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Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study

Background: Targeting the gut microbiota may become a new therapeutic to prevent and delay the progression of chronic kidney disease (CKD). Nonetheless, the causal relationship between specific intestinal flora and CKD is still unclear. Materials and Method: To identify genetically predicted microbi...

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Autores principales: Li, Ning, Wang, Yi, Wei, Ping, Min, Yu, Yu, Manshu, Zhou, Guowei, Yuan, Gui, Sun, Jinyi, Dai, Huibo, Zhou, Enchao, He, Weiming, Sheng, Meixiao, Gao, Kun, Zheng, Min, Sun, Wei, Zhou, Dong, Zhang, Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863044/
https://www.ncbi.nlm.nih.gov/pubmed/36678231
http://dx.doi.org/10.3390/nu15020360
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author Li, Ning
Wang, Yi
Wei, Ping
Min, Yu
Yu, Manshu
Zhou, Guowei
Yuan, Gui
Sun, Jinyi
Dai, Huibo
Zhou, Enchao
He, Weiming
Sheng, Meixiao
Gao, Kun
Zheng, Min
Sun, Wei
Zhou, Dong
Zhang, Lu
author_facet Li, Ning
Wang, Yi
Wei, Ping
Min, Yu
Yu, Manshu
Zhou, Guowei
Yuan, Gui
Sun, Jinyi
Dai, Huibo
Zhou, Enchao
He, Weiming
Sheng, Meixiao
Gao, Kun
Zheng, Min
Sun, Wei
Zhou, Dong
Zhang, Lu
author_sort Li, Ning
collection PubMed
description Background: Targeting the gut microbiota may become a new therapeutic to prevent and delay the progression of chronic kidney disease (CKD). Nonetheless, the causal relationship between specific intestinal flora and CKD is still unclear. Materials and Method: To identify genetically predicted microbiota, we used summary data from genome-wide association studies on gut microbiota in 18340 participants from 24 cohorts. Furthermore, we genetically predicted the causal relationship between 211 gut microbiotas and six phenotypes (outcomes) (CKD, estimated glomerular filtration rate (eGFR), urine albumin to creatinine ratio (UACR), dialysis, rapid progress to CKD, and rapid decline of eGFR). Four Mendelian randomization (MR) methods, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used to investigate the casual relationship between gut microbiotas and various outcomes. The result of IVW was deemed as the primary result. Then, Cochrane’s Q test, MR-Egger, and MR-PRESSO Global test were used to detect heterogeneity and pleiotropy. The leave-one method was used for testing the stability of MR results and Bonferroni-corrected was used to test the strength of the causal relationship between exposure and outcome. Results: Through the MR analysis of 211 microbiotas and six clinical phenotypes, a total of 36 intestinal microflora were found to be associated with various outcomes. Among them, Class Bacteroidia (=−0.005, 95% CI: −0.001 to −0.008, p = 0.002) has a strong causality with lower eGFR after the Bonferroni-corrected test, whereas phylum Actinobacteria (OR = 1.0009, 95%CI: 1.0003–1.0015, p = 0.0024) has a strong causal relationship with dialysis. The Cochrane’s Q test reveals that there is no significant heterogeneity between various single nucleotide polymorphisms. In addition, no significant level of pleiotropy was found according to MR-Egger and MR-PRESSO Global tests. Conclusions: Through the two-sample MR analysis, we identified the specific intestinal flora that has a causal relationship with the incidence and progression of CKD at the level of gene prediction, which may provide helpful biomarkers for early disease diagnosis and potential therapeutic targets for CKD.
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spelling pubmed-98630442023-01-22 Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study Li, Ning Wang, Yi Wei, Ping Min, Yu Yu, Manshu Zhou, Guowei Yuan, Gui Sun, Jinyi Dai, Huibo Zhou, Enchao He, Weiming Sheng, Meixiao Gao, Kun Zheng, Min Sun, Wei Zhou, Dong Zhang, Lu Nutrients Article Background: Targeting the gut microbiota may become a new therapeutic to prevent and delay the progression of chronic kidney disease (CKD). Nonetheless, the causal relationship between specific intestinal flora and CKD is still unclear. Materials and Method: To identify genetically predicted microbiota, we used summary data from genome-wide association studies on gut microbiota in 18340 participants from 24 cohorts. Furthermore, we genetically predicted the causal relationship between 211 gut microbiotas and six phenotypes (outcomes) (CKD, estimated glomerular filtration rate (eGFR), urine albumin to creatinine ratio (UACR), dialysis, rapid progress to CKD, and rapid decline of eGFR). Four Mendelian randomization (MR) methods, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used to investigate the casual relationship between gut microbiotas and various outcomes. The result of IVW was deemed as the primary result. Then, Cochrane’s Q test, MR-Egger, and MR-PRESSO Global test were used to detect heterogeneity and pleiotropy. The leave-one method was used for testing the stability of MR results and Bonferroni-corrected was used to test the strength of the causal relationship between exposure and outcome. Results: Through the MR analysis of 211 microbiotas and six clinical phenotypes, a total of 36 intestinal microflora were found to be associated with various outcomes. Among them, Class Bacteroidia (=−0.005, 95% CI: −0.001 to −0.008, p = 0.002) has a strong causality with lower eGFR after the Bonferroni-corrected test, whereas phylum Actinobacteria (OR = 1.0009, 95%CI: 1.0003–1.0015, p = 0.0024) has a strong causal relationship with dialysis. The Cochrane’s Q test reveals that there is no significant heterogeneity between various single nucleotide polymorphisms. In addition, no significant level of pleiotropy was found according to MR-Egger and MR-PRESSO Global tests. Conclusions: Through the two-sample MR analysis, we identified the specific intestinal flora that has a causal relationship with the incidence and progression of CKD at the level of gene prediction, which may provide helpful biomarkers for early disease diagnosis and potential therapeutic targets for CKD. MDPI 2023-01-11 /pmc/articles/PMC9863044/ /pubmed/36678231 http://dx.doi.org/10.3390/nu15020360 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Ning
Wang, Yi
Wei, Ping
Min, Yu
Yu, Manshu
Zhou, Guowei
Yuan, Gui
Sun, Jinyi
Dai, Huibo
Zhou, Enchao
He, Weiming
Sheng, Meixiao
Gao, Kun
Zheng, Min
Sun, Wei
Zhou, Dong
Zhang, Lu
Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title_full Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title_fullStr Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title_full_unstemmed Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title_short Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function—A Two-Sample Mendelian Randomization Study
title_sort causal effects of specific gut microbiota on chronic kidney diseases and renal function—a two-sample mendelian randomization study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863044/
https://www.ncbi.nlm.nih.gov/pubmed/36678231
http://dx.doi.org/10.3390/nu15020360
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