Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities
Nature Communications,
Journal Year:
2025,
Volume and Issue:
16(1)
Published: March 3, 2025
Genomics
can
provide
insight
into
the
etiology
of
type
2
diabetes
and
its
comorbidities,
but
assigning
functionality
to
non-coding
variants
remains
challenging.
Polygenic
scores,
which
aggregate
variant
effects,
uncover
mechanisms
when
paired
with
molecular
data.
Here,
we
test
polygenic
scores
for
cardiometabolic
comorbidities
associations
2,922
circulating
proteins
in
UK
Biobank.
The
genome-wide
score
associates
617
proteins,
75%
also
associate
another
score.
Partitioned
capture
distinct
disease
biology,
342
(20%
unique).
In
this
work,
identify
key
pathways
(e.g.,
complement
cascade),
potential
therapeutic
targets
FAM3D
diabetes),
biomarkers
diabetic
EFEMP1
IGFBP2)
through
causal
inference,
pathway
enrichment,
Cox
regression
clinical
trial
outcomes.
Our
results
are
available
via
an
interactive
portal
(
https://public.cgr.astrazeneca.com/t2d-pgs/v1/
).
Language: Английский
Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis
Frontiers in Digital Health,
Journal Year:
2025,
Volume and Issue:
7
Published: March 27, 2025
Background
Type
2
Diabetes
Mellitus
(T2DM)
remains
a
critical
global
health
challenge,
necessitating
robust
predictive
models
to
enable
early
detection
and
personalized
interventions.
This
study
presents
comprehensive
bibliometric
systematic
review
of
33
years
(1991-2024)
research
on
machine
learning
(ML)
artificial
intelligence
(AI)
applications
in
T2DM
prediction.
It
highlights
the
growing
complexity
field
identifies
key
trends,
methodologies,
gaps.
Methods
A
methodology
guided
literature
selection
process,
starting
with
keyword
identification
using
Term
Frequency-Inverse
Document
Frequency
(TF-IDF)
expert
input.
Based
these
refined
keywords,
was
systematically
selected
PRISMA
guidelines,
resulting
dataset
2,351
articles
from
Web
Science
Scopus
databases.
Bibliometric
analysis
performed
entire
tools
such
as
VOSviewer
Bibliometrix,
enabling
thematic
clustering,
co-citation
analysis,
network
visualization.
To
assess
most
impactful
literature,
dual-criteria
combining
relevance
impact
scores
applied.
Articles
were
qualitatively
assessed
their
alignment
prediction
four-point
scale
quantitatively
evaluated
based
citation
metrics
normalized
within
subject,
journal,
publication
year.
scoring
above
predefined
threshold
for
detailed
review.
The
spans
four
time
periods:
1991–2000,
2001–2010,
2011–2020,
2021–2024.
Results
findings
reveal
exponential
growth
publications
since
2010,
USA
UK
leading
contributions,
followed
by
emerging
players
like
Singapore
India.
Key
clusters
include
foundational
ML
techniques,
epidemiological
forecasting,
modelling,
clinical
applications.
Ensemble
methods
(e.g.,
Random
Forest,
Gradient
Boosting)
deep
Convolutional
Neural
Networks)
dominate
recent
advancements.
Literature
reveals
that,
studies
primarily
used
demographic
variables,
while
efforts
integrate
genetic,
lifestyle,
environmental
predictors.
Additionally,
advances
integrating
real-world
datasets,
trends
federated
learning,
explainability
SHAP
(SHapley
Additive
exPlanations)
LIME
(Local
Interpretable
Model-agnostic
Explanations).
Conclusion
Future
work
should
address
gaps
generalizability,
interdisciplinary
research,
psychosocial
integration,
also
focusing
clinically
actionable
solutions
applicability
combat
diabetes
epidemic
effectively.
Language: Английский
Usefulness of the Córdoba Equation for Estimating Body Fat When Determining the Level of Risk of Developing Diabetes Type 2 or Prediabetes
Medicina,
Journal Year:
2025,
Volume and Issue:
61(4), P. 613 - 613
Published: March 27, 2025
Background
and
Objectives:
Type
2
diabetes
(T2D)
prediabetes
represent
major
global
health
concerns,
with
obesity
being
a
key
risk
factor.
However,
recent
evidence
suggests
that
the
adipose
tissue
composition
distribution
play
more
critical
role
in
metabolic
dysfunction
than
total
body
weight
or
mass
index
(BMI).
This
study
evaluates
predictive
capacity
of
Córdoba
Equation
for
Estimating
Body
Fat
(ECORE-BF)
identifying
individuals
at
high
developing
T2D
prediabetes.
Materials
Methods:
A
cross-sectional
was
carried
out
involving
418,343
Spanish
workers.
fat
percentage
estimated
using
ECORE-BF
equation,
assessed
validated
models,
including
Finnish
Diabetes
Risk
Score
(FINDRISC),
QDiabetes
score
(QD-score),
others.
The
discriminatory
power
predicting
receiver
operating
characteristic
(ROC)
curve
analysis.
Results:
showed
strong
correlation
high-risk
classifications
across
all
scales.
area
under
ROC
(AUC)
exceeded
0.95
both
men
women,
demonstrating
accuracy.
Conclusions:
Adipose
distribution,
particularly
visceral
adiposity,
is
central
factor
dysfunction.
provides
cost-effective
alternative
large-scale
assessment.
Future
research
should
explore
impact
reduction
on
prevention
integration
estimation
scales
into
clinical
public
strategies.
Language: Английский
Clinical use of polygenic scores in type 2 diabetes: challenges and possibilities
Diabetologia,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 5, 2025
Language: Английский
Genetic Biomarkers for Periodontal Diseases: A Systematic Review
Journal Of Clinical Periodontology,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 8, 2025
ABSTRACT
Aims
To
identify
genetic
biomarkers
that
may
be
used
in
the
diagnosis,
prevention
or
management
of
different
forms
periodontal
disease.
Materials
and
Methods
Following
protocol
registration
PICOTS
(patient,
intervention,
comparison,
outcome,
time,
studies)
questions,
a
systematic
search
literature
was
conducted
(PudMed,
Ovid),
resulting
1592
papers
screened
by
two
reviewers.
Diagnostic
data
were
extracted
calculated
from
included
compared
with
clinically
determined
diagnoses,
disease
progression
and/or
response
to
treatment.
Results
A
total
607
articles
met
inclusion
criteria,
including
10
reporting
on
gingivitis
597
periodontitis.
Only
reported
diagnostic
performance
data,
while
for
41
large
candidate
gene
studies,
could
data.
No
study
using
chair‐side
tests
identified.
Low
moderate
values
sensitivity,
specificity,
positive
negative
predictive
value
accuracy
found.
Conclusion
test
clinical
emerged
prediction
resolution.
Thus,
potential
future
applications
polygenic
risk
scores
encode
susceptibility,
as
well
single‐marker
testing
monogenic
oligogenic
diseases,
are
discussed.
Language: Английский