Estimating bone marrow adiposity from head MRI and identifying its genetic architecture

Abstract Bone marrow adiposity changes radically through the lifespan, but this phenomenon is poorly characterised and understood in humans. Large datasets of magnetic resonance imaging (MRI) scans of the head have been collected to study the human brain, but also contain unexploited information about other organs. We developed an artificial neural network that localises calvarial bone marrow in T1-weighted MRI head scans, enabling us to study its composition in several large MRI datasets, and to model sex-dimorphic age trajectories, including the effect of menopause. We revealed high heritability in single-nucleotide polymorphism and twin data, and identified 41 genetic loci significantly associated with the trait, including six sex-specific loci. Integrating mapped genes with existing bone marrow single-cell RNA-sequencing data revealed patterns of adipogenic lineage differentiation and lipid loading. Finally, we identified significant genetic correlations with several human traits, including cognitive ability and Parkinson’s disease, which is intriguing in light of the recently discovered channels that link calvarial bone marrow to the meninges.

Authors

Institutions

Publication Details

Journal
eLife
Published
2026-11-04
DOI
https://doi.org/10.7554/elife.101499.3
Citations
1
Primary Topic
Adipose Tissue and Metabolism
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimating bone marrow adiposity from head MRI and identifying its genetic architecture

Lars Tjelta Westlye, Ole A. Andreassen, Martin Falck, Stener Nerland et al.
1 citations
eLife
Adipose Tissue and Metabolism
article

Estimating bone marrow adiposity from head MRI and identifying its genetic architecture

Lars Tjelta Westlye, Ole A. Andreassen, Martin Falck, Stener Nerland, Srdjan Djurovic, Kevin Sean O’Connell, Oleksandr Frei, Tobias Kaufmann, Timothy Hughes, P. M. Bjørnstad
article en
1 citations

Abstract

Abstract Bone marrow adiposity changes radically through the lifespan, but this phenomenon is poorly characterised and understood in humans. Large datasets of magnetic resonance imaging (MRI) scans of the head have been collected to study the human brain, but also contain unexploited information about other organs. We developed an artificial neural network that localises calvarial bone marrow in T1-weighted MRI head scans, enabling us to study its composition in several large MRI datasets, and to model sex-dimorphic age trajectories, including the effect of menopause. We revealed high heritability in single-nucleotide polymorphism and twin data, and identified 41 genetic loci significantly associated with the trait, including six sex-specific loci. Integrating mapped genes with existing bone marrow single-cell RNA-sequencing data revealed patterns of adipogenic lineage differentiation and lipid loading. Finally, we identified significant genetic correlations with several human traits, including cognitive ability and Parkinson’s disease, which is intriguing in light of the recently discovered channels that link calvarial bone marrow to the meninges.

eLifeVol. 13
Oslo University Hospital (NO), University of Oslo (NO), Diakonhjemmet Hospital (NO), University of Bergen (NO), University of Tübingen (DE)
Universitetet i Oslo, Norges Forskningsråd, Hangzhou Normal University, National Institutes of Health, McDonnell Center for Systems Neuroscience, Horizon 2020 Framework Programme, Helse Sør-Øst RHF, NIH Blueprint for Neuroscience Research
Good health and well-being
Openalex Percentile: Top 100%
Adipose Tissue and Metabolism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon