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慢性肾脏病生物标志物分类预测冠状动脉钙化。

Classification of chronic kidney disease biomarkers to predict coronary artery calcium.

机构信息

Washington State University, College of Nursing, Washington State University, Spokane, WA, USA.

出版信息

Kidney Blood Press Res. 2012;36(1):26-35. doi: 10.1159/000339024. Epub 2012 Jul 6.

Abstract

BACKGROUND/AIMS: The link between CKD and CAC has been mostly established by studies of patients who have abnormally high phosphorus levels and advanced CKD or end-stage renal disease. The aim of this study was to examine if there are distinct trajectory classes of serum phosphorus (controlling for eGFR) that are associated CAC in a relatively healthy, community sample.

METHODS

Phosphorus and eGFR were classified as a combined biomarker variable with 4 trajectory classes by growth mixture modeling. This classification variable was subsequently used to predict CAC as both a binary (i.e., onset) and continuous (i.e., accumulation) outcome using a two-part growth model.

RESULTS

Membership in one class of phosphorus trajectory versus the next lowest level was associated with a 97.9 Agatston unit increase in CAC (p <.001). The magnitude of this finding is similar in size as some primary risk factors for cardiovascular disease, including a 55.3 Agatston unit (p <.001) increase associated with age, and a--75.1 Agatston unit (p <.001) decrease associated with female gender.

CONCLUSIONS

Classification of phosphorus trajectories provides further definition for prediction of CAC within the conventional 'normal' range. Classifying trajectories may help determine clinically-relevant thresholds for interventions aimed at phosphorus reduction.

摘要

背景/目的:CKD 和 CAC 之间的联系主要是通过研究磷水平异常升高、CKD 或终末期肾病进展的患者来建立的。本研究的目的是检查在相对健康的社区样本中,是否存在与 CAC 相关的血清磷(控制 eGFR)的不同轨迹类别。

方法

通过增长混合建模,将磷和 eGFR 分类为一个联合生物标志物变量,具有 4 个轨迹类别。随后,使用双部分增长模型,将该分类变量用作 CAC 的二分类(即发病)和连续(即积累)结局的预测因子。

结果

与下一个最低水平的磷轨迹类别的成员相比,CAC 增加了 97.9 个 Agatston 单位(p <.001)。这一发现的大小与心血管疾病的一些主要危险因素相似,包括年龄增加 55.3 个 Agatston 单位(p <.001)和女性性别减少 75.1 个 Agatston 单位(p <.001)。

结论

磷轨迹的分类为在常规“正常”范围内预测 CAC 提供了进一步的定义。分类轨迹可能有助于确定旨在减少磷的干预措施的临床相关阈值。

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