Cardiovascular Diabetology最新文献

{"title":"Development and validation of prediction models for stroke and myocardial infarction in type 2 diabetes based on health insurance claims: does machine learning outperform traditional regression approaches?","authors":"Anna-Janina Stephan, Michael Hanselmann, Medina Bajramovic, Simon Schosser, Michael Laxy","doi":"10.1186/s12933-025-02640-9","DOIUrl":"https://doi.org/10.1186/s12933-025-02640-9","url":null,"abstract":"<p><strong>Background: </strong>Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning methods.</p><p><strong>Methods: </strong>We used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and calibration.</p><p><strong>Results: </strong>Among n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well calibrated.</p><p><strong>Conclusions: </strong>Discrimination performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive Predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature richness and complexity were exploited before the choice of algorithm could become critical to maximize performance. Future research might focus on the impact of different feature derivation approaches on performance ceilings. In the absence of other more powerful screening alternatives, applying transparent regression-based models in routine claims, though certainly imperfect, remains a promising scalable low-cost approach for population-based cardiovascular risk prediction and stratification.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"80"},"PeriodicalIF":8.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of body composition with left ventricular remodeling and outcomes in diabetic heart failure with reduced ejection fraction: assessment of sarcopenic obesity using cardiac MRI.","authors":"Ke Shi, Ge Zhang, Rong Xu, Xue-Ming Li, Li Jiang, Yue Gao, Hua-Yan Xu, Yuan Li, Ying-Kun Guo, Zhi-Gang Yang","doi":"10.1186/s12933-025-02639-2","DOIUrl":"10.1186/s12933-025-02639-2","url":null,"abstract":"<p><strong>Background: </strong>Obesity is common in the heart failure (HF) population and is regarded as an important risk factor for developing HF. Greater skeletal muscle mass has shown to be the underlying protective factor against cardiac failure. Since diabetic mellitus (DM) can impair muscle protein metabolism, leading to skeletal muscle wasting, accompanied by adipose tissue accumulation, sarcopenic obesity (SO) may be a high-risk phenotype with poor outcomes in this specific population, especially in HF with reduced ejection fraction (HFrEF). Thus, the aim of this study was to clarify the clinical profiles, left ventricular (LV) remodeling, and prognostic implications of SO in patients with HFrEF and DM.</p><p><strong>Methods: </strong>A total of 283 patients who underwent cardiac MRI were included. Thoracic skeletal muscle index (SMI) was served as a surrogate of skeletal muscle mass. Patients were stratified according to the median thoracic SMI (42.75 cm²/m²) and body mass index (25 kg/m²). Obesity in conjunction with a SMI lower than the median is referred to as SO. The LV volume and function, as well as the systolic strain, were measured. The clinical characteristics and cardiovascular outcomes (heart failure readmission, cardiovascular mortality and heart transplantation) were recorded.</p><p><strong>Results: </strong>Patients with SO had a greater level of amino-terminal pro-B-type natriuretic peptide and were more likely than nonsarcopenic patients with obesity to present with hypoproteinemia. Among patients with obesity, those with sarcopenia displayed greater LV expansion and more profound LV dysfunction, together with an increase in LV mass. During a median follow-up duration of 35.1 months, a total of 73 (25.8%) subjects reached the composite endpoint, with a worst outcome in the group of patients with SO (log-rank P = 0.04). Multivariable Cox analysis revealed that patients with SO had an approximately 3-fold greater risk of experiencing adverse outcomes than did those with neither sarcopenia nor obesity (hazard ratio: 3.03, 95% confidence interval: 1.39 to 6.63; P = 0.005).</p><p><strong>Conclusions: </strong>SO is a potentially high-risk phenotype with adverse LV remodeling and poor clinical outcomes in diabetic patients with HFrEF that may require more attention.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"79"},"PeriodicalIF":8.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.","authors":"Hanna Kwiendacz, Bi Huang, Yang Chen, Oliwia Janota, Krzysztof Irlik, Yang Liu, Marta Mantovani, Yalin Zheng, Mirela Hendel, Julia Piaśnik, Wiktoria Wójcik, Uazman Alam, Janusz Gumprecht, Gregory Y H Lip, Katarzyna Nabrdalik","doi":"10.1186/s12933-025-02615-w","DOIUrl":"10.1186/s12933-025-02615-w","url":null,"abstract":"<p><strong>Background: </strong>People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.</p><p><strong>Methods: </strong>We utilised machine learning (ML) model to predict CVEs in persons with DM and CKD from the Silesia Diabetes-Heart Project, a routine standard of care dataset. CVEs were defined as composite of nonfatal myocardial infarction, new onset heart failure, nonfatal stroke, incident atrial fibrillation, undergoing percutaneous coronary intervention or coronary artery bypass grafting, hospitalisation or death due to cardiovascular disease. Five ML models (Logistic regression [LR], Random forest [RF], Support vector classification [SVC], Light gradient boosting machine [LGBM], and eXtreme gradient boosting machine [XGBM]) were constructed. The predictive performance of the five ML models was compared and the model interpretability were evaluated by Shapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>A total of 1,116 people with DM and CKD out of 3,056 with DM were included (median age 67 [IQR 57-76] years; 57% men). The incidence of CVEs was 14.1% (157/1,116) during a median of 3.1 years follow-up period. Ten important features were identified through univariate Logistic regression, Boruta, and Least Absolute Shrinkage and Selection Operator [LASSO] regression. Among the five ML models based on these features, LGBM had the highest area under curve [AUC] (AUC = 0.740, 95% Confidence Interval [CI] 0.738-0.743), followed by LR (AUC = 0.621, 95% CI 0.618-0.623), RF (AUC = 0.707, 95% CI 0.704-0.709), SVC (AUC = 0.707, 95% CI 0.704-0.710), and XGBM (AUC = 0.710, 95% CI 0.707-0.713). Meanwhile, LGBM had relatively higher Recall (0.739), F1-score (0.820), and G-mean (0.826). The SHAP plot of LGBM revealed that estimated glomerular filtration rate (eGFR), age, and triglyceride glucose index were the three most important features for predicting CVEs.</p><p><strong>Conclusion: </strong>Ten features-based ML models, especially the LGBM model, had acceptable performance in predicting CVEs in persons with DM and CKD. A decrease in eGFR, aging, and elevated inflammatory markers significantly enhanced the predictive capability of the model. Future external validation of our model is required prior to implementation in a clinical environment.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"76"},"PeriodicalIF":8.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.","authors":"Yingjian Pei, Yajun Ma, Ying Xiang, Guitao Zhang, Yao Feng, Wenbo Li, Yinghua Zhou, Shujuan Li","doi":"10.1186/s12933-025-02644-5","DOIUrl":"10.1186/s12933-025-02644-5","url":null,"abstract":"<p><strong>Background: </strong>The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationship between the postoperative SHR index and all-cause mortality in patients undergoing cardiac surgery.</p><p><strong>Methods: </strong>Data for this study were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients were categorized into four groups based on postoperative SHR index quartiles. The primary outcome was 30-day all-cause mortality, while the secondary outcomes included in-hospital, 90-day and 360-day all-cause mortality. The SHR index was analyzed using quartiles, and Kaplan-Meier curves were generated to compare outcomes across groups. Cox proportional hazards regression and restricted cubic splines (RCS) were employed to assess the relationship between the SHR index and the outcomes. LASSO regression was used for feature selection. Six machine learning algorithms were used to predict in-hospital all-cause mortality and were further extended to predict 360-day all-cause mortality. The SHapley Additive exPlanations method was used for visualizing model characteristics and individual case predictions.</p><p><strong>Results: </strong>A total of 3,848 participants were included in the study, with a mean age of 68 ± 12 years and female participants comprised 30.6% (1,179). Higher postoperative SHR index levels were associated with an increased risk of in-hospital, 90-day and 360-day all-cause mortality as shown by Kaplan-Meier curves (log-rank P < 0.05). Cox regression analysis revealed that the highest postoperative SHR quartile was associated with a significantly higher risk of mortality at these time points (P < 0.05). RCS analysis demonstrated nonlinear relationships between the postoperative SHR index and all-cause mortality (P for nonlinear < 0.05). The Naive Bayes model achieves the highest area under the curve (AUC) for predicting both in-hospital mortality (0.7936) and 360-day all-cause mortality (0.7410).</p><p><strong>Conclusion: </strong>In patients undergoing cardiac surgery, higher postoperative SHR index levels were significantly associated with increased risk of in-hospital, 90-day and 360-day all-cause mortality. The SHR index may serve as a valid tool for assessing the severity after cardiac surgery and guiding treatment decisions.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"77"},"PeriodicalIF":8.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between the systemic immune-inflammation index and metabolic syndrome and its components: results from the multi-ethnic study of atherosclerosis (MESA).","authors":"Azra Ramezankhani, Maryam Tohidi, Farzad Hadaegh","doi":"10.1186/s12933-025-02629-4","DOIUrl":"10.1186/s12933-025-02629-4","url":null,"abstract":"<p><strong>Background: </strong>The Systemic Immune-Inflammation Index (SII) is a novel biomarker of systemic inflammation. We explored the association between the SII and metabolic syndrome (MetS) and its components in middle-aged and older adults.</p><p><strong>Methods: </strong>We included 2755 participants (1305 men) aged 45-84 years from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort from examination 5 (2010-2012). Logistic regression was employed to assess the relationship between the SII and MetS, as well as its components.</p><p><strong>Results: </strong>A total of 1082 participants (463 men) were diagnosed with MetS. On a continuous scale, the SII was positively associated with MetS (odds ratio (OR): 1.23, 95% confidence interval (CI): 1.05-1.46) and its components including hyperglycemia (1.23: 1.05-1.44) and elevated blood pressure (BP) (1.47: 1.14-1.89). When analyzed on a quartile scale, participants in the quartile 4 of SII had 32% and 63% higher prevalence of hyperglycemia and elevated BP, respectively, compared to those in the quartile 1 (P for trend: 0.021 and < 0.001, respectively). Additionally, we identified 40% higher prevalence of low HDL-C in quartile 2 of the SII compared to quartile 1 (1.40; 1.07-1.83) (P trend = 0.454). In subgroup analysis, general obesity status modified the relationship between SII and abdominal obesity, showing a positive association in obese individuals (1.72: 1.00-2.95) and a negative association (0.80: 0.66-0.97) in non-obese individuals (P for interaction = 0.009).</p><p><strong>Conclusions: </strong>Higher SII scores were associated with an increased likelihood of MetS, hyperglycemia, and high BP among middle-aged and older adults. Longitudinal studies are needed to determine the causal relationships between SII and the development of MetS, as well as to assess the potential role of SII as a screening tool in clinical practice.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"78"},"PeriodicalIF":8.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between the GMI/HbA1c ratio and preclinical carotid atherosclerosis in type 1 diabetes: impact of the fast-glycator phenotype across age groups.","authors":"Carlos Puig-Jové, Clara Viñals, Ignacio Conget, Carmen Quirós, Irene Vinagre, Belén Berrocal, Antonio-Jesús Blanco-Carrasco, Montserrat Granados, Alex Mesa, Tonet Serés-Noriega, Marga Giménez, Verónica Perea, Antonio J Amor","doi":"10.1186/s12933-025-02637-4","DOIUrl":"10.1186/s12933-025-02637-4","url":null,"abstract":"<p><strong>Background: </strong>Since the arrival of continuous glucose monitoring (CGM), the relationship between the glucose management indicator (GMI) and HbA1c has been a topic of considerable interest in diabetes research. This study aims to explore the association between the GMI/HbA1c ratio and the presence of preclinical carotid atherosclerosis in type 1 diabetes (T1D).</p><p><strong>Methods: </strong>Individuals with T1D and no prior history of cardiovascular disease were recruited from two centers. Carotid ultrasonography was performed using a standardized protocol and carotid plaques were defined as intima-media thickness ≥ 1.5 mm. CGM-derived data were collected from a 14-day report. A GMI/HbA1c ratio < 0.90 was selected to identify \"fast-glycator\" phenotype.</p><p><strong>Results: </strong>A total of 584 participants were included (319 women, 54.6%), with a mean age of 48.8 ± 10.7 years and a mean diabetes duration of 27.5 ± 11.4 years. Carotid plaques were present in 231 subjects (39.6%). Approximately 43.7% and 13.4% of participants showed absolute differences of ≥ 0.5 and ≥ 1.0 between 14-day GMI and HbA1c, respectively. Among patients ≥ 48 years, the fast-glycator phenotype was independently associated with presence of plaques (OR 2.27, 95%CI: 1.06-4.87), even after adjusting for non-specific and T1D-specific risk factors and statin treatment. No significant association was observed in younger subjects (p for interaction < 0.05).</p><p><strong>Conclusions: </strong>Fast-glycator phenotype is independently associated with atherosclerosis in T1D individuals aged ≥ 48 years, suggesting an age-related increase in the glycation risk. These findings highlight the potential of the GMI/HbA1c ratio for cardiovascular risk stratification in this population.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"75"},"PeriodicalIF":8.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac autonomic neuropathy is associated with ectopic fat distribution in autoimmune but not in type 2 diabetes.","authors":"Renata Risi, Rocco Amendolara, Angelo Lauria Pantano, Valeria Fassino, Luca D'Onofrio, Lucia Coraggio, Daniela Luverà, Davide Masi, Mikiko Watanabe, Lucio Gnessi, Raffaella Buzzetti, Ernesto Maddaloni","doi":"10.1186/s12933-025-02635-6","DOIUrl":"10.1186/s12933-025-02635-6","url":null,"abstract":"<p><strong>Background: </strong>Cardiac autonomic neuropathy (CAN) is a life-threatening complication of diabetes. While obesity is a well-known risk factor of dysautonomia, the association between CAN and body fat distribution has not been fully clarified, especially in autoimmune diabetes (AD).</p><p><strong>Aim: </strong>To evaluate if the association between CAN and body fat distribution differs between AD and type 2 diabetes (T2D).</p><p><strong>Methods: </strong>Body fat distribution was evaluated by Dual X-Ray Absorptiometry in 143 people with diabetes (44 with ADand 99 with T2D) undergoing clinical screening for CAN. The association of CAN with markers of ectopic fat distribution was evaluated in multivariate regression models adjusting for confounders and testing for the interaction between diabetes type and CAN.</p><p><strong>Results: </strong>A significant interaction between CAN and diabetes type was found with respect to markers of ectopic fat distribution. Specifically, people with CAN had significantly higher amount of visceral adipose tissue (530 [376-665]g versus 251[189-360]g, p = 0.001), total fat mass (22708[20200-27845]g versus 15434[12981-21879]g, p = 0,016), and trunk-to-leg ratio (0.88 [0.75-1.04] versus 0.70 [0.56-0.78], p = 0,023) compared to those without CAN only in participants with AD, but not in T2D (p-values for interaction < 0.05 for all comparisons).</p><p><strong>Conclusion: </strong>Ectopic fat distribution is more strongly associated with CAN in AD than in T2D. This highlights the distinct role of fat distribution in the cardiometabolic health of people with AD, suggesting the need for further studies to better understand the pathophysiology and implications of overweight in this population.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"74"},"PeriodicalIF":8.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clonal hematopoiesis of indeterminate potential, health indicators, and risk of cardiovascular diseases among patients with diabetes: a prospective cohort study.","authors":"Ying Sun, Yuefeng Yu, Lingli Cai, Bowei Yu, Wenying Xiao, Xiao Tan, Yu Wang, Yingli Lu, Ningjian Wang","doi":"10.1186/s12933-025-02626-7","DOIUrl":"10.1186/s12933-025-02626-7","url":null,"abstract":"<p><strong>Background: </strong>Clonal hematopoiesis of indeterminate potential (CHIP) was associated with diabetes and cardiovascular diseases (CVD). However, the effect of CHIP on CVD have not been evaluated among patients with diabetes, and whether maintaining the healthy indictors could mitigate the adverse influence was also unclear.</p><p><strong>Methods: </strong>A total of 22,239 adults from the UK Biobank with diabetes and available whole-exome sequence data, and free of CVD were included. Multivariable-adjusted Cox regressions were used to explore the associations of any CHIP (variant allele fraction ≥ 2%), large CHIP (variant allele fraction ≥ 10%), and the top 10 commonly mutated driver genes for CHIP and with risk of CVD. The joint associations between health indicators (body mass index [BMI], HbA1c, blood pressure [BP], and low-density lipoprotein cholesterol [LDL]) and CHIP were further investigated.</p><p><strong>Results: </strong>Over a median follow-up of 13.2 years, 5366 participants with diabetes developed CVD events. The hazard ratios (HRs) (95% confidence intervals [CIs]) of any CHIP and large CHIP were (1.21, 1.08-1.36) and (1.25, 1.09-1.43) for incident CVD, respectively. Significant associations between any CHIP and coronary heart disease (HR, 95%CI: 1.18, 1.03-1.36) and heart failure (1.73, 1.46-2.06) were observed, but not for stroke (1.14, 0.89-1.48). Gene-specific analyses suggested that the greatest association were for SF3B1 (HR, 95%CI: 2.50, 1.25-5.01) and TET2 (HR, 95%CI: 1.36, 1.07-1.77) with risk of CVD. There was no significant interaction between the four health indicators and CHIP in relation to incident CVD. Compared to patients without CHIP, those with any CHIP and ideal health indicators still exhibited significantly or nonsignificantly higher HRs (BMI: 1.18, 0.82-1.68; HbA1c: 1.12, 0.96-1.30; BP: 1.24, 1.03-1.49; LDL: 1.29, 1.09-1.53). Similar results were demonstrated using large CHIP.</p><p><strong>Conclusions: </strong>CHIP is independently associated with an increased risk of CVD in patients with diabetes, regardless of health indicator levels. Diabetic patients with CHIP but ideal health indicators still exhibited higher CVD risk compared with diabetic patients without CHIP.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"72"},"PeriodicalIF":8.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of sodium‒glucose cotransporter-2 inhibitors in patients with recent versus previous myocardial infarction: a systematic review and meta-analysis.","authors":"Pedro Gabriel Scardini, Eric Shih Katsuyama, Alonzo Armani Prata, Julia Marques Fernandes, Christian Ken Fukunaga, Wilson Falco Neto, Ana Carolina Covre Coan, Naieli Machado de Andrade, Abraão Santana Silva, Rafael Petri Pinheiro, Luciana Gioli Pereira, Remo H M Furtado","doi":"10.1186/s12933-024-02540-4","DOIUrl":"10.1186/s12933-024-02540-4","url":null,"abstract":"<p><strong>Background: </strong>Sodium‒glucose cotransporter 2 (SGLT2) inhibitors have been included in heart failure (HF) guidelines because of their benefits in reducing mortality and hospitalization rates. However, the timing and benefits of initiating SGLT2 inhibitors in patients after myocardial infarction (MI) remain controversial. Therefore, we aimed to perform a systematic review and meta-analysis comparing SGLT2 inhibitors with placebo in patients with MI.</p><p><strong>Methods: </strong>We performed a systematic review and meta-analysis to determine the impact of SGLT2 inhibitors in patients with recent or previous MI. We systematically searched PubMed, Cochrane, and Embase for RCTs comparing SGLT2 inhibitors versus placebo in patients with MI. The primary outcome was (1) HF hospitalization. In this analysis, we also included the following secondary outcomes: (2) major adverse cardiovascular events (MACE) defined as a composite of cardiovascular (CV) death, MI or stroke; and (3) all-cause mortality. A subgroup analysis was conducted for the primary outcome, comparing patients who had experienced an MI more than 8 weeks prior to study enrolment (previous MI) versus those who had experienced an MI within the preceding 8 weeks (acute MI). Risk ratios (RRs) and 95% confidence intervals (CIs) were pooled with a random effects model.</p><p><strong>Results: </strong>Our meta-analysis included 10 RCTs comprising 22,266 patients, of whom 11,339 (51.2%) had type 2 diabetes. The mean age was 62 years, and the median follow-up was 21 months. According to the pooled analysis, HF hospitalization rates were lower in patients on SGLT2 inhibitors compared with placebo (RR 0.77; 95% CI 0.69, 0.85; p < 0.001)). Differences in MACE were also observed in favor of SGLT2 inhibitors versus placebo (RR 0.88; 95% CI 0.79, 0.97; p = 0.012). There was no statistically significant difference in all-cause mortality between the groups (RR 0.88; 95% CI 0.78, 1.00; p = 0.058). Benefits of SGLT2 inhibitors for the primary outcome were consistent regardless of the timing of last MI, with no treatment by subgroup interaction (p for interaction = 0.56).</p><p><strong>Conclusion: </strong>In this meta-analysis of patients who experienced MI, the administration of SGLT2 inhibitors was associated with lower rates of hospitalization for HF. In addition, the treatment effect of SGLT2 inhibitors was consistent regardless of whether they were started in the recent versus previous MI setting.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"73"},"PeriodicalIF":8.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mural cell dysfunction contributes to diastolic heart failure by promoting endothelial dysfunction and vessel remodelling.","authors":"Mandy O J Grootaert, Alessandra Pasut, Jana Raman, Steven J Simmonds, Bram Callewaert, Ümare Col, Mieke Dewerchin, Peter Carmeliet, Stephane Heymans, Elizabeth A V Jones","doi":"10.1186/s12933-025-02623-w","DOIUrl":"10.1186/s12933-025-02623-w","url":null,"abstract":"<p><strong>Background: </strong>Heart failure with preserved ejection fraction (HFpEF) is a complex cardiovascular disease associated with metabolic comorbidities. Microvascular dysfunction has been proposed to drive HFpEF, likely via endothelial cell (EC) dysfunction, yet the role of the mural cells herein has never been explored.</p><p><strong>Methods: </strong>We used the diabetic db/db mouse given 1% salt as a new model of HFpEF and crossed then with PDGFRβ^tg/tg-CreERT2-EYFP^tg/tg mice to label the mural cells. We combined single-cell RNA sequencing, NichetNet analysis and histology to determine the role of mural cell dysfunction in HFpEF.</p><p><strong>Results: </strong>Db/db mice given 1% salt for 8 weeks developed diastolic dysfunction preceded by capillary density loss, pericyte loss and vessel regression. At 4 weeks of salt, hearts of db/db mice already showed EC dysfunction associated with an anti-angiogenic signature, and an increase in pericyte-EC intracellular space. Db/db + salt hearts were further characterised by increased ACTA2 expression, arteriole wall thickening and vessel enlargement. NicheNet analysis on the single cell transcriptomic data revealed little signalling from the ECs to the mural cells; instead, mural cells signalled strongly to ECs. Mechanistically, pericyte dysfunction induces an EC growth arrest via TNFα-dependent paracrine signalling and downstream signalling through STAT1.</p><p><strong>Conclusion: </strong>Mural cell dysfunction contributes to HFpEF by inducing coronary vessel remodelling, at least in part by reducing EC proliferation and inducing EC inflammation through TNFα-dependent paracrine signalling.</p>","PeriodicalId":9374,"journal":{"name":"Cardiovascular Diabetology","volume":"24 1","pages":"62"},"PeriodicalIF":8.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}