PDF(Pubmed)
Abstract：
BACKGROUND: Individuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown.
METHODS: Among a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients.
RESULTS: Deceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05-1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43-9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.
CONCLUSIONS: These findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.
METHODS: Among a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients.
RESULTS: Deceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05-1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43-9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.
CONCLUSIONS: These findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.
摘要：
背景：患有2型糖尿病（T2D）的个体面临增加的死亡风险，没有完全被典型的危险因素所捕获。通过DNA甲基化（DNAm）估算生物年龄，即表观遗传时钟，正在成为改善多种结果风险分层的可能工具。然而,这些工具是否独立于T2D患者的典型危险因素来预测死亡率尚不清楚.
方法：在一组568名T2D患者中，随访了16.8年，我们选择了一个由50名受试者组成的亚组，目前有27人幸存，23人死亡，通过质量检查，并在倾向评分匹配后平衡所有风险因素。我们使用InfiniumHumanMethylationEPICBeadChip（Illumina）分析了外周血白细胞中的DNAm，以通过先前验证的表观遗传时钟评估生物衰老，并评估所选炎症蛋白和血细胞计数的DNAm估计水平。我们使用两阶段残差结果回归分析检验了这些估计值与死亡率的关系，根据存活患者组的数据创建参考模型。
结果：用DNAmphenoAge算法表示的死亡受试者的表观遗传年龄中位数较高（57.49[54.72;60.58]岁。vs.53.40[49.73；56.75]年；p=0.012)，并加快了DunedinPoAm的衰老速度（1.05[1.02;1.11]vs.1.02[0.98；1.06]；p=0.012）。DNAmPhenoAge（HR1.16，95％CI1.05-1.28；p=0.004）和DunedinPoAm（HR3.65，95％CI1.43-9.35；p=0.007）显示出与死亡率无关的典型危险因素。3种慢性炎症相关蛋白的表观遗传学预测因子，即CXCL10、CXCL11和enRAGE，C反应蛋白甲基化风险评分和基于DNAm的耗尽CD8+T细胞计数的估计在死亡受试者中与存活者相比更高。
结论：这些研究结果表明，生物老化，通过现有的表观遗传工具估计，与T2D患者的死亡风险相关，独立于常见的危险因素，并且炎症蛋白水平的DNAm替代增加是死亡的T2D患者的特征。需要在更大的队列中复制，以评估这种方法改善T2D死亡风险的潜力。
方法：在一组568名T2D患者中，随访了16.8年，我们选择了一个由50名受试者组成的亚组，目前有27人幸存，23人死亡，通过质量检查，并在倾向评分匹配后平衡所有风险因素。我们使用InfiniumHumanMethylationEPICBeadChip（Illumina）分析了外周血白细胞中的DNAm，以通过先前验证的表观遗传时钟评估生物衰老，并评估所选炎症蛋白和血细胞计数的DNAm估计水平。我们使用两阶段残差结果回归分析检验了这些估计值与死亡率的关系，根据存活患者组的数据创建参考模型。
结果：用DNAmphenoAge算法表示的死亡受试者的表观遗传年龄中位数较高（57.49[54.72;60.58]岁。vs.53.40[49.73；56.75]年；p=0.012)，并加快了DunedinPoAm的衰老速度（1.05[1.02;1.11]vs.1.02[0.98；1.06]；p=0.012）。DNAmPhenoAge（HR1.16，95％CI1.05-1.28；p=0.004）和DunedinPoAm（HR3.65，95％CI1.43-9.35；p=0.007）显示出与死亡率无关的典型危险因素。3种慢性炎症相关蛋白的表观遗传学预测因子，即CXCL10、CXCL11和enRAGE，C反应蛋白甲基化风险评分和基于DNAm的耗尽CD8+T细胞计数的估计在死亡受试者中与存活者相比更高。
结论：这些研究结果表明，生物老化，通过现有的表观遗传工具估计，与T2D患者的死亡风险相关，独立于常见的危险因素，并且炎症蛋白水平的DNAm替代增加是死亡的T2D患者的特征。需要在更大的队列中复制，以评估这种方法改善T2D死亡风险的潜力。
