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Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes

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Abstract

Purpose

Modic changes (MC) on magnetic resonance imaging (MRI) have been associated with the development and severity of low back pain (LBP). The etiology of MC remains elusive, but it has been suggested that altered metabolism may be a risk factor. As such, this study aimed to identify metabolomic biomarkers for MC phenotypes of the lumbar spine via a combined metabolomic-genomic approach.

Methods

A population cohort of 3,584 southern Chinese underwent lumbar spine MRI. Blood samples were genotyped with single-nucleotide polymorphisms (SNP) arrays (n = 2,482) and serum metabolomics profiling using magnetic resonance spectroscopy (n = 757), covering 130 metabolites representing three molecular windows, were assessed. Genome-wide association studies (GWAS) were performed on each metabolite, to construct polygenic scores for predicting metabolite levels in subjects who had GWAS but not metabolomic data. Associations between predicted metabolite levels and MC phenotypes were assessed using linear/logistic regression and least absolute shrinkage and selection operator (LASSO). Two-sample Mendelian randomization analysis tested for causal relationships between metabolic biomarkers and MC.

Results

20.4% had MC (10.6% type 1, 67.2% type 2, 22.2% mixed types). Significant MC metabolomic biomarkers were mean diameter of very-low-density lipoprotein (VLDL)/low-density lipoprotein (LDL) particles and cholesterol esters/phospholipids in large LDL. Mendelian randomization indicated that decreased VLDL mean diameter may lead to MC.

Conclusions

This large-scale study is the first to address metabolomics in subject with/without lumbar MC. Causality studies implicate VLDL related to MC, noting a metabolic etiology. Our study substantiates the field of “spino-metabolomics” and illustrates the power of integrating metabolomics-genomics-imaging phenotypes to discover biomarkers for spinal disorders, paving the way for more personalized spine care for patients.

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Acknowledgements

This work was supported by grants from the Hong Kong Theme-Based Research Scheme (T12-708/12N), Hong Kong Research Grants Council (776613), and the Hansjorg Wyss Award by AO Spine International. The authors would like to thank Ms. Cora Bow, Yu Pei, and Kenneth MC Cheung from the Department of Orthopaedics and Traumatology, Hong Kong for their assistance with the cohort. The authors would also like thank Drs. Mika Ala-Korpela, Pasi Soininen and Antti J Kangas from the University of Eastern Finland, NMR Metabolomics Laboratory, Finland, for their assistance in analyzing the blood samples to obtain the metabolomic analytes.

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Authors and Affiliations

  1. Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

    Yiming Li & Pak C. Sham

  2. Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland

    Jaro Karppinen

  3. School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

    Kathryn S. E. Cheah & Danny Chan

  4. Centre for PanorOmic Sciences, State Key Laboratory of Brain and Cognitive Sciences, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China

    Pak C. Sham

  5. Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

    Dino Samartzis

  6. International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA

    Dino Samartzis

  7. Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60612, USA

    Dino Samartzis

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  1. Yiming Li
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Li, Y., Karppinen, J., Cheah, K.S.E. et al. Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes . Eur Spine J 31, 735–745 (2022). https://doi.org/10.1007/s00586-021-06995-x

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