Plasma metabolites and lipids associate with kidney function and kidney volume in hypertensive ADPKD patients early in the disease course

Role of MCP-1 as an inflammatory biomarker in nephropathy

The Monocyte chemoattractant protein-1 (MCP-1), also referred to as chemokine ligand 2 (CCL2), belongs to the extensive chemokine family and serves as a crucial mediator of innate immunity and tissue inflammation. It has a notable impact on inflammatory conditions affecting the kidneys. Upon binding to its receptor, MCP-1 can induce lymphocytes and NK cells' homing, migration, activation, differentiation, and development while promoting monocytes' and macrophages' infiltration, thereby facilitating kidney disease-related inflammation. As a biomarker for kidney disease, MCP-1 has made notable advancements in primary kidney diseases such as crescentic glomerulonephritis, chronic glomerulonephritis, primary glomerulopathy, idiopathic proteinuria glomerulopathy, acute kidney injury; secondary kidney diseases like diabetic nephropathy and lupus nephritis; hereditary kidney diseases including autosomal dominant polycystic kidney disease and sickle cell kidney disease. MCP-1 not only predicts the occurrence, progression, prognosis of the disease but is also closely associated with the severity and stage of nephropathy. When renal tissue is stimulated or experiences significant damage, the expression of MCP-1 increases, demonstrating a direct correlation with the severity of renal injury.

Metal mixture and osteoporosis risk: Insights from plasma metabolite profiling

The pathophysiology of osteoporosis (OP) is influenced by exposure to nonessential harmful metals and insufficient or excessive intake of necessary metals. Investigating multiple plasma metals, metabolites, and OP risk among older adults may reveal novel clues of underlying mechanisms for metal toxicity on bone mass. A total of 294 adults ≥ 55 years from Wuhan communities were included. Plasma concentrations of 23 metals and metabolites were measured via inductively coupled plasma-mass spectrometry and global metabolite detection. To investigate the relationships between plasma metals, OP risk, and OP-related metabolites, three different statistical techniques were used: generalized linear regression model, two-way orthogonal partial least-squares analysis (O2PLS), and weighted quantile sum (WQS). The mean ages were 66.82 and 66.21 years in OP (n = 115) and non-OP (n = 179) groups, respectively. Of all 2999 metabolites detected, 111 differential between-group members were observed. The OP risk decreased by 58.5% (OR=0.415, 95% CI: 0.237, 0.727) per quartile increment in the WQS index indicative of metal mixture exposure. Consistency remained for bone mineral density (BMD) measurements. The O2PLS model identified the top five OP-related metabolites, namely, DG(18:2_22:6), 3-phenoxybenzoic acid, TG(16:1_16:1_22:6), TG(16:0_16:0_20:4), and TG(14:1_18:2_18:3), contributing most to the joint covariation between the metal mixture and metabolites. Significant correlations between each of them and the metal mixture were found using WQS regression. Furthermore, the five metabolites mediated the associations of the metal mixtures, BMD, and OP risk. Our findings shed additional light on the mediation functions of plasma metabolites in the connection between multiple metal co-exposure and OP pathogenesis and offer new insights into the probable mechanisms underpinning the bone effects of the metal mixture.

Genome-wide meta-analysis identifies new candidate genes for sickle cell disease nephropathy

Sickle cell disease nephropathy (SCDN), a common SCD complication, is strongly associated with mortality. Polygenic risk scores calculated from recent transethnic meta-analyses of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) trended toward association with proteinuria and eGFR in SCD but the model fit was poor (R2 < 0.01), suggesting that there are likely unique genetic risk factors for SCDN. Therefore, we performed genome-wide association studies (GWAS) for 2 critical manifestations of SCDN, proteinuria and decreased eGFR, in 2 well-characterized adult SCD cohorts, representing, to the best of our knowledge, the largest SCDN sample to date. Meta-analysis identified 6 genome-wide significant associations (false discovery rate, q ≤ 0.05): 3 for proteinuria (CRYL1, VWF, and ADAMTS7) and 3 for eGFR (LRP1B, linc02288, and FPGT-TNNI3K/TNNI3K). These associations are independent of APOL1 risk and represent novel SCDN loci, many with evidence for regulatory function. Moreover, GWAS SNPs in CRYL1, VWF, ADAMTS7, and linc02288 are associated with gene expression in kidney and pathways important to both renal function and SCD biology, supporting the hypothesis that SCDN pathophysiology is distinct from other forms of kidney disease. Together, these findings provide new targets for functional follow-up that could be tested prospectively and potentially used to identify patients with SCD who are at risk, before onset of kidney dysfunction.

Biomarkers in Polycystic Kidney Disease: Are We There?

This article describes the use of prognostic, predictive, and response biomarkers that have been developed for autosomal dominant polycystic kidney disease and their use in clinical care or drug development. We focus on biochemical markers that can be assayed in patients' blood and urine and their association with the outcome of decreased glomerular filtration rate. There have been several studies on prognostic biomarkers. The most promising ones have been markers of tubular injury, inflammation, metabolism, or the vasopressin-urinary concentration axis. So far, none have been shown to be superior to kidney volume-based biomarkers. Several biomarkers are additive to kidney volume and genotype in prognostic models, but there have been few direct comparisons between the biochemical markers to identify the best ones. Moreover, there is a lack of uniformity in the statistical tools used to assess and compare biomarkers. There have been few reports of predictive and response biomarkers, and none are suitable surrogate endpoints. The U.S. Food and Drug Administration's Biomarker Qualification Program provides a regulatory pathway to approve biomarkers for use across multiple drug-development programs.

A Systematic Review of Reported Outcomes in ADPKD Studies

Introduction

Methods

Results

Conclusion

Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity

The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in group comparisons and effect size estimates. However, the imputation of missing data (the process of replacing missing data with estimated values such as a mean) may compromise the inherent intra-subject correlation of a metabolite across multiple biospecimens from the same subject, which in turn may compromise the efficacy of the statistical analysis of differential metabolites in biomarker discovery. We investigated imputation strategies when considering multiple biospecimens from the same subject. We compared a novel, but simple, approach that consists of combining the two biospecimen data matrices (rows and columns of subjects and metabolites) and imputes the two biospecimen data matrices together to an approach that imputes each biospecimen data matrix separately. We then compared the bias in the estimation of the intra-subject multi-specimen correlation and its effects on the validity of statistical significance tests between two approaches. The combined approach to multi-biospecimen studies has not been evaluated previously even though it is intuitive and easy to implement. We examine these two approaches for five imputation methods: random forest, k nearest neighbor, expectation-maximization with bootstrap, quantile regression, and half the minimum observed value. Combining the biospecimen data matrices for imputation did not greatly increase efficacy in conserving the correlation structure or improving accuracy in the statistical conclusions for most of the methods examined. Random forest tended to outperform the other methods in all performance metrics, except specificity.

Obesity, Weight Loss, Lifestyle Interventions, and Autosomal Dominant Polycystic Kidney Disease

Obesity remains a growing public health concern in industrialized countries around the world. The prevalence of obesity has also continued to rise in those with chronic kidney disease. Epidemiological data suggests those with overweight and obesity, measured by body mass index, have an increased risk for rapid kidney disease progression. Autosomal dominant polycystic kidney disease causes growth and proliferation of kidney cysts resulting in a reduction in kidney function in the majority of adults. An accumulation of adipose tissue may further exacerbate the metabolic defects that have been associated with ADPKD by affecting various cell signaling pathways. Lifestyle interventions inducing weight loss might help delay disease progression by reducing adipose tissue and systematic inflammation. Further research is needed to determine the mechanistic influence of adipose tissue on disease progression.

Metabolic Reprogramming and Reconstruction: Integration of Experimental and Computational Studies to Set the Path Forward in ADPKD

Metabolic reprogramming is a key feature of Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by changes in cellular pathways occurring in response to the pathological cell conditions. In ADPKD, a broad range of dysregulated pathways have been found. The studies supporting alterations in cell metabolism have shown that the metabolic preference for abnormal cystic growth is to utilize aerobic glycolysis, increasing glutamine uptake and reducing oxidative phosphorylation, consequently resulting in ADPKD cells shifting their energy to alternative energetic pathways. The mechanism behind the role of the polycystin proteins and how it leads to disease remains unclear, despite the identification of numerous signaling pathways. The integration of computational data analysis that accompanies experimental findings was pivotal in the identification of metabolic reprogramming in ADPKD. Here, we summarize the important results and argue that their exploitation may give further insights into the regulative mechanisms driving metabolic reprogramming in ADPKD. The aim of this review is to provide a comprehensive overview on metabolic focused studies and potential targets for treatment, and to propose that computational approaches could be instrumental in advancing this field of research.

Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data

Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: (i) eliminate or impute the missing values and apply statistical methods that require complete data and (ii) use statistical methods that specifically account for missing values without imputation (imputation-free methods). This study reviews the effect of sample size and percentage of missing values on statistical inference for multiple methods under these two strategies. With increasing missingness, the ability of imputation and imputation-free methods to identify differentially and non-differentially regulated compounds in a two-group comparison study declined. Random forest and k-nearest neighbor imputation combined with a Wilcoxon test performed well in statistical testing for up to 50% missingness with little bias in estimating the effect size. Quantile regression imputation accompanied with a Wilcoxon test also had good statistical testing outcomes but substantially distorted the difference in means between groups. None of the imputation-free methods performed consistently better for statistical testing than imputation methods.

Cardiovascular risk factors and the impact on prognosis in patients with chronic kidney disease secondary to autosomal dominant polycystic kidney disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent hereditary renal disease. There is an increased rate of cardiovascular disease (CVD) in ADPKD. In this study, we evaluate the prevalence of cardiovascular risk factors, the achievement rates for treatment goals and cardiovascular events (CVE) in ADPKD and their relations with asymptomatic CVD in CKD from other etiologies (CKDoe) and controls.

METHODS

We evaluated 2445 CKD patients (2010-2012). The information collected was: clinical, anthropometric and analytical parameters, treatments and CVD evaluation (intima-media thickness (IMT), atheromatous plaque presence and ankle-brachial index (ABI)). Laboratory, vital status, CVE and hospitalizations were collected for 4 years.

RESULTS

ADPKD patients had a worse renal function and worst achievement of blood pressure, higher parathormone levels but lower proteinuria compared to CKDoe. ADPKD patients presented lower IMT values than other groups, however, an intermediate rate of pathologic ABI and atheromatous plaque was present. More than half of the patients received statins, achieving LDL-c levels < 100 only in 50 and 39.8% of them (ADPKD and CKDoe respectively). The number of CVE during the follow-up period was low. In adjusted Cox regression model, ADPDK had the lowest occurrence of CVE of all three groups (HR:0.422, 95%CI 0.221-0.808, p = 0.009).

CONCLUSION

ADPKD patients show intermediate control rates of CVD. A better control of CVD risk seems to be related with a lower load of CVD compared to other groups, which may lead in the long term to a better prognosis. Further investigation is necessary to determine cardiovascular prognosis in ADPKD.

Metabolic profiling in children and young adults with autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most commonly inherited kidney disease. Although children with ADPKD show normal renal function, cyst development is already occurring. In this study, we aimed to identify markers and associated molecular pathways of disease progression in children and young adults with ADPKD. Plasma samples were collected during a 3-year randomized, double-blind, placebo-controlled, phase III clinical trial that was designed to test the efficacy of pravastatin on slowing down ADPKD progression in pediatric patients. Samples from 58 patients were available at baseline and at the 3-year endpoint of the study, respectively. Furthermore, plasma samples from 98 healthy children were used as controls. Metabolomic analysis was performed using liquid chromatography-tandem mass spectrometry and differences in metabolic profiles over time and within study groups were evaluated. While pravastatin therapy led to a decrease in a percent change of total kidney volume (HtTKV) in ADPKD patients, it had minimal effects on metabolite changes. Oxidative stress, endothelial dysfunction, inflammation and immune response were the most affected signaling pathways that distinguished healthy from diseased children. Pathway analysis revealed that metabolites in the arginine metabolism (urea and nitric oxide cycles), asparagine and glutamine metabolism, in the methylation cycle and kynurenine pathway were significantly changed between healthy and children with ADPDK and continued to diverge from the control levels while the disease progressed. Detected metabolite changes were primarily governed by disease progression, and less by pravastatin treatment. Identified metabolic pathways, from arginine and asparagine to kynurenine metabolism could present therapeutic targets and should be further investigated for potential to treat ADPKD progression at an early stage.

The relationship between blood metabolites of the tryptophan pathway and kidney function: a bidirectional Mendelian randomization analysis

Blood metabolites of the tryptophan pathway were found to be associated with kidney function and disease in observational studies. In order to evaluate causal relationship and direction, we designed a study using a bidirectional Mendelian randomization approach. The analyses were based on published summary statistics with study sizes ranging from 1,960 to 133,413. After correction for multiple testing, results provided no evidence of an effect of metabolites of the tryptophan pathway on estimated glomerular filtration rate (eGFR). Conversely, lower eGFR was related to higher levels of four metabolites: C-glycosyltryptophan (effect estimate = − 0.16, 95% confidence interval [CI] (− 0.22; − 0.1); p = 9.2e−08), kynurenine (effect estimate = − 0.18, 95% CI (− 0.25; − 0.11); p = 1.1e−06), 3-indoxyl sulfate (effect estimate = − 0.25, 95% CI (− 0.4; − 0.11); p = 6.3e−04) and indole-3-lactate (effect estimate = − 0.26, 95% CI (− 0.38; − 0.13); p = 5.4e−05). Our study supports that lower eGFR causes higher blood metabolite levels of the tryptophan pathway including kynurenine, C-glycosyltryptophan, 3-indoxyl sulfate, and indole-3-lactate. These findings aid the notion that metabolites of the tryptophan pathway are a consequence rather than a cause of reduced eGFR. Further research is needed to specifically examine relationships with respect to chronic kidney disease (CKD) progression among patients with existing CKD.

Metabolic Reprogramming in Autosomal Dominant Polycystic Kidney Disease: Evidence and Therapeutic Potential

Autosomal dominant polycystic kidney disease is characterized by progressive development and enlargement of kidney cysts, leading to ESKD. Because the kidneys are under high metabolic demand, it is not surprising that mounting evidence suggests that a metabolic defect exists in in vitro and animal models of autosomal dominant polycystic kidney disease, which likely contributes to cystic epithelial proliferation and subsequent cyst growth. Alterations include defective glucose metabolism (reprogramming to favor aerobic glycolysis), dysregulated lipid and amino acid metabolism, impaired autophagy, and mitochondrial dysfunction. Limited evidence supports that cellular kidney metabolism is also dysregulated in humans with autosomal dominant polycystic kidney disease. There are notable overlapping features and pathways among metabolism, obesity, and/or autosomal dominant polycystic kidney disease. Both dietary and pharmacologic-based strategies targeting metabolic abnormalities are being considered as therapies to slow autosomal dominant polycystic kidney disease progression and are attractive, particularly given the slowly progressive nature of the disease. Dietary strategies include daily caloric restriction, intermittent fasting, time-restricted feeding, a ketogenic diet, and 2-deoxy-glucose as well as alterations to nutrient availability. Pharmacologic-based strategies include AMP-activated kinase activators, sodium glucose cotransporter-2 inhibitors, niacinamide, and thiazolidenediones. The results from initial clinical trials targeting metabolism are upcoming and anxiously awaited within the scientific and polycystic kidney disease communities. There continues to be a need for additional mechanistic studies to better understand the role of dysregulated metabolism in autosomal dominant polycystic kidney disease and for subsequent translation to clinical trials. Beyond single-intervention trials focused on metabolic reprograming in autosomal dominant polycystic kidney disease, great potential also exists by combining metabolic-focused therapeutic approaches with compounds targeting other signaling cascades altered in autosomal dominant polycystic kidney disease, such as tolvaptan.

Chronic kidney disease: Biomarker diagnosis to therapeutic targets

Chronic kidney disease (CKD), characterized as renal dysfunction, is recognized as a major public health problem with high morbidity and mortality worldwide. Unfortunately, there are no obvious clinical symptoms in early stage disease until severe damage has occurred. Further complicating early diagnosis and treatment is the lack of sensitive and specific biomarkers. As such, novel biomarkers are urgently needed. Metabolomics has shown an increasing potential for identifying underlying disease mechanisms, facilitating clinical diagnosis and developing pharmaceutical treatments for CKD. Recent advances in metabolomics revealed that CKD was closely associated with the dysregulation of numerous metabolites, such as amino acids, lipids, nucleotides and glycoses, that might be exploited as potential biomarkers. In this review, we summarize recent metabolomic applications based on animal model studies and in patients with CKD and highlight several biomarkers that may play important roles in diagnosis, intervention and development of new therapeutic strategies.