Regulatory lymphocytes in thyroid orbitopathy and autoimmune thyroid diseases.

Thyroid orbitopathy (OT), as an organ­specific autoimmune disease, is a result of immune dysregulation leading to loss of control over inflammation directed against self­antigens. The source of the autoreactive lymphocytes is the impairment of central tolerance as well as their induction on the periphery by modified or sequestered by that time antigens. Active suppression by the various subpopulations of regulatory lymphocytes (Lreg) acts as a counterbalance to the proinflammatory factors and is aimed at dampening pathological reaction. Thereby, qualitative or quantitative shortfalls of Lreg play a critical role in the development of autoimmune diseases. Giving direction to Lreg­based therapy and restoring the dynamic balance seem to be of crucial importance, especially in diseases such as OT, where the causative self­antigen is not yet unequivocally elucidated. Technical difficulties with isolation and assessment of Lreg function in vitro as well as lack of unification of research protocols make the findings non­comparable, inconclusive and sometimes even conflicting. Lack of a Tregs' (regulatory T cells) specific set of surface markers makes the demethylation status analysis of TSDR (Treg specific demethylated region) FOXP3 (forkhead box P3) locus the most reliable method of their quantification. Despite numerous discrepancies between research findings, most of them point to Lreg's pivotal role in immune disturbances, which form the basis of OT and autoimmune thyroid diseases (AITD).

Branched chain amino acids: Passive biomarkers or the key to the pathogenesis of cardiometabolic diseases?

The metabolomic approach to research on lifestyle diseases has led to the discovery of new potential biomarkers of pathological conditions as well as key metabolic pathways that may become targets of therapeutic intervention. Current evidence supports plasma branched chain amino acids (BCAAs) as potential diagnostic and prognostic biomarkers of cardiometabolic diseases. However, the biological mechanisms of the associations that have been identified are still not completely understood and should be clarified before implementing BCAA-based biomarkers in the clinical setting. The most crucial issue that needs to be solved first is determining whether BCAA plasma profile disturbances are only passive biomarkers or whether they facilitate dysmetabolic processes. In this context, further research is also warranted to investigate the role of dietary BCAAs. Gaining this knowledge would be significant progress in molecular nutrition research, providing perspective for target therapeutic and prophylactic interventions. This paper provides a comprehensive review of the main hypotheses and mechanistic models that consider circulating BCAAs both as passive biomarkers and as contributors to cardiometabolic diseases.

Specific plasma amino acid disturbances associated with metabolic syndrome.

PURPOSE: The primary objective of the present study was to examine the association between branched chain and aromatic amino acid profiles (BCAA and AAA respectively) and the metabolic syndrome (MS), and to evaluate the clinical utility of these associations in the diagnostic process. METHODS: Two hundred and sixty three healthy men with MS [MS(+): n = 165] and without MS [MS(-): n = 98] were enrolled in the observational study. Anthropometrical, biochemical, and amino acid measurements were performed. The ability of the BCAA and AAA to discriminate subjects with MS and insulin resistance was tested. Based on logistic discrimination, a multivariate early MS diagnostic model was built, and its discrimination properties were evaluated. RESULTS: Two functionally independent amino acid clusters were identified. BCAA and phenylalanine differed significantly between MS(+) and MS(-) participants (P = 0.003). These factors were also found to be indicators of MS(+) individuals (AUC: 0.66; 95% CI: 0.5757-0.7469), and correlated with cardiometabolic factors. No statistically significant differences in amino acid concentrations between those with and without insulin resistance were noted, and none of the amino groups were indicators of insulin resistance. The proposed MS multivariate diagnostic model consisted of phenylalanine, insulin, leptin, and adiponectin, and had good discrimination properties [AUC 0.79; 95% CI: 0.7239-0.8646]. CONCLUSIONS: MS is associated with selective BCAA and AAA profile disturbances, which could be part of cardiometabolic disease pathogenesis and derive neither directly from insulin sensitivity impairment, nor obesity or muscle mass. The MS diagnostic model developed and described herein should be validated in future studies.