Infrared spectroscopy: shedding light on synovitis in patients with rheumatoid arthritis

J. M. G. Canvin, S. Bernatsky, C. A. Hitchon, M. Jackson1, M. G. Sowa1, J. R. Mansfield1, H. H. Eysel1, H. H. Mantsch1 and H. S. El-Gabalawy

Arthritis Centre, University of Manitoba and
1 Institute of Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Objectives. It is difficult to determine the extent of synovial involvement early in the course of rheumatoid arthritis. A spectroscopic technique was used to characterize the synovium of the small finger joints in both early and late rheumatoid arthritis. This synovium was also compared against normal joints.

Methods. Near-infrared spectroscopy assesses the absorption of near-infrared light by specific joints, giving a characteristic ‘fingerprint’ of the properties of the underlying tissues. Triple measurements by infrared spectroscopy were taken at the bilateral second and third metacarpophalangeal joints. Multivariate analysis was applied.

Results. Analysis was able to demonstrate relationships between the specific sources of spectral variation and joint tenderness or swelling as well as radiographic damage. Further use of multivariate analysis allowed recognition of the spectral patterns seen in early disease vs late rheumatoid arthritis and correct classification of over 74% of the joints.

Conclusions. The spectral regions where differences occurred were in the absorption bands related to tissue oxygenation status, allowing the provocative implication that this technique could be detecting ischaemic changes within the joint. Near-infrared spectroscopy may thus be able to provide us with some information about the biochemical changes associated with synovitis.

KEY WORDS: Rheumatoid arthritis, Early synovitis, Near infrared spectroscopy, Multivariate pattern recognition.


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Rheumatoid arthritis (RA) is a systemic autoimmune disorder causing a symmetrical inflammatory polyarthritis. Once the inflammatory process is activated there may be destruction of joints, which can, in some cases, be very aggressive and rapid. Commonly RA involves the small joints of the hands and feet; however, it is frequently hard to determine the extent of synovial involvement. Current modes of assessment, other than the clinical examination, include plain radiographs, which do not visualize soft tissue changes or very early damage, and magnetic resonance imaging, which does show early cartilage and bony destruction but is not easily available and is expensive [1]. Ultrasound imaging has also been used, but this has been confined to research units and is a more subjective assessment; microwave thermography has also been used in a research setting, with limited success [2, 3]. Near-infrared (NIR) spectroscopy has been used to examine the components of synovial fluid, and this research has evolved to aid the in vivo assessment of arthritis [2, 3].

Although RA has traditionally been thought of as one homogeneous disease, it has become increasingly obvious that there are many subgroups within this spectrum [6]. Numerous patients can have minimal disease for 20–30 yr, whereas some patients with a short duration of disease progress rapidly to joint destruction and total joint arthroplasty. Certain early prognostic features of patients are suggestive of more aggressive disease. These include specific genetic haplotypes, female gender, high rheumatoid factor titres, multiple joint involvement and early erosive damage [79]. However, no single feature predicts the severity of disease or the extent of involvement at a specific joint.

Studies have shown that indirect measures of disease activity, e.g. C-reactive protein and the erythrocyte sedimentation rate, can be correlated with increased destruction and resultant disability [10, 11]. A technique which could assess the extent of synovium involved may provide assistance in predicting outcome. It is also important to ascertain the burden of disease early on in the clinical assessment, as treatment protocols can then be customized to cope with more aggressive disease [12, 13]. Determining synovial involvement is often complicated by the discordance that is commonly found between the detection of swelling, tenderness and temperature of a joint with the plain radiographic assessment, which may not yet show any underlying damage [14].

NIR spectroscopy has been developed as a method to determine the different properties of tissues within a joint. Infrared light is passed via fibre-optic bundles through a tissue, allowing absorption of some of the light in a specific manner by the tissue and surrounding structures. A second, very minor, component of the light is non-preferentially scattered (diffusely scattered) over this area. The remaining light is reflected back. Analysis of either the reflected component or the transmitted components of the light can determine which wavelengths are absorbed by the tissue; this produces an infrared spectrum of the sample which may contain a spectral pattern characteristic of the tissue pathology.

Infrared spectroscopy will be important if it can truly detect significant differences between synovial signals. In the present work, we used infrared spectroscopy to compare populations of early RA and late RA patients and were able to identify early synovial changes in the actively inflamed joints. We included a control group of normal joints to ensure that the signals from RA and control joints were distinct.

This technique, which can non-invasively define joint involvement before it progressed to radiographically obvious disease, could aid in stratifying the disease burden and in prognosis. Ultimately, infrared spectroscopy could assist in prescribing individualized therapy. The equipment is portable, the size of a personal computer, and the technique is inexpensive. NIR spectroscopy was therefore investigated for its potential to aid the characterization of rheumatoid synovitis.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Patients
A cohort of 53 patients with RA (meeting the criteria of the American Rheumatism Association) [15] was studied. Of these, 28 had RA of duration less than 2 yr (early RA) and 25 had RA of duration greater than 2 yr (late RA). The control group consisted of 12 age- and gender-matched healthy subjects without RA. All the patients were between 18 and 70 yr of age. Patients were excluded if clinical or radiographic assessment showed that changes of osteoarthritis were superimposed.

Clinical assessment
Information was obtained on the patient's age, gender, ethnicity, smoking history and hand dominance. A total joint count (28 joints) was performed by one of two rheumatologists (JMGC, SB) using a graded scale (0=no findings, 3=maximal findings) for swelling, tenderness and damage. Damage was defined as loss of alignment, progressing to subluxation, or by restriction in the range of movement of the joint. Self-evaluation of morning stiffness, pain with a visual analogue scale and functional disability with the modified Health Assessment Questionnaire (HAQ) was performed. Erythrocyte sedimentation rate and C-reactive protein were determined. Radiographs of the hands were taken and scored at the eight target joints [second and third metacarpophalangeal (MCP) and second and third proximal interphalangeal (PIP) joints] by a panel of three rheumatologists, who were blinded to the results.

Near-infrared spectroscopy
Visible and NIR spectra were acquired using a Perstorp NIR Systems model 6500 scanning spectrometer (Silver Springs, MD, USA) equipped with a randomized fibre bundle with an active area of approximately 1 cm2. Scans were performed by one of three trained individuals. Reproducibility was ascertained by inter- and intra-technician measurements and was established before the study measurements. Three scans were performed on each patient, by one technician, and these triplicate spectra were collected at the wavelength in the area of interest between 400 and 1860 nm, at 2 nm resolution. Spectra were acquired using a fibre-optic probe that carried the NIR light from the spectrometer to the joints. A beam of low-intensity NIR light directed through a fibre-optic bundle was pressed lightly against the joint line of the target joints on the hand. By analysis of the reflected light, the wavelength of light absorbed by each joint was calculated.

Prior to any further analysis, the triplicate spectra from each joint were averaged, taking the median value for each wavelength, giving a total of 731 data points per spectrum. The spectra were partitioned into three classes—control, early RA and late RA—and a mean spectrum and the standard deviation for each data point were calculated for each class. Any spectrum greater than three standard deviations from the class mean was removed as an outlier. This left 94 control spectra, 205 early RA spectra and 193 late RA spectra, giving a total of 492 spectra.

Statistical analysis
Univariate t-tests and multivariate analyses were performed. The latter used principal components analysis (PCA) and linear discriminant analysis (LDA). PCA was used to partition the spectroscopic data into its independent sources (principal components) of variance and to determine if these sources of variation correlated with one or more clinical parameter. PCA finds the combination of wavelengths at which the spectral responses have the maximal variance. The first principal component accounts for the most variance in wavelength (PC1), the second (PC2) contributes the second greatest amount of variance in the data sets, but is independent of PC1. PCA can continue to further levels (PC3, PC4) to help explain the variance in wavelength.

Spectra were also classified by LDA of the optimal set of spectral subregions. An LDA algorithm was trained to recognize the patterns in these subregions, which were characteristic of early and late RA and control joints. This was performed for the MCP and PIP joint spectra independently. Further analysis was performed combining both MCP and PIP spectra. Two-thirds of the spectra were used in this training step. The remaining spectra were used as a test set to ascertain if the LDA algorithm could correctly predict whether the pattern corresponded to early RA, late RA or control joints.

To increase accuracy and decrease processing time, spectra were first preprocessed by selecting relevant features from the spectra by an optimal region selection algorithm developed by the Informatics Group of the National Research Council of Canada. Briefly, spectra were subdivided into a number of subregions. Combinations of these subregions were then used as input for the LDA to determine the minimum number of subregions that allowed classification of the spectra. In addition to reducing computation time, this region selection identifies specific diagnostic spectral features that can then be assigned to specific chemical species.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The baseline characteristics of all the patients are shown in Table 1Go; there were no significant differences between early RA and late RA other than the disease duration. The total joint count, radiographic score and laboratory parameters are summarized in Table 2Go. The total joint count was selected to provide an overview of the overall joint involvement in each group. Details of the target joint (second and third MCP and PIP) involvement are also included. The late RA group had significantly more damaged joints, both on clinical and radiographic evaluation, as would be expected. A representative NIR spectrum of a PIP joint is shown in Fig. 1Go. The spectra are plotted to show the amount of light absorbed by the joints at each wavelength; thus, peaks correspond to the wavelengths of light that are absorbed by different structures within the joint. As joint structures absorb characteristic wavelengths of light, each peak in the spectrum can be assigned to specific substances found within the joint. By comparison with the spectra of reference materials, we were able to assign the major peaks shown in Fig. 1Go. The mean spectra of all early RA, late RA and control joints are shown in Fig. 2Go. Superimposition of spectra on the same absorbance scale (inset, Fig. 2Go) suggests that subtle differences exist in absorptions arising from haemoglobin species and water. The high degree of variability between spectra of the scans from different subjects led to non-significant univariate statistical test results when comparing the reflectance response at individual wavelengths. To reliably ascertain whether there were significant differences, of diagnostic value, between the three patient groups, multivariate statistical methods (PCA and LDA) were used.


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TABLE 1. Baseline characteristics of patients

 

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TABLE 2. Clinical parameters of patients

 


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FIG. 1. Representative NIR spectrum of a PIP joint showing assignments of major absorptions.

 


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FIG. 2. Mean spectra of control, early and late RA joints. Areas highlighted in grey indicate spectral subregions containing the most diagnostic information. Assignment of absorptions in these subregions to individual chromophores is given in Table 4Go. Inset shows mean spectra plotted on the same absorbance scale.

 
Analysis of spectra of the eight target joints by PCA allowed each principal component, or source of spectral variation, to be correlated with clinical data, as shown in Table 3Go. Joint damage showed a small but statistically significant correlation with PC1. The clinical tenderness score correlated significantly with oxygenation status and tissue water content (hydration) as measured by NIR spectroscopy. Joint swelling correlated with PC4. Univariate t-tests indicate a significant difference in the values of both the PC1 and PC3 between the early and late RA groups.


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TABLE 3. Correlation of principle components (PC) with clinical data

 
Optimal region selection was then applied, allowing identification of a number of subregions (labelled 1–9 in Fig. 2Go), which allowed optimal classification. On the basis of assignments from the literature and laboratory studies of reference compounds, absorption bands in these regions can be attributed to oxy- and deoxyhaemoglobin, oxidized and reduced cytochrome aa3 and tissue lipids and proteins [16, 17] (Table 4Go).


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TABLE 4. Assignment of regions used in LDA (shaded regions in Fig. 2Go)

 
LDA was applied, using these selected regions to partition spectra into classes corresponding to disease states. The results of LDA as applied to PIP or MCP, or using both MCP and PIP joints, are shown in Table 5Go. When the trained algorithm was then applied to the test spectra for the PIP joints, 70.3% of early RA and 52.9% of late RA joints were correctly predicted, with an overall accuracy of 61.6%. The MCP joint alone had better sensitivity, 78.8% of early RA and 72.7% of late RA being correctly identified, with overall accuracy of 75.8%. The positive and negative predictive values were also improved compared with the PIP joint alone, all values being greater than 70%. Combining the spectral data for the PIP and MCP joints resulted in better overall performance of LDA. The combined data set had correct assignment in 77.3% of early RA and 71.2% of late RA, with overall accuracy of 74.3%. These results suggest that the technique is more sensitive to changes within the MCP joints than within the PIP joints.


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TABLE 5. Results of two-class LDA: early vs late RA

 
Finally, LDA was used to ascertain the discrimination between the three groups of early RA, late RA and control joints (Table 6Go). The specificity for the control group was 91.7%, providing acceptable accuracy for absence of disease in subjects with normal joints.


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TABLE 6. Three-class LDA: control, early and late RA, all joints

 


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
We describe a novel spectroscopic technique that has the potential to evaluate intra-articular metabolism non-invasively. The spectrometer and the attached hardware, which transmits the infrared signal and captures the reflected spectrum, are portable and were used effectively in a clinic setting. As with other non-invasive techniques, such as ultrasound imaging and Doppler studies, this procedure is well tolerated and multiple joints can be evaluated in an individual patient in one session. The MCP and PIP joints were studied as these joints were superficial, allowing adequate transmission, penetration and reflection of the infrared beam. Although this technique can potentially be applied to any joint, its sensitivity is related to the thickness of the overlying soft tissue. Moreover, the MCPs and PIPs are the joints most characteristically involved in RA. Correlation of measurements with radiographic findings is also easily achieved.

NIR spectroscopy is able to measure the wavelengths of NIR light that are absorbed by a sample, providing a characteristic spectrum of the sample. NIR light can be absorbed to promote low-lying, electronic transitions in the metal ions found in proteins such as haemoglobin, myoglobin and cytochromes. Both the oxidative state and the local environment influence the wavelength of light absorbed by the metal ions. Thus, oxy- and deoxymyoglobin, oxy- and deoxyhaemoglobin and reduced or oxidized cytochrome aa3 all absorb different wavelengths of NIR light. Consequently, the wavelengths of NIR light absorbed by tissues all provide direct chemical (compositional) and physiological (oxygenation and oxygen utilization) information. NIR spectroscopy can detect changes within the tissue that are indicative of its oxidative status, and this could allow the dynamic physiological assessment of a joint.

Spectra of all joints were dominated by absorption from water, as expected, and had contributions from oxy- and deoxyhaemoglobin, cytochrome aa3, lipids and proteins. Visually, the mean spectra of control, early RA and late RA joints exhibited a high degree of similarity and univariate analysis revealed no significant differences. However, the inadequacy of a univariate test is not surprising, as it is improbable that variations in the absorption of a single wavelength of light will provide clinically useful information. More plausibly, one would expect complex pattern changes in a variety of chemical species to be necessary to provide diagnostic information. Multivariate analysis of the complex spectral patterns is thus required to obtain diagnostic information from NIR spectra of joints.

One of the most straightforward multivariate techniques is PCA, which identifies sources of variance [principle components (PCs)]. PCs may then be correlated with clinical data in an attempt to assign sources of spectral variance to clinical parameters. When this approach was used, the data showed a correlation between joint damage and PC1 (the largest source of variance), suggesting that the greatest change in the spectral response of the joint is related to joint damage. However, the more subtle changes found in the subsequent PCs also provided important clinical correlates. Tenderness appeared to be correlated with PC2 and PC3, while swelling was associated with more subtle changes and was correlated with PC4. The correlation between PCs and the clinical or radiographic data suggests that information relating to inflammatory articular biochemical events is detectable in the visible NIR spectra.

The PCA established that clinically relevant information is potentially identifiable in the NIR spectrum. Analysis of NIR spectra using LDA has shown previously that the infrared spectrum of the tissue or fluids can predict the presence of a number of disease states, such as RA, Alzheimer's disease and a variety of forms of cancer [4, 5, 1820]. In this study we show that this type of analysis readily discriminates between normal joints and RA joints. Moreover, discrete spectral regions were identified which allowed distinction between early and late RA. These spectral regions are associated with the absorption features of water, cytochromes and haemoglobin species, suggesting that changes in oxidative status and water content of the joint are involved in the transition from early to late RA. Interestingly, similar changes have been reported previously in studies of animal models of acute tissue ischaemia [21]. This may suggest that the typical NIR spectra of early synovial lesions may reflect ischaemic changes within the joint. Although oxidative stress in rheumatoid synovitis has been shown in studies using a variety of techniques, the contribution of these processes to articular damage remains unclear [22]. Macromolecules in the synovial fluid, particularly hyaluronic acid, appear to have been broken down oxidatively into small oligosaccharides; likewise, proteins and lipids in rheumatoid synovial tissues and fluids appear to have undergone oxidative changes [23]. It has been shown that rheumatoid synovial fluids have the ischaemic characteristics of high lactate, low glucose, low partial pressure of oxygen (PO2) and high partial pressure of carbon dioxide (PCO2) levels [24].

The reactive oxygen species (ROS) that potentially mediate damage within the joint are generated by two principal mechanisms: an oxidative burst from activated intra-articular phagocytes and hypoxia or reperfusion injury within the synovium. Support for the generation of ROS by activated synovial neutrophils has been quite extensive [2527]. Currently, it is proposed that the end-products of oxidative stress may be important discriminators in the infrared spectra generated from synovial tissues. It is not clear why infrared spectroscopy in early rheumatoid joints demonstrates higher levels of oxidative stress compared with control and late RA joints. It is likely that the synovial compartments of the early rheumatoid patients had the most inflammatory activity, and thus the highest levels of oxidative stress. Alternatively, other mechanisms may be operative in the disease process, but are not detectable in advanced lesions. Analysis of synovial tissues using infrared microscopy is in progress and should help to define the cellular process.

Some studies, although using more invasive techniques with biopsies and magnetic resonance imaging, suggest that the clinical perception of joint involvement may be underestimated and quite inaccurate [2830]. It is therefore important to try to find modalities to detect early changes that are accurate and easy to perform.

In this preliminary study, we found infrared spectroscopy features that correlated joint damage, joint swelling and tenderness. It was further demonstrated that, by using LDA, NIR spectroscopy can effectively differentiate normal from rheumatoid joints with greater than 70% sensitivity. Furthermore, the technique is capable of differentiating advanced from early rheumatoid synovitis; it is easy to apply and LDA will be further refined with the acquisition of more data. It is hoped that a high enough level of discrimination can be achieved to detect the earliest changes of rheumatoid synovitis as well as to follow the effects of therapeutic interventions in a non-invasive manner.


    Acknowledgments
 
Funding was provided through a grant from the Health Sciences Foundation, Winnipeg, Manitoba.


    Notes
 
Correspondence to: J. M. G. Canvin, University of Manitoba, Arthritis Centre, RR-149, 800 Sherbrook Street, Winnipeg, MB R3A 1M4, Canada. E-mail: janice.m.canvin{at}gsk.com Back


    References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Submitted 8 December 2001; Accepted 28 May 2002





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