Evidence map›Paper›PMID 40155430›Full record

ArticleNature communications2025

Assessing the potential causal effects of 1099 plasma metabolites on 2099 binary disease endpoints.

Xianyong Yin, Jack Li, Debraj Bose, Jeffrey Okamoto, Annie Kwon, Anne U Jackson, Lilian Fernandes Silva, Anniina Oravilahti, Xiaomeng Chu, Heather M Stringham and 13 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

Xianyong YinDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China. xianyongyin@njmu.edu.cn.ORCID http://orcid.org/0000-0001-6454-2384
Jack LiDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Debraj BoseDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-3177-6608
Jeffrey OkamotoDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-9988-1618
Annie KwonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Anne U JacksonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-9672-2547
Lilian Fernandes SilvaInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.ORCID http://orcid.org/0000-0003-0225-842X
Anniina OravilahtiInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.ORCID http://orcid.org/0000-0001-8866-4482
Xiaomeng ChuDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-2991-6392
Lei LiuDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Ruyi PengDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Zhijie XiaDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Samuli RipattiInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-0504-1202
Mark DalyInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
Aarno PalotieInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-2527-5874
Laura J ScottDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-4886-5084
Charles F BurantDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-9189-5003
Eric B FaumanInternal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9739-0249
Xiaoquan WenDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-8990-2737
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA. boehnke@umich.edu.ORCID http://orcid.org/0000-0002-6442-7754
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland. markku.laakso@uef.fi.ORCID http://orcid.org/0000-0002-3394-7749
Jean MorrisonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA. jvmorr@umich.edu.ORCID http://orcid.org/0000-0003-4829-8283

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DAVID P OLSON · 2013 to 2026
$24.3M
University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Identifying Genes for Type 2 Diabetes: FUSIONU01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2009 to 2021
$11.6M
Identifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7M
Whole Genome Sequencing for Nephrotic Syndrome DiscoveryR01DK119380 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI Dongwon Lee, MATTHEW Gordon SAMPSON · 2019 to 2026
$3.7M
Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility: Administrative SupplementR35GM138121 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Xiaoquan Wen · 2020 to 2026
$2.4M
American Diabetes Association (ADA) 1-19-PDF-061NHGRI NIH HHS T32 HG000040NIDDK NIH HHS P30 DK020572NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R01 DK119380NIDDK NIH HHS U01 DK062370NIGMS NIH HHS R35 GM138121
6 · The paper itself

Abstract

Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Here, we perform two-sample Mendelian randomization to systematically infer the potential causal effects of 1099 plasma metabolites measured in 6136 Finnish men from the METSIM study on risk of 2099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen. We find evidence for 282 putative causal effects of 70 metabolites on 183 disease endpoints. We also identify 25 metabolites with potential causal effects across multiple disease domains, including ascorbic acid 2-sulfate affecting 26 disease endpoints in 12 disease domains. Our study suggests that N-acetyl-2-aminooctanoate and glycocholenate sulfate affect risk of atrial fibrillation through two distinct metabolic pathways and that N-methylpipecolate may mediate the putative causal effect of N6,N6-dimethyllysine on anxious personality disorder.

Indexed as

Atrial FibrillationBiomarkersCaprylatesFinlandHumansLysineMaleMendelian Randomization AnalysisMetabolomeMiddle AgedBiomarkersCaprylatesLysine

Identifiers

PMID40155430
PMCPMC11953310

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.