Journal of Diabetes and Metabolic Disorders最新文献

Introduction: Diabetes management often requires insulin therapy, yet adherence to daily injections can be challenging due to complexity, injection pain, and fear of hypoglycemia. Basal Insulin Fc (BIF) is a novel once-weekly insulin analog designed to simplify regimens, improve adherence, and enhance glycemic control. This meta-analysis evaluates the efficacy and safety of BIF compared to once-daily insulin degludec.

Methods: A systematic search of PubMed, Google Scholar, EBSCO, ScienceDirect, and the Cochrane Library, along with ClinicalTrials.gov, was conducted up to November 2024 to identify RCTs comparing BIF with insulin degludec. The search employed MeSH terms like "type 1 diabetes mellitus," "type 2 diabetes mellitus," "once weekly basal insulin Fc," and "insulin degludec." Studies were screened in accordance with PRISMA guidelines, and data on glycemic outcomes, safety, and patient demographics were extracted. Statistical analysis included pooled mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CIs) using random-effects models. Heterogeneity was assessed using the I² statistic, and sensitivity analyses were conducted for cases of high heterogeneity. Subgroup and meta-regression analyses assessed moderators such as diabetes type, insulin status, follow-up duration, and heterogeneity.

Results: Five RCTs with 2,562 participants (Type 1 and Type 2 diabetes) were included. BIF showed non-inferiority to degludec in HbA1c reduction (MD 0.03, p = 0.37) and percentage time in range (MD 0.56, p = 0.27). No significant differences were observed in self-monitored fasting blood glucose (MD 2.73, p = 0.40) or clinically significant hypoglycemia (RR 1.00, p = 0.95). However, BIF increased time spent below range (MD 0.30, p = 0.0004) and was associated with higher treatment-emergent adverse events (RR 1.12, p = 0.006). The subgroup analysis highlighted differences in hypoglycemia risks between Type 1 and Type 2 diabetes.

Conclusion: BIF offers comparable glycemic control to insulin degludec while reducing injection frequency, potentially enhancing adherence. However, increased hypoglycemia risks in certain subgroups and higher adverse event rates warrant further evaluation.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01602-y.

Purpose: Diabetes (DM) plus hypertension (HTN) is a prevalent multimorbidity pattern. However, it is unclear which other diseases frequently coexist with DM and HTN and their impact on walking speed. Therefore, we identified multimorbidity patterns in community-dwelling older adults with: i) DM, ii) HTN and iii) DM + HTN and we examined the association between multimorbidity patterns and walking speed.

Methods: This was a cross-sectional study. We included 5090 community-dwelling older adults, from the National Health and Aging Trends Study, a population-based study of older adults (≥ 65 years) in the U.S. We performed latent class analysis to identify multimorbidity patterns and then performed ANCOVA to examine the association between these multimorbidity patterns and walking speed.

Results: We identified 10 unique multimorbidity patterns: low multimorbidity, joint multimorbidity, cardiovascular-joint multimorbidity, psychological-joint multimorbidity, cardiovascular multimorbidity, cardiovascular-joint-respiratory multimorbidity, Metabolic-bone-joint multimorbidity, metabolic-cardiovascular-joint multimorbidity, metabolic-psychological-joint multimorbidity, metabolic-cardiovascular-joint-respiratory multimorbidity and metabolic-joint multimorbidity. Multimorbidity patterns with larger numbers of diseases and those that included psychological conditions (depression or anxiety) were associated with slower walking speeds compared to multimorbidity patterns with somatic conditions alone (e.g., arthritis).

Conclusions: At a population level, these multimorbidity patterns may help to identify subgroups of older adults with slower walking speed who may benefit from targeted assessment and management to improve their walking speed.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01598-5.

Objectives: The predictors of early re-admission of patients with diabetes mellitus (DM) have been studied with classical statistical techniques. Considering the increasing application of artificial intelligence to drive advances in medicine, this study aimed to leverage machine learning techniques to identify patients at risk of early re-admission after being admitted for hyperglycemic crises.

Methods: We extracted relevant data from a publicly available dataset of patients with DM who were admitted in U.S. hospitals from 1999 to 2008. The target variable was re-admission within 30 days. Point-biserial and chi-square tests were used to assess correlations between the input and target variables. Three machine learning models were initially deployed; the model with the best recall for the positive class was selected.

Results: The prevalence of early re-admission among the patients was 13.32%. Statistical tests revealed weak correlations between early re-admission and race, sex, age, use of antidiabetic medication, and numbers of non-laboratory procedures, medications, diagnoses, and visits to the emergency and inpatient departments in the previous year (all p < 0.05). Extreme gradient boosting classifier predicted early-re-admission with 79% recall for the positive class. The area under the receiver-operating characteristic curve was 0.78. Age and numbers of medications, emergency and inpatient visits in the previous year, and non-laboratory procedures, were the most important features for the model's prediction.

Conclusions: Our findings highlight the usefulness of machine learning in making clinical decisions in the management of patients with diabetes, especially when classical statistical methods do not yield much significant information.

Objectives: The global rise in type 2 diabetes mellitus (T2DM) poses challenges, particularly with the increasing burden of sarcopenia and poor glycemic control. Phase angle (PhA) is a promising biomarker for early detection and management of these conditions. This study aimed to evaluate PhA as an independent predictor of sarcopenia and glycemic control.

Methods: This cross-sectional study included older adults with T2DM hospitalized for diabetes education between April 2021 and March 2023. Measurements included PhA, muscle mass, body fat mass, grip strength, knee extension strength, physical function (Short Physical Performance Battery and 6-min walk distance), and glycemic control (fasting blood glucose and hemoglobin A1c [HbA1c]). Sarcopenia was defined as low muscle mass and physical function. Analyses included Pearson correlations, receiver operating characteristic curve analysis, and multivariate logistic regression.

Results: PhA was moderately correlated with muscle mass (r = 0.42, p < 0.001), grip strength (r = 0.43, p < 0.001), and body mass index (r = 0.27, p = 0.001), and inversely correlated with HbA1c (r = - 0.34, p < 0.001) and age (r = - 0.26, p = 0.003). PhA showed a strong predictive ability for sarcopenia (AUC = 0.83, 95% CI: 0.76-0.90, p < 0.001). Logistic regression indicated PhA as an independent predictor of sarcopenia (OR = 0.105, 95% CI: 0.031-0.353, p < 0.001) and glycemic control (OR = 0.380, 95% CI: 0.201-0.719, p = 0.003).

Conclusions: PhA is a non-invasive, practical tool for predicting sarcopenia and monitoring glycemic control. Routine integration of PhA could identify high-risk patients and guide interventions. Future research should validate its application in diverse settings.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01590-z.

Objectives: Sarcopenic obesity is a multifactorial disorder commonly found in elderly individuals. One contributing factor is gut microbiota dysbiosis. This study compared the abundance of certain bacteria in elderly individuals with obesity and sarcopenic obesity.

Methods: The study included 50 elderly individuals over 65 with a body mass index (BMI) of over 30 kg/m², both sexes. Participants were divided into two groups, each with 25 individuals, based on the diagnosis of sarcopenia using the EWGSOP2 criteria. Individuals with underlying diseases, those using antibiotics, and those with a history of gastrointestinal surgery were excluded. Stool samples were stored at -80 °C, and DNA was extracted using standard kits. Bacterial DNA sample quality was assessed using a Nanodrop device. Bacterial frequency was measured using qPCR. The log cfu for each bacteria was calculated and compared in both groups using an independent t-test. Spearman measured the correlation between bacterial genera and physical performance in SPSS 26.

Results: The case group had a significantly higher average age (70.96) than the control group (68.32). The average BMI was the same in both groups. The frequency of Escherichia (p-value = 0.046) and Bifidobacterium (p-value = 0.017) was significantly higher in the case group. There was no significant difference in the frequency of Lactobacillus and Akkermansia.

Conclusion: The study uncovered substantial differences in gut microbiota composition between elderly individuals experiencing sarcopenic obesity and those with obesity alone. The findings suggest that dysbiosis, characterized by an excessive presence of Bifidobacterium, Escherichia, and Akkermansia, may be associated with sarcopenic obesity.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01584-x.

Objectives: A major cause of death worldwide, cardiovascular disease (CVD) is largely caused by risk factors like smoking, high blood pressure, poor diets, and a lack of physical activity. To find clear trends in the dynamics of CVD risk over time, this study used an unsupervised learning approach to examine the relationship between the incidence of CVD in Iranian adults and the longitudinal trajectories of risk factors.

Methods: A total of 1872 adults aged 40-79 years, free of atherosclerotic cardiovascular disease (ASCVD) at baseline, were included in the Tehran Lipid and Glucose Study (TLGS). Longitudinal data spanning over 10 years were analyzed using clustering techniques to identify distinct trajectories of CVD risk factors. K-means clustering was applied after standardizing data using the TimeSeriesScalerMeanVariance method, and the optimal number of clusters was determined using silhouette scores.

Results: The risk factor trajectories were grouped into four different clusters. Compared to Cluster 4, which represents the low-risk group, Cluster 1, which represents the high-risk group, exhibited a significantly higher hazard of CVD events. The high-risk cluster showed a noteworthy 89% incidence of CVD during the first five years of follow-up. The results suggest that risk factor trajectories may better discriminate individuals at risk of CVD.

Conclusions: This study highlights the utility of trajectory-based clustering to identify high-risk individuals for CVD more effectively. Regular monitoring and longitudinal assessment of risk factor trajectories may improve the early identification of at-risk individuals and enable targeted prevention strategies to mitigate CVD incidence.

Objectives: Although plant-based diets (PBDs) are widely recognized for their cardiovascular benefits, their results on bone remain controversial. This study aimed to assess the association of PBDs with osteoporosis and fat indices in middle-aged adults.

Methods: This analysis included 9,295 adults from the Ravanser Non-Communicable Disease (RaNCD) cohort. Nutritional information was collected through a validated food frequency questionnaire (FFQ), which was used to derive overall, healthy, and unhealthy PBD indices. Participants underwent the bioelectrical impedance analysis (BIA) to measure body fat (BF), fat mass index (FMI), and visceral fat area (VFA).

Results: The highest tertile of healthy PBD was not associated with the odds of osteoporosis than the lowest tertile (OR for men: 1.07; 95%CI: 0.66-1.74 & OR for women: 1.24; 95%CI: 0.79-1.94). However, it was associated with a lower VFA (6.01 cm² for men and 13.64 cm² for women) than the lowest tertile. The highest tertile of overall and unhealthy PBDs was not associated with the odds of osteoporosis in men and women, while they were associated with a higher VFA [(3.22 cm² for men and 4.80 cm² for women) & (3.22 cm² for men and 11.78 cm² for women)] than the lowest tertile, respectively. A significant association was between PBD indices and BF and FMI in both sexes.

Conclusions: These findings suggest that while only healthy PBDs may contribute to improved fat distribution, they do not appear to influence osteoporosis risk. Longitudinal studies are needed to explore the long-term outcome of adherence to PBDs on bone.

Objectives: This study, for the first time, sought to investigate whether the interaction between the GRS consists of three SNPs (CAV-1, CRY-1, MC4R) and fat intake is associated with inflammatory markers among Iranian overweight and obese women.

Methods: This cross-sectional study was conducted with 246 overweight and obese women, aged 18-48 years. Three SNPs, including CAV-1 rs3807992, CRY-1 rs2287161, and MC4R rs17782313, were genotyped using PCR-RFLP to calculate the genetic risk score (GRS) for each participant. Dietary fat intake was measured using a validated semi-quantitative food frequency questionnaire (FFQ). C-reactive protein (CRP), interleukin-1β (IL-1β), transforming growth factor-β (TGF-β), monocyte chemoattractant protein-1 (MCP-1), plasminogen activator inhibitor-1 (PAI-1), and Galectin-3 (Gal-3) were assessed as the primary outcomes of the study.

Results: After controlling for confounding variables, a significant interaction between high total fat intake and high GRS, compared to the reference group, was found for TGF-β level (P-value: 0.028). A significant positive interaction between high GRS and high intakes of SFA intake (P-value: 0.013). A significant interaction between high GRS and high intakes of MUFA, compared to the reference group, was found for ghrelin level (P-value: 0.040) and MCP-1 level (P-value: 0.075). There was a significant interaction between high GRS and intakes of DHA, compared to the reference group, for Gal-3 level (P-value: 0.013) MCP-1 level (P-value: 0.020).

Conclusions: Consuming different types of fats can influence the interaction between GRS and inflammatory markers, suggesting further research is needed to fully understand this relationship.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-024-01542-z.

Purpose: This comprehensive study examines the multifaceted relationship between vitamin D and cancer, synthesizing key scientific advancements and global research trends to guide future investigations and address critical gaps in the field.

Methods: Publications on vitamin D and cancer were retrieved from Scopus up to November 2024. English-language original and review articles were analyzed using Excel, VOSviewer, and Scimago Graphica, focusing on publication trends, citation impacts, and research themes.

Results: A total of 11,442 publications (80.01% original articles, 19.98% reviews; 51.24% open access) were analyzed. The United States of America led in publications (38.3%) and citations (56.2%), followed by China (7.7%) and the United Kingdom (7.2%) in output, and the United Kingdom (10.6%) and Germany (6.4%) in citations. Countries with the highest citations per document were Belgium (103.4), Slovenia (87.9), and Puerto Rico (76.6). The most frequently studied cancers in relation to vitamin D were breast, colorectal, prostate, skin, lung, ovarian, pancreatic, gastric, hepatocellular, thyroid, leukemia, multiple myeloma, bladder, lymphoma, osteosarcoma, cervical, endometrial, and glioblastoma, respectively. Cluster analysis revealed key patterns related to vitamin D: Calcitriol's chemopreventive role in breast, prostate, and colorectal cancers, dietary vitamin D for its involvement in ovarian cancer, vitamin D for regulation of cancer-related hypercalcemia, vitamin D deficiency links to inflammation-obesity-cancer risk, VDR polymorphisms affecting outcomes in lung and colorectal cancers, and vitamin D's photoprotective effects on skin malignancies, and vitamin D in ulcerative colitis-related cancer. The most cited articles emphasized optimal vitamin D levels and cancer prevention.

Conclusion: This study highlights the extensive research on vitamin D and its complex links to cancer, emphasizing future prospects with a focus on precision medicine approaches, including targeted supplementation and genomic analyses, to better address individual variability in cancer prevention and treatment.