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Yang Z, Chen L, Huang Y, Dong J, Yan Q, Li Y, Qiu J, Li H, Zhao D, Liu F, Tang D, Dai Y. Proteomic profiling of laser capture microdissection kidneys from diabetic nephropathy patients. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1243:124231. [PMID: 38996754 DOI: 10.1016/j.jchromb.2024.124231] [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: 01/11/2024] [Revised: 05/23/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
Diabetic nephropathy (DN) remains the primary cause of end-stage renal disease (ESRD), warranting equal attention and separate analysis of glomerular, tubular, and interstitial lesions in its diagnosis and intervention. This study aims to identify the specific proteomics characteristics of DN, and assess changes in the biological processes associated with DN. 5 patients with DN and 5 healthy kidney transplant donor control individuals were selected for analysis. The proteomic characteristics of glomeruli, renal tubules, and renal interstitial tissue obtained through laser capture microscopy (LCM) were studied using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Significantly, the expression of multiple heat shock proteins (HSPs), tubulins, and heterogeneous nuclear ribonucleoproteins (hnRNPs) in glomeruli and tubules was significantly reduced. Differentially expressed proteins (DEPs) in the glomerulus showed significant enrichment in pathways related to cell junctions and cell movement, including the regulation of actin cytoskeleton and tight junction. DEPs in renal tubules were significantly enriched in glucose metabolism-related pathways, such as glucose metabolism, glycolysis/gluconeogenesis, and the citric acid cycle. Moreover, the glycolysis/gluconeogenesis pathway was a co-enrichment pathway in both DN glomeruli and tubules. Notably, ACTB emerged as the most crucial protein in the protein-protein interaction (PPI) analysis of DEPs in both glomeruli and renal tubules. In this study, we delve into the unique proteomic characteristics of each sub-region of renal tissue. This enhances our understanding of the potential pathophysiological changes in DN, particularly the potential involvement of glycolysis metabolic disorder, glomerular cytoskeleton and cell junctions. These insights are crucial for further research into the identification of disease biomarkers and the pathogenesis of DN.
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Affiliation(s)
- Zhiqian Yang
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China; Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China
| | - Liangmei Chen
- Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China
| | - Yingxin Huang
- Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China; Department of Nephrology, Xiaolan People's Hospital of Zhongshan, 528400, China
| | - Jingjing Dong
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China; Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China
| | - Qiang Yan
- Department of Organ Transplantation, 924 Hospital, Guilin 541002, China
| | - Ya Li
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China
| | - Jing Qiu
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China
| | - Haitao Li
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China
| | - Da Zhao
- The First Affiliated Hospital, School of Medicine, Anhui University of Science and Technology, Huainan 232001, Anhui, China
| | - Fanna Liu
- Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China.
| | - Donge Tang
- Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People' s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen 518020, China.
| | - Yong Dai
- Comprehensive Health Industry Research Center, Taizhou Research Institute, Southern University of Science and Technology, Taizhou 317000, China; The First Affiliated Hospital, School of Medicine, Anhui University of Science and Technology, Huainan 232001, Anhui, China.
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Hayward SJ, Chesnaye NC, Hole B, Aylward R, Meuleman Y, Torino C, Porto G, Szymczak M, Drechsler C, Dekker FW, Evans M, Jager KJ, Wanner C, Caskey FJ. Protein Biomarkers and Major Cardiovascular Events in Older People With Advanced CKD: The European Quality (EQUAL) Study. Kidney Med 2024; 6:100745. [PMID: 38162538 PMCID: PMC10757029 DOI: 10.1016/j.xkme.2023.100745] [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] [Indexed: 01/03/2024] Open
Abstract
Rationale & Objective Cardiovascular disease is the leading cause of morbidity and mortality in chronic kidney disease (CKD). We investigated 184 inflammatory and cardiovascular proteins to determine their potential as biomarkers for major cardiovascular events (MACEs). Study Design The European Quality (EQUAL) is an observational cohort study that enrolled people aged ≥65 years with an estimated glomerular filtration rate ≤20 mL/min/1.73 m2. Setting & Participants Recruited participants were split into the discovery (n = 611) and replication cohorts (n = 292). Exposure Levels of 184 blood proteins were measured at the baseline visit, and each protein was analyzed individually. Outcome MACE. Analytical Approach Cox proportional hazard models adjusted for age, sex, estimated glomerular filtration rate, previous MACE, and country were used to determine the risk of MACE. Proteins with false discovery rate adjusted P values of <0.05 in the discovery cohort were tested in the replication cohort. Sensitivity analyses were performed by adjusting for traditional risk factors, CKD-specific risk factors, and level of proteinuria and segregating atherosclerotic and nonatherosclerotic MACE. Results During a median follow-up of 2.9 years, 349 people (39%) experienced a MACE. Forty-eight proteins were associated with MACE in the discovery cohort; 9 of these were reproduced in the replication cohort. Three of these proteins maintained a strong association with MACE after adjustment for traditional and CKD-specific risk factors and proteinuria. Tenascin (TNC), fibroblast growth factor-23 (FGF-23), and V-set and immunoglobulin domain-containing protein 2 (VSIG2) were associated with both atherosclerotic and nonatherosclerotic MACE. All replicated proteins except carbonic anhydrase 1 and carbonic anhydrase 3 were associated with nonatherosclerotic MACE. Limitations Single protein concentration measurements and limited follow-up time. Conclusions Our findings corroborate previously reported relationships between FGF-23, vascular cell adhesion protein-1, TNC, and placental growth factor with cardiovascular outcomes in CKD. We identify 5 proteins not previously linked with MACE in CKD that may be targets for future therapies. Plain-Language Summary Kidney disease increases the risk of heart disease, stroke, and other vascular conditions. Blood tests that predict the likelihood of these problems may help to guide treatment, but studies are needed in people with kidney disease. We analyzed blood tests from older people with kidney disease, looking for proteins associated with higher risk of these conditions. Nine proteins were identified, of which 3 showed a strong effect after all other information was considered. This work supports previous research regarding 4 of these proteins and identifies 5 additional proteins that may be associated with higher risk. Further work is needed to confirm our findings and to determine whether these proteins can be used to guide treatment.
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Affiliation(s)
- Samantha J.L. Hayward
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Southmead Hospital, North Bristol NHS Trust, Bristol, United Kingdom
| | - Nicholas C. Chesnaye
- Amsterdam UMC, University of Amsterdam, ERA Registry, Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, The Netherlands
| | - Barnaby Hole
- Southmead Hospital, North Bristol NHS Trust, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ryan Aylward
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
| | - Yvette Meuleman
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Claudia Torino
- Institute of Clinical Physiology, National Research Council, Reggio Calabria, Italy
| | - Gaetana Porto
- GOM Bianchi Melacrino Morelli, Reggio Calabria, Italy
| | - Maciej Szymczak
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | | | - Friedo W. Dekker
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marie Evans
- Department of Clinical Sciences Intervention and Technology, Karolinska Institutet and Karolinska, Stockholm, Sweden
| | - Kitty J. Jager
- Amsterdam UMC, University of Amsterdam, ERA Registry, Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, The Netherlands
| | - Christoph Wanner
- Division of Nephrology, University Hospital of Wurzburg, Wurzburg, Germany
| | - Fergus J. Caskey
- Southmead Hospital, North Bristol NHS Trust, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - EQUAL investigators
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Southmead Hospital, North Bristol NHS Trust, Bristol, United Kingdom
- Amsterdam UMC, University of Amsterdam, ERA Registry, Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, The Netherlands
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Institute of Clinical Physiology, National Research Council, Reggio Calabria, Italy
- GOM Bianchi Melacrino Morelli, Reggio Calabria, Italy
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
- Division of Nephrology, University Hospital of Wurzburg, Wurzburg, Germany
- Department of Clinical Sciences Intervention and Technology, Karolinska Institutet and Karolinska, Stockholm, Sweden
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Rainey A, McKay GJ, English J, Thakkinstian A, Maxwell AP, Corr M. Proteomic analysis investigating kidney transplantation outcomes- a scoping review. BMC Nephrol 2023; 24:346. [PMID: 37993798 PMCID: PMC10666386 DOI: 10.1186/s12882-023-03401-0] [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: 08/08/2023] [Accepted: 11/16/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Kidney transplantation is the optimal treatment option for most patients with end-stage kidney disease given the significantly lower morbidity and mortality rates compared to remaining on dialysis. Rejection and graft failure remain common in transplant recipients with limited improvement in long-term transplant outcomes despite therapeutic advances. There is an unmet need in the development of non-invasive biomarkers that specifically monitor graft function and predict transplant pathologies that affect outcomes. Despite the potential of proteomic investigatory approaches, up to now, no candidate biomarkers of sufficient sensitivity or specificity have translated into clinical use. The aim of this review was to collate and summarise protein findings and protein pathways implicated in the literature to date, and potentially flag putative biomarkers worth validating in independent patient cohorts. METHODS This review followed the Joanna Briggs' Institute Methodology for a scoping review. MedlineALL, Embase, Web of Science Core Collection, Scopus and Google Scholar databases were searched from inception until December 2022. Abstract and full text review were undertaken independently by two reviewers. Data was collated using a pre-designed data extraction tool. RESULTS One hundred one articles met the inclusion criteria. The majority were single-centre retrospective studies of small sample size. Mass spectrometry was the most used technique to evaluate differentially expressed proteins between diagnostic groups and studies identified various candidate biomarkers such as immune or structural proteins. DISCUSSION Putative immune or structural protein candidate biomarkers have been identified using proteomic techniques in multiple sample types including urine, serum and fluid used to perfuse donor kidneys. The most consistent findings implicated proteins associated with tubular dysfunction and immunological regulatory pathways such as leukocyte trafficking. However, clinical translation and adoption of candidate biomarkers is limited, and these will require comprehensive evaluation in larger prospective, multicentre trials.
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Affiliation(s)
- Anna Rainey
- Centre for Public Health- Queen's University Belfast, Belfast, UK
| | - Gareth J McKay
- Centre for Public Health- Queen's University Belfast, Belfast, UK
| | - Jane English
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Michael Corr
- Centre for Public Health- Queen's University Belfast, Belfast, UK.
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Fujita Y, Shinkai S, Taniguchi Y, Miura Y, Tanaka M, Ohsawa I, Kitamura A, Ito M. Association Between Serum GDF15 Concentration and Total Mortality in Community-Dwelling Japanese Older Populations: The Involvement of Renal Dysfunction. J Gerontol A Biol Sci Med Sci 2023; 78:1701-1707. [PMID: 37190783 DOI: 10.1093/gerona/glad105] [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: 01/15/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Serum growth differentiation factor 15 (GDF15) is associated with age-related adverse outcomes. However, renal function has not been thoroughly evaluated in studies addressing the association between GDF15 and mortality. We aimed to clarify whether GDF15 is associated with total mortality after carefully controlling renal function markers. METHODS We divided 1 801 community-dwelling Japanese older adults into quartiles according to their serum GDF15 concentrations. The correlation of GDF15 with renal function and inflammation markers was assessed by calculating Spearman correlation coefficients. Cumulative survival rates of the quartiles were estimated. In a Cox regression analysis adjusted for confounders, the association between GDF15 and mortality was evaluated. The discriminative capacity of GDF15 for the prediction of mortality was assessed with receiver-operating characteristic analysis. RESULTS GDF15 was correlated with cystatin C (r = 0.394), β2-microglobulin (r = 0.382), C-reactive protein (r = 0.124), and interleukin-6 (r = 0.166). The highest GDF15 quartile showed poor survival compared to the others. Older adults with higher GDF15 were associated with an increased mortality risk, independent of demographics and clinically relevant variables (hazard ratio [95% confidence interval]: 1.98 [1.09-3.59]). This significant association disappeared when additionally adjusted for cystatin C (1.65 [0.89-3.05]) or β2-microglobulin (1.69 [0.91-3.12]). The ability to predict mortality was approximately comparable between GDF15 (area under the curve: 0.667), cystatin C (0.691), and β2-microglobulin (0.715). CONCLUSIONS Serum GDF15 is associated with total mortality in older Japanese after adjustment for major confounders. The increased mortality risk in older adults with higher GDF15 may be partly attributed to decreased renal function.
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Affiliation(s)
- Yasunori Fujita
- Biological Process of Aging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Shoji Shinkai
- Graduate School of Nutrition and Health Science, Kagawa Nutrition University, Sakado, Saitama, Japan
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Yu Taniguchi
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
| | - Yuri Miura
- Research Team for Mechanism of Aging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Masashi Tanaka
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ikuroh Ohsawa
- Biological Process of Aging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Akihiko Kitamura
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
- Health Town Development Science Center, Yao City Health Center, Yao, Osaka, Japan
| | - Masafumi Ito
- Biological Process of Aging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
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5
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Zhang W, Zhang L, Yang L, Xiao C, Wu X, Yan P, Cui H, Yang C, Zhu J, Wu X, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Zhang L, Yang C, Yao Y, Li J, Liu Z, Zhang B, Jiang X. Migraine, chronic kidney disease and kidney function: observational and genetic analyses. Hum Genet 2023; 142:1185-1200. [PMID: 37306871 PMCID: PMC10449948 DOI: 10.1007/s00439-023-02575-9] [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: 03/22/2023] [Accepted: 05/23/2023] [Indexed: 06/13/2023]
Abstract
Epidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We aimed to help avoid unnecessary interventions in individuals with migraine through the investigation of phenotypic and genetic relationships underlying migraine, CKD, and kidney function. We first evaluated phenotypic associations using observational data from UK Biobank (N = 255,896). We then investigated genetic relationships leveraging genomic data in European ancestry for migraine (Ncase/Ncontrol = 48,975/540,381), CKD (Ncase/Ncontrol = 41,395/439,303), and two traits of kidney function (estimated glomerular filtration rate [eGFR, N = 567,460] and urinary albumin-to-creatinine ratio [UACR, N = 547,361]). Observational analyses suggested no significant association of migraine with the risk of CKD (HR = 1.13, 95% CI = 0.85-1.50). While we did not find any global genetic correlation in general, we identified four specific genomic regions showing significant for migraine with eGFR. Cross-trait meta-analysis identified one candidate causal variant (rs1047891) underlying migraine, CKD, and kidney function. Transcriptome-wide association study detected 28 shared expression-trait associations between migraine and kidney function. Mendelian randomization analysis suggested no causal effect of migraine on CKD (OR = 1.03, 95% CI = 0.98-1.09; P = 0.28). Despite a putative causal effect of migraine on an increased level of UACR (log-scale-beta = 0.02, 95% CI = 0.01-0.04; P = 1.92 × 10-3), it attenuated to null when accounting for both correlated and uncorrelated pleiotropy. Our work does not find evidence supporting a causal association between migraine and CKD. However, our study highlights significant biological pleiotropy between migraine and kidney function. The value of a migraine prophylactic treatment for reducing future CKD in people with migraine is likely limited.
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Affiliation(s)
- Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Luo Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Urology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Jingwei Zhu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Xuan Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041 China
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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6
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Enoksen ITT, Rinde NB, Svistounov D, Norvik JV, Solbu MD, Eriksen BO, Melsom T. Validation of eGFR for Detecting Associations Between Serum Protein Biomarkers and Subsequent GFR Decline. J Am Soc Nephrol 2023; 34:1409-1420. [PMID: 37093083 PMCID: PMC10400103 DOI: 10.1681/asn.0000000000000147] [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: 09/16/2022] [Accepted: 04/01/2023] [Indexed: 04/25/2023] Open
Abstract
SIGNIFICANCE STATEMENT eGFR from creatinine, cystatin C, or both has been primarily used in search of biomarkers for GFR decline. Whether the relationships between biomarkers and eGFR decline are similar to associations with measured GFR (mGFR) decline has not been investigated. This study revealed that some biomarkers showed statistically significant different associations with eGFR decline compared with mGFR decline, particularly for eGFR from cystatin C. The findings indicate that non-GFR-related factors, such as age, sex, and body mass index, influence the relationship between biomarkers and eGFR decline. Therefore, the results of biomarker studies using eGFR, particularly eGFRcys, should be interpreted with caution and perhaps validated with mGFR. BACKGROUND Several serum protein biomarkers have been proposed as risk factors for GFR decline using eGFR from creatinine or cystatin C. We investigated whether eGFR can be used as a surrogate end point for measured GFR (mGFR) when searching for biomarkers associated with GFR decline. METHODS In the Renal Iohexol Clearance Survey, GFR was measured with plasma iohexol clearance in 1627 individuals without diabetes, kidney, or cardiovascular disease at baseline. After 11 years of follow-up, 1409 participants had one or more follow-up GFR measurements. Using logistic regression and interval-censored Cox regression, we analyzed the association between baseline levels of 12 serum protein biomarkers with the risk of accelerated GFR decline and incident CKD for both mGFR and eGFR. RESULTS Several biomarkers exhibited different associations with eGFR decline compared with their association with mGFR decline. More biomarkers showed different associations with eGFRcys decline than with eGFRcre decline. Most of the different associations of eGFR decline versus mGFR decline remained statistically significant after adjustment for age, sex, and body mass index, but several were attenuated and not significant after adjusting for the corresponding baseline mGFR or eGFR. CONCLUSIONS In studies of some serum protein biomarkers, eGFR decline may not be an appropriate surrogate outcome for mGFR decline. Although the differences from mGFR decline are attenuated by adjustment for confounding factors in most cases, some persist. Therefore, proposed biomarkers from studies using eGFR should preferably be validated with mGFR.
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Affiliation(s)
- Inger T. T. Enoksen
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
| | - Nikoline B. Rinde
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
| | - Dmitri Svistounov
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
| | - Jon V. Norvik
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
- Section of Nephrology, Clinic of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
| | - Marit D. Solbu
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
- Section of Nephrology, Clinic of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
| | - Bjørn O. Eriksen
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
- Section of Nephrology, Clinic of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
| | - Toralf Melsom
- Metabolic and Renal Research Group, UiT– The Arctic University of Norway, Tromsø, Norway
- Section of Nephrology, Clinic of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
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7
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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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8
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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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9
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Growth differentiation factor 15 (GDF-15) in kidney diseases. Adv Clin Chem 2023. [DOI: 10.1016/bs.acc.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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10
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Schmidt IM, Myrick S, Liu J, Verma A, Srivastava A, Palsson R, Onul IF, Stillman IE, Avillach C, Patil P, Waikar SS. The use of plasma biomarker-derived clusters for clinicopathologic phenotyping: results from the Boston Kidney Biopsy Cohort. Clin Kidney J 2023; 16:90-99. [PMID: 36726432 PMCID: PMC9871860 DOI: 10.1093/ckj/sfac202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Indexed: 02/04/2023] Open
Abstract
Background Protein biomarkers may provide insight into kidney disease pathology but their use for the identification of phenotypically distinct kidney diseases has not been evaluated. Methods We used unsupervised hierarchical clustering on 225 plasma biomarkers in 541 individuals enrolled into the Boston Kidney Biopsy Cohort, a prospective cohort study of individuals undergoing kidney biopsy with adjudicated histopathology. Using principal component analysis, we studied biomarker levels by cluster and examined differences in clinicopathologic diagnoses and histopathologic lesions across clusters. Cox proportional hazards models tested associations of clusters with kidney failure and death. Results We identified three biomarker-derived clusters. The mean estimated glomerular filtration rate was 72.9 ± 28.7, 72.9 ± 33.4 and 39.9 ± 30.4 mL/min/1.73 m2 in Clusters 1, 2 and 3, respectively. The top-contributing biomarker in Cluster 1 was AXIN, a negative regulator of the Wnt signaling pathway. The top-contributing biomarker in Clusters 2 and 3 was Placental Growth Factor, a member of the vascular endothelial growth factor family. Compared with Cluster 1, individuals in Cluster 3 were more likely to have tubulointerstitial disease (P < .001) and diabetic kidney disease (P < .001) and had more severe mesangial expansion [odds ratio (OR) 2.44, 95% confidence interval (CI) 1.29, 4.64] and inflammation in the fibrosed interstitium (OR 2.49 95% CI 1.02, 6.10). After multivariable adjustment, Cluster 3 was associated with higher risks of kidney failure (hazard ratio 3.29, 95% CI 1.37, 7.90) compared with Cluster 1. Conclusion Plasma biomarkers may identify clusters of individuals with kidney disease that associate with different clinicopathologic diagnoses, histopathologic lesions and adverse outcomes, and may uncover biomarker candidates and relevant pathways for further study.
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Affiliation(s)
- Insa M Schmidt
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Steele Myrick
- Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Jing Liu
- Division of Nephrology and National Clinical Research Center for Geriatrics, Kidney Research Institute, West China Hospital of Sichuan University, Chengdu, China
| | - Ashish Verma
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ingrid F Onul
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Isaac E Stillman
- Beth Israel Deaconess Medical Center, Harvard Medical School, Department of Pathology, Boston, MA, USA
| | - Claire Avillach
- Boston Medical Center, Department of Pathology, Boston, MA, USA
| | - Prasad Patil
- Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Sushrut S Waikar
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
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11
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Cui M, Cheng C, Zhang L. High-throughput proteomics: a methodological mini-review. J Transl Med 2022; 102:1170-1181. [PMID: 36775443 PMCID: PMC9362039 DOI: 10.1038/s41374-022-00830-7] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 07/06/2022] [Accepted: 07/10/2022] [Indexed: 11/15/2022] Open
Abstract
Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.
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Affiliation(s)
- Miao Cui
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pathology, Mount Sinai West, New York, NY, USA
| | - Chao Cheng
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA. .,Department of Medicine, Baylor College of Medicine, Houston, TX, USA. .,Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
| | - Lanjing Zhang
- Department of Biological Sciences, Rutgers University, Newark, NJ, USA. .,Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA. .,Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. .,Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.
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12
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Tang W, Zhang Y, Cui S, Yi F. The Growth Factors: Potential Biomarkers and Therapeutic Targets in Kidney Diseases. KIDNEY DISEASES (BASEL, SWITZERLAND) 2022; 8:368-380. [PMID: 36466071 PMCID: PMC9710479 DOI: 10.1159/000526208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/17/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Kidney diseases are a prevalent health problem worldwide. Although substantial progress has been made in understanding the pathophysiology of kidney disease, currently there is no satisfactory clinical treatment available to prevent or treat kidney disease. Therefore, strategies to establish early diagnosis, identify the key molecules, and develop novel therapeutic interventions to slow the progression of kidney diseases and reduce their complications are encouraged. SUMMARY The growth factors play a crucial role in the development of kidney diseases. The altered levels of growth factors are usually detected in circulation and urine in the disease course. A growing body of studies has suggested that growth factors, receptors, and related regulators are promising biomarkers for the diagnosis and/or prognosis and potential therapeutic targets for the treatment of kidney diseases. In this review, we summarize recent advances in the potential applications of growth factors for diagnostic biomarkers and therapeutic targets in kidney diseases and highlight their performances in clinical trials. KEY MESSAGES Most diagnostic and therapeutic strategies targeting growth factors are still far from clinical implementation. The better understanding of growth factor-regulated pathophysiology and the progress of new intervention approaches are expected to facilitate the clinical translation of growth factor-based diagnosis and therapy of kidney diseases.
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Affiliation(s)
- Wei Tang
- Department of Pharmacology, The Key Laboratory of Infection and Immunity of Shandong Province, School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Yufeng Zhang
- Department of Pharmacology, The Key Laboratory of Infection and Immunity of Shandong Province, School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Sijia Cui
- Department of Pharmacology, The Key Laboratory of Infection and Immunity of Shandong Province, School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Fan Yi
- Department of Pharmacology, The Key Laboratory of Infection and Immunity of Shandong Province, School of Basic Medical Sciences, Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital, Shandong University, Jinan, China
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13
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Pelle MC, Provenzano M, Busutti M, Porcu CV, Zaffina I, Stanga L, Arturi F. Up-Date on Diabetic Nephropathy. Life (Basel) 2022; 12:1202. [PMID: 36013381 PMCID: PMC9409996 DOI: 10.3390/life12081202] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 12/11/2022] Open
Abstract
Diabetes is one of the leading causes of kidney disease. Diabetic kidney disease (DKD) is a major cause of end-stage kidney disease (ESKD) worldwide, and it is linked to an increase in cardiovascular (CV) risk. Diabetic nephropathy (DN) increases morbidity and mortality among people living with diabetes. Risk factors for DN are chronic hyperglycemia and high blood pressure; the renin-angiotensin-aldosterone system blockade improves glomerular function and CV risk in these patients. Recently, new antidiabetic drugs, including sodium-glucose transport protein 2 inhibitors and glucagon-like peptide-1 agonists, have demonstrated additional contribution in delaying the progression of kidney disease and enhancing CV outcomes. The therapeutic goal is regression of albuminuria, but an atypical form of non-proteinuric diabetic nephropathy (NP-DN) is also described. In this review, we provide a state-of-the-art evaluation of current treatment strategies and promising emerging treatments.
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Affiliation(s)
- Maria Chiara Pelle
- Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Marco Busutti
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Clara Valentina Porcu
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Isabella Zaffina
- Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
| | - Lucia Stanga
- Oncology Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Franco Arturi
- Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
- Research Centre for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
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Lou X, Yang Z, Wu K, Li W, Hu W, Nie R, Tu P, Duan P. Elevated Serum Osteoprotegerin is Associated with Reduced Risks of Albuminuria and CKD Progression in Patients with Type 2 Diabetes. Diabetes Metab Syndr Obes 2022; 15:3831-3841. [PMID: 36530588 PMCID: PMC9756793 DOI: 10.2147/dmso.s390483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/26/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To analyze the correlation between serum osteoprotegerin (OPG) level and chronic kidney disease (CKD) at different CKD stages in patients with type 2 diabetes. METHODS All subjects were hospitalized patients with type 2 diabetes. Medical history collection, physical examinations, and blood and urine samples testing were performed. Stages of CKD (G1-5) were defined by eGFR, groups of persistent albuminuria (normal, microalbuminuria and massive albuminuria) were divided by UACR, and categories of CKD progression risks (low, moderate and high or very high risk) were recommended by the Kidney Disease: Improving Global Outcomes (KDIGO). Serum OPG level was determined by enzyme-linked immunosorbent assay in the central laboratory. RESULTS Four hundred and eighty-four patients were included in the study. The average level of OPG of all subjects was 941.30 (547.53-1332.62) pg/mL. The levels of OPG decreased gradually with the aggravation of albuminuria (P = 0.007, P for trend=0.003) and CKD progression (P = 0.001, P for trend=0.001). No differences were found between OPG levels and stages of CKD (P = 0.31). After the adjustment, each 100 pg/mL increase in OPG levels could reduce the risk of massive albuminuria (OR 0.92, 95% CI 0.86-0.99, P = 0.02) and the high or very high risk of CKD progression (OR 0.94, 95% CI 0.89-0.99, P = 0.04) by multivariate logistic regression analysis. No correlations were found between OPG and stages of CKD. CONCLUSION In patients with type 2 diabetes, elevated serum osteoprotegerin is associated with albuminuria and the risk of CKD progression, and may delay the progression of CKD.
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Affiliation(s)
- Xiaoyang Lou
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
- Department of Postgraduate Studies, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China
| | - Zhi Yang
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Kexia Wu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Weihong Li
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Wan Hu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Ronghui Nie
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
- Third Clinical School of Medicine, Jiangxi Medical College of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Ping Tu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Peng Duan
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
- Correspondence: Peng Duan, Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, Jiangxi, 330000, People’s Republic of China, Tel +86 13479111177, Email
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15
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Schmidt IM, Sarvode Mothi S, Wilson PC, Palsson R, Srivastava A, Onul IF, Kibbelaar ZA, Zhuo M, Amodu A, Stillman IE, Rennke HG, Humphreys BD, Waikar SS. Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease. Clin J Am Soc Nephrol 2022; 17:27-37. [PMID: 34759008 PMCID: PMC8763150 DOI: 10.2215/cjn.09380721] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/02/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND OBJECTIVES Biomarkers for noninvasive assessment of histopathology and prognosis are needed in patients with kidney disease. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Using a proteomics assay, we measured a multimarker panel of 225 circulating plasma proteins in a prospective cohort study of 549 individuals with biopsy-confirmed kidney diseases and semiquantitative assessment of histopathology. We tested the associations of each biomarker with histopathologic lesions and the risks of kidney disease progression (defined as ≥40% decline in eGFR or initiation of KRT) and death. RESULTS After multivariable adjustment and correction for multiple testing, 46 different proteins were associated with histopathologic lesions. The top-performing markers positively associated with acute tubular injury and interstitial fibrosis/tubular atrophy were kidney injury molecule-1 (KIM-1) and V-set and Ig domain-containing protein 2 (VSIG2), respectively. Thirty proteins were significantly associated with kidney disease progression, and 35 were significantly associated with death. The top-performing markers for kidney disease progression were placental growth factor (hazard ratio per doubling, 5.4; 95% confidence interval, 3.4 to 8.7) and BMP and activin membrane-bound inhibitor (hazard ratio, 3.0; 95% confidence interval, 2.1 to 4.2); the top-performing markers for death were TNF-related apoptosis-inducing ligand receptor-2 (hazard ratio, 2.9; 95% confidence interval, 2.0 to 4.0) and CUB domain-containing protein-1 (hazard ratio, 2.4; 95% confidence interval, 1.8 to 3.3). CONCLUSION We identified several plasma protein biomarkers associated with kidney disease histopathology and adverse clinical outcomes in individuals with a diverse set of kidney diseases. PODCAST This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_28_CJN09380721.mp3.
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Affiliation(s)
- Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Suraj Sarvode Mothi
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, Tennessee
| | - Parker C. Wilson
- Department of Pathology and Immunology, Washington University, St. Louis, Missouri
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Anand Srivastava
- Division of Nephrology and Hypertension, Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ingrid F. Onul
- Section of Nephrology, Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Zoe A. Kibbelaar
- Section of Nephrology, Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Min Zhuo
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
- Division of Nephrology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Afolarin Amodu
- Section of Nephrology, Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Isaac E. Stillman
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Helmut G. Rennke
- Department of Pathology, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Benjamin D. Humphreys
- Division of Nephrology, Department of Medicine, Washington University, St. Louis, Missouri
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
- Renal Division, Brigham & Women’s Hospital, Boston, Massachusetts
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16
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Bao X, Xu B, Borné Y, Orho-Melander M, Melander O, Nilsson J, Christensson A, Engström G. Growth differentiation factor-15 and incident chronic kidney disease: a population-based cohort study. BMC Nephrol 2021; 22:351. [PMID: 34706669 PMCID: PMC8554962 DOI: 10.1186/s12882-021-02558-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022] Open
Abstract
Background The relationship between growth differentiation factor 15 (GDF-15) and the development of chronic kidney disease (CKD) is still unclear. We sought to examine whether plasma GDF-15 was related to incident CKD and kidney function decline using a large prospective cohort study. Methods 4318 participants of the Malmö Diet and Cancer Study-Cardiovascular Cohort were examined in 1991-1994. Incidence of CKD was followed prospectively by linkage with national patient registers. Estimated glomerular filtration rate (eGFR) was available for all participants at baseline, and was re-measured in a subgroup of 2744 subjects after 16.6 ± 1.49 years. Incidence of CKD was examined in relation to GDF-15 using Cox regression analysis. Logistic regression was used to examine the association of GDF-15 with eGFR change and eGFR-based CKD. Models were carefully corrected for potential confounders including baseline eGFR, N-terminal pro-B-type natriuretic peptide, and competing risk from death. Results 165 patients developed CKD after 19.2 ± 4.04 years of follow-up. The adjusted hazard ratio (95% confidence interval, CI) for CKD in 4th versus 1st quartile of GDF-15 was 2.37 (1.33, 4.24) (p for trend < 0.01). Each per 1 standard deviation increase in GDF-15 was associated with a decline in eGFR of − 0.97 mL/min/1.73 m2 (95% CI, − 1.49 ~ − 0.45; p < 0.001). GDF-15 was also significantly associated eGFR-based CKD in 2713 subjects with baseline eGFR ≥60 mL/min/1.73 m2. Conclusions GDF-15 predicted incidence of CKD and eGFR decline in the general population, independent of a wide range of potential risk factors and competing risk of death.
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Affiliation(s)
- Xue Bao
- Department of Cardiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, China.,Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Biao Xu
- Department of Cardiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, China.
| | - Yan Borné
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Marju Orho-Melander
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Olle Melander
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Jan Nilsson
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Anders Christensson
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden
| | - Gunnar Engström
- Department of Clinical Sciences, Lund University, CRC 60:13, Jan Waldenströms gata 35, 205 02, Malmö, Sweden.
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17
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Nano J, Schöttker B, Lin JS, Huth C, Ghanbari M, Garcia PM, Maalmi H, Karrasch S, Koenig W, Rothenbacher D, Roden M, Meisinger C, Peters A, Brenner H, Herder C, Thorand B. Novel biomarkers of inflammation, kidney function and chronic kidney disease in the general population. Nephrol Dial Transplant 2021; 37:1916-1926. [PMID: 34612501 DOI: 10.1093/ndt/gfab294] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Inflammatory processes have been implicated in the development of chronic kidney disease (CKD). We investigated the association of a large panel of inflammatory biomarkers reflecting aspects of immunity with kidney function and CKD incidence. METHODS We used data from two independent population-based studies, KORA F4 (discovery, n = 1,110, mean age 70.3 years, 48.7% male) and ESTHER (replication, n = 1,672, mean age 61.9 years, 43.6% male). Serum levels of biomarkers were measured using proximity extension assay technology. The association of biomarkers with estimated glomerular filtration rate (eGFR) at baseline and with incident CKD was investigated using linear and logistic regression models adjusted for cardiorenal risk factors. Independent results from prospective analyses of both studies were pooled. The significance level was corrected for multiple testing by false-discovery rate (PFDR < 0.05.). RESULTS In the KORA F4 discovery study, 52 out of 71 inflammatory biomarkers were inversely associated with eGFR estimated based on serum creatinine. Top biomarkers included CD40, TNFRSF9 and IL10RB. Forty-two of these 52 biomarkers were replicated in the ESTHER study. Nine of the 42 biomarkers were associated with incident CKD independently of cardiorenal risk factors in the meta-analysis of the KORA (n = 142, mean follow-up of 6.5 years) and ESTHER (n = 103, mean follow-up of 8 years) studies. Pathway analysis revealed the involvement of inflammatory and immunomodulatory processes reflecting cross-communication of innate and adaptive immune cells. CONCLUSIONS Novel and known biomarkers of inflammation were reproducibly associated with kidney function. Future studies should investigate their clinical utility and underlying molecular mechanisms in independent cohorts.
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Affiliation(s)
- Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,German Center for Diabetes Research (DZD), Partner München-Neuherberg, München-Neuherberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.,Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Jie-Sheng Lin
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,German Center for Diabetes Research (DZD), Partner München-Neuherberg, München-Neuherberg, Germany
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Pamela Matias Garcia
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,German Center for Diabetes Research (DZD), Partner München-Neuherberg, München-Neuherberg, Germany
| | - Haifa Maalmi
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
| | - Stefan Karrasch
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich/Neuherberg, 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 Biostatistics, University of Ulm, Ulm, Germany
| | - Dietrich Rothenbacher
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.,Institute of Epidemiology and Biostatistics, University of Ulm, Ulm, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany.,Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, Munich, Germany.,Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,German Center for Diabetes Research (DZD), Partner München-Neuherberg, München-Neuherberg, Germany.,Chair of Epidemiology, IBE, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.,Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany.,Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.,German Center for Diabetes Research (DZD), Partner München-Neuherberg, München-Neuherberg, Germany
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18
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Greco F, Anastasi F, Pardini LF, Dilillo M, Vannini E, Baroncelli L, Caleo M, McDonnell LA. Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model. Molecules 2021; 26:5992. [PMID: 34641541 PMCID: PMC8512455 DOI: 10.3390/molecules26195992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/24/2021] [Accepted: 09/30/2021] [Indexed: 12/04/2022] Open
Abstract
Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset.
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Affiliation(s)
- Francesco Greco
- Institute of Life Sciences, Sant’Anna School of Advanced Studies, 56127 Pisa, Italy;
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
| | - Federica Anastasi
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
- NEST Laboratories, Scuola Normale Superiore, 56127 Pisa, Italy
| | - Luca Fidia Pardini
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Marialaura Dilillo
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
| | - Eleonora Vannini
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- Fondazione Umberto Veronesi, 20122 Milano, Italy
| | - Laura Baroncelli
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- IRCCS Fondazione Stella Maris, 56018 Calambrone, Italy
| | - Matteo Caleo
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- Dipartimento di Scienze Biomediche, Università di Padova, 35131 Padova, Italy
| | - Liam A. McDonnell
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
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19
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Tracz J, Luczak M. Applying Proteomics and Integrative "Omics" Strategies to Decipher the Chronic Kidney Disease-Related Atherosclerosis. Int J Mol Sci 2021; 22:7492. [PMID: 34299112 PMCID: PMC8305100 DOI: 10.3390/ijms22147492] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023] Open
Abstract
Patients with chronic kidney disease (CKD) are at increased risk of atherosclerosis and premature mortality, mainly due to cardiovascular events. However, well-known risk factors, which promote "classical" atherosclerosis are alone insufficient to explain the high prevalence of atherosclerosis-related to CKD (CKD-A). The complexity of the molecular mechanisms underlying the acceleration of CKD-A is still to be defied. To obtain a holistic picture of these changes, comprehensive proteomic approaches have been developed including global protein profiling followed by functional bioinformatics analyses of dysregulated pathways. Furthermore, proteomics surveys in combination with other "omics" techniques, i.e., transcriptomics and metabolomics as well as physiological assays provide a solid ground for interpretation of observed phenomena in the context of disease pathology. This review discusses the comprehensive application of various "omics" approaches, with emphasis on proteomics, to tackle the molecular mechanisms underlying CKD-A progression. We summarize here the recent findings derived from global proteomic approaches and underline the potential of utilizing integrative systems biology, to gain a deeper insight into the pathogenesis of CKD-A and other disorders.
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Affiliation(s)
| | - Magdalena Luczak
- European Centre for Bioinformatics and Genomics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland;
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20
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Matías-García PR, Wilson R, Guo Q, Zaghlool SB, Eales JM, Xu X, Charchar FJ, Dormer J, Maalmi H, Schlosser P, Elhadad MA, Nano J, Sharma S, Peters A, Fornoni A, Mook-Kanamori DO, Winkelmann J, Danesh J, Di Angelantonio E, Ouwehand WH, Watkins NA, Roberts DJ, Petrera A, Graumann J, Koenig W, Hveem K, Jonasson C, Köttgen A, Butterworth A, Prunotto M, Hauck SM, Herder C, Suhre K, Gieger C, Tomaszewski M, Teumer A, Waldenberger M. Plasma Proteomics of Renal Function: A Transethnic Meta-Analysis and Mendelian Randomization Study. J Am Soc Nephrol 2021; 32:1747-1763. [PMID: 34135082 PMCID: PMC8425654 DOI: 10.1681/asn.2020071070] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 02/24/2021] [Accepted: 03/22/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. METHODS A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR. RESULTS In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. CONCLUSIONS In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.
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Affiliation(s)
- Pamela R. Matías-García
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research, Munich, Germany
| | - Rory Wilson
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
| | - Qi Guo
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Shaza B. Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - James M. Eales
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
| | - Fadi J. Charchar
- School of Health and Life Sciences, Federation University Australia, Ballarat, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- Department of Physiology, University of Melbourne, Melbourne, Australia
| | - John Dormer
- Department of Cellular Pathology, University Hospitals of Leicester National Health Service Trust, Leicester, United Kingdom
| | - Haifa Maalmi
- Institute for Clinical Diabetology, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
| | - Pascal Schlosser
- Department of Data-Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Mohamed A. Elhadad
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research, Munich, Germany
| | - Jana Nano
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
| | - Sapna Sharma
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research, Munich, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
| | - Alessia Fornoni
- Department of Medicine, Katz Family Division of Nephrology and Hypertension, University of Miami Miller School of Medicine, Miami, Florida
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Juliane Winkelmann
- Institute of Neurogenomics, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Neurogenetics and Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Emanuele Di Angelantonio
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
| | - Willem H. Ouwehand
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Nicholas A. Watkins
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, United Kingdom
| | - David J. Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- National Health Service Blood and Transplant Oxford Centre, Oxford, United Kingdom
- Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Agnese Petrera
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute of Heart and Lung Research, Bad Nauheim, Germany
| | - Wolfgang Koenig
- German Center for Cardiovascular Research, Munich, Germany
- Klinik für Herz-Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Kristian Hveem
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Nord-Trøndelag Health Study HUNT Research Centre, Faculty of Medicine, Norwegian University of Science and Technology, Levanger, Norway
| | - Christian Jonasson
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Nord-Trøndelag Health Study HUNT Research Centre, Faculty of Medicine, Norwegian University of Science and Technology, Levanger, Norway
| | - Anna Köttgen
- Department of Data-Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Adam Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Marco Prunotto
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Stefanie M. Hauck
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, München-Neuherberg, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Christian Gieger
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research, Munich, Germany
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Manchester Heart Centre and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Alexander Teumer
- Department SHIP/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Center for Cardiovascular Research, partner site Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research, Munich, Germany
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21
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Lind L, Ärnlöv J, Sundström J. Plasma Protein Profile of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure in 2 Cohorts. J Am Heart Assoc 2021; 10:e017900. [PMID: 34096334 PMCID: PMC8477859 DOI: 10.1161/jaha.120.017900] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background The aim is to study common etiological pathways for 3 major cardiovascular diseases (CVD), as reflected in multiple proteins. Methods and Results Eighty-four proteins were measured using the proximity extension technique in 870 participants in the PIVUS (Prospective Investigation of Uppsala Seniors Study) cohort on 3 occasions (age 70, 75, and 80 years). The sample was followed for incident myocardial infarction, ischemic stroke or heart failure. The same proteins were measured in an independent validation sample, the ULSAM (Uppsala Longitudinal Study of Adult Men) cohort in 595 participants at age 77. During a follow-up of up to 15 years in PIVUS and 9 years in ULSAM, 222 and 167 individuals experienced a CVD. Examining associations with the 3 outcomes separately in a meta-analysis of the 2 cohorts, 6 proteins were related to incident myocardial infarction, 25 to heart failure, and 8 proteins to ischemic stroke following adjustment for traditional risk factors. Growth differentiation factor 15 and tumor necrosis factor-related apoptosis-inducing ligand receptor 2 were related to all 3 CVDs. Including estimated glomerular filtration rate in the models attenuated some of these relationships. Fifteen proteins were related to a composite of all 3 CVDs using a discovery/validation approach when adjusting for traditional risk factors. A selection of 7 proteins by lasso in PIVUS improved discrimination of incident CVD by 7.3% compared with traditional risk factors in ULSAM. Conclusions We discovered and validated associations of multiple proteins with incident CVD. Only a few proteins were associated with all 3 diseases: myocardial infarction, ischemic stroke, and heart failure.
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Affiliation(s)
- Lars Lind
- Department of Medical Sciences Uppsala University Uppsala Sweden
| | - Johan Ärnlöv
- Division of Family Medicine and Primary Care Department of Neurobiology, Care Sciences and Society Karolinska Institutet Huddinge Sweden.,School of Health and Social Sciences Dalarna University Falun Sweden
| | - Johan Sundström
- Department of Medical Sciences Uppsala University Uppsala Sweden.,The George Institute for Global HealthUniversity of New South Wales Sydney Australia
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22
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Yu KYC, Yung S, Chau MKM, Tang CSO, Yap DYH, Tang AHN, Ying SKY, Lee CK, Chan TM. Serum syndecan-1, hyaluronan and thrombomodulin levels in patients with lupus nephritis. Rheumatology (Oxford) 2021; 60:737-750. [PMID: 32793966 DOI: 10.1093/rheumatology/keaa370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/28/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVES We investigated circulating syndecan-1, HA and thrombomodulin levels in patients with biopsy-proven Class III/IV ± V LN and their clinico-pathological associations. Patients with non-renal SLE or non-lupus chronic kidney disease, and healthy subjects served as controls. METHODS Serum syndecan-1, HA and thrombomodulin levels were determined by ELISAs. RESULTS Syndecan-1, HA and thrombomodulin levels were significantly higher during active LN compared with remission (P < 0.01, for all), and correlated with the level of proteinuria, estimated glomerular filtration rate, anti-dsDNA antibodies, complement 3 and serum creatinine. Longitudinal studies showed that syndecan-1 and thrombomodulin levels increased prior to clinical renal flare by 3.6 months, while HA level increased at the time of nephritic flare, and the levels decreased in parallel with treatment response. Receiver operating characteristic curve analysis showed that syndecan-1 and thrombomodulin levels distinguished patients with active LN from healthy subjects, LN patients in remission, patients with active non-renal lupus and patients with non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.98, 0.91, 0.82 and 0.95, respectively, for syndecan-1; and area under curve of 1.00, 0.84, 0.97 and 0.79, respectively, for thrombomodulin). HA level distinguished active LN from healthy subjects, LN patients in remission and non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.82, 0.71 and 0.90, respectively) but did not distinguish between renal vs non-renal lupus. Syndecan-1 and thrombomodulin levels correlated with the severity of interstitial inflammation, while HA level correlated with chronicity grading in kidney biopsies of active LN. CONCLUSION Our findings suggest potential utility of serum syndecan-1, thrombomodulin and HA levels in clinical management, and their potential contribution to LN pathogenesis.
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Affiliation(s)
- Kelvin Y C Yu
- Department of Medicine, The University of Hong Kong, Hong Kong
| | - Susan Yung
- Department of Medicine, The University of Hong Kong, Hong Kong
| | - Mel K M Chau
- Department of Medicine, The University of Hong Kong, Hong Kong
| | - Colin S O Tang
- Department of Medicine, The University of Hong Kong, Hong Kong
| | - Desmond Y H Yap
- Department of Medicine, The University of Hong Kong, Hong Kong
| | | | - Shirley K Y Ying
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong
| | | | - Tak Mao Chan
- Department of Medicine, The University of Hong Kong, Hong Kong
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23
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Urinary Growth Differentiation Factor-15 (GDF15) levels as a biomarker of adverse outcomes and biopsy findings in chronic kidney disease. J Nephrol 2021; 34:1819-1832. [PMID: 33847920 DOI: 10.1007/s40620-021-01020-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 03/03/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Growth Differentiation Factor-15 (GDF15) is a member of the TGF-β superfamily. Increased serum GDF15 has been associated with increased risk of chronic kidney disease (CKD) progression. However, no prior studies have addressed the significance of urinary GDF15 in adult CKD. METHODS We measured serum and urinary GDF15 in a prospective cohort of 84 patients who underwent kidney biopsy and assessed their association with outcomes (survival, kidney replacement therapy) during a follow-up of 29 ± 17 months. RESULTS There was a statistically significant correlation between serum and urine GDF15 values. However, while serum GDF15 values increased with decreasing glomerular filtration rate, urinary GDF15 did not. Immunohistochemistry located kidney GDF15 expression mainly in tubular cells, and kidney GDF15 staining correlated with urinary GDF15 values. Urine GDF15 was significantly higher in patients with a histologic diagnosis of diabetic nephropathy than in diabetic patients without diabetic nephropathy. This was not the case for serum GDF15. Both serum and urine GDF15 were negatively associated with patient survival in multivariate models. However, when both urine and serum GDF15 were present in the model, lower urine GDF15 predicted patient survival [B coefficient (SEM) - 0.395 (0.182) p 0.03], and higher urine GDF15 predicted a composite of mortality or kidney replacement therapy [0.191 (0.06) p 0.002], while serum GDF15 was not predictive. Decision tree analysis yielded similar results. The area under the curve (AUC) of the receiver operating curve (ROC) for urine GDF15 as a predictor of mortality was 0.95 (95% CI 0.89-1.00, p < 0.001). CONCLUSIONS In conclusion, urinary GDF15 is associated with kidney histology patterns, mortality and the need for renal replacement therapy (RRT) in CKD patients who underwent a kidney biopsy.
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24
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Yepes‐Calderón M, Sotomayor CG, Pena M, Eisenga MF, Gans ROB, Berger SP, Moers C, Sugaya T, Doekharan D, Navis GJ, van den Born J, Bakker SJL. Urinary liver-type fatty acid-binding protein is independently associated with graft failure in outpatient kidney transplant recipients. Am J Transplant 2021; 21:1535-1544. [PMID: 32946659 PMCID: PMC8048636 DOI: 10.1111/ajt.16312] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/16/2020] [Accepted: 09/07/2020] [Indexed: 01/25/2023]
Abstract
Urinary liver-type fatty acid-binding protein (uL-FABP) is a biomarker of kidney hypoxia and ischemia, and thus offers a novel approach to identify early kidney insults associated with increased risk of graft failure in outpatient kidney transplant recipients (KTR). We investigated whether uL-FABP is associated with graft failure and whether it improves risk prediction. We studied a cohort of 638 outpatient KTR with a functional graft ≥1-year. During a median follow-up of 5.3 years, 80 KTR developed graft failure. uL-FABP (median 2.11, interquartile range 0.93-7.37 µg/24"/>h) was prospectively associated with the risk of graft failure (hazard ratio 1.75; 95% confidence interval 1.27-2.41 per 1-SD increment; P = .001), independent of potential confounders including estimated glomerular filtration rate and proteinuria. uL-FABP showed excellent discrimination ability for graft failure (c-statistic of 0.83) and its addition to a prediction model composed by established clinical predictors of graft failure significantly improved the c-statistic to 0.89 (P for F-test <.001). These results were robust to several sensitivity analyses. Further validation studies are warranted to evaluate the potential use of a risk-prediction model including uL-FABP to improve identification of outpatient KTR at high risk of graft failure in clinical care.
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Affiliation(s)
- Manuela Yepes‐Calderón
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Camilo G. Sotomayor
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Michelle Pena
- Departmant of Clinical Pharmacy and PharmacologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Michele F. Eisenga
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Rijk O. B. Gans
- Department of Internal MedicineUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Stefan P. Berger
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Cyril Moers
- Division of Transplantation SurgeryUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Takeshi Sugaya
- Department of MedicineSt. Marianna University School of MedicineKawasakiJapan
| | | | - Gerjan J. Navis
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Jaap van den Born
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
| | - Stephan J. L. Bakker
- Division of NephrologyUniversity Medical Center GroningenUniversity of GroningenGroningenthe Netherlands
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25
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Tavenier J, Andersen O, Nehlin JO, Petersen J. Longitudinal course of GDF15 levels before acute hospitalization and death in the general population. GeroScience 2021; 43:1835-1849. [PMID: 33763774 DOI: 10.1007/s11357-021-00359-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/19/2021] [Indexed: 11/29/2022] Open
Abstract
Growth differentiation 15 (GDF15) is a potential novel biomarker of biological aging. To separate the effects of chronological age and birth cohort from biological age, longitudinal studies investigating the associations of GDF15 levels with adverse health outcomes are needed. We investigated changes in GDF15 levels over 10 years in an age-stratified sample of the general population and their relation to the risk of acute hospitalization and death. Serum levels of GDF15 were measured three times in 5-year intervals in 2176 participants aged 30, 40, 50, or 60 years from the Danish population-based DAN-MONICA cohort. We assessed the association of single and repeated GDF15 measurements with the risk of non-traumatic acute hospitalizations. We tested whether changes in GDF15 levels over 10 years differed according to the frequency of hospitalizations within 2 years or survival within 20 years, after the last GDF15 measurement. The change in GDF15 levels over time was dependent on age and sex. Higher GDF15 levels and a greater increase in GDF15 levels were associated with an increased risk of acute hospitalization in adjusted Cox regression analyses. Participants with more frequent admissions within 2 years, and those who died within 20 years, after the last GDF15 measurement already had elevated GDF15 levels at baseline and experienced greater increases in GDF15 levels during the study. The change in GDF15 levels was associated with changes in C-reactive protein and biomarkers of kidney, liver, and cardiac function. Monitoring of GDF15 starting in middle-aged could be valuable for the prediction of adverse health outcomes.
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Affiliation(s)
- Juliette Tavenier
- Department of Clinical Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, DK-2650, Hvidovre, Denmark.
| | - Ove Andersen
- Department of Clinical Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, DK-2650, Hvidovre, Denmark.,Emergency Department, Copenhagen University Hospital Amager and Hvidovre, Kettegaard Alle 30, 2650, Hvidovre, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Jan O Nehlin
- Department of Clinical Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, DK-2650, Hvidovre, Denmark
| | - Janne Petersen
- Department of Clinical Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, DK-2650, Hvidovre, Denmark.,Center for Clinical Research and Prevention, Copenhagen University Hospital, Nordre Fasanvej 57, 2000, Frederiksberg, Denmark.,Section of Biostatistics, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1014, Copenhagen, Denmark
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26
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de Cos Gomez M, Benito Hernandez A, Garcia Unzueta MT, Mazon Ruiz J, Lopez del Moral Cuesta C, Perez Canga JL, San Segundo Arribas D, Valero San Cecilio R, Ruiz San Millan JC, Rodrigo Calabia E. Growth Differentiation Factor 15: A Biomarker with High Clinical Potential in the Evaluation of Kidney Transplant Candidates. J Clin Med 2020; 9:E4112. [PMID: 33419237 PMCID: PMC7766056 DOI: 10.3390/jcm9124112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/13/2020] [Accepted: 12/14/2020] [Indexed: 12/20/2022] Open
Abstract
Kidney transplantation implies a significant improvement in patient survival. Nevertheless, early mortality after transplant remains high. Growth differentiation factor 15 (GDF-15) is a novel biomarker under study as a mortality predictor in multiple scenarios. The aim of this study is to assess the utility of GDF-15 to predict survival in kidney transplant candidates. For this purpose, 395 kidney transplant recipients with pretransplant stored serum samples were included. The median GDF-15 was 5331.3 (50.49-16242.3) pg/mL. After a mean of 90.6 ± 41.5 months of follow-up, 82 (20.8%) patients died. Patients with higher GDF-15 levels (high risk tertile) had a doubled risk of mortality after adjustment by clinical characteristics (p = 0.009). After adjustment by EPTS (Estimated Post Transplant Survival score) the association remained significant for medium hazards ratios (HR) 3.24 95%CI (1.2-8.8), p = 0.021 and high risk tertiles HR 4.3 95%CI (1.65-11.54), p = 0.003. GDF-15 improved the prognostic accuracy of EPTS at 1-year (ΔAUC = 0.09, p = 0.039) and 3-year mortality (ΔAUC = 0.11, p = 0.036). Our study suggests an independent association between higher GDF-15 levels and mortality after kidney transplant, adding accuracy to the EPTS score, an established risk prediction model currently used in kidney transplant candidates.
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Affiliation(s)
- Marina de Cos Gomez
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Adalberto Benito Hernandez
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Maria Teresa Garcia Unzueta
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
- Clinical Analysis Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain
| | - Jaime Mazon Ruiz
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Covadonga Lopez del Moral Cuesta
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Jose Luis Perez Canga
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - David San Segundo Arribas
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
- Clinical Immunology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain
| | - Rosalia Valero San Cecilio
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Juan Carlos Ruiz San Millan
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
| | - Emilio Rodrigo Calabia
- Nephrology Department, Hospital Universitario Marques de Valdecilla, Avenida Valdecilla n 5, 39724 Santander, Spain; (A.B.H.); (J.M.R.); (C.L.d.M.C.); (J.L.P.C.); (R.V.S.C.); (J.C.R.S.M.); (E.R.C.)
- Valdecilla Biomedical Research Institute (IDIVAL), Cardenal Herrera Oria S/N, 39011 Santander, Spain; (M.T.G.U.); (D.S.S.A.)
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27
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Ali I, Ibrahim ST, Chinnadurai R, Green D, Taal M, Whetton TD, Kalra PA. A Paradigm to Discover Biomarkers Associated With Chronic Kidney Disease Progression. Biomark Insights 2020; 15:1177271920976146. [PMID: 33311975 PMCID: PMC7716058 DOI: 10.1177/1177271920976146] [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: 08/10/2020] [Accepted: 11/02/2020] [Indexed: 11/16/2022] Open
Abstract
Biomarker discovery in the field of risk prediction in chronic kidney disease (CKD) embraces the prospect of improving our ability to risk stratify future adverse outcomes and thereby guide patient care in a new era of personalised medicine. However, many studies that report biomarkers predictive of CKD progression share a key methodological limitation: failure to characterise patients' renal progression precisely. This weakens any observable association between a biomarker and an outcome poorly defined by a patient's change in renal function over time. In this commentary, we discuss the need for a better approach in this research arena and describe a compelling strategy that has the advantage of offering robust and meaningful biomarker exploration relevant to CKD progression.
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Affiliation(s)
- Ibrahim Ali
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.,Division of Cardiovascular Sciences, University of Manchester, Manchester, UK
| | - Sara T Ibrahim
- Internal Medicine and Nephrology Department, University of Alexandria, Alexandria, Egypt
| | - Rajkumar Chinnadurai
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK
| | - Darren Green
- Department of Acute Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK
| | - Maarten Taal
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Tony D Whetton
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.,Division of Cardiovascular Sciences, University of Manchester, Manchester, UK
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28
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Edfors R, Lindhagen L, Spaak J, Evans M, Andell P, Baron T, Mörtberg J, Rezeli M, Salzinger B, Lundman P, Szummer K, Tornvall P, Wallén HN, Jacobson SH, Kahan T, Marko-Varga G, Erlinge D, James S, Lindahl B, Jernberg T. Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction. J Intern Med 2020; 288:581-592. [PMID: 32638487 DOI: 10.1111/joim.13116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/30/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long-term outcomes. METHODS A total of 175 different biomarkers from MI patients enrolled in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes. RESULTS A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min-1 /1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor-1, adipocyte fatty acid-binding protein-4, TNF-related apoptosis-inducing ligand receptor 2, growth differentiation factor-15 and TNF receptor-2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization. CONCLUSION In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long-term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.
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Affiliation(s)
- R Edfors
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.,Bayer AB, Solna, Sweden
| | - L Lindhagen
- Uppsala Clinical Research Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - J Spaak
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - M Evans
- Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Renal Medicine, Karolinska Institutet, Stockholm, Sweden
| | - P Andell
- Department of Medicine, Unit of Cardiology, Karolinska Institutet, Stockholm, Sweden
| | - T Baron
- Uppsala Clinical Research Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - J Mörtberg
- Department of Clinical Sciences, Division of Renal Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - M Rezeli
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - B Salzinger
- Department of Clinical Sciences, Division of Renal Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - P Lundman
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - K Szummer
- Department of Medicine, Unit of Cardiology, Karolinska Institutet, Stockholm, Sweden
| | - P Tornvall
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - H N Wallén
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - S H Jacobson
- Department of Clinical Sciences, Division of Renal Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - T Kahan
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - G Marko-Varga
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - D Erlinge
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - S James
- Uppsala Clinical Research Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - B Lindahl
- Uppsala Clinical Research Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - T Jernberg
- From the, Department of Clinical Sciences, Division of Cardiovascular Medicine, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
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29
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Affiliation(s)
- Fatimah K Khalaf
- University of Toledo College of Medicine and Life Sciences Toledo OH
| | | | | | - David J Kennedy
- University of Toledo College of Medicine and Life Sciences Toledo OH
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30
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Lockhart SM, Saudek V, O’Rahilly S. GDF15: A Hormone Conveying Somatic Distress to the Brain. Endocr Rev 2020; 41:bnaa007. [PMID: 32310257 PMCID: PMC7299427 DOI: 10.1210/endrev/bnaa007] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 04/02/2020] [Indexed: 12/27/2022]
Abstract
GDF15 has recently gained scientific and translational prominence with the discovery that its receptor is a GFRAL-RET heterodimer of which GFRAL is expressed solely in the hindbrain. Activation of this receptor results in reduced food intake and loss of body weight and is perceived and recalled by animals as aversive. This information encourages a revised interpretation of the large body of previous research on the protein. GDF15 can be secreted by a wide variety of cell types in response to a broad range of stressors. We propose that central sensing of GDF15 via GFRAL-RET activation results in behaviors that facilitate the reduction of exposure to a noxious stimulus. The human trophoblast appears to have hijacked this signal, producing large amounts of GDF15 from early pregnancy. We speculate that this encourages avoidance of potential teratogens in pregnancy. Circulating GDF15 levels are elevated in a range of human disease states, including various forms of cachexia, and GDF15-GFRAL antagonism is emerging as a therapeutic strategy for anorexia/cachexia syndromes. Metformin elevates circulating GDF15 chronically in humans and the weight loss caused by this drug appears to be dependent on the rise in GDF15. This supports the concept that chronic activation of the GDF15-GFRAL axis has efficacy as an antiobesity agent. In this review, we examine the science of GDF15 since its identification in 1997 with our interpretation of this body of work now being assisted by a clear understanding of its highly selective central site of action.
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Affiliation(s)
- Samuel M Lockhart
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vladimir Saudek
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Stephen O’Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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31
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Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention. Sci Rep 2020; 10:7913. [PMID: 32404980 PMCID: PMC7220904 DOI: 10.1038/s41598-020-64636-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/02/2020] [Indexed: 12/15/2022] Open
Abstract
Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18–50 years old) with BMI 28–40 kg/m2, to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.
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Thorsteinsdottir H, Salvador CL, Mjøen G, Lie A, Sugulle M, Tøndel C, Brun A, Almaas R, Bjerre A. Growth Differentiation Factor 15 in Children with Chronic Kidney Disease and after Renal Transplantation. DISEASE MARKERS 2020; 2020:6162892. [PMID: 32089755 PMCID: PMC7026715 DOI: 10.1155/2020/6162892] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 10/29/2019] [Accepted: 01/04/2020] [Indexed: 12/23/2022]
Abstract
Growth differentiation factor 15 (GDF-15) is strongly associated with cardiovascular disease (CVD). The aim of our study was to evaluate plasma and urinary levels of GDF-15 after pediatric renal transplantation (Rtx) and in children with chronic kidney disease (CKD) and its associations to cardiovascular risk factors. In this cross-sectional study, GDF-15 was measured in plasma and urine from 53 children with a renal transplant and 83 children with CKD and related to cardiovascular risk factors (hypertension, obesity, and cholesterol) and kidney function. Forty healthy children served as a control group. Plasma levels of GDF-15 (median and range) for a Tx (transplantation) cohort, CKD cohort, and healthy controls were, respectively, 865 ng/L (463-3039 ng/L), 508 ng/L (183-3279 ng/L), and 390 ng/L (306-657 ng/L). The CKD and Tx cohorts both had significantly higher GDF-15 levels than the control group (p < 0.001). Univariate associations between GDF-15 and hyperuricemia (p < 0.001), elevated triglycerides (p = 0.028), low HDL (p = 0.038), and obesity (p = 0.028) were found. However, mGFR (p < 0.001) and hemoglobin (p < 0.001) were the only significant predictors of GDF-15 in an adjusted analysis. Urinary GDF-15/creatinine ratios were 448 ng/mmol (74-5013 ng/mmol) and 540 ng/mmol (5-14960 ng/mmol) in the Tx cohort and CKD cohort, respectively. In the CKD cohort, it was weakly correlated to mGFR (r = -0.343, p = 0.002). Plasma levels of GDF-15 are elevated in children with CKD and after Rtx. The levels were not associated with traditional cardiovascular risk factors but strongly associated with renal function.
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Affiliation(s)
- Hjordis Thorsteinsdottir
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Norway
- Institute of Clinical Medicine, University of Oslo, Norway
- Department of Pediatric Research, Oslo University Hospital, Norway
| | - Cathrin Lytomt Salvador
- Institute of Clinical Medicine, University of Oslo, Norway
- Department of Medical Biochemistry, Oslo University Hospital, Norway
| | - Geir Mjøen
- Department of Nephrology, Oslo University Hospital, Norway
| | - Anine Lie
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Norway
- Institute of Clinical Medicine, University of Oslo, Norway
| | - Meryam Sugulle
- Division of Gynaecology and Obstetrics, Oslo University Hospital, Norway
| | - Camilla Tøndel
- Department of Paediatrics, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Norway
| | - Atle Brun
- Laboratory for Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, University of Bergen, Norway
| | - Runar Almaas
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Norway
- Department of Pediatric Research, Oslo University Hospital, Norway
| | - Anna Bjerre
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Norway
- Institute of Clinical Medicine, University of Oslo, Norway
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Carlsson AC, Nowak C, Lind L, Östgren CJ, Nyström FH, Sundström J, Carrero JJ, Riserus U, Ingelsson E, Fall T, Ärnlöv J. Growth differentiation factor 15 (GDF-15) is a potential biomarker of both diabetic kidney disease and future cardiovascular events in cohorts of individuals with type 2 diabetes: a proteomics approach. Ups J Med Sci 2020; 125:37-43. [PMID: 31805809 PMCID: PMC7054929 DOI: 10.1080/03009734.2019.1696430] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background: Diabetic kidney disease (DKD) is a leading risk factor for end-stage renal disease and is one of the most important risk factors for cardiovascular disease in patients with diabetes. It is possible that novel markers portraying the pathophysiological underpinning processes may be useful.Aim: To investigate the associations between 80 circulating proteins, measured by a proximity extension assay, and prevalent DKD and major adverse cardiovascular events (MACE) in type 2 diabetes.Methods: We randomly divided individuals with type 2 diabetes from three cohorts into a two-thirds discovery and one-third replication set (total n = 813, of whom 231 had DKD defined by estimated glomerular filtration rate <60 mg/mL/1.73 m2 and/or urinary albumin-creatinine ratio ≥3 g/mol). Proteins associated with DKD were also assessed as predictors for incident major adverse cardiovascular events (MACE) in persons with DKD at baseline.Results: Four proteins were positively associated with DKD in models adjusted for age, sex, cardiovascular risk factors, glucose control, and diabetes medication: kidney injury molecule-1 (KIM-1, odds ratio [OR] per standard deviation increment, 1.65, 95% confidence interval [CI] 1.27-2.14); growth differentiation factor 15 (GDF-15, OR 1.40, 95% CI 1.16-1.69); myoglobin (OR 1.57, 95% CI 1.30-1.91), and matrix metalloproteinase 10 (MMP-10, OR 1.43, 95% CI 1.17-1.74). In patients with DKD, GDF-15 was significantly associated with increased risk of MACE after adjustments for baseline age, sex, microalbuminuria, and kidney function and (59 MACE events during 7 years follow-up, hazard ratio per standard deviation increase 1.43 [95% CI 1.03-1.98]) but not after further adjustments for cardiovascular risk factors.Conclusion: Our proteomics approach confirms and extends previous associations of higher circulating levels of GDF-15 with both micro- and macrovascular disease in patients with type 2 diabetes. Our data encourage additional studies evaluating the clinical utility of our findings.
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Affiliation(s)
- Axel C. Carlsson
- Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden
- CONTACT Axel C. Carlsson Department of Neurobiology Care Sciences and Society, Division for Family Medicine and Primary Care, 141 83 Huddinge, Sweden
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Carl Johan Östgren
- Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Fredrik H. Nyström
- Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Johan Sundström
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Juan Jesus Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ulf Riserus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Tove Fall
- Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden
- School of Health and Social Studies, Dalarna University, Falun, Sweden
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Hedman ÅK, Hage C, Sharma A, Brosnan MJ, Buckbinder L, Gan LM, Shah SJ, Linde CM, Donal E, Daubert JC, Mälarstig A, Ziemek D, Lund L. Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Heart 2020; 106:342-349. [DOI: 10.1136/heartjnl-2019-315481] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 11/08/2019] [Accepted: 11/18/2019] [Indexed: 01/08/2023] Open
Abstract
ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8–2.9); 5.7 (2.6–12.8); 2.9 (1.5–5.6); 2.7 (1.6–4.6); 2.1 (1.2–3.9)).ConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.
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Pérez-López L, Boronat M, Melián C, Brito-Casillas Y, Wägner AM. Animal Models and Renal Biomarkers of Diabetic Nephropathy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1307:521-551. [PMID: 32329028 DOI: 10.1007/5584_2020_527] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Diabetes mellitus (DM) is the first cause of end stage chronic kidney disease (CKD). Animal models of the disease can shed light on the pathogenesis of the diabetic nephropathy (DN) and novel and earlier biomarkers of the condition may help to improve diagnosis and prognosis. This review summarizes the most important features of animal models used in the study of DN and updates the most recent progress in biomarker research.
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Affiliation(s)
- Laura Pérez-López
- Institute of Biomedical and Health Research (IUIBS), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Mauro Boronat
- Institute of Biomedical and Health Research (IUIBS), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
- Department of Endocrinology and Nutrition, Complejo Hospitalario Universitario Insular Materno-Infantil, Las Palmas de Gran Canaria, Spain
| | - Carlos Melián
- Institute of Biomedical and Health Research (IUIBS), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
- Department of Animal Pathology, Veterinary Faculty, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Arucas, Las Palmas, Spain
| | - Yeray Brito-Casillas
- Institute of Biomedical and Health Research (IUIBS), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Ana M Wägner
- Institute of Biomedical and Health Research (IUIBS), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain.
- Department of Endocrinology and Nutrition, Complejo Hospitalario Universitario Insular Materno-Infantil, Las Palmas de Gran Canaria, Spain.
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Gómez-García EF, Cortés-Sanabria L, Cueto-Manzano AM, Medina-Zavala RS, Hernández-Ramos LE, Martínez-Ramírez HR, Vásquez-Jiménez JC, Mendoza-Carrera F. Interactions Between Diet Quality and Interleukin-6 Genotypes Are Associated With Metabolic and Renal Function Parameters in Mexican Patients With Type 2 Diabetes Mellitus. J Ren Nutr 2019; 30:223-231. [PMID: 31607549 DOI: 10.1053/j.jrn.2019.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/02/2019] [Accepted: 08/11/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE The aim of this study was to evaluate the interaction between diet quality and interleukin (IL)-6 genotypes and its association with metabolic and renal function parameters in Mexican patients with type 2 diabetes mellitus (T2DM). DESIGN AND METHODS Using an analytical cross-sectional design, 219 patients with T2DM (92 men; age 62 ± 10 years) were evaluated for selected metabolic and renal function parameters. Diet quality according to the Healthy Eating Index was evaluated and classified as good diet or poor diet in all patients. IL-6 serum concentrations and genotypes and haplotypes for IL6-597G > A (rs180097), -572G > C (rs180096), and -174G > C (rs180095) polymorphisms were determined. RESULTS Eighty-two percent of patients reported having a poor diet. Carriers of alleles -572C and -174C showed higher high-density lipoprotein cholesterol levels (44 ± 12 vs. 40 ± 9 mg/dL; P = .01) and lower total cholesterol levels (184 ± 33 vs. 197 ± 42 mg/dL; P = .03) than did those homozygous for G/G. Neither IL6 genotypes nor haplotypes were significantly associated with serum concentrations of IL-6. Some significant interactions between IL6 genotypes/haplotypes and diet quality were associated with body mass index, waist circumference, high-density lipoprotein cholesterol levels, and estimated glomerular filtration rate. CONCLUSIONS Interactions between diet quality and IL6 genotypes/haplotypes were associated with the main metabolic and renal function parameters in Mexican patients with T2DM. It will be important to consider genetic profiles in designing dietary portfolios and nutritional interventions for the management of such patients.
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Affiliation(s)
- Erika F Gómez-García
- Molecular Medicine Division, Centro de Investigación Biomédica de Occidente (CIBO), Jalisco Delegation, Instituto Mexicano del Seguro Social (IMSS), Guadalajara, Mexico; Medical Research Unit for Renal Diseases, Hospital de Especialidades, Centro Médico Nacional de Occidente, IMSS, Guadalajara, Mexico
| | - Laura Cortés-Sanabria
- Medical Research Unit for Renal Diseases, Hospital de Especialidades, Centro Médico Nacional de Occidente, IMSS, Guadalajara, Mexico
| | - Alfonso M Cueto-Manzano
- Medical Research Unit for Renal Diseases, Hospital de Especialidades, Centro Médico Nacional de Occidente, IMSS, Guadalajara, Mexico
| | - R Susana Medina-Zavala
- Medical Research Unit for Renal Diseases, Hospital de Especialidades, Centro Médico Nacional de Occidente, IMSS, Guadalajara, Mexico
| | - Luis Eduardo Hernández-Ramos
- Molecular Medicine Division, Centro de Investigación Biomédica de Occidente (CIBO), Jalisco Delegation, Instituto Mexicano del Seguro Social (IMSS), Guadalajara, Mexico
| | - Héctor Ramón Martínez-Ramírez
- Medical Research Unit for Renal Diseases, Hospital de Especialidades, Centro Médico Nacional de Occidente, IMSS, Guadalajara, Mexico
| | | | - Francisco Mendoza-Carrera
- Molecular Medicine Division, Centro de Investigación Biomédica de Occidente (CIBO), Jalisco Delegation, Instituto Mexicano del Seguro Social (IMSS), Guadalajara, Mexico.
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Deng X, Yang Q, Wang Y, Yang Y, Pei G, Zhu H, Wu J, Wang M, Zhao Z, Xu H, Zhou C, Guo Y, Yao Y, Zhang Z, Liao W, Zeng R. Association of plasma macrophage colony-stimulating factor with cardiovascular morbidity and all-cause mortality in chronic hemodialysis patients. BMC Nephrol 2019; 20:321. [PMID: 31419967 PMCID: PMC6697977 DOI: 10.1186/s12882-019-1510-z] [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: 01/12/2019] [Accepted: 07/31/2019] [Indexed: 11/23/2022] Open
Abstract
Background Cardiovascular disease (CVD) events are the main cause of death in long-term hemodialysis (HD) patients. Macrophage colony- stimulating factor (M-CSF) is actively involved in the formation of atherosclerosis and causes plaque instability, thrombosis and the development of acute coronary syndromes. However, little information is available on the role of M-CSF in HD patients. We aimed to investigate the association between plasma M-CSF levels and CVD events as well as all-cause mortality in patients undergoing long-term HD. Methods Fifty two HD patients and 8 healthy controls were recruited in this study. HD patients were followed up from September 2014 to May 2017. The primary end point was CVD event, the secondary outcome was death from any cause. Patients were divided into two groups with low and high M-CSF levels based on the optimal cut-off value determined by the ROC curve. Cox regression analyses were used to assess the predictive value of plasma M-CSF for CVD events and all-cause mortality in HD patients. We tested the levels of plasma M-CSF and other inflammatory cytokines in surviving HD patients using ELISA or CBA kit. Results The average plasma level of M-CSF in 52 patients was approximately twice that of healthy controls (992.4 vs. 427.2 pg/mL; p < 0.05). During 32 months of follow-up, 26 patients (50.0%) had at least one CVD event and 8 patients (15.4%) died. The mean plasma M-CSF concentration increased in survivors after follow-up compared to that detected at baseline (1277.8 ± 693.3 vs. 997.2 ± 417.4 pg/mL; p < 0.05). Multivariate Cox regression analysis showed that plasma M-CSF is an independent risk factor for CVD events in HD patients (p < 0.05). In the Cox regression model after adjusting for gender and age, high M-CSF levels were related to an increased risk of all-cause death (p < 0.05). We also found that M-CSF levels were positively correlated with IL-6 and IL-18 levels (both p < 0.05), which are the major pathogentic cytokines that contribute to HD-related CVD events. Conclusion M-CSF is a prognostic factor for CVD events and all-cause mortality in HD patients.
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Affiliation(s)
- Xuan Deng
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Qian Yang
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Yuxi Wang
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Yi Yang
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Guangchang Pei
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Han Zhu
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Jianliang Wu
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Meng Wang
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Zhi Zhao
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Huzi Xu
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Cheng Zhou
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Yi Guo
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Ying Yao
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Zhiguo Zhang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China
| | - Wenhui Liao
- Department of Geriatrics, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China.
| | - Rui Zeng
- Department of Nephrology, Tongji Hospital Affiliated with Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China.
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Scurt FG, Menne J, Brandt S, Bernhardt A, Mertens PR, Haller H, Chatzikyrkou C. Systemic Inflammation Precedes Microalbuminuria in Diabetes. Kidney Int Rep 2019; 4:1373-1386. [PMID: 31701047 PMCID: PMC6829192 DOI: 10.1016/j.ekir.2019.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/22/2019] [Accepted: 06/03/2019] [Indexed: 12/13/2022] Open
Abstract
Aim The aim of the case-control study was to investigate if serum biomarkers indicative of vascular inflammation and endothelial dysfunction can predict the development of microalbuminuria in patients with diabetes mellitus type 2. Methods Among participants enrolled in the ROADMAP (Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention) and observational follow-up (OFU) studies, a panel of 15 serum biomarkers was quantified from samples obtained at initiation of the study and tested for associations with the development of new-onset microalbuminuria during follow-up. A case-control study was conducted with inclusion of 172 patients with microalbuminuria and 188 matched controls. Nonparametric inferential, nonlinear regression, mediation, and bootstrapping statistical methods were used for the analysis. Results The median follow-up time was 37 months. At baseline, mean concentrations of C-X-C motif chemokine ligand 16 (CXCL-16), transforming growth factor (TGF)–β1 and angiopoietin-2 were higher in patients with subsequent microalbuminuria. In the multivariate analysis, after adjustment for age, sex, body mass index, glycated hemoglobin, duration of diabetes, low-density lipoprotein (LDL), smoking status, blood pressure, baseline urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), time of follow-up and cardiovascular disease, CXCL-16 (odds ratio [OR] 2.60, 95% confidence interval [CI] 1.71–3.96), angiopoietin-2 (OR 1.50, 95% CI 1.14–1.98) and TGF-β1 (OR 1.03, 95% CI 1.01–1.04) remained significant predictors of new-onset microalbuminuria (P < 0.001). Inclusion of these biomarkers in conventional clinical risk models for prediction of microalbuminuria increased the area under the curve (AUC) from 0.638 to 0.760 (P < 0.001). Conclusion In patients with type 2 diabetes, elevated plasma levels of CXCL-16, angiopoietin-2, and TGF-β1 are independently predictive of microalbuminuria. Thus, these serum markers improve renal risk models beyond established clinical risk factors.
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Affiliation(s)
- Florian G Scurt
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology, and Inflammation, Otto-von-Guericke University, Magdeburg, Germany
| | - Jan Menne
- Nephrology Section, Hanover Medical School, Hanover, Germany
| | - Sabine Brandt
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology, and Inflammation, Otto-von-Guericke University, Magdeburg, Germany
| | - Anja Bernhardt
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology, and Inflammation, Otto-von-Guericke University, Magdeburg, Germany
| | - Peter R Mertens
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology, and Inflammation, Otto-von-Guericke University, Magdeburg, Germany
| | - Hermann Haller
- Nephrology Section, Hanover Medical School, Hanover, Germany
| | - Christos Chatzikyrkou
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology, and Inflammation, Otto-von-Guericke University, Magdeburg, Germany.,Nephrology Section, Hanover Medical School, Hanover, Germany
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Targeted Proteomic Analysis Detects Acute T Cell-Mediated Kidney Allograft Rejection in Belatacept-Treated Patients. Ther Drug Monit 2019; 41:243-248. [PMID: 30883517 DOI: 10.1097/ftd.0000000000000587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND There is an unmet need for reliable minimally invasive diagnostic biomarkers for immunological allograft monitoring and for the detection of acute kidney transplant rejection. Here, targeted proteomic analysis was applied to compare 92 proteins in sera of belatacept-treated patients who had biopsy-proven, acute T-cell-mediated rejection (aTCMR) with patients without aTCMR. METHODS Proximity extension immunoassay was used to measure 92 inflammation-related protein concentrations in the prerejection and rejection sera of 11 patients with aTCMR and 9 patients without aTCMR. This assay uses 2 matched oligonucleotide-labeled antibody probes for each protein and polymerase chain reaction to measure normalized protein expression values. RESULTS Five proteins (CD5, CD8A, NCR1, TNFRSF4, and TNFRSF9) were expressed significantly higher in samples with aTCMR compared with samples without aTCMR (adjusted P-value < 0.014) and had a good predictive capacity for aTCMR [area under the curve in a receiver-operator curve ranged from 0.83 to 0.91 (P < 0.014)]. These proteins are associated with CD8 cytotoxic T-cell and NK cell functions. Nonhierarchical clustering analysis showed distinct clustering of samples with aTCMR and samples without aTCMR. This clustering was not found in prerejection samples (1 month after transplantation). In prerejection samples, IFN-γ was expressed at a significantly lower level (normalized protein expression value median -0.15, interquartile range: -0.27 to 0.04) than in samples of patients without rejection (median 0.13, interquartile range: -0.07 to 0.15, adjusted P-value = 0.00367). CONCLUSIONS Targeted proteomic analysis with proximity extension immunoassay is a promising minimally invasive technique to diagnose aTCMR in kidney transplant recipients.
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TRAIL, OPG, and TWEAK in kidney disease: biomarkers or therapeutic targets? Clin Sci (Lond) 2019; 133:1145-1166. [PMID: 31097613 PMCID: PMC6526163 DOI: 10.1042/cs20181116] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 04/19/2019] [Accepted: 04/29/2019] [Indexed: 12/11/2022]
Abstract
Ligands and receptors of the tumor necrosis factor (TNF) superfamily regulate immune responses and homeostatic functions with potential diagnostic and therapeutic implications. Kidney disease represents a global public health problem, whose prevalence is rising worldwide, due to the aging of the population and the increasing prevalence of diabetes, hypertension, obesity, and immune disorders. In addition, chronic kidney disease is an independent risk factor for the development of cardiovascular disease, which further increases kidney-related morbidity and mortality. Recently, it has been shown that some TNF superfamily members are actively implicated in renal pathophysiology. These members include TNF-related apoptosis-inducing ligand (TRAIL), its decoy receptor osteoprotegerin (OPG), and TNF-like weaker inducer of apoptosis (TWEAK). All of them have shown the ability to activate crucial pathways involved in kidney disease development and progression (e.g. canonical and non-canonical pathways of the transcription factor nuclear factor-kappa B), as well as the ability to regulate cell proliferation, differentiation, apoptosis, necrosis, inflammation, angiogenesis, and fibrosis with double-edged effects depending on the type and stage of kidney injury. Here we will review the actions of TRAIL, OPG, and TWEAK on diabetic and non-diabetic kidney disease, in order to provide insights into their full clinical potential as biomarkers and/or therapeutic options against kidney disease.
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Bidin MZ, Shah AM, Stanslas J, Seong CLT. Blood and urine biomarkers in chronic kidney disease: An update. Clin Chim Acta 2019; 495:239-250. [PMID: 31009602 DOI: 10.1016/j.cca.2019.04.069] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 04/17/2019] [Accepted: 04/17/2019] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Chronic kidney disease (CKD) is a silent disease. Most CKD patients are unaware of their condition during the early stages of the disease which poses a challenge for healthcare professionals to institute treatment or start prevention. The trouble with the diagnosis of CKD is that in most parts of the world, it is still diagnosed based on measurements of serum creatinine and corresponding calculations of eGFR. There are controversies with the current staging system, especially in the methodology to diagnose and prognosticate CKD. OBJECTIVE The aim of this review is to examine studies that focused on the different types of samples which may serve as a good and promising biomarker for early diagnosis of CKD or to detect rapidly declining renal function among CKD patient. METHOD The review of international literature was made on paper and electronic databases Nature, PubMed, Springer Link and Science Direct. The Scopus index was used to verify the scientific relevance of the papers. Publications were selected based on the inclusion and exclusion criteria. RESULT 63 publications were found to be compatible with the study objectives. Several biomarkers of interest with different sample types were taken for comparison. CONCLUSION Biomarkers from urine samples yield more significant outcome as compare to biomarkers from blood samples. But, validation and confirmation with a different type of study designed on a larger population is needed. More comparison studies on different types of samples are needed to further illuminate which biomarker is the better tool for the diagnosis and prognosis of CKD.
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Affiliation(s)
- Mohammad Zulkarnain Bidin
- Nephrology Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.
| | - Anim Md Shah
- Nephrology Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia; Nephrology Department, Serdang Hospital, Selangor, Malaysia
| | - J Stanslas
- Pharmacotherapeutics Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Christopher Lim Thiam Seong
- Nephrology Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia; Nephrology Department, Serdang Hospital, Selangor, Malaysia.
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Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R. Big science and big data in nephrology. Kidney Int 2019; 95:1326-1337. [PMID: 30982672 DOI: 10.1016/j.kint.2018.11.048] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 11/11/2018] [Accepted: 11/20/2018] [Indexed: 12/16/2022]
Abstract
There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.
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Affiliation(s)
- Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany; Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany; Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany.
| | - Markus M Rinschen
- Department II of Internal Medicine, and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany; Center for Mass Spectrometry and Metabolomics, The Scripps Research Institute, La Jolla, California, USA
| | - Jürgen Floege
- RWTH Aachen, Department of Nephrology and Clinical Immunology, Aachen, Germany
| | - Rafael Kramann
- RWTH Aachen, Department of Nephrology and Clinical Immunology, Aachen, Germany; Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands.
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Lind L, Sundström J, Larsson A, Lampa E, Ärnlöv J, Ingelsson E. Longitudinal effects of aging on plasma proteins levels in older adults - associations with kidney function and hemoglobin levels. PLoS One 2019; 14:e0212060. [PMID: 30802263 PMCID: PMC6388926 DOI: 10.1371/journal.pone.0212060] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/26/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND A targeted proteomics chip has been shown to be useful to discover novel associations of proteins with cardiovascular disease. We investigated how these proteins change with aging, and whether this change is related to a decline in kidney function, or to a change in hemoglobin levels. MATERIAL AND METHODS In the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study, including 1,016 participants from the general population aged 70 at baseline, 84 proteins were measured at ages 70, 75, 80. At these occasions, glomerular filtration rate (eGFR) was estimated and the hemoglobin levels were measured. RESULTS Sixty-one of the 84 evaluated proteins changed significantly during the 10-year follow-up (multiple testing-adjusted alpha = 0.00059), most showing an increase. The change in eGFR was inversely related to changes of protein levels for the vast majority of proteins (74%). The change in hemoglobin was significantly related to the change in 40% of the evaluated proteins, with no obvious preference of the direction of these relationships. CONCLUSION The majority of evaluated proteins increased with aging in adults. Therefore, normal ranges for proteins might be given in age-strata. The increase in protein levels was associated with the degree of reduction in eGFR for the majority of proteins, while no clear pattern was seen for the relationships between the proteins and the change in hemoglobin levels. Studies on changes in urinary proteins are warranted to understand the association between the reduction in eGFR and increase in plasma protein levels.
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Affiliation(s)
- Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- * E-mail:
| | - Johan Sundström
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala, Sweden
| | - Anders Larsson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Lampa
- Uppsala Clinical Research Center, Uppsala, Sweden
| | - Johan Ärnlöv
- School of Health and Social Studies, Dalarna University, Falun, Sweden
- Division of Family Medicine, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, United States of America
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States of America
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Abstract
Fatty acid-binding proteins (FABPs), a family of lipid chaperones, contribute to systemic metabolic regulation via several lipid signaling pathways. Fatty acid-binding protein 4 (FABP4), known as adipocyte FABP (A-FABP) or aP2, is mainly expressed in adipocytes and macrophages and plays important roles in the development of insulin resistance and atherosclerosis in relation to metabolically driven low-grade and chronic inflammation, referred to as ‘metaflammation’. FABP4 is secreted from adipocytes in a non-classical pathway associated with lipolysis and acts as an adipokine for the development of insulin resistance and atherosclerosis. Circulating FABP4 levels are associated with several aspects of metabolic syndrome and cardiovascular disease. Ectopic expression and function of FABP4 in cells and tissues are also related to the pathogenesis of several diseases. Pharmacological modification of FABP4 function by specific inhibitors, neutralizing antibodies or antagonists of unidentified receptors would be novel therapeutic strategies for several diseases, including obesity, diabetes mellitus, atherosclerosis and cardiovascular disease. Significant roles of FABP4 as a lipid chaperone in physiological and pathophysiological conditions and the possibility of FABP4 being a therapeutic target for metabolic and cardiovascular diseases are discussed in this review.
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Affiliation(s)
- Masato Furuhashi
- Department of Cardiovascular, Renal and Metabolic Medicine, Sapporo Medical University School of Medicine
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Chang WW, Liang W, Yao XM, Zhang L, Zhu LJ, Yan C, Jin YL, Yao YS. Tumour necrosis factor-related apoptosis-inducing ligand expression in patients with diabetic nephropathy. J Renin Angiotensin Aldosterone Syst 2019; 19:1470320318785744. [PMID: 29999450 PMCID: PMC6047249 DOI: 10.1177/1470320318785744] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Objective: The objective of this study was to evaluate the expression profile of tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) in patients with diabetic nephropathy (DN). Methods: A total of 126 Chinese subjects were enrolled in this study, including 42 patients with diabetes mellitus (DM), 42 patients with DN and 42 healthy controls. Real-time polymerase chain reaction was performed to analyze levels of TRAIL mRNA in peripheral blood mononuclear cells (PBMCs). Serum levels of soluble TRAIL (sTRAIL) and various cytokines were detected with a commercially available enzyme-linked immunosorbent assay kit. Results: Compared with the control group, the levels of TRAIL mRNA in PBMCs and sTRAIL in sera were both significantly decreased in the DM and DN patients (P < 0.05). Conversely, levels of interleukin (IL)-1, IL-6, tumour necrosis factor-α and monocyte chemotactic protein-1 were higher in the DN group than in the control group. Serum levels of TRAIL positively correlated with TRAIL mRNA levels in all of the subjects examined (P < 0.05). Conclusions: These results provide support and a theoretical basis for further research of TRAIL in regard to the pathogenesis of DN.
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Affiliation(s)
- Wei-Wei Chang
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, China
| | - Wei Liang
- 2 Department of Hospital Infection Management, Zhenjiang First People's Hospital, China
| | - Xin-Ming Yao
- 3 Department of Endocrine, the First Affiliated Hospital of Wannan Medical College, China
| | - Liu Zhang
- 4 Department of Hospital Infection Management Office, Wuhu Hospital of Traditional Chinese Medicine, China
| | - Li-Jun Zhu
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, China
| | - Chen Yan
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, China
| | - Yue-Long Jin
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, China
| | - Ying-Shui Yao
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, China
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Dendritic cell maturation in the corneal epithelium with onset of type 2 diabetes is associated with tumor necrosis factor receptor superfamily member 9. Sci Rep 2018; 8:14248. [PMID: 30250206 PMCID: PMC6155153 DOI: 10.1038/s41598-018-32410-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 09/03/2018] [Indexed: 12/29/2022] Open
Abstract
Type 2 diabetes mellitus is characterized by a low-grade inflammation; however, mechanisms leading to this inflammation in specific tissues are not well understood. The eye can be affected by diabetes; thus, we hypothesized that inflammatory changes in the eye may parallel the inflammation that develops with diabetes. Here, we developed a non-invasive means to monitor the status of inflammatory dendritic cell (DC) subsets in the corneal epithelium as a potential biomarker for the onset of inflammation in type 2 diabetes. In an age-matched cohort of 81 individuals with normal and impaired glucose tolerance and type 2 diabetes, DCs were quantified from wide-area maps of the corneal epithelial sub-basal plexus, obtained using clinical in vivo confocal microscopy (IVCM). With the onset of diabetes, the proportion of mature, antigen-presenting DCs increased and became organized in clusters. Out of 92 plasma proteins analysed in the cohort, tumor necrosis factor receptor super family member 9 (TNFRSF9) was associated with the observed maturation of DCs from an immature to mature antigen-presenting phenotype. A low-grade ocular surface inflammation observed in this study, where resident immature dendritic cells are transformed into mature antigen-presenting cells in the corneal epithelium, is a process putatively associated with TNFRSF9 signalling and may occur early in the development of type 2 diabetes. IVCM enables this process to be monitored non-invasively in the eye.
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Abstract
Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.
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Affiliation(s)
- Helen M Colhoun
- MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK.
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Affiliation(s)
- Stein Ivar Hallan
- Department of Clinical and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway; and Department of Nephrology, St. Olav University Hospital, Trondheim, Norway
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