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Friday 19 July 2019
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Expert analysis: Challenges in diagnosing connective tissue diseases


Connective tissue diseases (CTD) are a group of disorders involving the protein-rich tissue that supports organs and other parts of the body. However, more commonly the acronym CTD identifies systemic autoimmune rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and SLE-like diseases, Sjogren syndrome (SS), scleroderma (SSc), inflammatory autoimmune myopathies (AM), systemic autoimmune vasculitides (AV), mixed or undifferentiated connective tissue disease (MCTD/UCTD), and anti-phospholipid syndrome (APS).

All these disorders are also defined as systemic autoimmune rheumatic diseases (SARD) and are characterised by an autoimmune response against self-antigens that ends in chronic inflammation and irreversible tissue damage if untreated, with high direct and indirect costs for the social/health systems. SARD can affect several organs and tissues of the body; as a consequence, different specialists may see these patients but ultimately, rheumatologists or clinical immunologists are the main physicians providing care for SARD.

Classification and diagnosis 

The classification and the diagnosis of SARD are based on clinical signs and symptoms, however laboratory and instrumental parameters are also required.1 These tools improved our classification/diagnostic power; this is the case, for example, for RA with the new anti-citrullinated peptide antibodies and joint magnetic resonance and/or ultrasound Doppler evaluation.2 In addition, the histology or immune-phenotyping of the damaged tissues help in diagnosis but, unfortunately, are not always feasible or specific because of overlapping pathogenic pathways.3

Although each disease displays peculiar clinical pictures, some manifestations are not specific at all (such as thrombosis in APS) or frequently overlapping among different diseases (for example, inflammatory arthritis). Moreover, the clinical manifestations are non-specific particularly at the beginning of the clinical history making difficult the correct diagnosis. Consequently, biological biomarkers play a crucial role in directing physicians towards the right diagnosis. In particular, autoantibodies are offering the most reliable tools nowadays (Table 1). Additional tools, such as cytokines or genetic biomarkers (including non-coding RNA/DNA) are quite promising but not useful in practice at the moment.

Table 1. Autoantibodies in SARD



Rheumatoid factor (RF)

Anti-citrullinated protein antibodies (ACPA)

Rheumatoid arthritis

Anti-nuclear antibodies (ANA)

Anti-double stranded DNA antibodies (anti-dsDNA)

Anti-Sm antibodies (anti-Sm)

Lupus anticoagulant (LA)

Anti-cardiolipin antibodies (anti-CL)


Systemic lupus erythematosus


Anti-topoisomerase I

Anti-centromere A,B,C (anti-CENP A,B,C)

Anti-RNA polymerase III

Systemic sclerosis





Sjogren’s syndrome

Anti-U1 ribonucleoprotein (anti-U1 RNP)

Mixed connective tissue disease


Undifferentiated connective tissue disease



Anti-beta2-glycoprotein I antibodies (anti-b2GPI)

Antiphospholipid syndrome


Proteinase 3 (PR3)

Myeloperoxidase (MPO)

ANCA-associated vasculitides



Autoantibodies represent well-accepted classification criteria and, in some cases, they are also entry classification criteria; the most recent examples are represented by anti-phospholipid antibodies (aPL) for APS and anti-nuclear antibodies detectable by indirect immunofluorescence or equivalent solid phase assays for SLE.4,5 From this point of view, they represent mandatory inclusion criteria for clinical trials.

Autoantibodies in diagnosis                    

SARD are chronically evolving disorders characterised by the production of autoantibodies, even years before the appearance of the clinical manifestations. Because of the non-specific clinical symptoms at the beginning of the SARD, the detection of autoantibodies is a useful diagnostic tool. By contrast, early diagnosis is mandatory in order to start treatment before irreversible tissue damage takes place. This approach results in a better prognosis (that is, less organ damage) and in the reduction of the costs of the disease. The most common approach is the use of screening assays (for example, ANA detection) for supporting the suspect of the autoimmune origin of the disease and then the request for autoantibody profiles for identifying autoantibodies associated to SLE-, SSc-, SS- or AM-like disorders6 (Table 2).

Table 2. Autoantibodies and their associations with clinical manifestations in SARD


Disease association

Clinical manifestations

anti-topoisomerase I


ILD, digital ulcers, diffuse skin involvement, heart involvement

anti-CENP A, B, C


PAH, bowel involvement, digital ulcers, limited skin involvement

anti-RNA polymerase III


SRC, tendon friction rubs, severe diffuse skin involvement

anti-U3 RNP


PAH, myositis, heart involvement, diffuse skin involvement

anti-U1 RNP

SSc, SSc/PM overlap, SLE, MCTD

Myositis, PAH, arthritis, limited skin involvement


PM/DM, SSc/myositis overlap, SSc

Myositis, limited skin involvement



PAH, ILD, bowel involvement, limited skin involvement





PBC/lcSSc and PBC/SS overlap

Cholestatic liver disease


SLE, SSc, SLE/PM/SSc and PM/SSc overlap

Myositis, arthritis



Nephritis, disease activity



Haematologic disorder, photosensitivity, neonatal lupus, C2 deficiency

anti-aminoacyl-tRNA synthetases

(anti-Jo1, -PL7,

-PL12, -EJ,  -OJ,

-KS, -Zo, -YRS)


Myositis, ILD, mechanic’s hands

anti-ribosomal P protein


Neuropsychiatric manifestations



Drug-induced lupus



Drug-induced lupus, nephritis, disease activity



Active renal disease

anti-citrullinated proteins


Severe disease

LA, anti-CL, anti-b2GPI


Thrombosis, miscarriages

ILD, interstitial lung disease; SRC, scleroderma renal crisis; PAH, pulmonary arterial hypertension; PM, polymyositis; DM, dermatomyositis; lcSSc, limited cutaneous systemic sclerosis; SCLE, sub-acute cutaneous lupus erythematosus

Autoantibodies are also widely used for disease subgrouping. For example, SSc patients sub grouped according to the presence of anti-topoisomerase or anti-centromere autoantibodies display different clinical picture and evolution.7,8 The same is also the case for the autoantibodies detectable in a different AM.9

The presence of different autoantibody profiles is useful for risk stratification in some diseases. The best example is represented by APS, in which the number of tests positive for aPL (that is, lupus anticoagulant, anti-cardiolipin, anti-b2 glycoprotein) or the aPL titres are risk factors. In other words, the higher the number of positive tests (triple or double versus single positivity) or the antibody titres, the higher the risk for thrombosis or miscarriages.10

SARD subgrouping and risk profiling allow a better disease characterisation and ultimately the best treatment approach according to precision (or personalised) medicine.

In addition to their defined role in the diagnosis of AID, a number of autoantibodies represent biomarkers for damage or involvement of particular tissues or organs, making them important in defining relevant comorbidities. This is the case for anti-C1q antibodies closely associated with active renal disease in SLE11 (Table 2).

The presence of an autoantibody might be the determining factor for starting primary prophylactic therapy, even in the absence of overt clinical signs, in order to reduce the risk of the clinical manifestations. Anti-phospholipid antibodies are the most popular example, justifying antiplatelet therapy even in asymptomatic aPL-positive carriers or low-dose aspirin and heparin in pregnant women at high risk for miscarriages.10


Nowadays, autoantibody detection is performed by automated assays in high-throughput routine service laboratories. The availability of the new assays has uncovered many problems, including quality control, quality assurance, standardisation, analytical sensitivity/specificity, within and between laboratory reproducibility and clinical sensitivity. These aspects are all relevant to the overall clinical interpretation of the tests and several international standardisation initiatives are currently ongoing.12,13


  1. Meroni PL et al. Standardization of autoantibody testing: a paradigm for serology in rheumatic diseases. Nat Rev Rheumatol 2014;10(1):35–43.
  2. Aletaha D et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 2010;62(9):2569–81.
  3.  Barturen G et al. Moving towards a molecular taxonomy of autoimmune rheumatic diseases. Nat Rev Rheumatol 2018;14(3):180.
  4. Miyakis S et al. International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS). J Thromb Haemost 2006;4(2):295–306.
  5. Aringer M et al. Classification criteria for systemic lupus erythematosus (in press).
  6. Meroni PL, Borghi MO. Diagnostic laboratory tests for systemic autoimmune rheumatic diseases: unmet needs towards harmonization. Clin Chem Lab Med 2018;56(10):1743–8.
  7. Didier K et al. Autoantibodies associated with connective tissue diseases: What meaning for clinicians? Front Immunol 2018;9:541.
  8. Choi MY, Fritzler MJ. Progress in understanding the diagnostic and pathogenic  role of autoantibodies associated with systemic sclerosis. Curr Opin Rheumatol 2016;28(6):586–94.
  9. Palterer B et al. Bench to bedside review of myositis autoantibodies. Clin Mol Allergy 2018;16:5.
  10. Chighizola CB et al. The treatment of anti-phospholipid syndrome: A comprehensive clinical approach. J Autoimmun 2018;90:1–27.
  11. Gensous N et al; FHU ACRONIM. Predictive biological markers of systemic lupus erythematosus flares: a systematic literature review. Arthritis Res Ther 2017;19(1):238.
  12. Monogioudi E et al. Standardization of autoimmune testing – is it feasible? Clin Chem Lab Med 2018;56(10):1734–42.
  13. Conrad K et al. From autoantibody research to standardized diagnostic assays in the management of human diseases – report of the 12th Dresden Symposium on Autoantibodies. Lupus 2016;25(8):787–96.