 27, 2024

• Effects of genetic risk on incident type 2 diabetes and glycemia: the T2D-GENE lifestyle intervention trial.
  Maria Anneli Lankinen et al. J Clin Endocrinol Metab 2024
• A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk.
  Bradley Jermy et al. Nat Commun 2024 15(1) 5007
• Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.
  Mehrdad A Mizani et al. BMJ Open Diabetes Res Care 2024 12(3)
• Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.
  Andreas Leiherer et al. Int J Mol Sci 2024 25(10)
• Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores
  
• Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes.
  Yun-Ru Lai et al. Neurophysiol Clin 2024 54(4) 102982
• Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study.
  Ying Zhu et al. Diabetes Metab Syndr Obes 2024 171987-1997
• Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.
  Alberto Montesanto et al. Front Endocrinol (Lausanne) 2024 151359482
• Deriving Treatment Decision Support From Dutch Electronic Health Records by Exploring the Applicability of a Precision Cohort-Based Procedure for Patients With Type 2 Diabetes Mellitus: Precision Cohort Study.
  Xavier Pinho et al. Online J Public Health Inform 2024 16e51092
• Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes
  
• Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study.
  Ravi Mandla et al. Genome Med 2024 16(1) 63
• Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.
  Donatella Musetti et al. Eur J Ophthalmol 2024 11206721241248856
• Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score.
  Buu Truong et al. Med 2024
• Burden of Mendelian disorders in a large Middle Eastern biobank.
  Waleed Aamer et al. Genome Med 2024 16(1) 46
• Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness.
  Martin Bernstorff et al. Acta Psychiatr Scand 2024
• A New Tool to Identify Pediatric Patients with Atypical Diabetes Associated with Gene Polymorphisms.
  Sophie Welsch et al. Diabetes Metab J 2024
• Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
  Lianhua Liu et al. Front Endocrinol (Lausanne) 2024 151320335
• Multi-ancestry polygenic mechanisms of type 2 diabetes
  K Smith et al, Nature Medicine, March 6, 2024
• Machine learning based predictive model of Type 2 diabetes complications using Malaysian National Diabetes Registry: A study protocol.
  Mohamad Zulfikrie Abas et al. J Public Health Res 2024 13(1) 22799036241231786
• Ambitious survey of human diversity yields millions of undiscovered genetic variants Analysis of the ‘All of Us’ genomic data set begins to tackle inequities in genetics research.
  M Koslov, Nature, February 19, 2024
• AI-based diabetes care: risk prediction models and implementation concerns
  SCY Wang et al, NPJ Digital Medicine, February 15, 2024
• Some patients with type 2 diabetes may benefit from intensive glycaemic and blood pressure control: A post-hoc machine learning analysis of ACCORD trial data.
  Tianze Jiao et al. Diabetes Obes Metab 2024
• Familial Hypercholesterolemia in the Elderly: An Analysis of Clinical Profile and Atherosclerotic Cardiovascular Disease Burden from the Hellas-FH Registry.
  Christina Antza et al. Biomedicines 2024 12(1)
• Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank.
  Aeyeon Lee et al. Commun Biol 2024 7(1) 122
• Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis
  A Ahmad et al, Com Med January 22, 2024
• Genetic contributions to risk of adverse pregnancy outcomes.
  Zachary H Hughes et al. Curr Cardiovasc Risk Rep 2024 17(11) 185-193
• Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.
  Risa M Wolf et al. Nat Commun 2024 15(1) 421
• Mediating Factors in the Association of Maternal Educational Level With Pregnancy Outcomes: A Mendelian Randomization Study.
  Tormod Rogne et al. JAMA Netw Open 2024 1 (1) e2351166
• Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman.
  Khoula Al Sadi et al. Bioengineering (Basel) 2023 10(12)
• Implementing a Pharmacogenomic-driven Algorithm to Guide Antiplatelet Therapy among Caribbean Hispanics: A non-randomized prospective cohort study.
  Héctor Nuñez-Medina et al. medRxiv 2023
• Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.
  Panke Chen et al. BMC Cardiovasc Disord 2023 23(1) 585
• What Is Prediabetes?
  J Jin, JAMA Patient Corner, December 1, 2023
• Affected brother as the highest risk factor of type 1 diabetes development in children and adolescents: One center data before implementing type 1 diabetes national screening.
  Anna Wedrychowicz et al. Adv Clin Exp Med 2023
• Including diverse populations enhances the discovery of type 2 diabetes loci
  S Fatumo, Nat Rev Genetics, November 22, 2023
• The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.
  Jason L Vassy et al. Am J Hum Genet 2023 110(11) 1841-1852
• Association Between a First-Degree Family History and Self-Reported Personal History of Obesity, Diabetes, and Heart and Blood Conditions: Results From the All of Us Research Program.
  Danielle Rasooly et al. J Am Heart Assoc 2023 11 e030779
• Longitudinal Trajectories of Glycemic Control among U.S. Adults with Newly Diagnosed Diabetes.
  Rozalina G McCoy et al. Diabetes Res Clin Pract 2023 110989
• A scoping review of artificial intelligence-based methods for diabetes risk prediction.
  Farida Mohsen et al. NPJ Digit Med 2023 10 (1) 197
• Digital lifestyle treatment improves long-term metabolic control in type 2 diabetes with different effects in pathophysiological and genetic subgroups.
  Vishal A Salunkhe et al. NPJ Digit Med 2023 10 (1) 199
• Identification of monogenic diabetes in an Australian cohort using the Exeter maturity-onset diabetes of the young (MODY) probability calculator and next-generation sequencing gene panel testing.
  Sunita M C De Sousa et al. Acta Diabetol 2023
• Mobile Health and Preventive Medicine.
  Jill Waalen et al. Med Clin North Am 2023 107(6) 1097-1108
• Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study.
  Camilla Heisel Nyholm Thomsen et al. J Diabetes Sci Technol 2023 19322968231201400
• Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?
  Xiantong Zou et al. Digit Health 2023 920552076231203879
• Precision medicine of obesity as an integral part of type 2 diabetes management – past, present, and future
  L Szczerbinski et al. The Lancet Diabetes Endocr, October 4, 2023
• Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review
  LM Jacobsen et al, Comm Medicine, October 5, 2023
• Frequency of adding salt to foods, genetic susceptibility, and incident type 2 diabetes: a prospective cohort study.
  Yimin Zhao et al. J Clin Endocrinol Metab 2023
• Polygenic Scores for Longitudinal Prediction of Incident Type 2 Diabetes in an Ancestrally and Medically Diverse Primary Care Network.
  Ravi Mandla et al. medRxiv 2023
• Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.
  Xue Tao et al. Sci Rep 2023 13(1) 16437
• Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study.
  Shu Wang et al. BMJ Open 2023 13(8) e069018
• Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care.
  Andrew J Krentz et al. Metab Syndr Relat Disord 2023
• Utility of polygenic scores for differentiating diabetes diagnosis among patients with atypical phenotypes of diabetes.
  Liana K Billings et al. J Clin Endocrinol Metab 2023
• Cost effectiveness review of text messaging, smartphone application, and website interventions targeting T2DM or hypertension.
  Ruben Willems et al. NPJ Digit Med 2023 8 (1) 150
• The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials.
  Mozhgan Tanhapour et al. Acta Diabetol 2023
• Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.
  Yi-Tong Tong et al. Front Pharmacol 2023 141216182
• Australian parental perceptions of genomic newborn screening for non-communicable diseases.
  Sarah Casauria et al. Front Genet 2023 141209762
• Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data.
  Giorgio Gandaglia et al. Eur Urol 2023
• Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study.
  Xuelun Wu et al. Diabetes Metab Syndr Obes 2023 161987-2003
• A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.
  Megan M Shuey et al. EBioMedicine 2023 7 104674
• Machine learning approach to predict body weight in adults.
  Kazuya Fujihara et al. Front Public Health 2023 111090146
• Mendelian randomization evidence for the causal effects of socio-economic inequality on human longevity among Europeans.
  Chao-Jie Ye et al. Nat Hum Behav 2023
• The Utilization of Machine Learning Algorithms for Assisting Physicians in the Diagnosis of Diabetes.
  Linh Phuong Nguyen et al. Diagnostics (Basel) 2023 13(12)
• Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations.
  Niall J Lennon et al. medRxiv 2023
• Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.
  Ashwini Venkatasubramaniam et al. BMC Med Inform Decis Mak 2023 23(1) 110
• Accelerometer-measured intensity-specific physical activity, genetic risk and incident type 2 diabetes: a prospective cohort study.
  Mengyun Luo et al. Br J Sports Med 2023
• Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes.
  Soo Heon Kwak et al. Res Sq 2023
• Using Machine Learning and Artificial Intelligence to Predict Diabetes Mellitus Among Women Population.
  Ali Mamoon Alfalki et al. Curr Diabetes Rev 2023
• A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.
  Hong Pan et al. Front Med (Lausanne) 2023 101136653
• Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus.
  X Wu et al. J Endocrinol Invest 2023
• Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
  Oumer Abdulkadir Ebrahim et al. Sci Rep 2023 13(1) 7779
• Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.
  Joseph Mellor et al. Int J Med Inform 2023 175105072
• Polygenic risk score-based phenome-wide association study identifies novel associations for Tourette syndrome.
  Pritesh Jain et al. Transl Psychiatry 2023 13(1) 69
• Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program.
  Xin Wang et al. J Hum Genet
• Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes.
  William L Lowe et al. Int J Mol Sci 2023 24(7)
• Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.
  Antonio Agliata et al. Int J Mol Sci 2023 24(7)
• A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes.
  Rui Tao et al. Phys Eng Sci Med 2023
• A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure.
  Min Zhao et al. Comput Methods Programs Biomed 2023 235107537
• Genetics and epigenetics in the obesity phenotyping scenario.
  Khanh Trang et al. Reviews in endocrine & metabolic disorders 2023
• Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease.
  Mariella Gregorich et al. JAMA network open 2023 4 (4) e231870
• Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes.
  Rashid M Ansari et al. Healthcare (Basel, Switzerland) 2023 11(6)
• Hypercholesterolemia in the Malaysian Cohort Participants: Genetic and Non-Genetic Risk Factors.
  Nor Azian Abdul Murad et al. Genes 2023 14(3)
• Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms.
  Shuo-Ming Ou et al. BioData mining 2023 16(1) 8
• Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study.
  Madeleine L Smith et al. EBioMedicine 2023 90104503
• The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population.
  Lauren E Wedekind et al. Diabetologia 2023
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.
  Shruthi Chari et al. Artificial intelligence in medicine 2023 137102498
• Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes.
  Muhammad Shoaib et al. Genetic epidemiology 2023 2
• Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.
  Marie-Christine Fritzsche et al. Frontiers in genetics 2023 141098439
• Polygenic Risk of Prediabetes, Undiagnosed Diabetes, and Incident Type 2 Diabetes Stratified by Diabetes Risk Factors.
  Xiaonan Liu et al. Journal of the Endocrine Society 2023 7(4) bvad020
• The necessity of incorporating non-genetic risk factors into polygenic risk score models.
  Sipko van Dam et al. Scientific reports 2023 13(1) 1351
• Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges.
  Shuai Yuan et al. Diabetologia 2023
• An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial.
  You-Bin Lee et al. Diabetes care 2023
• ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study.
  Miao Hui et al. Journal of clinical medicine 2023 12(4)
• Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes.
  Chia-Tien Hsu et al. International journal of environmental research and public health 2023 20(4)
• Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study.
  Ilaria Gandin et al. PloS one 2023 18(2) e0281878
• Association of Hypertensive Disorders of Pregnancy With Future Cardiovascular Disease.
  Bilal Rayes et al. JAMA network open 2023 2 (2) e230034
• Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning.
  Hind Alamro et al. Frontiers in endocrinology 2023 131084656
• DNA methylation age acceleration is associated with risk of diabetes complications.
  Valentin Max Vetter et al. Communications medicine 2023 3(1) 21
• Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.
  Xiaomin Fu et al. Frontiers in endocrinology 2023 131061507
• Trialing precision medicine for type 2 diabetes
  SJ Pilla et al, Nature Medicine, February 6, 2023
• Loci for insulin processing and secretion provide insight into type 2 diabetes risk.
  K Alaine Broadaway et al. American journal of human genetics 2023
• Health in Our Hands: diabetes and substance use education through a new genomic framework for schools and communities.
  Modell Stephen M et al. Journal of community genetics 2023 1-15
• Prevalence of Diabetes and Its Association with Atherosclerotic Cardiovascular Disease Risk in Patients with Familial Hypercholesterolemia: An Analysis from the Hellenic Familial Hypercholesterolemia Registry (HELLAS-FH).
  Boutari Chrysoula et al. Pharmaceuticals (Basel, Switzerland) 2023 16(1)
• Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence.
  Ding Zhongao et al. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 2023
• Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases.
  Kiiskinen Tuomo et al. Nature medicine 2023
• The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study.
  Elashi Asma A et al. International journal of molecular sciences 2023 24(1)
• Machine Learning Approach to Drug Treatment Strategy for Diabetes Care.
  Fujihara Kazuya et al. Diabetes & metabolism journal 2023
• Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.
  Mansoori Amin et al. Scientific reports 2023 13(1) 663
• Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations.
  Sarnowski Chloé et al. Circulation. Genomic and precision medicine 2023 e003858
• Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes.
  Tomofuji Yoshihiko et al. Communications medicine 2023 3(1) 5
• Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes.
  Chang Lisa et al. Biological research for nursing 2023 10998004221147513
• Attitudes among Parents towards Return of Disease-Related Polygenic Risk Scores (PRS) for Their Children.
  Terek Shannon et al. Journal of personalized medicine 2022 12(12)
• Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study.
  Padilla-Martinez Felipe et al. International journal of molecular sciences 2022 23(24)
• Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials.
  Dawed Adem Y et al. The lancet. Diabetes & endocrinology 2022 11(1) 33-41
• MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus.
  Xu Zhigao et al. Frontiers in bioengineering and biotechnology 2022 101082794
• Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics.
  Li Mengting et al. Frontiers in public health 2022 101000622
• Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study.
  Hahn Seok-Ju et al. EBioMedicine 2022 86104383
• Risk of type 2 diabetes and KCNJ11 gene polymorphisms: a nested case-control study and meta-analysis.
  Moazzam-Jazi Maryam et al. Scientific reports 2022 12(1) 20709
• Predicting hypertension onset from longitudinal electronic health records with deep learning.
  Datta Suparno et al. JAMIA open 2022 5(4) ooac097
• Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach.
  Morgan-Benita Jorge et al. Diagnostics (Basel, Switzerland) 2022 12(11)
• The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.
  Qian Xu et al. Frontiers in public health 2022 101025271
• Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients.
  Kanda Eiichiro et al. Scientific reports 2022 12(1) 20012

More