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Zheng G, Cheng Y, Wang C, Wang B, Zou X, Zhou J, Peng L, Zeng T. Elucidating the causal nexus and immune mediation between frailty and chronic kidney disease: integrative multi-omics analysis. Ren Fail 2024; 46:2367028. [PMID: 39010723 PMCID: PMC11265307 DOI: 10.1080/0886022x.2024.2367028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/06/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Empirical research has consistently documented the concurrent manifestation of frailty and chronic kidney disease (CKD). However, the existence of a reverse causal association or the influence of confounding variables on these correlations remains ambiguous. METHODS Our analysis of 7,078 participants from National Health and Nutrition Examination Survey(NHANES) (1999-2018) applied weighted logistic regression and Mendelian Randomization (MR) to investigate the correlation between the frailty index (FI) and renal function. The multivariate MR analysis was specifically adjusted for type 2 diabetes and hypertension. Further analysis explored 3282 plasma proteins to link FI to CKD. A two-step network MR highlighted immune cells' mediating roles in the FI-CKD relationship. RESULT Genetically inferred FI and various renal function markers are significantly correlated, as supported by NHANES analyses. Multivariate MR analysis revealed a direct causal association between the FI and CKD. Additionally, our investigation into plasma proteins identified Tmprss11D and MICB correlated with FI and CKD, respectively. A two-step network MR to reveal 15 immune cell types, notably Central Memory CD4+ T cells and Lymphocytes, as crucial mediators between FI and CKD. CONCLUSION Our work establishes a causal connection between frailty and CKD, mediated by specific immune cell profiles. These findings highlight the importance of immune mechanisms in the frailty-CKD interplay and suggest that targeting shared risk factors and immune pathways could improve management strategies for these conditions. Our research contributes to a more nuanced understanding of frailty and CKD, offering new avenues for intervention and patient care in an aging population.
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Affiliation(s)
- Guanghao Zheng
- Department of Medicine, Graduate School of Nanchang University, Nanchang, China
| | - Yu Cheng
- Department of Medicine, Graduate School of Nanchang University, Nanchang, China
| | - Chenlong Wang
- Department of Central Laboratory, The Affiliated Huaian No.1 Peopele’s Hospital, Nanjing Medical University, Huai’an, China
| | - Bin Wang
- Department of Medicine, Graduate School of Nanchang University, Nanchang, China
| | - Xinchang Zou
- Department of Medicine, Graduate School of Nanchang University, Nanchang, China
| | - Jie Zhou
- Department of Medicine, Graduate School of Nanchang University, Nanchang, China
| | - Lifen Peng
- Molecular Experiment Center, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, China
| | - Tao Zeng
- Department of Urology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Galuška D, Pácal L, Chalásová K, Divácká P, Řehořová J, Svojanovský J, Hubáček JA, Lánská V, Kaňková K. T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects. Gene 2024; 927:148724. [PMID: 38909968 DOI: 10.1016/j.gene.2024.148724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 06/25/2024]
Abstract
This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population.
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Affiliation(s)
- David Galuška
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic; Department of Biochemistry, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
| | - Lukáš Pácal
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Katarína Chalásová
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petra Divácká
- Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic
| | - Jitka Řehořová
- Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic
| | - Jan Svojanovský
- Department of Internal Medicine, St. Anne's University Hospital, Brno, Czech Republic
| | - Jaroslav A Hubáček
- Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; 3rd Department of Internal Medicine, 1(st) Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Věra Lánská
- Department of Data Science, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Kateřina Kaňková
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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3
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Wang B, Li H, Wang N, Li Y, Song Z, Chen Y, Li X, Liu L, Chen H. The impact of homocysteine on patients with diabetic nephropathy: a mendelian randomization study. Acta Diabetol 2024:10.1007/s00592-024-02343-9. [PMID: 39105808 DOI: 10.1007/s00592-024-02343-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND/AIMS Homocysteine (Hcy) has been associated with an increased risk of diabetic nephropathy (DN) in patients, but there is still controversy. This study aims to investigate the causal relationship between plasma Hcy and DN. METHODS A Mendelian randomization (MR) study using data from 2 samples was employed to infer causal relationships. The aggregated genetic data associated with Hcy was derived from the largest genome-wide association study (GWAS) to date, involving 44,147 individuals of European ancestry.Data on SNP-diabetic nephropathy, creatinine, and urea nitrogen were obtained from the IEU GWAS database. The analysis method employed a fixed-effect or random-effect inverse variance-weighted approach to estimate effects.Additional analysis methods were used to assess stability and sensitivity. The potential for pleiotropy was evaluated using the MR-Egger intercept test. RESULTS Using 12 SNPs as instrumental variables, two-sample MR analysis revealed no evidence of a causal relationship between genetically predicted plasma Hcy levels and diabetic nephropathy, as well as creatinine and blood urea nitrogen levels. This finding is consistent with the results obtained from other testing methods. CONCLUSIONS Two-sample Mendelian Randomization analysis found no evidence of a causal relationship between plasma homocysteine levels and diabetic nephropathy, creatinine, or urea.
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Affiliation(s)
- Baiju Wang
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Han Li
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Na Wang
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Yuan Li
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Zihua Song
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Yajuan Chen
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Xiaobing Li
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China
| | - Lei Liu
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China.
| | - Hanwen Chen
- Department of General Medicine, Affiliated Hospital of Jining Medical University, 89 Guhuai Road, Rencheng District, Jining, 272029, Shandong, China.
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4
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Bustamante M, Balagué-Dobón L, Buko Z, Sakhi AK, Casas M, Maitre L, Andrusaityte S, Grazuleviciene R, Gützkow KB, Brantsæter AL, Heude B, Philippat C, Chatzi L, Vafeiadi M, Yang TC, Wright J, Hough A, Ruiz-Arenas C, Nurtdinov RN, Escaramís G, González JR, Thomsen C, Vrijheid M. Common genetic variants associated with urinary phthalate levels in children: A genome-wide study. ENVIRONMENT INTERNATIONAL 2024; 190:108845. [PMID: 38945087 DOI: 10.1016/j.envint.2024.108845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/02/2024]
Abstract
INTRODUCTION Phthalates, or dieters of phthalic acid, are a ubiquitous type of plasticizer used in a variety of common consumer and industrial products. They act as endocrine disruptors and are associated with increased risk for several diseases. Once in the body, phthalates are metabolized through partially known mechanisms, involving phase I and phase II enzymes. OBJECTIVE In this study we aimed to identify common single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) associated with the metabolism of phthalate compounds in children through genome-wide association studies (GWAS). METHODS The study used data from 1,044 children with European ancestry from the Human Early Life Exposome (HELIX) cohort. Ten phthalate metabolites were assessed in a two-void pooled urine collected at the mean age of 8 years. Six ratios between secondary and primary phthalate metabolites were calculated. Genome-wide genotyping was done with the Infinium Global Screening Array (GSA) and imputation with the Haplotype Reference Consortium (HRC) panel. PennCNV was used to estimate copy number variants (CNVs) and CNVRanger to identify consensus regions. GWAS of SNPs and CNVs were conducted using PLINK and SNPassoc, respectively. Subsequently, functional annotation of suggestive SNPs (p-value < 1E-05) was done with the FUMA web-tool. RESULTS We identified four genome-wide significant (p-value < 5E-08) loci at chromosome (chr) 3 (FECHP1 for oxo-MiNP_oh-MiNP ratio), chr6 (SLC17A1 for MECPP_MEHHP ratio), chr9 (RAPGEF1 for MBzP), and chr10 (CYP2C9 for MECPP_MEHHP ratio). Moreover, 115 additional loci were found at suggestive significance (p-value < 1E-05). Two CNVs located at chr11 (MRGPRX1 for oh-MiNP and SLC35F2 for MEP) were also identified. Functional annotation pointed to genes involved in phase I and phase II detoxification, molecular transfer across membranes, and renal excretion. CONCLUSION Through genome-wide screenings we identified known and novel loci implicated in phthalate metabolism in children. Genes annotated to these loci participate in detoxification, transmembrane transfer, and renal excretion.
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Affiliation(s)
- Mariona Bustamante
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | | | - Zsanett Buko
- Department of Oncological Science, Huntsman Cancer Institute, Salt Lake City, United States
| | - Amrit Kaur Sakhi
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maribel Casas
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lea Maitre
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sandra Andrusaityte
- Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Kristine B Gützkow
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Anne-Lise Brantsæter
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004, Paris, France
| | - Claire Philippat
- University Grenoble Alpes, Inserm U-1209, CNRS-UMR-5309, Environmental Epidemiology Applied to Reproduction and Respiratory Health Team, Institute for Advanced Biosciences, 38000, Grenoble, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Amy Hough
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Carlos Ruiz-Arenas
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
| | - Ramil N Nurtdinov
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Catalonia, Spain
| | - Geòrgia Escaramís
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Juan R González
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Cathrine Thomsen
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Martine Vrijheid
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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5
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Jones AC, Patki A, Srinivasasainagendra V, Tiwari HK, Armstrong ND, Chaudhary NS, Limdi NA, Hidalgo BA, Davis B, Cimino JJ, Khan A, Kiryluk K, Lange LA, Lange EM, Arnett DK, Young BA, Diamantidis CJ, Franceschini N, Wassertheil-Smoller S, Rich SS, Rotter JI, Mychaleckyj JC, Kramer HJ, Chen YDI, Psaty BM, Brody JA, de Boer IH, Bansal N, Bis JC, Irvin MR. Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations. J Am Soc Nephrol 2024:00001751-990000000-00377. [PMID: 39073889 DOI: 10.1681/asn.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Key Points
The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits.However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations.Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.
Background
CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.
Methods
This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.
Results
The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.
Conclusions
In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.
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Affiliation(s)
- Alana C Jones
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nicole D Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ninad S Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brittney Davis
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - James J Cimino
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, South Carolina
| | - Bessie A Young
- Division of Nephrology, University of Washington, Seattle, Washington
| | | | - Nora Franceschini
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Stephen S Rich
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Josyf C Mychaleckyj
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Holly J Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, Illinois
| | - Yii-Der I Chen
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Bruce M Psaty
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian H de Boer
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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Zhao X, Gao J, Kou K, Wang X, Gao X, Wang Y, Zhou H, Li F. Causal effects of plasma metabolites on chronic kidney diseases and renal function: a bidirectional Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1429159. [PMID: 39129920 PMCID: PMC11310041 DOI: 10.3389/fendo.2024.1429159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024] Open
Abstract
Background Despite the potential demonstrated by targeted plasma metabolite modulators in halting the progression of chronic kidney disease (CKD), a lingering uncertainty persists concerning the causal relationship between distinct plasma metabolites and the onset and progression of CKD. Methods A genome-wide association study was conducted on 1,091 metabolites and 309 metabolite ratios derived from a cohort of 8,299 unrelated individuals of European descent. Employing a bidirectional two-sample Mendelian randomization (MR) analysis in conjunction with colocalization analysis, we systematically investigated the associations between these metabolites and three phenotypes: CKD, creatinine-estimated glomerular filtration rate (creatinine-eGFR), and urine albumin creatinine ratio (UACR). In the MR analysis, the primary analytical approach employed was inverse variance weighting (IVW), and sensitivity analysis was executed utilizing the MR-Egger method and MR-pleiotropy residual sum and outlier (MR-PRESSO). Heterogeneity was carefully evaluated through Cochrane's Q test. To ensure the robustness of our MR results, the leave-one-out method was implemented, and the strength of causal relationships was subjected to scrutiny via Bonferroni correction. Results Our thorough MR analysis involving 1,400 plasma metabolites and three clinical phenotypes yielded a discerning identification of 21 plasma metabolites significantly associated with diverse outcomes. Specifically, in the forward MR analysis, 6 plasma metabolites were determined to be causally associated with CKD, 16 with creatinine-eGFR, and 7 with UACR. Substantiated by robust evidence from colocalization analysis, 6 plasma metabolites shared causal variants with CKD, 16 with creatinine-eGFR, and 7 with UACR. In the reverse analysis, a diminished creatinine-eGFR was linked to elevated levels of nine plasma metabolites. Notably, no discernible associations were observed between other plasma metabolites and CKD, creatinine-eGFR, and UACR. Importantly, our analysis detected no evidence of horizontal pleiotropy. Conclusion This study elucidates specific plasma metabolites causally associated with CKD and renal functions, providing potential targets for intervention. These findings contribute to an enriched understanding of the genetic underpinnings of CKD and renal functions, paving the way for precision medicine applications and therapeutic strategies aimed at impeding disease progression.
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Affiliation(s)
- Xiaodong Zhao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Jialin Gao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Kai Kou
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Xi Wang
- Department of Endocrinology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Gao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Yishu Wang
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Honglan Zhou
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Faping Li
- Department of Urology, The First Hospital of Jilin University, Changchun, China
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7
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Mandla R, Lorenz K, Yin X, Bocher O, Huerta-Chagoya A, Arruda AL, Piron A, Horn S, Suzuki K, Hatzikotoulas K, Southam L, Taylor H, Yang K, Hrovatin K, Tong Y, Lytrivi M, Rayner NW, Meigs JB, McCarthy MI, Mahajan A, Udler MS, Spracklen CN, Boehnke M, Vujkovic M, Rotter JI, Eizirik DL, Cnop M, Lickert H, Morris AP, Zeggini E, Voight BF, Mercader JM. Multi-omics characterization of type 2 diabetes associated genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.15.24310282. [PMID: 39072045 PMCID: PMC11275663 DOI: 10.1101/2024.07.15.24310282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
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Affiliation(s)
- Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kim Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
| | - Anthony Piron
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels (IB2), Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Diabetes and Inflammation Laboratory, Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Susanne Horn
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Kaiyuan Yang
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Karin Hrovatin
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Yue Tong
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Maria Lytrivi
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Miriam Cnop
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
- WEL Research Institute, Wavre, Belgium
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Andrew P. Morris
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
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8
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Si S, Liu H, Xu L, Zhan S. Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome. Genome Med 2024; 16:84. [PMID: 38898508 PMCID: PMC11186236 DOI: 10.1186/s13073-024-01356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome. METHODS We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy). RESULTS Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells. CONCLUSIONS Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.
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Affiliation(s)
- Shucheng Si
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Hongyan Liu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Lu Xu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China.
- Peking University Health Science Center, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan Road, Haidian District, Beijing, 100191, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
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9
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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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10
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Schreiber R, Ousingsawat J, Kunzelmann K. The anoctamins: Structure and function. Cell Calcium 2024; 120:102885. [PMID: 38642428 DOI: 10.1016/j.ceca.2024.102885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/22/2024]
Abstract
When activated by increase in intracellular Ca2+, anoctamins (TMEM16 proteins) operate as phospholipid scramblases and as ion channels. Anoctamin 1 (ANO1) is the Ca2+-activated epithelial anion-selective channel that is coexpressed together with the abundant scramblase ANO6 and additional intracellular anoctamins. In salivary and pancreatic glands, ANO1 is tightly packed in the apical membrane and secretes Cl-. Epithelia of airways and gut use cystic fibrosis transmembrane conductance regulator (CFTR) as an apical Cl- exit pathway while ANO1 supports Cl- secretion mainly by facilitating activation of luminal CFTR and basolateral K+ channels. Under healthy conditions ANO1 modulates intracellular Ca2+ signals by tethering the endoplasmic reticulum, and except of glands its direct secretory contribution as Cl- channel might be small, compared to CFTR. In the kidneys ANO1 supports proximal tubular acid secretion and protein reabsorption and probably helps to excrete HCO3-in the collecting duct epithelium. However, under pathological conditions as in polycystic kidney disease, ANO1 is strongly upregulated and may cause enhanced proliferation and cyst growth. Under pathological condition, ANO1 and ANO6 are upregulated and operate as secretory channel/phospholipid scramblases, partly by supporting Ca2+-dependent processes. Much less is known about the role of other epithelial anoctamins whose potential functions are discussed in this review.
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Affiliation(s)
- Rainer Schreiber
- Physiological Institute, University of Regensburg, University street 31, D-93053 Regensburg, Germany
| | - Jiraporn Ousingsawat
- Physiological Institute, University of Regensburg, University street 31, D-93053 Regensburg, Germany
| | - Karl Kunzelmann
- Physiological Institute, University of Regensburg, University street 31, D-93053 Regensburg, Germany.
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11
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Kunzelmann K, Ousingsawat J, Schreiber R. VSI: The anoctamins: Structure and function: "Intracellular" anoctamins. Cell Calcium 2024; 120:102888. [PMID: 38657371 DOI: 10.1016/j.ceca.2024.102888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/26/2024]
Abstract
Plasma membrane localized anoctamin 1, 2 and 6 (TMEM16A, B, F) have been examined in great detail with respect to structure and function, but much less is known about the other seven intracellular members of this exciting family of proteins. This is probably due to their limited accessibility in intracellular membranous compartments, such as the endoplasmic reticulum (ER) or endosomes. However, these so-called intracellular anoctamins are also found in the plasma membrane (PM) which adds to the confusion regarding their cellular role. Probably all intracellular anoctamins except of ANO8 operate as intracellular phospholipid (PL) scramblases, allowing for Ca2+-activated, passive transport of phospholipids like phosphatidylserine between both membrane leaflets. Probably all of them also conduct ions, which is probably part of their physiological function. In this brief overview, we summarize key findings on the biological functions of ANO3, 4, 5, 7, 8, 9 and 10 (TMEM16C, D, E, G, H, J, K) that are gradually coming to light. Compartmentalized regulation of intracellular Ca2+ signals, tethering of the ER to specific PM contact sites, and control of intracellular vesicular trafficking appear to be some of the functions of intracellular anoctamins, while loss of function and abnormal expression are the cause for various diseases.
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Affiliation(s)
- Karl Kunzelmann
- Physiological Institute, University of Regensburg, University street 31, D-93053, Regensburg, Germany.
| | - Jiraporn Ousingsawat
- Physiological Institute, University of Regensburg, University street 31, D-93053, Regensburg, Germany
| | - Rainer Schreiber
- Physiological Institute, University of Regensburg, University street 31, D-93053, Regensburg, Germany
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12
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Xue CC, Sim R, Chee ML, Yu M, Wang YX, Rim TH, Hyung PK, Woong KS, Song SJ, Nangia V, Panda-Jonas S, Wang NL, Hao J, Zhang Q, Cao K, Sasaki M, Harada S, Toru T, Ryo K, Raman R, Surya J, Khan R, Bikbov M, Wong IY, Cheung CMG, Jonas JB, Cheng CY, Tham YC. Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium. Ophthalmology 2024; 131:692-699. [PMID: 38160880 DOI: 10.1016/j.ophtha.2023.12.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/06/2023] [Accepted: 12/26/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE Chronic kidney disease (CKD) may elevate susceptibility to age-related macular degeneration (AMD) because of shared risk factors, pathogenic mechanisms, and genetic polymorphisms. Given the inconclusive findings in prior studies, we investigated this association using extensive datasets in the Asian Eye Epidemiology Consortium. DESIGN Cross-sectional study. PARTICIPANTS Fifty-one thousand two hundred fifty-three participants from 10 distinct population-based Asian studies. METHODS Age-related macular degeneration was defined using the Wisconsin Age-Related Maculopathy Grading System, the International Age-Related Maculopathy Epidemiological Study Group Classification, or the Beckman Clinical Classification. Chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 m2. A pooled analysis using individual-level participant data was performed to examine the associations between CKD and eGFR with AMD (early and late), adjusting for age, sex, hypertension, diabetes, body mass index, smoking status, total cholesterol, and study groups. MAIN OUTCOME MEASURES Odds ratio (OR) of early and late AMD. RESULTS Among 51 253 participants (mean age, 54.1 ± 14.5 years), 5079 had CKD (9.9%). The prevalence of early AMD was 9.0%, and that of late AMD was 0.71%. After adjusting for confounders, individuals with CKD were associated with higher odds of late AMD (OR, 1.46; 95% confidence interval [CI], 1.11-1.93; P = 0.008). Similarly, poorer kidney function (per 10-unit eGFR decrease) was associated with late AMD (OR, 1.12; 95% CI, 1.05-1.19; P = 0.001). Nevertheless, CKD and eGFR were not associated significantly with early AMD (all P ≥ 0.149). CONCLUSIONS Pooled analysis from 10 distinct Asian population-based studies revealed that CKD and compromised kidney function are associated significantly with late AMD. This finding further underscores the importance of ocular examinations in patients with CKD. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Can Can Xue
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Ralene Sim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Miao Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Park Kyu Hyung
- Department of Ophthalmology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Kang Se Woong
- Department of Ophthalmology of Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Su Jeong Song
- Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | | | - Ning Li Wang
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jie Hao
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qing Zhang
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Kai Cao
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Mariko Sasaki
- Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan; Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Takebayashi Toru
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Kawasaki Ryo
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Janani Surya
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Rehana Khan
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Mukharram Bikbov
- Ufa Eye Research Institute, Ufa, Bashkortostan, Russian Federation
| | - Ian Y Wong
- Department of Ophthalmology, The Hong Kong Sanatorium & Hospital, Hong Kong SAR, China
| | - Chui Ming Gemmy Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim Heidelberg University, Mannheim, Germany; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore; Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore; Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore.
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13
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Gagnon E, Bourgault J, Gobeil É, Thériault S, Arsenault BJ. Impact of loss-of-function in angiopoietin-like 4 on the human phenome. Atherosclerosis 2024; 393:117558. [PMID: 38703417 DOI: 10.1016/j.atherosclerosis.2024.117558] [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: 02/14/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Carriers of the E40K loss-of-function variant in Angiopoietin-like 4 (ANGPTL4), have lower plasma triglyceride levels as well as lower rates of coronary artery disease (CAD) and type 2 diabetes (T2D). These genetic data suggest ANGPTL4 inhibition as a potential therapeutic target for cardiometabolic diseases. However, it is unknown whether the association between E40K and human diseases is due to linkage disequilibrium confounding. The broader impact of genetic ANGPTL4 inhibition is also unknown, raising uncertainties about the safety and validity of this target. METHODS To assess the impact of ANGPLT4 inhibition, we evaluated whether E40K and other loss-of-function variants in ANGPTL4 influenced a wide range of health markers and diseases using 29 publicly available genome-wide association meta-analyses of cardiometabolic traits and diseases, as well as 1589 diseases assessed in electronic health records within FinnGen (n = 309,154). To determine whether these relationships were likely causal, and not driven by other correlated variants, we used the Bayesian fine mapping algorithm CoPheScan. RESULTS The CoPheScan posterior probability of E40K being the causal variant for triglyceride levels was 99.99 %, validating the E40K to proxy lifelong lower activity of ANGPTL4. The E40K variant was associated with lower risk of CAD (odds ratio [OR] = 0.84, 95 % CI = 0.81 to 0.87, p=3.6e-21) and T2D (OR = 0.91, 95 % CI = 0.87 to 0.95, p=2.8e-05) in GWAS meta-analyses, with results replicated in FinnGen. These significant results were also replicated using other rare loss-of-function variants identified through whole exome sequencing in 488,278 participants of the UK Biobank. Using a Mendelian randomization study design, the E40K variant effect on cardiometabolic diseases was concordant with lipoprotein lipase enhancement (r = 0.82), but not hepatic lipase enhancement (r = -0.10), suggesting that ANGPTL4 effects on cardiometabolic diseases are potentially mainly mediated through lipoprotein lipase. After correction for multiple testing, the E40K variant did not significantly increase the risk of any of the 1589 diseases tested in FinnGen. CONCLUSIONS ANGPTL4 inhibition may represent a potentially safe and effective target for cardiometabolic diseases prevention or treatment.
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Affiliation(s)
- Eloi Gagnon
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Jérome Bourgault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Émilie Gobeil
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Sébastien Thériault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada; Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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14
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Hou Y, Liu Q, Xiao Z, Li Y, Tian X, Wang Z. Association between chronic kidney disease and age-related macular degeneration: a Mendelian randomization study. Front Aging Neurosci 2024; 16:1399666. [PMID: 38872627 PMCID: PMC11169943 DOI: 10.3389/fnagi.2024.1399666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/20/2024] [Indexed: 06/15/2024] Open
Abstract
Purpose Observational studies have reported inconsistent results on the relationship between chronic kidney disease (CKD) and age-related macular degeneration (AMD). The primary objective of this study was to investigate the causal relationships between estimated glomerular filtration rate (eGFR), CKD, its common causes, and AMD among participants of European descent. Methods Genetic variants associated with eGFR, CKD and its common causes, encompassing diabetic nephropathy (DN), immunoglobulin A nephropathy (IgAN), and membranous nephropathy (MN) were obtained from previously published genome-wide association studies (GWAS) and FinnGen database. Summary statistics for early AMD, AMD, dry AMD, and wet AMD were acquired from the GWAS and FinnGen database. Inverse-variance-weighted (IVW) method was the main MR analysis. Sensitivity analyses were performed with Cochran's Q, MR-Egger intercept, and leave-one-out analysis. In addition, RadialMR was utilized to identify and remove outliers. Results IVW results showed that CKD, eGFR were not associated with any type of AMD (p > 0.05). DN (OR: 1.042, 95% CI: 1.002-1.083, p = 0.037) and MN (OR: 1.023, 95% CI: 1.007-1.040, p = 0.005) were associated with an increased risk of earl AMD. DN (OR: 1.111, 95% CI: 1.07-1.154, p = 4.87 × 10-8), IgAN (OR: 1.373, 95% CI: 1.097-1.719, p = 0.006), and MN (OR: 1.036, 95% CI: 1.008-1.064, p = 0.012) were associated with an increased risk of AMD. DN (OR: 1.090, 95% CI: 1.042-1.140, p = 1.57 × 10-4) and IgAN (OR: 1.480, 95% CI: 1.178-1.858, p = 7.55 × 10-4) were associated with an increased risk of dry AMD. The risk of wet AMD was associated with DN (OR: 1.107, 95% CI: 1.043-1.174, p = 7.56 × 10-4) and MN (OR: 1.071, 95% CI: 1.040-1.103, p = 5.48 × 10-6). Conclusion This MR study found no evidence of causal relationship between CKD and AMD. DN, IgAN, and MN may increase risk of AMD. This findings underscore the importance of ocular examinations in patients with DN, MN, and IgAN. More studies are needed to support the findings of our current study.
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Affiliation(s)
- Yawei Hou
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qinglin Liu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenwei Xiao
- Department of Nephrology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yameng Li
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xinyang Tian
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenguo Wang
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
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15
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Lu YQ, Wang Y. Multi-Omic Analysis Reveals Genetic Determinants and Therapeutic Targets of Chronic Kidney Disease and Kidney Function. Int J Mol Sci 2024; 25:6033. [PMID: 38892221 PMCID: PMC11172763 DOI: 10.3390/ijms25116033] [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: 04/17/2024] [Revised: 05/20/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Chronic kidney disease (CKD) presents a significant global health challenge, characterized by complex pathophysiology. This study utilized a multi-omic approach, integrating genomic data from the CKDGen consortium alongside transcriptomic, metabolomic, and proteomic data to elucidate the genetic underpinnings and identify therapeutic targets for CKD and kidney function. We employed a range of analytical methods including cross-tissue transcriptome-wide association studies (TWASs), Mendelian randomization (MR), summary-based MR (SMR), and molecular docking. These analyses collectively identified 146 cross-tissue genetic associations with CKD and kidney function. Key Golgi apparatus-related genes (GARGs) and 41 potential drug targets were highlighted, with MAP3K11 emerging as a significant gene from the TWAS and MR data, underscoring its potential as a therapeutic target. Capsaicin displayed promising drug-target interactions in molecular docking analyses. Additionally, metabolome- and proteome-wide MR (PWMR) analyses revealed 33 unique metabolites and critical inflammatory proteins such as FGF5 that are significantly linked to and colocalized with CKD and kidney function. These insights deepen our understanding of CKD pathogenesis and highlight novel targets for treatment and prevention.
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Affiliation(s)
| | - Yirong Wang
- School of Biology & Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China;
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16
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Hou Y, Xiao Z, Zhu Y, Li Y, Liu Q, Wang Z. Blood metabolites and chronic kidney disease: a Mendelian randomization study. BMC Med Genomics 2024; 17:147. [PMID: 38807172 PMCID: PMC11131213 DOI: 10.1186/s12920-024-01918-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/20/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Human blood metabolites have demonstrated close associations with chronic kidney disease (CKD) in observational studies. Nonetheless, the causal relationship between metabolites and CKD is still unclear. This study aimed to assess the associations between metabolites and CKD risk. METHODS We applied a two-sample Mendelian randomization (MR) analysis to evaluate relationships between 1400 blood metabolites and eight phenotypes (outcomes) (CKD, estimated glomerular filtration rate(eGFR), urine albumin to creatinine ratio, rapid progress to CKD, rapid decline of eGFR, membranous nephropathy, immunoglobulin A nephropathy, and diabetic nephropathy). The inverse variance weighted (IVW), MR-Egger, and weighted median were used to investigate the causal relationship. Sensitivity analyses were performed with Cochran's Q, MR-Egger intercept, MR-PRESSO Global test, and leave-one-out analysis. Bonferroni correction was used to test the strength of the causal relationship. RESULTS Through the MR analysis of 1400 metabolites and eight clinical phenotypes, a total of 48 metabolites were found to be associated with various outcomes. Among them, N-acetylleucine (OR = 0.923, 95%CI: 0.89-0.957, PIVW = 1.450 × 10-5) has a strong causal relationship with lower risk of CKD after the Bonferroni-corrected test, whereas Glycine to alanine ratio has a strong causal relationship with higher risk of CKD (OR = 1.106, 95%CI: 1.063-1.151, PIVW = 5.850 × 10-7). No horizontal pleiotropy and heterogeneity were detected. CONCLUSION Our study offers groundbreaking insights into the integration of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for CKD. It underscores 48 metabolites as potential causal candidates, meriting further investigation.
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Affiliation(s)
- Yawei Hou
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenwei Xiao
- Department of Nephrology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yushuo Zhu
- Department of Emergency and Critical Care Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yameng Li
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qinglin Liu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenguo Wang
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China.
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17
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Chen Z, Satake E, Pezzolesi MG, Md Dom ZI, Stucki D, Kobayashi H, Syreeni A, Johnson AT, Wu X, Dahlström EH, King JB, Groop PH, Rich SS, Sandholm N, Krolewski AS, Natarajan R. Integrated analysis of blood DNA methylation, genetic variants, circulating proteins, microRNAs, and kidney failure in type 1 diabetes. Sci Transl Med 2024; 16:eadj3385. [PMID: 38776390 DOI: 10.1126/scitranslmed.adj3385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Variation in DNA methylation (DNAmet) in white blood cells and other cells/tissues has been implicated in the etiology of progressive diabetic kidney disease (DKD). However, the specific mechanisms linking DNAmet variation in blood cells with risk of kidney failure (KF) and utility of measuring blood cell DNAmet in personalized medicine are not clear. We measured blood cell DNAmet in 277 individuals with type 1 diabetes and DKD using Illumina EPIC arrays; 51% of the cohort developed KF during 7 to 20 years of follow-up. Our epigenome-wide analysis identified DNAmet at 17 CpGs (5'-cytosine-phosphate-guanine-3' loci) associated with risk of KF independent of major clinical risk factors. DNAmet at these KF-associated CpGs remained stable over a median period of 4.7 years. Furthermore, DNAmet variations at seven KF-associated CpGs were strongly associated with multiple genetic variants at seven genomic regions, suggesting a strong genetic influence on DNAmet. The effects of DNAmet variations at the KF-associated CpGs on risk of KF were partially mediated by multiple KF-associated circulating proteins and KF-associated circulating miRNAs. A prediction model for risk of KF was developed by adding blood cell DNAmet at eight selected KF-associated CpGs to the clinical model. This updated model significantly improved prediction performance (c-statistic = 0.93) versus the clinical model (c-statistic = 0.85) at P = 6.62 × 10-14. In conclusion, our multiomics study provides insights into mechanisms through which variation of DNAmet may affect KF development and shows that blood cell DNAmet at certain CpGs can improve risk prediction for KF in T1D.
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Affiliation(s)
- Zhuo Chen
- Department of Diabetes Complications and Metabolism, Arthur Riggs Diabetes and Metabolism Research Institute and Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Eiichiro Satake
- Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, MA 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
| | - Marcus G Pezzolesi
- Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Zaipul I Md Dom
- Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, MA 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
| | - Devorah Stucki
- Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Hiroki Kobayashi
- Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, MA 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
- Division of Nephrology, Hypertension, and Endocrinology, Nihon University School of Medicine, Tokyo, Japan
| | - Anna Syreeni
- Folkhälsan Research Center, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, 00290, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Adam T Johnson
- Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Xiwei Wu
- Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
- Integrative Genomics Core, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Emma H Dahlström
- Folkhälsan Research Center, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, 00290, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Jaxon B King
- Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Per-Henrik Groop
- Folkhälsan Research Center, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, 00290, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - Stephen S Rich
- Center for Public Health Genomics and Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Niina Sandholm
- Folkhälsan Research Center, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, 00290, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Andrzej S Krolewski
- Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, MA 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
| | - Rama Natarajan
- Department of Diabetes Complications and Metabolism, Arthur Riggs Diabetes and Metabolism Research Institute and Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
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18
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Liu H, Xiang W, Wu W, Zhou G, Yuan J. Associations of systemic inflammatory regulators with CKD and kidney function: evidence from the bidirectional mendelian randomization study. BMC Nephrol 2024; 25:161. [PMID: 38730296 PMCID: PMC11088104 DOI: 10.1186/s12882-024-03590-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Previous observational studies have reported that systemic inflammatory regulators are related to the development of chronic kidney disease (CKD); however, whether these associations are causal remains unclear. The current study aimed to investigate the potential causal relationships between systemic inflammatory regulators and CKD and kidney function. METHOD We performed bidirectional two-sample Mendelian randomization (MR) analyses to infer the underlying causal associations between 41 systemic inflammatory regulators and CKD and kidney function. The inverse-variance weighting (IVW) test was used as the primary analysis method. In addition, sensitivity analyses were executed via the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test and the weighted median test. RESULTS The findings revealed 12 suggestive associations between 11 genetically predicted systemic inflammatory regulators and CKD or kidney function in the forward analyses, including 4 for CKD, 3 for blood urea nitrogen (BUN), 4 for eGFRcrea and 1 for eGFRcys. In the other direction, we identified 6 significant causal associations, including CKD with granulocyte-colony stimulating factor (GCSF) (IVW β = 0.145; 95% CI, 0.042 to 0.248; P = 0.006), CKD with stem cell factor (SCF) (IVW β = 0.228; 95% CI, 0.133 to 0.323; P = 2.40 × 10- 6), eGFRcrea with SCF (IVW β =-2.90; 95% CI, -3.934 to -1.867; P = 3.76 × 10- 8), eGFRcys with GCSF (IVW β =-1.382; 95% CI, -2.404 to -0.361; P = 0.008), eGFRcys with interferon gamma (IFNg) (IVW β =-1.339; 95% CI, -2.313 to -0.366; P = 0.007) and eGFRcys with vascular endothelial growth factor (VEGF) (IVW β =-1.709; 95% CI, -2.720 to -0.699; P = 9.13 × 10- 4). CONCLUSIONS Our findings support causal links between systemic inflammatory regulators and CKD or kidney function both in the forward and reverse MR analyses.
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Affiliation(s)
- Hailang Liu
- Department of Urology, Wuhan Integrated TCM & Western Medicine Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan No.1 Hospital, Wuhan, China
| | - Wei Xiang
- Department of Urology, Wuhan Integrated TCM & Western Medicine Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan No.1 Hospital, Wuhan, China
| | - Wei Wu
- Department of Urology, Wuhan Integrated TCM & Western Medicine Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan No.1 Hospital, Wuhan, China
| | - Gaofeng Zhou
- Department of Urology, Wuhan Integrated TCM & Western Medicine Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan No.1 Hospital, Wuhan, China.
| | - Jingdong Yuan
- Department of Urology, Wuhan Integrated TCM & Western Medicine Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan No.1 Hospital, Wuhan, China.
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Nusinovici S, Li H, Chong C, Yu M, Sørensen IMH, Bisgaard LS, Christoffersen C, Bro S, Liu S, Liu JJ, Chi LS, Wong TY, Tan GSW, Cheng CY, Sabanayagam C. Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease. J Nephrol 2024; 37:1007-1016. [PMID: 38308753 DOI: 10.1007/s40620-023-01872-w] [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: 07/05/2023] [Accepted: 12/26/2023] [Indexed: 02/05/2024]
Abstract
BACKGROUND The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction. METHODS Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance. RESULTS Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors. CONCLUSION The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.
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Affiliation(s)
- Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore.
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore.
| | - Hengtong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
| | - Crystal Chong
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
| | | | - Line Stattau Bisgaard
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christina Christoffersen
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Susanne Bro
- Department of Nephrology, Rigshospitalet University Hospital, Copenhagen, Denmark
| | - Sylvia Liu
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Jian-Jun Liu
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Lim Su Chi
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
- Saw Swee Hock School of Public Heath, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Landfors F, Henneman P, Chorell E, Nilsson SK, Kersten S. Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk. EUROPEAN HEART JOURNAL OPEN 2024; 4:oeae035. [PMID: 38895109 PMCID: PMC11182694 DOI: 10.1093/ehjopen/oeae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/30/2024] [Accepted: 04/26/2024] [Indexed: 06/21/2024]
Abstract
Aims APOC3, ANGPTL3, and ANGPTL4 are circulating proteins that are actively pursued as pharmacological targets to treat dyslipidaemia and reduce the risk of atherosclerotic cardiovascular disease. Here, we used human genetic data to compare the predicted therapeutic and adverse effects of APOC3, ANGPTL3, and ANGPTL4 inactivation. Methods and results We conducted drug-target Mendelian randomization analyses using variants in proximity to the genes associated with circulating protein levels to compare APOC3, ANGPTL3, and ANGPTL4 as drug targets. We obtained exposure and outcome data from large-scale genome-wide association studies and used generalized least squares to correct for linkage disequilibrium-related correlation. We evaluated five primary cardiometabolic endpoints and screened for potential side effects across 694 disease-related endpoints, 43 clinical laboratory tests, and 11 internal organ MRI measurements. Genetically lowering circulating ANGPTL4 levels reduced the odds of coronary artery disease (CAD) [odds ratio, 0.57 per s.d. protein (95% CI 0.47-0.70)] and Type 2 diabetes (T2D) [odds ratio, 0.73 per s.d. protein (95% CI 0.57-0.94)]. Genetically lowering circulating APOC3 levels also reduced the odds of CAD [odds ratio, 0.90 per s.d. protein (95% CI 0.82-0.99)]. Genetically lowered ANGPTL3 levels via common variants were not associated with CAD. However, meta-analysis of protein-truncating variants revealed that ANGPTL3 inactivation protected against CAD (odds ratio, 0.71 per allele [95%CI, 0.58-0.85]). Analysis of lowered ANGPTL3, ANGPTL4, and APOC3 levels did not identify important safety concerns. Conclusion Human genetic evidence suggests that therapies aimed at reducing circulating levels of ANGPTL3, ANGPTL4, and APOC3 reduce the risk of CAD. ANGPTL4 lowering may also reduce the risk of T2D.
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Affiliation(s)
- Fredrik Landfors
- Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University, B41, Norrlands universitetssjukhus, S-901 87 Umeå, Sweden
- Lipigon Pharmaceuticals AB, Tvistevägen 48C, S-907 36 Umeå, Sweden
| | - Peter Henneman
- Department of Human Genetics, Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Elin Chorell
- Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University, B41, Norrlands universitetssjukhus, S-901 87 Umeå, Sweden
| | - Stefan K Nilsson
- Lipigon Pharmaceuticals AB, Tvistevägen 48C, S-907 36 Umeå, Sweden
- Department of Medical Biosciences, Umeå University, B41, Norrlands universitetssjukhus, S-901 87 Umeå, Sweden
| | - Sander Kersten
- Nutrition, Metabolism, and Genomics group, Division of Human Nutrition and Health, Wageningen University, 6708WE Wageningen, the Netherlands
- Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA
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21
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Eoli A, Ibing S, Schurmann C, Nadkarni GN, Heyne HO, Böttinger E. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. Sci Rep 2024; 14:9642. [PMID: 38671065 PMCID: PMC11053134 DOI: 10.1038/s41598-024-59747-4] [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: 10/09/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n = 31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
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Affiliation(s)
- A Eoli
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482.
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany.
| | - S Ibing
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - C Schurmann
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - G N Nadkarni
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, New York City, NY, USA
| | - H O Heyne
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - E Böttinger
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
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22
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Zhou C, Peng Y, Zhan L, Zha Y. Causal relationship between basal metabolic rate and kidney function: a bidirectional two-sample mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1319753. [PMID: 38726345 PMCID: PMC11079271 DOI: 10.3389/fendo.2024.1319753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Background The relationship between basal metabolic rate (BMR) and Chronic kidney disease (CKD) remains unclear and controversial. In this study, we investigated the causal role of BMR in renal injury, and inversely, whether altered renal function causes changes in BMR. Methods In this two-sample mendelian randomization (MR) study, Genetic data were accessed from published genome-wide association studies (GWAS) for BMR ((n = 454,874) and indices of renal function, i.e. estimated glomerular filtration rate (eGFR) based on creatinine (n =1, 004, 040), CKD (n=480, 698), and blood urea nitrogen (BUN) (n =852, 678) in European. The inverse variance weighted (IVW) random-effects MR method serves as the main analysis, accompanied by several sensitivity MR analyses. We also performed a reverse MR to explore the causal effects of the above indices of renal function on the BMR. Results We found that genetically predicted BMR was negatively related to eGFR, (β= -0.032, P = 4.95*10-12). Similar results were obtained using the MR-Egger (β= -0.040, P = 0.002), weighted median (β= -0.04, P= 5.35×10-11) and weighted mode method (β= -0.05, P=9.92×10-7). Higher BMR had a causal effect on an increased risk of CKD (OR =1.36, 95% CI = 1.11-1.66, P =0.003). In reverse MR, lower eGFR was related to higher BMR (β= -0.64, P = 2.32×10-6, IVW analysis). Bidirectional MR supports no causal association was observed between BMR and BUN. Sensitivity analyses confirmed these findings, indicating the robustness of the results. Conclusion Genetically predicted high BMR is associated with impaired kidney function. Conversely, genetically predicted decreased eGFR is associated with higher BMR.
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Affiliation(s)
- Chaomin Zhou
- National Health Commission (NHC) Key Laboratory of Pulmonary Immune-related Diseases, Renal Division, Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang, China
- GuiZhou University, Medical College, Guiyang, China
| | - Yanzhe Peng
- GuiZhou University, Medical College, Guiyang, China
| | - Lin Zhan
- Research Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Yan Zha
- National Health Commission (NHC) Key Laboratory of Pulmonary Immune-related Diseases, Renal Division, Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang, China
- GuiZhou University, Medical College, Guiyang, China
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23
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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24
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Osborne AJ, Bierzynska A, Colby E, Andag U, Kalra PA, Radresa O, Skroblin P, Taal MW, Welsh GI, Saleem MA, Campbell C. Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease. NPJ Syst Biol Appl 2024; 10:28. [PMID: 38459044 PMCID: PMC10924093 DOI: 10.1038/s41540-024-00350-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/20/2024] [Indexed: 03/10/2024] Open
Abstract
Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.
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Affiliation(s)
- Amy J Osborne
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
| | - Agnieszka Bierzynska
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Elizabeth Colby
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Uwe Andag
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Stott Lane, Salford, M6 8HD, UK
| | - Olivier Radresa
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philipp Skroblin
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Maarten W Taal
- Centre for Kidney Research and Innovation, University of Nottingham, Derby, UK
| | - Gavin I Welsh
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Moin A Saleem
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Colin Campbell
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
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25
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Yang H, Cui Z, Quan Z. Effects of Metabolic Syndrome and Its Components on Chronic Kidney Disease and Renal Function: A Two-Sample Mendelian Randomization Study. Metab Syndr Relat Disord 2024; 22:114-122. [PMID: 37944108 DOI: 10.1089/met.2023.0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023] Open
Abstract
Objective: The association of metabolic syndrome (MetS) and its components with chronic kidney disease (CKD) and renal function remains controversial in observational studies. To comprehensively investigate the association between MetS and its components with CKD and renal function, a Mendelian randomization (MR) study was performed. Methods: The inverse variance weighting (IVW) of random effects was used as the main estimation method, while MR-Egger and weighted median analysis results were used for auxiliary judgments. Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and funnel plots were used to assess heterogeneity and pleiotropy. Results: The MR analyses of genetically predicted MetS and its components' association with CKD risk and renal function showed the following causal associations: hypertension with CKD risk; MetS and obesity with increased blood urea nitrogen and decreased estimated glomerular filtration rate based on cystatin C; hypertension and diabetes with increased urine albumin-creatinine ratio and increased risk of microalbuminuria; and CKD with increased triglyceride. Conclusion: Based on genetic data, this study demonstrated an association between hypertension and CKD risk and a causal association between other MetS components and renal function. The early diagnosis and prevention of MetS and its components might be essential for CKD management.
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Affiliation(s)
- Huazhao Yang
- Department of Preventive Medicine, College of Medicine, Yanbian University, Yanji, China
| | - Zhenhua Cui
- Department of Nephrology, Yanbian University Hospital, Yanji, China
| | - Zhenyu Quan
- Department of Preventive Medicine, College of Medicine, Yanbian University, Yanji, China
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26
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Harrer P, Inderhees J, Zhao C, Schormair B, Tilch E, Gieger C, Peters A, Jöhren O, Fleming T, Nawroth PP, Berger K, Hermesdorf M, Winkelmann J, Schwaninger M, Oexle K. Phenotypic and genome-wide studies on dicarbonyls: major associations to glomerular filtration rate and gamma-glutamyltransferase activity. EBioMedicine 2024; 101:105007. [PMID: 38354534 PMCID: PMC10875252 DOI: 10.1016/j.ebiom.2024.105007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The dicarbonyl compounds methylglyoxal (MG), glyoxal (GO) and 3-deoxyglucosone (3-DG) have been linked to various diseases. However, disease-independent phenotypic and genotypic association studies with phenome-wide and genome-wide reach, respectively, have not been provided. METHODS MG, GO and 3-DG were measured by LC-MS in 1304 serum samples of two populations (KORA, n = 482; BiDirect, n = 822) and assessed for associations with genome-wide SNPs (GWAS) and with phenome-wide traits. Redundancy analysis (RDA) was used to identify major independent trait associations. FINDINGS Mutual correlations of dicarbonyls were highly significant, being stronger between MG and GO (ρ = 0.6) than between 3-DG and MG or GO (ρ = 0.4). Significant phenotypic results included associations of all dicarbonyls with sex, waist-to-hip ratio, glomerular filtration rate (GFR), gamma-glutamyltransferase (GGT), and hypertension, of MG and GO with age and C-reactive protein, of GO and 3-DG with glucose and antidiabetics, of MG with contraceptives, of GO with ferritin, and of 3-DG with smoking. RDA revealed GFR, GGT and, in case of 3-DG, glucose as major contributors to dicarbonyl variance. GWAS did not identify genome-wide significant loci. SNPs previously associated with glyoxalase activity did not reach nominal significance. When multiple testing was restricted to the lead SNPs of GWASs on the traits selected by RDA, 3-DG was found to be associated (p = 2.3 × 10-5) with rs1741177, an eQTL of NF-κB inhibitor NFKBIA. INTERPRETATION This large-scale, population-based study has identified numerous associations, with GFR and GGT being of pivotal importance, providing unbiased perspectives on dicarbonyls beyond the current state. FUNDING Deutsche Forschungsgemeinschaft, Helmholtz Munich, German Centre for Cardiovascular Research (DZHK), German Federal Ministry of Research and Education (BMBF).
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Affiliation(s)
- Philip Harrer
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julica Inderhees
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; Bioanalytic Core Facility, Center for Brain, Behavior and Metabolism, University of Lübeck, Germany; German Centre for Cardiovascular Research (DZHK), Hamburg-Lübeck-Kiel, Germany
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany
| | - Barbara Schormair
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Erik Tilch
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Munich, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Olaf Jöhren
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; Bioanalytic Core Facility, Center for Brain, Behavior and Metabolism, University of Lübeck, Germany
| | - Thomas Fleming
- Department of Internal Medicine, University of Heidelberg, Heidelberg, Germany
| | - Peter P Nawroth
- Department of Internal Medicine, University of Heidelberg, Heidelberg, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Marco Hermesdorf
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Centre for Mental Health (DZPG), Munich-Augsburg, Germany
| | - Markus Schwaninger
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; German Centre for Cardiovascular Research (DZHK), Hamburg-Lübeck-Kiel, Germany
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany.
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27
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Sawanobori E, Shinohara R, Kobayashi A, Kanai H, Goto M, Otawa S, Horiuchi S, Kushima M, Yamagata Z, Inukai T. Mother-child correlation of kidney function: data from the Yamanashi Adjunct Study of Japan Environment and Children's Study (JECS). Pediatr Nephrol 2024; 39:789-797. [PMID: 37695441 DOI: 10.1007/s00467-023-06131-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/31/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND Individual variation in kidney function can be affected by both congenital and acquired factors, and kidney function in children is possibly correlated with that in their mothers. However, the mother-child correlation in kidney function remains directly unconfirmed. METHODS We conducted a cross-sectional study of 655 healthy pairs of 7- or 8-year-old children and their mothers as an adjunct study of a nationwide epidemiological study (Japan Environment and Children's Study). RESULTS Both serum creatinine level (all children, r = 0.324, p < 0.001; girls, r = 0.365, p < 0.001; boys, r = 0.278, p < 0.001) and estimated glomerular filtration rate (eGFR) (r = 0.274, p < 0.001; r = 0.352, p < 0.001; r = 0.195, p < 0.001, respectively) in children were weakly associated with their maternal values. In the single linear regression analyses, maternal values of serum creatinine and eGFR were significantly associated with the children's values. Moreover, several body composition values in children, such as weight-SDS, fat (%), and predicted muscle weight, were also significantly associated with kidney function values in children. In the multiple linear regression analysis for serum creatinine levels in children, in which weight-SDS and predicted muscle weight in children were selected as adjustment factors, maternal serum creatinine level showed a significant positive association (B = 0.214, p < 0.001 in the adjusted model). Moreover, in the multiple linear regression analysis for eGFR value in children, in which fat (%) and predicted muscle weight in children were selected as adjustment factors, maternal eGFR values showed a significant positive association (B = 0.319, p < 0.001). CONCLUSIONS We directly confirmed mother-child correlations in both serum creatinine levels and eGFR values, particularly in girls. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information.
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Affiliation(s)
- Emi Sawanobori
- Department of Pediatrics, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, Japan.
- Department of Pediatrics, National Hospital Organization Kofu National Hospital, 11-35 Tenjincho, Kofu, Yamanashi, Japan.
| | - Ryoji Shinohara
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
| | - Anna Kobayashi
- Department of Pediatrics, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, Japan
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
| | - Hiroaki Kanai
- Department of Pediatrics, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, Japan
| | - Miwa Goto
- Department of Pediatrics, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, Japan
| | - Sanae Otawa
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
| | - Sayaka Horiuchi
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
| | - Megumi Kushima
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
| | - Zentaro Yamagata
- Center for Birth Cohort Studies, Interdisciplinary Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan
- Department of Health Sciences, School of Medicine, University of Yamanashi, 1110, Shimokato, Chuo, Yamanashi, Japan
| | - Takeshi Inukai
- Department of Pediatrics, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, Japan
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28
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Fountoglou A, Deltas C, Siomou E, Dounousi E. Genome-wide association studies reconstructing chronic kidney disease. Nephrol Dial Transplant 2024; 39:395-402. [PMID: 38124660 PMCID: PMC10899781 DOI: 10.1093/ndt/gfad209] [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: 04/26/2023] [Indexed: 12/23/2023] Open
Abstract
Chronic kidney disease (CKD) is a major health problem with an increasing epidemiological burden, and is the 16th leading cause of years of life lost worldwide. It is estimated that more than 10% of the population have a variable stage of CKD, while about 850 million people worldwide are affected. Nevertheless, public awareness remains low, clinical access is inappropriate in many circumstances and medication is still ineffective due to the lack of clear therapeutic targets. One of the main issues that drives these problems is the fact that CKD remains a clinical entity with significant causal ambiguity. Beyond diabetes mellitus and hypertension, which are the two major causes of kidney disease, there are still many gray areas in the diagnostic context of CKD. Genetics nowadays emerges as a promising field in nephrology. The role of genetic factors in CKD's causes and predisposition is well documented and thousands of genetic variants are well established to contribute to the high burden of disease. Next-generation sequencing is increasingly revealing old and new rare variants that cause Mendelian forms of chronic nephropathy while genome-wide association studies (GWAS) uncover common variants associated with CKD-defining traits in the general population. In this article we review how GWAS has revolutionized-and continues to revolutionize-the old concept of CKD. Furthermore, we present how the investigation of common genetic variants with previously unknown kidney significance has begun to expand our knowledge on disease understanding, providing valuable insights into disease mechanisms and perhaps paving the way for novel therapeutic targets.
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Affiliation(s)
- Anastasios Fountoglou
- Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Constantinos Deltas
- School of Medicine and biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 2109, Cyprus
| | - Ekaterini Siomou
- Department of Pediatrics, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Evangelia Dounousi
- Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
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Govender MA, Stoychev SH, Brandenburg JT, Ramsay M, Fabian J, Govender IS. Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort. Clin Proteomics 2024; 21:15. [PMID: 38402394 PMCID: PMC10893729 DOI: 10.1186/s12014-024-09458-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/06/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. METHODS The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. RESULTS Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10- 45), innate immune system (q = 1.1 × 10- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. CONCLUSIONS The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.
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Affiliation(s)
- Melanie A Govender
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Stoyan H Stoychev
- Council for Scientific and Industrial Research, NextGen Health, Pretoria, South Africa
- ReSyn Biosciences, Edenvale, South Africa
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - June Fabian
- Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ireshyn S Govender
- Council for Scientific and Industrial Research, NextGen Health, Pretoria, South Africa.
- ReSyn Biosciences, Edenvale, South Africa.
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 124] [Impact Index Per Article: 124.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Arunachalam V, Lea R, Hoy W, Lee S, Mott S, Savige J, Mathews JD, McMorran BJ, Nagaraj SH. Novel genetic markers for chronic kidney disease in a geographically isolated population of Indigenous Australians: Individual and multiple phenotype genome-wide association study. Genome Med 2024; 16:29. [PMID: 38347632 PMCID: PMC10860247 DOI: 10.1186/s13073-024-01299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is highly prevalent among Indigenous Australians, especially those in remote regions. The Tiwi population has been isolated from mainland Australia for millennia and exhibits unique genetic characteristics that distinguish them from other Indigenous and non-Indigenous populations. Notably, the rate of end-stage renal disease is up to 20 times greater in this population compared to non-Indigenous populations. Despite the identification of numerous genetic loci associated with kidney disease through GWAS, the Indigenous population such as Tiwi remains severely underrepresented and the increased prevalence of CKD in this population may be due to unique disease-causing alleles/genes. METHODS We used albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) to estimate the prevalence of kidney disease in the Tiwi population (N = 492) in comparison to the UK Biobank (UKBB) (N = 134,724) database. We then performed an exploratory factor analysis to identify correlations among 10 CKD-related phenotypes and identify new multi-phenotype factors. We subsequently conducted a genome-wide association study (GWAS) on all single and multiple phenotype factors using mixed linear regression models, adjusted for age, sex, population stratification, and genetic relatedness between individuals. RESULTS Based on ACR, 20.3% of the population was at severely increased risk of CKD progression and showed elevated levels of ACR compared to the UKBB population independent of HbA1c. A GWAS of ACR revealed novel association loci in the genes MEG3 (chr14:100812018:T:A), RAB36 (rs11704318), and TIAM2 (rs9689640). Additionally, multiple phenotypes GWAS of ACR, eGFR, urine albumin, and serum creatinine identified a novel variant that mapped to the gene MEIS2 (chr15:37218869:A:G). Most of the identified variants were found to be either absent or rare in the UKBB population. CONCLUSIONS Our study highlights the Tiwi population's predisposition towards elevated ACR, and the collection of novel genetic variants associated with kidney function. These associations may prove valuable in the early diagnosis and treatment of renal disease in this underrepresented population. Additionally, further research is needed to comprehensively validate the functions of the identified variants/genes.
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Affiliation(s)
- Vignesh Arunachalam
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rodney Lea
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Wendy Hoy
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Simon Lee
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Susan Mott
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Judith Savige
- Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - John D Mathews
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Brendan J McMorran
- National Centre for Indigenous Genomics, The John Curtin of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia.
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Liang X, Liu H, Hu H, Zhou J, Abedini A, Navarro AS, Klötzer KA, Susztak K. Genetic Studies Highlight the Role of TET2 and INO80 in DNA Damage Response and Kidney Disease Pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578718. [PMID: 38370682 PMCID: PMC10871294 DOI: 10.1101/2024.02.02.578718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Genome-wide association studies (GWAS) have identified over 800 loci associated with kidney function, yet the specific genes, variants, and pathways involved remain elusive. By integrating kidney function GWAS, human kidney expression and methylation quantitative trait analyses, we identified Ten-Eleven Translocation (TET) DNA demethylase 2: TET2 as a novel kidney disease risk gene. Utilizing single-cell chromatin accessibility and CRISPR-based genome editing, we highlight GWAS variants that influence TET2 expression in kidney proximal tubule cells. Experiments using kidney-tubule-specific Tet2 knockout mice indicated its protective role in cisplatin-induced acute kidney injury, as well as chronic kidney disease and fibrosis, induced by unilateral ureteral obstruction or adenine diet. Single-cell gene profiling of kidneys from Tet2 knockout mice and TET2- knock-down tubule cells revealed the altered expression of DNA damage repair and chromosome segregation genes, notably including INO80 , another kidney function GWAS target gene itself. Remarkably both TET2- null and INO80- null cells exhibited an increased accumulation of micronuclei after injury, leading to the activation of cytosolic nucleotide sensor cGAS-STING. Genetic deletion of cGAS or STING in kidney tubules or pharmacological inhibition of STING protected TET2 null mice from disease development. In conclusion, our findings highlight TET2 and INO80 as key genes in the pathogenesis of kidney diseases, indicating the importance of DNA damage repair mechanisms.
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Dilixiati D, Kadier K, Lu JD, Xie S, Azhati B, Xilifu R, Rexiati M. Causal associations between prostate diseases, renal diseases, renal function, and erectile dysfunction risk: a 2-sample Mendelian randomization study. Sex Med 2024; 12:qfae002. [PMID: 38348104 PMCID: PMC10859556 DOI: 10.1093/sexmed/qfae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Background Previous observational studies have found a potential link between prostate disease, particularly prostate cancer (PCa), and kidney disease, specifically chronic renal disease (CKD), in relation to erectile dysfunction (ED), yet the causal relationship between these factors remains uncertain. Aim The study sought to explore the potential causal association between prostate diseases, renal diseases, renal function, and risk of ED. Methods In this study, 5 analytical approaches were employed to explore the causal relationships between various prostate diseases (PCa and benign prostatic hyperplasia), renal diseases (CKD, immunoglobulin A nephropathy, membranous nephropathy, nephrotic syndrome, and kidney ureter calculi), as well as 8 renal function parameters, with regard to ED. All data pertaining to exposure and outcome factors were acquired from publicly accessible genome-wide association studies. The methods used encompassed inverse variance weighting, MR-Egger, weighted median, simple mode, and weighted mode residual sum and outlier techniques. The MR-Egger intercept test was utilized to assess pleiotropy, while Cochran's Q statistic was employed to measure heterogeneity. Outcomes We employed inverse variance weighting MR as the primary statistical method to assess the causal relationship between exposure factors and ED. Results Genetically predicted PCa demonstrated a causal association with an elevated risk of ED (odds ratio, 1.125; 95% confidence interval, 1.066-1.186; P < .0001). However, no compelling evidence was found to support associations between genetically determined benign prostatic hyperplasia, CKD, immunoglobulin A nephropathy, membranous nephropathy, nephrotic syndrome, kidney ureter calculi, and the renal function parameters investigated, and the risk of ED. Clinical Implications The risk of ED is considerably amplified in patients diagnosed with PCa, thereby highlighting the importance of addressing ED as a significant concern for clinicians treating individuals with PCa. Strengths and Limitations This study's strength lies in validating the PCa-ED association using genetic analysis, while its limitation is the heterogeneity in study results. Conclusion The results of this study suggest a potential link between PCa and a higher risk of ED.
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Affiliation(s)
- Diliyaer Dilixiati
- Department of Urology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Kaisaierjiang Kadier
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Jian-De Lu
- Department of General Surgery, Children's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830010, China
| | - Shiping Xie
- Department of Urology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Baihetiya Azhati
- Department of Urology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Reyihan Xilifu
- Department of Nephrology, Children's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830054, China
| | - Mulati Rexiati
- Department of Urology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
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Oliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P, Chen VL, Halligan BD, Speliotes EK. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat Genet 2024; 56:212-221. [PMID: 38200128 PMCID: PMC10923176 DOI: 10.1038/s41588-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/28/2023] [Indexed: 01/12/2024]
Abstract
Insulin resistance (IR) is a well-established risk factor for metabolic disease. The ratio of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) is a surrogate marker of IR. We conducted a genome-wide association study of the TG:HDL-C ratio in 402,398 Europeans within the UK Biobank. We identified 369 independent SNPs, of which 114 had a false discovery rate-adjusted P value < 0.05 in other genome-wide studies of IR making them high-confidence IR-associated loci. Seventy-two of these 114 loci have not been previously associated with IR. These 114 loci cluster into five groups upon phenome-wide analysis and are enriched for candidate genes important in insulin signaling, adipocyte physiology and protein metabolism. We created a polygenic-risk score from the high-confidence IR-associated loci using 51,550 European individuals in the Michigan Genomics Initiative. We identified associations with diabetes, hyperglyceridemia, hypertension, nonalcoholic fatty liver disease and ischemic heart disease. Collectively, this study provides insight into the genes, pathways, tissues and subtypes critical in IR.
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Affiliation(s)
- Antonino Oliveri
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Ryan J Rebernick
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Annapurna Kuppa
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Asmita Pant
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Yanhua Chen
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Xiaomeng Du
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Kelly C Cushing
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Hannah N Bell
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Chinmay Raut
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Ponnandy Prabhu
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Vincent L Chen
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Brian D Halligan
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Elizabeth K Speliotes
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
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Wang J, Zeng D, Lin DY. Fitting the Cox proportional hazards model to big data. Biometrics 2024; 80:ujae018. [PMID: 38497824 PMCID: PMC10946235 DOI: 10.1093/biomtc/ujae018] [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: 04/21/2023] [Revised: 01/14/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024]
Abstract
The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions. We show that the final estimator has the same asymptotic distribution as the conventional maximum partial likelihood estimator using the whole dataset but requires only a small fraction of computation time. We demonstrate the usefulness of the proposed method through extensive simulation studies and an application to the UK Biobank data.
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Affiliation(s)
- Jianqiao Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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Scholz M, Horn K, Pott J, Wuttke M, Kühnapfel A, Nasr MK, Kirsten H, Li Y, Hoppmann A, Gorski M, Ghasemi S, Li M, Tin A, Chai JF, Cocca M, Wang J, Nutile T, Akiyama M, Åsvold BO, Bansal N, Biggs ML, Boutin T, Brenner H, Brumpton B, Burkhardt R, Cai J, Campbell A, Campbell H, Chalmers J, Chasman DI, Chee ML, Chee ML, Chen X, Cheng CY, Cifkova R, Daviglus M, Delgado G, Dittrich K, Edwards TL, Endlich K, Michael Gaziano J, Giri A, Giulianini F, Gordon SD, Gudbjartsson DF, Hallan S, Hamet P, Hartman CA, Hayward C, Heid IM, Hellwege JN, Holleczek B, Holm H, Hutri-Kähönen N, Hveem K, Isermann B, Jonas JB, Joshi PK, Kamatani Y, Kanai M, Kastarinen M, Khor CC, Kiess W, Kleber ME, Körner A, Kovacs P, Krajcoviechova A, Kramer H, Krämer BK, Kuokkanen M, Kähönen M, Lange LA, Lash JP, Lehtimäki T, Li H, Lin BM, Liu J, Loeffler M, Lyytikäinen LP, Magnusson PKE, Martin NG, Matsuda K, Milaneschi Y, Mishra PP, Mononen N, Montgomery GW, Mook-Kanamori DO, Mychaleckyj JC, März W, Nauck M, Nikus K, Nolte IM, Noordam R, Okada Y, Olafsson I, Oldehinkel AJ, Penninx BWJH, Perola M, Pirastu N, Polasek O, Porteous DJ, Poulain T, Psaty BM, Rabelink TJ, Raffield LM, Raitakari OT, Rasheed H, Reilly DF, Rice KM, Richmond A, Ridker PM, Rotter JI, Rudan I, Sabanayagam C, Salomaa V, Schneiderman N, Schöttker B, Sims M, Snieder H, Stark KJ, Stefansson K, Stocker H, Stumvoll M, Sulem P, Sveinbjornsson G, Svensson PO, Tai ES, Taylor KD, Tayo BO, Teren A, Tham YC, Thiery J, Thio CHL, Thomas LF, Tremblay J, Tönjes A, van der Most PJ, Vitart V, Völker U, Wang YX, Wang C, Wei WB, Whitfield JB, Wild SH, Wilson JF, Winkler TW, Wong TY, Woodward M, Sim X, Chu AY, Feitosa MF, Thorsteinsdottir U, Hung AM, Teumer A, Franceschini N, Parsa A, Köttgen A, Schlosser P, Pattaro C. X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements. Nat Commun 2024; 15:586. [PMID: 38233393 PMCID: PMC10794254 DOI: 10.1038/s41467-024-44709-1] [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: 05/01/2023] [Accepted: 12/30/2023] [Indexed: 01/19/2024] Open
Abstract
X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.
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Affiliation(s)
- Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Department of Medicine IV - Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - M Kamal Nasr
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Man Li
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Adrienne Tin
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Judy Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Bjørn Olav Åsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Thibaud Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Ben Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Jianwen Cai
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - John Chalmers
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Miao Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Miao Li Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Renata Cifkova
- Center for Cardiovascular Prevention, Charles University in Prague, First Faculty of Medicine and Thomayer University Hospital, Prague, Czech Republic
- Department of Medicine II, Charles University in Prague, First Faculty of Medicine, Prague, Czech Republic
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Graciela Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Katalin Dittrich
- University Hospital for Children and Adolescents, Pediatric Research Unit, Medical Faculty, University Medical Center, University of Leipzig, Leipzig, Germany
| | - Todd L Edwards
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Iceland School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Stein Hallan
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Pavel Hamet
- Montreal University Hospital Research Center, CHUM, Montréal, QC, Canada
- Medpharmgene, Montreal, QC, Canada
| | - Catharina A Hartman
- Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Jacklyn N Hellwege
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bernd Holleczek
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Nina Hutri-Kähönen
- Tampere Centre for Skills Training and Simulation, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Berend Isermann
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute for Laboratory Medicine, University of Leipzig, Leipzig, Germany
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
- Beijing Institute of Ophthalmology, Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
- Privatpraxis Prof Jonas und Dr Panda-Jonas, Heidelberg, Germany
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Wieland Kiess
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- University Hospital for Children and Adolescents, Pediatric Research Unit, Medical Faculty, University Medical Center, University of Leipzig, Leipzig, Germany
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Antje Körner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- University Hospital for Children and Adolescents, Pediatric Research Unit, Medical Faculty, University Medical Center, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Alena Krajcoviechova
- Center for Cardiovascular Prevention, Charles University in Prague, First Faculty of Medicine and Thomayer University Hospital, Prague, Czech Republic
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
- Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, IL, USA
| | - Bernhard K Krämer
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Mikko Kuokkanen
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, CO, USA
| | - James P Lash
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and The Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Hengtong Li
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Bridget M Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, and The Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | | | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC/Vrije Universiteit and GGZ inGeest, Amsterdam, the Netherlands
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, and The Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, and The Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA, USA
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Graz, Austria
- Synlab Academy, Synlab Holding Deutschland GmbH, Augsburg, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Yukinori Okada
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Albertine J Oldehinkel
- Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC/Vrije Universiteit and GGZ inGeest, Amsterdam, the Netherlands
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nicola Pirastu
- Centre for Global Health, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
- Biostatistics Unit - Population and Medical Genomics Programme, Genomics Research Centre, Human Technopole Palazzo Italia, Viale Rita Levi‑Montalcini, 1, 20157, Milan, Italy
| | | | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Tanja Poulain
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- University Hospital for Children and Adolescents, Pediatric Research Unit, Medical Faculty, University Medical Center, University of Leipzig, Leipzig, Germany
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ton J Rabelink
- Department of Internal Medicine, Section of Nephrology, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Center, Leiden, the Netherlands
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Humaira Rasheed
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Medicine and Laboratory Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | | | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Anne Richmond
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, 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 Center, Torrance, CA, USA
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Neil Schneiderman
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Mario Sims
- Department of Social Medicine, Population and Public Health, University of California at Riverside School of Medicine, Riverside, CA, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, Heidelberg University, Heidelberg, Germany
| | | | | | | | - Per O Svensson
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Cardiology and Intensive Care Medicine, University Hospital OWL of Bielefeld University, Campus Klinikum Bielefeld, Teutoburger Straße 50, 33604, Bielefeld, Germany
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute for Laboratory Medicine, University of Leipzig, Leipzig, Germany
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Johanne Tremblay
- Montreal University Hospital Research Center, CHUM, Montréal, QC, Canada
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chaolong Wang
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah H Wild
- Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - James F Wilson
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Mark Woodward
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
- The George Institute for Global Health, School of Public Health, Imperial College London, London, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Adriana M Hung
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology & Hypertension, Nashville, TN, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
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Li Q, Lin M, Deng Y, Huang H. The causal relationship between COVID-19 and estimated glomerular filtration rate: a bidirectional Mendelian randomization study. BMC Nephrol 2024; 25:21. [PMID: 38225574 PMCID: PMC10790484 DOI: 10.1186/s12882-023-03443-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Previous Mendelian studies identified a causal relationship between renal function, as assessed by estimated glomerular filtration rate (eGFR), and severe infection with coronavirus disease 2019 (COVID-19). However, much is still unknown because of the limited number of associated single nucleotide polymorphisms (SNPs) of COVID-19 and the lack of cystatin C testing. Therefore, in the present study, we aimed to determine the genetic mechanisms responsible for the association between eGFR and COVID-19 in a European population. METHODS We performed bidirectional Mendelian randomization (MR) analysis on large-scale genome-wide association study (GWAS) data; log-eGFR was calculated from the serum levels of creatinine or cystatin C by applying the Chronic Kidney Disease Genetics (CKDGen) Meta-analysis Dataset combined with the UK Biobank (N = 1,004,040) and on COVID-19 phenotypes (122,616 COVID-19 cases and 2,475,240 controls) from COVID19-hg GWAS meta-analyses round 7. The inverse-variance weighted method was used as the main method for estimation. RESULTS Analyses showed that the genetically instrumented reduced log-eGFR, as calculated from the serum levels of creatinine, was associated with a significantly higher risk of severe COVID-19 (odds ratio [OR]: 2.73, 95% confidence interval [CI]: 1.38-5.41, P < 0.05) and significantly related to COVID-19 hospitalization (OR: 2.36, 95% CI: 1.39-4.00, P < 0.05) or infection (OR: 1.24, 95% CI: 1.01-1.53, P < 0.05). The significance of these associations remained when using log-eGFR based on the serum levels of cystatin C as genetically instrumented. However, genetically instrumented COVID-19, regardless of phenotype, was not related to log-eGFR, as calculated by either the serum levels of creatinine or cystatin C. CONCLUSIONS Our findings suggest that genetical predisposition to reduced kidney function may represent a risk factor for COVID-19. However, a consistent and significant effect of COVID-19 on kidney function was not identified in this study.
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Affiliation(s)
- Qiuling Li
- Department of Nephrology, Blood Purification Center, Zhongshan City People's Hospital, Zhongshan, 528403, China
| | - Mengjiao Lin
- Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yinghui Deng
- Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| | - Haozhang Huang
- Department of Cardiology, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Department of Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China.
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Hughes O, Bentley AR, Breeze CE, Aguet F, Xu X, Nadkarni G, Sun Q, Lin BM, Gilliland T, Meyer MC, Du J, Raffield LM, Kramer H, Morton RW, Gouveia MH, Atkinson EG, Valladares-Salgado A, Wacher-Rodarte N, Dueker ND, Guo X, Hai Y, Adeyemo A, Best LG, Cai J, Chen G, Chong M, Doumatey A, Eales J, Goodarzi MO, Ipp E, Irvin MR, Jiang M, Jones AC, Kooperberg C, Krieger JE, Lange EM, Lanktree MB, Lash JP, Lotufo PA, Loos RJF, Ha My VT, Peralta-Romero J, Qi L, Raffel LJ, Rich SS, Rodriquez EJ, Tarazona-Santos E, Taylor KD, Umans JG, Wen J, Young BA, Yu Z, Zhang Y, Ida Chen YD, Rundek T, Rotter JI, Cruz M, Fornage M, Lima-Costa MF, Pereira AC, Paré G, Natarajan P, Cole SA, Carson AP, Lange LA, Li Y, Perez-Stable EJ, Do R, Charchar FJ, Tomaszewski M, Mychaleckyj JC, Rotimi C, Morris AP, Franceschini N. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas. CELL GENOMICS 2024; 4:100468. [PMID: 38190104 PMCID: PMC10794846 DOI: 10.1016/j.xgen.2023.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/31/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024]
Abstract
Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.
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Affiliation(s)
- Odessica Hughes
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles E Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department Health and Human Services, Bethesda, MD, USA; UCL Cancer Institute, University College London, London, UK
| | - Francois Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Girish Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bridget M Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thomas Gilliland
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mariah C Meyer
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jiawen Du
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Holly Kramer
- Division of Nephrology and Hypertension, Loyola University Chicago, Maywood, IL, USA
| | - Robert W Morton
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Nicole D Dueker
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lyle G Best
- Missouri Breaks Industries Research Inc., Eagle Butte, SD, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael Chong
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Ayo Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - James Eales
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Eli Ipp
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marguerite Ryan Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Minzhi Jiang
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alana C Jones
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jose E Krieger
- Laboratório de Genética e Cardiologia Molecular do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Matthew B Lanktree
- Division of Nephrology, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - James P Lash
- Division of Nephrology, Department of Medicine, University of Illinois, Chicago, IL, USA
| | - Paulo A Lotufo
- Center for Clinical and Epidemiological Research, Hospital Universitário, Universidade de São Paulo (USP), São Paulo, Brazil
| | - Ruth J F Loos
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vy Thi Ha My
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jesús Peralta-Romero
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Lihong Qi
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, USA
| | - Leslie J Raffel
- Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, Irvine, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Erik J Rodriquez
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eduardo Tarazona-Santos
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville MD and Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bessie A Young
- University of Washington School of Medicine, Seattle, WA, USA; Office of Healthcare Equity, UW Justice, Equity, Diversity, and Inclusion Center for Transformational Research (UW JEDI-CTR), University of Washington, Seattle, WA, USA; Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA; Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Zhi Yu
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Ying Zhang
- Center for American Indian Health Research, Department of Biostatistics and Epidemiology, Hudson College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma, OK, 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 Center, Torrance, CA USA
| | - Tanja Rundek
- Department of Neurology, Epidemiology and Public Health, Miller School of Medicine, University of Miami, Miami, FL, 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 Center, Torrance, CA USA
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, Houston, TX, USA
| | | | - Alexandre C Pereira
- Laboratório de Genética e Cardiologia Molecular do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil; Aging Division, Brigham Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Shelley A Cole
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eliseo J Perez-Stable
- National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fadi J Charchar
- School of Science, Psychology and Sport, Federation University, Ballarat, VIC, Australia; Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; Department of Physiology, University of Melbourne, Melbourne, VIC, Australia
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK; Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Josyf C Mychaleckyj
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Auwerx C, Jõeloo M, Sadler MC, Tesio N, Ojavee S, Clark CJ, Mägi R, Reymond A, Kutalik Z. Rare copy-number variants as modulators of common disease susceptibility. Genome Med 2024; 16:5. [PMID: 38185688 PMCID: PMC10773105 DOI: 10.1186/s13073-023-01265-5] [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: 07/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described. METHODS Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank. RESULTS We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS. CONCLUSIONS Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland
| | - Nicolò Tesio
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Sven Ojavee
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Charlie J Clark
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
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Yan P, Ke B, Fang X. Identification of molecular mediators of renal sarcopenia risk: a mendelian randomization analysis. J Nutr Health Aging 2024; 28:100019. [PMID: 38267164 DOI: 10.1016/j.jnha.2023.100019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/27/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Observational studies have shown an association between reduced renal function and the risk of sarcopenia. However, the causal relationship and the underlying biological mechanisms remain uncertain. Using a Mendelian randomization (MR) framework, we investigated the causal role of 27 hypothetical risk mediators, including metabolites, hormones, inflammation, and stress traits, on the risk of sarcopenia. METHODS Instrumental variables (IVs) to proxy renal function were identified by selecting single nucleotide polymorphisms (SNPs) reliably associated with creatinine and cystatin C-based glomerular filtration rate (GFR) in CKDGen summary data. IVs for putative risk traits and sarcopenia traits were constructed from relevant genome-wide association studies (GWAS). MR estimated effects were obtained using an inverse-variance weighted effects model, and various sensitivity analyses were performed. The mediating role of hypothetical risk factors in the relationship between GFR and sarcopenia was assessed through multivariate MR. RESULTS Genetically predicted reduced GFRcrea was associated with higher odds of appendicular lean mass (ALM) (odds ratio (OR): 0.64, 95% confidence interval (CI) 0.37 to 0.68) and grip strength (OR: 0.67; 95% CI 0.58 to 0.78). Likewise, GFRcys highlighted a causal relationship with ALM (OR: 0.52; 95% CI 0.42 to 0.65) and grip strength (OR: 0.66; 95% CI 0.59 to 0.74). Both estimated GFR (eGFR) were negatively associated with IGF-1, IL-16, 25(OH)D, triglycerides (range of effect size per standard deviation: -0.81 to -0.30), and positively correlated with HDL cholesterol (0.62, 0.31). There was a positive correlation between IGF-1, fasting insulin and ALM as well as grip strength (OR range: 1.04-1.67) and a negative correlation between serum CRP and ALM (OR: 0.95) as well as grip strength (OR: 0.98). Additionally, genetically predicted IL-1β (OR: 0.95) and total cholesterol (OR: 0.96) were negatively associated with ALM. We found evidence that IGF-1 mediates the relationship between eGFR and risk for muscle mass and strength. CONCLUSIONS This MR study provides insight into the potential causal mechanisms between renal function and the risk of sarcopenia and proposes IGF-1 as a potential target for the prevention of renal sarcopenia.
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Affiliation(s)
- Peng Yan
- Department of Nephrology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nangchang 330000, China
| | - Ben Ke
- Department of Nephrology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nangchang 330000, China.
| | - Xiangdong Fang
- Department of Nephrology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nangchang 330000, China.
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Cho JM, Koh JH, Kim SG, Lee S, Kim Y, Cho S, Kim K, Kim YC, Han SS, Lee H, Lee JP, Joo KW, Lim CS, Kim YS, Kim DK, Park S. Primary sclerosing cholangitis causally affects kidney function decline: A Mendelian randomization study. J Gastroenterol Hepatol 2024; 39:185-192. [PMID: 37726875 DOI: 10.1111/jgh.16355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/17/2023] [Accepted: 09/01/2023] [Indexed: 09/21/2023]
Abstract
BACKGROUND AND AIM The causal linkage between primary sclerosing cholangitis (PSC) and kidney function is unexplored despite their potential for long-term detrimental effects on kidney function. METHODS Two-sample summary-level Mendelian randomization (MR) study was conducted to identify the association between PSC and kidney function. The genetic variants were extracted from the PSC-specific multi-trait analyzed genome-wide association study (GWAS) of European ancestry. Summary-level data for kidney function traits, including estimated glomerular filtration rate (eGFR), annual eGFR decline, and chronic kidney disease (CKD), were obtained from the CKDGen consortium. Multiplicative random-effects inverse-variance weighted (MR-IVW), and a series of pleiotropy-robust analyses were performed to investigate the causal effects and ascertain their robustness. RESULTS Significant causal associations between genetically predicted PSC and kidney function traits were identified. Genetically predicted PSC was associated with decreased log-transformed eGFR (MR-IVW; beta = -0.41%; standard error [SE] = 0.02%; P < 0.001), increased rate of annual eGFR decline (MR-IVW; beta = 2.43%; SE = 0.18%; P < 0.001), and higher risk of CKD (MR-IVW; odds ratio = 1.07; 95% confidence interval = 1.06-1.08; P < 0.001). The main findings were supported by pleiotropy-robust analysis, including MR-Egger with bootstrapped error and weighted median. CONCLUSIONS Our study demonstrates that genetically predicted PSC is causally associated with kidney function impairment. Further studies are warranted to identify the underlying mechanisms.
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Affiliation(s)
- Jeong Min Cho
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jung Hun Koh
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Seong Geun Kim
- Department of Internal Medicine, Inje University Sanggye Paik Hospital, Seoul, South Korea
| | - Soojin Lee
- Department of Internal Medicine, Uijeongbu Eulji University Medical Center, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Yaerim Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, South Korea
| | - Semin Cho
- Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, South Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, South Korea
| | - Yong Chul Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Kwon Wook Joo
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Yon Su Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Kidney Research Institute, Seoul National University, Seoul, South Korea
| | - Sehoon Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
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Surapaneni AL, Schlosser P, Rhee EP, Cheng S, Jain M, Alotaiabi M, Coresh J, Grams ME. Eicosanoids and Related Metabolites Associated with ESKD in a Community-Based Cohort. KIDNEY360 2024; 5:57-64. [PMID: 38047655 PMCID: PMC10833602 DOI: 10.34067/kid.0000000000000334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Key Points High-throughput eicosanoid profiling can identify metabolites that may play a protective role in the development of kidney disease. In contrast to many other nonlipid metabolites, eicosanoid levels are minimally related with kidney filtration cross-sectionally. Background Eicosanoids are derivatives of polyunsaturated fatty acids and participate in the inflammatory response and the maintenance of endothelial function. Specific eicosanoids have been linked to various diseases, including hypertension and asthma, and may also reduce renal blood flow. A systematic investigation of eicosanoid-related metabolites and adverse kidney outcomes could identify key mediators of kidney disease and inform ongoing work in drug development. Methods Profiling of eicosanoid-related metabolites was performed in 9650 participants in the Atherosclerosis Risk in Communities Study (visit 2; mean age, 57 years). The associations between metabolite levels and the development of ESKD was investigated using Cox proportional hazards regression (n =256 events; median follow-up, 25.5 years). Metabolites with statistically significant associations with ESKD were evaluated for a potential causal role using bidirectional Mendelian randomization techniques, linking genetic instruments for eicosanoid levels to genomewide association study summary statistics of eGFR. Results The 223 eicosanoid-related metabolites that were profiled and passed quality control (QC) were generally uncorrelated with eGFR in cross-sectional analyses (median Spearman correlation, −0.03; IQR, −0.05 to 0.002). In models adjusted for multiple covariates, including baseline eGFR, three metabolites had statistically significant associations with ESKD (P value < 0.05/223). These included a hydroxyoctadecenoic acid, a dihydroxydocosapentaenoic acid, and arachidonic acid, with higher levels of the former two protective against ESKD and higher levels of arachidonic acid having a positive association with risk of ESKD. Mendelian randomization analyses suggested a causal role for the hydroxyoctadecenoic and arachidonic acid in determining eGFR. Spectral analysis identified the former metabolite as either 11-hydroxy-9-octadecenoic acid or 10-hydroxy-11-octadecenoic acid. Conclusions High-throughput eicosanoid profiling can identify metabolites that may play a protective role in the development of kidney disease.
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Affiliation(s)
- Aditya L. Surapaneni
- Division of Precision Medicine, New York University School of Medicine, New York, New York
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Eugene P. Rhee
- Endocrine Unit, Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts
| | - Susan Cheng
- National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, Massachusetts
- Division of Cardiology, Brigham and Women's Hospital, Boston, Massachusetts
- Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, California
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California, San Diego, California
| | - Mona Alotaiabi
- Departments of Medicine and Pharmacology, University of California, San Diego, California
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E. Grams
- Division of Precision Medicine, New York University School of Medicine, New York, New York
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
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Eun M, Kim D, Shin SI, Yang HO, Kim KD, Choi SY, Park S, Kim DK, Jeong CW, Moon KC, Lee H, Park J. Chromatin accessibility analysis and architectural profiling of human kidneys reveal key cell types and a regulator of diabetic kidney disease. Kidney Int 2024; 105:150-164. [PMID: 37925023 DOI: 10.1016/j.kint.2023.09.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/22/2023] [Accepted: 09/25/2023] [Indexed: 11/06/2023]
Abstract
Diabetes is the leading cause of kidney disease that progresses to kidney failure. However, the key molecular and cellular pathways involved in diabetic kidney disease (DKD) pathogenesis are largely unknown. Here, we performed a comparative analysis of adult human kidneys by examining cell type-specific chromatin accessibility by single-nucleus ATAC-seq (snATAC-seq) and analyzing three-dimensional chromatin architecture via high-throughput chromosome conformation capture (Hi-C method) of paired samples. We mapped the cell type-specific and DKD-specific open chromatin landscape and found that genetic variants associated with kidney diseases were significantly enriched in the proximal tubule- (PT) and injured PT-specific open chromatin regions in samples from patients with DKD. BACH1 was identified as a core transcription factor of injured PT cells; its binding target genes were highly associated with fibrosis and inflammation, which were also key features of injured PT cells. Additionally, Hi-C analysis revealed global chromatin architectural changes in DKD, accompanied by changes in local open chromatin patterns. Combining the snATAC-seq and Hi-C data identified direct target genes of BACH1, and indicated that BACH1 binding regions showed increased chromatin contact frequency with promoters of their target genes in DKD. Thus, our multi-omics analysis revealed BACH1 target genes in injured PTs and highlighted the role of BACH1 as a novel regulator of tubular inflammation and fibrosis.
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Affiliation(s)
- Minho Eun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Donggun Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - So-I Shin
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Hyun Oh Yang
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
| | - Kyoung-Dong Kim
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
| | - Sin Young Choi
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Sehoon Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chang Wook Jeong
- Department of Urology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kyung Chul Moon
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
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Wu PH, Lin J, Kuo MC, Yu SH. The eQTL analysis to discover unique eGFR-related SNPs for the Taiwanese population. J Nephrol 2024; 37:249-252. [PMID: 37856069 DOI: 10.1007/s40620-023-01793-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/26/2023] [Indexed: 10/20/2023]
Affiliation(s)
- Ping-Hsun Wu
- Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Johnathan Lin
- Institute of Precision Medicine, College of Medicine, National Sun Yat-Sen University, No.70 Lien-Hai Rd., Kaohsiung, 80424, Taiwan
- Institute of Biomedical Sciences, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Mei-Chuan Kuo
- Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Sung-Huan Yu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-Sen University, No.70 Lien-Hai Rd., Kaohsiung, 80424, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan.
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Feitosa MF, Lin SJ, Acharya S, Thyagarajan B, Wojczynski MK, Kuipers AL, Kulminski A, Christensen K, Zmuda JM, Brent MR, Province MA. Discovery of genomic and transcriptomic pleiotropy between kidney function and soluble receptor for advanced glycation end-products using correlated meta-analyses: The Long Life Family Study (LLFS). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.27.23300583. [PMID: 38234834 PMCID: PMC10793516 DOI: 10.1101/2023.12.27.23300583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end-products (AGE). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole-genome sequencing and correlated meta-analyses on combined genomewide association study (GWAS) p -values in 4,182 individuals (age range: 24-110) from the Long Life Family Study (LLFS). We also conducted transcriptome-wide association studies (TWAS) on whole blood in a subset of 1,209 individuals. We identified 59 pleiotropic GWAS loci ( p <5×10 -8 ) and 17 TWAS genes (Bonferroni- p <2.73×10 -6 ) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG , were associated with eGFR and sRAGE located within GWAS loci, lncRNA- KCNQ1OT1 and CACNA1A/CCDC130 , respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer-promoter variants with kidney function-related phenotypes at p <5×10 -8 . Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA-Europeans and TOPMed, suggesting better kidney function in healthy-aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and kidney diseases. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney- and aging-related signaling pathways.
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Gill D, Zagkos L, Gill R, Benzing T, Jordan J, Birkenfeld AL, Burgess S, Zahn G. The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development. BMC Med 2023; 21:504. [PMID: 38110950 PMCID: PMC10729503 DOI: 10.1186/s12916-023-03227-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/12/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Solute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts. METHODS The primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals. RESULTS We found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10-4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10-13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns. CONCLUSIONS This Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Primula Group Ltd, London, UK.
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | | | - Thomas Benzing
- Department II of Internal Medicine and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Cologne Excellence Cluster On Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Jens Jordan
- Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany
- Medical Faculty, University of Cologne, Cologne, Germany
| | - Andreas L Birkenfeld
- Department of Diabetology Endocrinology and Nephrology, Internal Medicine IV, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
- Division of Translational Diabetology, Institute of Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich, Eberhard Karls University Tübingen, Tübingen, Germany
- Department of Diabetes, School of Life Course Science and Medicine, King's College London, London, UK
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit at the University of Cambridge, Cambridge, UK
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Paul A, Lawlor A, Cunanan K, Gaheer PS, Kalra A, Napoleone M, Lanktree MB, Bridgewater D. The Good and the Bad of SHROOM3 in Kidney Development and Disease: A Narrative Review. Can J Kidney Health Dis 2023; 10:20543581231212038. [PMID: 38107159 PMCID: PMC10722951 DOI: 10.1177/20543581231212038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/19/2023] Open
Abstract
Purpose of review Multiple large-scale genome-wide association meta-analyses studies have reliably identified an association between genetic variants within the SHROOM3 gene and chronic kidney disease. This association extends to alterations in known markers of kidney disease including baseline estimated glomerular filtration rate, urinary albumin-to-creatinine ratio, and blood urea nitrogen. Yet, an understanding of the molecular mechanisms behind the association of SHROOM3 and kidney disease remains poorly communicated. We conducted a narrative review to summarize the current state of literature regarding the genetic and molecular relationships between SHROOM3 and kidney development and disease. Sources of information PubMed, PubMed Central, SCOPUS, and Web of Science databases, as well as review of references from relevant studies and independent Google Scholar searches to fill gaps in knowledge. Methods A comprehensive narrative review was conducted to explore the molecular mechanisms underlying SHROOM3 and kidney development, function, and disease. Key findings SHROOM3 is a unique protein, as it is the only member of the SHROOM group of proteins that regulates actin dynamics through apical constriction and apicobasal cell elongation. It holds a dichotomous role in the kidney, as subtle alterations in SHROOM3 expression and function can be both pathological and protective toward kidney disease. Genome-wide association studies have identified genetic variants near the transcription start site of the SHROOM3 gene associated with chronic kidney disease. SHROOM3 also appears to protect the glomerular structure and function in conditions such as focal segmental glomerulosclerosis. However, little is known about the exact mechanisms by which this protection occurs, which is why SHROOM3 binding partners remain an opportunity for further investigation. Limitations Our search was limited to English articles. No structured assessment of study quality was performed, and selection bias of included articles may have occurred. As we discuss future directions and opportunities, this narrative review reflects the academic views of the authors.
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Affiliation(s)
- Amy Paul
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Allison Lawlor
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Kristina Cunanan
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Pukhraj S. Gaheer
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton, ON, Canada
| | - Aditya Kalra
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Melody Napoleone
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Matthew B. Lanktree
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Nephrology, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Darren Bridgewater
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
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48
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Qin S, Wang M, Gill D, Zhang Z, Liu X. The mediating role of atrial fibrillation in causal associations between risk factors and stroke: a Mendelian randomization study. Epidemiol Health 2023; 46:e2024005. [PMID: 38404113 DOI: 10.4178/epih.e2024005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/15/2023] [Indexed: 02/27/2024] Open
Abstract
OBJECTIVES Atrial fibrillation (AF) contributes to stroke development and progression. We aimed to quantify the mediating role of AF in the causal associations between a wide range of risk factors and stroke via a Mendelian randomization (MR) framework. METHODS We assessed the associations of 108 traits with stroke and its subtypes in a 2-sample univariable MR approach, then conducted a bidirectional MR analysis between these 108 traits and AF to evaluate the presence and direction of their causal associations. Finally, to further investigate the extent to which AF mediated the effects of eligible traits on stroke, we applied multivariable and 2-step MR techniques in a mediation analysis where outcomes were restricted to stroke types causally affected by AF (any stroke [AS], any ischemic stroke [AIS], and cardioembolic stroke [CES]). RESULTS Among 108 traits, 42 were putatively causal for at least 1 stroke type; of these 42 traits, 20 that had no bidirectional relationship with AF were retained. Finally, 33 associations of 15 eligible traits were examined in the mediation analysis. The mediation analyses for AS, AIS, and CES each included 11 eligible traits. After AF adjustment, the direct effects of all traits on CES were attenuated to null (all p>0.05), while the associations with AS and AIS persisted for most traits (AF-mediated proportion: from 6.6% [95% confidence interval, 2.7 to 0.6] to 52.0% [95% confidence interval, 39.8 to 64.3]). CONCLUSIONS The causal associations between all eligible traits and CES were largely mediated through AF, while most traits affected AS and AIS independently of AF.
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Affiliation(s)
- Shanmei Qin
- Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Mengmeng Wang
- Department of Neurology, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, St Mary's Hospital, Imperial College London, London, UK
| | - Zhizhong Zhang
- Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xinfeng Liu
- Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Gagnon E, Paulin A, Mitchell PL, Arsenault BJ. Disentangling the impact of gluteofemoral versus visceral fat accumulation on cardiometabolic health using sex-stratified Mendelian randomization. Atherosclerosis 2023; 386:117371. [PMID: 38029505 DOI: 10.1016/j.atherosclerosis.2023.117371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND AIMS Individuals with a higher abdominal adipose tissue accumulation are at higher risk of developing cardiometabolic diseases. For a given body mass index (BMI), women typically present lower abdominal adipose tissue accumulation compared to men. Whether abdominal adiposity is a causal driver of cardiometabolic risk, or a mere marker of ectopic fat deposition is debated. METHODS We investigated the sex-specific and sex-combined impact of height and BMI-adjusted gluteofemoral adipose tissue (GFATadj) adjusted abdominal subcutaneous adipose tissue (ASATadj) and adjusted visceral adipose tissue (VATadj) on cardiometabolic traits and diseases using Mendelian randomization. RESULTS Leveraging genome-wide summary statistics on GFATadj, ASATadj and VATadj from 39,076 UK Biobank participants with full-body magnetic resonance imaging available, we found that GFATadj is associated with a more favourable cardiometabolic risk profile including lower low density lipoprotein (LDL) cholesterol, triglycerides, fasting glucose, fasting insulin, liver enzyme levels and blood pressure as well as higher high density lipoprotein (HDL) cholesterol levels. GFATadj also is negatively associated with ischemic stroke, coronary artery disease, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). ASATadj is not associated with cardiometabolic traits and diseases, whereas VATadj is associated with liver fat accumulation but not with NAFLD or other cardiometabolic traits or diseases. Although the absolute effect sizes of GFATadj on LDL cholesterol were more pronounced in women compared to men, most associations did not differ by sex. CONCLUSIONS The inability of subcutaneous fat depots to efficiently store energy substrates could be the causal factor underlying the association of visceral lipid deposition with cardiometabolic health.
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Affiliation(s)
- Eloi Gagnon
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Audrey Paulin
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Patricia L Mitchell
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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50
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Wang Z, Liu T, Li W, Yu G, Mi Z, Wang C, Liao X, Huai P, Chu T, Liu D, Sun L, Fu X, Sun Y, Wang H, Wang N, Liu J, Liu H, Zhang F. Genome-wide meta-analysis and fine-mapping prioritize potential causal variants and genes related to leprosy. MedComm (Beijing) 2023; 4:e415. [PMID: 38020709 PMCID: PMC10674079 DOI: 10.1002/mco2.415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/05/2023] [Accepted: 09/22/2023] [Indexed: 12/01/2023] Open
Abstract
To date, genome-wide association studies (GWASs) have discovered 35 susceptible loci of leprosy; however, the cumulative effects of these loci can only partially explain the overall risk of leprosy, and the causal variants and genes within these loci remain unknown. Here, we conducted out new GWASs in two independent cohorts of 5007 cases and 4579 controls and then a meta-analysis in these newly generated and multiple previously published (2277 cases and 3159 controls) datasets were performed. Three novel and 15 previously reported risk loci were identified from these datasets, increasing the known leprosy risk loci of explained genetic heritability from 23.0 to 38.5%. A comprehensive fine-mapping analysis was conducted, and 19 causal variants and 14 causal genes were identified. Specifically, manual checking of epigenomic information from the Epimap database revealed that the causal variants were mainly located within the immune-relevant or immune-specific regulatory elements. Furthermore, by using gene-set, tissue, and cell-type enrichment analyses, we highlighted the key roles of immune-related tissues and cells and implicated the PD-1 signaling pathways in the pathogenetic mechanism of leprosy. Collectively, our study identified candidate causal variants and elucidated the potential regulatory and coding mechanisms for genes associated with leprosy.
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Affiliation(s)
- Zhenzhen Wang
- Department of BiostatisticsSchool of Public HealthCheeloo College of MedicineShandong UniversityJinanShandongChina
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Tingting Liu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Wenchao Li
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Gongqi Yu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Zihao Mi
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Chuan Wang
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Xiaojie Liao
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Pengcheng Huai
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Tongsheng Chu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Dianchang Liu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Lele Sun
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Xi'an Fu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Yonghu Sun
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Honglei Wang
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Na Wang
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Jianjun Liu
- Department of Human Genetics, Genome Institute of SingaporeSingaporeSingapore
| | - Hong Liu
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
| | - Furen Zhang
- Shandong Provincial Key Lab for Dermatovenereology, Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongChina
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