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Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome
OBJECTIVE: Metabolic syndrome (MetS) refers to a cluster of risk factors for cardiovascular disease, including obesity, hypertension, dyslipidemia, and hyperglycemia. While sizable prior literature has examined associations between individual risk factors and quantitative measures of cortical thickn...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641920/ https://www.ncbi.nlm.nih.gov/pubmed/29062686 http://dx.doi.org/10.1016/j.nicl.2017.09.022 |
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author | Schwarz, Nicolette F. Nordstrom, Leslie K. Pagen, Linda H.G. Palombo, Daniela J. Salat, David H. Milberg, William P. McGlinchey, Regina E. Leritz, Elizabeth C. |
author_facet | Schwarz, Nicolette F. Nordstrom, Leslie K. Pagen, Linda H.G. Palombo, Daniela J. Salat, David H. Milberg, William P. McGlinchey, Regina E. Leritz, Elizabeth C. |
author_sort | Schwarz, Nicolette F. |
collection | PubMed |
description | OBJECTIVE: Metabolic syndrome (MetS) refers to a cluster of risk factors for cardiovascular disease, including obesity, hypertension, dyslipidemia, and hyperglycemia. While sizable prior literature has examined associations between individual risk factors and quantitative measures of cortical thickness (CT), only very limited research has investigated such measures in MetS. Furthermore, the relative contributions of these risk factors to MetS-related effects on brain morphology have not yet been studied. The primary goal of this investigation was to examine how MetS may affect CT. A secondary goal was to explore the relative contributions of individual risk factors to regional alterations in CT, with the potential to identify risk factor combinations that may underlie structural changes. METHODS: Eighteen participants with MetS (mean age = 59.78 years) were age-matched with 18 healthy control participants (mean age = 60.50 years). CT measures were generated from T1-weighted images and groups were contrasted using whole-brain general linear modeling. A follow-up multivariate partial least squares correlation (PLS) analysis, including the full study sample with complete risk factor measurements (N = 53), was employed to examine which risk factors account for variance in group structural differences. RESULTS: Participants with MetS demonstrated significantly reduced CT in left hemisphere inferior parietal, rostral middle frontal, and lateral occipital clusters and in a right hemisphere precentral cluster. The PLS analysis revealed that waist circumference, high-density lipoprotein cholesterol (HDL-C), triglycerides, and glucose were significant contributors to reduced CT in these clusters. In contrast, diastolic blood pressure showed a significantly positive association with CT while systolic blood pressure did not emerge as a significant contributor. Age was not associated with CT. CONCLUSION: These results indicate that MetS can be associated with regionally specific reductions in CT. Importantly, a novel link between a risk factor profile comprising indices of obesity, hyperglycemia, dyslipidemia and diastolic BP and localized alterations in CT emerged. While the pathophysiological mechanisms underlying these associations remain incompletely understood, these findings may be relevant for future investigations of MetS and might have implications for treatment approaches that focus on specific risk factor profiles with the aim to reduce negative consequences on the structural integrity of the brain. |
format | Online Article Text |
id | pubmed-5641920 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-56419202017-10-23 Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome Schwarz, Nicolette F. Nordstrom, Leslie K. Pagen, Linda H.G. Palombo, Daniela J. Salat, David H. Milberg, William P. McGlinchey, Regina E. Leritz, Elizabeth C. Neuroimage Clin Regular Article OBJECTIVE: Metabolic syndrome (MetS) refers to a cluster of risk factors for cardiovascular disease, including obesity, hypertension, dyslipidemia, and hyperglycemia. While sizable prior literature has examined associations between individual risk factors and quantitative measures of cortical thickness (CT), only very limited research has investigated such measures in MetS. Furthermore, the relative contributions of these risk factors to MetS-related effects on brain morphology have not yet been studied. The primary goal of this investigation was to examine how MetS may affect CT. A secondary goal was to explore the relative contributions of individual risk factors to regional alterations in CT, with the potential to identify risk factor combinations that may underlie structural changes. METHODS: Eighteen participants with MetS (mean age = 59.78 years) were age-matched with 18 healthy control participants (mean age = 60.50 years). CT measures were generated from T1-weighted images and groups were contrasted using whole-brain general linear modeling. A follow-up multivariate partial least squares correlation (PLS) analysis, including the full study sample with complete risk factor measurements (N = 53), was employed to examine which risk factors account for variance in group structural differences. RESULTS: Participants with MetS demonstrated significantly reduced CT in left hemisphere inferior parietal, rostral middle frontal, and lateral occipital clusters and in a right hemisphere precentral cluster. The PLS analysis revealed that waist circumference, high-density lipoprotein cholesterol (HDL-C), triglycerides, and glucose were significant contributors to reduced CT in these clusters. In contrast, diastolic blood pressure showed a significantly positive association with CT while systolic blood pressure did not emerge as a significant contributor. Age was not associated with CT. CONCLUSION: These results indicate that MetS can be associated with regionally specific reductions in CT. Importantly, a novel link between a risk factor profile comprising indices of obesity, hyperglycemia, dyslipidemia and diastolic BP and localized alterations in CT emerged. While the pathophysiological mechanisms underlying these associations remain incompletely understood, these findings may be relevant for future investigations of MetS and might have implications for treatment approaches that focus on specific risk factor profiles with the aim to reduce negative consequences on the structural integrity of the brain. Elsevier 2017-09-28 /pmc/articles/PMC5641920/ /pubmed/29062686 http://dx.doi.org/10.1016/j.nicl.2017.09.022 Text en © 2017 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Schwarz, Nicolette F. Nordstrom, Leslie K. Pagen, Linda H.G. Palombo, Daniela J. Salat, David H. Milberg, William P. McGlinchey, Regina E. Leritz, Elizabeth C. Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title | Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title_full | Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title_fullStr | Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title_full_unstemmed | Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title_short | Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
title_sort | differential associations of metabolic risk factors on cortical thickness in metabolic syndrome |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641920/ https://www.ncbi.nlm.nih.gov/pubmed/29062686 http://dx.doi.org/10.1016/j.nicl.2017.09.022 |
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