Journal of Diabetes and Metabolic Disorders最新文献

Introduction: Synbiotic supplements have been shown to affect type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), however; results remain inconclusive. Thus, the present study was designed to investigate the potential effect of multi-strain synbiotic supplements on liver enzymes, insulin resistance, anthropometric indices, and inflammatory markers in overweight/obese patients with NAFLD and T2DM.

Method: In a 12-week triple-blinded randomized controlled trial, 40 eligible overweight or obese adults with NAFLD and T2DM were randomly assigned to two groups to consume either synbiotic supplements or a placebo along a low-calorie diet. Participants were assessed for liver enzymes, anthropometric and glycemic indices, and lipid profiles before and after the study.

Result: After the study period, using intention-to-treat approach 20 individuals were included in the final analysis for each group. The intervention group showed significant reductions in within group analysis for insulin levels, weight, and BMI (P < 0.05). AST was reduced in both intervention and control groups. However, no significant differences were found for between-group analyses. Additionally, changes in inflammatory markers, lipid profiles, and insulin resistance indices were not statistically significant.

Conclusion: In the present study, synbiotic supplements showed improvements in insulin levels, weight, BMI, and AST. However, in comparison to the control group no beneficial effects were observed. Further studies are recommended to draw more definitive conclusions.

Objectives: Patients with phenylketonuria (PKU) are at increasing risk of metabolic disorders and atherosclerosis. This case-control study aimed to compare the values of atherosclerosis risk factors, including insulin resistance (IR), dyslipidemia, mean of platelet volume (MPV), and systemic inflammation in adult PKU patients.

Methods: Fifty patients with PKU were categorized into two groups: well-controlled (WC) (plasma Phe < 600 µmol/L) and poorly controlled (PC) (Phe > 600 µmol/L). Twenty-five age -, gender -, and BMI-matched healthy individuals were enrolled as the control group. Serum insulin, fasting blood sugar (FBS), and lipids were measured. The systemic inflammatory index (SII) and systemic inflammatory response index (SIRI) were calculated using the counts of neutrophils, lymphocytes, monocytes, and platelets.

Results: Both PKU groups had lower serum cholesterol, low-density lipoprotein, and high-density lipoprotein than the healthy subjects (p < 0.001). No significant difference was observed in IR between the PKU patients and the control group. MPV was significantly higher in the patients with PKU compared to the healthy controls. The levels of SII and SIRI were substantially lower in the WC group compared to the healthy control group and the PC group.

Conclusions: All in all, a higher level of dyslipidemia and MPV were observed in patients with PKU compared to healthy individuals. SII and SIRI were significantly lowered in the WC group compared to the PC group and healthy individuals, suggesting the role of adherence to a restricted diet in reducing the risk of systemic inflammation in patients with PKU.

Background and objectives: Social determinants of health (SDOH) play a critical role in the onset and progression of chronic kidney disease (CKD). Despite the well-established role of SDOH, previous studies have not fully incorporated these factors in predicting CKD in Type 2 diabetes patients. To bridge this gap, this study aimed to develop and evaluate the machine learning (ML) models that incorporate SDOH to enhance CKD risk prediction in Type 2 diabetes patients.

Methods: Data were obtained from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), a national survey that collects comprehensive health-related data from adults across the United States. Missing data were addressed using the K-nearest neighbor imputation method, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distributions. Potential predictive features were selected using correlation coefficient analysis. The dataset was partitioned into training (80%) and testing (20%) subsets, with a 3-fold cross-validation strategy applied to the training data. Seven ML models were developed for CKD risk prediction, including logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), eXtreme Gradient Boosting (XGBoost), and an artificial neural network (ANN). Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUROC), precision, recall, F1 score, accuracy, and false positive rate.

Results: The study included 19,912 Type 2 diabetes patients (weighted sample size: 818,878), among whom 2,924 (weighted 13.92%) had CKD, and 16,988 (weighted 86.08%) did not. Over half of the CKD group (50.4%) were aged 65 or older. The proportion of female patients was higher in both groups, comprising 53.8% of the CKD group and 50.5% of the non-CKD group. Among the ML models evaluated, the RF model demonstrated the highest predictive performance for CKD, with an AUROC of 0.89 (95% CI: 0.88 - 0.90), followed by the DT model (0.84, 95% CI: 0.83 - 0.85) and XGBoost (0.83, 95% CI: 0.82 - 0.84). The RF model achieved an accuracy of 0.81 (95%CI: 0.81 - 0.81), a precision of 0.79 (95%CI: 0.79 - 0.79), a recall of 0.85 (95%CI: 0.85 - 0.85), and an F1 score of 0.82 (95%CI: 0.82 - 0.82). Additionally, the RF model exhibited strong calibration, reinforcing its reliability as a predictive tool for CKD risk in individuals with Type 2 diabetes.

Conclusion: The study findings underscore the potential of ML models, particularly the RF model, in accurately predicting CKD among individuals with Type 2 diabetes. This approach not only enhances the precision of CKD prediction but also highlights the importance of addressing social and environmental disparities in disease prevention and management. Leveraging ML models with SDOH can lead to earlier interventions, more personalized treatment plans,

Background: Type 2 diabetes mellitus (T2DM) related mortality trends remain understudied among younger adults in the United States (US). This study aims to bridge this gap by using data from a large national database of mortality statistics.

Methods: Death certificate data from 1999 to 2020 were extracted from the CDC WONDER database for all the fatalities among US adults aged 25 to 64 years, where T2DM was listed as the underlying or contributing cause of death. Age-adjusted mortality rates (AAMR) per 1 million persons were calculated, and temporal mortality trends were evaluated by computing annual percent change (APC) in AAMRs using the Joinpoint log-linear regression model.

Results: A total of 272,155 T2DM-related deaths occurred among US adults aged 25-64 years from 1999 to 2020. The overall AAMR significantly increased from 37.6 in 1999 to 138.36 by 2020 with an APC of 4.8 (p < 0.01). Males had higher AAMR than females (80.13 vs. 51.20), and adults aged 55-64 years had a higher mortality rate than younger age groups. Among races, non-Hispanic American Indians/Alaska Natives had the highest AAMR (177.09). Mortality rates showed a significant upward trend across all genders, age groups, and racial subgroups. Nonmetropolitan areas had higher AAMR than metropolitan areas (83.08 vs. 61.97). AAMR varied substantially by state, with the highest AAMR in West Virginia (140.39) and the lowest in Massachusetts (23.06).

Conclusion: T2DM-related mortality has significantly increased among younger US adults over the last two decades. Higher mortality rates were observed among males, NH American Indians, and residents of rural areas and the Western regions.

Graphical abstract: Type 2 Diabetes Mellitus-related mortality among young adults aged 25-64 years in the United States from 1999 to 2020.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01626-4.

Objectives: Diabetes mellitus, a major cardiovascular risk factor, is a leading non-communicable disease globally. Self-management education can effectively prevent and control diabetes. We evaluated a localized health promotion model for diabetic patients through a field trial in a general hospital.

Methods: We enrolled 452 diabetic patients who visited our hospital's cardiology and internal medicine wards and randomly assigned them to two equal groups: intervention and control. The intervention group received initial and periodic education on diabetes management and lifestyle modification, as well as educational materials. The control group received only initial education and phone follow-ups. We measured the following outcomes after 6 and 12 months of discharge: glucose and lipid levels, smoking status, diet quality, rehospitalization rate, treatment cost, quality of life, and work absenteeism. The groups were then compared using chi-square, student t-test, and two-way repeated-measures analysis of variance.

Results: We enrolled 452 patients, randomized into two equal groups, and followed them for one year. Baseline demographic and clinical variables were similar between groups. The intervention group showed a significant reduction in BMI (P = 0.027), fasting blood glucose (P < 0.001), and HbA1c levels (P = 0.002) compared to the control group. The prevalence of hypertension, smoking, sedentary lifestyle, and inappropriate diet was significantly higher in the control group (P = 0.001 for all). The intervention group had fewer hospitalizations, work absences, and medical costs (P < 0.001, P = 0.001, and P < 0.001, respectively). No significant difference was observed in satisfaction rates between the groups.

Conclusions: Health promotion interventions could improve glucose control and other health indicators and reduce costs for diabetic patients.

Objectives: To evaluate the effects of a very-low-calorie diet (VLCD) on insulin resistance (IR), metabolic gene expression, and fatty acid profiles in obese patients (body mass index [BMI] ≥ 30 kg/m²) undergoing bariatric surgery compared to age- and sex-matched nonobese controls (BMI ≤ 25 kg/m²) undergoing elective abdominal surgery.

Methods: A total of 38 participants (21 obese and 17 nonobese controls) were recruited for this study. Obese patients underwent VLCD (800 kcal/day) for four weeks before surgery. Fasting blood samples and tissue biopsies were collected during surgery. Key parameters included IR (measured using HOMA-IR), metabolic gene expression (quantified via RT-PCR), and fatty acid composition (analyzed by gas chromatography). Data were compared between pre- and post-VLCD groups in the obese cohort.

Results: GLUT4 expression was reduced (1.57-fold, p = 0.025), whereas PDK4 (3.9-fold, p = 0.002), CPT1 (2.5-fold, p = 0.013), and AMPK (twofold, p = 0.004) expression were Correlation analysis revealed that GLUT4 was negatively correlated with BMI (r = -0.85), glucose (r = -0.94), and IR (r = -0.79), CPT1 was positively correlated with these parameters (BMI: r = 0.84, glucose: r = 0.92, IR: r = 0.82). VLCD significantly reduced monounsaturated fatty acids, including alpha-linolenic acid (p = 0.03) and erucic acid (p = 0.019). Postsurgical improvements included reductions in BMI (Δ = 6.21, p < 0.0001), glucose level (Δ = 6.94, p = 0.0007), and IR (Δ = 10.19, p = 0.0039).

Conclusion: VLCD modulated metabolic gene expression and fatty acid profiles, enhancing IR and metabolic health both pre- and post-surgery. This represents a critical strategy for optimizing the outcomes of obese patients undergoing bariatric surgery.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01625-5.

Background: Type 2 diabetes mellitus (T2DM) develops primarily from obesity as leptin (LEP) functions as an essential adipokine that controls metabolic regulation, energy balance activities, and glucose maintenance. The T2DM and obesity susceptibility traits are believed to be affected by genetic variations in the leptin receptor gene (LEPR), disrupting LEP signaling mechanisms. This case-control study investigates the association of these variants with T2DM risk in a Southeastern Iranian population.

Methods: A case-control study was conducted involving 450 T2DM patients and 450 matched healthy controls from Zahedan. Genomic DNA for this study was isolated from peripheral blood samples, and genotyping for the specified LEPR rs1137100, rs1137101, and rs1805094 polymorphisms was conducted using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Computational analysis created a gene-gene interaction network, highlighting LEPR as a central hub gene and detailing its interactions with related genes.

Results: Genetic models, such as codominant heterozygous (p-value = 0.009), dominant (p-value = 0.006), recessive (p-value = 0.008), and allelic (p-value = 0.011), all showed that the rs1137100 (A/G) polymorphism lowered the risk of T2DM. Several genetic models linked polymorphisms at the rs1137101 (G/A) and rs1805094 (G/C) loci to a higher risk of T2DM: The genetic models that were looked at were polymorphism rs1137101 (G/A) in codominant Homozygous (p-value = 0.031) and recessive (p-value = 0.028), as well as polymorphism rs1805094 (G/C) in codominant heterozygous (p-value = 0.009), dominant (p-value = 0.001), excess (p-value = 0.008), and allelic (p-value = 0.001). The research demonstrated a profound linkage disequilibrium (LD) among studied variants, especially in the LEPR haplotypes and across various blocks, with differing levels of association strength. The gene-gene interaction network for the LEPR gene highlights its strong associations with several key regulatory genes: LEP, PTPN11, STAT3, POMC, JAK2, IL6, and SOCS3.

Conclusion: We found a significant correlation between LEPR gene polymorphisms and the risk of T2DM, highlighting the prominent role of genetic factors in developing such a metabolic disorder. By elucidating the association between LEPR variations and susceptibility to T2DM, our findings enhance the understanding of molecular mechanisms involved in endocrine dysregulation and highlight the importance of including genetic profiling in clinical practice.

Background and purpose: Undesirable adherence to treatment is one of most important challenges in the control and treatment of type 2 diabetes. Therefore, the present study was conducted with the aim of determining the level of adherence to treatment and factors affecting it among patients with type 2 diabetes in Tehran.

Methods: This cross-sectional descriptive study was conducted in 2023 on 117 patients with diabetes referred to Imam Khomeini Hospital in Tehran. The data collection tool was the Hill-Bone Medication Adherence Scale (HB-MAS), which was completed by the participants. Version 16 SPSS and descriptive analysis at the statistical level of 0.05 were used to analyze the data.

Results: The mean age of the participants in this research was 45.06 ± 8.48 years. The results showed that 41.03% of the participants had the desirable treatment adherence and 58.97% had the undesirable treatment adherence. A significant relationship was observed between the compliance of patients with the variables of gender, level of education and job status (P < 0.05); But the variables of age, duration of illness and type of treatment had no significant relationship with treatment adherence.

Conclusion: The findings showed that the level of adherence to treatment in patients with type 2 diabetes in Tehran is not undesirable. It is better for the treatment team to take more effective measures to improve the awareness of patients in adherence to treatment. Planners and policy makers should also take more effective measures to empower patients to access health services.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01627-3.

Introduction: This study evaluated the risk of all-cause mortality among Type 2 Diabetes (T2D) patients in Malaysia, correlating it with glycosylated haemoglobin A1c (HbA1c), blood pressure (BP), and LDL-Cholesterol (LDL-C) - the ABC parameters. This would fill the evidence gap from middle-income countries like Malaysia.

Methods: This retrospective cohort study analysed data from National Diabetes Registry and death records for 90,933 T2D patients in southern Malaysia (2011-2021). ABC parameters were categorized into quantiles, and adjusted hazard ratios (aHR) were estimated using Cox regression with the lowest-risk quantile as reference.

Results: All-cause mortality showed a 'J-shaped' association across ABC parameters. For HbA1c, aHRs (95% CI) were 1.11 (1.03-1.19) and 1.51 (1.40-1.63) in the first and last deciles (reference: fourth decile). For BP and LDL-C (reference: third quantile), aHRs were 1.11 (1.05-1.17) and 1.19 (1.13-1.24) for systolic BP, and 1.08 (1.03-1.14) and 1.16 (1.11-1.22) for LDL-C at the lowest and highest quintiles. For diastolic BP, aHRs were 1.09 (1.02-1.16) and 1.11 (1.04-1.19) at the lowest and highest quartiles.

Conclusion: Maintaining optimal ABC parameters is crucial to reduce mortality in T2D patients. These findings fill critical gap in the literature, particularly for the Malaysian population.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01620-w.

Objectives: Heart failure (HF) is a complicated disease with several competing risks of interest, such as HF death and other causes. This study compares a Deep Neural Network Competing Risks (DNNCR) and Random Survival Forest (RSF) model to evaluate the predictive performance of time-to-event outcomes in HF patients with competing risks.

Methods: This study represents the retrospective analysis of 435 HF patients admitted to RCMRC, Tehran, Iran, between March and August 2018. After a five-year follow-up in 2023, predictions were analyzed based on Cause of death. This study employed RSF and DNN methods to account for competing risks in survival analysis. Then, model fitness was applied using C-index and IBS.

Results: The C-index of the results shows that DNNCR is superior to RSF in predicting survival outcomes for HF and other causes of death. Precisely, the C-index was 0.65 (0.04) for HF and 0.63 (0.02) for other causes of death in the DNNCR model, while in RSF, the C-index was 0.65 (0.04) for HF and 0.61 (0.03) for Other Causes. Additionally, calibration results showed via the IBS the finest performance of the DNNCR model at 0.16 for HF, followed by other causes with an IBS of 0.18.

Conclusions: The study shows that the DNNCR model outperforms RSF in predicting survival outcomes for HF patients, particularly in the presence of competing risks. The improved accuracy enables physicians to identify high-risk individuals and tailor treatment plans accordingly. Future research could utilize more diverse datasets to enhance DNNCR performance and integrate these models into clinical tools.

Graphical abstract: