Cardiovascular Diabetology最新文献

Cardiac organoids have emerged as pivotal models for cardiovascular disease research and drug development due to their ability to recapitulate the in vivo cardiac microenvironment, structure, and function. Imaging technologies are core tools for deciphering their three-dimensional (3D) complexity. This review outlines the key framework of cardiac organoid imaging research, focusing on pre-imaging sample processing, mainstream imaging technologies, image analysis workflows and applications in disease mechanisms and drug screening. We also provide guidelines for technique selection based on research objectives. Currently, long-term 4D imaging of cardiac organoids is still in its infancy. Future efforts should optimize imaging strategies and advance high-resolution dynamic techniques to deepen understanding of the temporal dynamics of cardiac pathology, providing technical support for cardiovascular research.

Background: Patients with type 2 diabetes (T2D) are at an increased risk of developing acute myocardial infarction (AMI), yet the role of 5-hydroxymethylcytosines (5hmC) changes in T2D-associated cardiovascular events remains poorly understood. Despite therapeutic advances, patients with T2D who suffer an AMI continue to exhibit markedly elevated cardiovascular risk and poor prognosis, underscoring the need for improved detection and risk assessment.

Methods: We evaluated genome-wide 5hmC modifications in circulating cell-free DNA (cfDNA) for their value as noninvasive biomarkers for AMI in T2D. Genome-wide mapping of 5hmC in plasma cfDNA were obtained using the 5hmC-Seal technique from 225 participants, including 57 T2D patients with AMI (T2D + AMI), and 168 T2D patients without AMI (T2D + non-AMI). A weighted 5hmC-based model was developed to compute an epigenetic score for each individual, which was first derived from baseline cross-sectional data to discriminate between the patients from the two groups. Subsequently, the utility of the epigenetic score for predicting composite cardiovascular outcomes (CCO) was assessed prospectively within the same cohort using 5-year longitudinal follow-up data.

Results: 255 differential 5hmC-gene bodies (P < 0.05) associated with T2D + AMI, involving pathways related to cardiac functions, such as heart process and heart contraction. A seven-feature weighted model based on 5hmC signatures was developed for distinguishing T2D + AMI from T2D + non-AMI, which achieved an area under the curve (AUC) of 99.2% (95% CI 98.1-100.0%) in training set and 95.6% (95% CI 89.8-100.0%) in testing set. Furthermore, we established and tested a 5hmC-derived weighted epigenetic score for predicting cardiovascular events in diabetic population (eSCORE-CARD) over a median follow-up of 5.3 years. Multivariate Cox regression analysis showed that the eSCORE-CARD values were significantly associated with an increased risk of CCO (HR = 4.25; 95% CI 1.41-12.80; P < 0.05). When combined with other established risk tools (SCORE2-Diabetes and SCORE2-OP), eSCORE-CARD demonstrated improved performance achieving an AUC of 76.4% (95% CI 65.9-86.8%), which holds promise for detection and prognosis of cardiovascular events in T2D.

Conclusions: Our work indicated specific 5hmC signatures implicated in AMI with T2D background, and provided the foundation for a comprehensive cardiovascular risk management tool for diabetic population.

Backgrounds: Despite numerous studies investigating the effects of antidiabetic medications on cardiovascular outcomes, the optimal second-line oral antidiabetic medication for atrial fibrillation (AF) prevention remains unclear. This study aims to compare the effects of second-line oral antidiabetic medications including sodium-glucose cotransporter 2 (SGLT2) inhibitors, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, or sulfonylureas, on the risk of incident AF in patients with type 2 diabetes.

Methods: This retrospective study analyzed data from the National Health Insurance Service data on adults with type 2 diabetes who simultaneously initiated metformin and second-line oral antidiabetic medication (SGLT2 inhibitors, thiazolidinediones, DPP-4 inhibitors, or sulfonylureas) between September 2014 and December 2017. Exact matching by sex and age categories was conducted in a 1:1:5:5 ratio corresponding to SGLT2 inhibitor, thiazolidinedione, DPP-4 inhibitor, and sulfonylurea users, with inverse probability of treatment weighting used to balance the baseline characteristics. The primary outcome was incident AF, which was analyzed using a Fine-Gray model treating all-cause mortality as a competing risk.

Results: During a mean follow-up of 6.2 years, 774 cases of AF occurred among the 36,744 participants (mean age 55.3 years; 33.6% female). Compared with SGLT2 inhibitors, the risk of AF was significantly higher in patients using thiazolidinediones (subdistribution hazard ratio [SHR], 1.22; 95% confidence interval [CI], 1.09-1.36), DPP-4 inhibitor (SHR, 1.14; 95% CI, 1.02-1.28), and sulfonylureas (SHR, 1.20; 95% CI, 1.07-1.34). However, pairwise comparisons among thiazolidinediones, DPP-4 inhibitors, and sulfonylureas revealed no significant differences in AF risk. Subgroup analyses revealed significant effect modifications according to age, hypertension status, and renal function.

Conclusions: This study showed that SGLT2 inhibitor use was associated with a significantly lower risk of AF than use of thiazolidinediones, DPP-4 inhibitors, and sulfonylureas.

Background: To develop a 10-year risk score to predict lower-limb amputation (LLA) in individuals with type 1 diabetes, and to assess whether this score also predicts kidney failure, and cardiovascular disease (CVD).

Methods: The LLA risk score was derived from GENEDIAB and GENESIS, two prospective French and Belgian cohorts of 828 individuals with type 1 diabetes. External validation was assessed in SURGENE, an independent cohort of 247 individuals with type 1 diabetes. LLA was defined as a non-traumatic amputation above the metatarsophalangeal joint, kidney failure as the need for renal replacement therapy, transplantation, or an estimated glomerular filtration rate (eGFR) < 15 ml/min/1.73 m², CVD as the occurrence of myocardial infarction, heart failure, or stroke, and major adverse cardiac event (MACE) as a composite of myocardial infarction, stroke, heart failure, or all-cause death.

Results: During 12 years of follow-up, LLA, kidney failure, CVD and MACE occurred in 71 (9%), 84 (11%), 138 (17%) and 265 (32.0%) of participants, respectively. Sex, diabetes duration, prior LLA, eGFR, and albuminuria were identified as independent determinants of incident LLA and were used to construct the risk score. Compared to participants in the lowest score, those in the highest score had a significantly increased risks of LLA (multivariable-adjusted HR 6.02 [95% CI, 1.92-18.89]), kidney failure (8.97 [3.95-20.37]), CVD (2.50 [1.34-4.64]) and MACE (1.91 [1.24-2.96]). The score demonstrated good discriminative performance for LLA (C-index 0.82 [0.77-0.87]), kidney failure (0.86 [0.82-0.91]), CVD (0.73 [0.68-0.78]), and MACE (0.71 [0.68-0.75]). Similar predictive performance was observed in the validation cohort.

Conclusion: This newly developed 10-year LLA risk score achieved excellent ability to identify individuals with type 1 diabetes at high risk for LLA, kidney failure, CVD, and MACE.

Background: In recent years, except for the well-known heart failure with reduced ejection fraction (HFrEF), the incidence of heart failure with preserved ejection fraction (HFpEF) and heart failure with mildly reduced ejection fraction (HFmrEF) among the classification of heart failure (HF) has been increasing. However, due to their complex mechanisms, current research remains insufficient to address clinical needs.

Methods and results: Utilizing wild-type (WT), miR-30a-5p knockout (KO), and overexpression (OE) murine models combined with estrogen modulation and ovariectomy (OVX), this study delineates sex-specific regulatory networks in HF pathogenesis. Female KO mice lost the inherent resistance of WT females to HFpEF induction via 24-week HFD/L-NAME, whereas males exhibited comparable HFpEF susceptibility regardless of genotype, developing hallmark phenotypes including diastolic dysfunction (E/E'), myocardial hypertrophy (heart weight/tibia length), cardiac fibrosis, and hepatic steatosis. Particularly, due to the reduced ejection fraction in KO mice, combined with HFD/L-NAME, the HF phenotype was ultimately manifested as impaired diastolic function and slightly reduced ejection fraction (with the characteristics of HFpEF and HFmrEF). Mechanistically, KO-HF females displayed significant estrogen axis disruption (plasma estradiol and the expression of ERα, ERβ, ESRRA, and PELP1 expression). OVX in WT females validated the importance of estrogen for HFpEF resistance. Transcriptomic profiling identified convergent targets across cardiac (ITGAD, ITGAM, FGA, and FGB) and hepatic tissues (APOA1 and APOB), revealing miR-30a-5p's orchestration of extracellular matrix remodeling (via ITGAD/ITGAM mechanotransduction),fibrinogen-mediated microvascular homeostasis, and APOB-driven metabolic dysregulation. Notably, OE intervention failed to mitigate OVX-induced cardiac/hepatic pathology, implicating estrogen-dependent miR-30a-5p functionality.

Conclusions: These findings establish miR-30a-5p as a crucial sex-specific regulator of HF (mainly HFpEF), operating through estrogen signaling to balance cardiac compliance and metabolic adaptation.

Background: Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known risk factors. Using machine learning to integrate a broader panel of features from electronic health records (EHRs) may improve post-angiography prognostication.

Methods: This retrospective model development and validation study was based on Danish EHR data. Icelandic EHR data were used for external test. Patients with a coronary angiography-confirmed diagnosis of coronary atherosclerosis between 2006 and 2016 were included for model development (n = 39,746). Time to all-cause mortality, the prediction target, was tracked until 2019, or up to 5 years, whichever came first. To model time-to-event data and deal with censoring, neural network-based discrete-time survival models were used. The model, PMHnet, uses 584 different features including clinical characteristics, laboratory tests, and diagnosis and procedure codes. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against the updated GRACE2.0 risk score and less feature-rich neural network models. Models were evaluated using hold-out data (n = 5000) and external validation data from Iceland. Feature importance and model explainability were assessed using SHAP analysis.

Results: On the test set (n = 5000), the tdAUC of PMHnet was 0.88 [ 0.86-0.90] (case count = 196) at six months, 0.88 [0.86-0.90] (cc = 261) at one year, 0.84 [0.82-0.86] (cc = 395) at three years, and 0.82 [0.80-0.84] (cc = 763) at five years. PMHnet showed similar performance in the Icelandic data. Compared to the GRACE2.0 score and intermediate models limited to GRACE2.0 features or single data modalities, PMHnet had significantly better model discrimination across all evaluated prediction timepoints.

Conclusions: More complex and feature-rich machine learning models can better predict all-cause mortality in ischemic heart disease and may be used by clinicians and patients to inform and guide treatment and management.

Background: Stroke is one of the advanced outcomes of cardiovascular kidney metabolic syndrome (CKM). The combined effects of inflammation, insulin resistance, and hypertension in stroke remain to be fully elucidated in population studies. This study investigates the association between the composite TyG inflammation index (c-reactive protein-triglyceride glucose index, CTI) and hypertension with long-term stroke.

Methods: This longitudinal cohort study included 9082 participants from the China Health and Retirement Longitudinal Study (CHARLS). The associations with stroke risk were assessed using Cox proportional hazards models and Kaplan-Meier analysis, while predictive performance was evaluated using ROC curves and time-dependent AUC. The contribution of each component was determined by Weighted Quantile Sum (WQS) regression. Subgroup analysis and sensitivity analysis were also conducted.

Results: Over a median 9-year follow-up, 811 (8.9%) incident stroke cases occurred. A significant dose-response relationship was observed, with the highest CTI-hypertension group exhibiting a fully adjusted hazard ratio of 3.19 (95% CI 2.62-3.88) for stroke compared to the lowest group. The combined CTI-hypertension model demonstrated superior predictive performance (AUC = 0.672) versus hypertension-alone (AUC = 0.661) or CTI-alone (AUC = 0.651) models. WQS analysis identified C-reactive protein (38.8%) and hypertension (31.7%) as the predominant risk factors.

Conclusion: Integrating CTI with hypertension significantly improves stroke risk stratification in CKM stage 0-3 populations, supporting its potential for early identification of high-risk individuals.