Introduction

Autoimmune diseases (ADs) are chronic conditions initiated by the loss of immunological tolerance to self-antigens and represent a heterogeneous group of disorders that affect specific target organs or multiple organ systems. The chronic nature of these diseases places a significant burden on the utilization of medical care, direct and indirect economic costs, and quality of life.

The mosaic of autoimmunity describes the multi-factorial origin and diversity of AD expression (1). This term implies that different combinations of many factors involved in autoimmunity produce several distinct clinical presentations that represent the wide spectrum of AD. The term “kaleidoscope of autoimmunity” portrays the possible change from one disease to another or the fact that more than one disease may coexist in the same individual or family (2). The fact that ADs share several clinical signs and symptoms (i.e. subphenotypes), physiopathological mechanisms, and genetic factors has been called the autoimmune tautology and indicates that they have several common mechanisms (3-7) (Table 1). Tautology (from Greek tauto, “the same” and logos, “word/idea”) is an obvious statement. In logic, tautology is a formula, which is true in every possible interpretation. Thus, autoimmune tautology means that one AD is similar to a second one, to a third one, and so on. ADs cannot be equal because the target cell and organ are different in each case, and the trigger factors and age at onset vary among ADs. Ten shared characteristics supporting this logically valid propositional theory are discussed below (Table 1).

Table 1

Common mechanisms of autoimmune diseases (the autoimmune tautology).

Women are more prone to autoimmunity

Almost 5% of the world population develops an AD (8). Of this 5%, approximately 80% are women (9), and it is considered the fourth leading cause of disability for them (10). The more frequent the AD and the later it appears, the more women are affected. Women tend to have a different age at onset and different disease activity than men. Also, female gender appears to be a risk factor for polyautoimmunity (11). The most convincing explanation of female biased autoimmunity remains the hormonal theory followed by genetic factors (12).

Shared subphenotypes

Subphenotypes are shared by ADs including signs and symptoms such as arthralgia, arthritis, alopecia, fatigue, photosensitivity, Raynaud’s phenomenon as are non-specific autoantibodies (e.g., antinuclear antibodies, rheumatoid factor, anti-Ro antibodies) and high levels of cytokines (e.g., TNF, IL-1, IL-6, IL-10, IL-17, etc.) which raises taxonomic concerns. ADs have a heterogeneous spectrum such that disease courses differ from patient to patient and, in addition, the disease goes through different phases within the same patient. Depending on the duration and activity of the disease, these subphenotypes might change. Mathematical approaches for precisely defining subphenotypes based on accurate clinical and immunological databases (13) combined with strengthening molecular genetic analyses have significant promise for a better understanding of ADs.

Polyautoimmunity

Polyautoimmunity is defined as the presence of more than one AD in a single patient. When three or more ADs coexist, this condition is called multiple autoimmune syndrome (14,15). Polyautoimmunity represents the effect of a single genotype on diverse phenotypes. Polyautoimmunity was observed in 34.4% of 1,083 patients belonging to four AD cohorts (11) with autoimmune thyroid disease and SS being the most frequent diseases encountered (11). Factors significantly associated with polyautoimmunity are female gender and familial autoimmunity (11).

Polyautoimmunity has been reported in most of the ADs including systemic lupus erythematosus (SLE) (41%) (16), systemic sclerosis (SSc) (26%) (17), primary biliary cirrhosis (32%) (18), vitiligo (27%) (19), myasthenia gravis (13%) (20), autoimmune thyroid disease (14%) (21), etc. Sardu et al. (8) confirmed a 5% prevalence of ADs in Sardinia. Among people with ADs, 95.6% were affected by one AD while the remaining 4.4% were affected by two ADs (8).

The main difference between polyautoimmunity and the overlapping syndromes lies in the fact that the former is the presence of two or more well-defined autoimmune conditions fulfilling validated classification criteria while the later is the partial presence of signs and symptoms of diverse ADs. Most of the cases of overlapping syndromes have been described in cross-sectional studies. As has been shown, there is a lag in the time interval between the first and the second AD. Just as in the mixed connective tissue disease (MCTD), the classical overlapping syndrome, in which some patients will develop SLE, SSc, or RA during the course of the disease, and some will presenting with a longstanding MCTD - incomplete sentence lacking main verb and the overlapping syndrome seems to have gotten lost! (22). In fact, long-term studies have shown that MCTD remains an overlapping syndrome in about 60% of the patients. The remaining 40% progress towards SSc (~20%), SLE (~10%), or rheumatoid arthritis (RA) (~5%) (23).For Iaccarino et al (24) polyautoimmunity is considered an “overlap syndrome” confined to “connective tissue diseases” [e.g., SLE, RA, SSc, polymyositis/dermatomyositis (PDM), and Sjögren’s syndrome (SS)]. These authors reduce the spectrum of polyautoimmunity to just the rheumatic diseases and omitted several other systemic and organ specific ADs that are also associated with each other and observed in clusters (Figure 1) (11). However, they highlight that, in some cases, polyautoimmunity may be related to a specific autoantibody which supports the hypothesis that these syndromes are not a mere association of two or more ADs in the same patient, but a well-defined clinical entity with specific clinical characteristics (24). Two of such cases are: 1) Anti-t-RNA synthetase syndrome, characterized by the clinical features of SSc, RA, and myositis and the presence of antibodies against aminoacyl-t-RNA synthetase; and 2) Scleromyositis, characterized by features of both SSc and PDM and the presence of anti-PM–Scl antibodies (24).

Figure 1

Cluster analysis dendogram of autoimmune diseases (ADs). Each node represents a stage in the clustering process. There were four clusters. The most hierarchical was composed of four ADs. AITD: autoimmune thyroid disease (including thyroiditis, Hashimoto (more...)

Familial aggregation

A primary characteristic of complex diseases is that affected individuals tend to cluster in families (familial aggregation, also referred to as recurrence risk or λ). The aggregation of a phenotype is observed when a disease occurs at a higher frequency in the relatives of an affected individual when compared to what is seen in the general population (14). Familial autoimmunity is defined as the presence of diverse ADs in multiple members of a nuclear family. Unlike familial autoimmune disease, which corresponds to the presence of one specific autoimmune disease in various members of a nuclear family, familial autoimmunity uses the term “autoimmune disease” as a trait that encompasses all accepted pathologies for which evidence suggests an autoimmune origin. Familial autoimmunity is more frequent than familial autoimmune disease (14,25) and represents the best model for the study of major autoimmunity genes (Figure 2).

Figure 2

How do autoimmune diseases cluster in families? A) Familial autoimmune disease. This classical concept indicates the same AD in diverse FDRs. In this case, a proband and a FDR (that is, the father) present with T1D. B) Familial autoimmunity. This new (more...)

Age of onset influences severity

The age at onset refers to the time period in which an individual experiences the first symptoms of a disease. In ADs, these symptoms can be subtle but are very relevant for diagnosis. They can appear during childhood, adulthood, or late in life and may vary depending on the age at onset. Early age at onset is the worst prognostic factor for some ADs such as SLE and Type 1 Diabetes Mellitus. For others, either it does not have a significant influence on the course of disease such as in the case of SS, or no unanimous consensus exists, e.g., RA and multiple sclerosis (26). It is noteworthy that late-onset traits are more sensitive to environmental variation than genetic influence.

Similar pathophysiology

Damage induced by T cells or B cells, or both, play a major pathogenic role in ADs. Similar immunopathological mechanisms lead to ADs (Figure 3). The autoimmune phenotype varies depending on the target cell and the affected organ. The predominant infiltrating cells include phagocytic macrophages, neutrophils, self-reactive CD4⁺ T helper cells, and self-reactive CD8⁺ cytolytic T cells along with smaller numbers of natural killer cells, mast cells, and dendritic cells. Among the T effector cells–Th1, Th17, and Th9 cells–contribute to pathogenesis of ADs (27).

Figure 3

The fourth-stage-model for the pathophysiology of autoimmune diseases (ADs) (the autoimmune tautology). Outline showing the plausible stages for a multifactorial etiology to develop over time. Each stage shows the known phenomena that, when it accumulates, (more...)

Immune cells damage tissues directly by killing cells or, indirectly, by releasing cytotoxic cytokines, prostaglandins, reactive nitrogen, or oxygen intermediates. Tissue macrophages and monocytes can act as antigen-presenting cells to initiate an autoimmune response or, as effector cells once an immune response has been initiated. Macrophages act as killer cells through antibody-dependent, cell-mediated cytotoxicity and by secreting Th1 cytokines, which act as protein signals between cells. Macrophages and neutrophils damage tissues (and microorganisms) by releasing highly cytotoxic proteins, e.g., nitric oxide and hydrogen peroxide. Cytokines and other mediators released by macrophages recruit other inflammatory cells such as neutrophils and T cells to the site of inflammation (28).

Regulatory T cell populations, including activated CD25+CD4+ regulatory T cells, exist in peripheral tissues and are important in controlling inflammation and autoimmune responses by killing autoreactive cells. These regulatory cells also secrete anti-inflammatory cytokines that further inhibit Th1 and Th17 immune responses, thereby reducing inflammation and autoimmune disease. If regulation of self-reactive T-cells and autoantibody production by regulatory T-cell populations is disrupted by environmental agents, e.g., infections or toxins, then AD may result (28).

Defects in tolerance leading to AD occur in one or multiple tolerance mechanisms (Figure 3). For example, changes in the apoptotic cell death process, which result in inappropriate cell death or survival or disturbances in clearing apoptotic cells, are thought to be involved in the pathogenesis of a number of ADs (28). An additional mechanism common to several ADs is the activation of the type I interferon system (29).

Autoantibodies appear long before clinical symptoms thus providing a good predictive marker for the potential to develop a disease. In fact, the risk of developing an AD goes from about 10% if one autoantibody is present to around 60–80% if three autoantibodies are present for a particular AD (30).

Similar environmental agents (The autoimmune ecology)

Ecology (from Greek: oikos, “house”; -logia, “study of”) is the scientific study of interactions between organisms and their environment. Therefore, the autoimmune ecology corresponds to the effects and relationships between all the environmental factors that may influence the risk and course of ADs (Table 2). Several environmental factors are common risk factors for ADs. Infectious agents are important in the pathogenesis of ADs since they are a major part of the environmental autoimmunity trigger. Although a latitudinal gradient of infectious agents exists (31), Epstein-Barr virus and cytomegalovirus are notorious as they are consistently associated with multiple ADs (32). On the other hand, some infections could be protective against AD development (33) (see Chapter 19). Smoking has also been consistently associated with several ADs including RA and SLE (34). This underlines the importance of smoking prevention and eradication not only in respiratory disorders but also in autoimmune conditions as well. Organic solvents have also shown to increase the risk of acquiring ADs (35).

Table 2

Factors involved into the autoimmune ecology.

Influence of ancestry

Genetic ancestry contributes to the clinical heterogeneity and variation in disease outcomes among AD patients (36,37). Amerindian ancestry has been associated with an increased number of risk alleles for SLE (38). Genetic studies of AD subphenotypes will need to carefully address issues of population substructure based on genetic ancestry (36). In addition, latitudinal gradients in allele frequencies due to ancestry may influence the observed effect of genotype on phenotype across populations (39).

Common genetic factors

The impact of genetic predisposition on susceptibility to ADs was first identified by the analysis of disease concordance rates between monozygotic twins (concordance rates ranged from about 15% to 57%) (40). The decrease in the concordance rates of siblings compared to the rate for monozygotic twins supports the presence of multiple genes contributing to the autoimmune phenotype onset. Several reports have confirmed that autoimmune phenotypes represent pleiotropic outcomes of nonspecific disease genes (3,41). Epistasis and pleiotropy are crucial in the understanding of the common genetic pathways of complex traits including ADs (see Box 1).

Box 1

Epistasis and Pleiotropy.

However, not all ADs share the same genetic susceptibility. Therefore, the genetic risk factors for ADs consist of two forms: those common to many ADs and those specific to a given disorder. Combinations of common and disease-specific alleles in HLA and non-HLA genes in interaction with epigenetic and environmental factors over time will determine the final clinical autoimmune phenotype (3). Yet only around 10% to 15% of the inherited risk for ADs can be explained at present (46). Most of the common variants, individually or in combination, confer relatively small increments in risk (1.1- to 1.5-fold) and explain only a small proportion of heritability (i.e., the proportion of phenotypic variation in a population that is attributable to genetic variation among individuals). The amount of heritability depends on the population under study because variations in both additive and non-additive genetic factors and the environmental variance are specific to the population (47). As a corollary, genetic results should be confirmed in different populations.

Commonalities among ADs should be investigated based on real clinical associations (16) rather than arbitrary reviews (Figure 1). An inaccurate conclusion about a limited genetic overlap between SLE and 16 ADs was drawn because most ADs included in the analysis were either not ADs (i.e. ankylosing spondylitis) or were rarely associated with SLE (i.e. Kawasaki disease, sarcoidosis, Behcet’s Disease, etc) (48). Some pitfalls and challenges of AD analysis as complex traits are summarized in Table 3.

Table 3

Pitfalls and challenges of complex trait analysis.

Similar treatment

What we have learned about the etiopathogenesis of ADs, which supports the view that common features of different clinical entities outnumber their differences, makes it possible to use similar treatments for various ADs despite specific variations and regimen tailoring (50). Similar biological and non biological therapies are used to treat diverse ADs.

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