Cargando…
Metabolomic profiling in small vessel disease identifies multiple associations with disease severity
Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337813/ https://www.ncbi.nlm.nih.gov/pubmed/35254405 http://dx.doi.org/10.1093/brain/awac041 |
_version_ | 1784759836216393728 |
---|---|
author | Harshfield, Eric L Sands, Caroline J Tuladhar, Anil M de Leeuw, Frank Erik Lewis, Matthew R Markus, Hugh S |
author_facet | Harshfield, Eric L Sands, Caroline J Tuladhar, Anil M de Leeuw, Frank Erik Lewis, Matthew R Markus, Hugh S |
author_sort | Harshfield, Eric L |
collection | PubMed |
description | Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches. |
format | Online Article Text |
id | pubmed-9337813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93378132022-08-01 Metabolomic profiling in small vessel disease identifies multiple associations with disease severity Harshfield, Eric L Sands, Caroline J Tuladhar, Anil M de Leeuw, Frank Erik Lewis, Matthew R Markus, Hugh S Brain Original Article Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches. Oxford University Press 2022-02-08 /pmc/articles/PMC9337813/ /pubmed/35254405 http://dx.doi.org/10.1093/brain/awac041 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Harshfield, Eric L Sands, Caroline J Tuladhar, Anil M de Leeuw, Frank Erik Lewis, Matthew R Markus, Hugh S Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title | Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title_full | Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title_fullStr | Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title_full_unstemmed | Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title_short | Metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
title_sort | metabolomic profiling in small vessel disease identifies multiple associations with disease severity |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337813/ https://www.ncbi.nlm.nih.gov/pubmed/35254405 http://dx.doi.org/10.1093/brain/awac041 |
work_keys_str_mv | AT harshfieldericl metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity AT sandscarolinej metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity AT tuladharanilm metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity AT deleeuwfrankerik metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity AT lewismatthewr metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity AT markushughs metabolomicprofilinginsmallvesseldiseaseidentifiesmultipleassociationswithdiseaseseverity |