Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort

A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes

Chronic inflammation is postulated to be involved in development of end stage renal disease (ESRD) in diabetes, but which specific circulating inflammatory proteins contribute to this risk remains unknown. To study this we examined 194 circulating inflammatory proteins in subjects from three independent cohorts with Type 1 and Type 2 diabetes. In each cohort we identified an extremely robust Kidney Risk Inflammatory Signature (KRIS) consisting of 17 novel proteins enriched for TNF Receptor Superfamily members that was associated with a 10-year risk of ESRD. All these proteins had a systemic, non-kidney source. Our prospective study findings provide strong evidence that KRIS proteins contribute to the inflammatory process underlying ESRD development in both types of diabetes. These proteins may be used as new therapeutic targets, new prognostic tests for high risk of ESRD and as surrogate outcome measures where changes in KRIS levels during intervention can reflect the tested therapy’s effectiveness.

Proteomic profiling of circulating proteins in subjects from three independent cohorts with type 1 and type 2 diabetes, identified an extremely robust inflammatory signature, consisting of 17 proteins enriched for TNF Receptor Superfamily members that was associated with a 10-year risk of end-stage renal disease.

Blood and urine biomarkers in chronic kidney disease: An update

INTRODUCTION

Chronic kidney disease (CKD) is a silent disease. Most CKD patients are unaware of their condition during the early stages of the disease which poses a challenge for healthcare professionals to institute treatment or start prevention. The trouble with the diagnosis of CKD is that in most parts of the world, it is still diagnosed based on measurements of serum creatinine and corresponding calculations of eGFR. There are controversies with the current staging system, especially in the methodology to diagnose and prognosticate CKD.

OBJECTIVE

The aim of this review is to examine studies that focused on the different types of samples which may serve as a good and promising biomarker for early diagnosis of CKD or to detect rapidly declining renal function among CKD patient.

METHOD

The review of international literature was made on paper and electronic databases Nature, PubMed, Springer Link and Science Direct. The Scopus index was used to verify the scientific relevance of the papers. Publications were selected based on the inclusion and exclusion criteria.

RESULT

63 publications were found to be compatible with the study objectives. Several biomarkers of interest with different sample types were taken for comparison.

CONCLUSION

Biomarkers from urine samples yield more significant outcome as compare to biomarkers from blood samples. But, validation and confirmation with a different type of study designed on a larger population is needed. More comparison studies on different types of samples are needed to further illuminate which biomarker is the better tool for the diagnosis and prognosis of CKD.

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

BACKGROUND

Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.

OBJECTIVE

To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches.

METHODS

The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked.

RESULTS

The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important.

CONCLUSIONS

Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.

Functional and Mechanistic Interrogation of BET Bromodomain Degraders for the Treatment of Metastatic Castration-resistant Prostate Cancer

PURPOSE

The bromodomain and extraterminal (BET)-containing proteins (BRD2/3/4) are essential epigenetic coregulators for prostate cancer growth. BRD inhibitors have shown promise for treatment of metastatic castration-resistant prostate cancer (mCRPC), and have been shown to function even in the context of resistance to next-generation AR-targeted therapies such as enzalutamide and abiraterone. Their clinical translation, however, has been limited by off-target effects, toxicity, and rapid resistance.

EXPERIMENTAL DESIGN

We have developed a series of molecules that target BET bromodomain proteins through their proteasomal degradation, improving efficacy and specificity of standard inhibitors. We tested their efficacy by utilizing prostate cancer cell lines and patient-derived xenografts, as well as several techniques including RNA-sequencing, mass spectroscopic proteomics, and lipidomics.

RESULTS

BET degraders function in vitro and in vivo to suppress prostate cancer growth. These drugs preferentially affect AR-positive prostate cancer cells (22Rv1, LNCaP, VCaP) over AR-negative cells (PC3 and DU145), and proteomic and genomic mechanistic studies confirm disruption of oncogenic AR and MYC signaling at lower concentrations than BET inhibitors. We also identified increases in polyunsaturated fatty acids (PUFA) and thioredoxin-interacting protein (TXNIP) as potential pharmacodynamics biomarkers for targeting BET proteins.

CONCLUSIONS

Compounds inducing the pharmacologic degradation of BET proteins effectively target the major oncogenic drivers of prostate cancer, and ultimately present a potential advance in the treatment of mCRPC. In particular, our compound dBET-3, is most suited for further clinical development.

Repeated administration of the NSAID meloxicam alters the plasma and urine lipidome

Non-steroidal anti-inflammatories (NSAIDs), such as meloxicam, are the mainstay for treating painful and inflammatory conditions in animals and humans; however, the repeated administration of NSAIDs can cause adverse effects, limiting the long-term administration of these drugs to some patients. The primary aim of this study was to determine the effects of repeated meloxicam administration on the feline plasma and urine lipidome. Cats (n = 12) were treated subcutaneously with either saline solution or 0.3 mg/kg body weight of meloxicam daily for up to 31 days. Plasma and urine lipidome were determined by LC-MS before the first treatment and at 4, 9 and 13 and 17 days after the first administration of meloxicam. The repeated administration of meloxicam altered the feline plasma and urine lipidome as demonstrated by multivariate statistical analysis. The intensities of 94 out of 195 plasma lipids were altered by the repeated administration of meloxicam to cats (p < 0.05). Furthermore, we identified 12 lipids in plasma and 10 lipids in urine that could serve as biomarker candidates for discriminating animals receiving NSAIDs from healthy controls. Expanding our understanding about the effects of NSAIDs in the body could lead to the discovery of mechanism(s) associated with intolerance to NSAIDs.

Lipidomics and Biomarker Discovery in Kidney Disease

Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown alterations in the lipidome that occur in metabolic disorders. A lipidomic approach serves as a powerful tool for biomarker discovery and gaining insight to molecular mechanisms of disease, especially when integrated with other -omics platforms (ie, transcriptomics, proteomics, and metabolomics) in the context of systems biology. In this review, we describe the workflow commonly applied to the conduct of lipidomic studies including important aspects of study design, sample preparation, biomarker identification and quantification, and data processing and analysis, as well as crucial considerations in clinical applications. We also review some recent studies of the application of lipidomic platforms that highlight the potential of lipid biomarkers and add to our understanding of the molecular basis of kidney disease.

Asymmetrical flow field-flow fractionation for improved characterization of human plasma lipoproteins

High- and low-density lipoproteins (HDL and LDL) are attractive targets for biomarker discovery. However, ultracentrifugation (UC), the current methodology of choice for isolating HDL and LDL, is tedious, requires large sample volume, results in sample loss, and does not readily provide information on particle size. In this work, human plasma HDL and LDL are separated and collected using semi-preparative asymmetrical flow field-flow fractionation (SP-AF4) and UC. The SP-AF4 and UC separation conditions, sample throughput, and liquid chromatography/mass spectrometry (LC/MS) lipidomic results are compared. Over 600 μg of total proteins is recovered in a single SP-AF4 run, and Western blot results confirm apoA1 pure and apoB100 pure fractions, consistent with HDL and LDL, respectively. The SP-AF4 separation requires ~ 60 min per sample, thus providing a marked improvement over UC which can span hours to days. Lipidome analysis of SP-AF4-prepared HDL and LDL fractions is compared to UC-prepared HDL and LDL samples. Over 270 lipids in positive MS mode and over 140 lipids in negative MS mode are identified by both sample preparation techniques with over 98% overlap between the lipidome. Additionally, lipoprotein size distributions are determined using analytical scale AF4 coupled with multiangle light scattering (MALS) and dynamic light scattering (DLS) detectors. These developments position SP-AF4 as a sample preparation method of choice for lipoprotein biomarker characterization and identification. Graphical abstract ᅟ.

An integrative systems biology approach for precision medicine in diabetic kidney disease

Current therapeutic approaches are ineffective in many patients with established diabetic kidney disease (DKD), an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large-scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed show that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to first identify patients at high risk of progression, and second to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

The CKD plasma lipidome varies with disease severity and outcome

BACKGROUND

Various alterations in lipid metabolism have been observed in patients with chronic kidney disease (CKD).

OBJECTIVES

To determine the levels of lipid species in plasma from CKD and hemodialysis (HD) patients and test their association with CKD severity and patient outcome.

METHODS

Seventy-seven patients with CKD stage 2 to HD were grouped into classes of CKD severity at baseline and followed-up for 3.5 years for the occurrence of transition to HD or death (combined outcome). Plasma levels of phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), sphingomyelins (SMs), and fatty acids were analyzed by flow-injection analysis coupled to tandem mass spectrometry or gas chromatography coupled with mass spectrometry. Kruskal Wallis rank tests and Cox regressions were used to analyze the association of lipids with CKD severity and the risk of combined outcome, respectively.

RESULTS

The plasma level of PCs, LPCs, and SMs was decreased in HD patients compared with nondialyzed CKD patients (all P < .05), whereas esterified and/or nonesterified fatty acids level did not change. Thirty-four lipids displayed significantly lower abundance in plasma of HD patients, whereas elaidic acid (C18:1ω9t) level was increased (P < .001). The total amount of LPCs and individual LPCs were associated with better outcome (P < .05). In particular, LPC 18:2 and LPC 20:3 were statistically associated with outcome in adjusted models (P < .05).

DISCUSSION

In HD patients, a reduction in plasma lipids is observed. Some of the alterations, namely reduced LPCs, were associated with the risk of adverse outcome. These changes could be related to metabolic dysfunctions.

Associations of the plasma lipidome with mortality in the acute respiratory distress syndrome: a longitudinal cohort study

Background

It is unknown if the plasma lipidome is a useful tool for improving our understanding of the acute respiratory distress syndrome (ARDS). Therefore, we measured the plasma lipidome of individuals with ARDS at two time-points to determine if changes in the plasma lipidome distinguished survivors from non-survivors. We hypothesized that both the absolute concentration and change in concentration over time of plasma lipids are associated with 28-day mortality in this population.

Methods

Samples for this longitudinal observational cohort study were collected at multiple tertiary-care academic medical centers as part of a previous multicenter clinical trial. A mass spectrometry shot-gun lipidomic assay was used to quantify the lipidome in plasma samples from 30 individuals. Samples from two different days were analyzed for each subject. After removing lipids with a coefficient of variation > 30%, differences between cohorts were identified using repeated measures analysis of variance. The false discovery rate was used to adjust for multiple comparisons. Relationships between significant compounds were explored using hierarchical clustering of the Pearson correlation coefficients and the magnitude of these relationships was described using receiver operating characteristic curves.

Results

The mass spectrometry assay reliably measured 359 lipids. After adjusting for multiple comparisons, 90 compounds differed between survivors and non-survivors. Survivors had higher levels for each of these lipids except for five membrane lipids. Glycerolipids, particularly those containing polyunsaturated fatty acid side-chains, represented many of the lipids with higher concentrations in survivors. The change in lipid concentration over time did not differ between survivors and non-survivors.

Conclusions

The concentration of multiple plasma lipids is associated with mortality in this group of critically ill patients with ARDS. Absolute lipid levels provided more information than the change in concentration over time. These findings support future research aimed at integrating lipidomics into critical care medicine.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0758-3) contains supplementary material, which is available to authorized users.

Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage.

Immunologic and endocrine functions of adipose tissue: implications for kidney disease

Excess adiposity can induce adverse sequelae in multiple cell types and organ systems. The transition from the lean to the obese state is characterized by fundamental cellular changes at the level of the adipocyte. These changes affect the local microenvironment within the respective adipose tissue but can also affect nonadipose systems. Adipocytes within fat pads respond to chronic nutrient excess through hyperplasia or hypertrophy, which can differentially affect interorgan crosstalk between various adipose depots and other organs. This crosstalk is dependent on the unique ability of the adipocyte to coordinate metabolic adjustments throughout the body and to integrate responses to maintain metabolic homeostasis. These actions occur through the release of free fatty acids and metabolites during times of energy need - a process that is altered in the obese state. In addition, adipocytes release a wide array of signalling molecules, such as sphingolipids, as well as inflammatory and hormonal factors (adipokines) that are critical for interorgan crosstalk. The interactions of adipose tissue with the kidney - referred to as the adipo-renal axis - are important for normal kidney function as well as the response of the kidney to injury. Here, we discuss the mechanistic basis of this interorgan crosstalk, which clearly has great therapeutic potential given the increasing rates of chronic kidney disease secondary to obesity and type 2 diabetes mellitus.

Impaired β-Oxidation and Altered Complex Lipid Fatty Acid Partitioning with Advancing CKD

Studies of lipids in CKD, including ESRD, have been limited to measures of conventional lipid profiles. We aimed to systematically identify 17 different lipid classes and associate the abundance thereof with alterations in acylcarnitines, a metric of β-oxidation, across stages of CKD. From the Clinical Phenotyping Resource and Biobank Core (CPROBE) cohort of 1235 adults, we selected a panel of 214 participants: 36 with stage 1 or 2 CKD, 99 with stage 3 CKD, 61 with stage 4 CKD, and 18 with stage 5 CKD. Among participants, 110 were men (51.4%), 64 were black (29.9%), and 150 were white (70.1%), and the mean (SD) age was 60 (16) years old. We measured plasma lipids and acylcarnitines using liquid chromatography-mass spectrometry. Overall, we identified 330 different lipids across 17 different classes. Compared with earlier stages, stage 5 CKD associated with a higher abundance of saturated C16-C20 free fatty acids (FFAs) and long polyunsaturated complex lipids. Long-chain-to-intermediate-chain acylcarnitine ratio, a marker of efficiency of β-oxidation, exhibited a graded decrease from stage 2 to 5 CKD (P<0.001). Additionally, multiple linear regression revealed that the long-chain-to-intermediate-chain acylcarnitine ratio inversely associated with polyunsaturated long complex lipid subclasses and the C16-C20 FFAs but directly associated with short complex lipids with fewer double bonds. We conclude that increased abundance of saturated C16-C20 FFAs coupled with impaired β-oxidation of FFAs and inverse partitioning into complex lipids may be mechanisms underpinning lipid metabolism changes that typify advancing CKD.

New perspectives on CKD-induced dyslipidemia

INTRODUCTION

Chronic kidney disease (CKD) is a world-wide health concern associated with a significantly higher cardiovascular morbidity and mortality. One of the principal cardiovascular risk factors is the lipid profile. CKD patients have a more frequent and progressive atheromatous disease that cannot be explained by the classical lipid parameters used in the daily clinical practice. Areas covered: The current review summarizes prevailing knowledge on the role of lipids in atheromathosis in CKD patients, including an overview of lipoprotein metabolism highlighting the CKD-induced alterations. Moreover, to obtain information beyond traditional lipid parameters, new state-of-the-art technologies such as lipoprotein subfraction profiling and lipidomics are also reviewed. Finally, we analyse the potential of new lipoprotein subclasses as therapeutic targets in CKD. Expert opinion: The CKD-induced lipid profile has specific features distinct from the general population. Besides quantitative alterations, renal patients have a plethora of qualitative lipid alterations that cannot be detected by routine determinations and are responsible for the excess of cardiovascular risk. New parameters, such as lipoprotein particle number and size, together with new biomarkers obtained by lipidomics will personalize the management of these patients. Therefore, nephrologists need to be aware of new insights into lipoprotein metabolism to improve cardiovascular risk assessment.