Advances in Food-as-Medicine interventions and their impact on future food production, processing, and supply chains
Advances in Nutrition,
Год журнала:
2025,
Номер
unknown, С. 100421 - 100421
Опубликована: Апрель 1, 2025
Food-as-Medicine
(FAM)
is
an
emerging
trend
among
medical
doctors,
health
insurers,
startups,
and
governmental
public-health
non-governmental
organizations.
FAM
implies
using
food
as
a
part
of
individual's
plan
to
prevent
or
help
treat
acute
chronic
conditions
diseases.
We
highlight
trends
hurdles
in
the
intervention
pyramid.
Our
viewpoint
indicate
how
interventions
might
change
future
demand
for
specific
groups,
their
transport
supply
chains,
technologies
used
process
them.
Based
on
national
guidelines,
dietary
can
many
diseases,
including
cardiovascular
disease,
cancers,
type
2
diabetes,
obesity.
R&D
services
offer
more
individualized
treatments.
This
challenging
given
inter-individual
variability
complexity
body's
response
related
factors,
such
habits,
genetics,
lifestyle,
biosphere.
Quantifying
improvements
essential
prove
added
value
compared
adopting
general
healthy
diet.
It
unclear
which
level
individualization
produces
largest
benefits
at
lowest
costs
patient,
healthcare
system,
climate.
support
complement
conventional
treatment.
They
will
require
shift
producing
health-promoting
foods,
whole
minimally-processed
selected
processed
foods.
The
processing
industry
chains
must
adapt
these
new
scenarios.
Auxiliary
methods
are
enablers,
delivery
services,
wearable
technology,
health-monitoring
apps,
data-driven
consumer
behavior
analysis.
Язык: Английский
Translational Algorithms for Technological Dietary Quality Assessment Integrating Nutrimetabolic Data with Machine Learning Methods
Nutrients,
Год журнала:
2024,
Номер
16(22), С. 3817 - 3817
Опубликована: Ноя. 7, 2024
Recent
advances
in
machine
learning
technologies
and
omics
methodologies
are
revolutionizing
dietary
assessment
by
integrating
phenotypical,
clinical,
metabolic
biomarkers,
which
crucial
for
personalized
precision
nutrition.
This
investigation
aims
to
evaluate
the
feasibility
efficacy
of
artificial
intelligence
tools,
particularly
(ML)
methods,
analyzing
these
biomarkers
characterize
food
nutrient
intake
predict
patterns.
Язык: Английский