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Liu Y, Xu K, Xiang Y, Ma B, Li H, Li Y, Shi Y, Li S, Bai Y. Role of MCP-1 as an inflammatory biomarker in nephropathy. Front Immunol 2024; 14:1303076. [PMID: 38239353 PMCID: PMC10794684 DOI: 10.3389/fimmu.2023.1303076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024] Open
Abstract
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.
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Affiliation(s)
- Yanlong Liu
- Heilongjiang Provincial Health Commission, Harbin, China
| | - Ke Xu
- Heilongjiang University of Chinese Medicine, The Second Clinical Medical College, Harbin, China
| | - Yuhua Xiang
- Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China
| | - Boyan Ma
- Heilongjiang University of Chinese Medicine, The Second Clinical Medical College, Harbin, China
| | - Hailong Li
- Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China
| | - Yuan Li
- The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yue Shi
- Heilongjiang University of Chinese Medicine, The Second Clinical Medical College, Harbin, China
| | - Shuju Li
- Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China
| | - Yan Bai
- Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China
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2
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Di D, Tooki T, Zhou H, Cui Z, Zhang R, Zhang JL, Yuan T, Liu Q, Zhou T, Luo X, Ling D, Wang Q. Metal mixture and osteoporosis risk: Insights from plasma metabolite profiling. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115256. [PMID: 37454484 DOI: 10.1016/j.ecoenv.2023.115256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/02/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Abstract
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.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tiaeki Tooki
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-Li Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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3
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Garrett ME, Soldano KL, Erwin KN, Zhang Y, Gordeuk VR, Gladwin MT, Telen MJ, Ashley-Koch AE. Genome-wide meta-analysis identifies new candidate genes for sickle cell disease nephropathy. Blood Adv 2023; 7:4782-4793. [PMID: 36399516 PMCID: PMC10469559 DOI: 10.1182/bloodadvances.2022007451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 10/11/2022] [Accepted: 10/29/2022] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
| | - Karen L. Soldano
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
| | - Kyle N. Erwin
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
| | - Yingze Zhang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | | | - Mark T. Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Marilyn J. Telen
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC
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Yu ASL, Landsittel DP. Biomarkers in Polycystic Kidney Disease: Are We There? ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:285-293. [PMID: 37088529 DOI: 10.1053/j.akdh.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 04/25/2023]
Abstract
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.
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Affiliation(s)
- Alan S L Yu
- Division of Nephrology and Hypertension and the Jared Grantham Kidney Institute, University of Kansas Medical Center, Kansas City, KS.
| | - Douglas P Landsittel
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN
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Jdiaa SS, Husainat NM, Mansour R, Kalot MA, McGreal K, Chebib FT, Perrone RD, Yu A, Mustafa RA. A Systematic Review of Reported Outcomes in ADPKD Studies. Kidney Int Rep 2022; 7:1964-1979. [PMID: 36090492 PMCID: PMC9459055 DOI: 10.1016/j.ekir.2022.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 11/26/2022] Open
Abstract
Introduction Methods Results Conclusion
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Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity. Metabolites 2022; 12:metabo12070671. [PMID: 35888795 PMCID: PMC9317643 DOI: 10.3390/metabo12070671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023] Open
Abstract
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.
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7
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Steele C, Nowak K. Obesity, Weight Loss, Lifestyle Interventions, and Autosomal Dominant Polycystic Kidney Disease. KIDNEY AND DIALYSIS 2022; 2:106-122. [PMID: 35350649 PMCID: PMC8959086 DOI: 10.3390/kidneydial2010013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
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.
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Affiliation(s)
- Cortney Steele
- Division of Renal Diseases and Hypertension, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Kristen Nowak
- Division of Renal Diseases and Hypertension, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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8
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Pagliarini R, Podrini C. Metabolic Reprogramming and Reconstruction: Integration of Experimental and Computational Studies to Set the Path Forward in ADPKD. Front Med (Lausanne) 2021; 8:740087. [PMID: 34901057 PMCID: PMC8652061 DOI: 10.3389/fmed.2021.740087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Roberto Pagliarini
- Molecular Basis of Cystic Kidney Disorders Unit, Division of Genetics and Cell Biology, IRCCS-San Raffaele Scientific Institute, Milan, Italy
| | - Christine Podrini
- Molecular Basis of Cystic Kidney Disorders Unit, Division of Genetics and Cell Biology, IRCCS-San Raffaele Scientific Institute, Milan, Italy
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Taylor S, Ponzini M, Wilson M, Kim K. Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data. Brief Bioinform 2021; 23:6361033. [PMID: 34472591 DOI: 10.1093/bib/bbab353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 07/27/2021] [Accepted: 08/10/2021] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- Sandra Taylor
- Division of Biostatistics, School of Medicine at the University of California, Davis, 2921 Stockton Boulevard, Suite 1400, Sacramento, CA 95817, USA
| | - Matthew Ponzini
- Division of Biostatistics, School of Medicine at the University of California, Davis, 2921 Stockton Boulevard, Suite 1400, Sacramento, CA 95817, USA
| | - Machelle Wilson
- Division of Biostatistics, School of Medicine at the University of California, Davis, 2921 Stockton Boulevard, Suite 1400, Sacramento, CA 95817, USA
| | - Kyoungmi Kim
- Division of Biostatistics, School of Medicine at the University of California, Davis, 2921 Stockton Boulevard, Suite 1400, Sacramento, CA 95817, USA
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Gorriz JL, Arroyo D, D'Marco L, Torra R, Tomás P, Puchades MJ, Panizo N, Pantoja J, Montomoli M, Llisterri JL, Pallares-Carratalá V, Valdivielso JM. Cardiovascular risk factors and the impact on prognosis in patients with chronic kidney disease secondary to autosomal dominant polycystic kidney disease. BMC Nephrol 2021; 22:110. [PMID: 33765945 PMCID: PMC7995703 DOI: 10.1186/s12882-021-02313-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/16/2021] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- José Luis Gorriz
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain.
| | - David Arroyo
- Department of Nephrology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Luis D'Marco
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain
| | - Roser Torra
- Inherited Kidney Diseases, Nephrology Department, Fundació Puigvert, Instituto de Investigaciones Biomédicas Sant Pau (IIB-Sant Pau), Medicine Department-Universitat Autónoma de Barcelona, REDinREN, nstituto de Investigación Carlos III, Barcelona, Spain
| | - Patricia Tomás
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain
| | - María Jesús Puchades
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain
| | - Nayara Panizo
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain
| | - Jonay Pantoja
- Department of Nephrology, University Dr Peset Hospital, Valencia, Spain
| | - Marco Montomoli
- Department of Nephrology, University Clinic Hospital, INCLIVA, University of Valencia, Av Blasco Ibañez 17, 46010, Valencia, Spain
| | | | - Vicente Pallares-Carratalá
- Health Surveillance Unit, Castellon Mutual Insurance Union, Castellon, Spain. Department of Medicine, Jaume I University, Castellon, Spain.
| | - José Manuel Valdivielso
- Vascular and Renal Translational Research Group, UDETMA, REDinREN del ISCIII, IRBLleida, Lleida, Spain, 2 Statistics Department, University of Lleida, Lleida, Spain
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11
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Baliga MM, Klawitter J, Christians U, Hopp K, Chonchol M, Gitomer BY, Cadnapaphornchai MA, Klawitter J. Metabolic profiling in children and young adults with autosomal dominant polycystic kidney disease. Sci Rep 2021; 11:6629. [PMID: 33758231 PMCID: PMC7988179 DOI: 10.1038/s41598-021-84609-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/01/2021] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
| | - Jost Klawitter
- Deparment of Anesthesiology, University of Colorado Denver Anschutz Medical Campus, 12705 E Montview Blvd., Bioscience 2, Suite 200, Aurora, CO, 80045-7109, USA
| | - Uwe Christians
- Deparment of Anesthesiology, University of Colorado Denver Anschutz Medical Campus, 12705 E Montview Blvd., Bioscience 2, Suite 200, Aurora, CO, 80045-7109, USA
| | - Katharina Hopp
- Division of Renal Diseases and Hypertension, University of Colorado School of Medicine, Aurora, CO, USA
| | - Michel Chonchol
- Division of Renal Diseases and Hypertension, University of Colorado School of Medicine, Aurora, CO, USA
| | - Berenice Y Gitomer
- Division of Renal Diseases and Hypertension, University of Colorado School of Medicine, Aurora, CO, USA
| | - Melissa A Cadnapaphornchai
- Division of Renal Diseases and Hypertension, University of Colorado School of Medicine, Aurora, CO, USA
- Presbyterian/St. Luke's Medical Center, Denver, CO, USA
| | - Jelena Klawitter
- Deparment of Anesthesiology, University of Colorado Denver Anschutz Medical Campus, 12705 E Montview Blvd., Bioscience 2, Suite 200, Aurora, CO, 80045-7109, USA.
- Division of Renal Diseases and Hypertension, University of Colorado School of Medicine, Aurora, CO, USA.
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12
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Cheng Y, Li Y, Benkowitz P, Lamina C, Köttgen A, Sekula P. The relationship between blood metabolites of the tryptophan pathway and kidney function: a bidirectional Mendelian randomization analysis. Sci Rep 2020; 10:12675. [PMID: 32728058 PMCID: PMC7391729 DOI: 10.1038/s41598-020-69559-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Yurong Cheng
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yong Li
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Paula Benkowitz
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Claudia Lamina
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Köttgen
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Peggy Sekula
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.
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Nowak KL, Hopp K. Metabolic Reprogramming in Autosomal Dominant Polycystic Kidney Disease: Evidence and Therapeutic Potential. Clin J Am Soc Nephrol 2020; 15:577-584. [PMID: 32086281 PMCID: PMC7133124 DOI: 10.2215/cjn.13291019] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Kristen L Nowak
- Division of Renal Diseases and Hypertension, Polycystic Kidney Disease Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Katharina Hopp
- Division of Renal Diseases and Hypertension, Polycystic Kidney Disease Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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Chronic kidney disease: Biomarker diagnosis to therapeutic targets. Clin Chim Acta 2019; 499:54-63. [PMID: 31476302 DOI: 10.1016/j.cca.2019.08.030] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 08/29/2019] [Accepted: 08/29/2019] [Indexed: 12/12/2022]
Abstract
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.
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