Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management
Frontiers in Public Health,
Journal Year:
2024,
Volume and Issue:
12
Published: Aug. 14, 2024
The
exposome
approach,
emphasizing
lifelong
environmental
exposures,
is
a
holistic
framework
exploring
the
intricate
interplay
between
genetics
and
environment
in
shaping
health
outcomes.
Complementing
this,
one
approach
recognizes
interconnectedness
of
human
ecological
within
shared
ecosystem,
extending
to
planetary
health,
which
encompasses
entire
planet.
Integrating
Disease
Surveillance
Systems
with
exposome,
signifies
paradigm
shift
management,
fostering
comprehensive
public
framework.
This
publication
advocates
for
combining
traditional
surveillance
health/planetary
proposing
three-step
approach:
analysis,
territorial
intervention
identified
issues,
an
analytical
phase
assessing
interventions.
Particularly
relevant
Latin
American
countries
facing
double
burden
diseases,
integrating
into
proves
cost-effective
by
leveraging
existing
data
measurements.
In
conclusion,
integration
approaches
presents
robust
monitoring
population
especially
regions
like
America
complex
challenges.
innovative
enables
tailored
interventions,
disease
outbreak
predictions,
understanding
links
environment,
offering
substantial
benefits
prevention
despite
Language: Английский
Weight of evidence evaluation of the metabolism disrupting effects of triphenyl phosphate using an expert knowledge elicitation approach
Toxicology and Applied Pharmacology,
Journal Year:
2024,
Volume and Issue:
489, P. 116995 - 116995
Published: June 11, 2024
Identification
of
Endocrine-Disrupting
Chemicals
(EDCs)
in
a
regulatory
context
requires
high
level
evidence.
However,
lines
evidence
(e.g.
human,
vivo,
vitro
or
silico)
are
heterogeneous
and
incomplete
for
quantifying
the
adverse
effects
mechanisms
involved.
To
date,
appraisal
metabolism-disrupting
chemicals
(MDCs),
no
harmonised
guidance
to
assess
weight
has
been
developed
at
EU
international
level.
explore
how
develop
this,
we
applied
formal
Expert
Knowledge
Elicitation
(EKE)
approach
within
European
GOLIATH
project.
EKE
captures
expert
judgment
quantitative
manner
provides
an
estimate
uncertainty
final
opinion.
As
proof
principle,
selected
one
suspected
MDC
-triphenyl
phosphate
(TPP)
-
based
on
its
related
endpoints
(obesity/adipogenicity)
relevant
metabolic
disruption
putative
Molecular
Initiating
Event
(MIE):
activation
peroxisome
proliferator
activated
receptor
gamma
(PPARγ).
We
conducted
systematic
literature
review
assessed
quality
with
two
independent
groups
experts
GOLIATH,
objective
categorising
properties
TPP,
by
applying
approach.
Having
followed
entire
process
separately,
both
arrived
same
conclusion,
designating
TPP
as
"suspected
MDC"
overall
agreement
exceeding
85%,
indicating
robust
reproducibility.
The
method
be
important
way
bring
together
scientists
diverse
expertise
is
recommended
future
work
this
area.
Language: Английский
Chemical exposome and children health: identification of dose-response relationships from meta-analyses and epidemiological studies
Environmental Research,
Journal Year:
2024,
Volume and Issue:
262, P. 119811 - 119811
Published: Aug. 17, 2024
Language: Английский
Science evolves but outdated testing and static risk management in the US delay protection to human health
Frontiers in Toxicology,
Journal Year:
2024,
Volume and Issue:
6
Published: Aug. 13, 2024
Science
evolves
but
outdated
testing
and
static
risk
management
in
the
US
delay
protection
to
human
health
Language: Английский
Towards Personalized Cardiometabolic Risk Prediction: A Fusion of Exposome and AI
Heliyon,
Journal Year:
2024,
Volume and Issue:
11(1), P. e40859 - e40859
Published: Dec. 20, 2024
The
influence
of
the
exposome
on
major
health
conditions
like
cardiovascular
disease
(CVD)
is
widely
recognized.
However,
integrating
diverse
factors
into
predictive
models
for
personalized
assessments
remains
a
challenge
due
to
complexity
and
variability
environmental
exposures
lifestyle
factors.
A
machine
learning
(ML)
model
designed
predicting
CVD
risk
introduced
in
this
study,
relying
easily
accessible
This
approach
particularly
novel
as
it
prioritizes
non-clinical,
modifiable
exposures,
making
applicable
broad
public
screening
assessments.
Assessments
were
conducted
using
both
internal
external
validation
groups
from
multi-center
cohort,
comprising
3,237
individuals
diagnosed
with
South
Korea
within
twelve
years
their
baseline
visit,
along
an
equal
number
participants
without
these
control
group.
Examination
109
variables
participants'
visits
spanned
physical
measures,
factors,
choices,
mental
events,
early-life
For
prediction,
Random
Forest
classifier
was
employed,
performance
compared
integrative
ML
clinical
variables.
Furthermore,
data
preprocessing
involved
normalization
handling
missing
values
enhance
accuracy.
model's
decision-making
process
advanced
explainability
method.
Results
indicated
comparable
between
exposome-based
model,
achieving
AUC
0.82(+/-)0.01,
0.70(+/-)0.01,
0.73(+/-)0.01.
study
underscores
potential
leveraging
early
intervention
strategies.
Additionally,
significant
identifying
pinpointed,
including
daytime
naps,
completed
full-time
education,
past
tobacco
smoking,
frequency
tiredness/unenthusiasm,
current
work
status.
Language: Английский