Diabetology & Metabolic Syndrome最新文献

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.

Aim: This study provided estimates of the burden of premature coronary artery disease (PCAD) attributed to high FPG and BMI by SDI regions and gender from 1990 to 2021, aiming to clarify the long-term patterns of future PCAD.

Methods: We first extracted data from the Global Burden of Disease 2021 study to analyze the number of DALYs, deaths, and ASRs associated with PCAD attributed to high FPG and BMI. The future incidence trend was predicted by the Bayesian age-period-cohort model.

Results: In 2021, the global age-standardized DALYs and mortality rate for PCAD attributed to high FPG were approximately 37.7 and 0.744 million per 100,000 population, respectively, with EAPCs of 0.897 and 0.880 from 1990. The PCAD ASRs attributed to high BMI showed a similar trend. SDI-related inequalities across regions have exacerbated over time. Across all years, males were more susceptible to PCAD related to high FPG and BMI than females. Up to 2050, the disease burden associated with high FPG levels is anticipated to maintain an upward trend, with high BMI expected to cause a steeper increase.

Conclusions: PCAD burden attributed to high FPG and BMI is heavily skewed toward lower SDI regions, and is projected to continuously increase until 2050. The rising global burden of PCAD underscores the critical need for targeted public health strategies and policies at multiple levels.

Background: The pathophysiology of metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM) is rooted in the gut-liver axis dysfunction. To enhance glycemic control and liver fat level, new duodenal mucosal ablation procedures, such as duodenal mucosal resurfacing (DMR), recellularization via electroporation therapy (ReCET), and photodynamic therapy, seek to reset duodenal signaling in a less invasive manner.

Methods: A systematic review (2016-2024) was carried out. Both observational studies and randomized controlled trials (RCTs) assessing duodenal mucosal treatments in adult patients with T2DM and/or MASLD were included. Changes in liver fat, insulin resistance, fasting plasma glucose (FPG), HbA1c, and safety profiles were key outcomes of interest.

Results: The sham-controlled REVITA-2 RCT showed that DMR is safe and produces clinically significant metabolic benefits: at 12 weeks, liver fat (MRI-PDFF) decreased by 5.4% versus 2.2% (p = 0.035) and HbA1c reduced by 6.6 mmol/mol vesus 3.3 mmol/mol in the sham group(p = 0.033) among European participants. Without significant safety concerns, duodenal mucosal ablation procedures were found to lower HbA1c, FPG, and liver fat in 15 studies (n = 317), including seven DMR and two ReCET reports. These techniques also lead to insulin cessation in 86% of patients.

Conclusion: Duodenal mucosal treatments, in particular DMR, appear to be viable minimally invasive approaches to treat the dual metabolic loads of T2DM and MASLD. Although long-term studies with histologic outcomes in MASLD are warranted to validate the efficacy and refine patient selection criteria, these methods seem safe and beneficial.