Editorial: Risk and protective factors in the natural history of autoimmunity

Autoimmune diseases, the third-most common category after cancer and heart disease, affecting at least 5% of the U.S. population (1), are severe, chronic, and costly to individuals and society. Preclinical or asymptotic autoimmunity may arise years prior diagnosis, occurs in the general population, and appears to be increasing, e.g., antinuclear antibodies rising in recent decades in the U.S.(2). But only some will develop symptoms and pathologies. Papers in this issue focus on risk and protective factors for asymptomatic or preclinical autoimmunity and disease. The relationship of autoimmunity with other diseases, especially cancer and infections, also has important clinical implications. These questions take on greater urgency, given the apparent rise in rates and costs of many autoimmune diseases (3).Clinical suspicion may lead to autoantibody testing, but low predictive probability can result in repeated, costly, unnecessary testing. Barnado et al., address this problem using electronic health records among antinuclear antibody (ANA)-positive persons, finding that greater likelihood of developing autoimmune diseases among those who were younger, female, had higher-titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes for relevant symptoms. In sum, this model is a useful clinical tool to identify high risk patients with a positive ANA who should receive further work-up, while reassuring lower-risk individuals and reducing unnecessary referrals.While autoantibodies are known to precede numerous autoimmune diseases, most studies lack longitudinal sampling and factors determining progression or regression are poorly understood.In children at risk of developing type 1 diabetes with disease-specific autoantibodies, Carry et al., found differences in DNA methylation comparing those who progressed to disease, and those who maintained autoantibodies, or sero-reverted. Candidate genes were related to diet and glucose levels, as well as immune and pancreatic beta cells, suggestive of potential environmental factors contributing to disease risk. More studies are needed that include exposure data as well as biomarkers in the progression of preclinical autoimmunity.In a cross-sectional analysis of cotinine (a marker of cigarette smoke exposure) and ANA prevalence among a representative population sample of the U.S. population Dinse et al., observed over the study periods (1988-1991, 1999-2004, 2011-2012), that the percent with ANA was highest (13.3-19.2%) for nonsmokers but non-trending; lower (11.1-15.5%) for "passive" smokers but steadily increasing; and lowest for active smokers but increasing from 7.4% in 1999-2004 to 13.3% in 2011-2012. The findings imply unmeasured environmental influences on ANA prevalence.In their review on cancer risk associated with connective tissue disease, Tonutti et al., explore the multiple complex interrelationships between these entities. The long-recognized increased cancer risk in many rheumatic conditions could develop for various reasons, including loss of immune tolerance due to oncogenesis; proinflammatory immune activation/autoimmunity that may promote oncogenesis; or immunosuppressive therapies that may decrease cancer surveillance. By contrast, autoimmunity may contribute to removal of constantly generated neoplasms. Incomplete data support all these theories, and further research is needed. In response, Jiayi Chen highlights the need for multidisciplinary collaborations, synthesizing across different diseases, and harmonizing methods for detecting autoantibodies.Female sex is associated with ANA prevalence and an increased risk of many autoimmune diseases. Investigating a polygenetic risk score for juvenile idiopathic arthritis (JIA), Haftorn et al., examined scores in a population-based study of 238 juvenile idiopathic arthritis cases vs. over 73,000 controls. Investigations into how to best model the genetic susceptibilities revealed strong sex differences, suggesting that generalized additive models (GAM) should employ sexstratification while general linear models can be applied successfully as well.Receptor (TLR7) signaling using published studies among people with SLE (as well as other autoimmune diseases). They found that the sex-bias among patients was explained by specific expressions, while inactivation of the X chromosome were also observed. Environmental factors examined were EBV infections and hormonal, mainly estrogen effects on B cells suggesting potential molecular pathways.In their overview, Choi et al. highlight diverse non-genetic risk and protective factors for systemic autoimmune rheumatic disorders and the complex interactions that may occur prior to disease development. Risk factors include airborne, waterborne, workplace/occupational, social, and behavioral factors, many of which have changed dramatically over recent decades and may help explain increases in autoimmunity and disease. Machine learning methods and multiomics have paved the way for better understanding of these risk factors, and expansions of these and other new technologies could allow for better preventative approaches in the future.Dåstøl et al., in a study of Juvenile idiopathic arthritis (JIA), explored the role of seafood and dietary contaminants in the context of a polygenic risk score. While they did not find evidence of associations between estimated intake of environmental contaminants and JIA risk of JIA, based on quantiles of fish intake of fish, or proxies for potential heavy metal exposures, patients with low genetic predisposition s had stronger and significant associations with environmental toxicants, suggestive of environmentally induced JIA. Some environmental factors may be considered triggers. Concerns have been raised that autoimmunity may develop following vaccine-specific immune activation and inflammatory responses. In their study of myositis patients Alhassan et al., in the pre-COVID era, found genetic risk and protective factors for myositis within 6 months of vaccination, including human leukocyte antigen alleles and immunoglobulin allotypes. Larger studies with greater genotyping and phenotyping are needed to personalize risk assessment and enhance vaccine safety.Infections are also possible triggers. In a global network of 74 healthcare organizations and nearly 4 million patients, Hileman et al. investigated incident autoimmune diseases up to 1 year after a diagnosed infection. They found elevated risk of eight autoimmune disease in those diagnosed with COVID-19, especially cutaneous vasculitis, polyarteritis nodosa, and hypersensitivity angiitis. A positive ANA was also more likely and predictive of risk following infection. They conclude that SARS-CoV-2 may be a potential trigger for some autoimmune