Lipidomic predictors of heart failure in chronic kidney disease

摘要

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.