Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of
Alzheimer's disease (AD), and inclusion criteria are based on
biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which
biomarkers should be combined to accurately predict conversion from
mild cognitive impairment (MCI) to AD
dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI),
amyloid- and
18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD
dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD
dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical
amyloid-β (Aβ) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on
18F-FDG SUVR. CSF measures included Aβ1-42, Aβ1-40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and
APOE ε4 genotype. As a result, faster
cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster
cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = -0.28 [p = 0.020]). Patients with MCI converted to AD
dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither
amyloid-PET nor CSF
biomarkers could discriminate short-term converters from non-converters.