Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis.

Abstract

Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for early diagnosis. This study aims to identify promising biomarkers for the early diagnosis of AL amyloidosis. Peripheral venous blood samples from 70 newly diagnosed systemic AL amyloidosis patients, 48 newly diagnosed multiple myeloma (MM) patients, and 29 healthy controls (HCs) were analyzed using high-performance liquid chromatography-mass spectrometry. Metabolomic profiling revealed distinct metabolic differences between the AL group and the controls (HCs and MM). Machine learning further identified that phytosphingosine and asymmetric dimethylarginine were significantly up-regulated in the AL group compared with HCs group, with area under curve (AUC) values of 0.990 and 0.904, sensitivity and specificity of (97%, 100%) and (88%, 93%), respectively. Compared with MM group, phytosphingosine was also significantly up-regulated in the AL group, with an AUC value of 0.779, sensitivity and specificity of (62%, 88%). Pathway analysis showed significant changes in starch and sucrose metabolism pathway, as well as pentose and glucuronate interconversions pathway between the AL and the controls. Metabolomics combined with machine learning identified phytosphingosine as a promising biomarker for early diagnosis of AL amyloidosis. Two metabolic pathways (starch and sucrose metabolism, pentose and glucuronate interconversions) may reflect the key pathological processes involved in the development and progression of AL amyloidosis. Further confirmation studies are warranted to validate its value in this field.