Lipidomic predictors of heart failure in chronic kidney disease

摘要 慢性肾脏病(CKD)是一个重大的公共卫生问题。心血管疾病(心血管疾病)S领衔 慢性肾脏病的发病和死亡原因，心力衰竭(HF)是一种主要的心血管事件。高频 以严重的心肌和全身脂代谢紊乱为特征。然而，时间 代谢改变的过程在功能、形态和临床表现方面不是 脂代谢紊乱在心力衰竭发病机制中的作用尚未得到证实。因此，它是 目前尚不清楚脂代谢改变是因果关系。我们假设脂类代谢的改变 途径预测CKD患者中的代谢事件HF，代谢事件HF是 这一过程始于脂代谢途径的改变，从而导致左心室(LV)的改变 配置，并最终表现为临床心衰。为了验证这一假设，我们将进行以下工作 具体目的：1)确定合并和不合并糖尿病的慢性肾脏病患者发生心力衰竭的脂类预测因素， 2)确定合并和不合并糖尿病的慢性肾脏病患者左室构型的脂体决定因素； 探讨CKD合并糖尿病和不合并糖尿病患者心血管疾病表型的差异脂质网络。 方法：这是一项关于心血管事件的前瞻性数据的观察性纵向研究。研究将会是 对作为训练子集的慢性肾功能不全队列(CRIC)的参与者和HF 密歇根大学的患者作为验证队列。纳入标准：基线EGFR&GT；30毫升/分钟， 注册时可获得200微米L血浆样本、一年级时超声心动图(CRIC)和心血管疾病结局 随访时的数据。抽样：实施纳入和排除标准后，来自CRIC的2931名患者 包括341名心力衰竭患者和1148名验证队列参与者，其中610名心力衰竭患者与 选择了538名无心衰的受试者。预测因子：基于质谱学的游离脂肪量化值 酸(FA)S、酰卡尼汀(AC)S以及由甘油脂、磷脂和 鞘磷脂。结果：AIMS 1和AIMS 3的主要结果是发生心力衰竭。目标2的主要结果是 超声心动图评价左室构型。分析：对于目标1，分析将包括利用 调整的COX回归模型，包括基线和结果前的血脂序贯测量 确定发生心衰的时变独立血脂预测因子。对于目标2，分析将包括利用 偏最小二乘判别分析、随机森林、混合线性模型和惩罚 用多项回归模型判别四类左室构型。对于目标3，分析将是 基于差示网络富集度分析(DNEA)的脂质差示网络识别 辨别研究结果。预期结果：预计由FA、ACS和 基线时的复杂血脂可预测心力衰竭的发生，并与左室形态有差异相关。它也是 预计DNEA将披露受研究结果不同调控的脂类代谢途径。

ABSTRACT Chronic kidney disease (CKD) is a major public health issue. Cardiovascular diseases (CVD)s are the leading causes of morbidity and mortality in CKD, with heart failure (HF) being a major cardiovascular (CV) event. HF is characterized by significant myocardial and systemic lipid metabolic derangements. However, the time course of metabolic alterations in regard to functional, morphological and clinical presentations is not established, nor the role of lipid metabolic derangements in pathogenesis of HF is investigated. Therefore, it is unclear if the lipid metabolic alterations are cause or effect. We hypothesize that alteration in lipid metabolic pathways predicts metabolic incident HF among CKD patients, and that metabolic incident HF is continuum of a process that starts with alterations in lipid metabolic pathways that leads to changes of left ventricular (LV) configuration, and eventually manifests as clinical HF. To test this hypothesis, we will pursue the following specific aims: 1) To identify the lipidomic predictors of incident HF in CKD patients with and without diabetes, 2) To determine the lipidomic determinants of LV configuration in CKD with and without diabetes, and 3) To explore differential lipid networks discriminating CVD phenotypes in CKD patients with and without diabetes. Methods: This is an observational longitudinal study with prospective data on CV events. Study will be performed on participants of the Chronic Renal Insufficiency Cohort (CRIC) as the training subset, and the HF patients at the University of Michigan as the validation cohort. Inclusion criteria: baseline eGFR>30 mL/min, availability of 200 µL of plasma sample at enrollment, echocardiography at year 1 (CRIC), and CVD outcome data at follow up. Sampling: After implementation of inclusion and exclusion criteria 2931 patients from CRIC including 341 with HF, and 1148 participants for validation cohort including 610 patients with HF matched by 538 participants without HF are selected. Predictors: mass spectrometry based quantified values of free fatty acids (FA)s, acylcarnitines (AC)s, and complex lipids consisting of glycerolipids, phospholipids, and sphingomyelins. Outcomes: Primary outcome for aims 1 and 3 is incident HF. Primary outcome for aim 2 is echocardiographic assessment of LV configurations. Analysis: For aim 1, the analysis will include utilization of adjusted Cox regression models with sequential lipidomic measurements at baseline and prior to outcome to identify time-varying independent lipid predictors of incident HF. For aim 2, the analysis will include utilization of partial least square-discriminant analysis (PLS-DA), random-forest, mixed linear models, and penalized multinomial regression models to discriminate 4 categories of LV configurations. For aim 3, the analysis will be based on Differential Network Enrichment Analysis (DNEA) to identify differential network of lipids discriminating the study outcomes. Expected outcomes: It is expected that a differential panel of FAs, ACs, and complex lipids at baseline predict incident HF and differentially correlate with LV morphology. It is also expected that DNEA will disclose lipid metabolic pathways that are differentially regulated by study outcomes.