Evidence map›Paper›PMID 41044249›Full record

ArticleNature genetics2025

A genetic map of human metabolism across the allele frequency spectrum.

Martijn Zoodsma, Carl Beuchel, Summaira Yasmeen, Leonhard Kohleick, Aakash Nepal, Mine Koprulu, Florian Kronenberg, Manuel Mayr, Alice Williamson, Maik Pietzner and 1 more

Abstract read
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Martijn ZoodsmaComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0003-3636-2209
Carl BeuchelComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0003-3224-3894
Summaira YasmeenComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0001-5470-2081
Leonhard KohleickComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0009-0002-4238-6602
Aakash NepalComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Mine KopruluPrecision Healthcare Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0001-6870-4539
Florian KronenbergInstitute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria.ORCID http://orcid.org/0000-0003-2229-1120
Manuel MayrNational Heart and Lung Institute, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-0597-829X
Alice WilliamsonComputational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0002-7599-9301
Maik Pietzner *Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. maik.pietzner@bih-charite.de.ORCID http://orcid.org/0000-0003-3437-9963
Claudia Langenberg *Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. claudia.langenberg@qmul.ac.uk.ORCID http://orcid.org/0000-0002-5017-7344

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101116072
6 · The paper itself

Abstract

Genetic studies of human metabolism have been limited in scale and allelic breadth. Here we provide a data-driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 traits) measured using proton nuclear magnetic resonance spectroscopy across the allele frequency spectrum in ~450,000 individuals. Trans-ancestral meta-analyses identify 29,824 locus-metabolite associations mapping to 753 regions with effects largely consistent between men and women and large ancestral groups represented in UK Biobank. We observe and classify extreme genetic pleiotropy, identify regulators of lipid metabolism, and assign effector genes at >100 loci through rare-to-common allelic series. We propose roles for genes less established in metabolic control (for example, SIDT2), genes characterized by phenotypic heterogeneity (for example, APOA1) and genes with specific disease relevance (for example, VEGFA). Our study demonstrates the value of broad, large-scale metabolomic phenotyping to identify and characterize regulators of human metabolism.

Indexed as

Gene FrequencyChromosome MappingFemaleGenetic PleiotropyGenome-Wide Association StudyHumansLipid MetabolismMaleMetabolomicsPhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID41044249
PMCPMC12513840

What OpenQuestion holds

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Registered trials

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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.