Liquid chromatography coupled to mass spectrometry-based metabolomics to differentiate the plasma metabolite and lipid profiles of patients diagnosed with Alzheimer’s disease under cholinesterase inhibitors

Mariana Marques1, Micael Dagom Lopes Oliveira1, Leda Leme Talib2, Wagner Farid Gattaz2, Alessandra Sussulini1,3

1. Unicamp, Universidade Estadual de Campinas; Cidade Universitária Zeferino Vaz - Barão Geraldo, Campinas - SP, 13083-970
2. USP, Universidade Estadual de São Paulo; R. da Reitoria, 374 - Butantã, São Paulo - SP, 05508-220
3. INCTBio, Instituto Nacional de Ciência e Tecnologia de Bioanalítica; Departamento de Química Analítica, Instituto de Química da Unicamp, Cidade Universitária Zeferino Vaz s/n, CEP: 13083-970, Campinas, SP

With the ageing of the population, dementia is considered by the World Health Organization as a considerable source of social and economic impacts since it affects not only the patients, but also their families and caretakers. Amongst the types of dementia that affect the elder citizens of society, Alzheimer's disease (AD) is its most common form, afflicting up to 70% of the elderly that present this type of syndrome. AD is a fatal neurodegenerative disease characterized by progressive memory loss, loss of cognitive ability, mood alterations, communication and rationality difficulties, as well as eventual loss of independent living. Currently, there is no cure for this disease, but some treatments may slow the symptoms progression and improve life quality of the patients. Cholinesterase inhibitors are the main class of pharmaceuticals used for the reduction of symptom effects. In this context, the objective of this research is to evaluate the metabolomic and lipidomic profile of blood plasma samples of patients diagnosed with AD before and after the use of cholinesterase inhibitors, aiming to elucidate the metabolic alterations that may assist diagnosis and optimize therapeutical strategies of symptom. For that purpose, liquid chromatography coupled to mass spectrometry was employed for data acquisition and biostatistical tools were used for analysis validation. From principal component analysis, it was observed that there was sufficient instrumental stability, ensuring that the subsequent analyses would not present instrumental bias. The application of PLS-DA provided VIP scores that pinpointed critical metabolic alterations, offering valuable insights into biochemical pathways implicated in Alzheimer’s disease.The analysis of the annotated metabolites and statistically significant pathways revealed that certain metabolite groups, such as fatty acids and amino acids, were present in both our results and the literature. Additionally, other metabolic pathways, such as the nitrogen biosynthesis and the arginine biosynthesis, were also relevant for this study.

Agradecimentos: I would like to thank CNPq for the financial support (GM-GD 2022 – CNPq Call No. 07/2022), which provided a full scholarship throughout my master's program. I am also grateful for the financial support from FAPESP (grant no. 2023/01800-2) and FAEPEX (grant nos. 2825/24 and 3455/24). This work was carried out with the support of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) – Funding Code 001. I would like to thank the Institute of Chemistry for the technical and infrastructure support. I am grateful to the patients who agreed to participate in this study, to the staff of HC-FMUSP for collecting the samples, and to the Neuroscience Laboratory LIM-27 for their collaboration, which enabled the use of patient samples. Finally, I would like to thank the LaBIOmics laboratory and Professor Alessandra Sussulini for their technical and intellectual support throughout this journey.