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Luo L, Chen G, Zhou Y, Xiang Y, Peng J. Dietary intake, antioxidants, minerals and vitamins in relation to childhood asthma: a Mendelian randomization study. Front Nutr 2024; 11:1401881. [PMID: 38846540 PMCID: PMC11153797 DOI: 10.3389/fnut.2024.1401881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/10/2024] [Indexed: 06/09/2024] Open
Abstract
Background Currently, there is limited and inconsistent evidence regarding the risk association between daily dietary intake, antioxidants, minerals, and vitamins with Childhood Asthma (CA). Therefore, this study employs Mendelian Randomization (MR) methodology to systematically investigate the causal relationships between daily dietary intake, serum antioxidants, serum minerals, and the circulating levels of serum vitamins with CA. Methods This study selected factors related to daily dietary intake, including carbohydrates, proteins, fats, and sugars, as well as serum antioxidant levels (lycopene, uric acid, and β-carotene), minerals (calcium, copper, selenium, zinc, iron, phosphorus, and magnesium), and vitamins (vitamin A, vitamin B6, folate, vitamin B12, vitamin C, vitamin D, and vitamin E), using them as Instrumental Variables (IVs). Genetic data related to CA were obtained from the FinnGen and GWAS Catalog databases, with the primary analytical methods being Inverse Variance Weighting (IVW) and sensitivity analysis. Results Following MR analysis, it is observed that sugar intake (OR: 0.71, 95% CI: 0.55-0.91, P: 0.01) is inversely correlated with the risk of CA, while the intake of serum circulating magnesium levels (OR: 1.63, 95% CI: 1.06-2.53, P: 0.03), fats (OR: 1.44, 95% CI: 1.06-1.95, P: 0.02), and serum vitamin D levels (OR: 1.14, 95% CI: 1.04-1.25, P: 0.02) are positively associated with an increased risk of CA. Conclusion This study identified a causal relationship between the daily dietary intake of sugars and fats, as well as the magnesium and vitamin D levels in serum, and the occurrence of CA. However, further in-depth research is warranted to elucidate the specific mechanisms underlying these associations.
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Affiliation(s)
- Liang Luo
- School of TCM Health Care, Leshan Vocational of Technical College, Leshan, Sicuan Province, China
| | - Guanglei Chen
- School of Basic Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China
| | - Yan Zhou
- School of TCM Health Care, Leshan Vocational of Technical College, Leshan, Sicuan Province, China
| | - YaJun Xiang
- School of TCM Health Care, Leshan Vocational of Technical College, Leshan, Sicuan Province, China
| | - Jing Peng
- School of TCM Health Care, Leshan Vocational of Technical College, Leshan, Sicuan Province, China
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Rodriguez-Hernandez Z, Gorski M, Tellez-Plaza M, Schlosser P, Wuttke M. metaGWASmanager: a toolbox for an automated workflow from phenotypes to meta-analysis in GWAS consortia. Bioinformatics 2024; 40:btae294. [PMID: 38688567 PMCID: PMC11096268 DOI: 10.1093/bioinformatics/btae294] [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: 12/15/2023] [Revised: 03/19/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
SUMMARY This article introduces the metaGWASmanager, which streamlines genome-wide association studies within large-scale meta-analysis consortia. It is a toolbox for both the central consortium analysis group and participating studies to generate homogeneous phenotypes, minimize unwanted variability from inconsistent methodologies, ensure high-quality association results, and implement time-efficient quality control workflows. The toolbox features a plug-in-based approach for customization of association testing. RESULTS The metaGWASmanager toolbox has been successfully deployed in both the CKDGen and MetalGWAS Initiative consortia across hundreds of participating studies, demonstrating its effectiveness in GWAS analysis optimization by automating routine tasks and ensuring the value and reliability of association results, thus, ultimately promoting scientific discovery. We provide a simulated data set with examples for script customization so that readers can reproduce the pipeline at their convenience. AVAILABILITY AND IMPLEMENTATION GitHub: https://github.com/genepi-freiburg/metaGWASmanager.
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Affiliation(s)
- Zulema Rodriguez-Hernandez
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institutes, Madrid, 28029, Spain
- Department of Biotechnology, Universitat Politècnica de València, Valencia, 46022, Spain
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center—University of Freiburg, Freiburg, 79106, Germany
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, 93053, Germany
| | - Maria Tellez-Plaza
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institutes, Madrid, 28029, Spain
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center—University of Freiburg, Freiburg, 79106, Germany
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, 21287, USA
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, 79104, Germany
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center—University of Freiburg, Freiburg, 79106, Germany
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Cho C, Kim B, Kim DS, Hwang MY, Shim I, Song M, Lee YC, Jung SH, Cho SK, Park WY, Myung W, Kim BJ, Do R, Choi HK, Merriman TR, Kim YJ, Won HH. Large-scale cross-ancestry genome-wide meta-analysis of serum urate. Nat Commun 2024; 15:3441. [PMID: 38658550 PMCID: PMC11043400 DOI: 10.1038/s41467-024-47805-4] [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: 08/08/2023] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.
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Affiliation(s)
- Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Kweon Cho
- Department of Pharmacology, Ajou University School of Medicine (AUSOM), Suwon, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyon K Choi
- Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tony R Merriman
- Biochemistry Department, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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54
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Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG ADVANCES 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [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: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
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Wang X, Wang X, Li Y, Wu C, Zhao B, Peng M, Chen W, Wang C. Response of Extremely Small Populations to Climate Change-A Case of Trachycarpus nanus in Yunnan, China. BIOLOGY 2024; 13:240. [PMID: 38666852 PMCID: PMC11048604 DOI: 10.3390/biology13040240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024]
Abstract
Climate change affects the geographical distribution of plant species. Rare Trachycarpus nanus with a narrow distribution range, high medicinal value and extremely small population is facing increasing extinction risks under global climate change. In this study, 96 recorded occurrences and 23 environmental factors are used to predict the potential suitable area of T. nanus based on the optimized MaxEnt (3.4.4) model and ArcGIS (10.7) software. The results show that when the parameters are FC = LQ and RM = 1, the MaxEnt model is optimal and AUC = 0.946. The distribution patterns were predicted in the past, present, and four future phases, i.e., 2021-2040 (2030), 2041-2060 (2050), 2061-2080 (2070), and 2081-2100 (2090). The main factors are the annual precipitation (bio12), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), precipitation of the wettest quarter (bio16), and isothermality (bio3). The potential distribution of T. nanus is primarily concentrated in central Chuxiong, encompassing a total potential suitable area of 5.65 × 104 km2. In historical periods, the total habitat area is smaller than that in the present. In the future, the potential suitable area is generally increased. The centroid analysis shows that T. nanus will move to a high-altitude area and to the southeast. But its dispersal capacity may not keep up with the climate change rate. Therefore, additional protection sites for this species should be appropriately established and the habitat connectivity should be enhanced.
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Affiliation(s)
- Xiaofan Wang
- Institute of Ecology and Geobotany, Yunnan University, Kunming 650504, China; (X.W.); (Y.L.); (B.Z.); (M.P.)
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
- Southwest United Graduate School, Yunnan University, Kunming 650092, China; (C.W.); (W.C.)
| | - Xuhong Wang
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
| | - Yun Li
- Institute of Ecology and Geobotany, Yunnan University, Kunming 650504, China; (X.W.); (Y.L.); (B.Z.); (M.P.)
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
| | - Changhao Wu
- Southwest United Graduate School, Yunnan University, Kunming 650092, China; (C.W.); (W.C.)
| | - Biao Zhao
- Institute of Ecology and Geobotany, Yunnan University, Kunming 650504, China; (X.W.); (Y.L.); (B.Z.); (M.P.)
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
| | - Mingchun Peng
- Institute of Ecology and Geobotany, Yunnan University, Kunming 650504, China; (X.W.); (Y.L.); (B.Z.); (M.P.)
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
| | - Wen Chen
- Southwest United Graduate School, Yunnan University, Kunming 650092, China; (C.W.); (W.C.)
| | - Chongyun Wang
- Institute of Ecology and Geobotany, Yunnan University, Kunming 650504, China; (X.W.); (Y.L.); (B.Z.); (M.P.)
- College of Ecology and Environment, Yunnan University, Kunming 650504, China;
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Lin B, Paterson AD, Sun L. Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank. PLoS Genet 2024; 20:e1011221. [PMID: 38656964 PMCID: PMC11073786 DOI: 10.1371/journal.pgen.1011221] [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: 12/05/2023] [Revised: 05/06/2024] [Accepted: 03/11/2024] [Indexed: 04/26/2024] Open
Abstract
Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.
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Affiliation(s)
- Boxi Lin
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Andrew D. Paterson
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lei Sun
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
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57
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Qiu Y, Li C, Huang Y, Wu C, Li F, Zhang X, Xia D. Exploring the causal associations of micronutrients on urate levels and the risk of gout: A Mendelian randomization study. Clin Nutr 2024; 43:1001-1012. [PMID: 38484526 DOI: 10.1016/j.clnu.2024.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/21/2024] [Accepted: 03/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND & AIMS Growing evidence has indicated a potential association between micronutrient levels, urate levels, and the risk of gout. However, the causal association underlying these associations still remains uncertain. Previous observational studies and randomized controlled trials investigating the association between micronutrients, urate levels, and the risk of gout have been limited in their scope and depth. The aim of this study was to utilize Mendelian randomization (MR) to investigate the causal associations between genetically predicted micronutrient levels, urate levels, and the risk of gout. METHODS In this study, we conducted a comprehensive examination of 10 specific micronutrients (vitamin B6, vitamin B12, vitamin C, vitamin D, folate, calcium, iron, copper, zinc, and selenium) as potential exposures. Two-sample MR analyses were performed to explore their causal associations with urate levels and the risk of gout. In these analyses, gout data were collected from the Global Biobank Meta-Analysis Initiative (N = 1,069,839, N cases = 30,549) and urate levels data from CKDGen Consortium (N = 288,649) by utilizing publicly available summary statistics from independent cohorts of European ancestry. We performed inverse-variance weighted MR analyses as main analyses, along with a range of sensitivity analyses, such as MR-Egger, weighted median, simple mode, weighted mode, Steiger filtering, MR-PRESSO, and Radial MR analysis, to ensure the robustness of our findings. RESULTS The results of our study indicate that there were negative associations between serum vitamin B12 and urate levels, as well as serum folate and the risk of gout. Specifically, we found a negative association between vitamin B12 levels and urate levels, with a β coefficient of -0.324 (95% CI -0.0581 to -0.0066, P = 0.0137) per one standard deviation (SD) increase. Similarly, a negative association was observed between folate levels and gout risk, with an odds ratio of 0.8044 (95% CI 0.6637 to 0.9750, P = 0.0265) per one SD increase. On the other hand, we identified positive associations between serum calcium levels and both urate levels and the risk of gout. Specifically, there was a positive association between serum calcium levels and urate levels (β coefficient: 0.0994, 95% CI 0.0519 to 0.1468, P = 4.11E-05) per one SD increase. Furthermore, a positive association was found between serum calcium levels and the risk of gout, with an odds ratio of 1.1479 (95% CI 1.0460 to 1.2598, P = 0.0036) per one SD increase. These findings were robust in extensive sensitivity analyses. By employing MR-PRESSO and Radial MR to eliminate outliers, the observed associations have been reinforced. No clear associations were found between the other micronutrients and the urate levels, as well as the risk of gout. CONCLUSION Our findings provided evidence that there were negative associations between serum vitamin B12 and urate levels, as well as serum folate and the risk of gout, while positive associations existed between the serum calcium levels and urate levels, as well as the risk of gout.
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Affiliation(s)
- Yu Qiu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cantao Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yan Huang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Chenxi Wu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Fenfen Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaoxi Zhang
- Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Daozong Xia
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
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Lou Y, Liu B, Jiang Z, Wen X, Song S, Xie Z, Mao Y, Shao T. Assessing the causal relationships of gut microbial genera with hyperuricemia and gout using two-sample Mendelian randomization. Nutr Metab Cardiovasc Dis 2024; 34:1028-1035. [PMID: 38403483 DOI: 10.1016/j.numecd.2024.01.021] [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: 05/24/2023] [Revised: 10/08/2023] [Accepted: 01/17/2024] [Indexed: 02/27/2024]
Abstract
BACKGROUND AND AIMS The causal relationship between gut microbiota and gout and hyperuricemia (HUA) has not been clarified. The objective of this research was to evaluate the potential causal effects of gut microbiota on HUA and gout using a two-sample Mendelian randomization (MR) approach. METHODS AND RESULTS Genetic instruments were selected using summary statistics from genome-wide association studies (GWASs) comprising a substantial number of individuals, including 18,473 participants for gut microbiome, 288,649 for serum urate (SU), and 763,813 for gout. Two-sample MR analyses were performed to determine the possible causal associations of gut microbial genera with the risk of HUA and gout using the inverse-variance weighted (IVW) method, and robustness of the results was confirmed by several sensitivity analyses. A reverse MR analysis was conducted on the bacterial taxa that were identified in forward MR analysis. Based on the results of MR analyses, Escherichia-Shigella (OR = 1.05; 95% CI, 1.01-1.08; P = 0.009) exhibited a positive association with SU levels, while Lachnospiraceae NC2004 group (OR = 0.95; 95% CI, 0.92-0.98; P = 0.001) and Family XIII AD3011 group (OR = 0.94; 95% CI, 0.90-0.99; P = 0.015) were associated with a reduced HUA risk. Moreover, Coprococcus 3 (OR = 1.17, 95% CI: 1.01-1.34, P = 0.031) was causally associated with a higher gout risk. In reverse MR analysis, no causal relationships were identified between these bacterial genera and HUA or gout. CONCLUSION This study provides evidence for a causal association between gut microbial genera and HUA or gout, and further investigations of the underlying mechanism are warranted.
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Affiliation(s)
- Yu Lou
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bin Liu
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhounan Jiang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xianghui Wen
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Siyue Song
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhijun Xie
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yingying Mao
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China.
| | - Tiejuan Shao
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
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Chen H, Shi D, Guo C, Zhang W, Guo Y, Yang F, Wang R, Zhang J, Fang Z, Yan Y, Mao S, Yao X. Can uric acid affect the immune microenvironment in bladder cancer? A single-center multi-omics study. Mol Carcinog 2024; 63:461-478. [PMID: 38018692 DOI: 10.1002/mc.23664] [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: 09/21/2023] [Revised: 11/05/2023] [Accepted: 11/13/2023] [Indexed: 11/30/2023]
Abstract
Metabolic abnormalities are one of the important factors in bladder cancer (BCa) progression and microenvironmental disturbance. As an important product of purine metabolism, uric acid's (UA) role in BCa metabolism and immunotherapy remains unclear. In this study, we conducted a retrospective analysis of a cohort comprising 39 BCa patients treated with PD-1 and 169 patients who underwent radical cystectomy at Shanghai Tenth People's Hospital. Kaplan-Meier curves and Cox regression analysis showed that the prognosis of patients with high UA is worse (p = 0.007), and high UA is an independent risk factor for cancer specific survival in patients with BCa (p = 0.025). We established a hyperuricemia mouse model with BCa subcutaneous xenografts in vivo. The results revealed that the subcutaneous tumors of hyperuricemia mice had a greater weight and volume in comparison with the control group. Through flow cytometric analysis, the proportion of CD8+ and CD4+ T cells in these subcutaneous tumors was seen to decline significantly. We also evaluated the relationship of UA and BCa by muti-omic analysis. UA related genes were significantly increased in the CD8+ T cell of non-responders to immunotherapy by single-cell sequencing. An 11-gene UA related signature was constructed and the risk score negatively correlated with various immune cells and immune checkpoints. Finally, a nomogram was established using a UA related signature to forecast the survival rate of patients with BCa. Collectively, this study demonstrated that UA was an independent prognostic biomarker for BCa and was associated with worse immunotherapy response.
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Affiliation(s)
- Haotian Chen
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Donghui Shi
- Department of Urology, Suzhou Wuzhong People's Hospital, Wuzhong, China
| | - Changfeng Guo
- Department of Logistic Support, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wentao Zhang
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Yadong Guo
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Fuhan Yang
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Ruiliang Wang
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Junfeng Zhang
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Zujun Fang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Shiyu Mao
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai, China
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Zhong J, Cai H, Zhang Z, Wang J, Xiao L, Zhang P, Xu Y, Tu W, Zhu W, Liu X, Sun W. Serum uric acid and prognosis of ischemic stroke: Cohort study, meta-analysis and Mendelian randomization study. Eur Stroke J 2024; 9:235-243. [PMID: 37905729 PMCID: PMC10916819 DOI: 10.1177/23969873231209620] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/08/2023] [Indexed: 11/02/2023] Open
Abstract
INTRODUCTION The role of serum uric acid (UA) levels in the functional recovery of ischemic stroke remains uncertain. To evaluate whether UA could predict clinical outcomes in patients with ischemic stroke. PATIENTS AND METHODS A three-stage study design was employed, combining a large-scale prospective cohort study, a meta-analysis and a Mendelian randomization (MR) analysis. Firstly, we conducted a cohort study using data from the Nanjing Stroke Registry Program (NSRP) to assess the association between UA levels and 3-month functional outcomes in ischemic stroke patients. Secondly, the meta-analysis was conducted to integrate currently available cohort evidence. Lastly, MR analysis was utilized to explore whether genetically determined UA had a causal link to the functional outcomes of ischemic stroke using summary data from the CKDGen and GISCOME datasets. RESULTS In the first stage, the cohort study included 5631 patients and found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke. In the second stage, the meta-analysis, including 10 studies with 14,657 patients, also showed no significant association between UA levels and stroke prognosis. Finally, in the third stage, MR analysis using data from 6165 patients in the GISCOME study revealed no evidence of a causal relationship between genetically determined UA and stroke functional outcomes. DISCUSSION AND CONCLUSION Our comprehensive triangulation approach found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke.
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Affiliation(s)
- Jinghui Zhong
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Huan Cai
- Department of Rehabilitation, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Zhizhong Zhang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Jinjing Wang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Lulu Xiao
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Pan Zhang
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yingjie Xu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Wenqing Tu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wusheng Zhu
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Xinfeng Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Wen Sun
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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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, American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. 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 PMCID: PMC12146881 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 845] [Impact Index Per Article: 845.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [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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Teerawattanapong N, Tangjarusritaratorn T, Narkdontri T, Santiprabhob J, Tangjittipokin W. Investigation of Monogenic Diabetes Genes in Thai Children with Autoantibody Negative Diabetes Requiring Insulin. Diabetes Metab Syndr Obes 2024; 17:795-808. [PMID: 38375489 PMCID: PMC10875177 DOI: 10.2147/dmso.s409713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/03/2024] [Indexed: 02/21/2024] Open
Abstract
Purpose The objective of this study was to clarify the phenotypic characteristics of monogenic diabetes abnormalities in Thai children with autoantibody-negative insulin. Patients and Methods Two hundred and thirty-one Thai type 1 diabetes (T1D) patients out of 300 participants with recent-onset diabetes were analyzed for GAD65 and IA2 pancreatic autoantibodies. A total of 30 individuals with T1D patients with negative autoantibody were screened for 32 monogenic diabetes genes by whole-exome sequencing (WES). Results All participants were ten men and twenty women. The median age to onset of diabetes was 8 years and 3 months. A total of 20 people with monogenic diabetes carried genes related to monogenic diabetes. The PAX4 (rs2233580) in ten patients with monogenic diabetes was found. Seven variants of WFS1 (Val412Ala, Glu737Lys, Gly576Ser, Cys673Tyr, Arg456His, Lys424Glu, and Gly736fs) were investigated in patients in this study. Furthermore, the pathogenic variant, rs115099192 (Pro407Gln) in the GATA4 gene was found. Most patients who carried PAX4 (c.575G>A, rs2233580) did not have a history of DKA. The pathogenic variant GATA4 variant (c.1220C>A, rs115099192) was found in a patient with a history of DKA. Conclusion This study demonstrated significant genetic overlap between autoantibody-negative diabetes and monogenic diabetes using WES. All candidate variants were considered disease risk with clinically significant variants. WES screening was the first implemented to diagnose monogenic diabetes in Thai children, and fourteen novel variants were identified in this study and need to be investigated in the future.
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Affiliation(s)
- Nipaporn Teerawattanapong
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Center of Research Excellence for Diabetes and Obesity, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Thanida Tangjarusritaratorn
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Tassanee Narkdontri
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Center of Research Excellence for Diabetes and Obesity, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Jeerunda Santiprabhob
- Siriraj Diabetes Center of Excellence, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Division of Endocrinology & Metabolism, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Watip Tangjittipokin
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Center of Research Excellence for Diabetes and Obesity, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
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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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Gaal OI, Liu R, Marginean D, Badii M, Cabău G, Hotea I, Nica V, Colcear D, Pamfil C, Merriman TR, Rednic S, Popp RA, Crișan TO, Joosten LAB. GWAS-identified hyperuricemia-associated IGF1R variant rs6598541 has a limited role in urate mediated inflammation in human mononuclear cells. Sci Rep 2024; 14:3565. [PMID: 38347000 PMCID: PMC10861580 DOI: 10.1038/s41598-024-53209-7] [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: 06/29/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
Gout is a common autoinflammatory joint diseases characterized by deposition of monosodium urate (MSU) crystals which trigger an innate immune response mediated by inflammatory cytokines. IGF1R is one of the loci associated with both urate levels and gout susceptibility in GWAS to date, and IGF-1-IGF-1R signaling is implicated in urate control. We investigate the role of IGF-1/IGF1R signaling in the context of gouty inflammation. Also, we test the gout and urate-associated IGF1R rs6598541 polymorphism for association with the inflammatory capacity of mononuclear cells. For this, freshly isolated human peripheral blood mononuclear cells (PBMCs) were exposed to recombinant IGF-1 or anti-IGF1R neutralizing antibody in the presence or absence of solubilized urate, stimulated with LPS/MSU crystals. Also, the association of rs6598541 with IGF1R and protein expression and with ex vivo cytokine production levels after stimulation with gout specific stimuli was tested. Urate exposure was not associated with IGF1R expression in vitro or in vivo. Modulation of IGF1R did not alter urate-induced inflammation. Developing urate-induced trained immunity in vitro was not influenced in cells challenged with IGF-1 recombinant protein. Moreover, the IGF1R rs6598541 SNP was not associated with cytokine production. Our results indicate that urate-induced inflammatory priming is not regulated by IGF-1/IGF1R signaling in vitro. IGF1R rs6598541 status was not asociated with IGF1R expression or cytokine production in primary human PBMCs. This study suggests that the role of IGF1R in gout is tissue-specific and may be more relevant in the control of urate levels rather than in inflammatory signaling in gout.
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Affiliation(s)
- Orsolya I Gaal
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ruiqi Liu
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dragoș Marginean
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
| | - Medeea Badii
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Georgiana Cabău
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
| | - Ioana Hotea
- Department of Rheumatology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Valentin Nica
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
| | - Doina Colcear
- Clinical Infectious Disease Hospital, Cluj-Napoca, Romania
| | - Cristina Pamfil
- Department of Rheumatology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Tony R Merriman
- Department of Microbiology, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Simona Rednic
- Department of Rheumatology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Radu A Popp
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
| | - Tania O Crișan
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania.
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Leo A B Joosten
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Str. Pasteur Nr.6, 400349, Cluj-Napoca, Romania
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Chuah MH, Leask MP, Topless RK, Gamble GD, Sumpter NA, Stamp LK, Merriman TR, Dalbeth N. Interaction of genetic variation at ADH1B and MLXIPL with alcohol consumption for elevated serum urate level and gout among people of European ethnicity. Arthritis Res Ther 2024; 26:45. [PMID: 38331848 PMCID: PMC10851571 DOI: 10.1186/s13075-024-03279-9] [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: 07/29/2023] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Alcohol consumption is a risk factor for hyperuricaemia and gout. Multiple single-nucleotide polymorphisms (SNPs) have been identified as associated with both alcohol consumption and serum urate or gout in separate genome-wide association studies (GWAS). This study aimed to identify and characterise interactions between these shared signals of genetic association and alcohol consumption for serum urate level, hyperuricaemia, and gout. METHODS This research was conducted using the UK Biobank resource. The association of alcohol consumption with serum urate and gout was tested among 458,405 European participants. Candidate SNPs were identified by comparing serum urate, gout, and alcohol consumption GWAS for shared signals of association. Multivariable-adjusted linear and logistic regression analyses were conducted with the inclusion of interaction terms to identify SNP-alcohol consumption interactions for association with serum urate level, hyperuricaemia, and gout. The nature of these interactions was characterised using genotype-stratified association analyses. RESULTS Alcohol consumption was associated with elevated serum urate and gout. For serum urate level, non-additive interactions were identified between alcohol consumption and rs1229984 at the ADH1B locus (P = 3.0 × 10-44) and rs6460047 at the MLXIPL locus (P = 1.4 × 10-4). ADH1B also demonstrated interaction with alcohol consumption for hyperuricaemia (P = 7.9 × 10-13) and gout (P = 8.2 × 10-9). Beer intake had the most significant interaction with ADH1B for association with serum urate and gout among men, while wine intake had the most significant interaction among women. In the genotype-stratified association analyses, ADH1B and MLXIPL were associated with serum urate level and ADH1B was associated with hyperuricaemia and gout among consumers of alcohol but not non-consumers. CONCLUSIONS In this large study of European participants, novel interactions with alcohol consumption were identified at ADH1B and MLXIPL for association with serum urate level and at ADH1B for association with hyperuricaemia and gout. The association of ADH1B with serum urate and gout may occur through the modulation of alcohol metabolism rate among consumers of alcohol.
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Affiliation(s)
- Min H Chuah
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Rd, Grafton, Auckland, New Zealand
| | - Megan P Leask
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ruth K Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Gregory D Gamble
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Rd, Grafton, Auckland, New Zealand
| | - Nicholas A Sumpter
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lisa K Stamp
- Department of Medicine, University of Otago Christchurch, Christchurch, New Zealand
| | - Tony R Merriman
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Rd, Grafton, Auckland, New Zealand.
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Yan Y, Liu H, Abedini A, Sheng X, Palmer M, Li H, Susztak K. Unraveling the epigenetic code: human kidney DNA methylation and chromatin dynamics in renal disease development. Nat Commun 2024; 15:873. [PMID: 38287030 PMCID: PMC10824731 DOI: 10.1038/s41467-024-45295-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 01/19/2024] [Indexed: 01/31/2024] Open
Abstract
Epigenetic changes may fill a critical gap in our understanding of kidney disease development, as they not only reflect metabolic changes but are also preserved and transmitted during cell division. We conducted a genome-wide cytosine methylation analysis of 399 human kidney samples, along with single-nuclear open chromatin analysis on over 60,000 cells from 14 subjects, including controls, and diabetes and hypertension attributed chronic kidney disease (CKD) patients. We identified and validated differentially methylated positions associated with disease states, and discovered that nearly 30% of these alterations were influenced by underlying genetic variations, including variants known to be associated with kidney disease in genome-wide association studies. We also identified regions showing both methylation and open chromatin changes. These changes in methylation and open chromatin significantly associated gene expression changes, most notably those playing role in metabolism and expressed in proximal tubules. Our study further demonstrated that methylation risk scores (MRS) can improve disease state annotation and prediction of kidney disease development. Collectively, our results suggest a causal relationship between epigenetic changes and kidney disease pathogenesis, thereby providing potential pathways for the development of novel risk stratification methods.
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Affiliation(s)
- Yu Yan
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Xin Sheng
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Matthew Palmer
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Hongzhe Li
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Pathology, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
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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, et alScholz 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] [Show More Authors] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Wu S, Li C, Li Y, Liu J, Rong C, Pei H, Li X, Zeng X, Mao W. SLC2A9 rs16890979 reduces uric acid absorption by kidney organoids. Front Cell Dev Biol 2024; 11:1268226. [PMID: 38269090 PMCID: PMC10806012 DOI: 10.3389/fcell.2023.1268226] [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: 08/01/2023] [Accepted: 12/14/2023] [Indexed: 01/26/2024] Open
Abstract
Introduction: The excretion and absorption of uric acid (UA) by the kidneys helps regulate serum UA levels. GLUT9, encoded by SLC2A9, is mainly expressed in the renal tubules responsible for UA absorption. SLC2A9 polymorphisms are associated with different serum UA levels. However, the lack of proper in vitro models has stalled research on the mechanisms of single nucleotide polymorphisms (SNPs) that affect UA metabolism in human urate transporters. Methods: In this study, we constructed a gene-edited human embryonic stem cells-9 (ESC-H9) derived kidney organoid bearing rs16890979, an SLC2A9 missense mutation with undetermined associations with hyperuricemia or hypouricemia. Kidney organoids derived from ESC-H9 with genetical overexpression (OE) and low expression (shRNA) of SLC2A9 to serve as controls to study the function of SLC2A9. The function of rs16890979 on UA metabolism was evaluated after placing the organoids to urate-containing medium and following histopathological analysis. Results: The kidney organoids with heterozygous or homozygous rs16890979 mutations showed normal SLC2A9 expression levels and histological distribution, phenotypically similar to the wild-type controls. However, reduced absorption of UA by the kidney organoids with rs16890979 mutants was observed. This finding together with the observation that UA absorption is increased in organoids with SLC2A9 overexpression and decreased in those with SLC2A9 knockdown, suggest that GLUT9 is responsible for UA absorption, and the rs16890979 SNP may compromise this functionality. Moreover, epithelial-mesenchymal transition (EMT) was detected in organoids after UA treatment, especially in the kidney organoid carrying GLUT9OE, suggesting the cytobiological mechanism explaining the pathological features in hyperuricosuria-related renal injury. Discussion: This study showing the transitional value of kidney organoid modeling the function of SNPs on UA metabolism. With a defined genetic background and a confirmed UA absorption function should be useful for studies on renal histological, cellular, and molecular mechanisms with this organoid model.
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Affiliation(s)
- Shouhai Wu
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Chuang Li
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Chinese Medicine for Prevention and Treatment of Refractory Chronic Diseases, Guangzhou, China
| | - Yizhen Li
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Junyi Liu
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Cuiping Rong
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hongfei Pei
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Xiong Li
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiang Zeng
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Lab of Stem Cell Biology and Innovative Research of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine/Guangdong Academy of Chinese Medicine, Guangzhou, China
- National Institute for Stem Cell Clinical Research, Guangdong Provincial Hospital of Chinese Medicine/The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Mao
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Chinese Medicine for Prevention and Treatment of Refractory Chronic Diseases, Guangzhou, China
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
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Zhang T, Xu X, Chang Q, Lv Y, Zhao Y, Niu K, Chen L, Xia Y. Ultraprocessed food consumption, genetic predisposition, and the risk of gout: the UK Biobank study. Rheumatology (Oxford) 2024; 63:165-173. [PMID: 37129545 DOI: 10.1093/rheumatology/kead196] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023] Open
Abstract
OBJECTIVE This study aimed to examine the interactions between ultraprocessed food (UPF) consumption and genetic predisposition with the risk of gout. METHODS This prospective cohort study analysed 181 559 individuals from the UK Biobank study who were free of gout at baseline. UPF was defined according to the NOVA classification. Assessment of genetic predisposition for gout was developed from a genetic risk score of 33 single nucleotide polymorphisms. Cox proportional hazards were used to estimate the associations between UPF consumption, genetic predisposition and the risk of gout. RESULTS Among the 181 559 individuals in the study, 1558 patients developed gout over 1 648 167 person-years of follow-up. In the multivariable adjustment model, compared with the lowest quartile of UPF consumption, the hazard ratio (HR) and 95% CI of the highest UPF consumption was 1.16 (1.01, 1.33) for gout risk, and there was a non-linear correlation between UPF consumption and the development of gout. In substitution analyses, replacing 20% of the weight of UPF in the daily intake with an equal amount of unprocessed or minimally processed food resulted in a 13% lower risk of gout (HR: 0.87; 95% CI: 0.79, 0.95). In the joint-effect analysis, the HR (95% CI) for gout was 1.90 (1.39, 2.60) in participants with high genetic predisposition and high UPF consumption, compared with those with low genetic predisposition and low UPF consumption. CONCLUSION In summary, UPF consumption was found to be associated with a higher risk of gout, particularly in those participants with genetic predisposition to gout. Our study indicated that reducing UPF consumption is crucial for gout prevention.
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Affiliation(s)
- Tingjing Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Wannan Medical College, Wuhu, China
| | - Xin Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Yanling Lv
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuhong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Kaijun Niu
- School of Public Health of Tianjin, University of Traditional Chinese Medicine, Tianjin, China
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
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Dai H, Hou T, Wang Q, Zhu Z, Zhu Y, Zhao Z, Li M, Xu Y, Lu J, Wang T, Ning G, Wang W, Bi Y, Zheng J, Xu M. The effect of metformin on urate metabolism: Findings from observational and Mendelian randomization analyses. Diabetes Obes Metab 2024; 26:242-250. [PMID: 37807832 DOI: 10.1111/dom.15310] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023]
Abstract
AIM To evaluate the effect of metformin on urate metabolism. MATERIALS AND METHODS Using the UK Biobank, we first performed association analyses of metformin use with urate levels, risk of hyperuricaemia and incident gout in patients with diabetes. To explore the causal effect of metformin on urate and gout, we identified genetic variants proxying the glycated haemoglobin (HbA1c)-lowering effect of metformin targets and conducted a two-sample Mendelian randomization (MR) utilizing the urate and gout genetic summary-level data from the CKDGen (n = 288 649) and the FinnGen cohort. We conducted two-step MR to explore the mediation effect of body mass index and systolic blood pressure. We also performed non-linear MR in the UK Biobank (n = 414 055) to show the results across HbA1c levels. RESULTS In 18 776 patients with type 2 diabetes in UK Biobank, metformin use was associated with decreased urate [β = -4.3 μmol/L, 95% confidence interval (CI) -7.0, -1.7, p = .001] and reduced hyperuricaemia risk (odds ratio = 0.87, 95% CI 0.79, 0.96, p = .004), but not gout. Genetically proxied averaged HbA1c-lowering effects of metformin targets, equivalent to a 0.62% reduction in HbA1c, was associated with reduced urate (β = -12.5 μmol/L, 95% CI -21.4, -4.2, p = .004). Body mass index significantly mediated this association (proportion mediated = 33.0%, p = .002). Non-linear MR results suggest a linear trend of the effect of metformin on urate reduction across various HbA1c levels. CONCLUSIONS The effect of metformin may reduce urate levels but not incident gout in the general population.
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Affiliation(s)
- Huajie Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianzhichao Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qi Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zheng Zhu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yijie Zhu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Moszczuk B, Życińska K, Mucha K. Asymptomatic Hyperuricemia: A Nephro-Rheumatological Perspective. Arch Immunol Ther Exp (Warsz) 2024; 72:aite-2024-0024. [PMID: 39612508 DOI: 10.2478/aite-2024-0024] [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: 06/11/2024] [Accepted: 10/18/2024] [Indexed: 12/01/2024]
Abstract
Hyperuricemia (HU) is a common disorder associated with gout, kidney injury, and high cardiovascular risk. However, whether high serum uric acid (sUA) is a causative factor or just comorbidity remains unclear. When asked if asymptomatic hyperuricemic patients need treatment, even artificial intelligence in the form of the GPT chat provides an ambivalent answer and refers us to a healthcare provider. We believe that such discrepancies stem from an incomplete understanding of the role that uric acid (UA) plays inside and outside the cell. With the rapid development of genomics, proteomics, immunology, and novel biomarkers, we are armed with new data to help us better understand the weight of inborn and environmental factors on an individual's UA concentrations. This review sums up the latest progress that has been made in the field of asymptomatic HU, compares the results presented by various research teams, and indicates new directions that emerge for future studies.
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Affiliation(s)
- Barbara Moszczuk
- Department of Clinical Immunology, Medical University of Warsaw, Warsaw, Poland
| | - Katarzyna Życińska
- Department of Rheumatology, Systemic Connective Tissue Diseases and Rare Diseases, Central Clinical Hospital MSWiA in Warsaw, Warsaw, Poland
- Department of Family Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Krzysztof Mucha
- Department of Transplantology, Immunology, Nephrology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
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Lu X, van der Meer TP, Kamali Z, van Faassen M, Kema IP, van Beek AP, Xu X, Huo X, Ani A, Nolte IM, Wolffenbuttel BHR, van Vliet-Ostaptchouk JV, Snieder H. A genome-wide association study of 24-hour urinary excretion of endocrine disrupting chemicals. ENVIRONMENT INTERNATIONAL 2024; 183:108396. [PMID: 38150807 DOI: 10.1016/j.envint.2023.108396] [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: 08/21/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
Ubiquitous exposure to environmental endocrine disrupting chemicals (EDCs) instigates a major public health problem, but much remains unknown on the inter-individual differences in metabolism and excretion of EDCs. To examine this we performed a two-stage genome-wide association study (GWAS) for 24-hour urinary excretions of four parabens, two bisphenols, and nine phthalate metabolites. Results showed five genome-wide significant (p-value < 5x10-8) and replicated single nucleotide polymorphisms (SNPs) representing four independent signals that associated with mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) and mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP). Three of the four signals were located on chromosome 10 in a locus harboring the cytochrome P450 (CYP) genes CYP2C9, CYP2C58P, and CYP2C19 (rs117529685, pMECPP = 5.38x10-25; rs117033379, pMECPP = 1.96x10-19; rs4918798, pMECPP = 4.01x10-71; rs7895726, pMEHHP = 1.37x10-15, r2 with rs4918798 = 0.93). The other signal was on chromosome 6 close to the solute carrier (SLC) genes SLC17A1, SLC17A3, SLC17A4, and SCGN (rs1359232, pMECPP = 7.6x10-16). These four SNPs explained a substantial part (8.3 % - 9.2 %) of the variance in MECPP in the replication cohort. Bioinformatics analyses supported a likely causal role of CYP2C9 and SLC17A1 in metabolism and excretion of MECPP and MEHHP. Our results provide biological insights into mechanisms of phthalate metabolism and excretion with a likely causal role for CYP2C9 and SLC17A1.
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Affiliation(s)
- Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands; Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Thomas P van der Meer
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands; Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan 81746-7346, Iran
| | - Martijn van Faassen
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Ido P Kema
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - André P van Beek
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Xijin Xu
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Xia Huo
- Laboratory of Environmental Medicine and Developmental Toxicology, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 510632, Guangdong, China
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands; Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan 81746-7346, Iran
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ, Groningen, the Netherlands.
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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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74
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Zhang Y, Song J, Lai Y, Li A, Zhang Y, Zhou H, Zhao W, Zong Z, Wu R, Li H. Association between the dietary inflammatory index and gout in the National Health and Nutrition Examination Survey 2007-2018. Heliyon 2023; 9:e22930. [PMID: 38058438 PMCID: PMC10696178 DOI: 10.1016/j.heliyon.2023.e22930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
Objective The aim of our study was to investigate whether the Dietary Inflammatory Index (DII) correlated with gout in American adults. Method The study used data from the 2007-2018 National Health and Nutrition Examination Survey, with 27,710 adults participating. Initially, multivariable analysis was performed, with controls for covariates, to assess the link of DII and gout. Then, restricted cubic splines (RCS) were applied to model the nonlinear relationship of DII and gout. Furthermore, propensity score matching (PSM) as a further study of potential relationships was established. Eventually, subgroup analysis was performed. Result Participants within the highest DII quartile would be more susceptible to increased risk of gout in the univariate regression model (Q4 vs. Q1, OR = 1.31, CI: 1.05-1.63). Additionally, a positive correlation was detected between gout risk and DII after adjusting on drinking, smoking, gender, race, age, and BMI. Based on RCS analysis, we observed that the risk of gout raised sharply as DII values increased, then flattened, and increased sharply again when the DII was greater than approximately 2.5. After performing the PSM, it was observed that DII correlated in a positive way to the presence of gout on a fully adjusted multivariable model. Subgroup analysis revealed that the link of DII and gout showed no statistical significance in females, blacks, Mexicans, nor in the population that smoked. Conclusion Greater degrees of pro-inflammation correlate with a higher risk of gout and might be a predisposing factor for gout. Hence, tactics fostering an anti-inflammatory diet for preventing and improving gout in adults should be regarded.
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Affiliation(s)
- Yujun Zhang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanchang University, 330006, Nanchang, China
- Nanchang University, 330006, Nanchang, China
| | - Jingjing Song
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanchang University, 330006, Nanchang, China
- Nanchang University, 330006, Nanchang, China
| | - Yizhong Lai
- Nanchang University, 330006, Nanchang, China
| | - Ao Li
- Queen Mary School, Nanchang University, 330006, Nanchang, China
| | - Yiwei Zhang
- Queen Mary School, Nanchang University, 330006, Nanchang, China
| | - Haonan Zhou
- Queen Mary School, Nanchang University, 330006, Nanchang, China
| | - Wentao Zhao
- The 3rd Clinical Department of China Medical University, 10159, Shenyang, China
| | - Zhen Zong
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, 1 MinDe Road, 330006, Nanchang, China
| | - Rui Wu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanchang University, 330006, Nanchang, China
| | - Hui Li
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanchang University, 330006, Nanchang, China
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75
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Nakayama A, Kurajoh M, Toyoda Y, Takada T, Ichida K, Matsuo H. Dysuricemia. Biomedicines 2023; 11:3169. [PMID: 38137389 PMCID: PMC10740884 DOI: 10.3390/biomedicines11123169] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
Gout results from elevated serum urate (SU) levels, or hyperuricemia, and is a globally widespread and increasingly burdensome disease. Recent studies have illuminated the pathophysiology of gout/hyperuricemia and its epidemiology, diagnosis, treatment, and complications. The genetic involvement of urate transporters and enzymes is also proven. URAT1, a molecular therapeutic target for gout/hyperuricemia, was initially derived from research into hereditary renal hypouricemia (RHUC). RHUC is often accompanied by complications such as exercise-induced acute kidney injury, which indicates the key physiological role of uric acid. Several studies have also revealed its physiological role as both an anti-oxidant and a pro-oxidant, acting as both a scavenger and a generator of reactive oxygen species (ROSs). These discoveries have prompted research interest in SU and xanthine oxidoreductase (XOR), an enzyme that produces both urate and ROSs, as status or progression biomarkers of chronic kidney disease and cardiovascular disease. The notion of "the lower, the better" is therefore incorrect; a better understanding of uric acid handling and metabolism/transport comes from an awareness that excessively high and low levels both cause problems. We summarize here the current body of evidence, demonstrate that uric acid is much more than a metabolic waste product, and finally propose the novel disease concept of "dysuricemia" on the path toward "normouricemia", or optimal SU level, to take advantage of the dual roles of uric acid. Our proposal should help to interpret the spectrum from hypouricemia to hyperuricemia/gout as a single disease category.
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Affiliation(s)
- Akiyoshi Nakayama
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa 359-8513, Japan
| | - Masafumi Kurajoh
- Department of Metabolism, Endocrinology and Molecular Medicine, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan
| | - Yu Toyoda
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa 359-8513, Japan
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo 113-8655, Japan
| | - Tappei Takada
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo 113-8655, Japan
| | - Kimiyoshi Ichida
- Department of Pathophysiology, Tokyo University of Pharmacy and Life Science, Hachioji 192-0392, Japan
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa 359-8513, Japan
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76
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Zhang Y, Tang Z, Tong L, Wang Y, Li L. Serum uric acid and risk of diabetic neuropathy: a genetic correlation and mendelian randomization study. Front Endocrinol (Lausanne) 2023; 14:1277984. [PMID: 38034019 PMCID: PMC10684953 DOI: 10.3389/fendo.2023.1277984] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
Background Previous observational studies have indicated an association between serum uric acid (SUA) and diabetic neuropathy (DN), but confounding factors and reverse causality have left the causality of this relationship uncertain. Methods Univariate Mendelian randomization (MR), multivariate MR and linkage disequilibrium score (LDSC) regression analysis were utilized to assess the causal link between SUA and DN. Summary-level data for SUA were drawn from the CKDGen consortium, comprising 288,648 individuals, while DN data were obtained from the FinnGen consortium, with 2,843 cases and 271,817 controls. Causal effects were estimated primarily using inverse variance weighted (IVW) analysis, supplemented by four validation methods, with additional sensitivity analyses to evaluate pleiotropy, heterogeneity, and result robustness. Results The LDSC analysis revealed a significant genetic correlation between SUA and DN (genetic correlation = 0.293, P = 2.60 × 10-5). The primary methodology IVW indicated that each increase of 1 mg/dL in SUA would increase DN risk by 17% (OR = 1.17, 95% CI 1.02-1.34, P = 0.02), while no causal relationship was found in reverse analysis (OR = 1.00, 95% CI 0.98~1.01, P = 0.97). Multivariate MR further identified that the partial effect of SUA on DN may be mediated by physical activity, low density lipoprotein cholesterol (LDL-C), insulin resistance (IR), and alcohol use. Conclusion The study establishes a causal link between elevated SUA levels and an increased risk of DN, with no evidence for a reverse association. This underscores the need for a comprehensive strategy in DN management, integrating urate-lowering interventions with modulations of the aforementioned mediators.
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Affiliation(s)
- Youqian Zhang
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China
| | - Zitian Tang
- Law School, Yangtze University, Jingzhou, Hubei, China
| | - Ling Tong
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China
| | - Yang Wang
- Department of Neurology, Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Li
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China
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Saadat M. Enrichment analysis and chromosomal distribution of gout susceptible loci identified by genome-wide association studies. EXCLI JOURNAL 2023; 22:1146-1154. [PMID: 38204969 PMCID: PMC10776878 DOI: 10.17179/excli2023-6481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/06/2023] [Indexed: 01/12/2024]
Abstract
Gout is an inherited and common inflammatory arthritic disease. Many researchers will identify polymorphic loci of gout susceptibility by conducting genome-wide association studies (GWAS). In the present study, the enrichment analysis and chromosomal distribution were performed using predicted polymorphic loci associated with gout risk. The polymorphic loci associated to gout were obtained from the GWAS database. Overall, this database contains 64,806 gout patients and 2,856,174 controls. Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using the Enrichr online server. A total of 110 common polymorphic protein-coding loci associated with gout risk were identified and included in the analysis. The results of the KEGG analysis showed that the gout-associated loci were mainly related to ABC transporters, endocrine and other factor-regulated calcium reabsorption, and gastric acid secretion pathways. The gene ontology analysis showed that the biological processes of the gout-associated loci were vascular transport, transport across the blood-brain barrier, positive regulation of transporter activity, and positive regulation of transcription by RNA polymerase II. The top cellular component was the external side of the apical plasma membrane. Statistical analysis revealed that the human chromosome segments 1q22, 4p16.1, 6p21.1-p21.2, 11q13.1-q13.2, 12q13.11-q13.3, and 12q24.1 had significantly bearing higher numbers of gout susceptibility loci.
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Affiliation(s)
- Mostafa Saadat
- Department of Biology, School of Science, Shiraz University, Shiraz 71467-13565, Iran
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Massimo G, Khambata RS, Chapman T, Birchall K, Raimondi C, Shabbir A, Dyson N, Rathod KS, Borghi C, Ahluwalia A. Natural mutations of human XDH promote the nitrite (NO 2-)-reductase capacity of xanthine oxidoreductase: A novel mechanism to promote redox health? Redox Biol 2023; 67:102864. [PMID: 37713777 PMCID: PMC10511815 DOI: 10.1016/j.redox.2023.102864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/17/2023] Open
Abstract
Several rare genetic variations of human XDH have been shown to alter xanthine oxidoreductase (XOR) activity leading to impaired purine catabolism. However, XOR is a multi-functional enzyme that depending upon the environmental conditions also expresses oxidase activity leading to both O2·- and H2O2 and nitrite (NO2-) reductase activity leading to nitric oxide (·NO). Since these products express important, and often diametrically opposite, biological activity, consideration of the impact of XOR mutations in the context of each aspect of the biochemical activity of the enzyme is needed to determine the potential full impact of these variants. Herein, we show that known naturally occurring hXDH mutations do not have a uniform impact upon the biochemical activity of the enzyme in terms of uric acid (UA), reactive oxygen species (ROS) and nitric oxide ·NO formation. We show that the His1221Arg mutant, in the presence of xanthine, increases UA, O2·- and NO generation compared to the WT, whilst the Ile703Val increases UA and ·NO formation, but not O2·-. We speculate that this change in the balance of activity of the enzyme is likely to endow those carrying these mutations with a harmful or protective influence over health that may explain the current equipoise underlying the perceived importance of XDH mutations. We also show that, in presence of inorganic NO2-, XOR-driven O2·- production is substantially reduced. We suggest that targeting enzyme activity to enhance the NO2--reductase profile in those carrying such mutations may provide novel therapeutic options, particularly in cardiovascular disease.
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Affiliation(s)
- G Massimo
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - R S Khambata
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - T Chapman
- LifeArc, Accelerator Building Open Innovation Campus, Stevenage, SG1 2FX, UK
| | - K Birchall
- LifeArc, Accelerator Building Open Innovation Campus, Stevenage, SG1 2FX, UK
| | - C Raimondi
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - A Shabbir
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Nicki Dyson
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - K S Rathod
- William Harvey Research Institute, Barts & the London Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - C Borghi
- Department of Medical and Surgical Sciences, Faculty of Medicine, University of Bologna, Via Massarenti, N.9, 40138, Italy
| | - A Ahluwalia
- Department of Medical and Surgical Sciences, Faculty of Medicine, University of Bologna, Via Massarenti, N.9, 40138, Italy.
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Li J, Song F. A causal relationship between antioxidants, minerals and vitamins and metabolic syndrome traits: a Mendelian randomization study. Diabetol Metab Syndr 2023; 15:194. [PMID: 37817280 PMCID: PMC10563368 DOI: 10.1186/s13098-023-01174-y] [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: 04/17/2023] [Accepted: 09/27/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND The available evidence regarding the association of antioxidants, minerals, and vitamins with the risk of metabolic syndrome (MetS) traits is currently limited and inconsistent. Therefore, the purpose of this Mendelian randomization (MR) study was to investigate the potential causal relationship between genetically predicted antioxidants, minerals, and vitamins, and MetS. METHODS In this study, we utilized genetic variation as instrumental variable (IV) to capture exposure data related to commonly consumed dietary nutrients, including antioxidants (β-carotene, lycopene, and uric acid), minerals (copper, calcium, iron, magnesium, phosphorus, zinc, and selenium), and vitamins (folate, vitamin A, B6, B12, C, D, E, and K1). The outcomes of interest, namely MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010), fasting blood glucose (FBG) (n = 281,416), triglycerides (n = 441,016), and high-density lipoprotein cholesterol (HDL-C) (n = 403,943), were assessed using pooled data obtained from the most comprehensive genome-wide association study (GWAS) available. Finally, we applied the inverse variance weighting method as the result and conducted a sensitivity analysis for further validation. RESULTS Genetically predicted higher iron (OR = 1.070, 95% CI 1.037-1.105, P = 2.91E-05) and magnesium levels (OR = 1.130, 95% CI 1.058-1.208, P = 2.80E-04) were positively associated with increased risk of MetS. For each component of MetS, higher level of genetically predicted selenium (OR = 0.971, 95% CI 0.957-0.986, P = 1.09E-04) was negatively correlated with HDL-C levels, while vitamin K1 (OR = 1.023, 95% CI 1.012-1.033, P = 2.90E-05) was positively correlated with HDL-C levels. Moreover, genetically predicted vitamin D (OR = 0.985, 95% CI 0.978-0.992, P = 5.51E-5) had a protective effect on FBG levels. Genetically predicted iron level (OR = 1.043, 95% CI 1.022-1.064, P = 4.33E-05) had a risk effect on TG level. CONCLUSIONS Our study provides evidence that genetically predicted some specific, but not all, antioxidants, minerals, and vitamins may be causally related to the development of MetS traits.
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Affiliation(s)
- Junxian Li
- Department of Blood Transfusion, Key Laboratory of Cancer Prevention and Therapy in Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, China
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology in Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology in Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin, China.
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80
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Wang M, Fan J, Huang Z, Zhou D, Wang X. Causal Relationship between Gut Microbiota and Gout: A Two-Sample Mendelian Randomization Study. Nutrients 2023; 15:4260. [PMID: 37836544 PMCID: PMC10574468 DOI: 10.3390/nu15194260] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Gout is a form of prevalent and painful inflammatory arthritis characterized by elevated serum urate (SUA) levels. The gut microbiota (GM) is believed to influence the development of gout and SUA levels. Our study aimed to explore the causal relationship between GM composition and gout, as well as SUA levels, utilizing a two-sample Mendelian Randomization (MR) approach. A total of 196 GM taxa from five levels were available for analysis. We identified five taxa associated with SUA levels and 10 taxa associated with gout. In reverse MR analysis, we discovered that gout affected the composition of five GM taxa, while SUA levels influenced the composition of 30 GM taxa. Combining existing research, our study unveiled a potential negative feedback loop between phylum Actinobacteria and SUA levels, establishing connections with gout. We also proposed two novel associations connecting GM taxa (genus Faecalibacterium and genus Prevotella9), SUA levels, and gout. These findings provide compelling evidence of causal relationships between specific GM taxa with SUA levels and gout, contributing valuable insights for the treatment of gout.
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Affiliation(s)
- Mengna Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
| | - Jiayao Fan
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhaohui Huang
- Affiliated Hospital of Jiangnan University, Wuxi 214062, China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xue Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
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Szrok-Jurga S, Czumaj A, Turyn J, Hebanowska A, Swierczynski J, Sledzinski T, Stelmanska E. The Physiological and Pathological Role of Acyl-CoA Oxidation. Int J Mol Sci 2023; 24:14857. [PMID: 37834305 PMCID: PMC10573383 DOI: 10.3390/ijms241914857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
Fatty acid metabolism, including β-oxidation (βOX), plays an important role in human physiology and pathology. βOX is an essential process in the energy metabolism of most human cells. Moreover, βOX is also the source of acetyl-CoA, the substrate for (a) ketone bodies synthesis, (b) cholesterol synthesis, (c) phase II detoxication, (d) protein acetylation, and (d) the synthesis of many other compounds, including N-acetylglutamate-an important regulator of urea synthesis. This review describes the current knowledge on the importance of the mitochondrial and peroxisomal βOX in various organs, including the liver, heart, kidney, lung, gastrointestinal tract, peripheral white blood cells, and other cells. In addition, the diseases associated with a disturbance of fatty acid oxidation (FAO) in the liver, heart, kidney, lung, alimentary tract, and other organs or cells are presented. Special attention was paid to abnormalities of FAO in cancer cells and the diseases caused by mutations in gene-encoding enzymes involved in FAO. Finally, issues related to α- and ω- fatty acid oxidation are discussed.
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Affiliation(s)
- Sylwia Szrok-Jurga
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Aleksandra Czumaj
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Jacek Turyn
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Areta Hebanowska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Julian Swierczynski
- Institue of Nursing and Medical Rescue, State University of Applied Sciences in Koszalin, 75-582 Koszalin, Poland;
| | - Tomasz Sledzinski
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Ewa Stelmanska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
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Zhang L, Zhang W, Xiao C, Wu X, Cui H, Yan P, Yang C, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Alfredsson L, Klareskog L, Yang Y, Yao Y, Li J, Liu Z, Yang C, Jiang X, Zhang B. Using human genetics to understand the epidemiological association between obesity, serum urate, and gout. Rheumatology (Oxford) 2023; 62:3280-3290. [PMID: 36734534 DOI: 10.1093/rheumatology/kead054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/31/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout. METHODS We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N = 806 834), waist-to-hip ratio (WHR; N = 697 734), WHR adjusted for BMI (WHRadjBMI; N = 694 649), serum urate (N = 288 649), and gout (Ncases = 13 179 and Ncontrols = 750 634). RESULTS Positive overall genetic correlations were observed for BMI (rg = 0.27, P = 6.62 × 10-7), WHR (rg = 0.22, P = 6.26 × 10-7) and WHRadjBMI (rg = 0.07, P = 6.08 × 10-3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene-tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI-gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR-gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance. CONCLUSION Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits.
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Affiliation(s)
- Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine and Center for Molecular Medicine, Karolinska Institutet at Karolinska University Hospital (Solna), Stockholm, Sweden
| | - Yanfang Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhenmi Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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83
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Wang Q, Liu YN, Zhang H, Zhang ZQ, Huang XY, Xiao WZ. Causal Association Between Tea Consumption and Gout: A Mendelian Randomization Study. Curr Med Sci 2023; 43:947-954. [PMID: 37755636 DOI: 10.1007/s11596-023-2778-6] [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: 06/14/2023] [Accepted: 07/25/2023] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited. We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization (MR). METHODS Genome-wide association studies in UK Biobank included 349 376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day. Summary statistics from the Chronic Kidney Disease Genetics consortium included 13 179 cases and 750 634 controls for gout. Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk. The inverse-variance weighted (IVW) method was used for primary analysis, and sensitivity analyses were also conducted to validate the potential causal effect. RESULTS In this study, the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method (OR: 0.90; 95% CI: 0.82-0.98). Similar results were found in weighted median methods (OR: 0.88; 95% CI: 0.78-1.00), while no significant associations were found in MR-Egger (OR: 0.89; 95% CI: 0.71-1.11), weighted mode (OR: 0.80; 95% CI: 0.65-0.99), and simple mode (OR: 1.01; 95% CI: 0.75-1.36). In addition, no evidence of pleiotropy was detected by MR-Egger regression (P=0.95) or MR-PRESSO analysis (P=0.07). CONCLUSION This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.
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Affiliation(s)
- Qi Wang
- Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China
| | - Yi-Ning Liu
- Human Phenome Institute, Zhangjiang Fudan International Innovation Centre, Fudan University, Shanghai, 200433, China
| | - Hui Zhang
- Human Phenome Institute, Zhangjiang Fudan International Innovation Centre, Fudan University, Shanghai, 200433, China
| | - Ze-Qun Zhang
- Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China
| | - Xiu-Ying Huang
- Department of Emergency, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
| | - Wen-Ze Xiao
- Department of Rheumatology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
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84
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Murdoch R, Mihov B, Horne AM, Petrie KJ, Gamble GD, Dalbeth N. Impact of Television Depictions of Gout on Perceptions of Illness: A Randomized Controlled Trial. Arthritis Care Res (Hoboken) 2023; 75:2151-2157. [PMID: 37038965 DOI: 10.1002/acr.25130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 03/06/2023] [Accepted: 04/04/2023] [Indexed: 04/12/2023]
Abstract
OBJECTIVE Gout is a chronic disease that can be effectively managed with long-term urate-lowering therapy. However, it is frequently portrayed on screen as an acute disease caused by a poor diet that should be managed with lifestyle changes. This study was undertaken to investigate the impact of a fictional television depiction of gout on perceptions of the disease and its management. METHODS In a randomized controlled single-blind study, 200 members of the public watched either a 19-minute commercial television comedy episode that depicted gout as an acute disease caused by poor diet and managed with lifestyle changes, or a control episode from the same television series that did not mention gout or other diseases. Participants completed a survey regarding their perceptions of gout, its likely causes, and management strategies. RESULTS Participants randomized to watch the gout-related episode believed gout had greater consequences (mean score of 7.1 versus 6.2 on an 11-point Likert scale; P < 0.001) and were more likely to rank the most important cause as poor eating habits compared to the control group (70% versus 38%; P < 0.001). They were also less likely to believe it is caused by genetic factors or chance. Participants watching the gout-related episode believed a change in diet would be a more effective management strategy (9.0 versus 8.4; P = 0.004) and long-term medication use would be less effective (6.9 versus 7.6; P = 0.007) compared to participants in the control group. CONCLUSION Television depictions of gout can perpetuate inaccurate beliefs regarding causes of the disease and underemphasize effective medical strategies required in chronic disease management.
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85
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Peng X, Li X, Xie B, Lai Y, Sosnik A, Boucetta H, Chen Z, He W. Gout therapeutics and drug delivery. J Control Release 2023; 362:728-754. [PMID: 37690697 DOI: 10.1016/j.jconrel.2023.09.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 09/12/2023]
Abstract
Gout is a common inflammatory arthritis caused by persistently elevated uric acid levels. With the improvement of people's living standards, the consumption of processed food and the widespread use of drugs that induce elevated uric acid, gout rates are increasing, seriously affecting the human quality of life, and becoming a burden to health systems worldwide. Since the pathological mechanism of gout has been elucidated, there are relatively effective drug treatments in clinical practice. However, due to (bio)pharmaceutical shortcomings of these drugs, such as poor chemical stability and limited ability to target the pathophysiological pathways, traditional drug treatment strategies show low efficacy and safety. In this scenario, drug delivery systems (DDS) design that overcome these drawbacks is urgently called for. In this review, we initially describe the pathological features, the therapeutic targets, and the drugs currently in clinical use and under investigation to treat gout. We also comprehensively summarize recent research efforts utilizing lipid, polymeric and inorganic carriers to develop advanced DDS for improved gout management and therapy.
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Affiliation(s)
- Xiuju Peng
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China
| | - Xiaotong Li
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China
| | - Bing Xie
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China
| | - Yaoyao Lai
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China
| | - Alejandro Sosnik
- Department of Materials Science and Engineering, Technion - Israel Institute of Technology, Technion City, Haifa 3200003, Israel
| | - Hamza Boucetta
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China
| | - Zhongjian Chen
- Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai 200443, China.
| | - Wei He
- Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing 2111198, PR China; Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai 200443, China.
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86
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Roman YM. The Role of Uric Acid in Human Health: Insights from the Uricase Gene. J Pers Med 2023; 13:1409. [PMID: 37763176 PMCID: PMC10532990 DOI: 10.3390/jpm13091409] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Uric acid is the final product of purine metabolism and is converted to allantoin in most mammals via the uricase enzyme. The accumulation of loss of function mutations in the uricase gene rendered hominoids (apes and humans) to have higher urate concentrations compared to other mammals. The loss of human uricase activity may have allowed humans to survive environmental stressors, evolution bottlenecks, and life-threatening pathogens. While high urate levels may contribute to developing gout and cardiometabolic disorders such as hypertension and insulin resistance, low urate levels may increase the risk for neurodegenerative diseases. The double-edged sword effect of uric acid has resurrected a growing interest in urate's antioxidant role and the uricase enzyme's role in modulating the risk of obesity. Characterizing both the effect of uric acid levels and the uricase enzyme in different animal models may provide new insights into the potential therapeutic benefits of uric acid and novel uricase-based therapy.
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Affiliation(s)
- Youssef M Roman
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
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87
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Thorpe HHA, Fontanillas P, Pham BK, Meredith JJ, Jennings MV, Courchesne-Krak NS, Vilar-Ribó L, Bianchi SB, Mutz J, 23andMe Research Team, Elson SL, Khokhar JY, Abdellaoui A, Davis LK, Palmer AA, Sanchez-Roige S. Genome-Wide Association Studies of Coffee Intake in UK/US Participants of European Ancestry Uncover Gene-Cohort Influences. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.09.23295284. [PMID: 37745582 PMCID: PMC10516045 DOI: 10.1101/2023.09.09.23295284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N=130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across thousands of biomarkers and health and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from UK Biobank (UKB; N=334,659). The results of these two GWAS were highly discrepant. We observed positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in UKB. Genetic correlations with cognition were negative in 23andMe but positive in UKB. The only consistent observations were positive genetic correlations with substance use and obesity. Our study shows that GWAS in different cohorts could capture cultural differences in the relationship between behavior and genetics.
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Affiliation(s)
- Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | | | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - 23andMe Research Team
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Sarah L Elson
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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88
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Robinson-Cohen C, Triozzi JL, Rowan B, He J, Chen HC, Zheng NS, Wei WQ, Wilson OD, Hellwege JN, Tsao PS, Gaziano JM, Bick A, Matheny ME, Chung CP, Lipworth L, Siew ED, Ikizler TA, Tao R, Hung AM. Genome-Wide Association Study of CKD Progression. J Am Soc Nephrol 2023; 34:1547-1559. [PMID: 37261792 PMCID: PMC10482057 DOI: 10.1681/asn.0000000000000170] [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: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 06/02/2023] Open
Abstract
SIGNIFICANCE STATEMENT Rapid progression of CKD is associated with poor clinical outcomes. Most previous studies looking for genetic factors associated with low eGFR have used cross-sectional data. The authors conducted a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD, focusing on longitudinal data. They identified three loci (two of them novel) associated with longitudinal eGFR decline. In addition to the known UMOD/PDILT locus, variants within BICC1 were associated with significant differences in longitudinal eGFR slope. Variants within HEATR4 also were associated with differences in eGFR decline, but only among Black/African American individuals without diabetes. These findings help characterize molecular mechanisms of eGFR decline in CKD and may inform new therapeutic approaches for progressive kidney disease. BACKGROUND Rapid progression of CKD is associated with poor clinical outcomes. Despite extensive study of the genetics of cross-sectional eGFR, only a few loci associated with eGFR decline over time have been identified. METHODS We performed a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD-defined by two outpatient eGFR measurements of <60 ml/min per 1.73 m 2 , obtained 90-365 days apart-from the Million Veteran Program and Vanderbilt University Medical Center's DNA biobank. The primary outcome was the annualized relative slope in outpatient eGFR. Analyses were stratified by ethnicity and diabetes status and meta-analyzed thereafter. RESULTS In cross-ancestry meta-analysis, the strongest association was rs77924615, near UMOD / PDILT ; each copy of the G allele was associated with a 0.30%/yr faster eGFR decline ( P = 4.9×10 -27 ). We also observed an association within BICC1 (rs11592748), where every additional minor allele was associated with a 0.13%/yr slower eGFR decline ( P = 5.6×10 -9 ). Among participants without diabetes, the strongest association was the UMOD/PDILT variant rs36060036, associated with a 0.27%/yr faster eGFR decline per copy of the C allele ( P = 1.9×10 -17 ). Among Black participants, a significantly faster eGFR decline was associated with variant rs16996674 near APOL1 (R 2 =0.29 with the G1 high-risk genotype); among Black participants with diabetes, lead variant rs11624911 near HEATR4 also was associated with a significantly faster eGFR decline. We also nominally replicated loci with known associations with eGFR decline, near PRKAG2, FGF5, and C15ORF54. CONCLUSIONS Three loci were significantly associated with longitudinal eGFR change at genome-wide significance. These findings help characterize molecular mechanisms of eGFR decline and may contribute to the development of new therapeutic approaches for progressive CKD.
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Affiliation(s)
- Cassianne Robinson-Cohen
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jefferson L Triozzi
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bryce Rowan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hua C Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neil S Zheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Otis D Wilson
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, Tennessee
| | - Jacklyn N Hellwege
- VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Philip S Tsao
- Department of Medicine, Division of Cardiovascular Medicine, VA Palo Alto Health Care System, Palo Alto, California
- Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital and Harvard School of Medicine, Boston, Massachusetts
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael E Matheny
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatrics Research Education and Clinical Care Service, VA Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Cecilia P Chung
- VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, Tennessee
- Division of Rheumatology and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Loren Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Edward D Siew
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - T Alp Ikizler
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Adriana M Hung
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, Tennessee
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89
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Joshi AD, McCormick N, Yokose C, Yu B, Tin A, Terkeltaub R, Merriman TR, Eliassen AH, Curhan GC, Raffield LM, Choi HK. Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A Population-Based, Prospective Study and Mendelian Randomization Analysis. Arthritis Rheumatol 2023; 75:1648-1657. [PMID: 37043280 PMCID: PMC10524152 DOI: 10.1002/art.42523] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 04/13/2023]
Abstract
OBJECTIVE To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate. METHODS We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases. RESULTS Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched-chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10-32 ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk. CONCLUSION This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.
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Affiliation(s)
- Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston MA USA
| | - Natalie McCormick
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Vancouver BC Canada
| | - Chio Yokose
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston TX USA
| | - Adrienne Tin
- Department of Medicine, University of Mississippi Medical Center, Jackson MS USA
| | - Robert Terkeltaub
- San Diego VA Healthcare Service and University of California San Diego, La Jolla, CA
| | - Tony R. Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham AL USA
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - A. Heather Eliassen
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston MA USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston MA USA
| | - Gary C. Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston MA USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill NC USA
| | - Hyon K. Choi
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Vancouver BC Canada
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90
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Naas S, Krüger R, Knaup KX, Naas J, Grampp S, Schiffer M, Wiesener M, Schödel J. Hypoxia controls expression of kidney-pathogenic MUC1 variants. Life Sci Alliance 2023; 6:e202302078. [PMID: 37316299 PMCID: PMC10267510 DOI: 10.26508/lsa.202302078] [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: 04/05/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 06/16/2023] Open
Abstract
The interplay between genetic and environmental factors influences the course of chronic kidney disease (CKD). In this context, genetic alterations in the kidney disease gene MUC1 (Mucin1) predispose to the development of CKD. These variations comprise the polymorphism rs4072037, which alters splicing of MUC1 mRNA, the length of a region with variable number of tandem repeats (VNTR), and rare autosomal-dominant inherited dominant-negative mutations in or 5' to the VNTR that causes autosomal dominant tubulointerstitial kidney disease (ADTKD-MUC1). As hypoxia plays a pivotal role in states of acute and chronic kidney injury, we explored the effects of hypoxia-inducible transcription factors (HIF) on the expression of MUC1 and its pathogenic variants in isolated primary human renal tubular cells. We defined a HIF-binding DNA regulatory element in the promoter-proximal region of MUC1 from which hypoxia or treatment with HIF stabilizers, which were recently approved for an anti-anemic therapy in CKD patients, increased levels of wild-type MUC1 and the disease-associated variants. Thus, application of these compounds might exert unfavorable effects in patients carrying MUC1 risk variants.
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Affiliation(s)
- Stephanie Naas
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - René Krüger
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Karl Xaver Knaup
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Julia Naas
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna and Medical University of Vienna, Wien, Austria
| | - Steffen Grampp
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mario Schiffer
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Wiesener
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Johannes Schödel
- Department of Nephrology and Hypertension, Uniklinikum Erlangen und Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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91
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Huang G, Li W, Zhong Y, Liao W, Zhang Z. Mendelian randomization to evaluate the causal relationship between liver enzymes and the risk of six specific bone and joint-related diseases. Front Immunol 2023; 14:1195553. [PMID: 37662902 PMCID: PMC10469508 DOI: 10.3389/fimmu.2023.1195553] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023] Open
Abstract
Background Studies of liver dysfunction in relation to bone and joint-related diseases are scarce, and its causality remains unclear. Our objective was to investigate whether serum liver enzymes are causally associated with bone and joint-related diseases using Mendelian randomization (MR) designs. Methods Genetic data on serum liver enzymes (alkaline phosphatase (ALP); alanine transaminase (ALT); gamma-glutamyl transferase (GGT)) and six common bone and joint-related diseases (rheumatoid arthritis (RA), osteoporosis, osteoarthritis (OA), ankylosing spondylitis, psoriatic arthritis, and gout) were derived from independent genome-wide association studies of European ancestry. The inverse variance-weighted (IVW) method was applied for the main causal estimate. Complementary sensitivity analyses and reverse causal analyses were utilized to confirm the robustness of the results. Results Using the IVW method, the positive causality between ALP and the risk of osteoporosis diagnosed by bone mineral density (BMD) at different sites was indicated (femoral neck, lumbar spine, and total body BMD, odds ratio (OR) [95% CI], 0.40 [0.23-0.69], 0.35 [0.19-0.67], and 0.33 [0.22-0.51], respectively). ALP was also linked to a higher risk of RA (OR [95% CI], 6.26 [1.69-23.51]). Evidence of potential harmful effects of higher levels of ALT on the risk of hip and knee OA was acquired (OR [95% CI], 2.48 [1.39-4.41] and 3.07 [1.49-6.30], respectively). No causal relationship was observed between GGT and these bone and joint-related diseases. The study also found that BMD were all negatively linked to ALP levels (OR [95% CI] for TBMD, FN-BMD, and LS-BMD: 0.993 [0.991-0.995], 0.993 [0.988-0.998], and 0.993 [0.989, 0.998], respectively) in the reverse causal analysis. The results were replicated via sensitivity analysis in the validation process. Conclusions Our study revealed a significant association between liver function and bone and joint-related diseases.
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Affiliation(s)
- Guiwu Huang
- Department of Joint Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Wenchang Li
- Department of Joint Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Yonglie Zhong
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weiming Liao
- Department of Joint Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Zhiqi Zhang
- Department of Joint Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
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92
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Liu Y, Jarman JB, Low YS, Augustijn HE, Huang S, Chen H, DeFeo ME, Sekiba K, Hou BH, Meng X, Weakley AM, Cabrera AV, Zhou Z, van Wezel G, Medema MH, Ganesan C, Pao AC, Gombar S, Dodd D. A widely distributed gene cluster compensates for uricase loss in hominids. Cell 2023; 186:3400-3413.e20. [PMID: 37541197 PMCID: PMC10421625 DOI: 10.1016/j.cell.2023.06.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 03/22/2023] [Accepted: 06/19/2023] [Indexed: 08/06/2023]
Abstract
Approximately 15% of US adults have circulating levels of uric acid above its solubility limit, which is causally linked to the disease gout. In most mammals, uric acid elimination is facilitated by the enzyme uricase. However, human uricase is a pseudogene, having been inactivated early in hominid evolution. Though it has long been known that uric acid is eliminated in the gut, the role of the gut microbiota in hyperuricemia has not been studied. Here, we identify a widely distributed bacterial gene cluster that encodes a pathway for uric acid degradation. Stable isotope tracing demonstrates that gut bacteria metabolize uric acid to xanthine or short chain fatty acids. Ablation of the microbiota in uricase-deficient mice causes severe hyperuricemia, and anaerobe-targeted antibiotics increase the risk of gout in humans. These data reveal a role for the gut microbiota in uric acid excretion and highlight the potential for microbiome-targeted therapeutics in hyperuricemia.
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Affiliation(s)
- Yuanyuan Liu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - J Bryce Jarman
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Hannah E Augustijn
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands; Institute of Biology, Leiden University, Leiden, the Netherlands
| | - Steven Huang
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Haoqing Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mary E DeFeo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kazuma Sekiba
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bi-Huei Hou
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xiandong Meng
- ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | | | | | - Zhiwei Zhou
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gilles van Wezel
- Institute of Biology, Leiden University, Leiden, the Netherlands; Netherlands Institute of Ecology, Wageningen, the Netherlands
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands; Institute of Biology, Leiden University, Leiden, the Netherlands
| | - Calyani Ganesan
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alan C Pao
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Saurabh Gombar
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Atropos Health, Palo Alto, CA, USA
| | - Dylan Dodd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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93
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Toyoda Y, Miyata H, Shigesawa R, Matsuo H, Suzuki H, Takada T. SVCT2/SLC23A2 is a sodium-dependent urate transporter: functional properties and practical application. J Biol Chem 2023; 299:104976. [PMID: 37390985 PMCID: PMC10374969 DOI: 10.1016/j.jbc.2023.104976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/10/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
Urate transporters play a pivotal role in urate handling in the human body, but the urate transporters identified to date do not account for all known molecular processes of urate handling, suggesting the presence of latent machineries. We recently showed that a urate transporter SLC2A12 is also a physiologically important exporter of ascorbate (the main form of vitamin C in the body) that would cooperate with an ascorbate importer, sodium-dependent vitamin C transporter 2 (SVCT2). Based on the dual functions of SLC2A12 and cooperativity between SLC2A12 and SVCT2, we hypothesized that SVCT2 might be able to transport urate. To test this proposal, we conducted cell-based analyses using SVCT2-expressing mammalian cells. The results demonstrated that SVCT2 is a novel urate transporter. Vitamin C inhibited SVCT2-mediated urate transport with a half-maximal inhibitory concentration of 36.59 μM, suggesting that the urate transport activity may be sensitive to physiological ascorbate levels in blood. Similar results were obtained for mouse Svct2. Further, using SVCT2 as a sodium-dependent urate importer, we established a cell-based urate efflux assay that will be useful for identification of other novel urate exporters as well as functional characterization of nonsynonymous variants of already-identified urate exporters including ATP-binding cassette transporter G2. While more studies will be needed to elucidate the physiological impact of SVCT2-mediated urate transport, our findings deepen understanding of urate transport machineries.
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Affiliation(s)
- Yu Toyoda
- Department of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Hiroshi Miyata
- Department of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Ryuichiro Shigesawa
- Department of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Hiroshi Suzuki
- Department of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Tappei Takada
- Department of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan.
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94
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Ji L, Shu P. A Mendelian randomization study of serum uric acid with the risk of venous thromboembolism. Arthritis Res Ther 2023; 25:122. [PMID: 37468959 PMCID: PMC10354911 DOI: 10.1186/s13075-023-03115-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Observational studies have linked hyperuricemia with venous thromboembolism (VTE). We aimed to investigate whether there are causal relationships between uric acid levels and VTE and its subtypes, including deep venous thrombosis (DVT) of the lower extremities and pulmonary embolism (PE). METHODS We utilized Mendelian randomization (MR) analysis to estimate the causal association in European individuals. We extracted two sets of polygenic instruments strongly associated (p < 5 × 10-8) with uric acid from the CKDGen consortium and UK biobank, respectively. Genetic associations with the risk of VTE, DVT, and PE were obtained from the FinnGen biobank. We used the inverse-variance weighted method as the preliminary estimate. Additionally, we employed MR-Egger, weighted median, and Mendelian randomization pleiotropy residual sum and outlier method as complementary assessments. Sensitivity analyses were performed to test for pleiotropic bias. RESULTS The genetically instrumented serum uric acid levels had no causal effects on VTE, DVT, and PE. Two sets of polygenic instruments used for exposure, along with three complementary MR methods, also yielded no significant association. CONCLUSIONS Our MR analysis provided no compelling evidence for a causal relationship of serum uric acid with the risk of VTE. This suggests that uric acid-lowering therapies in patients with hyperuricemia may not be effective in reducing the likelihood of developing VTE.
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Affiliation(s)
- Lixian Ji
- Department of Rheumatology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, China
| | - Peng Shu
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, China.
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95
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Li M, Wu X, Guo Z, Gao R, Ni Z, Cui H, Zong M, Van Bockstaele F, Lou W. Lactiplantibacillus plantarum enables blood urate control in mice through degradation of nucleosides in gastrointestinal tract. MICROBIOME 2023; 11:153. [PMID: 37468996 PMCID: PMC10354915 DOI: 10.1186/s40168-023-01605-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Lactobacillus species in gut microbiota shows great promise in alleviation of metabolic diseases. However, little is known about the molecular mechanism of how Lactobacillus interacts with metabolites in circulation. Here, using high nucleoside intake to induce hyperuricemia in mice, we investigated the improvement in systemic urate metabolism by oral administration of L. plantarum via different host pathways. RESULTS Gene expression analysis demonstrated that L. plantarum inhibited the activity of xanthine oxidase and purine nucleoside phosphorylase in liver to suppress urate synthesis. The gut microbiota composition did not dramatically change by oral administration of L. plantarum over 14 days, indicated by no significant difference in α and β diversities. However, multi-omic network analysis revealed that increase of L. plantarum and decrease of L. johnsonii contributed to a decrease in serum urate levels. Besides, genomic analysis and recombinant protein expression showed that three ribonucleoside hydrolases, RihA-C, in L. plantarum rapidly and cooperatively catalyzed the hydrolysis of nucleosides into nucleobases. Furthermore, the absorption of nucleobase by intestinal epithelial cells was less than that of nucleoside, which resulted in a reduction of urate generation, evidenced by the phenomenon that mice fed with nucleobase diet generated less serum urate than those fed with nucleoside diet over a period of 9-day gavage. CONCLUSION Collectively, our work provides substantial evidence identifying the specific role of L. plantarum in improvement of urate circulation. We highlight the importance of the enzymes RihA-C existing in L. plantarum for the urate metabolism in hyperuricemia mice induced by a high-nucleoside diet. Although the direct connection between nucleobase transport and host urate levels has not been identified, the lack of nucleobase transporter in intestinal epithelial cells might be important to decrease its absorption and metabolization for urate production, leading to the decrease of serum urate in host. These findings provide important insights into urate metabolism regulation. Video Abstract.
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Affiliation(s)
- Mengfan Li
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Food Structure and Function Research Group (FSF), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Xiaoling Wu
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zewang Guo
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Ruichen Gao
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zifu Ni
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Hualing Cui
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Minhua Zong
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Filip Van Bockstaele
- Food Structure and Function Research Group (FSF), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
| | - Wenyong Lou
- Lab of Applied Biocatalysis, School of Food Science and Engineering, South China University of Technology, Guangzhou, China.
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96
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Lake J, Warly Solsberg C, Kim JJ, Acosta-Uribe J, Makarious MB, Li Z, Levine K, Heutink P, Alvarado CX, Vitale D, Kang S, Gim J, Lee KH, Pina-Escudero SD, Ferrucci L, Singleton AB, Blauwendraat C, Nalls MA, Yokoyama JS, Leonard HL. Multi-ancestry meta-analysis and fine-mapping in Alzheimer's disease. Mol Psychiatry 2023; 28:3121-3132. [PMID: 37198259 PMCID: PMC10615750 DOI: 10.1038/s41380-023-02089-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/27/2023] [Accepted: 03/31/2023] [Indexed: 05/19/2023]
Abstract
Genome-wide association studies (GWAS) of Alzheimer's disease are predominantly carried out in European ancestry individuals despite the known variation in genetic architecture and disease prevalence across global populations. We leveraged published GWAS summary statistics from European, East Asian, and African American populations, and an additional GWAS from a Caribbean Hispanic population using previously reported genotype data to perform the largest multi-ancestry GWAS meta-analysis of Alzheimer's disease and related dementias to date. This method allowed us to identify two independent novel disease-associated loci on chromosome 3. We also leveraged diverse haplotype structures to fine-map nine loci with a posterior probability >0.8 and globally assessed the heterogeneity of known risk factors across populations. Additionally, we compared the generalizability of multi-ancestry- and single-ancestry-derived polygenic risk scores in a three-way admixed Colombian population. Our findings highlight the importance of multi-ancestry representation in uncovering and understanding putative factors that contribute to risk of Alzheimer's disease and related dementias.
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Affiliation(s)
- Julie Lake
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Caroline Warly Solsberg
- Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jonggeol Jeffrey Kim
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Preventive Neurology Unit, Centre for Prevention Diagnosis and Detection, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Juliana Acosta-Uribe
- Neuroscience Research Institute and the department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA
- Neuroscience Group of Antioquia, University of Antioquia, Medellín, Colombia
| | - Mary B Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Zizheng Li
- Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kristin Levine
- Data Tecnica International LLC, Washington, DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Peter Heutink
- Alector, Inc. 131 Oyster Point Blvd, Suite 600, South San Francisco, CA, 94080, USA
| | - Chelsea X Alvarado
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Dan Vitale
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Sarang Kang
- Gwangju Alzheimer's disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, 61452, Korea
- BK FOUR Department of Integrative Biological Sciences, Chosun University, Gwangju, 61452, Korea
| | - Jungsoo Gim
- Gwangju Alzheimer's disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, 61452, Korea
- BK FOUR Department of Integrative Biological Sciences, Chosun University, Gwangju, 61452, Korea
- Department of Biomedical Science, Chosun University, Gwangju, 61452, Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, 61452, Korea
- BK FOUR Department of Integrative Biological Sciences, Chosun University, Gwangju, 61452, Korea
- Department of Biomedical Science, Chosun University, Gwangju, 61452, Korea
- Korea Brain Research Institute, Daegu, 41062, Korea
| | - Stefanie D Pina-Escudero
- Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Cornelis Blauwendraat
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer S Yokoyama
- Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Hampton L Leonard
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
- Data Tecnica International LLC, Washington, DC, USA.
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA.
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
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97
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Lake BB, Menon R, Winfree S, Hu Q, Melo Ferreira R, Kalhor K, Barwinska D, Otto EA, Ferkowicz M, Diep D, Plongthongkum N, Knoten A, Urata S, Mariani LH, Naik AS, Eddy S, Zhang B, Wu Y, Salamon D, Williams JC, Wang X, Balderrama KS, Hoover PJ, Murray E, Marshall JL, Noel T, Vijayan A, Hartman A, Chen F, Waikar SS, Rosas SE, Wilson FP, Palevsky PM, Kiryluk K, Sedor JR, Toto RD, Parikh CR, Kim EH, Satija R, Greka A, Macosko EZ, Kharchenko PV, Gaut JP, Hodgin JB, Eadon MT, Dagher PC, El-Achkar TM, Zhang K, Kretzler M, Jain S. An atlas of healthy and injured cell states and niches in the human kidney. Nature 2023; 619:585-594. [PMID: 37468583 PMCID: PMC10356613 DOI: 10.1038/s41586-023-05769-3] [Citation(s) in RCA: 268] [Impact Index Per Article: 134.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/30/2023] [Indexed: 07/21/2023]
Abstract
Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
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Affiliation(s)
- Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Seth Winfree
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ricardo Melo Ferreira
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kian Kalhor
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Daria Barwinska
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Edgar A Otto
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Michael Ferkowicz
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Knoten
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Sarah Urata
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Laura H Mariani
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Abhijit S Naik
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Sean Eddy
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Bo Zhang
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Yan Wu
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Diane Salamon
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - James C Williams
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xin Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Paul J Hoover
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Teia Noel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anitha Vijayan
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | | | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sushrut S Waikar
- Section of Nephrology, Boston University School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Sylvia E Rosas
- Kidney and Hypertension Unit, Joslin Diabetes Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Francis P Wilson
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Paul M Palevsky
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - John R Sedor
- Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, OH, USA
| | - Robert D Toto
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Chirag R Parikh
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Eric H Kim
- Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
| | | | - Anna Greka
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
| | - Matthias Kretzler
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.
| | - Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
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98
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Benaglio P, Newsome J, Han JY, Chiou J, Aylward A, Corban S, Miller M, Okino ML, Kaur J, Preissl S, Gorkin DU, Gaulton KJ. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex immune trait variants using single nucleus ATAC-seq in peripheral blood. PLoS Genet 2023; 19:e1010759. [PMID: 37289818 DOI: 10.1371/journal.pgen.1010759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/25/2023] [Indexed: 06/10/2023] Open
Abstract
Gene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 13 individuals. Clustering chromatin accessibility profiles of 96,002 total nuclei identified 17 immune cell types and sub-types. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type using individuals of European ancestry which identified 6,901 caQTLs at FDR < .10 and 4,220 caQTLs at FDR < .05, including those obscured from assays of bulk tissue such as with divergent effects on different cell types. For 3,941 caQTLs we further annotated putative target genes of variant activity using single cell co-accessibility, and caQTL variants were significantly correlated with the accessibility level of linked gene promoters. We fine-mapped loci associated with 16 complex immune traits and identified immune cell caQTLs at 622 candidate causal variants, including those with cell type-specific effects. At the 6q15 locus associated with type 1 diabetes, in line with previous reports, variant rs72928038 was a naïve CD4+ T cell caQTL linked to BACH2 and we validated the allelic effects of this variant on regulatory activity in Jurkat T cells. These results highlight the utility of snATAC-seq for mapping genetic effects on accessible chromatin in specific cell types.
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Affiliation(s)
- Paola Benaglio
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jacklyn Newsome
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Jee Yun Han
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Joshua Chiou
- Biomedical Sciences Graduate Program. University of California San Diego, San Diego, California, United States of America
| | - Anthony Aylward
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Sierra Corban
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Michael Miller
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jaspreet Kaur
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - David U Gorkin
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
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99
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Lin K, McCormick N, Yokose C, Joshi AD, Lu N, Curhan GC, Merriman TR, Saag KG, Ridker PM, Buring JE, Chasman DI, Hu FB, Choi HK. Interactions Between Genetic Risk and Diet Influencing Risk of Incident Female Gout: Discovery and Replication Analysis of Four Prospective Cohorts. Arthritis Rheumatol 2023; 75:1028-1038. [PMID: 36512683 PMCID: PMC10238565 DOI: 10.1002/art.42419] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 11/08/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To examine whether the cross-sectional gene-diet interaction for prevalent hyperuricemia among women translates prospectively to risk of incident female gout. METHODS We analyzed the interaction between genetic predisposition and adherence to a healthy dietary pattern (i.e., Dietary Approaches to Stop Hypertension [DASH] score) on risk of incident female gout in 18,244 women from Nurses' Health Study (NHS; discovery) and 136,786 women from 3 additional prospective female cohorts from the US and UK (replication). Genetic risk score (GRS) was calculated from 114 urate-associated loci. RESULTS In the NHS and replication cohorts, association between diet and gout risk was larger and stronger among women with higher genetic risk. In all cohorts combined, compared to women with an unhealthy DASH score (less than the mean score), multivariable relative risk (RR) for incident gout among women with a healthy DASH score (greater than/equal to the mean score) was 0.67 (95% confidence interval [95% CI] 0.60-0.76) among higher GRS (greater than/equal to the mean score) and 0.91 (0.78-1.05) among lower GRS (P for multiplicative interaction = 0.001); multivariable RR for higher versus lower GRS was 2.03 (95% CI 1.80-2.29) and 1.50 (95% CI 1.31-1.71) among unhealthy and healthy DASH score groups, respectively. Additive interaction was also significant, in both the discovery and replication cohorts (P < 0.001), with 51% of the excess risk attributable to the additive gene-diet interaction in all cohorts combined. CONCLUSION The deleterious effect of genetic predisposition on risk of incident female gout was more pronounced among women with unhealthy diets, with nearly half the excess risk attributable to this gene-diet interaction. These data elucidate the important synergy of genetics and diet for female gout development.
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Affiliation(s)
- Kehuan Lin
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical Epidemiology Program, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, USA
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Natalie McCormick
- Clinical Epidemiology Program, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, USA
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Arthritis Research Canada, Vancouver, British Columbia, Canada
- Medicine, Harvard Medical School, Boston, MA, USA
| | - Chio Yokose
- Clinical Epidemiology Program, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, USA
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Medicine, Harvard Medical School, Boston, MA, USA
| | - Amit D. Joshi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Na Lu
- Arthritis Research Canada, Vancouver, British Columbia, Canada
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Gary C. Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Renal Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tony R. Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Kenneth G. Saag
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Paul M. Ridker
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Julie E. Buring
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B. Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hyon K Choi
- Clinical Epidemiology Program, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, USA
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Arthritis Research Canada, Vancouver, British Columbia, Canada
- Medicine, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
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100
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Topiwala A, Mankia K, Bell S, Webb A, Ebmeier KP, Howard I, Wang C, Alfaro-Almagro F, Miller K, Burgess S, Smith S, Nichols TE. Association of gout with brain reserve and vulnerability to neurodegenerative disease. Nat Commun 2023; 14:2844. [PMID: 37202397 PMCID: PMC10195870 DOI: 10.1038/s41467-023-38602-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/09/2023] [Indexed: 05/20/2023] Open
Abstract
Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kulveer Mankia
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Chapel Allerton Hospital, Leeds, UK
| | - Steven Bell
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Isobel Howard
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai, China
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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