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Kapoor A, Bartha R, Black SE, Borrie M, Freedman M, Gao F, Herrmann N, Mandzia J, Ozzoude M, Ramirez J, Scott CJM, Symons S, Fischer CE, Frank A, Seitz D, Wolf MU, Verhoeff NPLG, Naglie G, Reichman W, Masellis M, Mitchell SB, Tang-Wai DF, Tartaglia MC, Kumar S, Pollock BG, Rajji TK, Finger E, Pasternak SH, Swartz RH. Structural Brain Magnetic Resonance Imaging to Rule Out Comorbid Pathology in the Assessment of Alzheimer's Disease Dementia: Findings from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) Study and Clinical Trials Over the Past 10 Years. J Alzheimers Dis 2021; 74:747-757. [PMID: 32116253 PMCID: PMC7242844 DOI: 10.3233/jad-191097] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/OBJECTIVE Structural brain magnetic resonance imaging (MRI) is not mandatory in Alzheimer's disease (AD) research or clinical guidelines. We aimed to explore the use of structural brain MRI in AD/mild cognitive impairment (MCI) trials over the past 10 years and determine the frequency with which inclusion of standardized structural MRI acquisitions detects comorbid vascular and non-vascular pathologies. METHODS We systematically searched ClinicalTrials.gov for AD clinical trials to determine their neuroimaging criteria and then used data from an AD/MCI cohort who underwent standardized MRI protocols, to determine type and incidence of clinically relevant comorbid pathologies. RESULTS Of 210 AD clinical trials, 105 (50%) included structural brain imaging in their eligibility criteria. Only 58 (27.6%) required MRI. 16,479 of 53,755 (30.7%) AD participants were in trials requiring MRI. In the observational AD/MCI cohort, 141 patients met clinical criteria; 22 (15.6%) had relevant MRI findings, of which 15 (10.6%) were exclusionary for the study. DISCUSSION In AD clinical trials over the last 10 years, over two-thirds of participants could have been enrolled without brain MRI and half without even a brain CT. In a study sample, relevant comorbid pathology was found in 15% of participants, despite careful screening. Standardized structural MRI should be incorporated into NIA-AA diagnostic guidelines (when available) and research frameworks routinely to reduce diagnostic heterogeneity.
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Affiliation(s)
| | - Robert Bartha
- Robarts Research Institute and the Department of Medical Biophysics, the University of Western Ontario, London, ON, Canada
| | - Sandra E Black
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - Michael Borrie
- Parkwood Institute, St. Joseph's Health Care Center, London, ON, Canada
| | - Morris Freedman
- University of Toronto, Toronto, ON, Canada.,Rotman Research Institute of Baycrest Health Sciences, Toronto, ON, Canada.,Baycrest Health Sciences, Toronto, ON, Canada
| | - Fuqiang Gao
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Nathan Herrmann
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - Jennifer Mandzia
- Western University, London, ON, Canada.,London Health Sciences Centre, London, ON, Canada
| | - Miracle Ozzoude
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Sean Symons
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Corinne E Fischer
- Keenan Research Centre for Biomedical Research, the Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, ON, Canada
| | | | - Dallas Seitz
- Department of Psychiatry and Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Michael Uri Wolf
- University of Toronto, Toronto, ON, Canada.,Baycrest Health Sciences, Toronto, ON, Canada
| | | | - Gary Naglie
- University of Toronto, Toronto, ON, Canada.,Rotman Research Institute of Baycrest Health Sciences, Toronto, ON, Canada.,Baycrest Health Sciences, Toronto, ON, Canada
| | - William Reichman
- University of Toronto, Toronto, ON, Canada.,Baycrest Health Sciences, Toronto, ON, Canada
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - Sara B Mitchell
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - David F Tang-Wai
- University of Toronto, Toronto, ON, Canada.,University Health Network Memory Clinic, University of Toronto, Division of Neurology & Geriatric Medicine, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- University of Toronto, Toronto, ON, Canada.,Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Sanjeev Kumar
- University of Toronto, Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Bruce G Pollock
- University of Toronto, Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- University of Toronto, Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Elizabeth Finger
- Parkwood Institute, St. Joseph's Health Care Center, London, ON, Canada.,Western University, London, ON, Canada
| | - Stephen H Pasternak
- Robarts Research Institute and the Department of Medical Biophysics, the University of Western Ontario, London, ON, Canada.,Parkwood Institute, St. Joseph's Health Care Center, London, ON, Canada.,Western University, London, ON, Canada
| | | | - Richard H Swartz
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,University of Toronto, Toronto, ON, Canada
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Abstract
Current clinical criteria (DSM-IIIR and NINCDS-ADRDA) for the diagnosis of dementia and AD are reliable; however, these criteria remain to be validated by clinicians of different levels of expertise at different clinical settings. Structural neuroimaging has an important role in initial evaluation of dementia for ruling out potentially treatable causes. Although CT is the appropriate choice when brain tumors, subdural hematoma, or normal pressure hydrocephalus is suspected, MR imaging is more sensitive to the white-matter changes in vascular dementia. The diagnostic accuracy of PET, SPECT, 1H MRS, and MR volumetry of the hippocampus for distinguishing patients with AD from healthy elderly individuals is comparable to the accuracy of a pathologically confirmed clinical diagnosis. Sensitivity of PET for distinguishing patients with dementia with Lewy bodies from AD, however, is higher than that of clinical evaluation; similarly, SPECT and 1H MRS may be adjuncts to clinical evaluation for distinguishing patients with frontotemporal dementia from those with AD. Neuroimaging is valuable in predicting future development of AD in patients with MCI and in carriers of the ApoE epsilon 4 allele who are at a higher risk of developing AD than are cognitively normal elderly individuals. Quantitative MR techniques (e.g., MR volumetry, DWI, magnetization transfer MR imaging, and 1H MRS) and PET are sensitive to the structural and functional changes in the brains of patients with MCI, and hippocampal volumes on MR imaging are associated with future development of AD in these individuals. PET is also sensitive to the regional metabolic decline in the brains of carriers of the ApoE epsilon 4 allele. The longitudinal decrease of whole brain and hippocampal volumes on MR imaging, NAA levels on 1H MRS, cerebral glucose metabolism on PET, and cerebral blood flow on SPECT are associated with rate of cognitive decline in patients with AD. These neuroimaging markers may be useful for monitoring symptomatic progression in groups of patients with AD for drug trials. Furthermore, antemortem MR-based hippocampal volumes correlate with the pathologic stage of AD, and the rate of hippocampal volume loss on MR imaging correlates with clinical disease progression in the cognitive continuum from normal aging to MCI and to AD. Hence, as an in vivo correlate of pathologic involvement, structural imaging measures are potential surrogate markers for disease progression in patients with established AD and in patients with prodromal AD, who will benefit most from disease-modifying therapies underway.
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Affiliation(s)
- Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
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