Roux-en-Y gastric bypass (RYGB) procedure offers notable efficacy in treating type 2 diabetes mellitus (T2DM). The research focused on the glutamate receptor gene Grin3a in mediating the metabolic benefits of RYGB, particularly regarding lipid metabolism.
Public GEO datasets (GSE2508, GSE12050, and GSE35411) were analysed to assess Grin3a expression and its associated pathways. To assess RYGB efficacy, a T2DM model was induced in rats, with Grin3a knockdown, and AMPK activation. Metabolic parameters, insulin resistance, lipid profiles, adipose tissue morphology, and mitochondrial function in epididymal white adipose tissue (eWAT) were examined. The influence of Grin3a on lipid metabolism and the CaMKKβ/AMPK axis was evaluated in vitro using 3T3-L1 adipocytes.
Bioinformatic analyses revealed that Grin3a was diminished in visceral adipose tissue of obese or diabetic models and up-regulated following RYGB. Its expression was associated with lipid metabolism and oxidative phosphorylation pathways. In vivo, RYGB restored Grin3a levels and improved insulin sensitivity, lipid profiles, mitochondrial function, and adipose browning, while Grin3a knockdown attenuated these effects and AMPK activation partially reversed them. In vitro, Grin3a regulated lipid metabolism and mitochondrial function in adipocytes via the CaMKKβ/AMPK pathway.
RYGB improves lipid metabolism and mitochondrial homeostasis in T2DM by activating the Grin3a–CaMKKβ/AMPK axis, offering a potential target for metabolic intervention beyond surgery.
Continuous glucose monitoring (CGM) has become the standard of care for people with type 1 diabetes (T1D) and some people with type 2 diabetes (T2D). However, there is a lack of data regarding CGM use in people with T2D and chronic kidney disease (CKD), who are at increased risk of hypoglycaemia. We assess the use of CGM and glucose outcomes in this cohort.
Retrospective, observational analysis of adults on CGM attending a tertiary diabetes renal clinic between January 2023 and September 2024. People with T2D on multiple daily insulin injections (defined as 2 or more insulin injections per day) and CKD stage 3–5 (eGFR <60, on renal replacement therapy or post-renal transplant) were included. HbA1c was assessed pre- and post-initiation of CGM. CGM metrics were analysed for percentage times in ranges and other glycaemic metrics.
A total of 177 adults (median (interquartile range)) aged 64.0 (56.0–71.0) years were included. Time below range (<3.9 mmol/L) was significantly reduced (median (interquartile range)) from 1.0 (0.0–2.0)% to 0.0 (0.0–1.0)% following CGM initiation (p = 0.007). Hypoglycaemia leading to an insulin dose reduction was revealed by CGM in 91/177 participants (51.4%). Median HbA1c pre- and post-CGM was 64.0 (53.0–79.0) mmol/mol (8.0, 7.0–9.4%) and 62.0 (51.8–73.0) mmol/mol (7.8, 6.9–8.8%), respectively (p < 0.001). HbA1c reduction was observed in 114/177 participants (64.4%).
CGM use was associated with the identification of hypoglycaemia and improvements in HbA1c in people with T2D and CKD. These real-world data demonstrate the importance of CGM technology to high-risk individuals with diabetes and CKD.
Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learning could allow for better therapeutic strategies and improved treatment efficiency. This scoping review aimed to comprehensively review the machine learning models used to predict the need for pharmacological therapy in GDM.
Four electronic databases—Embase, Medline, IEEE Xplore and Web of Science—were searched for publications between 1 July 2007 and 31 August 2024. Studies predicting pharmacological therapy for GDM using machine learning were included. The Joanna Briggs Institute and PRISMA-ScR checklist was followed, and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to assess quality.
Included were 17 studies presenting 44 models, 61.4% (27/44) predicted any pharmacological therapy use and 38.6% (17/44) predicted insulin use alone. All were binary classifiers, and logistic regression was typically used. The overall area under the receiver operating curve had a median of 0.75. Common clinical variables were found to be predictors, such as history of GDM, gestational week at GDM diagnosis, pregestational body mass index, maternal age, HbA1c, fasting and 1 h glucose from 75 g oral glucose tolerance test. Though 65.9% of models were validated, there was a lack of external validation. There was no evidence of clinical application of the models.
Logistic regression with common clinical variables was often used to predict pharmacological therapy for GDM. Few models were externally validated or clinically applicable.
The aim of this study was to examine the psychometric properties and factor structure of the Danish translation of Diabetes Eating Problem Survey – Revised (DEPS-R) among adolescents with type 1 diabetes (T1D).
A total of 131 adolescents (53% women) with T1D aged 11–19 years completed DEPS-R and Youth Eating Disorder Examination Questionnaire (YEDE-Q). Additional anthropometric, biochemical, and medical data were obtained from medical records. Exploratory Factor Analysis was performed to examine the factor structure of DEPS-R.
The DEPS-R demonstrated good internal consistency (Cronbach α = 0.87) and was significantly correlated with YEDE-Q (r = 0.83; p < 0.01), HbA1c (r = 0.35; p < 0.01), zBMI (r = 0.35; p < 0.01), and age (r = 0.20; p < 0.05), indicating high convergent validity. The median DEPS-R score was 10 [5–18] for the entire sample; however, it was significantly higher among women (15 [9–21]) than men (6 [4–10]). Factor analysis revealed a three-factor structure accounting for 58% of the variance and indicated that item 4 contributed less strongly to the overall score.
This study supports the validity of DEPS-R as a screening tool for detecting the risk of disordered eating in adolescents with T1D but challenges the relevance of item 4. Future research is needed on updating the original DEPS-R, including a sensitivity analysis of DEPS-R in detecting the risk of serious eating disorder-related behaviour in T1D.
The interplay between angiogenic genetic variants and metabolic-vascular comorbidities in type 2 diabetes mellitus (T2DM) remains poorly elucidated. This cohort study investigated the synergistic effects of VEGFA-centric polymorphisms on hypertension (HTN) and dyslipidaemia (DYS) susceptibility in T2DM.
We enrolled 440 individuals stratified into three groups: T2DM-only (n = 258), T2DM+HTN (T2MH, n = 130) and T2DM+HTN+DYS (T2MH-DYS, n = 52). Genotyping of VEGFA rs3025020, ANGPT2 rs11137037/rs2442598, TGFB1 rs1800469 and AGTR1 rs5182 was performed using ligase-mediated hybridization and multiplex PCR. Phenotypic profiling included metabolic, haemodynamic and inflammatory biomarkers.
The T2MH-DYS group exhibited severe metabolic dysregulation, with elevated fasting glucose (12.60 ± 7.62 vs. 7.93 mmol/L, p = 0.017), triglycerides (median 2.48 vs. 1.68 mmol/L, p < 0.001) and systemic inflammation (CRP: 2.65 vs. 1.79 mg/L, p = 0.016). VEGFA rs3025020-TT genotype frequency was significantly enriched in T2MH-DYS (17.3% vs. 3.9% in T2DM, recessive model p < 0.001), conferring a 3.49-fold comorbidity risk (95% CI: 1.10–11.14, p = 0.034). ANGPT2 rs2442598-CC genotype demonstrated borderline protective effects (OR = 0.46, p = 0.057), while AGTR1 rs5182-TT correlated with milder HTN (Grade I: 72.2% vs. Grade III: 46.6%, p = 0.008). Haplotype analysis revealed synergistic risks: C-C-T (AGTR1-ANGPT2-VEGFA) increased T2MH-DYS susceptibility 3.58-fold (p = 0.0019), whereas C-T-C exhibited protection (OR = 0.333, p = 0.013).
These findings underscored VEGFA rs3025020 as a genetic hub coordinating multilocus angiogenic dysregulation, driving HTN and DYS pathogenesis in T2DM. Our results highlight the potential for genotype-guided anti-angiogenic therapies to mitigate metabolic–vascular deterioration in high-risk diabetic populations.
Severe type 1 diabetes (T1D) poses significant challenges due to absolute insulin deficiency, leading to glycemic instability and severe hypoglycaemic events (SHEs). Despite advancements in insulin therapy, some people experience profound instability and impaired quality of life. This article aims to highlight Lantidra (donislecel), the first FDA-approved allogeneic pancreatic islet cellular therapy, as a novel therapeutic option. The manuscript also discusses Lantidra's safety profile, regulatory pathway, and its therapeutic role within contemporary T1D management frameworks.
Isolation, purification, and transplantation protocols of allogeneic pancreatic islets were derived from the pivotal UIH-001 and UIH-002 clinical trials.
Pivotal clinical trials in individuals with brittle T1D and hypoglycaemic unawareness demonstrated that a significant proportion achieved insulin independence, with some lasting over 5 years. This was accompanied by a reduction in SHEs and improved HbA1c. The mechanism relies on islet engraftment in the liver, leading to neovascularization and functional beta-cell (β-cell) contribution to glucose homeostasis. However, the therapy carries substantial risks, including complications from the intraportal infusion and lifelong systemic immunosuppression. Consequently, careful patient selection and meticulous post-transplant management are critical.
Lantidra is currently reserved for highly selected adult T1D people with problematic hypoglycaemia and labile glycaemia unresponsive to other optimized therapies, where benefits may outweigh risks. Future research aims to improve graft survival, minimize immunosuppression, and explore alternative cell sources to expand β-cell replacement therapies.
To explore attitudes of people living with diabetes (PLD) and healthcare professionals (HCP) towards the use of automated retinal image analysis systems using artificial intelligence (AI) in NHS Diabetic Eye Screening Programmes (DESP) and how these perceptions vary by sociodemographic subgroups.
Two anonymous online surveys (28 questions for PLD and 21 for HCP) were developed to assess attitudes towards AI. Data were collected from four English DESPs, diabetes charities and patient groups between September and December 2023. Likert-scale responses were analysed using regression to examine subgroup differences.
A total of 1577 PLD and 262 HCP participated. Fifty-eight per cent of PLD believed AI would perform equally well in all subgroups, compared with 32% of HCP. Seventy-one per cent of HCP disagreed that AI could replace human grading, and 81% of PLD felt humans should remain responsible for screening outcomes. Both groups supported AI's efficiency but had concerns about data security, trust, job security and who would be responsible for AI errors. Linear regression of Likert scores showed women were less accepting of AI; PLD of Black and Asian ethnicities were more cautious of data security and impact on screening experience. HCP of Asian ethnicity generally held more negative views across themes. Those using more online applications had more positive views towards AI.
While both PLD and HCP recognise AI's potential benefits, concerns regarding security, job impact and errors highlight the need for targeted outreach based on sociodemographic factors.
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