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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Objective

To examine the association of biomarkers of inflammation with preclinical rheumatoid arthritis (RA).

Methods

A nested case–control study was performed using samples from 2 large, prospectively studied cohorts of women (the Women's Health Study [WHS] and the Nurses' Health Study [NHS]). Blood samples obtained prior to symptom onset in women who later developed RA were selected as incident RA cases, and 3 controls per case were randomly chosen, matched for age, menopausal status, postmenopausal hormone use, and day, time, and fasting status at the time of collection. Plasma was tested for levels of interleukin-6 (IL-6), soluble tumor necrosis factor receptor II (sTNFRII) (as a proxy for TNFα), and high-sensitivity C-reactive protein. Relationships between biomarkers and RA were assessed using conditional logistic regression models, adjusting for age, body mass index, smoking habits, ethnicity, and reproductive factors.

Results

In 93 incident cases in the NHS and 77 incident cases in the WHS, the mean time between blood collection and the onset of RA symptoms was 5.2 years (range 0.3–12 years). Median IL-6 and sTNFRII levels were significantly higher in preclinical RA cases compared with matched controls in the NHS (P = 0.03 and P = 0.003, respectively) though not in the WHS. Pooled analysis of the NHS and WHS cohorts demonstrated significant association of sTNFRII with RA (relative risk 2.0 [95% confidence interval 1.1–3.6], P for trend = 0.004), and a modest association of IL-6 with RA (relative risk 1.4 [95% confidence interval 0.8–2.5], P for trend = 0.06).

Conclusion

Levels of sTNFRII, a biomarker typically associated with active RA, were elevated up to 12 years prior to the development of RA symptoms and were positively associated with incident RA in these nested case–control studies. Studies with repeated assessments of biomarkers prior to RA development may provide further insight into the timing of biomarker elevation in preclinical RA.

Rheumatoid arthritis (RA) is the most common autoimmune inflammatory arthritis, affecting ∼1% of the population. Its etiology is unknown, but it is presumed to be an immunologic disease with contributing genetic factors (1–5) and environmental factors, such as cigarette smoking (6–15) and reproductive factors in women (16–18). A growing body of evidence suggests that RA develops in 3 phases: an asymptomatic period of genetic risk, a preclinical phase in which RA-related autoantibodies can be detected (19, 20), and a clinical phase with acute signs and symptoms of inflammatory arthritis (21). Similar phases of development have been proposed in other autoimmune diseases, such as type 1 diabetes mellitus (type 1 DM) and systemic lupus erythematosus (SLE) (22, 23).

In acute and chronic inflammation, cytokines are instrumental in regulating the magnitude and duration of the inflammatory response. Tumor necrosis factor α (TNFα) and interleukin-6 (IL-6) are pleiotropic cytokines produced predominantly by macrophages that initiate T cell and synovial proliferation, and are responsible for joint destruction in RA (24). Levels of both TNFα and IL-6 are elevated in the serum and joints during active RA (25–28). Expression of soluble TNF receptor II (sTNFRII) parallels TNFα levels and is a surrogate marker for inflammation (25, 26). In the present study, we investigated biomarkers of inflammatory and immune activity, including high-sensitivity C-reactive protein (hsCRP), IL-6, and sTNFRII (since TNFα degrades rapidly in stored samples [29]), during the preclinical phase of RA in 3 large cohorts of US women who were followed up prospectively for up to 12 years after blood collection and for whom there were extensive epidemiologic data on risk factors for RA. We hypothesized that women who developed RA would have evidence of immune activation prior to the first symptoms of RA, compared with women who did not develop signs and symptoms of RA.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

The Nurses' Health Study (NHS) was established in 1976 and enrolled 121,700 US female registered nurses, ages 30–55 years. A second NHS cohort, the NHSII, was established in 1989 and enrolled 116,609 female registered nurses, ages 25–42 years. The Women's Health Study (WHS) was a randomized, double-blind, placebo-controlled trial designed to evaluate the benefits and risks of low-dose aspirin and vitamin E in the primary prevention of cardiovascular disease and cancer among 39,876 female health professionals, ages ≥45 years (30–32). All women completed an initial questionnaire and have been followed up (biennially in the combined NHS cohorts and annually in the WHS) by questionnaire to update exposures and disease diagnoses.

From 1989 through 1990, 32,826 participants (ages 43–70 years) in the first NHS cohort provided plasma samples in heparinized tubes (33). They arranged to have their blood drawn and shipped with an icepack by overnight carrier. Upon arrival, the blood samples were centrifuged, and blood was divided into plasma, white blood cells, and red blood cells. Samples were stored in liquid nitrogen freezers with an electronic alarm system since the time of collection. From 1996 through 1999, 29,611 NHSII participants (ages 32–51 years) provided blood samples. From 1992 through 1995, 28,133 women in the WHS provided plasma samples in EDTA tubes. Collection and storage procedures for the Nurses' Health Study II and the WHS were similar to those described above for the first Nurses' Health Study. All aspects of this study were approved by the Partners' HealthCare Institutional Review Board.

Identification of RA.

RA case identification in the NHS (the original cohort and the NHSII combined) and WHS cohorts was a 2-step process (18). First, subjects who reported a physician diagnosis of RA received the Connective Tissue Disease Screening Questionnaire (CSQ) through the mail; the response rate was 77% in the NHS and 72% in the WHS (34, 35). For those who, based on the CSQ, had positive screening for RA or other connective tissue diseases (CTDs), including SLE, mixed connective tissue disease, scleroderma, polymyositis, dermatomyositis, or Sjögren's syndrome (34), medical records were requested and were received from 96% of subjects.

Two board-certified rheumatologists trained in chart abstraction conducted independent medical record reviews (each blinded with regard to the other's result). They examined the charts for components of the American College of Rheumatology (ACR; formerly, the American Rheumatism Association) classification criteria for RA (36), the date the first RA symptom was reported, evidence of RA-specific medication treatment, and the diagnosis made by the treating physician. (The specificity of CTD detection using a staged series design is very high, reducing misclassification of healthy subjects [37].) The reviewers met to discuss and resolve discrepancies and to determine a consensus diagnosis. Subjects who met 4 of the 7 ACR criteria for RA and for whom there was a consensus between the reviewers about a positive RA diagnosis were considered to have definite RA. For this nested case–control study, we also included a small number of subjects (5 cases in the NHS and 3 cases in the WHS) with only 3 documented ACR criteria for RA, a diagnosis of RA by their physician, and reviewer consensus on the diagnosis of RA. The presence of serum rheumatoid factor (RF) at diagnosis was determined from medical records.

Selection of cases and controls.

Eligible cases included all incident RA cases with a stored blood sample collected at least 3 months prior to the date of the first RA symptom documented in the medical record. The control group included subjects with a stored blood sample, excluding those with self-reported RA not confirmed by rheumatologist review and those with other self-reported CTDs. None of the controls developed RA or another rheumatic disease during the 12-year followup period.

Women who reported any cancer (except nonmelanoma skin cancer) at baseline or during followup were excluded as cases or controls, since cancer and its treatment can affect biomarker levels. Three controls for each confirmed RA case were randomly chosen from among subjects with stored blood, matched for cohort, birth year (±1 year), race/ethnicity, time of day and fasting status at the time of the blood collection, and menopausal status and postmenopausal hormone use on the date of collection. For premenopausal women in the NHSII, we also matched for timing of the blood sample within the menstrual cycle; for the WHS, we also matched for time since randomization.

Information on potential confounding variables.

All exposure information was self-reported on the mailed questionnaires administered every 2 years since 1976 in the original Nurses' Health Study and since 1989 in the NHSII, and annually since 1992 in the WHS. Because cigarette smoking is the strongest environmental risk factor for RA (6–15) and is associated with biomarker levels (thus meeting the definition of a classic confounder), we adjusted for smoking, coded as never smoker, past smoker, current smoker <15 cigarettes/day, and current smoker ≥15 cigarettes/day. Reproductive covariates were chosen based on our past findings of associations between reproductive factors and the risk of RA in the NHS (18). Age at menarche (<12 years versus ≥12 years), regularity of menses between the ages of 20 and 35 (regular, irregular), parity and duration of breastfeeding (nulliparous, parous and no breastfeeding, parous and 1–12 months breastfeeding, parous and ≥12 months breastfeeding), menopausal status and postmenopausal hormone use (premenopausal, postmenopausal and never use, postmenopausal and past use, postmenopausal and current use, dubious menopausal status), and body mass index (BMI) as a continuous variable were assessed as potential confounders of the biomarker and RA risk relationship.

For analyses of the WHS, the same potential confounders were included except for breastfeeding, since data were not available on this variable. For each control, a reference date corresponding to the date of RA onset in the matched case was assigned. Covariate data from all questionnaires were selected from the questionnaire preceding the reference date.

Laboratory assays.

The laboratory selected for this study has high assay precision and runs internal positive and negative quality control samples daily. The laboratory has undergone rigorous blinded pilot testing with aliquots from NHS quality control specimens in which aliquots were divided into 2 masked samples. Coefficients of variation were 0.07–6.1% for IL-6, 4.9–11.6% for sTNFRII, and 1.6–3.0% for CRP. For all assays, study samples were masked with regard to case/control status. Samples were labeled by number only, and matched case–control pairs handled together identically, shipped in the same batch, and assayed in the same run. The order within each case–control pair was random. Aliquots from pooled quality specimens, indistinguishable from study specimens, were interspersed randomly among case–control samples to monitor quality control.

IL-6 was measured with an ultrasensitive quantitative sandwich enzyme-linked immunosorbent assay (ELISA) (R&D Systems, Minneapolis, MN). IL-6 was quantified in pg/ml, and assay sensitivity was 0.94 pg/ml. As noted above, TNFα cannot be reliably measured in stored plasma as it degrades rapidly (29). Thus, for this study, we measured sTNFRII as a proxy for TNFα, since blood samples were shipped to our laboratory, and even though they arrived within 24 hours, most of the TNFα had likely degraded by this time. Soluble TNFRII stability was assessed in 17 blood samples, at baseline (zero-hour) and after delays in shipping of 24 hours and 36 hours, and the intraclass correlation coefficient (ICC) was found to be >75% for the comparison of 0–36 hours (38). Soluble TNFRII was measured with a quantitative sandwich ELISA (R&D Systems). At our laboratory, day-to-day assay variabilities in results of ELISAs with sTNFRII at concentrations of 54.8, 252, and 356 pg/ml are 8.8%, 3.7%, and 5.8%, respectively.

In addition, because of the potential for interference by RF in the ELISAs used for IL-6 and sTNFRII measurements (39), we conducted an additional pilot study, in which we selected 16 samples from RA patients with a range of RF titers (0–680). RF was depleted from these samples by affinity absorption with human IgG–conjugated Sepharose (IgG Sepharose 6 Fast Flow; GE Healthcare, Piscataway, NJ). Sera were diluted 1:1 with Tris buffered saline and incubated with an equal volume of IgG–Sepharose for >4 hours at 4°C. As a control, diluted sera were exposed to unconjugated Sepharose under identical conditions. Complete depletion of RF was confirmed by nephelometry (40). A subset of high-titer RF samples required 2 rounds of affinity absorption. Following RF depletion, 32 samples (16 predepletion and 16 postdepletion) were assayed for IL-6 and sTNFRII by technicians who were blinded with regard to sample status. ICCs were calculated as a measure of how well the predepletion and postdepletion biomarker levels agreed in the entire data set and in the RF-positive subset (an ICC near 1.0 indicates good agreement). ICCs were 0.99 for sTNFRII and 0.88 for IL-6. When results in 12 samples that were initially RF positive were compared with postdepletion results, ICCs were 0.98 for sTNRFII and 0.93 for IL-6, indicating excellent reproducibility.

In samples from the NHS, hsCRP in mg/dl was measured by high-sensitivity latex-enhanced immunonephelometric assay on a BN II analyzer (Dade-Behring, Newark, DE) with a coefficient of variation of <5% in our laboratory. Plasma from NHS samples was tested for anti–cyclic citrullinated peptide (anti-CCP) antibodies using the second generation Diastat ELISA (Axis-Shield Diagnostics, Dundee, UK), with positive anti-CCP defined as >5 units/ml. We tested 93 preclinical RA samples and 279 control samples for anti-CCP antibodies. Among 67 samples that were anti-CCP negative in the preclinical period, 43 were available from a second blood collection in 2001 (after onset of the first symptom of RA in 38 subjects [prevalent], and within 1 year before the first symptom in 5 subjects [incident]). The other 24 subjects who were anti-CCP negative had not provided a second blood sample. Anti-CCP assays were not performed in the WHS due to limited availability of samples.

Statistical analysis.

We calculated means (±SD) and medians with range for each biomarker (IL-6, sTNFRII, hsCRP, and anti-CCP antibodies). For any biomarker that was not normally distributed, we performed log transformation. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were determined using conditional logistic regression adjusted for potential confounders, comparing quartiles of biomarkers (log IL-6, sTNFRII, and log hsCRP) with cut points based on the control distribution within each study. (The OR appropriately estimates the relative risk [RR] [41], so we henceforth use “RR.”)

Trends were calculated using continuous biomarker values (log-transformed for IL-6 and hsCRP) and calculating the Wald statistic. High-sensitivity CRP was studied previously in the WHS as a predictor of RA and was found not to be significantly associated with RA risk (42); thus, only data on IL-6 and sTNFRII are presented for the WHS. For IL-6 and sTNFRII biomarkers, we pooled data from the NHS and the WHS, preserving the matching, keeping cohort-specific quartile cutoffs, and adjusting for covariates that were available in both data sets. SAS version 9.1 (SAS Institute, Cary, NC) was used for all analyses.

We conducted stratified analyses using unconditional polytomous logistic regression (PLR) among NHS and WHS samples to determine if the RRs across time interval groups differed, comparing a model in which the association between each biomarker and RA was held constant across time-interval groups versus a model in which the association was allowed to vary, using the likelihood ratio test (43). PLR has been used in prior biomarker analyses in the NHS (44) and other case–control studies of RA risk factors (45). Thus, although we had only 1 blood sample per subject collected prior to the first symptom of RA, we could employ this method to compare values for participants within each of these 3 time intervals before RA, comparing cases and controls and biomarker abnormalities.

We also used PLR to assess the relationship between 3 biomarkers (log IL-6, log hsCRP, sTNFRII) and the RR of anti-CCP–positive and anti-CCP–negative RA phenotypes in the NHS only, based on anti-CCP antibody status, which was measured in stored blood samples. Anti-CCP status was recorded based on a positive anti-CCP finding (>5 units/ml) from the blood samples collected prior to the onset of RA symptoms (93 incident blood samples) or among available samples from anti-CCP–negative subjects who had provided a second blood sample in 2000–2001 (n = 43 samples; 38 prevalent, 5 incident). In separate models, we assessed the same relationships with RF status in the NHS and the WHS cohorts, based on laboratory data from the medical record review. Because of the potential for misclassification of RF and anti-CCP variables, but assuming that seropositivity on either assay captured a more severe phenotype, we also performed an analysis stratified by seropositivity (the presence of RF or anti-CCP antibodies) versus seronegativity. All stratified models were adjusted for matching factors, cigarette smoking, and BMI.

To compare the frequency of abnormal levels of biomarkers in each of 3 time periods between blood collection and RA onset (0.3–<4 years, 4–<8 years, and 8–12 years), thresholds to define high versus low biomarker levels were determined based on the top quartile versus the bottom 3 quartiles among the control group, for the NHS and the WHS cohorts separately.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

In the NHS, we confirmed 93 incident RA cases, and in the WHS, we confirmed 77 incident RA cases, all with stored blood collected prior to onset of the first RA symptoms. The mean time between blood collection and the onset of RA symptoms was 5.1 years (range 0.4–12 years) in the NHS and 5.3 years (range 0.3–11 years) in the WHS (Table 1). Twenty-one percent of cases developed RA 8–12 years after blood collection, 42% 4–<8 years after blood collection, and 38% within the first 4 years (0.3–<4 years before onset of the first symptom of RA). In the NHS, 49 cases (53%) were determined by review of medical records to be RF positive from the time of diagnosis, and 26 cases (28%) were anti-CCP positive prior to RA onset. Among those who were ever seropositive, 13 (23%) had radiographic changes and 10 (18%) had rheumatoid nodules at RA diagnosis, compared with 7 (19%) with radiographic changes and 1 (3%) with rheumatoid nodules among those who were seronegative. Review of medical records revealed that 46 cases (60%) in the WHS were RF positive. The ethnic distribution was similar (>95% white in both studies). Distribution of other potential confounders across case–control groups was also similar in the NHS and the WHS cohorts.

Table 1. Characteristics of the preclinical RA cases and matched controls at the time of blood collection in the NHS and WHS*
 NHSWHS
RA cases (n = 93)Matched controls (n = 279)RA cases (n = 77)Matched controls (n = 227)
  • *

    Except where indicated otherwise, values are the number (%).

  • Rheumatoid factor (RF) positive at diagnosis, according to laboratory tests documented in the medical record.

  • Anti–cyclic citrullinated peptide (anti-CCP) level >5 units/ml according to laboratory testing performed in the Nurses' Health Study (NHS) prior to rheumatoid arthritis (RA) onset (not tested in the Women's Health Study [WHS]).

  • §

    Anti-CCP measured in subjects who were anti-CCP negative in the preclinical time period, and provided a second blood sample (sample available from 43 of 67 anti-CCP–negative subjects).

  • Erosions or periarticular osteopenia consistent with RA.

  • #

    Data on breastfeeding not available (NA) for the WHS.

White race92 (99)275 (99)74 (96)217 (96)
Age at blood collection, mean ± SD years54.6 ± 8.254.6 ± 8.155.4 ± 7.655.2 ± 7.4
Age at RA diagnosis, mean ± SD years60.2 ± 9.7NA61.3 ± 7.6NA
Time to RA onset, mean ± SD years5.1 ± 3.5NA5.3 ± 2.5NA
RF positive49 (53)NA46 (60)NA
Anti-CCP positive (preclinical)26 (28)0NANA
Anti-CCP positive (ever)§39 (42)NANANA
Seropositive (RF and/or anti-CCP)57 (61)NANANA
Radiographic changes49 (53)NA16 (21)NA
Smoking    
 Never smoker44 (47)124 (44)28 (36)107 (47)
 Past smoker45 (48)126 (45)42 (55)96 (42)
 Current smoker <15 cigarettes/day1 (1)11 (4)3 (4)8 (4)
 Current smoker ≥15 cigarettes/day3 (3)18 (6)4 (5)16 (7)
Parity and breastfeeding    
 Nulliparous4 (4)25 (9)10 (13)26 (11)
 Parous/no breastfeeding30 (32)69 (25)  
 Parous/≤1 year breastfeeding46 (49)130 (47)67 (87)#201 (89)
 Parous/>1 year breastfeeding13 (14)55 (20)  

In the NHS, median IL-6, sTNFRII, and anti-CCP levels were significantly higher in preclinical RA cases compared with matched controls (P = 0.03, P = 0.003, and P < 0.0001, respectively), but median hsCRP levels were not (P = 0.09) (Table 2). In the WHS, median IL-6 and mean and median sTNFRII levels were not significantly different in cases compared with controls.

Table 2. Inflammation biomarkers in preclinical RA cases compared with matched controls in the NHS and WHS*
 Cases (n = 93 [NHS]/ 77 [WHS])Controls (n = 279 [NHS]/ 227 [WHS])P
  • *

    Values are the median (range). RA = rheumatoid arthritis; NHS = Nurses' Health Study; WHS = Women's Health Study; IL-6 = interleukin-6; sTNFRII = soluble tumor necrosis factor receptor II; hsCRP = high-sensitivity C-reactive protein; anti-CCP = anti–cyclic citrullinated peptide.

  • By Wilcoxon's rank sum test.

NHS   
 IL-6, pg/ml1.35 (0.37–15.09)1.17 (0.30–13.45)0.03
 sTNFRII, pg/ml2,164 (1,306–5,528)1,989 (1,166–4,995)0.003
 hsCRP, mg/dl1.83 (0.11–27.78)1.30 (0.08–37.89)0.09
 Anti-CCP, units/ml3.0 (0–>100)2.0 (0–4.0)<0.0001
WHS   
 IL-6, pg/ml1.61 (0.38–19.42)1.39 (0.44–18.65)0.41
 sTNFRII, pg/ml2,203 (1,159–4,208)2,109 (1,144–6,555)0.45

In the NHS, when the highest and lowest quartiles were compared in multivariable adjusted models, 2 biomarkers were found to be associated with an increased risk of RA: IL-6 (RR 1.6 [95% CI 0.7–3.3], P for trend = 0.03) and sTNFRII (RR 2.9 [95% CI 1.2–7.1], P for trend = 0.01). High-sensitivity CRP was not significantly associated with RA (RR 1.5 [95% CI 0.7–3.6], P for trend = 0.26) (Table 3). For comparison, in a prior analysis of preclinical anti-CCP results in the same blood samples, anti-CCP positivity was found to be associated with a substantial increased relative risk of RA (RR 11.2 [95% CI 4.7–26.9]) (46). In the WHS, the IL-6 association was not replicated, and in pooled adjusted analysis of the NHS and the WHS, the P for trend was 0.06. Soluble TNFRII was not significantly associated with RA (RR 1.5 [95% CI 0.7–3.4], P for trend = 0.16) in the WHS. However, in pooled analysis of the NHS and the WHS, sTNFRII was significantly associated with RA (RR 2.0 [95% CI 1.1–3.6], P for trend = 0.004), after adjustment for potential confounders.

Table 3. Relative risk for association of quartiles of IL-6, sTNFRII, and hsCRP measured before RA symptom onset with RA in the NHS and WHS*
 Quartile 1Quartile 2Quartile 3Quartile 4P for trend
  • *

    RR = relative risk (with quartile 1 as referent); 95% CI = 95% confidence interval (see Table 2 for other definitions).

  • Log-transformed values, as not normally distributed.

  • Conditional logistic regression conditioned on matching factors, and adjusted for age, ethnicity, body mass index, cigarette smoking (never, past, current <15 cigarettes/day, current ≥15 cigarettes/day), parity, and breastfeeding.

  • §

    Conditional logistic regression conditioned on matching factors, and adjusted for age, ethnicity, body mass index, cigarette smoking (never, past, current <15 cigarettes/day, current ≥15 cigarettes/day), and parity.

NHS     
 IL-6     
  n, total (cases/controls)88 (19/69)92 (21/71)86 (17/69)106 (36/70) 
  Median in controls, pg/ml−0.4260.0170.3320.943 
  Unadjusted RR (95% CI)1.01.1 (0.5–2.1)0.9 (0.4–1.9)2.0 (1.0–3.9)0.01
  Adjusted RR (95% CI)1.00.8 (0.4–1.7)0.8 (0.3–1.7)1.6 (0.7–3.3)0.03
 sTNFRII     
  n, total (cases/controls)81 (12/69)90 (20/70)100 (30/70)101 (31/70) 
  Median in controls, pg/ml1,551.21,835.02,143.82,624.3 
  Unadjusted RR (95% CI)1.02.0 (0.8–4.6)3.0 (1.3–6.9)3.1 (1.3–7.3)0.004
  Adjusted RR (95% CI)1.02.0 (0.8–4.8)3.1 (1.3–7.5)2.9 (1.2–7.1)0.01
 hsCRP     
  n, total (cases/controls)85 (15/70)92 (22/70)98 (29/69)97 (27/70) 
  Median in controls, mg/dl−1.407−0.1800.7371.756 
  Unadjusted RR (95% CI)1.01.4 (0.7–2.9)2.0 (1.0–4.2)1.9 (0.9–4.0)0.06
  Adjusted RR (95% CI)1.01.3 (0.6–2.8)1.7 (0.8–3.6)1.5 (0.7–3.6)0.26
WHS     
 IL-6     
  n, total (cases/controls)76 (19/57)72 (16/56)77 (20/57)79 (22/57) 
  Median in controls, pg/ml−0.2700.1620.5451.139 
  Unadjusted RR (95% CI)1.00.9 (0.4–1.9)1.1 (0.5–2.2)1.1 (0.5–2.4)0.51
  Adjusted RR (95% CI)§1.00.8 (0.4–1.8)0.9 (0.4–2.0)1.1 (0.5–2.6)0.59
 sTNFRII     
  n, total (cases/controls)74 (18/56)73 (15/58)75 (19/56)82 (25/57) 
  Median in controls, pg/ml1,685.51,961.72,268.92,757.7 
  Unadjusted RR (95% CI)1.00.8 (0.4–1.8)1.2 (0.5–2.6)1.4 (0.7–3.1)0.21
  Adjusted RR (95% CI)§1.00.8 (0.4–1.8)1.2 (0.5–2.6)1.5 (0.7–3.4)0.16
NHS and WHS combined     
 IL-6     
  n, total (cases/controls)164 (38/126)164 (37/127)163 (37/126)185 (58/127) 
  Median in controls, pg/ml−0.3670.0720.4091.049 
  Unadjusted RR (95% CI)1.01.0 (0.6–1.6)1.0 (0.6–1.7)1.6 (0.9–2.5)0.02
  Adjusted RR (95% CI)§1.00.9 (0.5–1.5)0.9 (0.5–1.6)1.4 (0.8–2.5)0.06
 sTNFRII     
  n, total (cases/controls)155 (30/125)163 (35/128)175 (49/126)183 (56/127) 
  Median in controls, pg/ml1,610.41,905.82,204.22,689.3 
  Unadjusted RR (95% CI)1.01.2 (0.7–2.1)1.8 (1.0–3.2)2.0 (1.2–3.5)0.002
  Adjusted RR (95% CI)§1.01.2 (0.7–2.1)1.8 (1.0–3.2)2.0 (1.1–3.6)0.004

Spearman's correlation between the 4 biomarkers demonstrated strong associations between IL-6, sTNFRII, and hsCRP (data not shown). There were no significant correlations between anti-CCP level as a continuous variable and these 3 biomarkers. In analysis stratified by case status, the correlations for IL-6 and sTNFRII with hsCRP ranged from 0.40 to 0.45 (P < 0.0001) among RA cases and from 0.27 to 0.47 (P < 0.0001) among the controls. Because of the strong correlation among 3 biomarkers (IL-6, sTNFRII, and hsCRP), we performed additional multivariable analyses for each biomarker in the NHS, adjusting for the same covariates plus the other 2 biomarkers. In these analyses, the only biomarker that was independently associated with RA was sTNFRII (RR 3.1 [95% CI 1.2–8.0], P for trend = 0.02) (data not shown).

Using PLR models for stratified analysis of the time interval between blood sample collection and onset of RA symptoms, relative risk of RA showed a significant trend across quartiles of IL-6 concentration (P for trend = 0.003) in the shortest time interval (<4 years) (Table 4). In the time intervals designated as <4 years and 4–<8 years, relative risk of RA showed a significant trend across sTNFRII quartiles (P for trend = 0.01 and P for trend = 0.05, respectively). High-sensitivity CRP demonstrated no significant associations.

Table 4. Relative risk for association of quartiles of IL-6, sTNFRII, and hsCRP with RA in the NHS and WHS, by time interval between blood collection and RA symptom onset*
Biomarker, time between blood collection and RA onsetQuartile 1Quartile 2Quartile 3Quartile 4P for trendP for heterogeneity
  • *

    All models were adjusted for matching factors, cigarette smoking, and body mass index. 95% CI = 95% confidence interval (see Table 2 for other definitions).

  • Calculated using Wald's test, with the biomarker level as a continuous value.

  • Calculated using polytomous logistic regression and the likelihood ratio test, comparing a model in which relative risks (RRs [with quartile 1 as referent]) were constrained to be the same across case groups versus a model in which RRs were allowed to differ by case group.

IL-6      
 n, 0.3–<4 years/4–<8 years/8–12 years/controls13/19/6/12512/15/9/12612/20/5/12526/17/15/127  
  <4 years, RR (95% CI)1.00.9 (0.4–2.1)0.9 (0.4–2.0)1.9 (0.9–3.9)0.0030.07
  4–<8 years, RR (95% CI)1.00.8 (0.4–1.6)1.0 (0.5–2.0)0.8 (0.4–1.7)0.97 
  8–12 years, RR (95% CI)1.01.5 (0.5–4.3)0.8 (0.2–2.7)2.4 (0.9–6.4)0.29 
sTNFRII      
 n, 0.3–<4 years/4–<8 years/8–12 years/controls11/11/8/12514/13/7/12714/26/9/12624/21/10/125  
  <4 years, RR (95% CI)1.01.3 (0.5–2.9)1.2 (0.5–2.9)2.1 (1.0–4.6)0.010.77
  4–<8 years, RR (95% CI)1.01.2 (0.5–2.7)2.3 (1.1–4.9)1.9 (0.9–4.1)0.05 
  8–12 years, RR (95% CI)1.00.9 (0.3–2.4)1.2 (0.5–3.2)1.2 (0.5–3.2)0.51 
hsCRP      
 n, 0.3–<4 years/4–<8 years/8–12 years/controls7/6/2/7012/5/4/697/13/9/6912/7/8/70  
  <4 years, RR (95% CI)1.01.8 (0.7–5.0)1.0 (0.3–3.2)1.7 (0.6–5.0)0.430.73
  4–<8 years, RR (95% CI)1.00.9 (0.3–3.1)2.2 (0.8–6.3)1.2 (0.3–3.9)0.39 
  8–12 years, RR (95% CI)1.02.1 (0.4–12.1)4.6 (0.9–22.4)4.0 (0.8–20.5)0.13 

Using PLR analyses, we stratified for anti-CCP status (NHS only) and RF status (NHS and WHS). There was no association between IL-6 concentration quartile and anti-CCP–positive RA or anti-CCP–negative RA in PLR models adjusted for matching factors and potential confounders (BMI and cigarette smoking) (Table 5). The PLR models demonstrated a strong trend for association with anti-CCP–negative RA by sTNFRII quartile in a fully adjusted model (P for trend = 0.001), but not for an association with anti-CCP–positive RA. There was a strong trend for association with RF-negative RA by sTNFRII quartile (P for trend = 0.003), whereas an association was not seen for RF-positive RA. However, in PLR analyses stratified by seropositivity (i.e., RF positivity as documented in the medical record and/or anti-CCP positivity revealed in the first or second blood collection), there was no significant association between sTNFRII and seronegative RA. For hsCRP, there were no significant associations with RA phenotypes (data not shown).

Table 5. Relative risk for association of quartiles of IL-6 and sTNFRII measured before RA symptom onset with RA in the NHS and WHS, by RA phenotype*
Biomarker phenotypeQuartile 1Quartile 2Quartile 3Quartile 4P for trendP for heterogeneity
  • *

    All models were adjusted for matching factors, cigarette smoking, and body mass index. 95% CI = 95% confidence interval (see Table 2 for other definitions).

  • Calculated using Wald's test, with the biomarker level (log-transformed) as a continuous value.

  • Calculated using polytomous logistic regression and the likelihood ratio test, comparing a model in which relative risks (RRs [with quartile 1 as referent]) were constrained to be the same across case groups versus a model in which RRs were allowed to differ by case group.

  • §

    Analyses of anti-CCP phenotype and seropositive (Sero) phenotype (i.e., positivity for rheumatoid factor [RF] or anti-CCP) were performed in the NHS only. Anti-CCP positivity was defined as a positive result on either blood collection, anti-CCP negativity as a negative result on both blood collections, and anti-CCP data missing as a negative result on the first blood collection and missing data on the second blood collection.

IL-6      
 Anti-CCP phenotype§      
  n, +/−/missing/controls9/5/5/6813/4/3/714/8/5/6913/13/10/70  
  Anti-CCP+, RR (95% CI)1.01.4 (0.5–3.4)0.4 (0.1–1.5)1.3 (0.5–3.4)0.080.95
  Anti-CCP−, RR (95% CI)1.00.8 (0.2–2.9)1.6 (0.5–5.2)2.4 (0.8–7.2)0.23 
  Anti-CCP data missing, RR (95% CI)1.00.6 (0.1–2.5)1.0 (0.3–3.6)1.8 (0.6–5.8)0.15 
 RF phenotype      
  n, +/−/controls20/18/12522/14/12623/14/12529/29/127  
  RF+, RR (95% CI)1.01.1 (0.6–2.1)1.1 (0.6–2.1)1.4 (0.7–2.6)0.140.98
  RF−, RR (95% CI)1.00.8 (0.4–1.6)0.7 (0.4–1.6)1.5 (0.8–3.0)0.10 
 Seropositive phenotype§      
  n, +/−/controls13/6/6814/6/719/8/6920/16/70  
  Sero+, RR (95% CI)1.01.0 (0.4–2.3)0.7 (0.3–1.8)1.4 (0.6–3.2)0.07>0.99
  Sero−, RR (95% CI)1.00.9 (0.3–3.1)1.3 (0.4–4.1)2.4 (0.9–6.8)0.11 
sTNFRII      
 Anti-CCP phenotype§      
  n, +/−/missing/controls5/4/3/6910/6/4/7015/7/8/709/13/8/69  
  Anti-CCP+, RR (95% CI)1.02.0 (0.7–6.3)3.1 (1.0–9.1)1.8 (0.6–5.8)0.740.02
  Anti-CCP−, RR (95% CI)1.01.5 (0.4–5.7)1.8 (0.5–6.5)3.2 (1.0–10.7)0.001 
  Anti-CCP data missing, RR (95% CI)1.01.4 (0.3–6.3)2.7 (0.7–10.9)2.6 (0.7–10.7)0.32 
 RF phenotype      
  n, +/−/controls17/13/12519/16/12731/18/12627/28/125  
  RF+, RR (95% CI)1.01.0 (0.5–2.1)1.8 (1.0–3.5)1.6 (0.8–3.0)0.320.17
  RF−, RR (95% CI)1.01.2 (0.6–2.6)1.4 (0.6–2.9)2.1 (1.0–4.3)0.003 
 Seropositive phenotype§      
  n, +/−/controls7/5/6914/6/7019/11/7016/14/69  
  Sero+, RR (95% CI)1.02.0 (0.8–5.4)2.8 (1.1–7.2)2.3 (0.8–6.1)0.320.13
  Sero−, RR (95% CI)1.01.2 (0.4–4.2)2.3 (0.7–6.9)2.8 (0.9–8.4)>0.99 

We dichotomized levels of the 3 biomarkers based on the top quartile compared with the bottom 3 quartiles. Of note, the CRP cut point for the top quartile (3.14 mg/dl) is similar to the level recommended for cardiovascular risk stratification (3.0 mg/dl) (47). We compared the percentage of subjects with elevated biomarkers (i.e., top quartile) according to time before onset of RA symptoms (Figure 1). Among those with blood collected <4 years prior to onset of RA symptoms, a similar proportion were anti-CCP positive (46%) as compared with the proportions with elevated IL-6 (41%), sTNFRII (39%), and hsCRP (31%). Among those with blood collected 8–12 years before onset of RA symptoms, the proportion who were anti-CCP positive (4%) was lower than the proportions with high levels of the other biomarkers tested (IL-6 43%, sTNFRII 29%, hsCRP 35%).

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Figure 1. Prevalence of elevated preclinical levels of biomarkers in rheumatoid arthritis (RA) cases diagnosed 0.3–<4 years, 4–<8 years, and 8–12 years after blood collection in the combined Nurses' Health Study (NHS) cohort and the Women's Health Study (WHS). Data on interleukin-6 (IL-6) and soluble tumor necrosis factor receptor II (sTNFRII) are from both the NHS and the WHS, whereas data on anti–cyclic citrullinated peptide (anti-CCP) and high-sensitivity C-reactive protein (hsCRP) are from the NHS only. Thresholds for designation as elevated levels were as follows: anti-CCP >5 units/ml, IL-6 ≥2.21 mg/dl, sTNFRII ≥2,442.1 mg/dl, and hsCRP ≥3.14 mg/dl.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Using stored blood samples from women in the NHS and in the WHS, we have examined relationships between plasma levels of biomarkers of inflammation and the subsequent risk of RA and have found evidence for immune activation in preclinical RA cases compared with matched controls. Circulating levels of IL-6 and sTNFRII were higher in women in the NHS with preclinical RA than in controls (with blood collected up to 12 years prior to onset of RA symptoms) but were not significantly higher in the WHS. The lack of association between levels of the tested biomarkers and RA in the WHS study may be due to subjects having a less severe form of RA, as demonstrated by the lower frequency of radiographic changes, or to limited statistical power in this cohort. It is also possible that associations seen in the NHS were false-positives. Ultimately, replication of these investigations in other cohorts over time will be the best indication of the validity of our results. With 170 incident RA cases in the pooled models, we observed a modest (40%) increased risk when comparing the top and bottom quartiles of IL-6 concentrations, and a significant 100% increased RA risk comparing the top and bottom quartiles of sTNFRII concentrations. We were unable to demonstrate any significant association for hsCRP, as has been demonstrated in some studies (48, 49) but not in others. Our prior study of hsCRP in relation to the risk of RA in the WHS (42) and a study by others in a Finnish cohort (50) showed no significant association.

The levels of biomarkers of inflammation measured during the preclinical period in the current study are much lower than levels typically seen in patients with active RA (27, 51–54), suggesting that few subjects had active synovitis at the time of blood sampling. However, even modest elevations in these biomarkers were predictive in time intervals up to 8 years before the onset of RA symptoms. Analyses stratified by time interval between blood sampling and RA symptom onset suggested that IL-6 was associated with RA only in the shortest time interval (<4 years), while sTNFRII was associated with RA both in the shortest interval and with onset 4–<8 years after blood collection. However, this study design, with a single preclinical blood sample, does not allow us to delineate the time course of biomarker changes in the preclinical phase of RA in individual patients.

The 3 biomarkers (IL-6, sTNFRII, and hsCRP) were strongly correlated with one another, but showed no significant correlation with anti-CCP antibody levels. Elevation in the 3 biomarkers in the shortest time interval before RA onset was seen in ∼40% of the preclinical RA cases and was similar to the proportion with anti-CCP positivity. In the longest time interval (8–12 years), the proportion of cases with anti-CCP positivity was lower than the proportion with elevated levels of the other biomarkers. These data suggest that anti-CCP antibody production is a separate phenomenon from that of cytokine production.

The landmark finding of preclinical autoantibodies in RA (19, 20) has revolutionized thinking about the pathogenesis of this autoimmune disease (21). Studies suggest a complex series of events in which a genetically susceptible host is exposed to environmental risk factors that trigger autoreactivity with autoantibody production, and a second event or exposure that might trigger the onset of clinical symptoms. The interaction between genes and environmental factors, such as cigarette smoking, within the anti-CCP–positive RA subtype (55) provides clues to disease pathogenesis, but much of the complexity of RA etiopathogenesis has yet to be delineated. Whether smoking or other environmental factors precipitate autoreactivity is not clear. Our findings suggest that during the preclinical phase of autoantibody production, there is immune reactivity, with production of proinflammatory cytokines that are typically seen in symptomatic RA, namely, IL-6 and TNF. The findings of an association between sTNFRII and incident RA among RF-negative and anti-CCP–negative phenotypes, separately, was not consistent with findings of an analysis that combined subjects who were seronegative for both RF and anti-CCP, suggesting that the individual results could be due to a misclassification of cases who were seronegative on one assay but not on the other.

Other autoimmune diseases such as type 1 DM (56–58) and SLE (23) share these hypothesized stages of disease development: genetic susceptibility, and a preclinical phase with autoantibody production followed by a symptomatic clinical phase. Targeted therapies to prevent such diseases during the preclinical phase are being developed for type 1 DM (57) but have yet to be developed for either SLE or RA.

In a prospective cohort with serially obtained blood samples from blood bank donors in The Netherlands, subjects with preclinical RA, but not controls, were found to have a statistically significant increase in mean hsCRP levels over time, with the highest levels observed at the time of symptom onset (20, 59). Similar findings were demonstrated for another acute-phase reactant, secretory phospholipase A2. Time lag analysis did not show a clear pattern of whether serologic abnormalities preceded or appeared simultaneously with acute-phase reactant elevations. Our study was limited by a blood collection at only a single time point prior to RA onset, and thus is not comparable with that study. Two reported studies, assessing hsCRP levels in blood obtained at a single time point, did not demonstrate any association between hsCRP levels and the risk of RA in 90 preclinical RA cases prior to diagnosis (average 6.6 years) (42), or between CRP and risk of RA in 124 preclinical RA cases up to 20 years before disease onset (42, 50).

This study has several limitations. First, we used data set–specific quartile cut points in the analysis, precluding the ability to relate absolute levels to risk. We do not have data on long-term stability of marker levels from repeated blood collections among women in these studies, and thus we cannot determine whether a single sample measures the average long-term level of any biomarker. Data on biomarker reproducibility from a male cohort study with blood samples collected 4 years apart demonstrated ICCs of 0.47 for IL-6 and 0.78 for sTNFRII (60). The lower stability of IL-6 levels over time suggests that this could be responsible for the lack of significant association with RA in the present study. Further, the lack of repeated blood sampling prior to development of RA symptoms limits our ability to examine patterns of biomarker levels over time in any single subject.

RA diagnosis was validated by medical record review for the presence of 4 of 7 components of the ACR criteria. We included a small number of cases with a clinical diagnosis of RA, in which 2 reviewers agreed with the diagnosis, despite documentation of only 3 ACR criteria. Sensitivity analysis excluding these cases had yielded results similar to those obtained in the primary analysis. The date of the first RA symptom was documented by medical record review, which could be inaccurate, and it is not possible to determine definitively that the subjects were asymptomatic at the time of blood sample collection. There was potential for a misclassification of antibody status due to use of chart data from the time of diagnosis and blood samples collected at variable time points relative to RA diagnosis.

This study was also limited by the small sample size (170 incident RA cases) with stored samples; thus, power to detect small-to-moderate associations may be limited and may explain the lack of associations with seropositive phenotypes of RA in the stratified analyses. The annual incidence of RA is 30 per 100,000 in the NHS and 27.1 per 100,000 in the WHS, which is similar to findings in one population-based study (61) but lower than rates reported from other studies (62, 63), possibly due to the difficulty of obtaining responses from all self-reports, missing information on medical record reviews, or a “healthy participant” effect. The NHS and WHS cohorts largely comprise well-educated, white women, and these findings require replication in more diverse cohorts.

Another limitation is that we were unable to adjust for confounding due to infections and other inflammatory conditions, although we did exclude subjects with self-reported SLE or other CTDs prior to blood sample collection. Also, data on some reproductive variables were not available in the WHS. Finally, the possibility that RF could interfere with ELISAs has been raised by other investigators (39), although we found no evidence of interference by RF with the assays for IL-6 or sTNFRII used in this study.

Our finding of elevated levels of sTNFRII (a surrogate soluble receptor used to assess potential TNFα levels) among preclinical RA patients with no documented arthritis symptoms supports the hypothesis that RA develops in 3 phases: genetic susceptibility, and preclinical autoimmunity with immune activation followed by clinical symptoms. These results could have implications with regard to screening for biomarkers of inflammation of RA risk that could be used for risk counseling or for targeted interventions to prevent RA. Further studies with repeated blood collections in asymptomatic individuals, along with repeated assessments of environmental factors, may elucidate the pathway by which immune activation progresses to symptomatic RA.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Dr. Karlson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design.Karlson, Lee, Shadick, Manson, Costenbader.

Acquisition of data. Karlson, Tworoger, Lee, Buring, Costenbader.

Analysis and interpretation of data. Karlson, Chibnik, Tworoger, Shadick, Manson, Costenbader.

Manuscript preparation. Karlson, Chibnik, Tworoger, Lee, Shadick, Costenbader.

Statistical analysis. Chibnik, Tworoger.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

We thank the participants in the NHS and WHS cohorts for their dedication and continued participation in these longitudinal studies, as well as the staffs of the NHS and WHS for their assistance with this project. We also thank Drs. David Lee, Peter Schur, and Nader Rifai and Mr. Gary Bradwin for assistance with laboratory assays.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
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