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Ahmed A, Cule M, Bell JD, Sattar N, Yaghootkar H. Differing genetic variants associated with liver fat and their contrasting relationships with cardiovascular diseases and cancer. J Hepatol 2024; 81:921-929. [PMID: 38960375 DOI: 10.1016/j.jhep.2024.06.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND & AIMS The mechanisms underlying the association of steatotic liver disease with cardiovascular and cancer outcomes are poorly understood. We aimed to use MRI-derived measures of liver fat and genetics to investigate causal mechanisms that link higher liver fat to various health outcomes. METHODS We conducted a genome-wide association study on 37,358 UK Biobank participants to identify genetic variants associated with liver fat measured from MRI scans. We used a Mendelian randomisation approach to investigate the causal effect of liver fat on health outcomes independent of BMI, alcohol consumption and lipids using data from published genome-wide association studies and FinnGen. RESULTS We identified 13 genetic variants associated with liver fat that had differing effects on the risks of health outcomes. Genetic variants associated with impaired hepatic triglyceride export showed liver fat-increasing alleles to be correlated with a reduced risk of coronary artery disease and myocardial infarction but an elevated risk of type 2 diabetes, while variants associated with enhanced de novo lipogenesis showed liver fat-increasing alleles to be linked to a higher risk of myocardial infarction and coronary artery disease. Genetically higher liver fat content increased the risk of non-alcohol-related cirrhosis, hepatocellular carcinoma, and intrahepatic bile duct and gallbladder cancers, exhibiting a dose-dependent relationship, irrespective of the mechanism. CONCLUSION This study provides fresh insight into the heterogeneous effect of liver fat on health outcomes. It challenges the notion that liver fat per se is an independent risk factor for cardiovascular disease, underscoring the dependency of this association on the specific mechanisms that drive fat accumulation in the liver. However, excess liver fat, regardless of the underlying mechanism, appears to be causally linked to cirrhosis and cancers in a dose-dependent manner. IMPACT AND IMPLICATION This research advances our understanding of the heterogeneity in mechanisms influencing liver fat accumulation, providing new insights into how liver fat accumulation may impact various health outcomes. The findings challenge the notion that liver fat is an independent risk factor for cardiovascular disease and highlight the mechanistic effect of some genetic variants on fat accumulation and the development of cardiovascular diseases. This study is of particular importance for healthcare professionals including physicians and researchers, as well as patients, as it allows for more targeted and personalised treatment by understanding the relationship between liver fat and various health outcomes. The findings emphasise the need for a personalised management approach and a reshaping of risk assessment criteria. It also provides room for prioritising a clinical intervention aimed at reducing liver fat content (likely via intentional weight loss) that could help protect against liver-related fibrosis and cancer.
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Affiliation(s)
- Altayeb Ahmed
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, UK
| | | | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Hanieh Yaghootkar
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, UK.
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Strauss-Kruger M, Olinger E, Hofmann P, Wilson IJ, Mels C, Kruger R, Gafane-Matemane LF, Sayer JA, Ricci C, Schutte AE, Devuyst O. UMOD Genotype and Determinants of Urinary Uromodulin in African Populations. Kidney Int Rep 2024; 9:3477-3489. [PMID: 39698369 PMCID: PMC11652103 DOI: 10.1016/j.ekir.2024.09.015] [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: 03/02/2024] [Revised: 09/14/2024] [Accepted: 09/16/2024] [Indexed: 12/20/2024] Open
Abstract
Introduction Single-nucleotide polymorphisms (SNPs) in the UMOD -PDILT genetic locus are associated with chronic kidney disease (CKD) in European populations, through their effect on urinary uromodulin (uUMOD) levels. The genetic and nongenetic factors associated with uUMOD in African populations remain unknown. Methods Clinical parameters, 3 selected UMOD-PDILT SNPs and uUMOD levels were obtained in 1202 young Black and White adults from the African-PREDICT study and 1943 middle aged Black adults from the PURE-NWP-SA study, 2 cross-sectional, observational studies. Results Absolute uUMOD and uUMOD/creatinine levels were lower in Black participants compared to White participants. The prime CKD-risk allele at rs12917707 was more prevalent in Black individuals, with strikingly more risk allele homozygotes compared to White individuals. Haplotype analysis of the UMOD-PDILT locus predicted more recombination events and linkage disequilibrium (LD) fragmentation in Black individuals. Multivariate testing and sensitivity analysis showed that higher uUMOD/creatinine associated specifically with risk alleles at rs12917707 and rs12446492 in White participants and with higher serum renin and lower urine albumin-to-creatinine ratio in Black participants, with a significant interaction of ethnicity on the relationship between all 3 SNPs and uUMOD/creatinine. The multiple regression model explained a greater percentage of the variance of uUMOD/creatinine in White adults compared to Black adults (23% vs. 8%). Conclusion We evidenced ethnic differences in clinical and genetic determinants of uUMOD levels, in particular an interaction of ethnicity on the relationship between CKD-risk SNPs and uUMOD. These differences should be considered when analyzing the role of uromodulin in kidney function, interpreting genome-wide association studies (GWAS), and precision medicine recommendations.
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Affiliation(s)
- Michél Strauss-Kruger
- Hypertension in Africa Research Team, North-West University, Potchefstroom, North-West Province, South Africa
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, North-West Province, South Africa
| | - Eric Olinger
- Institute of Physiology, University of Zurich, Zurich, Switzerland
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Center for Human Genetics, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Patrick Hofmann
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Ian J. Wilson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Carina Mels
- Hypertension in Africa Research Team, North-West University, Potchefstroom, North-West Province, South Africa
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, North-West Province, South Africa
| | - Ruan Kruger
- Hypertension in Africa Research Team, North-West University, Potchefstroom, North-West Province, South Africa
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, North-West Province, South Africa
| | - Lebo F. Gafane-Matemane
- Hypertension in Africa Research Team, North-West University, Potchefstroom, North-West Province, South Africa
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, North-West Province, South Africa
| | - John A. Sayer
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Cristian Ricci
- African Unit for Transdisciplinary Health Research, North-West University, Potchefstroom, North-West Province, South Africa
| | - Aletta E. Schutte
- Hypertension in Africa Research Team, North-West University, Potchefstroom, North-West Province, South Africa
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, North-West Province, South Africa
- DSI-NRF Centre of Excellence in Human Development and SAMRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
- The George Institute for Global Health, Sydney, New South Wales, Australia
- School of Population Health, University of New South Wales; Sydney, New South Wales, Australia
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
- Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
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Dobrijevic E, van Zwieten A, Grant AJ, Loy CT, Craig JC, Teixeira-Pinto A, Wong G. Causal Relationship Between Kidney Function and Cancer Risk: A Mendelian Randomization Study. Am J Kidney Dis 2024; 84:686-695.e1. [PMID: 39084486 DOI: 10.1053/j.ajkd.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/07/2024] [Accepted: 05/17/2024] [Indexed: 08/02/2024]
Abstract
RATIONALE & OBJECTIVE Patients treated with kidney replacement therapy experience a 1.5- to 2-fold increased risk of cancer and cancer mortality compared with the general population. Whether this excess risk extends to people with earlier stage chronic kidney disease and whether reduced kidney function is causally related to cancer is unclear. STUDY DESIGN Two-sample Mendelian randomization (MR). SETTING & PARTICIPANTS Genome-wide association study (GWAS) summary statistics for estimated glomerular filtration rate (eGFR) (n=567,460) and urinary albumin-creatine ratio (UACR) (n=127,865) from the CKDGen consortium and cancer outcomes from the UK Biobank (n = 407,329). EXPOSURE eGFR and UACR. OUTCOME Overall cancer incidence, cancer-related mortality and site-specific colorectal, lung, and urinary tract cancer incidence. ANALYTICAL APPROACH Univariable and multivariable MR conducted for all outcomes. RESULTS The mean eGFR and median UACR were 91.4mL/min/1.73m2 and 9.32mg/g, respectively, in the CKDGen, and 90.4mL/min/1.73m2 and 9.29mg/g, respectively, in the UK Biobank. There were 98,093 cases of cancer, 15,850 cases of cancer-related death, 6,664 colorectal, 3584 lung, and 3,271 urinary tract cancer cases, respectively. The genetic instruments for eGFR and UACR comprised 34 and 38 variants, respectively. Genetically predicted kidney function (eGFR and UACR) was not associated with overall cancer risk or cancer death. The association between genetically predicted eGFR and UACR and overall cancer incidence had an odds ratio of 0.88 ([95% CI, 0.40-1.97], P=0.8) and 0.90 ([95% CI, 0.78-1.04], P=0.2) respectively, using the inverse-variance weighted method. An adjusted generalized additive model for eGFR and cancer demonstrated evidence of nonlinearity. However, there was no evidence of a causal association between eGFR and cancer in a stratified MR. LIMITATIONS To avoid overlapping samples a smaller GWAS for UACR was used, which reduced the strength of the instrument and may introduce population stratification. CONCLUSIONS Our study did not show a causal association between kidney function, overall cancer incidence, and cancer-related death. PLAIN-LANGUAGE SUMMARY Does reduced kidney function cause cancer? Patients with chronic kidney disease have been shown to have an increased risk of cancer and cancer-related death. However, it is not clear whether kidney disease is causally related to cancer or the association is due to other factors such as immune suppression and inflammation or a result of distortion of the analyses from unidentified variables (confounding). We used large, published genetic studies as well a database including 407,329 people in the United Kingdom in a series of Mendelian randomization analysis. Mendelian randomization uses the random assignment of genetic variants at birth to investigate causal relationships without confounding from measured and unmeasured confounders. We found that there is no evidence of a causal relationship between reduced kidney function and cancer.
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Affiliation(s)
- Ellen Dobrijevic
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Kidney Research, Children's Hospital at Westmead, Westmead, Australia.
| | - Anita van Zwieten
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Kidney Research, Children's Hospital at Westmead, Westmead, Australia
| | - Andrew J Grant
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Clement T Loy
- Macquarie Medical School, Macquarie University, Sydney, Australia
| | - Jonathan C Craig
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia
| | - Armando Teixeira-Pinto
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Kidney Research, Children's Hospital at Westmead, Westmead, Australia
| | - Germaine Wong
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Kidney Research, Children's Hospital at Westmead, Westmead, Australia; Centre for Transplant and Renal Research, Westmead Hospital, Westmead, Australia
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Pan W, Zhou L, Han R, Du X, Chen W, Jiang T. Causal associations between kidney function and aortic valve stenosis: a bidirectional Mendelian randomization analysis. Ren Fail 2024; 46:2417742. [PMID: 39440431 PMCID: PMC11500509 DOI: 10.1080/0886022x.2024.2417742] [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: 06/22/2024] [Revised: 09/17/2024] [Accepted: 10/12/2024] [Indexed: 10/25/2024] Open
Abstract
BACKGROUND Aortic valve stenosis (AVS) is currently the most common heart valve disease. The results of observational studies on the incidence of AVS in the renal dysfunction population are contradictory due to the short follow-up period and different diagnostic criteria, etc. This study aimed to explore the causal relationship between kidney function and AVS using Mendelian randomization (MR) analysis. METHODS We acquired summary statistics of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) from the CKDGen Consortium and a study on AVS from the FinnGen biobank. Univariate and multivariable MR analyses were conducted to evaluate the causal associations. The MR-Egger intercept and MR-PRESSO Global test were applied to assess the pleiotropic effects. The heterogeneity of MR results was tested by Cochran's Q statistic. Moreover, the Bonferroni and FDR corrections were performed for multiple tests. RESULTS Genetically predicted decreased eGFR may be associated with a raised risk of AVS (OR = 0.045, p = 1.317e-04 by IVW; OR = 0.002, p = 0.004 by MR-Egger, OR = 0.091, p = 0.057 by WM). The causal association still established after multiple comparisons. Quality control analyses indicated the absence of heterogeneity and pleiotropy in our MR research. In addition, the causality of eGFR and AVS remained significant in multivariable MR analysis after adjusting BMI, hypertension, T2DM, LDL-C, and smoking. CONCLUSION Our MR study discovered that reduced eGFR may be a causative risk factor for AVS. In addition, the evidence did not support a significant causal association of AVS on kidney function.
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Affiliation(s)
- Wanqian Pan
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Le Zhou
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Rui Han
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xiaojiao Du
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Weixiang Chen
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tingbo Jiang
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Wen C, Chen L, Jia D, Liu Z, Lin Y, Liu G, Zhang S, Gao B. Recent advances in the application of Mendelian randomization to chronic kidney disease. Ren Fail 2024; 46:2319712. [PMID: 38522953 PMCID: PMC10913720 DOI: 10.1080/0886022x.2024.2319712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/12/2024] [Indexed: 03/26/2024] Open
Abstract
OBJECTIVE Chronic kidney disease (CKD) is a condition influenced by both genetic and environmental factors and has been a focus of extensive research. Utilizing Mendelian randomization, researchers have begun to untangle the complex causal relationships underlying CKD. This review delves into the advances and challenges in the application of MR in the field of nephrology, shifting from a mere summary of its principles and limitations to a more nuanced exploration of its contributions to our understanding of CKD. METHODS Key findings from recent studies have been pivotal in reshaping our comprehension of CKD. Notably, evidence indicates that elevated testosterone levels may impair renal function, while higher sex hormone-binding globulin (SHBG) levels appear to be protective, predominantly in men. Surprisingly, variations in plasma glucose and glycated hemoglobin levels seem unaffected by genetically induced changes in the estimated glomerular filtration rate (eGFR), suggesting an independent pathway for renal function impairment. RESULTS Furthermore, lifestyle factors such as physical activity and socioeconomic status emerge as significant influencers of CKD risk and kidney health. The relationship between sleep duration and CKD is nuanced; short sleep duration is linked to increased risk, while long sleep duration does not exhibit a clear causal effect. Additionally, lifestyle factors, including diet, exercise, and mental wellness activities, play a crucial role in kidney health. New insights also reveal a substantial causal connection between both central and general obesity and CKD onset, while no significant links were found between genetically modified LDL cholesterol or triglyceride levels and kidney function. CONCLUSION This review not only presents the recent achievements of MR in CKD research but also illuminates the path forwards, underscoring critical unanswered questions and proposing future research directions in this dynamic field.
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Affiliation(s)
- Chaofan Wen
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Lanlan Chen
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Dan Jia
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Ziqi Liu
- Weifang Medical University, Weifang, Shandong Province, China
| | - Yidan Lin
- Herberger Institute for Design and Arts, Arizona State University, Tempe, AZ, USA
| | - Guan Liu
- Department of Pharmacology, Hebei Medical University, Shijiazhuang City, Hebei Province, China
| | - Shuo Zhang
- Department of Pharmacology, Hebei Medical University, Shijiazhuang City, Hebei Province, China
| | - Baoshan Gao
- Department of Urology and Surgery, the First Hospital of Jilin University, Changchun, Jilin Province, China
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Ho SC, Hoi-Yee Li G, Yu-Hung Leung A, Choon-Beng Tan K, Cheung CL. Effects of bone metabolism on hematopoiesis: A Mendelian randomization study. Osteoporos Sarcopenia 2024; 10:151-156. [PMID: 39835327 PMCID: PMC11742307 DOI: 10.1016/j.afos.2024.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 09/09/2024] [Accepted: 10/13/2024] [Indexed: 01/22/2025] Open
Abstract
Objectives Osteoblast is known to regulate hematopoiesis according to preclinical studies but the causal relationship in human remains uncertain. We aimed to evaluate causal relationships of bone mineral density (BMD) with blood cell traits using genetic data. Methods Summary statistics from the largest available genome-wide association study were retrieved for total body BMD (TBBMD), lumbar spine BMD (LSBMD), femoral neck BMD (FNBMD) and 29 blood cell traits including red blood cell, white blood cell and platelet-related traits. Using two-sample Mendelian randomization (MR) approach, inverse-variance weighted method was adopted as main univariable MR analysis. Multivariable MR (MVMR) analysis was conducted to evaluate whether the casual effect is independent of confounders. Results BMD was positively associated with reticulocyte-related traits, including high light scatter reticulocyte count and percentage, immature reticulocyte fraction, reticulocyte count and percentage, with causal effect estimate (beta) ranging from 0.023 to 0.064. Conversely, inverse association of BMD with hematocrit, hemoglobin, and red blood cell count was observed, with beta ranging from -0.038 to -0.019. The association remained significant in MVMR analysis after adjustment for confounders. For white blood cells, BMD was inversely associated with neutrophil count (beta: 0.029 to -0.019) and white blood cell count (beta: 0.024 to -0.02). Results across TBBMD, LSBMD, and FNBMD were consistent. Conclusions This study suggested bone metabolism had a causal effect on hematopoietic system in humans. Its causal effect on red blood cell traits was independent of confounders. Further studies on how improving bone health can reduce risk of hematological disorders are warranted.
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Affiliation(s)
- Shun-Cheong Ho
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Gloria Hoi-Yee Li
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong
| | - Anskar Yu-Hung Leung
- Department of Medicine, School of Clinical Medicine, Queen Mary Hospital, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Kathryn Choon-Beng Tan
- Department of Medicine, School of Clinical Medicine, Queen Mary Hospital, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Pak Shek Kok, Hong Kong
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Xiao R, Dong L, Xie B, Liu B. A Mendelian randomization study: physical activities and chronic kidney disease. Ren Fail 2024; 46:2295011. [PMID: 38178379 PMCID: PMC10773648 DOI: 10.1080/0886022x.2023.2295011] [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: 06/28/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
Increasing evidence has shown that physical activity is related to a lower risk of chronic kidney disease (CKD), thus indicating a potential target for prevention. However, the causality is not clear; specifically, physical activity may protect against CKD, and CKD may lead to a reduction in physical activity. Our study examined the potential bidirectional relationship between physical activity and CKD by using a genetically informed method. Genome-wide association studies from the UK Biobank baseline data were used for physical activity phenotypes and included 460,376 participants. For kidney function (estimated Glomerular Filtration Rate (eGFR) and CKD, with eGFR < 60 mL/min/1.73 m2), CKDGen Consortium data were used, which included 480,698 CKD participants of European ancestry. Mendelian randomization (MR) analysis was used to determine the causal relationship between physical activities and kidney function. Two-sample MR genetically predicted that heavy DIY (do it yourself) (e.g., weeding, lawn mowing, carpentry, and digging) decreased the risk of CKD (odds ratio [OR] = 0.287, 95% CI = 0.117-0.705, p = 0.0065) and improved the level of eGFR (β = 0.036, 95% CI = 0.005-0.067, p = 0.021). The bidirectional MR showed no reverse causality. It is worth noting that other physical activities, such as walking for pleasure, strenuous sports, light DIY (e.g., pruning and watering the lawn), and other exercises (e.g., swimming, cycling, keeping fit, and bowling), were not significantly correlated with CKD and eGFR. This study used genetic data to provide reliable and robust causal evidence that heavy physical activity (e.g., weeding, lawn mowing, carpentry, and digging) can protect kidney function and further lower the risk of CKD.
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Affiliation(s)
- Rui Xiao
- Department of General Practice, Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Li Dong
- Department of Nephrology and Rheumatology, Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Bo Xie
- Department of General Practice, Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Beizhong Liu
- Central Laboratory of Yongchuan Hospital, Chongqing Medical University, Chongqing, China
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Qing J, Zhang L, Li C, Li Y. Mendelian randomization analysis revealed that albuminuria is the key factor affecting socioeconomic status in CKD patients. Ren Fail 2024; 46:2367705. [PMID: 39010847 PMCID: PMC11776065 DOI: 10.1080/0886022x.2024.2367705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/31/2024] [Accepted: 06/08/2024] [Indexed: 07/17/2024] Open
Abstract
Previous studies indicate a strong correlation between the incidence of chronic kidney disease (CKD) and lower economic status. However, these studies often struggle to delineate a clear cause-effect relationship, leaving healthcare providers uncertain about how to manage kidney disease in a way that improves patients' financial outcomes. Our study aimed to explore and establish a causal relationship between CKD and socioeconomic status, identifying critical influencing factors. We utilized summary meta-analysis data from the CKDGen Consortium and UK Biobank. Genetic variants identified from these sources served as instrumental variables (IVs) to estimate the association between CKD and socioeconomic status. The presence or absence of CKD, estimated glomerular filtration rate (eGFR), and albuminuria were used as exposures, while income and regional deprivation were analyzed as outcomes. We employed the R packages 'TwoSampleMR' and 'Mendelianrandomization' to conduct both univariable and multivariable Mendelian randomization (MR) analyses, assessing for potential pleiotropy and heterogeneity. Our univariable MR analysis revealed a significant causal relationship between high levels of albuminuria and lower income (OR = 0.84, 95% CI: 0.73-0.96, p = 0.013), with no significant pleiotropy detected. In the multivariable MR analysis, both CKD (OR = 0.867, 95% CI: 0.786-0.957, p = 0.0045) and eGFR (OR = 0.065, 95% CI: 0.010-0.437, p = 0.0049) exhibited significant effects on income. This study underscores that higher albuminuria levels in CKD patients are associated with decreased income and emphasizes the importance of effective management and treatment of albuminuria in CKD patients to mitigate both social and personal economic burdens.
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Affiliation(s)
- Jianbo Qing
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan, China
- Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijuan Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Changqun Li
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
- Core Laboratory, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan, China
- Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
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Smiles WJ, Ovens AJ, Oakhill JS, Kofler B. The metabolic sensor AMPK: Twelve enzymes in one. Mol Metab 2024; 90:102042. [PMID: 39362600 PMCID: PMC11752127 DOI: 10.1016/j.molmet.2024.102042] [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: 08/02/2024] [Revised: 09/12/2024] [Accepted: 09/27/2024] [Indexed: 10/05/2024] Open
Abstract
BACKGROUND AMP-activated protein kinase (AMPK) is an evolutionarily conserved regulator of energy metabolism. AMPK is sensitive to acute perturbations to cellular energy status and leverages fundamental bioenergetic pathways to maintain cellular homeostasis. AMPK is a heterotrimer comprised of αβγ-subunits that in humans are encoded by seven individual genes (isoforms α1, α2, β1, β2, γ1, γ2 and γ3), permitting formation of at least 12 different complexes with personalised biochemical fingerprints and tissue expression patterns. While the canonical activation mechanisms of AMPK are well-defined, delineation of subtle, as well as substantial, differences in the regulation of heterogenous AMPK complexes remain poorly defined. SCOPE OF REVIEW Here, taking advantage of multidisciplinary findings, we dissect the many aspects of isoform-specific AMPK function and links to health and disease. These include, but are not limited to, allosteric activation by adenine nucleotides and small molecules, co-translational myristoylation and post-translational modifications (particularly phosphorylation), governance of subcellular localisation, and control of transcriptional networks. Finally, we delve into current debate over whether AMPK can form novel protein complexes (e.g., dimers lacking the α-subunit), altogether highlighting opportunities for future and impactful research. MAJOR CONCLUSIONS Baseline activity of α1-AMPK is higher than its α2 counterpart and is more sensitive to synergistic allosteric activation by metabolites and small molecules. α2 complexes however, show a greater response to energy stress (i.e., AMP production) and appear to be better substrates for LKB1 and mTORC1 upstream. These differences may explain to some extent why in certain cancers α1 is a tumour promoter and α2 a suppressor. β1-AMPK activity is toggled by a 'myristoyl-switch' mechanism that likely precedes a series of signalling events culminating in phosphorylation by ULK1 and sensitisation to small molecules or endogenous ligands like fatty acids. β2-AMPK, not entirely beholden to this myristoyl-switch, has a greater propensity to infiltrate the nucleus, which we suspect contributes to its oncogenicity in some cancers. Last, the unique N-terminal extensions of the γ2 and γ3 isoforms are major regulatory domains of AMPK. mTORC1 may directly phosphorylate this region in γ2, although whether this is inhibitory, especially in disease states, is unclear. Conversely, γ3 complexes might be preferentially regulated by mTORC1 in response to physical exercise.
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Affiliation(s)
- William J Smiles
- Research Program for Receptor Biochemistry and Tumour Metabolism, Department of Paediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria; Metabolic Signalling Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia.
| | - Ashley J Ovens
- Protein Engineering in Immunity & Metabolism, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia
| | - Jonathan S Oakhill
- Metabolic Signalling Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia; Department of Medicine, University of Melbourne, Parkville, Australia
| | - Barbara Kofler
- Research Program for Receptor Biochemistry and Tumour Metabolism, Department of Paediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria
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Zhu Y, Chen S, Chen Z, Wang Y, Fu G, Zhang W. Causal effect of lipoprotein(a) level on chronic kidney disease of European ancestry: a two-sample Mendelian randomization study. Ren Fail 2024; 46:2383727. [PMID: 39082753 PMCID: PMC11293262 DOI: 10.1080/0886022x.2024.2383727] [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: 03/17/2024] [Revised: 06/03/2024] [Accepted: 07/18/2024] [Indexed: 08/03/2024] Open
Abstract
INTRODUCTION Chronic kidney disease is a growing health issue, and the options of prevention and therapy remain limited. Although a number of observational studies have linked higher Lp(a) [lipoprotein(a)] levels to the kidney impairment, the causal relationship remains to be determined. The purpose of this study was to assess the causal association between Lp(a) levels and CKD. METHODS We selected eight single-nucleotide polymorphisms (SNPs) significantly associated with Lp(a) levels as instrumental variables. Genome-wide association study (GWAS) from CKDGen consortium yielded the summary data information for CKD. We designed the bidirectional two-sample Mendelian randomization (MR) analyses. The estimates were computed using inverse-variance weighted (IVW), simple median, weighted median, and maximum likelihood. MR-Egger regression was used to detect pleiotropy. RESULTS Fixed-effect IVW analysis indicated that genetically predicted Lp(a) levels were associated with CKD significantly (odds ratio, 1.039; 95% CI, 1.009-1.069; p = 0.010). The SNPs showed no pleiotropy according to result of MR-Egger test. Results from sensitivity analyses were consistent. In the inverse MR analysis, random-effect IVW method showed CKD had no causal effect on the elevated Lp(a) (odds ratio, 1.154; 95% CI, 0.845-1.576; p = 0.367). CONCLUSION In this bidirectional two-sample MR analysis, the causal deteriorating effects of genetically predicted plasma Lp(a) levels on the risk of CKD were identified. On the contrary, there is no evidence to support a causal effect of CKD on Lp(a) levels.
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Affiliation(s)
- Yunhui Zhu
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
| | - Songzan Chen
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
| | - Zhebin Chen
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
| | - Yao Wang
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
| | - Guosheng Fu
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
| | - Wenbin Zhang
- Department of Cardiology, Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
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61
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Jurgens SJ, Rämö JT, Kramarenko DR, Wijdeveld LFJM, Haas J, Chaffin MD, Garnier S, Gaziano L, Weng LC, Lipov A, Zheng SL, Henry A, Huffman JE, Challa S, Rühle F, Verdugo CD, Krijger Juárez C, Kany S, van Orsouw CA, Biddinger K, Poel E, Elliott AL, Wang X, Francis C, Ruan R, Koyama S, Beekman L, Zimmerman DS, Deleuze JF, Villard E, Trégouët DA, Isnard R, Boomsma DI, de Geus EJC, Tadros R, Pinto YM, Wilde AAM, Hottenga JJ, Sinisalo J, Niiranen T, Walsh R, Schmidt AF, Choi SH, Chang KM, Tsao PS, Matthews PM, Ware JS, Lumbers RT, van der Crabben S, Laukkanen J, Palotie A, Amin AS, Charron P, Meder B, Ellinor PT, Daly M, Aragam KG, Bezzina CR. Genome-wide association study reveals mechanisms underlying dilated cardiomyopathy and myocardial resilience. Nat Genet 2024; 56:2636-2645. [PMID: 39572784 PMCID: PMC11631763 DOI: 10.1038/s41588-024-01975-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 10/08/2024] [Indexed: 12/06/2024]
Abstract
Dilated cardiomyopathy (DCM) is a heart muscle disease that represents an important cause of morbidity and mortality, yet causal mechanisms remain largely elusive. Here, we perform a large-scale genome-wide association study and multitrait analysis for DCM using 9,365 cases and 946,368 controls. We identify 70 genome-wide significant loci, which show broad replication in independent samples and map to 63 prioritized genes. Tissue, cell type and pathway enrichment analyses highlight the central role of the cardiomyocyte and contractile apparatus in DCM pathogenesis. Polygenic risk scores constructed from our genome-wide association study predict DCM across different ancestry groups, show differing contributions to DCM depending on rare pathogenic variant status and associate with systolic heart failure across various clinical settings. Mendelian randomization analyses reveal actionable potential causes of DCM, including higher bodyweight and higher systolic blood pressure. Our findings provide insights into the genetic architecture and mechanisms underlying DCM and myocardial function more broadly.
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Affiliation(s)
- Sean J Jurgens
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Joel T Rämö
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Daria R Kramarenko
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- European Reference Network for rare low prevalence and complex diseases of the heart: ERN GUARD-Heart, Amsterdam, the Netherlands
| | - Leonoor F J M Wijdeveld
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Physiology, Amsterdam UMC location, Vrije Universiteit, Amsterdam, the Netherlands
| | - Jan Haas
- Department of Medicine III, Institute for Cardiomyopathies Heidelberg (ICH), University Hospital Heidelberg, Heidelberg, Germany
- Site Heidelberg/Mannheim, DZHK, Heidelberg, Germany
| | - Mark D Chaffin
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sophie Garnier
- Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics and Pathophysiology of Cardiovascular Disease, Sorbone Université, INSERM, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - Liam Gaziano
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Alex Lipov
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Sean L Zheng
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Albert Henry
- Institute of Cardiovascular Science, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA, USA
- Harvard Medical School, Boston, MA, USA
| | - Saketh Challa
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Frank Rühle
- Bioinformatics Core Facility, Institute of Molecular Biology gGmbH (IMB), Mainz, Germany
- Department of Genetic Epidemiology, Institute of Human Genetics, University of Münster, Münster, Germany
| | - Carmen Diaz Verdugo
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Krijger Juárez
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Constance A van Orsouw
- Department of Clinical Genetics, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Kiran Biddinger
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Edwin Poel
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Amanda L Elliott
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Harvard Medical School, Boston, MA, USA
- Department of Psychiatry and Center for Genomic Medicine, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Xin Wang
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Catherine Francis
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Richard Ruan
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satoshi Koyama
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Leander Beekman
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Dominic S Zimmerman
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Jean-François Deleuze
- CEA, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, Evry, France
- Laboratory of Excellence in Medical Genomics, GENMED, Evry, France
- Fondation Jean Dausset, Centre d'Etude du Polymorphisme Humain, Paris, France
| | - Eric Villard
- Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics and Pathophysiology of Cardiovascular Disease, Sorbone Université, INSERM, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - David-Alexandre Trégouët
- Laboratory of Excellence in Medical Genomics, GENMED, Evry, France
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, INSERM, Bordeaux, France
| | - Richard Isnard
- Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics and Pathophysiology of Cardiovascular Disease, Sorbone Université, INSERM, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
- APHP, Cardiology and Genetics Departments, Pitié-Salpêtrière Hospital, Paris, France
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC location, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rafik Tadros
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Yigal M Pinto
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- European Reference Network for rare low prevalence and complex diseases of the heart: ERN GUARD-Heart, Amsterdam, the Netherlands
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- European Reference Network for rare low prevalence and complex diseases of the heart: ERN GUARD-Heart, Amsterdam, the Netherlands
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- The Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Juha Sinisalo
- Department of Cardiology, Helsinki University Hospital, Helsinki, Finland
- Heart and Lung Center, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Teemu Niiranen
- Department of Internal Medicine, University of Turku, Helsinki, Finland
- Division of Medicine, Turku University Hospital, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Roddy Walsh
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Amand F Schmidt
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip S Tsao
- Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul M Matthews
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Saskia van der Crabben
- Department of Clinical Genetics, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Jari Laukkanen
- Department of Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Central Finland Biobank, Central Finland Health Care District, Jyväskylä, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ahmad S Amin
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- European Reference Network for rare low prevalence and complex diseases of the heart: ERN GUARD-Heart, Amsterdam, the Netherlands
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
| | - Philippe Charron
- Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics and Pathophysiology of Cardiovascular Disease, Sorbone Université, INSERM, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
- APHP, Cardiology and Genetics Departments, Pitié-Salpêtrière Hospital, Paris, France
| | - Benjamin Meder
- Department of Medicine III, Institute for Cardiomyopathies Heidelberg (ICH), University Hospital Heidelberg, Heidelberg, Germany
- Site Heidelberg/Mannheim, DZHK, Heidelberg, Germany
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
| | - Mark Daly
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
| | - Krishna G Aragam
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
| | - Connie R Bezzina
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands.
- European Reference Network for rare low prevalence and complex diseases of the heart: ERN GUARD-Heart, Amsterdam, the Netherlands.
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Wiegrebe S, Gorski M, Herold JM, Stark KJ, Thorand B, Gieger C, Böger CA, Schödel J, Hartig F, Chen H, Winkler TW, Küchenhoff H, Heid IM. Analyzing longitudinal trait trajectories using GWAS identifies genetic variants for kidney function decline. Nat Commun 2024; 15:10061. [PMID: 39567532 PMCID: PMC11579025 DOI: 10.1038/s41467-024-54483-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 11/11/2024] [Indexed: 11/22/2024] Open
Abstract
Understanding the genetics of kidney function decline, or trait change in general, is hampered by scarce longitudinal data for GWAS (longGWAS) and uncertainty about how to analyze such data. We use longitudinal UK Biobank data for creatinine-based estimated glomerular filtration rate from 348,275 individuals to search for genetic variants associated with eGFR-decline. This search was performed both among 595 variants previously associated with eGFR in cross-sectional GWAS and genome-wide. We use seven statistical approaches to analyze the UK Biobank data and simulated data, finding that a linear mixed model is a powerful approach with unbiased effect estimates which is viable for longGWAS. The linear mixed model identifies 13 independent genetic variants associated with eGFR-decline, including 6 novel variants, and links them to age-dependent eGFR-genetics. We demonstrate that age-dependent and age-independent eGFR-genetics exhibit a differential pattern regarding clinical progression traits and kidney-specific gene expression regulation. Overall, our results provide insights into kidney aging and linear mixed model-based longGWAS generally.
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Affiliation(s)
- Simon Wiegrebe
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
- Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany.
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Janina M Herold
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology, Diabetology, and Rheumatology, Traunstein Hospital, Southeast Bavarian Clinics, Traunstein, Germany
- KfH Kidney Centre Traunstein, Traunstein, Germany
| | - Johannes Schödel
- Department of Nephrology and Hypertension, Uniklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Hartig
- Theoretical Ecology, University of Regensburg, Regensburg, Germany
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Helmut Küchenhoff
- Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
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63
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Cai Y, Lv H, Yuan M, Wang J, Wu W, Fang X, Chen C, Mu J, Liu F, Gu X, Xie H, Liu Y, Xu H, Fan Y, Shen C, Ma X. Genome-wide association analysis of cystatin c and creatinine kidney function in Chinese women. BMC Med Genomics 2024; 17:272. [PMID: 39558362 PMCID: PMC11575226 DOI: 10.1186/s12920-024-02048-6] [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: 08/26/2024] [Accepted: 11/07/2024] [Indexed: 11/20/2024] Open
Abstract
BACKGROUND With increasing incidence and treatment costs, chronic kidney disease (CKD) has become an important public health problem in China, especially in females. However, the genetic determinants are very limited. The estimated glomerular filtration rate (eGFR) based on creatinine is commonly used as a measure of renal function but can be easily affected by other factors. In contrast, eGFR based on both creatinine and cystatin C (eGFRcr-cys) improved the diagnostic accuracy of CKD. To our knowledge, no genome-wide association analysis of eGFRcr-cys has been conducted in the Chinese population. METHODS By conducting a Genome-Wide association study(GWAS), a method used to identify associations between genetic regions (genomes) and traits/diseases, we examined the relationship between genetic factors and eGFRcr-cys in Chinese women, with 1983 participants and 3,838,121 variants included in the final analysis. RESULT One significant locus (20p11.21) was identified in the Chinese female population, which has been reported to be associated with eGFR based on cystatin C (eGFRcys) in the European population. More importantly, we found two new suggestive loci (1p31.1 and 11q24.2), which have not yet been reported. A total of three single nucleotide polymorphisms were identified as the most important variants in these regions, including rs2405367 (CST3), rs66588571(KRT8P21), and rs626995 (OR8B2). CONCLUSION We identified 3 loci 20p11.21, 1p31.1, and 11q24.2 to be significantly associated with eGFRcr-cys. These findings and subsequent functional analysis describe new biological clues related to renal function in Chinese women and provide new ideas for the diagnosis and treatment development of CKD.
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Affiliation(s)
- Yang Cai
- College of Public Health, Southwest Medical University, Luzhou, Sichuan, China
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Hongyao Lv
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Meng Yuan
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jiao Wang
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Wenhui Wu
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaoyu Fang
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Changying Chen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jialing Mu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fangyuan Liu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xincheng Gu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hankun Xie
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yu Liu
- Institute for the prevention and control of chronic non-communicable diseases, Center for Disease Control and Prevention of Jurong City, Jurong, China
| | - Haifeng Xu
- Institute for the prevention and control of chronic non-communicable diseases, Center for Disease Control and Prevention of Jurong City, Jurong, China
| | - Yao Fan
- Department of Clinical Epidemiology, Geriatric Hospital of Nanjing Medical University, Nanjing, China
| | - Chong Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Xiangyu Ma
- College of Public Health, Southwest Medical University, Luzhou, Sichuan, China.
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China.
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64
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Galuška D, Pácal L, Chalásová K, Divácká P, Řehořová J, Svojanovský J, Hubáček JA, Lánská V, Kaňková K. T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects. Gene 2024; 927:148724. [PMID: 38909968 DOI: 10.1016/j.gene.2024.148724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 06/25/2024]
Abstract
This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population.
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Affiliation(s)
- David Galuška
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic; Department of Biochemistry, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
| | - Lukáš Pácal
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Katarína Chalásová
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petra Divácká
- Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic
| | - Jitka Řehořová
- Department of Gastroenterology, University Hospital Brno-Bohunice, Brno, Czech Republic
| | - Jan Svojanovský
- Department of Internal Medicine, St. Anne's University Hospital, Brno, Czech Republic
| | - Jaroslav A Hubáček
- Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; 3rd Department of Internal Medicine, 1(st) Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Věra Lánská
- Department of Data Science, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Kateřina Kaňková
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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Liang X, Liu H, Hu H, Ha E, Zhou J, Abedini A, Sanchez-Navarro A, Klötzer KA, Susztak K. TET2 germline variants promote kidney disease by impairing DNA repair and activating cytosolic nucleotide sensors. Nat Commun 2024; 15:9621. [PMID: 39511169 PMCID: PMC11543665 DOI: 10.1038/s41467-024-53798-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 10/14/2024] [Indexed: 11/15/2024] Open
Abstract
Genome-wide association studies (GWAS) have identified over 800 loci associated with kidney function, yet the specific genes, variants, and pathways involved remain elusive. By integrating kidney function GWAS with human kidney expression and methylation quantitative trait analyses, we identified Ten-Eleven Translocation (TET) DNA demethylase 2 (TET2) as a novel kidney disease risk gene. Utilizing single-cell chromatin accessibility and CRISPR-based genome editing, we highlight GWAS variants that influence TET2 expression in kidney proximal tubule cells. Experiments using kidney/tubule-specific Tet2 knockout mice indicated its protective role in cisplatin-induced acute kidney injury, as well as in chronic kidney disease and fibrosis induced by unilateral ureteral obstruction or adenine diet. Single-cell gene profiling of kidneys from Tet2 knockout mice and TET2-knockdown tubule cells revealed the altered expression of DNA damage repair and chromosome segregation genes, notably including INO80, another kidney function GWAS target gene itself. Remarkably, both TET2-null and INO80-null cells exhibited an increased accumulation of micronuclei after injury, leading to the activation of cytosolic nucleotide sensor cGAS-STING. Genetic deletion of cGAS or STING in kidney tubules, or pharmacological inhibition of STING, protected TET2-null mice from disease development. In conclusion, our findings highlight TET2 and INO80 as key genes in the pathogenesis of kidney diseases, indicating the importance of DNA damage repair mechanisms.
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Affiliation(s)
- Xiujie Liang
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hailong Hu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Eunji Ha
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Jianfu Zhou
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrea Sanchez-Navarro
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Konstantin A Klötzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Penn/CHOP Kidney Innovation Center, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
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Hishida A, Nakatochi M, Sutoh Y, Nakano S, Momozawa Y, Narita A, Tanno K, Shimizu A, Hozawa A, Kinoshita K, Yamaji T, Goto A, Noda M, Sawada N, Ikezaki H, Nagayoshi M, Hara M, Suzuki S, Koyama T, Koriyama C, Katsuura-Kamano S, Kadota A, Kuriki K, Yamamoto M, Sasaki M, Iwasaki M, Matsuo K, Wakai K. GWAS Meta-analysis of Kidney Function Traits in Japanese Populations. J Epidemiol 2024; 34:526-534. [PMID: 38583947 PMCID: PMC11464852 DOI: 10.2188/jea.je20230281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/29/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Genetic epidemiological evidence for the kidney function traits in East Asian populations, including Japanese, remain still relatively unclarified. Especially, the number of genome-wide association studies (GWASs) for kidney traits reported still remains limited, and the sample size of each independent study is relatively small. Given the genetic variability between ancestries/ethnicities, implementation of GWAS with sufficiently large sample sizes in specific population of Japanese is considered meaningful. METHODS We conducted the GWAS meta-analyses of kidney traits by leveraging the GWAS summary data of the representative large genome cohort studies with about 200,000 Japanese participants (n = 202,406 for estimated glomerular filtration rate [eGFR] and n = 200,845 for serum creatinine [SCr]). RESULTS In the present GWAS meta-analysis, we identified 110 loci with 169 variants significantly associated with eGFR (on chromosomes 1-13 and 15-22; P < 5 × 10-8), whereas we also identified 112 loci with 176 variants significantly associated with SCr (on chromosomes 1-22; P < 5 × 10-8), of which one locus (more than 1 Mb distant from known loci) with one variant (CD36 rs146148222 on chromosome 7) for SCr was considered as the truly novel finding. CONCLUSION The present GWAS meta-analysis of the largest genome cohort studies in Japanese subjects provided some original genomic loci associated with kidney function, which may contribute to the possible development of personalized prevention of kidney diseases based on genomic information in the near future.
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Affiliation(s)
- Asahi Hishida
- Department of Public Health, Aichi Medical University School of Medicine, Aichi, Japan
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate, Japan
- Division of Biomedical Information Analysis, Iwate Medical University, Iwate, Japan
| | - Shiori Nakano
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Kozo Tanno
- Division of Clinical Research and Epidemiology, Iwate Medical University, Iwate, Japan
- Department of Hygiene and Preventive Medicine, Iwate Medical University, Iwate, Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate, Japan
- Division of Biomedical Information Analysis, Iwate Medical University, Iwate, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Atsushi Goto
- Department of Public Health, School of Medicine, Yokohama City University, Kanagawa, Japan
| | - Mitsuhiko Noda
- Department of Diabetes, Metabolism and Endocrinology, Ichikawa Hospital, International University of Health and Welfare, Chiba, Japan
- Department of Endocrinology and Diabetes, Saitama Medical University, Saitama, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Graduate School of Medical Sciences, Kyushu University, Kyushu University Hospital, Fukuoka, Japan
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Japan
| | - Mako Nagayoshi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Megumi Hara
- Department of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Aichi, Japan
| | - Teruhide Koyama
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Chihaya Koriyama
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Aya Kadota
- Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Shiga, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Iwate, Japan
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Iwate, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Aichi, Japan
- Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
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Kim S, Koppitch K, Parvez RK, Guo J, Achieng M, Schnell J, Lindström NO, McMahon AP. Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development. Dev Cell 2024; 59:2912-2930.e7. [PMID: 39121855 DOI: 10.1016/j.devcel.2024.07.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 04/02/2024] [Accepted: 07/12/2024] [Indexed: 08/12/2024]
Abstract
The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.
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Affiliation(s)
- Sunghyun Kim
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Riana K Parvez
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jinjin Guo
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - MaryAnne Achieng
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jack Schnell
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Nils O Lindström
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
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68
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Schuurmans IK, Dunn EC, Lussier AA. DNA methylation as a possible mechanism linking childhood adversity and health: results from a 2-sample mendelian randomization study. Am J Epidemiol 2024; 193:1541-1552. [PMID: 38754872 PMCID: PMC11538561 DOI: 10.1093/aje/kwae072] [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: 06/02/2023] [Revised: 03/07/2024] [Accepted: 05/14/2024] [Indexed: 05/18/2024] Open
Abstract
Childhood adversity is an important risk factor for adverse health across the life course. Epigenetic modifications, such as DNA methylation (DNAm), are a hypothesized mechanism linking adversity to disease susceptibility. Yet, few studies have determined whether adversity-related DNAm alterations are causally related to future health outcomes or if their developmental timing plays a role in these relationships. Here, we used 2-sample mendelian randomization to obtain stronger causal inferences about the association between adversity-associated DNAm loci across development (ie, birth, childhood, adolescence, and young adulthood) and 24 mental, physical, and behavioral health outcomes. We identified particularly strong associations between adversity-associated DNAm and attention-deficit/hyperactivity disorder, depression, obsessive-compulsive disorder, suicide attempts, asthma, coronary artery disease, and chronic kidney disease. More of these associations were identified for birth and childhood DNAm, whereas adolescent and young adulthood DNAm were more closely linked to mental health. Childhood DNAm loci also had primarily risk-suppressing relationships with health outcomes, suggesting that DNAm might reflect compensatory or buffering mechanisms against childhood adversity rather than acting solely as an indicator of disease risk. Together, our results suggest adversity-related DNAm alterations are linked to both physical and mental health outcomes, with particularly strong impacts of DNAm differences emerging earlier in development.
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Affiliation(s)
- Isabel K Schuurmans
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, 3000 CA Rotterdam, the Netherlands
| | - Erin C Dunn
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, United States
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, United States
| | - Alexandre A Lussier
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, United States
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, United States
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69
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Tan JHJ, Li Z, Porta MG, Rajaby R, Lim WK, Tan YA, Jimenez RT, Teo R, Hebrard M, Ow JL, Ang S, Jeyakani J, Chong YS, Lim TH, Goh LL, Tham YC, Leong KP, Chin CWL, SG10K_Health Consortium, Davila S, Karnani N, Cheng CY, Chambers J, Tai ES, Liu J, Sim X, Sung WK, Prabhakar S, Tan P, Bertin N. A Catalogue of Structural Variation across Ancestrally Diverse Asian Genomes. Nat Commun 2024; 15:9507. [PMID: 39496583 PMCID: PMC11535549 DOI: 10.1038/s41467-024-53620-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Collaborators] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/14/2024] [Indexed: 11/06/2024] Open
Abstract
Structural variants (SVs) are significant contributors to inter-individual genetic variation associated with traits and diseases. Current SV studies using whole-genome sequencing (WGS) have a largely Eurocentric composition, with little known about SV diversity in other ancestries, particularly from Asia. Here, we present a WGS catalogue of 73,035 SVs from 8392 Singaporeans of East Asian, Southeast Asian and South Asian ancestries, of which ~65% (47,770 SVs) are novel. We show that Asian populations can be stratified by their global SV patterns and identified 42,239 novel SVs that are specific to Asian populations. 52% of these novel SVs are restricted to one of the three major ancestry groups studied (Indian, Chinese or Malay). We uncovered SVs affecting major clinically actionable loci. Lastly, by identifying SVs in linkage disequilibrium with single-nucleotide variants, we demonstrate the utility of our SV catalogue in the fine-mapping of Asian GWAS variants and identification of potential causative variants. These results augment our knowledge of structural variation across human populations, thereby reducing current ancestry biases in global references of genetic variation afflicting equity, diversity and inclusion in genetic research.
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Affiliation(s)
- Joanna Hui Juan Tan
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Zhihui Li
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Mar Gonzalez Porta
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Nalagenetics, Singapore, Singapore
| | - Ramesh Rajaby
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Human Genome Center, University of Tokyo, Bunkyō, Japan
| | - Weng Khong Lim
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Duke-NUS Medical School, Singapore, Singapore
- SingHealth Duke-NUS Genomic Medicine Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Ye An Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rodrigo Toro Jimenez
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Renyi Teo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Maxime Hebrard
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Jack Ling Ow
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Shimin Ang
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Justin Jeyakani
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Yap Seng Chong
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute for Human Development and Potential (IHDP), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Tock Han Lim
- NHG Eye Institute, Tan Tock Seng Hospital, National Healthcare Group, Singapore, Singapore
| | - Liuh Ling Goh
- Personalised Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Khai Pang Leong
- Personalised Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
| | - Calvin Woon Loong Chin
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
- Cardiovascular ACP, Duke-NUS Medical School, Singapore, Singapore
| | | | - Sonia Davila
- SingHealth Duke-NUS Genomic Medicine Centre, Duke-NUS Medical School, Singapore, Singapore
- SingHealth Duke-NUS Institute of Precision medicine, Singapore Health Services, Singapore, Singapore
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore, Singapore
- Translational Medicine, Sidra Medicine, Ar-Rayyan, Qatar
| | - Neerja Karnani
- Human Development, Singapore Institute for Clinical Sciences, Singapore, Singapore
- Clinical Data Engagement, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - John Chambers
- Population and Global Health, Nanyang Technological University, Lee Kong Chian School of Medicine, Singapore, Singapore
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Precision Health Research, Singapore, Singapore
| | - E Shyong Tai
- Duke-NUS Medical School, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Precision Health Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jianjun Liu
- Laboratory of Human Genomics, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Wing Kin Sung
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Hong Kong Genome Institute, Hong Kong, Hong Kong
- Department of Chemical Pathology, Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
| | - Patrick Tan
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Duke-NUS Medical School, Singapore, Singapore.
- Precision Health Research, Singapore, Singapore.
| | - Nicolas Bertin
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.
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Collaborators
Khung Keong Yeo, Stuart Alexander Cook, Chee Jian Pua, Chengxi Yang, Tien Yin Wong, Charumathi Sabanayagam, Lavanya Raghavan, Tin Aung, Miao Ling Chee, Miao Li Chee, Hengtong Li, Jimmy Lee, Eng Sing Lee, Joanne Ngeow, Paul Eillot, Elio Riboli, Hong Kiat Ng, Theresia Mina, Darwin Tay, Nilanjana Sadhu, Pritesh Rajesh Jain, Dorrain Low, Xiaoyan Wang, Jin Fang Chai, Rob M Van Dam, Yik Ying Teo, Chia Wei Lim, Pi Kuang Tsai, Wen Jie Chew, Wey Ching Sim, Li-Xian Grace Toh, Johan Gunnar Eriksson, Peter D Gluckman, Yung Seng Lee, Fabian Yap, Kok Hian Tan,
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Gholipourshahraki T, Bai Z, Shrestha M, Hjelholt A, Hu S, Kjolby M, Rohde PD, Sørensen P. Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases. PLoS Genet 2024; 20:e1011463. [PMID: 39495786 PMCID: PMC11563439 DOI: 10.1371/journal.pgen.1011463] [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: 05/21/2024] [Revised: 11/14/2024] [Accepted: 10/17/2024] [Indexed: 11/06/2024] Open
Abstract
Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.
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Affiliation(s)
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Merina Shrestha
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Astrid Hjelholt
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
| | - Mads Kjolby
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Palle Duun Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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Jones AC, Patki A, Srinivasasainagendra V, Tiwari HK, Armstrong ND, Chaudhary NS, Limdi NA, Hidalgo BA, Davis B, Cimino JJ, Khan A, Kiryluk K, Lange LA, Lange EM, Arnett DK, Young BA, Diamantidis CJ, Franceschini N, Wassertheil-Smoller S, Rich SS, Rotter JI, Mychaleckyj JC, Kramer HJ, Chen YDI, Psaty BM, Brody JA, de Boer IH, Bansal N, Bis JC, Irvin MR. Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations. J Am Soc Nephrol 2024; 35:1558-1569. [PMID: 39073889 PMCID: PMC11543021 DOI: 10.1681/asn.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Key Points The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits. However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations. Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR. Background CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations. Methods This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study. Results The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs. Conclusions In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.
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Affiliation(s)
- Alana C. Jones
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hemant K. Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nicole D. Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ninad S. Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nita A. Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A. Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brittney Davis
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - James J. Cimino
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Leslie A. Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Ethan M. Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Donna K. Arnett
- Office of the Provost, University of South Carolina, Columbia, South Carolina
| | - Bessie A. Young
- Division of Nephrology, University of Washington, Seattle, Washington
| | | | - Nora Franceschini
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Stephen S. Rich
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Josyf C. Mychaleckyj
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Holly J. Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, Illinois
| | - Yii-Der I. Chen
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Bruce M. Psaty
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian H. de Boer
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Marguerite R. Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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Chen HL, Chiang HY, Chang DR, Cheng CF, Wang CCN, Lu TP, Lee CY, Chattopadhyay A, Lin YT, Lin CC, Yu PT, Huang CF, Lin CH, Yeh HC, Ting IW, Tsai HK, Chuang EY, Tin A, Tsai FJ, Kuo CC. Discovery and prioritization of genetic determinants of kidney function in 297,355 individuals from Taiwan and Japan. Nat Commun 2024; 15:9317. [PMID: 39472450 PMCID: PMC11522641 DOI: 10.1038/s41467-024-53516-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/12/2024] [Indexed: 11/02/2024] Open
Abstract
Current genome-wide association studies (GWAS) for kidney function lack ancestral diversity, limiting the applicability to broader populations. The East-Asian population is especially under-represented, despite having the highest global burden of end-stage kidney disease. We conducted a meta-analysis of multiple GWASs (n = 244,952) on estimated glomerular filtration rate and a replication dataset (n = 27,058) from Taiwan and Japan. This study identified 111 lead SNPs in 97 genomic risk loci. Functional enrichment analyses revealed that variants associated with F12 gene and a missense mutation in ABCG2 may contribute to chronic kidney disease (CKD) through influencing inflammation, coagulation, and urate metabolism pathways. In independent cohorts from Taiwan (n = 25,345) and the United Kingdom (n = 260,245), polygenic risk scores (PRSs) for CKD significantly stratified the risk of CKD (p < 0.0001). Further research is required to evaluate the clinical effectiveness of PRSCKD in the early prevention of kidney disease.
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Affiliation(s)
- Hung-Lin Chen
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Department of Biomedical Informatics, College of Medicine, China Medical University, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Department of Biomedical Informatics, College of Medicine, China Medical University, Taichung, Taiwan
| | - David Ray Chang
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chi-Fung Cheng
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Charles C N Wang
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chien-Yueh Lee
- Master Program in Artificial Intelligence, Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei, Taiwan
- Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Amrita Chattopadhyay
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Institute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Ting Lin
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Department of Biomedical Informatics, College of Medicine, China Medical University, Taichung, Taiwan
| | - Che-Chen Lin
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Pei-Tzu Yu
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Chien-Fong Huang
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Chieh-Hua Lin
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Hung-Chieh Yeh
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - I-Wen Ting
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Huai-Kuang Tsai
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Eric Y Chuang
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
- Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Adrienne Tin
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
| | - Fuu-Jen Tsai
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan.
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
- Division of Medical Genetics, China Medical University Children's Hospital, Taichung, Taiwan.
- Department of Medical Laboratory Science & Biotechnology, Asia University, Taichung, Taiwan.
| | - Chin-Chi Kuo
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
- Department of Biomedical Informatics, College of Medicine, China Medical University, Taichung, Taiwan.
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
- College of Medicine, China Medical University, Taichung, Taiwan.
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Chuang GT, Hsiung CN, Che TPH, Chang YC. Discovering Novel Loci of Chronic Kidney Disease via Principal Component Analysis-Based Multiple-Trait Genome-Wide Association Study. Am J Nephrol 2024; 56:198-210. [PMID: 39433025 PMCID: PMC11975323 DOI: 10.1159/000541982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 10/10/2024] [Indexed: 10/23/2024]
Abstract
INTRODUCTION Chronic kidney diseases (CKD) encompass a spectrum of complex pathophysiological processes. While numerous genome-wide association studies (GWASs) have focused on individual traits such as albuminuria, estimated glomerular filtration rate (eGFR), and eGFR change, there remains a paucity of genetic studies integrating these traits collectively for comprehensive evaluation. METHODS In this study, we performed individual GWASs for albuminuria, baseline eGFR, and eGFR slope utilizing data from non-diabetic individuals enrolled from the Taiwan Biobank (TWB). Subsequently, we employed principal component analysis to transform these three quantitative traits into principal components (PCs) and performed GWAS based on these principal components (PC-based GWAS). RESULTS The individual GWAS analyses of albuminuria, baseline eGFR, and eGFR slope identified 10, 13, and 210 candidate loci respectively, with 2, 3, and 99 of them representing previously reported loci. PC-based GWAS identified additional 20 novel candidate loci linked to CKD (p values ranging from 5.8 × 10-7 to 9.1 × 10-6). Notably, 4 of these 20 single nucleotide polymorphisms (rs9332641, rs10737429, rs117231653, and rs73360624) exhibited significant associations with kidney expression quantitative trait loci. CONCLUSION To our knowledge, this study represents the first PC-based GWAS integrating albuminuria, baseline eGFR, and eGFR slope. Our approach found 20 novel candidate loci suggestively associated with CKD, underscoring the value of integrating multiple kidney traits in unraveling the pathophysiology of this complex disorder.
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Affiliation(s)
- Gwo-Tsann Chuang
- Division of Nephrology, Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan,
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan,
| | - Chia-Ni Hsiung
- Program in Precision Medicine, National Tsing Hua University, Hsinchu, Taiwan
- Institute of Molecular Medicine, National Tsing Hua University, Hsinchu, Taiwan
| | - Tony Pan-Hou Che
- Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan
| | - Yi-Cheng Chang
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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74
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Huang Y, Plotnikov D, Wang H, Shi D, Li C, Zhang X, Zhang X, Tang S, Shang X, Hu Y, Yu H, Zhang H, Guggenheim JA, He M. GWAS-by-subtraction reveals an IOP-independent component of primary open angle glaucoma. Nat Commun 2024; 15:8962. [PMID: 39419966 PMCID: PMC11487129 DOI: 10.1038/s41467-024-53331-0] [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: 07/14/2023] [Accepted: 10/09/2024] [Indexed: 10/19/2024] Open
Abstract
The etiology of primary open angle glaucoma is constituted by both intraocular pressure-dependent and intraocular pressure-independent mechanisms. However, GWASs of traits affecting primary open angle glaucoma through mechanisms independent of intraocular pressure remains limited. Here, we address this gap by subtracting the genetic effects of a GWAS for intraocular pressure from a GWAS for primary open angle glaucoma to reveal the genetic contribution to primary open angle glaucoma via intraocular pressure-independent mechanisms. Seventeen independent genome-wide significant SNPs were associated with the intraocular pressure-independent component of primary open angle glaucoma. Of these, 7 are located outside known normal tension glaucoma loci, 11 are located outside known intraocular pressure loci, and 2 are novel primary open angle glaucoma loci. The intraocular pressure-independent genetic component of primary open angle glaucoma is associated with glaucoma endophenotypes, while the intraocular pressure-dependent component is associated with blood pressure and vascular permeability. A genetic risk score for the intraocular pressure-independent component of primary open angle glaucoma is associated with 26 different retinal micro-vascular features, which contrasts with the genetic risk score for the intraocular pressure-dependent component. Increased understanding of these intraocular pressure-dependent and intraocular pressure-independent components provides insights into the pathogenesis of glaucoma.
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Affiliation(s)
- Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK.
| | - Denis Plotnikov
- Central Research Laboratory, Kazan State Medical University, Kazan, Russia
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, UK
| | - Huan Wang
- Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Danli Shi
- Experimental Ophthalmology, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - Cong Li
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Shulin Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Hongyang Zhang
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, 510080, China.
| | | | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Experimental Ophthalmology, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China.
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Li C, Chen J, Chen Y, Zhang C, Yang H, Yu S, Song H, Fu P, Zeng X. The association between patterns of exposure to adverse life events and the risk of chronic kidney disease: a prospective cohort study of 140,997 individuals. Transl Psychiatry 2024; 14:424. [PMID: 39375339 PMCID: PMC11458756 DOI: 10.1038/s41398-024-03114-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 09/20/2024] [Accepted: 09/23/2024] [Indexed: 10/09/2024] Open
Abstract
Exposure to adverse life events is linked to somatic disorders. The study aims to evaluate the association between adverse events at varying life stages and the risk of chronic kidney disease (CKD), a condition affecting about 10% population worldwide. This prospective cohort study included 140,997 participants from the UK Biobank. Using survey items related to childhood maltreatment, adulthood adversity and catastrophic trauma, we performed latent class analysis to summarize five distinct patterns of exposure to adverse life events, namely "low-level exposure", "childhood exposure", "adulthood exposure", "sexual abuse" and "child-to-adulthood exposure". We used Cox proportional hazard regression to evaluate the association of patterns of exposure to adverse life events with CKD, regression-based mediation analysis to decompose the total effect, and gene-environment-wide interaction study (GEWIS) to identify interactions between genetic loci and adverse life events. During a median follow-up of 5.98 years, 2734 cases of incident CKD were identified. Compared with the "low-level exposure" pattern, "child-to-adulthood exposure" was associated with increased risk of CKD (hazard ratio 1.37, 95% CI 1.14 to 1.65). BMI, smoking and hypertension mediated 11.45%, 9.79%, and 4.50% of this total effect, respectively. Other patterns did not show significant results. GEWIS and subsequent analyses indicated that the magnitude of the association between adverse life events and CKD differed according to genetic polymorphisms, and identified potential underlying pathways (e.g., interleukin 1 receptor activity). These findings underscore the importance of incorporating an individual's psychological encounters and genetic profiles into the precision prevention of CKD.
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Affiliation(s)
- Chunyang Li
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Chen
- Central Laboratory, Sichuan Academy of Medical Science and Sichuan Provincial Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yilong Chen
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Chao Zhang
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huazhen Yang
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Shaobin Yu
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huan Song
- Center of Mental Health, West China Hospital, Sichuan University, Chengdu, China
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Ping Fu
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoxi Zeng
- Department of Nephrology and Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
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Lu K, Chiu KY, Chen IC, Lin GC. Identification of GTF2I Polymorphisms as Potential Biomarkers for CKD in the Han Chinese Population : Multicentric Collaborative Cross-Sectional Cohort Study. KIDNEY360 2024; 5:1466-1476. [PMID: 39024039 PMCID: PMC11556913 DOI: 10.34067/kid.0000000000000517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
Key Points Genetic factors are key players in CKD, with two linked single-nucleotide polymorphisms in the GTF2I gene, associated with CKD susceptibility in the Taiwanese population. Individuals with specific GTF2I genotypes (CT/TT for rs117026326 and CT/CC for rs73366469) show higher CKD prevalence and earlier onset. Men with the specific genotypes of rs117026326 and rs73366469 face a heightened CKD risk compared with women, particularly at lower eGFR. Background CKD poses a global health challenge, but its molecular mechanisms are poorly understood. Genetic factors play a critical role, and phenome-wide association studies and genome-wide association studies shed light on CKD's genetic architecture, shared variants, and biological pathways. Methods Using data from the multicenter collaborative precision medicine cohort, we conducted a retrospective prospectively maintained cross-sectional study. Participants with comprehensive information and genotyping data were selected, and genome-wide association study and phenome-wide association study analyses were performed using the curated Taiwan Biobank version 2 array to identify CKD-associated genetic variants and explore their phenotypic associations. Results Among 58,091 volunteers, 8420 participants were enrolled. Individuals with CKD exhibited higher prevalence of metabolic, cardiovascular, autoimmune, and nephritic disorders. Genetic analysis unveiled two closely linked single-nucleotide polymorphisms, rs117026326 and rs73366469, both associated with GTF2I and CKD (r 2 = 0.64). Further examination revealed significant associations between these single-nucleotide polymorphisms and various kidney-related diseases. The CKD group showed a higher proportion of individuals with specific genotypes (CT/TT for rs117026326 and CT/CC for rs73366469), suggesting potential associations with CKD susceptibility (P < 0.001). Furthermore, individuals with these genotypes developed CKD at an earlier age. Multiple logistic regression confirmed the independent association of these genetic variants with CKD. Subgroup analysis based on eGFR demonstrated an increased risk of CKD among carriers of the rs117026326 CT/TT genotypes (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07 to 1.24; P < 0.001; OR, 1.32, 95% CI, 1.04 to 1.66; P = 0.02, respectively) and carriers of the rs73366469 CT/CC genotypes (OR, 1.13; 95% CI, 1.05 to 1.21; P < 0.001; OR, 1.31; 95% CI, 1.08 to 1.58; P = 0.0049, respectively). In addition, men had a higher CKD risk than women at lower eGFR levels (OR, 1.35; 95% CI, 1.13 to 1.61; P < 0.001). Conclusions Our study reveals important links between genetic variants GTF2I and susceptibility to CKD, advancing our understanding of CKD development in the Taiwanese population and suggesting potential for personalized prevention and management strategies. More research is needed to validate and explore these variants in diverse populations.
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Affiliation(s)
- Kevin Lu
- College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
- Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Kun-Yuan Chiu
- Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou, Taiwan
| | - I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Guan-Cheng Lin
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
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77
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Yan Q, Liu M, Xie Y, Lin Y, Fu P, Pu Y, Wang B. Kidney-brain axis in the pathogenesis of cognitive impairment. Neurobiol Dis 2024; 200:106626. [PMID: 39122123 DOI: 10.1016/j.nbd.2024.106626] [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: 06/24/2024] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
The kidney-brain axis is a bidirectional communication network connecting the kidneys and the brain, potentially affected by inflammation, uremic toxin, vascular injury, neuronal degeneration, and so on, leading to a range of diseases. Numerous studies emphasize the disruptions of the kidney-brain axis may contribute to the high morbidity of neurological disorders, such as cognitive impairment (CI) in the natural course of chronic kidney disease (CKD). Although the pathophysiology of the kidney-brain axis has not been fully elucidated, epidemiological data indicate that patients at all stages of CKD have a higher risk of developing CI compared with the general population. In contrast to other reviews, we mentioned some commonly used medicines in CKD that may play a pivotal role in the pathogenesis of CI. Revealing the pathophysiology interactions between kidney damage and brain function can reduce the potential risk of future CI. This review will deeply explore the characteristics, indicators, and potential pathophysiological mechanisms of CKD-related CI. It will provide a theoretical basis for identifying CI that progresses during CKD and ultimately prevents and treats CKD-related CI.
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Affiliation(s)
- Qianqian Yan
- Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Mengyuan Liu
- Department of Anesthesiology, Air Force Hospital of Western Theater Command, PLA, Chengdu 610011, China
| | - Yiling Xie
- Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yimi Lin
- Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Ping Fu
- Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yaoyu Pu
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University, Chengdu 610041, China.
| | - Bo Wang
- Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu 610041, China.
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McCallum L, Lip S, McConnachie A, Brooksbank K, MacIntyre IM, Doney A, Llano A, Aman A, Caparrotta TM, Ingram G, Mackenzie IS, Dominiczak AF, MacDonald TM, Webb DJ, Padmanabhan S. UMOD Genotype-Blinded Trial of Ambulatory Blood Pressure Response to Torasemide. Hypertension 2024; 81:2049-2059. [PMID: 39077768 PMCID: PMC11460757 DOI: 10.1161/hypertensionaha.124.23122] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/19/2024] [Indexed: 07/31/2024]
Abstract
BACKGROUND UMOD (uromodulin) has been linked to hypertension through potential activation of Na+-K+-2Cl- cotransporter (NKCC2), a target of loop diuretics. We posited that hypertensive patients carrying the rs13333226-AA UMOD genotype would demonstrate greater blood pressure responses to loop diuretics, potentially mediated by this UMOD/NKCC2 interaction. METHODS This prospective, multicenter, genotype-blinded trial evaluated torasemide (torsemide) efficacy on systolic blood pressure (SBP) reduction over 16 weeks in nondiabetic, hypertensive participants uncontrolled on ≥1 nondiuretic antihypertensive for >3 months. The primary end point was the change in 24-hour ambulatory SBP (ABPM SBP) and SBP response trajectories between baseline and 16 weeks by genotype (AA versus AG/GG) due to nonrandomized groups at baseline (ClinicalTrials.gov: NCT03354897). RESULTS Of 251 enrolled participants, 222 received torasemide and 174 demonstrated satisfactory treatment adherence and had genotype data. The study participants were middle-aged (59±11 years), predominantly male (62%), obese (body mass index, 32±7 kg/m2), with normal eGFR (92±17 mL/min/1.73 m²) and an average baseline ABPM of 138/81 mm Hg. Significant reductions in mean ABPM SBP were observed in both groups after 16 weeks (AA, -6.57 mm Hg [95% CI, -8.44 to -4.69]; P<0.0001; AG/GG, -3.22 [95% CI, -5.93 to -0.51]; P=0.021). The change in mean ABPM SBP (baseline to 16 weeks) showed a difference of -3.35 mm Hg ([95% CI, -6.64 to -0.05]; P=0.048) AA versus AG/GG genotypes. The AG/GG group displayed a rebound in SBP from 8 weeks, differing from the consistent decrease in the AA group (P=0.004 for difference in trajectories). CONCLUSIONS Our results confirm a plausible interaction between UMOD and NKCC2 and suggest a potential role for genotype-guided use of loop diuretics in hypertension management. REGISTRATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT03354897.
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Affiliation(s)
- Linsay McCallum
- Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom (L.M.C., S.L., A.L., G.I., S.P.)
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
| | - Stefanie Lip
- Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom (L.M.C., S.L., A.L., G.I., S.P.)
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
| | - Alex McConnachie
- Robertson Centre for Biostatistics, School of Health and Wellbeing (A.M.C.), University of Glasgow, Scotland, United Kingdom
| | - Katriona Brooksbank
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
| | - Iain M. MacIntyre
- Clinical Pharmacology Unit and Research Centre, University of Edinburgh/BHF Centre of Research Excellence, United Kingdom (I.M.I., T.M.C., D.J.W.)
| | - Alexander Doney
- MEMO Research, University of Dundee, Ninewells Hospital and Medical School, United Kingdom (A.D., I.S.M., T.M.M.D.)
| | - Andrea Llano
- Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom (L.M.C., S.L., A.L., G.I., S.P.)
| | - Alisha Aman
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
| | - Thomas M. Caparrotta
- Clinical Pharmacology Unit and Research Centre, University of Edinburgh/BHF Centre of Research Excellence, United Kingdom (I.M.I., T.M.C., D.J.W.)
| | - Gareth Ingram
- Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom (L.M.C., S.L., A.L., G.I., S.P.)
| | - Isla S. Mackenzie
- MEMO Research, University of Dundee, Ninewells Hospital and Medical School, United Kingdom (A.D., I.S.M., T.M.M.D.)
| | - Anna F. Dominiczak
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
| | - Thomas M. MacDonald
- MEMO Research, University of Dundee, Ninewells Hospital and Medical School, United Kingdom (A.D., I.S.M., T.M.M.D.)
| | - David J. Webb
- Clinical Pharmacology Unit and Research Centre, University of Edinburgh/BHF Centre of Research Excellence, United Kingdom (I.M.I., T.M.C., D.J.W.)
| | - Sandosh Padmanabhan
- Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom (L.M.C., S.L., A.L., G.I., S.P.)
- School of Cardiovascular and Metabolic Health (L.M.C., S.L., K.B., A.A., A.F.D., S.P.), University of Glasgow, Scotland, United Kingdom
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79
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Thompson AS, Gaggl M, Bondonno NP, Jennings A, O'Neill JK, Hill C, Karavasiloglou N, Rohrmann S, Cassidy A, Kühn T. Adherence to a healthful plant-based diet and risk of mortality among individuals with chronic kidney disease: A prospective cohort study. Clin Nutr 2024; 43:2448-2457. [PMID: 39305755 DOI: 10.1016/j.clnu.2024.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/29/2024] [Accepted: 09/05/2024] [Indexed: 10/01/2024]
Abstract
BACKGROUND Plant-rich dietary patterns may protect against negative health outcomes among individuals with chronic kidney disease (CKD), although aspects of plant-based diet quality have not been studied. This study aimed to examine associations between healthful and unhealthful plant-based dietary patterns with risk of all-cause mortality among CKD patients for the first time. METHODS This prospective analysis included 4807 UK Biobank participants with CKD at baseline. We examined associations of adherence to both the healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI), calculated from repeated 24-h dietary assessments, with risk of all-cause mortality using multivariable Cox proportional hazard regression models. RESULTS Over a 10-year follow-up, 675 deaths were recorded. Participants with the highest hPDI scores showed a 34% lower risk of mortality [HRQ4vsQ1 (95% CI): 0.66 (0.52-0.83), ptrend <0.001]. Those with the highest uPDI scores had a 52% [1.52 (1.20-1.93), ptrend = 0.002] higher risk of mortality compared to participants with the lowest respective scores. In food group-specific analyses, higher wholegrain intakes were associated with a 29% lower mortality risk, while intakes of refined grains, and sugar-sweetened beverages were associated a 30% and 34% higher risk, respectively. CONCLUSIONS In CKD patients, a higher intake of healthy plant-based foods was associated with a lower risk of mortality, while a higher intake of less healthy plant-based foods was associated with a higher risk of mortality. These results underscore the importance of plant food quality and support the potential role of healthy plant food consumption in the treatment and management of CKD to mitigate unfavourable outcomes.
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Affiliation(s)
- Alysha S Thompson
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Martina Gaggl
- Medical University of Vienna, Center for Public Health, Public Health Nutrition, Vienna, Austria
| | - Nicola P Bondonno
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom; Danish Cancer Institute, Copenhagen, Denmark; Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Amy Jennings
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Joshua K O'Neill
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Claire Hill
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Nena Karavasiloglou
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland; Cancer Registry of the Cantons Zurich, Zug, Schaffhausen and Schwyz, Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland; European Food Safety Authority, Parma, Italy
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland; Cancer Registry of the Cantons Zurich, Zug, Schaffhausen and Schwyz, Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Aedín Cassidy
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom.
| | - Tilman Kühn
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom; Medical University of Vienna, Center for Public Health, Public Health Nutrition, Vienna, Austria; University of Vienna, Department of Nutritional Sciences, Vienna, Austria.
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80
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Loeb GB, Kathail P, Shuai RW, Chung R, Grona RJ, Peddada S, Sevim V, Federman S, Mader K, Chu AY, Davitte J, Du J, Gupta AR, Ye CJ, Shafer S, Przybyla L, Rapiteanu R, Ioannidis NM, Reiter JF. Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function. Nat Genet 2024; 56:2078-2092. [PMID: 39256582 DOI: 10.1038/s41588-024-01904-6] [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: 07/18/2023] [Accepted: 08/12/2024] [Indexed: 09/12/2024]
Abstract
Kidney failure, the decrease of kidney function below a threshold necessary to support life, is a major cause of morbidity and mortality. We performed a genome-wide association study (GWAS) of 406,504 individuals in the UK Biobank, identifying 430 loci affecting kidney function in middle-aged adults. To investigate the cell types affected by these loci, we integrated the GWAS with human kidney candidate cis-regulatory elements (cCREs) identified using single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq). Overall, 56% of kidney function heritability localized to kidney tubule epithelial cCREs and an additional 7% to kidney podocyte cCREs. Thus, most heritable differences in adult kidney function are a result of altered gene expression in these two cell types. Using enhancer assays, allele-specific scATAC-seq and machine learning, we found that many kidney function variants alter tubule epithelial cCRE chromatin accessibility and function. Using CRISPRi, we determined which genes some of these cCREs regulate, implicating NDRG1, CCNB1 and STC1 in human kidney function.
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Affiliation(s)
- Gabriel B Loeb
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Pooja Kathail
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Richard W Shuai
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Ryan Chung
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Reinier J Grona
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sailaja Peddada
- Laboratory for Genomics Research, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Volkan Sevim
- Laboratory for Genomics Research, San Francisco, CA, USA
- Target Discovery, GSK, San Francisco, CA, USA
| | - Scot Federman
- Laboratory for Genomics Research, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Karl Mader
- Laboratory for Genomics Research, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Audrey Y Chu
- Human Genetics and Genomics, GSK, Cambridge, MA, USA
| | | | - Juan Du
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander R Gupta
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine; Bakar Computational Health Sciences Institute; Parker Institute for Cancer Immunotherapy; Institute for Human Genetics; Department of Epidemiology & Biostatistics; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - Shawn Shafer
- Laboratory for Genomics Research, San Francisco, CA, USA
- Target Discovery, GSK, San Francisco, CA, USA
| | - Laralynne Przybyla
- Laboratory for Genomics Research, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Radu Rapiteanu
- Genome Biology, Research Technologies, GSK, Stevenage, UK
| | - Nilah M Ioannidis
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jeremy F Reiter
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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81
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Zhao P, Li Z, Xue S, Cui J, Zhan Y, Zhu Z, Zhang X. Proteome-wide mendelian randomization identifies novel therapeutic targets for chronic kidney disease. Sci Rep 2024; 14:22114. [PMID: 39333727 PMCID: PMC11437114 DOI: 10.1038/s41598-024-72970-3] [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: 09/19/2023] [Accepted: 09/12/2024] [Indexed: 09/29/2024] Open
Abstract
There is an urgent need to pinpoint novel targets for drug discovery in the context of chronic kidney disease (CKD), and the proteome represents a significant pool of potential therapeutic targets. To address this, we performed proteome-wide analyses using Mendelian randomization (MR) and colocalization techniques to uncover potential targets for CKD. We extracted summary-level data from the ARIC study, focusing on 7213 European American (EA) individuals and 4657 plasma proteins. To broaden our analysis, we incorporated genetic association data from Icelandic cohorts, thereby enhancing our investigation into the correlations with chronic kidney disease (CKD), creatinine-based estimated glomerular filtration rate (eGFRcrea), and estimated glomerular filtration rate (eGFR). We utilized genetic association data from the GWAS Catalog, including CKD (765,348, 625,219 European ancestry and 140,129 non-European ancestry), eGFRcrea (1,004,040, European ancestry), and eGFR (567,460, European ancestry). Employing MR analysis, we estimated the associations between proteins and CKD risk. Additionally, we conducted colocalization analysis to evaluate the existence of shared causal variants between the identified proteins and CKD. We detected notable correlations between levels predicted based on genetics of three circulating proteins and CKD, eGFRcrea, and eGFR. Notably, our colocalization analysis provided robust evidence supporting these associations. Specifically, genetically predicted levels of Transcription elongation factor A protein 2 (TCEA2) and Neuregulin-4 (NRG4) exhibited an inverse relationship with CKD risk, while Glucokinase regulatory protein (GCKR) showed an increased risk of CKD. Furthermore, our colocalization analysis also supported the associations of TCEA2, NRG4, and GCKR with the risk of eGFRcrea and eGFR.
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Affiliation(s)
- Pin Zhao
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China
| | - Zhenhao Li
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China
| | - Shilong Xue
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China
| | - Jinshan Cui
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China
| | - Yonghao Zhan
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China
| | - Zhaowei Zhu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China.
| | - Xuepei Zhang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, District of Erqi, Zhengzhou, 450052, Henan, People's Republic of China.
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82
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Fernandes Silva L, Vangipurapu J, Oravilahti A, Laakso M. Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort. Int J Mol Sci 2024; 25:10044. [PMID: 39337529 PMCID: PMC11432478 DOI: 10.3390/ijms251810044] [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: 08/24/2024] [Revised: 09/11/2024] [Accepted: 09/14/2024] [Indexed: 09/30/2024] Open
Abstract
Identification of the individuals having impaired kidney function is essential in preventing the complications of this disease. We measured 1009 metabolites at the baseline study in 10,159 Finnish men of the METSIM cohort and associated the metabolites with an estimated glomerular filtration rate (eGFR). A total of 7090 men participated in the 12-year follow-up study. Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Morrisville, NC, USA) on EDTA plasma samples obtained after overnight fasting. We applied liquid chromatography mass spectrometry (LC-MS/MS) to identify the metabolites (the Metabolon DiscoveryHD4 platform). We performed association analyses between the eGFR and metabolites using linear regression adjusted for confounding factors. We found 108 metabolites significantly associated with a decrease in eGFR, and 28 of them were novel, including 12 amino acids, 8 xenobiotics, 5 lipids, 1 nucleotide, 1 peptide, and 1 partially characterized molecule. The most significant associations were with five amino acids, N-acetylmethionine, N-acetylvaline, gamma-carboxyglutamate, 3-methylglutaryl-carnitine, and pro-line. We identified 28 novel metabolites associated with decreased eGFR in the 12-year follow-up study of the METSIM cohort. These findings provide novel insights into the role of metabolites and metabolic pathways involved in the decline of kidney function.
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Affiliation(s)
- Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland; (L.F.S.); (J.V.); (A.O.)
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland; (L.F.S.); (J.V.); (A.O.)
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland; (L.F.S.); (J.V.); (A.O.)
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland; (L.F.S.); (J.V.); (A.O.)
- Department of Medicine, Kuopio University Hospital, 70200 Kuopio, Finland
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83
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Liu B, Gao X, Teng H, Zhou H, Gao B, Li F. Association between GATM gene polymorphism and progression of chronic kidney disease: a mitochondrial related genome-wide Mendelian randomization study. Sci Rep 2024; 14:20346. [PMID: 39284843 PMCID: PMC11405879 DOI: 10.1038/s41598-024-68448-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/23/2024] [Indexed: 09/22/2024] Open
Abstract
Chronic Kidney Disease (CKD) stands as a substantial challenge within the global health landscape. The elevated metabolic demands essential for sustaining normal kidney function have propelled an increasing interest in unraveling the intricate relationship between mitochondrial dysfunction and CKD. However, the authentic causal relationship between these two factors remains to be conclusively elucidated. This study endeavors to address this knowledge gap through the Mendelian Randomization (MR) method. We utilized large-scale QTL datasets (including 31,684 eQTLs samples, 1980 mQTLs samples, and 35,559 pQTLs samples) to precisely identify key genes related to mitochondrial function as exposure factors. Subsequently, we employed GWAS datasets (comprising 480,698 CKD samples and 1,004,040 eGFRcrea samples) as outcome factors. Through a comprehensive multi-level analysis (encompassing expression, methylation, and protein quantification loci), we evaluated the causal impact of these genes on CKD and estimated glomerular filtration rate (eGFR). The integration and validation of diverse genetic data, complemented by the application of co-localization analysis, bi-directional MR analysis, and various MR methods, notably including inverse variance weighted, have collectively strengthened our confidence in the robustness of these findings. Lastly, we validate the outcomes through examination in human RNA sequencing datasets encompassing various subtypes of CKD. This study unveils significant associations between the glycine amidinotransferase (GATM) and CKD, as well as eGFR. Notably, an augmentation in GATM gene and protein expression corresponds to a diminished risk of CKD, whereas distinct methylation patterns imply an increased risk. Furthermore, a discernible reduction in GATM expression is observed across diverse pathological subtypes of CKD, exhibiting a noteworthy positive correlation with GFR. These findings establish a causal relationship between GATM and CKD, thereby highlighting its potential as a therapeutic target. This insight lays the foundation for the development of potential therapeutic interventions for CKD, presenting substantial clinical promise.
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Affiliation(s)
- Bin Liu
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China
| | - Xin Gao
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China
| | - Haolin Teng
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China
| | - Honglan Zhou
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China
| | - Baoshan Gao
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China
| | - Faping Li
- Department of Urology II, The First Hospital of Jilin University, Changchun, 130021, Jilin, China.
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84
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Zhang X, Jiang Z, Ma J, Qi Y, Li Y, Zhang Y, Liu Y, Wei C, Chen Y, Liu P, Peng Y, Tan J, Han Y, Zeng S, Cai C, Shen H. Leveraging large-scale genetic data to assess the causal impact of COVID-19 on multisystemic diseases. JOURNAL OF BIG DATA 2024; 11:129. [DOI: 10.1186/s40537-024-00997-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 09/02/2024] [Indexed: 01/02/2025]
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85
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Ahmad S, Imtiaz MA, Mishra A, Wang R, Herrera-Rivero M, Bis JC, Fornage M, Roshchupkin G, Hofer E, Logue M, Longstreth WT, Xia R, Bouteloup V, Mosley T, Launer LJ, Khalil M, Kuhle J, Rissman RA, Chene G, Dufouil C, Djoussé L, Lyons MJ, Mukamal KJ, Kremen WS, Franz CE, Schmidt R, Debette S, Breteler MMB, Berger K, Yang Q, Seshadri S, Aziz NA, Ghanbari M, Ikram MA. Genome-wide association study meta-analysis of neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration. Commun Biol 2024; 7:1103. [PMID: 39251807 PMCID: PMC11385583 DOI: 10.1038/s42003-024-06804-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 08/29/2024] [Indexed: 09/11/2024] Open
Abstract
Neurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels could provide new insights into molecular mechanisms underlying neurodegenerative disorders. In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 (SLC39A11). We observe association of three loci at 1q43 (FMN2), 12q14, and 12q21 with blood NfL levels in the meta-analysis of African-American ancestry. In the trans-ethnic meta-analysis, we identify three additional genome-wide significant loci at 1p32 (FGGY), 6q14 (TBX18), and 4q21. In the post-GWAS analyses, we observe the association of higher NfL polygenic risk score with increased plasma levels of total-tau, Aβ-40, Aβ-42, and higher incidence of Alzheimer's disease in the Rotterdam Study. Furthermore, Mendelian randomization analysis results suggest that a lower kidney function could cause higher blood NfL levels. This study uncovers multiple genetic loci of blood NfL levels, highlighting the genes related to molecular mechanism of neurodegeneration.
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Affiliation(s)
- Shahzad Ahmad
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands
- Oxford-GSK Institute of Computational and Molecular Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine (NDM), University of Oxford, Oxford, OX3 7BN, UK
| | - Mohammad Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Ruiqi Wang
- Boston University, Boston, MA, 02215, USA
| | - Marisol Herrera-Rivero
- Department of Genetic Epidemiology, Institute of Human Genetics, University of Münster, Münster, Germany
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave #1360, Seattle, WA, 98101, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street Houston, Houston, 77030, TX, USA
| | - Gennady Roshchupkin
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, Fifth Floor, Graz, 8036, Austria
| | - Mark Logue
- National Center for PTSD, Behavioral Sciences Division at VA Boston Healthcare System, Boston, 150 South Huntington Avenue, Boston, MA, 02130, USA
- Department of Psychiatry and Biomedical Genetics, Boston University School of Medicine, Boston, 72 East Concord Street E200, Boston, MA, 02118, USA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, 3980 15th Ave NE Seattle, Seattle, WA, 98195, USA
| | - Rui Xia
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street Houston, Houston, 77030, TX, USA
| | - Vincent Bouteloup
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Thomas Mosley
- MIND Center, University of Mississippi Medical Center, Jackson, 2500 North State Street, Jackson, MS, 39216, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, NIA Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Michael Khalil
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Jens Kuhle
- Research Center for Clinical Neuroimmunology and Neuroscience University Hospital, Spitalstrasse 2, CH-4031, Basel, Switzerland
| | - Robert A Rissman
- Department of Physiology and Neuroscience, Alzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, California, USA
| | - Genevieve Chene
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Carole Dufouil
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Luc Djoussé
- Brigham and Women's Hospital, Harvard Medical School, Boston, 75 FRANCIS STREET, BOSTON MA 02115, MA, Boston, USA
| | - Michael J Lyons
- Department of Psychological & Brain Sciences, Boston University, Boston, 64 Cummington Mall # 149, Boston, MA, 02215, USA
| | - Kenneth J Mukamal
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, 330 Brookline Avenue Boston, MA, 02215, USA
| | - William S Kremen
- Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Carol E Franz
- Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
- CHU de Bordeaux, Department of Neurology, Institute for Neurodegenerative Diseases, F-33000, Bordeaux, France
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Institut für Epidemiologie und Sozialmedizin Albert-Schweitzer-Campus 1, Gebäude D3 48149, Münster, Germany
| | - Qiong Yang
- Boston University, Boston, MA, 02215, USA
| | - Sudha Seshadri
- Boston University, Boston, MA, 02215, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - N Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, 53127, Bonn, Germany
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands.
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86
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Hou Y, Li Y, Xiao Z, Wang Z. Causal effects of obstructive sleep apnea on chronic kidney disease and renal function: a bidirectional Mendelian randomization study. Front Neurol 2024; 15:1323928. [PMID: 39296957 PMCID: PMC11408330 DOI: 10.3389/fneur.2024.1323928] [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: 11/24/2023] [Accepted: 08/23/2024] [Indexed: 09/21/2024] Open
Abstract
Background Observational studies have suggested an association between obstructive sleep apnea (OSA), chronic kidney disease (CKD), and renal function, and vice versa. However, the results from these studies are inconsistent. It remains unclear whether there are causal relationships and in which direction they might exist. Methods We used a two-sample Mendelian randomization (MR) method to investigate the bidirectional causal relation between OSA and 7 renal function phenotypes [creatinine-based estimated glomerular filtration rate (eGFRcrea), cystatin C-based estimated glomerular filtration rate (eGFRcys), blood urea nitrogen (BUN), rapid progress to CKD, rapid decline of eGFR, urinary albumin to creatinine ratio (UACR) and CKD]. The genome-wide association study (GWAS) summary statistics of OSA were retrieved from FinnGen Consortium. The CKDGen consortium and UK Biobank provided GWAS summary data for renal function phenotypes. Participants in the GWAS were predominantly of European ancestry. Five MR methods, including inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were used to investigate the causal relationship. The IVW result was considered the primary outcome. Then, Cochran's Q test and MR-Egger were used to detect heterogeneity and pleiotropy. The leave-one-out analysis was used for testing the stability of MR results. RadialMR was used to identify outliers. Bonferroni correction was applied to test the strength of the causal relationships (p < 3.571 × 10-3). Results We failed to find any significant causal effect of OSA on renal function phenotypes. Conversely, when we examined the effects of renal function phenotypes on OSA, after removing outliers, we found a significant association between BUN and OSA using IVW method (OR: 2.079, 95% CI: 1.516-2.853; p = 5.72 × 10-6). Conclusion This MR study found no causal effect of OSA on renal function in Europeans. However, genetically predicted increased BUN is associated with OSA development. These findings indicate that the relationship between OSA and renal function remains elusive and requires further investigation.
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Affiliation(s)
- Yawei Hou
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yameng Li
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenwei Xiao
- Department of Nephrology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhenguo Wang
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
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87
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Hsi RS, Zhang S, Triozzi JL, Hung AM, Xu Y, Bejan CA. Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease. EUR UROL SUPPL 2024; 67:38-44. [PMID: 39156495 PMCID: PMC11327546 DOI: 10.1016/j.euros.2024.07.109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2024] [Indexed: 08/20/2024] Open
Abstract
Background and objective Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system. Methods We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures. Key findings and limitations The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the UMOD gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23, p = 2.7 × 10-9). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; NXPH1, rs79970906 and rs4725104). The UMOD single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p < 0.05). No associations were found between UMOD variants and disease severity. Limitations include an omitted variable bias and a misclassification bias. Conclusions and clinical implications We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-h urine parameters and stone composition, but not disease severity. Patient summary We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.
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Affiliation(s)
- Ryan S. Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jefferson L. Triozzi
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
| | - Cosmin A. Bejan
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
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88
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Ge YM, Peng SL, Wang Q, Yuan J. Causality between Celiac disease and kidney disease: A Mendelian Randomization Study. Medicine (Baltimore) 2024; 103:e39465. [PMID: 39213254 PMCID: PMC11365674 DOI: 10.1097/md.0000000000039465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/03/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024] Open
Abstract
Celiac disease, characterized as an autoimmune disorder, possesses the capacity to affect multiple organs and systems. Earlier research has indicated an increased risk of kidney diseases associated with celiac disease. However, the potential causal relationship between genetic susceptibility to celiac disease and the risk of kidney diseases remains uncertain. We conducted Mendelian randomization analysis using nonoverlapping European population data, examining the link between celiac disease and 10 kidney traits in whole-genome association studies. We employed the inverse variance-weighted method to enhance statistical robustness, and results' reliability was reinforced through rigorous sensitivity analysis. Mendelian randomization analysis revealed a genetic susceptibility of celiac disease to an increased risk of immunoglobulin A nephropathy (OR = 1.44; 95% confidence interval [CI] = 1.17-1.78; P = 5.7 × 10-4), chronic glomerulonephritis (OR = 1.15; 95% CI = 1.08-1.22; P = 2.58 × 10-5), and a decline in estimated glomerular filtration rate (beta = -0.001; P = 2.99 × 10-4). Additionally, a potential positive trend in the causal relationship between celiac disease and membranous nephropathy (OR = 1.37; 95% CI = 1.08-1.74; P = 0.01) was observed. Sensitivity analysis indicated the absence of pleiotropy. This study contributes novel evidence establishing a causal link between celiac disease and kidney traits, indicating a potential association between celiac disease and an elevated risk of kidney diseases. The findings provide fresh perspectives for advancing mechanistic and clinical research into kidney diseases associated with celiac disease.
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Affiliation(s)
- Ya-mei Ge
- Clinical College of Chinese Medicine, Hubei University of Chinese Medicine, Hubei, Wuhan, China
| | - Shuang-li Peng
- Department of Traditional Chinese Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, China
| | - Qiong Wang
- Clinical College of Chinese Medicine, Hubei University of Chinese Medicine, Hubei, Wuhan, China
| | - Jun Yuan
- Department of Nephrology, Renmin Hospital of Wuhan University, Hubei, Wuhan, China
- First Clinical College, Hubei University of Chinese Medicine, Hubei, Wuhan, China
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89
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Zhao H, Yuan H, Wang E. Causal Effects of Kidney Function and Chronic Kidney Disease on Alzheimer's Disease by Analyzing Large-Scale Genome-Wide Association Study Datasets. J Alzheimers Dis 2024:JAD240807. [PMID: 39213079 DOI: 10.3233/jad-240807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Background Alzheimer's disease (AD) is the leading cause of dementia. Genetic components play an important role in AD and have been widely evaluated by genome-wide association studies (GWAS) and exome sequencing, and some common and rare genetic variants have been identified. In addition to genetic factors, environment factors have a role in AD. Growing evidence from observational studies linked impaired kidney function to cognitive impairment and AD; however, there are inconsistences in these findings. Objective To determine the causal effects of impaired kidney function and chronic kidney disease (CKD) on AD. Methods Mendelian randomization (MR) methods have been widely used to infer causal associations between exposure and outcome. Here, we conducted an MR study to investigate the causal effects of impaired kidney function and CKD on the risk of AD by analyzing large-scale GWAS datasets from FinnGen and CKD Genetics (CKDGen) Consortium. Results We found no significant but a suggestive effect of CKD on decreased risk of AD using inverse-variance weighted (IVW) (p = 8.46E-02) and simple mode (p = 7.60E-02) methods. We identified a statistically significant effect of the estimated glomerular filtration rate (eGFR) on increased risk of AD using IVW (p = 1.11E-02), weighted median regression (p = 5.60E-03), and weighted mode (p = 2.45E-02) methods. Conclusions Together, our findings indicate that high eGFR levels may increase the risk of AD. These findings need to be verified in future studies.
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Affiliation(s)
- Hainan Zhao
- Department of Nephrology, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
| | - Hongxia Yuan
- Department of Nephrology, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
| | - Ermin Wang
- Department of Nephrology, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
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90
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Liu G. Assessment of the effect of the SLC5A2 gene on eGFR: a Mendelian randomization study of drug targets for the nephroprotective effect of sodium-glucose cotransporter protein 2 inhibition. Front Endocrinol (Lausanne) 2024; 15:1418575. [PMID: 39268232 PMCID: PMC11390543 DOI: 10.3389/fendo.2024.1418575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 08/14/2024] [Indexed: 09/15/2024] Open
Abstract
Aim Sodium-glucose cotransporter protein 2 (SGLT2) inhibitors have been shown to have renoprotective effects in clinical studies. For further validation in terms of genetic variation, drug-targeted Mendelian randomization (MR) was used to investigate the causal role of SGLT2 inhibition on eGFR effects. Methods Genetic variants representing SGLT2 inhibition were selected as instrumental variables. Drug target Mendelian randomization analysis was used to investigate the relationship between SGLT2 inhibitors and eGFR. The IVW method was used as the primary analysis method. As a sensitivity analysis, GWAS pooled data from another CKDGen consortium was used to validate the findings. Results MR results showed that hemoglobin A1c (HbA1c) levels, regulated by the SLC5A2 gene, were negatively correlated with eGFR (IVW β -0.038, 95% CI -0.061 to -0.015, P = 0.001 for multi-ancestry populations; IVW β -0.053, 95% CI -0.077 to -0.028, P = 2.45E-05 for populations of European ancestry). This suggests that a 1-SD increase in HbA1c levels, regulated by the SLC5A2 gene, is associated with decreased eGFR. Mimicking pharmacological inhibition by lowering HbA1c per 1-SD unit through SGLT2 inhibition reduces the risk of eGFR decline, demonstrating a renoprotective effect of SGLT2 inhibitors. HbA1c, regulated by the SLC5A2 gene, was negatively correlated with eGFR in both validation datasets (IVW β -0.027, 95% CI -0.046 to -0.007, P=0.007 for multi-ancestry populations, and IVW β -0.031, 95% CI -0.050 to -0.011, P=0.002 for populations of European origin). Conclusions The results of this study indicate that the SLC5A2 gene is causally associated with eGFR. Inhibition of SLC5A2 gene expression was linked to higher eGFR. The renoprotective mechanism of SGLT2 inhibitors was verified from the perspective of genetic variation.
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Affiliation(s)
- Gailing Liu
- Department of Nephrology, People’s Hospital of Zhengzhou University, He’nan Provincial People’s Hospital, He’nan Provincial Key Laboratory of Kidney Disease and Immunology, Zhengzhou, China
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91
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Vivante A. Genetics of Chronic Kidney Disease. N Engl J Med 2024; 391:627-639. [PMID: 39141855 DOI: 10.1056/nejmra2308577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Affiliation(s)
- Asaf Vivante
- From the Department of Pediatrics and the Pediatric Nephrology Unit, Edmond and Lily Safra Children's Hospital, and the Nephro-Genetics Clinic and Genetic Kidney Disease Research Laboratory, Sheba Medical Center, Tel Hashomer, and the Faculty of Medicine, Tel Aviv University, Tel Aviv - all in Israel
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92
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Kim Y, Jo J, Ji Y, Bae E, Lee K, Paek JH, Jin K, Han S, Lee JP, Kim DK, Lim CS, Won S, Lee J. Impact of hyperuricemia on CKD risk beyond genetic predisposition in a population-based cohort study. Sci Rep 2024; 14:18466. [PMID: 39122851 PMCID: PMC11316130 DOI: 10.1038/s41598-024-69420-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 08/05/2024] [Indexed: 08/12/2024] Open
Abstract
The bidirectional effect of hyperuricemia on chronic kidney disease (CKD) underscores the importance of hyperuricemia as a risk factor for CKD. We evaluated the effect of hyperuricemia on the presence and development of CKD after considering genetic background by calculating polygenic risk scores (PRSs). We employed genome-wide association study summary statistics-excluding the United Kingdom Biobank (UKB) datasets among published CKD Gen Consortium papers-to calculate the PRSs for CKD in white background subjects. To validate PRS performance, we divided the UKB into two datasets to validate and test the data. We used logistic regression analysis to evaluate the association between hyperuricemia and CKD, and performed Kaplan-Meier survival analysis exclusively for subjects with available follow-up data. In total, 438,253 clinical data and 4,307,940 single nucleotide polymorphisms from 459,155 samples were included. We observed a significant positive association between PRS and CKD and the presence and development of CKD. Hyperuricemia significantly increased CKD risk (adjusted odds ratio 1.55, 95% confidence interval 1.48-1.61). The impact of hyperuricemia on CKD was maintained irrespective of PRS range. In addition, negative interaction between hyperuricemia and PRS for CKD was found. Survival analysis indicates that the presence of hyperuricemia significantly increased the risk of CKD development. The PRS for CKD thoroughly reflects the risk of CKD development. Hyperuricemia is a significant indicator of CKD risk, even after incorporating the genetic risk score for CKD. Irrespective of genetic risk, patients with a prospective risk of developing CKD require uric acid monitoring and management.
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Affiliation(s)
- Yaerim Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Eunjin Bae
- Department of Internal Medicine, Gyeongsang National University College of Medicine, Jinju, Republic of Korea
| | - Kwangbae Lee
- Korea Medical Institute, Seoul, Republic of Korea
| | - Jin Hyuk Paek
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Kyubok Jin
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Seungyeup Han
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea.
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Boramae Medical Center 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.
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93
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Siew ED, Hellwege JN, Hung AM, Birkelo BC, Vincz AJ, Parr SK, Denton J, Greevy RA, Robinson-Cohen C, Liu H, Susztak K, Matheny ME, Velez Edwards DR. Genome-wide association study of hospitalized patients and acute kidney injury. Kidney Int 2024; 106:291-301. [PMID: 38797326 PMCID: PMC11260539 DOI: 10.1016/j.kint.2024.04.019] [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] [Revised: 03/15/2024] [Accepted: 04/05/2024] [Indexed: 05/29/2024]
Abstract
Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.
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Affiliation(s)
- Edward D Siew
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA.
| | - Jacklyn N Hellwege
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adriana M Hung
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Bethany C Birkelo
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Andrew J Vincz
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Sharidan K Parr
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Jason Denton
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA
| | - Robert A Greevy
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cassianne Robinson-Cohen
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Hongbo Liu
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Katalin Susztak
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Michael E Matheny
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Digna R Velez Edwards
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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94
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Chen Z, Xu LL, Du W, Ouyang Y, Gu X, Fang Z, Yu X, Li J, Xie L, Jin Y, Ma J, Wang Z, Pan X, Zhang W, Ren H, Wang W, Chen X, Zhou XJ, Zhang H, Chen N, Xie J. Uromodulin and progression of IgA nephropathy. Clin Kidney J 2024; 17:sfae209. [PMID: 39145144 PMCID: PMC11322676 DOI: 10.1093/ckj/sfae209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND This study investigates the link between genetic variants associated with kidney function and immunoglobulin A (IgA) nephropathy (IgAN) progression. METHODS We recruited 961 biopsy-proven IgAN patients and 651 non-IgAN end-stage renal disease (ESRD) patients from Ruijin Hospital. Clinical and renal pathological data were collected. The primary outcome was the time to ESRD. A healthy population was defined as estimated glomerular filtration rate >60 mL/min/1.73 m2 without albuminuria or hematuria. Fifteen single-nucleotide polymorphisms (SNPs) were selected from a genome-wide association study of kidney function and genotyped by the SNaPshot. Immunohistochemistry in renal tissue and ELISA in urine samples were performed to explore the potential functions of genetic variations. RESULTS The rs77924615-G was independently associated with an increased risk for ESRD in IgAN patients after adjustments for clinical and pathologic indices, and treatment (adjusted hazard ratio 2.10; 95% confidence interval 1.14-3.88). No significant differences in ESRD-free survival time were found among different genotypes in non-IgAN ESRD patients (log-rank, P = .480). Moreover, rs77924615 exhibited allele-specific enhancer activity by dual-luciferase reporter assay. Accordingly, the urinary uromodulin-creatinine ratio (uUCR) was significantly higher in healthy individuals with rs77924615 AG or GG than in individuals with AA. Furthermore, uromodulin expression in tubular epithelial cells was higher in patients with rs77924615 AG or GG. Finally, we confirmed that an increased uUCR (P = .009) was associated with faster IgAN progression. CONCLUSION The SNP rs77924615, which modulates the enhancer activity of the UMOD gene, is associated with renal function deterioration in IgAN patients by increasing uromodulin levels in both the renal tubular epithelium and urine.
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Affiliation(s)
- Zijin Chen
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lin-lin Xu
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Wen Du
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Ouyang
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangchen Gu
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengying Fang
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xialian Yu
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junru Li
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lin Xie
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanmeng Jin
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Ma
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhaohui Wang
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxia Pan
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Zhang
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Ren
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiming Wang
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaonong Chen
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xu-jie Zhou
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Hong Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Nan Chen
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingyuan Xie
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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95
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Zhao X, Gao J, Kou K, Wang X, Gao X, Wang Y, Zhou H, Li F. Causal effects of plasma metabolites on chronic kidney diseases and renal function: a bidirectional Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1429159. [PMID: 39129920 PMCID: PMC11310041 DOI: 10.3389/fendo.2024.1429159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024] Open
Abstract
Background Despite the potential demonstrated by targeted plasma metabolite modulators in halting the progression of chronic kidney disease (CKD), a lingering uncertainty persists concerning the causal relationship between distinct plasma metabolites and the onset and progression of CKD. Methods A genome-wide association study was conducted on 1,091 metabolites and 309 metabolite ratios derived from a cohort of 8,299 unrelated individuals of European descent. Employing a bidirectional two-sample Mendelian randomization (MR) analysis in conjunction with colocalization analysis, we systematically investigated the associations between these metabolites and three phenotypes: CKD, creatinine-estimated glomerular filtration rate (creatinine-eGFR), and urine albumin creatinine ratio (UACR). In the MR analysis, the primary analytical approach employed was inverse variance weighting (IVW), and sensitivity analysis was executed utilizing the MR-Egger method and MR-pleiotropy residual sum and outlier (MR-PRESSO). Heterogeneity was carefully evaluated through Cochrane's Q test. To ensure the robustness of our MR results, the leave-one-out method was implemented, and the strength of causal relationships was subjected to scrutiny via Bonferroni correction. Results Our thorough MR analysis involving 1,400 plasma metabolites and three clinical phenotypes yielded a discerning identification of 21 plasma metabolites significantly associated with diverse outcomes. Specifically, in the forward MR analysis, 6 plasma metabolites were determined to be causally associated with CKD, 16 with creatinine-eGFR, and 7 with UACR. Substantiated by robust evidence from colocalization analysis, 6 plasma metabolites shared causal variants with CKD, 16 with creatinine-eGFR, and 7 with UACR. In the reverse analysis, a diminished creatinine-eGFR was linked to elevated levels of nine plasma metabolites. Notably, no discernible associations were observed between other plasma metabolites and CKD, creatinine-eGFR, and UACR. Importantly, our analysis detected no evidence of horizontal pleiotropy. Conclusion This study elucidates specific plasma metabolites causally associated with CKD and renal functions, providing potential targets for intervention. These findings contribute to an enriched understanding of the genetic underpinnings of CKD and renal functions, paving the way for precision medicine applications and therapeutic strategies aimed at impeding disease progression.
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Affiliation(s)
- Xiaodong Zhao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Jialin Gao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Kai Kou
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Xi Wang
- Department of Endocrinology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Gao
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Yishu Wang
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Honglan Zhou
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Faping Li
- Department of Urology, The First Hospital of Jilin University, Changchun, China
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96
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Du J, Shui H, Chen R, Dong Y, Xiao C, Hu Y, Wong NK. Neuraminidase-1 (NEU1): Biological Roles and Therapeutic Relevance in Human Disease. Curr Issues Mol Biol 2024; 46:8031-8052. [PMID: 39194692 DOI: 10.3390/cimb46080475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/24/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024] Open
Abstract
Neuraminidases catalyze the desialylation of cell-surface glycoconjugates and play crucial roles in the development and function of tissues and organs. In both physiological and pathophysiological contexts, neuraminidases mediate diverse biological activities via the catalytic hydrolysis of terminal neuraminic, or sialic acid residues in glycolipid and glycoprotein substrates. The selective modulation of neuraminidase activity constitutes a promising strategy for treating a broad spectrum of human pathologies, including sialidosis and galactosialidosis, neurodegenerative disorders, cancer, cardiovascular diseases, diabetes, and pulmonary disorders. Structurally distinct as a large family of mammalian proteins, neuraminidases (NEU1 through NEU4) possess dissimilar yet overlapping profiles of tissue expression, cellular/subcellular localization, and substrate specificity. NEU1 is well characterized for its lysosomal catabolic functions, with ubiquitous and abundant expression across such tissues as the kidney, pancreas, skeletal muscle, liver, lungs, placenta, and brain. NEU1 also exhibits a broad substrate range on the cell surface, where it plays hitherto underappreciated roles in modulating the structure and function of cellular receptors, providing a basis for it to be a potential drug target in various human diseases. This review seeks to summarize the recent progress in the research on NEU1-associated diseases and highlight the mechanistic implications of NEU1 in disease pathogenesis. An improved understanding of NEU1-associated diseases should help accelerate translational initiatives to develop novel or better therapeutics.
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Affiliation(s)
- Jingxia Du
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Hanqi Shui
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Rongjun Chen
- Clinical Pharmacology Section, Department of Pharmacology, Shantou University Medical College, Shantou 515041, China
| | - Yibo Dong
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Chengyao Xiao
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Yue Hu
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Nai-Kei Wong
- Clinical Pharmacology Section, Department of Pharmacology, Shantou University Medical College, Shantou 515041, China
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97
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Zhang T, Cui Y, Jiang S, Jiang L, Song L, Huang L, Li Y, Yao J, Li M. Shared genetic correlations between kidney diseases and sepsis. Front Endocrinol (Lausanne) 2024; 15:1396041. [PMID: 39086896 PMCID: PMC11288879 DOI: 10.3389/fendo.2024.1396041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 07/02/2024] [Indexed: 08/02/2024] Open
Abstract
Background Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated. Methods Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis. Results In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them. Conclusions This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.
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Affiliation(s)
- Tianlong Zhang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Ying Cui
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Siyi Jiang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Lu Jiang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Lijun Song
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Lei Huang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Yong Li
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Jiali Yao
- Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China
| | - Min Li
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
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Mandla R, Lorenz K, Yin X, Bocher O, Huerta-Chagoya A, Arruda AL, Piron A, Horn S, Suzuki K, Hatzikotoulas K, Southam L, Taylor H, Yang K, Hrovatin K, Tong Y, Lytrivi M, Rayner NW, Meigs JB, McCarthy MI, Mahajan A, Udler MS, Spracklen CN, Boehnke M, Vujkovic M, Rotter JI, Eizirik DL, Cnop M, Lickert H, Morris AP, Zeggini E, Voight BF, Mercader JM. Multi-omics characterization of type 2 diabetes associated genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.15.24310282. [PMID: 39072045 PMCID: PMC11275663 DOI: 10.1101/2024.07.15.24310282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
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Affiliation(s)
- Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kim Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
| | - Anthony Piron
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels (IB2), Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Diabetes and Inflammation Laboratory, Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Susanne Horn
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Kaiyuan Yang
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Karin Hrovatin
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Yue Tong
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Maria Lytrivi
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Miriam Cnop
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
- WEL Research Institute, Wavre, Belgium
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Andrew P. Morris
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
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Huang Y, Xu S, Wan T, Wang X, Jiang S, Shi W, Ma S, Wang H. The Combined Effects of the Most Important Dietary Patterns on the Incidence and Prevalence of Chronic Renal Failure: Results from the US National Health and Nutrition Examination Survey and Mendelian Analyses. Nutrients 2024; 16:2248. [PMID: 39064691 PMCID: PMC11280344 DOI: 10.3390/nu16142248] [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: 06/19/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND We aimed to comprehensively assess the relationship of specific dietary patterns and various nutrients with chronic kidney disease (CKD) and its progression. METHODS The observational study data were from the NHANES 2005-2020. We calculated four dietary pattern scores (healthy eating index 2020 (HEI-2020), dietary inflammatory index (DII), alternative mediterranean diet (aMed), and dietary approaches to stop hypertension (DASH)) and the intakes of various nutrients and defined CKD, CKD-very high risk, and kidney dialysis. Associations between dietary patterns and nutrients and disease were assessed by means of two logistic regression models. Two-sample MR was performed with various food and nutrients as the exposure and CKD, kidney dialysis as the outcome. Sensitivity analyses were conducted to verify the reliability of the results. RESULTS A total of 25,167 participants were included in the analyses, of whom 4161 had CKD. HEI-2020, aMed, and DASH were significantly negatively associated with CKD and CKD-very high risk at higher quartiles, while DII was significantly positively associated. A higher intake of vitamins and minerals may reduce the incidence and progression of CKD to varying degrees. The MR results, corrected for false discovery rates, showed that a higher sodium intake was associated with a higher prevalence of CKD (OR: 3.91, 95%CI: 2.55, 5.99). CONCLUSIONS Adhering to the three dietary patterns of HEI-2020, aMed, and DASH and supplementing with vitamins and minerals benefits kidney health.
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Affiliation(s)
- Yanqiu Huang
- Department of Nephrology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China;
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (S.X.); (T.W.); (S.J.)
| | - Shiyu Xu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (S.X.); (T.W.); (S.J.)
| | - Tingya Wan
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (S.X.); (T.W.); (S.J.)
| | - Xiaoyu Wang
- Department of Gastroenterology, Shanghai Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China;
| | - Shuo Jiang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (S.X.); (T.W.); (S.J.)
| | - Wentao Shi
- Clinical Research Unit, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China;
| | - Shuai Ma
- Department of Nephrology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China;
| | - Hui Wang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (S.X.); (T.W.); (S.J.)
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100
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- 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
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - 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; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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