Cardiovascular Diabetology最新文献

Background: Biological aging varies across individuals and tissues, influencing chronic diseases, including heart failure (HF). Emerging proteome techniques enable quantification of organ-specific aging acceleration (OAA), but whether OAA relates to HF severity and differs by sex remains unclear. We aim to assess the sex-related association between OAA of heart, artery and kidneys and HF severity, and to investigate relevant cardiometabolic risk factors of organ aging.

Methods: In 556 participants from the HELPFul cohort, we estimated predicted biological age for heart, artery, and kidneys using plasma proteomics and calculated OAA as the deviation from chronological age. Associations between OAA and HF stage, echocardiographic parameters, and cardiometabolic risk factors were evaluated using regression models. Composite indices, including triglyceride-glucose body mass index (TyG-BMI), c-reactive protein-triglyceride glucose index and triglyceride-to-HDL cholesterol ratio were assessed for associations with advanced OAA.

Results: Mean age was 63 ± 9 years; 65% were women. Patients were classified as HF stage A (35%), B (29%) and C/D (36%). Heart OAA was significantly associated with advanced HF (Stage C/D) in both sexes (OR = 1.12, 95% CI 1.03 to 1.23 in women; OR = 1.18, 95% CI 1.05 to 1.32 in men), while artery OAA was linked to HF only in women (OR = 1.10, 95% CI 1.01 to 1.18). Multi-organ aging (≥ 2 organs with advanced OAA) conferred over three-fold higher odds of being in Stage C/D. Heart OAA correlated with impaired cardiac structure and function, particularly reduced ejection fraction in men and increased left ventricular mass index in both sexes. Diabetes emerged as the most relevant factor of artery and kidney OAA. TyG-BMI was significantly associated with advanced kidney OAA, only in women (z-scored OR = 1.88, 95% CI 1.45 to 2.45).

Conclusions: Proteome-derived organ aging correlates with HF severity, with possible sex-related patterns. Diabetes and higher TyG-BMI are associated with faster organ aging, which may reflect shared aging mechanisms between metabolic dysfunction and HF.

Background: Statin therapy has been associated with increased risk of type 2 diabetes (T2D). We investigated the relationship between Low-Density Lipoprotein Cholesterol (LDL-C) plasma concentrations and incident T2D and evaluated the modifying effect of statin therapy in a large population-based cohort.

Methods: Individuals free of T2D and cardiovascular disease at baseline were followed longitudinally for the development of new-onset T2D. Cox proportional hazards models were applied to evaluate the associations of LDL-C levels and statin therapy with T2D risk.

Results: From a population of 202,545 individuals, we selected 13,674 participants free of T2D and cardiovascular disease (of whom 52% were on statins), who were followed for a median of 71.6 months (IQR 34.5-149.9), during which 1,819 (13%) developed incident T2D. Cox multiple regression analysis revealed a significant inverse association between LDL-C plasma levels and incident T2D (p < 0.001). When stratifying LDL-C into quartiles [i.e. low (< 84 mg/dL), medium (≥ 84 to < 107 mg/dL), high (≥ 107 to < 131 mg/dL), and very high (≥ 131 mg/dL)], we observed that patients with LDL-C < 84 mg/dL had the highest risk of developing T2D. The interaction between statin therapy and T2D incidence was significant only in the very high LDL-C group, where statin users had a greater risk than non-users (p = 0.018); in the other three LDL-C groups, statin therapy did not significantly modify the association between LDL-C and T2D risk.

Conclusions: Taken together, our findings demonstrate a strong inverse association between LDL-C and incident T2D in the general population. The increased risk of T2D at lower LDL-C levels appears to be independent of statin use, supporting the role of LDL-C as a potential biomarker of T2D susceptibility.

Background: Early detection of prediabetes is crucial for diabetes prevention, yet it remains challenging due to its asymptomatic nature and low screening rates. This study aimed to develop and rigorously validate artificial intelligence (AI) models to identify individuals with prediabetes solely using electrocardiograms (ECGs).

Methods: We defined prediabetes/diabetes based on fasting plasma glucose ≥ 110 mg/dL, hemoglobin A_1c ≥ 6.0%, or ongoing diabetes treatment. From a primary cohort of 16,766 health checkup records, 269 ECG features were extracted to develop a novel AI model. The final model was subsequently evaluated using an internal held-out test dataset and an independent external validation cohort (n = 2,456). SHAP (SHapley Additive exPlanations) was applied to assess feature importance and clinical interpretability.

Results: The best-performing model, a LightGBM-based algorithm we termed DiaCardia, achieved an area under the receiver operating characteristic curve (AUROC) of 0.851 in the internal test dataset (sensitivity: 85.7%, specificity: 70.0%). The model demonstrated robust generalizability, achieving an AUROC of 0.785 in the external validation cohort. Furthermore, DiaCardia maintained substantial predictive ability (AUROC: 0.789) after adjustment for six major confounders using propensity score matching. Higher R-wave amplitude in leads aVL and I, and smaller peak interval dispersion were prominent predictors. Notably, a version of DiaCardia using only single-lead (lead I) ECG data achieved a comparable AUROC of 0.844 (sensitivity: 82.3%; specificity: 70.2%).

Conclusions: This study establishes that an AI model, DiaCardia, can accurately identify individuals with prediabetes from an ECG alone, with performance that is robust across different patient cohorts and independent of major clinical confounders. Our highly generalizable, single-lead DiaCardia model offers a promising solution for scalable prediabetes screening via wearable devices, potentially enabling early, home-based detection and transforming diabetes prevention strategies.

Background: Post-transplant diabetes mellitus (PTDM) affects up to 34% of heart transplant recipients (HTR) within five years, increasing the risk of adverse outcomes. Although the oral glucose tolerance test (OGTT) is the most sensitive method for detecting prediabetes and diabetes, it is burdensome. This study evaluates the diagnostic accuracy of exhaled breath analysis using an electronic nose (eNose) as a non-invasive alternative for detecting prediabetes or PTDM.

Methods: HTR > one year post-transplant undergoing OGTT were included, along with fasting HTR with known PTDM as a control group. Exhaled breath was analyzed before glucose loading using the SpiroNose. Diagnostic performance of eNose parameters alone and combined with clinical variables was assessed using multivariate logistic regression.

Results: Seventy-six HTR were included (29% female, median age 56 years, median 7.8 years post-transplant). Of these, 18 had normal glucose tolerance, 33 had prediabetes, and 25 had PTDM (including 8 newly diagnosed). Overall, 76% had prediabetes or PTDM. eNose alone differentiated normal from abnormal glucose tolerance (prediabetes or PTDM) with an AUROC of 0.70 (95% CI 0.57-0.83), 68% accuracy, 69% sensitivity, 67% specificity, 40% negative predictive value (NPV), and 87% positive predictive value (PPV). Combining eNose with clinical parameters improved diagnostic performance (AUROC 0.88, 95% CI 0.81-0.96), achieving 78% accuracy, 71% sensitivity, 100% specificity, 51% NPV, and 100% PPV.

Conclusions: Prediabetes or PTDM is common in HTR. eNose technology, especially when combined with clinical data, shows promise as a non-invasive screening tool with high specificity and PPV. This approach may reduce OGTT burden, pending further confirmation.

Terazosin (TZ), a well-known antagonist of the α1-adrenergic receptor (α1-AR), has demonstrated protective effects on vascular endothelial cells (ECs) and reduced vascular stiffness in clinical studies. Endothelial dysfunction and oxidative stress are central drivers of cardiometabolic diseases such as diabetes, where sustained ROS burden accelerates EC senescence and barrier failure. These findings suggest its potential role in combating vascular aging and atherosclerosis; however, the underlying mechanisms remain partially understood. In this study, we investigated whether TZ can prevent atherosclerosis in ApoE^-/- mice fed a high-cholesterol diet and aimed to elucidate the mechanisms involved. Our results showed that TZ significantly reduced plaque size, EC senescence, vascular permeability, and reactive oxygen species (ROS) levels, effectively inhibiting atherosclerosis independently of α1-AR signaling. In cultured primary human umbilical vein ECs (HUVECs), TZ inhibited EC senescence via the Pgk1/Hsp90 pathway. It enhanced the interaction between Hsp90 and the antioxidant enzyme peroxiredoxin 1 (Prdx1), leading to lower ROS levels-a key driver of cellular senescence. These findings were confirmed in atherosclerotic ApoE^-/- mice. Furthermore, senescent ECs exhibited increased levels of vascular endothelial growth factor A (VEGFA) and decreased levels of angiostatin, contributing to higher vascular permeability and exacerbating atherosclerosis. TZ effectively reversed these changes. Overall, our study demonstrates that TZ primarily alleviates EC senescence and atherosclerosis through the Pgk1/Hsp90/Prdx1 pathway, highlighting Pgk1 activation as a strategy that may also mitigate endothelial dysfunction and oxidative stress in broader cardiometabolic contexts (e.g., diabetes), suggesting that TZ is a promising senomorphic agent for treating vascular aging and atherosclerosis in clinical settings and that Pgk1-targeted interventions could have implications beyond atherosclerosis.

Background: Type 2 diabetes (T2D), affecting approximately 12% of the global population and over 30% of older adults, is among the most prevalent and fast-growing risk factors for neurodegenerative disorders. Evidence is lacking on whether specific glucose-lowering agents may reduce the risk of neurodegeneration onset in people living with T2D.

Methods: In this retrospective cohort study, we utilized the TriNetX platform, which contains electronic health records of over 170 million people worldwide. We propensity-score matched (1:1) people with T2D lacking evidence of neurodegeneration who initiated glucagon-like peptide-1 receptor agonists (GLP-1 RAs) or dipeptidyl peptidase-4 inhibitors (DPP4i) (2010-2021). In a separate analytical cohort, we compared individuals initiating GLP-1 RA with those initiating basal insulin. Follow-up continued for ≤ 5 years. We used Cox proportional-hazard regression models to assess the risk of the composite outcome of developing new neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, dementia subtypes, and other synucleinopathies. We also assessed each component individually. Analyses were repeated among subgroups defined by sex, age, and the specific GLP-1 RA initiated.

Results: Overall, 214,442 matched individuals initiated GLP-1 RAs or DPP4i (109,731 women, mean age 58.6 years [SD 12], and mean HbA1c 7.7% [1.4]). During a 4.0-year mean follow-up, neurodegenerative disorder onset occurred in 2,393 (2.2%) and 3,062 (2.9%) people initiating GLP-1 RAs and DPP4i, respectively (hazard ratio of 0.81 [95% CI 0.77 to 0.86]; absolute risk difference - 0.6% [- 0.8 to - 0.5]). The associations were separately observed among women (0.78 [0.72 to 0.84]) and men (0.90 [0.83 to 0.98]), individuals aged ≥ 65 years old (0.82 [0.78 to 0.87]) or < 65 years old (0.84 [0.70 to 1.00]), and in those initiating semaglutide (0.75 [0.67 to 0.84]), liraglutide (0.77 [0.70 to 0.84]), or dulaglutide (0.82 [0.77 to 0.88]). The hazard ratios for dementia, Alzheimer's disease, vascular dementia, and Parkinson's disease onset were 0.76 [0.72 to 0.81], 0.77 [0.68 to 0.87], 0.75 [0.67 to 0.85], and 1.04 [0.93 to 1.17] with GLP-1 RAs versus DPP4i, respectively. The results were in the same direction when comparing individuals initiating GLP-1 RAs with those initiating basal insulin.

Conclusions: In a real-world cohort of people living with T2D with a multinational representation, the initiation of GLP-1 RAs, compared to DPP4i or basal insulin, was associated with a lower risk of new-onset neurodegeneration. These data support the rationale for dedicated clinical trials to assess the potential neuroprotective properties of GLP-1 RAs in this population.

Background: Urinary albumin excretion is an established marker of cardiovascular (CV) risk in people with type 2 diabetes mellitus (T2DM). However, its prognostic significance within the normoalbuminuric range (< 30 mg/g) remains uncertain, particularly regarding sex-specific differences. This study examined whether urinary albumin-to-creatinine ratio (UACR; KDIGO A1 range) is associated with coronary artery disease (CAD) severity and 6-year major adverse cardiovascular events (MACE) in women and in men with T2DM and preserved kidney function (eGFR > 60 mL/min/1.73 m², UACR < 30 mg/g), treating sex differences as a co-primary objective.

Methods: We conducted a retrospective cohort study involving adults with T2DM who underwent diagnostic coronary angiography. Baseline associations between log-transformed UACR, CAD severity, CV risk factors, and inflammatory markers were evaluated using multivariable linear regression. MACE (defined as non-fatal myocardial infarction or unstable angina requiring urgent revascularization, stroke, or CV death) were recorded during 6-year of follow-up. Cox proportional hazards models, adjusted for age, sex, hypertension, smoking, BMI, lipid profile, hs-CRP, and ACEI/ARB use, were used to assess UACR-MACE associations.

Results: We included 420 adults (180 women, 42.9%) with a mean of age 65.3 ± 10.7 years and a median UACR 7.56 mg/g (IQR 4.12-15.5). Significant CAD was present in 310 participants (73.8%), and 78 experienced MACE during follow-up (35.5%). Higher UACR was independently associated with greater coronary stenosis (adjusted R² = 0.090, p < 0.001). Kaplan-Meier analysis showed a significantly higher incidence of MACE in the highest UACR tertile (log-rank p = 0.039). In multivariable Cox models adjusted for age, sex, hypertension, smoking, lipid profile, hs-CRP, SSI, and ACEI/ARB use, higher log-UACR independently predicted MACE (adjusted HR 1.67, 95% CI 1.35-2.10; p < 0.01). In sex-stratified Cox models, higher log-UACR predicted MACE in both sexes and remained independently associated in multivariable analyses (adjusted HR 1.67, 95% CI 1.35-2.10; p < 0.01). Associations were directionally stronger in women, who showed higher cumulative event rates across UACR tertiles, although the formal UACR × sex interaction did not reach statistical significance.

Conclusions: Within the normoalbuminuric range, UACR is associated with greater CAD burden and higher 6-year MACE risk, with sex-specific differences. These findings suggest potential sex-related variation in the prognostic value of high-normal albuminuria, particularly among women, warranting validation in larger and more diverse cohorts.

Background: Using serial coronary CT angiography (CCTA) imaging, we aimed to characterize baseline coronary plaque characteristics and quantify 10-year coronary plaque progression, including high-risk and low-density plaque presence, in patients with and without type 2 diabetes.

Methods: A total of 299 patients underwent CCTA with a median scan interval of 10.2 [IQR 8.7-11.2] years. Patients who underwent coronary artery bypass grafting and vessels revascularized by percutaneous coronary intervention were excluded (n = 32). Scans were analyzed using atherosclerosis imaging-quantitative CCTA analysis (AI-QCT; Cleerly Inc.). Associations between diabetic status, baseline and follow-up plaque burden and characteristics were evaluated using multivariable regression adjusted for cardiovascular risk factors, statin use, baseline plaque volumes, and scanner settings.

Results: In total, 267 patients were included (mean age 57 ± 7 years; 43% were women), 44 (16.5%) had type 2 diabetes (HbA1c 56 ± 14 mmol/mol). At baseline, patients with diabetes had a higher percent atheroma volume (PAV) compared to non-diabetic individuals (5.1% [1.7, 10.9] versus 2.2% [0.5, 5.8]). Adjusted for cardiovascular risk factors, patients with diabetes had a higher plaque burden at both baseline and follow-up. After adjustment for cardiovascular risk factors and baseline plaque volumes, individuals with diabetes had a more than threefold higher rate of plaque progression. After 10 years of follow-up, patients with diabetes had a higher prevalence of both high-risk plaque (OR 2.75; 95% CI 1.38-5.48; p = 0.004) and low-density plaque (OR 2.88; 95% CI 1.45-5.70; p = 0.002).

Conclusions: Patients with diabetes had a more than threefold higher rate of coronary plaque progression and an increased development of high-risk plaque.