Lowe, Val J.

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.

AbstractObjectivePrimary progressive apraxia of speech (PPAOS) is associated with imaging abnormalities in the lateral premotor cortex (LPC) and supplementary motor area (SMA). It is not known whether greater involvement of these regions in either hemisphere is associated with demographics, presenting, and/or longitudinal features.MethodsIn 51 prospectively recruited PPAOS patients who completed [18F]‐fluorodeoxyglucose (FDG) positron emission tomography (PET), we classified patients as left‐dominant, right‐dominant, or symmetric, based on visual assessment of the LPC and SMA on FDG‐PET. SPM and statistical analyses of regional metabolic values were performed. Diagnosis of PPAOS was made if apraxia of speech was present and aphasia absent. Thirteen patients completed ioflupane‐123I (dopamine transporter [DAT]) scans. We compared cross‐sectional and longitudinal clinicopathological, genetic, and neuroimaging characteristics across the three groups, with area under the receiver‐operating curve (AUROC) determined as a measure of effect size.ResultsIn all, 49% of the PPAOS patients were classified as left‐dominant, 31% as right‐dominant, and 20% as symmetric, which was supported by results from the SPM and regional analyses. There were no differences in baseline characteristics. Longitudinally, right‐dominant PPAOS showed faster rates of progression of ideomotor apraxia (AUROC 0.79), behavioral disturbances (AUROC 0.84), including disinhibition symptoms (AUROC 0.82) and negative behaviors (AUROC 0.82), and parkinsonism (AUROC 0.75) compared to left‐dominant PPAOS. Symmetric PPAOS showed faster rates of dysarthria progression compared to left‐dominant (AUROC 0.89) and right‐dominant PPAOS (AUROC 0.79). Five patients showed abnormal DAT uptake. Braak neurofibrillary tangle stage differed across groups (p = 0.01).ConclusionsPatients with PPAOS and a right‐dominant pattern of hypometabolism on FDG‐PET have the fastest rates of decline of behavioral and motor features.

Abstract Staging the severity of Alzheimer’s disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer’s clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1–2, 3–4, 5–6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1–42 and Aβ1–40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78–0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72–0.85 versus C = 0.73–0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer’s disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.