Cardiovascular Diabetology最新文献

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: 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: 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: High blood pressure(BP) is a modifiable risk factor for premature mortality, adverse cardiovascular outcomes and kidney diseases in individuals with type 2 diabetes(T2D). We studied the age-specific associations between BP and incident cardiovascular disease(CVD), chronic kidney disease(CKD), kidney failure, and all-cause death among individuals with T2D.

Methods: We included individuals with T2D who underwent structured diabetes assessment between 2000 and 2022 in Hong Kong Special Administrative Region, People's Republic of China. Participants were stratified by baseline age(18-44 years, 45-59 years, 60-74 years, and 75 years or older). Cox proportional hazard model was used to estimate hazard ratios for the risk of incident CVD, CKD, kidney failure and all-cause death associated with systolic blood pressure(SBP) and diastolic blood pressure(DBP) categories, referenced to SBP 120-129 mmHg and DBP 70-79 mmHg within each age stratum. Non-linear association between SBP/ DBP and clinical outcomes was modelled using restricted cubic splines(RCS).

Results: We included 429,740 individuals with T2D (mean age 61.9 years, 52.7% men, 75.3% pre-existing hypertension). SBP above 120-129 mmHg and DBP above 70-79 mmHg were associated with a proportional increase in risks for CVD, CKD, kidney failure and death across ages adjusted for demographics, diabetes duration, BMI, smoking status, HbA1c, lipids, albuminuria, history of CVD, CKD and use of BP-lowering medications. The strength of the risk associations was greatest in youngest age group and declined with increasing age. Among individual components of CVD, risks conferred by an incremental increase in SBP or DBP were high for hemorrhagic stroke. A 10 mmHg or 1-SD increase in SBP / DBP conferred a 1.2 to 1.5-fold increase in hazards for hemorrhagic stroke among individuals aged 18-44 years (p-interaction < 0.001). RCS indicated variable linear and nonlinear associations between SBP/DBP and CVD, kidney disease, or death across age categories.

Conclusions: We observed heterogeneity in the relationship between BP and various clinical outcomes across ages in a diabetes population. Risk associations were strongest among young individuals, emphasising the importance of BP management in this population.

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.

Background: Semaglutide has demonstrated the ability to reduce the risk of progression to type 2 diabetes in patients with myocardial infarction and overweight or obesity without diabetes. However, implementation of semaglutide treatment in daily clinical care is challenging due to costs and limited supply. Thus, it is essential to identify patients who are most likely to benefit from this treatment. Therefore, we aimed to investigate the 5-year risk of progression to type 2 diabetes in SELECT-eligible patients with a recent myocardial infarction and overweight or obesity without diabetes, and to assess the estimated preventive potential of semaglutide in a real-world cohort.

Methods: We included patients registered in the Western Denmark Heart Registry with first-time myocardial infarction and body mass index (BMI) ≥ 27 kg/m² without diabetes, thus meeting the eligibility criteria of the SELECT trial. The cohort was grouped by glycemic status at baseline: HbA1c 6.0-6.4% (prediabetes_6.0-6.4%), 5.7-6.4% (prediabetes_5.7-6.4%), or < 5.7% (normoglycemia). A Cox proportional hazards regression model was used to calculate cause-specific hazard ratios (HR), adjusted for sex, age, hypertension, and smoking. The main endpoint was progression to type 2 diabetes. The 5-year number-needed-to-treat (NNT₅) with semaglutide was estimated based on the SELECT trial.

Results: The cohort comprised 7398 patients with median follow-up of 4.7 (Q1-Q3 2.8-5.0) years. At baseline, 1385 (19%) patients had prediabetes_6.0-6.4%, 3751 (51%) had prediabetes_5.7-6.4%, and 3647 (49%) had normoglycemia. The median BMI was similar across the groups: 30 kg/m² (Q1-Q3 28-33), 30 kg/m² (Q1-Q3 28-32), and 29 kg/m² (Q1-Q3 28-31). The 5-year risks of progression to type 2 diabetes were 54%, 30%, and 5%, respectively. Compared with normoglycemia, those with prediabetes_6.0-6.4% were associated with an 18-fold increased risk of progression to type 2 diabetes (HR 18.3, 95% CI 15.2-22.1) and the estimated NNT₅ with semaglutide to prevent one case of type 2 diabetes was 2.7 in this subgroup.

Conclusions: In real-world patients with recent myocardial infarction and overweight or obesity, prediabetes was much stronger associated with progression to type 2 diabetes than previously reported in the SELECT trial. Further, we estimated that treatment with semaglutide might be able to substantially reduce progression to type 2 diabetes.

Background: The COLCOT trial showed that patients with diabetes may benefit from low-dose colchicine, suggesting a potential interplay between insulin resistance (IR) and inflammation. Whether their combined assessment improves mortality risk stratification in the general population remains unclear.

Methods: We analyzed 50,654 adults from NHANES 1999-2018 linked to the National Death Index. IR and inflammation were assessed using estimated glucose disposal rate (eGDR) and the log₂-transformed aggregate index of systemic inflammation (AISI), respectively. Survey-weighted Cox proportional hazards models were used for all-cause mortality. For cardiovascular (CVD) mortality, cumulative incidence functions (CIFs) were estimated with Gray's test for between-group comparisons, and Fine-Gray subdistribution hazard models were fitted treating non-CVD death as a competing event. Discrimination was assessed using time-dependent ROC curves at 5 and 10 years. Robustness was evaluated through sensitivity analyses excluding immune-modifying conditions/treatments, applying a 24-month lag, and excluding extreme absolute lymphocyte counts.

Results: Over a median follow-up of 120 months, 6,936 all-cause deaths and 2,170 CVD deaths occurred. Higher eGDR was inversely associated with mortality (all-cause HR per 1-unit increase 0.90, 95% CI 0.88-0.92; CVD sHR 0.88, 95% CI 0.85-0.91), whereas higher log₂(AISI) was positively associated (all-cause HR per doubling 1.10, 95% CI 1.06-1.15; CVD sHR 1.13, 95% CI 1.06-1.20). In joint analyses, participants with low eGDR (≤ 8.40) and high log₂(AISI) (> 7.98) had the highest risks of all-cause mortality (HR 1.58, 95% CI 1.38-1.81) and CVD mortality (cause-specific HR 2.09, 95% CI 1.58-2.77; Fine-Gray sHR 2.13, 95% CI 1.66-2.74), with graded separation of CIFs (Gray's test P < 0.001). The combined model showed improved discrimination (AUCs at 5/10 years: all-cause 0.705/0.723; CVD 0.754/0.769). Results were consistent across sensitivity analyses.

Conclusion: In a nationally representative U.S. cohort, eGDR and log₂(AISI) were independently and jointly associated with all-cause and CVD mortality. Their combined assessment improves risk stratification and may help identify individuals most likely to benefit from targeted preventive and anti-inflammatory strategies.