The status of care for youth with type 1 diabetes within and coming from humanitarian crises settings: a narrative review.

Background: Humanitarian crises bring unique, and potentially growing challenges to people with type 1 diabetes (T1D). We aimed to determine, in youth with T1D (mean age (± 1SD) 0-17.9 years) within and coming from humanitarian crises settings (HCS), the reported prevalence that meet international consensus targets for glycaemic, blood pressure and lipid management, and incidence of severe hypoglycaemia or diabetic ketoacidosis.

Trends in the incidence of young-adult-onset diabetes by diabetes type: a multi-national population-based study from an international diabetes consortium.

Background: Population-based incidence data on young-adult-onset type 1 diabetes and type 2 diabetes are limited. We aimed to examine secular trends in the incidence of diagnosed type 1 diabetes and type 2 diabetes with an age of onset between 15 and 39 years.

Methods: In this multicountry aggregate data analysis, we assembled eight administrative datasets from high-income jurisdictions and countries (Australia, Denmark, Finland, Hungary, Japan, Scotland, South Korea, and Spain [Catalonia]) that had appropriate data available from an international diabetes consortium (GLOBODIAB) describing incidence by diabetes type among people aged 15-39 years from 2000 to 2020.

Management of Pain in People Living With Chronic Limb Threatening Ischemia: Highlights From a Rapid Umbrella Review.

Peripheral artery disease is a complex health condition. It is associated with atherosclerotic occlusive lesions in the arteries limiting normal blood flow, mostly involving the lower extremities, leading to chronic limb-threatening ischemia (CLTI). Chronic unrelenting ischemic leg pain can be debilitating and distressing, contributing to poor health-related quality of life.

The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus.

Background: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process.