A Cohort Study of Rheumatoid Arthritis Patients - ACS Publications

Aug 11, 2010 - Herein we present results of a metabolic phenotyping study that monitored ... A cohort study including 47 patients with RA (23 with act...
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H NMR Spectroscopy-Based Interventional Metabolic Phenotyping: A Cohort Study of Rheumatoid Arthritis Patients Michael B. Lauridsen,† Henning Bliddal,*,‡,§ Robin Christensen,§,| Bente Danneskiold-Samsøe,‡,§,⊥ Robert Bennett,# Hector Keun,∇ John C. Lindon,∇ Jeremy K. Nicholson,∇ Mikkel H. Dorff,¶ Jerzy W. Jaroszewski,[ Steen H. Hansen,[ and Claus Cornett[

Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark, The Parker Institute, Copenhagen University Hospital, Frederiksberg, Denmark, Institute of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark, Faculty of Health & Science, University of Copenhagen, Copenhagen, Denmark, Oregon Health & Sciences University, Portland, Oregon, Faculty of Medicine, Imperial College London, London, United Kingdom, Department of Heamatology, The Finsen Centre, Rigshospitalet, Copenhagen, Denmark, and Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark Received March 26, 2010 1

H NMR spectroscopy-based metabolic phenotyping was used to identify biomarkers in the plasma of patients with rheumatoid arthritis (RA). Forty-seven patients with RA (23 with active disease at baseline and 24 in remission) and 51 healthy subjects were evaluated during a one-year follow-up with assessments of disease activity (DAS-28) and 1H NMR spectroscopy of plasma samples. Discriminant analysis provided evidence that the metabolic profiles predicted disease severity. Cholesterol, lactate, acetylated glycoprotein, and lipid signatures were found to be candidate biomarkers for disease severity. The results also supported the link between RA and coronary artery disease. Repeated assessment using mixed linear models showed that the predictors obtained from metabolic profiles of plasma at baseline from patients with active RA were significantly different from those of patients in remission (P ) 0.0007). However, after 31 days of optimized therapy, the two patient groups were not significantly different (P ) 0.91). The metabolic profiles of both groups of RA patients were different from the healthy subjects. 1H NMR-based metabolic phenotyping of plasma samples in patients with RA is well suited for discovery of biomarkers and may be a potential approach for disease monitoring and personalized medication for RA therapy. Keywords: metabonomics • phenotyping • NMR • rheumatoid arthritis • chemometrics • disease monitoring • personalized medicine

Introduction Rheumatoid arthritis (RA) is an chronic inflammatory condition with major socio-economic consequences.1,2 There is currently no available cure and all treatment is aimed at pain relief and reduction of disease progression,3 although a longerlasting remission may be induced by early, aggressive therapy.4 Diagnosis is based on a range of different immunological and clinical findings.5 The disease mechanism is suggested to * To whom correspondence should be addressed. Henning Bliddal, The Parker Institute, Copenhagen University Hospital, Frederiksberg, Denmark. E-mail: [email protected]. † Faculty of Life Sciences, University of Copenhagen. ‡ Aalborg University. § Copenhagen University Hospital. | University of Southern Denmark. ⊥ Faculty of Health & Science, University of Copenhagen. # Oregon Health & Sciences University. ∇ Imperial College London. ¶ The Finsen Centre. [ Faculty of Pharmaceutical Sciences, University of Copenhagen. 10.1021/pr1002774

 2010 American Chemical Society

originate in unknown antigens present in the arthritic joint that initiate a complex immune response.6 Identification of reliable biomarkers in the blood of RA patients, which reflect both diagnosis and disease severity, would provide an important new tool for maximizing patient care. Numerous proteomic7-9 and genomic10 studies have provided several biomarker candidates and insight into the pathology of rheumatoid arthritis. In the realm of metabonomics, differences in the lactate/alanine ratio of synovial fluid (SF) were reported to discriminate between RA and osteoarthritis.11 Elevated concentrations of N-acetylated “acute phase” glycoproteins and advanced glycation endproducts (AGE) in SF correlated with having RA, as did the creatinine concentration.12,13 Decreased glucose, chylomicron and VLDL concentrations and increased levels of ketone bodies in SF relative to serum reflected higher levels of anaerobic metabolism and enhanced metabolic consumption of lipids in the inflammatory exudates of RA patients14 and rodents.15 Increased lipid metabolism was associated with an elevated Journal of Proteome Research 2010, 9, 4545–4553 4545 Published on Web 08/11/2010

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Experimental Procedures

Figure 1. Typical unfiltered water suppressed 1D 1H NMR spectra of human blood plasma for a symptomatic patient with active RA (joint inflammation) (red), a RA patient in remission (blue), and a healthy subject (black), including assignment of selected metabolites. Magnifications of lactate and N-acetyl glycoprotein signals illustrate differences among groups. Spectra are acquired with the NOESYPRESAT pulse sequence. For experimental conditions, see the Experimental Procedures section.

activity of phospholipase A2 in human SF and plasma.16 Of possible relevance to the current study is the link between RA and coronary artery disease (CAD) due to levels of serum lipoprotein A (LpA), triglyceride and High Density Lipoprotein (HDL), which independently discriminated RA patients and healthy controls.17 The anti/proinflammatory nature of HDLs has been studied also18 and showed that proinflammatory HDL levels in RA patients were associated with an increased risk of CAD. Finally, a number of candidate markers for RA have been proposed in rodent models.15,19 The homeostasis of the organism is influenced by environmental factors such as age, ethnicity, lifestyle, stress levels, gut microflora, and diet, that is, factors not entirely defined by the genome.20 The characteristic metabolite profile associated with a certain condition, the metabolic phenotype, can be obtained using a metabonomics approach. Metabonomics provides real biological end points expressed as metabolite levels and is defined as “the quantitative measurement of the time-related metabolic responses of living systems to pathophysiological stimuli or genetic modification”.21,22 Nuclear magnetic resonance (NMR) spectroscopic- and mass spectrometry (MS)based platforms have contributed significantly to biomarker research.23,24 The typical 1H NMR spectra of symptomatic patients with active RA, RA patients in remission and healthy subjects with assignment of major metabolites is shown in Figure 1, illustrating differences among patient groups and healthy subjects. Herein we present results of a metabolic phenotyping study that monitored metabolic changes of plasma composition in patients with active RA at baseline over a one-year period and compare these changes to those observed in healthy controls as well as patients in remission, undergoing treatment with TNF-alpha inhibitors. We utilized high-resolution 1H NMR spectroscopy in combination with multivariate data analysis to extract relevant information from the vast amount of data represented by the spectra. These results highlight possibilities of metabolic phenotyping in personalized drug therapy and drug management. 4546

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Study Design. A cohort study including 47 patients with RA (23 with active inflammation and 24 in remission (according to a DAS-28-crp < 2.6))25 and 51 healthy controls was studied. For each subject, samples were collected three times with a 6 months frequency, supported by a questionnaire providing demographic and clinical information. All samples were drawn at the same time in the morning, between 8 and 9 a.m., and all subjects were fasting from the evening before, 10 p.m. For symptomatic patients, samples were collected more frequently (0, 2, 4 weeks, 6 months and 12 months) as a consequence of clinical practice. The patients were all enrolled at Frederiksberg Hospital during a two-year period (May 2005-May 2007), while controls were recruited at two different hospitals in Copenhagen, but all samples were collected at the same hospital and processed at the same laboratory to avoid any bias. Patients. Patients with rheumatoid arthritis (according to ACR criteria)5 and not suffering from any other chronic diseases were included in the study. Two patient groups were included: (i) Patients in remission who underwent treatment with TNFalpha inhibitors in combination with DMARD’s (disease modifying anti-rheumatic drugs) during the entire study period;26 (ii) symptomatic patients with active RA (joint inflammation) were recruited from the out-patients clinic, Frederiksberg Hospital before change of therapy due to insufficient response. The active patients were in general treated with change or addition of DMARD (at baseline, 11 patients in the active group received methotrexate, 5 received salazopyrine and 6 patients received prednisone), while only as the exception treated with TNF-alpha inhibitors within the study period. At the time of inclusion, patients in the active group were not treated with TNF-alpha inhibitors. All patients in remission received antiTNF-alpha inhibitors at baseline and of these 12 received methotrexate. The healthy control participants were recruited from hospital and university staff; these healthy individuals had no disease symptoms and did not use any medication. Excessive alcohol consumption, presence of other chronic diseases and pregnancy resulted in exclusion. Good Clinical Practice. The study was conducted in accordance with ethical principles of Good Clinical Practice and the Declaration of Helsinki. The local ethics committee (The municipalities of Copenhagen and Frederiksberg) approved study protocols (KF 01-258/04) prior to the investigation and written informed consent was obtained from all participating subjects. Variables. At each visit, assessment of demographic (sex, age, and body mass index [BMI]), clinical (presence of erosions, DAS-28, Health Assessment Questionnaire index [HAQ]) and biochemical information (hemoglobin, leukocytes, thrombocytes, urea, creatinine, orosomucoid, erythrocyte sedimentation rate [ESR], C-reactive protein [CRP] and rheumatoid factor) was performed by means of a questionnaire and laboratory measurements. Sample Preparation. Blood was collected into heparinized vials, allowed to stand for 30-60 min and then centrifuged at 3000× g at 4 °C for 11 min to produce plasma and stored at or below -25 °C for a period of up to 19 months until a sufficient number of samples were collected. All samples were treated in identical ways. For NMR analysis, plasma was thawed at room temperature and 500 µL was mixed with 55 µL of saline (0.9%) prepared from deuterated water (99.9 atom % of deuterium). The sample was stirred and centrifuged at 10 000×

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Cohort Study of Rheumatoid Arthritis Patients g for 10 min. Subsequently, 0.5 µL of the supernatant was transferred to a 5 mm NMR tube, and the tubes were stored for 1 month at or below -25 °C until NMR analysis. NMR Analysis. 1H NMR spectra were acquired at 300 K with a Bruker Avance 600 MHz spectrometer operating at 600.44 MHz for 1H and equipped with a 5 mm broad-band inverseconfiguration probe. Samples were randomly analyzed in automation with a B-ACS 60 sample changer system. Plasma samples were analyzed with a total of three standard 1H NMR experiments: (a) water suppressed 1D NMR spectrum using the NOESYPRESAT pulse sequence (128 transients) providing an unfiltered view of the sample; (b) Carr-Purcell-Meiboom-Gill (CPMG) spin-echo sequence with presaturation, which allows for acquisition of spectra with suppression of peaks from macromolecular species (128 transients, spin-echo diffusion time 128 ms); (c) diffusion-edited spectra suppressing peaks from small molecules were acquired using a bipolar pulse-pair longitudinal eddy current delay (BPP-LED) pulse sequence with spoil gradients immediately following the 90° pulses after the bipolar gradient pulse pairs (32 transients, spectral width 20 ppm, gradient pulses were sine shaped and 1.7 ms in duration at 95% maximum strength (53 G cm-1), the eddy current recovery time (Te) was 5 ms, and the time interval between the bipolar gradients (τ) was 0.2 ms. A diffusion time of 100 ms was used). Irradiation of the solvent (water) resonance was applied during presaturation delay (2.0 s) for all spectra and for the water suppressed 1D NMR spectra also during the mixing time (0.1 s). The spectral width was 20 ppm for all spectra. The NMR data were apodized with an exponential linebroadening of 1.0 Hz prior to Fourier transformation, which resulted in 64k real data points for the water suppressed 1D NMR spectra and 32k real data points for the CPMG and diffusion-edited spectra. Phase correction and baseline correction was performed with NMRproc ver. 0.3 software (Drs. Hector Keun and Timothy Ebbels, Imperial College London). Data [-3.0 to 13.0 ppm] were imported into Matlab ver. 7.0 software (MathWorks, Natick, MA), where interpolation of the spectra onto a common chemical shift axis (digital resolution ) 0.00025 ppm) was performed. Water-suppressed 1D NMR spectra and CPMG spectra were calibrated to the R-glucose anomeric proton doublet (δ ) 5.23) and the diffusion-edited plasma spectra to the acetyl resonance from the glycoprotein singlet (δ ) 2.04). Included spectral regions and the discarded residual solvent region, respectively, were as follows: Water suppressed 1D NMR spectra: δ 0.2-10.6 and δ 4.5-5.0; CPMG spectra: δ 0.0-10.0 and δ 4.2-5.2; diffusion-edited spectra: δ -0.5 to 10.0 and δ 4.5-5.0. Normalization to total area was performed by calculating, for each spectrum individually, the ratio between each variable and the sum of each spectrum after removal of regions specified above. In addition to analysis on full-resolution data, a data reduction strategy by “binning”, that is, summing data point intensities within equally sized spectral segments (0.01 ppm) was employed. Assignment of resonances was done by comparison to literature values.23,27-31 Chemometrics. Principal Component Analysis (PCA) and Orthogonal Projection to Least Squares Discriminant Analysis (OPLS-DA)32 calculations were performed with Simca P+ ver. 11.5 (Umetrics, Umeå, Sweden) using the autofit function on binned (0.01 ppm) and full-resolution data sets. The data were mean-centered or scaled to unit variance (UV) prior to analysis. UV-scaled loadings from OPLS-DA analysis were subjected to a back-scaling procedure multiplying each variable with the corresponding standard deviation33 using in-house written

Matlab code (Dr. Henrik Toft). The resulting loadings were displayed colored with the absolute value of the original UVscaled loading in order to carry both covariance and correlation information. These loadings are created with a digital resolution of 0.001 ppm. Validation of the results was performed by 7-fold cross validation, achieved by repeatedly excluding one-seventh of the observations and predicting them back into the calculated model. The validation is reported in terms of Q2 computed during cross-validation, and permutation testing was used to evaluate the significance of these validations. Age and sex were identified as sources of potential selection bias. Therefore, in a subsequent validation step, an age- and sex-matched testset of patients in remission was used to verify results. Computation of RA predictors (Ypred) used in the mixed model (described below) are performed in two steps. First, an OPLSDA model are created on samples obtained from patients with active inflammation and healthy control subjects only, that is, samples from the group of patients included with active inflammation at monitoring time 0 and all healthy control subjects. Second, this model is used to compute RA predictors for all symptomatic patients, all patients in remission and all healthy control subjects, for all monitoring times. Statistical Methods. We applied a likelihood-based approach to general linear mixed models, handling the repeated (longitudinal) measures in a statistical model.34 The MIXED procedure of the SAS system (SAS 9.1.3; SAS Institute Inc., Cary, NC) provides a rich selection of covariance structures through the RANDOM and REPEATED statements.35 The factor [Participant] was considered as a random effects factor. The assessment of the group (RA[remission], RA[active], Control) and time (0, 31, 182, 365 days) effects was of interest in testing for a possible interaction and both main effects and the interaction between them were considered as systematic factors. Subject ) participant specifies that the correlation structure should only be used between measurements on the same participant. Type ) sp(gau)(time) specifies that the spatial Gaussian type of correlation should be used and that the observation times are in the time variable in the data set.36

Results Sample Characteristics. After a 19-month period of sample collection, 50 patients and 52 healthy control subjects had been enrolled. Failure to follow up was 2% in the patient group and 33% in the control group. The distribution of failure to followup in the control group was 29% at 182 days and 71% at 365 days. On the basis of previous metabonomics studies, we anticipated that these numbers were sufficient to obtain statistically valid conclusions, and thus, data acquisition was initiated. Investigation of the clinical data gathered through questionnaires identified diabetic and hypertensive subjects who were excluded, ultimately leading to 47 patients and 51 healthy control subjects at baseline (Figure 2). In addition, subsequent data analysis identified one outlying sample from the symptomatic patient group that was excluded, because of ineffective water suppression, during data analysis as described above (see Chemometric methods). Characteristics of the study population showing demographic, clinical and biochemical variables at baseline are presented in Table 1. The disease impact as estimated by the Journal of Proteome Research • Vol. 9, No. 9, 2010 4547

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significance of Q was explored in a permutation test calculating 999 PLS-DA models, with equal model dimensionality as compared to the original OPLS-DA model. R2Y and Q2 obtained from the PLS-DA model reflecting RA (Q2 ) 0.41) was compared to 999 permuted models and indicated reasonable validity of the original model (Q2 linear regression intercept ) -0.1 and R2Y linear regression intercept ) 0.1). Models calculated with full-resolution UV-scaled data (digital resolution 0.00025 ppm) showed moderate improvements of the predictive ability with respect to RA (Q2 ) 0.51) and increased the interpretability of the models. Evaluation of model statistics and a scores plot from the full resolution OPLS-DA model, created on the selected subset, demonstrated good discriminatory properties as visualized in Figure 3A.

Figure 2. Diagram that show flow of samples. Number of individuals (n) is shown for patients with active inflammation at the time of enrollment (RA+), patients in remission (RA-) and healthy subjects (HS). As a consequence of failure to follow up, only 37 and 15 healthy subjects are available for analysis, at monitoring time 182 days and 365 days, respectively.

self-reported health assessment questionnaire, HAQ, was moderate in both groups without difference. Pattern Recognition Analysis of 1H NMR Spectra of Plasma. Mean-centered and binned (0.01 ppm) water suppressed 1D NMR data, normalized to total area, were subjected to unsupervised modeling by PCA that provided an overview of data and a means of detecting outliers, which arose due to poor water suppression or extreme amounts of certain metabolites, for example, lipids. The PCA analysis of the entire data set showed major interpersonal variations in the first four PC’s, amounting to 95% of the total variation in the data set. Thorough investigation of subsequent PC’s did not reveal grouping according to the presence of RA, most likely due to the very subtle nature of differences between patients and controls. To enhance probability of discrimination between groups, observations from patients with active inflammation at the time of enrollment (t ) 0 days) and healthy controls were modeled by PCA. Inspection of scores plots did not show separation between these two groups, highlighting the presence of variations not related to the disease (age, gender, diet, medicine intake, etc.). Ultimately, metabolic differences attributable solely to RA were investigated with a supervised approach (OPLS-DA). The case-control subset used above for PCA (23 patients with active inflammation at monitoring time t ) 0, and 51 healthy subjects, monitoring time t ) 0, 182, and 365 days) was selected. The age and gender distribution within these two groups varied significantly. A Student’s t test of age distributions and a χ2test of gender distributions between groups indicated a potential bias (age, P < 0.0001; gender, P < 0.005), limiting the interpretability of the results, and thus stressing the importance of proper experimental design with respect to these factors. OPLS-DA was applied in an effort to extract systematic parts of information discriminating patients and controls. Here, binned data were used to speed up computation. In one predictive and three orthogonal components, the model accounted for 93% of the total variation of which 5% were related to separation of the groups. The predictive power of the model was evaluated in terms of Q2 that indicated a model with moderate predictive power (Q2 ) 0.44). In addition, the 4548

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Furthermore, the full-resolution UV-scaled model was recalculated omitting the healthy subjects at monitoring time 182 and 365 days. These results showed high similarity with the result from the model including all healthy subjects, in terms of predictive power (Q2 ) 0.34) and retrieved biochemical information (correlation coefficient between the predictive loading of the two models was 0.96). An OPLS-DA model with female subjects only (Q2 ) 0.46), afford a predictive loading that by visual comparison, shows high degree of similarity with the model calculated with both genders, thereby supporting the latter model with respect to the proposed markers. A model with male subjects only (Q2 ) 0.25) could not to the same extent verify the results obtained, but this is most likely attributed to the very limited percentage of symptomatic male patients present (Table 1). Biomarker Identification. Identification of potential biomarkers associated with RA was based on an investigation of the back-scaled OPLS-DA loading plot (Figure 3B) and by comparison to similar OPLS-DA models of age and gender. Elevated amounts of cholesterol C-21 (δ 0.91, broad multiplet), lactate (δ 1.33, doublet and δ 4.11, quartet), acetylated glycoprotein (δ 2.04, NHCOCH3, broad singlet), unsaturated lipid (δ 2.77, CdCCH2CdC, broad multiplet) and decreased amounts of HDL (δ 0.80-0.84, broad multiplet) and an unassigned quartet (δ 2.46) were discriminative for RA. Disease Monitoring. Application of the OPLS-DA derived “predictors” (Ypred) for all eligible patients and healthy subjects, throughout the entire course of the study, was evaluated in a linear mixed model handling the repeated measures (i.e., clustered within subjects) and data missing at random in an attempt to monitor the disease throughout the study period. The potential diagnostic value of the 1H NMR based metabolic phenotyping approach in RA was explored and evaluated based on this analysis (Figure 4A). Significant interaction between Group and Time was observed for the “predictors” Ypred (P < 0.0001, Figure 4A) and DAS-28 (P ) 0.0004, Figure 4B); the pattern of Ypred obtained from the 1H NMR metabolic profiles of plasma compared to the equivalent pattern of DAS-28 scores could not verify all results from the linear mixed model. As anticipated and illustrated in Figure 4, the healthy control subjects were significantly different from the rheumatoid arthritis patients. Furthermore, Ypred and DAS-28 for the healthy controls was throughout the entire course of the study significantly lower than Ypred and DAS-28 obtained from the two patient groups, suggesting that the applied treatment interventions are not sufficient to completely remove the underlying metabolic profile associated with RA (Figure 4A and B). On the other hand, a significant difference between active patients and patients

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Table 1. Demographic, Clinical, and Biochemical Characteristics at Baseline for Active Patients (RA+), Patients in Remission (RA-), and Healthy Subjects (HS) RA + (n ) 23)

Age (Years) Female, % BMI, kg/m2 DAS-28a HAQ Disease duration, years Predicted RA status, Ypred Rheumatoid factor (+), % ESR, arbitrary unit Orosomucoid, g/L CRP, mg/L Leukocytes, 109/L Thrombocytes, 109/L Hemoglobine, mmol/L Urea, mmol/L Creatinine, µmol/L a

HS (n ) 51)

RA- (n ) 24)

mean

SD

median

[min;max]

mean

SD

median

[min;max]

mean

SD

median

59 91 27 5.0 0.8 7 0.8 30 15 0.91 6.4 6.1 268 8.3 5.0 67

12 6 1.3 0.6 8 0.2 11 0.23 7.6 1.2 51 0.6 1.6 10

59 25 5.2 0.6 3 0.8 12 0.81 3.9 6.2 271 8.2 5.2 65

[32;77] [20;45] [2.2;6.8] [0;2.1] [0;25] [0.5;1.1] [3;50] [0.59;1.47] [0.7;30] [4.2;9.1] [189;397] [7.3;9.4] [2.7;9.2] [50;92]

42 51 24 1.2 0 0.1 0 6 0.69 1.1 5.6 240 8.8 5.2 74

12 4 0.1 0 0.2 4 0.13 0.90 1.5 49 0.8 1.3 12

42 24 1.2 0 0.1 5 0.70 0.70 5.2 237 8.8 4.9 73

[24;64] [17;36] [1.0;1.7] [0;0] [-0.5;0.5] [1;17] [0.42;0.92] [0.0;5.8] [3.7;12.2] [93;362] [6.5;10.3] [3.3;9.8] [46;113]

57 87 24 2.6 0.9 14 0.6 71 17 0.90 3.1 6.7 261 8.4 5.0 64

15 3 1.0 0.2 10 0.2 12 0.21 2.7 1.8 39 0.6 1.5 8

61 24 2.6 0.9 11 0.5 15 0.88 2.3 6.2 260 8.5 5.1 63

[min;max]

[24;79] [19;31] [1.3;4.6] [0.6;1.3] [1;39] [0.2;1.3] [3;50] [0.59;1.29] [0.7;11.0] [4.2;11.1] [208;368] [7.5;9.6] [1.6;8.7] [50;83]

DAS-28 is estimated for healthy controls using the formula:

DAS - 28 ) 0.56 × √TJC + 0.28 × √SJC + 0.36 × ln(CRP + 1) + 0.014 × (VAS + 0.96) where TJC is tender joint count, SJC is swollen joint count, CRP is C-reactive protein, and VAS is general health measured as a number between 1 and 10 mm; see www.panlar.org.

Figure 3. (a) OPLS-DA scores plot showing separation of active patients (red dots) at the time of inclusion (t ) 0 days) and healthy controls (black dots) (t ) 0, 182, and 365 days). Full resolution data (0.00025 ppm) were used for computation. t1[p]: predictive component containing all information responsible for group separation. t2[o]: first orthogonal component containing variation orthogonal to group separation. (b) Back-scaled loading plot showing 1H NMR resonances responsible for clustering observed in the corresponding scores plot (Figure 3A). Intensities show influence of variable in the OPLS-DA model (positive, high concentration in RA group; negative, low concentration in RA group) and colors show importance of variable for discrimination of groups (red, most important; blue, not important). Digital resolution: 0.001 ppm.

in remission, at the time of inclusion (monitoring time 0 days), was observed for Ypred (Figure 4A; P ) 0.0007, group mean difference 0.24 [SE: 0.07]) and for DAS-28 (Figure 4B; P < 0.0001 group mean difference 2.3 [SE: 0.4]). Within the first month of optimized treatment for the active patients, Ypred was observed to decrease to the same level as for the patients in remission (Figure 4A; P ) 0.91 comparing groups at day 31 versus baseline; group mean difference 0.01 [SE: 0.07]), despite an unchanged level of DAS-28 (Figure 4B; P < 0.0001 comparing groups at day 31 versus baseline, group mean DAS-28 difference 2.1 [SE: 0.4]). The decrease of Ypred for active patients was consistent throughout the one-year study period; however for DAS-28 the pattern was not similar when comparing the active patients with patients in remission, as DAS-28 was significantly

different throughout the one-year study period (P < 0.02). In addition, during the one year study period CRP levels for the active patients were, after an initial “lag-phase”, observed to approach that of patients in remission (monitoring time t ) 0 days, group mean difference 3.71 mg/L [SE: 1.80] and monitoring time t ) 365 days, group mean difference 1.58 mg/L [SE: 1.61]), while the CRP level for all patients stayed different from that of the healthy subjects, throughout the entire study period.

Discussion Disease Monitoring Based on Human Plasma 1H NMR Spectroscopic Metabolic Profiles. Herein we report that the 1 H NMR plasma metabolic profiles of RA patients, irrespective Journal of Proteome Research • Vol. 9, No. 9, 2010 4549

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Figure 4. (A) Mean values and standard errors of OPLS-DA derived predictive values of all individuals throughout the entire course of the study, evaluated in a linear mixed model. (B) Mean values and standard errors of DAS-28 plotted throughout the entire course of study. Black squares, Patients enrolled with active joint inflammation (n ) 21 and t ) 0, 14, 31, 182, and 365 days); white squares, Patients enrolled in remission (n ) 24 and t ) 0, 182, and 365 days); black triangles, Healthy subjects (n ) 51 and t ) 0, 182, and 365 days). For each part of the figure, a magnification show the same mean values and standard errors for t ) 0, 14, and 31 days for all individuals.

of TNF-alpha inhibition, were significantly different from healthy subjects. Furthermore, the baseline metabolic profiles of patients with active RA were significantly different from RA patients in remission. First, this observation indicates that the state of inflammation in RA patients is reflected in the 1H NMR spectra. Second, upon optimization of treatment in the active group, the metabolic profile approached that of patients in remission, suggesting that this group was receiving suboptimal treatment prior to study inclusion. The few patients in the symptomatic patient group, who were given TNF-alpha inhibitors in the first part of the study, cannot have been of much importance for the improvement in metabolic profiles of the whole group. Thus it would appear that the improved metabolic profile was indeed related to biological changes associated with more efficacious treatment, rather than a profile change that is merely related to a drug effect on the metabolic profiles. Later in the observation period, another two patients in the active group were given TNF-alpha inhibitors; however, this also did not influence the observed patterns described by the linear mixed model. We note, however, that the results must be interpreted with caution due to the different gender and age profile for patients and healthy subjects and due to the relative low number of samples included in this preliminary study. These findings are in agreement with the fact that treatment does not cure RA, as patients in remission (treated with TNFalpha inhibitors) and symptomatic patients, after initiation of an optimized treatment, are similar and do not resemble the healthy controls, with respect to the metabolic predictors. Therefore we conclude that, with neither TNF-alpha inhibitors nor conventional DMARDs, the underlying pathology for RA is not fully reversed. Interestingly, the correlation of our predictors to DAS-28 is poor and reflects the complex nature of RA, that is, not all mechanisms involved in the pathology of RA are covered in the current methodology. It should be noted that we employed the traditional DAS-score, without joints on the feet, which may 4550

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be a confounder in some materials.37 Another source of bias may be reflected by the relatively low HAQ of our “active” patients. This indicates that while not sufficiently treated to reach DAS-28 remission, the patients in this group were on average not typical candidates for treatment with TNF-alpha inhibitors. A usual HAQ at baseline before biologics would be in the range of 1.5,38 while our patients had just below 1.0, which indicated a moderate disease impact on daily activities. Interestingly, for the active patients, the significant decrease of CRP after the one-year period indicated that the treatment with DMARDs showed an effect of treatment. Despite contradictory results in the literature, there is a growing awareness of the metabolic changes in RA leading to increased risks of atherogenic cardiovascular disease.39,40 Oxidative damage to HDL molecules may result in the production of “pro-inflammatory HDL”,41 which may be relevant to the reports that long-term treatment with TNF-alpha inhibitors does not produce a sustainable effect on HDL levels.42,43 As RA patients exhibit a larger proportion of pro-inflammatory HDL, it is important to evaluate these modified molecules as well as total HDL levels. In the current analysis, lactate levels indirectly reflected active inflammation through increased oxidative damage, thus supporting the potential application of this methodology in monitoring and evaluating inflammation as one predictor of accelerated atherogensis. Potential Biomarkers of Rheumatoid Arthritis. Interpretation of back-scaled loading plots demonstrated, as a consequence of improved visualization, interesting metabolic patterns highlighting subtle differences between RA and healthy controls attributable to neither gender nor age. Metabolites responsible for discrimination of RA such as lactate, acetylated glycoprotein, cholesterol (C-21), HDL and an unidentified resonance (δ 2.46) were in good accordance with previously published work.44,45 Elevated concentrations of plasma lactate have previously been identified as a consequence of oxidative damage and lowered synovial pH.46 Interestingly, lactate did

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Cohort Study of Rheumatoid Arthritis Patients not appear important for discrimination of rheumatoid arthritis in an arthritic mouse model,19 thereby highlighting possible discrepancies between human and mouse models. Increased amounts of glycoprotein concentrations are a consequence of degradation of the synovial membrane, resulting in diffusion of these components from the inflamed joint to plasma.44 Additionally, the immune response associated with RA may also lead to an increase in glycoprotein levels.47 Increased amounts of cholesterol in plasma have previously been reported in patients suffering from RA48 and is supported by the observed lower HDL levels. Removal of cholesterol from the bloodstream is primarily mediated by HDL.49 In summary, reduced levels of HDL increase the risk of CAD due to increased cholesterol levels, in agreement with the finding of elevated cholesterol (C-21) levels in RA patients. The resonance corresponding to CHdCH lipid (broad singlet, δ 5.29) was, by visual comparison to models of age and gender, difficult to attribute only to RA and therefore not proposed as a potential biomarker. Verification of the discriminative property of lactate could also be found by comparison to OPLS-DA of mean-centered and binned data. Strong correlation to age and gender for the phosphatidylcholine NCH3 signal (δ 3.22, singlet) and phosphatidylcholine NCH2 signal (broad multiplet, δ 3.66) ruled out that these signals were attributable to RA. OPLS-DA models of diffusion edited spectra, suppressing the peaks from low molecular-weight molecules, and OPLS-DA models of CPMG spectra, suppressing the peaks from large molecular-weight molecules, supported the findings from the unfiltered 1D NMR spectra (results not shown). Validation of OPLS-DA Model Used to Calculate Predictors of Rheumatoid Arthritis. The proposed biomarkers were subjected to a thorough validation scheme to ensure that the observed differences are not caused by age and gender alone. Two different strategies were used to evaluate possible interference of age and gender in the OPLS-DA model: (1) Assessment of back-scaled loading plot similarities among models of RA, age and gender and (2) test set validation. Visual assessment of back-scaled loadings shows that the metabolic profiles of RA did not correlate strongly to the metabolic profiles of age and gender (see Supporting Information Figure S1). Specifically, high correlation of the HDL signal is observed only in the model of RA, whereas the models of age and gender show very low correlation for this signal. Choline show high correlation in models of RA and gender and therefore this compound is not proposed as a marker of RA. Interestingly, the LDL signal is observed to be important for models of age and gender, but not for discrimination of RA patients and healthy subjects. Test-set validation is conservative and robust and here the test-set is biased toward age and gender, that is, mean age and gender distributions of the test-set (patients in remission) is very similar to that of the symptomatic patient group, but different from the control group (Table 1). The mean value of the predictors (Ypred) of the test-set, calculated by application of the created OPLS-DA model, are different from the symptomatic patients used in the model and this clearly demonstrates that the model does not reflect age and gender only. We note that the treatment of the two patient groups with DMARD’s and anti-TNF-alpha differs significantly. However, as shown previously, both interventions result in comparable effects on the lipid profile, that is, HDL and total cholesterol levels,42,48 but long-term effects appear controversial. Our results also provide a putative link between RA and an elevated risk of CAD. Despite confounding effects of age and

gender, it was still possible to extract useful information from human plasma that reveals subtle metabolic changes related to RA, indicating the major potential of metabonomic technologies in discovery of potential biomarkers and treatment monitoring. Importantly, our results suggest that amelioration of the metabolic profile does not seem to depend on TNF-alpha inhibition alone, but may also be possible with traditional DMARDs (Figure 4A). This is quite in line with the notion that methotrexate should be tried before TNF-alpha inhibitors at all stages of the disease.50 The treatment related changes in the metabolic profiles were not reflected by the clinical evaluation as exemplified by DAS28 (Figure 4B). Comparable findings that show no correlation between lipid levels and DAS-28 have been reported.48 A similar dissociation between clinical impression and a surrogate marker of disease activity has recently been presented in another patient population, which showed synovitis activity by ultrasound Doppler test, while supposedly being in remission according to DAS-28.51 Such results would possibly lead to more objective measurements of arthritis activity. Our results indicated that the metabolic profile may provide additional information to the clinical evaluation as reflected by DAS-28 and CRP. Further studies on the metabolism are required to elucidate this issue. One hypothesis may be that the metabolism alters before a change in clinical symptoms is evident, for example, by a “lag-phase”. In conclusion, changes in 1H NMR-derived metabolic profiles of rheumatoid arthritis patients are different from healthy individuals, and treatment tends to improve the profiles, albeit they do not return to normal. The preliminary results reported here are encouraging and require further evaluation with larger numbers in a multicenter setting.

Acknowledgment. The study was supported with grants from the Oak Foundation, Velux Fonden, the Danish Rheumatism Association, Aase og Ejnar Danielsens Fond and Direktør Jacob Madsen og Hustru Olga Madsens Fond. Supporting Information Available: Back-scaled loading plots obtained from OPLS-DA models of age and gender are available for comparison to the back-scaled loading plot from the OPLS-DA model of RA. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) van Jaarsveld, C. H.; Jacobs, J. W.; Schrijvers, A. J.; Heurkens, A. H.; Haanen, H. C.; Bijlsma, J. W. Direct cost of rheumatoid arthritis during the first six years: a cost-of-illness study. Br. J. Rheumatol. 1998, 37, 837–847. (2) Albers, J. M.; Kuper, H. H.; van Riel, P. L.; Prevoo, M. L.; van’t Hof, M. A.; van Gestel, A. M.; Severens, J. L. Socio-economic consequences of rheumatoid arthritis in the first years of the disease. Rheumatology (Oxford) 1999, 38, 423–430. (3) Goldring, M. B. Update on the biology of the chondrocyte and new approaches to treating cartilage diseases. Best Pract. Res. Clin. Rheumatol. 2006, 20, 1003–1025. (4) van der Kooij, S. M.; Goekoop-Ruiterman, Y. P. M.; de VriesBouwstra, J. K.; Guler-Yuksel, M.; Zwinderman, A. H.; Kerstens, P. J. S. M.; van der Lubbe, P. A. H. M.; de Beus, W. M.; Grillet, B. A. M.; Ronday, H. K.; Huizinga, T. W. J.; Breedveld, F. C.; Dijkmans, B. A. C.; Allaart, C. F. Drug-free remission, functioning and radiographic damage after 4 years of response-driven treatment in patients with recent-onset rheumatoid arthritis. Ann. Rheum. Dis. 2009, 68, 914–921. (5) Arnett, F. C.; Edworthy, S. M.; Bloch, D. A.; Mcshane, D. J.; Fries, J. F.; Cooper, N. S.; Healey, L. A.; Kaplan, S. R.; Liang, M. H.; Luthra, H. S.; Medsger, T. A.; Mitchell, D. M.; Neustadt, D. H.; Pinals, R. S.;

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