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Millions of people all round the world suffer from various autoimmune disorders. T1D, MS, SLE, autoimmune diseases of the heart, liver, intestine and other internal organs, etc., all represent severe manifestations, which deteriorate the quality of life and cause physical disability and even death in patients with chronic illnesses. Every year, the largest world economies incur megabuck losses associated with medical services, insurance and drug procurement, not to mention the ever decreasing size of the able-bodied population. The development of preclinical diagnostic algorithms for autoimmune diseases, their implementation and introduction into routine clinical practice will help detect tissue or organ pathologies at the stages where their reversal is still possible. Early implementation of causal therapy allows the physician to compensate for the lack of one or another organ and ensures complete recovery or significant improvement of the patient’s health status. However, in developing updated preventive protocols, the investigator is faced with a necessity to solve fundamental problems in order to understand:  Who should be selected for examination?  What exactly needs to be checked?  When should the examination be performed?  And, last but not least, what are the analytical procedures for mass-scale monitoring? While constructing preclinical monitoring algorithms, one should also take into consideration the diseases suffered by patients’ relatives. One of the cornerstones in preventive and predictive medicine is screening for genetic abnormalities or hereditary predisposition. All-encompassing analysis of gene units and construction of individual genetic maps not only facilitate the assessment of individual risks for each concrete patient, but also allows prediction of disease development in first-degree-relatives. Therefore, today’s objective demands include not only large-scale monitoring of definite cohorts of the general population, but, rather, identification of high risk cohorts coupled with genetic abnormalities and/or shifts and social factors (residence, place of employment, occupation, living conditions, etc.). However, even a comprehensive analysis is inadequate without a set

Preclinical and Predictive Algorithms in Monitoring Patients with Autoimmune Diseases and Their Relatives-at-Risks