Ramanan, Vijay K

Abstract There is increasing interest in Alzheimer’s disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ1–42 and Aβ1–40 (used as Aβ42/Aβ40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants’ brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65–80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R² = 0.15) to MRI (R² = 0.18) and cardiovascular measures (R² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R² = 0.26 and 0.27). For amyloid positive individuals Aβ42/Aβ40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ42/Aβ40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population’s socio-demographics, brain and cardiovascular health.

Abstract Predominant limbic degeneration has been associated with various underlying aetiologies and an older age, predominant impairment of episodic memory and slow clinical progression. However, the neurological syndrome associated with predominant limbic degeneration is not defined. This endeavour is critical to distinguish such a syndrome from those originating from neocortical degeneration, which may differ in underlying aetiology, disease course and therapeutic needs. We propose a set of clinical criteria for a limbic-predominant amnestic neurodegenerative syndrome that is highly associated with limbic-predominant age-related TDP-43 encephalopathy but also other pathologic entities. The criteria incorporate core, standard and advanced features, including older age at evaluation, mild clinical syndrome, disproportionate hippocampal atrophy, impaired semantic memory, limbic hypometabolism, absence of neocortical degeneration and low likelihood of neocortical tau, with degrees of certainty (highest, high, moderate and low). We operationalized this set of criteria using clinical, imaging and biomarker data to validate its associations with clinical and pathologic outcomes. We screened autopsied patients from Mayo Clinic and Alzheimer’s Disease Neuroimaging Initiative cohorts and applied the criteria to those with an antemortem predominant amnestic syndrome (Mayo, n = 165; Alzheimer’s Disease Neuroimaging Initiative, n = 53) and who had Alzheimer’s disease neuropathological change, limbic-predominant age-related TDP-43 encephalopathy or both pathologies at autopsy. These neuropathology-defined groups accounted for 35, 37 and 4% of cases in the Mayo cohort, respectively, and 30, 22 and 9% of cases in the Alzheimer’s Disease Neuroimaging Initiative cohort, respectively. The criteria effectively categorized these cases, with Alzheimer’s disease having the lowest likelihoods, limbic-predominant age-related TDP-43 encephalopathy patients having the highest likelihoods and patients with both pathologies having intermediate likelihoods. A logistic regression using the criteria features as predictors of TDP-43 achieved a balanced accuracy of 74.6% in the Mayo cohort, and out-of-sample predictions in an external cohort achieved a balanced accuracy of 73.3%. Patients with high likelihoods had a milder and slower clinical course and more severe temporo-limbic degeneration compared to those with low likelihoods. Stratifying patients with both Alzheimer’s disease neuropathological change and limbic-predominant age-related TDP-43 encephalopathy from the Mayo cohort according to their likelihoods revealed that those with higher likelihoods had more temporo-limbic degeneration and a slower rate of decline and those with lower likelihoods had more lateral temporo-parietal degeneration and a faster rate of decline. The implementation of criteria for a limbic-predominant amnestic neurodegenerative syndrome has implications to disambiguate the different aetiologies of progressive amnestic presentations in older age and guide diagnosis, prognosis, treatment and clinical trials.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.