Background The triglyceride–glucose (TyG) index, which is a reliable surrogate marker of insulin resistance (IR), has been associated with cardiovascular diseases. However, evidence of the impact of the TyG index on the severity of coronary artery disease (CAD) is limited. This study investigated the relationship between the TyG index and CAD severity of individuals with different glucose metabolic statuses. Methods This study enrolled 2792 participants with CAD in China between January 1, 2018 and December 31, 2021. All participants were divided into groups according to the tertiles of the TyG index as follows: T1 group, TyG index < 6.87; T2 group, TyG index ≥ 6.87 to < 7.38; and T3 group, TyG index ≥ 7.38. The glucose metabolic status was classified as normal glucose regulation, pre-diabetes mellitus (pre-DM), and diabetes mellitus according to the standards of the American Diabetes Association. CAD severity was determined by the number of stenotic vessels (single-vessel CAD versus multi-vessel CAD). Results We observed a significant relationship between the TyG index and incidence of multi-vessel CAD. After adjusting for sex, age, body mass index, smoking habits, alcohol consumption, hypertension, estimated glomerular filtration rate, antiplatelet drug use, antilipidemic drug use, and antihypertensive drug use in the logistic regression model, the TyG index was still an independent risk factor for multi-vessel CAD. Additionally, the highest tertile of the TyG group (T3 group) was correlated with a 1.496-fold risk of multi-vessel CAD compared with the lowest tertile of the TyG group (T1 group) (odds ratio [OR], 1.496; 95% confidence interval [CI], 1.183–1.893; P < 0.001) in the multivariable logistic regression model. Furthermore, a dose–response relationship was observed between the TyG index and CAD severity (non-linear P = 0.314). In the subgroup analysis of different glucose metabolic statuses, the T3 group (OR, 1.541; 95% CI 1.013–2.344; P = 0.043) were associated with a significantly higher risk of multi-vessel CAD in individuals with pre-DM. Conclusions An increased TyG index was associated with a higher risk of multi-vessel CAD. Our study indicated that TyG as an estimation index for evaluating IR could be a valuable predictor of CAD severity, especially for individuals with pre-DM.

No abstract

Background Stress hyperglycemia is strongly associated with poor clinical outcomes in patients with acute coronary syndrome (ACS). Recently, the stress hyperglycemia ratio (SHR) has been proposed to represent relative hyperglycemia. Studies regarding the relationship between SHR and mortality in coronary artery disease (CAD) are limited. This study aimed to clarify the association between SHR and in-hospital mortality in patients with CAD. Methods A total of 19,929 patients with CAD who were hospitalized in Beijing Hospital were enrolled in this study. Patients with an estimated glomerular filtration rate < 30 ml/min, cancer, or missing blood glucose/HbA1c data were excluded; therefore, 8,196 patients were included in the final analysis. The patients were divided into three groups based on tertiles of SHR: T1 group (SHR < 0.725, n = 2,732), T2 group (0.725 ≤ SHR < 0.832, n = 2,730), and T3 group (SHR ≥ 0.832, n = 2,734). The primary endpoint was in-hospital mortality. Results The overall in-hospital mortality rate was 0.91% (n = 74). After adjusting for covariates, SHR was significantly associated with in-hospital mortality in patients with CAD [odds ratio (OR) = 17.038; 95% confidence interval (CI) = 9.668–30.027; P < 0.001], and the T3 group had a higher risk of in-hospital mortality (OR = 4.901; 95% CI = 2.583–9.297; P < 0.001) compared with T1 group. In the subgroup analysis, the T3 group had an increased risk of mortality among patients with pre-diabetes mellitus (pre-DM) (OR = 9.670; 95% CI = 1.886–49.571; P = 0.007) and diabetes mellitus (DM) (OR = 5.023; 95% CI = 2.371–10.640; P < 0.001) after adjustments for covariates. The relationship between SHR and in-hospital mortality among patients with ACS and chronic coronary syndrome was consistent with the main finding. SHR and in-hospital mortality exhibited a dose-response relationship, and the risk of in-hospital mortality increased when the SHR index was above 1.20. Moreover, the area under the curve of SHR for predicting in-hospital mortality in patients with CAD was 0.741. Conclusion SHR is significantly associated with in-hospital mortality in patients with CAD. SHR may be an effective predictor of in-hospital mortality in patients with CAD, especially for those with pre-DM and DM.

Objective Mounting evidence has linked microbiome and metabolome to systemic autoimmunity and cardiovascular diseases (CVDs). Takayasu arteritis (TAK) is a rare disease that shares features of immune‐related inflammatory diseases and CVDs, about which there is relatively limited information. This study was undertaken to characterize gut microbial dysbiosis and its crosstalk with phenotypes in TAK. Methods To address the discriminatory signatures, we performed shotgun sequencing of fecal metagenome across a discovery cohort (n = 97) and an independent validation cohort (n = 75) including TAK patients, healthy controls, and controls with Behçet's disease (BD). Interrogation of untargeted metabolomics and lipidomics profiling of plasma and fecal samples were also used to refine features mediating associations between microorganisms and TAK phenotypes. Results A combined model of bacterial species, including unclassified Escherichia, Veillonella parvula, Streptococcus parasanguinis, Dorea formicigenerans, Bifidobacterium adolescentis, Lachnospiraceae bacterium 7 1 58FAA, Escherichia coli, Streptococcus salivarius, Klebsiella pneumoniae, Bifidobacterium longum, and Lachnospiraceae Bacterium 5 1 63FAA, distinguished TAK patients from controls with areas under the curve (AUCs) of 87.8%, 85.9%, 81.1%, and 71.1% in training, test, and validation sets including healthy or BD controls, respectively. Diagnostic species were directly or indirectly (via metabolites or lipids) correlated with TAK phenotypes of vascular involvement, inflammation, discharge medication, and prognosis. External validation against publicly metagenomic studies (n = 184) on hypertension, atrial fibrillation, and healthy controls, confirmed the diagnostic accuracy of the model for TAK. Conclusion This study first identifies the discriminatory gut microbes in TAK. Dysbiotic microbes are also linked to TAK phenotypes directly or indirectly via metabolic and lipid modules. Further explorations of the microbiome–metagenome interface in TAK subtype prediction and pathogenesis are suggested.

BackgroundThere have been no studies of the effect of non-alcoholic fatty liver disease (NAFLD) on cardiovascular events (CVEs) in patients with pre-diabetes (pre-DM), and diabetes mellitus (DM). We performed a community-based cohort study to evaluate the relationship between NAFLD and CVEs in patients with glucose metabolism disorder.MethodsWe enrolled 71,852 participants from the Kailuan study who had not experienced CVEs, after excluding alcohol abuse and other liver diseases. NAFLD was assessed using abdominal ultrasonography. Besides, participants were categorized by glucose metabolism status [normal glucose regulation (NGR), pre-DM, and DM]. All subjects were followed up for the occurrence of CVEs.ResultsDuring a median of 13.01 (0.64) years of follow-up, 6,037 CVEs occurred. NAFLD was present in 22,525 (31.3%), and compared with participants without NAFLD, those with NAFLD had a 12.3% [95% confidence interval (CI) 1.059–1.191, P &lt; 0.001] higher risk of CVEs, after adjustment for potential confounders. The hazard ratios for patients with mild, moderate, and severe NAFLD were 1.104 (95% CI 1.035–1.179, P &lt; 0.001), 1.149 (95% CI 1.055–1.251, P &lt; 0.001), and 1.235 (95% CI 1.059–1.441, P &lt; 0.001), respectively. Moreover, participants with pre-DM plus NAFLD and participants with DM plus NAFLD had 1.267-fold (95% CI 1.151–1.395, P &lt; 0.001) and 1.829-fold (95% CI 1.666–2.008, P &lt; 0.001) higher risks of CVEs, respectively, compared with those with NGR and no NAFLD. The addition of the combination of NAFLD and glucose metabolism status to the crude Cox model increased the C-statistic by 0.0066 (0.0053–0.0080, P &lt; 0.001).ConclusionsNAFLD is associated with higher risks of CVEs. Moreover, NAFLD is an independent predictor of CVEs in patients with pre-DM and DM, suggesting that NAFLD may provide greater risk predictive value for patients with glucose metabolism disorder.