Cerebrospinal Fluid Inflammatory Markers in Alzheimer's Disease: Influence of Comorbidities

Ying Wang; Ceren Emre; Helena Gyllenhammar-Schill; Karin Fjellman; Helga Eyjolfsdottir; Maria Eriksdotter; Marianne Schultzberg; Erik Hjorth

doi:10.2174/1567205018666210330162207

Cerebrospinal Fluid Inflammatory Markers in Alzheimer's Disease: Influence of Comorbidities

Curr Alzheimer Res. 2021;18(2):157-170. doi: 10.2174/1567205018666210330162207.

Authors

Ying Wang¹, Ceren Emre¹, Helena Gyllenhammar-Schill², Karin Fjellman³, Helga Eyjolfsdottir⁴, Maria Eriksdotter⁴, Marianne Schultzberg¹, Erik Hjorth¹

Affiliations

¹ Department of Neurobiology, Care Sciences & Society, Karolinska Institutet, Center for Alzheimer Research, BioClinicum J9:20, Division of Neurogeriatrics, Visionsgatan 4, SE-171 64 Solna, Sweden.
² Danderyd Hospital, Geriatric Clinic, SE-185 57 Danderyd, Sweden.
³ Karolinska University Hospital, Theme Clinical Pharmacology, SE-141 86 Huddinge, Sweden.
⁴ Karolinska University Hospital, Theme Aging, SE-141 86 Huddinge, Sweden.

PMID: 33784960
DOI: 10.2174/1567205018666210330162207

Abstract

Background: Alzheimer's disease (AD) develops into dementia after several years, and subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are used as intermediary diagnoses of increasing severity. Inflammation is an important part of AD pathology and provides potential novel biomarkers and treatment targets.

Objective: To identify novel potential biomarkers of AD in cerebrospinal fluid (CSF) and create a molecular pattern of inflammatory factors providing differentiation between AD and SCI.

Methods: We analyzed 43 inflammatory-related mediators in CSF samples from a cohort of SCI and AD cases vetted for confounding factors (Training cohort). Using multivariate analysis (MVA), a model for discrimination between SCI and AD was produced, which we then applied to a larger nonvetted cohort (named Test cohort). The data were analyzed for factors showing differences between diagnostic groups and factors that differed between the vetted and non-vetted cohorts. The relationship of the factors to the agreement between model and clinical diagnosis was investigated.

Results: A good MVA model able to discriminate AD from SCI without including tangle and plaque biomarkers was produced from the Training cohort. The model showed 50% agreement with clinical diagnosis in the Test cohort. Comparison of the cohorts indicated different patterns of factors distinguishing SCI from AD. As an example, soluble interleukin (IL)-6Rα showed lower levels in AD cases in the Training cohort, whereas placental growth factor (PlGF) and serum amyloid A (SAA) levels were higher in AD cases of the Test cohort. The levels of p-tau were also higher in the Training cohort.

Conclusion: This study provides new knowledge regarding the involvement of inflammation in AD by indicating different patterns of factors in CSF depending on whether potential confounding comorbidities are present or not, and presents sIL-6Rα as a potential new biomarker for improved diagnosis of AD.

Keywords: Alzheimer's disease; biomarker; cerebrospinal fluid; diagnosis; inflammation; multivariate analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Alzheimer Disease* / cerebrospinal fluid
Alzheimer Disease* / pathology
Amyloid / blood
Cognitive Dysfunction / diagnosis*
Comorbidity*
Female
Humans
Inflammation*
Male
Middle Aged
Sensitivity and Specificity
tau Proteins

Substances

Amyloid
tau Proteins