4

Crosscutting Issues in Autoimmune Diseases

In Chapter 1 and Chapter 3, the committee highlighted unique and heterogeneous features of specific autoimmune diseases. In contrast, this chapter examines common features found among many autoimmune diseases. Because discussion applies to autoimmune diseases as a category of diseases, the 11 diseases of focus as well as other autoimmune diseases, such as alopecia areata, juvenile idiopathic arthritis, myocarditis, spondyloarthropathy, systemic sclerosis (also known as scleroderma), and vasculitis, are referenced.

COMMONALITIES ACROSS AUTOIMMUNE DISEASES

While every autoimmune disease has its unique characteristics, many of them also share common features that researchers believe—and evidence supports—suggest that there are common mechanisms involved in the etiology of autoimmune diseases. For example, the fact that autoimmune diseases tend to occur in family clusters suggests that there are common genetic and environmental factors involved in triggering autoimmunity and disease symptoms. Research shows that genetic factors alone cannot explain the etiology of any autoimmune disease, and that environmental agents or insults—infectious microorganisms and respiratory and dietary exposures are leading candidates for several autoimmune diseases—are interacting with those genetic factors to produce the disease state. Certain types of proinflammatory immune responses and the presence of autoantibodies are two other common features. Finally,

multiple autoimmune diseases show sex differences in disease incidence and severity. The discussion below highlights how these commonalities, as well as common mechanistic pathways, can serve as therapeutic targets.

Genetics

Research suggests strongly that autoimmunity leading to autoimmune disease requires at least one, but likely more than one, gene (Sogkas et al., 2021) that interacts with environmental factors. The gene–environment relationship is complex, with environmental changes altering genes through epigenetic modifications that not only affect that individual but subsequent offspring (Cavalli and Heard, 2019; Surace and Hedrich, 2019). Epigenetic factors could contribute to early onset of some autoimmune diseases such juvenile idiopathic arthritis (Meyer et al., 2015). Individuals with one autoimmune disease are more likely to develop another autoimmune disease (Cojocaru et al., 2010), and autoimmune diseases, although often not the same autoimmune disease, typically cluster in families (Shamim and Miller, 2000). In addition, autoantibodies are more likely to develop in family members, whether symptomatic or not, of an individual with an autoimmune disease (James et al., 2019; Leslie et al., 2001). These findings, along with the observation that autoimmune diseases run in families, lead the research community to believe that common mechanisms increase autoimmunity in genetically susceptible individuals.

Human leukocyte antigen (HLA), or HLA haplotype, is the best available predictor of developing an autoimmune disease (Bacon et al., 1974; Cooper et al., 1999; Matzaraki et al., 2017) and is directly related to increased autoantibodies in family members (Wong and Wen, 2003), though it does not appear to be the only region of the genome where different autoimmune diseases have genes in common (Cho and Gregersen, 2011; Cooper et al., 1999). Many of the genes that confer susceptibility to autoimmune disease and that different autoimmune diseases share regulate the immune response (Sogkas et al., 2021). Some autoimmune diseases with different clinical characteristics, including which organs are affected, share genetic variants, indicating that these diseases may share some common pathways of immune dysfunction (see Figure 4-1 and Figure 4-2). This supports the suggestion that the specific organs targeted in individuals with similar genetic risk variants, such as in families with multiple affected individuals, may require an additional factor such as an environmental exposure for disease to progress. In addition to the 11 diseases of focus, Figure 4-1 and Figure 4-2 include systemic sclerosis, spondyloarthropathy, primary sclerosing cholangitis, and juvenile

idiopathic arthritis, further highlighting commonalities among autoimmune diseases.

Determining genetic risk for autoimmune disease is complex because many different genes may be associated with the risk for the disease. Polygenic risk scores are used to convert the complexity of the multiple genetic risk variants into a single quantified measure (NHGRI, 2020). Studies defining polygenic risk scores for specific autoimmune diseases have demonstrated potential utility in diagnosis, prognosis, and molecular subsetting of autoimmune diseases. Recent studies in systemic lupus erythematosus (SLE) found associations of increased organ damage and mortality in individuals with high genetic risk scores (Reid et al., 2020). Another study focused on genes in different immunological pathways in order to identify different molecular subsets of individuals with SLE based on pathway-specific genetic risk scores (Sandling et al., 2021). Genetic risk scores have been used in studies of other autoimmune diseases such as type 1 diabetes (Sharp et al., 2018), multiple sclerosis (de Mol et al., 2020; van Pelt et al., 2013), and systemic sclerosis (Bossini-Castillo et al., 2021). In inflammatory bowel disease (IBD), genetic risk scoring is less well-established but shows some promise (Abakkouy and Cleynen, 2021). Advantages of genetic risk scores include the ease of a standardized measurement and the absence of disease activity-based variability that can complicate measures of other disease-associated biomarkers (Sharp et al., 2018). Disadvantages include risk scores that represent relative risk rather than absolute risk, and that their applicability to the general population will require studying more diverse populations. Much of the available genomics data are based on individuals of European ancestry, which makes the relevance of these risk scores less clear for other populations (NHGRI, 2020). Moreover, the overlap in genetic risk between autoimmune diseases such as systemic sclerosis and SLE, Sjögren’s disease, or rheumatoid arthritis may preclude the ability to accurately distinguish the diseases based on genetic risk scores alone (Bossini-Castillo et al., 2021). Thus, additional studies are needed to overcome the limitations of polygenic risk scores before they can be used clinically, but such studies are warranted to help shift the diagnosis and treatment of autoimmune diseases to prediction and prevention.

Despite clear associations between genetic variants of immune-related genes and specific autoimmune diseases, a complete understanding of the specific effects of these variants in the pathogenesis of these diseases is lacking. The altered biological functions of the genetic variants and their roles in immune dysregulation remain gaps in our knowledge of the genotype–phenotype relationships of disease-associated genetic variants. An important focus of future research will be to systematically map the biologic function of genetic variants for the different autoimmune diseases. Defining the functions of variant-related immune pathways is

critical as this knowledge can inform clinical approaches to modulate the dysregulated or overactive immune pathways. The same pathway may or may not function the same way across diseases. For example, the pathogenic role of tumor necrosis factor (TNF) in rheumatoid arthritis, inflammatory bowel disease, and psoriasis led to targeting therapies to block TNF to treat these diseases (Jang et al., 2021), but TNF inhibitors have negative effects in the treatment of multiple sclerosis (Kemanetzoglou and Andreadou, 2017). In some individuals without multiple sclerosis, treatment with TNF inhibitors can cause multiple sclerosis-like demyelination (Kemanetzoglou and Andreadou, 2017). Despite effectively treating psoriasis in many individuals, the use of TNF inhibitors may also induce psoriasis (Mazloom et al., 2020). Similarly, genetic variants may have opposite effects in different autoimmune diseases and be associated with protective effects in some autoimmune diseases but with increased risk in others (Dendrou et al., 2016; Wang et al., 2010). Beyond associations of gene

variants with autoimmune diseases, altered expression of genes through epigenetic modifications may drive differential effects of immune-related genes (Mazzone et al., 2019).

Environmental Factors

Studies of the incidence of autoimmune disease in identical and fraternal twins show that autoimmune diseases may require an environmental signal or exposure to develop (Figure 4-3). Nationwide or population-based twin registry or cohort studies comparing the incidence of SLE (Ulff-Møller et al., 2018), type 1 diabetes (Nistico et al., 2012), multiple sclerosis (Willer et al., 2003), and Graves’ disease (Brix et al., 2001) find probandwise concordance rates of 25 to 45 percent in identical twins and much lower rates, ranging from 5 to 16 percent, in fraternal twins, implying strong genetic factors and the need for a “second hit”—presumably environmental exposure—for the development of autoimmune disease. Investigators found a lower concordance rate of 9.1 percent in identical twins in rheumatoid arthritis, similar to the 6.4 percent concordance rate seen in fraternal twins (Svendsen et al., 2013). In systemic sclerosis, identical and fraternal twins both had a 5 percent concordance rate (Feghali-Bostwick et al., 2003), suggesting more environmental and less genetic influence on disease development.

Because they depend on diagnosis definition and duration of observation, concordance rates may mislead. For example, in a restudy that applied new technology to examine twin concordance rates fifteen years

after an earlier study, the researchers showed that SLE markers had been present in the well twins of the earlier study (Reichlin et al., 1992), and thus the well twins had been serologically but not clinically concordant with their siblings. The restudy also showed that some well twins developed clinical illness, becoming clinically concordant, decades after their twin siblings were first diagnosed. These observations strongly support the “second-hit” hypothesis, which holds that a genetic profile requires an additional exposure event to become clinical illness. Examples of exposures associated with autoimmune diseases include infectious agents, pesticides, solvents, endocrine-disrupting chemicals, occupational exposure to respirable particulates and fibers, and personal factors such as cigarette-smoking history and diet preferences (Cooper et al., 2009; Cunningham, 2019; Diegel et al., 2018; Hahn et al., 2022; Khan and Wang, 2019; Parks et al., 1999; Popescu et al., 2021).

Infections

For some autoimmune diseases, such as rheumatic fever, myocarditis, multiple sclerosis, rheumatoid arthritis, and SLE, research has identified a causal link between bacterial or viral infection and the development of disease (Bjornevik et al., 2022; Cunningham, 2019; Fairweather et al., 2001, 2003; Fechtner et al., 2021; Jog et al., 2019; Lanz et al., 2022; McClain et al., 2005). For example, data have found that infection with or reactivation of Epstein-Barr virus precedes development of multiple sclerosis, rheumatoid arthritis, and SLE (Bjornevik et al., 2022; Fechtner et al., 2021; Jog et al., 2019; Lanz et al., 2022; McClain et al., 2005).

Additionally, research has found associations between type 1 diabetes and coxsackievirus and cytomegalovirus infections (Fairweather et al., 2001; Rodriguez-Calvo, 2019); multiple sclerosis and Epstein-Barr virus (Bjornevik et al., 2022) and measles infections (Mentis et al., 2017; Persson et al., 2014; Tucker and Andrew Paskauskas, 2008); and rheumatoid arthritis and mycobacteria and Epstein-Barr virus infections (Fairweather et al., 2012). However, most of the associations between infectious agents and autoimmune diseases are considered to be circumstantial. Linking an infection to an autoimmune disease is problematic because there is usually an extended period between the infection and the appearance of clinical autoimmune disease. The best evidence that infectious agents can cause autoimmune disease comes from animal studies in which researchers administered self-antigens with adjuvant incorporating non-infectious microbial antigens (Fairweather et al., 2001). A variation on the classic experimental autoimmune model uses an injection of heart- or salivary gland-derived cytomegalovirus or coxsackievirus B3 as the trigger to

induce myocarditis, which is associated with the development of autoantibodies against cardiac myosin (Fairweather et al., 2001).

Some of the best clinical evidence that viral infections can cause an autoimmune disease comes from the ability of SARS-CoV-2 to cause myocarditis, which occurs in approximately 4.5 percent of COVID-19 cases (Kawakami et al., 2021). SARS-CoV-2 has also been associated with the production of multiple autoantibodies and the development of autoimmune diseases besides myocarditis such as antiphospholipid syndrome, Guillain-Barré syndrome, and Kawasaki disease (Dotan et al., 2021). The production of autoantibodies after viral infections is a feature of animal models of virally induced autoimmune disease (Fairweather et al., 2001), further indicating this as an important area for future investigation. Multiple diverse microorganisms have been associated with a single autoimmune disease—for example, several different viral infections are associated with myocarditis—which suggests that infectious agents induce autoimmune disease through common mechanisms (Fairweather and Rose, 2006). Similarly, infection by one microorganism may induce different autoimmune diseases. Coxsackievirus is associated with type 1 diabetes (Rodriguez-Calvo, 2019), autoimmune thyroiditis (Hashimoto’s thyroiditis) (Mori and Yoshida, 2010), and myocarditis (Tam, 2006), and in children, SARS-CoV-2 infection is linked to myocarditis (Boehmer et al., 2021) and Kawasaki-like inflammatory disease (Dotan et al., 2021) as well as multisystem inflammatory syndrome (Consiglio et al., 2020). The inflammatory response to infection may play a greater role in causing autoimmunity than the specific infectious agent; the inflammation activates common pathogenic mechanisms that cause autoimmune disease. Inherent in this observation is that common innate immune factors such as Toll-like receptor 2 (TLR2) and/or TLR4 are able to initiate the autoimmune response to infectious agents (Mohammad Hosseini et al., 2015).

There are a number of theories for how infections or other environmental agents can trigger autoimmunity and autoimmune disease. These include (Cavalli and Heard, 2019; Fairweather and Rose, 2006; Surace and Hedrich, 2019):

• direct viral damage;
• release of cryptic self-peptides, which are antigens that are normally exposed to the immune system in such low amounts that the body has not developed self-tolerance¹ to them;

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¹ Self-tolerance refers to the immune system’s tolerance to the body’s own antigens (self-antigens). By this mechanism, the immune system prevents the production of functional B cells and T cells that react to self-antigens and prevents the body from directing an immune response against itself. Impairment of this mechanism leads to autoimmunity and production of autoantibodies against self-antigens (Hale et al., 2005).

• antigenic spread, which occurs when the immune system’s response to a microbial antigen expands to include similar self-antigens;
• molecular mimicry, which occurs when a microbial antigen closely resembles a self-antigen;
• epigenetics;
• bystander activation, which occurs when the immune system is activated in an antigen-independent manner and then produces a cytokine storm; and
• an adjuvant effect, which occurs when a microbial antigen or chemical adjuvant activates the innate immune response to self-tissue. Support for the latter comes from animal experiments in which researchers induce autoimmune disease using self-peptides and adjuvants; examples include inducing rheumatoid arthritis by administering collagen with an adjuvant, multiple sclerosis by inoculating with myelin basic protein with an adjuvant, and myocarditis by injecting cardiac myosin with an adjuvant.

Other hypotheses hold that co-infections with bacteria and viruses are necessary to induce autoimmune disease or that autoimmunity may occur commonly as a normal response needed to clear damaged self (Root-Bernstein and Fairweather, 2015).

Respiratory Exposures: Silica, Asbestos, and Tobacco Smoke

The research pertaining to respirable silica exposure and systemic autoimmune diseases—rheumatoid arthritis, SLE, systemic sclerosis, and various forms of small-vessel vasculitis—spans more than 100 years (Parks et al., 1999). A robust set of studies, with highly consistent findings from a variety of populations and settings and using different study designs, have demonstrated at least a 2- to 4-fold increased risk of developing each of those diseases associated with occupational exposure to silica (Figure 4-4). The figure’s inclusion of data on 2 of the 11 diseases of focus as well as systemic sclerosis and vasculitis highlights the risk silica exposure carries across autoimmune diseases.

Studies have also investigated immune-related effects of silica in the lung. Silica stimulates macrophages and also leads to their destruction, releasing intracellular self-antigens and stimulating proinflammatory cytokines, including interleukin 1 (IL-1) and TNF, and eventually activating the innate and adaptive immune response in the lungs and other tissues (Bates et al., 2015; Brown et al., 2003; Chauhan et al., 2021; Cooper et al., 2008; Foster et al., 2019). Studies of a breed of mice susceptible to SLE have examined the mechanisms by which inhaled silica induces both

development and exacerbation of SLE and activates immune responses outside the lung (Bates et al., 2015; Brown et al., 2003; Chauhan et al., 2021; Foster et al., 2019). Examining silica in combination with other exposures has also been revealing. A recent study using a breed of mice that is not prone to autoimmune disease found that an early-life exposure to lymphocytic choriomeningitis virus followed later by a single high-intensity airway exposure to silica in the adult mice produced lupus-related autoantibodies and kidney lesions (Gonzalez-Quintial et al., 2019).

Recent research has expanded the interest in respirable exposures to other particles and fibers. Several studies have examined systemic autoimmune diseases and autoantibodies in populations exposed to various forms of asbestos and asbestiform particles in Montana and in Australia, Italy, Korea, and Sweden (Carey et al., 2021; Diegel et al., 2018; Ilar et al., 2019; Lee et al., 2020; Noonan et al., 2006; Pfau et al., 2018; Rapisarda et al., 2018; Reid et al., 2018), and experimental studies have examined the effects of asbestos on macrophages, T cells, and cytokine profiles (Ferro et al., 2014; Khaliullin et al., 2020; Kumagai-Takei et al., 2020). An emerging area of research is exploring the effects on inflammation and autoimmune disease of a severe and sudden high-intensity mixture of respirable exposures, or the combination of these exposures with highly stressful conditions, such as experienced during the September 11, 2001, World Trade Center attacks and during natural disasters such as earthquakes and tsunamis. With respect to the respirable occupational exposures noted above, the question of relevant exposure level—the combination of intensity and duration needed to induce autoimmune disease—and whether current occupational exposure limits are sufficient to prevent adverse autoimmune disease, is a crucial question that further research needs to address.

Cigarette smoking is a well-studied risk factor for many forms of cancer, cardiovascular disease, and other conditions, and for some autoimmune diseases there are relatively strong data supporting an association, including evidence of a dose–response relationship between the intensity and duration of cigarette smoking and the likelihood of developing an autoimmune disease. This is seen, for example, with rheumatoid arthritis, particularly in people with rheumatoid factor or anti-citrullinated protein peptide antibodies (Costenbader and Karlson, 2006; Liu et al., 2019), and Graves’ disease, particularly in people with Graves’ ophthalmopathy (Vestergaard, 2002). Studies have identified weaker associations between current smoking and SLE and multiple sclerosis (Belbasis et al., 2015; Parisis et al., 2019). Somewhat paradoxically, smoking is associated with an increased risk and severity of Crohn’s disease, but with a reduced risk of developing ulcerative colitis (Piovani et al., 2021); smoking cessation has been shown to increase the risk of developing ulcerative colitis in the Western world (Ananthakrishnan et al., 2020). Studies in Western cohorts,

however, have consistently demonstrated that smoking increases the risk and severity of Crohn’s disease (Ananthakrishnan et al., 2020). Research has also found that smoking is associated with severity, progression, risk of flares, or treatment effectiveness in several other autoimmune diseases (Costenbader and Karlson, 2006; Hedström et al., 2021; Parisis et al., 2019; Simmons et al., 2021).

Other Occupational and Environmental Exposures

Studies have examined the relationship of exposure to the broad category of solvents and the development of several autoimmune diseases. In 2010, an expert panel concluded that evidence from epidemiologic studies provided “confidence that solvent exposure contributes to the development of systemic sclerosis (scleroderma),” while the panel considered a link between solvent exposure and multiple sclerosis to be “likely but requiring confirmation” (Miller et al., 2012; Parks et al., 2014). Regarding specific solvents, studies of trichloroethylene exposure in animal models found disease acceleration in lupus-prone mice, markers of inflammatory response, and the development of autoimmune hepatitis, inflammatory skin lesions, and alopecia. Human studies have produced similar findings in workers exposed to trichloroethylene (Cooper et al., 2009). There is a need for further research focusing on the effects of specific solvents in experimental and epidemiologic studies.

Endocrine-disrupting chemicals—bisphenol A (BPA), a variety of phthalates, polybrominated flame retardants, perchlorate, perfluorinated chemicals, polychlorinated biphenyls, dioxins, and some types of pesticides and herbicides, among others—are another broad category of compounds of particular relevance to the general population because of the potential for exposure through environmental contamination as well as through food, food packaging, and personal care and home products (Diamanti-Kandarakis et al., 2009). Exposure to endocrine-disrupting chemicals in utero also carries the potential to affect DNA methylation and genomic imprinting; this early-life exposure can carry risk for disease even in adulthood (Robles-Matos et al., 2021). Biomonitoring data from the National Health and Nutrition Examination Survey have revealed a high prevalence and widely varying levels of exposure to many of these compounds in the U.S. population (EPA, 2021). Endocrine-disrupting chemicals such as phenols (e.g., BPA), parabens, and phthalates have been shown to influence sex hormone activity and alter immune responses in animals and may influence sex differences in autoimmune diseases as has been found for viral myocarditis in mice (Bruno et al., 2019; Castro-Correia et al., 2018; Edwards et al., 2018; Popescu et al., 2021). Compounds within this category may exhibit estrogenic, antiandrogenic, or thyroid

hormone effects, including disruption to hormone binding, synthesis, secretion, transport and metabolism. There is a growing body of research pertaining to effects of specific compounds or classes of chemical compounds on immune function (Nowak et al., 2019), but there has been relatively little focused research to date within the context of autoimmunity or autoimmune diseases.

Dietary Exposures

As Chapter 3 described, autoimmune diseases share several diet-related risk factors (Table 4-1), suggesting common etiologies and mechanisms. Several of the shared dietary risk factors, including breast milk, gluten, vitamin D, omega-3 fatty acids, and sugars, play a significant role in shaping the gut microbiome (Hamilton-Williams et al., 2021), and researchers theorize that these dietary components may interact with the microbiome to stimulate a mucosal immune response. Alternatively, or in addition, dietary intake influences the inflammatory state of the body, which may also play a role in the development of autoimmune diseases. Although vitamin D is considered a vitamin, it is actually a steroid hormone that binds to the vitamin D receptor with downstream transcriptional regulation of many genes (Tintut and Demer, 2021), and similar to other steroid hormones, vitamin D strongly influences immune responses (Park and Han, 2021).

Research has also found that proinflammatory dietary factors, such as red meat and sugar-sweetened beverages, are associated with increased risk of rheumatoid arthritis, inflammatory bowel disease, and type 1 diabetes, whereas anti-inflammatory dietary factors, such as omega-3 fatty acids, have been associated with decreased risk of type 1 diabetes and rheumatoid arthritis. The recently published Vitamin D and Omega 3 Trial found that supplementation with vitamin D led to a 22 percent reduction in all incident autoimmune diseases, and supplementation with marine omega-3 fatty acids (fish oil) with or without vitamin D led to a (nonsignificant) 15 percent reduction in all incident autoimmune diseases during 5 years of randomized follow-up of more than 25,800 older adults from the general population (Hahn et al., 2022). The study was not adequately powered to examine the effect of supplementation on individual autoimmune diseases. The list of shared dietary risk factors in Table 4-1 was limited to those observed in prospective studies. It is likely that additional prospective investigations of diet in other autoimmune diseases would uncover more shared dietary risk factors, thereby providing clues into shared etiologies and mechanisms. Additionally, systematic collection of longitudinal data on dietary exposures would enable examination of their role in development, progression, and mitigation of autoimmune diseases.

TABLE 4-1 Shared Dietary Risk Associations Across Autoimmune Diseases

NOTES: Dietary factors listed in the left column are associated with autoimmune diseases listed across the top as indicated by a filled black box.

MS, multiple sclerosis; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; T1D, type 1 diabetes; UC, ulcerative colitis; 25OHD, 25-hydroxyvitamin D.

SOURCES: No breast feeding: T1D (Lund-Blix et al., 2017), UC and Crohn’s disease (Barclay et al., 2009; Klement et al., 2004; Ng et al., 2015). Lower omega-3 fatty acids: T1D (Norris et al., 2020), RA (Di Giuseppe et al., 2014; Gan et al., 2016, 2017a,b). Lower Vitamin D/25OHD: T1D (Miettinen et al., 2020), Crohn’s disease (Ananthakrishnan et al., 2012), SLE (Hassanalilou et al., 2018; Young et al., 2017), MS (Munger et al., 2006). Higher gluten: celiac disease (Andrén Aronsson et al., 2019), T1D (Norris et al., 2020). Sugar/sugar-sweetened beverage intake: T1D (Norris et al., 2020), UC (Racine et al., 2016), RA (Hu et al., 2014). Higher meat/animal protein: Crohn’s disease and UC (Hou et al., 2011; Jantchou et al., 2010), RA (Pattison et al., 2004).

The preceding discussion of environmental factors provides a foundation for considering potential approaches to preventing the occurrence of autoimmune diseases. Some approaches are based outside of the health care system such as advertising efforts to discourage smoking or work-site interventions to reduce levels of respirable silica exposure. Other approaches rely on health care provider interactions with individuals, including efforts to identify people at high(er) risk for developing autoimmune diseases; monitor biomarkers of disease development such as type 1 diabetes-linked antibodies; apply exposure interventions; and use pharmaceutical agents that research shows can reduce disease risk, as seen in ongoing studies for type 1 diabetes and rheumatoid arthritis.

Inflammation, Autoantibodies, and Mechanisms of Autoimmune Disease

The presence of autoantibodies is a common feature of autoimmune diseases, as is the presence of inflammatory cells such as mononuclear phagocytes, autoreactive T lymphocytes and autoantibody-producing B cells. Autoantibodies play a key role in disease diagnosis, and they may be pathogenic in some cases when they bind to self-tissues, activate the complement cascade, and instigate cell destruction and/or clearing cell debris (Burbelo et al., 2021; Daha et al., 2011). They can also interact with and alter the function of cell-surface receptors, as is the case in myasthenia

gravis and Graves’ disease (Chain et al., 2020; George et al., 2021; Kojima et al., 2021).

Immune system cells induced during inflammation can play a major pathogenic role in many autoimmune diseases, either by killing cells directly or by releasing cytotoxic cytokines, prostaglandins, or reactive nitrogen or oxygen intermediates. At the same time, other immune system cells, such as suppressor or regulatory T cells, function as a counterbalance in controlling inflammation and autoimmune responses by killing self-reactive T cells and releasing anti-inflammatory cytokines. Disrupting the regulation of self-reactive T cells and autoantibody production by regulatory T cells may result in the development of autoimmunity. Similarly, breaking tolerance against self-antigens may trigger the development of autoimmune disease (Abbas et al., 2004; Marx et al., 2021; Nelson, 2004). Self-tolerance develops through carefully regulated mechanisms, and defects in one or more of these mechanisms may lead to autoimmune disease development.

Inoculating animals with self-antigen in combination with adjuvants that activate the immune response enables investigators to produce animal models that recreate many of the features of human autoimmune diseases (see Chapter 3 for more information on animal models for specific autoimmune diseases). Injecting adjuvants without self-antigen or self-antigen without adjuvants does not usually result in the development of autoimmune disease. Thus, in the context of presentation of self-antigen that occurs presumably as a result of viral infection or chemical exposure, it is necessary to activate the innate immune system in order to develop the adaptive immune response (i.e., a T cell and B cell response) that progresses to autoimmune disease. Adjuvant-stimulated collagen-induced rheumatoid arthritis and cardiac myosin-induced myocarditis are examples of this type of animal model (Fontes et al., 2017). The self-antigens that are responsible for causing autoimmune disease have been identified for a number of autoimmune diseases including cardiac myosin for myocarditis (Myers et al., 2013), myelin basic protein or myelin oligodendrocyte glycoprotein for multiple sclerosis (Martinsen and Kursula, 2022; Yannakakis et al., 2016), and collagen for rheumatoid arthritis (Allen et al., 2019; Liang et al., 2019). However, the autoantigens associated with most autoimmune diseases remain unknown. Identifying new autoantigens is necessary in order to develop new animal models of disease, to better detect autoantibodies and autoreactive T cells, and to develop new and more effective therapies.

Increased production of the proinflammatory cytokines TNF and IL-1b are also involved in the development of some autoimmune diseases. Injecting strains of mice that are genetically susceptible to developing autoimmunity with either of these cytokines, or with certain adjuvants

(i.e., lipopolysaccharide that binds to TLR4 releasing IL-1β) that stimulate their production, can accelerate autoimmune disease. Repeating the same experiment with genetically resistant mouse strains can break self-tolerance and lead to autoimmunity (Lane et al., 1992, 1993). This suggests that environmental factors can overcome genetic resistance to developing autoimmune disease.

Disease Predictors

A number of biomarkers obtained from serum, tissues, or other sources have been used to predict the development of an autoimmune disease, to confirm diagnosis, or to predict the outcome of disease, such as autoantibodies, cytokines, microRNAs, and T cell–B cell interactions.

Autoantibodies are common in autoimmune diseases, and, while their role in the disease pathogenesis is not always clear, detecting them in clinical specimens may aid in diagnosing or, in some cases, predicting disease. In type 1 diabetes, perhaps the most studied autoimmune disease, autoantibodies that target intracellular autoantigens prior to the patient developing autoimmune pathology are detectable many years prior to the onset of clinical symptoms (Burbelo et al., 2021; Leslie et al., 2001). The risk of developing type 1 diabetes within 5 years increases from approximately 10 percent when a blood sample shows one autoantibody to 60 to 80 percent when it shows three autoantibodies (Notkins, 2004; Ziegler et al., 2013). In SLE, autoantibodies can be detected in blood samples taken years before the clinical features of the disease appear (Arbuckle et al., 2003), and researchers have proposed that detecting autoantibodies or other measures of inflammation may be predictors of disease (Lambers et al., 2021). Similarly, studies have detected autoantibodies up to 20 years before diagnosis of Sjögren’s disease (Theander et al., 2015), and years in advance of clinical manifestations or diagnosis of other autoimmune diseases, including rheumatoid arthritis, systemic sclerosis, celiac disease, thyroid disease, and others (Burbelo et al., 2021; Leslie et al., 2001).

The ability to reliably predict the development of autoimmune diseases is an opportunity to intervene to prevent or delay development of clinical manifestations of disease and to improve outcomes. New, exciting data are now available for many autoimmune diseases, some more advanced than others, and additional studies are needed to better define the complex components driving risk to developing disease.

In type 1 diabetes, research has included focusing on genetic risk factors, exposures in infancy or before birth, and development of autoantibodies in the first years of life (Anand et al., 2021; Bonifacio et al., 2021; Frederiksen et al., 2013; Jacobsen et al., 2015; Krischer et al., 2019; Ng et

al., 2022; Ziegler et al., 2013). Such studies have made predicting disease development reliable enough to justify preventive intervention in those at high risk. The Global Platform for the Prevention of Autoimmune Diabetes Primary Oral Insulin Trial is randomizing infants with a > 10 percent risk for developing multiple autoantibodies, which in turn predicts high risk for type 1 diabetes, to receive daily oral insulin or placebo. To determine whether the intervention prevents development of autoantibodies or progression to diabetes, investigators will follow the infants up to 7 years (Ziegler et al., 2019). Because the trial participants have not yet developed evidence of autoimmunity but are at risk for doing so, it is a primary prevention trial (Primavera et al., 2020). In a secondary prevention trial, nondiabetic relatives of individuals with type 1 diabetes who were at high risk for development of disease (defined as having two or more autoantibodies and dysglycemia but not overt diabetes) were randomized to a T cell-targeting therapy or placebo and monitored for the progression to clinical diagnosis of diabetes (Herold et al., 2019). Treatment delayed progression to diagnosis of diabetes. Given that treatment is not risk free, justification for its use requires highly reliable prediction of high-risk individuals.

Similar studies, although less advanced, are in progress to define predictive measures and preemptive interventions for rheumatoid arthritis (Deane and Holers, 2021). Genetic risk scores and certain autoantibodies inform the predictive power; documented dietary and infectious exposures can enhance prediction and provide targets for intervention (Primavera et al., 2020). In addition, recent data in the study of SLE indicate that antibody to Ro52 may predict which pre-diagnosis patients will progress to diagnosable disease (Munoz-Grajales et al., 2021).

Cytokine profiles can also be used as biomarkers. For example, the type I, II, and III interferon (IFN) cytokines are involved in SLE (Idborg and Oke, 2021), and most patients with SLE have an augmented expression of IFN-regulated genes, termed an “IFN gene signature,” with type I IFNs contributing most to the signature (Capecchi et al., 2020; Ramaswamy et al., 2021). Circulating levels of IFN-α correlate with disease activity and potentially could be used as a disease biomarker (Idborg and Oke, 2021). However, levels of IFN-γ have been shown to rise years before both type I IFN activation and clinical presentation of SLE. In addition, the patterns of cytokine increases vary with different clinical manifestations of SLE. Further research in this area will likely yield novel biomarkers.

Another potential biomarker is to use a combination of cytokines (soluble ST2, IL-6), immune cell responses (Th17), and micro RNAs to diagnose and/or predict worse outcome in myocarditis. Soluble ST2 is a cytokine in the TLR4/IL-1R family that has been found to be elevated in men and male mice with myocarditis and to be associated with worse

outcome (Coronado et al., 2019). The cytokines IL-1β and IL-6, necessary for the development of Th17-type immune responses, have been found to be elevated in men with myocarditis where Th17 responses were found to be associated with poor outcome, but only in men (Myers et al., 2016). In addition, a microRNA associated with Th17 cells was identified in viral and autoimmune animal models of myocarditis that enabled researchers to distinguish myocarditis from other forms of inflammatory heart disease (Blanco-Domínguez et al., 2021). These immune pathways (TLR4/IL-1β/IL-6/Th17) are common to many autoimmune diseases and indicate the importance of identifying common pathogenic mechanisms that may reveal new biomarkers and therapeutic strategies to prevent disease.

Many autoimmune diseases involve pathologic T cell–B cell interactions, and more specifically, implicate PD-1^hi CXCR5-T peripheral helper cells, which suggests that these T helper cells might be used as biomarkers of disease progression and as a target for therapy (Marks and Rao, 2022). Biomarkers also may be used to predict disease-activity changes such as flares of disease, as was recently reported using cytokines to identify renal and non-renal disease flares in individuals with SLE (Ruchakorn et al., 2019).

The most effective disease predictors will likely need to be combinations of biomarkers and/or algorithms rather than an individual biomarker.

Sex Differences

Most autoimmune diseases display a sex difference in incidence and severity of disease (Cooper and Stroehla, 2003). These differences may result from the differential effects of sex hormones; microchimerism, which is the presence of two genetically distinct cell populations in the same individual; specific genes on the X or Y chromosomes; X chromosome inactivation; and sex differences in the body’s response to environmental factors. Of these, researchers consider sex hormones to be a

major contributor to the sex differences in autoimmune diseases because of their essential role in the normal physiologic function of cells, tissues, and organs and their well-defined and extensive role in regulating the immune system in normal and pathological conditions. Sex steroid hormone receptors are expressed in or on the surface of immune cells (Buskiewicz et al., 2016), and those expressed on the surface in particular influence the immune response, as they likely contribute to immune signaling by innate and adaptive immune receptors in a sex-specific manner (Benten et al., 1999a,b; Schlegel et al., 1999).

Estrogen activates B cells, resulting in increased antibody and autoantibody responses to infection, vaccines, and autoantigens (Fischinger et al., 2019). In contrast, androgens such as testosterone and dehydroepiandrosterone decrease B cell maturation, reduce B cell synthesis of antibody, and suppress autoantibody production in humans with SLE, for example (Cook, 2008; Flanagan et al., 2017; Gubbels Bupp and Jorgensen, 2018; Regitz-Zagrosek et al., 2008). Estrogen’s ability to increase autoantibodies and T and B cell responses, which are adaptive immune system activities, could play a key role in the increased incidence and severity of many autoimmune diseases that occur more often in women than men, such as autoimmune thyroiditis, Sjögren’s disease, SLE, and systemic sclerosis (Cooper and Stroehla, 2003; Fairweather et al., 2012), while androgen’s ability to increase Toll-like receptors, mast cells, and macrophages, which are innate immune system activities, may drive autoimmune diseases in men (Fairweather et al., 2008; Gubbels Bupp and Jorgensen, 2018). However, while there is evidence that estrogen influences immune signaling pathways pivotal in autoimmune disease and that it affects antibody and autoantibody levels in autoimmune disease in different animal and tissue culture models with normal and diseased cells, the fact that estrogen levels rise substantially during pregnancy and that autoimmune disease activity does not increase during pregnancy, and in some diseases such as rheumatoid arthritis, decreases during pregnancy, suggests that more research is needed to understand the role of estrogen in autoimmune disease (Moulton, 2018). Additionally, other hormones that alter the immune response besides estrogen are released during pregnancy such as progesterone (Desai and Brinton, 2019; Piccinni et al., 2021; Ziegler et al., 2018), making it more complicated to understand the role of a specific hormones in the progression of autoimmune disease.

Environmental exposures can affect sex hormone activity. For example, endocrine disruptors such as BPA have been shown to alter sex hormone expression and ratio on/in immune and other cell types in a sex-specific manner (Böckers et al., 2020; Bruno et al., 2019), and there have been few studies on the role of endocrine-disrupting chemicals in autoimmune diseases (Bruno et al., 2019).

Sex hormones are not the only factors driving sex differences in autoimmune diseases. Other factors besides estrogen that could increase autoimmune disease in women include genes expressed on X chromosomes, as appears to be the case in SLE, for example (Umiker et al., 2014), and differential responses to stress, especially early in childhood (Dube et al., 2009). The global effect of sex hormones on immune cell phenotype and function that promotes systemic or organ-specific autoimmunity is a common mechanism that occurs among different autoimmune diseases in adults. The role of onset of autoimmunity prior to puberty also requires study.

Complications, Comorbidities, and Co-occurring or Overlapping Autoimmune Diseases

People with autoimmune diseases commonly have complications, which are “sequelae or predictable consequences of a particular diagnosis” (Iezzoni, 2013); complications are considered a direct result of the autoimmune disease or its treatment and increase the risk of developing another health problem. Examples of complications that adversely affect prognosis and treatment are cardiovascular disease in patients with diabetes and colon cancer in patients with ulcerative colitis. Patients with autoimmune diseases may also suffer comorbidities, which “are diseases with unrelated etiology” (Iezzoni, 2013) that do not share causes, symptoms, or laboratory abnormalities with the autoimmune disease. A patient may also develop a co-occurring autoimmune disease, which is one that does not share symptoms or laboratory test results with the coexisting autoimmune disease. And finally, patients with autoimmune disease may suffer from an overlapping autoimmune disease, defined as the simultaneous presence of clinical and laboratory features of multiple autoimmune diseases—for example, SLE and rheumatoid arthritis, commonly known as “rhupus” (Antonini et al., 2020).

Complications of Autoimmune Diseases

Complications of autoimmune diseases (Table 4-2) can be serious. Depression and anxiety are examples of complications that occur across a majority of autoimmune disorders. The occurrence of depression and/or anxiety in the broad range of autoimmune conditions may result from the psychological effects or from the social determinants of living with an autoimmune disorder (Martz et al., 2021), but research has not ruled out the possibility that the direct central nervous system effects of rheumatoid arthritis, SLE, and multiple sclerosis may play a role in inducing depression (Bai et al., 2016; Vallerand et al., 2018, 2019). Cardiovascular disorders, stroke, lymphoma, and renal failure complicate a number of autoimmune disorders. Sometimes complications are specific to the organ targeted by the autoimmune disorder. Hepatocellular carcinoma and portal hypertension, for example, result from cirrhosis, which occurs as a consequence of primary biliary cholangitis. It is critical for clinicians to be aware of the complications of the diagnosed autoimmune disease, to undertake appropriate screening for them, and to engage patients in self-management so that patients are knowledgeable about potential complications and successful preventive strategies, and thus will bring their symptoms to medical attention (Bodenheimer et al., 2002).

Co-occurring and Overlapping Autoimmune Disease

Table 4-3 shows the likelihood of increased risk for co-occurrence of a second autoimmune illness for the autoimmune diseases examined in Chapter 3. There is one exception: risk is decreased for co-occurring multiple sclerosis and rheumatoid arthritis (Somers et al., 2009). Study of the patterns of co-occurrence may inform knowledge of pathophysiology and mechanistic pathways for all autoimmune illnesses. For instance, both genetic susceptibility and environmental agents, including diet, likely play a role in the pathogenesis of autoimmune diseases, but how they affect clinical phenotype is unknown. As with complications, it is critical for clinicians and patients to be aware of the symptoms of a second autoimmune disease when the patient has a diagnosed autoimmune disease that has a higher association with one or more other autoimmune diseases.

TABLE 4-2 Complications and Comorbidities Exhibited by More Than One Autoimmune Disease

TABLE 4-3 Autoimmune Diseases That Commonly Co-occur

* Inverse association between rheumatoid arthritis and multiple sclerosis (Somers et al., 2009).

NOTES: Choose an index disease in the far-left column and read horizontally across the row to identify co-occurring diseases. AITD, autoimmune thyroid diseases; APS, antiphospholipid syndrome; IBD, inflammatory bowel disease; MS, multiple sclerosis; PBC, primary biliary cholangitis; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; T1D, type 1 diabetes.

SOURCE: Chapter 3.

Common Mechanistic Pathways as Therapeutic Targets

Despite the considerable variation in clinical symptoms or targeted organs among the different autoimmune diseases, the specific mechanisms of immune-mediated damage that leads to the different clinical pictures may share common immune pathways. As discussed above, cytokines are key mediators of immune cell effector functions, either in stimulating other immune cells to propagate disease or in directly affecting target organ cells to cause damage. The identification of specific proinflammatory signaling pathways and their cytokines as pathogenic in autoimmune diseases has resulted in targeted therapeutics that aim to block their effects. Identifying TNF as an inflammatory cytokine in synovial fluid from individuals with rheumatoid arthritis and then demonstrating that blocking TNF ameliorated disease in a mouse model of inflammatory arthritis led to the study of TNF inhibitors in the treatment of rheumatoid arthritis (Feldmann and Maini, 2010). The Food and Drug Administration (FDA) has approved TNF inhibitors for treating a number of different autoimmune diseases in adults and children including rheumatoid arthritis, juvenile idiopathic arthritis, plaque psoriasis and psoriatic arthritis, ankylosing spondylitis, inflammatory bowel disease (Crohn’s disease and ulcerative colitis), and uveitis (FDA, 2021). While TNF inhibitors have had a significant effect on the management of these autoimmune diseases for many individuals, they are not effective for everyone. Studies of those individuals who fail to respond adequately to currently available medications may help to elucidate additional cytokines or other immune pathways that can be targeted therapeutically.

As noted above, IL-1 is another cytokine that plays a pathogenic role in the inflammation associated with some autoimmune diseases. Clinical studies have shown that IL-1 inhibitors are effective and often life-saving in dampening the hyperinflammatory response in autoinflammatory diseases such as systemic juvenile idiopathic arthritis, monogenic autoinflammatory diseases such as cryopyrin-associated periodic syndromes, and cytokine storm syndromes including the hyperinflammatory response that may occur in children following SARS-CoV-2 infection (Jesus and Goldbach-Mansky, 2014; Malcova et al., 2021; Mehta et al., 2020; Tanner and Wahezi, 2020).

Type I interferons, a family of molecules that includes 17 different cytokines, have relevant roles in autoimmune diseases, as well as a key

role in anti-viral immune responses (Lazear et al., 2019). This latter role, along with the association of type I interferons with autoimmune diseases, may support the idea that viral infections participate in triggering autoimmunity. However, research has also suggested that dysregulation of the type I interferon system or stimulation of type I interferon production by endogenous triggers may be involved in the etiology of autoimmunity. One extreme example of the latter is the class of autoinflammatory diseases grouped as interferonopathies, which result from monogenic inborn errors of immunity that lead to upregulation of a type I interferon response (Sogkas et al., 2021).

Research has implicated type I interferons as playing a role in several non-monogenic autoimmune diseases based on measurements of interferons directly or, more commonly, with the detection of the interferon signature—a collection of genes that are upregulated in response to interferons. SLE is the prototypical type I interferon-associated autoimmune disease, but studies have also associated type I interferons with diseases such as Sjögren’s disease, idiopathic inflammatory myopathies, and systemic sclerosis (Jiang et al., 2020). Many of the disease risk-associated gene variants shared among autoimmune diseases are also associated with type I interferon-mediated signaling pathways (Figure 4-1). Additional support for a pathogenic role for type I interferons came from the development of autoimmune manifestations following treatment with type I interferons for cancer or viral hepatitis infections. These manifestations included effects on immune-mediated thrombocytopenia, vasculitis, autoimmune thyroid disease, autoimmune hepatitis, Raynaud phenomenon, rheumatoid arthritis, myositis, psoriasis, interstitial nephritis, autoimmune colitis, and SLE, and product inserts for type I interferon drugs noted them as potential adverse effects of treatment (FDA, 2021). Other autoimmune diseases, such as multiple sclerosis, may actually improve with type I interferon treatment, suggesting that the role of type I interferons in driving autoimmunity is not absolute (FDA, 2021).

The pathogenic role of type I interferons in multiple autoimmune diseases or monogenetic autoinflammatory diseases has led to developing treatments to target the type I interferon pathway; these include blocking type I interferons or the type I interferon receptor directly, or through small molecule inhibitors such as the Janus kinase (JAK) inhibitors that target key signaling components of the type I interferon signaling pathway. FDA has approved JAK inhibitors for the treatment of multiple autoimmune diseases including rheumatoid arthritis, juvenile idiopathic arthritis, psoriatic arthritis, and ulcerative colitis (Harrington et al., 2020; O’Shea, 2021; Troncone et al., 2020), and clinical trials are ongoing in other autoimmune diseases such as systemic sclerosis, Sjögren’s disease, SLE, idiopathic inflammatory myopathies, alopecia areata, vitiligo, psoriasis,

Crohn’s disease, spondyloarthritis/ankylosing spondylitis, and vasculitis (NIH, 2021). The JAK family comprises four different kinases (JAK1, JAK2, JAK3, tyrosine kinase 2) that may each be blocked somewhat selectively by different drugs. Different JAKs or combinations of JAKs are used by different cytokine receptors to mediate signaling, so JAK inhibitors may be used to block signaling through multiple different inflammatory cytokine receptors as appropriate for specific diseases. Additional drugs specifically targeting other inflammatory cytokines, including but not limited to IFN-g, IL-17, IL-23, and IL-12/23 shared subunit, are in use or in development for treating multiple different autoimmune diseases that share these specific inflammatory cytokines in the disease process (Baker and Isaacs, 2018).

Further studies to identify common immune pathways in different autoimmune diseases or subsets of different diseases will continue to provide insight into potentially targetable molecules that could lead to treatments for multiple different autoimmune diseases.

Animal Models

Animal models have played a critical role in defining common genetic and environmentally-induced mechanisms that drive the pathogenesis of autoimmune diseases as described in this chapter. Chapter 3 describes some of these animal models for the autoimmune diseases that are the focus of this report, but a detailed description of the many animal models investigators are using and the key findings and discoveries that studies using these models have produced is outside the scope of this report.

Existing animal models form a critical component of autoimmune disease research by providing mechanistic understanding of autoimmune diseases and preclinical data needed for translational research. For example, environmental causes of several human autoimmune diseases including celiac disease (Wu et al., 2021), rheumatic fever (de Loizaga and Beaton, 2021), and myocarditis (Boehmer et al., 2021) are known, and these

diseases could be used as models to accelerate identification of triggers and primary prevention for others. The ability to directly study effects of genetic modification, environmental effects, or potentially therapeutic immunomodulatory agents on the function of the immune system or the development of autoimmunity has been invaluable. Moreover, the ability to study immune cells and non-immune cells harvested directly from the target organs may provide a basis for subsequent studies in human tissues, which are often much less readily available or accessible. The committee wants to emphasize the importance of existing animal models that continue to provide valuable understanding of disease pathogenesis as well as the need to develop animal models for many autoimmune diseases where animal models do not currently exist and the need to continue to develop new animal models that answer specific and more translational research questions.

Future advances in tissue culture, such as 3D organoids, or organs-on-a-chip, which can model organs and systems such as the immune system (Park et al., 2020; Polini et al., 2019), as well as novel culture, animal, and other models, such as in silico confirmatory trials that use computer simulation to model virtual cohorts of patients (Pappalardo et al., 2019), may provide additional insight into autoimmune disease mechanisms or effective therapies.

MAXIMIZING ADVANCES IN TECHNOLOGY

Over the past decade, the availability of innovative technologies has completely changed the research environment and has come to dominate research on autoimmune diseases. These technological advances and the knowledge they have generated include the ability to perform more complex analyses of biological systems; knowledge about genomics and epigenomics; cytokine profiling; microbiomics, proteomics, metabolomics, and the study of changes in these “omics”; and generation of artificial intelligence. Fine-mapping genetic and epigenetic causal autoimmune disease variants, for example, enables researchers to identify clustering

among autoimmune diseases (Farh et al., 2015). As an example, a genetic region containing the gene for the enzyme tyrosine kinase 2 was identified in genome-wide association studies as linked to many autoimmune diseases, including psoriasis, SLE, and Crohn’s disease (Makin, 2021). A disease-associated gene variant modifies the enzyme, which plays a role in cytokine function. Therapy to block enzyme function greatly reduced skin lesions in psoriasis, and a phase III trial in psoriasis and early-stage trials in Crohn’s disease, SLE, and psoriatic arthritis are under way.

The ability to study gene expression profiles in immune cells at the single-cell level provides insights into the complex processes within heterogeneous tissue samples (Yofe et al., 2020). Moreover, combining the transcriptomics with proteomics on a single cell level (such as with flow or mass cytometry) can provide tremendous detail of complex subsets of immune and non-immune cells contributing to the inflammatory state within affected tissue. Analyses of synovial fluid from individuals with rheumatoid arthritis compared with non-autoimmune arthritis (osteoarthritis) combined single-cell RNA sequencing with mass cytometry and identified 18 unique cell populations—including different subsets of T cells, B cells, monocytes, and fibroblasts—and discovered the specific subsets expressing genes for key inflammatory cytokines IL-6 (expressed by fibroblasts) and IL-1b (expressed by pro-inflammatory monocytes) (Zhang et al., 2019). Another study utilizing mass cytometry coupled analyses from labial minor salivary gland biopsy samples with blood samples from individuals with Sjögren’s disease to define immunopheno-types of cells from the affected organs and the peripheral blood (Mingueneau et al., 2016). The study identified different disease clusters based on profiles of cells within the affected tissue and peripheral blood including associations with disease activity parameters and potential pathogenic cells to be targeted therapeutically. In another study, single cell sequencing to define T-cell receptor gene expression to identify clones of T cells expanded within affected tissue samples of individuals with Sjögren’s disease identified common T-cell receptor sequences within and between individual samples, strongly suggesting common antigen-driven immune response between different individuals (Joachims et al., 2016). The techno-logic advances afforded by single cell RNA sequencing provides a level of detail not possible through other measures to identify specific clonotypes of T cells in the absence of known antigens even from small samples such as cells isolated from biopsy specimens routinely obtained for diagnostic purposes. The analyses of such complex data sets are continually evolving, with recent study demonstrating the utility of latent factor modeling of single cell RNA sequencing data to define new pathways active in synovial tissues of individuals with rheumatoid arthritis and in the kidneys of individuals with SLE (Palla and Ferrero, 2020). Additional single

cell RNA sequencing studies have been performed in rheumatoid arthritis, SLE, systemic sclerosis, psoriatic arthritis, spondyloarthritis, Sjögren’s disease, Kawasaki disease, multiple sclerosis, and type 1 diabetes (Kuret et al., 2021; Zhao et al., 2021). A possible avenue is to use high-throughput screening of thousands of autoantigens in parallel rather than single autoantigens or panels of single autoantigens. The continued development of new technologies and complex data-analysis methodologies will continue to gain unprecedented detailed knowledge of the immunopathologic processes in autoimmune diseases.

Gene-editing techniques are available to study the effects of insertion/deletion/alteration of genes of interest on immune cell function and disease models (Pavlovic et al., 2020). On a public health level, the use of these omics are being incorporated into defining the exposome, which will describe the effects of lifetime environmental exposures on health and disease (DeBord et al., 2016; Manrai et al., 2017).

Given the complexities of autoimmunity on multiple levels, researchers are using multi-omics approaches to provide a more comprehensive understanding of the immune response and the interplay between different components (cells, cytokines, genes, metabolism) (Chu et al., 2021). Such multi-omics approaches have demonstrated that distinct molecular clusters characterize individual systemic autoimmune diseases despite common clinical manifestations and that such clusters are similar across different systemic autoimmune diseases despite differences in clinical presentation (Barturen et al., 2021). These advanced technologies are generating datasets of increasing size and complexity, requiring more complex analyses that include systems modeling and machine learning, which have also advanced during this period and are providing additional insight into the complexities of autoimmune diseases (Brown et al., 2018).

Artificial intelligence (AI) and machine learning, for example, can be used to search electronic health records. An examination of electronic health records of 167,109 patients with rheumatoid arthritis found that the risk for lower-tract gastrointestinal perforation associated with treatment with tocilizumab and tofacitinib to be more than twice that of anti-TNF (Xie et al., 2016). These tools can be used for precision medicine, a term that encompasses determining disease treatment for individuals; prospective public health approaches that target disease prevention; and precision public health approaches to classify patients into at-risk or disease subsets for interventions (Conrad et al., 2020). In addition, phenome-wide association studies match genetic variants to corresponding phenotypes in electronic health records to gain insight into outcomes and disease risk.

Regardless of the kind of data examined or the investigative purpose, the value of output depends on the rigor of the analytic methods used

to collect and interpret it. Big data analytics involves “integration of heterogeneous data, data quality control, analysis, modeling, interpretation and validation” (Ristevski and Chen, 2018), and the skill sets needed to fulfill these needs are essential to robust research. In addition, sound use of big data includes planning for optimum usability; in the case of electronic health records, for example, this would mean to share, clean, and archive data.

A multidisciplinary approach to longitudinal data collection in diverse population-based cohorts is needed in order to provide a strong foundation for integrated basic, translational, and clinical research necessary to address the complex and challenging questions relating to development, progression, and treatment of autoimmune diseases. Immunology, genetics, and clinical expertise in a variety of specialties including environmental health, epidemiology, and data science are among the disciplines needed to optimize the design, conduct, and analysis of this research. Existing and newly developed large data sets should apply rapid improvements in big data-analysis tools, including AI and machine learning, to advance knowledge in autoimmune diseases. For example, technical advances should be harnessed to increase information on sub-populations. Technologic tools could be used, among other things, to:

• examine genetic variation via genome-wide association studies;
• examine health disparities including the molecular underpinnings of differences in disease across race and ethnicity and how these might impact treatment;
• study environmental triggers for disease onset, disease flares, disease progression, and poor outcomes;
• elucidate immunophenotype immune cell pathways;
• identify metabolic pathways;
• decipher within and across disease-homogenous subsets; and
• compare persons with minimal disease or disease that has not yet manifested with those with severe disease or poor outcomes in order to expose key pathways to target in autoimmunity.

New and evolving technologies are supporting and advancing basic science, and translational research, to which the National Center for Advancing Translational Sciences is dedicated, is developing and using innovative technologies and methodologies to better search and interpret basic science findings in order to identify opportunities for enhancing diagnostics and therapeutics. The accruing knowledge will guide clinical research, from informing the types of samples collected and tests performed in observational studies to the design of intervention trials.

SUMMARY AND RESEARCH IMPLICATIONS

Characteristics that distinguish autoimmune diseases from each other and those that are common across autoimmune diseases provide valuable information about the pathogenesis of disease. These similarities and differences provide insights to better understand the epidemiology of disease, early predictors/biomarkers of disease, diagnosis, whether a disease is predicted to primarily affect an organ or have a systemic distribution, the occurrence of a flare, and the type of immune response that is important in driving disease, for example. The descriptions of common features across autoimmune diseases that this chapter provides lead to several research opportunities.

One implication of considering autoimmune diseases according to commonalities is that considering autoimmune diseases as a group according to common mechanisms of disease might provide novel insights beyond those gained by the more common approach that focuses on the affected organ or system. Examples of mechanistic themes that are common across autoimmune diseases that this chapter discusses include sex differences in incidence, severity, and type of immune response and the role of type I interferons in increasing autoimmune pathology (Barrat et al., 2019; Psarras et al., 2017). The current tendency of many researchers to focus on the organ or system may stem from the National Institutes of Health’s (NIH’s) organization of study sections based on that scheme. Animal models that better represent the clinical pathology and course of disease would enable studies that explore these common mechanisms. As a personalized medicine approach to health care (i.e., genomics/pharmacogenomics) becomes more common in medical practice, it becomes increasingly important to understand common mechanisms of autoimmune disease given that autoimmune disease may influence other coexisting inflammatory illnesses that are increasingly being considered as autoinflammatory diseases, including cardiovascular diseases such as

atherosclerosis. In addition, it is common for a patient with one autoimmune disease to have another autoimmune disease.

Many of the genes that confer susceptibility to autoimmune disease and that different autoimmune diseases share regulate the immune response, and some autoimmune diseases with very different clinical characteristics share genetic variants (Sogkas et al., 2021) (see Figure 4-1 and Figure 4-2). Fine-mapping genetic and epigenetic causal autoimmune disease variants can detect clustering among autoimmune diseases (Farh et al., 2015). Studying the function of genetic variant-related immune pathways will be critical to revealing the genotype–phenotype relationships that could provide targets for therapies. Polygenic risk scores are useful, but still have limitations, one being that the genomics data they are based on would likely need to include more diverse populations in order to be more broadly relevant.

The number and type of autoantibodies predict progression to autoimmune disease, but research is lacking in this area across autoimmune diseases. Insight can be gained by better characterizing autoantibodies that exist in many autoimmune diseases as well as those that are distinct for a particular disease and using those similarities and differences to better diagnose disease, predict the risk of developing an autoimmune disease, and predict the progression of disease. Cytokine profiles and microRNAs, for example, have demonstrated similar opportunities (Blanco-Domínguez et al., 2021; Idborg and Oke, 2021), and research on these and other novel biomarkers is called for.

For some autoimmune diseases, Black, Hispanic, and Indigenous people have an increased risk and greater severity of disease (Izmirly et al., 2021), while for other diseases, the highest risk occurs among White populations (Dabelea et al., 2014). Research pertaining to racial and ethnic patterns needs to fully address the spectrum of experiences represented by these findings, including living and work environments, social and economic factors, and a wide variety of stressors. To date, few studies focusing on race and ethnicity have used this lens.

Although sex differences in autoimmune diseases have been known and studied for some time, large gaps in defining sex and age differences and similarities remain. The field would benefit from research on sex differences in epidemiology, risk-factor identification and assessment, diagnostic criteria, coexisting illnesses, outcomes, and other topics. The committee emphasizes that sex differences need to be examined within the context of age because of the significant changes in sex hormone levels that occur across the lifespan in females and males. There is a gap in research across the lifespan for most autoimmune diseases.

An understanding of potential environmental causes of autoimmune diseases that is based on a systematic study of exposures including

dietary factors over time is needed to devise effective approaches to prevent and/or treat disease, including disease flares. Gene and environment interactions are also understudied. Twin concordance studies indicate the importance of both factors in contributing to autoimmune disease (Brix et al., 2001; Nistico et al., 2012; Ulff-Møller et al., 2018; Willer et al., 2003) research to better understand this relationship is needed. Current research indicates that environmental factors drive disease in individuals with genetic susceptibility (Bjornevik et al., 2022; Mehri et al., 2020; Robles-Matos et al., 2021; Vestergaard, 2002), yet the mechanisms behind the relationship remain unclear. It will be important to establish study sections that support research that examines interactions between genes and multiple environmental factors such as infections and chemicals. Currently an unintentional and artificial barrier exists, with grants for environmental topics going to the National Institute of Environmental Health Sciences, for example, and grants for infectious diseases going to the National Institute of Allergy and Infectious Diseases. This type of research may be important for efforts to develop effective intervention strategies to prevent development of disease in people who are at higher risk for developing disease.

Based on the known sex differences in disease for most autoimmune diseases and the well-described effect of sex hormones on immune cells and antibodies (Cook, 2008; Fischinger et al., 2019; Flanagan et al., 2017; Gubbels Bupp and Jorgensen, 2018; Regitz-Zagrosek et al., 2008), an area that lacks clinical and basic research studies includes the effect of exogenous hormone endocrine disruptors such as BPA and bisphenol S, which replaced BPA in plastic drinking bottles, on autoimmune disease. Also needed are studies on other hormone influencing factors such as those in diet—soy, for example—and medications such as birth control pills.

That research has linked several different infectious agents and chemicals to one autoimmune disease and one infectious agent to several different diseases suggests that common innate immune mechanisms are critical in driving disease. More research is needed to better characterize these common pathways across multiple autoimmune diseases clinically and through basic research. A better understanding of the mechanisms that occur commonly in many autoimmune diseases could fundamentally improve understanding of the pathogenesis of autoimmune disease and benefit patients with these conditions. Current technologies and methodologies such as multi-omics approaches, have already contributed greatly to this end, and continued development of innovative tools can support and drive further advances.

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