eLife· 2026Q1
Estimating bone marrow adiposity from head MRI and identifying its genetic architecture
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- Q1SCImago
- 2026year
Short summary
An artificial neural network can estimate bone marrow adiposity from head MRI scans, revealing 41 associated genetic loci, six of which are sex-specific.
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Key points
- An artificial neural network was developed to localize calvarial bone marrow in head MRI scans and assess its adiposity.
- Bone marrow adiposity shows sex-dimorphic age trajectories, including an effect of menopause.
- High heritability of bone marrow adiposity was found, with 41 genetic loci significantly associated, including six sex-specific loci.
- Genetic correlations were identified between bone marrow adiposity and cognitive ability, as well as Parkinson's disease.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. eLife, 2026 · DOI ↗
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Field: Physiology
Physiology (medical)Medicine