Serum antibodies to periodontal pathogens prior to rheumatoid arthritis diagnosis: A case-control study

Seminars in Arthritis and Rheumatism 59 (2023) 152176

Available online 11 February 2023

nc-nd/4.0/).

Serum antibodies to periodontal pathogens prior to rheumatoid arthritis

diagnosis: A case-control study

Joyce A. Lee

, Ted R. Mikuls

, Kevin D. Deane

, Harlan R. Sayles

, Geoffrey M. Thiele

, Jess

D. Edison

, Brandie D. Wagner

, Marie L. Feser

, Laura K. Moss

, Lindsay B. Kelmenson

William H. Robinson

, Jeffrey B. Payne

Department of Surgical Specialties, Division of Periodontics, College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, USA

Department of Internal Medicine, Division of Rheumatology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA

Medicine, Veterans Affairs Nebraska-Western Iowa Health Care System, Omaha, NE, USA

Department of Internal Medicine, Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE USA

Department of Internal Medicine, Rheumatology Service, Walter Reed National Military Medical Center, Bethesda, MD, USA

Department of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Geriatrics Research Education and Clinical Center, Veterans Affairs Palo Alto Healthcare System and Division of Immunology/Rheumatology, Stanford University

School of Medicine, Palo Alto, CA, USA

ARTICLE INFO

Keywords:

Rheumatoid arthritis

Periodontitis

ACPA

Rheumatoid factor

Porphyromonas gingivalis

Prevotella intermedia

ABSTRACT

Objectives: 1) To quantify the association between anti-Porphyromonas gingivalis serum antibody concentrations

and the risk of developing rheumatoid arthritis (RA), and 2) to quantify the associations among RA cases between

anti-P. gingivalis serum antibody concentrations and RA-specic autoantibodies. Additional anti-bacterial anti-

bodies evaluated included anti-Fusobacterium nucleatum and anti-Prevotella intermedia.

Methods: Serum samples were acquired pre- and post- RA diagnosis from the U.S. Department of Defense Serum

Repository (n = 214 cases, 210 matched controls). Using separate mixed-models, the timing of elevations of anti-

P. gingivalis, anti-P. intermedia, and anti-F. nucleatum antibody concentrations relative to RA diagnosis were

compared in RA cases versus controls. Associations were determined between serum anti-CCP2, ACPA ne

specicities (vimentin, histone, and alpha-enolase), and IgA, IgG, and IgM RF in pre-RA diagnosis samples and

anti-bacterial antibodies using mixed-effects linear regression models.

Results: No compelling evidence of case-control divergence in serum anti-P. gingivalis, anti-F. nucleatum, and anti-

P. intermedia was observed. Among RA cases, including all pre-diagnosis serum samples, anti-P. intermedia was

signicantly positively associated with anti-CCP2, ACPA ne specicities targeting vimentin, histone, alpha-

enolase, and IgA RF (p<0.001), IgG RF (p = 0.049), and IgM RF (p = 0.004), while anti-P. gingivalis and anti-

F. nucleatum were not.

Conclusions: No longitudinal elevations of anti-bacterial serum antibody concentrations were observed in RA

patients prior to RA diagnosis compared to controls. However, anti-P. intermedia displayed signicant associa-

tions with RA autoantibody concentrations prior to RA diagnosis, suggesting a potential role of this organism in

progression towards clinically-detectable RA.

Introduction

It has been hypothesized that rheumatoid arthritis (RA) may be

initiated in mucosal tissues, including the periodontium [1]. Periodon-

titis (PD) is a biolm-driven inammatory disease of the soft and hard

tissues in the oral cavity resulting from an interaction between the host

immune response and a dysbiotic oral microbiota, ultimately leading to

tooth loss [2]. Over the past few decades, there has been an increased

awareness of the relationship between RA and PD. Both diseases share

similar inammatory pathways and risk factors [3]. Several studies have

* Corresponding author at: Professor and F. Gene and Rosemary Dixon Endowed Chair in Dentistry, Department of Surgical Specialties, College of Dentistry,

Department of Internal Medicine, College of Medicine, University of Nebraska Medical Center, 4000 East Campus Loop South, Lincoln, NE, USA.

E-mail address: [email protected] (J.B. Payne).

Contents lists available at ScienceDirect

Seminars in Arthritis and Rheumatism

journal homepage: www.elsevier.com/locate/semarthrit

https://doi.org/10.1016/j.semarthrit.2023.152176

Seminars in Arthritis and Rheumatism 59 (2023) 152176

demonstrated PD as a risk factor for RA [4–6]. It has been speculated

that this relationship may be mediated through the oral periodontal

pathogen, Porphyromonas gingivalis [7].

P. gingivalis is a gram-negative anaerobe recognized as a keystone

pathogen in the pathogenesis of PD [8,9]. Uniquely, it is the only pro-

karyote that can express a functional bacterial peptidyl arginine dei-

minase (PAD) enzyme (often termed PPAD) as a primary virulence

factor, thus serving as a microbe of interest in its role with RA and PD

[10]. The discovery of P. gingivalis PAD led to the hypothesis that

P. gingivalis PAD-mediated protein citrullination at affected periodontal

sites can launch a sequence of events that culminate in the generation of

anti-citrullinated protein antibodies (ACPAs) and, eventually, in the

clinical manifestation of RA [11]. However, while P. gingivalis is a

periodontal pathogen implicated in RA pathogenesis, other bacterial

species involved in PD, such as Prevotella intermedia and Fusobacterium

nucleatum, may also inuence development of RA [12,13].

We hypothesized that circulating concentrations of antibody to

P. gingivalis would be higher in samples from individuals later devel-

oping RA compared to controls. Anti-bacterial serologies may be used as

a surrogate of exposure to periodontal pathogens and we have previ-

ously reported associations between serum antibody to P. gingivalis and

RA-related autoantibody expression among patients without clinically

apparent RA, but with a higher risk of future disease [14]. Moreover, we

postulated that, among those with RA, anti-P. gingivalis antibodies would

be associated with the presence of RA-related autoantibodies prior to

diagnosis. The purpose of this study was to: 1) quantify the association

between anti-P. gingivalis serum antibody concentrations and the risk of

developing RA, and 2) quantify the associations among RA cases be-

tween anti-P. gingivalis serum antibody concentrations and RA-specic

autoantibodies. Additional anti-bacterial antibodies evaluated

included anti-P. intermedia and anti-F. nucleatum to determine whether

associations observed were specic to P. gingivalis or related to a broader

dysbiosis that may be observed in PD.

Methods

Patient population

Study participants consisted of military personnel participating in

the U.S. Department of Defense Serum Repository (DoDSR). Since 1996,

DoDSR has been collecting serum samples to observe health history in

the military population and further understand the risks of deployment

concerning subsequent injuries or chronic illnesses [15].

Active-duty personnel with ≥2 RA diagnostic codes (≥1 from a

rheumatologist) were screened from the military’s electronic medical

records [16]. The records were further examined to obtain the date of

diagnosis and fulllment of the 1987 American College of Rheuma-

tology classication criteria [17]. Serum samples were acquired prior to

and after RA diagnosis for up to four samples per case, a minimum of two

samples and up to three samples from pre-diagnosis, collected at

different time points, and one sample from post-diagnosis. This study

utilized 214 RA cases who received a diagnosis of RA between 1995 and

2012. Out of these cases, 212 met the 1987 RA classication criteria and

the other two cases were diagnosed by a board-certied rheumatologist.

These RA cases were chosen because there was a clear date of RA

diagnosis recorded, adequate information to evaluate the clinical course

of their RA after diagnosis, and two or more pre-diagnosis and one

post-diagnosis serum samples with adequate volumes available for

analysis.

Controls were selected and matched to each case based on age (at

time of RA diagnosis for their matched cases), sex, ethnicity, enlistment

region, and duration of sample storage. Exclusions for the controls were

a history of RA or other inammatory arthritis [16].

Four of these controls were subsequently excluded due to insufcient

information available to exclude inammatory arthritis, leaving a total

of 210 controls evaluable for the analysis. These cases and controls were

included in earlier DoDSR studies by our group [16,18].

Clinical data collected included: age at time of diagnosis, sex,

ethnicity, smoking status (those with missing data after chart review

were imputed as never smokers), sample collection timing relative to RA

diagnosis, follow-up time and RA medications received post-RA diag-

nosis, radiographic erosions, and number of samples tested [16].

Serum autoantibody assays

ACPA was determined using a commercially-available second-gen-

eration anti-CCP2 ELISA (Diastat, Axis-Shield Diagnostics, Dundee,

Scotland); CCP2 positivity was based on the manufacturer’s recom-

mendation at a level of > 5 U/ml. Serum samples also were evaluated for

26 specic ACPAs using a bead-based multiplex antigen array that

measures antibody reactivity to a panel of putative citrullinated auto-

antigens [19]. To reduce the chance of false discovery, analyses of

antigen-specic ACPAs were limited to antibodies targeting citrullinated

forms of vimentin, alpha-enolase, and histone, which are autoantigens

consistently implicated in RA pathogenesis [20–22]. IgA rheumatoid

factor (RF), IgG RF, and IgM RF concentrations (IU/ml) were deter-

mined using ELISA (Inova Diagnostics, San Diego, CA). RF positivity was

based on concentrations for each isotype (IgA RF, IgG RF, and IgM RF)

determined to be present in < 2% of controls.

Serum bacterial antibodies

Serum concentrations of IgG antibodies to outer membrane antigens

(OMA) of P gingivalis, P. intermedia, and F. nucleatum were measured by

ELISA, as described in a previous publication from our group [14].

Ethical considerations

The Institutional Review Boards approved the study protocol at the

DoDSR, Walter Reed National Military Medical Center, and the Uni-

versity of Colorado Multiple Institutional Review Board.

Statistical analyses

Participant characteristics were compared between RA and control

groups using chi-square tests, exact chi-square tests, t-tests, or Wilcoxon

rank sum tests as necessary. Autoantibodies and bacterial antibodies

were log (base 2) transformed for all analyses. The primary analysis

investigated the associations between anti-P. gingivalis serum antibodies

and RA diagnosis (i.e., case status). Anti-P. intermedia and anti-

F. nucleatum were evaluated to determine whether associations observed

were specic to P. gingivalis or conversely related to a broader dysbiosis

observed in PD. Initial analyses compared anti-bacterial antibody con-

centrations and biomarkers (i.e., anti-CCP2, ACPA ne specicities

targeting vimentin, histone and alpha-enolase, and RF isotypes) be-

tween groups in the pre-RA diagnosis sample that was closest to diag-

nosis, and the post-RA diagnosis sample using Wilcoxon rank sum tests.

The timing of elevations in anti-bacterial concentrations were evaluated

in RA cases versus controls in a manner previously described [16,18].

Briey, we used mixed models for each bacterial concentration with a

continuous time effect modeled using B-splines and assuming a multi-

variate normal distribution for random subject intercepts and slopes. At

each month prior to diagnosis, we compared autoantibody concentra-

tions to identify the rst instance where concentrations differed signif-

icantly (p <0.05) between cases and controls. These multiple

comparisons were using a stepdown Holm-simulated method. Correla-

tions between anti-bacterial antibody concentrations were evaluated by

Pearson correlation coefcient.

Secondary analyses examined potential associations between anti-

P. gingivalis, anti-P. intermedia, and anti-F. nucleatum antibody concen-

trations and biomarkers (i.e., anti-CCP2, ACPA ne specicities target-

ing vimentin, histone and alpha-enolase, and RF isotypes) within the RA

J.A. Lee et al.

Seminars in Arthritis and Rheumatism 59 (2023) 152176

group. These analyses were completed using both unadjusted and

adjusted mixed-effects linear regression models with either RF or ACPA

as the dependent variable, a xed effect for each of the anti-bacterial

antibodies in turn, and random subject intercepts. The adjusted

models also included terms for age, sex, and smoking status.

All analyses were performed utilizing SAS v9.4 (SAS Institute, Cary,

NC).

Results

Participant characteristics and autoantibody values

Patient characteristics and median autoantibody concentrations of

the participants are shown in Table 1. RA cases were slightly more likely

than controls to be ever smokers (32% vs. 23%, p = 0.05); however,

when analysis was limited to non-missing data, RA cases and controls

did not differ with respect to ever smokers (p = 0.15). Higher median

serum concentrations of anti-CCP2, ACPA ne specicities targeting

vimentin, histone, alpha-enolase, and IgA, IgG and IgM RF isotypes were

observed for the immediate/closest pre-diagnosis sample and post-RA

diagnosis sample in RA cases versus controls (p<0.001). Likewise,

anti-CCP2 positivity and IgA, IgG, and IgM RF isotype positivity were

signicantly higher for the immediate/closest pre-diagnosis sample and

post-RA diagnosis sample in RA cases versus controls (p<0.001)

(Table 1).

Serum anti-bacterial antibodies in RA cases versus controls

Median anti-P. gingivalis serum antibody concentrations were not

signicantly different between RA cases and controls with respect to the

immediate/closest pre-diagnosis or post-diagnosis samples (Table 2).

Median anti-P. intermedia serum antibody concentrations were signi-

cantly higher in RA cases than controls for the immediate/closest pre-

diagnosis sample (p = 0.008), but not in the post-diagnosis sample. In

contrast, median anti-F. nucleatum serum antibody concentrations were

lower in RA cases than controls in the immediate/closest pre-diagnosis

sample (p = 0.045) but did not differ in post-diagnosis samples.

Association between pre-RA diagnosis serum anti-bacterial antibody

concentrations and future RA case status

Temporal relationships of anti-P. gingivalis, anti-P. intermedia, and

anti-F. nucleatum serum antibody concentrations with RA cases and

controls are shown in Fig. 1. No evidence of case-control divergence in

anti-P. gingivalis and anti-P. intermedia was observed during the pre-RA

diagnosis period. Anti-F. nucleatum displayed evidence of slight case-

control divergence at 13 years, 7 months prior to diagnosis, with the

controls having higher anti-bacterial antibodies than the cases, but

values reconverged and were not signicantly different at all later time

points. Correlations among the anti-bacterial serum antibody concen-

trations were moderately strong and positive (r = 0.46–0.66; data not

shown).

Autoantibody concentrations among RA cases and associations with anti-

bacterial antibodies

In analyses limited to RA cases, using data from all pre-diagnosis

observations, anti-P. gingivalis and anti-F. nucleatum serum antibody

concentrations were not signicantly associated with any of the RA

autoantibodies in either unadjusted analyses or in multivariable models

adjusted for age, sex, and smoking status (Table 3).

However, higher anti-P. intermedia serum antibody concentrations

were signicantly associated with higher concentrations of anti-CCP2,

ACPA ne specicities targeting vimentin, histone, alpha-enolase, and

IgA RF autoantibodies (p<0.001) in both unadjusted and adjusted an-

alyses. Anti-P. intermedia serum antibody concentrations were also

Table 1

Patient characteristics and autoantibody values.

Characteristic RA Cases

n = 214

Controls

n = 210

p-value

Age at time of diagnosis, mean (SD) 36.8 (7.9) 36.7 (8.0) 0.89

Sex, n (%)

0.93

Female 102 (48) 101 (48)

Male 112 (52) 109 (52)

Ethnicity, n (%)

1.00

White 123 (59) 122 (60)

Black 58 (28) 55 (27)

Hispanic 18 (9) 18 (9)

Asian 5 (2) 5 (2)

American Indian 4 (2) 4 (2)

Other 1 (0) 1 (0)

Ever Smoker, n (%)

68 (32) 49 (23) 0.05

Anti-CCP2, U/ml, median (IQ range)

Immediate / closest pre-diagnosis

sample

59 (2, 216) 0.3 (0.1, 1.0) <0.001

Post diagnosis sample 52 (3, 204) 0.4 (0.1, 0.9) <0.001

Anti-CCP2, U/ml, n (% positive)

Immediate / closest pre-diagnosis

sample

152 (72) 3 (1) <0.001

Post diagnosis sample 153 (72) 0 (0) <0.001

ACPA against vimentin, MFI, median

(IQ range)

Immediate / closest pre-diagnosis

sample

288 (93,

1725)

60 (47, 79) <0.001

Post diagnosis sample 397 (86,

1849)

53 (47, 68) <0.001

ACPA against histone, MFI, median

(IQ range)

Immediate / closest pre-diagnosis

sample

591 (133,

2563)

91 (71, 126) <0.001

Post diagnosis sample 546 (122,

2478)

77 (62, 108) <0.001

ACPA against alpha-enolase, MFI,

median (IQ range)

Immediate / closest pre-diagnosis

sample

310 (108,

4431)

82 (67, 103) <0.001

Post diagnosis sample 406 (112,

3640)

78 (65, 98) <0.001

IgA RF, IU/ml, median (IQ range)

Immediate / closest pre-diagnosis

sample

5.8 (2.1,

27.1)

1.3 (0.9, 2.0) <0.001

Post diagnosis sample 5.7 (1.8,

29.3)

1.2 (0.9, 2.0) <0.001

IgA RF, IU/ml, n (% positive)

Immediate / closest pre-diagnosis

sample

86 (41) 3 (1) <0.001

Post diagnosis sample 86 (40) 4 (4) <0.001

IgG RF, IU/ml, median (IQ range)

Immediate / closest pre-diagnosis

sample

6.4 (4.7,

11.6)

4.5 (3.5, 5.7) <0.001

Post diagnosis sample 6.7 (4.2,

11.7)

4.4 (3.3, 5.8) <0.001

IgG RF, IU/ml, n (% positive)

Immediate / closest pre-diagnosis

sample

40 (19) 5 (2) <0.001

Post diagnosis sample 35 (16) 1 (1) <0.001

IgM RF, IU/ml, median (IQ range)

Immediate / closest pre-diagnosis

sample

30 (8, 105) 3.8 (2.1, 7.0) <0.001

Post diagnosis sample 30 (8, 105) 3.6 (2.2, 7.7) <0.001

IgM RF, IU/ml, n (% positive)

Immediate / closest pre-diagnosis

sample

112 (53) 7 (3) <0.001

Post diagnosis sample 112 (53) 4 (4) <0.001

RA medications (Ever Used), n (%)

Methotrexate 187 (88) – –

Anti-TNF inhibitor 157 (74) – –

Radiographic erosions, n (%) 95 (45) – –

Number of samples tested, per

individual, n (%)

–

2 0 (0) 1 (0)

3 3 (1) 102 (49)

4 211 (99) 107 (51)

(continued on next page)

J.A. Lee et al.

Seminars in Arthritis and Rheumatism 59 (2023) 152176

signicantly associated with IgG RF (p = 0.047, 0.049) and IgM RF (p =

0.003, 0.004) for unadjusted and adjusted values, respectively.

Discussion

This study shows serum anti-P. intermedia antibodies demonstrated

signicant associations with anti-CCP2, ACPA ne specicities targeting

vimentin, histone, alpha-enolase, and IgA, IgG, and IgM RF autoanti-

body concentrations prior to RA diagnosis even after adjusting for age,

sex, and smoking. In contrast, anti-P. gingivalis and anti-F. nucleatum

serum antibody concentrations were not signicantly associated with

RA autoantibodies. Additionally, no longitudinal elevations of anti-

P. gingivalis, anti-P. intermedia, and anti-F. nucleatum serum antibody

concentrations were observed in RA patients prior to a diagnosis of RA

compared to controls.

Prior studies have evaluated serum anti-P. gingivalis antibody con-

centrations in association with pre-RA case status [23–25]. Fisher et al.

evaluated a southern European population prior to the onset of RA and

reported the association between smoking and antibodies to P. gingivalis

arginine gingipain (RgpB), and citrullinated PPAD peptides with the risk

of RA and pre-RA autoimmunity [23]. Median timing from blood sam-

pling to diagnosis in pre-RA cases was seven years. Their results showed

that smoking was signicantly associated with an increased risk of RA

before clinical onset of disease and former smoking was associated with

ACPA positivity in pre-RA cases. Antibodies to RgpB and PPAD peptides

were not associated with risk of RA or with pre-RA autoimmunity.

Similar to our study, P. gingivalis antibody was not associated with

pre-RA autoimmunity or risk of RA and the authors suggested this or-

ganism may not play a role in the association between PD and RA in this

cohort [23].

A study by Johansson analyzed a Northern Swedish population and

investigated whether anti-P. gingivalis antibody levels pre-dated the

onset of RA symptoms and ACPA production [24]. The median time

blood samples pre-dated RA symptoms was approximately ve years. In

contrast to the Fisher et al. study [23], their data demonstrated signif-

icantly increased anti-RgpB IgG levels in pre-symptomatic patients and

in RA patients compared with controls. Enhanced levels of antibodies to

a citrullinated PPAD peptide (anti-CPP3) were also found in both

pre-symptomatic and RA individuals. Interestingly, no signicant asso-

ciation was noted between anti-RgpB and anti-CPP3 antibodies. This

study supported a relationship between P. gingivalis and RA by

Table 1 (continued )

Span of pre-RA samples in years,

mean (SD)

− 5.1 (5.7) – –

Span, oldest to newest sample, in

years, mean (SD)

12.8 (5.2) 12.2 (4.9) 0.30

ACPA = anti-citrullinated protein antibodies.

MFI = mean uorescent intensity.

t-test.

Pearson chi-square test.

Exact Pearson chi-square test.

Wilcoxon

rank-sum test.

Each ethnicity group was missing values for 5 cases and 5 controls.

Data missing regarding ‘ever smoking’ in 5 cases and 89 controls (imputed as

never smokers); when analysis limited to non-missing data, ever smoking

observed in 33% of cases and 41% of controls (p = 0.15).

Immediate pre-diagnosis samples available for 212 cases and 207 controls;

post-diagnosis samples available for 214 cases and 112 controls.

Among 214 cases and 112 controls with a post diagnosis/index date sample.

Table 2

Serum anti-bacterial antibodies in RA cases versus controls.

Serum anti-bacterial antibodies RA Cases

n = 214

Controls

n = 210

value

Anti-P. gingivalis, ug/ml, median (IQ

range)

Immediate / closest pre-diagnosis

sample

47 (29, 83) 50 (30, 86) 0.633

Post diagnosis sample 50 (31, 84) 53 (30, 87) 0.913

Anti-P. intermedia, ug/ml, median

(IQ range)

Immediate / closest pre-diagnosis

sample

331 (258,

412)

313 (220,

379)

0.008

Post diagnosis sample 372 (289,

445)

371 (289,

423)

0.404

Anti-F. nucleatum, ug/ml, median (IQ

range)

Immediate / closest pre-diagnosis

sample

57 (34, 86) 61 (39, 105) 0.045

Post diagnosis sample 57 (33, 96) 67 (42, 97) 0.192

Immediate pre-diagnosis samples available for 212 cases and 207 controls;

post-diagnosis samples available for 214 cases and 112 controls.

Fig. 1. Pre-rheumatoid arthritis diagnosis concentrations of serum anti-bacterial antibodies (RA cases shown with solid lines, controls shown with dashed lines).

J.A. Lee et al.

Seminars in Arthritis and Rheumatism 59 (2023) 152176

demonstrating increased concentrations of anti-P. gingivalis antibodies in

RA patients compared to controls, detectable years before symptom

development [24].

Manoil et al. measured serum IgG antibodies against selected peri-

odontal pathogens, including P. gingivalis, to determine whether they

were associated with early symptoms or RA development [25]. This

study did not nd an association between serum IgG titers against in-

dividual periodontal pathogens and specic preclinical phases of RA

development. However, the authors found an association between cu-

mulative IgG titers against periodontal pathogens and ACPA-positivity.

These data suggest that synergy among periodontal pathogens, rather

than specic bacterial associations, may be associated with ACPA

development [25].

Our results may differ from prior reports given differences in the

populations studied. In the present study, antibody to P. gingivalis was

directed against outer membrane antigens (OMA), rather than only to

specic P. gingivalis virulence factors seen in the other two studies [23,

24]. Also, the majority of our study population was male and consisted

of active United States military personnel compared to individuals

residing in Northern [24] or Southern Europe [23]. Furthermore, the

mean age of the RA cases in the European studies were around 50 years

old and had a high percentage of ever smokers, ranging from 59% [23]

to 67% [24], while our RA participants averaged 37 years old and had

lower smoking prevalence of 32%. With the differences in age at disease

onset, our younger cohort could suggest a high genetic burden for RA.

That high genetic risk could potentially attenuate the importance of

environmental factors in this population, such as smoking and bacterial

infection leading to PD [26]. We did not determine HLA-SE in the cur-

rent study, although a previous publication by our group found no ev-

idence of an interaction of PD with HLA-DRB1 SE positivity [26].

In our previous study, relationship of P. gingivalis with RA autoan-

tibodies in individuals at “high risk” for RA was examined [14]. Patients

were considered autoantibody positive with one or more positive

autoantibody tests and high-risk individuals were either ACPA-positive

or were positive on two or more RF assays. No patients satised the

1987 American College of Rheumatology RA classication criteria [17].

Anti-P. gingivalis concentrations were higher in both the high-risk and

autoantibody positive groups than in the autoantibody negative group.

There were no differences between groups with respect to

anti-P. intermedia or anti-F. nucleatum. The majority of this cohort was

slightly older and predominantly female when compared to our

younger, male population and could account for the different associa-

tions with serum anti-bacterial antibodies [14]. These contrasting con-

clusions suggest additional research is needed to further explore

whether antibody concentrations to the pathogen P. gingivalis may be

increased prior to onset of RA symptoms and linked to the development

of RA.

Although P. gingivalis is the most studied periodontal microorganism

in the pathogenesis of RA, it has been suggested that P. intermedia may

also play a role in RA progression, albeit by a different mechanism. A

study by Schwenzer et al. suggested that, since P. intermedia does not

express a PAD, its ability to induce ACPA differs from P. gingivalis [12]

potentially through a mechanism whereby degradation of neutrophil

extracellular traps (NETs) by nucleases from P. intermedia leads to the

release of PADs [27] and increases the pathogenicity of this organism

[28]. Kimura et al. [29] evaluated synovitis and its association with

periodontal pathogens and established biomarkers of RA. Greater

P. intermedia antibody titer was observed in active RA patients and RA

patients in clinical remission with subclinical synovitis, detected by ul-

trasound, compared to RA patients in clinical remission without sub-

clinical synovitis. An association of P. intermedia antibody titer and

disease activity of RA, specically synovitis, was proposed. The mech-

anism suggested by the authors is activation of macrophages by

P. intermedia which initiates production of IL-6 and TNF-

, inammatory

cytokines that play a role in periodontal and joint destruction. Of note,

Scher et al. reported that Prevotella and Leptotrichia species were the only

characteristic taxa in the oral microbiota in the new-onset RA group

irrespective of PD status and were completely absent in the oral

microbiota of controls [30]. While other investigators were unable to

demonstrate a relationship between P. intermedia and RA [31,32], our

study observed a strong association with anti-CCP2, certain ACPA

specicities as well as several isotypes of RF and highlights a need to

further explore the potential role of P. intermedia in RA pathogenesis

and, in particular, the generation of these RA-related autoantibodies.

Limited studies exist evaluating anti-F. nucleatum antibody concen-

trations with RA. One study analyzed saliva samples of early RA patients

and found microbiota rich in F. nucleatum when compared to healthy

controls and proposed the oral microbiota may be useful in detecting

Table 3

Associations of all pre-diagnosis autoantibody sample concentrations among RA

cases with serum anti-bacterial antibodies.

Unadjusted Adjusted

Dependent

Variable

Anti-P. gingivalis

coefcient (95%

CI)

p-value Anti-P. gingivalis

coefcient (95%

CI)

p-value

Anti-CCP2 0.101 (− 0.274,

0.475)

0.597 0.105 (− 0.274,

0.484)

0.586

ACPA against

vimentin

0.020 (− 0.180,

0.220)

0.845 0.032 (− 0.170,

0.233)

0.758

ACPA against

histone

0.168 (− 0.027,

0.363)

0.092 0.173 (− 0.024,

0.371)

0.085

ACPA against

alpha-

enolase

0.073 (− 0.132,

0.277)

0.485 0.094 (− 0.111,

0.299)

0.367

IgA RF − 0.066 (− 0.244,

0.112)

0.465 − 0.060 (− 0.239,

0.119)

0.510

IgG RF 0.038 (− 0.057,

0.133)

0.434 0.040 (− 0.056,

0.137)

0.411

IgM RF 0.019 (− 0.158,

0.195)

0.835 0.030 (− 0.144,

0.205)

0.733

Dependent

Variable

Anti-P. intermedia

coefcient (95%

CI)

value

Anti-P. intermedia

coefcient (95%

CI)

value

Anti-CCP2 1.838 (1.210,

2.466)

<0.001 1.869 (1.235,

2.503)

<0.001

ACPA against

vimentin

0.792 (0.457,

1.127)

<0.001 0.827 (0.490,

1.163)

<0.001

ACPA against

histone

0.739 (0.410,

1.068)

<0.001 0.758 (0.426,

1.090)

<0.001

ACPA against

alpha-

enolase

0.790 (0.443,

1.136)

<0.001 0.840 (0.492,

1.187)

<0.001

IgA RF 0.525 (0.236,

0.814)

<0.001 0.536 (0.245,

0.827)

<0.001

IgG RF 0.163 (0.002,

0.325)

0.047 0.163 (0.001,

0.326)

0.049

IgM RF 0.454 (0.157,

0.750)

0.003 0.442 (0.146,

0.737)

0.004

Dependent

Variable

Anti-F. nucleatum

coefcient (95%

CI)

value

Anti-F. nucleatum

coefcient (95%

CI)

value

Anti-CCP2 0.066 (− 0.326,

0.459)

0.740 0.081 (− 0.314,

0.476)

0.687

ACPA against

vimentin

0.055 (− 0.154,

0.264)

0.604 0.048 (− 0.161,

0.256)

0.655

ACPA against

histone

0.036 (− 0.169,

0.241)

0.731 0.041 (− 0.165,

0.247)

0.693

ACPA against

alpha-

enolase

0.179 (− 0.034,

0.392)

0.100 0.181 (− 0.032,

0.393)

0.095

IgA RF − 0.090 (− 0.275,

0.094)

0.337 − 0.081 (− 0.266,

0.104)

0.388

IgG RF 0.080 (− 0.020,

0.179)

0.117 0.084 (− 0.016,

0.184)

0.098

IgM RF − 0.046 (− 0.231,

0.138)

0.622 − 0.024 (− 0.206,

0.158)

0.799

ACPA = anti-citrullinated protein antibodies.

All measures in this table were log base 2 transformed.

Models were adjusted for age, sex, and smoking.

J.A. Lee et al.

Seminars in Arthritis and Rheumatism 59 (2023) 152176

risk assessment for early onset of RA [13]. In looking at subgingival

biolm of RA patients, F. nucleatum was found in higher concentrations

in aCCP-positive patients with RA versus controls, though this nding

was not statistically signicant [33]. In a separate study, F. nucleatum

was found in the synovial uid of RA patients derived from both native

and prosthetic joints. Identical clones of the bacteria were found in the

same patient’s plaque sample, and it was proposed that F. nucleatum can

translocate from the oral cavity to the synovial cavity [34]. In contrast,

our data does not provide compelling evidence to support a role of

F. nucleatum in RA development. In contrast to prior reports, our results

demonstrated only a slight case-control divergence of anti-F. nucleatum

prior to RA diagnosis; however, controls had initially higher concen-

trations that reconverged to no longer be statistically signicant than

concentrations in RA cases. When compared to P. gingivalis and P.

intermedia, potential mechanisms linking F. nucleatum and RA risk

remain poorly understood.

There are limitations in this study. The participants were military

personnel with a relatively high proportion of men to women (52% vs.

48%, respectively) and a younger age of RA onset (37 years old). Thus,

these results may not be generalizable to other RA populations [35]. A

majority of RA cases utilized methotrexate and/or biologics (88% and

74%, respectively), which could have impacted these results. Further-

more, there were only 112 control patient samples available for post-RA

diagnosis evaluations. The lack of a difference in anti-P. intermedia

concentrations in RA cases versus controls post-RA diagnosis needs to be

interpreted with caution in light of this smaller sample size available for

analysis. In addition, most of the pre-RA serum samples were collected

within 5 years of diagnosis, which could have limited our ability to

detect earlier differences in anti-bacterial or autoantibody elevations

[18]. In future studies, more frequent serum sample collection over

more extended time periods would provide an even more comprehen-

sive look at the autoantibody and anti-bacterial responses potentially

leading to RA onset. PD status was not determined in this study and,

therefore, we were unable to associate periodontal status with the pa-

tients’ systemic response against the periodontal pathogens investi-

gated. Moreover, the taxa could exert a local response without triggering

a serum IgG response; therefore, null associations should be carefully

considered. Finally, future studies also should focus on the plethora of

inammatory reactions occurring in the gingival tissues that have the

potential to stimulate autoantibody production associated with RA.

Conclusion

In conclusion, no longitudinal elevations of serum anti-bacterial

antibody concentrations were observed in RA patients prior to a diag-

nosis of RA compared to controls. However, anti-P. intermedia displayed

a signicant association with RA autoantibody concentrations prior to

RA diagnosis, suggesting a potential role of this organism in progression

towards clinically-detectable RA.

Funding statement

Dr. Deane receives research support from the Rheumatology

Research Foundation, Department of Defense Congressionally Directed

Medical Research Program grants PR120839 and PR191079, NIH/

NCATS Colorado CTSA Grant Number UL1 TR001082 and NIH/NIAMS

P30 AR079369.

Dr. Mikuls receives research support from the VA (Merit grant

BX004600), the US Department of Defense (PR200793), and NIH/

NIGMS (U54GM115458).

Disclosure statement

Dr. Deane has received kits for testing RA-related autoantibodies

from Werfen through research relationships. There are no other nan-

cial disclosures for the other authors.

Military disclaimer

The identication of specic products or scientic instrumentation is

considered an integral part of the scientic endeavor and does not

constitute endorsement or implied endorsement on the part of the

author(s), DoD, or any component agency. The views expressed in this

article are those of the author(s) and do not necessarily reect the

ofcial policy of the Department of Defense or the U.S. Government.

CRediT authorship contribution statement

Joyce A. Lee: Conceptualization, Investigation, Methodology,

Visualization, Writing – original draft, Writing – review & editing. Ted

R. Mikuls: Conceptualization, Funding acquisition, Investigation,

Methodology, Project administration, Supervision, Visualization,

Writing – original draft, Writing – review & editing. Kevin D. Deane:

Conceptualization, Data curation, Funding acquisition, Investigation,

Project administration, Resources, Writing – review & editing. Harlan

R. Sayles: Formal analysis, Validation, Visualization, Writing – original

draft, Writing – review & editing. Geoffrey M. Thiele: Data curation,

Investigation, Methodology, Resources, Validation, Writing – review &

editing. Jess D. Edison: Data curation, Investigation, Project adminis-

tration, Supervision, Writing – review & editing. Brandie D. Wagner:

Methodology, Software, Writing – review & editing. Marie L. Feser:

Project administration, Resources, Writing – review & editing. Laura K.

Moss: Data curation, Writing – review & editing. Lindsay B. Kelmen-

son: Data curation, Writing – review & editing. William H. Robinson:

Data curation, Investigation, Project administration, Resources, Vali-

dation, Writing – review & editing. Jeffrey B. Payne: Conceptualiza-

tion, Investigation, Methodology, Project administration, Supervision,

Visualization, Writing – original draft, Writing – review & editing.

Data Availability

Data requests can be made to the authors although use is restricted

based on Department of Defense guidelines.

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