Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis
-
Objective: To explore the relationship of aortic medial amyloid with biochemical and micromechanical properties of the aortic wall in aneurysm patients. Methods: Human aortic tissues removed during aneurysm surgery from tricuspid (idiopathic degenerative aneurysm, DA) and bicuspid valve (BAV) patients were subjected to oscillatory nanoindentation experiments to determine localised mechanical properties of the tissue (shear storage modulus, G´ and shear loss modulus, G˝). Collagen, elastin, matrix metalloproteinase 2 and glycosaminoglycans concentrations were determined, along with relative levels of aortic medial amyloid-related factors (medin, milk fat globule-EGF factor 8, oligomers and fibrils). ⋯ Conclusions: We identified a group of DA patients with high amyloid oligomers and altered micromechanical and structural properties of the vessel wall. We propose these findings as a cause for aneurysm formation in these patients. Amyloid is not found in BAV patients, suggesting at least two distinct mechanisms for pathogenesis.
-
Background: Atrial fibrillation (AF) commonly affects patients with cardiac amyloidosis (CA). Amyloid deposition within the left atrium may be responsible for the subtype of AF in either permanent or non-permanent form. The prognostic implications of AF and its clinical subtype according to the type of CA are still controversial in this population. ⋯ Subtype of AF had no impact on cardiovascular mortality. Conclusions: AF is common in patients with CA. However, AF and clinical subtype of AF have no impact on all-cause mortality, whatever the type of amyloidosis.
-
Objective: Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotein profiles accompany the disease. ⋯ Sensitivity and specificity of the risk model were predicted to be 82%, and were assessed at 79 and 72%, respectively. Total, low density lipoprotein and high density lipoprotein cholesterol levels were significantly lower, and SAA levels and triglyceride-to-HDL ratios were significantly higher in cases versus controls. Conclusion: Machine learning is a powerful approach to identifying macaques at risk of AA amyloidosis, which is accompanied by increased circulating SAA and altered lipoprotein profiles.