Diabetology

Background/Objectives: The Diabetes-Specific Dementia Risk Score (DSDRS) integrates clinical and metabolic markers to predict cognitive decline in Type 2 diabetes mellitus (T2DM), yet its association with key modifiable risk factors remains unvalidated in Latin American cohorts. This study aimed to assess the relationship between the DSDRS and clinical, biochemical, anthropometric, and neuropsychological variables in older adults with T2DM. Methods: A cross-sectional study of 291 Mexican adults aged ≥ 60 with T2DM was conducted, including assessments of cognitive function (MoCA), depression (BDI), sleep quality (AIS), and metabolic parameters. Results: Higher DSDRS correlated with lower cognitive scores, poorer sleep quality, reduced muscle mass, and smoking status. Multiple regression explained 32% of DSDRS variance, highlighting these factors as significant contributors. Conclusions: The DSDRS reflects multifactorial influences on dementia risk in older adults with T2DM and may aid early identification of individuals at increased cognitive vulnerability in this population. Full article

Background/Objectives: Contemporary diabetes guidelines increasingly emphasize broader cardiometabolic risk stratification beyond glycemic control, but the national proportion of Peruvian adults with diagnosed diabetes who show obesity or hypertension is unknown. We estimated the prevalence of overweight/obesity, obesity, and hypertension; quantified the proportion meeting at least one cardiometabolic-profile criterion; and described recent acquisition of diabetes medications. Methods: We performed a cross-sectional analysis of the Peruvian Demographic and Family Health Survey (ENDES) 2022–2024. Adults aged ≥30 years with a prior medical diagnosis of diabetes were included (n = 2599; weighted population estimate = 1.51 million) from 49,363 eligible adults (weighted population estimate = 19.91 million). Survey-weighted prevalences of overweight (body mass index [BMI] ≥ 25 kg/m²), obesity (BMI ≥ 30 kg/m²), hypertension (primary definition: systolic blood pressure [SBP] ≥ 140 mmHg or diastolic blood pressure [DBP] ≥ 90 mmHg), and diabetes medication purchase in the previous 12 months were estimated. Results: Mean age was 60.0 years (95% confidence interval [CI]: 59.2–60.8), and 55.9% were women. Overall, 81.5% (95% CI: 79.2–83.7) had overweight/obesity, 41.1% (95% CI: 38.0–44.3) had obesity, and 31.8% (95% CI: 28.7–34.9) had hypertension by the primary survey definition. Among participants with complete data for obesity and hypertension (n = 2534; weighted population estimate = 1.45 million), 58.5% (95% CI: 55.4–61.6) met at least one cardiometabolic-profile criterion. Independently, 78.9% (95% CI: 76.4–81.3) reported purchasing diabetes medications, with lower proportions in rural populations and among uninsured adults. Conclusions: Nearly six in ten Peruvian adults with diagnosed diabetes had obesity or hypertension, a population-level profile consistent with substantial cardiometabolic complexity and the potential need for broader risk-oriented management beyond glucose alone. Persistent gaps in medication acquisition suggest that therapeutic modernization and equitable continuity of basic pharmacologic treatment should be addressed simultaneously. Full article

Background/Objectives: Acute care use (ACU)—emergency department visits, inpatient hospitalizations, and observation stays—drives morbidity and costs among adults with diabetes. We developed and evaluated machine-learning models to predict 1-year ACU risk using electronic health record (EHR) data and neighborhood-level data. Methods: We performed a retrospective cohort study using de-identified EHR data from a large urban academic health center, including adults (≥18 years) with diabetes (N = 23,052). The index date was defined as one year before each patient’s last encounter, and ACU was assessed during the subsequent year. We modeled 180 predictors spanning demographics, Area Deprivation Index (ADI), prior healthcare utilization, vitals/BMI, comorbidities, medications, and laboratory results. Decision tree and gradient-boosted models (XGBoost, LightGBM, CatBoost) were tuned with Optuna using 8-fold stratified cross-validation, optimizing area under the receiver operating characteristic curve (AUC). To improve class-balanced classification performance under outcome imbalance, we selected post hoc probability thresholds that maximized Macro F1 and quantified interpretability with permutation feature importance. Results: ACU occurred in 30.53% of patients (7039/23,052). Boosted models achieved AUC ≈ 0.78, with LightGBM performing best (AUC = 0.7839). Macro F1–optimized thresholds (<0.5; typically 0.375–0.40) improved class-balanced performance versus a 0.5 cutoff. Across boosted models, prior utilization features dominated, followed by discharge-related factors and neighborhood deprivation; comorbidities and laboratory results contributed. Conclusions: In this single urban academic health-system cohort of adults with diabetes, EHRbased boosted models demonstrated moderate discrimination for predicting 1-year ACU and identified interpretable predictive signals. Threshold optimization improved class-balanced statistical performance. Prior utilization, care transitions, and neighborhood deprivation emerged as dominant predictive features. External and temporal validation are needed before broader application. Full article

Background: Diabetic neuropathy (DN) is a common complication of diabetes mellitus that remains frequently undetected by conventional diagnostic methods. Sudomotor dysfunction, reflecting small-fiber impairment, has emerged as a potential early marker. SUDOSCAN, a rapid and non-invasive device measuring electrochemical skin conductance (ESC), has been proposed as a screening tool for early DN. The objective of this study was to systematically evaluate the diagnostic performance and clinical utility of SUDOSCAN in the early detection of DN. Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines. Studies assessing SUDOSCAN-derived ESC in adults with diabetes were included. Data on diagnostic accuracy, correlations with established neuropathy measures, and clinical applicability were extracted. Where feasible, pooled sensitivity and specificity were estimated using a random-effects model. Results: Fifteen studies (n = 7343 participants) were included in the qualitative synthesis, with five of them contributing to the quantitative analysis. Reduced ESC values were consistently associated with DN, including early and asymptomatic cases. Pooled sensitivity and specificity for detecting DN were 0.81 (95% CI 0.73–0.87) and 0.73 (95% CI 0.57–0.85), respectively. ESC values correlated with neuropathy severity scores and autonomic dysfunction measures. However, substantial heterogeneity was observed due to variability in diagnostic criteria, ESC thresholds, and study populations. Conclusions: SUDOSCAN is a feasible, rapid, and non-invasive tool for detecting DN, particularly in the early-stage or small-fiber disease. It shows promise as a screening and adjunctive diagnostic modality, especially when combined with established clinical tools. Nevertheless, the lack of standardized thresholds limits its standalone use. Full article

Gestational diabetes (GDM) is associated with long-term risk of diabetes and cardiovascular disease. Offspring of mothers with GDM also have elevated cardiometabolic risk in their lifetime. This article reviews risk factors that may predict progression to Type 2 or Type 1 diabetes after history of GDM, recurrence risk of GDM in a future pregnancy and discusses what evidence is available for risk mitigation in reducing long-term adverse health outcomes in both mothers with GDM and their children. Full article

Background/Objectives: Angiogenic T (Tang) cells support endothelial repair and vascular homeostasis. This cross-sectional study compared circulating Tang cell levels in young adults with T1DM vs. healthy controls, and assessed associations between Tang cells and continuous glucose monitoring (CGM) metrics. Methods: Sixty-five young adults with T1DM and 55 healthy controls were enrolled at the University of Campania “Luigi Vanvitelli,” Naples, Italy. Clinical and biochemical data were collected. Tang cells (CD3⁺CD31⁺CD184⁺) were quantified by flow cytometry as absolute counts and percentage of CD3⁺ T cells. In T1DM, CGM metrics from the preceding 14 days were analyzed, including time in range (TIR), time above range (TAR), and time below range (TBR). Results: Individuals with T1DM had higher fasting glucose and HbA1c than controls. Total CD3⁺ T cell counts were lower in T1DM. Tang cells were significantly reduced in T1DM both as absolute number and percentage (21% [10–31] vs. 48% [39–62]; p < 0.001). In multivariable analyses, Tang cell percentage was positively associated with TIR and inversely associated with HbA1c and TAR. Conclusions: Young adults with T1DM exhibit significantly reduced circulating Tang cells. Associations with CGM metrics support a link between real-world glucose control and endothelial vascular health. Full article

Background/Objectives: Uncontrolled diabetes and associated comorbidities disproportionately affect African American (AA) adults. Medication adherence is key to diabetes control yet is often suboptimal, particularly among AA adults. This study examined associations between patient characteristics and adherence among AA adults with uncontrolled diabetes and compared two medication adherence instruments for predicting diabetes control. Methods: This cross-sectional analysis used baseline data from the Management of Diabetes in Everyday Life (MODEL) study, a clinical trial to improve diabetes self-care among AA adults with uncontrolled diabetes. Internal consistency of the 12-item Adherence to Medication Refills and Medications Scale for diabetes medications (ARMS-D) was evaluated by comparing its Cronbach α to the standardized Cronbach α calculated from MODEL data. Associations with variables were examined using correlations, t-tests, or ANOVA, as appropriate. Stepwise multiple regression identified predictors of diabetes control assessed by hemoglobin A1c (HbA1c). Results: Among 665 participants (mean age = 54 years, HbA1c = 10.24%; 67% female; 73% high health literacy), 75% reported perfect adherence on the Summary of Diabetes Self-Care Activities Medications Subscale (SDSCA-MS) versus 7.3% on ARMS-D. ARMS-D showed strong internal consistency (α = 0.81). Lower adherence by ARMS-D was associated with younger age, higher social complexity, and depression (all p ≤ 0.001). ARMS-D score, age, depression, and insulin, dipeptidyl peptidase 4 inhibitor, and sodium-glucose co-transporter 2 inhibitor use predicted baseline HbA1c. Conclusions: This study demonstrates that younger age, depression, and high social complexity are associated with lower medication adherence measured using the ARMS-D. Adherence gaps identified by ARMS-D may validly predict diabetes control and help guide interventions to improve diabetes care in AA adults with uncontrolled diabetes. Full article

Background: Diabetes is the eighth-leading cause of death in the U.S. and poor blood glucose (BG) management is associated with serious long-term complications. While educational interventions have been shown to improve health outcomes among individuals with diabetes, evidence regarding the effectiveness of gamification remains inconsistent. The purpose of this study is to evaluate existing systematic reviews on the effectiveness of gamification interventions for blood glucose management among individuals with diabetes. Method: A systematic literature search was conducted using electronic databases including Medline, Embase, Cochrane library, APA PsycInfo, Web of Science, and Campbell systematic reviews. Studies published in English with gamification as the primary intervention and BG or HbA1c as primary outcomes were included in the review. Studies were excluded if they involved gestational diabetes, used gamification alongside other interventions, or were classified as gray literature. The quality of each review was assessed using a modified AMSTAR 2 tool. Results: Of 382 articles screened, eight systematic reviews were included in the final review. In the quality assessment, four reviews fulfilled 11 out of 13 (84.6%) of the critical appraisal items. All (100%) of the reviews demonstrated reduction in HbA1c; however, the reduction was statistically significant in only one review. Conclusions: Gamification shows potential in assisting with glycemic control, with reviews finding a decrease in HbA1c among patients with diabetes. More rigorous, large-scale studies need to be done to understand gamification as a method of diabetes management and long-term outcomes. Full article

Background: Kidney transplant recipients (KTRs) with diabetes mellitus (DM) are at high cardiovascular risk, and heart failure (HF) is a major concern. Dapagliflozin has proven benefits in HF, but data in KTRs are scarce. Methods: Retrospective analysis of adult KTRs with DM and HF who received dapagliflozin. Data were collected at baseline, defined as dapagliflozin initiation, and at the most recent follow-up (April 2024). Outcomes included changes in LVEF, renal function, metabolic and hemodynamic parameters, hospitalizations, and adverse events. Results: In 32 KTRs (median age 60.5 years, 66% male), after a median follow-up of 2.2 years, left ventricular ejection fraction (LVEF) did not change significantly (60% to 58%, p = 0.28). Systolic BP decreased by 5 mmHg (p < 0.001) and diastolic BP by 3 mmHg (p = 0.034). HbA1c decreased from 7.3 [6.6–8.1] to 6.8 [6.3–7.7]% (p = 0.034), while LDL-c and triglycerides decreased (p = 0.013 and p < 0.001). Body weight and BMI also decreased (both p < 0.001). Renal function, as assessed by eGFR, remained stable (p = 0.633), with no major renal safety signals. No deaths, severe hypoglycemia, or ketoacidosis occurred; urinary tract infections in 12%. Conclusions: In this exploratory cohort, dapagliflozin use was followed by stable graft function and changes in metabolic and hemodynamic parameters. No significant change in LVEF was observed. Given the observational design, small sample size, limited echocardiographic data, and absence of a control group, causal inference is not possible. These hypothesis-generating findings require confirmation in prospective controlled trials of KTRs with diabetes and heart failure. Full article

Background/Objectives: Family dyad-focused support plays an important role in health outcomes and has been incorporated in diabetes interventions to enhance diabetes self-management. The purpose of this meta-analysis was to examine the pooled efficacy of dyad-focused diabetes interventions on glycemic control for people with Type 2 diabetes (T2D). Methods: The PRISMA was used to guide the study. Four databases (PubMed, Embase, CINAHL, and Scopus) were searched from inception through 18 December 2025 to identify randomized controlled trials (RCTs) that tested glycemic control (A1C) of family-based diabetes interventions for people with T2D. Controlled vocabulary and key terms included “Diabetes, Mellitus, Type 2,” “type 2 diabetes,” “self-management,” “self-care,” “spouses,” “dyad,” “caregiver,” and “partner support”. Studies were excluded if they were not RCTs, contained peer-support dyads, lacked family/friend dyads, or did not measure glycemic control. Results: Nineteen eligible RCTs were included in this meta-analysis. Short-term efficacy (3–6 months) was reported in 17 studies with a total of 863 dyads; long-term efficacy (9–12+ months) was reported in 9 studies with a total of 707 dyads. Compared to the control groups, dyad-focused interventions showed a statistically significant reduction in A1C in both short-term (−0.50; 95% CI −0.72, −0.28) and long-term (−0.52; 95% CI −0.77, −0.27) measurement points (p < 0.001). The meta-analyses showed low or moderate detectable heterogeneity (I² = 38%, 45%). The funnel plot suggested no obvious evidence of publication bias. Conclusions: Meta-analysis provided evidence on the efficacy of family dyad-focused diabetes intervention on improving glycemic control. Future large RCTs are warranted to explore innovative strategies to better incorporate family/friend dyad support in diabetes self-management. Full article

Background: The pharmacological management of type 2 diabetes mellitus has become increasingly complex due to expanding therapeutic options and the high prevalence of multimorbidity in affected patients. As a result, the risk of drug–drug and drug–disease interactions has grown significantly, with potential implications for glycemic control, safety, and treatment outcomes. Objective: This narrative review provides a comprehensive, class-based overview of clinically relevant interactions associated with antidiabetic medications, highlighting their mechanisms, clinical consequences, and practical management strategies. Methods: A targeted literature search was conducted using major medical databases to identify clinical studies, meta-analyses, pharmacovigilance reports, and evidence-based guidelines concerning interactions related to key antidiabetic drug classes. Interactions were categorized as pharmacokinetic, pharmacodynamic, or disease-related. Results: Significant variability exists across antidiabetic drug classes in terms of interaction profile and clinical relevance. Metformin presents interaction risks mainly through renal impairment or co-administration with drugs affecting lactate metabolism. Sulfonylureas and glinides are strongly associated with hypoglycemia-enhancing interactions. DPP-4 inhibitors generally exhibit a low interaction burden, whereas GLP-1 receptor agonists may interact through delayed gastric emptying. SGLT2 inhibitors require caution in patients with diuretics or conditions predisposing them to dehydration or ketoacidosis. Insulin remains highly sensitive to pharmacodynamic interactions with a broad spectrum of therapies. Underlying renal, hepatic, and cardiovascular conditions further modify the interaction risk. Conclusions: Understanding class-specific interaction profiles is essential for personalized and safe diabetes management. Careful medication review, close metabolic monitoring, and individualized dose adjustments can mitigate the risk of harmful interactions. Further research is needed to elucidate interactions in populations with advanced multimorbidity and polypharmacy. Full article

Diabetes mellitus (DM) is increasing worldwide and places a substantial burden on health systems through its complications. Background/Objectives: To identify factors associated with DM-related complications in adults receiving primary care in Rio Branco, Acre, western Brazilian Amazon. Methods: Population-based cross-sectional study in 30 Family Health Strategy (FHS) units; 324 participants, weighted to represent 2245 adults with DM. Four binary outcomes were analyzed: self-reported stroke, electrocardiographic (ECG) abnormalities, microangiopathy, and any complication. Associations were estimated through using Poisson regression with robust variance. Results: About 72% of participants had at least one complication. Any complication was independently associated with male sex (PR = 1.23), age ≥ 60 years (PR = 1.25), hypertension (PR = 1.34), illiteracy (PR = 1.18), and ≤3 medical appointments in the previous 12 months (PR = 1.46). Distinct factors emerged for each individual outcome. Conclusions: DM complications were highly prevalent and associated with multifactorial determinants, supporting risk stratification, early detection, and targeted interventions in primary care. Full article

Background/Objectives: Diabetic retinopathy (DR) is one of the most common microvascular complications in type 2 diabetes mellitus (T2DM), in which oxidative stress, inflammation and angiogenic pathways are associated with the development and progression beyond glycemic control. Serum trace element levels (Cu, Zn, Fe, Mg, Cr, Mn, Cd, and Se), antioxidant enzyme activities (superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px)) were measured in patients with T2DM, with and without DR, as well as in healthy controls, and their associations with the presence and severity of DR were evaluated. Methods: 61 T2DM patients, 27 healthy controls. Patients with T2DM were classified into T2DM without DR (n = 30) and T2DM with DR (n = 31). Non-proliferative DR (NPDR, n = 19) and proliferative DR (PDR, n = 12) were classified as the T2DM with DR group. Inductively coupled plasma–mass spectrometry (ICP-MS) was used to quantify serum trace elements. SOD and GSH-Px activities were measured using colorimetric assays. Results: Significant differences were observed in trace element levels and antioxidant enzyme activities among the study groups (p < 0.001 to 0.05). The DR subgroup had lower levels of Cr, Cu and Se compared to the T2DM without DR group; Cd, Zn and Mn were also higher in the T2DM with DR than in the T2DM without DR group. Fe levels were significantly higher in the PDR subgroup than in the T2DM without DR group (p < 0.001). The PDR group showed greater declines of Cr, Cu and GSH-Px compared to NPDR while higher values for Mn, Fe, and Zn were obtained (p < 0.001). Several biomarkers remained significantly associated with DR after adjustment for metabolic variables. Correlation analysis between trace elements, and antioxidant enzymes showed significant associations. Conclusions: Trace element imbalance, and reduced antioxidant enzyme activities may contribute to the development and progression of DR in T2DM. These findings suggest that oxidative stress and micronutrient imbalance may be linked to DR-related biochemical alterations. Full article

Background: Type 2 diabetes mellitus (T2DM) is strongly associated with atherosclerotic cardiovascular disease (ASCVD), yet the independent contribution of diabetes duration to cardiovascular burden remains incompletely understood. While prolonged disease exposure is presumed to increase vascular risk, the extent to which this association is independent of chronological aging and metabolic factors remains unclear. Methods: We conducted a real-world, cross-sectional study including 250 adults with T2DM followed in a tertiary outpatient clinic. Diabetes duration was analyzed both as a continuous variable and across four predefined strata (0–4, 5–9, 10–14, and ≥15 years). The primary outcome was the presence of a composite ASCVD endpoint. Logistic regression models were constructed in unadjusted, adjusted (age, sex, BMI, HbA1c), and extended forms including additional cardiometabolic variables. Interaction, nonlinear, and sensitivity analyses were also performed. Results: ASCVD prevalence increased numerically across duration strata (76.9%, 83.3%, 86.5%, and 93.8%, respectively), although the linear trend did not reach statistical significance (p = 0.118). In unadjusted analysis, each additional year of diabetes was associated with increased odds of ASCVD (OR 1.09; 95% CI 1.02–1.17; p = 0.012), but this association was attenuated after adjustment (OR 1.04; 95% CI 0.96–1.13; p = 0.328) and remained non-significant in extended models (OR 1.05; 95% CI 0.95–1.15; p = 0.347). Conclusions: In this high-risk clinical cohort, the association between diabetes duration and prevalent ASCVD was attenuated after multivariable adjustment, particularly after accounting for age and cardiometabolic covariates. These findings suggest substantial overlap between chronological aging, cumulative metabolic exposure, and cardiovascular burden in patients with T2DM. Due to the cross-sectional design and potential residual confounding, the results should be interpreted as hypothesis-generating. Full article

Background: Epilepsy is a frequent neurological comorbidity in type 2 diabetes. Sodium–glucose cotransporter-2 inhibitors (SGLT2i) exert metabolic, vascular, and anti-inflammatory actions beyond glucose lowering, suggesting potential neuroprotective properties. We assessed whether SGLT2i use is associated with a reduced incidence of epilepsy compared with dipeptidyl peptidase-4 inhibitors (DPP-4i). Methods: We emulated a target trial using a retrospective observational cohort of adults with type 2 diabetes initiating SGLT2i or DPP-4i from a large real-world database. Propensity scores were estimated using a SuperLearner algorithm, and stabilized inverse probability of treatment weights were applied to balance baseline characteristics. Weighted Kaplan–Meier and Cox regression models were used to estimate hazard ratios (HRs) for incident epilepsy. Results: Among 176,728 patients (mean age 68 years; 39% women), 43% received SGLT2i. The weighted incidence of epilepsy was 2.05 versus 2.45 per 1000 person-years for SGLT2i and DPP-4i, respectively. SGLT2i treatment was associated with a significantly lower risk of epilepsy (HR 0.72, 95% CI 0.61–0.86; p < 0.001). Conclusions: In this large real-world study, initiation of SGLT2 inhibitors was associated with a lower incidence of epilepsy compared with DPP-4 inhibitors. The absolute difference in event rates was small (2.05 vs. 2.45 cases per 1000 person-years), and residual confounding cannot be excluded. These findings should therefore be regarded as hypothesis-generating and warrant prospective research to confirm causality and clarify potential mechanisms. Full article

Background: The prevalence of GDM is increasing and is associated with maternal health and neonatal complications. Therapeutic intervention for this condition is important for the health of both mothers and their unborn children. Objective: The present meta-analysis evaluates the effects of pharmacological, nutritional, and physical activity interventions on maternal and neonatal outcomes in women with GDM, including glucometabolic control, weight gain, blood pressure, lipid profiles, and pregnancy complications. Methods: Multiple databases were systematically searched for studies investigating GDM interventions and their effects on maternal and neonatal outcomes, including at least one of the following endpoints: 2 h postprandial glycemia, FBG, HbA1c, triglycerides, cholesterol, weight gain, blood pressure, cesarean delivery, preeclampsia, gestational age at delivery, neonatal hypoglycemia, neonatal complications, birth weight, preterm birth, Apgar score at 5 min, macrosomia, and NICU admission. Initial screening identified 204 records, which were narrowed to 17 studies meeting the eligibility criteria for inclusion in the meta-analysis following multi-author relevance review. Six reviewers independently extracted data and resolved discrepancies through consensus. Study quality was appraised by two reviewers using the Cochrane Risk of Bias tool, and data were analyzed using the RevMan Web software with random-effects models. Results: Pharmacological, nutritional, and physical activity interventions in women with gestational diabetes mellitus demonstrated statistically significant reductions in gestational weight gain and cesarean delivery rates. No statistically significant effects were observed for HbA1c, fasting blood glucose, 2 h postprandial glucose, lipid profiles, or blood pressure. Several outcomes, including preeclampsia, neonatal hypoglycemia, neonatal complications, and NICU admission, showed non-significant trends toward benefit, but these findings were based on limited data and should be interpreted cautiously. No meaningful effects were observed for gestational age at delivery, neonatal birth weight, preterm birth, Apgar score, or macrosomia. Substantial heterogeneity was present across metabolic outcomes, limiting the interpretability of pooled estimates. Conclusions: Nutritional and physical activity interventions significantly reduce HbA1c, gestational weight gain, and cesarean delivery in women with GDM, with protective trends for preeclampsia and neonatal complications. However, effects on lipid profiles and blood pressure remain inconsistent. Personalized, multimodal strategies integrating pharmacological, nutritional, and lifestyle modifications are necessary for optimal GDM management. Full article

Background: Diabetes is a growing public health crisis across the Arab region, where rapid urbanization, dietary transitions, and physical inactivity have contributed to some of the highest diabetes rates globally. Despite a growing recognition of the problem, most diabetes prevention efforts in the region remain small-scale or insufficiently adapted to the sociocultural realities of adults living in the UAE. Evidence-based diabetes prevention strategies, such as the United States’ Centers for Disease Control Diabetes Prevention Program (DPP), reduce the risk of developing diabetes but remain underutilized. Methods: The objectives of this study were to (1) describe the systematic cultural adaptation of the Evidence-based Intervention for Diabetes Prevention (EID) using the Framework for Reporting Adaptations and Modifications–Expanded (FRAME), and (2) assess the preliminary acceptability of the adapted materials through formative focus groups. Results: Materials were culturally tailored to address both deep and surface structures. Deep structure adaptations incorporated Arab cultural values, social norms, and religious practices, including Ramadan-specific content. The original 26-session curriculum was condensed to 12 weekly sessions based on prior research and stakeholder input. Surface-level adaptations included translation into Arabic and development of culturally relevant educational videos. Three formative focus groups (n = 7 total participants) provided preliminary findings of strong acceptability of simplified, culturally relevant, and digitally supported materials. Conclusions: This work will inform the adaptation of an evidence-based lifestyle change program aimed at preventing type 2 diabetes in high-risk individuals to better meet the needs of adults living in the UAE. While some countries have created their own national diabetes prevention efforts, like the United Kingdom, there is notably no similar program in the Arab world. Full article

Background: A bidirectional association between diabetes and oral health is well established, yet oral self-care is overlooked in diabetes management. Health Belief Model (HBM)-guided oral care interventions have exhibited promising outcomes in the literature but have not been used to guide oral self-care interventions designed for people with diabetes (PWD). Positioned at the early conceptualization and design stage of such a program, this developmental study was to identify self-perceived needs in oral self-care practices and to obtain preliminary feedback among PWD about the blueprint of a new program—DiaOral^©. Methods: We conducted semi-structured interviews with 15 PWD recruited from a large healthcare system, with a goal to recruit patients from racially/ethnically diverse urban/suburban zip codes. Interviews explored participants’ oral self-care practices in relation to diabetes. Sample DiaOral^© content and images on a blueprint were presented and feedback was solicited. Braun and Clarke’s reflexive thematic analysis was used to code and interpret transcripts, aligning emerging themes with HBM constructs through team-based consensus. Results: Three major themes and 27 sub-themes emerged: (1) lack of knowledge on optimal oral care, (2) low perceived importance of preventive care and oral health in diabetes, and (3) low self-efficacy for performing effective oral self-care. Participants expressed satisfaction with the content and their perceived confidence and interest potentially in using the DiaOral^© program based on their preliminary review of the blueprint. Conclusions: Findings support the relevance of HBM constructs in shaping oral self-care among PWD. This developmental study suggests that the DiaOral^© blueprint is ready to move forward to website prototype development. Future work will finalize the program and evaluate its efficacy among PWD. Full article

Background: Pre-existing diabetes mellitus is prevalent among critically ill adults and can influence initial glycemic targets, therapeutic decisions, and early risk stratification in the intensive care unit (ICU). However, diabetes status may be distributed across heterogeneous electronic health record (EHR) sources and may be incomplete at the time of ICU admission, particularly for inter-facility transfers. Methods: Using the public WiDS Datathon 2021 tabular release derived from the Global Open-Source Severity of Illness Score (GOSSIS) initiative, we conducted a retrospective machine-learning benchmarking study for admission-time identification of documented diabetes status in ICU patients. Candidate predictors included demographics, admission characteristics, anthropometrics, day-1 physiologic and laboratory summaries, APACHE-related variables, comorbidity indicators, and site descriptors. We compared CatBoost, random forest, tuned XGBoost, tuned LightGBM, histogram-based gradient boosting, and a soft-voting ensemble combining XGBoost, LightGBM, and histogram-based gradient boosting. Because class imbalance was a central concern, the final workflow emphasized model-intrinsic class weighting and threshold-aware evaluation rather than synthetic oversampling. Results: In the primary leakage-mitigated random validation split, the voting ensemble achieved the highest overall balance, with AUROC 0.8539, precision 0.5671, recall 0.6690, and F1-score 0.6138. Tuned LightGBM was the most sensitivity-oriented individual model, achieving recall 0.7677 and AUROC 0.8537, although with lower precision and a less favorable Brier score. Ablation analyses clarified the source of this performance: removing leakage-prone and APACHE-related variables caused only modest decreases in discrimination, whereas the strict reduced model that also excluded glucose-like predictors produced a marked decline, with LightGBM AUROC falling to 0.7432 and the voting ensemble AUROC falling to 0.7448. These findings, together with SHAP analyses identifying day-1 glucose maximum, day-1 glucose minimum, BMI, age, hemoglobin, and related clinical variables as major contributors, indicate that glucose-related admission variables remained the dominant predictive signal. In grouped hospital validation, tuned LightGBM maintained recall of 0.7684 while AUROC decreased modestly to 0.8443, indicating preserved case detection under stricter site separation but reduced precision. Precision–recall analysis further showed that average precision decreased from 0.622 under random validation to 0.551 under grouped validation; at a high-sensitivity grouped-site operating point, a probability threshold of 0.4537 achieved recall of 0.8001 with precision of 0.4314. Calibration curves and Brier scores showed that predicted probabilities were imperfectly calibrated. Conclusions: Although the dominance of glucose-related predictors is clinically plausible for identifying documented diabetes status, early glycemic measurements in critically ill patients may also partly capture acute stress physiology, treatment-related effects, monitoring intensity, or other forms of acute dysglycemia rather than chronic diabetes status alone. Therefore, these findings support gradient-boosted and ensemble models as reproducible tools for ICU admission-time phenotyping of documented diabetes status, but the proposed system should be interpreted primarily as a screening-oriented phenotyping aid for chart review, cohort enrichment, or workflow support, not as a stand-alone diagnostic tool. Further external validation, recalibration, threshold selection matched to intended use, and clinical review are needed before deployment. Full article