RESUMO
Introduction: Kidney transplant recipients (KTRs) have an increased risk of cardiovascular (CV) events (CVEs) compared with the general population. The impact of insulin resistance on CV risk after transplantation is not well defined. Methods: We tested whether triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, may predict posttransplant CVEs in a cohort of 715 consecutive KTRs all included 1 year after transplant. Results: Follow-up was 9.1 ± 4.6 years. Mean TyG at inclusion was 4.75 ± 0.29 (median, 4.73 [4.14-5.84]). In multiple regression analysis, having a TyG above the median value was associated with higher body mass index (BMI), low high-density lipoprotein (HDL) cholesterol level, and greater urinary protein excretion. A total of 127 CVEs (17.7%) occurred during the study period. In univariate analysis, TyG was strongly associated with CVE occurrence (hazard ratio [HR] 2.06, 95% CI 1.42-3.50, for each increase of 0.1 in TyG, P < 0.001). The best predictive value was 4.87 (HR 6.32, 95% CI 3.30-12.11, P < 0.001). The risk of CVE gradually increased with higher TyG index (quartile 2, HR 1.71, 95% CI 0.84-5.20, P = 0.139; quartile 3, HR 3.12, 95% CI 1.61-6.02, P < 0.001; quartile 4, HR 7.46, 95% CI 4.03-13.80, P < 0.001, vs. quartile 1). TyG remained associated with CVE in multivariate analysis (HR 2.11, 95% CI 1.22-3.68, for each increase of 0.1 in TyG, P < 0.001). Conclusion: Insulin resistance, as measured by the TyG index is strongly associated with CVE in KTRs. Improving insulin sensitivity seems to be a major issue to prevent CV morbidity and mortality in this high-risk population.
RESUMO
Background: Acute rejection persists as a frequent complication after kidney transplantation. Defining an at-risk immune profile would allow better preventive approaches. Methods: We performed unsupervised hierarchical clustering analysis on pre-transplant immunological phenotype in 1113 renal transplant recipients from the ORLY-EST cohort. Results: We identified three immune profiles correlated with clinical phenotypes. A memory immune cluster was defined by memory CD4+T cell expansion and decreased naïve CD4+T cell. An activated immune cluster was characterized by an increase in CD8+T cells and a decreased CD4/CD8 ratio. A naïve immune cluster was mainly defined by increased naïve CD4+T cells. Patients from the memory immune profile tend to be older and to have diabetes whereas those from the activated immune profile were younger and more likely to have pre-transplant exposure to CMV. Patients from the activated immune profile were more prone to experience acute rejection than those from other clusters [(HR=1.69, 95%IC[1.05-2.70], p=0.030) and (HR=1.85; 95%IC[1.16-3.00], p=0.011). In the activated immune profile, those without previous exposure to CMV (24%) were at very high risk of acute rejection (27 vs 16%, HR=1.85; 95%IC[1.04-3.33], p=0.039). Conclusion: Immune profile determination based on principal component analysis defines clinically different sub-groups and discriminate a population at high-risk of acute rejection.