Combination of ¹⁸F-FDG PET and Cerebrospinal Fluid Biomarkers as a Better Predictor of the Progression to Alzheimer's Disease in Mild Cognitive Impairment Patients

Article type: Research Article

Authors: Choo, IL Han^{a; b} | Ni, Ruiqing^a | Schöll, Michael^a | Wall, Anders^c | Almkvist, Ove^d | Nordberg, Agneta^{a; e; *}

Affiliations: [a] Alzheimer Neurobiology Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden | [b] Department of Neuropsychiatry, School of Medicine, Chosun University, Gwangju, Republic of Korea | [c] PET Center, Section of Nuclear Medicine & PET, Department of Radiology, Oncology and Radiation Sciences, Uppsala University, Uppsala, Sweden | [d] Department of Psychology, Stockholm University, Stockholm, Sweden | [e] Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden

Correspondence: [*] Correspondence to: Prof. Agneta Nordberg, MD, PhD, Department of Neurobiology, Care Sciences and Society, Division of Alzheimer Neurobiology, Karolinska Institutet, Karolinska University Hospital Huddinge, Novum Floor 5, S-141 86 Stockholm, Sweden. Tel.: +46 8 585 854 67; Fax: +46 8 585 854 70; E-mail: agneta.k.nordberg@ki.se.

Abstract: The biomarker-based new diagnostic criteria have been proposed for Alzheimer's disease (AD) spectrum. However, any biomarker alone has not been known to have satisfactory AD predictability. We explored the best combination model with baseline demography, neuropsychology, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), cerebrospinal fluid (CSF) biomarkers, and apolipoprotein E (APOE) genotype evaluation to predict progression to AD in mild cognitive impairment (MCI) patients. A longitudinal clinical follow-up (mean, 44 months; range, 1.6–161.7 months) of MCI patients was done. Among 83 MCI patients, 26 progressed to AD (MCI-AD) and 51 did not deteriorate (MCI-Stable). We applied that univariate and multivariate logistic regression analyses, and multistep model selection for AD predictors including biomarkers. In univariate logistic analysis, we selected age, Rey Auditory Verbal Retention Test, parietal glucose metabolic rate, CSF total tau, and presence or not of at least one APOE ε4 allele as predictors. Through multivariate stepwise logistic analysis and model selection, we found the combination of parietal glucose metabolic rate and total tau representing the best model for AD prediction. In conclusion, our findings highlight that the combination of regional glucose metabolic assessment by PET and CSF biomarkers evaluation can significantly improve AD predictive diagnostic accuracy of each respective method.

Keywords: Biomarkers, combination, mild cognitive impairment, predictor

DOI: 10.3233/JAD-2012-121489

Journal: Journal of Alzheimer's Disease, vol. 33, no. 4, pp. 929-939, 2013