Prediction of Future Rheumatoid Arthritis




Rheumatoid arthritis (RA) results from an interaction between genetic susceptibility and environmental factors. Several of these factors are known, such as family history of RA, high birth weight, smoking, silica exposure, alcohol nonuse, obesity, diabetes mellitus, rheumatoid factor, anti-citrullinated protein antibody, and genetic variants such as the shared epitope and protein tyrosine phosphatase nonreceptor type 22. The impact of these factors can be modeled in the 2 main groups at risk of RA: family members of patients with RA and seropositive persons with or without arthralgia. Current models have the potential to select individuals for preventive strategies.


Key points








  • Risk factors for rheumatoid arthritis (RA) include family history, birth weight, smoking, silica, alcohol nonuse, obesity, diabetes mellitus, autoantibodies, and genetic variants.



  • Symptoms, antibodies, and inflammatory biomarkers can be useful in late at-risk stages, and genetic scores plus environmental factors more useful in early at-risk stages.



  • Prediction models of RA can help to select candidates for intervention studies.



  • The best target populations for screening are relatives of patients with RA and (seropositive) patients with arthralgia. However, only a minority of persons at risk can thus be recognized.



  • Screening for RA risk is still experimental, because there is no validated screening tool and no proven therapy to prevent disease.






Introduction


Rheumatoid arthritis (RA) on average becomes clinically manifest around the age of 55 years. During the healthy part of life, the risk of future RA is determined by genetic, reproductive and environmental factors ( Fig. 1 , green bar). Over time, people at risk for RA may pass through a phase of autoimmunity, accompanied by subclinical inflammation, followed by a symptomatic phase, which may last a few months to several years. In the symptomatic phase, markers of autoimmunity and inflammation increase before the onset of clinical arthritis. Therefore, prediction can be based on different characteristics in the asymptomatic phase and in the symptomatic phase.




Fig. 1


The evolution of RA from health to disease. ACPA, anti–citrullinated protein antibody; RF, rheumatoid factor. anti-CarP, anti-carbamylated protein antibodies.


The expectation that intervening in the preclinical phase of RA could be beneficial is based on the success of treatment of RA within 1 to 2 years after onset of clinical disease. The new criteria for RA from 2010 with a focus on early signs such as involvement of even only a few small joints together with serology and acute phase reactants facilitate treatment in the earliest clinical phase, and the further characterization of the preclinical phase offers new opportunities for intervention studies even before clinically apparent arthritis occurs. Because RA is the most prevalent inflammatory rheumatic disease, with a high burden for the patient and society, it seems the ideal candidate rheumatic disease for screening and intervention programs. However, a lot of steps need to be taken before such programs can be offered to persons at risk.


This article summarizes the present knowledge on risk factors for RA, including genetic, reproductive, and hormonal factors; environmental exposures; biomarkers; personal characteristics and symptoms; and how these can be combined in risk models attempting to increase the accuracy of the prediction of RA. Genetic risk and gene-environmental interactions are dealt with elsewhere in this issue and are only mentioned here in relation to their roles in prediction models. Risk scores from such models require further validation, but could be used to select candidates for intervention studies.




Introduction


Rheumatoid arthritis (RA) on average becomes clinically manifest around the age of 55 years. During the healthy part of life, the risk of future RA is determined by genetic, reproductive and environmental factors ( Fig. 1 , green bar). Over time, people at risk for RA may pass through a phase of autoimmunity, accompanied by subclinical inflammation, followed by a symptomatic phase, which may last a few months to several years. In the symptomatic phase, markers of autoimmunity and inflammation increase before the onset of clinical arthritis. Therefore, prediction can be based on different characteristics in the asymptomatic phase and in the symptomatic phase.




Fig. 1


The evolution of RA from health to disease. ACPA, anti–citrullinated protein antibody; RF, rheumatoid factor. anti-CarP, anti-carbamylated protein antibodies.


The expectation that intervening in the preclinical phase of RA could be beneficial is based on the success of treatment of RA within 1 to 2 years after onset of clinical disease. The new criteria for RA from 2010 with a focus on early signs such as involvement of even only a few small joints together with serology and acute phase reactants facilitate treatment in the earliest clinical phase, and the further characterization of the preclinical phase offers new opportunities for intervention studies even before clinically apparent arthritis occurs. Because RA is the most prevalent inflammatory rheumatic disease, with a high burden for the patient and society, it seems the ideal candidate rheumatic disease for screening and intervention programs. However, a lot of steps need to be taken before such programs can be offered to persons at risk.


This article summarizes the present knowledge on risk factors for RA, including genetic, reproductive, and hormonal factors; environmental exposures; biomarkers; personal characteristics and symptoms; and how these can be combined in risk models attempting to increase the accuracy of the prediction of RA. Genetic risk and gene-environmental interactions are dealt with elsewhere in this issue and are only mentioned here in relation to their roles in prediction models. Risk scores from such models require further validation, but could be used to select candidates for intervention studies.




Methods


We searched the PubMed database on January 29, 2014, for the terms risk, prediction, and development in relation to RA. After excluding articles not directly related to prediction of RA, such as studies on prevalence, diagnosis, treatment, outcome, or comorbidities of RA, more than 200 articles remained on this topic after screening 2000 abstracts. Additional articles were added that were found after the search date until May 1, 2014, by screening rheumatologic journals.




Risk factors: the building blocks of prediction


The current evidence on risk factors for RA is summarized in Table 1 . Besides the factors reported in the table, many others have been investigated for their association with the risk of RA, but these studies have led to negative, inconclusive, or conflicting results. Among these are variables such as silicone implants ; consumption of coffee, tea, or red meat ; geographic area ; and socioeconomic status. In contrast, some of the factors that have statistically significant associations with RA show opposite directions of risk in different studies. Examples of such cases are age at menarche, breastfeeding, and parity. This uncertainty makes the value of such variables questionable, even if they have been included in prediction models, as is the case with parity and breastfeeding in the model by Lahiri and colleagues.



Table 1

Overview of evidence on risk factors for the development of RA








































Risk Factor Comments
Family history Risk increases with number of affected family members
The longer the disease duration and the higher the age of the proband, the higher the risk
Some studies did not find an association between relatives with RA and risk of RA
Genetic factors Around 60 risk loci for RA are known, explaining 16% of total susceptibility
65% of RA risk is thought to be heritable
Reproductive and hormonal factors Risk is 2–4 times higher in women
A protective effect of oral contraceptives is suggested
High birth weight (more than 4 kg) increases risk
Lower risk during pregnancy, compensated by an increased risk in the first postpartum year
Complications during pregnancy may be related to a higher risk
Inconclusive or conflicting results for breastfeeding, age at menarche, irregular menstrual cycles and age at menopause, postmenopausal hormone use, lower testosterone levels, parity, age at first childbirth, low birth weight, and being small for gestational age
Environmental factors Smoking is the most established risk factor
Smoking interacts with the strongest genetic risk factor ( HLA-SE ) in a dose-dependent manner to increase the risk of seropositive RA
Alcohol consumption (even in small quantities) protects
High consumption of olive oil and fish (oil) protects
Inconclusive results were found for vitamin D intake and ultraviolet B exposure, antioxidant and trace element intake, and exposure to toxic elements
Occupations and occupational exposures Farmers, blue collar workers, and hairdressers are at increased risk
Silica exposure gives increased risk
Exposures that could not be related to RA: asbestos, mineral oil, organic dust, herbicides, insecticides, carbamates, organophosphates, carbaryl, glyphosate, malathion, and ambient air pollution
Infections and vaccinations Frequent infections may predispose
One study reported increased risk after influenza vaccination
Risks could not be quantified for: Ebstein-Barr virus infection, hepatitis C, HIV, Yersinia enterocolitica , mycoplasma, or Porphyromonas gingivalis infection of the gums, and for immunization (other than influenza)
Comorbidities Diabetes types 1 and 2 and inflammatory lung disorders increase risk
Schizophrenia is protective
Obesity and the related condition obstructive sleep apnea syndrome increase the risk
Dyslipidemia is present before RA and predicts RA
Other associations, such as for thyroid disease, are inconclusive
Autoantibodies Status and levels of (isotypes of) RF and ACPA associate with RA risk
Higher levels and the combination of RF and ACPA confer a higher risk
Additional predictive ability independent of RF and ACPA was shown for anti–carbamylated protein antibodies and anti–peptidyl arginine deiminase type 4 antibodies
Other biomarkers in blood Several acute phase reactants and cytokines are increased in pre-RA or at-risk cohorts
TNF (receptor), cartilage oligomeric matrix protein, and a high interferon gene score are quantified risk factors
Imaging Ultrasonography abnormalities (mainly power Doppler signal) in seropositive patients with arthralgia were predictive of arthritis at the joint level in 1 study and at the patient level in another study
Technetium bone scintigraphy is predictive of RA in patients with arthralgia and can exclude inflammatory joint disease
Macrophage-targeted positron emission tomography predicts arthritis in ACPA-positive patients with arthralgia
The predictive capacity of MRI in arthralgia is not yet clear
Symptoms Predictive symptoms in combination with the presence of autoantibodies: duration <12 mo, intermittent symptoms, arthralgia in upper and lower extremities, morning stiffness ≥1 h, self-reported joint swelling, tenderness of hand or foot joints, and morning stiffness ≥30 min

Abbreviations: ACPA, anti–citrullinated protein antibody; HIV, human immunodeficiency virus; MRI, magnetic resonance imaging; RF, rheumatoid factor; TNF, tumor necrosis factor.


In conclusion, there are not many risk factors with strong and confirmed associations with RA. Among these are family history of RA, high birth weight, smoking, silica exposure, alcohol nonuse, obesity, diabetes mellitus, rheumatoid factor (RF), anti–citrullinated protein antibody (ACPA), and genetic variants such as the shared epitope (SE) and protein tyrosine phosphatase nonreceptor type 22 (PTPN22).




Prediction rules: putting the blocks together


In a manner similar to the way clinical characteristics, signs, and symptoms can be combined to diagnose a disease in a patient, the potential risk factors for a given disease can be combined by statistical modeling of variables measured in an at-risk population in order to produce prediction rules. The advantage of such models is that they clarify the relative impact of the individual variables and quantify the overall risk for individuals coming from that population. The validity of these models can then be further confirmed by testing them in other populations.


Recently, several prediction models have been published that attempt to quantify progression to RA ( Table 2 ). Two of these models were based on large population studies, of which 1 was designed for investigating other diseases as well. One of these used clinical characteristics to predict either seropositive or seronegative RA, the other used the combination of clinical characteristics, autoantibodies, and a genetic risk score containing multiple genes (see Table 2 for the variables in the models). Both studies achieve good prediction. However, it is uncertain whether these values can be reproduced in smaller populations.



Table 2

Prediction models of RA


































First Author and Year (Ref.) Cohort; Variables Numbers Results
Van de Stadt et al, 2013 Seropositive patients with arthralgia
Prediction rule variables: alcohol consumption, family history, symptoms <12 mo, intermittent, in upper and lower extremities, VAS ≥ 50, morning stiffness ≥1 h, swollen joints reported by patient, autoantibody status
Arthralgia: 374 (131 developed arthritis) Prediction rule: AUC 0.82 (CI 0.75–0.89)
Intermediate-risk vs low-risk group: HR 4.52 (CI 2.42–8.77)
High-risk vs low-risk group: HR 14.86 (CI 8.40–28)
de Hair et al, 2013 Seropositive patients with arthralgia
Predictive variables: smoking and BMI
Arthralgia: 55 (15 developed arthritis) Smoking (ever vs never) and risk of RA: HR 9.6 (CI 1.3–73)
Obesity (BMI ≥ 25 vs <25) and risk of RA: HR 5.6 (CI 1.3–25)
Lahiri et al, 2014 European Prospective Investigation of Cancer, Norfolk, United Kingdom
40–79 y
Prediction rule variables: alcohol consumption, smoking, occupation, BMI, diabetes mellitus, parity
Total participants: 25,455 (184 developed IP, 138 developed RA) Pack-years smoking in men and risk of IP: HR 1.21 (CI 1.08–1.37)
Seropositive in men and risk of IP: HR 1.24 (CI 1.10–1.41)
Having DM (I or II) and risk of IP: HR 2.54 (CI 1.26–5.09)
Alcohol and risk of IP (per unit/d): HR 0.36 (CI 0.15–0.89)
Overweight vs normal-weight and risk of seronegative IP: HR 2.75 (CI 1.39–5.46)
Parity ≥2 vs no children and risk of IP: HR 2.81 (CI 1.37–5.76)
Breastfeeding for every 52 wk and risk of IP: HR 0.66 (CI 0.46–0.94)
Sparks et al, 2014 NHS, United States, women 30–55 y
EIRA, Sweden, 18–70 y
Prediction rule variables: family history, alcohol consumption, smoking, BMI, parity, autoantibody status, genetic risk score
RA cases: 1625
Controls: 1381
NHS seropositive RA (model family history, epidemiologic, genetic): AUC 0.74 (CI 0.70–0.78)
NHS seropositive RA and positive family history: AUC 0.82 (CI 0.74–0.90)
EIRA ACPA-positive RA (model family history, epidemiologic, genetic): AUC 0.77 (CI 0.75–0.80)
EIRA ACPA-positive RA and positive family history: AUC 0.83 (CI 0.76–0.91)
EIRA ACPA-positive RA and positive family history, high genetic susceptibility, smoking, and increased BMI: OR 21.73 (CI 10–44)
Rakieh et al, 2014 Yorkshire, United Kingdom
ACPA-positive patients with arthralgia
Prediction rule variables: joint tenderness, morning stiffness ≥30 min, high positive autoantibodies, positive ultrasonographic power
Doppler signal
Arthralgia: 100 (50 developed RA) Power Doppler model: Harrell C 0.67 (CI 0.59–0.74)
Progression to IA:
Low risk (0 points) 0%
Moderate risk (1–2 points) 31%
High risk (≥3 points) 62%

Abbreviations: AUC, area under the receiver operating characteristic curve; BMI, body mass index; CI, confidence interval; DM, diabetes mellitus; EIRA, Epidemiological Investigation of RA; HR, hazard ratio; IA, inflammatory arthritis; IP, inflammatory polyarthritis; NHS, Nurses Health Study; OR, odds ratio; VAS, visual analogue scale.


Three other studies investigated the development of RA in ACPA-positive and/or RF-positive patients with arthralgia. The patients were partly recruited in primary care, and partly in the rheumatology clinic. The models were based on clinical characteristics, symptoms, and antibody characteristics, in 1 study supplemented by ultrasonographic power Doppler signal (see Table 2 ). All 3 models provide good discrimination between persons who do or do not develop RA. However, they require ongoing validation as other studies select and follow such cohorts of people at risk for RA. Similar studies from North America designed to predict RA in first-degree relatives of patients with RA are underway but have not yet gathered enough arthritis cases to enable the construction of prediction models. These studies are hampered by the low frequency of autoantibodies or of increased biomarkers in relatives of patients with RA.


Measuring the risk of RA is also a matter of timing. During the early at-risk stage, before the onset of autoimmunity, clinicians can only measure genetic susceptibility and environmental factors (see the left part of Fig. 1 ). The predictive capability of models in this situation is becoming good, with areas under the curve of 72% to 77% for the prediction of ACPA-positive RA. However, the measured risk is a lifetime risk, which makes it an abstract figure for the individual person at risk. Prediction including a time frame becomes possible nearer to the onset of clinical RA, when the aspects of symptoms, autoimmunity, and inflammation can be taken into account. In the Amsterdam risk model, points can be gathered for clinical characteristics, symptoms, and serology, with more points for high levels of ACPA or positivity for both ACPA and RF. The more points, the higher the risk and the sooner the onset of arthritis can be expected ( Fig. 2 ). This prediction reflects studies in pre-RA blood donors, in which autoantibody levels increase during the 1 to 3 years before the onset of clinical arthritis. In an US cohort of 81 patients with clinical RA from whom stored serum was available from 1 to 12 years before disease onset, a biomarker profile including autoantibodies and cytokines was identified that predicts the imminent onset of clinical arthritis within 2 years. Autoantibody epitope spreading by itself in the preclinical phase also predicts progression to classifiable RA.




Fig. 2


Flowchart search strategy.




Screening strategies


Many medical, ethical, and economic issues need to be addressed before screening for risk of future RA can be offered to certain categories of unaffected persons. Basic requirements for screening groups of people to predict a disease are (1) a defined population to test; (2) the existence of an asymptomatic (or nonspecific symptomatic) phase; (3) the availability of a test with good accuracy, low rates of side effects, and low cost; and (4) the availability of a cost-effective intervention in the at-risk phase. Only the second requirement of an asymptomatic phase is clearly fulfilled at present. Regarding items 3 and 4, no single test can identify those at risk for RA and no intervention exists with proven efficacy in the at-risk situation. All efforts to predict RA and treat persons with an increased risk for RA are therefore currently regarded as investigational. The test for RA will eventually be a validated, cost-effective, and accurate prediction rule that is easy to apply. For comparison, consider the screening programs for colonic cancer, which have recently been established in several countries. All persons more than a certain age are offered screening, which leads to huge numbers of colonoscopies. The high cost of this procedure and the possibility of serious side effects need to be weighed against the benefit of removing polyps that would cause a high morbidity and mortality if left unnoticed.


Regarding item 1, careful consideration is needed to decide which population(s) should be screened or tested. The choices from general to specific are general population, relatives of patients with RA, persons with musculoskeletal symptoms, or persons with RA-specific autoimmunity. Because RA is not highly prevalent in most populations, with the possible exception of North American native peoples, at this time it is not practical to test the general population for RA. Two recognizable target groups then remain: relatives of patients with RA and persons with musculoskeletal symptoms. The latter are found both in general practice and in rheumatology clinics. After history taking and physical examination, it must be decided which patients should proceed to further testing for RA risk, and which test to use. At present most clinicians use the RF and/or ACPA test, which are widely available and easy to perform. Except for patients with only RF positivity just above the reference range, the results give useful information. The question of who to test in general practice cannot accurately be answered at this time. This question requires structured longitudinal follow-up of patients in general practice, or the following of cohorts with clinically suspect arthralgia in rheumatology clinics.




Summary


There is a trend toward increasingly sophisticated prediction models for RA in different stages of risk. However, further work is needed to combine patient-level information with the published promising biomarkers into more robust models. For example, models for relatives of patients with RA, reflecting the early at-risk stage, depend largely on personal characteristics and genetic risk, whereas models for patients with arthralgia that reflect the late at-risk stage need to include patient-related and symptom characteristics in combination with biomarkers of autoimmunity and inflammation. In view of the vague and unspecific first symptoms of many patients who later develop RA, it will be necessary to better characterize and measure these symptoms in future models.


However, because much is known about the risks for developing RA, it is already possible to use this information to design preventive interventions in persons at high risk for RA. At least in the late preclinical stage, several such interventions are currently being tested or planned.


Disclosures: None.



1 S.A. Turk and M.H. van Beers-Tas contributed equally to this work.


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