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Estimation of blood-based biomarkers of glial activation related to neuroinflammation

BACKGROUND: Neuroinflammation is a well-known feature of Alzheimer’s disease (AD), and a blood-based test for estimating the levels of neuroinflammation would be expected. In this study, we examined and validated a model using blood-based biomarkers to predict the level of glial activation due to ne...

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Detalles Bibliográficos
Autores principales: Yasuno, Fumihiko, Watanabe, Atsushi, Kimura, Yasuyuki, Yamauchi, Yumeka, Ogata, Aya, Ikenuma, Hiroshi, Abe, Junichiro, Minami, Hiroyuki, Nihashi, Takashi, Yokoi, Kastunori, Hattori, Saori, Shimoda, Nobuyoshi, Kasuga, Kensaku, Ikeuchi, Takeshi, Takeda, Akinori, Sakurai, Takashi, Ito, Kengo, Kato, Takashi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650015/
https://www.ncbi.nlm.nih.gov/pubmed/36388135
http://dx.doi.org/10.1016/j.bbih.2022.100549
Descripción
Sumario:BACKGROUND: Neuroinflammation is a well-known feature of Alzheimer’s disease (AD), and a blood-based test for estimating the levels of neuroinflammation would be expected. In this study, we examined and validated a model using blood-based biomarkers to predict the level of glial activation due to neuroinflammation, as estimated by (11)C-DPA-713 positron emission tomography (PET) imaging. METHODS: We included 15 patients with AD and 10 cognitively normal (CN) subjects. Stepwise backward deletion multiple regression analysis was used to determine the predictors of the TSPO-binding potential (BP(ND)) estimated by PET imaging. The independent variables were age, sex, diagnosis, apolipoprotein E4 positivity, body mass index and the serum concentration of blood-based biomarkers, including monocyte chemotactic protein 1 (MCP-1), fractalkine, chitinase 3-like protein-1 (CHI3L1), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), and clusterin. RESULTS: Sex, diagnosis, and serum concentrations of MCP1 and sTREM2 were determined as predictors of TSPO-BP(ND) in the Braak1-3 area. The serum concentrations of MCP1 and sTREM2 correlated positively with TSPO-BP(ND). In a leave one out (LOO) cross-validation (CV) analysis, the model gave a LOO CV R(2) of 0.424, which indicated that this model can account for approximately 42.4% of the variance of brain TSPO-BP(ND.) CONCLUSIONS: We found that the model including serum MCP-1 and sTREM2 concentration and covariates of sex and diagnosis was the best for predicting brain TSPO-BP(ND). The detection of neuroinflammation in AD patients by blood-based biomarkers should be a sensitive and useful tool for making an early diagnosis and monitoring disease progression and treatment effectiveness.