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Cañadas-Garre M, Baños-Jaime B, Maqueda JJ, Smyth LJ, Cappa R, Skelly R, Hill C, Brennan EP, Doyle R, Godson C, Maxwell AP, McKnight AJ. Genetic variants affecting mitochondrial function provide further insights for kidney disease. BMC Genomics 2024; 25:576. [PMID: 38858654 PMCID: PMC11163707 DOI: 10.1186/s12864-024-10449-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a complex disorder that has become a high prevalence global health problem, with diabetes being its predominant pathophysiologic driver. Autosomal genetic variation only explains some of the predisposition to kidney disease. Variations in the mitochondrial genome (mtDNA) and nuclear-encoded mitochondrial genes (NEMG) are implicated in susceptibility to kidney disease and CKD progression, but they have not been thoroughly explored. Our aim was to investigate the association of variation in both mtDNA and NEMG with CKD (and related traits), with a particular focus on diabetes. METHODS We used the UK Biobank (UKB) and UK-ROI, an independent collection of individuals with type 1 diabetes mellitus (T1DM) patients. RESULTS Fourteen mitochondrial variants were associated with estimated glomerular filtration rate (eGFR) in UKB. Mitochondrial variants and haplogroups U, H and J were associated with eGFR and serum variables. Mitochondrial haplogroup H was associated with all the serum variables regardless of the presence of diabetes. Mitochondrial haplogroup X was associated with end-stage kidney disease (ESKD) in UKB. We confirmed the influence of several known NEMG on kidney disease and function and found novel associations for SLC39A13, CFL1, ACP2 or ATP5G1 with serum variables and kidney damage, and for SLC4A1, NUP210 and MYH14 with ESKD. The G allele of TBC1D32-rs113987180 was associated with higher risk of ESKD in patients with diabetes (OR:9.879; CI95%:4.440-21.980; P = 2.0E-08). In UK-ROI, AGXT2-rs71615838 and SURF1-rs183853102 were associated with diabetic nephropathies, and TFB1M-rs869120 with eGFR. CONCLUSIONS We identified novel variants both in mtDNA and NEMG which may explain some of the missing heritability for CKD and kidney phenotypes. We confirmed the role of MT-ND5 and mitochondrial haplogroup H on renal disease (serum variables), and identified the MT-ND5-rs41535848G variant, along with mitochondrial haplogroup X, associated with higher risk of ESKD. Despite most of the associations were independent of diabetes, we also showed potential roles for NEMG in T1DM.
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Affiliation(s)
- Marisa Cañadas-Garre
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK.
- Genomic Oncology Area, Centre for Genomics and Oncological Research: Pfizer, GENYO, University of Granada-Andalusian Regional Government, PTS Granada. Avenida de La Ilustración 114, 18016, Granada, Spain.
- Hematology Department, Hospital Universitario Virgen de Las Nieves, Avenida de Las Fuerzas Armadas 2, 18014, Granada, Spain.
- Instituto de Investigación Biosanitaria de Granada (Ibs.GRANADA), Avda. de Madrid, 15, 18012, Granada, Spain.
| | - Blanca Baños-Jaime
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
- Instituto de Investigaciones Químicas (IIQ), Centro de Investigaciones Científicas Isla de La Cartuja (cicCartuja), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Sevilla, Avda. Américo Vespucio 49, 41092, Seville, Spain
| | - Joaquín J Maqueda
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
- Experimental Oncology Laboratory, IRCCS Rizzoli Orthopaedic Institute, 40136, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, 40126, Bologna, Italy
| | - Laura J Smyth
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
| | - Ruaidhri Cappa
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
| | - Ryan Skelly
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
| | - Claire Hill
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
| | - Eoin P Brennan
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
- School of Medicine, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Ross Doyle
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
- School of Medicine, University College Dublin, Dublin, D04 V1W8, Ireland
- Mater Misericordiae University Hospital, Eccles St, Dublin, D07 R2WY, Ireland
| | - Catherine Godson
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
- School of Medicine, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Alexander P Maxwell
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
- Regional Nephrology Unit, Belfast City Hospital, Level 11Lisburn Road, Belfast, BT9 7AB, UK
| | - Amy Jayne McKnight
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health,, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK
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Kim JY, Chun SY, Lim H, Chang TI. Association between familial aggregation of chronic kidney disease and its incidence and progression. Sci Rep 2023; 13:5131. [PMID: 36991140 PMCID: PMC10060248 DOI: 10.1038/s41598-023-32362-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
This study aimed to examine the association between familial aggregation of chronic kidney disease (CKD) and risk of CKD development and its progression. This nationwide family study comprised 881,453 cases with newly diagnosed CKD between 2004 and 2017 and 881,453 controls without CKD matched by age and sex, using data from the Korean National Health Insurance Service with linkage to the family tree database. The risks of CKD development and disease progression, defined as an incident end-stage renal disease (ESRD), were evaluated. The presence of any affected family member with CKD was associated with a significantly higher risk of CKD with adjusted ORs (95% CI) of 1.42 (1.38-1.45), 1.50 (1.46-1.55), 1.70 (1.64-1.77), and 1.30 (1.27-1.33) for individuals with affected parents, offspring, siblings, and spouses, respectively. In Cox models conducted on patients with predialysis CKD, risk of incident ESRD was significantly higher in those with affected family members with ESRD. The corresponding HRs (95% CI) were 1.10 (1.05-1.15), 1.38 (1.32-1.46), 1.57 (1.49-1.65), and 1.14 (1.08-1.19) for individuals listed above, respectively. Familial aggregation of CKD was strongly associated with a higher risk of CKD development and disease progression to ESRD.
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Affiliation(s)
- Jae Young Kim
- Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, 100 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10444, Republic of Korea
- Department of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung-Youn Chun
- Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Hyunsun Lim
- Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Tae Ik Chang
- Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, 100 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10444, Republic of Korea.
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Pan Y, Sun X, Mi X, Huang Z, Hsu Y, Hixson JE, Munzy D, Metcalf G, Franceschini N, Tin A, Köttgen A, Francis M, Brody JA, Kestenbaum B, Sitlani CM, Mychaleckyj JC, Kramer H, Lange LA, Guo X, Hwang SJ, Irvin MR, Smith JA, Yanek LR, Vaidya D, Chen YDI, Fornage M, Lloyd-Jones DM, Hou L, Mathias RA, Mitchell BD, Peyser PA, Kardia SLR, Arnett DK, Correa A, Raffield LM, Vasan RS, Cupple LA, Levy D, Kaplan RC, North KE, Rotter JI, Kooperberg C, Reiner AP, Psaty BM, Tracy RP, Gibbs RA, Morrison AC, Feldman H, Boerwinkle E, He J, Kelly TN. Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease. Hum Mol Genet 2023; 32:1048-1060. [PMID: 36444934 PMCID: PMC9990994 DOI: 10.1093/hmg/ddac290] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022] Open
Abstract
Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.
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Affiliation(s)
- Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Xiao Sun
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Xuenan Mi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Zhijie Huang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Yenchih Hsu
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James E Hixson
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Donna Munzy
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nora Franceschini
- Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Adrienne Tin
- University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center – University of Freiburg, Freiburg 79106, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Michael Francis
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | | | - Jennifer A Brody
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Bryan Kestenbaum
- University of Washington, Department of Medicine, Division of Nephrology, Kidney Research Institute, Seattle, WA 98195, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA 22903, USA
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL 60153, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL 35233, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dhananjay Vaidya
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY 40506, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27516, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - L Adrienne Cupple
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Kari E North
- Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Department of Health Services, University of Washington, Seattle, WA 98195, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Harold Feldman
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiang He
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
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Zhong Y, Wu Y, Yang Y, Chen Y, Hui R, Zhang M, Zhang W. Association of MPPED2 gene variant rs10767873 with kidney function and risk of cardiovascular disease in patients with hypertension. J Hum Genet 2023; 68:393-398. [PMID: 36797372 DOI: 10.1038/s10038-022-01118-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 02/18/2023]
Abstract
Changes in kidney function and the progression of chronic kidney disease (CKD) are associated with the risk of cardiovascular disease (CVD) and influenced by genetic factors. However, the association between genetic variants and kidney function in patients treated with antihypertensive drugs remains uncertain. This study aimed to examine the association between 30 variants locating at the 22 genes and the risk of kidney function evaluated by the estimated glomerular filtration rate (eGFR) in 1911 patients with hypertension from a Chinese community-based longitudinal cohort (including 1220 participants with CKD and 691 without CKD at baseline). By using multivariate linear regression analysis after adjustment for age, sex, traditional cardiovascular risk factors, and the use of antihypertensive drugs, as well as after correction for multiple comparison, patients with rs10767873T allele of the metallophosphoesterase domain containing 2 (MPPED2) gene were associated with higher level of eGFR (β = 0.041, p = 0.01) and lower levels of serum creatinine (β = -0.068, p = 0.001) and serum uric acid (β = -0.047, p = 0.02). But variant rs10767873 was not found to be associated with the risk of CKD, regardless of the types of antihypertensive drugs used. During a median 2.25-year follow-up, 152 CVD events were documented. Interestingly, patients with the rs10767873TT genotype had an increased risk of CVD events (hazard ratio, 1.74, 95% confidence interval, 1.11 to 2.73; p = 0.02) compared with patients carrying the wild-type genotype of rs10767873CC. In conclusion, our findings suggest variant rs10767873 of the MPPED2 gene is associated with kidney function and risk of CVD in Chinese hypertensive patients.
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Affiliation(s)
- Yixuan Zhong
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Yiyi Wu
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China.,The First Affiliated Hospital of Anhui University of Science and Technology (The First People's Hospital of Huainan City), Huainan, 232000, Anhui, China
| | - Yunyun Yang
- The First Affiliated Hospital of Xiamen University; Clinical laboratory; Xiamen Key Laboratory of Genetic Testing, Xiamen, 361000, Fujian, China
| | - Yu Chen
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Rutai Hui
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Mei Zhang
- The First Affiliated Hospital of Anhui University of Science and Technology (The First People's Hospital of Huainan City), Huainan, 232000, Anhui, China.
| | - Weili Zhang
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China. .,Central-China Branch of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Hospital, Zhengzhou, 450046, China.
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Yan P, Ke B, Song J, Fang X. Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis. Front Genet 2023; 14:1111976. [PMID: 36814902 PMCID: PMC9939663 DOI: 10.3389/fgene.2023.1111976] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Background: Chronic kidney disease (CKD) is a heterogeneous disease with multiple etiologies, risk factors, clinical manifestations, and prognosis. The aim of this study was to identify different immune-related molecular clusters in CKD, their functional immunological properties, and to screen for promising diagnostic markers. Methods: Datasets of 440 CKD patients were obtained from the comprehensive gene expression database. The core immune-related genes (IRGs) were identified by weighted gene co-expression network analysis. We used unsupervised clustering to divide CKD samples into two immune-related subclusters. Then, functional enrichment analysis was performed for differentially expressed genes (DEGs) between clusters. Three machine learning methods (LASSO, RF, and SVM-RFE) and Venn diagrams were applied to filter out 5 significant IRGs with distinguished subtypes. A nomogram diagnostic model was developed, and the prediction effect was verified using calibration curve, decision curve analysis. CIBERSORT was applied to assess the variation in immune cell infiltration among clusters. The expression levels, immune characteristics and immune cell correlation of core diagnostic markers were investigated. Finally, the Nephroseq V5 was used to assess the correlation among core diagnostic markers and renal function. Results: The 15 core IRGs screened were differentially expressed in normal and CKD samples. CKD was classified into two immune-related molecular clusters. Cluster 2 is significantly enriched in biological functions such as leukocyte adhesion and regulation as well as immune activation, and has a severe immune prognosis compared to cluster 1. A nomogram diagnostic model with reliable prediction of immune-related clusters was developed based on five signature genes. The core diagnostic markers LYZ, CTSS, and ISG20 were identified as playing an important role in the immune microenvironment and were shown to correlate meaningfully with immune cell infiltration and renal function. Conclusion: Our study identifies two subtypes of CKD with distinct immune gene expression patterns and provides promising predictive models. Along with the exploration of the role of three promising diagnostic markers in the immune microenvironment of CKD, it is anticipated to provide novel breakthroughs in potential targets for disease treatment.
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Association of Single Nucleotide Polymorphisms in KCNA10 and SLC13A3 Genes with the Susceptibility to Chronic Kidney Disease of Unknown Etiology in Central Indian Patients. Biochem Genet 2023:10.1007/s10528-023-10335-7. [PMID: 36696070 DOI: 10.1007/s10528-023-10335-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023]
Abstract
Global rise in the prevalence of endemic chronic kidney disease of unknown etiology (CKDu) possess major health issues. The prevalence of CKDu is also rising in the Indian population. Besides environmental factors, genetic factors play an important role in the predisposition to CKDu. In the present study, we have analyzed the association of single nucleotide polymorphisms (SNPs) in three genes with the susceptibility to CKDu. This was a case-control study with a total of 180 adult subjects (CKD = 60, CKDu = 60, Healthy = 60) from central India. We performed KASP genotyping assay to determine the allele frequency of SNP genotypes. We used the odds ratio (OR) to assess the association of individual SNPs, rs34970857 of KCNA10, rs6066043 of SLC13A3, and rs2910164 of miR-146a with CKDu and CKD susceptibility. In the case of rs34970857 of the KCNA10 gene, we noted a significantly increased OR for CKDu versus healthy control (Dominant model; CKDu versus control, CT + CC versus TT, OR = 3.96, p = 0.004). In the recessive and homozygous model, we observed significantly increased OR for rs6066043 of SLC13A3 gene, CKDu versus healthy control {(Recessive model; CKDu versus control, GG versus AA + GA, OR = 2.41, p = 0.03; homozygous model, GG versus AA, OR = 3.54, p = 0.04)}. CC genotype of rs34970857 of the KCNA10 gene and the GG genotype of the SLC13A3 gene are significantly associated with the susceptibility of CKDu.
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Xia J, Hou Y, Cai A, Xu Y, Yang W, Huang M, Mou S. An integrated co-expression network analysis reveals novel genetic biomarkers for immune cell infiltration in chronic kidney disease. Front Immunol 2023; 14:1129524. [PMID: 36875100 PMCID: PMC9981626 DOI: 10.3389/fimmu.2023.1129524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/06/2023] [Indexed: 02/19/2023] Open
Abstract
Background Chronic kidney disease (CKD) is characterized by persistent damage to kidney function or structure. Progression to end-stage leads to adverse effects on multiple systems. However, owing to its complex etiology and long-term cause, the molecular basis of CKD is not completely known. Methods To dissect the potential important molecules during the progression, based on CKD databases from Gene Expression Omnibus, we used weighted gene co-expression network analysis (WGCNA) to identify the key genes in kidney tissues and peripheral blood mononuclear cells (PBMC). Correlation analysis of these genes with clinical relevance was evaluated based on Nephroseq. Combined with a validation cohort and receiver operating characteristic curve (ROC), we found the candidate biomarkers. The immune cell infiltration of these biomarkers was evaluated. The expression of these biomarkers was further detected in folic acid-induced nephropathy (FAN) murine model and immunohistochemical staining. Results In total, eight genes (CDCP1, CORO1C, DACH1, GSTA4, MAFB, TCF21, TGFBR3, and TGIF1) in kidney tissue and six genes (DDX17, KLF11, MAN1C1, POLR2K, ST14, and TRIM66) in PBMC were screened from co-expression network. Correlation analysis of these genes with serum creatinine levels and estimated glomerular filtration rate from Nephroseq showed a well clinical relevance. Validation cohort and ROC identified TCF21, DACH1 in kidney tissue and DDX17 in PBMC as biomarkers for the progression of CKD. Immune cell infiltration analysis revealed that DACH1 and TCF21 were correlated with eosinophil, activated CD8 T cell, activated CD4 T cell, while the DDX17 was correlated with neutrophil, type-2 T helper cell, type-1 T helper cell, mast cell, etc. FAN murine model and immunohistochemical staining confirmed that these three molecules can be used as genetic biomarkers to distinguish CKD patients from healthy people. Moreover, the increase of TCF21 in kidney tubules might play important role in the CKD progression. Discussion We identified three promising genetic biomarkers which could play important roles in the progression of CKD.
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Affiliation(s)
- Jia Xia
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yutong Hou
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Anxiang Cai
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingjie Xu
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Yang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Masha Huang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Mou
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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8
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Wang J, Li D, Sun Y, Tian Y. Air pollutants, genetic factors, and risk of chronic kidney disease: Findings from the UK Biobank. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 247:114219. [PMID: 36306611 DOI: 10.1016/j.ecoenv.2022.114219] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/10/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Experiment studies have suggested the emerging role of air pollutants in chronic kidney disease (CKD). However, only a few population studies conducted in Asia and North America have assessed their association, and the conclusions remained controversial. This study aims to investigate the effect of air pollutants exposure on CKD in the European population and first explores the modification effect of genetic risk on this association. METHODS 458,968 participants from the UK Biobank were included in this study. Cox proportional hazards model was used to assess the associations of air pollutants (PM2.5, PM10, NO2, and NOx) with incident CKD. A genetic risk score of 53 single nucleotide polymorphisms was constructed to represent the genetic susceptibility to CKD. To assess the interaction effect between air pollutants and the genetic risk, we added a multiplicative interaction term and did a stratified analysis. RESULTS During a median follow-up of 11.7 years, 16,637 incidents of CKD were identified. We observed positive associations between air pollutants exposure and CKD risk with the HRs for CKD were 1.09 (1.07, 1.11), 1.08 (1.06, 1.10), 1.05 (1.03, 1.07), 1.06 (1.04, 1.08) with per IQR (interquartile range) increment in PM2.5, PM10, NO2, and NOx, respectively. Stratified analysis showed that the associations between air pollutants and CKD were modest and marginal in the high genetic risk population (P > 0.05), while the associations were statistically significant in the low and intermediate genetic risk groups. CONCLUSIONS Our study indicated that exposure to various air pollutants, including PM2.5, PM10, NO2, and NOx, was associated with an elevated risk of CKD. This finding provide evidence that formulating strategies to improve air quality can be helpful to reduce the burden of CKD.
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Affiliation(s)
- Jianing Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dankang Li
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yu Sun
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yaohua Tian
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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9
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Koraishy FM, Mann FD, Waszczuk MA, Kuan PF, Jonas K, Yang X, Docherty A, Shabalin A, Clouston S, Kotov R, Luft B. Polygenic association of glomerular filtration rate decline in world trade center responders. BMC Nephrol 2022; 23:347. [PMID: 36307804 PMCID: PMC9615399 DOI: 10.1186/s12882-022-02967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The factors associated with estimated glomerular filtrate rate (eGFR) decline in low risk adults remain relatively unknown. We hypothesized that a polygenic risk score (PRS) will be associated with eGFR decline. METHODS We analyzed genetic data from 1,601 adult participants with European ancestry in the World Trade Center Health Program (baseline age 49.68 ± 8.79 years, 93% male, 23% hypertensive, 7% diabetic and 1% with cardiovascular disease) with ≥ three serial measures of serum creatinine. PRSs were calculated from an aggregation of single nucleotide polymorphisms (SNPs) from a recent, large-scale genome-wide association study (GWAS) of rapid eGFR decline. Generalized linear models were used to evaluate the association of PRS with renal outcomes: baseline eGFR and CKD stage, rate of change in eGFR, stable versus declining eGFR over a 3-5-year observation period. eGFR decline was defined in separate analyses as "clinical" (> -1.0 ml/min/1.73 m2/year) or "empirical" (lower most quartile of eGFR slopes). RESULTS The mean baseline eGFR was ~ 86 ml/min/1.73 m2. Subjects with decline in eGFR were more likely to be diabetic. PRS was significantly associated with lower baseline eGFR (B = -0.96, p = 0.002), higher CKD stage (OR = 1.17, p = 0.010), decline in eGFR (OR = 1.14, p = 0.036) relative to stable eGFR, and the lower quartile of eGFR slopes (OR = 1.21, p = 0.008), after adjusting for established risk factors for CKD. CONCLUSION Common genetic variants are associated with eGFR decline in middle-aged adults with relatively low comorbidity burdens.
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Affiliation(s)
- Farrukh M Koraishy
- Division of Nephrology, Department of Medicine, Stony Brook University, 100 Nicolls Road, HSCT16-080E, Stony Brook, NY, USA.
| | - Frank D Mann
- Department of Family, Population, and Preventative Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University, North Chicago, IL, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Katherine Jonas
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Xiaohua Yang
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anna Docherty
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Andrey Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Sean Clouston
- Department of Family, Population, and Preventative Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Benjamin Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
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10
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Alaamery M, Alghamdi J, Massadeh S, Alsawaji M, Aljawini N, Albesher N, Alghamdi B, Almutairi M, Hejaili F, Alfadhel M, Baz B, Almuzzaini B, Almutairi AF, Abdullah M, Quintana FJ, Sayyari A. Analysis of chronic kidney disease patients by targeted next-generation sequencing identifies novel variants in kidney-related genes. Front Genet 2022; 13:886038. [PMID: 36035137 PMCID: PMC9407681 DOI: 10.3389/fgene.2022.886038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Despite the enormous economic and societal burden of chronic kidney disease (CKD), its pathogenesis remains elusive, impeding specific diagnosis and targeted therapy. Herein, we sought to elucidate the genetic causes of end-stage renal disease (ESRD) and identify genetic variants associated with CKD and related traits in Saudi kidney disease patients. We applied a genetic testing approach using a targeted next-generation sequencing gene panel including 102 genes causative or associated with CKD. A total of 1,098 Saudi participants were recruited for the study, including 534 patients with ESRD and 564 healthy controls. The pre-validated NGS panel was utilized to screen for genetic variants, and then, statistical analysis was conducted to test for associations. The NGS panel revealed 7,225 variants in 102 sequenced genes. Cases had a significantly higher number of confirmed pathogenic variants as classified by the ClinVar database than controls (i.e., individuals with at least one allele of a confirmed pathogenic variant that is associated with CKD; 279 (0.52) vs. 258 (0.45); p-value = 0.03). A total of 13 genetic variants were found to be significantly associated with ESRD in PLCE1, CLCN5, ATP6V1B1, LAMB2, INVS, FRAS1, C5orf42, SLC12A3, COL4A6, SLC3A1, RET, WNK1, and BICC1, including four novel variants that were not previously reported in any other population. Furthermore, studies are necessary to validate these associations in a larger sample size and among individuals of different ethnic groups.
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Affiliation(s)
- Manal Alaamery
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
- Saudi Human Genome Program, National Center for Genomic Technologies and Bioinformatics, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- KACST-BWH Centre of Excellence for Biomedicine, Joint Centres of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- *Correspondence: Manal Alaamery, ; Abdullah Sayyari,
| | | | - Salam Massadeh
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
- Saudi Human Genome Program, National Center for Genomic Technologies and Bioinformatics, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- KACST-BWH Centre of Excellence for Biomedicine, Joint Centres of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Mona Alsawaji
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Nora Aljawini
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
- Saudi Human Genome Program, National Center for Genomic Technologies and Bioinformatics, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- KACST-BWH Centre of Excellence for Biomedicine, Joint Centres of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Nour Albesher
- KACST-BWH Centre of Excellence for Biomedicine, Joint Centres of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- Department of Biological Sciences, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Bader Alghamdi
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Mansour Almutairi
- Developmental Medicine Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Fayez Hejaili
- Department of Internal Medicine, Division of Nephrology, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Majid Alfadhel
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Batoul Baz
- Saudi Human Genome Program, National Center for Genomic Technologies and Bioinformatics, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Bader Almuzzaini
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Adel F. Almutairi
- Science and Technology Unit, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Mubarak Abdullah
- Department of Medicine, Ministry of the National Guard–Health Affairs, Riyadh, Saudi Arabia
| | - Francisco J. Quintana
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Abdullah Sayyari
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Medicine, Ministry of the National Guard–Health Affairs, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- *Correspondence: Manal Alaamery, ; Abdullah Sayyari,
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11
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Galectin 3 (LGALS3) Gene Polymorphisms Are Associated with Biochemical Parameters and Primary Disease in Patients with End-Stage Renal Disease in Serbian Population. J Clin Med 2022; 11:jcm11133874. [PMID: 35807161 PMCID: PMC9267120 DOI: 10.3390/jcm11133874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/16/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Galectin 3 plays a significant role in the development of chronic renal failure, particularly end-stage renal disease (ESRD). The aim of our study was to investigate the association between Gal-3 and biochemical parameters and primary disease in ESRD patients, by exploring the polymorphisms LGALS3 rs4644, rs4652, and rs11125. A total of 108 ESRD patients and 38 healthy controls were enrolled in the study. Genotyping of LGALS3 gene rs4644, rs4652, and rs11125 polymorphisms was performed by polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP). By multivariate logistic regression analysis, we found that LGALS3 rs4644 CC and rs4652 AA genotypes were significantly associated with a higher risk for lower hemoglobin, higher level of parathyroid hormone, and also occurrence of diabetes mellitus and arterial hypertension. The CAA haplotype was significantly more common in patients with diabetes, low hemoglobin level, and normal PTH level. It has been observed as well that the ACT haplotype was more common in patients with low glomerular filtration, low PTH, and normal hemoglobin level. We found that the LGALS3 rs4644 and rs4652 gene polymorphism may be involved in the pathogenesis and appearance of complications in ESRD patients and thus could be considered a new genetic risk factor in this population.
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12
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Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA. J Immunol Res 2022; 2022:4615292. [PMID: 35571562 PMCID: PMC9095404 DOI: 10.1155/2022/4615292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/24/2022] Open
Abstract
Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.
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13
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Lieberman KV, Chang AR, Block GA, Robinson K, Bristow SL, Morales A, Mitchell A, McCalley S, McKay J, Pollak MR, Aradhya S, Warady BA, Pollak MR, Aradhya S, Warady BA. The KIDNEYCODE Program: Diagnostic Yield and Clinical Features of Individuals with CKD. KIDNEY360 2022; 3:900-909. [PMID: 36128480 PMCID: PMC9438426 DOI: 10.34067/kid.0004162021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/14/2022] [Indexed: 01/10/2023]
Abstract
Background Despite increasing recognition that CKD may have underlyi ng genetic causes, genetic testing remains limited. This study evaluated the diagnostic yield and phenotypic spectrum of CKD in individuals tested through the KIDNEYCODE sponsored genetic testing program. Methods Unrelated individuals who received panel testing (17 genes) through the KIDNEYCODE sponsored genetic testing program were included. Individuals had to meet at least one of the following eligibility criteria: eGFR ≤90 ml/min per 1.73m2 and hematuria or a family history of kidney disease; or suspected/biopsy-confirmed Alport syndrome or FSGS in tested individuals or relatives. Results Among 859 individuals, 234 (27%) had molecular diagnoses in genes associated with Alport syndrome (n=209), FSGS (n=12), polycystic kidney disease (n=6), and other disorders (n=8). Among those with positive findings in a COL4A gene, the majority were in COL4A5 (n=157, 72 hemizygous male and 85 heterozygous female individuals). A positive family history of CKD, regardless of whether clinical features were reported, was more predictive of a positive finding than was the presence of clinical features alone. For the 248 individuals who had kidney biopsies, a molecular diagnosis was returned for 49 individuals (20%). Most (n=41) individuals had a molecular diagnosis in a COL4A gene, 25 of whom had a previous Alport syndrome clinical diagnosis, and the remaining 16 had previous clinical diagnoses including FSGS (n=2), thin basement membrane disease (n=9), and hematuria (n=1). In total, 491 individuals had a previous clinical diagnosis, 148 (30%) of whom received a molecular diagnosis, the majority (89%, n=131) of which were concordant. Conclusions Although skewed to identify individuals with Alport syndrome, these findings support the need to improve access to genetic testing for patients with CKD-particularly in the context of family history of kidney disease, hematuria, and hearing loss.
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Affiliation(s)
- Kenneth V. Lieberman
- Division of Pediatric Nephrology, Joseph M. Sanzari Children's Hospital of the Hackensack Meridian Health Network, Hackensack, New Jersey
| | - Alexander R. Chang
- Division of Nephrology, Geisinger Medical Center, Danville, Pennsylvania
| | - Geoffrey A. Block
- Division of Clinical Research, Reata Pharmaceuticals, Inc., Plano, Texas
| | | | - Sara L. Bristow
- Department of Clinical Genomics, Invitae, San Francisco, California,Department of Medical Affairs, Invitae, San Francisco, California
| | - Ana Morales
- Department of Clinical Genomics, Invitae, San Francisco, California,Department of Medical Affairs, Invitae, San Francisco, California
| | - Asia Mitchell
- Department of Clinical Genomics, Invitae, San Francisco, California,Department of Medical Affairs, Invitae, San Francisco, California
| | - Stephen McCalley
- Division of Medical Affairs, Reata Pharmaceuticals, Inc., Plano, Texas
| | - Jim McKay
- Division of Medical Affairs, Reata Pharmaceuticals, Inc., Plano, Texas
| | - Martin R. Pollak
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Swaroop Aradhya
- Department of Clinical Genomics, Invitae, San Francisco, California,Department of Medical Affairs, Invitae, San Francisco, California
| | - Bradley A. Warady
- Division of Pediatric Nephrology, Children’s Mercy Kansas City, Kansas City, Missouri
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Abstract
Based on the high incidence of chronic kidney disease (CKD) in recent years, a better early prediction model for identifying high-risk individuals before end-stage renal failure (ESRD) occurs is needed. We conducted a nested case–control study in 348 subjects (116 cases and 232 controls) from the “Tianjin Medical University Chronic Diseases Cohort”. All subjects did not have CKD at baseline, and they were followed up for 5 years until August 2018. Using multivariate Cox regression analysis, we found five nongenetic risk factors associated with CKD risks. Logistic regression was performed to select single nucleotide polymorphisms (SNPs) from which we obtained from GWAS analysis of the UK Biobank and other databases. We used a logistic regression model and natural logarithm OR value weighting to establish CKD genetic/nongenetic risk prediction models. In addition, the final comprehensive prediction model is the arithmetic sum of the two optimal models. The AUC of the prediction model reached 0.894, while the sensitivity was 0.827, and the specificity was 0.801. We found that age, diabetes, and normal high values of urea nitrogen, TGF-β, and ADMA were independent risk factors for CKD. A comprehensive prediction model was also established, which may help identify individuals who are most likely to develop CKD early.
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15
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Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol 2021; 32:3161-3173. [PMID: 34548389 PMCID: PMC8638405 DOI: 10.1681/asn.2020111599] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 08/29/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. METHODS We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. RESULTS The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. CONCLUSIONS A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts,Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Centre–University of Freiburg, Freiburg, Germany
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Jingsha Chen
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Aditya Surapaneni
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Linda Zhou
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | | | - Ron C. Hoogeveen
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Dan E. Arking
- McKusick-Nathans Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
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16
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Guo JH, Shi JM, Shi GP, Wang Y, Chu XF, Wang ZD, Yao S, Sun XH, Wang XF, Zhu YS, Jiang XY. Association Study of Mitochondrial DNA Haplogroup D and C5178A Polymorphisms with Chronic Kidney Disease. Genet Test Mol Biomarkers 2021; 25:546-550. [PMID: 34406848 DOI: 10.1089/gtmb.2020.0306] [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] [Indexed: 12/17/2022] Open
Abstract
Objective: To explore the associations of common mitochondrial DNA polymorphisms with chronic kidney disease (CKD). Methods: Data from 286 longevous individuals aged 95 years or older from the longevity arm from the Rugao Longevity and Ageing Study (RuLAS) were used. Twenty-eight common haplogroups defined by 33 single nucleotide polymorphisms were genotyped using SNaPshot minisequencing reaction assays. The creatinine-based estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Results: The prevalence of CKD was 23.6% among the longevous participants aged 95 years and older. The D haplogroup (67.37 ± 14.72 vs. 70.65 ± 11.07, p = 0.045), the D5 haplogroup (60.86 ± 18.36 vs. 70.34 ± 11.53, p = 0.002), and the 5178A allele (67.23 ± 14.48 vs. 70.75 ± 11.10, p = 0.029) were associated with lower eGFR levels compared with noncarriers. The D5 haplogroup (13.8% vs. 3.6%, p = 0.005) was significantly higher, while D haplogroup (35.4% vs. 24%, p = 0.067) and the 5178A allele (36.9% vs. 24.9%, p = 0.056) were borderline significantly higher in CKD individuals than those without CKD. Further, after adjusting for multiple covariates, the D haplogroup, the D5 haplogroup, and the 5178A allele were associated with increased odds of CKD with odds ratios of 1.93 (95% confidence interval [CI]: 1.00-3.72, p = 0.050), 4.76 (95% CI: 1.49-15.22, p = 0.009) and 2.04 (95% CI: 1.05-3.96, p = 0.035), respectively. Conclusions: The D and D5 haplogroups, as well as the 5178A allele are associated with decreased eGFR levels and an increased risk of CKD in a longevous population.
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Affiliation(s)
| | | | | | - Yong Wang
- Rugao People's Hospital, Rugao, Jiangsu, China
| | | | | | - Shun Yao
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xue-Hui Sun
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Feng Wang
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Xiao-Yan Jiang
- Key Laboratory of Arrhythmias of the Ministry of Education of China, Department of Pathology and Pathophysiology, Tongji University School of Medicine, Shanghai, China
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17
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Shamshirgaran Y, Jonebring A, Svensson A, Leefa I, Bohlooly-Y M, Firth M, Woollard KJ, Hofherr A, Rogers IM, Hicks R. Rapid target validation in a Cas9-inducible hiPSC derived kidney model. Sci Rep 2021; 11:16532. [PMID: 34400685 PMCID: PMC8368200 DOI: 10.1038/s41598-021-95986-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/02/2021] [Indexed: 12/24/2022] Open
Abstract
Recent advances in induced pluripotent stem cells (iPSCs), genome editing technologies and 3D organoid model systems highlight opportunities to develop new in vitro human disease models to serve drug discovery programs. An ideal disease model would accurately recapitulate the relevant disease phenotype and provide a scalable platform for drug and genetic screening studies. Kidney organoids offer a high cellular complexity that may provide greater insights than conventional single-cell type cell culture models. However, genetic manipulation of the kidney organoids requires prior generation of genetically modified clonal lines, which is a time and labor consuming procedure. Here, we present a methodology for direct differentiation of the CRISPR-targeted cell pools, using a doxycycline-inducible Cas9 expressing hiPSC line for high efficiency editing to eliminate the laborious clonal line generation steps. We demonstrate the versatile use of genetically engineered kidney organoids by targeting the autosomal dominant polycystic kidney disease (ADPKD) genes: PKD1 and PKD2. Direct differentiation of the respective knockout pool populations into kidney organoids resulted in the formation of cyst-like structures in the tubular compartment. Our findings demonstrated that we can achieve > 80% editing efficiency in the iPSC pool population which resulted in a reliable 3D organoid model of ADPKD. The described methodology may provide a platform for rapid target validation in the context of disease modeling.
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Affiliation(s)
- Yasaman Shamshirgaran
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Jonebring
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Svensson
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Isabelle Leefa
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Mohammad Bohlooly-Y
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Mike Firth
- Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Kevin J Woollard
- Bioscience Renal, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Alexis Hofherr
- Early Clinical Development, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Ian M Rogers
- Department of Physiology, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Soham & Shaila Ajmera Family Transplant Centre, University Health Network, Toronto, ON, M5G 2C4, Canada
- Department of Obstetrics and Gynecology, University of Toronto, Toronto, ON, M5G1E2, Canada
| | - Ryan Hicks
- BioPharmaceuticals R&D Cell Therapy, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
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18
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Giandalia A, Giuffrida AE, Gembillo G, Cucinotta D, Squadrito G, Santoro D, Russo GT. Gender Differences in Diabetic Kidney Disease: Focus on Hormonal, Genetic and Clinical Factors. Int J Mol Sci 2021; 22:5808. [PMID: 34071671 PMCID: PMC8198374 DOI: 10.3390/ijms22115808] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 02/07/2023] Open
Abstract
Diabetic kidney disease (DKD) is one of the most serious complications of both type 1 (T1DM) and type 2 diabetes mellitus (T2DM). Current guidelines recommend a personalized approach in order to reduce the burden of DM and its complications. Recognizing sex and gender- differences in medicine is considered one of the first steps toward personalized medicine, but the gender issue in DM has been scarcely explored so far. Gender differences have been reported in the incidence and the prevalence of DKD, in its phenotypes and clinical manifestations, as well as in several risk factors, with a different impact in the two genders. Hormonal factors, especially estrogen loss, play a significant role in explaining these differences. Additionally, the impact of sex chromosomes as well as the influence of gene-sex interactions with several susceptibility genes for DKD have been investigated. In spite of the increasing evidence that sex and gender should be included in the evaluation of DKD, several open issues remain uncovered, including the potentially different effects of newly recommended drugs, such as SGLT2i and GLP1Ras. This narrative review explored current evidence on sex/gender differences in DKD, taking into account hormonal, genetic and clinical factors.
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Affiliation(s)
- Annalisa Giandalia
- Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
| | - Alfio Edoardo Giuffrida
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
| | - Guido Gembillo
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
- Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, 98125 Messina, Italy
| | - Domenico Cucinotta
- Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
| | - Giovanni Squadrito
- Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
| | - Domenico Santoro
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
| | - Giuseppina T Russo
- Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
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19
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Fawzy MS, Abu AlSel BT, Toraih EA. Analysis of microRNA processing machinery gene (DROSHA, DICER1, RAN, and XPO5) variants association with end-stage renal disease. J Clin Lab Anal 2020; 34:e23520. [PMID: 32770606 PMCID: PMC7755820 DOI: 10.1002/jcla.23520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 01/29/2023] Open
Abstract
Background MicroRNA (miRNA) processing machinery gene variant was associated with several diseases. We aimed to explore for the first time the association of machinery gene (DROSHA rs10719A/G; DICER1 rs3742330A/G; RAN rs14035C/T; and XPO5 rs11077T/G) variants with the susceptibility and phenotype of end‐stage renal disease (ESRD). Method A total of 281 participants (98 ESRD patients and 183 healthy volunteers) were enrolled. Real‐Time TaqMan allelic discrimination assay was applied for the genotyping of the specified variants. Multiple logistic regression models, univariate, multivariate, and principal component analyses were carried out. Results Carrying one DICER1 rs3742330*G allele conferred protection against developing ESRD [heterozygote comparison: OR = 0.30, 95% CI = 0.15‐0.62, dominant model: OR = 0.35, 95% CI = 0.17‐0.70]. Similarly, for XPO5 rs11077T/G, homozygote and heterozygote carriers of G variant were less likely to develop ESRD [homozygote comparison: adjusted OR = 0.23, 95% CI = 0.11‐0.50, and heterozygote comparison: OR = 0.50, 95% CI = 0.22‐0.92, and allelic model: OR = 0.46, 95% CI = 0.34‐0.70]. RAN rs14035*TT subjects were 5 times more likely to develop ESRD while being heterozygote (CT) have twice the risk [homozygote comparison: 5.18, 95% CI = 2.28‐11.8, heterozygote comparison: OR = 2.12, 95% CI = 1.10‐409]. Subgroup analysis also detected DICER1 rs3742330*A, XPO5 rs11077*T, and RAN rs14035*T as genetic risk determinants for ESRD development in both sex and age categories. Two genotype combinations of DROSHA/DICER1/XPO5/RAN were associated with increased susceptibility to ESRD [A‐A‐T‐T: OR = 9.49, 95%CI = 2.48‐36.31 (P = .001) and G‐A‐T‐T: OR = 5.92, 95%CI = 1.77‐19.83 (P = .004), respectively]. Conclusion Variants and combined genotypes of DICER1 rs3742330, XPO5 rs11077, and RAN rs14035 might be associated with ESRD development in the study population. Integrating molecular analysis in ESRD risk stratification is warranted.
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Affiliation(s)
- Manal S Fawzy
- Department of Biochemistry, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia.,Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Baraah T Abu AlSel
- Department of Microbiology, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia
| | - Eman A Toraih
- Department of Surgery, School of Medicine, Tulane University, New Orleans, LA, USA.,Genetics Unit, Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
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20
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Randi EB, Vervaet B, Tsachaki M, Porto E, Vermeylen S, Lindenmeyer MT, Thuy LTT, Cohen CD, Devuyst O, Kistler AD, Szabo C, Kawada N, Hankeln T, Odermatt A, Dewilde S, Wenger RH, Hoogewijs D. The Antioxidative Role of Cytoglobin in Podocytes: Implications for a Role in Chronic Kidney Disease. Antioxid Redox Signal 2020; 32:1155-1171. [PMID: 31910047 DOI: 10.1089/ars.2019.7868] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Aims: Cytoglobin (CYGB) is a member of the mammalian globin family of respiratory proteins. Despite extensive research efforts, its physiological role remains largely unknown, but potential functions include reactive oxygen species (ROS) detoxification and signaling. Accumulating evidence suggests that ROS play a crucial role in podocyte detachment and apoptosis during diabetic kidney disease. This study aimed to explore the potential antioxidative renal role of CYGB both in vivo and in vitro. Results: Using a Cygb-deficient mouse model, we demonstrate a Cygb-dependent reduction in renal function, coinciding with a reduced number of podocytes. To specifically assess the putative antioxidative function of CYGB in podocytes, we first confirmed high endogenous CYGB expression levels in two human podocyte cell lines and subsequently generated short hairpin RNA-mediated stable CYGB knockdown podocyte models. CYGB-deficient podocytes displayed increased cell death and accumulation of ROS as assessed by 2'7'-dichlorodihydrofluorescein diacetate assays and the redox-sensitive probe roGFP2-Orp1. CYGB-deficient cells also exhibited an impaired cellular bioenergetic status. Consistently, analysis of the CYGB-dependent transcriptome identified dysregulation of multiple genes involved in redox balance, apoptosis, as well as in chronic kidney disease (CKD). Finally, genome-wide association studies and expression studies in nephropathy biopsies indicate an association of CYGB with CKD. Innovation: This study demonstrates a podocyte-related renal role of Cygb, confirms abundant CYGB expression in human podocyte cell lines, and describes for the first time an association between CYGB and CKD. Conclusion: Our results provide evidence for an antioxidative role of CYGB in podocytes.
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Affiliation(s)
- Elisa B Randi
- Department of Medicine/Physiology, University of Fribourg, Fribourg, Switzerland.,Institute of Physiology and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland
| | - Benjamin Vervaet
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Maria Tsachaki
- National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland.,Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Elena Porto
- Institute of Organismal and Molecular Evolutionary Biology, University of Mainz, Mainz, Germany
| | - Stijn Vermeylen
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Maja T Lindenmeyer
- Institute of Physiology and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland.,Nephrological Center, Medical Clinic and Policlinic IV, University of Munich, Munich, Germany
| | - Le Thi Thanh Thuy
- Department of Hepatology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Clemens D Cohen
- Institute of Physiology and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland.,Nephrological Center, Medical Clinic and Policlinic IV, University of Munich, Munich, Germany
| | - Olivier Devuyst
- Institute of Physiology and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland
| | - Andreas D Kistler
- Division of Nephrology, Kantonsspital Frauenfeld, Frauenfeld, Switzerland
| | - Csaba Szabo
- Chair of Pharmacology, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Norifumi Kawada
- Department of Hepatology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Thomas Hankeln
- Institute of Organismal and Molecular Evolutionary Biology, University of Mainz, Mainz, Germany
| | - Alex Odermatt
- National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland.,Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Sylvia Dewilde
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Roland H Wenger
- Institute of Physiology and Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland
| | - David Hoogewijs
- Department of Medicine/Physiology, University of Fribourg, Fribourg, Switzerland.,National Centre of Competence in Research (NCCR) "Kidney.CH", Zurich, Switzerland
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21
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Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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22
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Adam KM, Mohammed AM, Elamin AA. Non-diabetic end-stage renal disease in Saudis associated with polymorphism of MYH9 gene but not UMOD gene. Medicine (Baltimore) 2020; 99:e18722. [PMID: 32011449 PMCID: PMC7220318 DOI: 10.1097/md.0000000000018722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The prevalence of risk factors of chronic kidney disease in Saudi Arabia has augmented an already serious public health problem, therefore, determination of genetic variants associated with the risk of the disease presents potential screening tools that help reducing the incidence rates and promote effective disease management.The aim of the present study is to determine the association of UMOD and MYH9 genetic variants with the risk of non-diabetic end-stage renal disease (ESRD) in the Saudi population.Two single nucleotide polymorphisms (SNP), rs12917707 in gene UMOD and rs4821480 in gene MYH9 were genotyped in 154 non-diabetic ESRD Saudi patients and 123 age-matched healthy controls using Primers and Polymerase chain reaction conditions (PCR), Sanger sequencing, and TaqMan Pre-designed SNP Genotyping Assay. The association of these genetic variants with the risk of the disease and other renal function determinants was assessed using statistical tools such as logistic regression and One-way Analysis of Variance tests.The genotypic frequency of the two SNPs showed no deviation from Hardy-Weinberg equilibrium, the minor allele frequency of UMOD SNP was 0.13 and MYH9 SNP was 0.08. rs4821480 in MYH9 was significantly associated with the risk of non-diabetic ESRD (OR = 3.86; 95%CI: 1.38-10.82, P value .010), while, rs12917707 showed lack of significant association with the disease, P value .380. and neither of the 2 SNPs showed any association with the renal function determinants, serum albumin, and alkaline phosphatase enzyme.
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23
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Cañadas-Garre M, Anderson K, Cappa R, Skelly R, Smyth LJ, McKnight AJ, Maxwell AP. Genetic Susceptibility to Chronic Kidney Disease - Some More Pieces for the Heritability Puzzle. Front Genet 2019; 10:453. [PMID: 31214239 PMCID: PMC6554557 DOI: 10.3389/fgene.2019.00453] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/30/2019] [Indexed: 12/12/2022] Open
Abstract
Chronic kidney disease (CKD) is a major global health problem with an increasing prevalence partly driven by aging population structure. Both genomic and environmental factors contribute to this complex heterogeneous disease. CKD heritability is estimated to be high (30-75%). Genome-wide association studies (GWAS) and GWAS meta-analyses have identified several genetic loci associated with CKD, including variants in UMOD, SHROOM3, solute carriers, and E3 ubiquitin ligases. However, these genetic markers do not account for all the susceptibility to CKD, and the causal pathways remain incompletely understood; other factors must be contributing to the missing heritability. Less investigated biological factors such as telomere length; mitochondrial proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded genes; structural variants, such as copy number variants (CNVs), insertions, deletions, inversions and translocations are poorly covered and may explain some of the missing heritability. The sex chromosomes, often excluded from GWAS studies, may also help explain gender imbalances in CKD. In this review, we outline recent findings on molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes) that typically have received less attention than gene polymorphisms. Shorter telomere length has been associated with renal dysfunction and CKD progression, however, most publications report small numbers of subjects with conflicting findings. CNVs have been linked to congenital anomalies of the kidney and urinary tract, posterior urethral valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA biomarkers for CKD comes primarily from case reports, therefore the data are scarce and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene, mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found associations between X-chromosome and renal function (rs12845465 and rs5987107). No loci in the Y-chromosome have reached genome-wide significance. In conclusion, despite the efforts to find the genetic basis of CKD, it remains challenging to explain all of the heritability with currently available methods and datasets. Although additional biomarkers have been investigated in less common suspects such as telomeres, CNVs, mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more comprehensive approaches, particularly through the integration of multiple -"omics" data, are needed.
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Affiliation(s)
- Marisa Cañadas-Garre
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Kerry Anderson
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Ruaidhri Cappa
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Ryan Skelly
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Laura Jane Smyth
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Amy Jayne McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
| | - Alexander Peter Maxwell
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom
- Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom
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24
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Genetic risk score raises the risk of incidence of chronic kidney disease in Korean general population-based cohort. Clin Exp Nephrol 2019; 23:995-1003. [PMID: 30955190 DOI: 10.1007/s10157-019-01731-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 03/19/2019] [Indexed: 01/27/2023]
Abstract
BACKGROUND Chronic kidney disease (CKD) is a common disease, affecting about 10% of the general population. The genetic component about CKD incidence in Asian population is not well known. The aim of the study is to find the genetic loci associated with incident CKD and to figure out the effect of genetic variation on the development of CKD. METHODS We conducted a genome-wide association (GWA) study regarding the development of CKD based on two population-based cohorts of Korean Genome Epidemiology Study. 3617 Koreans from two different cohorts, aged 40-49 years without CKD at initial visit, were included in our analysis. We used 2510 individuals in Ansan as the discovery set and another 1107 individuals from Ansung as the replication set. At baseline, members of both cohorts provided information on creatinine, and DNA samples were collected for genotyping. Single nucleotide polymorphisms that surpassed a significance threshold of P < 5 × 10-3 were selected. RESULTS A total of 281 among 3617 developed CKD during the follow-up period. Incident CKD group was older (P < 0.001), included more female (P < 0.001), and had more hypertension and diabetes (P < 0.001). We identified 12 SNPs that are associated with incident CKD in the GWA study and made genetic risk score using these SNPs. In multiple Cox regression analysis, genetic risk score was still a significant associated factor (HR 1.311, CI 1.201, 1.431, P < 0.001). CONCLUSIONS We identified several loci highly associated with incident CKD. The findings suggest the need for further investigations on the genetic propensity for incident CKD.
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Vilander LM, Vaara ST, Kaunisto MA, Pettilä V, Study Group TF. Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients. J Clin Med 2019; 8:jcm8030342. [PMID: 30862128 PMCID: PMC6463106 DOI: 10.3390/jcm8030342] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 02/26/2019] [Accepted: 03/04/2019] [Indexed: 12/19/2022] Open
Abstract
Acute kidney injury (AKI) is a syndrome with high incidence among the critically ill. Because the clinical variables and currently used biomarkers have failed to predict the individual susceptibility to AKI, candidate gene variants for the trait have been studied. Studies about genetic predisposition to AKI have been mainly underpowered and of moderate quality. We report the association study of 27 genetic variants in a cohort of Finnish critically ill patients, focusing on the replication of associations detected with variants in genes related to inflammation, cell survival, or circulation. In this prospective, observational Finnish Acute Kidney Injury (FINNAKI) study, 2647 patients without chronic kidney disease were genotyped. We defined AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We compared severe AKI (Stages 2 and 3, n = 625) to controls (Stage 0, n = 1582). For genotyping we used iPLEXTM Assay (Agena Bioscience). We performed the association analyses with PLINK software, using an additive genetic model in logistic regression. Despite the numerous, although contradictory, studies about association between polymorphisms rs1800629 in TNFA and rs1800896 in IL10 and AKI, we found no association (odds ratios 1.06 (95% CI 0.89–1.28, p = 0.51) and 0.92 (95% CI 0.80–1.05, p = 0.20), respectively). Adjusting for confounders did not change the results. To conclude, we could not confirm the associations reported in previous studies in a cohort of critically ill patients.
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Affiliation(s)
- Laura M Vilander
- Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine,University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland.
| | - Suvi T Vaara
- Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine,University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland.
| | - Mari A Kaunisto
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki,000014 Helsinki, Finland.
| | - Ville Pettilä
- Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine,University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland.
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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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Osman WM, Jelinek HF, Tay GK, Khandoker AH, Khalaf K, Almahmeed W, Hassan MH, Alsafar HS. Clinical and genetic associations of renal function and diabetic kidney disease in the United Arab Emirates: a cross-sectional study. BMJ Open 2018; 8:e020759. [PMID: 30552240 PMCID: PMC6303615 DOI: 10.1136/bmjopen-2017-020759] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Within the Emirati population, risk factors and genetic predisposition to diabetic kidney disease (DKD) have not yet been investigated. The aim of this research was to determine potential clinical, laboratory and reported genetic loci as risk factors for DKD. RESEARCH DESIGN AND METHODS Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD, and clinical and laboratory data were obtained. Following adjustments for possible confounders, a logistic regression model was developed to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were then used to study the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with vitamin D (25-OH cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine and 73 SNPs with blood urea. RESULTS Patients with DKD, as compared with those without the disease, were mostly men (52%vs38% for controls), older (67vs59 years) and had significant rates of hypertension and dyslipidaemia. Furthermore, patients with DKD had T2DM for a longer duration of time (16vs10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (p=0.02, OR=3.12, 95% CI 1.21 to 8.02). Among the replicated associations of the genetic loci with different renal function traits, the most notable included SHROOM3 with levels of serum creatinine, eGFR and DKD (Padjusted=0.04, OR=1.46); CASR, GC and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. CONCLUSIONS Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.
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Affiliation(s)
- Wael M Osman
- Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- School of Community Health, Charles Sturt University, Albury, New South Wales, Australia
- Clinical Medicine, Macquarie University, Sydney, New South Wales, Australia
| | - Guan K Tay
- Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
- School of Health and Medical Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Psychiatry and Clinical Neurosciences, University of Western Australia, Western Australia, Australia
- Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ahsan H Khandoker
- Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kinda Khalaf
- Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Wael Almahmeed
- Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
- Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates
| | - Mohamed H Hassan
- Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
| | - Habiba S Alsafar
- Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
- Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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Cañadas-Garre M, Anderson K, McGoldrick J, Maxwell AP, McKnight AJ. Genomic approaches in the search for molecular biomarkers in chronic kidney disease. J Transl Med 2018; 16:292. [PMID: 30359254 PMCID: PMC6203198 DOI: 10.1186/s12967-018-1664-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/14/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD. MAIN BODY Conventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management. CONCLUSION CKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases.
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Affiliation(s)
- M. Cañadas-Garre
- Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen’s University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB Northern Ireland UK
| | - K. Anderson
- Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen’s University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB Northern Ireland UK
| | - J. McGoldrick
- Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen’s University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB Northern Ireland UK
| | - A. P. Maxwell
- Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen’s University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB Northern Ireland UK
- Regional Nephrology Unit, Belfast City Hospital, Belfast, UK
| | - A. J. McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Belfast City Hospital, Queen’s University of Belfast, c/o University Floor, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB Northern Ireland UK
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29
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George C, Yako YY, Okpechi IG, Matsha TE, Kaze Folefack FJ, Kengne AP. An African perspective on the genetic risk of chronic kidney disease: a systematic review. BMC MEDICAL GENETICS 2018; 19:187. [PMID: 30340464 PMCID: PMC6194564 DOI: 10.1186/s12881-018-0702-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 10/02/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND Individuals of African ethnicity are disproportionately burdened with chronic kidney disease (CKD). However, despite the genetic link, genetic association studies of CKD in African populations are lacking. METHODS We conducted a systematic review to critically evaluate the existing studies on CKD genetic risk inferred by polymorphism(s) amongst African populations in Africa. The study followed the HuGE handbook and PRISMA protocol. We included studies reporting on the association of polymorphism(s) with prevalent CKD, end-stage renaldisease (ESRD) or CKD-associated traits. Given the very few studies investigating the effects of the same single nucleotide polymorphisms (SNPs) on CKD risk, a narrative synthesis of the evidence was conducted. RESULTS A total of 30 polymorphisms in 11 genes were investigated for their association with CKD, ESRD or related traits, all using the candidate-gene approach. Of all the included genes, MYH9, AT1R and MTHFR genes failed to predict CKD or related traits, while variants in the APOL1, apoE, eNOS, XPD, XRCC1, renalase, ADIPOQ, and CCR2 genes were associated with CKD or other related traits. Two SNPs (rs73885319, rs60910145) and haplotypes (G-A-G; G1; G2) of the apolipoprotein L1 (APOL1) gene were studied in more than one population group, with similar association with prevalent CKD observed. The remaining polymorphisms were investigated in single studies. CONCLUSION According to this systematic review, there is currently insufficient evidence of the specific polymorphisms that poses African populations at an increased risk of CKD. Large-scale genetic studies are warranted to better understand susceptibility polymorphisms, specific to African populations.
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Affiliation(s)
- Cindy George
- Non-Communicable Diseases Research Unit, South African Medical Research Council, Parow Valley, PO Box 19070, Cape Town, South Africa.
| | - Yandiswa Y Yako
- Department of Human Biology, Faculty of Health Sciences, Walter Sisulu University, Mthatha, South Africa
| | - Ikechi G Okpechi
- Department of Medicine, Division of Nephrology and Hypertension, University of Cape Town, Cape Town, South Africa.,Kidney and Hypertension Research Unit, University of Cape Town, Cape Town, South Africa
| | - Tandi E Matsha
- Department of Biomedical Sciences, Faculty of Health and Wellness Science, Cape Peninsula University of Technology, Bellville, Cape Town, South Africa
| | - Francois J Kaze Folefack
- Faculty of Medicine and Biomedical Sciences, University of Yaounde I, Yaounde, Cameroon.,Medicine Unit, Yaounde University Teaching Hospital, Yaounde, Cameroon
| | - Andre P Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, Parow Valley, PO Box 19070, Cape Town, South Africa
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30
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Zusi C, Trombetta M, Bonetti S, Dauriz M, Boselli ML, Trabetti E, Malerba G, Penno G, Zoppini G, Bonora E, Solini A, Bonadonna RC. A renal genetic risk score (GRS) is associated with kidney dysfunction in people with type 2 diabetes. Diabetes Res Clin Pract 2018; 144:137-143. [PMID: 30153470 DOI: 10.1016/j.diabres.2018.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/12/2018] [Accepted: 08/22/2018] [Indexed: 11/29/2022]
Abstract
This study aims to investigate whether renal and cardiovascular phenotypes in Italian patients with type 2 diabetes (T2D) could be influenced by a number of disease risk SNPs recently found in genome-wide association studies (GWAS). In 1591 Italian subjects with T2D: (1) 47 SNPs associated to kidney function and/or chronic kidney disease (CKD) and 49 SNPs associated to cardiovascular disease (CVD) risk were genotyped; (2) urinary albumin/creatinine (A/C) ratio, glomerular filtration rate (eGFR) and lipid profile were assessed; (3) a standard electrocardiogram was performed; (4) two genotype risk scores (GRS) were computed (a renal GRS calculated selecting 39 SNPs associated with intermediate traits of kidney damage and a cardiovascular GRS determined selecting 42 SNPs associated to CVD risk phenotypes). After correction for multiple comparisons, the renal GRS was not associated to A/C ratio (p = 0.33), but it was significantly related to decreased eGFR (p = 0.005). No association between the cardiovascular GRS and electrocardiogram was detected. Thus, in Italian patients with T2D a renal GRS might predict the decline in glomerular function, suggesting that the clock of diabetes associated CKD starts ticking long before hyperglycemia. Our data support the feasibility of gene-based prediction of complications in people with T2D.
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Affiliation(s)
- Chiara Zusi
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy
| | - Maddalena Trombetta
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy; Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Hospital Trust of Verona, Verona, Italy.
| | - Sara Bonetti
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy
| | - Marco Dauriz
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy
| | - Maria L Boselli
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy
| | - Elisabetta Trabetti
- Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Verona, Italy
| | - Giovanni Malerba
- Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Verona, Italy
| | - Giuseppe Penno
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Giacomo Zoppini
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy
| | - Enzo Bonora
- Department of Medicine, Section of Endocrinology, Diabetes and Metabolism, University of Verona, Verona, Italy; Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Hospital Trust of Verona, Verona, Italy
| | - Anna Solini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
| | - Riccardo C Bonadonna
- Department of Medicine and Surgery, University of Parma, Parma, Italy; Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliera Universitaria di Parma, Parma, Italy
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31
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Dhande IS, Cranford SM, Zhu Y, Kneedler SC, Hicks MJ, Wenderfer SE, Braun MC, Doris PA. Susceptibility to Hypertensive Renal Disease in the Spontaneously Hypertensive Rat Is Influenced by 2 Loci Affecting Blood Pressure and Immunoglobulin Repertoire. Hypertension 2018; 71:700-708. [PMID: 29437896 PMCID: PMC5843527 DOI: 10.1161/hypertensionaha.117.10593] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 12/02/2017] [Accepted: 12/27/2017] [Indexed: 12/11/2022]
Abstract
High blood pressure exerts its deleterious effects on health largely through acceleration of end-organ diseases. Among these, progressive loss of renal function is particularly important, not only for the direct consequences of kidney damage but also because loss of renal function is associated with amplification of other adverse cardiovascular outcomes. Genetic susceptibility to hypertension and associated end-organ disease is non-Mendelian in both humans and in a rodent model, the spontaneously hypertensive rat (SHR). Here, we report that hypertensive end-organ disease in the inbred SHR-A3 line is attributable to genetic variation in the immunoglobulin heavy chain on chromosome 6. This variation coexists with variation in a 10 Mb block on chromosome 17 that contains genetic variation in 2 genes involved in immunoglobulin Fc receptor signaling. Substitution of these genomic regions into the SHR-A3 genome from the closely related, but injury-resistant, SHR-B2 line normalizes both biomarker and histological measures of renal injury. Our findings indicate that genetic variation leads to a contribution by immune mechanisms hypertensive end-organ injury and that, in this rat model, disease is influenced by differences in germ line antibody repertoire.
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Affiliation(s)
- Isha S Dhande
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Stacy M Cranford
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Yaming Zhu
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Sterling C Kneedler
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - M John Hicks
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Scott E Wenderfer
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Michael C Braun
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX
| | - Peter A Doris
- From the Institute of Molecular Medicine, University of Texas HSC at Houston (I.S.D., S.M.C., Y.Z., S.C.K., P.A.D.); and Department of Pediatrics (S.E.W., M.C.B.) and Department of Pathology and Immunology (M.J.H.), Baylor College of Medicine, Houston, TX.
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Prudente S, Di Paola R, Copetti M, Lucchesi D, Lamacchia O, Pezzilli S, Mercuri L, Alberico F, Giusti L, Garofolo M, Penno G, Cignarelli M, De Cosmo S, Trischitta V. The rs12917707 polymorphism at the UMOD locus and glomerular filtration rate in individuals with type 2 diabetes: evidence of heterogeneity across two different European populations. Nephrol Dial Transplant 2018; 32:1718-1722. [PMID: 27448670 DOI: 10.1093/ndt/gfw262] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 06/04/2016] [Indexed: 11/12/2022] Open
Abstract
Background UMOD variability has been associated at a genome-wide level of statistical significance with glomerular filtration rate (GFR) in Swedish individuals with type 2 diabetes (T2D; n = 4888). Whether this finding is extensible also to diabetic patients from other populations deserves further study. Our aim was to investigate the relationship between UMOD variability and GFR in patients with T2D from Italy. Methods Genotyping of the single nucleotide polymorphism (SNP) rs12917707 at the UMOD locus has been carried out in 3087 individuals from four independent Italian cohorts of patients with T2D by TaqMan allele discrimination. Results In none of the four study cohorts was rs12917707 significantly associated with GFR (P > 0.05 for all). Similar results were obtained when the four samples were pooled and analyzed together (β = 0.83, P = 0.19). Such effect was strikingly smaller than that previously reported in Swedish patients (P for heterogeneity = 1.21 × 10-7). Conclusions The previously reported strong association between rs12917707 and GFR in diabetic patients from Sweden is not observed in Italian diabetic patients, thus clearly pointing to a heterogeneous effect across the two different samples. This suggests that UMOD is a strong genetic determinant of kidney function in patients with T2D in some, but not all, populations.
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Affiliation(s)
- Sabrina Prudente
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Rosa Di Paola
- Research Unit of Diabetes and Endocrine Diseases, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Massimiliano Copetti
- Unit of Biostatistics, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Daniela Lucchesi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Olga Lamacchia
- Unit of Endocrinology, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Serena Pezzilli
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.,Department of Experimental Medicine, 'Sapienza' University, Rome, Italy
| | - Luana Mercuri
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Federica Alberico
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Laura Giusti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Monia Garofolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Giuseppe Penno
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Mauro Cignarelli
- Unit of Endocrinology, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Salvatore De Cosmo
- Department of Medical Sciences, IRCCS Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Italy
| | - Vincenzo Trischitta
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.,Research Unit of Diabetes and Endocrine Diseases, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.,Department of Experimental Medicine, 'Sapienza' University, Rome, Italy
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33
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Liu HM, He JY, Zhang Q, Lv WQ, Xia X, Sun CQ, Zhang WD, Deng HW. Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 2018; 293:225-235. [PMID: 29038864 PMCID: PMC5819009 DOI: 10.1007/s00438-017-1381-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 10/06/2017] [Indexed: 01/19/2023]
Abstract
Genome-wide association studies (GWAS) have been shown to have the potential of explaining more of the "missing heritability" of complex human phenotypes by improving statistical approaches. Here, we applied a genetic-pleiotropy-informed conditional false discovery rate (cFDR) to capture additional polygenic effects associated with estimated glomerular filtration rate (creatinine) (eGFRcrea) and type 2 diabetes (T2D). The cFDR analysis improves the identification of pleiotropic variants by incorporating potentially shared genetic mechanisms between two related traits. The Q-Q and fold-enrichment plots were used to assess the enrichment of SNPs associated with eGFRcrea or T2D, and Manhattan plots were used for showing chromosomal locations of the significant loci detected. By applying the cFDR method, we newly identified 74 loci for eGFRcrea and 3 loci for T2D with the cFDR criterion of 0.05 compared with previous related GWAS studies. Four shared SNPs were detected to be associated with both eGFRcrea and T2D at the significant conjunction cFDR level of 0.05, and these shared SNPs had not been reported in previous studies. In addition, we used DAVID analysis to perform functional analysis of the shared SNPs' annotated genes and found their potential hidden associations with eGFRcrea and T2D. In this study, the cFDR method shows the feasibility to detect more genetic variants underlying the heritability of eGFRcrea and T2D, and the overlapping SNPs identified could be regarded as candidate loci that provide a thread of genetic mechanisms between eGFRcrea and T2D in future research.
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Affiliation(s)
- Hui-Min Liu
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Jing-Yang He
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Qiang Zhang
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Wan-Qiang Lv
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Xin Xia
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Chang-Qing Sun
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Wei-Dong Zhang
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China.
| | - Hong-Wen Deng
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China.
- Department of Biostatistics and Data Science, Tulane Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
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Devuyst O, Pattaro C. The UMOD Locus: Insights into the Pathogenesis and Prognosis of Kidney Disease. J Am Soc Nephrol 2017; 29:713-726. [PMID: 29180396 DOI: 10.1681/asn.2017070716] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The identification of genetic factors associated with kidney disease has the potential to provide critical insights into disease mechanisms. Genome-wide association studies have uncovered genomic regions associated with renal function metrics and risk of CKD. UMOD is among the most outstanding loci associated with CKD in the general population, because it has a large effect on eGFR and CKD risk that is consistent across different ethnic groups. The relevance of UMOD for CKD is clear, because the encoded protein, uromodulin (Tamm-Horsfall protein), is exclusively produced by the kidney tubule and has specific biochemical properties that mediate important functions in the kidney and urine. Rare mutations in UMOD are the major cause of autosomal dominant tubulointerstitial kidney disease, a condition that leads to CKD and ESRD. In this brief review, we use the UMOD paradigm to describe how population genetic studies can yield insight into the pathogenesis and prognosis of kidney diseases.
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Affiliation(s)
- Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland; and
| | - Cristian Pattaro
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
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35
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Piras D, Zoledziewska M, Cucca F, Pani A. Genome-Wide Analysis Studies and Chronic Kidney Disease. KIDNEY DISEASES 2017; 3:106-110. [PMID: 29344505 DOI: 10.1159/000481886] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/02/2017] [Indexed: 11/19/2022]
Abstract
In recent years, the very high worldwide prevalence of chronic kidney disease (CKD) has led some authors to talk of an "epidemic." The progression of CKD varies considerably among individuals despite similar aetiologies, optimal blood pressure, and glycaemic control. Over the last decade, through genome-wide association studies (GWAS), more than 50 genetic loci have been identified in association with CKD. Understanding the genetic basis of CKD could provide a better knowledge of the biology of the involved pathways, thus potentially leading to novel tools for the diagnosis, prevention, and therapy of CKD. In this review, we will analyse the role of GWAS in the study of CKD.
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Affiliation(s)
- Doloretta Piras
- Divisione di Nefrologia e Dialisi, Azienda Ospedaliera G. Brotzu, Cagliari, Italy
| | | | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari, Italy.,Università degli Studi di Sassari, Sassari, Italy
| | - Antonello Pani
- Divisione di Nefrologia e Dialisi, Azienda Ospedaliera G. Brotzu, Cagliari, Italy
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36
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Akrawi DS, PirouziFard M, Fjellstedt E, Sundquist J, Sundquist K, Zöller B. Heritability of End-Stage Renal Disease: A Swedish Adoption Study. Nephron Clin Pract 2017; 138:157-165. [PMID: 29131054 DOI: 10.1159/000484327] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 10/17/2017] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS The heritability of end-stage renal disease (ESRD) among adoptees has not been examined so far. By studying adoptees and their biological and adoptive parents, it is possible to differentiate between the genetic causes and environmental causes of familial aggregation. This nationwide study aimed to disentangle the genetic and shared environmental contribution to the familial transmission of ESRD. METHODS We performed a family study for Swedish-born adoptees (born between 1945 until 1995) and their biological and adoptive parents. The Swedish Multi-Generation Register was linked to the National Patient Registry for the period 1964-2012. ESRD was defined as patients in active uremic care, that is, chronic dialysis or kidney transplantation. OR for ESRD was determined for adoptees with an affected biological parent with ESRD compared with adoptees without a biological parent with ESRD. The OR for ESRD was also calculated in adoptees with an adoptive parent with ESRD compared with adoptees with an adoptive parent without ESRD. Moreover, heritability for ESRD was estimated with Falconer's regression. RESULTS A total of 111 adoptees, 463 adoptive parents, and 397 biological parents were affected by ESRD. The OR for ESRD was 6.41 in adoptees (95% CI 2.96-13.89) of biological parents diagnosed with ESRD. The OR for ESRD was 2.40 in adoptees (95% CI 0.76-7.60) of adoptive parents diagnosed with ESRD. The heritability of ESRD was 59.5 ± 18.2%. CONCLUSION The family history of ESRD in a biological parent is an important risk factor for ESRD. The high heritability indicates that genetic factors play an important role in understanding the etiology of ESRD.
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Affiliation(s)
- Delshad Saleh Akrawi
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - MirNabi PirouziFard
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Erik Fjellstedt
- Department of Nephrology and Transplantation, SUS University Hospital, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Kristina Sundquist
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Bengt Zöller
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
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37
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Levin A, Tonelli M, Bonventre J, Coresh J, Donner JA, Fogo AB, Fox CS, Gansevoort RT, Heerspink HJL, Jardine M, Kasiske B, Köttgen A, Kretzler M, Levey AS, Luyckx VA, Mehta R, Moe O, Obrador G, Pannu N, Parikh CR, Perkovic V, Pollock C, Stenvinkel P, Tuttle KR, Wheeler DC, Eckardt KU. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. Lancet 2017; 390:1888-1917. [PMID: 28434650 DOI: 10.1016/s0140-6736(17)30788-2] [Citation(s) in RCA: 571] [Impact Index Per Article: 81.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 02/09/2017] [Accepted: 02/15/2017] [Indexed: 12/18/2022]
Abstract
The global nephrology community recognises the need for a cohesive plan to address the problem of chronic kidney disease (CKD). In July, 2016, the International Society of Nephrology hosted a CKD summit of more than 85 people with diverse expertise and professional backgrounds from around the globe. The purpose was to identify and prioritise key activities for the next 5-10 years in the domains of clinical care, research, and advocacy and to create an action plan and performance framework based on ten themes: strengthen CKD surveillance; tackle major risk factors for CKD; reduce acute kidney injury-a special risk factor for CKD; enhance understanding of the genetic causes of CKD; establish better diagnostic methods in CKD; improve understanding of the natural course of CKD; assess and implement established treatment options in patients with CKD; improve management of symptoms and complications of CKD; develop novel therapeutic interventions to slow CKD progression and reduce CKD complications; and increase the quantity and quality of clinical trials in CKD. Each group produced a prioritised list of goals, activities, and a set of key deliverable objectives for each of the themes. The intended users of this action plan are clinicians, patients, scientists, industry partners, governments, and advocacy organisations. Implementation of this integrated comprehensive plan will benefit people who are at risk for or affected by CKD worldwide.
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Affiliation(s)
- Adeera Levin
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
| | - Marcello Tonelli
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joseph Bonventre
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Josef Coresh
- Johns Hopkins University Bloomberg School of Public Health, George W Comstock Center for Public Health Research and Prevention, Baltimore, MD, USA; Johns Hopkins University School of Medicine, Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD, USA
| | - Jo-Ann Donner
- International Society of Nephrology, Brussels, Belgium
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Ron T Gansevoort
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Meg Jardine
- The George Institute for Global Health, Sydney, NSW, Australia; Concord Repatriation General Hospital, Concord, NSW, Australia
| | - Bertram Kasiske
- Hennepin County Medical Center, Minneapolis, MN, USA; University of Minnesota, Minneapolis, MN, USA
| | - Anna Köttgen
- Division of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Matthias Kretzler
- Department of Internal Medicine and Department of ComputationalMedicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Andrew S Levey
- Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Valerie A Luyckx
- Institute of Biomedical Ethics and Klinik für Nephrologie University Hospital, University of Zurich, Zurich, Switzerland
| | - Ravindra Mehta
- Department of Medicine, University of California, San Diego, CA, USA
| | - Orson Moe
- Department of Internal Medicine and Charles and Jane Pak Center of Mineral Metabolism and Clinical Research, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gregorio Obrador
- Faculty of Health Sciences, Universidad Panamericana, Mexico City, Mexico
| | - Neesh Pannu
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Chirag R Parikh
- Program of Applied Translational Research, Department of Medicine, Yale University, New Haven, CT, USA; Veterans Affairs Medical Center, West Haven, CT, USA
| | - Vlado Perkovic
- The George Institute for Global Health, Sydney, NSW, Australia; University of Sydney, Sydney, NSW, Australia
| | - Carol Pollock
- Kolling Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Peter Stenvinkel
- Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Katherine R Tuttle
- Providence Medical Research Center, Providence Health Care Kidney Research Institute, Nephrology Division and Institute for Translational Health Sciences, University of Washington, Spokane, WA, USA
| | - David C Wheeler
- Centre for Nephrology, Royal Free Hospital, University College London, London, UK
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany
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Obrador GT, Schultheiss UT, Kretzler M, Langham RG, Nangaku M, Pecoits-Filho R, Pollock C, Rossert J, Correa-Rotter R, Stenvinkel P, Walker R, Yang CW, Fox CS, Köttgen A. Genetic and environmental risk factors for chronic kidney disease. Kidney Int Suppl (2011) 2017; 7:88-106. [PMID: 30675423 DOI: 10.1016/j.kisu.2017.07.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In order to change the current state of chronic kidney disease knowledge and therapeutics, a fundamental improvement in the understanding of genetic and environmental causes of chronic kidney disease is essential. This article first provides an overview of the existing knowledge gaps in our understanding of the genetic and environmental causes of chronic kidney disease, as well as their interactions. The second part of the article formulates goals that should be achieved in order to close these gaps, along with suggested timelines and stakeholders that are to be involved. A better understanding of genetic and environmental factors and their interactions that influence kidney function in healthy and diseased conditions can provide novel insights into renal physiology and pathophysiology and result in the identification of novel therapeutic or preventive targets to tackle the global public health care problem of chronic kidney disease.
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Affiliation(s)
- Gregorio T Obrador
- Department of Epidemiology, Biostatistics and Public Health, Universidad Panamericana School of Medicine, Mexico City, Mexico
| | - Ulla T Schultheiss
- Institute of Genetic Epidemiology, Medical Center and Faculty of Medicine-University of Freiburg, Freiburg, Germany.,Renal Division, Department of Medicine IV, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Matthias Kretzler
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Robyn G Langham
- Monash Rural Health, Monash University, Clayton VIC, Australia
| | - Masaomi Nangaku
- Department of Hemodialysis and Apheresis, Division of Nephrology and Endocrinology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Roberto Pecoits-Filho
- Department of Internal Medicine, School of Medicine, Pontificia Universidade Catolica do Paraná, Curitiba, Brazil
| | - Carol Pollock
- Kolling Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
| | | | - Ricardo Correa-Rotter
- Department of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zuibrán, Mexico City, Mexico
| | - Peter Stenvinkel
- Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Robert Walker
- Department of Medicine, University of Otago, Dunedin, New Zealand
| | - Chih-Wei Yang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Caroline S Fox
- Genetics and Pharmacogenomics, Merck Research Laboratories, Boston, Massachusetts, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Medical Center and Faculty of Medicine-University of Freiburg, Freiburg, Germany
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Pattaro C. Genome-wide association studies of albuminuria: towards genetic stratification in diabetes? J Nephrol 2017; 31:475-487. [PMID: 28918587 DOI: 10.1007/s40620-017-0437-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Accepted: 09/02/2017] [Indexed: 12/16/2022]
Abstract
Genome-wide association studies (GWAS) have been very successful in unraveling the polygenic structure of several complex diseases and traits. In the case of albuminuria, despite the large sample size achieved by some studies, results look sparse with a limited number of loci reported so far. This review searched for GWAS studies of albumin excretion, albuminuria, and proteinuria. The resulting picture sets elements of uniqueness for albuminuria GWAS with respect to other complex traits. So far, very few loci associated with albuminuria have been validated by means of genome-wide significant evidence or formal replication. With rare exceptions, the validated loci are ethnicity specific. Within a given ethnicity, variants are common and have relatively large effects, especially in the presence of diabetes. In most cases, the identified variants were functional and a biological involvement of the target genes in renal damage was established. Recently reported variants associated with albuminuria in diabetes may be potentially combined into a genetic risk score, making it possible to rank diabetic patients by increasing risk of albuminuria. Validation of this model is required. To expand the understanding of the biological basis of albumin excretion regulation, future initiatives should achieve larger sample sizes and favor a transethnic study design.
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Affiliation(s)
- Cristian Pattaro
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100, Bolzano, Italy.
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40
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Wu HH, Kuo CF, Li IJ, Weng CH, Lee CC, Tu KH, Liu SH, Chen YC, Yang CW, Luo SF, See LC, Yu KH, Huang LH, Zhang W, Doherty M, Tian YC. Family Aggregation and Heritability of ESRD in Taiwan: A Population-Based Study. Am J Kidney Dis 2017; 70:619-626. [PMID: 28663061 DOI: 10.1053/j.ajkd.2017.05.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 05/01/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND Aggregation of end-stage renal disease (ESRD) has been observed in families of European origin, as well as those of African origin. However, it is not well documented if this disease aggregates in Asian families. Furthermore, the contribution of genetic factors and shared environmental factors to family aggregation remains unclear. STUDY DESIGN Population-based cross-sectional cohort study. SETTING & PARTICIPANTS All 23,422,955 individuals registered in the Taiwan National Health Insurance Research Database in 2013. Among these, 47.45%, 57.45%, 47.29%, and 1.51% had a known parent, child, sibling, or twin, respectively. We identified 87,849 patients who had a diagnosis of ESRD. PREDICTOR Family history of ESRD. OUTCOMES & MEASUREMENTS ESRD and heritability defined as the proportion of phenotypic variance attributable to genetic factors. RESULTS Having an affected first-degree relative with ESRD was associated with an adjusted relative risk of 2.46 (95% CI, 2.32-2.62). Relative risks were 96.38 (95% CI, 48.3-192.34) for twins of patients with ESRD, 2.15 (95% CI, 2.02-2.29) for parents, 2.78 (95% CI, 2.53-3.05) for offspring, 4.96 (95% CI, 4.19-5.88) for siblings, and 1.66 (95% CI, 1.54-1.78) for spouses without genetic similarities. Heritability in this study was 31.1% to 11.4% for shared environmental factors and 57.5% for nonshared environmental factors. LIMITATIONS This was a registry database study and we did not have detailed information about clinical findings or the definite causes of ESRD. CONCLUSIONS This whole population-based family study in Asia confirmed, in a Taiwanese population, that a family history of ESRD is a strong risk factor for this disease. Moderate heritability was noted and environmental factors were related to disease. Family history of ESRD is an important piece of clinical information.
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Affiliation(s)
- Hsin Hsu Wu
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chang Fu Kuo
- Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - I Jung Li
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Cheng Hao Weng
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Cheng Chia Lee
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Kun Hua Tu
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Shou Hsuan Liu
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Yung Chang Chen
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chih Wei Yang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Shue Fen Luo
- Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Lai Chu See
- Biostatistics Core Laboratory, Molecular Medicine Research Centre, Chang Gung University, Taoyuan, Taiwan
| | - Kuang Hui Yu
- Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Lu Hsiang Huang
- Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Weiya Zhang
- Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Michael Doherty
- Biostatistics Core Laboratory, Molecular Medicine Research Centre, Chang Gung University, Taoyuan, Taiwan
| | - Ya Chung Tian
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Abdel-Hady Algharably E, Beige J, Kreutz R, Bolbrinker J. Effect of UMOD genotype on long-term graft survival after kidney transplantation in patients treated with cyclosporine-based therapy. THE PHARMACOGENOMICS JOURNAL 2017; 18:227-231. [PMID: 28418009 DOI: 10.1038/tpj.2017.14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 01/31/2017] [Accepted: 02/14/2017] [Indexed: 11/09/2022]
Abstract
The genetic rs12917707-G>T variant in uromodulin (UMOD) has been associated with renal function, chronic kidney disease and hypertension with the minor T-allele showing a protective effect. Hypertension and nephrotoxicity are adverse effects of chronic cyclosporine treatment. We tested whether UMOD rs12917707-T in donor kidneys associates with long-term graft survival in 393 Caucasian patients with stable graft function for more than 10 weeks after kidney transplantation treated with a cyclosporine-based maintenance therapy (mean graft survival 9 years). Presence of the donor T-allele had no effect on blood pressure, serum creatinine 1 year after transplantation, and on number of acute graft rejections during the first year. No significant effect on overall graft survival was observed in Kaplan-Meier analysis (P=0.65). In death-censored adjusted multivariate analysis, presence of donor T-allele associated with a significant lower hazard ratio of 0.67 (95% confidence interval: 0.46-0.97, P=0.05) for graft loss. This protective effect of the donor T-allele on graft loss observed in multivariate adjusted analysis justifies further investigations including patients treated with similar or other immunosuppressive regimens.
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Affiliation(s)
- E Abdel-Hady Algharably
- Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Department of Clinical Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
| | - J Beige
- Faculty of Medicine, Martin-Luther-University Halle/Wittenberg, Halle, Germany.,Department of Medicine Nephrology, Klinikum St. Georg, Leipzig, Germany
| | - R Kreutz
- Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - J Bolbrinker
- Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, Berlin, Germany
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42
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Bontha SV, Maluf DG, Mueller TF, Mas VR. Systems Biology in Kidney Transplantation: The Application of Multi-Omics to a Complex Model. Am J Transplant 2017; 17:11-21. [PMID: 27214826 DOI: 10.1111/ajt.13881] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 04/15/2016] [Accepted: 05/12/2016] [Indexed: 01/25/2023]
Abstract
In spite of reduction of rejection rates and improvement in short-term survival post-kidney transplantation, modest progress has occurred in long-term graft attrition over the years. Timely identification of molecular events that precede clinical and histopathological changes might help in early intervention and thereby increase the graft half-life. Evolution of "omics" tools has enabled systemic investigation of the influence of the whole genome, epigenome, transcriptome, proteome and microbiome on transplant function and survival. In this omics era, systemic approaches, in-depth clinical phenotyping and use of strict validation methods are the key for further understanding the complex mechanisms associated with graft function. Systems biology is an interdisciplinary holistic approach that focuses on complex and dynamic interactions within biological systems. The complexity of the human kidney transplant is unlikely to be captured by a reductionist approach. It appears essential to integrate multi-omics data that can elucidate the multidimensional and multilayered regulation of the underlying heterogeneous and complex kidney transplant model. Herein, we discuss studies that focus on genetic biomarkers, emerging technologies and systems biology approaches, which should increase the ability to discover biomarkers, understand mechanisms and stratify patients and responses post-kidney transplantation.
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Affiliation(s)
- S V Bontha
- Translational Genomics Transplant Laboratory, Division of Transplant, Department of Surgery, University of Virginia, Charlottesville, VA
| | - D G Maluf
- Translational Genomics Transplant Laboratory, Division of Transplant, Department of Surgery, University of Virginia, Charlottesville, VA
| | - T F Mueller
- Division of Nephrology, University Hospital, Zürich, Switzerland
| | - V R Mas
- Translational Genomics Transplant Laboratory, Division of Transplant, Department of Surgery, University of Virginia, Charlottesville, VA
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43
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Sun L, Zou LX, Chen MJ. Make Precision Medicine Work for Chronic Kidney Disease. Med Princ Pract 2017; 26:101-107. [PMID: 28152529 PMCID: PMC5588375 DOI: 10.1159/000455101] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 12/13/2016] [Indexed: 02/03/2023] Open
Abstract
Precision medicine is based on accurate diagnosis and tailored intervention through the use of omics and clinical data together with epidemiology and environmental exposures. Precision medicine should be achieved with minimum adverse events and maximum efficacy in patients with chronic kidney disease (CKD). In this review, the breakthroughs of omics in CKD and the application of systems biology are reviewed. The potential role of transforming growth factor-β1 in the targeted intervention of renal fibrosis is discussed as an example of how to make precision medicine work for CKD.
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Affiliation(s)
- Ling Sun
- *Ling Sun, Department of Nephrology, Xuzhou Central Hospital, Medical College of Southeast University, Xuzhou City, Jiangsu Province (China), E-Mail
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44
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Wuttke M, Köttgen A. Insights into kidney diseases from genome-wide association studies. Nat Rev Nephrol 2016; 12:549-62. [PMID: 27477491 DOI: 10.1038/nrneph.2016.107] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Over the past decade, genome-wide association studies (GWAS) have considerably improved our understanding of the genetic basis of kidney function and disease. Population-based studies, used to investigate traits that define chronic kidney disease (CKD), have identified >50 genomic regions in which common genetic variants associate with estimated glomerular filtration rate or urinary albumin-to-creatinine ratio. Case-control studies, used to study specific CKD aetiologies, have yielded risk loci for specific kidney diseases such as IgA nephropathy and membranous nephropathy. In this Review, we summarize important findings from GWAS and clinical and experimental follow-up studies. We also compare risk allele frequency, effect sizes, and specificity in GWAS of CKD-defining traits and GWAS of specific CKD aetiologies and the implications for study design. Genomic regions identified in GWAS of CKD-defining traits can contain causal genes for monogenic kidney diseases. Population-based research on kidney function traits can therefore generate insights into more severe forms of kidney diseases. Experimental follow-up studies have begun to identify causal genes and variants, which are potential therapeutic targets, and suggest mechanisms underlying the high allele frequency of causal variants. GWAS are thus a useful approach to advance knowledge in nephrology.
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Affiliation(s)
- Matthias Wuttke
- Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics, Faculty of Medicine, and Medical Centre - University of Freiburg, Berliner Allee 29, 79110 Freiburg, Germany.,Department of Medicine IV, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany
| | - Anna Köttgen
- Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics, Faculty of Medicine, and Medical Centre - University of Freiburg, Berliner Allee 29, 79110 Freiburg, Germany.,Department of Medicine IV, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, Maryland, USA
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Nadkarni GN, Horowitz CR. Genomics in CKD: Is This the Path Forward? Adv Chronic Kidney Dis 2016; 23:120-4. [PMID: 26979150 DOI: 10.1053/j.ackd.2016.01.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Accepted: 01/26/2016] [Indexed: 01/13/2023]
Abstract
Recent advances in genomics and sequencing technology have led to a better understanding of genetic risk in CKD. Genetics could account in part for racial differences in treatment response for medications including antihypertensives and immunosuppressive medications due to its correlation with ancestry. However, there is still a substantial lag between generation of this knowledge and its adoption in routine clinical care. This review summarizes the recent advances in genomics and CKD, discusses potential reasons for its underutilization, and highlights potential avenues for application of genomic information to improve clinical care and outcomes in this particularly vulnerable population.
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Harder JL, Hodgin JB, Kretzler M. Integrative Biology of Diabetic Kidney Disease. KIDNEY DISEASES 2015; 1:194-203. [PMID: 26929927 DOI: 10.1159/000439196] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND The leading cause of ESRD in the U.S. is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for alternative approaches to identify persons at risk, and to search for more effective therapies to halt progression of DKD. Identification of novel therapies is critically dependent on a more comprehensive understanding of the pathophysiology of DKD, specifically at the molecular level. A more expansive and exploratory view of DKD is needed to complement more traditional research approaches that have focused on single molecules. SUMMARY In recent years, sophisticated research methodologies have emerged within systems biology that should allow for a more comprehensive disease definition of DKD. Systems biology provides an inter-disciplinary approach to describe complex interactions within biological systems including how these interactions influence systems' functions and behaviors. Computational modeling of large, system-wide, quantitative data sets is used to generate molecular interaction pathways, such as metabolic and cell signaling networks. KEY MESSAGES Importantly, interpretation of data generated by systems biology tools requires integration with enhanced clinical research data and validation using model systems. Such an integrative biological approach has already generated novel insights into pathways and molecules involved in DKD. In this review, we highlight recent examples of how combining systems biology with traditional clinical and model research efforts results in an integrative biology approach that has significantly added to the understanding of the complex pathophysiology of DKD.
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Affiliation(s)
- Jennifer L Harder
- Department of Internal Medicine, the Division of Nephrology, University of Michigan, Ann Arbor, Michigan
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Matthias Kretzler
- Department of Internal Medicine, the Division of Nephrology, University of Michigan, Ann Arbor, Michigan ; Department of Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, Michigan
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47
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Gorski M, Tin A, Garnaas M, McMahon GM, Chu AY, Tayo BO, Pattaro C, Teumer A, Chasman DI, Chalmers J, Hamet P, Tremblay J, Woodward M, Aspelund T, Eiriksdottir G, Gudnason V, Harris TB, Launer LJ, Smith AV, Mitchell BD, O'Connell JR, Shuldiner AR, Coresh J, Li M, Freudenberger P, Hofer E, Schmidt H, Schmidt R, Holliday EG, Mitchell P, Wang JJ, de Boer IH, Li G, Siscovick DS, Kutalik Z, Corre T, Vollenweider P, Waeber G, Gupta J, Kanetsky PA, Hwang SJ, Olden M, Yang Q, de Andrade M, Atkinson EJ, Kardia SLR, Turner ST, Stafford JM, Ding J, Liu Y, Barlassina C, Cusi D, Salvi E, Staessen JA, Ridker PM, Grallert H, Meisinger C, Müller-Nurasyid M, Krämer BK, Kramer H, Rosas SE, Nolte IM, Penninx BW, Snieder H, Fabiola Del Greco M, Franke A, Nöthlings U, Lieb W, Bakker SJL, Gansevoort RT, van der Harst P, Dehghan A, Franco OH, Hofman A, Rivadeneira F, Sedaghat S, Uitterlinden AG, Coassin S, Haun M, Kollerits B, Kronenberg F, Paulweber B, Aumann N, Endlich K, Pietzner M, Völker U, Rettig R, Chouraki V, Helmer C, Lambert JC, Metzger M, Stengel B, Lehtimäki T, Lyytikäinen LP, Raitakari O, Johnson A, Parsa A, Bochud M, Heid IM, Goessling W, Köttgen A, Kao WHL, Fox CS, Böger CA. Genome-wide association study of kidney function decline in individuals of European descent. Kidney Int 2015; 87:1017-29. [PMID: 25493955 PMCID: PMC4425568 DOI: 10.1038/ki.2014.361] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 09/04/2014] [Accepted: 09/11/2014] [Indexed: 11/08/2022]
Abstract
Genome-wide association studies (GWASs) have identified multiple loci associated with cross-sectional eGFR, but a systematic genetic analysis of kidney function decline over time is missing. Here we conducted a GWAS meta-analysis among 63,558 participants of European descent, initially from 16 cohorts with serial kidney function measurements within the CKDGen Consortium, followed by independent replication among additional participants from 13 cohorts. In stage 1 GWAS meta-analysis, single-nucleotide polymorphisms (SNPs) at MEOX2, GALNT11, IL1RAP, NPPA, HPCAL1, and CDH23 showed the strongest associations for at least one trait, in addition to the known UMOD locus, which showed genome-wide significance with an annual change in eGFR. In stage 2 meta-analysis, the significant association at UMOD was replicated. Associations at GALNT11 with Rapid Decline (annual eGFR decline of 3 ml/min per 1.73 m(2) or more), and CDH23 with eGFR change among those with CKD showed significant suggestive evidence of replication. Combined stage 1 and 2 meta-analyses showed significance for UMOD, GALNT11, and CDH23. Morpholino knockdowns of galnt11 and cdh23 in zebrafish embryos each had signs of severe edema 72 h after gentamicin treatment compared with controls, but no gross morphological renal abnormalities before gentamicin administration. Thus, our results suggest a role in the deterioration of kidney function for the loci GALNT11 and CDH23, and show that the UMOD locus is significantly associated with kidney function decline.
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Affiliation(s)
- Mathias Gorski
- 1] Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany [2] Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Maija Garnaas
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Gearoid M McMahon
- 1] Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA [2] NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Bamidele O Tayo
- Department of Public Health Services, Loyola Medical Center, Loyola University Chicago, Maywood, Illinois, USA
| | - Cristian Pattaro
- Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affiliated to the University of Lübeck, Bolzano, Italy
| | - Alexander Teumer
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - John Chalmers
- George Institute for Global Health, University of Sydney, Sydney, New South Wales, Australia
| | - Pavel Hamet
- Centre de recherche du Centre hospitalier de l'Université de Montréal, University of Montreal, Montreal, Quebec, Canada
| | - Johanne Tremblay
- CHUM Research Center- Technopôle Angus, Montreal, Québec, Canada
| | - Marc Woodward
- George Institute for Global Health, University of Sydney, Sydney, New South Wales, Australia
| | - Thor Aspelund
- 1] Icelandic Heart Association, Research Institute, Kopavogur, Iceland [2] University of Iceland, Reykjavik, Iceland
| | | | - Vilmundur Gudnason
- 1] Icelandic Heart Association, Research Institute, Kopavogur, Iceland [2] University of Iceland, Reykjavik, Iceland
| | - Tamara B Harris
- Intramural Research Program, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland, USA
| | - Lenore J Launer
- Intramural Research Program, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland, USA
| | - Albert V Smith
- 1] Icelandic Heart Association, Research Institute, Kopavogur, Iceland [2] University of Iceland, Reykjavik, Iceland
| | - Braxton D Mitchell
- 1] Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA [2] Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jeffrey R O'Connell
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Alan R Shuldiner
- 1] Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA [2] Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Josef Coresh
- 1] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA [2] Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, Maryland, USA
| | - Man Li
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Paul Freudenberger
- Institute of Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | - Edith Hofer
- Department of Neurology, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | | | - Elizabeth G Holliday
- Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, CReDITSS, HMRI, Callaghan, New South Wales, Australia
| | - Paul Mitchell
- Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia
| | - Jie Jin Wang
- Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia
| | | | - Guo Li
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
| | - David S Siscovick
- 1] Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA [2] New York Academy of Medicine, New York, New York, USA
| | - Zoltan Kutalik
- 1] Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland [2] Department of Medical Genetics, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tanguy Corre
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Internal Medicine Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Gérard Waeber
- Internal Medicine Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Jayanta Gupta
- Perelman School of Medicine at the University of Pennsylvania, Center for Clinical Epidemiology and Biostatistics
| | - Peter A Kanetsky
- Perelman School of Medicine at the University of Pennsylvania, Center for Clinical Epidemiology and Biostatistics
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA
| | - Matthias Olden
- 1] Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany [2] NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA
| | - Qiong Yang
- 1] NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA [2] Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | | | | | | | - Jeanette M Stafford
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Jingzhong Ding
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | | | - Daniele Cusi
- 1] Department of Health Science, University of Milano, Milano, Italy [2] Division of Nephrology, San Paolo Hospital, Milano, Italy
| | - Erika Salvi
- Department of Health Science, University of Milano, Milano, Italy
| | - Jan A Staessen
- 1] Department of Epidemiology, Maastricht University, Maastricht, The Netherlands [2] Studies Coordinating Centre, Division of Hypertension and Cardiovascular Rehabilitation, Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Harald Grallert
- 1] Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany [2] Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany [3] German Center for Diabetes Research, Neuherberg, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- 1] DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany [2] Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany [3] Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany [4] Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
| | - Bernhard K Krämer
- University Medical Centre Mannheim, 5th Department of Medicine, University of Heidelberg, Mannheim, Germany
| | - Holly Kramer
- Department of Public Health Services, Loyola Medical Center, Loyola University Chicago, Maywood, Illinois, USA
| | - Sylvia E Rosas
- Joslin Diabetes Center and Beth Israel Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ilja M Nolte
- 1] Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands [2] Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology (FA40), University Medical Center Groningen, Groningen, The Netherlands
| | - Brenda W Penninx
- 1] Department of Psychiatry/EMGO Institute/Neuroscience Campus, VU University Medical Centre, Amsterdam, The Netherlands [2] EMGO Institute Vumc, NESDA, Amsterdam, The Netherlands
| | - Harold Snieder
- 1] Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands [2] Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology (FA40), University Medical Center Groningen, Groningen, The Netherlands
| | - M Fabiola Del Greco
- Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affiliated to the University of Lübeck, Bolzano, Italy
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel, Germany
| | - Ute Nöthlings
- 1] Popgen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany [2] Section for Epidemiology, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank popgen, Christian-Albrechts University, Kiel, Germany
| | - Stephan J L Bakker
- University Medical Center Groningen, Department of Nephrology, University of Groningen, Groningen, The Netherlands
| | - Ron T Gansevoort
- University Medical Center Groningen, Department of Nephrology, University of Groningen, Groningen, The Netherlands
| | - Pim van der Harst
- University Medical Center Groningen, Department of Cardiology, University of Groningen, Groningen, The Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Stefan Coassin
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Margot Haun
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Barbara Kollerits
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Bernhard Paulweber
- First Department of Internal Medicine, Paracelsus Private Medical University Salzburg, Salzburg, Austria
| | - Nicole Aumann
- Department SHIP/KEF, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Karlhans Endlich
- Institute of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Mike Pietzner
- Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Greifswald-Karlsburg, Germany
| | - Vincent Chouraki
- Inserm, U744, Institut Pasteur de Lille, Université Lille-Nord de France, CHR&U de Lille, Service d'épidémiologie régional, CHRU, Lille, France
| | - Catherine Helmer
- Inserm, U897, Université Bordeaux 2, ISPED, ISPED, Université Bordeaux 2, Bordeaux, France
| | - Jean-Charles Lambert
- Inserm, U744, Institut Pasteur de Lille, Université Lille-Nord de France, Institut Pasteur, Lille, France
| | - Marie Metzger
- Inserm, U1018, University Paris-Sud, CESP Team 10, Villejuif, France
| | - Benedicte Stengel
- Inserm, U1018, University Paris-Sud, CESP Team 10, Villejuif, France
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | | | - Olli Raitakari
- 1] Department of Clinical Physiology, Turku University Hospital, Turku, Finland [2] Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Andrew Johnson
- NHLBI Cardiovascular Epidemiology and Human Genomics Branch, Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA
| | - Afshin Parsa
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Murielle Bochud
- Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Epalinges, Switzerland
| | - Iris M Heid
- 1] Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany [2] Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfram Goessling
- 1] Divisions of Genetics and Gastroenterology, Department of Medicine, Brigham and Women's Hospital, and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA [2] Harvard Stem Cell Institute, Harvard University and Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Anna Köttgen
- 1] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA [2] Renal Division, Freiburg University Clinic, Germany, Freiburg, Germany
| | - W H Linda Kao
- 1] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA [2] Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, Maryland, USA
| | - Caroline S Fox
- 1] NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts, USA [2] Department of Endocrinology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
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Understanding multicellular function and disease with human tissue-specific networks. Nat Genet 2015; 47:569-76. [PMID: 25915600 PMCID: PMC4828725 DOI: 10.1038/ng.3259] [Citation(s) in RCA: 543] [Impact Index Per Article: 60.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 03/06/2015] [Indexed: 12/17/2022]
Abstract
Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, reveal genes’ changing functional roles across tissues, and illuminate disease-disease relationships. We introduce NetWAS, which combines genes with nominally significant GWAS p-values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than one hundred human tissues and cell types.
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Genetic variants in nicotinic acetylcholine receptor genes jointly contribute to kidney function in American Indians: the Strong Heart Family Study. J Hypertens 2014; 32:1042-8; discussion 1049. [PMID: 24569419 DOI: 10.1097/hjh.0000000000000151] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Cigarette smoking negatively affects kidney function. Genetic variants in the nicotinic acetylcholine receptor (nAChR) genes have been associated with nicotine dependence, and are likely to influence renal function and related traits. Whereas each single variant may only exert a small effect, the joint contribution of multiple variants to the risk of disease could be substantial. METHODS Using a gene-family approach, we investigated the joint association of 61 tagging SNPs in seven genes encoding the nAChRs with kidney function in 3620 American Indians participating in the Strong Heart Family Study, independent of known risk factors. Kidney function was evaluated by estimated glomerular filtration rate, urinary albumin/creatinine ratio, albuminuria and chronic kidney disease. The joint impact of smoking-related variants was assessed using the weighted truncated product method. RESULTS Multiple SNPs showed marginal individual effect on renal function variability, and only a few survive multiple comparison correction. In contrast, a gene-family analysis considering the joint impact of all 61 SNPs reveals significant associations of the nAChR gene family with kidney function variables including estimated glomerular filtration rate, urinary albumin/creatinine ratio, and albuminuria (all Ps ≤ 0.0001) after adjusting for established risk factors including cigarette smoking. CONCLUSION Genetic variants in nAChR genes jointly contribute to renal function or kidney damage in American Indians. The effects of these genetic variants on kidney function or damage are independent of traditional risk factors including cigarette smoking per se.
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50
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Akrawi DS, Li X, Sundquist J, Sundquist K, Zöller B. Familial risks of kidney failure in Sweden: a nationwide family study. PLoS One 2014; 9:e113353. [PMID: 25423475 PMCID: PMC4244139 DOI: 10.1371/journal.pone.0113353] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 10/22/2014] [Indexed: 02/03/2023] Open
Abstract
Background The value of family history as a risk factor for kidney failure has not been determined in a nationwide setting. Aim This nationwide family study aimed to determine familial risks for kidney failure in Sweden. Methods The Swedish multi-generation register on 0–78-year-old subjects were linked to the Swedish patient register and the Cause of death register for 1987–2010. Individuals diagnosed with acute kidney failure (n = 10063), chronic kidney failure (n = 18668), or unspecified kidney failure (n = 3731) were included. Kidney failure patients with cystic kidney disease, congenital kidney and urinary tract malformations, urolithiasis, and rare inherited kidney syndromes, and hyperoxaluria were excluded. Standardized incidence ratios (SIRs) were calculated for individuals whose parents/siblings were diagnosed with kidney failure compared to those whose parents or siblings were not. Results The concordant (same disease) familial risks (sibling/parent history) were increased for chronic kidney failure SIR = 2.02 (95% confidence interval, CI 1.90–2.14) but not for acute kidney failure SIR = 1.08 (95% CI 0.94–1.22) and for unspecified kidney failure SIR = 1.25 (95% CI 0.94–1.63). However, the discordant (different disease) familial risk for acute kidney failure SIR = 1.19 (95% CI 1.06–1.32) and unspecified kidney failure SIR = 1.63 (95% CI 1.40–1.90) was significantly increased in individuals with a family history of chronic kidney failure. The familial risk for chronic kidney failure was similar for males SIR = 2.04 (95% CI 1.90–2.20) and females SIR = 1.97 (95% CI 1.78–2.17). Familial risks for chronic kidney failure were highest at age of 10–19 years SIR = 6.33 (95% CI 4.16–9.22). Conclusions The present study shows that family history is an important risk factor for chronic kidney failure but to a lower degree for acute kidney failure and unspecified kidney failure.
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Affiliation(s)
- Delshad Saleh Akrawi
- Center for Primary Care Research, Lund University/Region Skåne, Malmö, Sweden
- * E-mail:
| | - Xinjun Li
- Center for Primary Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Care Research, Lund University/Region Skåne, Malmö, Sweden
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kristina Sundquist
- Center for Primary Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Bengt Zöller
- Center for Primary Care Research, Lund University/Region Skåne, Malmö, Sweden
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