Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: A longitudinal cohort study in the Netherlands

Xanthe M E Matthijssen; Ellis Niemantsverdriet; Tom W J Huizinga; Annette H M van der Helm–van Mil

doi:10.1371/journal.pmed.1003296

. 2020 Sep 22;17(9):e1003296. doi: 10.1371/journal.pmed.1003296

Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: A longitudinal cohort study in the Netherlands

Xanthe M E Matthijssen ^1,^*, Ellis Niemantsverdriet ¹, Tom W J Huizinga ¹, Annette H M van der Helm–van Mil ¹

Editor: Carlomaurizio Montecucco²

PMCID: PMC7508377 PMID: 32960885

Abstract

Background

Based on different genetic and environmental risk factors and histology, it has been proposed that rheumatoid arthritis (RA) consists of 2 types: autoantibody-positive and autoantibody-negative RA. However, until now, this remained hypothetical. To assess this hypothesis, we studied whether the long-term outcomes differed for these 2 groups of RA patients.

Methods and findings

In the Leiden Early Arthritis Clinic cohort, 1,285 consecutive RA patients were included between 1993 and 2016 and followed yearly. Treatment protocols in routine care improved over time, irrespective of autoantibody status, and 5 inclusion periods were used as instrumental variables: 1993–1996, delayed mild disease-modifying antirheumatic drug (DMARD) initiation (reference period); 1997–2000, early mild DMARDs; 2001–2005, early methotrexate; 2006–2010, early methotrexate followed by treat-to-target adjustments; 2011–2016, similar to 2006–2010 plus additional efforts for very early referral. Three long-term outcomes were studied: sustained DMARD-free remission (SDFR) (persistent absence of clinical synovitis after DMARD cessation), mortality, and functional disability measured by yearly Health Assessment Questionnaire (HAQ). Treatment response in the short term (disease activity) was measured by Disease Activity Score–28 with erythrocyte sedimentation rate (DAS28-ESR). Linear mixed models and Cox regression were used, stratified for autoantibody positivity, defined as IgG anti-CCP2 and/or IgM rheumatoid factor positivity. In total, 823 patients had autoantibody-positive RA (mean age 55 years, 67% female); 462 patients had autoantibody-negative RA (age 60 years, 64% female). Age, gender, and percentage of autoantibody-positive patients were stable throughout the inclusion periods. Disease activity significantly decreased over time within both groups. SDFR rates increased after introduction of treat-to-target (hazard ratio [HR] 2006–2010 relative to 1993–1996: 3.35 [95% CI 1.46 to 7.72; p = 0.004]; HR 2011–2016: 4.57 [95% CI 1.80 to 11.6; p = 0.001]) in autoantibody-positive RA, but not in autoantibody-negative RA. In autoantibody-positive RA, mortality decreased significantly after the introduction of treat-to-target treatment adjustments (HR 2006–2010: 0.56 [95% CI 0.34 to 0.92; p = 0.023]; HR 2011–2016: 0.33 [95% CI 0.14 to 0.77; p = 0.010]), but not in autoantibody-negative RA (HR 2006–2010: 0.79 [95% CI 0.40 to 1.56; p = 0.50]; HR 2011–2016: 0.36 [95% CI 0.10 to 1.34; p = 0.13]). Similarly, functional disability improved in autoantibody-positive RA for the periods after 2000 relative to 1993–1996 (range −0.16 [95% CI −0.29 to −0.03; p = 0.043] to −0.32 [95% CI −0.44 to −0.20; p < 0.001] units of improvement), but not in autoantibody-negative RA (range 0.10 [95% CI −0.12 to 0.31; p = 0.38] to −0.13 [95% CI −0.34 to 0.07; p = 0.20] units of improvement). Limitations to note were that treatment was not randomized—but it was protocolized and instrumental variable analysis was used to obtain comparable groups—and that a limited spread of ethnicities was included.

Conclusions

Although disease activity has improved in both autoantibody-positive and autoantibody-negative RA in recent decades, the response in long-term outcomes differed. We propose that it is time to subdivide RA into autoantibody-positive RA (type 1) and autoantibody-negative RA (type 2), in the hope that this leads to stratified treatment in RA.

In a longitudinal cohort study, Xanthe Matthijssen and colleagues investigate differences between autoantibody-positive and -negative RA.

Author summary

Why was this study done?

Patients with rheumatoid arthritis (RA) have different risk factors and histology (microscopic anatomy) depending on the presence or absence of autoantibodies (anti-citrullinated protein antibodies and rheumatoid factor).
Because it is suspected that RA with and without autoantibodies are 2 distinct diseases with different pathophysiology, we hypothesized that these 2 types of RA will have reacted differently to improvements in treatment strategies that have taken place over the last decades.

What did the researchers do and find?

Since its start in 1993, the inclusion criteria of the Leiden Early Arthritis Clinic cohort have not changed, and included RA patients have remained similar, apart from earlier diagnosis; therefore, RA patients from different years were comparable. Treatment protocols enhanced over time, but were similar for patients with and without autoantibodies.
We studied the changes in disease activity and 3 long-term outcomes of RA patients with and without autoantibodies over time (inclusion period was a proxy for treatment strategy).
We found that while disease activity improved in both patient groups, the long-term outcomes (the possibility to permanently stop medication, mortality, and functional disability) only improved in RA patients with autoantibodies.

What do these findings mean?

The disconnection between improvement in disease activity and subsequent improvement in long-term outcomes in RA without autoantibodies suggests that the underlying pathogenesis of RA with and without autoantibodies is different.
We propose that it is time to formally subdivide RA into type 1 (with autoantibodies) and type 2 (without autoantibodies).

Introduction

Careful clinical observations over time have led to the description of diseases. In addition, the subdividing of diseases has also been based on clinical observations, with differences in pathogenetic etiology identified subsequently. For instance, the subdividing of diabetes into type 1 and type 2 was based on differences in clinical presentation (young patients versus older and obese patients); this distinction was confirmed by treatment response to insulin, and subsequently fueled targeted etiological studies [1].

Rheumatoid arthritis (RA) is considered a syndrome. During the last decade it was observed that there are differences in RA patients with and without autoantibodies (such as rheumatoid factor [RF] and anti-citrullinated protein antibodies [ACPAs]). Autoantibody-positive RA has a different genetic background [2], different environmental risk factors [3,4], slight differences in the preclinical symptomatic phase and first clinical presentation [5–7], differences in histology [8], differences in the synovial fluid cytokine profile [9], and, when left untreated, more severe joint destruction [5]. Nonetheless, the etiology and pathophysiology of RA is still incompletely understood. It is unclear if there is 1 pathophysiological genesis—in which the presence of autoantibodies is promoted by certain genetic factors and where autoantibodies act as a “severity” factor—or, alternatively, if there are 2 different mechanisms of disease development. When distinct disease mechanisms exist, treatment response may differ. Whether autoantibody-positive and autoantibody-negative RA have different mechanisms can therefore be addressed by clinical evaluation of long-term results in response to changes in treatment strategy.

Slight differences in the effect of some drugs have been described between autoantibody-positive and autoantibody-negative RA patients based on trial data [10–13], but these are based on selected groups of RA patients with a limited follow-up duration. We will take advantage of a large longitudinal cohort including incident RA patients without selection followed over the last 25 years; to our knowledge, this is currently the largest observational cohort of RA. Treatment of RA has changed over time, and the same improvements in strategies (e.g., earlier treatment initiation and treat-to-target treatment adjustments) have been applied in both autoantibody-positive and autoantibody-negative patients. To evaluate whether autoantibody-positive RA and autoantibody-negative RA are 2 disease types, we studied the associations between changing treatment strategies and disease activity in the short term as well as 3 long-term outcomes.

Methods

Longitudinal cohort

The Leiden Early Arthritis Clinic (EAC) cohort is a population-based inception cohort including all consecutive patients newly presenting with recent-onset arthritis, that was started in 1993 and has been described in [14]. Inclusion criteria were presence of synovitis determined at physical examination by a rheumatologist and symptom duration of <2 years. The department of rheumatology in the Leiden University Medical Center is the only center for rheumatic diseases in a semi-rural area with >400,000 inhabitants. Since the start of the cohort, general practitioners (GPs) were informed on the relevance of early referral, and patients referred with suspicion of early arthritis were seen with priority, generally within 2 weeks. Of note, in line with Dutch GP guidelines, autoantibodies were rarely determined in primary care [15]. Written informed consent was obtained from all participants. The study was approved by the local medical ethics committee (Commissie Medische Ethiek of the Leiden University Medical Center; B19.008).

For this study we selected patients with RA (clinical diagnosis plus fulfillment of 1987 American College of Rheumatology criteria). The use of the 1987 criteria (instead of the 2010 criteria) excluded influences of temporal changes in views on diagnosing RA and of the inverse relationship between presence of autoantibodies and degree of inflammation on the classification [16,17]. Between 24 February 1993 and 31 December 2016, 1,377 patients enrolled in the cohort were classified with RA.

At the first visit, rheumatologists and patients completed questionnaires (including the Health Assessment Questionnaire [HAQ] Disability Index), swollen and tender joint counts were performed, and blood samples were taken for routine diagnostic laboratory screening (including erythrocyte sedimentation rate [ESR] and immunoglobulin M RF [positive if ≥3.5 IU/ml]). From 2006 onward, ACPA was measured (before 2009, anti-CCP2, Eurodiagnostica, positive if ≥25 U/ml; from 2009 onward, EliA, Phadia, positive if ≥7U/ml). In patients included before 2006, ACPA status was assessed retrospectively on stored baseline serum samples using the Eurodiagnostica assay. Since seroconversion is rare, repeated ACPA and/or RF measurements during follow-up were not studied [18]. In 6 patients, autoantibody status was not available; consequently, they were excluded from the analyses (S1 Fig).

Protocolized follow-up visits were performed twice in the first year and yearly thereafter, as long as patients were treated at the outpatient clinic. Follow-up ended in case of death, release from care due to sustained DMARD-free remission (SDFR), moving to another area, or withdrawal of informed consent while remaining treated. As data were collected at regular rheumatologist visits, withdrawal of informed consent was rare. Data from Statistics Netherlands from our region showed that moving away from the Leiden area was also infrequent (<3% annually) [19]. Inherent to the design, follow-up was shorter in the more recent inclusion periods. The majority of missed follow-up visits (not due to inclusion date) was due to mortality or SDFR.

Definition of autoantibody-positive and autoantibody-negative

Patients with ACPA and/or RF were categorized as autoantibody-positive (type 1); double-negative patients were categorized as autoantibody-negative (type 2). For practical reasons, the distinction between type 1 and type 2 is based on the autoantibodies that are currently used in the clinic. It could be that if more factors were included, e.g., other autoantibodies or other factors such as obtained from histology, a better division into groups would be obtained [20–23]. Our primary goal, however, was to investigate the main distinction into autoantibody-positive and autoantibody-negative RA as it is used in clinical practice.

Treatment

Patients were treated in routine care according to protocols. Of 1,377 RA patients, 86 were treated within randomized clinical trials that were not in line with the treatment guidelines at that time and were excluded, leaving 1,285 RA patients for analyses (S1 Fig). Temporal changes in treatment strategies concerned the initial start as well as treatment adjustments over time; improvements in both aspects of treatment are reflected by inclusion period as proxy. Patients included between 24 February 1993 and 31 December 1996 (n = 168) received initial nonsteroidal anti-inflammatory drugs (NSAIDs) and started mild disease-modifying antirheumatic drugs (DMARDs) with delay. Patients included between 1 January 1997 and 31 December 2000 (n = 185) were treated early with DMARDs but not with methotrexate (e.g., hydroxychloroquine and sulfasalazine) [24]. Patients included between 1 January 2001 and 31 December 2005 (n = 207) started early with methotrexate [25]. From 2006 onwards, early methotrexate was followed by treat-to-target treatment adjustments, indicating treatment adjustments in case of increased Disease Activity Score (DAS) (1 January 2006–31 December 2010, n = 335) [26]. Furthermore, because the value of very early treatment became even more apparent in 2010, and as GP delay contributed most to the total delay in our region [27], from 2011 onwards, on top of the existing regimen, additional efforts were undertaken to further reduce referral delay by instituting an early arthritis recognition clinic, which is a screening clinic for the presence of inflammatory arthritis (1 January 2011–31 December 2016, n = 390) [27–29].

In line with the absence of guidelines that initial treatment should be adapted to autoantibody status [30,31], initial treatment choices were not directed by autoantibodies. Subsequent treatment decisions were targeted at DAS; this was independent of patient characteristics. Thus, protocols were similar for type 1 and 2.

Anti-TNF was the first biologic that became available in the early 2000s for RA patients whose treatment failed on ≥2 conventional DMARDs [32]. Over time other biologics were registered, though the indication remained similar in the Netherlands. S1 Table provides information about the use of biologics during different follow-up durations, for type 1 and 2 separately. The usage was slightly higher in type 1, especially after introduction of treat-to-target.

Outcomes

Disease activity reflected the direct results of treatment, as measured with the Disease Activity Score–28 with erythrocyte sedimentation rate (DAS28-ESR) [33]. Since 2006, treatment has been aimed at this short-term target to eventually improve long-term outcomes. Three long-term outcomes were studied: SDFR, mortality, and functional disability. SDFR was defined as the sustained absence of synovitis (by physical examination) after discontinuation of DMARD therapy (including biologics and systemic or intra-articular corticosteroids) for the entire follow-up after DMARD withdrawal, and this follow-up had to continue for at least 1 year after DMARD stop [34]. This stringent and innovative definition of long-term remission is the opposite of disease persistence and has become increasingly achievable [35]. After achievement of SDFR, patients were followed for median 5.5 years, to verify its sustainability. Patients who achieved DMARD-free remission but developed a late flare during subsequent follow-up (n = 23) were not considered as being in SDFR. All medical files of patients with ≥1 year of follow-up were retrospectively explored for SDFR until April 2017. Mortality status was obtained from the civic registries on 1 June 2018. Functional disability is one of the most important outcomes from the patients’ perspective [36], and was measured yearly with the HAQ, ranging from 0 (no disability) to 3 (severe disability) [37,38].

Statistical analyses

Main analyses were done for type 1 and 2 RA separately. Inclusion period was used as an instrumental variable for treatment strategy. Within each type, improvements over time were compared to the reference period (inclusion in 1993–1996).

Next, improvements over time compared to the reference period were compared between the 2 types by including an interaction term in the models to quantify the difference in improvement over time between the 2 types.

Time to SDFR was analyzed with Cox regression. SDFR status was censored at the date of data extraction (e.g., revision of the medical files) or at an earlier date when patients were lost to follow-up or had died.

Mortality was analyzed with Cox regression; follow-up was censored at the date of data extraction. Mortality was not compared to the general population because excess mortality in RA relative to the general population requires >10 years of follow-up to become apparent [39,40]; this follow-up duration was absent for the recent inclusion periods.

Missing data on DAS28-ESR (complete DAS28-ESR missing for 0% of patients at baseline and 3% of patients at follow-up visits) and HAQ (missing for 13% at baseline and 22% at annual follow-up visits) for attended visits were imputed using multivariate multiple imputation with predictive mean matching (100 cycles, 30 datasets). DAS28-ESR and HAQ were analyzed with linear mixed models. Because both outcomes rapidly decreased within the first year, the first year was analyzed separately from the remaining follow-up [41–43]. The slope of decrease in the first year was analyzed with a random intercept and an identity covariance matrix. The course after the first year was analyzed with a random intercept, random slope, and continuous auto-regressive covariance matrix of order 1. Estimated marginal means were calculated. Percentages of DAS28-ESR remission (<2.6) at 1 and 3 years were tested with chi-squared tests [44].

To minimize the influence of the association of the studied exposure and follow-up duration, analyses were truncated at 15 years of follow-up, and follow-up duration was not included as a covariate in any of the analyses. All analyses were corrected for age and gender to improve model fit. As none of the measured baseline covariates were true confounders of the relationship between treatment strategy and outcomes, because they were not associated with the exposure or regarded to be in the causal path (see S1 Text and S2 Fig for explanation), no other corrections were made.

No formal prospective analysis plan was written or submitted prior to performing the analyses. The widths of the confidence intervals have not been adjusted for multiplicity, and p-values < 0.05 were considered significant. R 3.6.1 with packages described in S2 Text was used. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (See S1 Checklist).

Sensitivity analyses

In a sensitivity analysis RA was defined according to the 2010 criteria.

In response to requests during peer review, to assess whether the difference in age at onset between the disease types might influence the results, patients aged <65 years at diagnosis were analyzed in a sensitivity analysis.

For SDFR and mortality, a sensitivity analysis was done to address differences in symptom duration at baseline, as patients could not have presented themselves to the EAC if the studied event (SDFR or death) had already happened. To assess the influence of this possible left truncation, correction for left truncation was applied.

Finally, data for both disease types were plotted by inclusion period for all outcomes; this was done for illustration.

Results

Baseline characteristics

In total, 823 patients had type 1 RA; their mean age at first presentation was 55 years, and 67% were female (Table 1). In total, 462 patients had type 2 RA; their mean age at first presentation was 60 years, and 64% were female. Age, gender, and percentage of RA types were stable throughout the inclusion periods (p = 0.59, p = 0.28, and p = 0.42, respectively), showing that similar RA patients were included over time. Within both RA types, patients presented with shorter symptom duration, lower numbers of swollen and tender joints, and lower acute phase reactants in more recent inclusion periods, reflecting that earlier presentation was paralleled with less severe disease (Table 1).

Table 1. Characteristics of patients with type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA at first presentation to the Leiden Early Arthritis Clinic.

Characteristic	Inclusion period					p-Value
Characteristic	1993–1996	1997–2000	2001–2005	2006–2010	2011–2016	p-Value
Type 1 RA	(n = 112, 67%)	(n = 118, 64%)	(n = 129, 62%)	(n = 203, 61%)	(n = 261, 67%)
Women, n (%)	77 (69)	82 (70)	91 (71)	136 (67)	167 (64)	0.70
Age in years, mean (SD)	56 (16)	55 (16)	55 (15)	54 (15)	56 (15)	0.63
Symptom duration, days, median (IQR)	153 (84–306)	156 (84–304)	147 (72–264)	146 (61–270)	103 (53–227)	0.006
Current smoker, n (%)	35 (33)	35 (33)	29 (27)	40 (22)	74 (30)	0.21
28 SJC, median (IQR)	6 (3–10)	7 (4–12)	4 (2–7)	4 (2–7)	4 (2–7)	<0.001
28 TJC, median (IQR)	7 (3–13)	7 (3–14)	7 (3–12)	6 (3–11)	5 (2–9)	<0.001
ESR, median (IQR)	46 (26–70)	32 (20–54)	30 (18–55)	29 (14–42)	29 (14–41)	<0.001
VAS general health, median (IQR)	43 (17–70)	44 (26–66)	53 (34–72)	56 (29–72)	70 (50–80)	<0.001
DAS28-ESR, median (IQR)	5.5 (4.2–6.5)	5.2 (4.2–6.1)	5.2 (4.3–6.0)	4.9 (4.2–6.0)	4.8 (4.1–5.7)	0.02
HAQ, median (IQR)	1.0 (0.6–1.4)	0.8 (0.4–1.6)	1.0 (0.6–1.6)	1.0 (0.5–1.5)	1.0 (0.5–1.5)	0.12
Type 2 RA	(n = 56, 33%)	(n = 67, 36%)	(n = 78, 38%)	(n = 132, 39%)	(n = 129, 33%)
Women, n (%)	38 (68)	41 (61)	57 (73)	80 (61)	79 (61)	0.34
Age in years, mean (SD)	56 (15)	59 (19)	60 (14)	61 (16)	62 (14)	0.16
Symptom duration, days, median (IQR)	126 (61–220)	92 (62–219)	120 (74–234)	109 (59–176)	85 (45–189)	0.06
Current smoker, n (%)	17 (30)	11 (18)	14 (20)	24 (21)	28 (22)	0.52
28 SJC, median (IQR)	9 (4–14)	12 (7–19)	6 (3–10)	6 (3–10)	6 (3–10)	<0.001
28 TJC, median (IQR)	9 (3–19)	13 (6–20)	11 (5–19)	9 (4–13)	7 (3–11)	<0.001
ESR, median (IQR)	40 (22–56)	28 (16–47)	27 (16–47)	31 (9–46)	25 (11–41)	0.008
VAS general health, median (IQR)	46 (25–63)	50 (26–62)	56 (36–75)	64 (44–79)	70 (60–80)	<0.001
DAS28-ESR, median (IQR)	5.6 (4.5–6.3)	5.8 (4.8–6.5)	5.6 (4.4–6.7)	5.3 (4.4–6.3)	5.2 (4.4–6.0)	0.19
HAQ, median (IQR)	1.1 (0.8–1.6)	0.9 (0.5–1.4)	1.1 (0.8–1.8)	1.1 (0.8–1.5)	1.0 (0.6–1.5)	0.15

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p-Value for results of Kruskal–Wallis H test (Fisher’s exact test for proportions and ANOVA for normally distributed variables). The percentage of patients with type 1 or 2 RA for the different inclusion periods was stable over time (p = 0.42). SJC and TJC are the number of swollen and tender joints, respectively, out of 28 joints assessed. The VAS general health is a self-reported assessment, ranging from 0 to 100. DAS28-ESR ranges from 2 to 9.4, with higher scores indicating more disease activity. HAQ (HAQ Disability Index) ranges from 0 to 3, with higher scores indicating more disability.

DAS28-ESR, Disease Activity Score–28 with erythrocyte sedimentation rate; ESR, erythrocyte sedimentation rate; HAQ, Health Assessment Questionnaire; IQR, interquartile range; RA, rheumatoid arthritis; SD, standard deviation; SJC, swollen joint count; TJC, tender joint count; VAS, visual analogue scale.

Disease activity

In type 1 RA, DAS28-ESR improved in the first year and during subsequent follow-up (Fig 1; Table 2). The percentage of patients achieving DAS28-ESR remission (<2.6) significantly increased, e.g., from 13% in the oldest inclusion period to 50% at year 1 and 61% at year 3 in the most recent period (S3 Fig).

Table 2. Disease activity during the first year and subsequent follow-up, and long-term outcomes (sustained DMARD-free remission, mortality, and functional disability) by inclusion period compared to the reference period for type 1 (autoantibody-positive) RA.

Inclusion period	DAS28-ESR, slope in first year		DAS28-ESR over time after first year		Sustained DMARD free remission		Mortality		HAQ, slope in first year		HAQ over time, after first year
Inclusion period	Relative mean difference^a	p-val	Relative mean difference^b	p-val	Hazard ratio^c	p-val	Hazard ratio^c	p-val	Relative mean difference^a	p-val	Relative mean difference^b	p-val
1993–1996	Ref^d		Ref^d		Ref		Ref		Ref^d		Ref^d
1997–2000	−0.38 (−0.87; 0.10)	0.12	−0.41 (−0.66; −0.16)	0.002	1.14 (0.42; 3.05)	0.80	0.74 (0.47; 1.15)	0.18	0.01 (−0.19; 0.21)	0.89	−0.02 (−0.15; 0.11)	0.58
2001–2005	−1.70 (−2.21; −1.20)	<0.001	−0.86 (−1.12; −0.61)	<0.001	1.66 (0.67; 4.12)	0.27	0.71 (0.46; 1.11)	0.13	−0.28 (−0.49; −0.07)	0.009	−0.16 (−0.29; −0.03)	0.043
2006–2010	−1.62 (−2.08; −1.17)	<0.001	−1.04 (−1.28; −0.80)	<0.001	3.35 (1.46; 7.72)	0.004	0.56 (0.34; 0.92)	0.023	−0.33 (−0.51; −0.14)	0.001	−0.32 (−0.44; −0.20)	<0.001
2011–2016	−1.54 (−1.96; −1.12)	<0.001	−1.07 (−1.32; −0.83)	<0.001	4.57 (1.80; 11.6)	0.001	0.33 (0.14; 0.77)	0.010	−0.29 (−0.46; −0.12)	0.001	−0.26 (−0.38; −0.14)	0.008

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Bold value indicate p-values < 0.05.

^aDifference in slope in the first year in the inclusion period compared to 1993–1996; analyzed with linear mixed models corrected for age and gender. A negative number indicates a steeper slope.

^bDifference in mean over time in the inclusion period compared to 1993–1996; analyzed with linear mixed models corrected for age and gender.

^cHazard ratios compared to 1993–1996; analyzed with Cox regression corrected for age and gender.

^dThe estimated marginal mean, adjusted for age and gender, in type 1 RA for inclusion period 1993–1996 was −0.34 (95% CI −0.70 to 0.03) for the slope in DAS28-ESR in the first year, 3.58 (95% CI 3.39 to 3.76) for DAS28-ESR over time after the first year, −0.15 (95% CI −0.29 to 0.00) for slope in HAQ in the first year, and 0.78 (95% CI 0.68 to 0.88) for HAQ over time after the first year.

DAS28-ESR, Disease Activity Score–28 with erythrocyte sedimentation rate; DMARD, disease-modifying antirheumatic drug; HAQ, Health Assessment Questionnaire; p-val, p-value.

In type 2 RA, DAS28-ESR also improved, especially in the first year (Fig 2; Table 3). DAS28-ESR remission percentages increased from 32% in the oldest inclusion period to 54% at year 1 and 71% at year 3 in the most recent period (S3 Fig).

Table 3. Disease activity during the first year and subsequent follow-up, and long-term outcomes (sustained DMARD-free remission, mortality, and functional disability) by inclusion period compared to the reference period for type 2 (autoantibody-negative) RA.

Inclusion period	DAS28-ESR, slope in first year		DAS28-ESR over time after first year		Sustained DMARD free remission		Mortality		HAQ, slope in first year		HAQ over time, after first year
Inclusion period	Relative mean difference^a	p-val	Relative mean difference^b	p-val	Hazard ratio^c	p-val	Hazard ratio^c	p-val	Relative mean difference^a	p-val	Relative mean difference^b	p-val
1993–1996	Ref^d		Ref^d		Ref		Ref		Ref^d		Ref^d
1997–2000	−0.53 (−1.30; 0.24)	0.18	0.08 (−0.32; 0.49)	0.69	0.61 (0.32; 1.18)	0.14	0.67 (0.35; 1.30)	0.24	0.16 (−0.13; 0.44)	0.29	0.03 (−0.19; 0.24)	0.81
2001–2005	−0.88 (−1.66; −0.11)	0.025	−0.03 (−0.43; 0.37)	0.89	0.80 (0.43; 1.48)	0.48	0.57 (0.28; 1.13)	0.11	0.05 (−0.25; 0.35)	0.75	0.10 (−0.12; 0.31)	0.38
2006–2010	−0.78 (−1.48; −0.08)	0.029	−0.26 (−0.63; 0.11)	0.17	1.11 (0.63; 1.97)	0.71	0.79 (0.40; 1.56)	0.50	0.02 (−0.24; 0.28)	0.87	−0.09 (−0.28; 0.10)	0.34
2011–2016	−1.08 (−1.75; −0.41)	0.002	−0.44 (−0.84; −0.04)	0.030	1.89 (0.97; 3.67)	0.060	0.36 (0.10; 1.34)	0.13	−0.02 (−0.27; 0.23)	0.89	−0.13 (−0.34; 0.07)	0.20

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Bold values indicate p-values < 0.05.

^aDifference in slope in the first year in the inclusion period compared to 1993–1996; analyzed with linear mixed models corrected for age and gender. A negative number indicates a steeper slope.

^bDifference in mean over time in the inclusion period compared to 1993–1996; analyzed with linear mixed models corrected for age and gender.

^cHazard ratios compared to 1993–1996; analyzed with Cox regression and corrected for age and gender.

^dThe estimated marginal mean, adjusted for age and gender, in type 2 RA for inclusion period 1993–1996 was −1.27 (95% CI −1.81 to −0.72) for the slope in DAS28-ESR in the first year, 2.70 (95% CI 2.40 to 3.01) for DAS28-ESR over time after the first year, −0.46 (95% CI −0.67 to −0.25) for slope in HAQ in the first year, and 0.62 (95% CI 0.47 to 0.78) for HAQ over time after the first year.

DAS28-ESR, Disease Activity Score–28 with erythrocyte sedimentation rate; DMARD, disease-modifying antirheumatic drug; HAQ, Health Assessment Questionnaire; p-val, p-value.

Sustained DMARD-free remission

In type 1 RA, SDFR significantly increased over time, especially since the start of treat-to-target (Fig 1; Table 2). In type 2 RA, there was no significant increase in SDFR (Fig 2; Table 3).

Mortality

Compared to the reference period, mortality decreased significantly in type 1 RA since the start of treat-to-target (Fig 1; Table 2). No significant association was found in type 2 RA (Fig 2; Table 3), although hazard ratios were in the same direction as in type 1 RA.

Functional disability

In type 1 RA, functional disability improved over time since the start of early methotrexate as the standard treatment, both in the first year and the subsequent years (Fig 1; Table 2). In type 2, in contrast, improvement was absent (Fig 2; Table 3).

Comparison of improvement of type 1 and type 2

To assess whether more improvement was indeed observed in type 1 RA compared to type 2 RA, change with respect to the reference period was compared between the 2 disease types by adding an interaction term to the models. More improvement for the outcomes DAS28-ESR over time, SDFR, and functional disability was observed in type 1 RA (Table 4). This improvement was statistically significant for these outcomes in the inclusion period 2006–2010 (early methotrexate followed by treat-to-target treatment adjustments).

Table 4. Differences in improvement of disease outcomes between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) rheumatoid arthritis with enhanced treatment strategies over 25 years.

Inclusion period	DAS28-ESR, slope in first year		DAS28-ESR over time after first year		Sustained DMARD free remission		Mortality		HAQ, slope in first year		HAQ over time, after first year
Inclusion period	Relative mean difference^a	p-val	Relative mean difference^b	p-val	Hazard ratio^c	p-val	Hazard ratio^d	p-val	Relative mean difference^a	p-val	Relative mean difference^b	p-val
1993–1996	Ref^d		Ref^d		Ref		Ref		Ref^d		Ref^d
1997–2000	0.14 (−0.75; 1.04)	0.75	−0.46 (−0.94; 0.03)	0.068	1.80 (0.55; 5.92)	0.33	1.02 (0.47; 2.23)	0.96	−0.14 (−0.49; 0.21)	0.42	−0.06 (−0.30; 0.19)	0.65
2001–2005	−0.82 (−1.73; 0.08)	0.073	−0.70 (−1.18; −0.22)	0.004	2.10 (0.70; 6.28)	0.18	1.22 (0.54; 2.73)	0.64	−0.33 (−0.69; 0.03)	0.069	−0.21 (−0.46; 0.04)	0.095
2006–2010	−0.82 (−1.64; 0.00)	0.050	−0.70 (−1.14; −0.25)	0.002	2.93 (1.08; 7.90)	0.034	0.82 (0.37; 1.83)	0.63	−0.35 (−0.66; −0.05)	0.024	−0.22 (−0.44; 0.00)	0.046
2011–2016	−0.47 (−1.23; 0.29)	0.22	−0.55 (−1.04; −0.05)	0.030	2.10 (0.71; 6.22)	0.18	1.11 (0.26; 4.85)	0.89	−0.27 (−0.56; 0.02)	0.064	−0.11 (−0.35; 0.13)	0.37

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Bold values indicate p-values < 0.05. The overall p-value of the interaction term in the models (e.g., the p-value for difference in improvement between the 2 subtypes over all inclusion periods) was 0.072 for DAS28-ESR slope in first year, <0.001 for DAS28-ESR over time after first year, 0.28 for sustained DMARD-free remission, 0.91 for mortality, 0.016 for HAQ slope in first year, and 0.10 for HAQ over time after first year.

^aAdditional improvement in type 1 relative to type 2. A negative number corresponds to additional change downward in type 1 relative to the reference period (e.g., more decrease in the first year with respect to the reference period). Since lower DAS28-ESR/HAQ is better, a negative number indicates more improvement in type 1.

^bAdditional improvement in type 1 relative to type 2. A negative number corresponds to additional change downward of the mean after the first year in type 1 relative to the reference period. Since lower DAS28-ESR/HAQ is better, a negative number indicates more improvement in type 1.

^cAdditional improvement in type 1 relative to type 2. A number above 1 corresponds to additional SDFR in type 1 relative to the reference period. Since more SDFR is better, a number above 1 indicates more improvement in type 1.

^dAdditional improvement in type 1 relative to type 2. A number below 1 corresponds to less mortality in type 1 relative to the reference period. Since lower mortality is better, a number below 1 indicates more improvement in type 1.

DAS28-ESR, Disease Activity Score–28 with erythrocyte sedimentation rate; DMARD, disease-modifying antirheumatic drug; HAQ, Health Assessment Questionnaire p-val, p-value.

Sensitivity analyses

According to the 2010 criteria, 1,421 patients had RA, 957 type 1 and 474 type 2 (S4 Fig). Due to the composition of these criteria, type 2 RA required ≥11 involved joints for classification [16,17]. Indeed, this group had high joint counts, especially high tender joints in the latest periods, when acute phase reactants and swollen joint counts at diagnosis decreased (S2 Table). This possibly resulted in incomparability in disease activity between the periods within type 2 RA. Results for type 1 classified by the 2010 criteria were similar to those when RA was classified according to the 1987 criteria. For type 2 little improvement in DAS28-ESR was present, and effect sizes of long-term outcomes were in line with the main results (S3 and S4 Tables).

Analyses were repeated in patients aged <65 years at diagnosis; similar results were obtained except for a non-significant improvement in mortality in type 1 RA, possibly caused by a lower number of events (S5 and S6 Tables).

Effect sizes for the outcomes SDFR and mortality after correction for left truncation were similar (S7 Table).

For illustration, head-to-head comparisons between type 1 and type 2 RA within the inclusion periods are shown in S5–S8 Figs.

Discussion

Summary of findings

During the last 25 years, the treatment of RA has changed in several aspects. We studied outcomes of RA and observed that improved treatment strategies were paralleled by reduced disease activity in autoantibody-positive and autoantibody-negative RA, but resulting significant improvements in long-term outcomes—SDFR, mortality, and functional disability—were only present in autoantibody-positive RA and not in autoantibody-negative RA. In line with these findings, DAS28-ESR, SDFR, and functionality showed greater improvements over the last 25 years within autoantibody-positive than within autoantibody-negative RA. Especially the introduction of treat-to-target treatment adjustments was associated with significantly greater improvements in autoantibody-positive RA than in autoantibody-negative RA. The disconnection between improvements in disease activity and in several long-term outcomes suggests that the underlying pathogenesis of autoantibody-positive and autoantibody-negative RA is different. We therefore propose that the time has come to subdivide RA into type 1 and type 2.

Comparisons with other studies

Subdivisions of disease are ideally underpinned with identified differences in etiopathology. However, clinical observations have frequently been the basis of subdivisions of diseases and have preceded the identification of pathophysiological mechanisms. Both types of RA have a different genetic background. Whereas >100 genetic risk factors are identified for type 1, few genetic factors have been related to type 2 RA [45]. Known environmental risk factors are associated with predominantly 1 of the 2 types [3,4]. These data, together with observed differences in histology [8], may also point towards different underlying mechanisms.

Etiopathogenetic research in the last decade has focused mostly on autoantibody-positive RA, but a causal relationship for the autoantibodies has not been proven. Further pathogenic research is needed for both type 1 and type 2 RA.

Strengths and limitations of this study

We have studied the autoantibodies that are in daily use in clinical practice (ACPA and RF). Several new autoantibodies have recently been identified; most co-occur in patients who also harbor ACPA or RF [20–23]. A small percent of ACPA- and RF-negative patients were found to be positive for novel autoantibodies, leaving the so-called serological gap largely unchanged. There was insufficient power to assess which autoantibodies are optimal for the characterization of type 1 RA. It is a subject for further research to determine whether the division can be optimized by incorporation of recently identified autoantibodies or other markers (e.g., obtained from histology) [46].

Autoantibody positivity was determined with the cutoffs that are also used in daily clinical practice in our hospital. Some patients might have values just around the cutoff at baseline and therefore might change in autoantibody positivity over time. Previous research in the EAC cohort has shown that seroconversion towards autoantibody negativity is rare, even when SDFR is achieved, and that seroconversion was mostly caused by fluctuations of levels around the cutoff [18]. Similarly, data from our cohort show that seroconversion from autoantibody negativity to autoantibody positivity is also infrequent (2% after 1 year of follow-up; S9 Fig). Thus, autoantibody status is quite stable after diagnosis.

Patients with type 2 RA had a clinical diagnosis of RA, fulfilled classification criteria, and lacked ACPA and RF. It has been suggested that autoantibody-negative RA is heterogeneous in nature. We find it important to formally consider autoantibody-negative RA as a separate entity, but we cannot exclude the possibility that type 2 RA consists of different subtypes. This is beyond the scope and power of this study.

To assess the response to improved treatment strategies without exposing patients to outdated and less effective treatments, historical data were used, and inclusion period was used as an instrumental variable for treatment strategy. As an alternative to randomization, instrumental variable analysis uses a proxy (inclusion period) to create groups with comparable patients that receive different treatment strategies. Between these groups, treatment strategies can be compared without confounding by indication, under the assumption that allocation to the groups is random. Since the inclusion criteria of the Leiden EAC have not changed over time, year of RA diagnosis was assumed random. Importantly, initial treatment protocols and treat-to-target protocols were similar for patients with and without autoantibodies, making the instrument similar for both patient groups.

Treatment was targeted at DAS remission since 2006, and was never targeted at autoantibodies (notably, ACPA results became available for rheumatologists in this study from 2006 onwards). While type 2 RA had a slightly higher baseline DAS28-ESR and in type 1 mean DAS28-ESR over time decreased more, mean DAS28-ESR and remission rates were similar or better in type 2 RA in all periods. Observed differences in long-term outcomes are therefore unlikely to be the result of better adherence to treat-to-target in autoantibody-positive patients. Also, the finding that patients with autoantibodies more often required biologics to achieve DAS28-ESR remission (S1 Table) merely underlines the difference between the types.

Progression of joint destruction was not studied as outcome, because the natural course of type 2 RA involves little structural damage and a lack of improvement can also be explained by the inability to measure this [5]. The long-term outcomes studied here, on the other hand, had the potential for indicating improvement, also in patients with type 2 RA.

Mortality was studied without adjusting for mortality in the general population because excess mortality in RA is heavily dependent on follow-up duration, which differs between the inclusion cohorts [40]. Although a significant improvement in mortality was observed in type 1 RA and not in type 2 RA, effect sizes were in the same direction. Analyses of longer follow-up in larger cohorts that also adjust for mortality in the general population are needed to determine if excess mortality is reduced differently between the 2 groups.

In current treatment strategies SDFR is not targeted. Although innovative, this is an interesting outcome from an immunological perspective that resembles “cure.” Prolonged follow-up duration is required to determine the sustainability of DMARD-free remission after DMARD cessation. An advantage of our data is that we had median 5.5 years of follow-up after DMARD stop.

RA was defined according to the 1987 criteria (not the 2010 criteria) to exclude influences of temporal changes in rheumatologists’ views on diagnosing RA. Furthermore, autoantibodies load heavily in the 2010 criteria. It is known that much inflammation is needed in the absence of autoantibodies to fulfill the 2010 criteria [16,17]. Further, in our data, higher tender joint counts were needed to classify RA in recent periods, possibly resulting in incomparability in DAS28-ESR within the current set of autoantibody-negative 2010-criteria RA patients. Nonetheless, similar results in long-term outcomes were found.

Future implications

Possible implications of formal subdivision of RA are execution of more focused pathogenetic studies, development of treatment protocols adapted to disease type, and performance of trials by disease type. Ultimately a better distinction leads to improved personalized care.

Conclusion

In sum, to our knowledge this is the first long-term study in a large cohort of RA patients with data on 25 years of follow-up. Based on the demonstrated differences in long-term outcomes, and supported by previous findings on risk factors, we propose to subgroup RA into type 1 and type 2, in the hope that this leads to stratified treatment in RA.

Supporting information

S1 Checklist. STROBE Statement—checklist of items that should be included in reports of cohort studies.

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S1 Fig. Flowchart of patient inclusion.

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S2 Fig. Directed acyclic graph of causal mechanisms to identify potential sources of confounding.

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S3 Fig. Percentage of patients achieving DAS28-ESR remission (<2.6) after 1 and 3 years of follow-up in type 1 (autoantibody-positive) RA and type 2 (autoantibody-negative) RA.

(A) Type 1 RA; (B) type 2 RA.

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S4 Fig. Flowchart of RA patients fulfilling the 1987 and/or 2010 criteria.

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S5 Fig. Disease activity compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S6 Fig. Sustained DMARD-free remission compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S7 Fig. Mortality compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S8 Fig. Functional disability compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S9 Fig. Autoantibody status over time in RA patients who were autoantibody-negative at diagnosis, showing that conversion to autoantibody positivity is rare.

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S1 Table. Biologic use (prevalence within different follow-up durations) by inclusion period, showing slightly more biologic use in type 1 (autoantibody-positive) RA than type 2 (autoantibody-negative) RA.

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S2 Table. Characteristics of patients with type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA at first presentation to the EAC that fulfilled the 2010 criteria.

(A) Type 1 RA; (B) type 2 RA.

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S3 Table. Disease activity during the first year and subsequent follow-up and long-term outcomes—sustained DMARD-free remission, mortality, and functional disability—by inclusion period compared to the reference period for type 1 (autoantibody-positive) RA fulfilling 2010 criteria.

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S4 Table. Disease activity during the first year and subsequent follow-up and long-term outcomes—sustained DMARD-free remission, mortality, and functional disability—by inclusion period compared to the reference period for type 2 (autoantibody-negative) RA fulfilling 2010 criteria.

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S5 Table. Disease activity during the first year and subsequent follow-up and long-term outcomes—sustained DMARD-free remission, mortality, and functional disability—by inclusion period compared to the reference period for type 1 (autoantibody-positive) patients aged <65 years at diagnosis.

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S6 Table. Disease activity during the first year and subsequent follow-up and long-term outcomes—sustained DMARD-free remission, mortality, and functional disability—by inclusion period compared to the reference period for type 2 (autoantibody-negative) patients aged <65 years at diagnosis.

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S7 Table. Long-term outcomes in type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA after correction for left truncation.

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S1 Text. Confounding.

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S2 Text. Additional R-packages used.

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Abbreviations

ACPA: anti-citrullinated protein antibody
DAS: Disease Activity Score
DAS28-ESR: Disease Activity Score–28 with erythrocyte sedimentation rate
DMARD: disease-modifying antirheumatic drug
EAC: Early Arthritis Clinic
GP: general practitioner
HAQ: Health Assessment Questionnaire
HR: hazard ratio
RA: rheumatoid arthritis
RF: rheumatoid factor
SDFR: sustained DMARD-free remission

Data Availability

Our dataset, used for the analyses, contains potentially identifying and sensitive patient information. Therefore our data cannot be made publicly available because it is prohibited by Dutch law in the regulation “Algemene verordening gegevensbescherming” and does not comply with the study protocol as it was submitted to the local ethics committee. Inquiries can be directed to eac@lumc.nl.

Funding Statement

The research leading to these results has received funding from the from the Dutch Arthritis Foundation and European Research Council (ERC, https://erc.europa.eu/) under the European Union’s Horizon 2020 research and innovation programme (Starting grant, agreement No 714312, AHMvdHvM). The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

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PLoS Med. doi: 10.1371/journal.pmed.1003296.r001

Decision Letter 0

Helen Howard

3 Feb 2020

Dear Dr Matthijssen,

Thank you for submitting your manuscript entitled "Long-term outcomes of a 25-year longitudinal cohort study indicate that Rheumatoid Arthritis can be divided in type 1 and type 2" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Helen Howard, for Clare Stone PhD

Acting Editor-in-Chief

PLOS Medicine

plosmedicine.org

PLoS Med. doi: 10.1371/journal.pmed.1003296.r002

Decision Letter 1

Emma Veitch

2 May 2020

Dear Dr. Matthijssen,

Thank you very much for submitting your manuscript "Long-term outcomes of a 25-year longitudinal cohort study indicate that Rheumatoid Arthritis can be divided in type 1 and type 2" (PMEDICINE-D-20-00279R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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We expect to receive your revised manuscript by May 25 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

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Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of Clare Stone, PhD, Acting Chief Editor,

PLOS Medicine

plosmedicine.org

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Requests from the editors:

*In responding to the reviewers' points, the issue raised by the statistical reviewer (that the analysis currently does not include a between-group comparison but only within-groups) should be fully dealt with, as this seems crucial.

*Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative (ie not state the findings), rather beginning with the main concept if possible. Please place the study design in the subtitle (ie, after a colon) - eg in this case "prospective cohort".

*In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*In the methods section, where you currently state that local ethical approval was obtained, would suggest also naming the ethics board/committee that gave approval (this is stated in some of the additional information with the paper, but good if it can be updated in the paper itself as well).

*Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

*When you cover limitations in the Discussion, it would be good to say something about the intended purpose of the instrumental variable analysis in this study. How well does the use of time period fulfill the assumptions of such an analysis? How well does this approach begin to deal with some of the limitations of a non-instrumented-for analysis?

*Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. 1 Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

*Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

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Comments from the reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

Matthijssen et al present an analysis of data from a large cohort of RA patients. They use period of entry to the cohort as a proxy for the type of treatment received, and analyse four long term outcomes (disease activity, remission, survival, and disability). This review considers the statistical aspects of the paper.

The use of time period as an instrumental variable is acceptable, even though the authors subsume earlier diagnosis within improved treatment protocols. The methods of analysis used for each outcome are also good.

For me, the main problem with the paper is the lack of comparison between the two disease subtypes. Results are generally presented for each outcome, within each subtype, but do not compare the two types directly. The authors conclude, based on statistically significant results within one group but not the other, that the two groups are different. This is not enough; the results should be presented in such a way as to test whether the two groups are different. There is no statistical evidence presented that supports the main conclusion of the paper.

In the abstract, it is not clear why two HRs are presented for SDFR rates in type 1 RA. Only when looking at the tables is it obvious what these HRs represent. Also, when reporting HAQ improvements, it is not clear what is being reported. Also, the confidence limits are the wrong way round (negative changes are being reported as improvements, by removing the minus signs).

-----------------------------------------------------------

Reviewer #2: Interesting but on review of this revision I think the designation of Type 1 and Type 2 is not quite right. Really this is just ACPA and/or RF positive vs. ACPA and/or RF negative RA and without trying to bring in other autoantibodies, even if not standard in RA diagnostic yet (e.g. anti-CarP, ANAs, other), or features of disease (nodules etc) it seems too much of a stretch to label these subsets Type 1 and 2 and trying to make a case for any nomenclature change. I think still very interesting but perhaps revise to just state that this is analyses of ACPA and/or RF positive vs. negative, and then in the discussion section discuss how this may for the basis for future classification.

Literature supports that subjects who are seroneg at initial diagnosis may later become positive. was this evaluated?

please list the specific ACPA and RF assays used and also comment on how seropositivity may differ based on the test used

Please provide analyses of how various elements of disease activity measures may differ between disease subsets. for example, is CRP or swollen joints comprising more of the score in one group vs. global scores in another

please discuss how diagnostic certainty may influence the use of biologics - this can be separate issue from disease severity. for example, a practitioner may be more willing to use expensive and perhaps toxic meds for seropositive RA because they are sure of their diagnosis based on the blood test.

1987 criteria don't include ACPA - how was this handled in RF negative, ACPA positive subjects? could they have been excluded because they may not have met criteria?

-----------------------------------------------------------

Reviewer #3: Matthijssen and co-authors report here on the long term outcomes of autoantibody-positive and -negative rheumatoid arthritis (RA) over 15 years of follow-up. Outcomes of interest are drug-free remission, functional disability and mortality. Data were retrieved from the large Leiden Early Arthritis inception cohort. All analyses were adjusted for the inclusion period, taking into account important modifications that have occurred in the management of RA over the years (earlier diagnosis, use of methotrexate as first DMARD, treat-to-target strategies). Collectively, the Authors show that all the outcomes analyzed ameliorated over time in autoantibody-positive but not autoantibody-negative RA.

Data arising from the current research work are of interest as they open the interesting perspective on whether autoantibody-positive and -negative RA should be managed as two different diseases entities. However, a number of confounders should be carefully evaluated before drawing definitive conclusions.

Major points to consider:

1) All the outcomes analyzed are strongly dependent of the treatment strategy adopted. Although the Authors show similar trends in the reduction of disease activity in autoantibody-positive and -negative patients, it cannot be excluded that differences in the observed outcomes may depend on different treatment strategies. Patients with autoantibody-negative RA may indeed receive less frequent combination therapy with biological drugs. This appears clearly evident from Table S1. Also the use of glucocorticoids may differ. It is therefore important that the Authors provide information on (and correct their analyses for):

- the trends in drug-free remission, functional disability and mortality in patients treated with csDMARDs and in those also receiving bDMARDs stratified for the autoantibody status. Indeed, I believe that adjusting analyses for inclusion period may not take into account the use of bDMARDs. From Table S1, it appears that bDMARDs have increased in autoantibody-positive but not in -negative RA in more recent times.

- the use of glucocorticoids in autoantibody-positive and -negative patients

2) As the Authors acknowledge, autoantibody-negative RA may itself be a heterogeneous disease entity. Among the various confounders, elderly-onset RA has peculiar clinical features in presentation and outcomes. From Table 1, it appears autoantibody-negative patients are (as expected) older. I would therefore suggest that the Authors perform a subanalysis on patients (both autoantibody-positive and -negative) aged less than 65 years. I am aware that this could limit the frequency of observed events for the outcome of mortality, but other outcomes remain valid.

Additional points:

The scope of the current study was a within-subgroup comparison of outcomes over time, and not a direct comparison between autoantibody-positive and -negative RA. However, from the Kaplain Maier curves presented, it seems that mortality and functional disability have remained poorer in autoantibody-negative RA compared to autoantibody-positive even after the adoption of T2T. To avoid misinterpretations, it would be important to provide the reader with numbers of in the Kaplan Meier curves. Is the percentage of losts to follow-up different between autoantibody-positive and -negative RA? This would help understanding whether the current analyses have selected particular patient populations.

-----------------------------------------------------------

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Sep 22;17(9):e1003296. doi: 10.1371/journal.pmed.1003296.r003

Author response to Decision Letter 1

20 May 2020

Attachment

Submitted filename: Point by point FINAL.docx

Click here for additional data file.^{(183.6KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003296.r004

Decision Letter 2

Artur Arikainen

6 Jul 2020

Dear Dr. Matthijssen,

Thank you very much for re-submitting your manuscript "Differences in improvement of disease outcomes between autoantibody-positive and autoantibody-negative rheumatoid arthritis with enhanced treatment strategies over 25 years: A longitudinal prospective cohort study" (PMEDICINE-D-20-00279R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jul 13 2020 11:59PM.

Sincerely,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Please update your title to: "Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: a longitudinal cohort study in the Netherlands"

2. Please add line numbers in the margin throughout your manuscript.

3. The Data Availability Statement (DAS) requires revision. For each data source used in your study:

a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number).

b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.

c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author).

4. Abstract:

a. Please quantify your results with 95% CIs and p values.

b. Please provide a p value here or in the main Results section to support this statement: “…percentage of autoantibody-positive patients were constant throughout the inclusion periods”, as it appears to have varied over time.

c. Please remove or replace “idem”.

d. Please include another limitation at the end of ‘Methods and findings’, eg. limited spread of ethnicities.

e. Please consider add the following to the Conclusion (from your main Conclusion section): “…, in the hope that this leads to stratified treatment in RA.”

5. Author Summary:

a. Add “(RA)” after the first instance of ‘rheumatoid arthritis’.

b. Please clarify ‘histology’ for a non-scientist audience.

c. Please correct typo: “distict”

6. Please delete this sentence, as it is somewhat irrelevant to RA: “Careful clinical observations over time have led to the description of diseases, originally by the name of the doctor who made the observation.”

7. Please move the reference callouts to before punctuation, and do not include spaces within the callout, eg: “…different environmental risk factors [3,4],…”

8. Methods:

a. Please include the exact date ranges (including day and month) for each period of patient recruitment/inclusion.

b. Please replace “As suggested by the reviewer,…” with “In response to requests during peer review,…”

c. Please mention explicitly that there was no prospective study protocol or analysis plan.

9. Results:

a. Please quantify your results with 95% CIs and p values.

b. In the Figures and Tables, please label the groups as “autoantibody-positive” and “autoantibody-negative” (or similar), rather than (or in addition to) types 1 and 2, since your study appears to be introducing this typology.

10. Given that your study design cannot show causation, please replace all instances of “effect” with “association” or similar.

11. The terms gender and sex are not interchangeable (as discussed in http://www.who.int/gender/whatisgender/en/ ); please use the appropriate term.

12. Discussion:

a. Please make the first paragraph a summary of your study’s findings.

b. Please correct typo: “Advantageous”

13. PLOS does not permit references to unpublished data. Please remove any such claims, or do one of the following:

a) If you are the owner of the data relevant to this claim, please provide the data in accordance with the PLOS data policy, and update your Data Availability Statement as needed.

b) If the data not shown refer to a study from another group that has not been published, please cite personal communication in your manuscript text (it should not be included in the reference section). Please provide the name of the individual, the affiliation, and date of communication. The individual must provide PLOS Medicine written permission to be named for this purpose.

c) For any other circumstance, please contact the journal office ASAP.

14. Please remove the Footnotes section – all relevant information should be included on the submission form or in the main manuscript text instead.

15. Table 1 footnotes contain an extra semicolon.

16. Table 1: Please define EAC.

17. Figure 1 (and check others): Ensure all abbreviations are defined, eg. MTX, T2T.

18. Tables 2, 3, 4: Please give exact p values where p>0.001.

19. Please upload the STROBE checklist as a separate file, named “S1 Checklist”.

----------

Comments from Reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

I thank the authors for their responses to my original comments. Comparing the changes over time in outcomes between the two groups of patients is very useful. However, the authors now need to use these findings in their conclusions.

Table 4 shows quite clearly that the rate of decline of DAS28 after the first year is greater in Type 1 patients.

However, there is no evidence to suggest any difference between groups in the rate at which mortality has reduced over time. This needs to be carried forward into the conclusions of the paper. Many of the results are reported and interpreted as outcomes improving in Type 1 but not Type2 patients, but the results shown in Table 4 show no evidence of a difference between the groups for mortality. Looking at Tables 2 and 3, the hazard ratios relative to the earliest time period are all less than 1, and in some instances lower for Type 2 than for Type 1 patients, but the differences between them are not large. The fact that in one group, the hazard ratios are significantly lower than one, whilst in the other they are not, may simply reflect the different sample sizes. In order to claim a difference between the groups, the statistical evidence (as shown in Table 4) should be referred to.

It is interesting, for all the other outcomes, that there appear to be significantly greater improvements in the 2006-10 period for Type 1 compared to Type 2 patients (even for DAS28 improvement during the first year of treatment, the difference is on the borderline of p=0.05), and whilst none of the interactions at other time points reach formal statistical significance, there appear to be clear trends towards better improvements in all outcomes other than mortality from 2001 to 2016. However, other than for the rate of change in DAS28 after the first year of treatment, there is little to suggest any differences between the groups in response to treatment in 1997-2000.

As stated above, when discussing the differences between groups in the improvements over time, the authors cannot simply report a significant improvement for one group and not the other, and draw conclusions from that. They must refer to the results in Table 4 to determine for which outcomes, and during which time periods, there is evidence of a difference between the two groups.

Finally, it may be useful to report p-values from likelihood ratio tests, comparing models with and without interaction terms, to give a single p-value for each outcome, to assess whether the changes over time differ between the two groups. This would be in addition to the information already reported in Table 4; maybe a separate row.

Reviewer #3: The authors have addressed my major points. Non further revision is required

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Sep 22;17(9):e1003296. doi: 10.1371/journal.pmed.1003296.r005

Author response to Decision Letter 2

13 Aug 2020

Attachment

Submitted filename: point by point part2.docx

Click here for additional data file.^{(14.7KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003296.r006

Decision Letter 3

Artur Arikainen

18 Aug 2020

Dear Dr. Matthijssen,

On behalf of my colleagues and the academic editor, Dr. Carlomaurizio Montecucco, I am delighted to inform you that your manuscript entitled "Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: a longitudinal cohort study in the Netherlands" (PMEDICINE-D-20-00279R3) has been accepted for publication in PLOS Medicine.

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

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This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE Statement—checklist of items that should be included in reports of cohort studies.

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S1 Fig. Flowchart of patient inclusion.

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S2 Fig. Directed acyclic graph of causal mechanisms to identify potential sources of confounding.

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S3 Fig. Percentage of patients achieving DAS28-ESR remission (<2.6) after 1 and 3 years of follow-up in type 1 (autoantibody-positive) RA and type 2 (autoantibody-negative) RA.

(A) Type 1 RA; (B) type 2 RA.

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S4 Fig. Flowchart of RA patients fulfilling the 1987 and/or 2010 criteria.

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S5 Fig. Disease activity compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S6 Fig. Sustained DMARD-free remission compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S7 Fig. Mortality compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S8 Fig. Functional disability compared between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA.

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S9 Fig. Autoantibody status over time in RA patients who were autoantibody-negative at diagnosis, showing that conversion to autoantibody positivity is rare.

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S2 Table. Characteristics of patients with type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA at first presentation to the EAC that fulfilled the 2010 criteria.

(A) Type 1 RA; (B) type 2 RA.

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S7 Table. Long-term outcomes in type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA after correction for left truncation.

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S1 Text. Confounding.

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S2 Text. Additional R-packages used.

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Attachment

Submitted filename: Point by point FINAL.docx

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Data Availability Statement

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PERMALINK

Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: A longitudinal cohort study in the Netherlands

Xanthe M E Matthijssen

Ellis Niemantsverdriet

Tom W J Huizinga

Annette H M van der Helm–van Mil

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Longitudinal cohort

Definition of autoantibody-positive and autoantibody-negative

Treatment

Outcomes

Statistical analyses

Sensitivity analyses

Results

Baseline characteristics

Table 1. Characteristics of patients with type 1 (autoantibody-positive) and type 2 (autoantibody-negative) RA at first presentation to the Leiden Early Arthritis Clinic.

Disease activity

Fig 1. Disease activity and long-term outcomes in type 1 RA.

Table 2. Disease activity during the first year and subsequent follow-up, and long-term outcomes (sustained DMARD-free remission, mortality, and functional disability) by inclusion period compared to the reference period for type 1 (autoantibody-positive) RA.

Fig 2. Disease activity and long-term outcomes in type 2 RA.

Table 3. Disease activity during the first year and subsequent follow-up, and long-term outcomes (sustained DMARD-free remission, mortality, and functional disability) by inclusion period compared to the reference period for type 2 (autoantibody-negative) RA.

Sustained DMARD-free remission

Mortality

Functional disability

Comparison of improvement of type 1 and type 2

Table 4. Differences in improvement of disease outcomes between type 1 (autoantibody-positive) and type 2 (autoantibody-negative) rheumatoid arthritis with enhanced treatment strategies over 25 years.

Sensitivity analyses

Discussion

Summary of findings

Comparisons with other studies

Strengths and limitations of this study

Future implications

Conclusion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Helen Howard

Roles

Decision Letter 1

Emma Veitch

Roles

Author response to Decision Letter 1

Decision Letter 2

Artur Arikainen

Roles

Author response to Decision Letter 2

Decision Letter 3

Artur Arikainen

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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