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Medical Journal
15th Jan, 2026
Diabetes Journals
In type 1 diabetes, a condition that necessitates lifelong exogenous insulin replacement, there is heavy reliance on technology-assisted insulin delivery and glucose monitoring. Yet, people living with type 1 diabetes still face dysglycemia, weight gain, vascular complications, ketoacidosis and severe hypoglycemia, and psychological distress. Cardiovascular and kidney disease remain the leading causes of morbidity and mortality, yet traditional risk factors (smoking, hypertension, hyperlipidemia, obesity, hyperglycemia) incompletely explain this excess burden. Emerging evidence highlights the role of insulin resistance, inflammation, and endothelial dysfunction exacerbated by current subcutaneous insulin therapies in type 1 diabetes, independent of overweight/obesity status. This has fueled interest in addressing metabolic challenges in type 1 diabetes through novel insulin analogs, adjunctive noninsulin therapies, and integrated technologies. Our review explores the potential synergy between technologies and adjunctive therapeutics to address unique physiologic drivers of metabolic dysfunction in type 1 diabetes. Innovations such as multihormonal systems, dynamic glucose and ketone monitoring, and automated insulin titration hold promise. However, leveraging emerging insights from nutrient-stimulated hormone-based therapies and other drug classes such as insulin-sensitizing agents and sodium–glucose cotransporter 2 inhibitors could pave the way for designing combination type 1 diabetes–specific therapies. Large, placebo-controlled trials are needed to progress the field toward use of combination therapies that reduce metabolic and vascular complications and ease patient burden in type 1 diabetes.
OBJECTIVE Although continuous glucose monitoring (CGM) reduces hypoglycemia and may improve impaired awareness of hypoglycemia (IAH), its effectiveness in older adults at high risk remains unknown. RESEARCH DESIGN AND METHODS This post hoc analysis of the WISDM study focuses on CGM use over 52 weeks. IAH was assessed using the Clarke original score (Clarke-full) and its subscales, Hypoglycemia Awareness Factor (HAF) and Severe Hypoglycemia Experienced Factors (SHEF), at baseline, 26 weeks, and 52 weeks. RESULTS After 26 weeks ( n = 184) and 52 weeks ( n = 94) of CGM use, Clarke-SHEF decreased significantly ( P = 0.02 and P < 0.0001, respectively), whereas Clarke-full and Clarke-HAF remained unchanged. After 52 weeks, Clarke-full but not Clarke-HAF improved in the IAH subgroup, highlighting the importance of selecting the appropriate scoring method for IAH. CONCLUSIONS In older adults with type 1 diabetes, CGM improves hypoglycemia; however, its role in improving IAH is variable, depending on the scoring method. This study highlights the limitations of the Clarke score.
Many studies have reported that radiation exposure may increase the risk of type 2 diabetes ( 1 ). Insulin resistance in adipose tissue is considered an early and critical defect in impaired glucose metabolism because it increases serum free fatty acid (FFA) levels and consequently contributes to the progression of insulin resistance and β-cell dysfunction. However, whether radiation exposure induces insulin resistance in adipose tissue remains unclear. Thus, this study aimed to investigate the relationship between radiation exposure and insulin resistance in adipose tissue and serum FFA levels, along with the relationship between radiation exposure and hepatic and muscle insulin resistance, and β-cell function.
OBJECTIVE Continuous glucose monitoring (CGM) is widely used to monitor glucose levels in patients with diabetes and guide insulin dosing in outpatients. In inpatient care, special regulatory requirements necessitate CGM accuracy as a prerequisite for its integration into clinical decision support. RESEARCH DESIGN AND METHODS To meet the specific demands of in-hospital care, CGM accuracy was retrospectively evaluated in 226 patients using paired CGM and point-of-care glucose measurements, assessed via mean absolute relative difference (MARD), Clarke Error Grid (CEG) analysis, and a modified version of the U.S. Food and Drug Administration agreement rule. A dynamic, patient-specific algorithm incorporating time lag correction and linear modeling was developed to minimize MARD and applied in a second cohort of 24 patients within the clinical workflow. RESULTS Data analysis showed an initial MARD of 10.30%, with 99.02% of data points located in zones A and B of the CEG. The application of the patient-specific optimization algorithm improved the MARD by 4.33%. Evaluation of the patient-specific algorithm on the second inpatient cohort demonstrated a 5.58% reduction in intrapersonal MARD, indicating its potential applicability within clinical workflows. CONCLUSIONS Patient-specific algorithmic refinements of CGM data demonstrate the potential to adequately address the unique demands of inpatient diabetes care by reducing intrapersonal MARD, paving the way for the adoption of CGM systems into hospital environments.
OBJECTIVE In May 2022, California expanded full-scope Medicaid (Medi-Cal) to low-income undocumented immigrants aged 50 years or older, which provided access to newer type 2 diabetes (T2D) medications. This study examined whether the expansion led to more prescriptions of newer therapies like glucagon-like peptide 1 receptor agonists and sodium–glucose cotransporter 2 inhibitors among older undocumented immigrants. RESEARCH DESIGN AND METHODS We used patient records between January 2019 to June 2023 from two Federally Qualified Health Centers (FQHCs) in Los Angeles County. We compared prescriptions among 1 ) older undocumented immigrants newly eligible for Medi-Cal, 2 ) younger undocumented immigrants not eligible for Medi-Cal, and 3 ) documented patients ( n = 20,420 encounters and 4,601 patients). We used generalized linear mixed models with patient-level random intercepts to examine whether the patient groups differed in their likelihood of being prescribed newer medications and if there were changes over time. RESULTS The odds of being prescribed newer classes of drugs was significantly lower for both the older and younger undocumented patients than documented patients at baseline. Prescriptions for newer T2D medications increased over time for all patients, but the monthly rate of increase in the odds was 6% higher for the older undocumented group compared with the documented patient group. CONCLUSIONS Medi-Cal expansion was effective in changing prescription patterns for older undocumented immigrants with T2D. Although the older undocumented immigrants were prescribed newer drugs at a much lower level than were documented immigrants, they ended at a similar level as the documented patients by the end of the study period.
OBJECTIVE To examine whether age at type 2 diabetes onset affects disease progression, assessed by changes in glycemic control and clinical biomarkers during follow-up. RESEARCH DESIGN AND METHODS Participants in the Kerala Diabetes Prevention Program (K-DPP) and U.S. Diabetes Prevention Program (US-DPP) who developed type 2 diabetes during the trial were analyzed. Data on fasting plasma glucose (FPG), hemoglobin A 1c (HbA 1c ), triglycerides (TGs), HDL, LDL, BMI, blood pressure, and estimated glomerular filtration rate (eGFR) were collected at diabetes onset and end of follow-up. Linear and mixed-effects regressions assessed the association and rate of biomarker change by age at onset. RESULTS We included 802 US-DPP (mean age 52.6 years) and 146 K-DPP participants (mean age 47.7 years). Younger-onset participants had a higher BMI at onset and end of follow-up (mean follow-up 7.9 and 7.6 years for US-DPP and K-DPP, respectively), with a relatively small BMI change over time in US-DPP participants. In fully adjusted models, FPG and HbA 1c at onset were not associated with age at onset. Both measures increased faster in younger-onset participants, although the association was not significant in K-DPP participants. In US-DPP participants, younger age at onset was associated with higher eGFR and lower HDL and systolic blood pressure (SBP); similar directions were seen in K-DPP participants, but the association with HDL was nonsignificant. SBP fell slightly in older-onset US-DPP participants during follow-up but not in younger-onset participants. CONCLUSIONS Younger-onset diabetes was associated with greater adiposity, lower HDL, and better SBP and eGFR at onset, with differences largely persisting during follow-up. During follow-up, glycemia increased slightly faster in individuals with younger-onset diabetes.
OBJECTIVE We evaluated the prognostic and clinical utility of kidneyintelX.dkd, a biomarker-based risk score, in patients with type 2 diabetes and a broad range of chronic kidney disease (CKD) by assessing its association with kidney outcomes at baseline and longitudinally, comparing it with the established Kidney Disease Improving Global Outcomes (KDIGO) risk classification, and examining its responsiveness to canagliflozin. RESEARCH DESIGN AND METHODS We measured tumor necrosis factor receptor-1 (TNFR-1), TNFR-2, and kidney injury molecule-1 (KIM-1) in banked plasma samples at baseline and year 1 and calculated kidneyintelX.dkd scores of participants with CKD G1-G3b from two large randomized controlled trials (Canagliflozin Cardiovascular Assessment Study [CANVAS] and Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation [CREDENCE]). We assessed concordance between KDIGO and kidneyintelX.dkd risk levels, evaluated associations of baseline and 1-year changes in kidneyintelX.dkd with kidney outcomes, and examined treatment effects of canagliflozin versus placebo. RESULTS Mean kidneyintelX.dkd scores increased across higher KDIGO risk re was independently associated with kidney outcomes and more strongly predictive than KDIGO classification. At 1 year, canagliflozin significantly lowered kidneyintelX.dkd score versus placebo, and longitudinal reductions by 1 year were associated with lower subsequent risk of kidney outcomes, independent of changes in estimated glomerular filtration rate or urinary albumin-to-creatinine ratio. Absolute risk reductions with canagliflozin were largest among those at high kidneyintelX.dkd risk. CONCLUSIONS The kidneyintelX.dkd score adds prognostic value beyond clinical classification, reflects canagliflozin treatment response, and helps identify individuals most likely to benefit from therapy. These findings support a role for the kidneyintelX.dkd score in personalized risk assessment and monitoring in type 2 diabetes and CKD in prospective studies and clinical practice.
OBJECTIVE To evaluate inhaled technosphere insulin (TI) in children with diabetes. RESEARCH DESIGN AND METHODS A total of 230 youth 4–17 years old with type 1 (98%) or type 2 (2%) diabetes treated with multiple daily injections of insulin were randomly assigned 1:1 to TI or rapid-acting analog (RAA) insulin plus continuation of long-acting basal insulin and continuous glucose monitoring (CGM) for 26 weeks. The primary outcome was change in HbA 1c, tested for noninferiority with margin of 0.4%. RESULTS In intent-to-treat analysis, mean HbA 1c (% ± SD) was 8.22 ± 0.87 at baseline and 8.41 ± 1.38 at 26 weeks with TI and 8.21 ± 0.96 and 8.21 ± 1.10, respectively, with RAA (adjusted difference = 0.18; 95% CI −0.07, 0.43; noninferiority P = 0.091). CGM-measured time in range 70–180 mg/dL was not significantly different between groups (adjusted difference −2.2%; 95% CI −7.0, 2.7; P = 0.38). Two severe hypoglycemic events occurred in the TI group and one in the RAA group. Change in forced expiration volume in 1 s from baseline to 26 weeks did not differ comparing TI and RAA ( P = 0.53). The TI group reported greater treatment satisfaction ( P = 0.004) and had less gain in weight and BMI percentile ( P = 0.009) than did the RAA group. CONCLUSIONS The primary analysis did not meet the prespecified criteria for HbA 1c noninferiority. However, TI use was safe over 26 weeks without affecting pulmonary function and was associated with greater treatment satisfaction and less weight gain compared with RAA, supporting TI as a treatment option for some pediatric patients with type 1 diabetes.
OBJECTIVE Autoimmune polyendocrine syndrome type 1 (APS-1) is a rare, monogenic autoimmune disorder that may manifest as type 1 diabetes (T1D). Teplizumab, an anti-CD3 monoclonal antibody, delays progression of stage 2 T1D, but its effects in APS-1–associated diabetes are unknown. RESEARCH DESIGN AND METHODS We report clinical responses of two adolescents with APS-1 and stage 2 T1D who received 14-day courses of teplizumab. In one patient, pancreatic MRI and spectral immune cell phenotyping were performed before and after treatment. RESULTS Both patients exhibited improved glycemia. One who briefly required insulin recovered insulin independence 2 weeks after therapy. Pancreatic volume transiently increased, and circulating lymphocytes showed changes in homing receptors and senescence markers in the individual who underwent those studies. Nonpancreatic APS-1 manifestations were unchanged. CONCLUSIONS Teplizumab may preserve β-cell function in APS-1–associated T1D. Larger studies are needed to define efficacy, durability, and immunologic and tissue mechanisms in this rare context.
OBJECTIVE Glucagon-like peptide 1 agonists (GLP-1s) compared with dipeptidyl peptidase 4 inhibitors (DPP-4s) are associated with reduced risk of dementia in the general population with diabetes, but whether this association is true for patients requiring hemodialysis is unknown. RESEARCH DESIGN AND METHODS Using the U.S. Renal Data System and Medicare Parts A, B, and D claims data from 2011 to 2021, we used the active comparator, new-user design to evaluate incident dementia comparing GLP-1s versus DPP-4s among individuals with both diabetes and hemodialysis dependence. We used inverse probability of treatment weights (IPTW) to balance baseline characteristics and Fine-Gray models to estimate subdistribution hazard ratios (sHRs) accounting for competing risks of death and kidney transplantation. We estimated intention-to-treat and as-treated effects. RESULTS We identified 3,619 GLP-1 users and 11,502 DPP-4 users. After IPTW, the average individual was 63 years old, 63% were White, and mean BMI was 31 kg/m 2. The median (interquartile interval) follow-up was 1.5 (0.6–2.9) years, and 2,014 patients received a dementia diagnosis. In the intention-to-treat analysis, the IPTW-sHR for dementia was 0.82 (95% CI 0.67–0.98), and after 2 years of follow-up, the cumulative incidence of dementia was 10.2% on GLP-1s vs 11.2% on DPP-4s. As-treated and subgroup analyses were consistent. GLP-1s were also associated with an increased risk of ketoacidosis (sHR 1.52, 95% CI 1.14–2.02; 2-year cumulative incidence: 3.1% vs. 2.2%). CONCLUSIONS In patients with diabetes requiring hemodialysis, GLP-1s (vs. DPP-4s) may be a promising therapy to reduce the risk of dementia.
OBJECTIVE Older adults with type 2 diabetes are at high risk for frailty. The effects of glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium–glucose cotransporter 2 inhibitors (SGLT-2is) on frailty remain uncertain. RESEARCH DESIGN AND METHODS Using a 7% random sample of Medicare data, we compared new users of dipeptidyl peptidase 4 inhibitors (DPP-4is), GLP-1RAs, SGLT-2is, and sulfonylureas on 1-year frailty progression, measured by a claims-based frailty index (CFI) (range: 0–1; higher scores indicate greater frailty). Mediation analyses assessed whether cardiovascular or safety events explained differences in frailty progression. RESULTS Compared with DPP-4i users, the mean CFI change (95% CI) was significantly lower for GLP-1RA (−0.007 [−0.011, −0.004]) and SGLT-2i (−0.005 [−0.008, −0.002]) users; no difference was found for sulfonylurea users. These associations were minimally mediated by cardiovascular or safety events. CONCLUSIONS GLP-1RAs and SGLT-2is may slow frailty progression through mechanisms independent of cardiovascular benefits. Future trials should confirm these preliminary findings.
OBJECTIVE We examined the associations of inflammatory and insulinemic diets with type 2 diabetes (T2D) risk among women with a history of gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS We followed 4,318 women with a history of GDM in the Nurses’ Health Study II for incident T2D between 1991 and 2019. Empirical dietary inflammatory pattern (EDIP) and empirical dietary index for hyperinsulinemia (EDIH) scores were calculated using prevalidated methods. Cox models were used to calculate hazard ratios (HRs) and 95% CIs for the risk of T2D. Additionally, we estimated the least squares means of cardiometabolic biomarkers according to EDIP and EDIH quintiles in a subset of participants who were free of T2D at the time of blood collection. RESULTS During 84,174 person-years of follow-up, 1,037 women developed T2D. After adjusting for major covariates, including BMI, higher EDIP and EDIH scores, which indicated greater dietary inflammatory and insulinemic potential, were associated with an increased risk of T2D. Compared with those for the lowest quintile, adjusted HRs (95% CIs) for the highest quintile were 1.32 (1.06, 1.64) for EDIP and 1.44 (1.14, 1.82) for EDIH (both P trend < 0.05). Higher EDIP scores were significantly associated with higher concentrations of insulin and lower levels of HDL cholesterol, and EDIH scores were significantly positively associated with insulin and C-peptide concentrations. CONCLUSIONS Among women with a history of GDM, those with greater dietary inflammatory and insulinemic potential were found to be at a higher risk of T2D and to have unfavorable cardiometabolic profiles.
OBJECTIVE Clinical heterogeneity in youth-onset type 2 diabetes is less understood than that of adult-onset type 2 diabetes. We performed phenotypic clustering of youth-onset type 2 diabetes to determine whether clusters provided clinical utility. RESEARCH DESIGN AND METHODS We performed data-driven clustering in a diverse subset of autoantibody-negative, clinician-diagnosed type 2 diabetes before age 20 years in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) ( n = 525) and the SEARCH for Diabetes in Youth (SEARCH) ( n = 333) studies. Participants were clustered using 1 ) similar variables as previously described in adults and 2 ) novel routinely available clinical variables. We assessed the effectiveness of the clusters, as well as that of simple clinical measures, to predict treatment response in the TODAY clinical trial. RESULTS There were three youth-onset type 2 diabetes clusters: 1 ) youth-onset insulin-deficient diabetes (YIDD-T2), 2 ) youth-onset insulin-resistant diabetes, and 3 ) intermediate youth-onset diabetes. These clusters had differential responses to therapies and risk of treatment failure in the TODAY study, with those in the YIDD-T2 cluster experiencing the highest rate of treatment failure, regardless of treatment arm. YIDD-T2 also had high rates of type 2 diabetes complications. We then generated three novel clusters, with different rates of treatment failure, using variables available in routine clinical practice. Compared with both clustering methods, simple clinical measures performed comparably or better at predicting treatment response and complications. CONCLUSIONS Youth-onset type 2 diabetes can be characterized into reproducible clusters that demonstrate differential response to treatments and risk of complications. Nevertheless, cluster membership did not add clinical utility beyond simple clinical measures for predicting outcomes.
OBJECTIVE Hypoglycemia is a hazardous diabetes-related emergency. We aimed to develop a machine learning (ML) approach for noninvasive hypoglycemia detection using voice data. RESEARCH DESIGN AND METHODS We collected voice data (540 recordings) with a smartphone in standardized euglycemia and hypoglycemia in two sequential clinical studies in people with type 1 diabetes. Using these data, we trained and evaluated an ML approach to detect hypoglycemia solely based on voice features. RESULTS Twenty-two individuals were included (11 female, age 37.3 ± 12.4 years, HbA 1c 7.1 ± 0.5%). The ML approach detected hypoglycemia noninvasively with high accuracy (area under the receiver operating characteristic curve 0.90 ± 0.12 for reading a text aloud and 0.87 ± 0.15 for rapid repetition of syllables [diadochokinetic task]). CONCLUSIONS An ML approach exclusively based on voice data allows for noninvasive hypoglycemia detection, corroborating the potential of ML-based approaches to infer acute health states through voice.
OBJECTIVE Glucagon-like peptide 1 receptor agonists (GLP-1RAs) have cardiovascular benefits, but whether this is via metabolic improvements or direct effect remains controversial. This study aimed to explore the causal link between GLP-1RAs and myocardial infarction (MI) and quantify the contribution of metabolic improvements. RESEARCH DESIGN AND METHODS Mendelian randomization (MR) was applied to assess the causal relationship between GLP-1RAs and MI, and two-step MR analysis was applied to quantify the mediating role of metabolic traits. The direct effect of GLP-1RAs on MI was evaluated by multivariate Mendelian randomization (MVMR). Genetic variants associated with GLP-1 receptor (GLP-1R) expression (proxying GLP-1RAs) were used as instrumental variables. Genome-wide association studies (GWAS) data for metabolic traits glycated hemoglobin (HbA 1c ), BMI, lipid profile, and blood pressure were sourced from the Million Veteran Program, serving as mediators. GWAS data for type 2 diabetes mellitus (T2DM) were obtained from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, and data for MI were sourced from the UK Biobank/Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus The Coronary Artery Disease (CARDIoGRAMplusC4D), serving as outcomes. All GWAS data were restricted to European ancestry. RESULTS Higher GLP-1R expression was correlated with a lower risk of T2DM (odds ratio 0.94 [95% CI 0.92, 0.97]) and MI (0.97 [0.95, 1.00]). Metabolic improvements mediated this association: HbA 1c (36.67% [3.89, 69.44]), BMI (28.86% [2.62, 55.10]), triglycerides (18.52% [1.47, 35.57]), HDL-cholesterol (18.28% [1.45, 35.12]), and systolic blood pressure (11.55% [0.33, 22.76]). No direct effect of GLP-1R expression on MI was observed after adjusting for metabolic traits (β = −0.003, P = 0.12). CONCLUSIONS GLP-1RAs protect against MI primarily through metabolic improvements, with no direct effect independent of these pathways. These findings support prioritizing metabolic improvements to reduce cardiovascular risk with GLP-1RAs.
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