Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis.

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-ß42/40, 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.

Cross-scanner harmonization methods for structural MRI may need further work: A comparison study.

The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.

Can integration of Alzheimer's plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?

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, Aß1-42 and Aß1-40 (used as Aß42/Aß40 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 Aß42/Aß40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aß42/Aß40 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.

Associations of Neurodegeneration Biomarkers in Cerebrospinal Fluid with Markers of Alzheimer's Disease and Vascular Pathology.

BACKGROUND: The National Institute on Aging-Alzheimer's Association Research Framework proposes defining Alzheimer's disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear. OBJECTIVE: To determine whether cerebrospinal fluid (CSF)-neurodegenerative biomarkers (N) map differentially to Alzheimer's disease pathology measured by Aß42 (an indicator of amyloidosis, [A]), p-tau (an indicator of tau deposition, [T]), and MRI vascular pathology indicators (measured by white-matter integrity, infarcts, and microbleeds [V]). METHODS: Participants were from Mayo Clinic Study of Aging (MCSA) with CSF measures of NfL, Ng, t-tau, Aß42, and p-tau and available MRI brain imaging. Linear models assessed associations between CSF neurodegeneration (N) markers, amyloid markers (A), tau (T), and vascular pathology (V). RESULTS: Participants (n = 408) had a mean age of 69.2±10.7; male, 217 (53.2%); cognitively unimpaired, 359 (88%). All three neurodegeneration biomarkers correlated with age (p < 0.001 for NfL and t-tau, p = 0.018 for Ng). Men had higher CSF-NfL levels; women had higher Ng (p < 0.001). NfL and t-tau levels correlated with infarcts (p = 0.009, p = 0.034 respectively); no biomarkers correlated with white-matter integrity. N biomarkers correlated with p-tau levels (T, p < 0.001). Higher Aß42 levels associated with higher N-biomarker levels but only among cognitively unimpaired (A, p < 0.001). CONCLUSION: The influence of vascular pathology in the general population on CSF (N) biomarkers is modest, with greater influence of infarcts than white-matter disruption. Neurodegeneration markers more closely correlated with tau than amyloid markers.

Long-term effects of premenopausal bilateral oophorectomy with or without hysterectomy on physical aging and chronic medical conditions.

OBJECTIVE: We examined the long-term effects of premenopausal bilateral oophorectomy (PBO) with or without concurrent or preceding hysterectomy on physical and cognitive function and on odds of chronic conditions. METHODS: We enrolled 274 women with PBO with or without concurrent or preceding hysterectomy and 240 referents aged 55 years and older who were residents of Olmsted County, MN as of the PBO or index date. Chronic conditions were assessed via medical record abstraction. Cognitive diagnoses were based on neurocognitive testing. A physical function assessment included measures of strength and mobility. Multivariable regression models compared characteristics for women with PBO <46 years, PBO 46-49 years, and referent women with adjustments for age and other confounders. RESULTS: The clinical visits (median age, 67 years) were a median of 22 years after the PBO or index date. Of 274 women with PBO, 161 (59%) were <46 years at PBO and 113 (41%) were 46-49 years. Compared with referents, women with a history of PBO <46 years had increased odds of arthritis (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.06-2.55), asthma (OR, 1.74; 95% CI, 1.03-2.93), obstructive sleep apnea (OR, 2.00; 95% CI, 1.23-3.26), and bone fractures (OR, 2.86; 95% CI, 1.17-6.98), and walked a shorter mean distance on a 6-minute walk test ( b = -18.43; P = 0.034). Compared with referents, women with a history of PBO at age 46-49 years had increased odds of arthritis (OR, 1.92; 95% CI, 1.16-3.18) and obstructive sleep apnea (OR, 2.21; 95% CI, 1.33-3.66). There were no significant differences in cognitive status in women with PBO compared with referents. CONCLUSIONS: Women with a history of PBO with or without concurrent or preceding hysterectomy, especially at age <46 years, have more chronic conditions in late mid-life compared with referents.

Association of plasma glial fibrillary acidic protein (GFAP) with neuroimaging of Alzheimer's disease and vascular pathology.

Introduction: Plasma glial fibrillary acidic protein (GFAP) may be associated with amyloid burden, neurodegeneration, and stroke but its specificity for Alzheimer's disease (AD) in the general population is unclear. We examined associations of plasma GFAP with amyloid and tau positron emission tomography (PET), cortical thickness, white matter hyperintensities (WMH), and cerebral microbleeds (CMBs). Methods: The study included 200 individuals from the Mayo Clinic Study of Aging who underwent amyloid and tau PET and magnetic resonance imaging and had plasma GFAP concurrently assayed; multiple linear regression and hurdle model analyses were used to investigate associations controlling for age and sex. Results: GFAP was associated with amyloid and tau PET in multivariable models. After adjusting for amyloid, the association with tau PET was no longer significant. GFAP was associated with cortical thickness, WMH, and lobar CMBs only among those who were amyloid-positive. Discussion: This cross-sectional analysis demonstrates the utility of GFAP as a plasma biomarker for AD-related pathologies.

Association of plasma biomarkers of amyloid and neurodegeneration with cerebrovascular disease and Alzheimer's disease.

The objective of this study was to determine the differential mapping of plasma biomarkers to postmortem neuropathology measures. We identified 64 participants in a population-based study with antemortem plasma markers (amyloid-ß [Aß] x-42, Aßx-40, neurofilament light [NfL], and total tau [T-tau]) who also had neuropathologic assessments of Alzheimer's and cerebrovascular pathology. We conducted weighted linear-regression models to evaluate relationships between plasma measures and neuropathology. Higher plasma NfL and Aß42/40 ratio were associated with cerebrovascular neuropathologic scales (p < 0.05) but not with Braak stage, neuritic plaque score, or Thal phase. Plasma Aß42/40 and NfL explained up to 18% of the variability in cerebrovascular neuropathologic scales. In participants predominantly with modest levels of Alzheimer's pathologic change, biomarkers of amyloid and neurodegeneration were associated with cerebrovascular neuropathology. NfL is a non-specific marker of brain injury, therefore its association with cerebrovascular neuropathology was expected. The association between elevated Aß42/40 and cerebrovascular disease pathology needs further investigation but could be due to the use of less specific amyloid-ß assays (x-40, x-42).