# Relationship between Investigative Biomarkers and Radiographic Grading in Patients with Knee Osteoarthritis

Authors: Eduardo Anitua; Mikel Sánchez; Maria de la Fuente; Juan Azofra; Mar Zalduendo; Jose J. Aguirre; Isabel Andía
Journal: International Journal of Rheumatology
 (2009)

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## Abstract

Objective. To examine new investigative biomarkers and their relevance for radiographic severity in knee osteoarthritis. Methods. The group comprised 63 patients with 73 knees examined. Patients were divided according to radiographic severity to allow for comparison of biomarker levels. Hyaluronic acid (HA), matrix metalloproteases (MMP-1, MMP-3 and MMP-13), tissue inhibitors of metalloproteases (TIMP-1 and TIMP-2), platelet-derived growth factor (PDGF-AB), transformed growth factor (TGF-β), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF) and insulin-like growth factor (IGF-I) were measured on synovial fluid and in plasma releasate at a single time point. Principal component analysis (PCA) followed by analysis of covariance were applied to evaluate data. Results.  Four different groups of biomarker were identified in plasma releasates. The first (platelet number, PDGF-AB and TGF-β) and second groups (HA and IGF-I) were related to radiographic severity, P=.005 and P=.022, respectively. The third (MMP-1 and TIMP-2) and fourth groups (MMP-3 and TIMP-1) represented the catabolic balance, but were not associated to radiographic grading. Three different clusters of biomarkers were found in synovial fluid but did not show any significant association to radiographic grading. Conclusions. New imaging approaches to assess structural deterioration and correlation with biomarker levels are warranted to advance in OA research.


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## Body

## 1. Introduction

Despite the vast increase in
molecular knowledge accrued during the last few years, a major breakthrough in
OA therapy has not emerged [1]. Limiting factors in current efforts are
somewhat attributed to the poor understanding of the molecular basis of disease
progression and the lack of dynamic biomarkers that reflect specific biological
or pathological processes [2].Accordingly, a number of studies
have focused on finding objective tissue-specific indicators of pathogenic
processes in both synovial and peripheral fluids. A major breakthrough in this
research was the finding of panels of biomarkers released into urine and serum
specifically reflecting the breakdown of major cartilage macromolecules and
bone turnover [3]. Other investigative biomarkers reflecting
degradative mechanisms (MMPs and TIMPs) of cartilage or representing essential
cell-to-cell or cell-to-matrix signaling (Growth Factors, GFs) in serum or
plasma are also under examination [4, 5].The present cross-sectional study
was undertaken in order to contrast the levels of GFs in addition to hyaluronic
acid (HA), matrix metalloproteases (MMP-1, MMP-3, MMP-13), tissue inhibitors of
metalloproteases (TIMP-1 and TIMP-2) in both the Preparation Rich in Growth
Factor (PRGF) releasate and synovial fluid (SF) from patients with OA. We
hypothesized that the composition of plasma releasate and/or SF may influence
radiographic severity of OA. Therefore, patients were divided according to
radiographic severity to allow for comparison of biomarker level.


## 2. Methods

The local Ethics Committee approved the study and all patients signed a
detailed informed consent form. The study was conducted with 63 consecutive
patients with clinical and radiographic evidence of knee OA, according to the
ACR criteria, [6] and with joint effusion detected clinically. These
patients were a subset of a larger prospective clinical study aiming to
evaluate the efficacy of PRGF for the treatment of knee OA. Idiopathic but not
secondary posttraumatic or inflammatory OA were included. Patients with
generalized OA or arthroscopic lavage in the year previous to treatment, or
intra-articular treatment within the previous three months were excluded.
Anterior-posterior weight bearing radiographs were scored for Ahlbäck
radiographic severity grade by two trained observers in consensus.


### 2.1. Blood and Synovial Fluid Sampling

To obtain PRGF from the patients, fasting venous blood was withdrawn
into 9 mL tubes containing 3.8% (wt/volt) sodium citrate. PRGF was prepared by
centrifugation at 640 g for 8 minutes. The platelet count in peripheral blood and
PRGF was determined using the hematological analyzer MICROS 60 from ABX
(Abingdon, UK). Plasma releasates were obtained after plasma coagulation with
calcium at a final concentration of 22.8 mM followed by 1 hour incubation at
37C°. Longer times of incubation did not change the releasate composition.All patients presented joint effusion,11.5±9.5 cc, range 2–40 cc. One
aliquot was used to estimate cell counts and the remaining volume was
centrifuged at 2000 g for 10 minutes. All samples were stored at −80C°.


### 2.2. Measurements

HA was determined by an
enzyme-linked binding protein assay (Corgenix Inc, CO, USA). The total amount
of TIMP-1, MMP-1, MMP-13, and MMP-3 was measured by the corresponding one step
sandwich enzyme immunoassay (EIA) from Amersham Biosciences (UK,
Buckinghamshire, England) and BioSource International,
USA (MMP-3). Enzyme-linked immunosorbent assay (ELISA) kits were used for
determining PDGF-AB, VEGF, HGF, IGF-I, and TGF-β1 concentrations (R&D
Systems, Abingdon, UK).


### 2.3. Statistical Analysis

Results are presented as median and arithmetic mean± standard
deviations. The Pearson coefficient was used to evaluate the associations
between plasma and SF biomarker concentrations. Factor analysis by the principal component (PCA) method was carried out
to determine associations between molecular markers and reduce the data of the
biochemical markers that are correlated. Components with Eigen values
>1 were extracted. The interpretability of these markers was examined after
applying a Varimax rotation with Kaiser Normalization. Further analysis of
parametric samples was performed using the General Linear Method (GLM) approach
to ANCOVA. Each principal factor representing combined biomarkers was used as a
dependent variable; the Ahlbäck score in addition to sex was used as an
independent variable; age and BMI were entered as covariables. Statistical
analyses were performed using SPSS version 15.0 (SPSS, Chicago, IL, USA).


## 2.1. Blood and Synovial Fluid Sampling

To obtain PRGF from the patients, fasting venous blood was withdrawn
into 9 mL tubes containing 3.8% (wt/volt) sodium citrate. PRGF was prepared by
centrifugation at 640 g for 8 minutes. The platelet count in peripheral blood and
PRGF was determined using the hematological analyzer MICROS 60 from ABX
(Abingdon, UK). Plasma releasates were obtained after plasma coagulation with
calcium at a final concentration of 22.8 mM followed by 1 hour incubation at
37C°. Longer times of incubation did not change the releasate composition.All patients presented joint effusion,11.5±9.5 cc, range 2–40 cc. One
aliquot was used to estimate cell counts and the remaining volume was
centrifuged at 2000 g for 10 minutes. All samples were stored at −80C°.


## 2.2. Measurements

HA was determined by an
enzyme-linked binding protein assay (Corgenix Inc, CO, USA). The total amount
of TIMP-1, MMP-1, MMP-13, and MMP-3 was measured by the corresponding one step
sandwich enzyme immunoassay (EIA) from Amersham Biosciences (UK,
Buckinghamshire, England) and BioSource International,
USA (MMP-3). Enzyme-linked immunosorbent assay (ELISA) kits were used for
determining PDGF-AB, VEGF, HGF, IGF-I, and TGF-β1 concentrations (R&D
Systems, Abingdon, UK).


## 2.3. Statistical Analysis

Results are presented as median and arithmetic mean± standard
deviations. The Pearson coefficient was used to evaluate the associations
between plasma and SF biomarker concentrations. Factor analysis by the principal component (PCA) method was carried out
to determine associations between molecular markers and reduce the data of the
biochemical markers that are correlated. Components with Eigen values
>1 were extracted. The interpretability of these markers was examined after
applying a Varimax rotation with Kaiser Normalization. Further analysis of
parametric samples was performed using the General Linear Method (GLM) approach
to ANCOVA. Each principal factor representing combined biomarkers was used as a
dependent variable; the Ahlbäck score in addition to sex was used as an
independent variable; age and BMI were entered as covariables. Statistical
analyses were performed using SPSS version 15.0 (SPSS, Chicago, IL, USA).


## 3. Results

The mean age of the participants was66±11 years (range 44–88) and 57%
were females. Subjects had an average BMI of 29.07±4.12 kg/m2 (range
22–40). The right knee was affected in 26 patients and the left in 27
while 10 patients had both knees affected. Of the 73 knee radiographs evaluated
according to Ahlbäck classification: eleven (15%) graded Ahlbäck I, 22 (30%)
graded Ahlbäck II, 27 (37%) graded Ahlbäck III, and 13 (18%) graded Ahlbäck IV.Table1 shows the biomarker concentrations in
both plasma releasate and synovial fluid.Table 1
Levels of hyaluronic acid (HA), matrix metalloproteases (MMP-1, MMP-3,
and MMP-13), tissue inhibitors of metalloproteases (TIMP-1 and TIMP-2),
platelet-derived growth factor (PDGF-AB), transformed growth factor (TGF-beta),
vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), and
insulin-like growth factor (IGF-I) were measured on knee synovial fluid and in
platelet-rich plasma (PRP) releasate at a single point in time. Results are
shown as mean± SD, (median), and range (ng-pg/mL). ND not detected. MMP*-13
was measurable only in 13 of the 63 patients.
PRGFSynovial fluidLeukocyte countND<400/μLPlatelet (x106)
/mL385±133ND(356)217−690PDGF-AB (ng/mL)15.33±7.48ND(14.19)3600−46725TGF-beta1 (ng/mL)27.02±11.170.75±0.56(24.75)8.39−57.55(0.62)0.24−3.46VEGF (pg/mL)200±142993±533(169)10−681(857)304−2544HGF (pg/mL)472±210672±445(413)56−1115(592)212−3768IGF-I (ng/mL)56.0±21.051.5±20.0(53.4)20.0−117.0(53.0)6−100HA (μg/mL)72±661452±694(50)4−367(1385)363−3625TIMP-1 (ng/mL)48.6±11.5868.6±511.6(46.0)34.0−86.0(720.0)51−3218TIMP-2 (ng/mL)53.0±24.8120.2±47.0(59.0)3−88(117.0)32−220MMP-1 (ng/mL)2.55±1.1224.7±40.6(2.45)0.3−6.2(13.0)1.0−250.0MMP-3 (ng/mL)7.17±3.69571±494(6.43)1.96−27.18(466)25−2056MMP-13* (ng/mL)ND0.056±0.024(0.054)0.033−0.119A marked degree of correlation (Pearson product
moment correlation coefficient,r>0.5) was observed for plasmatic IGF-I with
IGF-I in synovial fluid (r>0.577, P=.000) but not for other GFs, suggesting
local synthesis of these factors and/or variable clearance kinetics.Marked correlations were observed for
platelet-secreted growth factors (TGF-β, PDGF, HGF, and VEGF) within plasma
releasates (data not shown) while a moderate degree of correlation (r>0.3)
was observed between TGF-β and VEGF (0.340, P=.007) and between VEGF and HGF
(0.265, P=.035) within the synovium.The levels of plasmatic but not synovial HA
associated with IGF-I in both fluids, with Pearson coefficients of−0.472, P=.000
(synovial fluid) and −0.415, P=.001
(plasma).


### 3.1. Associations of Biochemical Markers by the Principal Component Analysis: Relationship to Radiographic Severity

#### 3.1.1. Plasma Releasates

The correlated biomarkers were reduced to four
independent factors explaining 70% of the total cumulative variance (Table2). The platelet count in PRGF, PDGF-AB,
and TGF-β 1 was loaded together in the first factor accounted for 24.79% of
the variance. The second factor (IGF-I and HA) explains 15.91% of the variance.
The third and fourth factors are represented by typical biomarkers of the
catabolic balance, MMP-1/TIMP-2 and MMP-3/TIMP-1, and accounted for 14.97% and 14.68% of the
variance, respectively. To test our hypothesis that the radiographic status
entails different biomarker levels we used ANCOVA.  In ANCOVA analyses we ascertained that the
first (PDGF-AB and TGF-β) and the second (IGF-I and HA) factors representing
the combined biomarkers are relevant to radiographic severity (P=.005 and
P=.002, resp.).Table 2
Principal component analysis coefficients of independent molecular marker factors
in plasma from 63 patients with knee OA. Molecular markers were grouped into
factors of related measures by principal component analysis using a Varimax
rotation with Kaiser Normalization. Components with Eigen values>1 were
extracted. Primary components of each factor are shown in bold type.
BiomarkerFactor1234Platelet count0.846−0.2550.1060.061PDGF-AB0.8410.183−0.1860.177TGF-beta10.853−0.2940.0240.164VEGF0.5990.3810.354−0.069HGF0.2930.356−0.4890.505MMP-10.112−0.0110.6850.091TIMP-2−0.037−0.0140.8280.213MMP-3−0.025−0.0580.1380.832TIMP-10.3180.1270.2460.726IGF-I0.020-0.8210.0540.044HA−0.1310.774−0.0040.137


#### 3.1.2. Synovial Fluid

The
correlated biomarkers in synovial fluid were reduced to three factors; these
together explain 51.84% of the total cumulative variance. The first factor
comprised IGF-I and TIMP-2 and explains 20.4% of the variance. Angiogenic
signaling factors (TGF-β1, VEGF, and HGF) accounted for 17% of the variance. HA and
MMP-3 segregated in the third factor explained 14% of the variance. Any of
these factors showed a significant connection to Ahlbäck grading.


## 3.1. Associations of Biochemical Markers by the Principal Component Analysis: Relationship to Radiographic Severity

### 3.1.1. Plasma Releasates

The correlated biomarkers were reduced to four
independent factors explaining 70% of the total cumulative variance (Table2). The platelet count in PRGF, PDGF-AB,
and TGF-β 1 was loaded together in the first factor accounted for 24.79% of
the variance. The second factor (IGF-I and HA) explains 15.91% of the variance.
The third and fourth factors are represented by typical biomarkers of the
catabolic balance, MMP-1/TIMP-2 and MMP-3/TIMP-1, and accounted for 14.97% and 14.68% of the
variance, respectively. To test our hypothesis that the radiographic status
entails different biomarker levels we used ANCOVA.  In ANCOVA analyses we ascertained that the
first (PDGF-AB and TGF-β) and the second (IGF-I and HA) factors representing
the combined biomarkers are relevant to radiographic severity (P=.005 and
P=.002, resp.).Table 2
Principal component analysis coefficients of independent molecular marker factors
in plasma from 63 patients with knee OA. Molecular markers were grouped into
factors of related measures by principal component analysis using a Varimax
rotation with Kaiser Normalization. Components with Eigen values>1 were
extracted. Primary components of each factor are shown in bold type.
BiomarkerFactor1234Platelet count0.846−0.2550.1060.061PDGF-AB0.8410.183−0.1860.177TGF-beta10.853−0.2940.0240.164VEGF0.5990.3810.354−0.069HGF0.2930.356−0.4890.505MMP-10.112−0.0110.6850.091TIMP-2−0.037−0.0140.8280.213MMP-3−0.025−0.0580.1380.832TIMP-10.3180.1270.2460.726IGF-I0.020-0.8210.0540.044HA−0.1310.774−0.0040.137


### 3.1.2. Synovial Fluid

The
correlated biomarkers in synovial fluid were reduced to three factors; these
together explain 51.84% of the total cumulative variance. The first factor
comprised IGF-I and TIMP-2 and explains 20.4% of the variance. Angiogenic
signaling factors (TGF-β1, VEGF, and HGF) accounted for 17% of the variance. HA and
MMP-3 segregated in the third factor explained 14% of the variance. Any of
these factors showed a significant connection to Ahlbäck grading.


## 3.1.1. Plasma Releasates

The correlated biomarkers were reduced to four
independent factors explaining 70% of the total cumulative variance (Table2). The platelet count in PRGF, PDGF-AB,
and TGF-β 1 was loaded together in the first factor accounted for 24.79% of
the variance. The second factor (IGF-I and HA) explains 15.91% of the variance.
The third and fourth factors are represented by typical biomarkers of the
catabolic balance, MMP-1/TIMP-2 and MMP-3/TIMP-1, and accounted for 14.97% and 14.68% of the
variance, respectively. To test our hypothesis that the radiographic status
entails different biomarker levels we used ANCOVA.  In ANCOVA analyses we ascertained that the
first (PDGF-AB and TGF-β) and the second (IGF-I and HA) factors representing
the combined biomarkers are relevant to radiographic severity (P=.005 and
P=.002, resp.).Table 2
Principal component analysis coefficients of independent molecular marker factors
in plasma from 63 patients with knee OA. Molecular markers were grouped into
factors of related measures by principal component analysis using a Varimax
rotation with Kaiser Normalization. Components with Eigen values>1 were
extracted. Primary components of each factor are shown in bold type.
BiomarkerFactor1234Platelet count0.846−0.2550.1060.061PDGF-AB0.8410.183−0.1860.177TGF-beta10.853−0.2940.0240.164VEGF0.5990.3810.354−0.069HGF0.2930.356−0.4890.505MMP-10.112−0.0110.6850.091TIMP-2−0.037−0.0140.8280.213MMP-3−0.025−0.0580.1380.832TIMP-10.3180.1270.2460.726IGF-I0.020-0.8210.0540.044HA−0.1310.774−0.0040.137


## 3.1.2. Synovial Fluid

The
correlated biomarkers in synovial fluid were reduced to three factors; these
together explain 51.84% of the total cumulative variance. The first factor
comprised IGF-I and TIMP-2 and explains 20.4% of the variance. Angiogenic
signaling factors (TGF-β1, VEGF, and HGF) accounted for 17% of the variance. HA and
MMP-3 segregated in the third factor explained 14% of the variance. Any of
these factors showed a significant connection to Ahlbäck grading.


## 4. Discussion

In the
present study, we have compared growth factor contents (PDGF-AB, TGF-β1, IGF-I,
VEGF, and HGF) in synovial fluid and platelet-rich plasma releasate from OA
patients. The rationale for measuring GFs in OA stems from the original
therapeutic option, presently under investigation, that is based on the
intra-articular application of autologous PRGF [7]. Initially, we have
compared the levels of GFs in PRGF and synovial fluid in OA to clarify the
rationale of our hypothesis. Platelets contain high levels of TGF-β and PDGF,
which may allow the possibility of using them as a vehicle for GF
supplementation within the capsular joint [8]. Additional research in
animal models indicates that TGF-β is crucial for cartilage maintenance and a
deficiency results in OA-like changes [9], although this issue has not
been confirmed in humans.We have also
examined a further group of molecules including HA, MMP-1, MMP-3, MMP-13,
TIMP-1, and TIMP-2. Together these molecules have been listed as investigative
and/or burden biomarkers according to the BIPED terminology [10]. Every
single one may be representative of specific molecular mechanisms primarily
involving synovial turnover, angiogenic signaling, and metabolic conditions in
OA.Because
analyses of single molecules do not reflect the complexity of disease
progression, a multivariate approach is required to better illustrate the
complex dynamic networks that participate in the disease. In the present study,
blood biomarkers were investigated in PRGF releasate as an alternative to
serum. Both are fluid components that remain after the clotting process of
plasma or full blood is completed. The former may be better in the study of
PDGF and TGF-β since it does not contain leukocytes, improving homogeneity of
the fluid and reducing variability. The principal component analysis in this
fluid segregated (i) platelet-secreted factors possibly associated to
angiogenesis (PDGF-AB and TGF-β), (ii) HA and IGF-I likely related to synovium
turnover and cartilage or bone metabolism, (iii) MMP-1/TIMP2, and (iv)
MMP-3/TIMP-1, which may reflect the catabolic status of the joint. Among the
principal factors found in plasma, TGF-β1 and PDGF showed the most consistent
association with OA severity. An association of serum TGF-β1 to radiographic
severity has also been reported previously, although those samples were
collected after 12 hours of daily activities [5].Another
finding showed a significant connection of HA and IGF-I to radiographic
severity. HA has been previously associated with morphological progression of
knee OA [11], whereas a systemic role for growth hormone and IGF-I has
been previously described in the pathogenesis and progression of OA [12].
Despite these significant findings there are some caveats in the present study.
First, plasma and synovial fluid results did not correlate. In fact, to truly
understand the interactions and influence of food intake, circadian and
activity-related variations in biomarker concentrations could help in defining
more precisely the usefulness of these biomarkers and the most appropriate body
fluid for analyses.In contrast to current agreement of
the great potential value of biomarker assessment in SF, we have only found a
single component with clear biological interpretation, namely, the association
of TGF-β1, HGF, and VEGF, which may reflect angiogenesis in the synovium. It is
difficult to determine why SF biomarkers did not show any association to OA
severity. It is possible that this failure may reflect the limited value of
standard radiography. In addition, rapid changes in the joint in response to
local perturbations along with the rapid turnover of synovial fluid and
variations in the efficiency of clearance from the joint compartment may
increase the inconsistency in synovial fluid measurements.



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*Source: 747432-2009-04-06.xml*