OBJECTIVE Patients with chronic kidney disease (CKD) show features of premature ageing. We aimed to evaluate the association between biological ageing and adverse outcomes, including end-stage kidney disease (ESKD), cardiovascular diseases (CVD) and all-cause mortality, in patients with CKD. METHODS 23 435 participants with CKD and free of related adverse outcomes at baseline from the UK Biobank were included. Leukocyte telomere length (LTL) was measured by quantitative polymerase chain reaction assay. Clinical biomarker-based biological ages were quantified using Klemera-Doubal method biological age (KDM-BA) and PhenoAge algorithms. RESULTS During a median follow-up of 12 years, 3417 incident CVD, 383 incident ESKD and 3195 all-cause mortality were recorded. Per SD increment of KDM-BA acceleration was associated with a 56% [95% confidence interval (CI): 41%-73%], 26% (95% CI: 21%-31%) and 39% (95% CI: 34%-44%) increase in the risk of incident ESKD, incident CVD and all-cause mortality, respectively. Similar results were found for PhenoAge acceleration. LTL (per SD increment) was inversely associated with the risk of incident CVD [hazard ratio (HR): 0.96, 95% CI: 0.92-0.99] and all-cause mortality (HR: 0.94, 95% CI: 0.91-0.98) and was not significantly associated with the risk of incident ESKD (HR: 0.96, 95% CI: 0.86-1.06). Adding KDM-BA acceleration or PhenoAge acceleration, but not LTL, to the traditional validated clinical prediction models significantly improved the predictive performance for incident ESKD, all-cause mortality and CVD. CONCLUSION In patients with CKD, both KDM-BA acceleration and PhenoAge acceleration were associated with an increased risk of ESKD, CVD and all-cause mortality, and KDM-BA or PhenoAge may be a better predictor on adverse outcomes than LTL.

中文翻译：

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基于临床生物标志物的生物衰老和慢性肾病患者不良结局的风险。


目的 慢性肾脏病 （CKD） 患者表现出过早衰老的特征。我们旨在评估 CKD 患者生物衰老与不良结局之间的关联，包括终末期肾病 （ESKD） 、心血管疾病 （CVD） 和全因死亡率。方法 纳入 23 435 名来自英国生物样本库的基线无相关不良结局的 CKD 参与者。通过定量聚合酶链反应测定法测量白细胞端粒长度 （LTL）。使用 Klemera-Doubal 方法生物年龄 （KDM-BA） 和 PhenoAge 算法量化基于临床生物标志物的生物年龄。结果 在中位随访 12 年期间，记录了 3417 例 CVD 事件、383 例 ESKD 事件和 3195 例全因死亡率。KDM-BA 加速的每 SD 增量与事件 ESKD 、事件 CVD 和全因死亡率风险分别增加 56% [95% 置信区间 （CI）：41%-73%]、26% （95% CI：21%-31%） 和 39% （95% CI：34%-44%）相关。PhenoAge 加速也发现了类似的结果。LTL （每 SD 增量） 与事件 CVD 风险 [风险比 （HR）： 0.96， 95% CI： 0.92-0.99] 和全因死亡率 （HR： 0.94， 95% CI： 0.91-0.98） 呈负相关，与事件 ESKD 风险无显著相关性 （HR： 0.96， 95% CI： 0.86-1.06）。在传统的经过验证的临床预测模型中加入 KDM-BA 加速或 PhenoAge 加速，而不是 LTL，显著提高了对事件 ESKD、全因死亡率和 CVD 的预测性能。 结论 在 CKD 患者中，KDM-BA 加速和 PhenoAge 加速均与 ESKD 、 CVD 和全因死亡风险增加相关，KDM-BA 或 PhenoAge 可能比 LTL 更好地预测不良结局。