Diabetology & Metabolic Syndrome最新文献

Background: The association between vitamin D status and mortality risk among adults with cardiovascular-kidney-metabolic (CKM) syndrome stages 0-3 requires further elucidation. We investigated the relationship between serum 25-hydroxyvitamin D [25(OH)D] levels and all-cause and cause-specific mortality among United States adults diagnosed with CKM syndrome stages 0-3.

Methods: Data from 37,551 adults presenting with CKM syndrome stages 0-3 were examined using National Health and Nutrition Examination Survey records (2001-2018). Death outcomes were determined via National Death Index linkage through December 31, 2019. Cox proportional hazards modeling and two-piecewise Cox regression approaches were utilized to assess nonlinear relationships between serum 25(OH)D levels and mortality risk, incorporating stratified analyses for high-risk population identification.

Results: Throughout 337,921 person-years of observation, we documented 4,039 deaths from all causes, encompassing 1,078 cardiovascular disease (CVD)-related deaths. Following multivariable adjustment, reduced serum 25(OH)D levels demonstrated significant associations with elevated all-cause and CVD mortality risk through nonlinear patterns. L-shaped dose-response curves emerged, revealing mortality risk plateaus at 52.20 nmol/L for all-cause mortality and 53.90 nmol/L for CVD mortality. Individuals maintaining 25(OH)D levels above these threshold values exhibited 2% reduced all-cause mortality risk (hazard ratio [HR] 0.98, 95% confidence interval [CI] 0.97-0.99) and 2% decreased CVD mortality risk (HR 0.98, 95% CI 0.97-0.99) relative to participants below these cutoff points.

Conclusions: Nonlinear relationships between serum 25(OH)D levels and both all-cause and CVD mortality were demonstrated among United States adults with CKM syndrome stages 0-3. These findings warrant confirmation through randomized controlled trials.

Aims: To investigate the role and underlying mechanisms of miR-4687-5p in diabetic retinopathy (DR) development.

Methods: A total of 180 DR patients were enrolled and stratified into non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) groups. Concurrently, 100 type 2 diabetes mellitus (T2DM) patients and 100 healthy volunteers were recruited as control groups. DR cellular models were established by treating ARPE-19 cells with high glucose (HG). Reverse transcription quantitative polymerase chain reaction was used to detect gene expression. An in vitro DR cell model was constructed to explore the potential mechanism of miR-4867-5p in DR.

Results: Serum miR-4687-5p expression was significantly elevated in DR patients compared to healthy controls and T2DM patients, with the highest levels observed in PDR patients. This miRNA exhibited excellent diagnostic value for DR. miR-4687-5p expression positively correlated with clinical parameters and pro-inflammatory cytokines in DR patients. In DR cell models, miR-4687-5p overexpression inhibited cell viability, promoted apoptosis, and exacerbated inflammatory responses, whereas miR-4687-5p knockdown exerted opposite effects. Mechanistically, miR-4687-5p contributes to DR progression by negatively regulating SIRT2 to modulate cell function and inflammation.

Conclusions: miR-4687-5p is expected to become a biomarker for the early diagnosis of DR and a potential therapeutic target.

Background: Obesity, particularly central adiposity, is a major risk factor for impaired glucose metabolism and type 2 diabetes. The biological mechanisms linking body fat distribution to glucose regulation remain incompletely understood. Therefore, we investigated the associations between body fat distribution and plasma markers of glucose metabolism in a population-based sample and examined the mediating role of hepatocyte growth factor (HGF) in this connection.

Methods: The analysis was based on data from 238 participants of the MEGA study (German acronym for metabolic health study Augsburg) conducted between 2018 and 2021. Anthropometric measurements and a body composition analysis via bioelectrical impedance analysis (BIA) were conducted. HGF was measured from EDTA plasma based on the Proximity Extension Assay (Olink inflammation panel). Multivariable linear regression models were chosen to examine the associations between standardized anthropometric and BIA measurements and the outcome variables in non-diabetic individuals. Obesity measures included body mass index (BMI), waist circumference (WC), visceral adipose tissue (VAT), and waist-to-hip ratio (WHR), relative fat mass (RFMV), and absolute fat mass (AFMV). The outcome variables included fasting glucose, two-hour OGTT glucose, and HbA1c concentrations. Mediation effects of HGF between obesity measures and glucose parameters were assessed.

Results: The observed positive associations between obesity measures and glucose metabolism were stronger for variables that are indicative of central obesity (WC, WHR, VAT), when compared to indices of general obesity (BMI, RFMV, AFMV). Fasting glucose showed the strongest positive association with WC (β = 5.10 mg/dL per one standard deviation increase; 95% CI 1.86-5.77). For two-hour plasma glucose, the strongest associations were observed with WHR (β = 21.00; 95% CI 14.57-27.44). HGF mediated between 7.0% and 9.1% of the total effects with two-hour glucose levels but not the associations with the other outcomes. No associations were found with HbA1c levels after accounting for multiple testing.

Conclusions: These findings suggest HGF may contribute to the metabolic effects of different obesity measures on post-load glucose regulation, providing insights into obesity-related glucose dysregulation and potential targets for early intervention. However, the causal role of HGF remains unproven; further studies on the exact pathophysiological mechanisms are necessary.

Background: The reliability of hemoglobin A1c (HbA1c) as a glycemic marker is limited in chronic kidney disease (CKD), which has led to increased interest in glycated albumin (GA). This study aimed to compare the associations of GA and HbA1c with mortality.

Methods: This prospective study used data from the National Health and Nutrition Examination Survey (1999-2004). Cox proportional hazards regression analysis with weight adjustment and restricted cubic spline regression were used to examine the associations between GA/HbA1c and mortality. The discriminative performance of GA and HbA1c for mortality was assessed via the concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: A total of 1,532 (62.97%) all-cause deaths and 473 (19.44%) cardiovascular (CV) deaths among the 2,433 participants with CKD occurred over a median of 12.46 years of follow-up. The associations between GA and all-cause mortality as well as CV mortality showed a positive linear pattern (P_nonlinearity = 0.18 and 0.29, respectively), whereas HbA1c exhibited a U-shaped relationship (P_nonlinearity = 0.02 for all-cause mortality and 0.04 for CV mortality). GA showed higher C-index values than HbA1c in the associations with both all-cause (C-index: 0.60 (95% CI 0.59-0.62) vs. 0.55 (95% CI 0.53-0.57), P < 0.001) and CV mortality (C-index: 0.62 (95% CI 0.60-0.64) vs. 0.56 (95% CI 0.53-0.58), P < 0.001), particularly in early CKD patients. However, no difference existed in advanced CKD patients. Compared with HbA1c, adding GA to the existing risk models resulted in a greater C-index and significant improvements in the NRI and IDI for their associations with all-cause mortality. (Framingham CVD Risk Score + GA: C-index, 0.70; Framingham CVD Risk Score + HbA1c: C-index, 0.69, P = 0.04; NRI, 20% (95% CI 1%-36%); IDI, 0.3% (95% CI 0.3%-0.6%); Framingham Heart Age, C-index 0.68 vs. 0.67 (P = 0.04); NRI 12% (95% CI 3%-24%); IDI 1.3% (95% CI 0.5%-2.3%). Similar patterns were observed for CV mortality.

Conclusions: GA is more strongly associated with mortality than HbA1c in patients with CKD, particularly in the early stages, highlighting its potentially complementary value in clinical practice.

Background: The impact of discordance between remnant cholesterol (RC) and low-density lipoprotein cholesterol (LDL-c) on diabetes, diabetic kidney disease (DKD), diabetic retinopathy (DR) and cardiovascular disease (CVD) remains unclear. This study aims to explore the association between the discordance and these outcomes, using data from the National Health and Nutrition Examination Survey (NHANES) 1999.1-2020.3 and Clinical Medical College & Affiliated Hospital of Chengdu University.

Methods: We prespecified a ± 15 percentile-point cutoff for discordance between RC and LDL-c, defined as RC percentile minus LDL-c percentile. Using this rule, 11,826 NHANES participants (cohort 1) and 306 participants from the Clinical Medical College & Affiliated Hospital of Chengdu University (cohort 2) were categorized as low discordance ( ≤ - 15), concordant (- 15 to + 15), or high discordance ( ≥ + 15). Key variables were screened by the Boruta algorithm. Logistic regression analysis models, restricted cubic spline (RCS), and subgroup analyses were used to assess the associations of discordance with outcomes. Receiver operating characteristic (ROC) and three machine learning models were used to assess the predictive value of the discordance for outcomes.

Results: High discordance was significantly associated with increased risks of diabetes (OR -cohort 1: 2.371, 95% CI: 1.848-3.055; OR -cohort 2: 4.064, 95% CI: 1.750-10.020), DKD (OR: 2.593, 95% CI: 1.930-3.521), DR (OR: 2.205, 95% CI: 1.404-3.556), and CVD (OR -cohort 1: 2.299, 95% CI: 1.900-2.791; OR -cohort 2: 3.220, 95% CI: 1.266-8.175). In cohort 1, the RCS analysis showed linear relationships for these outcomes. Hypertension, HOMA-IR ≥ 3.1 and HOMA-β < 100 were identified as significant modifiers in subgroup analyses. In cohort 2, the RCS analysis showed linear relationships for diabetes and non-linear for CVD. All machine learning models demonstrated great predictive value of the discordance for diabetes and CVD.

Conclusion: The discordance is a significant predictor of diabetes, diabetic microvascular diseases, and cardiovascular disease.

Key message: (1)The discordance between RC and LDL-c is significantly associated with diabetes, diabetic microvascular diseases, and cardiovascular disease.(2) In NHANES, the RCS analysis showed linear relationships for diabetes, DKD, DR and CVD. In clinical cohort, the RCS analysis showed linear relationships for diabetes and non-linear for CVD.(3)The association of discordance with diabetes, DKD and CVD was more prevalent in individuals with hypertension and HOMA-IR ≥3.1, while individuals with hypertension were more sensitive to DR and individuals with HOMA-β <100 were more sensitive to diabetes.(4)The discordance showed the certain predictive ability for all outcomes.

Background: Both genetic predisposition and air pollution may lead to type 2 diabetes mellitus (T2D). However, the synergistic effects of gene by environment interactions between air pollution exposure and genetic susceptibility to T2D remain underexplored in Asian populations. This study assessed the association between air pollutants, including fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), and T2D, while considering polygenic risk scores (PRS).

Methods: Data were obtained from 104,554 participants in the Taiwan Biobank. Air pollutant concentrations were estimated using satellite-based models, and long-term trends were represented by slopes derived from linear regression models. The PRS for T2D was constructed from East Asian-specific genome-wide association study summary statistics (AGEN consortium) using the clumping and thresholding method. Logistic regression models were applied to examine associations of T2D with air pollution and PRS, expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction was evaluated using the relative excess risk due to interaction (RERI), and multiplicative interaction was tested via cross-product terms in logistic models.

Results: Every 1 µg/m³ per year increase in PM_2.5 concentrations was significantly associated with increased T2D risk (OR: 1.036, 95% CI: 1.003-1.071). A positive exposure-response relationship between PRS and T2D was observed, with individuals in the highest PRS quartile showing significantly higher risk (OR: 1.385, 95% CI: 1.279,1.499). The association between PM_2.5 slope and T2D was slightly stronger among those with the highest genetic risk; however, the additive interaction was weak and borderline significant (RERI: 0.144, 95% CI: 0.008-0.319).

Conclusions: Both worsening PM_2.5 exposure and PRS were associated with T2D. The observed PM_2.5 and PRS interaction was weak and should be interpreted cautiously. Our findings highlight the importance of improving air quality and adopting personalized prevention strategies for individuals with high genetic risk.