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This article discusses a study that examined the associations between different pathologies and gray matter atrophy in neurodegenerative diseases. The study used a dataset of 125 dementia patients with both antemortem MRI scans and postmortem histopathology data. The researchers compared different models to assess the impact of various pathologies on atrophy, including a polypathologic model, a model based on the patient's primary diagnosis, and a protein-agnostic model. The results showed that the polypathologic model provided the best fits in both the training and testing datasets. Tau, TDP-43, and -synuclein burden were found to be inversely associated with regional volumes, while amyloid was not. Additionally, the study found that gliosis and neuronal loss explained residual variance in atrophy and mediated the effects of tau, TDP-43, and -synuclein on atrophy. The findings highlight the importance of antemortem biomarkers for detecting mixed pathology in neurodegenerative diseases.
Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer’s pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-, tau, TDP-43, and -synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient’s primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and -synuclein burden were inversely associated with regional volumes, but amyloid- was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and -synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.