Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches
Abstract Aβ-amyloid deposition is a key feature of Alzheimer’s disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-12-01
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| Series: | Acta Neuropathologica Communications |
| Online Access: | https://doi.org/10.1186/s40478-019-0858-4 |
| _version_ | 1818332171442585600 |
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| author | S. B. Wharton D. Wang C. Parikh F. E. Matthews C. Brayne P. G. Ince on behalf of the Cognitive Function and Ageing Neuropathology Study Group |
| author_facet | S. B. Wharton D. Wang C. Parikh F. E. Matthews C. Brayne P. G. Ince on behalf of the Cognitive Function and Ageing Neuropathology Study Group |
| author_sort | S. B. Wharton |
| collection | DOAJ |
| description | Abstract Aβ-amyloid deposition is a key feature of Alzheimer’s disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposition, and in combination with Braak neurofibrillary tangle staging also allows derivation of primary age-related tauopathy (PART). We sought to determine whether Thal Aβ phase predicts dementia better than CERAD in a population-representative cohort (n = 186) derived from the Cognitive Function and Ageing Study (CFAS). Cerebral amyloid angiopathy (CAA) was quantitied as the number of neuroanatomical areas involved and cases meeting criteria for PART were defined to determine if they are a distinct pathological group within the ageing population. Agreement with the Thal scheme was excellent. In univariate analysis Thal phase performed less well as a predictor of dementia than CERAD, Braak or CAA. Logistic regression, decision tree and linear discriminant analysis were performed for multivariable analysis, with similar results. Thal phase did not provide a better explanation of dementia than CERAD, and there was no additional benefit to including more than one assessment of Aβ in the model. Number of areas involved by CAA was highly correlated with assessment based on a severity score (p < 0.001). The presence of capillary involvement (CAA type I) was associated with higher Thal phase and Braak stage (p < 0.001). CAA was not associated with microinfarcts (p = 0.1). Cases satisfying pathological criteria for PART were present at a frequency of 10.2% but were not older and did not have a higher likelihood of dementia than a comparison group of individuals with similar Braak stage but with more Aβ. They also did not have higher hippocampal-tau stage, although PART was weakly associated with increased presence of thorn-shaped astrocytes (p = 0.048), suggesting common age-related mechanisms. Thal phase is highly applicable in a population-representative setting and allows definition of pathological subgroups, such as PART. Thal phase, plaque density, and extent and type of CAA measure different aspects of Aβ pathology, but addition of more than one Aβ measure does not improve dementia prediction, probably because these variables are highly correlated. Machine learning predictions reveal the importance of combining neuropathological measurements for the assessment of dementia. |
| first_indexed | 2024-12-13T13:31:30Z |
| format | Article |
| id | doaj.art-412ebe85176c457386793a930503e7db |
| institution | Directory Open Access Journal |
| issn | 2051-5960 |
| language | English |
| last_indexed | 2024-12-13T13:31:30Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Acta Neuropathologica Communications |
| spelling | doaj.art-412ebe85176c457386793a930503e7db2022-12-21T23:44:09ZengBMCActa Neuropathologica Communications2051-59602019-12-017111210.1186/s40478-019-0858-4Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approachesS. B. Wharton0D. Wang1C. Parikh2F. E. Matthews3C. Brayne4P. G. Ince5on behalf of the Cognitive Function and Ageing Neuropathology Study GroupSheffield Institute for Translational Neuroscience, University of SheffieldSheffield Institute for Translational Neuroscience, University of SheffieldSheffield Institute for Translational Neuroscience, University of SheffieldInstitute for Health and Society, University of NewcastleInstitute of Public Health, University of CambridgeSheffield Institute for Translational Neuroscience, University of SheffieldAbstract Aβ-amyloid deposition is a key feature of Alzheimer’s disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposition, and in combination with Braak neurofibrillary tangle staging also allows derivation of primary age-related tauopathy (PART). We sought to determine whether Thal Aβ phase predicts dementia better than CERAD in a population-representative cohort (n = 186) derived from the Cognitive Function and Ageing Study (CFAS). Cerebral amyloid angiopathy (CAA) was quantitied as the number of neuroanatomical areas involved and cases meeting criteria for PART were defined to determine if they are a distinct pathological group within the ageing population. Agreement with the Thal scheme was excellent. In univariate analysis Thal phase performed less well as a predictor of dementia than CERAD, Braak or CAA. Logistic regression, decision tree and linear discriminant analysis were performed for multivariable analysis, with similar results. Thal phase did not provide a better explanation of dementia than CERAD, and there was no additional benefit to including more than one assessment of Aβ in the model. Number of areas involved by CAA was highly correlated with assessment based on a severity score (p < 0.001). The presence of capillary involvement (CAA type I) was associated with higher Thal phase and Braak stage (p < 0.001). CAA was not associated with microinfarcts (p = 0.1). Cases satisfying pathological criteria for PART were present at a frequency of 10.2% but were not older and did not have a higher likelihood of dementia than a comparison group of individuals with similar Braak stage but with more Aβ. They also did not have higher hippocampal-tau stage, although PART was weakly associated with increased presence of thorn-shaped astrocytes (p = 0.048), suggesting common age-related mechanisms. Thal phase is highly applicable in a population-representative setting and allows definition of pathological subgroups, such as PART. Thal phase, plaque density, and extent and type of CAA measure different aspects of Aβ pathology, but addition of more than one Aβ measure does not improve dementia prediction, probably because these variables are highly correlated. Machine learning predictions reveal the importance of combining neuropathological measurements for the assessment of dementia.https://doi.org/10.1186/s40478-019-0858-4 |
| spellingShingle | S. B. Wharton D. Wang C. Parikh F. E. Matthews C. Brayne P. G. Ince on behalf of the Cognitive Function and Ageing Neuropathology Study Group Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches Acta Neuropathologica Communications |
| title | Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| title_full | Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| title_fullStr | Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| title_full_unstemmed | Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| title_short | Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| title_sort | epidemiological pathology of aβ deposition in the ageing brain in cfas addition of multiple aβ derived measures does not improve dementia assessment using logistic regression and machine learning approaches |
| url | https://doi.org/10.1186/s40478-019-0858-4 |
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