Journal of the American Society for Mass Spectrometry,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 25, 2025
Untargeted
metabolomics
often
produce
large
datasets
with
missing
values.
These
values
are
derived
from
biological
or
technical
factors
and
can
undermine
statistical
analyses
lead
to
biased
interpretations.
Imputation
methods,
such
as
k-Nearest
Neighbors
(kNN)
Random
Forest
(RF)
regression,
commonly
used,
but
their
effects
vary
depending
on
the
type
of
data,
e.g.,
Missing
Completely
At
(MCAR)
Not
(MNAR).
Here,
we
determined
impacts
degree
data
accuracy
kNN
RF
imputation
using
two
datasets:
a
targeted
metabolomic
dataset
spiked-in
standards
an
untargeted
dataset.
We
also
assessed
effect
compositional
approaches
(CoDA),
centered
log-ratio
(CLR)
transform,
interpretation
since
these
methods
increasingly
being
used
in
metabolomics.
Overall,
found
that
performed
more
accurately
when
proportion
across
samples
for
metabolic
feature
was
low.
However,
imputations
could
not
handle
MNAR
generated
wildly
inflated
imputed
where
none
should
exist.
Furthermore,
show
had
strong
impact
imputation,
which
affected
results.
Our
results
suggest
be
extreme
caution
even
modest
levels
especially
missingness
is
unknown.
Advances in Nutrition,
Год журнала:
2024,
Номер
15(4), С. 100199 - 100199
Опубликована: Март 1, 2024
Within
twenty
years,
the
number
of
adults
in
United
States
over
age
65
is
expected
to
more
than
double
and
85
triple.
The
risk
for
most
chronic
diseases
disabilities
increases
with
age,
so
this
demographic
shift
carries
significant
implications
individual,
health
care
providers,
population
health.
Strategies
that
delay
or
prevent
onset
age-related
are
becoming
increasingly
important.
Although
considerable
progress
has
been
made
understanding
contribution
nutrition
healthy
aging,
it
become
apparent
much
remains
be
learned,
especially
since
aging
process
highly
variable.
Most
federal
programs
research
studies
define
all
as
'older'
do
not
account
physiological
metabolic
changes
occur
throughout
older
adulthood
influence
nutritional
needs.
Moreover,
adult
racially
ethnically
diverse,
cultural
preferences
other
social
determinants
need
considered.
Research
Centers
Collaborative
Network
(RCCN)
sponsored
a
1.5-day
multi-disciplinary
workshop
included
sessions
on
Dietary
Patterns
Health
Disease,
Timing
Targeting
Interventions,
Disparities
Social
Context
Diet
Food
Choice.
agenda
presentations
can
found
at
https://www.rccn-aging.org/nutrition-2023-rccn-workshop.
Here
we
summarize
workshop's
themes
discussions
highlight
gaps
if
filled
will
considerably
advance
our
role
aging.
Journal of the American Society for Mass Spectrometry,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 25, 2025
Untargeted
metabolomics
often
produce
large
datasets
with
missing
values.
These
values
are
derived
from
biological
or
technical
factors
and
can
undermine
statistical
analyses
lead
to
biased
interpretations.
Imputation
methods,
such
as
k-Nearest
Neighbors
(kNN)
Random
Forest
(RF)
regression,
commonly
used,
but
their
effects
vary
depending
on
the
type
of
data,
e.g.,
Missing
Completely
At
(MCAR)
Not
(MNAR).
Here,
we
determined
impacts
degree
data
accuracy
kNN
RF
imputation
using
two
datasets:
a
targeted
metabolomic
dataset
spiked-in
standards
an
untargeted
dataset.
We
also
assessed
effect
compositional
approaches
(CoDA),
centered
log-ratio
(CLR)
transform,
interpretation
since
these
methods
increasingly
being
used
in
metabolomics.
Overall,
found
that
performed
more
accurately
when
proportion
across
samples
for
metabolic
feature
was
low.
However,
imputations
could
not
handle
MNAR
generated
wildly
inflated
imputed
where
none
should
exist.
Furthermore,
show
had
strong
impact
imputation,
which
affected
results.
Our
results
suggest
be
extreme
caution
even
modest
levels
especially
missingness
is
unknown.
