Cardiovascular Diabetology最新文献

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.

Background: The atherogenic index of plasma (AIP) is known to be associated with atherosclerotic burden. However, the prognostic value of AIP in patients with severe coronary artery calcification (CAC) undergoing rotational atherectomy (RA) remains unclear. This study aimed to evaluate the relationship between AIP and adverse outcomes in this patient population and to explore relevant risk factors using explainable machine learning methods.

Methods: This study included patients with severe CAC who underwent RA between January 2017 and October 2024, with a median follow-up of 40.55 months. Patients were divided into three groups according to baseline AIP tertiles. The primary endpoints were cardiovascular death or all-cause death; secondary endpoints included non-fatal myocardial infarction, target vessel revascularization, and stroke. Cox regression and restricted cubic splines were used to assess the association between AIP and endpoint events. Kaplan-Meier survival analysis and log-rank tests were employed to compare differences between groups. LASSO regression was used for feature selection, and six machine learning algorithms were applied to construct predictive models for cardiac death. Finally, the SHAP method was used to interpret the model.

Results: In a cohort of 513 participants (58.28% male), multivariable Cox analysis showed that compared with the lowest AIP tertile, the highest AIP tertile was associated with significantly increased risks of various adverse events: cardiovascular death (HR 2.40, 95% CI 1.40-4.13), all-cause death (HR 1.68, 95% CI 1.13-2.51), non-fatal myocardial infarction (HR 2.27, 95% CI 1.20-4.29), target vessel revascularization (HR 1.74, 95% CI 1.04-2.90), and stroke (HR 1.92, 95% CI 1.00-3.69). Restricted cubic spline analysis indicated a dose-response relationship between AIP and the risk of adverse outcomes. Subgroup analysis suggested that the association between AIP and mortality was stronger in elderly patients, those with cardiac dysfunction, or those with poor glycemic control. Among the six machine learning algorithms, the random forest model demonstrated the best predictive performance for cardiac death (AUC = 0.800). SHAP analysis identified AIP as one of the key features driving the model's predictions. Kaplan-Meier curves revealed that patients in the high-AIP group had worse long-term clinical outcomes.

Conclusion: AIP was independently associated with adverse outcomes in patients with severe CAC undergoing RA. The integration of this low-cost, readily available biomarker into explainable machine learning frameworks offers a promising avenue for enhancing risk prediction models.

Background: Chronic kidney disease (CKD) is a risk factor for cardiovascular (CV) events in patients with heart failure (HF). It is unclear whether type 2 diabetes (T2D), closely intertwined with both HF and CKD, modifies the association between cardiovascular outcomes and CKD in HF patients, and whether this association differs according to ejection fraction (EF).

Methods: HF patients enrolled in the Swedish Heart Failure Registry from January 2017 to December 2021 were analyzed. Linkage with the National Diabetes Registry and other population registries provided extensive baseline information. Patients were stratified by T2D status and CKD stages, defined by estimated glomerular filtration rate (eGFR: <30, 30-44, 45-59, ≥ 60 ml/min/1.73 m²). The primary outcome was the composite of time to first HF hospitalization (HHF) or CV death. Secondary outcomes were major adverse CV events (MACE, i.e. CV death, non-fatal myocardial infarction and stroke), CV death and all-cause death. Multivariable Cox regression models assessed the associations between eGFR and outcomes according to T2D, including interaction testing. A subgroup analysis was conducted by EF.

Results: Of 36,597 patients included, 8,053 (22%) had T2D, 23,562 (64.4%), 7122 (19.4%), 4477 (12.2%), 1436 (4.0%), were in the four eGFR categories (eGFR ≥ 60, 45-59, 30-44, and < 30 mL/min/1.73 m², respectively), and 53%, 25%, 22% had HF with reduced, mildly-reduced, and preserved EF, respectively. Across eGFR, patients with vs. without T2D were younger, more often male, with higher CV comorbidity and more frequent use of cardio-renal drugs. Across EF categories, T2D patients had higher prevalence of CKD. Lower eGFR categories were progressively associated with higher risk of the primary outcome, independently of T2D. This was consistent across EF, except in HFpEF with eGFR < 30 ml/min/1.73 m², where the magnitude of the association in T2D group was smaller than in non-T2D (p-interaction < 0.01). Risks of MACE, CV death and all-cause mortality were higher for lower eGFR categories, with lower hazards in T2D group (p-interaction < 0.01).

Conclusions: In a contemporary HF cohort, decreased kidney function was associated with a progressively higher risk of HHF/CV death, and T2D was not a risk modifier. Renal protection should therefore be implemented in HF regardless of T2D.

Background: Elevated urinary albumin-to-creatinine ratio (UACR) within the normal range (< 30 mg/g) and the triglyceride-glucose (TyG) index are associated with cardiovascular disease (CVD) incidence, yet their joint effect remains underexplored.

Methods: This prospective cohort study used data from the 2016 Shandong-MOH Salt and Hypertension (SMASH) project, linked to CVD records until September 30, 2023, including 14,481 adults with normal UACR. Participants were stratified by TyG index and UACR quantiles. Multivariable Cox proportional hazards models and restricted cubic splines (RCS) were employed to evaluate individual and joint effects on overall CVD, coronary heart disease (CHD), and stroke. The incremental predictive value was assessed using the C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). Additionally, an exploratory mediation analysis was performed to examine the potential bidirectional effects between the TyG index and UACR.

Results: Mean age was 41.75 ± 13.06 years, with median follow-up of 7.2 years. Among 420 incident overall CVD cases (309 stroke, 111 CHD), Compared to the Low TyG & Low UACR group, the High TyG & High UACR group demonstrated the highest risks for overall CVD (HR = 2.632, 95% CI: 1.695-4.085, P < 0.001), CHD (HR = 2.680, 95% CI: 1.144-6.282, P = 0.023), and stroke (HR = 2.628, 95% CI: 1.573-4.392, P < 0.001). The TyG index showed nonlinear associations with overall CVD and stroke risk but a linear association with CHD, while UACR exhibited linear positive correlations with all outcomes. The model combining the TyG index and UACR significantly enhanced the predictive ability for CVD events. Mediation analysis revealed that elevated UACR significantly mediated 4.9% of the association between TyG index and CVD, while elevated TyG index mediated 11.2% of the association between UACR and CVD.

Conclusion: The study demonstrates that both elevated UACR (within normal range) and higher TyG index are jointly associated with increased CVD risk, with evidence suggesting potential bidirectional mediation. Their combined assessment provides significant incremental predictive value, supporting its integration into high-risk population screening for precise CVD prevention and management.

Background & aims: Pediatric metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly prevalent among children with overweight or obesity, yet its early diagnosis remains a major clinical challenge. This study aimed to identify circulating inflammatory proteins associated with MASLD and to develop a proteomic risk score (ProScore) to improve diagnostic accuracy.

Methods: In this cross-sectional study of 161 children (median age 8.5 years) with overweight or obesity, MASLD was assessed by vibration-controlled transient elastography, with 42 cases identified. Plasma concentrations of 92 inflammation-related proteins were quantified using a high-throughput proximity extension assay. The ProScore was compared with eleven conventional anthropometric/metabolic indices (WHtR, METS-IR, SPISE, PNFI, VAI, LAP, TyG, TyG-ALT, TyG-WC, TyG-WHtR, and TyG-BMI) and a genetic risk score (GRS). Six machine learning algorithms were employed and diagnostic performance was assessed using area under the curve (AUC) with fivefold cross-validation.

Results: Fifteen proteins were significantly associated with MASLD. A six-protein panel (FGF-21, CDCP1, CD244, OPG, Flt3L, MCP-1) achieved the highest diagnostic accuracy (AUC = 0.84), exceeding that of all conventional indices (AUC = 0.65-0.78; all P < 0.05). ProScore performance remained robust in school-based validation (AUC = 0.83), with no substantial improvement when combined with conventional indices. Diagnostic accuracy was higher in children with lower GRS (AUC = 0.92) than in those with higher GRS (AUC = 0.80; P = 0.003).

Conclusions: A proteomic signature of systemic inflammation provides accurate, non-invasive identification of MASLD in at-risk children, outperforming conventional metabolic and genetic tools, and may have utility in clinical and public health settings.

Background: The relationship between plasma lipoprotein(a) [Lp(a)] levels and metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear. The aim of this study was to examine the combined effects of Lp(a) levels on liver and vascular damage.

Methods: The study was conducted using the Liver-Bible cohort of individuals with metabolic dysfunction (n = 859, 808 with genomic information) and the Milan Biobank (n = 6963). Genome-wide association studies (GWAS) and polygenic risk scores (PRS) were used to evaluate the inherited factors influencing plasma Lp(a) levels.

Results: In the Liver-Bible cohort, genetic variation in the LPA gene was the strongest determinant of Lp(a), followed by liver stiffness measurement (LSM). Additionally, circulating Lp(a) levels, but not genetic predisposition, were inversely related to LSM, suggesting that MASLD severity may affect Lp(a) secretion. Among participants with more severe insulin resistance (n = 250), Lp(a) levels (odds ratio 6.7, 95% CI 1.0-53.0, p = 0.046) and LSM (odds ratio 13.7, 95% CI 1.4-172.2, p = 0.023) were associated with greater prevalence of carotid atherosclerotic plaques, regardless of traditional cardiovascular risk factors. In the Milan Biobank, genetically predicted higher Lp(a) levels tended to increase the risk of liver-related outcomes, whereas genetically predicted MASLD was associated with lower circulating Lp(a) levels.

Conclusions: The results of this study suggest that liver damage is more likely the cause of reduced plasma Lp(a) levels rather than a consequence. Assessing plasma Lp(a) levels and the extent of liver damage could improve the prediction of vascular damage.

Background: Type 2 diabetes mellitus (T2DM) is a major risk factor for atherosclerosis and cardiovascular events (CVEs), partly due to increased platelet activation and inflammation. Neutrophil-derived cathepsin G (CatG), a prothrombotic protease, may play a role in this process by promoting platelet aggregation. However, its association with CVEs in T2DM has not been previously explored. This study aimed to evaluate whether circulating CatG levels independently predict CVEs in patients with T2DM.

Methods: We included 485 T2DM patients from two prospective cohorts (PLINIO and ATHERO-AF studies). The primary outcome was a composite of cardiovascular death, non-fatal coronary and cerebrovascular events, and peripheral artery events. Cardiovascular death, all-cause death, non-fatal coronary and cerebrovascular events were tested as secondary outcomes. Multivariate Cox-regression was used to assess associations between the top (V) CatG quintile and outcomes. A subgroup analysis was conducted in 312 patients with available neutrophil count data and after a propensity score matching, to test the correlation between CatG and plasma soluble P-selectin (sP-selectin), an in vivo marker of platelet activation.

Results: During the follow-up yielding for 2,437.6 person-years, 86 CVEs occurred. Patients developing CVEs had higher CatG (2.9 [1.9-4.4] ng/mL vs. 2.1 [1.6-2.6] ng/mL; p < 0.001) compared to CVEs-free patients. The CVEs incidence rate in patients in the V CatG quintile was 10.4% per year (V quintile versus each other quintile: p < 0.001). V CatG quintile associated with increased CVEs (adjusted Hazard Ratio (aHR) 6.081 [95% confidence interval (CI) 3.887-9.514], p < 0.001) and its component incidence, including cardiovascular mortality or non-fatal coronary events or all-cause mortality. The association between higher CatG levels and CVEs remained significant after adjustment for neutrophil count (aHR 4.051 [95% CI 2.098-7.820], p < 0.001). Neutrophil count was also independently associated with CVEs (aHR 1.177 [95% CI 1.009-1.372], p = 0.038). Finally, in the propensity score matching analysis CatG independently correlated with sP-selectin (Beta: 0.443; p < 0.001).

Conclusions: Circulating CatG is an independent predictor of cardiovascular events in T2DM, suggesting a novel biomarker linking inflammation to athero-thrombosis.

Pre-registered clinical trial number: NCT01882114, NCT04036357.