Cardiovascular Diabetology最新文献

Background: The estimated glucose disposal rate (eGDR), an established measure of peripheral insulin sensitivity, contributes to stratifying the risk of cardio-cerebrovascular events. Nevertheless, the association between long-term eGDR exposure and stroke incidence throughout all stages (0-4) of cardiovascular-kidney-metabolic (CKM) syndrome remains unknown.

Methods: A cohort of 5248 individuals was drawn from the China Health and Retirement Longitudinal Study (CHARLS). For each participant, eGDR values for the years 2012 and 2015 were calculated using the equation: 21.158 - [0.090 × WC (cm)] - [3.407 × HTN (presence = 1)] - [0.551 × HbA1c (%)]. Cumulative eGDR was calculated as (eGDR₂₀₁₂ + eGDR₂₀₁₅)/2* time (2015-2012). K-means clustering was used to analyse eGDR values from both 2012 and 2015 to identify distinct change patterns. To assess associations with stroke risk, we utilised multivariable logistic regression and restricted cubic spline models.

Results: During the 2015-2018 follow-up period, a total of 336 incident stroke cases were documented. Four distinct eGDR change patterns were identified. In fully adjusted models, compared with the participants in the persistent low pattern (Class 2), those in the moderate-high stable (OR 0.43, 95% CI: 0.31-0.58), stable high (OR 0.29, 0.19-0.43), and rapid decrease (OR 0.66, 0.47-0.91) patterns exhibited significantly lower stroke risk. Furthermore, each 1-unit increase in cumulative eGDR was associated with a 5% reduction in stroke odds (OR 0.95, 0.93-0.96). Restricted cubic spline analysis confirmed a linear inverse relationship between cumulative eGDR and stroke risk (P < 0.001; P for nonlinearity = 0.259).

Conclusion: Cumulative eGDR is inversely associated with stroke risk across all CKM syndrome stages (0-4). This observation suggests that prolonged eGDR surveillance may be associated with improved risk stratification in this population.

Background: Diabetes mellitus (DM), which consists of type I and type 2 diabetes (T1D and T2D), is a known risk factor for myocardial infarction (MI) and negatively impacts post-MI outcomes. However, the mechanisms by which DM exacerbates cardiac remodeling in T1D versus T2D have not been well defined. Here, we assessed acute and chronic post-MI outcomes in T1D and T2D mice, focusing on immune and metabolic pathways.

Methods: T1D was induced in adult male mice by a single high dose of streptozotocin (STZ), and T2D induced by high fat/fructose feeding and multiple low STZ doses. Two weeks following STZ administration, MI was induced by permanent coronary artery ligation, and mice were studied at days (D) 0, 3, 7, and 28 post-MI. Cardiac function was assessed by echocardiography.

Results: Compared to non-diabetic mice, T1D and T2D mice had worse cardiac dysfunction after MI, including increased wall thinning and decreased ejection fraction, despite similar infarct sizes. T1D mice also displayed acute pulmonary congestion. By RNA-sequencing analysis, T1D and T2D mice displayed upregulation of genes associated with canonical chemokine/monocyte-mediated inflammatory pathways, and downregulation of genes associated with extracellular matrix remodeling. T1D and T2D delayed activation of M2-like (CD206+) macrophages in the heart, and impaired normal collagen and elastin deposition after MI. T2D also increased expression of genes associated with T cell activation, and increased CD8 + T cells in the infarct. T1D and T2D hearts showed signs of impaired glucose and ketone oxidation, and T1D hearts had increased markers of fatty acid oxidation. Extracted D3 cardiac macrophages from T1D and T2D mice exhibited higher basal oxygen consumption, and increased M1 markers and chemokine expression. Plasma from T1D and T2D mice increased chemokine expression (Ccl2, Ccl7, Cxcl1) in cultured bone marrow macrophages, and T2D plasma impaired mitochondrial function.

Conclusions: DM promotes adverse cardiac remodeling, which is associated with activation of overlapping and unique inflammatory pathways, impaired ECM remodeling, remote metabolic remodeling, and alterations in macrophage metabolism. Our results provide novel insights into potential therapeutic pathways for DM patients suffering from MI.

Background: Markers of insulin resistance, such as the triglyceride-glucose (TyG) index and estimated glucose disposal rate (eGDR), have been extensively linked to cardiometabolic multimorbidity (CMM). However, the roles of lipid metabolism indicators, including the atherogenic index of plasma (AIP) and remnant cholesterol (RC), remain less clearly defined. This study aimed to evaluate both the individual and combined effects of insulin resistance and dyslipidemia on the risk of CMM.

Methods: Data were derived from the English Longitudinal Study of Ageing. A composite metabolic index integrating the TyG, eGDR, AIP, and RC was developed using principal component analysis. The associations of individual and composite indices with incident CMM were examined using multivariable Cox proportional hazards models, while their predictive performance was assessed via receiver operating characteristic (ROC) and net reclassification improvement (NRI) analysis.

Results: Over a 6.8-year follow-up period, 552 cases of CMM occurred among 4232 participants. After multivariable adjustment, each standard deviation (SD) increase in TyG, AIP, and RC was linked to a higher risk of CMM by 30.8% (HR = 1.308; 95% CI: 1.202-1.422), 22.2% (HR = 1.222; 95% CI: 1.117-1.338), and 7.6% (HR = 1.076; 95% CI: 1.025-1.129), respectively. In contrast, eGDR and the composite metabolic index were linked to 35.0% (HR = 0.650; 95% CI: 0.565-0.747) and 37.4% (HR = 0.626; 95% CI: 0.554-0.707) lower risks of CMM. The eGDR and CompositeIndex showed high Population attributable fraction (PAF) of 56.3% (95% CI: 47.3-63.4) and 38.3% (95% CI: 29.8-47.8), respectively. Dose-response analyses showed near-linear relationships for all indices. ROC and NRI analysis further indicated that the CompositeIndex offered highest discrimination with a modest improvement for CMM (AUC = 0.754, 95% CI: 0.737-0.778; NRI = 0.066 (0.027-0.106). The associations were more pronounced among participants younger than 65 years and consistent across sex.

Conclusions: The integrated index combining insulin resistance and lipid dysregulation was associated with incident CMM and provided modest improvements in risk discrimination and reclassification beyond traditional risk factors.

Objective: Cardiometabolic multimorbidity (CMM) is a growing global health challenge. Whether the baseline or cumulative triglyceride glucose and Chinese visceral adiposity index product (TyG-CVAI) can predict incident CMM remains unclear.

Methods: We constructed two prospective cohorts from the China Health and Retirement Longitudinal Study (CHARLS): Cohort 1 (n = 8895 patients) to assess the association of the baseline TyG-CVAI with CMM and Cohort 2 (n = 5839 patients) to assess the association of the cumulative TyG-CVAI with CMM. The cumulative TyG-CVAI was calculated as the average TyG-CVAI between baseline and the 2015 wave multiplied by the exposure time. Incident CMM was confirmed via a self-reported physician diagnosis, medication use, and clinical data. Cox regression models were used to estimate hazard ratios (HRs). Nonlinearity was assessed using restricted cubic splines, and predictive performance was evaluated by performing a receiver operating characteristic (ROC) curve analysis.

Results: During follow-up, 875 and 492 incident CMM cases were documented in Cohort 1 and Cohort 2, respectively. Both the baseline and cumulative TyG-CVAI showed graded, positive associations with the CMM risk. Compared with the lowest quartile, the highest quartile was associated with significantly increased risks (baseline: HR = 1.93, 95% CI = 1.46-2.54; cumulative: HR = 1.76, 95% CI = 1.22-2.53). Significant nonlinear relationships with threshold effects were observed for both indices (P for nonlinearity < 0.001). Furthermore, compared with their individual components (TyG or CVAI), both the baseline and cumulative TyG-CVAI demonstrated superior predictive ability for CMM, as indicated by a larger area under the ROC curve.

Conclusions:  Both the baseline and cumulative TyG-CVAI are independent and nonlinear predictors of incident CMM, outperforming TyG or CVAI alone. This easily obtainable metric may enhance risk stratification and help identify high-risk individuals for early preventive intervention.

Background: Cardiovascular-kidney-metabolic (CKM) syndrome imposes a substantial global health burden, with most adults clustered in early stages 0-3. Insulin resistance (IR), as a core manifestation of metabolic dysfunction, is thought to play a pivotal role in CKM progression and cardiovascular disease (CVD) development, but the relative impact of diverse IR surrogates and the mediating role of biological ageing acceleration remain unclear.

Method: This prospective analysis included 6318 participants with CKM syndrome stages 0-3 from the China Health and Retirement Longitudinal Study (CHARLS). We evaluated twelve insulin resistance surrogates in relation to incident CVD using multivariable-adjusted logistic regression, restricted cubic splines (RCS), and quantile-based models. Mediation analyses assessed whether biological aging acceleration mediated the association between IR indices and new-onset CVD.

Results: 1231 (19.5%) of 6318 participants with CKM stages 0-3 developed new-onset CVD. All IR surrogates demonstrated significant associations with CVD risk, with elevated TyG-derived indices, METS-IR, CTI, and TG/HDL-C showing positive associations whereas eGDR exhibited an inverse relationship (all P-trend < 0.05). RCS analyses revealed nonlinear relationships for METS-IR, CTI, and eGDR. Significant modification effects were observed by biological ageing acceleration, gender, and CKM stage. Mediation analyses indicated that biological aging acceleration accounted for 14.9-16.4% of the TyG-ABSI-CVD association and 1.3-4.2% of other IR-CVD relationships.

Conclusions: Multiple IR surrogate indices independently predict cardiovascular disease in CKM stages 0-3, with biological aging acceleration mediating this association. Integrating these measures into risk stratification could enable early identification and targeted intervention for high-risk individuals.

Background: The atherogenic index of plasma (AIP) serves as a crucial indicator for assessing atherosclerotic risk. It reflects the degree of dyslipidaemia and cardiovascular disease (CVD) risk. The cardiometabolic index (CMI) provides a comprehensive evaluation of obesity-related metabolic risk, acting as a key biomarker for predicting multiple cardiometabolic diseases. The relationship between AIP and CMI in patients with non-alcoholic fatty liver disease (NAFLD) and mortality or CVD risk remains unclear.

Methods: This study included 5792 adult (≥ 18 years) NAFLD patients from the US National Health and Nutrition Examination Survey (NHANES, 1999-2018). Weighted logistic regression and Cox proportional hazards models were employed to investigate the association between AIP, CMI and all-cause mortality, CVD mortality and CVD risk. Restricted cubic spline (RCS) curves assessed non-linear associations. Subgroup analyses and mediation analyses examined the effect modifiers and mediators. The incremental predictive value of AIP and CMI was evaluated. Sensitivity analyses were conducted to validate the robustness.

Results: During follow-up, 721 all-cause deaths (including 241 CVD deaths) and 726 total CVD events were recorded. After adjusting for confounding factors, patients in the highest quartiles of AIP and CMI had a significantly higher risk of specific CVD events. The strongest association was observed for CHF (AIP: OR = 3.157, 95% CI 1.684, 5.922, p < 0.001; CMI: OR = 3.604, 95% CI 1.843, 7.047, p < 0.001), followed by heart attack and CHD. CMI consistently demonstrated a stronger effect than AIP. RCS analysis indicates a non-linear relationship between CMI and CHD, angina pectoris. Subgroup analysis revealed that both AIP and CMI demonstrated high predictive value for all-cause mortality in the 40-60 age cohort. Mediation analysis revealed that Mets, NLR, hypertension and HOMA-IR partially mediated the aforementioned associations. The inclusion of AIP and CMI partially improved the predictive capability of the basic model. Sensitivity analyses validated the robustness of these findings.

Conclusions: In patients with NAFLD, CMI is a stronger predictor of non-fatal CVD than AIP. While both indices show limited value for predicting mortality, CMI holds promise as a practical supplementary tool for clinical risk assessment.

Background: Mitochondrial dysfunction is a hallmark of diabetic kidney disease (DKD), yet its regulatory mechanisms remain poorly defined. Mitochondrial transcription factor A (TFAM), a central regulator of mitochondrial homeostasis, undergoes lysine 76 (K76) acetylation, but the functional significance of this modification in DKD has not been established.

Methods: We collected kidney tissues from DKD patients and DKD mice, and assessed TFAM acetylation in HK-2 cells and primary renal tubular cells under high-glucose conditions. In addition, to investigate the potential mechanism of TFAM acetylation in mitochondrial damage within the kidney, we explored relevant pathways using proteomics and utilized streptozotocin (STZ)-induced DKD mouse models with tubular-specific expression of TFAM wild-type and mutant forms to examine kidney injury. Moreover, we identified TFAM K76 acetylation-specific inhibitors through high-throughput virtual screening and thoroughly validated them in HK-2 cells, primary cells, and DKD mice, confirming the critical role of TFAM acetylation in DKD-related kidney injury.

Results: Here, we identify TFAM K76 acetylation as a critical mediator of mitochondrial injury in DKD. TFAM K76 acetylation was markedly elevated in kidney tissues from DKD patients and diabetic mouse models, correlating with mitochondrial damage, inflammation, and fibrosis under hyperglycemic conditions. In vivo, overexpression of acetylation-mimetic TFAM K76Q in renal tubular epithelial cells aggravated renal injury and ultrastructural damage, whereas its deacetylation attenuated these effects. Mechanistically, TFAM K76 acetylation impaired oxidative phosphorylation and excessively activated autophagy, further exacerbating mitochondrial damage. We identified sirtuin 3 (SIRT3) as an upstream deacetylase that regulates this modification. Importantly, through high-throughput virtual screening, we discovered a novel small-molecule inhibitor (C14) that selectively reduces TFAM K76 acetylation and effectively alleviates hyperglycemia-induced mitochondrial dysfunction, inflammation, and fibrosis in both in vitro and in vivo models.

Conclusions: Collectively, our findings define TFAM K76 acetylation as a pathogenic driver of DKD and propose C14 as a promising therapeutic candidate targeting mitochondrial metabolism.

Background/aims: Current clinical risk tools in type 1 diabetes do not include left ventricular dysfunction or inflammation, potentially limiting early risk detection. We aimed to evaluate the associations and predictive value of combining echocardiography with inflammatory biomarkers for mortality and major adverse cardiovascular events (MACE).

Methods: In a prospective cohort of individuals with type 1 diabetes without known cardiovascular disease, we evaluated whether subclinical left ventricular dysfunction, defined by an elevated ratio of early mitral inflow velocity to early diastolic mitral annular velocity (E/e') or impaired global longitudinal strain (GLS), combined with elevated levels of an inflammatory biomarker (interleukin-6 [IL-6], soluble urokinase-plasminogen-activator-receptor [suPAR], or high-sensitivity C-reactive-protein [hsCRP]), was associated with all-cause mortality and MACE. Cox models were adjusted for all 10 variables included in the Steno T1 Risk Engine variables: age, sex, systolic blood pressure, duration of diabetes, HbA1c, low-density lipoprotein, estimated glomerular filtration rate, albuminuria status, smoking, and physical activity. C-statistics and net reclassification improvement were assessed.

Results: Among 876 participants (51% male, median age 50 years), 114 deaths occurred over 14.5 years of follow-up. Elevated E/e' combined with IL-6 or suPAR, but not hsCRP, was independently associated with mortality. Compared with individuals with E/e' <8 and non-elevated IL-6, the hazard ratio (HR) for E/e' 8-13 with elevated IL-6 was 2.5 (95% CI 1.4 to 4.6, P < 0.01), and for E/e' ≥13 with elevated IL-6 was 3.4 (1.5-7.6; P < 0.01). Corresponding HRs for suPAR were 2.4 (1.2 to 4.7, P < 0.01) and 3.9 (1.8 to 8.5, P < 0.01). Adding E/e' and an inflammatory biomarker increased the C-statistic from 0.839 (Steno T1 Risk Engine alone) to 0.887 (E/e' and IL6) and 0.868 (E/e' and suPAR). Findings were similar for GLS and with MACE as the outcome.

Conclusions: Echocardiography combined with inflammatory biomarkers synergistically identifies individuals with type 1 diabetes, without known cardiovascular disease, who are at high risk of mortality and MACE.

Background: Myocardial infarction (MI) remains a leading cause of global mortality, with risk varying substantially across demographic and clinical subgroups. Although SCORE2 is widely implemented for cardiovascular risk stratification, the extent to which clinical subgroup specific plasma proteomics can further refine personalized MI risk prediction remains uncertain.

Methods: SCORE2-Pro, a clinical subgroup-stratified plasma proteome prediction model was built stratified by sex, age, smoking status, non-high-density lipoprotein (non-HDL) cholesterol, and systolic blood pressure. In 51,010 UK Biobank participants (aged 40-69 years; 54.9% female) without MI at baseline, 70% were used for model development, and the remaining 30% for an internal hold-out validation. We used light gradient boosting machine classifiers and Cox proportional hazards models to identify top-predictive protein combinations and stratification strategies for MI.

Results: The SCORE2-Pro model revealed distinct and highly effective protein panels for each subgroup. Compared with the clinical model, SCORE2-Pro remarkably enhanced predictive performance across demographic and clinical subgroups. A 9-protein model in females improved AUC by + 0.061 (P = 1.51 × 10^-3), with a net reclassification index (NRI) of + 0.125 (P = 2.69 × 10^-6). Similarly, a 7-protein model for middle-aged subpopulation demonstrated an AUC improvement of + 0.036 (P = 0.033) with an NRI of + 0.127 (P = 8.09 × 10^-11). Notably, in high-risk populations, SCORE2-Pro model significantly improved reclassification performance compared with the clinical model (NRI: + 0.121, P = 0.020).

Conclusions: By adopting a clinical subgroup stratification approach using factors derived from SCORE2, we identified subgroup-specific proteomic signatures for MI that considerably improve predictive accuracy and reclassification beyond traditional clinical models.

Background: Estrogen therapy and androgen‑deprivation were once combined to treat prostate cancer (PrCa). Clinical studies later showed that prolonged estrogen exposure in androgen‑deprived men raises cardiovascular disease (CVD) risk, yet the metabolic pathways responsible remain unclear.

Methods: We generated an androgen‑deprived, 17β‑estradiol (E2)-treated mouse model by gonadectomizing male C57BL/6 J mice and implanting sub‑cutaneous delayed‑release E2 or vehicle pellets. Mice received a Western‑style diet and were housed at thermoneutrality to accelerate CVD‑risk phenotypes. Metabolic profiling included hyperinsulinemic‑euglycemic clamps, oral lipid and pyruvate tolerance tests, flow cytometry of immune cells, and single‑nucleus RNA sequencing of liver tissue.

Results: In hypogonadal males, E2 treatment induced several metabolic disturbances. During clamps, E2‑treated mice showed markedly elevated gluconeogenesis, corroborated by higher glucose peaks and AUC during pyruvate tolerance testing and by up‑regulation of hepatic Pck1 mRNA. Triglyceride (TG) clearance, which improves with E2 in females, was impaired in E2‑treated males: oral lipid‑tolerance testing revealed prolonged TG excursions, reduced maximal lipase activity, lower non‑lipase clearance at 6 h post-OLTT, and decreased free‑fatty‑acid peak levels. Hepatic lipase, VLDL clearance receptors Ldlr and Lrp1, and microsomal triglyceride transfer protein (MTP) transcripts were down‑regulated. SnRNA‑seq showed suppression of lipid‑clearance genes with E2 treatment in males. Subcutaneous adipocytes were hypertrophic, and flow cytometry identified increased TNFα‑positive macrophages, an inflammatory milieu that could promote insulin resistance. Cardiac morphology was modestly altered; E2‑treated males exhibited a larger left‑ventricular end‑diastolic diameter, while ejection fraction and arterial pressure remained unchanged.

Conclusion: Estradiol administration in androgen‑deprived male mice produces a constellation of metabolic derangements-including enhanced hepatic gluconeogenesis, impaired TG clearance, and inflammatory adipocyte hypertrophy-that likely underlie the increased CVD risk observed clinically. The identified molecular nodes (PEPCK, hepatic lipase, LRP1, LDLR, MTP, and adipose‑macrophage TNFα) provide potential targets for mitigating estrogen‑induced CVD risk while preserving its therapeutic benefit for PrCa.