Disease-associated autoantibodies become more and more important for routine diagnostics as well as basic and applied research. As many of these autoantibodies are detectable long time before clinical manifestations, they may be used to predict the development of the appropriate disease. However their potential role in the very early diagnosis or risk assessment of disease development remains to be further studied. The current knowledge, the facts and perspectives regarding the prediction of organ specific and systemic autoimmune diseases are discussed in Chapter 1. For disease prediction and early intervention it is necessary to understand the pathologic processes leading to autoimmune diseases. For instance, components of the innate immune system (e.g. Toll-like receptors) can have a dramatic impact on autoantibody response and disease pathogenesis, either by promoting or by regulating disease (Chapter 2). The different effects of autoantibodies in immune homeostasis and autoimmune manifestations are discussed in Chapters 3 to 6.1 (natural autoantibodies as catalytic or protective immunoglobulins, autoantibodies against protective molecules, macromolecular complexes, receptor structures and ion channels). Reviews and news regarding autoantibodies in organ specific (Chapter 6) and systemic autoimmune diseases (Chapter 7) follow. The main focus of Chapter 7 is the pathologic, diagnostic and prognostic relevance of autoantibodies against citrullinated proteins or peptides. Chapter 9 deals with methodical aspects and diagnostic stategies starting with general comments on early diagnosis of autoimmune rheumatic diseases. Technologies for the identification of novel autoantibodies as well as for the determination of autoantibodies and autoantibody profiles were presented. Improvement of autoantibody analyses by autoantigen designing and technological innovations were discussed. Optimized, standardized and cost-effective multiparametric assays are the prerequisite for a probable future use of autoantibodies for the more accurate prediction of diseases.weiterlesen