Search Results (4,017)

Aims: This study aimed to examine the association between three-year changes in body mass index (BMI) and the risk of new-onset or progressive diabetic kidney disease (DKD) among people with type 2 diabetes and a normal BMI at baseline. Methods: A total of 416 people with type 2 diabetes (T2DM) and a normal BMI were enrolled from the Chongqing Diabetes Registry (CDR, NCT03692884) cohort and were followed for incident DKD until 2025. The change in BMI at the three-year follow-up was classified as follows: stable BMI (<5% change), decreased BMI (≥5% reduction), and increased BMI (≥5% gain). Cox proportional hazards models were used to analyze the association between BMI change categories and DKD risk. Results: During a mean follow-up of 3.4 years, people with an increased BMI exhibited a significantly higher risk of DKD onset or progression compared with people with a stable BMI [HR = 1.67, 95%CI: 1.15–2.43, p = 0.007]. Each 1% increase in BMI was significantly associated with an increased risk of DKD onset or progression [HR = 1.05, 95%CI: 1.02–1.07, p < 0.001]. This association remained significant after multivariable adjustment. Time-dependent receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of this indicator reached 0.683–0.729 for the prediction of new-onset or progressive DKD risk over 3–5 years. In subgroup analyses, decreased BMI was associated with a lower risk of DKD among people aged <60 years [HR = 0.21; 95% CI: 0.04–0.96; p = 0.044]. Conclusions: A ≥5% increase in BMI in three years may be a risk factor for new-onset or progressive DKD among people with T2DM and normal BMI. Conversely, a ≥5% decrease in BMI may be associated with renal protection in non-elderly individuals within the population. Full article

The diagnosis of type 2 diabetes using classical clinical and laboratory biomarkers (HbA1c, glucose, lipids, BMI, and blood pressure) is a classification by symptoms and does not provide insight into the underlying pathophysiological disorders (insulin resistance, ß-cell dysfunction, visceral adipose tissue hormonal secretion, and chronic systemic inflammation). A better understanding of these disorders may help in the selection of appropriate and potentially more successful personalized therapeutic interventions. Based on extensive clinical trial experience, a method for individual phenotyping and consecutive personalized diabetes therapy has been developed in our practice, which we have been using for more than 15 years and would like to share for discussion and debate. In this Part 1, the pathophysiological background and diagnostic approach to phenotyping is described. A consecutive Part 2 will present the translation of the phenotyping result into a personalized diabetes therapy, and another consecutive Part 3 will provide more comprehensive real-world patient observations when practicing this concept. This article is intended as a discussion/concept paper and does not present unpublished patient-level outcome data or formal effectiveness analyses. Prospective validation studies are needed to evaluate the clinical utility of this phenotype-based framework. Full article

Managing blood glucose in type 1 diabetes (T1D) remains a daily clinical challenge, and accurate short-term prediction of glucose levels can meaningfully improve insulin dosing decisions while reducing the risk of dangerous hypoglycaemic episodes. Although numerous machine learning approaches have been proposed for this task, comparing their relative merits is difficult because published studies differ widely in datasets, preprocessing choices, and evaluation criteria. In this work, we address this research gap by benchmarking ten machine learning methods—from a naïve persistence baseline through classical linear regressors, gradient-boosted ensembles, and recurrent neural networks to a novel hybrid that couples LightGBM with stochastic differential equation (SDE)-based glucose–insulin simulation—on two multi-patient datasets comprising 34 T1D subjects, across prediction horizons of 15, 30, 60, and 120 min. Every method is trained and tested under identical preprocessing and temporal splitting conditions to ensure a fair comparison. The proposed Hybrid LightGBM-SDE model consistently outperforms all alternatives, recording RMSE values of 22.42 mg/dL at 15 min, 28.74 mg/dL at 30 min, 33.89 mg/dL at 60 min, and 37.22 mg/dL at 120 min—an improvement of between 13.6% and 27.0% relative to standalone LightGBM. At the clinically important 30 min horizon, 99.7% of predictions lie within the acceptable A and B zones of the Clarke Error Grid. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 10⁻¹⁰), and SHAP-based analysis shows that the SDE-derived simulation features are among the most influential predictors, especially at longer horizons. All source code and evaluation scripts are publicly released to support reproducibility. Due to temporary data access constraints, all experiments reported here use physics-based synthetic datasets generated from the Bergman minimal model, replicating the structural properties of the D1NAMO and HUPA-UCM collections; validation on the original clinical recordings is planned. Among the two synthetic datasets, the D1NAMO-equivalent cohort (nine patients) proves more challenging, with systematically higher per-patient RMSE variance. The clinically acceptable prediction accuracy at the 30 min horizon (99.7% in Clarke zones A + B) suggests potential for integration into insulin dosing decision-support systems. Full article

Background: Screening for presymptomatic type 1 diabetes (T1D) reduces the risk of diabetic ketoacidosis (DKA) and allows for early intervention with disease-modifying therapies. Despite the rising incidence of T1D in Bulgaria, screening initiatives remain limited. This pilot study aims to evaluate the feasibility of selective T1D screening in high-risk children and identify potential clinical associations with islet autoimmunity. Methods: The study targeted a recruitment of 250 children aged 0–18 years (200 with a relative with T1D and 50 without). Screening for islet autoantibodies (AABs), including glutamic acid decarboxylase (GADA), insulin (IAA), insulinoma-associated-2 (IA-2A), zinc transporter-8 (ZnT8A), and islet cell cytoplasmic autoantibodies (ICAs), was performed via chemiluminescence immunoassay (CLIA). Participants testing positive for one or more AABs were scheduled for longitudinal immunological and metabolic follow-up to evaluate the persistence of autoimmunity and disease progression. Results: Between October 2024 and February 2026, the pilot study recruited 210 participants (84% of the 250 target), including 160 children with a relative (target 200) and 50 without a family history of T1D (target 50). Within the high-risk group, seven children (4.4%) tested positive for a single autoantibody (3 GADA, 2 ZnT8A, 1 IA-2A, and 1 IAA), while no autoantibodies were detected in the group without a relative. No cases of multiple autoantibody positivity or stage 3 T1D were identified in either group. Furthermore, no statistically significant associations were observed between autoantibody positivity and secondary factors, including breastfeeding, allergic status, a high-glycemic diet, frequent illness, and personal history of autoimmune disease. Conclusions: The findings validate the feasibility of selective T1D screening in Bulgaria, driven by high public interest and successful recruitment across both high-risk and general population cohorts. While this exploratory study found no significant clinical correlations, it establishes a vital roadmap for larger, longitudinal research. Ultimately, this pilot framework provides a scalable model for implementing standardized early detection to reduce the burden of T1D on the national healthcare system. Full article

Background: Pacemakers enable continuous long-term surveillance of atrial fibrillation detected by implanted devices. Circulating biomarkers reflecting endothelial dysfunction, inflammation, and myocardial stress may help identify patients at risk for atrial fibrillation (AF) progression and higher arrhythmic burden. Methods: This analysis included patients from the prospective ACaSA study (NCT05127720) with a dual chamber pacemaker (Microport^® BOREA DR or TEO DR) and monitored weekly via remote monitoring technology (SMARTVIEW^®). Individuals with permanent AF or single-chamber systems were excluded. Baseline plasma concentrations of angiopoietin-2 (ANGPT2), growth differentiation factor-15 (GDF-15), fibroblast growth factor-23 (FGF-23), bone morphogenetic protein-10 (BMP10), and tumor necrosis factor–related apoptosis-inducing ligand receptor-2 (TRAIL-R2) were quantified using enzyme-linked immunosorbent assays. N-terminal pro-B-type natriuretic peptide (NT-proBNP) was measured using electrochemiluminescence immunoassay. Biomarkers were log₂-transformed, with values below assay detection limits imputed at half the lower limit of detection. Two endpoints were assessed following a 30-day blanking period: (1) progression to persistent AF, defined as ≥7 consecutive days with >99% daily AF burden, analyzed using Cox regression; and (2) AF burden, calculated as total AF time normalized to monitored days and categorized as <25%, 25–75%, or >75%, analyzed using multinomial logistic regression. Multivariable models were adjusted for age, sex, heart failure, diabetes, and prior myocardial infarction; Cox models were limited to age, sex, and heart failure due to fewer events. Results: A total of 223 patients were included (median age 75 years; 37.2% women). During follow-up, 28 patients (13.3%) progressed to persistent AF. Higher baseline ANGPT2 was the strongest predictor of progression (HR per doubling 1.83, 95% CI 1.27–2.66, p = 0.001), followed by GDF-15 (HR 1.52, 95% CI 1.03–2.24, p = 0.036). In the burden analysis, ANGPT2 demonstrated a pronounced graded relationship with arrhythmic load, with markedly increased odds of high (>75%) AF burden (OR 8.31, 95% CI 2.63–26.26, p < 0.001). GDF-15 independently predicted both medium (OR 2.05, p = 0.025) and high burden (OR 2.32, p = 0.037). NT-proBNP displayed a borderline association with high burden (OR 2.02, p = 0.061). No significant associations were observed for FGF-23, BMP10, or TRAIL-R2. Conclusions: In continuously monitored pacemaker patients, ANGPT2 and GDF-15 emerged as key biomarkers associated with AF disease severity. ANGPT2 was strongly linked to both progression to persistent AF and high AF burden, whereas GDF-15 consistently predicted higher AF burden and also contributed to risk of progression. These findings highlight endothelial and inflammatory pathways as potential markers of atrial disease progression. Full article

Background: Ectopic fat deposition has been demonstrated to play a critical role in the onset and progression of renal dysfunction. However, research on renal parenchymal fat deposition and its association with renal dysfunction in type 2 diabetes mellitus (T2DM) remains limited, particularly regarding its association with early kidney injury. The present study aimed to further investigate the relationship between renal fat fraction (FF) and biomarkers of kidney injury, thereby providing new evidence for the potential link between intrarenal fat accumulation and early renal impairment in T2DM. Methods: This cross-sectional study enrolled 60 patients with T2DM. Renal FF was quantitatively assessed using magnetic resonance imaging (MRI). Clinical characteristics, body composition parameters, and biochemical indices were collected. Levels of kidney injury biomarkers, including tumor necrosis factor receptors 1 (TNF-R1), tumor necrosis factor receptors 2 (TNF-R2), chitinase-3-like protein 1 (YKL-40), and kidney injury molecule-1 (KIM-1), were measured using enzyme-linked immunosorbent assay (ELISA). To evaluate the correlations between fat distribution and inflammatory biomarkers, Pearson correlation analysis was performed. Furthermore, linear regression analysis was conducted to explore the associations between renal FF and kidney injury biomarkers with adjustments for potential confounders such as smoking status, diabetes duration, and visceral fat. Lasso regression was used to screen variables. Results: The results demonstrated that renal FF was significantly positively correlated with serum YKL-40 (r = 0.3, p = 0.021), TNF-R1 (r = 0.246, p = 0.042), and urinary KIM-1 (r = 0.396, p = 0.004), indicating a close association between renal fat accumulation and early kidney injury biomarkers. In regression analyses adjusted for age, sex, and duration of diabetes, the associations between renal FF and these biomarkers remained significant. After further adjustment for potential confounders, including smoking history, alcohol consumption, hypertension, renin-angiotensin-aldosterone system (RAAS) inhibitors, sodium-dependent glucose transporters 2 (SGLT2) inhibitors, glucagon-Like Peptide-1 (GLP-1) receptor agonists, and lipid-lowering drugs, renal FF remained significantly associated with TNF-R1 (β = 0.327, p = 0.015), KIM-1 (β = 0.352, p = 0.021), and YKL-40 (β = 0.275, p = 0.025). Moreover, even after additional adjustment for visceral fat, the associations of renal FF with TNF-R1 and KIM-1 persisted. After using the Benjamini–Hochberg procedure for false discovery rate, the relationship between renal FF and KIM-1 had a significant difference. Variables of age and gender were excluded to build the parsimonious modeling using Lasso regression. It suggested that renal fat accumulation may contribute to kidney injury independently of visceral adiposity. Conclusions: The study systematically demonstrates a significant association between renal FF and early biomarkers of kidney injury in T2DM, which may suggest the potential role of renal fat accumulation in the pathogenesis of diabetic nephropathy. These findings provide clinical data support for the development of a fat-targeted intervention study. Future research should further elucidate the long-term mechanistic role of renal FF in diabetic nephropathy, as well as its potential value in early diagnosis and therapeutic applications. Full article

The disproportionately severe disease course of diabetic patients with SARS-CoV-2 infection was repeatedly observed by clinicians during the COVID-19 pandemic. The overlap between metabolic impairment, viral pathophysiology, and chronic inflammation created a pattern that urged deeper examination. The aim of this paper was to review and synthesize evidence regarding the interaction between diabetes mellitus and COVID-19. We synthesized evidence across mechanistic pathways (immune dysregulation, chronic inflammation, ACE2/DPP-4-related signaling, endothelial dysfunction, and pancreatic involvement) and key clinical outcomes (severity, intensive care unit (ICU) admission, mortality, dysglycaemia/new-onset diabetes, and DKA). This systematic search was conducted in PubMed, Clinical Key, and Google Scholar. The eligibility criteria included papers on adults (≥18 years) with pre-existing diabetes mellitus (type 1 or type 2) or newly diagnosed diabetes/hyperglycemia and confirmed SARS-CoV-2 infection, published between January 2020 and October 2025, in English language. The PRISMA guidelines were used for data extraction. We identified 412 articles, out of which only 30 met all the inclusion criteria. Diabetes was consistently evoked as a major risk factor for severe COVID-19, being associated with higher susceptibility to pneumonia, respiratory failure, ICU admission, and mortality. The explanation lies in the impaired immune system, endothelial dysfunction, and metabolic repercussions imposed by hyperglycemia. Several antidiabetic drugs appeared protective in multiple cohorts. In conclusion, the accumulated evidence underscores the tight interplay between metabolic disease and COVID-19. Essentially, the clinical management of these patients would be a thoughtful selection of antidiabetic therapy and close metabolic monitoring. Full article

Background: The ketogenic diet has gained substantial popularity in recent years, and an increasing number of caregivers of children with type 1 diabetes are considering it as a nutritional strategy to improve glycemic control. Reported benefits include fewer postprandial glucose fluctuations, lower insulin requirements, and reduced insulin-associated weight gain. However, the use of this diet in children with type 1 diabetes remains highly debated, and scientific evidence regarding its safety and long-term effects in the pediatric population is limited. This narrative review aims to explore the motivations that lead parents to initiate a ketogenic diet in their children with type 1 diabetes and to summarize current knowledge on its potential metabolic and developmental consequences. Methods: A narrative review of the literature was conducted, including original research articles, case reports, and existing reviews addressing the use of ketogenic diets in children with type 1 diabetes. Clinical observations and published accounts of family experiences were also examined to contextualize emerging concerns and motivations. Results: Parents most commonly adopt a ketogenic diet for their children due to the desire for tighter glucose control, concerns about insulin-related weight gain, and the influence of information shared on social media. Some observational data suggest improvements in glycemic stability and reduced insulin requirements under ketogenic dietary regimens, while available evidence also highlights several potential risks, including dyslipidemia, increased susceptibility to hypoglycemia, slowed linear growth, and possible neurocognitive and psychosocial effects. Long-term safety data remain scarce, and current findings are insufficient to establish clear clinical recommendations. Conclusions: Interest in ketogenic diets among families of children with type 1 diabetes is growing, yet existing evidence suggests that the diet may pose significant metabolic and developmental risks in this population. Further well-designed studies are needed to evaluate its safety and efficacy. This review may assist clinicians in counseling families and underscores the need for evidence-based guidelines regarding restrictive dietary patterns in youth with type 1 diabetes. Full article

Background: Individuals with type 1 diabetes (T1D) have an increased fracture risk, but no clear biomarkers have been linked to this risk. ApoA1 and ApoB were selected due to their association with metabolic disturbances in T1D. Copeptin was included given emerging evidence that ADH influences bone remodeling and glucose metabolism. The aim of this study was to identify biomarkers associated with fractures in patients with T1D. Methods: This prospective, population-based study included 473 individuals with T1D and 465 individuals without diabetes. Fasting blood samples were collected at baseline, and fracture outcomes were assessed after approximately 10 years. ApoA1, ApoB, CRP, GFR, copeptin, and HbA1c were analyzed. Cox regression was used to evaluate associations with fracture risk, and results were calculated per unit increase. Results: In total, 91 fractures occurred. A Kaplan–Meier analysis was performed to compare fracture risk between the control group and individuals with T1D. The results demonstrated a higher risk of fractures over time in patients with T1D compared to controls (p-value 0.037). When we divided the population by patient/control status, we found that, after adjustment for all investigated variables (HbA1c, GFR, CRP, copeptin, age, smoking, cortisone treatment, physical activity, lipid-lowering medication, and gender), both ApoA1 (HR 4.290, CI 1.871–9.837, p-value < 0.001) and ApoB (HR 7.625, CI 1.995–29.138, p-value 0.003) remained independently associated with fracture risk in the T1D group. Conclusions: Higher ApoA1 and ApoB levels are associated with increased fracture risk in individuals with T1D, independently of confounders. Additionally, individuals with T1D have a higher overall fracture risk compared to controls. Full article

Background/Objectives: Diabetes mellitus is a common chronic disease that may lead to multimorbidity and high drug use. Therefore, this study aims to examine the prevalence of polypharmacy and hyperpolypharmacy among adult patients diagnosed with type 2 diabetes mellitus (T2DM) with its associated factors. Methods: This is a retrospective cross-sectional study conducted from 1 May 2023 to 31 October 2024. The outcomes in our study were polypharmacy (from five to nine drugs) and hyperpolypharmacy (≥10 drugs). Baseline and demographic characteristics, along with multinomial logistic regression, were used to analyze the data. Results: The total number of patients with T2DM was 2435. The prevalence rate of polypharmacy was 46.98%, while hyperpolypharmacy was 24.27%. Older age was significantly associated with a higher risk of polypharmacy [OR = 1.031, 95% (1.022–1.040)] and hyperpolypharmacy [OR = 1.037, 95% (1.026–1.049)]. In addition, patients with higher levels of hemoglobin A1c showed a significantly higher risk of polypharmacy and hyperpolypharmacy ([OR = 1.162, 95% (1.105–1.221)] and [OR = 1.284, 95% (1.209–1.364)], respectively). The comorbidities that increased the odds of hyperpolypharmacy were hypertension [OR = 2.136, 95% (1.449–3.148)], pulmonary disease [OR = 2.375, 95% (1.292–4.367)], mental disorders [OR = 6.269; 95% (3.284–11.964], and congestive heart failure [OR = 8.014, 95% (2.768–23.200)]. Conclusions: The prevalence of polypharmacy and hyperpolypharmacy is high in patients with T2DM. The predictors that may play a significant role in increasing the risk of hyperpolypharmacy are the poor control of HbA1c and the coexistence of comorbidities. Providing proper prescribing of patients’ therapy plans can improve individuals’ health outcomes. Therefore, this study highlights the important role of primary care physicians in coordinating care, along with clinical pharmacists, in the identification of polypharmacy. Full article

Type 1 diabetes (T1D) is an autoimmune disease characterized by T-cell-mediated destruction of pancreatic β-cells. Antigen-specific peptide immunotherapy represents a promising strategy to restore immune tolerance. Reliable identification of relevant T-cell epitopes requires accurate prediction of peptide binding to disease-associated major histocompatibility complex (MHC) molecules. In this study, we developed and validated artificial intelligence (AI)-driven machine learning (ML) predictive models for peptides binding to the NOD mouse-specific MHC class I molecules H-2D^b and H-2K^d and the class II molecule I-A^g7. Balanced datasets of experimentally validated binders and non-binders were compiled, divided into training and test sets, and used to construct position-specific logo models and supervised ML classifiers based on z-scale physicochemical descriptors. External validation demonstrated moderate predictive performance for the logo models (ROC AUC 0.685–0.738), whereas AI models, including Random Forest, Support Vector Machine, and Gradient Boosting, achieved substantially improved discrimination (ROC AUC 0.888–0.906). The validated models were applied to the major T1D autoantigens glutamic acid decarboxylase 65, insulin-1, insulin-2 and zinc transporter 8 and predicted multiple binders, with some overlapping with previously reported immunodominant regions. Selected binders were prioritized for further synthesis and in vivo immunogenicity testing in NOD mice. Full article

Background/Objectives: Sleep is critical for young adults, particularly those with type 1 diabetes (T1D), who face unique challenges in achieving recommended sleep and diabetes health targets. The purpose of this study guided by the theoretical framework of self-regulation theory is to explore how these individuals navigate self-regulatory processes in their sleep behaviors through mechanisms of self-monitoring, self-judgment, and self-evaluation. Methods: A qualitative descriptive design was implemented using semi-structured interviews with 34 young adults (ages 18–30) living with T1D. Data were collected through focused interviews, sleep diaries, actigraphy, and continuous glucose monitoring, followed by thematic analysis to identify sleep behavior self-regulation patterns. Results: Three primary themes were identified: (1) Sleep Behavior Self-Monitoring—highlighting participants’ awareness of their sleep habits and the diabetes-related impacts on these habits; (2) Sleep Behavior Self-Judgment—reflecting how personal and societal standards inform their evaluation of sleep health; (3) Sleep Behavior Self-Evaluation—showing emotional responses associated with sleep out-comes, where good sleep led to positive feelings and motivation, while poor sleep resulted in frustration. Conclusions: Understanding sleep behavior self-regulation among young adults with T1D is crucial for improving sleep health and diabetes management. Targeted interventions incorporating sleep education and self-regulatory strategies may enhance both perceived sleep quality and overall well-being in this population. Full article

Background: In type 2 diabetes mellitus (T2DM), bone and microvascular complications may be linked. Methods: The University of California Davis (UCD) polygenic T2DM and Sprague Dawley healthy control (CTL) rats (N = 48) were divided equally into diabetic and age-matched groups: (1) pre-diabetes, (2) diabetes onset, (3) early-stage T2DM, and (4) late-stage T2DM. Body mass, HbA1c, fasted blood glucose and femoral and tibial lengths were measured. Bones were scanned (μCT; 15 µm) to assess trabecular microarchitecture and density and mid-shaft cortical thickness (Ct.Th, µm), density and porosity. Ossified vessel volume (OsVV, %) and thickness (OsV.Th, µm) were also analyzed. A GLM determined significance at p < 0.05. Body mass and HbA1c were higher (p < 0.05) in all T2DM groups and blood glucose became elevated (p < 0.05) in early-stage T2DM and late-stage T2DM. Results: Tibiae and femora were longer (p < 0.05) with diabetes. Tibial bone volume was lower (p < 0.05) in pre-diabetes (4 ± 1% vs. CTL, 9 ± 2%) and late-stage T2DM (5 ± 2% vs. CTL, 8 ± 2%), and femoral bone volume was lower (p < 0.05) in pre-diabetes (7 ± 1% vs. 12 ± 4%). Cortical density (tibia) was lower (p < 0.05) in pre-diabetes and early-stage T2DM. Trabecular density in the femur was lower (p < 0.05) in all T2DM groups and cortical density was reduced (p < 0.05) in pre-diabetes, diabetes onset, and late-stage T2DM. OsVV in both bones were lower (p < 0.05) during early-stage T2DM. Tibial OsV.Th was higher (p < 0.05) in pre-diabetes (69 ± 14 µm vs. CTL, 56 ± 13 µm) and late-stage T2DM (80 ± 10 µm vs. CTL, 59 ± 13 µm) and higher (p < 0.05) in the femur at diabetes onset (58 ± 14 µm vs. CTL, 40 ± 10 µm). Conclusions: Trabecular and cortical bone varied as diabetes progressed, and the thicker ossified vessels may represent microangiopathy. Full article

Background/Objectives: Continuous glucose monitoring (CGM) has transformed diabetes management by enabling high-resolution assessment of glucose dynamics, with well-established use in type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D), and expanding applications across broader populations, including non-insulin-treated T2D and gestational diabetes. However, real-world implementation remains constrained by economic barriers, fragmented reimbursement, workflow challenges, and limited capacity for continuous data interpretation. This review examines key barriers to CGM implementation and synthesizes current evidence on pharmacist-integrated CGM care as an emerging model to support CGM adoption across clinical and community-based settings. Methods: A narrative literature review was conducted to synthesize evidence on pharmacist-integrated CGM services in diabetes care. Literature was identified through structured searches of PubMed, Embase, and the Cochrane Library, supplemented by Google Scholar and citation tracking, covering publications from January 2010 to December 2025. Studies were selected based on predefined criteria, including those reporting clinical outcomes, pharmacist involvement, or health system and implementation factors related to CGM use. Relevant survey-based and real-world studies were also considered to capture healthcare professionals’ perspectives and implementation experiences. Evidence was synthesized thematically across clinical, behavioral, and health system domains. Results: Available evidence suggests that pharmacist-integrated CGM care is associated with improvements in glycemic management, including increased time in range, reduced glycemic variability, and more timely pharmacotherapy optimization. Pharmacist involvement may also support patient education, self-management, and engagement with digital health technologies, and facilitate ongoing data interpretation and treatment adjustment between clinical encounters. However, evidence remains heterogeneous and geographically limited, with predominantly retrospective and pilot studies and few randomized trials, constraining the robustness and external validity of the findings. Further studies are needed to confirm its clinical effectiveness, comparative effectiveness, and economic value. Conclusions: Pharmacist-integrated CGM represents a promising and operationally feasible approach to supporting CGM use in routine diabetes care. While current evidence indicates potential benefits in glycemic management and care delivery processes, further research and implementation efforts are required to support its effective and sustainable adoption across diverse healthcare settings. Full article