Hindawi
Disease Markers
Volume 2019, Article ID 1814304, 12 pages
https://doi.org/10.1155/2019/1814304
Research Article
Inflammation-Related Patterns in the Clinical Staging and
Severity Assessment of Chronic Kidney Disease
Simona Mihai ,1 Elena Codrici ,1 Ionela D. Popescu,1 Ana-Maria Enciu ,1,2
Elena Rusu,3,4 Diana Zilisteanu,3,4 Laura G. Necula,5 Gabriela Anton,5
and Cristiana Tanase1,6
1
Biochemistry-Proteomics Department, Victor Babes National Institute of Pathology, Splaiul Independentei 99-101, 050096 Sector 5,
Bucharest, Romania
2
Cellular and Molecular Medicine Department, Carol Davila University of Medicine and Pharmacy, No. 8 B-dul Eroilor Sanitari,
050474 Sector 5, Bucharest, Romania
3
Fundeni Clinic of Nephrology, Carol Davila University of Medicine and Pharmacy, Sos Fundeni 258, 022328 Sector 2,
Bucharest, Romania
4
Nephrology Department, Fundeni Clinical Institute, Sos Fundeni 258, 022328 Sector 2, Bucharest, Romania
5
Molecular Virology Department, Stefan S. Nicolau Institute of Virology, Sos Mihai Bravu 285, 030304 Sector 3, Bucharest, Romania
6
Titu Maiorescu University, Cajal Institute, Faculty of Medicine, Strada Dâmbovnicului 22, 040441, Sector 4, Bucharest, Romania
Correspondence should be addressed to Elena Codrici; raducan.elena@gmail.com
Received 12 June 2019; Revised 2 August 2019; Accepted 10 August 2019; Published 7 October 2019
Guest Editor: Christos Chadjichristos
Copyright © 2019 Simona Mihai et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Chronic kidney disease (CKD) is an irreversible loss of kidney function, and it represents a major global public health burden due to
both its prevalence and its continuously increasing incidence. Mineral bone disorders (MBDs) constitute a hallmark of CKD, and
alongside cardiovascular complications, they underlie a poor prognosis for these patients. Thus, our study focused on novel CKD
biomarker patterns and their impact on the clinical staging of the disease. As a first testing approach, the relative expression
levels of 105 proteins were assessed by the Proteome Profiler Cytokine Array Kit for pooled CKD stage 2–4 serum samples to
establish an overall view regarding the proteins involved in CKD pathogenesis. Among the molecules that displayed significant
dysregulation in the CKD stages, we further explored the involvement of Dickkopf-related protein 1 (Dkk-1), a recognised
inhibitor of the Wnt signalling pathway, and its crosstalk with 1,25OH2D3 (calcitriol) as new players in renal bone and vascular
disease. The serum levels of these two molecules were quantified by an ELISA (76 samples), and the results reveal decreasing
circulating levels of Dkk-1 and calcitriol in advanced CKD stages, with their circulating expression showing a downward trend
as the CKD develops. In the next step, we analysed the inflammation and MBD biomarkers’ expression in CKD (by xMAP
array). Our results show that the molecules involved in orchestrating the inflammatory response, interleukin-6 (IL-6) and
tumour necrosis factor alpha (TNFα), as well as the mineral biomarkers osteoprotegerin (OPG), osteocalcin (OC), osteopontin
(OPN), and fibroblast growth factor 23 (FGF-23), correlate with Dkk-1 and calcitriol, raising the possibility of them being
potential useful CKD biomarkers. These results reveal the impact of different biomarker patterns in CKD staging and severity,
thus opening up novel approaches to be explored in CKD clinical management.
1. Introduction
Chronic kidney disease (CKD) represents a major global disease that covers all degrees of injured renal function, with a
rising incidence and prevalence of kidney failure resulting
in poor outcomes and high economic costs. According to
the Kidney Disease Improving Global Outcomes (KDIGO)
2017 Clinical Practice Guideline Update for the Diagnosis,
Evaluation, Prevention, and Treatment of Chronic Kidney
Disease: Mineral and Bone Disorder (CKD-MBD), the
disease is defined as “abnormalities of the kidney structure
or function, present for more than 3 months, with
2
implications for health” [1]. The characteristic features of
CKD are the progressive and irreversible loss of renal function, which results in extensive kidney damage, leading
unconditionally to end-stage renal disease (ESRD). Over the
last 10 years, CKD has reached epidemic proportions, with
a constant increase in terms of both prevalence and
incidence, and it has been classified by the Global Burden of
Disease Study as “the 12th most common cause of death,
accounting for 1.1 million deaths worldwide.” Overall, its
poor prognoses ranked CKD as “the 17th leading cause of
global year loss of life and the 3rd largest increase of any
major cause of death” [2, 3].
Cardiovascular disease (CVD) is noted as the main cause
of morbidity and mortality in these patients, while CKD is
considered an accelerator of cardiovascular events and an
independent risk factor for CVD. It was also shown that all
CKD stages are accompanied by an elevated risk of cardiovascular complications and a decreased quality of life [4].
The causes of high cardiovascular mortality related to
CKD have been attributed in part to CKD-MBD syndrome,
which generates a unique environment that accelerates
vascular calcification (VC)—the pathological deposition of
calcium phosphate in the vasculature’s medial layer. Even
in the early CKD stages, the systemic mineral metabolism
and bone composition begin to alter; thus, the dysregulation
of mineral metabolism is considered a key player in CKD
pathophysiology.
An imbalance in the kidney-vascular-bone axis, a multifaceted active process, is induced by mineral metabolism
disorders and also by local inflammation; nevertheless, the
most extensive mineral disorders are experienced by patients
suffering from CKD [5].
The discovery of Wnt inhibitors, among them Dickkopfrelated protein 1 (Dkk-1), released during renal repair as
crucial components of mineral bone disorder (MBD) pathogenesis, suggests that additional pathogenic factors need to be
explored [6, 7].
Elucidating the signalling pathways involved in vascular
smooth muscle cell calcification holds the promise of being
able to unravel novel therapeutic approaches counteracting
the progression of MBDs in CKD.
Various factors mediate the VC mechanisms including
disturbances in the serum calcium/phosphate balance, systemic and local inflammation, the receptor activator of
nuclear factor kappa B (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) triad, aldosterone, microRNAs,
osteogenic transdifferentiation, and the effects of vitamins
[8]. The emerging role of 1,25-dihydroxyvitamin D3 (calcitriol, 1,25OH2D3) in CKD has been extensively explored,
since vitamin D deficiency/insufficiency is known to be
common among patients with CKD or in those undergoing
dialysis. Vitamin D has pleiotropic effects on the immune,
cardiovascular, and neurological systems, and many extrarenal organs have the enzymatic capability to convert 25OHD3
to 1,25OH2D3. It was also hypothesised that serum
1,25OH2D3 and 25OHD expressions tend to positively correlate, together with the renal function, as well [9].
Persistent low-grade inflammation is currently considered an essential part of CKD and as a traditional risk factor
Disease Markers
for renal pathology, hugely contributing to the development
of all-cause mortality in these patients [10]. The role of
proinflammatory cytokine overexpression inside the renal
patient’s landscape has drawn considerable attention, and
various studies have explored the potential link between
inflammatory status and renal function decline [11, 12]. A
challenging theory regarding the direct consequence of
inflammation on the progression of both CKD and CVD
was developed based on the supposition of this association
between markers of inflammation and an estimated glomerular filtration rate (eGFR) imbalance [13].
Despite the accessibility to the studies published in the
past few years, the KDIGO Guideline Committee underlines
the lack of strong clinical proof, emphasizing the critical role
of understanding the mechanisms underlying the disease’s
development, yet stressing the need for comprehensive, accurate clinical trials in this direction [14].
Considering the aforementioned aspects, in this study,
the correlation between the severity of CKD and inflammatory factors, MBD biomarkers, and other novel biomarkers
with an impact on CKD’s pathophysiology was investigated
to reveal potential proteome patterns that better characterise
the condition characteristic of each stage of CKD.
2. Materials and Methods
2.1. Patients and Samples
2.1.1. Study Population. We included 56 patients in our
cross-sectional study who were diagnosed with CKD according to the KDIGO Guidelines alongside 20 normal controls.
The CKD patients were divided into three groups based on
the CKD staging criteria as follows: 16 patients with CKD
stage 4 (25% female and 75% male; mean age 63 ± 14:8),
26 with CKD stage 3 (31% female and 69% male; mean
age 68 ± 8:5), and 14 with CKD stage 2 (29% female and
71% male; mean age 65 ± 10:3). Written informed consent
was obtained from all subjects prior to their inclusion in
the study according to the Helsinki Declaration and Ethics
Committee that approved this study.
Patients with acute infections, acute heart failure and significant heart valvular disease, chronic use of glucocorticoids
and immunosuppressive agents, and known malignancy were
excluded from our study. In addition, in order to avoid
the potential bias, patients undergoing vitamin D synthetic
analogue treatment were also excluded.
2.1.2. Clinical and Biochemical Assessment. On the day the
blood samples were collected, clinical and anthropometric
data were gathered: age, sex, weight, height, medical history,
and concomitant treatment. Laboratory tests were performed
on admission, namely, haemoglobin, haematocrit, serum creatinine, urea, uric acid, glucose, total cholesterol, triglycerides, alkaline phosphatase, phosphate, calcium, albumin,
and fibrinogen. The eGFR was calculated based on the
CKD-epidemiology collaboration (EPI) equation. Urinary
protein excretion was determined from a 24 h urine sample.
The blood samples were harvested the morning after a
12 h fast. After a standard centrifugation, the serum was aliquoted and stored at −80°C pending further analysis.
Disease Markers
2.2. Human Dot-Blot Proteome Profiler. Semiquantitative
immunodetection of serum cytokines, chemokines, growth
factors, angiogenesis markers, and other soluble proteins
was performed using the immuno-dot-blot method in the
Proteome Profiler Human XL Cytokine Array Kit (ARY022B,
R&D Systems, Inc., Abingdon, UK). A number of 105 captured antibodies, along with reference controls, were spotted
in duplicate on nitrocellulose membranes and incubated
overnight with 100 mL of pooled serum samples. Each of
the four pools was obtained by mixing the serum samples
from CKD patients in stages 4, 3, and 2, respectively; the
4th pool was assigned to control sera. The protocol recommended by the manufacturer was followed accordingly. The
membranes were incubated with biotinylated detection
antibodies, streptavidin-horseradish peroxidase (HRP), and
chemoluminescent detection reagents. Chemiluminescence
signals, corresponding to the amount of protein bound, were
detected using the MicroChemi 4.2 System (DNR BioImaging Systems, Israel), and the intensity of the chemiluminescence signals (pixel densities) was measured using ImageJ
1.42 software (National Institute of Health, Bethesda, MD,
USA). For each measured analyte, the average signal of the
duplicate spots was determined and normalised to the average signal of the reference spots after being corrected with
the background signal.
2.3. ELISA Immunoassay. Dkk-1 serum levels were assessed
using the Quantikine ELISA Human Dkk-1 Immunoassay
Kit (R&D Systems, Inc., USA) according to the manufacturer’s protocol. The quantitative determination of the calcitriol (1,25OH2D3 (1,25-dihydroxyvitamin D3)) serum levels
was made using the EIAab ELISA General Calcitriol Kit
(Wuhan EIAab Science Co., Ltd., China), and the manufacturer’s instructions were followed accordingly. The optical
densities were measured using an Anthos Zenyth 3100
Microplate Multimode Detector.
2.4. Luminex xMAP Array Analysis. The Luminex xMAP
array procedure was performed according to the manufacturer’s instructions. The serum levels of the 6-plex analytes
were simultaneously quantified on the Luminex 200 multiplexing platform. The Luminex xMAP array technique is
based on proprietary colour-coded microspheres coated with
specific capture antibodies. After the analytes from the serum
samples were captured by the bead cocktail, a biotinylated
detection antibody was added. The reaction mixture was then
incubated with the reporter molecule conjugate (streptavidin-phycoerythrin (SA-PE)) to complete the reaction on the
surface of the microspheres. After the reaction steps had been
completed, the microspheres were passed rapidly through a
red laser which excited the internal dyes, thus identifying
each unique microsphere set. The green laser excited PE,
the fluorescent dye on the reporter molecule, which was
directly correlated with the amount of analyte found in the
sample. All the acquired data was processed by high-speed
digital-signal processors and by xPONENT 3.1 software, generating results expressed in pg/mL.
Cytokine levels and BMD biomarkers were assayed using
the MILLIPLEX MAP Human Bone Magnetic Bead Panel Kit
3
(Merck-Millipore, Billerica, MA, USA), which comprises a
cocktail of six analytes: proinflammatory cytokines IL-6
and TNF-α and the MBD biomarkers OPG, osteocalcin
(OCN), osteopontin (OPN), and fibroblast growth factor
23 (FGF-23).
For all the biological specimens, duplicate samples were
used and their average concentrations were taken into consideration for further statistical analysis.
2.5. Statistical Analysis. As a first statistical approach, we
applied the Kolmogorov-Smirnov and D’Agostino and Pearson normality tests to all the CKD and control samples under
analysis. The Kolmogorov-Smirnov test was used to evaluate
the normality of the data distribution. The groups presented
with a nonnormal distribution (p < 0:0001); therefore, nonparametric statistical tests were used for further analysis.
The groups were not homogeneous in terms of age and gender, but according to the results obtained after applying the
Chi-square test, they did not influence the level of the analysed molecules; age was expressed as the mean ± SD. The
differences between the variables were analysed using the
Kruskal-Wallis test (a one-way analysis of variance) followed
by a Bonferroni post hoc test to compare the results inside the
different CKD stage groups. The Chi-square test for trends
was applied to reveal the differences in molecule expression
between the various CKD stages. The differences between
the nominal variables were analysed using Chi-square tests
(r, p). A value of p < 0:05 was considered statistically significant (∗ p < 0:05, ∗∗ p < 0:01, and ∗∗∗ p < 0:001). Spearman’s
correlation analysis was used to evaluate the correlations
between the analysed markers (r, p). GraphPad Prism version
5 software for Windows was used for the statistical analysis.
3. Results and Discussion
3.1. Proteome Profiler for CKD Clinical Staging by Dot-Blot
Array Assessment. An overall perspective on the multiple
proteins that are differentially expressed in the CKD stages
and thus potentially influence CKD’s pathophysiology was
gained by performing semiquantitative dot-blot immunodetection [15, 16]. Out of 105 molecules included in the Proteome Profiler Human XL Cytokine Array Kit, 24 relevant
molecules were identified as expressing significant levels in
CKD patients versus the control group. At first glance, the
dot-blot analysis revealed that molecules orchestrating the
inflammatory response were significantly overexpressed in
CKD; moreover, the multianalyte screening showed different
patterns of expression depending on the CKD stage (as illustrated in Figure 1). The integrated relative pixel density of
these molecules trended towards a progressive pattern of
expression, exhibiting gradual amounts depending on the
stage of renal disease. The most significant expression level
for proteins was identified in CKD stage 4. Among the
proteins that displayed a significant fold change versus the
control (about a 1.5-fold change), markers for inflammatory
response were identified, reflecting the high significance of
the inflammatory component in CKD. Among them, IL-6,
IL-8, IL-12, IL-18, interferon gamma (IFN-γ), the regulated
upon activation normal T-cell expressed and secreted
4
Disease Markers
1.2
Integrated relative density of pixels
⁎⁎
⁎⁎
⁎⁎
⁎⁎
⁎
⁎⁎
1.0
⁎⁎
0.8
⁎⁎
⁎⁎
⁎⁎
⁎⁎
⁎
0.6
⁎⁎
⁎⁎
0.4
0.2
Control
CKD stage 2
CD30
TFF3
IGFBP-2
Lipocalin2
PAI-1
PDGF-AA
GDF-15
uPAR
IL-18
MMP-9
RAGE
RANTES
IP-10
IFNgamma
Leptin
IL-8
SDF-1a
C-reactive protein
ICAM-1
OPN
IL-12p70
IL-6
Vit.D BP
Dkk-1
0.0
CKD stage 3
CKD stage 4
Figure 1: Original dot-blot membranes of the Proteome Profiler corresponding to different CKD stages and the control. The representative
molecules that exhibited significant fold changes versus the control and were the subject of further analysis have been marked accordingly.
(RANTES), the receptor for advanced glycation end products
(RAGE), intercellular adhesion molecule 1 (ICAM-1), inducible protein 10 (IP-10), plasminogen activator inhibitor 1
(PAI-1), platelet-derived growth factor (PDGF), and others
were identified as having a place in the CKD proteome
pattern, as shown in Figure 1.
Persistent, low-grade inflammation constitutes a common
feature of the disease, which accompanies CKD from its onset
[17]. Inflammatory biomarkers such as C-reactive protein and
IL-6 are known to independently predict mortality in these
patients. The origins of inflammation in kidney disease are
multifactorial, including the imbalance between proinflammatory increased production, induced on the one hand by various
sources of inflammatory stimuli (oxidative stress, acidosis,
comorbidities, genetic and epigenetic influences, etc.) and on
the other hand by their insufficient elimination due to
impaired glomerular filtration [18]. IL-6 hastens the development of CKD not only by aggravating kidney injury but also
by initiating its complications, especially the cardiovascular
ones. It is well established that IL-6 initiates the endothelial
injury mostly by reducing endothelial nitric oxide synthase
and adiponectin (an antiatherogenic adipokine) expression,
thus contributing to the increased incidence of cardiovascular
events in CKD patients. Taken together, an increased IL-6
level not only is a consequence of CKD but also acts as a trigger
for CKD-related complications [19].
Mediators of inflammation have been shown to be at high
levels in CKD patients. IL-12 and IL-18 are elevated during
the earlier stages of CKD, and the association with eGFR
suggests that IL-18 is mainly dependent upon renal clearance,
as suggested by Yong et al. [20].
The urokinase receptor system, a key regulator at the
intersection between inflammation, immunity, and coagulation [21], has also been shown to significantly increase in
CKD patients. Nuclear factor kappa B (NF-κB), a pivotal
mediator of inflammatory responses through triggering the
prototypical proinflammatory signalling pathway, appears
to mediate renal inflammation in different cell types including renal cells, innate immune cells, and lymphocytes [22,
23]. It was shown that NF-κB also controls several genes
involved in inflammation, and RAGE (an advanced glycation
end-product-specific receptor) itself seems to be upregulated
by NF-κB [24].
The pleiotropic cytokine OPN is increased in early
CKD stages, and its circulatory level increases with the
severity of the disease stage. OPN is an important factor
in bone remodelling, as it is involved in the pathogenesis
of both kidney and cardiovascular diseases. Barreto et al.
reported a positive correlation between OPN levels and
the clinical outcomes of CKD patients depending on their
inflammatory status [25].
The interplay between different proteins involved in
inflammation and the MBD profile is depicted in Figures 1
and 2. Among the molecules that exhibited significant downregulation in CKD stage 4 versus the control (with about a
1.5-fold change, p < 0:05—illustrated in Figure 3), Dkk-1
and vitamin D binding protein (vit D BP) showed the
highest potential and were chosen for further analyses.
The proinflammatory cytokine IL-6 and the MBD biomarker
OPN, with significant increases in CKD, were also subject to
further analyses.
3.2. Dkk-1 Was Negatively Correlated with CKD Clinical
Staging. Recent studies emphasize the close connection
between CKD and cardiovascular complications, as well
as the presence of a dysregulated Wnt signalling pathway
in CVD.
[26, 27]. Based on these facts, we explored the circulating
expression of Dkk-1, a recognised inhibitor of the Wnt–βcatenin signalling pathway, in modulating the renal disease
course. Targeting the Wnt signalling cascade aligns with
innovative therapeutic CVD strategies [28, 29].
Disease Markers
Reference
5
IL-6
DKK-1
Reference
Reference
IL-6
DKK-1
Reference
CKD
stage 2
Control
Reference Vit. D
BP
OPN
Reference Vit. D
BP
OPN
Reference
IL-6
DKK-1
Reference
Reference
IL-6
DKK-1
Reference
CKD
stage 3
CKD
stage 4
Reference Vit. D
BP
OPN
Reference Vit. D
BP
OPN
Figure 2: Serum protein profiling in CKD stages 2–4 versus the control. The integrated relative density of the pixels was calculated for each
molecule after normalisation to the average signal of the reference spots. The molecules showed an ascending trend of expression according to
the severity of the disease; Dkk-1 and vit D BP showed a descending trend.
2.5
1.5
1
−1.5
TFF3
CD30
IGFBP-2
PAI-1
Lipocalin2
GDF-15
PDGF-AA
IL-18
uPAR
RAGE
MMP-9
IP-10
RANTES
IFNgamma
IL-8
Leptin
SDF-1a
OPN
IL-6
C-reactive protein
−1
ICAM-1
−0.5
IL-12p70
0
Dkk-1
0.5
Vit.D BP
Fold changeCKD stage 4 versus control
2
Figure 3: The fold change in protein expression in CKD stage 4 versus the control. The average for the control group was established at 1.0,
and for each analysed molecule, the fold change was expressed as the CKD stage 4/control ratio.
The significant downregulation of Dkk-1 in CKD stage
4, determined via a dot-blot analysis, was further confirmed by running a quantitative ELISA. Our results
showed a statistically significant decreased expression of
Dkk-1 in CKD patients compared to the control group
(p < 0:05, Figure 4).
Relative serum Dkk-1 levels decreased even in the early
stages of CKD, with a 1.05-fold decrease in stage 2 versus
the control and a 1.3-fold decrease in stage 3. Dkk-1 circulat-
ing levels showed a downward trend, culminating in stage 4,
where a significant 2.36-fold decrease was recorded versus the
control. Recent studies have also reported that serum Dkk-1
levels were lower in CKD patients as compared with controls
and that Dkk-1 levels had a tendency to decrease with
the progressive development of CKD [30]. Interestingly,
Behets et al. reported lower levels in CKD patients than in
the controls, but Dkk-1 levels were not associated with the
laboratory parameters of mineral metabolism. Since these
Disease Markers
Dkk-1
1.5
Relative serum calcitriol levels
Relative serum Dkk-1 levels
6
1.0
0.5
0.0
Stage 2
Stage 3
Stage 4
Figure 4: Dkk-1 fold change expression in CKD stages 4, 3, and 2
versus the control, assessed by ELISA.
correlations were applied only to haemodialysis patients, it
was hypothesised that Dkk-1 targeted different regulatory
mechanisms inside the Wnt–β-catenin signalling pathway
[31]. Thus, Dkk-1 seems to have distinct effects depending
on the cell type, which is in line with the different effects of
Wnt–β-catenin signalling. Increasing evidence indicates that
the Wnt–β-catenin signalling pathway has important roles in
skeletal development and bone mass equilibrium. It was
found that Wnt activation increases bone formation and
reduces bone desorption; therefore, a disturbed Wnt–βcatenin signalling pathway may be involved in CKD-MBD
pathophysiology [32, 33]. Since VC is a hallmark feature of
chronic inflammatory disorders, it has been shown that
CKD aggravates vascular inflammation [34]. In a study conducted by Jang et al., the role of Dkk-1 in mediating the
inflammatory response was investigated, thus exploring the
implications of the Wnt signalling pathway in promoting
immune responses or inflammation by triggering NF-κB
activity. In this study, lipopolysaccharide- (LPS-) induced
inflammatory responses were found to be prevented by
Dkk-1 in a dose-dependent manner in human bronchial
epithelial cells and human umbilical vein endothelial cells
(HUVEC). Therefore, LPS-induced expression of the proinflammatory cytokines IL-6 and IL-8 was inhibited by
Dkk-1. Other proinflammatory genes such as TNF-α and
IL-1β were also downregulated by Dkk-1, a secreted Wnt
antagonist [35].
Exploring the potential of the Wnt–β-catenin signalling
pathway inhibitor Dkk-1 in predicting the severity of CKD
and elucidating its role in CKD-MBD pathophysiology is
thus a promising strategy for further studies.
3.3. Calcitriol Levels Decrease with Increasing CKD Stage. A
vitamin D deficiency is a common condition associated with
kidney disease. Many clinical studies have highlighted how a
vitamin D deficiency is an important risk factor for CKD
patients [36, 37].
Since dot-blot screening revealed significantly decreased
levels of vitamin D BP, we thus measured the most active
vitamin D metabolite in the kidneys: calcitriol (1,25-dihydroxyvitamin D3 (1,25OH2D3)).
Experimental studies have established that calcitriol and
vitamin D receptors are decisive regulators of the heart in
terms of structure and function. In addition, clinical studies
Calcitriol
1.5
1.0
0.5
0.0
Stage 2
Stage 3
Stage 4
Figure 5: Calcitriol fold change expression in CKD stages 4, 3, and 2
versus the control, assessed by ELISA.
have correlated vitamin D deficiency with CVD. Emerging
evidence has highlighted that calcitriol is significantly
involved in CVD-related signalling pathways, particularly in
the Wnt signalling pathway [38].
Our results revealed that relative serum calcitriol levels
started to decrease even in the early CKD stages, showing
a 1.15-fold decrease in stage 2 compared to the control
condition, a 1.5-fold decrease in stage 3, and a 2.24-fold
decrease in stage 4, respectively (as depicted in Figure 5),
thus gradually decreasing as the disease develops. In the
early CKD stages, the physiologic FGF-23 secretion from
the osteocytes causes inhibition of 1-α-hydroxylase and
stimulation of 24-hydroxylase in proximal renal tubules,
thereby decreasing calcitriol production. As CKD evolves,
the decrease in the functioning nephron mass combined
with hyperphosphatemia and high FGF-23 levels also
results in calcitriol deficiency [6, 39]. Since inflammation
has emerged to be at the core of CKD pathophysiology,
it was also hypothesised that vitamin D has a potential
role in modulating inflammatory cytokines and oxidative
stress, but the molecular mechanisms still remain unclear
[40]. Recent studies have shown that vitamin D supplementation among CKD patients undergoing dialysis had
beneficial effects on several genes related to inflammation
and oxidative stress. The downward trend in calcitriol concentrations in the CKD groups could be related to various
inflammatory and MBD factors, thus providing the basis
for future clinical assessments.
3.4. Multiplexing Showed Inflammatory and Mineral Bone
Disorder Biomarker Levels to Be Positively Correlated with
Disease Severity. Among the many contributors to CKD’s
poor prognosis, systemic low-grade inflammation is one
of the major players with an impact on the uremic phenotype in CKD. This chronic condition is fuelled by several
independent mechanisms, among which the mediators of
inflammation, IL-6 and TNFα, play important roles. We
have simultaneously quantified the serum levels of IL-6
and TNFα using the Luminex multiplexing xMAP array
platform, assaying via a preconfigured cytokine kit. Our
results revealed that relative serum IL-6 levels started to
increase in CKD early stage 2, showing a 1.9-fold change
compared to the control, with an ascending trend, presenting with a 6.3-fold increase in stage 3 and an 11-fold
increase in stage 4 (Figure 6(a)). Analysing the circulating
Relative serum TNF𝛼 levels
7
Relative serum IL-6 levels
Disease Markers
IL-6
25
20
15
10
5
0
Stage 2
Stage 3
TNF𝛼
15
10
5
0
Stage 2
Stage 4
OPG
6
4
2
0
Stage 2
Stage 3
Stage 4
OC
15
10
5
0
Stage 2
OPN
20
Stage 3
Stage 4
(d)
Relative serum FGF-23 levels
Relative serum OPN levels
(c)
15
10
5
0
Stage 2
Stage 4
(b)
Relative serum OC levels
Relative serum OPG levels
(a)
Stage 3
Stage 3
Stage 4
(e)
FGF-23
50
40
30
20
10
0
Stage 2
Stage 3
Stage 4
(f)
Figure 6: Fold change in serum IL-6 (a), TNFα (b), OPG (c), OC (d), OPN (e), and FGF-23 (f) expressions in CKD stages 4, 3, and 2 versus
the control, assessed by xMAP array.
expression of TNFα, we also observed an ascending trend,
with a 3.3-fold increase in stage 4 versus the control, and
as for CKD stages 3 and 4, the increases were 1.8-fold
and 1.7-fold, respectively (Figure 6(b)).
Given the fact that various cytokines mediate the inflammatory response, the extent to which inflammation plays a
role in raising the risk of MBDs in CKD remains unclear.
Regarding the MBD molecules, we analysed the serum levels
for OPG, OC, OPN, and FGF-23. All these biomarkers presented with an upward trend of expression, correlated with
disease severity. In CKD stage 4, the circulatory levels showed
the most significant differences compared to the control, as
follows: for OPG, a 3.14-fold increase; for OC, a 4.6-fold
increase; for OPN, a 7-fold increase; and for FGF-23, a 17fold increase. Our results suggest that the serum levels of
the above-mentioned molecules start to increase progressively, even from the CKD early stage 2, as depicted in
Figures 6(c)–6(f).
Since all the analysed biomarkers expressed the highest
concentrations in the most advanced stage of the disease,
and given that the circulatory trend increases as the disease
evolves, we considered it necessary to further analyse the possible correlations between these molecules that had a potential impact on CKD pathogenesis.
3.5. Correlations between Orchestrators of Inflammatory
Response and Biomarkers of Mineral and Bone
Disorders in CKD
3.5.1. The Trend for Biomarker Expression Was Modified
Depending on the CKD Stage. The pathophysiologic interplay between mediators of inflammation and the molecules
involved in MBDs was further analysed to establish potential
significant correlations at each stage of renal disease. By
applying the Chi-square test for trends, it was found that each
CKD stage had its own unique biomarker signature.
In CKD stage 4, we found a strong positive correlation
between Dkk-1 and calcitriol and a negative correlation
between Dkk-1 and IL-6, OPG, OC, OPN, and FGF-23
(p < 0:001, Chi-square test for trends). Renal function
(eGFR) was positively correlated with Dkk-1 in CKD stage
4 (p < 0:001).
Yeremenko et al. also observed an inverse correlation
between Dkk-1 and IL-6 in a study on inflamed arthritic
joints, potentially reflected by the differential regulation of
Dkk-1 production by TNFα and IL-6 [41]. Besides, it was
suggested that there were other recognised signalling pathways that Dkk-1 utilises other than the well-known canonical
Wnt pathway [42]. Another study highlighted that the
8
production of proinflammatory cytokines IL-4 and IL-10 was
notably reduced by Dkk-1 inhibitor treatment, suggesting
that Dkk-1 utilises the MAPK and mTOR signalling pathway
components to induce type 2 cell-mediated immune
responses or inflammation [43]. In a study conducted by
Malysheva et al., it was shown that proinflammatory cytokine
IL-6 repressed the activation of the Wnt signalling pathway
in human synoviocyte cells, and together with TNFα and
Dkk-1, it inhibited the activation of the Wnt response [44].
It was also found that calcitriol distinctly regulated two
genes encoding the extracellular Wnt inhibitors Dkk-1 and
Dkk-4 via an indirect transcriptional mechanism. Thus, calcitriol increases the expression of Dkk-1 RNA and protein,
acting as a tumour suppressor in human colon cancer cells
harbouring endogenous mutations in the Wnt–β-catenin
pathway [45].
Moreover, in CKD stage 4, the serum calcitriol concentrations were significantly correlated with proinflammatory
cytokine TNFα (p < 0:01, Chi-square test) and the MBD
markers OC, OPN, and FGF-23.
Our findings support the hypothesis that Dkk-1 could be
a useful biomarker for CKD severity, together with calcitriol,
both expressing the lowest levels in CKD stage 4.
According to recent studies, serum Dkk-1 levels were
lower in CKD patients, displaying different kinetics depending on the disease stage [31].
We also obtained significant correlations between Dkk-1
and calcitriol in CKD stages 3 and 2 and with several proinflammatory and MBD markers, as follows: Dkk-1 and OPG,
OPN, and FGF-23 (p < 0:001, Chi-square test for trends) in
CKD stages 3 and 2; calcitriol and TNFα (p < 0:01) in CKD
stage 3; and Dkk-1 and OPG and FGF-23 (p < 0:001) in
CKD stage 2.
According to our results, Dkk-1, calcitriol, mediators of
inflammation, and MBD markers showed significant
interactions, also being correlated with the severity of
CKD. How the relative balance between Dkk-1 and other
cytokines determines Wnt signalling and the pattern of
inflammation in CKD’s different stages needs to be further
investigated.
3.5.2. Strong Correlations between Dkk-1 and Calcitriol,
Inflammatory Cytokines, and Renal Function in the CKD
Patient Groups. The investigation of correlations in the
CKD patient groups was examined by applying the χ2 test
(χ2 , p) for serum levels of all the above-mentioned markers,
and strong correlations were found between Dkk-1 and calcitriol (χ2 = 21:4, p < 0:001). Furthermore, Dkk-1 was also
strongly correlated with the mediators of inflammation IL-6
(χ2 = 13:7, p < 0:001) and TNFα (χ2 = 10:4, p = 0:001) and
with the MBD biomarker FGF-23 (χ2 = 10, p = 0:001).
Calcitriol expression in the CKD patient groups was correlated with IL-6 (χ2 = 4:4, p < 0:05) and FGF-23 (χ2 = 5:5,
p = 0:01). Regarding renal function, we found a strong correlation between eGFR and Dkk-1 (χ2 = 8:48, p < 0:01) and calcitriol (χ2 = 8:36, p < 0:01), indicating the increased potential
for these two molecules in terms of assessing the severity of
the disease.
Disease Markers
In order to reveal the significant biomarker correlations
between the CKD stages, we performed Spearman correlation tests (r, p value). In advanced CKD stage 4, we obtained
significant correlations, as follows: TNFα and Dkk-1
(r = 0:50, p < 0:05), OPG (r = 0:58, p < 0:05), and OPN
(r = 0:66, p = 0:001). The MBD biomarkers OPG and OPN
were also correlated (r = 0:51, p < 0:05).
Other studies also supported the interactions between
the key players of bone metabolism, Dkk-1 and OPG, in
modulating the Wnt signalling pathway by balancing out
bone absorption and reconstruction. TNF-α, a key inducer
of Dkk-1, alongside OPG emerged as independent predictors of osteoarthritis severity. TNF-α, Dkk-1, and OPG
were considered as valuable biomarkers in predicting the
severity of the disease. The study also supported
inflammation-induced Dkk-1 and OPG in osteoarthritis
pathogenesis [46].
In CKD stage 3, correlations between the proinflammatory biomarkers TNFα and OPG (r = 0:6, p = 0:001) and
FGF-23 (r = 0:57, p < 0:01) are highlighted. In CKD early
stage 2, we found a strong negative correlation between
Dkk-1 and FGF-23 (r = −0:84, p < 0:001); moderate correlations were also observed between calcitriol and IL-6
(r = 0:53, p < 0:05), TNFα (r = 0:58, p < 0:05), OPG
(r = 0:71, p < 0:05), and FGF-23 (r = 0:52, p < 0:05). The
mediators of inflammation, IL-6 and TNFα, were also moderately correlated (r = 0:58, p < 0:05), and a moderate correlation was found between IL-6 and OPG (r = 0:61, p = 0:01).
In CKD, a complex network between Dkk-1, calcitriol,
mediators of inflammation, and MBD markers exists, but
the level at which it can affect the course of the disease
remains in question.
3.5.3. Significant Differences between Dkk-1, Calcitriol,
Mineral Disorders, Inflammatory Markers, and Renal
Function, Depending on CKD Stages. By applying the
Kruskal-Wallis one-way analysis of variance, we obtained
significant differences in the circulating expression of Dkk1, calcitriol, and eGFR in CKD patients (p < 0:0001). The post
hoc analysis showed that levels of Dkk-1, calcitriol, and eGFR
were significantly different between CKD stage 4 and stage 3,
CKD stages 4 and 2, and CKD stages 3 and 2, respectively
(p < 0:0001), highlighting the potential of these two markers
in evaluating the severity of the disease.
Significant differences in IL-6 were observed in CKD
patients (p < 0:0001). Bonferroni’s multiple comparison test
showed that IL-6 was significantly different between CKD
stage 4 and stage 3, CKD stages 4 and 2 (p < 0:0001), and
CKD stages 3 and 2 (p < 0:05). TNFα showed a significant
variance in CKD patients (p < 0:01), and the differences
between the stages were as follows: CKD stage 4 and stage 3
and CKD stages 4 and 2 (p < 0:05), according to our post
hoc analysis.
FGF-23 and OC presented with significant differences in
the CKD group (p < 0:0001), and the comparisons between
stages were only significant between CKD stage 4 and stage
3 and CKD stages 4 and 2 (p < 0:0001).
According to our results, we can conclude that a crosstalk
between Dkk-1, calcitriol, mineral disorders, inflammation,
Disease Markers
9
PDGFA
IL19
IL12B
GDF15
IL6R
PLAT
PDGFRA
MMP2
IL18
TNFRSF8
TIMP2
STAT3
TIMP3
IFNG
LCN2
CCL5
IL10
CCR5
JAK2
PLAUR
SERPINE1
IL6
MMP9
CXCR2
ICAM1
CYP24A1
CRP
CXCL8
AHSG
FGF23
CXCR1
CXCL10
SPP1
LEP
GC
TFF3
VDR
DKK1
CXCL12
BGLAP
TNFRSF11B
MOKT21
KREMEN2
DKKL1
MSX2
KREMEN1
Figure 7: Functional interaction between different molecules involved in inflammation and MBDs in CKD. The coloured nodes are
represented by query proteins and the first shell of interactors. Edges represent protein-protein functional associations, assigned with
different colour codes, as follows: a blue edge indicates known interactions from curated databases, a pink edge indicates known
interactions that have been experimentally determined, a green edge indicates predicted interactions in the gene neighbourhood, a red
edge indicates predicted interactions for gene fusions, a blue-ink edge indicates predicted interactions for gene cooccurrences, a lightgreen edge indicates other interactions derived from text mining, and a black edge indicates gene coexpression derived from other
databases. Abbreviations: CYP24A1: calcitriol, 1,25-dihydroxyvitamin D3, and 1,25OH2D3; SPP1: osteopontin, OPN; TNFRSF11B:
osteoprotegerin, OPG; BGLAP: osteocalcin, OC; CXCL8: IL-8, interleukin-8; GC: vitamin D binding protein, DBP; VDR: vitamin D receptor.
and renal function is present in CKD, thus influencing CKD
pathophysiology. Inflammation, the hallmark feature of
chronic diseases, seems to be a common mediator for both
kidney function and subsidiary MBDs. Because of its insidious nature, CKD silently evolves alongside other chronic
conditions, exhibiting different biomarker patterns depending on disease severity.
3.6. Functional Interplay between Markers of Inflammation
and Mineral Bone Disorders in CKD. Considering the rel-
evant proteins revealed by dot-blot immunodetection
screening, the functional interactions between the molecules involved in shaping the different patterns of CKD
have been put together by employing the STRING databases. The interactions include functional associations
between multiple molecules stemming from computational
prediction, knowledge transfer between organisms, and
interactions derived from other databases. Stronger evidence
for an association is represented by a thicker network edge, as
depicted in Figure 7.
10
Since CKD commonly arises alongside other comorbidities (such as hypertension, diabetes, and CVD) and the diagnosis of isolated CKD represents the exception rather than
the rule, an integrative patient assessment is the best clinical
approach [47]. Detailed characterisation of kidney disease is
needed to better understand the molecular relationships
underlying the pathophysiology of disease and to design
CKD biomarker patterns characteristic of the various CKD
stages, thus moving towards personalised care for each individual patient.
A potential limitation of our study is its cross-sectional
design, given the relatively small number of patients included
in our study. Therefore, further intense research is necessary
to completely decipher the underlying mechanisms behind
the connections between the analysed molecules in order to
better characterise the cytokine patterns in CKD.
4. Conclusions
As highlighted in our study, a functional interplay occurs
between markers of inflammation and MBDs in CKD
depending on disease severity. In spite of the advances in
CKD pathophysiology, there is an emerging need for novel
biomarkers to better characterise the different patterns of
nephropathy at each CKD stage. Out of all the analysed molecules, Dkk-1 and calcitriol were found to significantly correlate with CKD clinical staging, exhibiting the lowest levels in
CKD stage 4. Since inflammation has emerged at the core of
the pathophysiology of CKD, our results revealed significant
correlations between Dkk-1 and calcitriol and proinflammatory cytokines, starting with the early CKD stages. The MBD
biomarkers OPG, OPN, OC, and FGF-23 were significantly
correlated with Dkk-1 and calcitriol, as well as with the mediators of inflammation IL-6 and TNFα. In view of these findings, Dkk-1 and calcitriol could be considered as potential
useful biomarkers for CKD severity. Nevertheless, further
studies are needed to clearly unravel the complex networking
between Dkk-1, calcitriol, the mediators of inflammation,
and MBD markers to design promising biomarker patterns
for CKD, starting with its early stages.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no competing interests.
Authors’ Contributions
All authors contributed equally to this work.
Acknowledgments
The study was supported by the Ministry of Research and
Innovation in Romania, under Program 1: The Improvement
of the National System of Research and Development, Subprogram 1.2: Institutional Excellence-Projects of Excellence
Disease Markers
Funding in RDI, Contract No. 440 7PFE/16.10.2018, grant
COP A 1.2.3, ID: P_40_197/2016, and PN 19.29.01.04.
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