Diabetology & Metabolic Syndrome最新文献

Aim: To explore the topological organization alterations of functional connectomes in type 2 diabetes (T2DM) patients with and without mild cognitive impairment (MCI), and compare these with structural connectomes changes.

Methods: Twenty-six T2DM patients with MCI (DM-MCI), 26 without cognitive impairment (DM-NC), and 28 healthy controls were included. Diffusion tensor imaging (DTI) and resting-state functional MRI images were acquired. Networks were constructed and graph-theory based network measurements were calculated. The global network parameters and nodal efficiencies were compared across the three groups using one-way ANOVA and a false-discovery rate correction was applied for multiple comparisons. Partial correlation analyses were performed to investigate relationships between network parameters, cognitive performance and clinical variables.

Results: In the structural connectome, the DM-MCI group exhibited significantly decreased global efficiency (E_glob) and local efficiency (E_loc) compared to the DM-NC and control groups. In the functional connectome, the DM-MCI group exhibited increased E_loc and clustering coefficient (Cp) compared to the controls. No significant differences were found in E_glob, E_loc, or Cp between the DM-NC and the control group, both in structural and functional connectomes. Nodal efficiencies decreased in some brain regions of structural and functional networks in the DM-MCI and DM-NC groups, but increased in five regions in functional network, some of which were involved in the default-mode network.

Conclusion: Unlike the consistently decreased global properties and nodal efficiencies in the structural connectome of T2DM patients, increases in E_loc, Cp, and nodal efficiencies in the functional connectome may be viewed as a compensatory mechanism due to functional plasticity and reorganization. Altered nodal efficiency can hint at cognitive decrements at an early stage in T2DM patients.

Background: The possible role of the insulinemic potential of diet in the etiology of type 2 diabetes (T2D) has recently received significant attention in observational studies. This meta-analysis aimed to synthesize available evidence and quantify the potential association between the empirical dietary index for hyperinsulinemia (EDIH) score and T2D risk.

Methods: Various electronic databases, including Scopus, PubMed, and Web of Science, were comprehensively searched up to January 2024 using related keywords to identify relevant studies. The hazard ratios (HR) or odds ratios were extracted from eligible cohort studies, and a random-effects model with an inverse variance weighting method was used to calculate the pooled effect size, which was expressed as HR.

Results: The analysis included six cohort studies (four publications), with sample sizes ranging from 3,732 to 90,786 individuals aged 20 to 79 years. During follow-up periods of 5 to over 20 years, 31,284 T2D incidents were identified. The pooled results showed that a higher EDIH score was associated with an increased risk of T2D incidence (HR: 1.47; 95%CI 1.21-1.77; I² = 91.3%). Significant publication bias was observed in the present meta-analysis (P = 0.020). Geographical region and follow-up period can be as sources of heterogeneity (P_{heterogeneity} <0.001).

Conclusion: Our meta-analysis of observational studies suggested that a diet with a higher EDIH score may be associated with an increased risk of incidence of T2D.

Backgroud: The association between the atherogenic index of plasma (AIP) and stroke risk is uncertain. Overweight and obese individuals frequently develop atherosclerosis, suggesting AIP may mediate the relationship between body mass index (BMI) and stroke risk. This study investigates whether AIP mediates the BMI-stroke association and evaluates the interaction effects of AIP and BMI on stroke risk in middle-aged and older Chinese adults.

Method: This study analyzes data from the China Health and Retirement Longitudinal Study (CHARLS), an ongoing nationally representative prospective cohort study that began in 2011. It includes 8 598 middle-aged and older Chinese adults without stroke at baseline. A mediation analysis, employing a novel two-stage regression method, was conducted to evaluate the indirect effect of BMI on stroke through AIP.

Results: During a median follow-up of 7.1 years, 615 (7.2%) participants developed a stroke. After adjusting for confounders, AIP was significantly associated with stroke risk (hazard ratio [HR] per 1-SD increase, 1.24; 95% CI 1.14-1.35). Mediation analysis indicated that compared to normal weight, obesity similarly raised stroke risk by 78.0% (HR 1.78, 95% CI 1.40-2.27), with 29.67% (95% CI 14.27-45.08%) of the association mediated through AIP (HR 1.15, 95% CI 1.08-1.23). No significant multiplicative or additive interactions were observed between BMI and AIP on stroke.

Conclusions: This study found that the AIP appeared to be associated with stroke risk and mediates the association between obesity and stroke among middle-aged and older Chinese adults.

Background: By 2045, it is expected that 693 million individuals worldwide will have diabetes and with greater risk of morbidity, mortality, loss of vision, renal failure, and a decreased quality of life due to the devastating effects of macro- and microvascular complications. As such, clinical variables and glycemic control alone cannot predict the onset of vascular problems. An increasing body of research points to the importance of genetic predisposition in the onset of both diabetes and diabetic vascular complications.

Objectives: Purpose of this article is to review these approaches and narrow down genetic findings for Diabetic Mellitus and its consequences, highlighting the gaps in the literature necessary to further genomic discovery.

Material and methods: In the past, studies looking for genetic risk factors for diabetes complications relied on methods such as candidate gene studies, which were rife with false positives, and underpowered genome-wide association studies, which were constrained by small sample sizes.

Results: The number of genetic findings for diabetes and diabetic complications has over doubled due to the discovery of novel genomics data, including bioinformatics and the aggregation of global cohort studies. Using genetic analysis to determine whether diabetes individuals are at the most risk for developing diabetic vascular complications (DVC) might lead to the development of more accurate early diagnostic biomarkers and the customization of care plans.

Conclusions: A newer method that uses extensive evaluation of single nucleotide polymorphisms (SNP) in big datasets is Genome-Wide Association Studies (GWAS).

Latent Autoimmune Diabetes in Adults (LADA) is a type of diabetes mellitus often overlooked in clinical practice for its dual resemblance to Type 1 Diabetes Mellitus (T1DM) in pathogenesis and to Type 2 Diabetes Mellitus (T2DM) in clinical presentation. To better understand LADA's distinctiveness from T1DM and T2DM, we conducted a comprehensive review encompassing etiology, pathology, clinical features, treatment modalities, and prognostic outcomes. With this comparative lens, we propose that LADA defies simple classification as either T1DM or T2DM. The specific treatments for the disease are limited and should be based on the therapies of T1DM or T2DM that address specific clinical issues at different stages of the disease. It is crucial to identify LADA cases potentially misdiagnosed as T2DM, warranting prompt screening for poor blood sugar control, short-term blood sugar deterioration, and other conditions. If the prognosis for LADA is similar to T2DM, it can be managed as T2DM. However, if the prognosis fundamentally differs, early LADA screening is crucial to optimize patient outcomes and enhance research on tailored treatments. The pathogenesis of LADA is clear, so the prognosis may be the key to determining whether it can be classified as T2DM, which is also the direction of future research. On the one hand, this paper aims to provide suggestions for the clinical screening and treatment of LADA based on the latest progress and provide worthy directions for future research on LADA.

Background: Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach.

Method: This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score.

Results: XGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score.

Conclusion: The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.

Background: The pathogenesis of type 2 diabetes mellitus is somewhat associated with lipid metabolism. We aim to assess the impact of lipid-lowering drugs (HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors) on type 2 diabetes mellitus and its complications through a two-sample Mendelian randomization (MR) study.

Method: We identified suitable genetic instruments from the GWAS database that represent the expression levels of three genes, interpreting reduced genetically proxied gene expression as indicative of lipid-lowering drug use. We evaluated the causal relationships among these variables employing a two-sample Mendelian randomization approach, with the Inverse Variance Weighted (IVW) analysis serving as the primary method. Coronary artery disease was utilized as a positive control to validate the reliability of the selected genetic instruments.

Result: Increased genetically proxied HMGCR expression is significantly associated with a reduced risk of type 2 diabetes mellitus (OR = 0.64, 95%CI = 0.55-0.74), which was replicated in the FinnGen study with consistent results (OR = 0.65, 95%CI = 0.53-0.80). Increased genetically proxied HMGCR expression is associated with a reduced risk of diabetic retinopathy (OR = 0.23, 95%CI = 0.12-0.44) and diabetic nephropathy (OR = 0.35, 95%CI = 0.17-0.71). In contrast, increased genetically proxied PCSK9 expression is associated with a decreased risk of diabetic coma (OR = 0.70, 95%CI = 0.50-0.98), diabetic neuropathy (OR = 0.24, 95%CI = 0.14-0.42), diabetic retinopathy (OR = 0.67, 95%CI = 0.48-0.96), diabetic cardiovascular diseases (OR = 0.62, 95%CI = 0.38-0.99), and diabetic nephropathy (OR = 0.62, 95%CI = 0.41-0.95).

Conclusions: This Mendelian randomization study suggests an association between HMGCR and the pathogenesis of type 2 diabetes mellitus, with increased genetically proxied HMGCR expression reducing the risk of type 2 diabetes mellitus, while PCSK9 and NPC1L1 show no significant association with type 2 diabetes mellitus. These findings may offer more reasonable lipid-lowering drug options for patients with dyslipidemia.

Background: To analyze the association between the hemoglobin glycation index (HGI) and the long-term prognosis of patients with coronary artery disease (CAD).

Methods: HGI represented the difference between laboratory measured Hemoglobin A1c (HbA1c) and predicted HbA1c based on a liner regression between Hb1Ac and fasting plasma glucose (FPG). A total of 10 598 patients who treated with percutaneous coronary intervention (PCI) were stratified into three groups (low HGI group: HGI<-0.506, medium HGI group: -0.506 ≤ HGI < 0.179, and high HGI group: HGI ≥ 0.179). The primary endpoints includes all-cause mortality (ACM) and cardiac mortality (CM). The secondary endpoints were major adverse cardiac events (MACEs) and major adverse cardiac and cerebrovascular events (MACCEs).

Results: A total of 321 ACMs, 243 CMs, 774 MACEs, and 854 MACCEs were recorded during a 60-month follow-up period. After adjusting for confounders using a multivariate Cox regression analysis, the patients in the low HGI group had a significantly increased risk of ACM (adjusted HR = 1.683, 95%CI:1.179-2.404, P = 0.004) and CM (HR = 1.604, 95%CI:1.064-2.417, P = 0.024) as compared with patients in the medium HGI group. Similarly, the patients in the high HGI group had an increased risk of MACEs (HR = 1.247, 95% CI: 1.023-1.521, P = 0.029) as compared with patients in the medium HGI group. For ACM, CM, and MACEs, a U-shaped relation were found among these three groups. However, we did not find significant differences in the incidence of MACCEs among these three groups.

Conclusion: The present study indicates that HGI could be an independent predictor for the risk of mortality and MACEs in patients with CAD.

Background: The triglyceride-glucose (TyG) index is identified as an alternative indicator of insulin resistance (IR) and is associated with macro- and micro-vascular diseases among patients with type 2 diabetes mellitus. The relationship between the TyG index and IR and its impact on diabetic kidney disease (DKD) remains unclear among adults with type 1 diabetes mellitus(T1DM).

Methods: This study comprised a cross-sectional analysis using data from the Guangdong T1DM Translational Medicine Study (GTT) and a longitudinal analysis using data from the type 1 diabetes (T1D) Exchange registry study. Correlation analysis was used to investigate the association between the TyG index and IR. Logistic regression and Cox proportional hazards regression were performed to explore the impact of the TyG index on DKD risk.

Results: The GTT Study included 836 adults (216 with DKD and 620 without DKD). A significant correlation existed between the TyG index and the estimated glucose disposal rate (r=-0.64, p < 0.01). The TyG index was a risk factor for DKD after confounder adjustment (OR = 1.34, 95% CI:1.03-1.74). The T1D Exchange registry study included 8,771 adults (2,050 with DKD and 6,721 without DKD). After adjusting confounding factors, the TyG index was identified as an independent risk factor for DKD at enrollment, with the highest risk of DKD incidence observed in the highest TyG tertile group (OR = 1.92, 95%CI:1.67-2.20). During a median follow-up of 44.58(21.84, 67.09) months, the risk of developing DKD was increased by 32% at every 1 SD increase of the TyG index over time among participants without DKD at enrollment.

Conclusions: The TyG index could be used to assess IR and was identified as an independent risk factor of DKD among adults with T1DM.

Objective: To assess the effectiveness of an exercise intervention, in addition to standard care, in preventing or delaying insulin prescription in women with gestational diabetes mellitus (GDM).

Design: Randomised controlled trial.

Setting: University hospital.

Population: Pregnant women at 25-35 weeks of gestation diagnosed with GDM.

Methods: Women in the intervention group participated in weekly, supervised, 30-45 min exercise sessions and were encouraged to accumulate more than 5000 steps per day, tracked by a pedometer, in addition to receiving usual care. The control group received standard care only.

Main outcome measure: Insulin prescription.

Results: From February 2008 through April 2013, 109 women were randomized into the intervention group (n = 57) or the usual care group (n = 52). Two women in the intervention group were excluded from the analysis (one was randomised in error and one was lost to follow-up). Six women never attended the exercise sessions, and two attended fewer than two sessions. However, two-third of women were considered as compliant to the intervention (attended more than 50% of sessions and/or averaged more than 5000 steps/day). The incidence of insulin prescription did not differ between the groups: 31 women (56%) in the intervention group versus 24 women (46%) in the control group (RR 1.22, 95% CI 0.84 to 1.78). The median time from randomization to insulin prescription was also similar between groups (14 days in the intervention group and 13 days in the control group).

Conclusion: This study did not demonstrate that an exercise program reduces or delays insulin prescription in women with GDM. Low adherence to the intervention, a small sample size, and the short duration of the program may explain the lack of observed benefit.

Registered: At clinicaltrials.gov, NCT03174340, 02/06/2017.