Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies
Yulia K. Komleva,
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K. A. Shpiliukova,
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N. I. Bondar
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et al.
Frontiers in Aging Neuroscience,
Journal Year:
2025,
Volume and Issue:
17
Published: March 19, 2025
The
global
extension
of
human
lifespan
has
intensified
the
focus
on
aging,
yet
its
underlying
mechanisms
remain
inadequately
understood.
article
highlights
aspects
genetic
susceptibility
to
impaired
brain
bioenergetics,
trends
in
age-related
gene
expression
related
neuroinflammation
and
senescence,
impact
stem
cell
exhaustion
quiescence
accelerated
aging.
We
also
review
accumulation
senescent
cells,
mitochondrial
dysfunction,
metabolic
disturbances
as
central
pathological
processes
emphasizing
how
these
factors
contribute
inflammation
disrupt
cellular
competition
defining
aging
trajectory.
Furthermore,
we
discuss
emerging
therapeutic
strategies
future
potential
integrating
advanced
technologies
refine
assessments.
combination
several
methods
including
analysis,
neuroimaging
techniques,
cognitive
tests
digital
twins,
offer
a
novel
approach
by
simulating
monitoring
individual
health
trajectories,
thereby
providing
more
accurate
personalized
insights.
Conclusively,
estimation
trajectories
is
crucial
for
understanding
managing
processes,
potentially
transforming
preventive
improve
outcomes
populations.
Language: Английский
Brain Age Gap as a Predictive Biomarker: Linking Aging, Lifestyle, and Neuropsychiatric Health
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 16, 2025
Abstract
Background
The
brain
age
gap
(BAG),
a
neuroimaging-derived
biomarker
of
accelerated
aging,
faces
translational
challenges
due
to
model
inaccuracies
and
unclear
disease-mechanism
linkages.
We
systematically
evaluated
BAG's
clinical
relevance
across
neuropsychiatric
disorders,
cognitive
trajectories,
mortality,
lifestyle
interventions.
Methods
Using
multi-cohort
data
(UK
Biobank
[n
=
38,967],
Alzheimer’s
Disease
Neuroimaging
Initiative
[ADNI;
n
1,402],
Parkinson’s
Progression
Markers
[PPMI;
1,182]),
we
developed
3D
Vision
Transformer
(3D-ViT)
for
whole-brain
estimation.
Survival
analyses,
restricted
cubic
splines,
stratified
regressions
assessed
BAG’s
associations
with
cognition,
16
mortality.
Lifestyle
modulation
effects
were
quantified
through
longitudinal
BAG
progression.
Results
demonstrated
robust
predictive
accuracy,
achieving
mean
absolute
error
(MAE)
2.68
years
in
the
UK
cohort
2.99–3.20
external
validation
cohorts
(ADNI/PPMI).
Per
1-year
increment
was
linearly
associated
elevated
risks
Alzheimer's
disease
(HR
1.165,
95%
CI
1.086–1.249;
+16.5%
risk/year),
mild
impairment
1.040,
1.030–1.050;
+4.0%),
all-cause
mortality
1.12,
1.09–1.15;
+12%;
all
p
<
0.001).
Individuals
highest
quartile
(Q4)
faced
substantially
amplified
risks:
2.8-fold
2.801),
6.4-fold
multiple
sclerosis
6.417),
1.5-fold
major
depressive
disorder
1.466).
Notably,
prodromal
Parkinson's
exhibited
paradoxical
rejuvenation
(mean
Δ=−1.441
years,
0.001),
contrasting
nonsignificant
incident
cases
1.830,
0.154).
Cognitive
decline
followed
nonlinear
critical
thresholds
domain-specific
emerging
at
Q4
(BAG
>
2.48
years).
interventions
synergistically
attenuated
progression
advanced
neurodegeneration
(Q3–Q4;
0.05),
particularly
smoking
cessation,
moderated
alcohol
consumption,
physical
activity.
Interpretation:
robustly
predicts
multimorbidity,
Its
stage-dependent
modifiability
underscore
utility
risk
stratification
personalized
prevention
strategies.
Language: Английский
Brain Age Prediction in Type II GM1 Gangliosidosis
Connor Lewis,
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Selby I. Chipman,
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Precilla D’Souza
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et al.
medRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 25, 2025
GM1
gangliosidosis
is
an
inherited,
progressive,
and
fatal
neurodegenerative
lysosomal
storage
disorder
with
no
approved
treatment.
We
calculated
a
predicted
brain
ages
Brain
Structures
Age
Gap
Estimation
(BSAGE)
for
81
MRI
scans
from
41
Type
II
patients
897
556
neurotypical
controls
(NC)
utilizing
BrainStructuresAges
,
machine
learning
analysis
pipeline.
NC
showed
whole
aging
at
rate
of
0.83
per
chronological
year
compared
1.57
in
juvenile
12.25
late-infantile
patients,
accurately
reflecting
the
clinical
trajectories
two
disease
subtypes.
Accelerated
distinct
was
also
observed
throughout
midbrain
structures
including
thalamus
caudate
nucleus,
hindbrain
cerebellum
brainstem,
ventricles
to
NC.
Predicted
age
BSAGE
both
correlated
cross-sectional
longitudinal
assessments,
indicating
their
importance
as
surrogate
neuroimaging
outcome
measures
trials
gangliosidosis.
Language: Английский