% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{NicolaisenSobesky:1048426,
author = {Nicolaisen-Sobesky, Eliana and Maleki Balajoo, Somayeh and
Mahdipour, Mostafa and Mihalik, Agoston and Olfati, Mahnaz
and Hoffstaedter, Felix and Mourao-Miranda, Janaina and
Tahmasian, Masoud and Eickhoff, Simon B. and Genon, Sarah},
title = {{C}ardiometabolic health and physical robustness map onto
distinct patterns of brain structure and neurotransmitter
systems},
journal = {PLoS biology},
volume = {23},
number = {11},
issn = {1544-9173},
address = {Lawrence, KS},
publisher = {PLoS},
reportid = {FZJ-2025-04637},
pages = {e3003498 -},
year = {2025},
abstract = {The link between brain health and risk/protective factors
for non-communicable diseases (such as high blood pressure,
high body mass index, diet, smoking, physical activity,
etc.) is increasingly acknowledged. However, the specific
effects that these factors have on brain health are still
poorly understood, delaying their implementation in
precision brain health. Here, we studied the multivariate
relationships between risk factors for non-communicable
diseases and brain structure, including cortical thickness
(CT) and gray matter volume (GMV). Furthermore, we adopted a
systems-level perspective to understand such relationships,
by characterizing the cortical patterns (yielded in
association to risk factors) with regards to brain
morphological and functional features, as well as with
neurotransmitter systems. Similarly, we related the pattern
of risk/protective factors dimensions with a peripheral
marker of inflammation. First, we identified latent
dimensions linking a broad set of risk factors for
non-communicable diseases to parcel-wise CT and GMV across
the whole cortex. Data was obtained from the UK Biobank (n =
7,370, age range = 46-81 years). We used regularized
canonical correlation analysis (RCCA) embedded in a machine
learning framework. This approach allows us to capture
inter-individual variability in a multivariate association
and to assess the generalizability of the model. The brain
patterns (captured in association with risk/protective
factors) were characterized from a multi-level perspective,
by performing correlations (spin tests) between them and
different brain patterns of structure, function, and
neurotransmitter systems. The association between the
risk/protective factors pattern and C-reactive protein (CRP,
a marker of inflammation) was examined using Spearman
correlation. We found two significant and partly replicable
latent dimensions. One latent dimension linked
cardiometabolic health to brain patterns of CT and GMV and
was consistent across sexes. The other latent dimension
linked physical robustness (including non-fat mass and
strength) to patterns of CT and GMV, with the association to
GMV being consistent across sexes and the association to CT
appearing only in men. The CT and GMV patterns of both
latent dimensions were associated to the binding potentials
of several neurotransmitter systems. Finally, the
cardiometabolic health dimension was correlated to CRP,
while physical robustness was only very weakly associated to
it. We observed robust, multi-level and multivariate links
between both cardiometabolic health and physical robustness
with respect to CT, GMV, and neurotransmitter systems.
Interestingly, we found that cardiometabolic health and
physical robustness are associated with not only increases
in CT or GMV, but also with decreases of CT or GMV in some
brain regions. Our results also suggested a role for
low-grade chronic inflammation in the association between
cardiometabolic health and brain structural health. These
findings support the relevance of adopting a holistic
perspective in health, by integrating neurocognitive and
physical health. Moreover, our findings contribute to the
challenge to the classical conceptualization of
neuropsychiatric and physical illnesses as categorical
entities. In this perspective, future studies should further
examine the effects of risk/protective factors on different
brain regions in order to deepen our understanding of the
clinical significance of such increased and decreased CT and
GMV.},
cin = {INM-7},
ddc = {610},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5251 - Multilevel Brain Organization and Variability
(POF4-525)},
pid = {G:(DE-HGF)POF4-5251},
typ = {PUB:(DE-HGF)16},
doi = {10.1371/journal.pbio.3003498},
url = {https://juser.fz-juelich.de/record/1048426},
}