1H 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,[ andClaus Cornett[

Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark, Center for Sensory-MotorInteraction, Aalborg University, Aalborg, Denmark, The Parker Institute, Copenhagen University Hospital,

Frederiksberg, Denmark, Institute of Sports Science and Clinical Biomechanics, University of SouthernDenmark, 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

1H NMR spectroscopy-based metabolic phenotyping was used to identify biomarkers in the plasma ofpatients with rheumatoid arthritis (RA). Forty-seven patients with RA (23 with active disease at baselineand 24 in remission) and 51 healthy subjects were evaluated during a one-year follow-up withassessments of disease activity (DAS-28) and 1H NMR spectroscopy of plasma samples. Discriminantanalysis 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 assessmentusing mixed linear models showed that the predictors obtained from metabolic profiles of plasma atbaseline 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 significantlydifferent (P ) 0.91). The metabolic profiles of both groups of RA patients were different from the healthysubjects. 1H NMR-based metabolic phenotyping of plasma samples in patients with RA is well suitedfor discovery of biomarkers and may be a potential approach for disease monitoring and personalizedmedication for RA therapy.

Keywords: metabonomics • phenotyping • NMR • rheumatoid arthritis • chemometrics • diseasemonitoring • personalized medicine

IntroductionRheumatoid arthritis (RA) is an chronic inflammatory condi-

tion with major socio-economic consequences.1,2 There iscurrently no available cure and all treatment is aimed at painrelief and reduction of disease progression,3 although a longer-lasting remission may be induced by early, aggressive therapy.4

Diagnosis is based on a range of different immunological andclinical findings.5 The disease mechanism is suggested to

originate in unknown antigens present in the arthritic joint thatinitiate a complex immune response.6 Identification of reliablebiomarkers in the blood of RA patients, which reflect bothdiagnosis and disease severity, would provide an important newtool for maximizing patient care. Numerous proteomic7-9 andgenomic10 studies have provided several biomarker candidatesand insight into the pathology of rheumatoid arthritis. In therealm of metabonomics, differences in the lactate/alanine ratioof synovial fluid (SF) were reported to discriminate betweenRA and osteoarthritis.11 Elevated concentrations of N-acetylated“acute phase” glycoproteins and advanced glycation end-products (AGE) in SF correlated with having RA, as did thecreatinine concentration.12,13 Decreased glucose, chylomicronand VLDL concentrations and increased levels of ketone bodiesin SF relative to serum reflected higher levels of anaerobicmetabolism and enhanced metabolic consumption of lipids inthe inflammatory exudates of RA patients14 and rodents.15

Increased lipid metabolism was associated with an elevated

* To whom correspondence should be addressed. Henning Bliddal, TheParker Institute, Copenhagen University Hospital, Frederiksberg, Denmark.E-mail: parker@frh.regionh.dk.

† 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 Journal of Proteome Research 2010, 9, 4545–4553 4545Published on Web 08/11/2010

activity of phospholipase A2 in human SF and plasma.16 Ofpossible relevance to the current study is the link between RAand coronary artery disease (CAD) due to levels of serumlipoprotein A (LpA), triglyceride and High Density Lipoprotein(HDL), which independently discriminated RA patients andhealthy controls.17 The anti/proinflammatory nature of HDLshas been studied also18 and showed that proinflammatory HDLlevels in RA patients were associated with an increased risk ofCAD. Finally, a number of candidate markers for RA have beenproposed in rodent models.15,19

The homeostasis of the organism is influenced by environ-mental factors such as age, ethnicity, lifestyle, stress levels, gutmicroflora, and diet, that is, factors not entirely defined by thegenome.20 The characteristic metabolite profile associated witha certain condition, the metabolic phenotype, can be obtainedusing a metabonomics approach. Metabonomics provides realbiological end points expressed as metabolite levels and isdefined as “the quantitative measurement of the time-relatedmetabolic responses of living systems to pathophysiologicalstimuli or genetic modification”.21,22 Nuclear magnetic reso-nance (NMR) spectroscopic- and mass spectrometry (MS)-based platforms have contributed significantly to biomarkerresearch.23,24 The typical 1H NMR spectra of symptomaticpatients with active RA, RA patients in remission and healthysubjects with assignment of major metabolites is shown inFigure 1, illustrating differences among patient groups andhealthy subjects.

Herein we present results of a metabolic phenotyping studythat monitored metabolic changes of plasma composition inpatients with active RA at baseline over a one-year period andcompare these changes to those observed in healthy controlsas well as patients in remission, undergoing treatment withTNF-alpha inhibitors. We utilized high-resolution 1H NMRspectroscopy in combination with multivariate data analysisto extract relevant information from the vast amount of datarepresented by the spectra. These results highlight possibilitiesof metabolic phenotyping in personalized drug therapy anddrug management.

Experimental Procedures

Study Design. A cohort study including 47 patients with RA(23 with active inflammation and 24 in remission (accordingto a DAS-28-crp < 2.6))25 and 51 healthy controls was studied.For each subject, samples were collected three times with a 6months frequency, supported by a questionnaire providingdemographic and clinical information. All samples were drawnat the same time in the morning, between 8 and 9 a.m., andall subjects were fasting from the evening before, 10 p.m. Forsymptomatic patients, samples were collected more frequently(0, 2, 4 weeks, 6 months and 12 months) as a consequence ofclinical practice. The patients were all enrolled at FrederiksbergHospital during a two-year period (May 2005-May 2007), whilecontrols were recruited at two different hospitals in Copen-hagen, but all samples were collected at the same hospital andprocessed at the same laboratory to avoid any bias.

Patients. Patients with rheumatoid arthritis (according toACR criteria)5 and not suffering from any other chronic diseaseswere included in the study. Two patient groups were included:(i) Patients in remission who underwent treatment with TNF-alpha inhibitors in combination with DMARD’s (disease modi-fying anti-rheumatic drugs) during the entire study period;26

(ii) symptomatic patients with active RA (joint inflammation)were recruited from the out-patients clinic, FrederiksbergHospital before change of therapy due to insufficient response.The active patients were in general treated with change oraddition of DMARD (at baseline, 11 patients in the active groupreceived methotrexate, 5 received salazopyrine and 6 patientsreceived prednisone), while only as the exception treated withTNF-alpha inhibitors within the study period. At the time ofinclusion, patients in the active group were not treated withTNF-alpha inhibitors. All patients in remission received anti-TNF-alpha inhibitors at baseline and of these 12 receivedmethotrexate. The healthy control participants were recruitedfrom hospital and university staff; these healthy individuals hadno disease symptoms and did not use any medication. Exces-sive alcohol consumption, presence of other chronic diseasesand pregnancy resulted in exclusion.

Good Clinical Practice. The study was conducted in ac-cordance with ethical principles of Good Clinical Practice andthe Declaration of Helsinki. The local ethics committee (Themunicipalities of Copenhagen and Frederiksberg) approvedstudy protocols (KF 01-258/04) prior to the investigation andwritten informed consent was obtained from all participatingsubjects.

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]) andbiochemical information (hemoglobin, leukocytes, thromb-ocytes, urea, creatinine, orosomucoid, erythrocyte sedimenta-tion rate [ESR], C-reactive protein [CRP] and rheumatoid factor)was performed by means of a questionnaire and laboratorymeasurements.

Sample Preparation. Blood was collected into heparinizedvials, allowed to stand for 30-60 min and then centrifuged at3000× g at 4 °C for 11 min to produce plasma and stored at orbelow -25 °C for a period of up to 19 months until a sufficientnumber of samples were collected. All samples were treatedin identical ways. For NMR analysis, plasma was thawed atroom temperature and 500 µL was mixed with 55 µL of saline(0.9%) prepared from deuterated water (99.9 atom % ofdeuterium). The sample was stirred and centrifuged at 10 000×

Figure 1. Typical unfiltered water suppressed 1D 1H NMR spectraof human blood plasma for a symptomatic patient with activeRA (joint inflammation) (red), a RA patient in remission (blue),and a healthy subject (black), including assignment of selectedmetabolites. Magnifications of lactate and N-acetyl glycoproteinsignals illustrate differences among groups. Spectra are acquiredwith the NOESYPRESAT pulse sequence. For experimentalconditions, see the Experimental Procedures section.

research articles Lauridsen et al.

4546 Journal of Proteome Research • Vol. 9, No. 9, 2010

g for 10 min. Subsequently, 0.5 µL of the supernatant wastransferred to a 5 mm NMR tube, and the tubes were storedfor 1 month at or below -25 °C until NMR analysis.

NMR Analysis. 1H NMR spectra were acquired at 300 K witha Bruker Avance 600 MHz spectrometer operating at 600.44MHz for 1H and equipped with a 5 mm broad-band inverse-configuration probe. Samples were randomly analyzed inautomation with a B-ACS 60 sample changer system. Plasmasamples were analyzed with a total of three standard 1H NMRexperiments: (a) water suppressed 1D NMR spectrum using theNOESYPRESAT pulse sequence (128 transients) providing anunfiltered view of the sample; (b) Carr-Purcell-Meiboom-Gill(CPMG) spin-echo sequence with presaturation, which allowsfor acquisition of spectra with suppression of peaks frommacromolecular species (128 transients, spin-echo diffusiontime 128 ms); (c) diffusion-edited spectra suppressing peaksfrom small molecules were acquired using a bipolar pulse-pairlongitudinal eddy current delay (BPP-LED) pulse sequence withspoil gradients immediately following the 90° pulses after thebipolar gradient pulse pairs (32 transients, spectral width 20ppm, gradient pulses were sine shaped and 1.7 ms in durationat 95% maximum strength (53 G cm-1), the eddy currentrecovery time (Te) was 5 ms, and the time interval betweenthe bipolar gradients (τ) was 0.2 ms. A diffusion time of 100ms was used). Irradiation of the solvent (water) resonance wasapplied during presaturation delay (2.0 s) for all spectra andfor the water suppressed 1D NMR spectra also during themixing time (0.1 s). The spectral width was 20 ppm for allspectra. The NMR data were apodized with an exponential line-broadening of 1.0 Hz prior to Fourier transformation, whichresulted in 64k real data points for the water suppressed 1DNMR spectra and 32k real data points for the CPMG anddiffusion-edited spectra. Phase correction and baseline cor-rection 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. 7.0software (MathWorks, Natick, MA), where interpolation of thespectra onto a common chemical shift axis (digital resolution) 0.00025 ppm) was performed. Water-suppressed 1D NMRspectra and CPMG spectra were calibrated to the R-glucoseanomeric proton doublet (δ ) 5.23) and the diffusion-editedplasma spectra to the acetyl resonance from the glycoproteinsinglet (δ ) 2.04). Included spectral regions and the discardedresidual solvent region, respectively, were as follows: Watersuppressed 1D NMR spectra: δ 0.2-10.6 and δ 4.5-5.0; CPMGspectra: 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 wasperformed by calculating, for each spectrum individually, theratio between each variable and the sum of each spectrum afterremoval of regions specified above. In addition to analysis onfull-resolution data, a data reduction strategy by “binning”, thatis, summing data point intensities within equally sized spectralsegments (0.01 ppm) was employed. Assignment of resonanceswas done by comparison to literature values.23,27-31

Chemometrics. Principal Component Analysis (PCA) andOrthogonal 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 onbinned (0.01 ppm) and full-resolution data sets. The data weremean-centered or scaled to unit variance (UV) prior to analysis.UV-scaled loadings from OPLS-DA analysis were subjected toa back-scaling procedure multiplying each variable with thecorresponding standard deviation33 using in-house written

Matlab code (Dr. Henrik Toft). The resulting loadings weredisplayed colored with the absolute value of the original UV-scaled loading in order to carry both covariance and correlationinformation. These loadings are created with a digital resolutionof 0.001 ppm. Validation of the results was performed by 7-foldcross validation, achieved by repeatedly excluding one-seventhof the observations and predicting them back into the calcu-lated model. The validation is reported in terms of Q2 computedduring cross-validation, and permutation testing was used toevaluate the significance of these validations. Age and sex wereidentified as sources of potential selection bias. Therefore, ina subsequent validation step, an age- and sex-matched test-set of patients in remission was used to verify results. Com-putation of RA predictors (Ypred) used in the mixed model(described below) are performed in two steps. First, an OPLS-DA model are created on samples obtained from patients withactive inflammation and healthy control subjects only, that is,samples from the group of patients included with activeinflammation at monitoring time 0 and all healthy controlsubjects. Second, this model is used to compute RA predictorsfor all symptomatic patients, all patients in remission and allhealthy control subjects, for all monitoring times.

Statistical Methods. We applied a likelihood-based approachto general linear mixed models, handling the repeated (longi-tudinal) measures in a statistical model.34 The MIXED proce-dure of the SAS system (SAS 9.1.3; SAS Institute Inc., Cary, NC)provides a rich selection of covariance structures through theRANDOM and REPEATED statements.35 The factor [Participant]was considered as a random effects factor. The assessment ofthe group (RA[remission], RA[active], Control) and time (0, 31,182, 365 days) effects was of interest in testing for a possibleinteraction and both main effects and the interaction betweenthem were considered as systematic factors. Subject ) partici-pant specifies that the correlation structure should only be usedbetween measurements on the same participant. Type )sp(gau)(time) specifies that the spatial Gaussian type of cor-relation should be used and that the observation times are inthe time variable in the data set.36

Results

Sample Characteristics. After a 19-month period of samplecollection, 50 patients and 52 healthy control subjects had beenenrolled. Failure to follow up was 2% in the patient group and33% in the control group. The distribution of failure to follow-up in the control group was 29% at 182 days and 71% at 365days. On the basis of previous metabonomics studies, weanticipated that these numbers were sufficient to obtainstatistically valid conclusions, and thus, data acquisition wasinitiated. Investigation of the clinical data gathered throughquestionnaires identified diabetic and hypertensive subjectswho were excluded, ultimately leading to 47 patients and 51healthy control subjects at baseline (Figure 2). In addition,subsequent data analysis identified one outlying sample fromthe symptomatic patient group that was excluded, because ofineffective water suppression, during data analysis as describedabove (see Chemometric methods).

Characteristics of the study population showing demo-graphic, clinical and biochemical variables at baseline arepresented in Table 1. The disease impact as estimated by the

Cohort Study of Rheumatoid Arthritis Patients research articles

Journal of Proteome Research • Vol. 9, No. 9, 2010 4547

self-reported health assessment questionnaire, HAQ, was mod-erate in both groups without difference.

Pattern Recognition Analysis of 1H NMR Spectra ofPlasma. Mean-centered and binned (0.01 ppm) water sup-pressed 1D NMR data, normalized to total area, were subjectedto unsupervised modeling by PCA that provided an overviewof data and a means of detecting outliers, which arose due topoor water suppression or extreme amounts of certain me-tabolites, for example, lipids. The PCA analysis of the entiredata set showed major interpersonal variations in the first fourPC’s, amounting to 95% of the total variation in the data set.Thorough investigation of subsequent PC’s did not revealgrouping according to the presence of RA, most likely due tothe very subtle nature of differences between patients andcontrols. To enhance probability of discrimination betweengroups, observations from patients with active inflammationat the time of enrollment (t ) 0 days) and healthy controls weremodeled by PCA. Inspection of scores plots did not showseparation between these two groups, highlighting the presenceof variations not related to the disease (age, gender, diet,medicine intake, etc.).

Ultimately, metabolic differences attributable solely to RAwere investigated with a supervised approach (OPLS-DA). Thecase-control subset used above for PCA (23 patients with activeinflammation at monitoring time t ) 0, and 51 healthy subjects,monitoring time t ) 0, 182, and 365 days) was selected. Theage and gender distribution within these two groups variedsignificantly. A Student’s t test of age distributions and a �2-test of gender distributions between groups indicated a po-tential bias (age, P < 0.0001; gender, P < 0.005), limiting theinterpretability of the results, and thus stressing the importanceof proper experimental design with respect to these factors.OPLS-DA was applied in an effort to extract systematic partsof information discriminating patients and controls. Here,binned data were used to speed up computation. In onepredictive and three orthogonal components, the model ac-counted for 93% of the total variation of which 5% were relatedto separation of the groups. The predictive power of the modelwas evaluated in terms of Q2 that indicated a model withmoderate predictive power (Q2 ) 0.44). In addition, the

significance of Q2 was explored in a permutation test calculating999 PLS-DA models, with equal model dimensionality ascompared to the original OPLS-DA model. R2Y and Q2 obtainedfrom the PLS-DA model reflecting RA (Q2 ) 0.41) was comparedto 999 permuted models and indicated reasonable validity ofthe original model (Q2 linear regression intercept ) -0.1 andR2Y linear regression intercept ) 0.1). Models calculated withfull-resolution UV-scaled data (digital resolution 0.00025 ppm)showed moderate improvements of the predictive ability withrespect to RA (Q2 ) 0.51) and increased the interpretability ofthe models. Evaluation of model statistics and a scores plotfrom the full resolution OPLS-DA model, created on theselected subset, demonstrated good discriminatory propertiesas visualized in Figure 3A.

Furthermore, the full-resolution UV-scaled model was re-calculated omitting the healthy subjects at monitoring time 182and 365 days. These results showed high similarity with theresult from the model including all healthy subjects, in termsof predictive power (Q2 ) 0.34) and retrieved biochemicalinformation (correlation coefficient between the predictiveloading of the two models was 0.96). An OPLS-DA model withfemale subjects only (Q2 ) 0.46), afford a predictive loadingthat by visual comparison, shows high degree of similarity withthe model calculated with both genders, thereby supportingthe latter model with respect to the proposed markers. A modelwith male subjects only (Q2 ) 0.25) could not to the same extentverify the results obtained, but this is most likely attributed tothe very limited percentage of symptomatic male patientspresent (Table 1).

Biomarker Identification. Identification of potential biom-arkers associated with RA was based on an investigation of theback-scaled OPLS-DA loading plot (Figure 3B) and by com-parison to similar OPLS-DA models of age and gender. Elevatedamounts 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 (δ2.46) 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 alinear mixed model handling the repeated measures (i.e.,clustered within subjects) and data missing at random in anattempt to monitor the disease throughout the study period.The potential diagnostic value of the 1H NMR based metabolicphenotyping approach in RA was explored and evaluated basedon this analysis (Figure 4A).

Significant interaction between Group and Time was ob-served for the “predictors” Ypred (P < 0.0001, Figure 4A) andDAS-28 (P ) 0.0004, Figure 4B); the pattern of Ypred obtainedfrom the 1H NMR metabolic profiles of plasma compared tothe equivalent pattern of DAS-28 scores could not verify allresults from the linear mixed model. As anticipated andillustrated in Figure 4, the healthy control subjects weresignificantly different from the rheumatoid arthritis patients.Furthermore, Ypred and DAS-28 for the healthy controls wasthroughout the entire course of the study significantly lowerthan Ypred and DAS-28 obtained from the two patient groups,suggesting that the applied treatment interventions are notsufficient to completely remove the underlying metabolicprofile associated with RA (Figure 4A and B). On the other hand,a significant difference between active patients and patients

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

research articles Lauridsen et al.

4548 Journal of Proteome Research • Vol. 9, No. 9, 2010

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

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

Discussion

Disease Monitoring Based on Human Plasma 1H NMRSpectroscopic Metabolic Profiles. Herein we report that the1H NMR plasma metabolic profiles of RA patients, irrespective

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

RA + (n ) 23) HS (n ) 51) RA- (n ) 24)

mean SD median [min;max] mean SD median [min;max] mean SD median [min;max]

Age (Years) 59 12 59 [32;77] 42 12 42 [24;64] 57 15 61 [24;79]Female, % 91 - - - 51 - - - 87 - - -BMI, kg/m2 27 6 25 [20;45] 24 4 24 [17;36] 24 3 24 [19;31]DAS-28a 5.0 1.3 5.2 [2.2;6.8] 1.2 0.1 1.2 [1.0;1.7] 2.6 1.0 2.6 [1.3;4.6]HAQ 0.8 0.6 0.6 [0;2.1] 0 0 0 [0;0] 0.9 0.2 0.9 [0.6;1.3]Disease duration, years 7 8 3 [0;25] - - - - 14 10 11 [1;39]Predicted RA status, Ypred 0.8 0.2 0.8 [0.5;1.1] 0.1 0.2 0.1 [-0.5;0.5] 0.6 0.2 0.5 [0.2;1.3]Rheumatoid factor (+), % 30 - - - 0 - - - 71 - -ESR, arbitrary unit 15 11 12 [3;50] 6 4 5 [1;17] 17 12 15 [3;50]Orosomucoid, g/L 0.91 0.23 0.81 [0.59;1.47] 0.69 0.13 0.70 [0.42;0.92] 0.90 0.21 0.88 [0.59;1.29]CRP, mg/L 6.4 7.6 3.9 [0.7;30] 1.1 0.90 0.70 [0.0;5.8] 3.1 2.7 2.3 [0.7;11.0]Leukocytes, 109/L 6.1 1.2 6.2 [4.2;9.1] 5.6 1.5 5.2 [3.7;12.2] 6.7 1.8 6.2 [4.2;11.1]Thrombocytes, 109/L 268 51 271 [189;397] 240 49 237 [93;362] 261 39 260 [208;368]Hemoglobine, mmol/L 8.3 0.6 8.2 [7.3;9.4] 8.8 0.8 8.8 [6.5;10.3] 8.4 0.6 8.5 [7.5;9.6]Urea, mmol/L 5.0 1.6 5.2 [2.7;9.2] 5.2 1.3 4.9 [3.3;9.8] 5.0 1.5 5.1 [1.6;8.7]Creatinine, µmol/L 67 10 65 [50;92] 74 12 73 [46;113] 64 8 63 [50;83]

a 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 healthycontrols (black dots) (t ) 0, 182, and 365 days). Full resolution data (0.00025 ppm) were used for computation. t1[p]: predictive componentcontaining all information responsible for group separation. t2[o]: first orthogonal component containing variation orthogonal to groupseparation. (b) Back-scaled loading plot showing 1H NMR resonances responsible for clustering observed in the corresponding scoresplot (Figure 3A). Intensities show influence of variable in the OPLS-DA model (positive, high concentration in RA group; negative, lowconcentration in RA group) and colors show importance of variable for discrimination of groups (red, most important; blue, notimportant). Digital resolution: 0.001 ppm.

Cohort Study of Rheumatoid Arthritis Patients research articles

Journal of Proteome Research • Vol. 9, No. 9, 2010 4549

of TNF-alpha inhibition, were significantly different fromhealthy subjects. Furthermore, the baseline metabolic profilesof patients with active RA were significantly different from RApatients in remission. First, this observation indicates that thestate of inflammation in RA patients is reflected in the 1H NMRspectra. Second, upon optimization of treatment in the activegroup, the metabolic profile approached that of patients inremission, suggesting that this group was receiving suboptimaltreatment prior to study inclusion. The few patients in thesymptomatic patient group, who were given TNF-alpha inhibi-tors in the first part of the study, cannot have been of muchimportance for the improvement in metabolic profiles of thewhole group. Thus it would appear that the improved metabolicprofile was indeed related to biological changes associated withmore efficacious treatment, rather than a profile change thatis merely related to a drug effect on the metabolic profiles. Laterin the observation period, another two patients in the activegroup were given TNF-alpha inhibitors; however, this also didnot influence the observed patterns described by the linearmixed model. We note, however, that the results must beinterpreted with caution due to the different gender and ageprofile for patients and healthy subjects and due to the relativelow number of samples included in this preliminary study.

These findings are in agreement with the fact that treatmentdoes not cure RA, as patients in remission (treated with TNF-alpha inhibitors) and symptomatic patients, after initiation ofan optimized treatment, are similar and do not resemble thehealthy controls, with respect to the metabolic predictors.Therefore we conclude that, with neither TNF-alpha inhibitorsnor conventional DMARDs, the underlying pathology for RAis not fully reversed.

Interestingly, the correlation of our predictors to DAS-28 ispoor and reflects the complex nature of RA, that is, not allmechanisms involved in the pathology of RA are covered inthe current methodology. It should be noted that we employedthe traditional DAS-score, without joints on the feet, which may

be a confounder in some materials.37 Another source of biasmay be reflected by the relatively low HAQ of our “active”patients. This indicates that while not sufficiently treated toreach DAS-28 remission, the patients in this group were onaverage not typical candidates for treatment with TNF-alphainhibitors. A usual HAQ at baseline before biologics would bein 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 decreaseof CRP after the one-year period indicated that the treatmentwith DMARDs showed an effect of treatment.

Despite contradictory results in the literature, there is agrowing awareness of the metabolic changes in RA leading toincreased risks of atherogenic cardiovascular disease.39,40

Oxidative damage to HDL molecules may result in the produc-tion of “pro-inflammatory HDL”,41 which may be relevant tothe reports that long-term treatment with TNF-alpha inhibitorsdoes not produce a sustainable effect on HDL levels.42,43 AsRA patients exhibit a larger proportion of pro-inflammatoryHDL, it is important to evaluate these modified molecules aswell as total HDL levels. In the current analysis, lactate levelsindirectly reflected active inflammation through increasedoxidative damage, thus supporting the potential application ofthis methodology in monitoring and evaluating inflammationas one predictor of accelerated atherogensis.

Potential Biomarkers of Rheumatoid Arthritis. Interpreta-tion of back-scaled loading plots demonstrated, as a conse-quence of improved visualization, interesting metabolic pat-terns highlighting subtle differences between RA and healthycontrols attributable to neither gender nor age. Metabolitesresponsible for discrimination of RA such as lactate, acetylatedglycoprotein, cholesterol (C-21), HDL and an unidentifiedresonance (δ 2.46) were in good accordance with previouslypublished work.44,45 Elevated concentrations of plasma lactatehave previously been identified as a consequence of oxidativedamage and lowered synovial pH.46 Interestingly, lactate did

Figure 4. (A) Mean values and standard errors of OPLS-DA derived predictive values of all individuals throughout the entire course ofthe study, evaluated in a linear mixed model. (B) Mean values and standard errors of DAS-28 plotted throughout the entire course ofstudy. 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 365days). For each part of the figure, a magnification show the same mean values and standard errors for t ) 0, 14, and 31 days for allindividuals.

research articles Lauridsen et al.

4550 Journal of Proteome Research • Vol. 9, No. 9, 2010

not appear important for discrimination of rheumatoid arthritisin an arthritic mouse model,19 thereby highlighting possiblediscrepancies between human and mouse models. Increasedamounts of glycoprotein concentrations are a consequence ofdegradation of the synovial membrane, resulting in diffusionof these components from the inflamed joint to plasma.44

Additionally, the immune response associated with RA may alsolead to an increase in glycoprotein levels.47 Increased amountsof cholesterol in plasma have previously been reported inpatients suffering from RA48 and is supported by the observedlower HDL levels. Removal of cholesterol from the bloodstreamis primarily mediated by HDL.49 In summary, reduced levelsof HDL increase the risk of CAD due to increased cholesterollevels, in agreement with the finding of elevated cholesterol(C-21) levels in RA patients. The resonance corresponding toCHdCH lipid (broad singlet, δ 5.29) was, by visual comparisonto models of age and gender, difficult to attribute only to RAand therefore not proposed as a potential biomarker. Verifica-tion of the discriminative property of lactate could also befound by comparison to OPLS-DA of mean-centered andbinned data. Strong correlation to age and gender for thephosphatidylcholine NCH3 signal (δ 3.22, singlet) and phos-phatidylcholine NCH2 signal (broad multiplet, δ 3.66) ruled outthat these signals were attributable to RA.

OPLS-DA models of diffusion edited spectra, suppressing thepeaks from low molecular-weight molecules, and OPLS-DAmodels of CPMG spectra, suppressing the peaks from largemolecular-weight molecules, supported the findings from theunfiltered 1D NMR spectra (results not shown).

Validation of OPLS-DA Model Used to CalculatePredictors of Rheumatoid Arthritis. The proposed biomarkerswere subjected to a thorough validation scheme to ensure thatthe observed differences are not caused by age and genderalone. Two different strategies were used to evaluate possibleinterference of age and gender in the OPLS-DA model: (1)Assessment of back-scaled loading plot similarities amongmodels of RA, age and gender and (2) test set validation. Visualassessment of back-scaled loadings shows that the metabolicprofiles of RA did not correlate strongly to the metabolicprofiles of age and gender (see Supporting Information FigureS1). Specifically, high correlation of the HDL signal is observedonly in the model of RA, whereas the models of age and gendershow very low correlation for this signal. Choline show highcorrelation in models of RA and gender and therefore thiscompound is not proposed as a marker of RA. Interestingly,the LDL signal is observed to be important for models of ageand gender, but not for discrimination of RA patients andhealthy subjects. Test-set validation is conservative and robustand here the test-set is biased toward age and gender, that is,mean age and gender distributions of the test-set (patients inremission) is very similar to that of the symptomatic patientgroup, but different from the control group (Table 1). The meanvalue of the predictors (Ypred) of the test-set, calculated byapplication of the created OPLS-DA model, are different fromthe symptomatic patients used in the model and this clearlydemonstrates that the model does not reflect age and genderonly. We note that the treatment of the two patient groups withDMARD’s and anti-TNF-alpha differs significantly. However,as shown previously, both interventions result in comparableeffects on the lipid profile, that is, HDL and total cholesterollevels,42,48 but long-term effects appear controversial.

Our results also provide a putative link between RA and anelevated risk of CAD. Despite confounding effects of age and

gender, it was still possible to extract useful information fromhuman plasma that reveals subtle metabolic changes relatedto RA, indicating the major potential of metabonomic tech-nologies in discovery of potential biomarkers and treatmentmonitoring.

Importantly, our results suggest that amelioration of themetabolic profile does not seem to depend on TNF-alphainhibition alone, but may also be possible with traditionalDMARDs (Figure 4A). This is quite in line with the notion thatmethotrexate should be tried before TNF-alpha inhibitors atall stages of the disease.50

The treatment related changes in the metabolic profiles werenot reflected by the clinical evaluation as exemplified by DAS-28 (Figure 4B). Comparable findings that show no correlationbetween lipid levels and DAS-28 have been reported.48 A similardissociation between clinical impression and a surrogatemarker of disease activity has recently been presented inanother patient population, which showed synovitis activity byultrasound Doppler test, while supposedly being in remissionaccording to DAS-28.51 Such results would possibly lead tomore objective measurements of arthritis activity.

Our results indicated that the metabolic profile may provideadditional information to the clinical evaluation as reflectedby DAS-28 and CRP. Further studies on the metabolism arerequired to elucidate this issue. One hypothesis may be thatthe metabolism alters before a change in clinical symptoms isevident, for example, by a “lag-phase”.

In conclusion, changes in 1H NMR-derived metabolic profilesof rheumatoid arthritis patients are different from healthyindividuals, and treatment tends to improve the profiles, albeitthey do not return to normal. The preliminary results reportedhere are encouraging and require further evaluation with largernumbers in a multicenter setting.

Acknowledgment. The study was supported withgrants from the Oak Foundation, Velux Fonden, the DanishRheumatism Association, Aase og Ejnar Danielsens Fondand Direktør Jacob Madsen og Hustru Olga Madsens Fond.

Supporting Information Available: Back-scaled load-ing plots obtained from OPLS-DA models of age and genderare available for comparison to the back-scaled loading plotfrom the OPLS-DA model of RA. This material is available freeof 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 arthritisduring 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 conse-quences 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 andnew 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 Vries-Bouwstra, 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, functioningand radiographic damage after 4 years of response-driven treat-ment 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.;

Cohort Study of Rheumatoid Arthritis Patients research articles

Journal of Proteome Research • Vol. 9, No. 9, 2010 4551

Schaller, J. G.; Sharp, J. T.; Wilder, R. L.; Hunder, G. G. TheAmerican-Rheumatism-Association 1987 Revised Criteria for theClassification of Rheumatoid-Arthritis. Arthritis Rheum. 1988, 31,315–324.

(6) Choy, E. H.; Panayi, G. S. Cytokine pathways and joint inflamma-tion in rheumatoid arthritis. N. Engl. J. Med. 2001, 344, 907–916.

(7) de Seny, D.; Fillet, M.; Meuwis, M. A.; Geurts, P.; Lutteri, L.;Ribbens, C.; Bours, V.; Wehenkel, L.; Piette, J.; Malaise, M.; Merville,M. P. Discovery of new rheumatoid arthritis biomarkers using thesurface-enhanced laser desorption/ionization time-of-flight massspectrometry ProteinChip approach. Arthritis Rheum. 2005, 52,3801–3812.

(8) Garnero, P.; Delmas, P. D. Noninvasive techniques for assessingskeletal changes in inflammatory arthritis: bone biomarkers. Curr.Opin.Rheumatol. 2004, 16, 428–434.

(9) Liao, H.; Wu, J.; Kuhn, E.; Chin, W.; Chang, B.; Jones, M. D.; O’Neil,S.; Clauser, K. R.; Karl, J.; Hasler, F.; Roubenoff, R.; Zolg, W.; Guild,B. C. Use of mass spectrometry to identify protein biomarkers ofdisease severity in the synovial fluid and serum of patients withrheumatoid arthritis. Arthritis Rheum. 2004, 50, 3792–3803.

(10) Lopez-Pedrera, C.; Barbarroja, N.; Villalba, J. M. Novel biomarkersof atherosclerosis and cardiovascular risk in autoimmune diseases:Genomics and proteomics approaches. Proteomics Clin. Appl.2009, 3, 213–225.

(11) Meshitsuka, S.; Yamazaki, E.; Inoue, M.; Hagino, H.; Teshima, R.;Yamamoto, K. Nuclear magnetic resonance studies of synovialfluids from patients with rheumatoid arthritis and osteoarthritis.Clin. Chim. Acta 1999, 281, 163–167.

(12) Williamson, M. P.; Humm, G.; Crisp, A. J. H-1 Nuclear Magnetic-Resonance Investigation of Synovial-Fluid Components in Osteo-Arthritis, Rheumatoid-Arthritis and Traumatic Effusions. BritishJ. Rheumatol. 1989, 28, 23–27.

(13) Miyata, T.; Ishiguro, N.; Yasuda, Y.; Ito, T.; Nangaku, M.; Iwata,H.; Kurokawa, K. Increased pentosidine, an advanced glycationend product, in plasma and synovial fluid from patients withrheumatoid arthritis and its relation with inflammatory markers.Biochem. Biophys. Res. Commun. 1998, 244, 45–49.

(14) Naughton, D.; Whelan, M.; Smith, E. C.; Williams, R.; Blake, D. R.;Grootveld, M. An investigation of the abnormal metabolic statusof synovial fluid from patients with rheumatoid arthritis by highfield proton nuclear magnetic resonance spectroscopy. FEBS Lett.1993, 317, 135–138.

(15) Claxson, A.; Grootveld, M.; Chander, C.; Earl, J.; Haycock, P.;Mantle, M.; Williams, S. R.; Silwood, C. J. L.; Blake, D. R.Examination of the metabolic status of rat air pouch inflammatoryexudate by high field proton NMR spectroscopy. Biochim. Biophys.Acta-Mol. Basis Dis. 1999, 1454, 57–70.

(16) Fuchs, B.; Schiller, J.; Wagner, U.; Häntzschel, H.; Arnold, K. Thephosphatidylcholine/lysophosphatidylcholine ratio in humanplasma is an indicator of the severity of rheumatoid arthritis:Investigations by 31P NMR and MALDI-TOF MS. Clin. Biochem.2005, 38, 925–933.

(17) Dursunoglu, D.; Evrenguel, H.; Polat, B.; Tanriverdi, H.; Cobankara,V.; Kaftan, A.; Kilic, M. Lp(a) lipoprotein and lipids in patients withrheumatoid arthritis: serum levels and relationship to inflamma-tion. Rheumatol. Int. 2005, 25, 241–245.

(18) McMahon, M.; Grossman, J.; FitzGerald, J.; Dahlin-Lee, E.; Wallace,D. J.; Thong, B. Y.; Badsha, H.; Kalunian, K.; Charles, C.; Navab,M.; Fogelman, A. M.; Hahn, B. H. Proinflammatory high-densitylipoprotein as a biomarker for atherosclerosis in patients withsystemic lupus erythematosus and rheumatoid arthritis. ArthritisRheum. 2006, 54, 2541–2549.

(19) Weljie, A. M.; Dowlatabadi, R.; Miller, B. J.; Vogel, H. J.; Jirik, F. R.An Inflammatory Arthritis-Associated Metabolite Biomarker Pat-tern Revealed by 1H NMR Spectroscopy. J. Proteome Res. 2007, 6,3456–3464.

(20) Lindon, J. C.; Holmes, E.; Bollard, M. E.; Stanley, E. G.; Nicholson,J. K. Metabonomics technologies and their applications in physi-ological monitoring, drug safety assessment and disease diagnosis.Biomarkers 2004, 9, 1–31.

(21) Nicholson, J. K.; Lindon, J. C.; Holmes, E. Metabonomics: under-standing the metabolic responses of living systems to pathophysi-ological stimuli via multivariate statistical analysis of biologicalNMR spectroscopic data. Xenobiotica 1999, 29, 1181–1189.

(22) Nicholson, J. K.; Lindon, J. C. Systems biology - Metabonomics.Nature 2008, 455, 1054–1056.

(23) Lindon, J. C.; Nicholson, J. K.; Everett, J. R. NMR spectroscopy ofbiofluids. Annu. Rep. NMR Spectrosc. 1999, 38, 1–88.

(24) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Metabonomics and itsrole in drug development and disease diagnosis. Exp. Rev. Mol.Diagn. 2004, 4, 189–199.

(25) Fransen, J.; Creemers, M. C. W.; Van Riel, P. L. C. M. Remission inrheumatoid arthritis: agreement of the disease activity score(DAS28) with the ARA preliminary remission criteria. Rheumatology2004, 43, 1252–1255.

(26) Furst, D. E.; Keystone, E. C.; Kirkham, B.; Fleischmann, R.; Mease,P.; Breedveld, F. C.; Smolen, J. S.; Kalden, J. R.; Burmester, G. R.;Braun, J.; Emery, P.; Winthrop, K.; Bresnihan, B.; De Benedetti,F.; Doerner, T.; Gibofsky, A.; Schiff, M. H.; Sieper, J.; Singer, N.;Van Riel, P. L. C. M.; Weinblatt, M. E.; Weisman, M. H. Updatedconsensus statement on biological agents for the treatment ofrheumatic diseases, 2008. Ann. Rheum. Dis. 2008, 67, 2–25.

(27) Tang, H.; Wang, Y.; Nicholson, J. K.; Lindon, J. C. Use of relaxation-edited one-dimensional and two dimensional nuclear magneticresonance spectroscopy to improve detection of small metabolitesin blood plasma. Anal. Biochem. 2004, 325, 260–272.

(28) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon,J. C. 750 MHz 1H and 1H-13C NMR Spectroscopy of Human BloodPlasma. Anal. Chem. 1995, 67, 793–811.

(29) Ala-Korpela, M. 1H NMR spectroscopy of human blood plasma.Prog. Nucl. Magn. Reson. Spectrosc. 1995, 27, 475–554.

(30) Ala-Korpela, M.; Lankinen, N.; Salminen, A.; Suna, T.; Soininen,P.; Laatikainen, R.; Ingman, P.; Jauhiainen, M.; Taskinen, M. R.;Heberger, K.; Kaski, K. The inherent accuracy of H-1 NMRspectroscopy to quantify plasma lipoproteins is subclass depend-ent. Atherosclerosis 2007, 190, 352–358.

(31) Liu, M.; Tang, H.; Nicholson, J. K.; Lindon, J. Use of 1H NMR-determined diffusion coefficients to characterize lipoprotein frac-tions in human blood plasma. Magn. Reson. Chem. 2002, 40, S83–S88.

(32) Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemometrics 2002, 16, 119–128.

(33) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon,J. C.; Nicholson, J. K.; Holmes, E. Evaluation of the OrthogonalProjection on Latent Structure Model Limitations Caused byChemical Shift Variability and Improved Visualization of BiomarkerChanges in 1H NMR Spectroscopic Metabonomic Studies. Anal.Chem. 2005, 77, 517–526.

(34) Diggle, P. J. An Approach to the Analysis of Repeated Measure-ments. Biometrics 1988, 44, 959–971.

(35) Littell, R. C.; Pendergast, J.; Natarajan, R. Modelling covariancestrucutre in the analysis of repeated measures data. Stat. Med.2000, 19, 1793–1819.

(36) Littell, R. C.; Miliken, G. A.; Stroup, W. W.; Wolfinger, R. D.;Schabenberger, O. SAS for Mixed Models, 2nd ed.; SAS InstituteInc: Cary, NC, 2006.

(37) Landewe, R.; van der Heijde, D.; van der Linden, S.; Boers, M.Twenty-eight-joint counts invalidate the DAS28 remission defini-tion owing to the omission of the lower extremity joints: acomparison with the original DAS remission. Ann. Rheum. Dis.2006, 65, 637–641.

(38) BSRBR Control Centre Consortium; Symmons, D. P. M.; Lunt, M.;Hyrich, K. L.; Deighton, C.; Watson, K. D. on behalf of the BritishSociety for Rheumatology Biologics Register. Benefit of anti-TNFtherapy in rheumatoid arthritis patients with moderate diseaseactivity. Rheumatology (Oxford) 2009, 48, 1323–1327.

(39) Peters, M. J. L.; Nielen, M. M. J.; Raterman, H. G.; Verheij, R. A.;Schellevis, F. G.; Nurmohamed, M. T. Increased CardiovascularDisease in Patients with Inflammatory Arthritis in Primary Care:A Cross-sectional Observation. J. Rheumatol. 2009, 36, 1866–1868.

(40) Schimmel, E. K.; Yazici, Y. Increased lipid levels but unchangedatherogenic index in rheumatoid arthritis patients treated withbiologic disease modifying antirheumatic drugs: published experi-ence. Clin. Exp. Rheum. 2009, 27, 446–451.

(41) Navab, M.; Anantharamaiah, G. M.; Reddy, S. T.; Van Lenten, B. J.;Ansell, B. J.; Fogelman, A. M. Mechanisms of disease: proathero-genic HDL - an evolving field. Nature Clin. Pract. Endocrinol.Metab. 2006, 2, 504–511.

(42) Peters, M. J. L.; Vis, M.; van Halm, V. P.; Wolbink, G. J.; Voskuyl,A. E.; Lems, W. F.; Dijkmans, B. A. C.; Twisk, J. W. R.; de Koning,M. H. M. T.; van de Stadt, R. J.; Nurmohamed, M. T. Changes inlipid profile during infliximab and corticosteroid treatment inrheumatoid arthritis. Ann. Rheum. Dis. 2007, 66, 958–961.

(43) Popa, C.; van den Hoogen, F. H. J.; Radstake, T. R. D. J.; Netea,M. G.; Eijsbouts, A. E.; Den Heijer, M.; van der Meer, J. W. M.;Van Riel, P. L. C. M.; Stalenhoef, A. F. H.; Barrera, P. Modulationof lipoprotein plasma concentrations during long-term anti-TNFtherapy in patients with active rheumatoid arthritis. Ann. Rheum.Dis. 2007, 66, 1503–1507.

(44) Naughton, D. P.; Haywood, R.; Blake, D. R.; Edmonds, S.; Hawkes,G. E.; Grootveld, M. A comparative evaluation of the metabolic

research articles Lauridsen et al.

4552 Journal of Proteome Research • Vol. 9, No. 9, 2010

profiles of normal and inflammatory knee-joint synovial fluids byhigh resolution proton NMR spectroscopy. FEBS Lett. 1993, 332,221–225.

(45) Sattar, N.; McCarey, D. W.; Capell, H.; McInnes, I. B. ExplainingHow “High-Grade” Systemic Inflammation Accelerates VascularRisk in Rheumatoid Arthritis. Circulation 2003, 108, 2957–2963.

(46) Parkes, H. G.; Grootveld, M. C.; Henderson, E. B.; Farrell, A.; Blake,D. R. Oxidative Damage to Synovial-Fluid from the InflamedRheumatoid Joint Detected by H-1-Nmr Spectroscopy. J. Pharm.Biomed. Anal. 1991, 9, 75–82.

(47) Rudd, P. M.; Elliott, T.; Cresswell, P.; Wilson, I. A.; Dwek, R. A.Glycosylation and the immune system. Science 2001, 291, 2370–2376.

(48) Georgiadis, A. N.; Papavasiliou, E. C.; Lourida, E. S.; Alamanos, Y.;Kostara, C.; Tselepis, D.; Drosos, A. A. Atherogenic lipid profile isa feature characteristic of patients with early rheumatoid arthritis:effect of early treatment - a prospective, controlled study. ArthritisRes. Ther. 2006, 8, R82.

(49) Bakkaloglu, A.; Kirel, B.; Ozen, S.; Saatci, U.; Topaloglu, R.; Besbas,N. Plasma lipids and lipoproteins in juvenile chronic arthritis. Clin.Rheumatol. 1996, 15, 341–345.

(50) Saag, K. G.; Teng, G. G.; Patkar, N. M.; Anuntiyo, J.; Finney, C.;Curtis, J. R.; Paulus, H. E.; Mudano, A.; Pisu, M.; Elkins-Melton,M.; Outman, R.; Allison, J. J.; Almazor, M. S.; Bridges, S. L.;Chatham, W. W.; Hochberg, M.; Maclean, C.; Mikuls, T.; Moreland,L. W.; O’Dell, J.; Turkiewicz, A. M.; Furst, D. E. American Collegeof Rheumatology 2008 recommendations for the use of nonbio-logic and biologic disease-modifying antirheumatic drugs inrheumatoid arthritis. Arthritis Care Res. 2008, 59, 762–784.

(51) Brown, A. K.; Conaghan, P. G.; Karim, Z.; Quinn, M. A.; Ikeda, K.;Peterfy, C. G.; Hensor, E.; Wakefield, R. J.; O’Connor, P. J.; Emery,P. An Explanation for the Apparent Dissociation Between ClinicalRemission and Continued Structural Deterioration in RheumatoidArthritis. Arthritis Rheum. 2008, 58, 2958–2967.

PR1002774

Cohort Study of Rheumatoid Arthritis Patients research articles

Journal of Proteome Research • Vol. 9, No. 9, 2010 4553