Cardiovascular Diabetology最新文献

Background: Thymoma-associated myasthenia gravis (MG) is a clinically significant but uncommon condition, affecting up to half of thymoma patients and associated with worse outcomes than either disease alone. Reliable biomarkers for early risk stratification remain scarce. The triglyceride-glucose (TyG) index and triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio are established biomarkers reflecting insulin resistance and dyslipidemia. However, their clinical associations with thymoma-associated MG remain incompletely characterized.

Methods: A total of 501,954 UK Biobank participants were included. After exclusions, 422,397 (313 cases) were analyzed for TyG index and 422,691 (314 cases) for TG/HDL-C ratio. Exposures were stratified into quartiles, and assessed continuously per unit increase. Cox proportional hazards models estimated hazard ratios (HRs), restricted cubic splines (RCS) assessed non-linear associations, and subgroup analyses were stratified by body mass index (BMI). Sensitivity analyses examined TyG index and MG alone.

Results: An elevated TyG index was associated with increased risk of thymoma-associated MG. Compared to Q1, Q4 had higher risk (HR = 1.66, 95% CI: 1.20-2.31, P = 0.003); the overall HR per unit increase was 1.42 (95% CI: 1.17-1.73, P = 0.0005). TG/HDL-C ratio showed similar patterns: Q4 vs Q1 HR = 1.54 (95% CI: 1.11-2.15, P = 0.01); overall HR per unit increase was 1.06 (95% CI: 1.02-1.10, P = 0.002). Non-linear relationships were observed, with suggested inflection points at TyG index 8.7 and TG/HDL-C ratio 2.8, rather than strict thresholds. Subgroup analyses revealed stronger associations in normal-weight and obese participants, although tests for interaction were not statistically significant. Sensitivity analyses confirmed consistent associations between TyG index and MG risk, including for isolated MG.

Conclusion: Elevated TyG index and TG/HDL-C ratio were independently associated with higher thymoma-associated MG risk in this large prospective cohort, with evidence of non-linear relationships and BMI-related heterogeneity. These findings provide novel epidemiologic evidence linking metabolic markers of insulin resistance with thymoma-associated MG, but clinical translation requires further validation.

Background: Type 2 diabetes mellitus (T2DM) is a major driver of coronary artery disease (CAD). Prior studies often conflate T2DM- and CAD-specific metabolic alterations, limiting insights into CAD pathogenesis in T2DM. This study aimed to distinguish CAD-unique signatures from T2DM-specific dysmetabolism, and to identify potential metabolic biomarkers for CAD risk escalation in T2DM patients.

Methods: We performed an untargeted plasma metabolomic study with 123 healthy controls (HCs), 50 T2DM patients without CAD, and 155 T2DM patients with CAD. T2DM_CAD was defined as T2DM diagnosed at least 5 years prior to CAD, with coronary angiography-confirmed stenosis (> 30%) in major coronary arteries. Differential metabolites were identified via intergroup comparisons, with T2DM-specific and CAD-specific signatures distinguished based on unique expression patterns. Machine learning models were developed to evaluate the discriminatory performance of these metabolites for CAD.

Results: Plasma metabolomic profiling identified distinct metabolic patterns across the three cohorts. Metabolites specific to T2DM were enriched in carbohydrates and certain lipid species, reflecting disturbances in glucose and lipid metabolism. CAD-specific metabolites were predominantly lipids and organic acids, with notable involvement in amino acid and fatty acid metabolic pathways. Several metabolites changed progressively from HCs through T2DM to T2DM_CAD, reflecting advancing metabolic dysregulation, whereas others showed opposing trends, suggesting compensatory or protective adaptations. Integration of key metabolites with clinical parameters in machine learning models effectively distinguished between study groups, demonstrating promising performance for CAD risk assessment in T2DM patients.

Conclusions: These findings disentangle T2DM- and CAD-specific metabolic disturbances and identify escalation/de-escalation features of CAD risk in diabetic patients, which are potential candidates for future risk stratification pending validation.

Background: Limited data are available on the risk of pancreatic adverse events among people with obesity or type 2 diabetes mellitus (T2DM) initiating glucagon-like peptide-1 receptor agonist (GLP-1 RA).

Methods: Retrospective study utilizing data from a federated research network (TriNetX). Adult people (≥ 18 years) with a diagnosis of obesity (body mass index ≥ 30 kg/m²) or T2DM (ICD-10-CM: E11) between 2018 and 2024 were subdivided in two mutually exclusive cohorts: (1) GLP-1 RA Users; and (2) Non-GLP-1 RA Users. Primary outcomes were 1-year risk of all-cause death and a composite outcome (acute pancreatitis, chronic pancreatitis). Secondary outcomes included the individual components of the composite outcome and pancreatic cancer. Cox regression analyses were employed to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) before and after 1:1 propensity score matching (PSM). Sensitivity analyses stratified follow-up into early (first 6 months) and late (last 6 months) phases. Subgroup analyses were performed based on age (≥ 65 or < 65 years), sex, and the history of smoking and alcohol use, hypertriglyceridemia, cholelithiasis, heart failure, and chronic kidney disease.

Results: We identified 1,562,626 people who initiated treatment with GLP-1 RA (mean age 55.3 ± 14.0 years; 59.2% female) and 18,652,572 those who did not (mean age 50.6 ± 18.7 years; 54.5% female). Before PSM, GLP-1 RA Users were older, more frequently female, and exhibited a higher burden of endocrine, metabolic and gastrointestinal disorders. After PSM, GLP-1 RA use was associated with a substantially lower risk of all-cause death (HR 0.554, 95% CI 0.542-0.566), and small increased risk of the composite outcome (HR 1.062, 95% CI 1.023-1.102) and acute pancreatitis (HR 1.058, 95% CI 1.015-1.103), with no differences in chronic pancreatitis or pancreatic cancer. The excess risk of acute pancreatitis was more pronounced during the early phase of follow-up (first 6 months). Subgroup analyses showed a higher reduction in death and composite outcome among people aged < 65 years. Additional significant interactions were observed for all-cause death in females and in people with a history of smoking, alcohol use, heart failure, chronic kidney disease, or cholelithiasis.

Conclusion: GLP-1 RA use was associated with substantially reduced all-cause death but a small increased risk of acute pancreatitis, particularly during early treatment. The survival benefit was more pronounced in younger people and those with cardiometabolic comorbidities, highlighting the need for a careful risk-benefit evaluation when prescribing GLP-1 RAs in high-risk individuals.

Background: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized.

Objective: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications.

Methods: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE).

Results: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype.

Conclusion: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Background: Left ventricular (LV) dilatation has been found to be associated with poor prognosis in patients with reduced left ventricular ejection fraction (LVEF). However, the relationship between LV dilatation and biventricular myocardial dysfunction in patients with diabetes mellitus (DM) and reduced LVEF unclear.

Methods: From September 2017 to March 2025, 265 clinically diagnosed patients with DM who underwent cardiac magnetic resonance (CMR) scanning in our hospital were included. According to LVEF and LV dilatation status, these patients were divided into three groups: 122 DM with preserved LVEF (DMpEF) group, 51 DM with reduced LVEF and no LV dilatation (DMrEF-NLVD) group, and 92 DM with dilated cardiomyopathy (DMrEF-DCM) group. Biventricular strain parameters, including left/right ventricular global radial strain (LV-/RVGRS), left/right ventricular global circumferential strain (LV-/RVGCS), and left/right ventricular global longitudinal strain (LV-/RVGLS) were evaluated and compared among the three groups. Additionally, multiple linear regression analysis was performed to assess the independent effect of LV dilatation on biventricular strains in DM patients with reduced LVEF.

Results: Significant differences were observed in both left and right ventricular strain parameters among the three groups. For left ventricular function, LV global strains progressively declined from the DMpEF group to the DMrEF-NLVD group and further to the DMrEF-DCM group (all p < 0.001). For right ventricular function, RV global strains were significantly more impaired in the DMrEF-DCM group and the DMrEF-NLVD group compared with the DMpEF group (all p < 0.05). The DMrEF-DCM group had decreased RVGCS (- 6.18 ± 3.52 vs. - 8.89 ± 4.15, p < 0.001) compared with the DMrEF-NLVD group. In DM patients with reduced LVEF, multivariable linear regression analysis revealed that LV dilatation was independently associated with reduced LVGCS (β = 0.176, p = 0.009).

Conclusions: In DM patients with reduced LVEF, LV dilatation was associated with biventricular dysfunction and deformation injury. LV dilatation was found to be independently associated with impaired LVGCS, the further decrease of LVGCS in DM patients with reduced LVEF.

Background: The atherogenic index of plasma (AIP) and obesity indices have been introduced as cost-effective indicators for cardiovascular disease (CVD) risk. We aimed to investigate the association between cumulative changes in AIP and its obesity-related derivatives (AIP, AIP-BMI, AIP-WC, AIP-WHtR, AIP-BRI, and AIP-CVAI) and CVD using a nationally representative cohort.

Methods: A total of 4,519 CVD-free participants from the China Health and Retirement Longitudinal Study were included. Based on repeated measurement data from Waves 1 and 3, we classified the control levels of AIP and its obesity-related derivatives using k-means clustering and evaluated their cumulative exposure. A multi-model joint analysis strategy was adopted to systematically assess the associations of cumulative changes in AIP and its obesity-related derivatives with CVD, the component contributions, and the mediating role of glycometabolic factors.

Results: Over 8 years' median follow-up, the study found that both AIP and its obesity-related derivatives (including baseline levels and cumulative changes) exhibited linear positive associations with CVD events. Compared to baseline assessments, evaluating the cumulative changes in AIP and its obesity-related derivatives further enhanced the assessment of CVD risk. Compared to individuals with better control level or lowest cumulative exposure, those with poor control level or highest cumulative exposure of AIP, AIP-BMI, AIP-WC, AIP-WHtR, AIP-BRI, and AIP-CVAI exhibited odds ratios of 1.37/1.34, 1.48/1.43, 1.44/1.42, 1.36/1.42, 1.43/1.42, and 1.51/1.42, respectively. Notably, diabetic status further amplified the impact of cumulative changes in AIP and its obesity-related derivatives on CVD risk. Further weighted quantile sum analysis revealed that cumulative triglycerides and cumulative obesity indices exhibited the highest relative contribution weights to CVD events. Finally, mediation analysis demonstrated that cumulative haemoglobin A1c partially mediated the CVD risk associated with cumulative AIP and its obesity-related derivatives.

Conclusions: AIP and its obesity-related derivatives (particularly AIP-CVAI) exhibited significant positive associations with incident CVD risk. The strength of these associations dynamically influenced with cumulative changes in these metrics and significantly modified by diabetic status. Comprehensive analysis using weighted quantile sum and mediation models revealed that cumulative glucose effect partially mediated these associations, while sustained exposure to triglycerides and obesity emerged as primary drivers.

Background: Pacemaker recipients are predisposed to heart failure (HF), yet evidence guiding preventive pharmacotherapy in this population remains unexplored. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have redefined HF management across a broad spectrum of cardiometabolic phenotypes. This study evaluated the association between SGLT2i therapy and clinical outcomes after pacemaker implantation for atrioventricular block.

Methods: Patients receiving conventional pacemakers between 2016 and 2024 were retrospectively analyzed and stratified by baseline SGLT2i therapy. Exclusions included sinus node dysfunction or iatrogenic pacing indications, single-chamber devices, and estimated glomerular filtration rate < 20 mL/min/1.73m². Events within 3 months post-implantation were omitted to reduce peri-procedural confounding. After propensity score matching, Cox regression assessed associations between SGLT2i use and all-cause mortality and HF hospitalization.

Results: Among 11,518 eligible patients, propensity score matching yielded two well-balanced cohorts of 1,226 SGLT2i users and non-SGLT2i users. Over three years, death occurred in 124 (10.1%) in the SGLT2i users and in 194 (15.8%) in the non-SGLT2i group (hazard ratio [HR], 0.62; 95% confidence interval [CI], 0.50 to 0.78; P < 0.001). HF hospitalization occurred in 132 (10.7%) in the SGLT2i group, and in 280 (22.8%) in the non-SGLT2i group (HR, 0.50; 95% CI, 0.41 to 0.62; P < 0.001). Subgroup analyses demonstrated consistent effects across strata.

Conclusions: SGLT2i therapy was associated with reduced risk of all-cause mortality and HF-related hospitalizations following pacemaker implantation. Future randomized studies are needed to confirm this association.

Background: The Time In Range (TIR) represents the amount of time spent by a given individual in the range close to normoglycemia, i.e. 70-180 mg/dl. On the basis of studies demonstrating an association of TIR with the incidence of diabetes complications, guidelines recommend a target of at least 70% of TIR for most people with diabetes. However, no study has explored the effect of variable degrees of TIR on molecular mechanisms relevant for the development of diabetes complications.

Methods: We exposed endothelial cells and monocytes to increasing percentages of TIR, i.e. 50%, 70%, 85% by changing cell media twice a day as appropriate, as well as to constant normoglycemia (i.e. fixed 100 mg/dl of glucose for endothelial cells) and hyperglycemia (i.e. 500 mg/dl glucose), evaluating the development of senescence, of the associated pro-inflammatory response, and monocytes adhesion to endothelial cells as a functional assay. We then assessed the expression of a plethora of markers of senescence and inflammation at the mRNA level in peripheral blood mononuclear cells (PBMC)s derived from individuals with early (i.e. 1-year post-diagnosis) type 1 diabetes (T1D, n = 37), categorized according to the TIR (< or > 70%) observed in the previous 14 days, comparing the two groups through ANCOVA adjusted for HbA1c. As a confirmatory analysis, we also compared the expression of the same markers in people with Time Above Range (TAR), considered as the whole time above 180 mg/dl, ≥ vs < 30%. Correlations between TIR values and the expression of the same markers were tested through linear regression.

Results: Constant hyperglycemia promoted the development of senescence in endothelial cells and induced inflammatory responses in both endothelial cells and monocytes, promoting also monocytes adhesion to endothelial cells. A TIR of 70%, but not of 50%, suppressed these effects while a TIR of 85% did not provide additional benefit. Data from people with T1D mirrored such results, as demonstrated by the higher expression of p16, a marker of senescence, and of IL-6, MCP-1, and CXCL1, three inflammatory mediators, in PBMCs from individuals with TIR < 70% and compared with those with TIR > 70%, independently of HbA1c. Similar results were obtained when comparing people with TAR ≥ vs < 30%. When considered as a continuous variable, TIR values were correlated with p16, IL-6, and CXCL1.

Conclusions: A TIR above 70% is associated with attenuated pro-senescence and pro-inflammatory effects of hyperglycemia. These molecular results support the TIR target currently recommended by guidelines, especially for people with T1D.

Background and aim: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM.

Methods: Among 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (< 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations.

Results: A total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance.

Conclusion: This study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.

Background: The triglyceride glucose (TyG) index and various obesity indices have been proven to be cost-effective indicators of cardiovascular disease (CVD) risk. This study aims to systematically investigate and compare the associations between longitudinal trajectories of TyG index combined with classical and novel obesity indices (TyG-BMI, TyG-WC, TyG-WHtR, TyG-WWI, TyG-ABSI, TyG-BRI, TyG-CVAI) and CVD.

Methods: The study sample comprised 3505 non-CVD participants from the CHARLS national cohort. Longitudinal data from Waves 1 and 3 of the national surveys were used to quantify cumulative exposure and trajectories of TyG and its obesity derivatives. A multi-model analytical framework (including logistic regression, spline regression, and weighted quantile sum regression models) was constructed to systematically examine the strength of associations between trajectories of TyG and its obesity derivatives and CVD, and the contribution of each component.

Results: During 8-year median follow-up, 411 CVD cases occurred. The study demonstrated that compared to static baseline values, longitudinal assessment of cumulative exposure and trajectory of TyG and its obesity derivatives enhanced predictive capacity for CVD. Notably, combinations of TyG with classical obesity index WC and novel obesity index CVAI (TyG-WC and TyG-CVAI) exhibited superior performance for CVD risk assessment. Compared to participants with well-controlled trajectories and low exposure levels, those with poorly controlled or highest cumulative exposure to TyG index, TyG-BMI, TyG-WC, TyG-WHtR, TyG-WWI, TyG-ABSI, TyG-BRI, and TyG-CVAI had odds ratios of 1.61/1.40, 2.13/1.70, 2.00/1.78, 1.77/1.59, 1.31/1.36, 1.37/1.30, 1.76/1.56, and 2.00/1.72, respectively. Finally, weighted quantile sum regression results indicated that cumulative exposure to obesity and triglycerides contributed most to CVD risk among all metabolic indices, suggesting that simultaneous regulation of triglycerides and obesity may be critical for reducing CVD risk.

Conclusion: In this cohort study, the longitudinal trajectories of TyG and its obesity derivatives were closely associated with CVD. Comparatively, the combinations of TyG with classical obesity index WC and novel obesity index CVAI (TyG-WC and TyG-CVAI) exhibited superior performance for CVD risk assessment, with this risk primarily driven by obesity and triglycerides.