Cardiovascular Diabetology最新文献

Background: The C-reactive protein-triglyceride-glucose index (CTI), a novel biomarker integrating inflammation and insulin resistance, has been linked to cardiovascular disease. However, its association with stroke risk across varying levels of renal function remains unclear.

Methods: This prospective cohort study included 8,808 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS), covering the period 2011 to 2018. CTI was calculated as 0.412×Ln (CRP [mg/L]) + Ln (TG [mg/dL]×FPG [mg/dL])/2. Multivariable Cox models and restricted cubic splines were employed to assess the association between CTI and incident stroke. We stratified the analysis by estimated glomerular filtration rate (eGFR) and introduced interaction terms between CTI and eGFR levels to evaluate potential effect modification.

Results: The mean age of the participants was 59.48 (9.41) years, and 4098 (46.5%) were male. During a median follow-up of 7 years, 404 (4.6%) stroke events occurred. In the fully adjusted model, each 1-unit increase in CTI was associated with a 15% higher stroke risk [hazard ratios (HR) = 1.15, 95% confidence interval (CI) = 1.00-1.32]. A significant positive linear dose-response relationship was observed (P _overall <0.001, P _non-linear= 0.289). Notably, we observed significant interaction between CTI and eGFR levels on stroke risk (P = 0.037). The association was markedly stronger in individuals with mildly reduced renal function (eGFR 60-89 mL/min/1.73 m²), where the highest CTI quartile (Q4) had an HR of 2.73 (95% CI: 1.16-6.40) compared to the Q1. Conversely, no significant associations were observed in participants with preserved (eGFR ≥ 90 mL/min/1.73 m²) or moderately-to-severely reduced renal function (eGFR < 60 mL/min/1.73 m²).

Conclusion: Elevated CTI levels are associated with an increased risk of stroke in middle-aged and older Chinese adults, particularly among individuals with mild renal impairment. By combining inflammatory and metabolic markers, CTI might offer potential clinical value for risk assessment.

Background: Humans with type 2 diabetes and/or metabolic dysfunction-associated steatotic liver disease (MASLD) are at higher risk of ST-segment elevation myocardial infarction (STEMI) and worse prognosis. However, mechanisms, prognostic factors and risk subtypes in humans with STEMI and (pre)diabetes with or without MASLD, are not fully understood.

Methods: The DIabetes and ST-segment Elevation Myocardial Infarction (DISTEMI) study is a prospective longitudinal cohort study, recruiting humans with different degrees of glucose tolerance after recent STEMI. This cohort study has the primary objective to detect changes in glycemia and insulin sensitivity derived from the oral glucose tolerance test (OGTT) and their relationships to cardiac function. Secondary objectives address tissue-specific insulin sensitivity and organ function, focusing on adipose tissue, liver and heart. Exploratory objectives comprise multiomic analyses and measures of mitochondrial function and quality of life. At 2 and 12 months after STEMI, participants undergo comprehensive cardiometabolic phenotyping (OGTT, modified Botnia clamp-test, magnetic resonance imaging/spectroscopy/elastography, high-resolution respirometry). Magnetic resonance-based techniques are employed to assess cardiovascular function and structure, adipose tissue distribution, skeletal muscle and hepatic lipid deposition and fibrosis, and hepatic energy metabolism. Exploratory analyses include multiomics of blood, urine, and stool samples. Multiomics analyses shall allow detecting biomarkers for stratification of cardiovascular disease risk. Currently, 100 participants have been included in DISTEMI, of whom 29% have type 2 diabetes.

Conclusion: The DISTEMI study integrates comprehensive cardiometabolic phenomic with multiomic profiling to identify cardiometabolic STEMI subtypes and predictors of outcomes, and to improve precision risk stratification and targeted prevention.

Trial registration: NCT05046483.

Background: Individuals with cardiovascular-kidney-metabolic (CKM) syndrome exhibit a substantially elevated risk of all-cause and cardiovascular-specific mortality. Although estimated glucose disposal rate (eGDR) and a body shape index (ABSI) are commonly used indicators for assessing insulin resistance and atherosclerotic risk, respectively, evidence regarding their combined effect on all-cause and cardiovascular-specific mortality in patients with CKM syndrome remains insufficient. Investigating this combined impact may help improve risk stratification in this population.

Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999-2018), including 18,186 individuals with stage 0-4 CKM syndrome. Cox proportional hazards models, Kaplan-Meier curves and subgroup analyses were used to evaluate the associations between eGDR and ABSI and mortality risk. The integrated discrimination improvement (IDI) and net reclassification index (NRI) were used to assess the incremental prognostic value of eGDR and ABSI. Finally, six machine learning algorithms were applied to develop predictive models.

Results: During the follow-up period, a total of 2536 all-cause mortality and 790 cardiovascular-specific mortality were documented. After multivariable adjustment, both low eGDR and high ABSI independently predicted mortality risk. Combined analysis revealed that individuals with both Low-eGDR and High-ABSI had the highest mortality risk: all-cause mortality hazard ratio (HR) = 2.79 (95% CI 2.30-3.38) and cardiovascular-specific mortality HR = 4.53 (95% CI 2.96-6.92). However, the interaction effect was not statistically significant. Among the six machine learning algorithms, XGBoost demonstrated the best performance, with areas under the curve (AUC) of 0.877 and 0.850 for predicting all-cause and cardiovascular-specific mortality, respectively.

Conclusion: Both eGDR and ABSI are independent and combined predictors of mortality risk among individuals with CKM syndrome. Their combined use significantly improves risk stratification and machine learning models provide an effective tool for precise risk assessment in this population.

Epicardial adipose tissue (EAT) is a metabolically active fat depot located between the myocardium and visceral pericardium, directly interacting with cardiomyocytes and coronary vasculature. Emerging evidence suggests that EAT plays a significant role in the pathophysiology and prognosis of heart failure (HF) across different left ventricular ejection fraction (LVEF) phenotypes. This review summarizes current data on the prognostic role of EAT in HF, including volume, thickness, and density, measured by echocardiography, computed tomography, and cardiac magnetic resonance imaging. In HF with preserved (HFpEF) and mildly reduced ejection fraction (HFmrEF), increased EAT consistently associates with adverse outcomes, including higher rates of hospitalization and mortality, independent of systemic obesity. In HF with reduced ejection fraction (HFrEF), the relationship is more complex, with studies showing both protective and detrimental associations depending on EAT quantity, density, and spatial distribution. EAT also appears to contribute to ventricular arrhythmogenesis, particularly in patients with preserved myocardial structure. Mechanistically, EAT may promote inflammation, fibrosis, and electrophysiological remodeling, while moderate amount EAT may exert metabolic or mechanical protection. Overall, EAT emerges as a promising imaging-derived biomarker for risk stratification in HF, highlighting the need for phenotype-specific evaluation and potential therapeutic targeting. Future studies should focus on EAT quality, remodeling, and its interaction with myocardial tissue to guide individualized HF management.

Background: The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitations of traditional obesity metrics. TyG-ABSI, an index combining Triglyceride-Glucose (TyG) with A Body Shape Index (ABSI), is a novel marker reflecting both functional insulin resistance and structural visceral adiposity. However, its predictive value for PAD remains unexplored in large prospective cohorts.

Methods: We included 390,274 adults from the UK Biobank. Baseline characteristics were analyzed across TyG-ABSI quartiles and PAD status. Associations between TyG-related indices and incident PAD were assessed using multivariable-adjusted Cox regression, Kaplan-Meier survival curves, and restricted cubic splines. Robustness was evaluated via Fine-Gray competing risk models, propensity score matching, subgroup analyses, and external validation in the NHANES database. Consensus k-means clustering, integrating biochemical and insulin resistance markers, identified metabolic phenotypes and stratified PAD risk. Feature selection (LASSO, Boruta, and Minimum Redundancy Maximum Relevance [mRMR]) guided the development of six machine learning models (logistic regression, GBM, XGBoost, AdaBoost, LightGBM, and neural network) for PAD prediction, with interpretability assessed via SHAP analysis.

Results: Higher TyG-ABSI and related indices were strongly associated with increased PAD incidence (cumulative incidence at 15 years: 4.16% in the top quartile vs. 0.98% in the bottom quartile; fully-adjusted Hazard Ratio [HR] per 1-SD increase for TyG-ABSI: 1.22, 95% Confidence Interval [CI] 1.17-1.27), which were robust in the NHANES external validation cohort. Clustering analysis revealed four distinct metabolic subgroups, with the highest PAD risk in the insulin resistance/glucose dysfunction cluster (HR vs. healthy phenotype: 7.48, 95% CI 6.82-8.21). Feature selection identified 19 key predictors. Logistic regression provided the most stable and generalizable prediction (validation Area Under the Curve [AUC] = 0.788, 95% CI 0.778-0.798), demonstrating superior generalizability compared to complex ensemble methods. SHAP analysis demonstrated TyG-ABSI, age, and neutrophil count as leading predictors for incident PAD and confirmed the interpretability of the model.

Conclusion: TyG-ABSI is a robust, independent predictor of long-term PAD risk. Data-driven phenotyping and interpretable machine learning facilitate more precise risk stratification. Logistic regression offers optimal performance and interpretability, holding potential clinical utility for individualized PAD risk prediction.

Background: Diabetic cardiomyopathy (DCM) involves cardiac dysfunction/remodeling with mitochondrial stress and impaired mitochondrial proteostasis. The role of dual-specificity phosphatases (DUSPs) in these processes remains unclear. We examined whether Dusp15 modulates diabetic cardiac injury and whether mtHsp70/mito-UPR-linked proteostasis is involved.

Methods: DCM was induced in mice by high-fat diet (HFD) combined with low-dose streptozotocin (STZ). We studied cardiomyocyte-specific Dusp15 knockout (Dusp15^Cko) mice, a Dusp15 gain-of-function line, and high-glucose-treated HL-1 cardiomyocytes. Cardiac function/remodeling were assessed by echocardiography and molecular/histological analyses. Dusp15-mtHsp70 signaling was interrogated by protein interaction assays and mtHsp70 Thr116 genetic models.

Results: Dusp15 was reduced in diabetic hearts and associated with impaired contractility. Dusp15 gain-of-function improved cardiac function and reduced remodeling/inflammation, whereas Dusp15^Cko worsened diabetic injury, indicating a cardiomyocyte-necessary role for Dusp15. Dusp15 associated with mtHsp70 and supported mtHsp70-linked mitochondrial proteostasis/mito-UPR in cardiomyocytes. Genetically, mtHsp70^T116A knock-in mice were substantially protected from diabetic cardiac dysfunction/remodeling. Finally, dapagliflozin (DAPA) improved diabetic cardiac outcomes, and its benefit was reduced in Dusp15^Cko mice, suggesting Dusp15 as an important mediator.

Conclusion: Dusp15 is a stress-responsive regulator that protects against diabetic cardiac dysfunction and remodeling through mtHsp70-associated mito-UPR signaling. Targeting the Dusp15-mtHsp70 axis may represent a therapeutic strategy for diabetic cardiomyopathy.

The prevalence of chronic diseases is increasing dramatically, but the metabolism, particularly the pervasive carbonyl stress that accompanies many of these conditions, is rarely considered a potential cause. Reactive carbonyl species are spontaneously generated through a variety of endogenous metabolic reactions, and contain highly reactive carbonyl groups. Methylglyoxal, a prime reactive carbonyl, has been linked to cardiovascular disease, diabetes and its complications, obesity, chronic kidney disease, and ageing. Its detoxification is mainly regulated by the glyoxalase system; however, surprisingly, studies in mice, zebrafish and drosophila with a knockout of glyoxalase 1 showed viable animals with only minor metabolic phenotypes. Importantly, compensatory mechanisms for other potential methylglyoxal-detoxifying enzymes, including aldehyde dehydrogenases and aldo reductases, were identified in glyoxalase 1 knockout animals. Subsequent knockout studies of different Aldehyde-Dehydrogenases and Aldo-Keto-Reductases have demonstrated that Glyoxalase 1 does not solely regulate the metabolism of reactive carbonyl species and organ functions. Instead, other reactive carbonyl species, together with their corresponding detoxification enzymes, exhibit distinct organ susceptibility. These detoxifying enzyme systems are interconnected at multiple levels in a complex and redundant manner, and their dysregulation can lead to chronic pathological conditions. Conceptually, the review aims to focus on future cardiovascular research investigating the specificity of different reactive carbonyl species to their respective detoxification systems and the interplay and organ-specific regulation of these detoxification pathways. The future goal is to develop reactive carbonyl species profiles and markers of inadequate detoxification in order to identify new patient subgroups. Another future challenge will be to establish reactive carbonyl species profiles and corresponding enzyme system activities as biomarkers for predicting, diagnosing and monitoring chronic diseases in translational and clinical contexts. Ultimately, we suggest to develop potent and specific reactive carbonyl species scavengers, as well as detoxifying enzyme activators, and define new patient subgroups with different treatment needs and prognoses.

Background: Cardiac remodeling is common in individuals with type 2 diabetes (T2D) and is influenced by glycemic and metabolic factors. However, myocardial steatosis, a proposed contributor to diabetic cardiomyopathy, has been inconsistently related to glycemic control. This study aimed to characterize longitudinal changes in myocardial triglyceride content (MTGC) and cardiac remodeling following glycemic optimization in newly diagnosed T2D.

Methods: In this uncontrolled, exploratory longitudinal study, twenty adults with newly diagnosed T2D underwent a 12-month standardized glycemic optimization protocol including insulin, metformin, and empagliflozin, in addition to nutritional and lifestyle counseling. Cardiac magnetic resonance imaging (CMR) and proton magnetic resonance spectroscopy (¹H-MRS) were performed at baseline and after 12 months to assess cardiac structure, function, and MTGC. Longitudinal changes and associations between clinical, biochemical, and imaging parameters were assessed.

Results: Participants (mean age 54.8 ± 9 years, 72.3% male) achieved significant reductions in HbA1c levels, body mass index (BMI) and waist circumference (WC). No significant changes in MTGC were found at follow-up (p = 0.23). CMR evaluation revealed increases in left ventricle (LV) ejection fraction (59.0% [54.8-61.5] vs. 63.1% [56.9-66.3], p = 0.01) and decreases in ventricular volumes: (LV) end-systolic volume (29.9 mL/m² [26.4-35.1] vs. 27.3 mL/m² [22.5-31.7]; p = 0.007), right ventricular (RV) end-systolic volume (30.6 mL/m² [25.9-35.7] vs. 28.7 mL/m² [25.5-32.6], p = 0.02) and RV end-diastolic volume (76.5 mL/m² [64.6-82.4] vs. 72.4 mL/m² [66.1-77.7], p = 0.03). The indexed LV mass increased (46.1 g/m² [35.1-54.2] vs. 49.5 [39.5-54.3], p = 0.006). No associations were found between HbA1c improvement and the MTGC or CMR parameters. Reductions in BMI and WC were associated with greater left atrial strain (ρ = - 0.78 and - 0.77; p < 0.001), whereas reductions in WC were also associated with greater LV end-diastolic volume (ρ = -0.59; p = 0.024).

Conclusions: In patients with newly diagnosed T2D, 12 months of glycemic optimization was associated with changes in cardiac remodeling parameters despite no detectable changes in myocardial steatosis. The observed cardiac changes were more closely associated with concurrent reductions in adiposity markers than with changes in HbA1c, emphasizing weight management as a key target for early prevention of diabetic cardiomyopathy.

Background: Previous research identifies the atherosclerotic index of plasma (AIP) as a key marker for cardiovascular risk, but its role in predicting outcomes in type B aortic dissection (TBAD) patients after thoracic endovascular aortic repair (TEVAR) is uncertain. This study aimed to investigate the association between AIP and long-term outcomes in TBAD patients after TEVAR.

Methods: This retrospective cohort study included 1335 patients with TBAD who underwent TEVAR. Patients were stratified into tertiles based on AIP levels. The primary endpoints were aortic-related adverse events (ARAEs) at 1 and 5 years after TEVAR. Cox regression analyses were used to evaluate the independent effect of AIP on outcomes. Kaplan-Meier (KM) analysis was conducted to compare the incidence of ARAEs among different groups. Restricted cubic spline (RCS) models were utilized to investigate the nonlinear relationship between AIP and ARAEs, and subgroup analyses assessed the stability of this association. Time-dependent receiver operating characteristic (ROC) curves were applied to assess the predictive accuracy of AIP for ARAEs over a 5-year period.

Results: The KM analysis revealed a significantly higher incidence of ARAEs in the high AIP group compared to the low AIP group (P < 0.001). However, no statistically significant differences were found in all-cause mortality and major adverse cardiovascular and cerebrovascular events (MACCEs) (all P > 0.05). Cox regression analysis demonstrated that a high level of AIP was associated with an increased risk of ARAEs (all P < 0.001). Additionally, RCS analysis indicated a linear relationship between AIP and the risk of ARAEs. In subgroup analyses, the timing of operation showed a significant interaction with 1-year ARAEs (P for interaction = 0.008). Time-dependent ROC analysis demonstrated an area under the curve approaching 0.8 throughout the 5-year period.

Conclusion: Our research indicates that AIP is independently associated with 1-year and 5-year ARAEs in patients with TBAD following TEVAR, providing a novel metabolic perspective for the prognostic evaluation of this population.