The metabolomic profile associated with clustering of cardiovascular risk factors A multi-sample evaluation
(2022) In PLoS ONE 17(9 September).- Abstract
Background A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS. Methods and findings 791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmo , n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of... (More)
Background A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS. Methods and findings 791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmo , n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of MetS (blood pressure, waist circumference, glucose, HDL-cholesterol and triglycerides) at a false discovery rate of 0.05 with adjustments for BMI and several life-style factors. They represented different metabolic classes, such as amino acids, simple carbohydrates, androgenic steroids, corticosteroids, co-factors and vitamins, ceramides, carnitines, fatty acids, phospholipids and metabolonic lactone sulfate. All 15 metabolites were related to insulin sensitivity (Matsuda index) in POEM, but only Palmitoyl-oleoyl-GPE (16:0/18:1), a glycerophospholipid, was related to incident cardiovascular disease over 8.6 years follow-up in the EpiHealth sample following adjustment for cardiovascular risk factors (HR 1.32 for a SD change, 95%CI 1.07 1.63). Conclusion A complex metabolic profile was related to all cardiovascular risk factors included in MetS independently of BMI. This profile was also related to insulin sensitivity, which provide further support for the importance of insulin sensitivity as an important underlying mechanism in the clustering of cardiovascular risk factors.
(Less)
- author
- Lind, Lars ; Sundstrom, Johan ; Elmstahl, Solve LU ; Dekkers, Koen F. ; Smith, J. Gustav LU ; Engstrom, Gunnar LU ; Fall, Tove and Arnlov, Johan
- organization
-
- Geriatrics (research group)
- EpiHealth: Epidemiology for Health
- WCMM-Wallenberg Centre for Molecular Medicine
- Heart Failure and Mechanical Support (research group)
- Cardiovascular Epigenetics (research group)
- Cardiology
- EXODIAB: Excellence of Diabetes Research in Sweden
- Molecular Epidemiology and Cardiology (research group)
- Cardiovascular Research - Epidemiology (research group)
- publishing date
- 2022-09
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 17
- issue
- 9 September
- article number
- e0274701
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- scopus:85137906925
- pmid:36107885
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0274701
- language
- English
- LU publication?
- yes
- id
- 41838c16-0cc7-4a1a-9eb6-ec4ed7d93d11
- date added to LUP
- 2022-12-05 15:27:10
- date last changed
- 2024-04-18 15:57:10
@article{41838c16-0cc7-4a1a-9eb6-ec4ed7d93d11, abstract = {{<p>Background A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS. Methods and findings 791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmo , n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of MetS (blood pressure, waist circumference, glucose, HDL-cholesterol and triglycerides) at a false discovery rate of 0.05 with adjustments for BMI and several life-style factors. They represented different metabolic classes, such as amino acids, simple carbohydrates, androgenic steroids, corticosteroids, co-factors and vitamins, ceramides, carnitines, fatty acids, phospholipids and metabolonic lactone sulfate. All 15 metabolites were related to insulin sensitivity (Matsuda index) in POEM, but only Palmitoyl-oleoyl-GPE (16:0/18:1), a glycerophospholipid, was related to incident cardiovascular disease over 8.6 years follow-up in the EpiHealth sample following adjustment for cardiovascular risk factors (HR 1.32 for a SD change, 95%CI 1.07 1.63). Conclusion A complex metabolic profile was related to all cardiovascular risk factors included in MetS independently of BMI. This profile was also related to insulin sensitivity, which provide further support for the importance of insulin sensitivity as an important underlying mechanism in the clustering of cardiovascular risk factors.</p>}}, author = {{Lind, Lars and Sundstrom, Johan and Elmstahl, Solve and Dekkers, Koen F. and Smith, J. Gustav and Engstrom, Gunnar and Fall, Tove and Arnlov, Johan}}, issn = {{1932-6203}}, language = {{eng}}, number = {{9 September}}, publisher = {{Public Library of Science (PLoS)}}, series = {{PLoS ONE}}, title = {{The metabolomic profile associated with clustering of cardiovascular risk factors A multi-sample evaluation}}, url = {{http://dx.doi.org/10.1371/journal.pone.0274701}}, doi = {{10.1371/journal.pone.0274701}}, volume = {{17}}, year = {{2022}}, }