Frontiers in clinical diabetes and healthcare最新文献

Background: With the prevalence of Type 2 Diabetes (T2D) projected to increase, understanding its potential consequences on healthcare systems is crucial for adequately preparing society to address this growing challenge. In 2019, Malmö, Sweden's third-largest city, joined the Cities for Better Health global initiative to tackle the multifaceted challenges associated with T2D, including its significant economic burden on the healthcare system and the broader community. Understanding the economic burden of T2D on the healthcare system will facilitate optimisation of the initiatives undertaken by the programme. Therefore, this study aimed to estimate the costs associated with primary care, hospital care, and work absenteeism due to diabetes-related complications among people with T2D residing in Malmö.

Methods: In order to estimate the cost for the City of Malmö, we expanded a model (Andersson et al., 2020) developed to estimate the cost of T2D on a national level, using retrospective data from 1997-2016. The costs were estimated by using NordDRG weights and national reference prices. Primary care costs for Region Skåne were added to the model. Data on healthcare utilisation, work absence, and socioeconomic factors were collected from Swedish national and regional registers. The method was expanded to include Malmö-specific adjustments for demographics, employment, and education, as well as regional primary care costs.

Results: The prevalence of T2D in Malmö was 5.4%, and diabetes complications were: diabetic retinopathy (49.9%), diabetic kidney disease (19.1%), angina pectoris (13.7%), ischaemic heart disease (10.9%), and myocardial infarction (10.5%). Total excess costs for T2D in primary care were €12.7 million. The lowest primary care excess costs were in the age group 16-34 and the highest in the age group 65-74. Estimated overall hospital-based costs for T2D were €38.8 million, and costs related to macrovascular and microvascular complications were €18.1 million and €16.4 million, respectively. Estimated total cost due to absence from work related to T2D complications was €15.4 million. The complication costs were higher for men, except for neuropathy.

Conclusions: These findings may support city-level healthcare planning and preventive interventions, as Malmö is facing substantial costs both in monetary terms and in reduced quality of life. Younger persons increasingly develop diabetes complications, which needs to be considered when allocating resources for primary prevention, treatment of complications, and municipality costs within a near future.

Aim and objectives: To investigate the Cutaneous Signs of Insulin Resistance, namely acanthosis nigricans (AN) and acrochordon (AC), in individuals with Central Obesity (CO-CSIR) as physical predictors of metabolic syndrome (MetS), underlying adipose tissue pathology, and consequent pathophysiological traits in South Asians.

Methods: In this study, 371 participants were recruited in a tertiary care facility and grouped based on the presence of cutaneous signs (AC and/or AN) and central obesity. Each participant was assessed for MetS, T2D, as well as other demographic, biochemical, and radiological parameters. Additionally, we conducted transcriptome profiling in adipose depots for selected individuals to investigate whether there are modules of co-expressed genes that show a correlation with cutaneous sign(s) and MetS/T2D, in order to decipher the link between these signs and metabolic derangement.

Results: ANOVA analyses revealed significant differences among groups with varying cutaneous signs and W:H ratios, particularly highlighting the combined predictive capability of these markers. Post hoc tests further confirmed these findings, showing substantial differences in MetS, T2D, and HOMA-IR between these groups. Sensitivity-specificity analyses demonstrated that CO-CSIR provides a more balanced and accurate prediction of MetS status compared to either CO or CSIR alone. Furthermore, in predicting MetS status based on the number of MetS components (from 5 to ≥1), it also performed well. WGCNA analysis in visceral fat revealed modules of co-expressed genes significantly correlated with AC and MetS, indicating a link between the adipose tissue molecular pathology and the cutaneous signs.

Conclusion: CO-CSIR is a promising physical sign for predicting MetS and the underlying adipose tissue-driven dysmetabolism in South Asians.

The rapid advancement of digital health technologies-such as continuous glucose monitors, automated insulin delivery systems, and telehealth platforms-has transformed diabetes management. However, a persistent digital divide continues to amplify health disparities based on socioeconomic status, geography, and age. Generational gaps are especially notable. "Digital immigrants" (older adults) often face significant barriers to technology adoption, such as reduced digital literacy, lower smartphone ownership, and difficulties using remote care platforms. Individuals aged 80 and above have much lower uptake of continuous glucose monitors and telemedicine, even when cost is not a barrier. Health literacy and language challenges further increase the impact and reduce the use of digital health tools among older adults. In contrast, "digital natives"-younger generations with strong technological skills-are well positioned to help bridge this divide. This mini-review summarizes current evidence on disparities in digital diabetes care and proposes an innovative, student-led solution. We advocate for empowering digital native health sciences students to lead digital health literacy initiatives and serve as technology mentors for both patients and clinicians. By establishing student-led digital literacy centers, academic institutions can promote intergenerational collaboration. This approach can turn the generational divide from a barrier into an opportunity. Ultimately, student-led initiatives offer a sustainable, community-based pathway to equitable adoption of digital diabetes technologies and improved health outcomes.

Background and purpose: Diabetes mellitus and dyslipidemia are major risk factors for atherosclerosis. Hypoechoic plaques, which indicate vulnerable or unstable plaques, may rupture and lead to ischemic stroke, cognitive impairment, increased adverse cardiac events, and even death. This study aimed to investigate the correlation between plasma lipid levels and the characteristics of atherosclerotic plaques in adult patients with type 2 diabetes mellitus.

Methods: A retrospective analysis was conducted on adult patients with type 2 mellitus who were hospitalized in the Department of Endocrinology at Affiliated Hospital of Hebei University between January 2017 and December 2021.Patients were categorized into two groups based on arterial ultrasound results. Statistical analyses were performed to compare plasma lipid levels and plaque characteristics across the groups.

Results: 1) Statistically significant differences were observed among the two groups in terms of gender, hypertension, age, duration of diabetes mellitus, plaque location, triglycerides (TG),total cholesterol (TC), Apolipoprotein A1 (Apo A1),very-low-density lipoprotein (VLDL), VLDL/apolipoprotein B(ApoB), high-density lipoprotein cholesterol (HDL)/ApoA1 (P<0.05). 2) Univariate and multivariate regression analyses revealed that VLDL,VLDL/ApoB and HDL/ApoA1 were correlated with plaque stability, the higher the levels of VLDL,VLDL/ApoB and HDL/ApoA1,the more likely they were to be hypoechoic plaque group (OR>1, P<0.05). 3) VLDL, VLDL/ApoB and HDL/ApoA1 showed predictive value for determining whether patients with type 2 diabetes had stable plaques. The area under the receiver operating characteristic (ROC) curve for VLDL, VLDL/ApoB and HDL/ApoA1 respectively were 0.789,0.779 and 0.728.

Conclusion: In clinical practice, the characteristics of atherosclerotic plaques and lipid profiles should be jointly evaluated to guide targeted treatment and effectively reduce the risk of atherosclerotic cardiovascular disease.

Objective: Diabetes mellitus (DM) is a prevalent chronic disease that can lead to severe microvascular complications. Among these, diabetic nephropathy (DN) remains a leading cause of end-stage renal disease worldwide. Glycemic variability, reflecting fluctuations in blood glucose, has been suggested as a potential predictor of DM complications. This study aimed to investigate whether visit-to-visit HbA1c variability contributes to the development and progression of DN in patients with DM.

Methods: In this retrospective cohort study, 228 patients were selected from 2,000 individuals diagnosed with DM between January 2007 and December 2017. A total of 80 patients without DN at baseline (ODN) and 148 patients with DN at baseline (WDN) were included in the study. HbA1c was measured 2-4 times per year over 3-5 years. Mean, standard deviation (SD), and coefficient of variation (CV) of HbA1c were calculated. Annual urea, creatinine, and albumin/protein levels were recorded. Logistic regression identified independent risk factors.

Results: DN developed in 47 (58.8%) patients in the ODN group, whereas progression occurred in 44 (29.7%) patients in the WDN group. In the ODN group, higher HbA1c mean, SD, CV, hypertension, and albuminuria were significantly associated with DN onset (p<0.05). Logistic regression analysis confirmed HbA1c variability and hypertension as independent predictors. No significant association was found between HbA1c variability and DN progression.

Conclusions: Variability in HbA1c is linked to the onset of DN but not its progression. These findings highlight the need for strategies targeting glycemic stability in DM management. Larger, multicenter prospective studies are warranted to confirm these results.

Introduction: Prediabetes is a highly prevalent metabolic condition that significantly increases the risk of developing type 2 diabetes and cardiovascular disease. Despite its clinical importance, over 80% of individuals with prediabetes remain undiagnosed. Voice analysis has emerged as a non-invasive, accessible method for disease screening, with prior work showing promising results in detecting hypertension and type 2 diabetes from acoustic features. This study investigates whether voice-based machine learning models can identify individuals with prediabetes and evaluates the generalizability of these models across populations.

Methods: Participants were recruited from clinical sites in India and a community college in Canada. All participants recorded the same spoken phrase multiple times daily via a mobile app, and glycemic status was assessed using HbA1c levels. Voice recordings were preprocessed to remove silence and trimmed to exclude potentially uninformative sections. A total of 167 acoustic features were extracted from each sample using Librosa, scipy, and parselmouth. Features were averaged per participant. Sex-specific models were developed under six experimental configurations varying by dataset balance (age/BMI-matched vs. unbalanced) and BMI inclusion. Feature selection was conducted using L1-regularized logistic regression (LASSO), and SMOTE was applied during training to address class imbalance. Twelve machine learning classifiers were evaluated using leave-one-subject-out cross-validation (LOSO-CV) on the India dataset. Final models were tested on a holdout India subset and the independent Canada dataset.

Results: In cross-validation, the best female model (XGBoost, balanced, no BMI) achieved a balanced accuracy of 0.78, and the best male model (Random Forest, balanced, no BMI) achieved 0.68. However, holdout set testing identified different optimal configurations for generalization: the male XGBoost model trained on an unbalanced dataset outperformed the cross-validated model. In the Canada dataset, models failed to generalize effectively, with several configurations unable to correctly identify prediabetic participants.

Discussion: Voice-based prediction models show potential for prediabetes screening in controlled populations, but their performance declines when applied across geographic or demographic boundaries. These findings highlight the need for more diverse training data and population-specific model tuning to support real-world applicability.

Objective: To estimate the prevalence of inadequate glycaemic control and identify factors associated with it among people with type 2 diabetes mellitus (T2DM) living in low- and middle-income countries (LMICs).

Methods: A systematic literature search was conducted in the Medline, Embase, CINAHL, PsychINFO, and Global Health databases for articles published between 1 January 2001 and 15 April 2025. Information was descriptively summarised following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. The quality of the articles was assessed using the Newcastle-Ottawa Scale. Random effects model was used to obtain the pooled proportion of inadequate glycaemic control. Heterogeneity (I²) was tested, sensitivity analyses were performed, and publication bias was examined using Egger's regression test.

Results: Among 12,985 records, 62 studies from 28 countries involving 176,349 participants were reviewed. The estimated pooled proportion of inadequate glycaemic control (glycosylated haemoglobin [HbA1c] ≥7%) was 69% (95% confidence interval [CI]: 66%-72%, p <0.001, I² = 99.10%), with no publication bias (Egger's test, p = 0.489). A number of factors were associated with inadequate glycaemic control (overall p < 0.001), including education below secondary level (OR: 1.47, 95% CI: 0.98-1.97), rural residence (OR: 1.80, 95% CI: 1.33-2.28), obesity (OR: 1.17, 95% CI: 1.11-1.22), use of oral glucose-lowering drugs and/or insulin (OR: 4.06, 95% CI: 2.58-5.54 and OR: 2.44, 95% CI: 1.70-3.19, respectively), non-adherence to diet (OR: 2.13, 95% CI: 1.33-2.93) and treatment (OR: 2.08, 95% CI: 1.61-2.54), and physical inactivity (OR: 2.15, 95% CI: 1.35-2.95).

Conclusion: More than two-thirds of people with T2DM in LMICs have inadequate glycaemic control. Urgent interventions are needed, focusing on sociodemographic, lifestyle, and treatment-related factors.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD: 42023390577.

Recent literature shows that GLP-1 receptor agonists are highly effective for weight loss and improving metabolic and cardiovascular health, often surpassing the results of lifestyle interventions alone, such as exercise and diet modification. However, long-term weight maintenance is more successful when exercise is included, as stopping GLP-1 therapy alone often leads to weight regain, while exercise helps preserve muscle mass and sustain weight loss. Combining GLP-1 receptor agonists with structured lifestyle changes, especially increased protein intake and strength training, can mitigate muscle loss and enhance overall outcomes. As a result, future obesity management is likely to prioritize integrated approaches that combine pharmacotherapy with lifestyle interventions, rather than replacing lifestyle changes with medication alone.

A diverse range of disorders resulting from various pathophysiological mechanisms are represented by rare forms of diabetes. To date, variants in at least 25 different genes have been identified. Although these forms account for only approximately 6% of all diabetes cases, accurate diagnosis is essential for effective treatment and personalized disease management. Most of these subtypes are monogenic, syndromic, or related to structural abnormalities, providing crucial insights into the genetic and physiological underpinnings of glucose regulation. Clinical clues, such as an early age at onset, the absence of autoantibodies, atypical disease progression, low insulin requirements, and the presence of multi-organ involvement, may indicate a non-classical diabetes phenotype. The classification and recognition of these rare types are clinically significant, especially as advances in genetic technologies continue to expand our understanding of disease mechanisms and therapeutic options. Significantly, the study of rare diabetes forms contributes not only to individualized care but also to the development of novel therapeutic strategies for more common types such as type 1 and type 2 diabetes. The improved understanding of beta-cell function and its genetic regulation through these models has enabled the emergence of precision medicine approaches that extend beyond conventional glycemic control. Mitochondrial diabetes results from mitochondrial defects that impair energy metabolism in pancreatic β-cells, while endoplasmic reticulum (ER) stress-induced by the accumulation of misfolded proteins-triggers β-cell apoptosis. These convergent mechanisms disrupt insulin secretion and glycemic homeostasis, driving diabetes pathogenesis. Elucidating the molecular interplay between mitochondrial dysfunction and ER stress may advance the understanding of disease progression and facilitate the development of targeted therapeutic strategies. This review summarizes the current knowledge on rare forms of diabetes, emphasizing their diagnostic value and therapeutic potential.