Serum Inflammatory Profile in Hereditary Transthyretin Amyloidosis: Mechanisms and Possible Therapeutic Implications.
Marco LuigettiAngela RomanoValeria GuglielminoMaria Ausilia SciarroneFrancesca VitaliCarmine CarboneGeny PiroAndrea SabinoNicola De StefanoDomenico PlantoneGuido PrimianoPublished in: Brain sciences (2022)
Hereditary transthyretin (ATTRv) amyloidosis is a severe, progressive, and heterogeneous multisystemic condition due to mutations in the TTR gene. Although multiple aspects of its molecular pathophysiological mechanisms have been elucidated over the years, it is possible to hypothesize different pathogenetic pathways. Indeed, we extensively investigated the serum levels of several molecules involved in the immune response, in a cohort of ATTRv patients and healthy controls (HCs). Sixteen ATTRv patients and twenty-five HCs were included in the study. IFN-alpha levels were higher in ATTRv patients than in HCs, as well as IFN-gamma levels. By contrast, IL-7 levels were lower in ATTRv patients than in HCs. No significant difference between groups was found regarding IL-1Ra, IL-6, IL-2, IL-4, and IL-33 levels. Correlation analysis did not reveal any significant correlation between IFN-α, IFN-γ, IL-7, and demographic and clinical data. Larger and longitudinal studies using ultrasensitive methods to perform a full cytokine profiling are needed to better elucidate the role of inflammation in ATTRv pathogenesis and to test the reliability of these molecules as possible biomarkers in monitoring patients' progression.
Keyphrases
- end stage renal disease
- immune response
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- dendritic cells
- prognostic factors
- gene expression
- rheumatoid arthritis
- oxidative stress
- magnetic resonance imaging
- magnetic resonance
- multiple sclerosis
- multidrug resistant
- genome wide
- cross sectional
- high resolution
- machine learning
- mass spectrometry
- single cell
- systemic sclerosis
- single molecule
- big data
- interstitial lung disease
- data analysis