Evidence map›Paper›PMID 32454124›Full record

ArticleKidney international2020

A bidirectional Mendelian randomization study supports causal effects of kidney function on blood pressure.

Zhi Yu, Josef Coresh, Guanghao Qi, Morgan Grams, Eric Boerwinkle, Harold Snieder, Alexander Teumer, Cristian Pattaro, Anna Köttgen, Nilanjan Chatterjee and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Kidney international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 3 pooled it
24.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

61 citing papers in PubMed, 3 syntheses or guidelines pooled it, 137 citations in OpenAlex.

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1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 4 countries.

Zhi YuDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Josef CoreshDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Guanghao QiDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Morgan GramsWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Eric BoerwinkleHuman Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.
Harold SniederDepartment of Epidemiology, University Medical Centre Groningen, Groningen, The Netherlands.
Alexander TeumerInstitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany.
Cristian PattaroEurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy.
Anna KöttgenDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Adrienne TinDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA. Electronic address: atin1@jhu.edu.
Johns Hopkins University · USBaylor College of Medicine · USEurac Research · ITUniversitätsmedizin Greifswald · DEUniversity Medical Center Groningen · NL

Funding

CHARGE Consortium: Omics Discovery for CVD and Aging PhenotypesR01HL105756 · NHLBI · UNIVERSITY OF WASHINGTON · PI Bruce M Psaty, NICHOLAS L SMITH · 2011 to 2026
$9.5M
Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk PredictionR01HG010480 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2019 to 2023
$2.8M
Identifying Novel Biological Pathways for Gout using DNA Methylation and GeneticsR01AR073178 · NIAMS · UNIVERSITY OF MISSISSIPPI MED CTR · PI TIN, ADRIENNE · 2018 to 2023
$2.5M
NHGRI NIH HHS R01 HG010480NHLBI NIH HHS HHSN268201100009CNHLBI NIH HHS HHSN268201100009INHLBI NIH HHS R01 HL105756NIAMS NIH HHS R01 AR073178
6 · The paper itself

Abstract

Blood pressure and kidney function have a bidirectional relation. Hypertension has long been considered as a risk factor for kidney function decline. However, whether intensive blood pressure control could promote kidney health has been uncertain. The kidney is known to have a major role in affecting blood pressure through sodium extraction and regulating electrolyte balance. This bidirectional relation makes causal inference between these two traits difficult. Therefore, to examine the causal relations between these two traits, we performed two-sample Mendelian randomization analyses using summary statistics of large-scale genome-wide association studies. We selected genetic instruments more likely to be specific for kidney function using meta-analyses of complementary kidney function biomarkers (glomerular filtration rate estimated from serum creatinine [eGFRcr], and blood urea nitrogen from the CKDGen Consortium). Systolic and diastolic blood pressure summary statistics were from the International Consortium for Blood Pressure and UK Biobank. Significant evidence supported the causal effects of higher kidney function on lower blood pressure. Based on the mode-based Mendelian randomization method, the effect estimates for one standard deviation (SD) higher in log-transformed eGFRcr was -0.17 SD unit (95 % confidence interval: -0.09 to -0.24) in systolic blood pressure and -0.15 SD unit (95% confidence interval: -0.07 to -0.22) in diastolic blood pressure. In contrast, the causal effects of blood pressure on kidney function were not statistically significant. Thus, our results support causal effects of higher kidney function on lower blood pressure and suggest preventing kidney function decline can reduce the public health burden of hypertension.

Indexed as

HypertensionMendelian Randomization AnalysisBlood PressureGenome-Wide Association StudyHumansKidneybiomarkersblood pressurechronic kidney diseasegenome-wide association studykidney functionMendelian randomization

Identifiers

PMID32454124
PMCPMC7784392
OpenAlexW3028085141

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.