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Cancer survivors have an increased risk of developing Type 2 diabetes compared to the general population. Patients treated with cisplatin, a common chemotherapeutic agent, are more likely to develop metabolic syndrome and Type 2 diabetes than age- and sex-matched controls. Surprisingly, the impact of cisplatin on pancreatic islets has not been reported. Our study aimed to determine if mouse islet function is adversely affected by systemic (in vivo) or direct (in vitro) exposure to cisplatin. In vivo cisplatin exposure led to deficits in glucose-stimulated plasma insulin levels in both male and female mice, despite no differences in glucose tolerance. In vitro cisplatin exposure to mouse islets dysregulated insulin release and reduced oxygen consumption in a non-sex specific manner. When shifting our focus to male mouse islets, cisplatin altered the expression of genes related to insulin production, oxidative stress, and the Bcl-2 family as early as 6-hours post-exposure. Genome-wide expression analysis confirmed the pronounced downregulation of genes within the insulin secretion pathway in cisplatin-exposed mouse islets. Data from 3 human organ donors confirms that the detrimental effects of cisplatin on insulin secretion and gene expression are reproduced in human islets. Our findings indicate that cisplatin exposure causes significant defects in insulin secretion and may have lasting effects on islet health., (© 2025 by the American Diabetes Association.)

Individuals with excessive adipose tissue and type 2 diabetes mellitus (T2DM) face a heightened risk of cardiovascular morbidity and mortality. Metabolic surgery is an effective therapy for people with severe obesity to achieve significant weight loss. Additionally, metabolic surgery improves blood glucose levels and can lead to T2DM remission, reducing major adverse cardiovascular outcomes (MACE). Glucagon-like peptide 1 (GLP-1) receptor agonists (GLP-1RAs) are a class of medication that effectively reduce body weight and MACE in patients with T2DM. This review explores the potential mechanisms underlying the cardioprotective benefits of metabolic surgery and GLP-1RA-based therapies and discusses recent evidence and emerging therapies in this dynamic area of research., Competing Interests: Declaration of interests The authors have no interests to declare., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca 2+ -ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community., Competing Interests: Declaration of Interests ALG’s spouse is an employee of Genentech and holds stock options in Roche AP serves on the Scientific Advisory Committee for Encellin Inc. JX is founder of XiaLab Analytics, which provides omics data science training and support. All other authors confirm no relevant interests to declare.

Aims: Severe aortic valve stenosis (AS) is defined by an aortic valve area (AVA) <1 cm2 or an AVA indexed to body surface area (BSA) <0.6 cm/m2, despite little evidence supporting the latter approach and important intrinsic limitations of BSA indexation. We hypothesized that AVA indexed to height (H) might be more applicable to a wide range of populations and body morphologies and might provide a better predictive accuracy., Methods and Results: In 1298 patients with degenerative AS and preserved ejection fraction from three different countries and continents (derivation cohort), we aimed to establish an AVA/H threshold that would be equivalent to 1.0 cm2 for defining severe AS. In a distinct prospective validation cohort of 395 patients, we compared the predictive accuracy of AVA/BSA and AVA/H. Correlations between AVA and AVA/BSA or AVA/H were excellent (all R2 > 0.79) but greater with AVA/H. Regressions lines were markedly different in obese and non-obese patients with AVA/BSA (P < 0.0001) but almost identical with AVA/H (P = 0.16). AVA/BSA values that corresponded to an AVA of 1.0 cm2 were markedly different in obese and non-obese patients (0.48 and 0.59 cm2/m2) but not with AVA/H (0.61 cm2/m for both). Agreement for the diagnosis of severe AS (AVA < 1 cm2) was significantly higher with AVA/H than with AVA/BSA (P < 0.05). Similar results were observed across the three countries. An AVA/H cut-off value of 0.6 cm2/m [HR = 8.2(5.6-12.1)] provided the best predictive value for the occurrence of AS-related events [absolute AVA of 1 cm2: HR = 7.3(5.0-10.7); AVA/BSA of 0.6 cm2/m2 HR = 6.7(4.4-10.0)]., Conclusion: In a large multinational/multiracial cohort, AVA/H was better correlated with AVA than AVA/BSA and a cut-off value of 0.6 cm2/m provided a better diagnostic and prognostic value than 0.6 cm2/m2. Our results suggest that severe AS should be defined as an AVA < 1 cm2 or an AVA/H < 0.6 cm2/m rather than a BSA-indexed value of 0.6 cm2/m2., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)