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Diabetes PHGKB

Specific PHGKB|Diabetes PHGKB|PHGKB

Last Posted: Mar 29, 2023
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Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model.
Roomasa Channa et al. NPJ digital medicine 2023 3 (1) 53

We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP.

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes.
Muhammad Shoaib et al. Genetic epidemiology 2023 2

We evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC]?=?0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC?=?0.792) and separation in MGI (AUC?=?0.686).

Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges.
Shuai Yuan et al. Diabetologia 2023 2

Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarize the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes.

Beyond genetic screening-functionality-based precision medicine in monogenic obesity.
Antje Körner et al. The lancet. Diabetes & endocrinology 2023 2 (3) 143-144

Most genes causing monogenic obesity are implicated in the central energy regulatory circuits of the leptin-melanocortin pathway. Even though monogenic obesity is still a rare disease entity, identifying these patients is important since there are now promising treatment options such as setmelanotide, a melanocortin receptor agonist, which was recently approved by the US Food and Drug Administration and European Medicines Agency


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Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec. External Web Site Icon
Rahimi Samira, et al. Annals of family medicine 2023 0 0. (21 Suppl 1)
Machine learning for post-acute pancreatitis diabetes mellitus prediction and personalized treatment recommendations. External Web Site Icon
Jun Zhang et al. Scientific reports 2023 13(1) 4857
Clinical Applications of Artificial Intelligence in Glaucoma. External Web Site Icon
Siamak Yousefi et al. Journal of ophthalmic & vision research 2023 18(1) 97-112
Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative. External Web Site Icon
Zang Chengxi, et al. Research square 2023 0 0.
A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails. External Web Site Icon
Yun-Nam Chan et al. Scientific reports 2023 13(1) 4184
Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. External Web Site Icon
Aretha Zhu et al. Journal of telemedicine and telecare 2023 1357633X231158832
The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review. External Web Site Icon
Arfan Ahmed et al. Journal of medical Internet research 2023 25e40259
The severity of COVID-19 in hypertensive patients is associated with mirSNPs in the 3' UTR of ACE2 that associate with miR-3658: In silico and in vitro studies. External Web Site Icon
Safdar Muhammad, et al. Journal of Taibah University Medical Sciences 2023 0 0. (5) 1030-1047
Association of triglyceride-glucose index with atherosclerotic cardiovascular disease and mortality among familial hypercholesterolemia patients. External Web Site Icon
Jun Wen et al. Diabetology & metabolic syndrome 2023 15(1) 39
Clinical Spectrum of LMNA-Associated Type 2 Familial Partial Lipodystrophy: A Systematic Review. External Web Site Icon
Antia Fernandez-Pombo et al. Cells 2023 12(5)
Hyperglycemia in Turner syndrome: Impact, mechanisms, and areas for future research. External Web Site Icon
Cameron Mitsch et al. Frontiers in endocrinology 2023 141116889
Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. External Web Site Icon
Yang Yang et al. European radiology 2023
Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms. External Web Site Icon
Shuo-Ming Ou et al. BioData mining 2023 16(1) 8
Pathways of Coagulopathy and Inflammatory Response in SARS-CoV-2 Infection among Type 2 Diabetic Patients. External Web Site Icon
Akácsos-Szász Orsolya-Zsuzsa, et al. International journal of molecular sciences 2023 0 0. (5)
Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders. External Web Site Icon
Waszczuk Monika A, et al. PloS one 2023 0 0. (3) e0282271
CIC 2022: Identifying high need primary care patients using nursing knowledge and machine learning methods. External Web Site Icon
Sharon Hewner et al. Applied clinical informatics 2023
Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms. External Web Site Icon
Ning Wang et al. Journal of diabetes 2023
Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study. External Web Site Icon
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. External Web Site Icon
Lauren E Wedekind et al. Diabetologia 2023
Unmet needs in clinical trials in CKD: questions we have not answered and answers we have not questioned. External Web Site Icon
Adeera Levin et al. Clinical kidney journal 2023 16(3) 437-441
Artificial intelligence and body composition. External Web Site Icon
Prasanna Santhanam et al. Diabetes & metabolic syndrome 2023 17(3) 102732
Identification of High Likelihood of Dementia in Population-Based Surveys using Unsupervised Clustering: a Longitudinal Analysis. External Web Site Icon
Amin Gharbi-Meliani et al. medRxiv : the preprint server for health sciences 2023
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes. External Web Site Icon
Shruthi Chari et al. Artificial intelligence in medicine 2023 137102498
Safety of COVID-19 Vaccines among Patients with Type 2 Diabetes Mellitus: Real-World Data Analysis. External Web Site Icon
Kim Hye Jun, et al. Diabetes & metabolism journal 2023 0 0.
Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges. External Web Site Icon
Marie-Christine Fritzsche et al. Frontiers in genetics 2023 141098439
Identification of individuals at high-risk for pancreatic cancer using a digital patient-input tool combining family cancer history screening and new-onset diabetes. External Web Site Icon
Derk C F Klatte et al. Preventive medicine reports 2023 31102110
Polygenic Risk of Prediabetes, Undiagnosed Diabetes, and Incident Type 2 Diabetes Stratified by Diabetes Risk Factors. External Web Site Icon
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. External Web Site Icon
Sipko van Dam et al. Scientific reports 2023 13(1) 1351
Risk of Underlying Diseases and Effectiveness of Drugs on COVID-19 Inpatients Assessed Using Medical Claims in Japan: Retrospective Observational Study. External Web Site Icon
Mitsushima Shingo, et al. International journal of general medicine 2023 0 0. 657-672
The Impact of LEP rs7799039 Polymorphism and Obesity on the Severity of Coronavirus Disease-19. External Web Site Icon
Mohamed Amal Ahmed, et al. Diabetes, metabolic syndrome and obesity : targets and therapy 2023 0 0. 515-522

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About Diabetes PHGKB

Diabetes PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to diabetes...more

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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.

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