Youth-onset type 2 diabetes (T2D) is a growing public health concern. Its genetic basis and relationship to other forms of diabetes are largely unknown. To gain insight into the genetic architecture and biology of youth-onset T2D, we analyzed exome sequences of 3,005 youth-onset T2D cases and 9,777 ancestry matched adult controls. We identified (a) monogenic diabetes variants in 2.1% of individuals; (b) two exome-wide significant ( < 4.3×10) common coding variant associations (in and ); (c) three exome-wide significant ( < 2.5×10) rare variant gene-level associations (, , ); and (d) rare variant association enrichments within 25 gene sets broadly related to obesity, monogenic diabetes, and β-cell function. Many association signals were shared between youth-onset and adult-onset T2D but had larger effects for youth-onset T2D risk (1.18-fold increase for common variants and 2.86-fold increase for rare variants). Both common and rare variant associations contributed more to youth-onset T2D liability variance than they did to adult-onset T2D, but the relative increase was larger for rare variant associations (5.0-fold) than for common variant associations (3.4-fold). Youth-onset T2D cases showed phenotypic differences depending on whether their genetic risk was driven by common variants (primarily related to insulin resistance) or rare variants (primarily related to β-cell dysfunction). These data paint a picture of youth-onset T2D as a disease genetically similar to both monogenic diabetes and adult-onset T2D, in which genetic heterogeneity might be used to sub-classify patients for different treatment strategies.
Purpose Of Review: Obesity is recognized as a "gateway" chronic, progressive disease of dysfunctional adipocytes. Glucagon-like peptide-1 receptor agonist-based therapies (GLP1BTs), including glucagon-like peptide-1 receptor agonists (GLP-1 RAs) with/without glucose-dependent insulinotropic polypeptide (GIP), have demonstrated clinically significant weight loss and health gains in adults, hence interest in using them in younger and older people. Therefore, reviewing the role of GLP1BTs in these populations is pertinent and timely.
OF WHAT IS NEW/DIFFERENT Updates since the 2022 ISPAD guidelines on this topic include: • Diagnostic algorithm for youth with new onset type 2 diabetes (T2D). • Algorithms and tables for treatment, management, and assessment of co-morbidities and complications. • Recommendations on recently approved pharmacologic therapies for the treatment of youth-onset T2D and management strategies.
Importance: Whether COVID-19 contributes to youth-onset diabetes is controversial, and research in Asia is lacking.
Objective: To explore the incidence and severity of diabetes among youths during the COVID-19 pandemic in South Korea.
Design, Setting, And Participants: This cohort study used claims data for January 1, 2017, through February 28, 2022, from the National Health Insurance Service database in South Korea.
Objective: There has been little to no qualitative research done with adolescents and young adults (AYA) with type 2 diabetes (T2D) that can guide creation of interventions for this demographic. Using qualitative research methods, a novel mind-body intervention called Intervention for Early Onset Type 2 Diabetes (INTEND) has been developed for AYA aged 15 to 20 years, with the goal of improving self-management and coping skills, by enhancing routine care with augmented education coupled with mind-body skills.
Method: Qualitative interviews with AYA 15 to 20 years of age with T2D, their parents, and professionals caring specifically for this population were done through a focus group model.
Key Points: Proteomics analyses identified seven proteins predictive of time to development of albuminuria among youth with type 2 diabetes in the Treatment Options for Type 2 Diabetes in Adolescents and Youth cohort, 118 proteins predictive of time to development of hyperfiltration, and three proteins predictive of time to rapid eGFR decline. Seven proteins were predictive of all three outcomes (SEM4A, PSB3, dihydroxyphenylalanine decarboxylase, C1RL1, T132A, pyruvate carboxylase, and C1-esterase inhibitor) and have been implicated in immune regulatory mechanisms, metabolic dysregulation, proteostasis, and cellular signaling pathways. Elastic net Cox proportional hazards model identified distinct multiprotein signatures (38–68 proteins) of time to albuminuria, hyperfiltration, and rapid eGFR decline with concordance for models with clinical covariates and selected proteins between 0.
