Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow‐Up in the General Population Article Swipe
Filip Ottosson
,
Gunnar Engström
,
Marju Orho‐Melander
,
Olle Melander
,
Peter M. Nilsson
,
Madeleine Johansson
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1161/jaha.123.033442
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1161/jaha.123.033442
Background Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality. Methods and Results We included 6865 individuals from 2 Swedish population‐based cohorts. Untargeted plasma metabolomics was performed by liquid‐chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid‐femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite‐predicted aortic stiffness and risk of new‐onset cardiovascular disease, cardiovascular mortality, and all‐cause mortality were calculated. Metabolite‐predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R 2 =0.27 and R 2 =0.39, respectively). Metabolite‐predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low‐density lipoprotein. After 23 years of follow‐up, metabolite‐predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new‐onset coronary artery disease, cardiovascular mortality, and all‐cause mortality. Conclusions Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new‐onset coronary artery disease, cardiovascular mortality, and all‐cause mortality after more than 23 years of follow‐up in the general population.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1161/jaha.123.033442
- https://www.ahajournals.org/doi/pdf/10.1161/JAHA.123.033442
- OA Status
- gold
- Cited By
- 12
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394954651
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394954651Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1161/jaha.123.033442Digital Object Identifier
- Title
-
Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow‐Up in the General PopulationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-19Full publication date if available
- Authors
-
Filip Ottosson, Gunnar Engström, Marju Orho‐Melander, Olle Melander, Peter M. Nilsson, Madeleine JohanssonList of authors in order
- Landing page
-
https://doi.org/10.1161/jaha.123.033442Publisher landing page
- PDF URL
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https://www.ahajournals.org/doi/pdf/10.1161/JAHA.123.033442Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.ahajournals.org/doi/pdf/10.1161/JAHA.123.033442Direct OA link when available
- Concepts
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Pulse wave velocity, Arterial stiffness, Internal medicine, Cardiology, Medicine, Metabolome, Population, Coronary artery disease, Blood pressure, Metabolite, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.largely | 24 |
| abstract_inverted_index.predict | 108, 234 |
| abstract_inverted_index.profile | 37 |
| abstract_inverted_index.valeric | 142, 229 |
| abstract_inverted_index.absolute | 92 |
| abstract_inverted_index.accuracy | 158 |
| abstract_inverted_index.assessed | 76 |
| abstract_inverted_index.cohorts. | 63 |
| abstract_inverted_index.coronary | 207, 239 |
| abstract_inverted_index.cystine, | 226 |
| abstract_inverted_index.cystine. | 146 |
| abstract_inverted_index.directly | 77 |
| abstract_inverted_index.disease, | 121, 209, 241 |
| abstract_inverted_index.incident | 47 |
| abstract_inverted_index.included | 56 |
| abstract_inverted_index.operator | 96 |
| abstract_inverted_index.systolic | 179 |
| abstract_inverted_index.vascular | 9, 21 |
| abstract_inverted_index.velocity | 82 |
| abstract_inverted_index.(AIx@75). | 89 |
| abstract_inverted_index.(vascular | 41 |
| abstract_inverted_index.Increased | 1 |
| abstract_inverted_index.increased | 203, 235 |
| abstract_inverted_index.metabolic | 232 |
| abstract_inverted_index.mortality | 126, 246 |
| abstract_inverted_index.performed | 68 |
| abstract_inverted_index.predicted | 150 |
| abstract_inverted_index.pressure, | 181 |
| abstract_inverted_index.selection | 95 |
| abstract_inverted_index.shrinkage | 93 |
| abstract_inverted_index.stiffness | 3, 40, 74, 115, 131, 152, 169, 195, 217 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Untargeted | 64 |
| abstract_inverted_index.associated | 6, 44, 137, 201, 219 |
| abstract_inverted_index.correlated | 175 |
| abstract_inverted_index.glutamate, | 144 |
| abstract_inverted_index.indirectly | 85 |
| abstract_inverted_index.mechanisms | 18 |
| abstract_inverted_index.metabolism | 223 |
| abstract_inverted_index.metabolome | 149 |
| abstract_inverted_index.mortality, | 123, 211, 243 |
| abstract_inverted_index.mortality. | 51, 214 |
| abstract_inverted_index.positively | 136 |
| abstract_inverted_index.regression | 98 |
| abstract_inverted_index.stiffness. | 110 |
| abstract_inverted_index.underlying | 17 |
| abstract_inverted_index.unexplored | 25 |
| abstract_inverted_index.Conclusions | 215 |
| abstract_inverted_index.all‐cause | 125, 213, 245 |
| abstract_inverted_index.calculated. | 128 |
| abstract_inverted_index.follow‐up | 253 |
| abstract_inverted_index.independent | 11 |
| abstract_inverted_index.individuals | 58 |
| abstract_inverted_index.investigate | 33 |
| abstract_inverted_index.metabolites | 104 |
| abstract_inverted_index.metabolomic | 36 |
| abstract_inverted_index.new‐onset | 119, 206, 238 |
| abstract_inverted_index.population. | 29, 257 |
| abstract_inverted_index.Associations | 111 |
| abstract_inverted_index.augmentation | 87 |
| abstract_inverted_index.disturbances | 233 |
| abstract_inverted_index.follow‐up, | 192 |
| abstract_inverted_index.lipoprotein. | 187 |
| abstract_inverted_index.metabolomics | 66 |
| abstract_inverted_index.particularly | 138, 220 |
| abstract_inverted_index.low‐density | 186 |
| abstract_inverted_index.significantly | 174, 200 |
| abstract_inverted_index.spectrometry. | 72 |
| abstract_inverted_index.cardiovascular | 48, 120, 122, 210, 242 |
| abstract_inverted_index.respectively). | 166 |
| abstract_inverted_index.acylcarnitines, | 140, 225 |
| abstract_inverted_index.carotid‐femoral | 79 |
| abstract_inverted_index.dimethylguanidino | 141, 228 |
| abstract_inverted_index.(arteriosclerosis) | 4 |
| abstract_inverted_index.population‐based | 62 |
| abstract_inverted_index.Metabolite‐predicted | 129, 167 |
| abstract_inverted_index.metabolite‐predicted | 113, 193 |
| abstract_inverted_index.liquid‐chromatography | 70 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.97193914 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |