NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China.
Special Medical Service Center, Zhujiang Hospital, Southern Medical University, No. 253, Middle Industrial Avenue, Haizhu District, Guangzhou, Guangdong, China.
BMC Geriatr. 2024 Feb 26;24(1):193. doi: 10.1186/s12877-024-04760-5.
There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD).
This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH.
The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.
目前缺乏评估心血管健康因素（CVH）指标对慢性肾脏病（CKD）长期影响的相关研究。
本研究旨在探讨老年人 CVH 指标的长期变化，并探讨 CVH 指标轨迹与 CKD 之间的关系。
共纳入 27635 名来自社区的年龄在 60 岁以上的天津慢性肾脏病队列研究参与者。这些参与者在 2014 年 1 月 1 日至 2018 年 12 月 31 日期间完成了五次年度体检，并在 2019 年 1 月 1 日至 2021 年 12 月 31 日期间进行了后续随访。采用基于群组的轨迹模型来建立 CVH 指标轨迹，以预测 CKD 风险。还通过逻辑回归和限制立方样条回归模型探讨了基线 CVH、CVH 变化（ΔCVH）与 CKD 风险之间的关系。此外，还使用似然比检验来比较不同模型的拟合优度。
在参与者中确定了六种不同的 CVH 指标轨迹：低稳定（11.19%）、低-中稳定（30.58%）、中稳定（30.54%）、中-高降低（5.46%）、中-高稳定（18.93%）和高稳定（3.25%）。在校正潜在混杂因素后，较高的 CVH 指标轨迹与 CKD 风险降低相关（趋势 P<0.001）。与低稳定组相比，高稳定组 CKD 风险降低了 46%。所有敏感性分析，包括调整基线 CVH 和从总 CVH 中去除每个 CVH 成分，均得出一致的结果。此外，似然比检验表明，CVH 轨迹模型的拟合优于基线 CVH 和ΔCVH。
较高的 CVH 指标轨迹和 CVH 指标的改善与 CKD 风险降低相关。本研究强调了改善 CVH 以实现老年人 CKD 的一级预防的重要性。
