Cardiovascular Diabetology最新文献

Background: Cardio-renal-metabolic (CRM) comorbidity, including cardiovascular disease, chronic kidney disease, and type 2 diabetes mellitus, is prevalent in the population and closely associated with biological aging. However, longitudinal evidence and potential proteomics mediator remain limited.

Methods: We studied 330,177 UK Biobank participants free of CRM diseases at baseline. Biological aging was measured by KDM-BA, PhenoAge, their accelerations, and frailty status, and its effects on CRM progression, including no CRM disease to first, double, and triple CRM diseases, were evaluated using multistate proportional hazards model. In the subpopulation with proteomics data (n = 35,118), 2911 plasma proteins were profiled, and mediation analyses were performed to identify potential mediators.

Results: All five biological aging indicators significantly predicted CRM progression. For example, each standard deviation increase in PhenoAge was associated with hazard ratios of 1.42 [95% confidence interval (CI) 1.40-1.44], 1.26 (95% CI 1.22-1.31), and 1.24 (95% CI 1.12-1.37) for the transitions to first, double, and triple CRM disease, respectively. Mediation analyses identified nine circulating key proteins that statistically mediated the associations between biological aging and CKM progression, with GDF15, ADM, and HAVCR1 showing the largest mediated proportion (13.10-43.23%). The neutralizing antibody ponsegroumab for GDF15 is currently undergoing clinical evaluation.

Conclusion: Biological aging was strongly associated with the progression of CRM comorbidity, and these associations were partly accounted for by specific circulating proteins. These findings highlight the potential of aging-centered strategies and proteomic biomarkers for improving the risk prediction of CRM health and identifying therapeutic targets.

Background: Type 2 diabetes mellitus (T2DM) is a growing global health challenge. Conventional diagnostic tools have limited sensitivity and specificity for early-stage disease. In this context, long non-coding RNAs (lncRNAs) have emerged as promising biomarkers for T2DM risk. However, studies exploring their predictive value remain limited. This study aimed to evaluate the potential of circulating lncRNAs in T2DM development and to assess their interaction with dietary interventions.

Methods: The study included 462 non-diabetic participants from the CORDIOPREV study, followed for 5 years under Mediterranean or low-fat diet interventions. Expression levels of 22 circulating lncRNAs were quantified by qPCR. A series of statistical and machine learning methods were applied.

Results: Random forest analysis identified four lncRNAs (XIST, LINC01116, CASC2, LINC01370) as predictive of T2DM incidence. The combination of these lncRNAs with clinical variables significantly improved prediction performance (AUC = 0.730) compared to models with Hb1Ac (p = 0.015) or clinical variables alone (p < 0.001). Regarding the constructed lncRNA score from the previous model, a lower lncRNA score was associated with a reduced risk of developing T2DM (HR 0.37 (0.24-0.59), p < 0.001), and showed an inverse correlation with the disposition index among individuals following a Mediterranean diet (r = - 0.18, p = 0.009).

Conclusion: Circulating lncRNAs, particularly integrated into an epigenetic score, represent promising predictive biomarkers for T2DM development. The interaction with dietary intervention-especially the Mediterranean diet-supports their potential use in guiding personalized dietary strategies for T2DM prevention in high-risk populations.

Aims: This study aimed to assess the impacts of overweight and type 2 diabetes (T2D) on early hemodynamic and left ventricular (LV) changes using comprehensive cardiovascular magnetic resonance imaging (CMR) in three age- and sex-similar groups: lean individuals (LI group), BMI-similar overweight controls without T2D (CWO group) and overweight individuals with uncomplicated T2D (T2D group).

Methods: Middle-aged participants included 13 in the LI group, 37 in the CWO group, and 28 in the T2D group. Baseline assessments included cardiac and vascular function measured by CMR, thoracic echocardiography, insulin sensitivity, clinical measures, and cardiorespiratory fitness. 4D flow CMR was used to evaluate diastolic function via valve tracking and intraventricular flow analysis. Group differences were analyzed using ANOVA or Kruskal-Wallis tests, with Holm-Bonferroni correction applied for pairwise comparisons. Linear correlation and multivariate regression analyses were performed to evaluate the associations between CMR-derived diastolic variables and echocardiographic and clinical measures.

Results: Cardiorespiratory fitness was significantly lower in both the CWO and T2D groups compared with the LI group (all P < 0.001), with no difference between CWO and T2D. 4D flow CMR identified significant differences in the E/A velocity ratio between LI and CWO groups (P = 0.029), and in E/A velocity, flow, and vorticity ratios between CWO and T2D groups (P = 0.025, 0.031, and 0.034, respectively). The E/A vorticity ratio correlated moderately with conventional echocardiographic diastolic indices, such as MV E/A velocity ratio (Spearman's rho = 0.35, 95% CI: 0.07-0.60, P = 0.010; adjusted for age and sex). In multivariate regression, age (β =  - 0.014, P = 0.001), hypertension (β =  - 0.130, P = 0.015), heart rate (β =  - 0.009, P = 0.001), and circumferential peak early diastolic strain rate (β = 0.431, P = 0.002) emerged as independent determinants of the E/A vorticity ratio.

Conclusion: The present study suggests that the 4D flow CMR-derived E/A vorticity ratio may serve as a potential LV diastolic biomarker, given its capability in differentiating T2D-specific changes and stronger associations with clinical measures as well as CMR and echocardiographic parameters.

Trial registration:

Clinicaltrials: gov Identifier: NCT03419195 and NCT04791371.

Frailty and cardiometabolic disorders are highly prevalent in the aging population and frequently coexist, amplifying each other's adverse effects. Frailty, defined by decreased physiological reserves and heightened vulnerability to stressors, often occurs alongside cardiometabolic conditions such as diabetes, hypertension, and cardiovascular disease. The intersection of these conditions poses substantial clinical challenges, impacting morbidity, mortality, and quality of life. Understanding the shared pathophysiological mechanisms underlying frailty and cardiometabolic disorders is critical for guiding effective prevention and management strategies. This systematic review, registered in PROSPERO (CRD420251164236), documents current knowledge on the definitions, epidemiology, and pathophysiology of frailty in the context of cardiometabolic disorders and highlights the main clinical implications of their coexistence. Additionally, we discuss evidence-based strategies for assessment, prevention, and management, emphasizing the importance of an integrated approach to improve outcomes in older adults. These insights aim to inform about targeted interventions that can mitigate risk, enhance resilience, and optimize patient care.

Background: Stress hyperglycemia, reflected by the stress hyperglycemia ratio (SHR), is increasingly recognized as a marker of adverse cardiovascular outcomes in both diabetic and non-diabetic patients. Stress-induced hyperglycemia arises from acute metabolic and inflammatory stress responses and may signify impaired glycemic resilience. Heart failure with preserved ejection fraction (HFpEF) commonly coexists with metabolic abnormalities such as hyperglycemia, prediabetes, and diabetes, while physical frailty-frequent in older adults-is mechanistically linked to both dysglycemia and HFpEF. In this study, we aimed to investigate the association between SHR and physical performance in frail older adults with HFpEF.

Methods: We conducted a prospective observational study enrolling consecutive frail adults aged > 65 years with a confirmed diagnosis of HFpEF and Montreal cognitive assessment (MoCA) score < 26. Frailty was defined by ≥ 3 of 5 Fried criteria (low physical activity, unintentional weight loss, exhaustion, weakness, and slowness). SHR was calculated as the ratio of admission plasma glucose (mmol/L) to estimated chronic glucose derived from HbA1c (%). Participants were stratified into two groups: SHR ≤ 1 and SHR > 1. Physical function was assessed by gait speed (m/s).

Results: Of 295 screened individuals, 204 met inclusion criteria and completed the study. Patients with SHR > 1 demonstrated significantly reduced physical performance compared with those with SHR ≤ 1 (mean gait speed 0.65 ± 0.20 m/s vs. 0.72 ± 0.20 m/s, p = 0.0004).

Conclusions: A higher SHR was independently associated with poorer physical function in frail older adults with HFpEF. These findings suggest that stress-related dysglycemia may contribute to functional decline in this population, highlighting the potential utility of SHR as a metabolic marker of frailty severity and cardiovascular vulnerability.

Background: Disrupted cardiomyocyte energy metabolism is a hallmark of heart failure with preserved ejection fraction (HFpEF). Succinate, a key intermediate of the tricarboxylic acid cycle, is markedly decreased in HFpEF myocardium. In addition to its metabolic role, succinate functions as a signaling molecule that activates GPR91 to regulate metabolic and immune pathways. However, the precise contributions and mechanisms of cardiomyocyte succinate-GPR91 signaling in HFpEF pathogenesis remain incompletely understood.

Methods: HFpEF models were established in wild-type (WT), global GPR91 knockout (Gpr91^-/-), and cardiomyocyte-specific GPR91 knockout (Gpr91^ΔCM) mice, with or without succinate supplementation. Cardiac structure, function, and metabolic phenotypes were assessed using echocardiography, histology, and molecular assays. Transcriptome sequencing of myocardial tissues was performed to identify pathways regulated by succinate-GPR91 signaling. Mechanistic studies in human AC16 cardiomyocytes were conducted to validate pathway regulation and clarify downstream molecular mechanisms. Additionally, rescue experiments were performed to confirm the functional relevance of succinate-GPR91 signaling in cardiomyocyte metabolism and HFpEF progression.

Results: Cardiac succinate levels and GPR91 expression were markedly decreased in HFpEF mice. Succinate supplementation restored systemic metabolism, improved diastolic function, and attenuated myocardial hypertrophy and fibrosis in WT HFpEF mice, whereas these protective effects were abolished in both Gpr91^-/- and Gpr91^ΔCM mice. Transcriptomic analysis demonstrated that succinate activated AMPK signaling and enriched pathways related to glucose-lipid metabolism and NAD⁺ biosynthesis in Gpr91^fl/fl but not in Gpr91^ΔCM hearts. Mechanistically, succinate enhanced AMPK phosphorylation and NAD⁺ production via Gq-mediated signaling, thereby promoting cardiomyocyte metabolic reprogramming.

Conclusion: These findings identify the succinate-GPR91 axis as a critical regulator of cardiometabolic homeostasis and a potential therapeutic target in HFpEF.

Background: The mechanisms underlying cardiorenal benefits of finerenone remain unclear. This mechanistic trial aimed to evaluate the effects of finerenone on vascular stiffness, as assessed using the cardio-ankle vascular index (CAVI), and cardiorenal biomarkers in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD).

Methods: Eligible patients with T2D and CKD (estimated glomerular filtration rate [eGFR], 25 to < 90 mL/min/1.73 m²; urinary albumin-to-creatinine ratio [UACR], 30 to < 3500 mg/g Cr) were randomly allocated to receive either dose-adjusted finerenone or matching placebo. The primary endpoint was the change in CAVI at week 24. The key secondary endpoint was the proportional change in UACR from baseline over 24 weeks. As an exploratory analysis, changes in circulating proteins were measured by using the Olink® Target 96 Cardiovascular III and Inflammation panels.

Results: This investigator-initiated, multicentre, prospective, two-arm parallel, placebo-controlled, double-blind, randomised clinical trial was conducted at 13 sites in Japan. Among 102 patients randomised, 101 (66.3% men; median age, 73 years; eGFR, 56.2 mL/min/1.73 m²; and UACR, 193.8 mg/g Cr) were analysed. Changes in CAVI at week 24 were - 0.023 (95% confidence interval [CI], - 0.299 to 0.254) for finerenone and 0.011 (95% CI, - 0.245 to 0.267) for placebo. The group difference was - 0.057 (95% CI, - 0.428 to 0.314; P = 0.760). Compared with placebo, finerenone led to a 29% reduction in UACR levels at weeks 12 (group ratio 0.706 [95% CI, 0.504 to 0.989; P = 0.043]) and 24 (0.709 [95% CI, 0.506 to 0.994; P = 0.046]). Finerenone also resulted in an early and sustained eGFR decline over 24 weeks, without increasing levels of urinary biomarkers of acute tubular injury. Finerenone, compared with placebo, was associated with nominal changes in the expression of 11 proteins among the 181 circulating proteins tested.

Conclusions: Finerenone did not affect changes in vascular stiffness but led to a significant and sustained reduction in albuminuria in patients with T2D and CKD. The clinical benefits of finerenone may result from lowering intraglomerular pressure rather than from its effect on vascular stiffness.

Registration: ClinicalTrial.gov (NCT05887817) and Japan Registry of Clinical Trials (jRCTs021230011).

Diabetic cardiomyopathy (DbCM) is a progressive cardiac disorder characterized by left ventricular dysfunction in the presence of diabetes mellitus. With the rising global prevalence of diabetes, early detection and intervention are crucial to prevent transition to overt heart failure. Cardiac magnetic resonance (CMR) has emerged as a powerful non-invasive imaging modality, providing comprehensive insights into myocardial structure, function, and tissue characteristics. This review highlights the role of multiparametric CMR, including T1 mapping, late gadolinium enhancement, strain analysis, perfusion imaging, and spectroscopy, in identifying key pathological features of DbCM such as diffuse fibrosis, microvascular dysfunction, steatosis, and subclinical systolic/diastolic impairment. Furthermore, we discuss how these imaging biomarkers can stratify risk, monitor disease progression, and evaluate treatment efficacy, particularly in the context of comorbidities and emerging therapies such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. Future directions include the integration of artificial intelligence for automated analysis and the development of molecular imaging probes for targeted detection of early disease pathways. CMR holds significant promise for translating advanced imaging biomarkers into clinical practice, enabling personalized management of DbCM.

Background: Coronary artery disease (CAD) is a major contributor to cardiovascular events in individuals with diabetes. Quantification of coronary atherosclerotic burden is now feasible from coronary computed tomography angiography (CTA) whereas positron emission tomography (PET) enables quantitative assessment of myocardial perfusion. We studied the prognostic implications of quantitatively measured coronary plaque burden and myocardial perfusion in diabetic vs. non-diabetic patients with suspected CAD.

Methods: In this observational cohort study, 1311 symptomatic patients with suspected CAD underwent coronary CTA and [¹⁵O]H₂O PET perfusion imaging. Coronary plaque burden was quantified using artificial intelligence-based analysis and reported as percent atheroma volume (PAV). Myocardial perfusion was assessed as regional stress myocardial blood flow (sMBF), with abnormal perfusion defined as ≥ 2 adjacent segments with sMBF < 2.3 ml/g/min. The composite endpoint was all-cause death, myocardial infarction (MI), or unstable angina pectoris (UAP) over 7 years.

Results: Among the 1311 patients, 251 (19%) had diabetes and 134 (10%) experienced an adverse event during follow-up. The annual event rate was low (0.8% [95% CI 0.6-1.1%]) in non-diabetic patients with normal myocardial perfusion and increased significantly with the presence of either diabetes (2.3% [95% CI 1.4-3.8%]), abnormal perfusion (2.6% [95% CI 2.1-3.3%]), or both (3.2% [95% CI 2.1-4.8%]) (p < 0.001). Among patients with normal myocardial perfusion, those with diabetes had two-fold PAV as compared with non-diabetic individuals (median 8.2% vs. 4.1%, p < 0.001). In multivariable Cox regression models, both PAV (HR 1.03 [95% CI 1.01-1.05] per 1% increase, p < 0.001) and regional sMBF (HR 1.04 [95% CI 1.01-1.07] per 0.1 ml/g/min decrease, p = 0.016) were independent predictors of adverse outcome in non-diabetic patients. In diabetic patients, only PAV (HR 1.04 [95% CI 1.01-1.07], p = 0.014) was predictive, whereas sMBF was not.

Conclusions: Coronary atherosclerotic plaque burden appears as an important predictor of long-term cardiovascular outcomes both in diabetic and non-diabetic patients. In patients with diabetes, normal myocardial perfusion does not necessarily imply low event risk, partly attributable to higher coronary plaque burden. Quantitative imaging methods for detailed CAD phenotyping shed light on the complex relationship between diabetes and clinical outcomes.

Background: The heterogeneous and complex nature of prediabetes presents a major challenge in identifying individuals predisposed to developing incident diabetes and related complications. We aimed to identify phenotypic subgroups of prediabetes at risk and to explore their distinct associations with cardiometabolic outcomes.

Methods: This study included 79,000 individuals with prediabetes from the three large-scale prospective cohorts in China. Phenotypic heterogeneity was identified using a soft-clustering algorithm based on the proximity network derived from uniform manifold approximation and projection (UMAP), combined with graph-clustering and Gaussian mixture models. Associations between phenotype probabilities and the incidence of type 2 diabetes (T2D), cardiovascular disease (CVD), and kidney events were assessed to evaluate risk differences across the identified profiles.

Results: Six phenotypic profiles were identified, including five with distinct metabolic features (representing ~ 70% of the total population), and one without significant features. These profiles demonstrated substantial differences in both baseline cardiometabolic burden and future disease risk. For instance, individuals with a 20% higher probability of belonging to the hypertensive profile had a 9, 6, and 12% higher risk of T2D, CVD, and CKD, respectively, while the profile with high lipids, creatinine, and liver enzyme was associated with an 10% increased risk of T2D and kidney events. Moreover, incorporating phenotypic probabilities into multivariable models significantly improved the prediction of disease risks (likelihood ratio test, P < 0.05).

Conclusions: Prediabetes exhibits substantial phenotypic heterogeneity, and delineation of distinct metabolic profiles enables refined risk stratification and informs precision prevention strategies.