Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content
Diabetes PHGKB

Specific PHGKB|Diabetes PHGKB|PHGKB

Last Posted: Jul 28, 2022
spot light Spotlight

Precision Medicine in Diabetes, Current Research and Future Perspectives
R Franceschi, J Per Medicine, July 28, 2022

Recently the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) have jointly released an expert opinion-based consensus report on precision medicine. The report defines precision diabetes medicine as “an approach to optimize the diagnosis, prediction, prevention, or treatment of diabetes by integrating multidimensional data, accounting for individual differences”, and it is characterized by six categories; precision diagnosis, precision therapeutics, precision prevention, precision treatment, precision prognosis and precision monitoring. Precision medicine in diabetes utilizes the individual’s unique genetic makeup, environment or context data (that can be collected from clinical records, wearable technology, genomics and other ‘omics data) and allows one to appreciate individual characteristics, differences, circumstances and preferences

Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations
T Ge et al, Genome Medicine, June 29, 2022

We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts.

Enhancing self-management in type 1 diabetes with wearables and deep learning
T Zhu et al, NPJ Digital Medicine, June 27, 2022

Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband, we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later.

Extending precision medicine tools to populations at high risk of type 2 diabetes.
Misra Shivani et al. PLoS medicine 2022 5 (5) e1003989

The generation of ethnicity-specific T2D PS for every subethnic group remains aspirational, as the large sample sizes needed to do this robustly are prohibitive (the genetic ancestry of the Indian subcontinent, for example, is more diverse than the whole of Europe. Thus, strategies to utilise existing scores derived from other populations, or leveraging multi-ancestry GWAS, have predominated. Attempts to apply a PS derived in a population of one ancestry to another ethnic group have shown variable performance.

news Latest News and Publications
Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis. External Web Site Icon
Edward Justin A et al. Cardiovascular diabetology 2022 21(1) 58
Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients. External Web Site Icon
Lee Jisoo et al. BMC medical informatics and decision making 2022 22(1) 115
Long-term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network. External Web Site Icon
Jaloli Mehrad et al. Journal of diabetes science and technology 2022 19322968221092785
Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach. External Web Site Icon
Chen Wansu et al. Journal of clinical gastroenterology 2022
Association of Lipoprotein (a) in Coronary Artery Disease in Young Individuals. External Web Site Icon
Patted Aishwarya et al. The Journal of the Association of Physicians of India 2022 70(4) 11-12
Development of a clinical polygenic risk score assay and reporting workflow. External Web Site Icon
Hao Limin et al. Nature medicine 2022
Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases. External Web Site Icon
Lu Tianyuan et al. Genetics in medicine : official journal of the American College of Medical Genetics 2022
Preconception leisure-time physical activity and family history of stroke and myocardial infarction associate with preterm delivery: findings from a Norwegian cohort. External Web Site Icon
Engen Tone et al. BMC pregnancy and childbirth 2022 22(1) 341
Risk Factor Analysis for Predicting the Onset of Rotator Cuff Calcific Tendinitis Based on Artificial Intelligence. External Web Site Icon
Dong Shengtao et al. Computational intelligence and neuroscience 2022 20228978878
Hospitalizations of Children Aged 5-11 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 2020-February 2022. External Web Site Icon
Shi Dallas S, et al. MMWR. Morbidity and mortality weekly report 2022 0 0. (16) 574-581
A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program. External Web Site Icon
Okada Akira et al. Diabetes care 2022
Artificial Intelligence Algorithms in Diabetic Retinopathy Screening. External Web Site Icon
Zafar Sidra et al. Current diabetes reports 2022
Prediction of COVID-19 severity from clinical and biochemical markers: a single-center study from Saudi Arabia. External Web Site Icon
Alshanbari H M, et al. European review for medical and pharmacological sciences 2022 0 0. (7) 2592-2601
Impact of the ABCD-GENE Score on Clopidogrel Clinical Effectiveness after PCI: A Multi-site, Real-world Investigation. External Web Site Icon
Thomas Cameron D et al. Clinical pharmacology and therapeutics 2022
Prevalence and Patient Outcomes of Adult Primary Hypercholesterolemia and Dyslipidemia in the UK: Longitudinal Retrospective Study Using a Primary Care Dataset from 2009 to 2019. External Web Site Icon
Bilitou Aikaterini et al. ClinicoEconomics and outcomes research : CEOR 2022 14189-203
Hospitalizations of Children Aged 5–11 Years with Laboratory-Confirmed COVID-19 — COVID-NET, 14 States, March 2020–February 2022
DS Shi et al, MMWR, April 19, 2022
A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus. External Web Site Icon
Lee Suehyun et al. Endocrinology and metabolism (Seoul, Korea) 2022
Development of COVID 19 vaccine: A summarized review on global trials, efficacy, and effectiveness on variants. External Web Site Icon
Prakash Satyendra, et al. Diabetes & metabolic syndrome 2022 0 0. (4) 102482
The relationship of early- and late-onset Alzheimer's disease genes with COVID-19. External Web Site Icon
Sirin Seda, et al. Journal of neural transmission (Vienna, Austria : 1996) 2022 0 0.
Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease. External Web Site Icon
David Satish Kumar et al. Journal of healthcare engineering 2022 20227378307


Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.