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: Sep 16, 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
Metabolomics can provide new insights into perinatal nutrition. External Web Site Icon
Pintus Roberta et al. Acta paediatrica (Oslo, Norway : 1992) 2021
Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning. External Web Site Icon
Elghamrawy Sally M, et al. International journal of imaging systems and technology 2021 0 0.
Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning. External Web Site Icon
Li Qingqing et al. Frontiers in physiology 2021 12714195
The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity. External Web Site Icon
Höskuldsdóttir Gudrún et al. BMC endocrine disorders 2021 21(1) 183
Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. External Web Site Icon
Banoei Mohammad M, et al. Critical care (London, England) 2021 0 0. (1) 328
Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C. External Web Site Icon
Feldman Theodore C et al. International journal of medical informatics 2021 154104562
Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome. External Web Site Icon
Yuan Siyi et al. Frontiers in medicine 2021 8720926
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. 688071
Susceptibility of Blood Groups Infection with COVID-19 Disease Among Sudanese Patients Suffering from Different Chronic Diseases. External Web Site Icon
Faroug Mohamed Malaz, et al. Pakistan journal of biological sciences : PJBS 2021 0 0. (7) 815-820
[Accuracy of artificial intelligence compared to trained medical technologists in diabetic retinopathy screening]. External Web Site Icon
Ibáñez-Bruron María C et al. Revista medica de Chile 2021 149(4) 493-500


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.