Cardiovascular Diabetology最新文献

Diabetic cardiomyopathy (DCM) is a significant complication of diabetes mellitus, often leading to heart failure and increased mortality. While its pathogenesis remains incompletely understood, key contributors include chronic hyperglycemia, hyperlipidemia, insulin resistance, myocardial fibrosis, oxidative stress, mitochondrial dysfunction, and aberrant cell death pathways. Emerging evidence highlights ferroptosis, an iron-dependent form of regulated cell death, as a critical player in DCM progression. This review synthesizes current knowledge on the mechanistic links between ferroptosis and DCM, focusing on endothelial dysfunction, myocardial fibrosis, oxidative stress, and mitochondrial damage. We also discuss promising therapeutic strategies targeting ferroptosis to alleviate DCM, including pharmacological inhibitors, natural compounds, and non-coding RNAs, while identifying gaps for future research.

Background: Patients with obesity exhibit high prevalence of heart failure with preserved or mildly reduced ejection fraction (HFpEF or HFmrEF). Recently, the mineralocorticoid receptor antagonist finerenone demonstrated benefits in this population. Glucagon-like peptide-1 analog aided weight loss also showed benefits. We hypothesized that finerenone would improve cardiovascular parameters when combined with weight loss in previously obese mice.

Methods: Mice were fed a high-fat diet (HFD) for 24 weeks, and were compared to mice fed a HFD for 16 weeks before returning to a standard diet for a further 8 weeks (rSD), with or without finerenone treatment 1 mg.kg^-1.day^-1 (rSD + F). Glycaemia and insulin tolerance were assessed, cardiac left ventricle (LV) function was evaluated by ultrasound and invasive hemodynamics, myocardial perfusion reserve (MPR) was measured by MRI, and exercise capacity was evaluated on a treadmill.

Results: After 16 weeks of HFD, compared to controls, obese mice had hyperglycaemia and insulin resistance, no difference in LV fractional shortening but increased LV filling pressure, impaired LV compliance, and reduced exercise capacity. After 24 weeks, this obesity phenotype was worsened by a reduction in MPR, LV fractional shortening, and cardiac output. After returning to standard diet, compared to the HFD group, body weight decreased in both the rSD and rSD + F groups, with improvements in glycaemia, insulin resistance, LV stroke volume, and LV compliance. In the rSD + F group, compared to rSD group, finerenone further improved LV compliance (34% improvement) (LVEDPVR: Ctl 1.19 ± 0.25, HFD 4.75 ± 0.31^***, rSD 3.44 ± 0.39^†, rSD + F 2.27 ± 0.23^†††,§; ^***P < 0.001 vs C, ^{†, †††}P < 0.05 and < 0.001 vs H, ^§P < 0.05 vs HS), as well as LV interstitial collagen fibrosis (-47%), and was necessary to improve LV fractional shortening, cardiac output, LV filling pressure (-30%) (LVEDP: Ctl 2.73 ± 0.17, HFD 4.73 ± 0.34^***, rSD 4.53 ± 0.33, rSD + F 3.18 ± 0.26^††,§; ^* vs C,^† vs H,^§ vs rSD), MPR (+ 28%), and exercise capacity (+ 43%). Benefits of finenerone were likely due, in part, to reduced LV mRNA and protein expression (-49%) of the protein tyrosine-phosphatase PTP-1B, a downstream negative regulator of the insulin receptor, associated with increased phosphorylation level (+ 82%) of AKT kinase.

Conclusion: In the mouse model of HFD-induced obesity and heart failure, finerenone improves weight loss-induced recovery of cardiac function.

Background: Arterial Hypertension (AH) and Type 2 Diabetes (T2DM) are common in obesity. However, there is no data on the influence of different degrees of obesity on the frequency of the two diseases.

Patients and methods: In this cross-sectional study 3,486 patients (867 men and 2,619 women, aged 42.6 ± 11.48 years, minimum BMI 30.0 kg/m², mean BMI 41.9 ± 6.62 kg/m²), were evaluated at presentation for bariatric surgery (BS). BMI was expressed as a continuous variable, as BMI quartiles (BMIQ), as full obesity class (I-III), as reduced obesity class (II-III), Frequency of AH and T2DM, and of a comorbidity score (CS: AH + T2DM) was recorded, and univariable and multivariable stepwise regression analysis was employed to evaluate the association of BMI indexes with AH, T2DM, and CS.

Results: AH, T2DM and CS showed increasing frequency with increasing BMI quartiles (p < 0.001), and with age (p < 0.001). AH and T2DM were frequently associated. At multivariable analysis, age and BMIQ were associated with AH (p < 0.001), T2DM (p < 0.001), and with CS (p < 0.001), more than BMI (NS for CS) full obesity class and reduced obesity class and BMI (NS for T2DM and for CS). The same results were observed at receiver operating curves (ROC), with greater area (AUC) for BMIQ than for BMI and obesity classes.

Conclusion: The frequency of AH and T2DM increases with increasing BMI levels. BMIQ outperforms BMI and obesity classes in modelling. This finding supports the use of fine stratification of BMI in clinical risk assessment. More specific studies are required to fully understand the impact of different BMI thresholds in determining the health status of obese patients, and to reduce the risks of BS in patients with extreme obesity.

Background: It is unclear whether the time-varying mediation effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on the three-point major adverse cardiovascular event (3P-MACE) outcomes, including nonfatal stroke, nonfatal myocardial infarction, and cardiovascular death, via HbA_1c, urine albumin-to-creatinine ratio (UACR), and systolic blood pressure (SBP) vary by types of GLP-1RAs, and patient subgroups.

Methods: A causal mediation analysis was applied to assess the mediation effects of HbA_1c, UACR, and SBP on the 3P-MACE with liraglutide and semaglutide over time, using the LEADER and SUSTAIN-6 trials. We further explored the heterogeneity in mediation effects in subsets of vulnerable patients, including those with an estimated glomerular filtration rate (eGFR) level of < 60 ml/min/1.73 m² or established cardiovascular diseases (CVDs).

Results: Individual-level data consisting of 9340 (liraglutide: 4668; placebo: 4672) and 3297 (semaglutide: 1648; placebo: 1649) subjects from the LEADER and SUSTAIN-6 trials, respectively, was utilized for this study. The study population aged around 64.2-64.4/64.4-64.8 years old, with 64.0%-64.5%/58.5%-63.0% of males, and having a diabetes duration of 12.8-12.9/13.2-14.3 years in the LEADER/SUSTAIN-6 trials, respectively. Among study populations, those established CVDs (e.g., heart failure, stroke, myocardial infarction) accounted for 14.0%-35.4% in the LEADER trial, whereas 13.7%-61.9% in the SUSTAIN-6 trial. At the end of each trial, HbA_1c contributed the most to liraglutide's/semaglutide's effect on 3P-MACE (38.2%, 95% confidence interval: 13.8%-194.5%/51.8%, 29.6%-86.7%), followed by UACR (17.9%, 8.5%-61.9%/12.4%, 6.1%-32.4%) and SBP (6.8%, 2.2%-31.0%/6.7%, 2.9%-11.7%), respectively. Over the trial period, the effect of liraglutide mediated by HbA_1c increased from 31.3% to 38.2%, whereas the effects of semaglutide mediated by HbA_1c remained stable from 52.5% to 51.8%. For patients with eGFR < 60 ml/min/1.73 m², HbA_1c was the leading mediator (35.2%) of semaglutide-associated 3P-MACE, while the mediation effects via HbA_1c (7.2%), UACR (11.8%), and SBP (5.1%) on liraglutide-associated 3P-MACE seemed similar and smaller. For patients with established CVDs, HbA_1c was the leading mediator of liraglutide- (25.3%) and semaglutide-associated 3P-MACE (51.2%).

Conclusions: The HbA_1c-mediated effects of liraglutide and semaglutide varied over time and across patient subgroups. HbA_1c consistently explained a larger proportion of mediation compared to UACR and SBP, with patterns suggesting that mediation through HbA_1c may differ by underlying risk factors.

Background: Triglyceride-glucose (TyG) index is considered an alternative indicator of insulin resistance and is associated with cardiovascular diseases. However, its association with subclinical myocardial injury in a general population without known cardiovascular disease has not been investigated, nor has the effect of diabetes.

Methods: Individuals without known cardiovascular disease were included from the Atherosclerosis Risk in Communities (ARIC) cohort. The baseline TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The association between the baseline TyG index and subclinical myocardial injury [high-sensitivity cardiac troponin T (hs-cTnT≥ 14 ng/L)] was assessed using both cross-sectional and prospective cohort designs.

Results: A total of 11,478 participants were involved in the cross-sectional study (mean age, 56.78 years; 42.44% male) and 8801 participants were involved in the prospective cohort study (mean age, 56.57 years; 41.36% male). Both linear (r = 0.13, p < 0.001) and logistic regression analyses (adjusted odds ratio [aOR] = 1.33, p < 0.001) showed a positive association between the TyG index and baseline hs-cTnT level, which was consistent in diabetic participants (aOR = 1.64, p = 0.020) but not significant in non-diabetic participants (aOR = 0.89, p = 0.374). After a six-year follow-up, a U-shaped association between the TyG index and incidence of hs-cTnT elevation was observed among the overall participants. Further subgroup analyses showed an L-shaped (aOR = 0.72, p = 0.006) and a J-shaped (aOR = 2.09, p < 0.001) association between the TyG index and incidence of hs-cTnT elevation in participants without and with diabetes, respectively.

Conclusion: A U-shaped association between the TyG index and the incidence of subclinical myocardial injury was observed for the first time. Diabetes may be a critical modifier of the association between the TyG index and subclinical myocardial injury. Considering the risk stratification value of the TyG index based on diabetes status may hold significant clinical value.

Background: The comparison between conventional glycemic markers and continuous glucose monitoring (CGM) in relation to adverse outcomes in the elderly with type 2 diabetes remains unclear. We aimed to assess associations of 1,5-anhydroglucitol (1,5-AG) and CGM metrics with carotid intima-media thickness (CIMT) as a surrogate of cardiovascular disease.

Methods: The study included 2509 adults aged ≥ 60 years with type 2 diabetes. CIMT was measured by high-resolution ultrasonography, with abnormal CIMT defined as a mean thickness of ≥ 1.0 mm. Time in range (TIR), mean sensor glucose (MSG), time above range (TAR, > 10.0 mmol/L), standard deviation (SD), and coefficient of variation (CV) were calculated from CGM data.

Results: The median serum 1,5-AG was 3.9 (2.0, 8.0) μg/mL, and the prevalence of abnormal CIMT was 44.2% (n = 1,109). The prevalence of abnormal CIMT decreased across ascending 1,5-AG categories (P for trend < 0.01). In the fully adjusted model, each 1-standard deviation decrease in 1,5-AG conferred 10% higher odds of abnormal CIMT. Compared with 1,5-AG ≥ 10.0 μg/mL, 1,5-AG < 6.0 μg/mL was associated with an odds ratio of 1.25 (95% CI 1.00-1.55) for abnormal CIMT. Among CGM metrics, TIR, MSG, and TAR _> 10.0, but not CV or SD, were significantly associated with abnormal CIMT. The C-statistics for 1,5-AG in predicting abnormal CIMT were comparable to those for TIR, MSG, and TAR _>10.0 (all P > 0.05).

Conclusions: In older adults with type 2 diabetes, 1,5-AG demonstrated a performance comparable to CGM for detecting abnormal CIMT, supporting its potential as a clinical biomarker for identifying subclinical atherosclerosis.

Background: The atherogenic index of plasma (AIP) has been widely investigated in relation to cardiovascular disease (CVD). However, its association with major adverse cardiovascular events (MACE) among individuals with metabolic syndrome (MetS) remains unclear. We aimed to evaluate the association of AIP with incident MACE and its component endpoints among individuals with MetS.

Methods: We analyzed data from 131,736 UK Biobank participants with MetS who were free of MACE at baseline. The primary outcome was MACE, defined as the first occurrence of acute myocardial infarction (AMI), cardiovascular death (CV death), unstable angina (UA), stroke, or heart failure (HF). Cumulative incidence was estimated using Kaplan-Meier methods, and the association between baseline AIP and MACE was explored with multivariable Cox proportional hazards models. Additionally, nonlinearity in this association was examined with restricted cubic splines (RCS). The incremental predictive value of AIP for MACE was evaluated using the change in C-index ([Formula: see text]C-index), the net reclassification index (NRI), and the integrated discrimination improvement (IDI). Robustness of the findings was evaluated in prespecified subgroup and sensitivity analyses.

Results: During a median of 13.4 years of follow-up, 19,140 of the 131,736 participants with MetS experienced MACE (14.5%). Each standard deviation (SD) increase in AIP was associated with higher risks across multiple cardiovascular endpoints in this population. In fully adjusted models, AIP was associated with MACE (hazard ratio (HR), 1.07; 95% confidence interval (CI), 1.05,1.08), AMI (1.16; 1.12,1.19), and UA (1.16; 1.13,1.19), with no clear evidence of association for stroke (1.03; 1.00,1.06), CV death (1.01; 0.97,1.05), or HF (1.01; 0.98,1.03). Kaplan-Meier curve analysis demonstrated significant differences in the incidences of MACE, AMI, UA, CV death, and HF across AIP groups in the entire cohort (all log-rank P < 0.05), whereas stroke showed no significant difference (log-rank P > 0.05). RCS analyses indicated U-shaped associations for MACE, AMI, HF, and CV death and a J-shaped association for UA (all P for nonlinearity < 0.05), whereas the association with stroke was linear (P for nonlinearity > 0.05). Moreover, AIP improved reclassification metrics (NRI/IDI) and discriminative ability (C-index). Sensitivity analyses corroborated these primary findings.

Conclusions: This observational study showed that higher AIP was associated with increased risks of MACE and its component endpoints, highlighting its potential as a biomarker for risk stratification among individuals with MetS.

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.