Gender Differences in the Incidence of Nephropathy and Changes in Renal Function in Patients with Type 2 Diabetes Mellitus: A Retrospective Cohort Study
Diabetes Metabolic Syndrome and Obesity,
Год журнала:
2024,
Номер
Volume 17, С. 943 - 957
Опубликована: Фев. 1, 2024
This
research
aims
to
examine
and
scrutinize
gender
variations
in
the
incidence
of
diabetic
nephropathy
(DN)
trajectory
renal
function
type
2
diabetes
mellitus
(T2DM)
patients.
Язык: Английский
The threshold effect of triglyceride glucose index on diabetic kidney disease risk in patients with type 2 diabetes: unveiling a non-linear association
Frontiers in Endocrinology,
Год журнала:
2024,
Номер
15
Опубликована: Июнь 13, 2024
Background
Previous
studies
have
confirmed
that
the
triglyceride
glucose
(TyG)
index,
recognized
as
a
reliable
marker
of
insulin
resistance,
is
an
important
risk
factor
for
diabetic
kidney
disease
(DKD).
However,
it
still
unclear
whether
DKD
continues
to
increase
linearly
with
elevation
TyG
index.
This
study
aimed
thoroughly
investigated
intrinsic
relationship
between
index
and
in
type
2
diabetes
(T2D).
Methods
cross-sectional
included
933
patients
T2D
China,
who
were
categorized
into
non-DKD
groups
stratified
by
levels.
Logistic
regression
analysis
identified
independent
factors
DKD.
The
association
was
evaluated
using
restricted
cubic
spline
(RCS)
curves
analysis.
R
package
‘CatPredi’
utilized
determine
optimal
cut-off
point
followed
threshold
effect
Results
prevalence
33.01%.
After
adjusting
confounding
factors,
prominent
clinical
DKD,
showing
highest
odds
ratio
(OR
1.57
(1.26
-
1.94),
P<0.001).
RCS
revealed
non-linear
interval
risk.
When
≤
9.35,
plateaued
at
low
level;
however,
when
>
increased
gradually
rising
Among
each
1-unit
associated
1.94-fold
(OR=1.94
(1.10
3.43),
P=0.022).
Conclusion
presented
initially
stable
level,
then
above
9.35.
Язык: Английский
A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital
BMC Medical Informatics and Decision Making,
Год журнала:
2025,
Номер
25(1)
Опубликована: Март 26, 2025
Abstract
Background
Diabetic
kidney
disease
(DKD)
is
a
serious
complication
of
diabetes
mellitus
(DM),
with
patients
typically
remaining
asymptomatic
until
reaching
an
advanced
stage.
We
aimed
to
develop
and
validate
predictive
model
for
DKD
in
initial
diagnosis
type
2
(T2DM)
using
real-world
data.
Methods
retrospectively
examined
data
from
3,291
(1740
men,
1551
women)
newly
diagnosed
T2DM
at
Ningbo
Municipal
Hospital
Traditional
Chinese
Medicine
(2011–2023).
The
dataset
was
randomly
divided
into
training
validation
cohorts.
Forty-six
readily
available
medical
characteristics
the
electronic
records
were
used
prediction
models
based
on
linear,
non-linear,
SuperLearner
approaches.
Model
performance
evaluated
area
under
curve
(AUC).
SHapley
Additive
exPlanation
(SHAP)
interpret
best-performing
models.
Results
Among
3291
participants,
563
(17.1%)
during
median
follow-up
2.53
years.
exhibited
highest
AUC
(0.7138,
95%
confidence
interval:
[0.673,
0.7546])
holdout
internal
set
predicting
any
Top-ranked
features
WBC_Cnt*,
Neut_Cnt,
Hct,
Hb.
High
WBC_Cnt,
low
high
Hb
levels
associated
increased
risk
DKD.
Conclusions
developed
validated
T2DM.
Using
routinely
clinical
measurements,
could
predict
hospital
visits.
Prediction
accuracy
SHAP-based
interpretability
may
help
improve
early
detection,
targeted
interventions,
prognosis
DM.
Язык: Английский
Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis
Frontiers in Endocrinology,
Год журнала:
2024,
Номер
15
Опубликована: Июль 3, 2024
Objective
This
study
systematically
reviews
and
meta-analyzes
existing
risk
prediction
models
for
diabetic
kidney
disease
(DKD)
among
patients
with
type
2
diabetes,
aiming
to
provide
references
scholars
in
China
develop
higher-quality
models.
Methods
We
searched
databases
including
National
Knowledge
Infrastructure
(CNKI),
Wanfang
Data,
VIP
Chinese
Science
Technology
Journal
Database,
Biomedical
Literature
Database
(CBM),
PubMed,
Web
of
Science,
Embase,
the
Cochrane
Library
studies
on
construction
DKD
diabetes
patients,
up
until
28
December
2023.
Two
researchers
independently
screened
literature
extracted
evaluated
information
according
a
data
extraction
form
bias
assessment
tool
model
studies.
The
area
under
curve
(AUC)
values
were
meta-analyzed
using
STATA
14.0
software.
Results
A
total
32
included,
31
performing
internal
validation
22
reporting
calibration.
incidence
rate
ranged
from
6.0%
62.3%.
AUC
0.713
0.949,
indicating
have
fair
excellent
accuracy.
overall
applicability
included
was
good;
however,
there
high
bias,
mainly
due
retrospective
nature
most
studies,
unreasonable
sample
sizes,
conducted
single
center.
Meta-analysis
yielded
combined
0.810
(95%
CI:
0.780–0.840),
good
predictive
performance.
Conclusion
Research
is
still
its
initial
stages,
lack
clinical
application.
Future
efforts
could
focus
constructing
high-performance,
easy-to-use
based
interpretable
machine
learning
methods
applying
them
settings.
Registration
systematic
review
meta-analysis
following
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
(PRISMA)
statement,
recognized
guideline
such
research.
registration
https://www.crd.york.ac.uk/prospero/
,
identifier
CRD42024498015.
Язык: Английский
A New Potent Inhibitor against α-Glucosidase Based on an In Vitro Enzymatic Synthesis Approach
Molecules,
Год журнала:
2024,
Номер
29(4), С. 878 - 878
Опубликована: Фев. 16, 2024
Inhibiting
the
activity
of
intestinal
α-glucosidase
is
considered
an
effective
approach
for
treating
type
II
diabetes
mellitus
(T2DM).
In
this
study,
we
employed
in
vitro
enzymatic
synthesis
to
synthesize
four
derivatives
natural
products
(NPs)
discovery
therapeutic
drugs
T2DM.
Network
pharmacology
analysis
revealed
that
betulinic
acid
derivative
P3
exerted
its
effects
treatment
T2DM
through
multiple
targets.
Neuroactive
ligand–receptor
interaction
and
calcium
signaling
pathway
were
identified
as
key
pathways
involved
action
compound
The
results
molecular
docking,
dynamics
(MD)
simulations,
binding
free
energy
calculations
indicate
exhibits
a
more
stable
lower
(−41.237
kcal/mol)
with
compared
acarbose.
addition,
demonstrates
excellent
characteristics
various
pharmacokinetic
prediction
models.
Therefore,
holds
promise
lead
development
warrants
further
exploration.
Finally,
performed
site-directed
mutagenesis
achieve
targeted
derivative.
This
work
practical
strategy
discovering
novel
anti-hyperglycemic
from
NPs
synthesized
technology,
providing
potential
insights
into
drug
development.
Язык: Английский
Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis
Current Medical Research and Opinion,
Год журнала:
2024,
Номер
unknown, С. 1 - 31
Опубликована: Окт. 30, 2024
The
purpose
of
this
study
was
to
conduct
a
systematic
investigation
the
potential
artificial
intelligence
(AI)
models
in
prediction,
detection
diagnostic
biomarkers,
and
progression
diabetic
kidney
disease
(DKD).
In
addition,
we
compared
performance
non-logistic
regression
(LR)
machine
learning
(ML)
conventional
LR
prediction
models.
Язык: Английский
Two-Dimensional Ultrasound-Based Radiomics Nomogram for Diabetic Kidney Disease: A Pilot Study
International Journal of General Medicine,
Год журнала:
2024,
Номер
Volume 17, С. 1877 - 1885
Опубликована: Май 1, 2024
Objective:
To
establish
a
radiomics
nomogram
based
on
two-dimensional
ultrasound
for
risk
assessment
of
diabetic
kidney
disease
(DKD)
in
patients
with
type
2
diabetes
mellitus
(T2DM).
Methods:
This
study
retrospectively
collected
images
and
clinical
data
from
52
T2DM
who
underwent
renal
biopsy
our
hospital
January
2023
to
August
2023.
Based
the
pathological
results,
all
were
categorized
into
two
groups:
DKD
(n=33)
non-DKD
(n=19).
The
radiomic
features
segmented
pictures
retrieved
selected
calculate
each
patient's
rad-score.
A
predictive
rad-score
was
then
constructed
validated
calibration
curve.
Results:
computed
five
imaging
characteristics
extracted
images.
developed
rad-score,
retinopathy,
duration
diabetes,
glycosylated
hemoglobin.
Moreover,
showed
outstanding
capability,
discrimination
as
well
therapeutic
usefulness.
Conclusion:
We
patientsThe
model
has
been
proven
have
good
performance,
showing
its
potential
identifying
assisting
making
appropriate
early
interventions.
Keywords:
disease,
ultrasound,
machine
learning,
Язык: Английский