Associations between HLA, PTPN22, CTLA4 genotypes and RA phenotypes of autoantibody status, age at diagnosis, and erosions in a large cohort study

Abstract

Background

HLA-DRB1 shared epitope (HLA-SE), PTPN22 and CTLA4 alleles are associated with CCP+ RA.

Objective

We examined associations between HLA-SE, PTPN22, CTLA4 genotypes and RA phenotypes in a large cohort to (a) replicate prior associations with CCP status, and (b) determine associations with radiographic erosions and age of diagnosis.

Methods

689 RA patients from the Brigham RA Sequential Study (BRASS) were genotyped for HLA-SE, PTPN22 (rs2476601) and CTLA4 (rs3087243). Association between genotypes and CCP, RF erosive phenotypes and age at diagnosis were assessed with multivariable models adjusting for age, sex, and disease duration. Novel causal pathway analysis was used to test the hypothesis that genetic risk factors and CCP are in the causal pathway for predicting erosions.

Results

In multivariable analysis, presence of any HLA-SE was strongly associated with CCP+ (OR 3.05 (2.18–4.25)), and RF+ (OR 2.53 (1.83–3.5)) phenotypes; presence of any PTPN22 T allele was associated with CCP+ (OR 1.81 (1.24–2.66)) and RF+ phenotypes (1.84 (1.27–2.66)). CTLA4 was not associated with CCP or RF phenotypes. While HLA-SE was associated with erosive RA phenotype (OR 1.52 (1.01–2.17)), this was no longer significant after conditioning on CCP. PTPN22 and CTLA4 were not associated with erosive phenotype. Presence of any HLA-SE was associated with on average 3.6 years earlier diagnosis compared with absence of HLA-SE (41.3 vs. 44.9 years, p=0.003) and PTPN22 was associated with 4.2 years earlier age of diagnosis (39.5 vs. 43.6 years, p=0.002). CTLA4 genotypes were not associated with age at diagnosis of RA.

Conclusion

In this large clinical cohort, we replicated the association between HLA-SE, and PTPN22 but not CTLA4 with CCP+ and RF+ phenotypes. We also found evidence for associations between HLA-SE, and PTPN22 and earlier age at diagnosis. Since HLA-SE is associated with erosive phenotype in unconditional analysis, but is not significant after conditioning on CCP, this suggests that CCP is in the causal pathway for predicting erosive phenotype.

Genetic factors are thought to be responsible for up to 50%−60% of RA risk.[1–3] Two genes have been unequivocally associated with RA susceptibility (HLA-DRB1 and PTPN22), while other genes demonstrate strong, but inconclusive risk (eg. CTLA, PADI4).[4] Although the HLA associations with RA are complex[5, 6], the majority of the genetic signal from HLA is explained by alleles at the HLA-DRB1 locus[7], and account for approximately 30% of the genetic risk of RA[1]. In individuals of European ancestry, the associated HLA-DRB1 alleles share a region of sequence similarity or “shared epitope” at amino acid positions 70–74 in the third hypervariable region of the HLA-DRB1 molecule[1] (HLA-SE). Outside the HLA, the only genetic polymorphism that has been associated with RA susceptibility in populations of European ancestry and replicated across multiple independent studies is PTPN22. [3, 8–14] A missense allele (C➔T) is associated with an increased risk of RA (rs2476601), with a summary odds ratio of 1.68 (1.53–1.84) from a meta-analysis.[15] Both the HLA-SE alleles, and PTPN22 allele are more strongly associated with the phenotype citric citrullinated peptide positive (CCP+) RA.[4, 16–19] Cytotoxic T lymphocyte-associated antigen 4 (CTLA4) gene has an A➔G single nucleotide polymorphism (SNP) in the 3’ untranslated region (rs3087243), that is associated with increased risk of RA in several populations[20–22], although the OR is much more modest (OR=1.20). In a large replication study CTLA4 was more strongly associated with CCP+ RA.[4]

Phenotyping of disease subgroups in rheumatoid arthritis (RA), such CCP status, is important in studies of RA genetic and environmental epidemiology.[17, 23–25] Environmental risk factors for RA differ in CCP+ and CCP- subgroups.[25] HLA-SE demonstrates a significant gene-environment interaction with cigarette smoking for susceptibility to CCP+ RA[23, 26] and for CCP antibody status in RA[24] but not for CCP- RA.

Studies of genetic predictors of individual RA phenotypes have suggested associations between several genes and autoantibody status (CCP, RF), erosive disease and age at onset of RA.[4, 16, 17, 23, 27–30] Several prior studies have suggested that the association between HLA and erosions is due solely to CCP status; PTPN22 and CTLA4 have not been extensively studied for these phenotypes. We sought to test whether these genetic factors are associated radiographic erosions and early age of diagnosis using data from a large observational RA cohort. Thus, our goal was to (a) replicate association of HLA, PTPN22, CTLA4 with CCP phenotype; (b) determine if genotypes predicted radiographic erosions independent of the CCP association; and (c) to study whether age of diagnosis is associated with genetic variation. We performed conventional association testing as well as novel causal pathway analysis as proposed by Cooper[31] and Li[32] to test the hypothesis that genetic risk factors and CCP are in the causal pathway in predicting erosions.

PATIENTS AND METHODS

Study Population

BRASS (Brigham Rheumatoid Arthritis Sequential Study) is a prospective observational study of 968 RA patients receiving care at the Brigham and Women’s Hospital. Goals of the study are to: 1) determine and validate biomarkers that predict drug response and toxicity in RA; 2) determine and validate biomarkers that predict disease activity and prognosis in RA; and, 3) evaluate the natural history of treated RA by measuring clinical, functional and economic outcomes. Baseline evaluation includes demographic and clinical information, assessment of functional status, disease activity, comorbidity, laboratory testing and hand radiographs. A physical exam, including joint examination, assessment of pain and disease activity by MD and the patient are collected at baseline and yearly. Samples of blood for immunophenoptyping, including C reactive protein (CRP), cytokines, chemokines, and rheumatoid factor (RF), anti-cyclic citrullinated peptide (CCP) as well as blood specimens for DNA/RNA testing are collected and stored at baseline and yearly. During follow-up, patients are mailed a self-administered questionnaire every 6 months to collect information on disease severity, functional status, resource utilization, level of fatigue, employment status, medications, adverse events and intercurrent health events. Hand radiographs, PA views of the hands are performed at baseline and years 3 and 5 during the study. This analysis was limited to the baseline radiographs. The study was approved by the Partners Institutional Review Committee.

Laboratory Methods

Blood was collected at the baseline visit for genotyping. Samples were genotyped for HLA-SE alleles by low-resolution genotyping, and for PTPN22, and CTLA4 (CT60 allele) by Sequenom genotyping (San Diego, CA). HLA-SE alleles 01, 04, 10, and 14 were considered positive. Rheumatoid factor testing was performed by immunoturbidimetric technique on the Cobas Integra 700 analyzer (Roche Diagnostics -Indianapolis, IN), using reagents and calibrators from Roche. Anti-cyclic citrullinated peptide antibodies (CCP) were measured using a second generation ELISA assay (Inova Diagnostics, Inc. – San Diego, CA) with a titer of >20 considered as positive. Radiograph reports were reviewed by a study rheumatologist for evidence of erosions and coded as erosion present or absent.

Statistical analysis

Association between genotypes and dichotomous phenotypes for CCP, RF and erosive RA at baseline were assessed with logistic regression models adjusting for age, sex, and disease duration. Association between genotype and age at diagnosis of RA phenotype was assessed with general linear models adjusting for sex.

Since statistical correlation does not imply a causal relationship, we performed causal pathway analysis by the method described by Li, et al.[32] In brief, Cooper’s Local Causal Discovery algorithm (Cooper’s LCD) was used to explore the potential causal pathway between genotypes and phenotypes (Figure 1).[31] Each causal pathway analysis tested 3 variables: (x) genotype, (y) phenotype, and (z) phenotype. From prior knowledge we know the genotype (x), for example, HLA-SE genotype, cannot be caused by any intermediate phenotypes (y, z), for example, RF and CCP antibody status. If the pairwise unconditional correlations exists between the 3 variables, but the genotype (x) and one phenotype (z) is un-correlated conditional on the other phenotype (y), only one causal path can be derived from x→y→z (Figure 1). We conducted causal pathway analyses of the association between HLA-SE (x), CCP (y), and RF (z), and between HLA-SE (x), CCP (y), and erosion (z).

Figure 1.

Causal Pathway Analysis

RESULTS

The BRASS research study began enrollment in March 2003, and has enrolled 968 patients to date. Genotype data and autoantibody status were available for 728 subjects at the time of this analysis. For this analysis, we included only Caucasian subjects, resulting in a sample of 689 Caucasian RA patients. Among these 689 patients, mean age is 58.0 (SD ±13.8) years, mean disease duration 15.4 (SD ±12.8) years, 110 (15.9%) are recent diagnosis RA, defined as < 2 years disease duration, 560 (81.2%) are female of whom 179 (32%) are in the premenopausal age range (age<51). Education level is 21.5% with high school or less and 78.5% with some college education. At baseline, 323 (47%) of patients report starting a new therapy for RA within the prior 12 months (Table 1). Baseline radiographs demonstrated presence of RA erosions in 374/627(59.7%) subjects with radiographs. There were 61.8% RF positive subjects, and 66.5% CCP positive subjects. Mean age at diagnosis of RA was 42.58 (±15.1). Genotype frequencies were similar to other RA cohorts (Table 2).[4]

Table 1.

Characteristics of 689 Caucasian subjects in the BRASS cohort genetic analyses

Mean age (SD)	58.0±13.8
Mean age at RA diagnosis (SD)	42.6±15.1
Mean years disease duration (SD)	15.4±12.8
Percent female	81.2
Education, percent
< High School	3.1
High School	18.4
Technical college, professional school	26.2
College	26.4
Graduate	25.9
Percent early onset RA (duration < 2 yrs)	15.9
Percent CCP+	66.5
Percent RF+	61.8
Percent CCP+ and RF+	55.8
Percent with erosions	59.7

Table 2.

Genotypes for HLA-SE, PTPN22 and CTLA 4 among 689 Caucasian rheumatoid arthritis subjects in the BRASS cohort

Gene	Genotype	N (%)
HLA-SE	None	237 (34.7%)
	Single copy	272 (39.9%)
	Double copy	173 (25.3%)
PTPN22	CC	488 (74.4%)
	CT	150 (22.9%)
	TT	18 (2.7%)
CTLA4	AA	116 (17.7%)
	AG	320 (47.8%)
	GG	220 (33.5%)

We first attempted to replicate the finding that alleles within three genes, HLA-DRB1, PTPN22 and CTLA4, are associated with CCP+ RA. We created contingency tables for genotypes dichotomized by presence of any HLA-SE (single copy or double copy), the T allele of PTPN22 (single or double copy), the G allele of CTLA4 (single or double copy) and presence/absence of CCP and RF phenotypes. We tested for significance using logistic regression models assuming a multiplicative model for genotype, and adjusting for age, sex, and disease duration (Table 3). Presence of any HLA-SE was strongly associated with CCP+ phenotype (OR 3.05, 95% CI 2.19–4.25, p=2.9 × 10⁻⁹) and RF+ phenotype (OR 2.53, 95% CI 1.83–3.5, p=4.2 × 10⁻⁷); presence of any PTPN22 T allele was associated with CCP+ phenotype (OR 1.81, 95% CI 1.24–2.66, p=0.006) and RF+ phenotype (OR 1.84, 95% CI 1.27–2.66, p=0.002); and CTLA4 was not associated with CCP (OR 1.04, 95% CI 0.7–1.55) or RF phenotypes (OR=0.94, 95% CI 0.64–1.39).

Table 3.

Genotype phenotype associations with autoantibody status in the BRASS study (n=689 Caucasian subjects)

		CCP Phenotype			RF Phenotype
Gene	Genotype	% CCP+	P-value†	OR‡ (95% CI)	% RF+	P-value	OR (95%CI)
HLA-SE	None	49.8			47.2
	Single copy	72.0	1.2 × 10−9	2.05(1.63–2.59)	67.8	1.2 × 10−6	1.71(1.38–2.13)
	Double copy	80.6			72.2
	Any SE	75.3	2.9 × 10−9	3.05(2.19–4.25)	69.5	4.2 × 10−7	2.53(1.83–3.5)
PTPN22	CC	63.4			58.2
	CT	75.0	0.008	1.67(1.14–2.43)	71.8	0.003	1.73(1.21–2.47)
	TT	81.3			76.5
	CT or TT	75.6	0.006	1.81(1.24–2.66)	72.3	0.002	1.84(1.27–2.66)
CTLA4	AA	63.5			61.7
	AG	65.8	0.14	1.20 (0.94–1.53)	61.2	0.48	1.09(0.86–1.37)
	GG	69.0			62.8
	AG or GG	67.1	0.30	1.04(0.7–1.55)	61.8	0.74	0.94(0.64–1.39)

^†Logistic regression models for genotype as predictor of phenotype, adjusted for age, sex and disease duration, using a multiplicative model for genotype.

^‡OR is for each additional copy of HLA-SE, T allele of PTPN22, or G allele of CTLA4

We next sought to determine whether these genetic variants were associated with two markers of disease severity, radiographic erosions and age of diagnosis. Presence of any HLA-SE was associated with erosive RA phenotype in unadjusted logistic regression analysis (p=4 × 10⁻⁴), however this association was less strong in a logistic regression model that adjusted for age, sex and disease duration (OR 1.52, 95% CI 1.01–2.17, p=0.02) (Table 4). Presence of any PTPN22 T allele was not associated with erosive phenotype (OR = 1.14, 95% CI 0.77–1.71), nor was CTLA4 (OR=1.34, 95% CI 0.87–2.15) (Table 4).

Table 4.

Genotype phenotype association with erosive RA phenotype in the BRASS study (n=689 Caucasian subjects)

		Erosion Phenotype
Gene	Genotype	% Erosive	P-value*	OR (95%CI) ‡	Adjusted P-value†	Adjusted OR (95%CI)
HLA-SE	None	50
	Single copy	61.9		1.5 (1.21–1.86)	0.005	1.38(1.11–1.73)
	Double copy	68.9	2.0 × 10−4
	Any SE	64.7	4.0 × 10−4	1.89 (1.36–2.63)	0.02	1.52 (1.01–2.17)
PTPN22	CC	58.6
	CT	59.3	0.11	1.30 (0.94–1.80)	0.2	1.26(0.89–1.78)
	TT	88.9
	CT or TT	62.7	0.37	1.17 (0.82–1.67)	0.51	1.14 (0.77–1.71)
CTLA4	AA	56.0
	AG	63.3	0.81	0.97 (0.77–1.23)	0.69	1.05(0.82–1.35)
	GG	56.7
	AG or GG	60.6	0.43	1.19 (0.8–1.76)	0.17	1.34 (0.87–2.15)

^*Logistic regression models for genotype as predictor of phenotype, using a multiplicative model for genotype.

^†Logistic regression models for genotype as predictor of phenotype, adjusted for age, sex and disease duration, using a multiplicative model for genotype.

^‡OR is for each additional copy of HLA-SE, T allele of PTPN22, or G allele of CTLA4

Using general linear regression models for genotype as a predictor of age at diagnosis of RA, adjusted for sex, presence of any HLA-SE was on average associated with 3.6 years earlier age at diagnosis of RA compared with absence of HLA-SE (41.3 vs. 44.9 years, p=0.002) (Table 5). PTPN22 was on average associated with 4.2 years earlier age at diagnosis of RA (39.5 vs. 43.6 p=0.002) with the earliest age at diagnosis in those with the TT genotype (37.8 years). Adjusting for sex slightly attenuated these relationships. CTLA4 genotypes were not associated with age at diagnosis of RA in this dataset.

Table 5.

RA genotypes as predictors of age at diagnosis of RA in the BRASS study (n=689)

Genotype	Presence of genotype	Age at RA diagnosis ± SD	Difference†	Unadjusted P-value	Adjusted P-value*
HLA SE	None	44.9 (±16.7)
	Single copy	41.0 (±14.7)		0.02	0.02
	Double copy	41.9 (±13.9)
	Any SE	41.3 (±14.4)	3.56	0.003	0.002
PTPN22	CC	43.6 (±14.9)
	CT	39.7 (±14.4)		0.002	0.002
	TT	37.8 (±16.8)
	CT or TT	39.5 (±14.7)	4.19	0.002	0.002
CTLA4	AA	42.9 (±15.7)
	AG	42.1 (±15.4)		0.72	0.64
	GG	43.2 (±14.0)
	AG or GG	42.5 (±15.9)	0.35	0.82	0.84

^*General linear regression model for genotype as predictor of age at diagnosis, adjusted for sex

^†Difference between no HLA-SE and double copy of HLA-SE, difference between CC and any T allele of PTPN22, and AA and any G allele of CTLA4

We conducted two causal pathway analyses, adapted from Li, et al.[32], and illustrated in Figure 1. We asked whether a genetic variant (HLA-SE) contributed to RF phenotype, independent of CCP status (Causal Pathway 1) as a replication of the Li, et al analysis. We also asked whether HLA-SE contributed to erosion phenotype, independent of CCP status (Causal Pathway 2).

The first step in a causal pathway analysis[32] is to test unconditional associations between variables. We demonstrated strong relationships (p< 0.001) between all variables (Table 6).

Table 6.

Unconditional association between variables

	χ2	OR (95%CI)	p-value
Causal Pathway 1
HLA-SE and CCP	44.57	3.1 (2.2 – 4.3)	<1.0 × 10−4
CCP and RF	278.71	24.9 (16.2 – 38.2)	<1.0 × 10−4
HLA-SE and RF	31.95	2.5 (1.8 – 3.5)	<1.0 × 10−4
Causal Pathway 2
HLA-SE and CCP	44.57	3.1 (2.2 – 4.3)	<1.0 × 10−4
CCP and Erosion	58.65	3.8(2.7–5.4)	<1.0 × 10−4
HLA-SE and Erosion	12.57	1.8(1.3–2.6)	4.0 × 10−4

Conditional analysis of Causal Pathway 1, of the association between HLA-SE (x), CCP (y), and RF (z) demonstrated that HLA-SE (x) and RF (z) are not associated when conditioning on CCP (y) (Table 7). These results are similar to those shown in the causal pathway analysis by Li, et al.[32] Therefore the evidence supports a causal pathway from HLA-SE → CCP → RF, but not directly from HLA-SE → RF.

Table 7.

Conditional analysis of causal pathways for HLA-SE, RF and CCP phenotypes in RA

		Conditioned on	OR0†	OR1†	ORmh*	p-value
x and y	SE and CCP	RF (z)	2.9 (1.6 – 5.2)	1.9 (1.0 – 3.8)	2.5 (1.6 – 3.8)	<1.0 × 10−4
y and z	CCP and RF	SE (x)	32.0 (15.5 – 66)	18.9 (10.9 – 32.5)	23.0 (14.9 – 35.5)	<1.0 × 10−4
x and z	SE and RF	CCP (y)	1.9 (0.9 – 3.7)	1.2 (0.7 – 2.1)	1.4 (0.9 – 2.2)	0.09

^†OR₀ = Odds ratio for strata where conditioned on variable = 0 (unexposed)

^‡OR₁ = Odds ratio for strata where conditioned on variable = 1 (exposed)

^*OR_mh = Mantel-Haenszel Odds ratio

Conditional analysis of Causal Pathway 2, of the association between HLA-SE (x), CCP (y), and erosion (z) demonstrated that HLA-SE (x) and erosion (z) are not associated when conditioning on CCP (y) (Table 8). Therefore the evidence supports a causal pathway from HLA-SE → CCP→ erosion, but not directly from HLA-SE → erosion. Since the analysis presented in Table 7 demonstrated that SE (x) and RF(y) are not associated when conditioning on CCP, we did not test for a causal pathway from SE → RF → erosion.

Table 8.

Conditional analysis of causal pathways for HLA-SE, CCP and erosion phenotypes in RA

		Conditioned on	OR0†	OR1†	ORmh*	p-value
x and y	SE and CCP	Erosion (z)	4.5(2.6–7.8)	1.9(1.1–3.3)	3.0(2.1–4.3)	<1.0 × 10−4
y and z	CCP and Erosion	SE (x)	6.0(3.3–10.8)	2.5(1.6–4.0)	3.5(2.5–5.1)	<1.0 × 10−4
x and z	SE and Erosion	CCP (y)	2.2(1.2–3.9)	0.9(0.6–1.5)	1.4(0.9–1.9)	0.11

^†OR₀ = Odds ratio for strata where conditioned on variable = 0 (unexposed)

^‡OR₁ = Odds ratio for strata where conditioned on variable = 1 (exposed)

^*OR_mh = Mantel-Haenszel Odds ratio

DISCUSSION

In this large observational RA cohort, we demonstrated strong associations between two RA genetic risk factors (HLA SE, PTPN22) and RA phenotypes. HLA-SE was strongly associated with CCP, RF, and erosive phenotypes, even after adjusting for age, sex, and disease duration. We demonstrated that PTPN22 was strongly associated with CCP and RF phenotypes, but not with erosive RA. We were unable to show any genotype-phenotype associations for CTLA-4, perhaps due to limited power as the published OR for susceptibility for CTLA-4 is 1.2 whereas for PTPN22 is 1.75 and for HLA-SE is 3.0. We found strong correlations between HLA-SE and both CCP and RF phenotypes, however, the HLA-SE association with the CCP phenotype was stronger than for RF phenotype. In this clinical cohort, using causal pathway analysis we replicated prior findings from the North American Rheumatoid Arthritis Consortium (NARAC) [18, 32], and the Leiden Early Arthritis Clinic (EAC)[17] that suggest that HLA-SE is causally associated with the CCP phenotype, and CCP is causally associated with the RF phenotype, but HLA-SE is not causally associated with the RF phenotype.

Our findings for HLA-SE and erosions in unconditional analyses are consistent with a meta-analysis of 30 studies published from prospective cohorts and cross-sectional studies involving 3,240 RA patients demonstrating an odds ratio of 2.0 (95% CI 1.8–2.2) for association of HLA-SE and erosions.[27] Our causal pathway analysis extends these observations to study the role of CCP antibodies. In our cross-sectional study, CCP is strongly associated with erosions, but HLA-SE is not associated with erosions, after conditioning on CCP status, suggesting that there is no causal pathway directly from HLA-SE to erosions. Our causal pathway findings are consistent with two studies from the Leiden EAC.[17, 30] Among RA patients in the EAC prospective cohort there were large differences in rates of erosion progression between CCP+RA and CCP-RA, with an additional effect of the HLA-SE on erosion progression only among the CCP+ group but no association for HLA-SE with erosions among the CCP- group.[17] Analysis of the undifferentiated arthritis patients in the EAC cohort followed prospectively for the development of RA demonstrated that HLA-SE alleles are primarily a risk factor for development of anti-CCP antibodies and are not an independent risk factor for progression to RA, after adjusting for CCP status.[30]

We found evidence of 3.6 years earlier age of diagnosis with HLA-SE in this large clinical cohort with a mean disease duration of 15 years. This work replicates other studies in which HLA-SE was associated with 6 years earlier age at onset in a seropositive RA cohort with < 15 months of disease duration in the US [28] as well as in a Korean population in which the specific allele, HLA DRB1*0405, was associated with 4 years earlier onset.[33] We found evidence for 4.2 years earlier diagnosis of RA for PTPN22, which is similar to the findings of 2 years earlier age of onset in samples from North America and Sweden.[4] In a population from the UK, PTPN22 was associated with 8.6 years earlier onset in homozygotes, and 4.7 years earlier onset in heterozygotes.[34]

The BRASS cohort is a well-educated, primarily Caucasian population, with long disease duration, treated at a tertiary referral center in the United States, all factors that may limit generalizability. Although disease duration is similar to that in the North American Rheumatoid Arthritis Consortium (NARAC), the rates of seropositivity and erosive disease are lower, since by design NARAC recruited more severe RA patients.[32] However age, disease duration, rates of erosive disease, and seropositivity are similar to those reported in the National Databank study of >14,000 patients enrolled from rheumatology practices across the US[35, 36], suggesting that BRASS subjects are more similar to RA patients seen in the community. The causal pathway approach is a statistical method that requires a number of assumptions, as discussed in detail by Cooper et al[31], and it is possible that our dataset does not meet all of the assumptions. The approach does allow for the presence of potential confounders, as long as the variable “x”, in this case genotype, is not caused by the confounder.

In conclusion, genotype-phenotype analysis of a large clinical cohort demonstrates the importance of considering phenotypes when studying genetic predictors. We replicated association of both HLA-SE, and PTPN22 with CCP+ RA compared with CCP- RA as well as associations with earlier diagnosis of RA, but were unable to demonstrate any associations for CTLA-4. Of the 3 genes studied, only HLA-SE was associated with radiographic erosions but this association was not independent of CCP status. The novel causal pathway analyses confirms prior studies that demonstrate the importance of antibodies to CCP in the pathogenesis of joint damage in RA and provides support to recent calls[17, 23] for considering CCP+ RA as a separate clinical entity within the overall RA phenotype.