Comparing PET and MRI Biomarkers Predicting Cognitive Decline in Preclinical Alzheimer Disease

Danielle V Mayblyum¹, J Alex Becker¹, Heidi I L Jacobs¹, Rachel F Buckley¹, Aaron P Schultz¹, Jorge Sepulcre¹, Justin S Sanchez¹, Zoe B Rubinstein¹, Samantha R Katz¹, Kirsten A Moody¹, Patrizia Vannini¹, Kathryn V Papp¹, Dorene M Rentz¹, Julie C Price¹, Reisa A Sperling¹, Keith A Johnson¹, Bernard J Hanseeuw²

Affiliations

¹ From the Department of Radiology (D.V.M., J.A.B., H.I.L.J., J.S., J.S.S., Z.B.R., S.R.K., K.A.M., J.C.P., K.A.J., B.J.H.), Massachusetts General Hospital, Gordon Center for Medical Imaging and Athinoula A. Martinos Center for Biomedical Imaging, Boston; Faculty of Health, Medicine and Life Sciences (H.I.L.J.), School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, the Netherlands; Department of Neurology (R.F.B., P.V., K.V.P., D.M.R., R.A.S., K.A.J.), Massachusetts General Hospital, Harvard Medical School, Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston; The Florey Institute (R.F.B.) and Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Victoria Australia; Department of Neurology (A.P.S., B.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Neurology (B.J.H.), Cliniques Universitaires Saint-Luc, Brussels, Belgium.
² From the Department of Radiology (D.V.M., J.A.B., H.I.L.J., J.S., J.S.S., Z.B.R., S.R.K., K.A.M., J.C.P., K.A.J., B.J.H.), Massachusetts General Hospital, Gordon Center for Medical Imaging and Athinoula A. Martinos Center for Biomedical Imaging, Boston; Faculty of Health, Medicine and Life Sciences (H.I.L.J.), School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, the Netherlands; Department of Neurology (R.F.B., P.V., K.V.P., D.M.R., R.A.S., K.A.J.), Massachusetts General Hospital, Harvard Medical School, Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston; The Florey Institute (R.F.B.) and Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Victoria Australia; Department of Neurology (A.P.S., B.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Neurology (B.J.H.), Cliniques Universitaires Saint-Luc, Brussels, Belgium. bhanseeuw@mgh.harvard.edu.

PMID: 33952655
PMCID: PMC8253562
DOI: 10.1212/WNL.0000000000012108

Comparing PET and MRI Biomarkers Predicting Cognitive Decline in Preclinical Alzheimer Disease

Danielle V Mayblyum et al. Neurology. 2021.

. 2021 Jun 14;96(24):e2933-e2943.

doi: 10.1212/WNL.0000000000012108.

Authors

Affiliations

¹ From the Department of Radiology (D.V.M., J.A.B., H.I.L.J., J.S., J.S.S., Z.B.R., S.R.K., K.A.M., J.C.P., K.A.J., B.J.H.), Massachusetts General Hospital, Gordon Center for Medical Imaging and Athinoula A. Martinos Center for Biomedical Imaging, Boston; Faculty of Health, Medicine and Life Sciences (H.I.L.J.), School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, the Netherlands; Department of Neurology (R.F.B., P.V., K.V.P., D.M.R., R.A.S., K.A.J.), Massachusetts General Hospital, Harvard Medical School, Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston; The Florey Institute (R.F.B.) and Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Victoria Australia; Department of Neurology (A.P.S., B.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Neurology (B.J.H.), Cliniques Universitaires Saint-Luc, Brussels, Belgium.
² From the Department of Radiology (D.V.M., J.A.B., H.I.L.J., J.S., J.S.S., Z.B.R., S.R.K., K.A.M., J.C.P., K.A.J., B.J.H.), Massachusetts General Hospital, Gordon Center for Medical Imaging and Athinoula A. Martinos Center for Biomedical Imaging, Boston; Faculty of Health, Medicine and Life Sciences (H.I.L.J.), School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, the Netherlands; Department of Neurology (R.F.B., P.V., K.V.P., D.M.R., R.A.S., K.A.J.), Massachusetts General Hospital, Harvard Medical School, Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston; The Florey Institute (R.F.B.) and Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Victoria Australia; Department of Neurology (A.P.S., B.J.H.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Neurology (B.J.H.), Cliniques Universitaires Saint-Luc, Brussels, Belgium. bhanseeuw@mgh.harvard.edu.

PMID: 33952655
PMCID: PMC8253562
DOI: 10.1212/WNL.0000000000012108

Abstract

Objective: To compare how structural MRI, fluorodeoxyglucose (FDG), and flortaucipir (FTP) PET signals predict cognitive decline in high-amyloid vs low-amyloid participants with the goal of determining which biomarker combination would result in the highest increase of statistical power for prevention trials.

Methods: In this prospective cohort study, we analyzed data from clinically normal adults from the Harvard Aging Brain Study with MRI, FDG, FTP, and Pittsburgh compound B (PiB)-PET acquired within a year and prospective cognitive evaluations over a mean 3-year follow-up. We focused analyses on predefined regions of interest: inferior temporal, isthmus cingulate, hippocampus, and entorhinal cortex. Cognition was assessed with the Preclinical Alzheimer's Cognitive Composite. We evaluated the association between biomarkers and cognitive decline using linear mixed-effect models with random intercepts and slopes, adjusting for demographics. We generated power curves simulating prevention trials.

Results: Data from 131 participants (52 women, age 73.98 ± 8.29 years) were analyzed in the study. In separate models, most biomarkers had a closer association with cognitive decline in the high-PiB compared to the low-PiB participants. A backward stepwise regression including all biomarkers demonstrated that only neocortical PiB, entorhinal FTP, and entorhinal FDG were independent predictors of subsequent cognitive decline. Power analyses revealed that using both high PiB and low entorhinal FDG as inclusion criteria reduced 3-fold the number of participants needed in a hypothetical trial compared to using only high PiB.

Discussion: In preclinical Alzheimer disease, entorhinal hypometabolism is a strong and independent predictor of subsequent cognitive decline, making FDG a potentially useful biomarker to increase power in clinical trials.

Classification of evidence: This study provides Class II evidence that in people with preclinical Alzheimer disease, entorhinal hypometabolism identified by FDG-PET is predictive of subsequent cognitive decline.

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Figures

**Figure 1. Regional FTP PET Measures Are Predictive of Cognitive Decline in High-PiB Individuals**
Prospective cognitive decline (Preclinical Alzheimer's Cognitive Composite performance expressed in SD per year) was predicted using flortaucipir (FTP) PET biomarkers in linear mixed-effects models with a random intercept and slope per participant. Analyses were split between the low–Pittsburgh compound B (PiB) group (blue) and high-PiB group (red). Biomarkers included FTP PET partial volume-corrected (PVC) standard uptake value ratio (SUVr) values in the (A) entorhinal cortex, (B) inferior temporal, (C) isthmus cingulate, and (D) hippocampus. Interactions between PiB group and FTP PET biomarkers are included on the top of each graph. Models were adjusted for age, sex, and education. A Bonferroni-corrected p value was applied (13 biomarkers, p < 0.0038), and model outputs that survived the correction are in bold.

**Figure 2. Regional FDG PET Measures Are Predictive of Cognitive Decline in High-PiB Individuals**
Prospective cognitive decline (Preclinical Alzheimer's Cognitive Composite performance expressed in SD per year) was predicted using fluorodeoxyglucose (FDG) PET biomarkers in linear mixed-effects models with a random intercept and slope per participant. Analyses were split between the low–Pittsburgh compound B (PiB) group (blue) and high-PiB group (red). Biomarkers included FDG PET partial volume–corrected (PVC) standard uptake value ratio (SUVr) values in the (A) entorhinal cortex, (B) inferior temporal, (C) isthmus cingulate, and (D) hippocampus. Interactions between PiB group and FDG PET biomarkers are included on the top of each graph. Models were adjusted for age, sex, and education. A Bonferroni-corrected p value was applied (13 biomarkers, p < 0.0038), and model outputs that survived the correction are in bold.

**Figure 3. Regional MRI Measures Are Predictive of Cognitive Decline in High-PiB Individuals**
Prospective cognitive decline (Preclinical Alzheimer's Cognitive Composite performance expressed in SD per year) was predicted using various MRI biomarkers in linear mixed-effects models with a random intercept and slope per participant. Analyses were split between the low–Pittsburgh compound B (PiB) group (blue) and high-PiB group (red). MRI biomarkers used to predict cognitive decline were thickness measures in millimeters in the (A) entorhinal cortex, (B) inferior temporal, and (C) isthmus cingulate, as well as (D) hippocampal volume (cubic millimeters). Interactions between PiB group and MRI biomarkers are included on the top of each graph. Models were adjusted for age, sex, and education. A Bonferroni-corrected p value was applied (13 biomarkers, p < 0.0038), and model outputs that survived the correction are in bolded.

**Figure 4. Entorhinal FDG Predicts Subsequent Cognitive Decline Independently of PiB and FTP**
Backward stepwise regressions identified the strongest predictors of subsequent cognitive decline in the (A) entire sample, (B) low–Pittsburgh compound B (PiB) subgroup, and (C) high-PiB subgroup. We used a liberal threshold in this test (2-tailed p ≤ 0.10) and removed nonsignificant predictors from the model. (A) PiB frontal, lateral temporal, and retrosplenial cortices (FLR), entorhinal cortex (EC) flortaucipir (FTP), EC fluorodeoxyglucose (FDG), and age were independently predictive of cognitive decline in the entire sample. (B) Age was the only predictor of cognitive decline in the low-PiB group. (C) EC FDG, EC FTP, and hippocampus volume (HV) were independently predictive in the high-PiB group. Although PiB and FTP estimates are negative, they are presented in the positive range to facilitate the comparison with FDG and HV.

**Figure 5. Higher Statistical Power With Entorhinal FDG than With FTP**
The number of high–Pittsburgh compound B (PiB) clinically normal individuals per arm (y-axis) that are needed for detecting a given slope reduction on the Preclinical Alzheimer Cognitive Composite (PACC5; x-axis) with a 90% power and α = 0.05 in a 4-year trial with annual assessments, using Pittsburgh compound B (PiB) alone (black dotted line), PiB and entorhinal cortex (EC) flortaucipir (FTP) (black plain line), PiB and EC fluorodeoxyglucose (FDG) (red dotted line), or PiB, EC FTP, and EC FDG (red plain line) as inclusion criteria. For this analysis, FDG (standard uptake value ratio [SUVr] threshold 0.81) and FTP (SUVr threshold 1.52) signals were dichotomized using a median split in the high-PiB group.

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References

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Comparing PET and MRI Biomarkers Predicting Cognitive Decline in Preclinical Alzheimer Disease

Affiliations

Comparing PET and MRI Biomarkers Predicting Cognitive Decline in Preclinical Alzheimer Disease

Authors

Affiliations

Abstract

Figures

References

Grants and funding

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Full Text Sources

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Research Materials