Diabetes Research and Clinical Practice

Objective: Type 1 diabetes is related to an increased risk of structural brain alterations, cerebral microbleeds (CMBs), and cognitive deficits. We explored brain volumes and their direct and combined associations with CMBs on cognitive performance in middle-aged individuals with type 1 diabetes. Research Design and Methods: Adults with type 1 diabetes (n=163; mean age 46+/-8 years; diabetes duration 31+/-10 years; 53% women) and 48 matched controls underwent brain MRI and clinical and neuropsychological evaluations. Volumetric MRI measures adjusted to intracranial volume included total brain volume (TBV), white matter volume (WMV), and total volumes of cortex, thalamus, hippocampus, nucleus accumbens, and choroid plexus. Results: Individuals with type 1 diabetes had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, and larger choroid plexus compared to controls (Cohen d=0.39-0.54). Those with type 1 diabetes and 3 or more CMBs had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, compared to those with 0-2 CMBs (Cohen d=0.54-0.92). We found no direct associations between brain volumes and processing speed or executive functions. However, TBV, WMV, nucleus accumbens, and choroid plexus volumes had significant negative synergistic interactions with CMBs on processing speed and executive functions (standardized betas: -0.61 to -0.51 and 0.54 to 0.75, FDR-corrected p=0.006-0.048). Conclusions: Smaller global and regional brain volumes and larger choroid plexus volumes were found in middle-aged individuals with type 1 diabetes compared to healthy controls. Together with CMB burden, structural brain volumetric alterations were associated with accelerated cognitive deficits.

BackgroundType II diabetes (T2DM) is one of the most prevalent metabolic disorders, and its multisystemic health consequences are widely known. Due to skeletal muscles ability to sequester a vast amount of glucose, muscle function and exercise have become a subject of much research into strategies to prevent and treat T2DM. Myokines are bioactive molecules released by muscle during contraction and involved in several biological processes such as metabolism, inflammation and behavior. Irisin, a recently discovered myokine, has been implicated in a vast array of physiological roles, including the ability to induce fat beiging. Since beige and brown fat both serve important roles in metabolic regulation, irisins role in the context of T2DM is the subject of ongoing investigations. MethodsWe systematically reviewed articles indexed in PubMed, Scopus and Web of Science that were published between 2011 and 2024, and compared circulating irisin levels in patients affected by T2DM and healthy subjects. As part of our systematic review of the literature, we performed meta-analysis of the data across all included articles, as well as stratified by body mass index (BMI), country of origin and by average irisin concentration in the control group. ResultsWe discovered great variability across the included studies in the average irisin levels detected, which spanned four orders of magnitude, hence the attempt at reducing variability by stratifying based on average levels in the control group. While the statistical power of our meta-analysis was decreased by the great variability in reported irisin concentrations, we nonetheless detected a consistent trend of decreased irisin concentration in T2DM patients compared with healthy controls, regardless of BMI, country of origin or average irisin concentration in the control group. ConclusionWith almost 60 articles included, ours is the first extensive systematic review and meta-analysis of irisin in T2DM, yet a highly statistically significant association between circulating irisin levels and T2DM could not be established due to the great variability of the data across include articles. Nonetheless we noticed a trend that is independent of BMI, suggesting a direct relationship between T2DM and irisin that is likely not secondary to diabetic sarcopenia. While our work encourages further research into irisins potential role in T2DM pathogenesis, the reproducibility of irisin detection methods in biological samples should be determined and standardized protocols should be made available to the research and clinical communities.

BackgroundThe coronavirus disease 2019 (Covid-19) spreads rapidly around the world. ObjectiveTo evaluate the association between comorbidities and the risk of death in patients with COVID-19, and to further explore potential sex-specific differences. MethodsWe analyzed the data from 18,465 laboratory-confirmed cases that completed an epidemiological investigation in Hubei Province as of February 27, 2020. Information on death was obtained from the Infectious Disease Information System. The Cox proportional hazards model was used to estimate the association between comorbidities and the risk of death in patients with COVID-19. ResultsThe median age for COVID-19 patients was 50.5 years. 8828(47.81%) patients were females. Severe cases accounted for 20.11% of the study population. As of March 7, 2020, a total of 919 cases deceased from COVID-19 for a fatality rate of 4.98%. Hypertension (13.87%), diabetes (5.53%), and cardiovascular and cerebrovascular diseases (CBVDs) (4.45%) were the most prevalent comorbidities, and 27.37% of patients with COVID-19 reported having at least one comorbidity. After adjustment for age, gender, address, and clinical severity, patients with hypertension (HR 1.55, 95%CI 1.35-1.78), diabetes (HR 1.35, 95%CI 1.13-1.62), CBVDs (HR 1.70, 95%CI 1.43-2.02), chronic kidney diseases (HR 2.09, 95%CI 1.47-2.98), and at least two comorbidities (HR 1.84, 95%CI 1.55-2.18) had significant increased risks of death. And the association between diabetes and the risk of death from COVID-19 was prominent in women (HR 1.69, 95%CI 1.27-2.25) than in men (HR 1.16, 95%CI 0.91-1.46) (P for interaction = 0.036). ConclusionAmong laboratory-confirmed cases of COVID-19 in Hubei province, China, patients with hypertension, diabetes, CBVDs, chronic kidney diseases were significantly associated with increased risk of death. The association between diabetes and the risk of death tended to be stronger in women than in men. Clinicians should increase their awareness of the increased risk of death in COVID-19 patients with comorbidities.

BackgroundParalytic ileus (PI), a condition characterized by reduced bowel motor activity without physical obstruction, can be affected by complications from type 2 diabetes (T2D) and anti-diabetic medications. It is unclear of the causal associations of glucagon-like peptide-1 receptor agonists (GLP-1RAs) with the risk of PI in the context of T2D management. MethodsTo investigate the causal relationship of GLP-1RAs with PI, we conducted a 2-sample mendelian randomization (MR) study based on summary statistics from genome-wide association studies (GWAS). Genetic variants in the GLP1R were identified as genetical proxies of GLP-1RAs by the glycemic control therapy, based on genetic associations with glycated hemoglobin (GWAS n=344,182) and T2D (ncases/controls=228,499/1,178,783). The effects of GLP-1RAs were estimated for PI risk (ncases/controls=517/182,423) using GWAS data from the FinnGen project. ResultsBased on MR analysis, GLP-1RAs are causally associated with a decreased risk of PI (OR per 1 mmol/mol decrease in glycated hemoglobin: 0.21; 95% confidence interval [CI]=0.06-0.69). The magnitude of these benefit exceeded those expected from improved glycemic control more generally. ConclusionsOur studys findings show that GLP-1RAs are causally associated with a lower risk for PI, which provides information to guide clinicians in the selection of appropriate therapies for individuals with T2D while mitigating the risk of developing PI. Investigating the underlying mechanisms that contribute to the lower PI risk associated with GLP-1RAs is essential for a deeper understanding of these associations.

Disposition index (DI) is an informative measure of {beta}-cell function adjusted for insulin resistance, but its assessment is procedurally demanding, requiring dynamic testing with timed sampling and insulin or C-peptide-based estimation of insulin sensitivity and secretion. A simple glucose-only metric derived from the oral glucose tolerance test (OGTT) could provide a practical approach to estimating DI. We developed the Recovery-Burden Index (RBI), a glucose-only geometric metric that quantifies post-peak glucose recovery relative to total glucose excursion during OGTT. Using densely sampled venous OGTT profiles with measured DI, RBI was evaluated for prediction of continuous DI by leave-one-out (LOO) cross-validated R2 and for discrimination of DI-defined {beta}-cell dysfunction by AUROC. Performance was compared with conventional glycemic metrics. RBI predicted continuous DI more accurately than conventional glycemic metrics, with LOO R2 of 0.43, Pearson r = 0.70, and Spearman{rho} = 0.75. RBI30-180 performed similarly, with cross-validated R2 of 0.42, Pearson r = 0.72, and Spearman{rho} = 0.75. RBI also discriminated DI-defined {beta}-cell dysfunction, with AUROC values of 0.90 for RBI and 0.91 for RBI30-180. Reduced sampling schedules preserved much of the RBI signal, whereas truncation at 120 min attenuated continuous DI prediction, supporting the contribution of late recovery-phase information. RBI extracts {beta}-cell-relevant information from the OGTT glucose profile using a single transparent glucose-only index. These findings highlight post-peak recovery as a key feature for estimating DI-associated {beta}-cell compensation and support further validation of RBI in extended or CGM-augmented OGTT settings.

Objective: To determine whether continuous glucose monitoring (CGM) identifies clinically relevant glycemic heterogeneity and subclinical end-organ alterations in adults without diabetes. Research Design and Methods: We analyzed 1,017 AI-READI Year 3 participants without diabetes (558 with normoglycemia and 459 with prediabetes by A1C). Fifty-two metrics from 10-day blinded CGM were reduced to nonredundant glycemic axes. Partial Spearman correlations between representative CGM metrics and clinical measures across 13 domains were adjusted for age, sex, and BMI and controlled for false discovery rate. CGM-derived subphenotypes were identified using unsupervised UMAP-HDBSCAN-based clustering. Results: Among 462 glycemic-clinical associations tested, 99 (21.4%) remained significant after false discovery rate correction. Hyperglycemia-related metrics, including mean glucose, time above range, and time in tight range, showed more associations than variability metrics. The strongest signals involved cardiometabolic, cardiovascular, and cognitive measures. Greater hyperglycemia and glucose excursions were associated with lower language performance, slower processing speed, and lower cognitive efficiency ({rho} {approx} -0.10 to -0.14; all P < 0.01). Clustering identified four reproducible glycemic subphenotypes: Healthy, Mild Hyperglycemia, High Variability, and Hyperglycemia. CGM phenotypes reclassified A1C-defined groups: 58.1% of participants with normoglycemia fell into dysglycemic phenotypes, whereas 18.8% of participants with prediabetes fell into more favorable phenotypes. The Hyperglycemia phenotype had the most adverse cardiometabolic profile and lower cognitive performance. Conclusions: In adults without diabetes, CGM revealed glycemic patterns associated with distinct subclinical alterations. CGM-based phenotyping may complement A1C for characterizing early dysglycemia and selecting individuals for longitudinal risk-stratification studies.

AIMSUsing machine learning integrated with clinical and metabolomic data to identify biomarkers associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR), and to improve the performance of DKD/DR detection models beyond traditional risk factors. METHODSWe examined a population-based cross-sectional sample of 2,772 adults with type 1 or type 2 diabetes from Singapore Epidemiology of Eye Diseases study (SEED, 2004-2011). LASSO logistic regression (LASSO) and gradient boosting decision tree (GBDT) were used to select markers of prevalent DKD (defined as an eGFR < 60ml/min/1.73m2) and prevalent DR (defined as an ETDRS severity level [&ge;] 20) from an expanded set of 19 established risk factors and 220 NMR-quantified circulating metabolites. Risk assessment models were developed based on the variable selection results and externally validated in UK Biobank (n=5,843, 2007-2010). Model performance (AUC with 95% CI, sensitivity, and specificity) of machine learning was compared to that of traditional logistic regression adjusted for age, gender, diabetes duration, HbA1c%, systolic BP, and BMI. RESULTSSEED participants had a median age of 61.7 years, with 49.1% female, 20.2% having DKD, and 25.4% having DR. UK Biobank participants had a median age of 61.0 years, with 39.2% female, 6.4% having DKD, and 5.7% having DR. Both algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with CVD, hypertension medication, and three metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density-lipoprotein); While DR was additionally associated with HbA1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional logistic regression in both internal (AUC: 0.832-0.838 vs. 0.743 for DKD, and 0.779-0.790 vs. 0.764 for DR) and external validation (AUC: 0.737-0.790 vs. 0.692 for DKD, and 0.778 vs. 0.760 for DR). CONCLUSIONSMachine-learned biomarkers suggested insulin resistance to be a primary factor associated with diabetic microvascular complications. Integrating machine learning with biomedical big data enabled biomarker discovery from a wide range of correlated variables, which may facilitate our understanding of the disease mechanisms and improve disease screening.

Type 2 diabetes mellitus (T2DM) is a global burgeoning health problem that increases the risk of atherosclerotic cardiovascular disease (ASCVD). Infiltration and oxidative modification of low-density lipoprotein (LDL) cholesterol in the arterial wall and chronic inflammation comprise central pathogenetic mechanisms in ASCVD. Scavenger receptors, particularly Stabilin-1 (Stab1) and Stabilin-2 (Stab2), are pivotal in the clearance of oxidized LDL (oxLDL) cholesterol and pro-atherogenic ligands from circulation. However, their role in atherosclerosis development in the spectrum of T2DM remains poorly characterized. We assessed circulating levels of Stab1, Stab2, and their ligands (TGFbI, Periostin and Reelin) in a cohort of 33 T2DM and 21 non-diabetic individuals, stratified by their atherosclerotic plaque burden as assessed by high-resolution vascular ultrasound. Associations between stabilins, their ligands and conventional cardiovascular risk factors were evaluated. Stab1 levels were significantly elevated in individuals with higher atherosclerotic plaque burden (p<0.05), while Reelin levels were marginally elevated, both in the total study cohort and among T2DM patients. Stab1 levels positively correlated with body mass index and inversely correlated with total cholesterol, LDL, and high-density lipoprotein (HDL) cholesterol levels. Our findings indicate that Stab1 may serve as a marker of dysregulated lipid metabolism and increased atherosclerotic plaque burden in individuals with T2DM. Larger prospective studies are warranted to establish the prognostic and potentially therapeutic value of Stab1 and to clarify its mechanistic role in diabetic atherosclerosis.

Prescription of sodium glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) among patients with guideline-directed indications remains limited with substantial inter-prescriber variability. In this prospective study of US adults, we used administrative claims database of individuals with type 2 diabetes and compelling indications for SGLT-2i and GLP-1RA to evaluate the impact of healthcare visits with certain specialty providers on the initiation of these medications. These specialties included family medicine, internal medicine, cardiology, endocrinology, and nephrology. Overall, 294,988 individuals eligible for SGLT-2i and 198,525 for GLP-1RA were identified. In 2019-2020, SGLT-2i and GLP-1RA were initiated in 10.4% and 16.7% of eligible individuals, respectively. After accounting for patient characteristics and comorbidities, healthcare visit with endocrinologists was associated with the highest rate of initiation of either drug across specialties (OR=2.16 [2.08-2.24] for SGLT-2i, and 2.76 [2.64-2.88] for GLP-1RA). Healthcare visits with cardiologists and with family medicine and internal medicine physicians were only modestly associated with initiation of SGLT-2i and GLP-1RA. The study highlights the need for broad education for expansion of the use of these medications rather than focus on dedicated specialty clinics.

Type 2 diabetes mellitus (T2DM) is a major health and economic burden because of the seriousness of the disease and its complications. Improvements in short- and long-term glycemic control is the goal of diabetes treatment. To investigate the longitudinal management of T2DM at Geisinger, we interrogated the electronic health record (EHR) information and identified a T2DM cohort including 125,477 patients using the Electronic Medical Records and Genomics Network (eMERGE) T2DM phenotyping algorithm. We investigated the annual anti-diabetic medication usage and the overall glycemic control using hemoglobin A1c (HbA1c). Metformin remains the most frequently medication despite the availability of the new classes of anti-diabetic medications. Median value of HbA1c decreased to 7% in 2002 and since remained stable, indicating a good glycemic management in Geisinger population. Using metformin as a pilot study, we identified three groups of patients with distinct HbA1c trajectories after metformin treatment. The variabilities in metformin response is mainly explained by the baseline HbA1c. The pharmacogenomic analysis of metformin identified a missense variant rs75740279 (Leu/Val) for STAU2 associated with the metformin response. This strategy can be applied to study other anti-diabeticmedications. Such research will facilitate the translational healthcare for better T2DM management.

ObjectiveTo investigate the relationship between history of metformin use and delirium risk, as well as long-term mortality. MethodsIn this retrospective cohort study, subjects recruited between January 2016 and March 2020 were analyzed. Logistic regression analysis was performed to investigate the relationship between metformin use and delirium. Log-rank analysis and Cox proportional hazards model were used to investigate the relationship between metformin use and 3-year mortality. ResultsThe data from 1404 subjects were analyzed. 242 subjects were categorized into a DM-without-metformin group, and 264 subjects were categorized into a DM-with-metformin group. Prevalence of delirium was 36.0% in the DM-without-metformin group, and 29.2% in the DM-with-metformin group. A history of metformin use reduced the risk of delirium in patients with DM (OR, 0.50 [95% CI, 0.32 to 0.79]) after controlling for age, sex, and dementia status, body mass index (BMI), and insulin use. The 3-year mortality in the DM-without-metformin group (survival rate, 0.595 [95% CI, 0.512 to 0.669]) was higher than in the DM-with-metformin group (survival rate, 0.695 [95% CI, 0.604 to 0.770]) (p=0.035). A history of metformin use decreased the risk of 3-year mortality after adjustment for age, sex, Charlson Comorbidity Index, BMI, history of insulin use, and delirium status (HR, 0.69 [95% CI, 0.48 to 0.98]). ConclusionsIt was found that metformin usage was associated with decreased delirium prevalence and lower 3-year mortality. The potential benefit of metformin on delirium risk and mortality were shown.

BackgroundType 1 diabetes mellitus (T1DM) is associated with elevated dementia risk, but the mechanisms are not well understood. Prior studies suggest that co-occurring diabetes-related complications and other comorbidities may further increase dementia risk, but these studies are few and typically have small samples. Whether diabetes-related complications and other comorbidities modify the effect of T1DM on dementia risk remains unclear. MethodsData are from participants of the All of Us (AoU) cohort ages [&ge;] 50 years, with complete baseline surveys, linked electronic health records (EHRs), and either T1DM or no DM. Enrollment began in 2017, with data available through October 2023, including information prior to enrollment in AoU. Incident dementia was identified based on ICD-9, ICD-10, and SNOMED codes in participants EHRs. Baseline clinical comorbidities (diabetes complications and eye diseases, other vascular and metabolic comorbidities, and mental health conditions) were also identified using participants EHRs, classifying each as present if at least one diagnostic code occurred on or before the baseline survey. ResultsAmong 232,429 participants (mean [SD] age 64.5 [9.0] years; 57.3% women), 2.3% had a T1DM diagnosis. Participants averaged 1.67 total comorbidities (SD = 2.08). T1DM and each comorbidity was associated with higher dementia incidence. T1DM was associated with higher dementia incidence among individuals with no comorbidities (HR = 1.77; 95% CI: 0.95-3.30), though the CI included 1. Each additional comorbidity increased risk (HR = 1.22; 95% CI: 1.19-1.26), with some evidence that the effect of T1DM differed by the number of comorbidities (HR = 0.94; 95% CI:0.86-1.01). The combined estimated effect of T1DM and most comorbidities was less than multiplicative. Depression was an exception; the dementia HR for individuals with both T1DM and depression (HR = 5.47; 95% CI: 4.23, 7.08) roughly reflected what would have been expected based on the HR for T1DM (HR = 2.01; 95% CI: 1.38, 2.92) times the HR for depression among those without T1DM (HR = 2.55; 95% CI: 2.22, 2.95). ConclusionOur findings suggest that T1DM and common comorbidities independently increase dementia risk, though their combined effects are generally less than multiplicative. However, depression in the context of T1DM is associated with major elevations in dementia risk.

Background Data-driven type 2 diabetes (T2D) subtypes differ in their risks of dementia and stroke. We examined whether their metabolomic profiles also differed and whether subtype-related metabolic patterns were associated with dementia, stroke, and all-cause mortality. Methods We analyzed NMR-based metabolomic profiles across previously defined T2D subtypes in the UK Biobank. Subtype-related metabolites were summarized using principal component analysis (PCA), and their associations with incident dementia, stroke, and all-cause mortality were examined using Cox models. Attenuation analyses and two-sample Mendelian randomization further assessed subtype-outcome relationships and the potential causal relevance of outcome-associated metabolites. Results Among 7,671 individuals (mean age 59.85 years; 37% female), the first five PCs explained 76.7% of variance in subtype-related metabolites and mainly reflected lipid and lipoprotein signatures. After adjustment for T2D subtype and confounders, the HDL-remodeling PC increased risks of all-cause dementia (HR 1.17, 95% CI 1.08-1.27), VaD (HR 1.18, 95% CI 1.05-1.32), and all-cause mortality (HR 1.16, 95% CI 1.13-1.19). Lower scores on the LDL cholesterol-enriched axis increase risks of all-cause dementia (HR 0.75, 95% CI 0.62-0.91) and mortality (HR 0.76, 95% CI 0.69-0.83). The VLDL/LDL-enriched PC was inversely associated with mortality (HR 0.93, 95% CI 0.88-0.98). No significant stroke results were observed. Adjustment for the PCA-derived metabolomic patterns generally attenuated subtype-outcome associations, MR analyses identified 197 metabolite-outcome associations that remained significant after FDR correction. Conclusions Metabolomic profiling showed that the metabolic signatures differed across data-driven T2D subtypes and highlighted lipid and lipoprotein remodeling as a major metabolic feature associated with dementia, stroke, and all-cause mortality.

AimsIdeal glycemic control is of great importance for diabetic patients during public health emergencies of infectious diseases as long-term hyperglycemic are not only associated with chronic complications but also vital drivers of common and life-threatening infections. The present study was designed to investigate the changes of blood glucose levels in elderly subjects with type 2 diabetes(T2D) during COVID-19 outbreak. MethodsThis retrospective study focused on the T2D outpatients at Fujian Provincial Hospital aged 65 years old and above who received baseline test for fasting plasma glucose and/or glycated hemoglobin (HbA1c) between January 1, 2019 and March 8, 2019 and were followed up on fasting plasma glucose and/or HbA1c in the same period in 2020. The baseline and follow-up data were analyzed with the paired-samples T-test. ResultsA total of 135 elderly subjects with T2D with baseline and follow-up fasting plasma glucose and 50 elderly subjects with T2D with baseline and follow-up HbA1c were analyzed, respectively. The baseline and follow-up fasting plasma glucose were 7.08 {+/-} 1.80 and 7.48{+/-}2.14 mmol/L, respectively (P=0.008). The baseline and follow-up HbA1c were 7.2{+/-}1.7% and 7.4{+/-}1.8%, respectively (P=0.158). ConclusionsElderly subjects with T2D had higher fasting plasma glucose levels during COVID-19 outbreak. We should pay more attension to the management of diabetics during public health emergencies.

Abstract Objective: To identify clinical and genetic factors associated with variation in glycemic response to glucagon-like peptide-1 receptor agonist (GLP-1RA) therapy among adults with type 2 diabetes, with a focus on common GLP1R variations, polygenic risk load, and pancreas-specific regulation annotation. Research Design and Methods: We conducted a retrospective cohort study using electronic health record (EHR)-linked biobank data from the All of Us research workbench platform that included 5784 adults with type 2 diabetes who initiated GLP-1RA therapy. Baseline HbA1c was measured within 3 months before medication initiation, and follow-up HbA1c was measured after 3 months. The patients with type 2 diabetes were classified as good responders (HbA1c reduction [&ge;] 2.5 percentage point) or poor responders (HbA1c reduction <0.5 percentage point). Models adjusted for demographic characteristics, anthropometric and metabolic measures, blood pressure, body mass index (BMI), lipid profile, liver function tests, polygenic risk score, and GLP1R variant carrier status were compared between the two groups. Common GLP1R variations were further investigated for carrier frequency and associated HbA1c levels before and after medication use. Results: The cohort included 3194 good responders and 2590 poor responders. Good responders were younger than poor responders (55.2 vs. 58.6 years) and had significantly higher glycemic improvement. HbA1c levels fell from 9.2% to 6.3% in good responders and 8.4% to 8.1% in poor responders, resulting in an absolute HbA1c reduction of 2.9% and 0.3%, respectively. Good responders also showed larger decreases in fasting glucose, BMI, systolic and diastolic blood pressure, triglycerides, total cholesterol, LDL cholesterol, and liver enzymes, as well as minor improvements in HDL-C. After multivariable adjustment, Poor responders had a greater T2D polygenic risk score (0.38 vs. 0.21), more GLP1R coding variant carrier status (10.1% vs. 8.0%), and a higher overall GLP1R variant burden (22.8% vs. 19.2%). Variant-level studies revealed rs2268650 and rs2003132 enrichment among poor responders, with negative post-treatment HbA1c patterns in carriers, whereas good-response carriers showed significant HbA1c improvement. Conclusions: Response to GLP-1RA in T2D is associated with baseline clinical and metabolic status, as well as inherited genetic susceptibility, which includes common GLP1R variation and a larger polygenic risk burden. Integrating clinical and pharmacogenomic profiling may improve patient classification and provide insight into treatment failure in poor responders.

Background: Diabetes mellitus and hypertension are increasingly prevalent non-communicable diseases that often coexist due to their interrelated pathophysiology and commonalities of risk factors. Effective management of these two comorbid conditions often involves polypharmacy, defined as the concurrent use of five or more medications, which for therapeutically relevant outcomes requires a high-medication adherence. Limited data exist on the extent of polypharmacy and its impact on adherence among Tanzanian patients with these comorbidities. This study therefore aimed at evaluating the prevalence of polypharmacy and its impact on medication adherence levels among this population. Methodology: A cross-sectional study involving 396 outpatients was conducted at Muhimbili National Hospital. Consecutive sampling was used to recruit eligible participants. Data was collected using structured-questionnaire which captured information on socio-demographics, clinical characteristics and adherence behaviors. Polypharmacy was defined as using five or more medications. Medication adherence was assessed using the Medication Adherence Report Scale (MARS-5). Multivariable logistic regression was performed to identify factors associated with adherence. Results: 71% of the study participants were on five or more medications, indicating high polypharmacy prevalence, with a median of six medications. Medication adherence was reported at 55.1%. Factors associated with lower adherence included moderate (APR: 0.83, P = 0.001) and high fasting glucose (APR: 0.66, P < 0.001), herbal medicine use (APR: 0.72, P < 0.001), and uncontrolled blood pressure. Conclusion: This study reveals a high prevalence of polypharmacy with moderate medication adherence among patients with comorbid T2DM and hypertension. These findings suggest a targeted intervention utilizing such as patient education and medication reviews are essential to improve adherence and management in Tanzania.

Introduction. Diabetes is a growing global health challenge, necessitating effective management strategies. Glycosylation, a highly regulated post-translational protein modification, has emerged as a pivotal factor in diabetes pathophysiology. However, the modulation of protein glycosylation by antidiabetic treatment is still largely unknown. This study explored the longitudinal effects of four distinct antidiabetic therapies - metformin, insulin, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and glucagon-like peptide-1 receptor agonists (GLP-1RA) - on plasma protein and immunoglobulin G (IgG) glycosylation in patients with type 2 diabetes (T2D). Research Design and Methods. Plasma protein and IgG N-glycans were enzymatically released, purified and chromatographically profiled in a cohort of 124 patients, examined at four time points, to assess therapy-induced glycan alterations. Linear mixed models adjusting for covariates and multiple testing (FDR<0.05) were used to investigate the associations between plasma protein and IgG N-glycosylation and antidiabetic therapy. Results. Our findings reveal that metformin, SGLT2 inhibitors, and GLP-1RA induce significant alterations in IgG glycosylation, including the increased core fucosylation and galactosylation, features associated with a reduced inflammatory IgG potential. Notably, IgG monogalactosylation, previously linked to cardioprotective effects in women, was elevated in response to GLP-1RA and SGLT2 inhibitor treatments. Plasma protein glycosylation changes were more limited, with distinct alterations observed for each therapy. Metformin and GLP-1RA similarly reduced certain fucosylated and sialylated glycans, while SGLT2 inhibitors decreased a high-mannose glycan, previously positively associated with diabetes progression. Insulin therapy had a minimal effect on protein glycosylation, with only one plasma glycan significantly altered. Conclusions. Our findings emphasise the importance of protein glycosylation as a dynamic and responsive marker in T2D treatment. The distinct glycan alterations observed in response to metformin, SGLT2 inhibitors, and GLP-1 receptor agonists provide novel insights into the molecular effects of these therapies, potentially contributing to the development of glycan-based biomarkers for personalized diabetes management.

BackgroundNovel coronavirus (SARS-CoV-2) infects human lung tissue cells through angiotensin-converting enzyme-2 (ACE2), and the body sodium is an important factor for regulating the expression of ACE2. Through a systematic review, meta-analysis and retrospective cohort study, we found that the low blood sodium population may significantly increase the risk and severity of SARS-CoV-2 infection. MethodsWe extracted the data of serum sodium concentrations of patients with COVID-19 on admission from the articles published between Jan 1 and April 28, 2020, and analyzed the relationship between the serum sodium concentrations and the illness severity of patients. Then we used a cohort of 244 patients with COVID-19 for a retrospective analysis. ResultsWe identified 36 studies, one of which comprised 2736 patients.The mean serum sodium concentration in patients with COVID-19 was 138.6 mmol/L, which was much lower than the median level in population (142.0). The mean serum sodium concentration in severe/critical patients (137.0) was significantly lower than those in mild and moderate patients (140.8 and 138.7, respectively). Such findings were confirmed in a retrospective cohort study, of which the mean serum sodium concentration in all patients was 137.5 mmol/L, and the significant differences were found between the mild (139.2) and moderate (137.2) patients, and the mild and severe/critical (136.6) patients. Interestingly, such changes were not obvious in the serum chlorine and potassium concentrations. ConclusionsThe low sodium state of patients with COVID-19 may not be the consequence of virus infection, but could be a physiological state possibly caused by living habits such as low salt diet and during aging process, which may result in ACE2 overexpression, and increase the risk and severity of COVID-19. These findings may provide a new idea for the prevention and treatment of COVID-19.

Continuous glucose monitoring (CGM) is increasingly used for inpatient glycemic surveillance, but evidence in non-critical care wards remains limited, particularly in real-world public healthcare settings. Intermittent capillary glucose testing may fail to detect transient, nocturnal, or asymptomatic dysglycemia. We sought to evaluate whether CGM improves detection of clinically significant dysglycemia compared with seven-point capillary glucose monitoring in hospitalized patients with type 2 diabetes mellitus or hyperglycemia. This is a prospective, observational, non-randomized, real-world study performed in a tertiary referral center in Mexico. 56 hospitalized patients were included: 28 underwent flash CGM and 28 underwent seven-point capillary glucose monitoring. Patients were followed for up to 6 hospitalization days. The main analytical focus was detection of clinically significant dysglycemia, including hypoglycemia <70 mg/dL, clinically significant hypoglycemia <54 mg/dL, and severe hyperglycemia >250 mg/dL. Secondary outcomes included time in range, mean daily glucose, insulin requirements, infectious complications, length of stay, and mortality. CGM detected more hypoglycemia <70 mg/dL than capillary monitoring (71.4% vs 35.7%, p=0.005), more clinically significant hypoglycemia <54 mg/dL (median 3 [IQR 0-6.5] vs 0, p=0.030), and more severe hyperglycemia >250 mg/dL (median 8.5 [IQR 0.5-17] vs 0 [IQR 0-9.52], p=0.030). Time in range was not significantly different between groups (59.86 +/- 23.46% vs 69.28 +/- 24.99%, p=0.151). After adjustment for age, diabetes duration, and admission hyperglycemia, CGM remained associated with hypoglycemia detection (OR 4.7, 95% CI 1.2-19.0, p=0.027). We concluded that CGM improved detection of clinically significant dysglycemia during up to 6 hospitalization days. Although CGM did not improve time in range or short-term clinical outcomes, it provided superior glycemic surveillance compared with intermittent capillary glucose testing.

BackgroundTime-restricted eating (TRE) shows promise for metabolic health, but its effectiveness in glucose management among individuals with type 2 diabetes, and its role in modulating glucose responses to dietary intake remain poorly understood. ObjectiveWe aimed to identify temporal associations of TRE and dietary intake with 24-hour glucose dynamics. We further examined the interactions between eating windows and carbohydrate intake. MethodsClinical information, dietary records, and continuous glucose monitoring data from 90 Chinese adults with type 2 diabetes were analyzed. Two digital biomarkers were developed to characterize duration and regularity of eating patterns: a binary indicator for eating windows <10 hours (TRE10) and a continuous measure of deviation from an individuals median eating window (TWD). Linear mixed-effects models and functional data analysis were used to examine independent and temporal associations of glucose levels with TRE and dietary intakes. ResultsTRE10 was associated with reduced mean amplitude of glycemic excursions (MAGE) ({beta} = -7.15, P = 0.031) and glucose standard deviation (SD) ({beta} = -2.16, P = 0.035), with the strongest associations around 09:00. TWD was positively associated with glycemic coefficient of variation (CV) ({beta} = 0.36, P = 0.050) and higher glucose levels between 07:00 and 08:00. Carbohydrate intake was significantly associated with time in range (TIR) and glycemic variability, with notable glucose changes at 10:00 and 21:00. Dietary vitamin D intake was linked to reduced glycemic area under the curve (AUC) ({beta} = -0.29, P = 0.040), with pronounced effects at 12:00 and 20:00. Additionally, eating windows < 10 hours attenuated carbohydrate-induced glucose spikes in the morning but amplified glucose responses in the early afternoon. ConclusionsIn adults with type 2 diabetes, eating windows <10 hours and consistent eating windows improved glycemic control, with distinct time-of-day effects. These findings support integrating timing-based nutritional strategies into personalized diabetes management.