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Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction
BACKGROUND: Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity‐associated cardiometabolic traits. We sought to identify proteomic signatures of obesity...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483498/ https://www.ncbi.nlm.nih.gov/pubmed/34219465 http://dx.doi.org/10.1161/JAHA.120.020215 |
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author | Lau, Emily S. Paniagua, Samantha M. Zarbafian, Shahrooz Hoffman, Udo Long, Michelle T. Hwang, Shih‐Jen Courchesne, Paul Yao, Chen Ma, Jiantao Larson, Martin G. Levy, Daniel Shah, Ravi V. Ho, Jennifer E. |
author_facet | Lau, Emily S. Paniagua, Samantha M. Zarbafian, Shahrooz Hoffman, Udo Long, Michelle T. Hwang, Shih‐Jen Courchesne, Paul Yao, Chen Ma, Jiantao Larson, Martin G. Levy, Daniel Shah, Ravi V. Ho, Jennifer E. |
author_sort | Lau, Emily S. |
collection | PubMed |
description | BACKGROUND: Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity‐associated cardiometabolic traits. We sought to identify proteomic signatures of obesity and examine overlap with related cardiometabolic traits, including abdominal adiposity, insulin resistance, and adipose depots. METHODS AND RESULTS: We measured 71 circulating cardiovascular disease protein biomarkers in 6981 participants (54% women; mean age, 49 years). We examined the associations of obesity, computed tomography measures of adiposity, cardiometabolic traits, and incident metabolic syndrome with biomarkers using multivariable regression models. Of the 71 biomarkers examined, 45 were significantly associated with obesity, of which 32 were positively associated and 13 were negatively associated with obesity (false discovery rate q<0.05 for all). There was significant overlap of biomarker profiles of obesity and cardiometabolic traits, but 23 biomarkers, including melanoma cell adhesion molecule (MCAM), growth differentiation factor‐15 (GDF15), and lipoprotein(a) (LPA) were unique to metabolic traits only. Using hierarchical clustering, we found that the protein biomarkers clustered along 3 main trait axes: adipose, metabolic, and lipid traits. In longitudinal analyses, 6 biomarkers were significantly associated with incident metabolic syndrome: apolipoprotein B (apoB), insulin‐like growth factor‐binding protein 2 (IGFBP2), plasma kallikrein (KLKB1), complement C2 (C2), fibrinogen (FBN), and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP); false discovery rate q<0.05 for all. CONCLUSIONS: We found that the proteomic architecture of obesity overlaps considerably with associated cardiometabolic traits, implying shared pathways. Despite overlap, hierarchical clustering of proteomic profiles identified 3 distinct clusters of cardiometabolic traits: adipose, metabolic, and lipid. Further exploration of these novel protein targets and associated pathways may provide insight into the mechanisms responsible for the progression from obesity to cardiometabolic disease. |
format | Online Article Text |
id | pubmed-8483498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84834982021-10-06 Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction Lau, Emily S. Paniagua, Samantha M. Zarbafian, Shahrooz Hoffman, Udo Long, Michelle T. Hwang, Shih‐Jen Courchesne, Paul Yao, Chen Ma, Jiantao Larson, Martin G. Levy, Daniel Shah, Ravi V. Ho, Jennifer E. J Am Heart Assoc Original Research BACKGROUND: Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity‐associated cardiometabolic traits. We sought to identify proteomic signatures of obesity and examine overlap with related cardiometabolic traits, including abdominal adiposity, insulin resistance, and adipose depots. METHODS AND RESULTS: We measured 71 circulating cardiovascular disease protein biomarkers in 6981 participants (54% women; mean age, 49 years). We examined the associations of obesity, computed tomography measures of adiposity, cardiometabolic traits, and incident metabolic syndrome with biomarkers using multivariable regression models. Of the 71 biomarkers examined, 45 were significantly associated with obesity, of which 32 were positively associated and 13 were negatively associated with obesity (false discovery rate q<0.05 for all). There was significant overlap of biomarker profiles of obesity and cardiometabolic traits, but 23 biomarkers, including melanoma cell adhesion molecule (MCAM), growth differentiation factor‐15 (GDF15), and lipoprotein(a) (LPA) were unique to metabolic traits only. Using hierarchical clustering, we found that the protein biomarkers clustered along 3 main trait axes: adipose, metabolic, and lipid traits. In longitudinal analyses, 6 biomarkers were significantly associated with incident metabolic syndrome: apolipoprotein B (apoB), insulin‐like growth factor‐binding protein 2 (IGFBP2), plasma kallikrein (KLKB1), complement C2 (C2), fibrinogen (FBN), and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP); false discovery rate q<0.05 for all. CONCLUSIONS: We found that the proteomic architecture of obesity overlaps considerably with associated cardiometabolic traits, implying shared pathways. Despite overlap, hierarchical clustering of proteomic profiles identified 3 distinct clusters of cardiometabolic traits: adipose, metabolic, and lipid. Further exploration of these novel protein targets and associated pathways may provide insight into the mechanisms responsible for the progression from obesity to cardiometabolic disease. John Wiley and Sons Inc. 2021-07-03 /pmc/articles/PMC8483498/ /pubmed/34219465 http://dx.doi.org/10.1161/JAHA.120.020215 Text en © 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Research Lau, Emily S. Paniagua, Samantha M. Zarbafian, Shahrooz Hoffman, Udo Long, Michelle T. Hwang, Shih‐Jen Courchesne, Paul Yao, Chen Ma, Jiantao Larson, Martin G. Levy, Daniel Shah, Ravi V. Ho, Jennifer E. Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title | Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title_full | Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title_fullStr | Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title_full_unstemmed | Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title_short | Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction |
title_sort | cardiovascular biomarkers of obesity and overlap with cardiometabolic dysfunction |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483498/ https://www.ncbi.nlm.nih.gov/pubmed/34219465 http://dx.doi.org/10.1161/JAHA.120.020215 |
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