Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease

Abstract

Background and purpose: Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.

Materials and methods: ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Abeta42, t-tau, p-tau, and ratios of t-tau/Abeta42 and p-tau/Abeta42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.

Results: Hippocampal volume, retrosplenial thickness, and t-tau/Abeta42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.

Conclusions: All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.

Fig 1.

The regions of interest used are the following: 1) hippocampus and 2) entorhinal, 3) parahippocampal, 4) retrosplenial, 5) precuneus, 6) inferior parietal, 7) supramarginal, 8) middle temporal, 9) lateral orbitofrontal, and 10) medial orbitofrontal cortices.

Fig 2.

Comparison of ROC curves for using 1 versus a combination of 2 and all 3 variables shown to be unique predictors of NC-versus-AD classification. Yellow is the predicted probability based on hippocampal volume alone (AUC = 0.900, SE = 0.033). Blue is the predicted probability based on hippocampal volume and t-tau/Aβ42 ratio (AUC = 0.950, SE = 0.022). Red is the predicted probability based on hippocampal volume, t-tau/Aβ42 ratio, and retrosplenial cortical thickness (AUC = 0.961, SE = 0.018).

Fig 3.

The regression plots for 2-year change in scores in the MCI group significantly (P < .05) predicted from MR imaging morphometry and PET metabolism variables. A, CDR change predicted from retrosplenial cortical thickness. B and C, MMSE change predicted from retrosplenial cortical metabolism (B) and retrosplenial cortical thickness (C). D, Delayed logical memory change predicted from hippocampal volume.