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@PHDTHESIS{JaimesCampos:1018195,
author = {Jaimes Campos, Mayra Alejandra},
othercontributors = {Jankowski, Joachim and Rauen, Thomas},
title = {{P}roteomics-guided interventions in kidney and
cardiovascular diseases},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2025-07745},
pages = {16 Seiten, Seite 873-883 : Illustrationen},
year = {2025},
note = {Dissertation, Rheinisch-Westfälische Technische Hochschule
Aachen, 2025, Kumulative Dissertation},
abstract = {Kidney and cardiovascular diseases are causing a large
proportion of morbidity and mortality in the population.
Diagnosis of these diseases often occurs at an advanced
stage when the disease is well-established and, in some
cases, incurable. While current interventions aim to slow
disease progression, the risk of advancement remains
significant, and existing clinical tools are unable to
predict individual treatment responses reliably. Early
diagnosis followed by targeted and personalized intervention
represents a promising approach to cope with these
challenges. Previous studies have shown that urinary
proteomics enables the identification of naturally occurring
peptides associated with the onset and progression of kidney
and cardiovascular diseases. Moreover, urinary peptides have
been shown to reflect the impact of specific interventions.
This thesis aimed to investigate proteomics-guided
interventions for improving the management of kidney and
cardiovascular diseases. The thesis consisted of two studies
based on analysing urinary proteomics data obtained using
capillary electrophoresis coupled with mass spectrometry
(CE-MS). (1) The first study investigated urinary peptide
biomarkers to predict the response to renin-angiotensin
system inhibitors (RASi) treatment to prevent diabetic
kidney disease (DKD) progression. Additionally, the study
evaluated the comparability of four different approaches and
equations for estimating the outcome parameter and estimated
glomerular filtration rate (eGFR). Analysis of 199 urinary
proteomics datasets from diabetic patients treated with RASi
identified 227 peptides that differ between patients with
controlled and uncontrolled kidney function. Response to
RASi treatment (controlled kidney function) or lack thereof
(uncontrolled kidney function) was determined by the annual
decline in eGFR, measured as the eGFR percentage slope
between visits. 189 of 227 peptides were combined in a
support vector machine-based proteomics model able to
predict non-response to treatment and DKD progression in two
independent cohorts treated with RASi (PRIORITY, n = 468,
AUC= 0.60; DIRECT-Protec-2, n = 194, AUC= 0.633). This study
also revealed substantial differences in kidney function
classification depending on the GFR equation used despite
the same sample set. (2) The second study evaluated
previously established urinary proteomics models (CKD273,
HF2, and CAD160) to predict renal or cardiovascular events
in a cohort of 5,585 subjects. Subsequently, an in-silico
treatment approach was applied to assess the impact of
common interventions, including ´mineralocorticoid receptor
antagonists´ (MRAs), ´sodium-glucose co-transporter 2
inhibitors´ (SGLT2i), dipeptidyl peptidase-4 inhibitors´
(DPP4i), ´angiotensin receptor blockers´ (ARB),
´glucagon-like peptide-1 receptor agonists´ (GLP1 RAs),
olive oil, and exercise, on individual urinary proteomic
profiles. The classifiers demonstrated significant
predictive value for heart failure, coronary artery disease,
and chronic kidney disease events, with respective hazard
ratios of 2.59, 1.71, and 4.12. The application of proteomic
models after the in-silico treatment indicated different
individual responses to interventions, supporting an
approach based on a personalized, proteomics-guided personal
intervention for each individual. Collectively, the results
of this thesis demonstrated the potential of urinary
proteomics to guide patient treatment and provide insights
into the potential impact of specific drugs and
interventions on the outcomes of kidney and cardiovascular
diseases at the personalized level. These results open the
way for further investigation into the clinical benefits of
these approaches in prospective trials.},
cin = {531010-3 ; 932310},
ddc = {610},
cid = {$I:(DE-82)531010-3_20140620$},
pnm = {DisCo-I - Discovering Collagen I degradation process in
chronic diseases with fibrotic component (101072828)},
pid = {G:(EU-Grant)101072828},
typ = {PUB:(DE-HGF)11},
url = {https://publications.rwth-aachen.de/record/1018195},
}
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