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: Oct 19, 2021
spot light Spotlight

Innovative new model predicts glucose levels without poking or prodding
L Wedland et al, NPJ Digital Medicine, August 20, 2021

Non-invasive glucose monitoring once seemed like an impossible challenge, but today’s technology and the incredible work of Bent et al. promise a future in which daily fingersticks are obsolete. The new challenge will be realizing that promise in an accessible and practical way that benefits all patients.

Preventing type 1 diabetes in childhood.
Dayan Colin M et al. Science (New York, N.Y.) 2021 7 (6554) 506-510

Type 1 diabetes (T1D) is an autoimmune disease in which the insulin-producing ß cells of the pancreas are destroyed by T lymphocytes. Recent studies have demonstrated that monitoring for pancreatic islet autoantibodies, combined with genetic risk assessment, can identify most children who will develop T1D when they still have sufficient ß cell function to control glucose concentrations without the need for insulin.

Immunotherapy: Building a bridge to a cure for type 1 diabetes.
Bluestone Jeffrey A et al. Science (New York, N.Y.) 2021 7 (6554) 510-516

Over the past two decades, research has identified multiple immune cell types and soluble factors that destroy insulin-producing ß cells. These insights into disease pathogenesis have enabled the development of therapies to prevent and modify T1D. In this review, we highlight the key events that initiate and sustain pancreatic islet inflammation in T1D, the current state of the immunological therapies, and their advantages for the treatment of T1D.

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients
Y Deng et al, NPJ Digital Medicine, July 14, 2021

We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5?min to 1?h.

news Latest News and Publications
Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning. External Web Site Icon
Kumar Deepak et al. Sensors (Basel, Switzerland) 2021 21(19)
Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks. External Web Site Icon
Chen Hsing-Chung et al. Sensors (Basel, Switzerland) 2021 21(19)
Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes. External Web Site Icon
Salna Ilze et al. Journal of diabetes and its complications 2021 108072
Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications. External Web Site Icon
Butt Umair Muneer et al. Journal of healthcare engineering 2021 20219930985
SARS-CoV-2 driving rapid change in adult cystic fibrosis services: the role of the clinical nurse specialist. External Web Site Icon
Dunk Rachel, et al. BMJ open quality 2021 0 0. (4)
Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas. External Web Site Icon
Dawed Adem Y et al. Diabetes care 2021
Effect of Digital Health Among People With Type 2 Diabetes Mellitus During the COVID-19 Pandemic in Japan. External Web Site Icon
Sankoda Akiko, et al. Journal of diabetes science and technology 2021 0 0. 19322968211050040
MBL deficiency-causing B allele (rs1800450) as a risk factor for severe COVID-19. External Web Site Icon
Speletas Matthaios, et al. Immunobiology 2021 0 0. (6) 152136
Corrigendum: ACE Gene Variants Rise the Risk of Severe COVID-19 in Patients With Hypertension, Dyslipidemia or Diabetes: A Spanish Pilot Study. External Web Site Icon
Íñiguez María, et al. Frontiers in endocrinology 2021 0 0. 771445
Effective methods of diabetic retinopathy detection based on deep convolutional neural networks. External Web Site Icon
Gu Yunchao et al. International journal of computer assisted radiology and surgery 2021
Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records. External Web Site Icon
Mantena Sreekar et al. Journal of clinical monitoring and computing 2021
Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis. External Web Site Icon
Nadarajah Ramesh et al. Heart (British Cardiac Society) 2021
Comparison of Multidrug Use in the General Population and among Persons with Diabetes in Denmark for Drugs Having Pharmacogenomics (PGx) Based Dosing Guidelines. External Web Site Icon
Westergaard Niels et al. Pharmaceuticals (Basel, Switzerland) 2021 14(9)
Family history recording in UK general practice: the lIFeLONG study. External Web Site Icon
Dineen Molly et al. Family practice 2021
Association of Vitamin D receptor gene polymorphisms and clinical/severe outcomes of COVID-19 patients. External Web Site Icon
Abdollahzadeh Rasoul, et al. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases 2021 0 0. 105098
Role of microRNAs in COVID-19 with implications for therapeutics. External Web Site Icon
Arghiani Nahid, et al. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2021 0 0. 112247
Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods. External Web Site Icon
Hara Konan et al. PloS one 2021 16(9) e0254394
Quantifying representativeness in randomized clinical trials using machine learning fairness metrics. External Web Site Icon
Qi Miao et al. JAMIA open 2021 4(3) ooab077
The Importance of Close Follow-Up in Patients with Early-Grade Diabetic Retinopathy: A Taiwan Population-Based Study Grading via Deep Learning Model. External Web Site Icon
Lee Chia-Cheng et al. International journal of environmental research and public health 2021 18(18)
Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. External Web Site Icon
Wong Kenneth Chi-Yin, et al. JMIR public health and surveillance 2021 0 0. (9) e29544


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.