Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database

Abstract

Background: The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in diagnosing Alzheimer's disease (AD) and predicting the cognitive evolution of patients with mild cognitive impairment (MCI) is still debated.

Methods: Subjects from the Alzheimer's Disease Neuroimaging Initiative 2 with complete baseline cognitive assessment (Mini Mental State Examination, Clinical Dementia Rating [CDR] and Alzheimer's Disease Assessment Scale-Cognitive Subscale [ADAS-cog] scores), CSF collection (amyloid-β_1-42 [Aβ], tau and phosphorylated tau) and ¹⁸F-florbetapir scans were included in our cross-sectional cohort. Among these, patients with MCI or substantial memory complaints constituted our longitudinal cohort and were followed for 30 ± 16 months. PET amyloid deposition was quantified using relative retention indices (standardised uptake value ratio [SUVr]) with respect to pontine, cerebellar and composite reference regions. Diagnostic and prognostic performance based on PET and CSF was evaluated using ROC analysis, multivariate linear regression and survival analysis with the Cox proportional hazards model.

Results: The cross-sectional study included 677 participants and revealed that pontine and composite SUVr values were better classifiers (AUC 0.88, diagnostic accuracy 85%) than CSF markers (AUC 0.83 and 0.85, accuracy 80% and 75%, for Aβ and tau, respectively). SUVr was a strong independent determinant of cognition in multivariate regression, whereas Aβ was not; tau was also a determinant, but to a lesser degree. Among the 396 patients from the longitudinal study, 82 (21%) converted to AD within 22 ± 13 months. Optimal SUVr thresholds to differentiate AD converters were quite similar to those of the cross-sectional study. Composite SUVr was the best AD classifier (AUC 0.86, sensitivity 88%, specificity 81%). In multivariate regression, baseline cognition (CDR and ADAS-cog) was the main predictor of subsequent cognitive decline. Pontine and composite SUVr were moderate but independent predictors of final status and CDR/ADAS-cog progression rate, whereas baseline CSF markers had a marginal influence. The adjusted HRs for AD conversion were 3.8 (p = 0.01) for PET profile, 1.2 (p = ns) for Aβ profile and 1.8 (p = 0.03) for tau profile.

Conclusions: Semi-quantitative amyloid PET appears more powerful than CSF markers for AD grading and MCI prognosis in terms of cognitive decline and AD conversion.

Keywords: ADNI; Alzheimer’s disease; Amyloid PET; CSF markers; MCI.

Fig. 1

Patient flow diagram in the cross-sectional and longitudinal cohorts. AD Alzheimer’s disease, MCI Mild cognitive impairment, SMC Significant memory complaint

Fig. 2

Distribution of baseline standardised uptake value ratio (SUVr) values by baseline status (control subjects, significant memory complaint [SMC], mild cognitive impairment [MCI], Alzheimer’s disease [AD]) in the cross-sectional cohort and by last known status (normal, MCI, AD) in the longitudinal cohort. Boxes represent IQRs. Whiskers correspond to mean ± 1.5 SD. CTL Control, NL Normal, ns Not significant

Fig. 3

Last known status in the longitudinal cohort (patients with significant memory complaint/patients with mild cognitive impairment [MCI]) according to baseline positron emission tomography (PET) profile (assessed using composite standardised uptake value ratio with a cut-off at 0.89) and cerebrospinal fluid profile in terms of amyloid-β_1–42 (Aβ/tau) combination. AD Alzheimer’s disease, NL Normal

Fig. 4

Evolution of cognitive scores in the longitudinal cohort (patients with significant memory complaint/mild cognitive impairment [MCI]) according to baseline composite standardised uptake value ratio (SUVr). From left to right: Mean annual change in Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-cog). a Scatterplot in which marker colour refers to last known status. The vertical dashed grey line indicates the optimal SUVr cut-off. The black dashed curve stands for the quadratic regression. ρ Spearman’s rank correlation. b Score distribution according to positron emission tomography (PET) profile based on composite SUVr (cut-off 0.89). Boxes represent mean with 95% CI. Whiskers represent mean ± 1.5 SD. ns Not significant, NL Normal, AD Alzheimer’s disease

Fig. 5

Kaplan-Meier curves for conversion to Alzheimer’s disease (AD) in patients with significant memory complaint/mild cognitive impairment (MCI) according to baseline positron emission tomography (PET) profile (composite standardised uptake value ratio [SUVr]) and baseline cerebrospinal fluid (CSF) Amyloid-β_1–42 (Aβ) and total tau profiles. ns Not significant. Cox model accounts for age, sex, apolipoprotein E status, baseline cognitive scores, PET profile, CSF Aβ and tau profiles