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Wen C, Chen L, Jia D, Liu Z, Lin Y, Liu G, Zhang S, Gao B. Recent advances in the application of Mendelian randomization to chronic kidney disease. Ren Fail 2024; 46:2319712. [PMID: 38522953 PMCID: PMC10913720 DOI: 10.1080/0886022x.2024.2319712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/12/2024] [Indexed: 03/26/2024] Open
Abstract
OBJECTIVE Chronic kidney disease (CKD) is a condition influenced by both genetic and environmental factors and has been a focus of extensive research. Utilizing Mendelian randomization, researchers have begun to untangle the complex causal relationships underlying CKD. This review delves into the advances and challenges in the application of MR in the field of nephrology, shifting from a mere summary of its principles and limitations to a more nuanced exploration of its contributions to our understanding of CKD. METHODS Key findings from recent studies have been pivotal in reshaping our comprehension of CKD. Notably, evidence indicates that elevated testosterone levels may impair renal function, while higher sex hormone-binding globulin (SHBG) levels appear to be protective, predominantly in men. Surprisingly, variations in plasma glucose and glycated hemoglobin levels seem unaffected by genetically induced changes in the estimated glomerular filtration rate (eGFR), suggesting an independent pathway for renal function impairment. RESULTS Furthermore, lifestyle factors such as physical activity and socioeconomic status emerge as significant influencers of CKD risk and kidney health. The relationship between sleep duration and CKD is nuanced; short sleep duration is linked to increased risk, while long sleep duration does not exhibit a clear causal effect. Additionally, lifestyle factors, including diet, exercise, and mental wellness activities, play a crucial role in kidney health. New insights also reveal a substantial causal connection between both central and general obesity and CKD onset, while no significant links were found between genetically modified LDL cholesterol or triglyceride levels and kidney function. CONCLUSION This review not only presents the recent achievements of MR in CKD research but also illuminates the path forwards, underscoring critical unanswered questions and proposing future research directions in this dynamic field.
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Affiliation(s)
- Chaofan Wen
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Lanlan Chen
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Dan Jia
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Ziqi Liu
- Weifang Medical University, Weifang, Shandong Province, China
| | - Yidan Lin
- Herberger Institute for Design and Arts, Arizona State University, Tempe, AZ, USA
| | - Guan Liu
- Department of Pharmacology, Hebei Medical University, Shijiazhuang City, Hebei Province, China
| | - Shuo Zhang
- Department of Pharmacology, Hebei Medical University, Shijiazhuang City, Hebei Province, China
| | - Baoshan Gao
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
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Schlosser P, Surapaneni AL, Borisov O, Schmidt IM, Zhou L, Anderson A, Deo R, Dubin R, Ganz P, He J, Kimmel PL, Li H, Nelson RG, Porter AC, Rahman M, Rincon-Choles H, Shah V, Unruh ML, Vasan RS, Zheng Z, Feldman HI, Waikar SS, Köttgen A, Rhee EP, Coresh J, Grams ME. Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression. J Am Soc Nephrol 2024; 35:923-935. [PMID: 38640019 PMCID: PMC11230725 DOI: 10.1681/asn.0000000000000343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
Key Points Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation. Background Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. Methods We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study (N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569). Results We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10−5). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P value = 2.6×10−5). Conclusions This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Aditya L. Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
| | - Oleg Borisov
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Amanda Anderson
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Rajat Deo
- Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruth Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Peter Ganz
- Division of Cardiology, University of California, San Francisco, San Francisco, California
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert G. Nelson
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
- Research Division, Joslin Diabetes Center, Boston, Massachusetts
| | - Anna C. Porter
- Renal Service, Wellington Regional Hospital, Wellington, New Zealand
| | - Mahboob Rahman
- Department of Kidney Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | | | - Vallabh Shah
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Mark L. Unruh
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Ramachandran S. Vasan
- University of Texas Health Sciences Center, San Antonio, Texas
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Optimal Aging Institute, Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, New York
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
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Kim T, Surapaneni AL, Schmidt IM, Eadon MT, Kalim S, Srivastava A, Palsson R, Stillman IE, Hodgin JB, Menon R, Otto EA, Coresh J, Grams ME, Waikar SS, Rhee EP. Plasma Proteins Associated with Chronic Histopathologic Lesions on Kidney Biopsy. J Am Soc Nephrol 2024; 35:910-922. [PMID: 38656806 PMCID: PMC11230715 DOI: 10.1681/asn.0000000000000358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
Key Points Proteomic profiling identified 35 blood proteins associated with chronic histopathologic lesions in the kidney. Testican-2 was expressed in the glomerulus, released by the kidney into circulation, and inversely associated with glomerulosclerosis severity. NELL1 was expressed in tubular epithelial cells, released by the kidney into circulation, and inversely associated with interstitial fibrosis and tubular atrophy severity. Background The severity of chronic histopathologic lesions on kidney biopsy is independently associated with higher risk of progressive CKD. Because kidney biopsies are invasive, identification of blood markers that report on underlying kidney histopathology has the potential to enhance CKD care. Methods We examined the association between 6592 plasma protein levels measured by aptamers and the severity of interstitial fibrosis and tubular atrophy (IFTA), glomerulosclerosis, arteriolar sclerosis, and arterial sclerosis among 434 participants of the Boston Kidney Biopsy Cohort. For proteins significantly associated with at least one histologic lesion, we assessed renal arteriovenous protein gradients among 21 individuals who had undergone invasive catheterization and assessed the expression of the cognate gene among 47 individuals with single-cell RNA sequencing data in the Kidney Precision Medicine Project. Results In models adjusted for eGFR, proteinuria, and demographic factors, we identified 35 proteins associated with one or more chronic histologic lesions, including 20 specific for IFTA, eight specific for glomerulosclerosis, and one specific for arteriolar sclerosis. In general, higher levels of these proteins were associated with more severe histologic score and lower eGFR. Exceptions included testican-2 and NELL1, which were associated with less glomerulosclerosis and IFTA, respectively, and higher eGFR; notably, both of these proteins demonstrated significantly higher levels from artery to renal vein, demonstrating net kidney release. In the Kidney Precision Medicine Project, 13 of the 35 protein hits had cognate gene expression enriched in one or more cell types in the kidney, including podocyte expression of select glomerulosclerosis markers (including testican-2) and tubular expression of several IFTA markers (including NELL1). Conclusions Proteomic analysis identified circulating proteins associated with chronic histopathologic lesions, some of which had concordant site-specific expression within the kidney.
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Affiliation(s)
- Taesoo Kim
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Aditya L. Surapaneni
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Michael T. Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Sahir Kalim
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Anand Srivastava
- Division of Nephrology, University of Illinois Chicago, Chicago, Illinois
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Isaac E. Stillman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jeffrey B. Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Edgar A. Otto
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Josef Coresh
- Departments of Population Health and Medicine, New York University Grossman School of Medicine, New York, New York
| | - Morgan E. Grams
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Eugene P. Rhee
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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4
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Si S, Liu H, Xu L, Zhan S. Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome. Genome Med 2024; 16:84. [PMID: 38898508 PMCID: PMC11186236 DOI: 10.1186/s13073-024-01356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome. METHODS We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy). RESULTS Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells. CONCLUSIONS Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.
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Affiliation(s)
- Shucheng Si
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Hongyan Liu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Lu Xu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China.
- Peking University Health Science Center, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan Road, Haidian District, Beijing, 100191, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
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5
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Zhang Z, Cao B, Wu Q. Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico. Metab Syndr Relat Disord 2024. [PMID: 38742978 DOI: 10.1089/met.2024.0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024] Open
Abstract
Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.
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Affiliation(s)
- Zekai Zhang
- Second College of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Beibei Cao
- Academy of Paediatrics, Nanjing Medical University, Nanjing, China
| | - Qiutong Wu
- Second College of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Xu X, Khunsriraksakul C, Eales JM, Rubin S, Scannali D, Saluja S, Talavera D, Markus H, Wang L, Drzal M, Maan A, Lay AC, Prestes PR, Regan J, Diwadkar AR, Denniff M, Rempega G, Ryszawy J, Król R, Dormer JP, Szulinska M, Walczak M, Antczak A, Matías-García PR, Waldenberger M, Woolf AS, Keavney B, Zukowska-Szczechowska E, Wystrychowski W, Zywiec J, Bogdanski P, Danser AHJ, Samani NJ, Guzik TJ, Morris AP, Liu DJ, Charchar FJ, Tomaszewski M. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 2024; 15:2359. [PMID: 38504097 PMCID: PMC10950894 DOI: 10.1038/s41467-024-46132-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.
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Affiliation(s)
- Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | | | - James M Eales
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Sebastien Rubin
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - David Scannali
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Sushant Saluja
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - David Talavera
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Havell Markus
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Lida Wang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Maciej Drzal
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Akhlaq Maan
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Abigail C Lay
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Priscilla R Prestes
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Jeniece Regan
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Avantika R Diwadkar
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Matthew Denniff
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Grzegorz Rempega
- Department of Urology, Medical University of Silesia, Katowice, Poland
| | - Jakub Ryszawy
- Department of Urology, Medical University of Silesia, Katowice, Poland
| | - Robert Król
- Department of General, Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - John P Dormer
- Department of Cellular Pathology, University Hospitals of Leicester, Leicester, UK
| | - Monika Szulinska
- Department of Obesity, Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - Marta Walczak
- Department of Internal Diseases, Metabolic Disorders and Arterial Hypertension, Poznan University of Medical Sciences, Poznan, Poland
| | - Andrzej Antczak
- Department of Urology and Uro-oncology, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - Pamela R Matías-García
- Institute of Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Adrian S Woolf
- Division of Cell Matrix Biology and Regenerative Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Royal Manchester Children's Hospital and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary, Manchester, UK
| | | | - Wojciech Wystrychowski
- Department of General, Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Joanna Zywiec
- Department of Internal Medicine, Diabetology and Nephrology, Zabrze, Medical University of Silesia, Katowice, Poland
| | - Pawel Bogdanski
- Department of Obesity, Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - A H Jan Danser
- Department of Internal Medicine, Division of Pharmacology and Vascular Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Tomasz J Guzik
- Department of Internal Medicine, Jagiellonian University Medical College, Kraków, Poland
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Fadi J Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Physiology, University of Melbourne, Melbourne, Australia
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary, Manchester, UK.
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7
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Arredondo-Damián JG, Martínez-Soto JM, Molina-Pelayo FA, Soto-Guzmán JA, Castro-Sánchez L, López-Soto LF, Candia-Plata MDC. Systematic review and bioinformatics analysis of plasma and serum extracellular vesicles proteome in type 2 diabetes. Heliyon 2024; 10:e25537. [PMID: 38356516 PMCID: PMC10865249 DOI: 10.1016/j.heliyon.2024.e25537] [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: 11/13/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Background Type 2 diabetes (T2D) is a complex metabolic ailment marked by a global high prevalence and significant attention in primary healthcare settings due to its elevated morbidity and mortality rates. The pathophysiological mechanisms underlying the onset and progression of this disease remain subjects of ongoing investigation. Recent evidence underscores the pivotal role of the intricate intercellular communication network, wherein cell-derived vesicles, commonly referred to as extracellular vesicles (EVs), emerge as dynamic regulators of diabetes-related complications. Given that the protein cargo carried by EVs is contingent upon the metabolic conditions of the originating cells, particular proteins may serve as informative indicators for the risk of activating or inhibiting signaling pathways crucial to the progression of T2D complications. Methods In this study, we conducted a systematic review to analyze the published evidence on the proteome of EVs from the plasma or serum of patients with T2D, both with and without complications (PROSPERO: CRD42023431464). Results Nine eligible articles were systematically identified from the databases, and the proteins featured in these articles underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We identified changes in the level of 426 proteins, with CST6, CD55, HBA1, S100A8, and S100A9 reported to have high levels, while FGL1 exhibited low levels. Conclusion These proteins are implicated in pathophysiological mechanisms such as inflammation, complement, and platelet activation, suggesting their potential as risk markers for T2D development and progression. Further studies are required to explore this topic in greater detail.
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Affiliation(s)
| | | | | | | | - Luis Castro-Sánchez
- University Center for Biomedical Research, University of Colima, Colima, Colima, Mexico
- CONAHCYT-University of Colima, Colima, Colima, Mexico
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8
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Lanktree MB, Perrot N, Smyth A, Chong M, Narula S, Shanmuganathan M, Kroezen Z, Britz-Mckibbin P, Berger M, Krepinsky JC, Pigeyre M, Yusuf S, Paré G. A novel multi-ancestry proteome-wide Mendelian randomization study implicates extracellular proteins, tubular cells, and fibroblasts in estimated glomerular filtration rate regulation. Kidney Int 2023; 104:1170-1184. [PMID: 37774922 DOI: 10.1016/j.kint.2023.08.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 08/15/2023] [Accepted: 08/25/2023] [Indexed: 10/01/2023]
Abstract
Estimated glomerular filtration rate (eGFR) impacts the concentration of plasma biomarkers confounding biomarker association studies of eGFR with reverse causation. To identify biomarkers causally associated with eGFR, we performed a proteome-wide Mendelian randomization study. Genetic variants nearby biomarker coding genes were tested for association with plasma concentration of 1,161 biomarkers in a multi-ancestry sample of 12,066 participants from the Prospective Urban and Rural Epidemiological (PURE) study. Using two-sample Mendelian randomization, individual variants' effects on biomarker concentration were correlated with their effects on eGFR and kidney traits from published genome-wide association studies (GWAS). Genetically altered concentrations of 22 biomarkers were associated with eGFR above a Bonferroni-corrected significance threshold. Five biomarkers were previously identified by GWAS (UMOD, FGF5, LGALS7, NINJ1, COL18A1). Nine biomarkers were within 1 Mb of the lead GWAS variant but the gene for the biomarker was unidentified as the candidate for the GWAS signal (INHBC, TNFRSF11A, TCN2, PXN1, PRTN3, PSMD9, TFPI, ITGB6, CA3). Single-cell transcriptomic data indicated the 22 biomarkers are expressed in kidney tubules, collecting duct, fibroblasts, and immune cells. Pathway analysis showed significant enrichment of identified biomarkers in the extracellular kidney parenchyma. Thus, using genetic regulators of biomarker concentration via proteome-wide Mendelian randomization, we identified 22 biomarkers that appear to causally impact eGFR in either a beneficial or adverse manner. The current study provides rationale for novel therapeutic targets for eGFR and emphasized a role for extracellular proteins produced by tubular cells and fibroblasts for impacting eGFR.
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Affiliation(s)
- Matthew B Lanktree
- Population Health Research Institute, Hamilton, Ontario, Canada; Division of Nephrology, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
| | - Nicolas Perrot
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Andrew Smyth
- Population Health Research Institute, Hamilton, Ontario, Canada; HRB Clinical Research Facility Galway, University of Galway, Galway, Ireland
| | - Michael Chong
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sukrit Narula
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Meera Shanmuganathan
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Zachary Kroezen
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Philip Britz-Mckibbin
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Mario Berger
- Bayer AG, Pharmaceuticals Research & Development, Pharma Research Center, Wuppertal, Germany
| | - Joan C Krepinsky
- Division of Nephrology, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Salim Yusuf
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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9
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Downie ML, Desjarlais A, Verdin N, Woodlock T, Collister D. Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience. Can J Kidney Health Dis 2023; 10:20543581231209012. [PMID: 37920777 PMCID: PMC10619345 DOI: 10.1177/20543581231209012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/10/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose of review Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review. Sources of information Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics." Methods A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience. Key findings Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy. Limitations There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory. Implications Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.
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Affiliation(s)
- Mallory L. Downie
- McGill University Health Center Research Institute, Montreal, QC, Canada
| | - Arlene Desjarlais
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - Nancy Verdin
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - Tania Woodlock
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - David Collister
- Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
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10
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Lin JS, Nano J, Petrera A, Hauck SM, Zeller T, Koenig W, Müller CL, Peters A, Thorand B. Proteomic profiling of longitudinal changes in kidney function among middle-aged and older men and women: the KORA S4/F4/FF4 study. BMC Med 2023; 21:245. [PMID: 37407978 DOI: 10.1186/s12916-023-02962-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/26/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Due to the asymptomatic nature of the early stages, chronic kidney disease (CKD) is usually diagnosed at late stages and lacks targeted therapy, highlighting the need for new biomarkers to better understand its pathophysiology and to be used for early diagnosis and therapeutic targets. Given the close relationship between CKD and cardiovascular disease (CVD), we investigated the associations of 233 CVD- and inflammation-related plasma proteins with kidney function decline and aimed to assess whether the observed associations are causal. METHODS We included 1140 participants, aged 55-74 years at baseline, from the Cooperative Health Research in the Region of Augsburg (KORA) cohort study, with a median follow-up time of 13.4 years and 2 follow-up visits. We measured 233 plasma proteins using a proximity extension assay at baseline. In the discovery analysis, linear regression models were used to estimate the associations of 233 proteins with the annual rate of change in creatinine-based estimated glomerular filtration rate (eGFRcr). We further investigated the association of eGFRcr-associated proteins with the annual rate of change in cystatin C-based eGFR (eGFRcys) and eGFRcr-based incident CKD. Two-sample Mendelian randomization was used to infer causality. RESULTS In the fully adjusted model, 66 out of 233 proteins were inversely associated with the annual rate of change in eGFRcr, indicating that higher baseline protein levels were associated with faster eGFRcr decline. Among these 66 proteins, 21 proteins were associated with both the annual rate of change in eGFRcys and incident CKD. Mendelian randomization analyses on these 21 proteins suggest a potential causal association of higher tumor necrosis factor receptor superfamily member 11A (TNFRSF11A) level with eGFR decline. CONCLUSIONS We reported 21 proteins associated with kidney function decline and incident CKD and provided preliminary evidence suggesting a potential causal association between TNFRSF11A and kidney function decline. Further Mendelian randomization studies are needed to establish a conclusive causal association.
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Affiliation(s)
- Jie-Sheng Lin
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Agnese Petrera
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Tanja Zeller
- University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg, Hamburg, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Christian L Müller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Helmholtz AI, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Department of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany
- Center for Computational Mathematics, Flatiron Institute, New York, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany.
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11
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Akwo EA, Robinson-Cohen C. Mendelian randomization and the association of fibroblast growth factor-23 with heart failure with preserved ejection fraction. Curr Opin Nephrol Hypertens 2023; 32:305-312. [PMID: 37016957 PMCID: PMC10313786 DOI: 10.1097/mnh.0000000000000888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
PURPOSE OF REVIEW Observational data provide compelling evidence for elevated fibroblast growth factor-23 (FGF23) as a risk factor for heart failure (HF), particularly heart failure with preserved ejection fraction (HFpEF). Given the limitations of observational studies, uncertainties persist regarding the causal role of FGF23 in the pathogenesis of HF and HFpEF. Recently, Mendelian randomization (MR) studies have been performed to examine causal associations between FGF23 and HF phenotypes. RECENT FINDINGS The current review describes the methodological basis of the MR techniques used to examine the causal role of FGF23 on HF phenotypes, highlighting the importance of large-scale multiomics data. The findings from most of the MR studies indicate an absence of evidence of a causal effect of FGF23 on the risk of HF in general population settings. However, analysis using individual-level data showed a strong association between genetically-predicted FGF23 and HFpEF in individuals with a genetic predisposition to low estimated glomerular filtration (eGFR). SUMMARY Evidence from MR analysis suggests a causal role of FGF23 in the pathogenesis of HFpEF in low eGFR settings - a finding supported by experimental, clinical, and epidemiological data. While future MR studies of FGF23 and HFpEF could provide further evidence, randomized trials of FGF23-lowering agents could provide the most definitive answers on the association in chronic kidney disease populations.
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Affiliation(s)
- Elvis A. Akwo
- Vanderbilt O’Brien Kidney Center, Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
| | - Cassianne Robinson-Cohen
- Vanderbilt O’Brien Kidney Center, Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
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12
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Schlosser P, Grams ME, Rhee EP. Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease. Mol Cell Proteomics 2023; 22:100550. [PMID: 37076045 PMCID: PMC10326701 DOI: 10.1016/j.mcpro.2023.100550] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/20/2023] [Accepted: 03/28/2023] [Indexed: 04/21/2023] Open
Abstract
Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
| | - Morgan E Grams
- Division of Precision Medicine, Department of Medicine, New York University, New York, New York, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
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13
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Kiernan E, Surapaneni A, Zhou L, Schlosser P, Walker KA, Rhee EP, Ballantyne CM, Deo R, Dubin RF, Ganz P, Coresh J, Grams ME. Alterations in the Circulating Proteome Associated with Albuminuria. J Am Soc Nephrol 2023; 34:1078-1089. [PMID: 36890639 PMCID: PMC10278823 DOI: 10.1681/asn.0000000000000108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/05/2023] [Indexed: 03/10/2023] Open
Abstract
SIGNIFICANCE STATEMENT We describe circulating proteins associated with albuminuria in a population of African American Study of Kidney Disease and Hypertension with CKD (AASK) using the largest proteomic platform to date: nearly 7000 circulating proteins, representing approximately 2000 new targets. Findings were replicated in a subset of a general population cohort with kidney disease (ARIC) and a population with CKD Chronic Renal Insufficiency Cohort (CRIC). In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in the Black group, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. LMAN2, TNFSFR1B, and members of the ephrin superfamily had the strongest associations. Pathway analysis also demonstrated enrichment of ephrin family proteins. BACKGROUND Proteomic techniques have facilitated understanding of pathways that mediate decline in GFR. Albuminuria is a key component of CKD diagnosis, staging, and prognosis but has been less studied than GFR. We sought to investigate circulating proteins associated with higher albuminuria. METHODS We evaluated the cross-sectional associations of the blood proteome with albuminuria and longitudinally with doubling of albuminuria in the African American Study of Kidney Disease and Hypertension (AASK; 38% female; mean GFR 46; median urine protein-to-creatinine ratio 81 mg/g; n =703) and replicated in two external cohorts: a subset of the Atherosclerosis Risk in Communities (ARIC) study with CKD and the Chronic Renal Insufficiency Cohort (CRIC). RESULTS In cross-sectional analysis, 104 proteins were significantly associated with albuminuria in AASK, of which 67 of 77 available proteins were replicated in ARIC and 68 of 71 available proteins in CRIC. Proteins with the strongest associations included LMAN2, TNFSFR1B, and members of the ephrin superfamily. Pathway analysis also demonstrated enrichment of ephrin family proteins. Five proteins were significantly associated with worsening albuminuria in AASK, including LMAN2 and EFNA4, which were replicated in ARIC and CRIC. CONCLUSIONS Among individuals with CKD, large-scale proteomic analysis identified known and novel proteins associated with albuminuria and suggested a role for ephrin signaling in albuminuria progression.
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Affiliation(s)
- Elizabeth Kiernan
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, New York
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, Maryland
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Rajat Deo
- Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruth F. Dubin
- Division of Nephrology, University of Texas—Southwestern, Dallas, Texas
| | - Peter Ganz
- Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California San Francisco, San Francisco, California
| | - Josef Coresh
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, New York
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14
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Malmgren L, Öberg C, den Bakker E, Leion F, Siódmiak J, Åkesson A, Lindström V, Herou E, Dardashti A, Xhakollari L, Grubb G, Strevens H, Abrahamson M, Helmersson-Karlqvist J, Magnusson M, Björk J, Nyman U, Ärnlöv J, Ridefelt P, Åkerfeldt T, Hansson M, Sjöström A, Mårtensson J, Itoh Y, Grubb D, Tenstad O, Hansson LO, Olafsson I, Campos AJ, Risch M, Risch L, Larsson A, Nordin G, Pottel H, Christensson A, Bjursten H, Bökenkamp A, Grubb A. The complexity of kidney disease and diagnosing it - cystatin C, selective glomerular hypofiltration syndromes and proteome regulation. J Intern Med 2023; 293:293-308. [PMID: 36385445 PMCID: PMC10107454 DOI: 10.1111/joim.13589] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Estimation of kidney function is often part of daily clinical practice, mostly done by using the endogenous glomerular filtration rate (GFR)-markers creatinine or cystatin C. A recommendation to use both markers in parallel in 2010 has resulted in new knowledge concerning the pathophysiology of kidney disorders by the identification of a new set of kidney disorders, selective glomerular hypofiltration syndromes. These syndromes, connected to strong increases in mortality and morbidity, are characterized by a selective reduction in the glomerular filtration of 5-30 kDa molecules, such as cystatin C, compared to the filtration of small molecules <1 kDa dominating the glomerular filtrate, for example water, urea and creatinine. At least two types of such disorders, shrunken or elongated pore syndrome, are possible according to the pore model for glomerular filtration. Selective glomerular hypofiltration syndromes are prevalent in investigated populations, and patients with these syndromes often display normal measured GFR or creatinine-based GFR-estimates. The syndromes are characterized by proteomic changes promoting the development of atherosclerosis, indicating antibodies and specific receptor-blocking substances as possible new treatment modalities. Presently, the KDIGO guidelines for diagnosing kidney disorders do not recommend cystatin C as a general marker of kidney function and will therefore not allow the identification of a considerable number of patients with selective glomerular hypofiltration syndromes. Furthermore, as cystatin C is uninfluenced by muscle mass, diet or variations in tubular secretion and cystatin C-based GFR-estimation equations do not require controversial race or sex terms, it is obvious that cystatin C should be a part of future KDIGO guidelines.
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Affiliation(s)
- Linnea Malmgren
- Department of Clinical Sciences Malmö, Clinical and Molecular Osteoporosis Research Unit, Lund University, Malmö, Sweden.,Department of Geriatrics, Skåne University Hospital, Malmö, Sweden
| | - Carl Öberg
- Department of Clinical Sciences Lund, Division of Nephrology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Emil den Bakker
- Department of Pediatrics, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Felicia Leion
- Department of Clinical Chemistry, Skåne University Hospital, Lund University, Lund, Sweden
| | - Joanna Siódmiak
- Department of Laboratory Medicine, Faculty of Pharmacy, Ludwik Rydygier Collegium Medicum (Nicolaus Copernicus University in Torun), Bydgoszcz, Poland
| | - Anna Åkesson
- Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden.,Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden
| | - Veronica Lindström
- Department of Clinical Chemistry, Skåne University Hospital, Lund University, Lund, Sweden
| | - Erik Herou
- Department of Cardiothoracic Surgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - Alain Dardashti
- Department of Cardiothoracic Surgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - Liana Xhakollari
- Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Nephrology, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Gabriel Grubb
- Department of Radiology, Skåne University Hospital, Lund, Sweden
| | - Helena Strevens
- Department of Clinical Sciences Lund, Department of Obstetrics and Gynaecology, Lund University, Lund, Sweden
| | - Magnus Abrahamson
- Department of Clinical Chemistry, Skåne University Hospital, Lund University, Lund, Sweden
| | | | - Martin Magnusson
- Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Cardiology, Skåne University Hospital, Malmö, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden.,Hypertension in Africa Research Team (HART), North West University, Potchefstroom, South Africa
| | - Jonas Björk
- Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden.,Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden
| | - Ulf Nyman
- Department of Translational Medicine, Division of Medical Radiology, University of Lund, Malmö, Sweden
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society (NVS), Family Medicine and Primary Care Unit, Karolinska Institute, Huddinge, Sweden.,School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Peter Ridefelt
- Department of Medical Sciences, Clinical Chemistry, Uppsala University Hospital, Uppsala, Sweden
| | - Torbjörn Åkerfeldt
- Department of Medical Sciences, Clinical Chemistry, Uppsala University Hospital, Uppsala, Sweden
| | - Magnus Hansson
- Department of Clinical Chemistry, Karolinska University Hospital, Huddinge, Sweden
| | - Anna Sjöström
- Department of Clinical Chemistry, Karolinska University Hospital, Huddinge, Sweden
| | - Johan Mårtensson
- Department of Physiology and Pharmacology, Section of Anaesthesia and Intensive Care, Karolinska Institute, Stockholm, Sweden
| | - Yoshihisa Itoh
- Clinical Laboratory, Eiju General Hospital, Life Extension Research Institute, Tokyo, Japan
| | - David Grubb
- Department of Cardiothoracic Surgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - Olav Tenstad
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Lars-Olov Hansson
- Department of Clinical Chemistry, Karolinska University Hospital, Huddinge, Sweden
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali - National University Hospital of Iceland, Reykjavik, Iceland
| | - Araceli Jarquin Campos
- Faculty of Medical Sciences, Private University in the Principality of Liechtenstein, Triesen, Liechtenstein
| | - Martin Risch
- Central Laboratory, Cantonal Hospital Graubünden, Chur, Switzerland
| | - Lorenz Risch
- Faculty of Medical Sciences, Private University in the Principality of Liechtenstein, Triesen, Liechtenstein.,University Institute of Clinical Chemistry, University Hospital and University of Bern, Inselspital, Bern, Switzerland
| | - Anders Larsson
- Department of Medical Sciences, Clinical Chemistry, Uppsala University Hospital, Uppsala, Sweden
| | | | - Hans Pottel
- Department of Public Health and Primary Care, Katholieke Universiteit Leuven Campus Kulak Kortrijk, Kortrijk, Belgium
| | - Anders Christensson
- Department of Nephrology, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Henrik Bjursten
- Department of Cardiothoracic Surgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - Arend Bökenkamp
- Department of Pediatric Nephrology, Emma Children's Hospital, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Anders Grubb
- Department of Clinical Chemistry, Skåne University Hospital, Lund University, Lund, Sweden
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15
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Wen D, Zhou L, Zheng Z, Surapaneni A, Ballantyne CM, Hoogeveen RC, Shlipak MG, Waikar SS, Vasan RS, Kimmel PL, Dubin RF, Deo R, Feldman HI, Ganz P, Coresh J, Grams ME, Rhee EP. Testican-2 Is Associated with Reduced Risk of Incident ESKD. J Am Soc Nephrol 2023; 34:122-131. [PMID: 36288905 PMCID: PMC10101586 DOI: 10.1681/asn.2022020216] [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: 11/14/2022] [Revised: 08/23/2022] [Accepted: 11/14/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Testican-2 was recently identified as a podocyte-derived protein that is released into circulation by the kidneys and is positively correlated with eGFR and eGFR slope. However, whether higher testican-2 levels are associated with lower risk of ESKD is unknown. METHODS Aptamer-based proteomics assessed blood testican-2 levels among participants in the African American Study of Kidney Disease and Hypertension (AASK, n =703), the Chronic Renal Insufficiency Cohort (CRIC) study ( n =3196), and the Atherosclerosis Risk in Communities (ARIC) study ( n =4378). We compared baseline characteristics by testican-2 tertile and used Cox proportional hazards models to study the association of testican-2 with incident ESKD. RESULTS Higher testican-2 levels were associated with higher measured GFR (mGFR) in AASK, higher eGFR in the CRIC and ARIC studies, and lower albuminuria in all cohorts. Baseline testican-2 levels were significantly associated with incident ESKD in Cox proportional hazards models adjusted for age, sex, and race (model 1) and model 1+mGFR or eGFR+comorbidities (model 2). In model 3 (model 2+proteinuria), the associations between testican-2 (per SD increase) and incident ESKD were AASK (hazard ratio [HR]=0.84 [0.72 to 0.98], P =0.023), CRIC (HR=0.95 [0.89 to 1.02], P =0.14), ARIC (HR=0.54 [0.36 to 0.83], P =0.0044), and meta-analysis (HR=0.92 [0.86 to 0.98], P =0.0073). CONCLUSIONS Across three cohorts spanning >8000 individuals, testican-2 is associated with kidney health and prognosis, with higher levels associated with reduced risk of ESKD.
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Affiliation(s)
- Donghai Wen
- Nephrology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Christie M. Ballantyne
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Ron C. Hoogeveen
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Michael G. Shlipak
- Kidney Health Research Collaborative, Department of Medicine, San Francisco Veterans Affairs Health Care System
- University of California, San Francisco, California
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Paul L. Kimmel
- Division of Kidney Urologic and Hematologic Diseases, National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Ruth F. Dubin
- Division of Nephrology, San Francisco VA Medical Center, University of California, San Francisco, California
| | - Rajat Deo
- Division of Cardiology, Electrophysiology Section, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter Ganz
- Cardiovascular Division, Zuckerberg San Francisco General Hospital, University of California, San Francisco, California
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Eugene P. Rhee
- Nephrology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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16
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Zhou XJ, Zhong XH, Duan LX. Integration of artificial intelligence and multi-omics in kidney diseases. FUNDAMENTAL RESEARCH 2023; 3:126-148. [PMID: 38933564 PMCID: PMC11197676 DOI: 10.1016/j.fmre.2022.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 10/18/2022] Open
Abstract
Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
| | - Xu-Hui Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Li-Xin Duan
- The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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17
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Tomaszewski M, Morris AP, Howson JMM, Franceschini N, Eales JM, Xu X, Dikalov S, Guzik TJ, Humphreys BD, Harrap S, Charchar FJ. Kidney omics in hypertension: from statistical associations to biological mechanisms and clinical applications. Kidney Int 2022; 102:492-505. [PMID: 35690124 PMCID: PMC9886011 DOI: 10.1016/j.kint.2022.04.045] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/10/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023]
Abstract
Hypertension is a major cardiovascular disease risk factor and contributor to premature death globally. Family-based investigations confirmed a significant heritable component of blood pressure (BP), whereas genome-wide association studies revealed >1000 common and rare genetic variants associated with BP and/or hypertension. The kidney is not only an organ of key relevance to BP regulation and the development of hypertension, but it also acts as the tissue mediator of genetic predisposition to hypertension. The identity of kidney genes, pathways, and related mechanisms underlying the genetic associations with BP has started to emerge through integration of genomics with kidney transcriptomics, epigenomics, and other omics as well as through applications of causal inference, such as Mendelian randomization. Single-cell methods further enabled mapping of BP-associated kidney genes to cell types, and in conjunction with other omics, started to illuminate the biological mechanisms underpinning associations of BP-associated genetic variants and kidney genes. Polygenic risk scores derived from genome-wide association studies and refined on kidney omics hold the promise of enhanced diagnostic prediction, whereas kidney omics-informed drug discovery is likely to contribute new therapeutic opportunities for hypertension and hypertension-mediated kidney damage.
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Affiliation(s)
- Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK; Manchester Heart Centre and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK.
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
| | - Joanna M M Howson
- Department of Genetics, Novo Nordisk Research Centre Oxford, Novo Nordisk Ltd, Oxford, UK
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - James M Eales
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Sergey Dikalov
- Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tomasz J Guzik
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK; Department of Internal and Agricultural Medicine, Jagiellonian University College of Medicine, Kraków, Poland
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Stephen Harrap
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Victoria, Australia
| | - Fadi J Charchar
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Victoria, Australia; Health Innovation and Transformation Centre, School of Science, Psychology and Sport, Federation University Australia, Ballarat, Victoria, Australia; Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
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18
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Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection. Proteomes 2022; 10:proteomes10030024. [PMID: 35893765 PMCID: PMC9326686 DOI: 10.3390/proteomes10030024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 02/07/2023] Open
Abstract
Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume, proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the 78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Proteomics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclinical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.
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19
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Keshawarz A, Hwang SJ, Lee GY, Yu Z, Yao C, Köttgen A, Levy D. Cardiovascular disease protein biomarkers are associated with kidney function: The Framingham Heart Study. PLoS One 2022; 17:e0268293. [PMID: 35544531 PMCID: PMC9094507 DOI: 10.1371/journal.pone.0268293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background Biomarkers common to chronic kidney disease (CKD) and cardiovascular disease (CVD) may reflect early impairments underlying both diseases. Methods We evaluated associations of 71 CVD-related plasma proteins measured in 2,873 Framingham Heart Study (FHS) Offspring cohort participants with cross-sectional continuous eGFR and with longitudinal change in eGFR from baseline to follow-up (ΔeGFR). We also evaluated the associations of the 71 CVD proteins with the following dichotomous secondary outcomes: prevalent CKD stage ≥3 (cross-sectional), new-onset CKD stage ≥3 (longitudinal), and rapid decline in eGFR (longitudinal). Proteins significantly associated with eGFR and ΔeGFR were subsequently validated in 3,951 FHS Third Generation cohort participants and were tested using Mendelian randomization (MR) analysis to infer putatively causal relations between plasma protein biomarkers and kidney function. Results In cross-sectional analysis, 37 protein biomarkers were significantly associated with eGFR at FDR<0.05 in the FHS Offspring cohort and 20 of these validated in the FHS Third Generation cohort at p<0.05/37. In longitudinal analysis, 27 protein biomarkers were significantly associated with ΔeGFR at FDR<0.05 and 12 of these were validated in the FHS Third Generation cohort at p<0.05/27. Additionally, 35 protein biomarkers were significantly associated with prevalent CKD, five were significantly associated with new-onset CKD, and 17 were significantly associated with rapid decline in eGFR. MR suggested putatively causal relations of melanoma cell adhesion molecule (MCAM; -0.011±0.003 mL/min/1.73m2, p = 5.11E-5) and epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1; -0.006±0.002 mL/min/1.73m2, p = 0.0001) concentration with eGFR. Discussion/conclusions Eight protein biomarkers were consistently associated with eGFR in cross-sectional and longitudinal analysis in both cohorts and may capture early kidney impairment; others were implicated in association and causal inference analyses. A subset of CVD protein biomarkers may contribute causally to the pathogenesis of kidney impairment and should be studied as targets for CKD treatment and early prevention.
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Affiliation(s)
- Amena Keshawarz
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Gha Young Lee
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Zhi Yu
- Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Chen Yao
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- * E-mail:
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20
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Chen TK, Surapaneni AL, Arking DE, Ballantyne CM, Boerwinkle E, Chen J, Coresh J, Köttgen A, Susztak K, Tin A, Yu B, Grams ME. APOL1 Kidney Risk Variants and Proteomics. Clin J Am Soc Nephrol 2022; 17:684-692. [PMID: 35474272 PMCID: PMC9269576 DOI: 10.2215/cjn.14701121] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/17/2022] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND OBJECTIVES The APOL1 risk variants (G1 and G2) are associated with kidney disease among Black adults, but the clinical presentation is heterogeneous. In mouse models and cell systems, increased gene expression of G1 and G2 confers cytotoxicity. How APOL1 risk variants relate to the circulating proteome warrants further investigation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Among 461 African American Study of Kidney Disease and Hypertension (AASK) participants (mean age: 54 years; 41% women; mean GFR: 46 ml/min per 1.73 m2), we evaluated associations of APOL1 risk variants with 6790 serum proteins (measured via SOMAscan) using linear regression models. Covariates included age, sex, percentage of European ancestry, and protein principal components 1-5. Associated proteins were then evaluated as mediators of APOL1-associated risk for kidney failure. Findings were replicated among 875 Atherosclerosis Risk in Communities (ARIC) study Black participants (mean age: 75 years; 66% women; mean eGFR: 67 ml/min per 1.73 m2). RESULTS In the AASK study, having two (versus zero or one) APOL1 risk alleles was associated with lower serum levels of APOL1 (P=3.11E-13; P=3.12E-06 [two aptamers]), APOL2 (P=1.45E-10), CLSTN2 (P=2.66E-06), MMP-2 (P=2.96E-06), SPOCK2 (P=2.57E-05), and TIMP-2 (P=2.98E-05) proteins. In the ARIC study, APOL1 risk alleles were associated with APOL1 (P=1.28E-11); MMP-2 (P=0.004) and TIMP-2 (P=0.007) were associated only in an additive model, and APOL2 was not available. APOL1 high-risk status was associated with a 1.6-fold greater risk of kidney failure in the AASK study; none of the identified proteins mediated this association. APOL1 protein levels were not associated with kidney failure in either cohort. CONCLUSIONS APOL1 risk variants were strongly associated with lower circulating levels of APOL1 and other proteins, but none mediated the APOL1-associated risk for kidney failure. APOL1 protein level was also not associated with kidney failure.
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Affiliation(s)
- Teresa K. Chen
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Aditya L. Surapaneni
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Josef Coresh
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Data Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Adrienne Tin
- Department of Medicine, The University of Mississippi Medical Center, Jackson, Mississippi
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Medicine, New York University Langone School of Medicine, New York, New York
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