The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity
Abstract Introduction At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer’s disease (pAD), mild cog...
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Adis, Springer Healthcare
2025-04-01
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| Series: | Neurology and Therapy |
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| Online Access: | https://doi.org/10.1007/s40120-025-00741-x |
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| author | James E. Galvin Michael J. Kleiman Heather M. Harris Paul W. Estes |
| author_facet | James E. Galvin Michael J. Kleiman Heather M. Harris Paul W. Estes |
| author_sort | James E. Galvin |
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| description | Abstract Introduction At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer’s disease (pAD), mild cognitive impairment (MCI) due to Alzheimer’s disease (MCI-AD), AD, and MCI and dementia due to non-AD etiologies enrolled in the Bio-Hermes Study. Methods A total of 887 individuals completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. Three Cognivue Clarity subtests differentiated between True Controls and pAD, and between cognitive impairment due to AD versus non-AD processes. This finding was leveraged to develop an amyloid-specific marker, combining the three subtests with age using machine learning to create the 4-point Cognivue Amyloid Risk Measure (CARM). Results Cognivue Clarity discriminated cognitively normal from cognitively impaired individuals (p < 0.001, Cohen’s d = 0.732). The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen’s d = 0.618) and blood-based biomarkers (p’s < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR: 3.67; 95% CI 2.76–4.89). CARM categories differentiated between True Controls, pAD, MCI-AD, AD, and cognitive impairment due to non-AD etiologies (χ 2 = 137.6, p < 0.001) with the majority of True Controls and non-AD etiologies being in CARM1/CARM2, and the majority of pAD, MCI-AD, and AD being in CARM3/CARM4. Conclusions Cognivue Clarity detects individuals with cognitive impairment, and a derivation benchmarked against amyloid PET was used to develop the CARM to predict the presence of amyloid. Combining the CARM and the Cognivue Clarity overall score could help identify individuals with and without cognitive impairment due to AD or non-AD etiologies, help screen for treatment protocols with anti-amyloid therapies, enrich clinical trial recruitment, and help to identify pAD for prevention studies. Trial Registration ClinicalTrials. gov identifier, NCT04733989. |
| format | Article |
| id | doaj-art-556e20c997e543eb9dcdd9c566809742 |
| institution | OA Journals |
| issn | 2193-8253 2193-6536 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Adis, Springer Healthcare |
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| spelling | doaj-art-556e20c997e543eb9dcdd9c5668097422025-08-20T02:33:32ZengAdis, Springer HealthcareNeurology and Therapy2193-82532193-65362025-04-0114386588010.1007/s40120-025-00741-xThe Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue ClarityJames E. Galvin0Michael J. Kleiman1Heather M. Harris2Paul W. Estes3Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of MedicineDepartment of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of MedicineCognivue, IncCognivue, IncAbstract Introduction At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer’s disease (pAD), mild cognitive impairment (MCI) due to Alzheimer’s disease (MCI-AD), AD, and MCI and dementia due to non-AD etiologies enrolled in the Bio-Hermes Study. Methods A total of 887 individuals completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. Three Cognivue Clarity subtests differentiated between True Controls and pAD, and between cognitive impairment due to AD versus non-AD processes. This finding was leveraged to develop an amyloid-specific marker, combining the three subtests with age using machine learning to create the 4-point Cognivue Amyloid Risk Measure (CARM). Results Cognivue Clarity discriminated cognitively normal from cognitively impaired individuals (p < 0.001, Cohen’s d = 0.732). The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen’s d = 0.618) and blood-based biomarkers (p’s < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR: 3.67; 95% CI 2.76–4.89). CARM categories differentiated between True Controls, pAD, MCI-AD, AD, and cognitive impairment due to non-AD etiologies (χ 2 = 137.6, p < 0.001) with the majority of True Controls and non-AD etiologies being in CARM1/CARM2, and the majority of pAD, MCI-AD, and AD being in CARM3/CARM4. Conclusions Cognivue Clarity detects individuals with cognitive impairment, and a derivation benchmarked against amyloid PET was used to develop the CARM to predict the presence of amyloid. Combining the CARM and the Cognivue Clarity overall score could help identify individuals with and without cognitive impairment due to AD or non-AD etiologies, help screen for treatment protocols with anti-amyloid therapies, enrich clinical trial recruitment, and help to identify pAD for prevention studies. Trial Registration ClinicalTrials. gov identifier, NCT04733989.https://doi.org/10.1007/s40120-025-00741-xAlzheimer’s diseaseAmyloid PETBio-HermesCognivueCognitive assessmentDigital biomarker |
| spellingShingle | James E. Galvin Michael J. Kleiman Heather M. Harris Paul W. Estes The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity Neurology and Therapy Alzheimer’s disease Amyloid PET Bio-Hermes Cognivue Cognitive assessment Digital biomarker |
| title | The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity |
| title_full | The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity |
| title_fullStr | The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity |
| title_full_unstemmed | The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity |
| title_short | The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity |
| title_sort | cognivue amyloid risk measure carm a novel method to predict the presence of amyloid with cognivue clarity |
| topic | Alzheimer’s disease Amyloid PET Bio-Hermes Cognivue Cognitive assessment Digital biomarker |
| url | https://doi.org/10.1007/s40120-025-00741-x |
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