Christopher J. Fowler

Alzheimer's disease (AD) is characterized by amyloid-β (Aβ) for ∼20 years prior to dementia onset. Phthalates, routinely added to plastics to increase flexibility, and bisphenol monomers, have been shown to interfere with neural activity/brain function. Phthalate and bisphenol exposure did not show association of individual urinary biomarkers with Aβ or clinical phenotype (mild cognitive impairment (MCI)/AD). Assessing biomarkers in quartiles, low mono(2-ethylhexyl) phthalate (MEHP), but not other biomarkers of di(2-ethylhexyl) phthalate (DEHP), was associated with Aβ+ and with having MCI/AD (q < 0.05), while high Bisphenol S (BPS) was associated with Aβ+ (q < 0.05). Mixture analysis shows significant association with MCI/AD but not Aβ.

In early Alzheimer disease (AD), β-amyloid (Aβ) deposition is associated with volume loss in the basal forebrain (BF) and cognitive decline. However, the extent to which Aβ-related BF atrophy manifests as cognitive decline is not understood. This study sought to characterize the relationship between BF atrophy and the decline in memory and attention in patients with early AD. Participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study who completed Aβ-PET imaging and repeated MRI and cognitive assessments were included. At baseline, participants were classified based on their clinical dementia stage and Aβ status, yielding groups that were cognitively unimpaired (CU) Aβ-, CU Aβ+, and mild cognitive impairment (MCI) Aβ+. Linear mixed-effects models were used to assess changes in volumetric measures of BF subregions and the hippocampus and changes in AIBL memory and attention composite scores for each group compared with CU Aβ- participants. Associations between Aβ burden, brain atrophy, and cognitive decline were evaluated and explored further using mediation analyses. The cohort included 476 participants (72.6 ± 5.9 years, 55.0% female) with longitudinal data from a median follow-up period of 6.1 years. Compared with the CU Aβ- group (n = 308), both CU Aβ+ (n = 107) and MCI Aβ+ (n = 61) adults showed faster decline in BF and hippocampal volumes and in memory and attention (Cohen d = 0.73-1.74). Rates of atrophy in BF subregions and the hippocampus correlated with cognitive decline, and each individually mediated the impact of Aβ burden on memory and attention decline. When all mediators were considered simultaneously, hippocampal atrophy primarily influenced the effect of Aβ burden on memory decline (β [SE] = -0.139 [0.032], proportion mediated [PM] = 28.0%) while the atrophy of the posterior nucleus basalis of Meynert in the BF (β [SE] = -0.068 [0.029], PM = 13.1%) and hippocampus (β [SE] = -0.121 [0.033], PM = 23.4%) distinctively influenced Aβ-related attention decline. These findings highlight the significant role of BF atrophy in the complex pathway linking Aβ to cognitive impairment in early stages of AD. Volumetric assessment of BF subregions could be essential in elucidating the relationships between the brain structure and behavior in AD.

Background: Alzheimer’s disease (AD) is a multifactorial disease including pre‐clinical, prodromal and clinical phases. CSF “A/T/N” (Ab42, pTau181, tTau) biomarkers help characterize the biological state of AD. The primary aim was to assess contributions of eight CSF markers for predicting changes in cognition.Method: CSF from 237 participants (CN: 176[74.3%], MCI: 33[13.9%], AD: 28[11.8%]) of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing was used to measure Ab42 (GenII assay), pTau181, and tTau (Elecsys®) along with neurogranin, neurofilament light (NFL), α‐synuclein, glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells‐2 (sTREM2), s100 calcium‐binding protein B (s100B), and interleukin‐6 (IL‐6) from the NeuroToolKit (NTK) panel of exploratory prototype assays (all Roche Diagnostics International Ltd). NTK biomarker concentrations were measured amongst each of four ATN groups at baseline; 1) A‐/T‐/N‐ (reference group), 2) A+/T±/N± (amyloid+), 3) A‐/T±/N± (amyloid‐), and 4) A+/T+/N+. Change in cognition was assessed using the pre‐clinical Alzheimer’s cognitive composite (PACC) and the clinical dementia rating sum of boxes (CDR‐SB) scores, in all four ATN groups, stratified by low/high NTK biomarker over a minimum of 36 months. The primary outcome was defined as change in cognition in amyloid+ participants modulated by the addition of NTK markers to ATN groups.Result: A+/T+/N+ participants had faster cognitive decline on both PACC and CDR‐SB over time compared to all other groups (Figure 1A, PACC; Figure 1B, CDR‐SB). Within the amyloid+ group, participants with high sTREM2 (K+) had a faster rate of decline in PACC compared to those with low sTREM2 (K‐, p = 0.04; Figure 1C), whilst participants with high α‐synuclein had a faster increase in CDR‐SB compared with those with low α‐synuclein (p = 0.005; Figure 1D). Amongst participants who were A+/T+/N+, those with high YKL40 had a significantly faster rate of decline in PACC (p = 0.017; Figure 1E), and a faster increase in CDR‐SB, (p = 0.017; Figure 1F) compared with those who with low YKL40.Conclusion: Biomarkers of microglial activation (sTREM2), synaptic function and synuclein metabolism (α‐synuclein), and astrocytic activation (YKL‐40) may be appropriate to discern rates of cognitive change within amyloid+ participants. Findings are being validated in a separate population.

Background: It has been shown that Aß is a normal component in urine (Ghiso et al, FEBS Lett, 1997) and may change with disease status (Takata et al, Neurosci Lett, 2008), however, research on amyloid in urine has been neglected for long time.Method: We examined β amyloid proteins in urine from cognitive normal and dementia patients including Alzheimer’s disease (AD) and frontotemporal dementia (FTD) using Western blotting.Result: Several protein fragments were identified including the 12KD and 14KD bands recognized by the anti‐APP C‐termini antibody (Ab369), 14KD and 28KD bands by W0‐2 (Figure 1), and 28KD, 56KD and 68KD bands by Aducanumab (Figure 2) and anti‐APP N‐termini antibody (22C11). Further analysis with immunoprecipitation and mass spectrometry confirmed the presence of various APP fragments and Aß in urine. One type of peptide was found to have high affinity to Aß and APP fragments using microscale thermophoresis and circular dichroism spectroscopy. With the assistance from AIBL, we further invented a lateral flow immunoassay device with colloidal gold nanoparticles coated with anti‐APP/Aß antibodies. In the early clinical trials with aged people and children, 95% of dementia patients, 90% of MCI, 85% of SMC showed positivity while 90% of healthy children were negative. The second quality examination was performed in Oriental (Dongfeng) Hospital (Pudong, Shanghai) in Mar‐June 2022. The examination of detection limit, critical threshold, repeatability, tolerance to interferences (creatinine, protein, glucose, nitrites, pH) was performed and all passed. Furthermore, 43 of 49 cases showed positive (87.7%) while only 1 out of 30 controls was positive (3.3%). Since then, the device has been tested in a dozen of large hospitals and pathology laboratories in China, all provided positive feedback. The preliminary results from 175 AIBL participants showed overall 83.8% positivity to clinically diagnosed MCI (n = 37), while for those Aβ PET scan negative MCI (n = 17), positivity was 94.1% (Figure 3).Conclusion: Our results indicates the high accuracy of this device, which has high potential to be used as an early screening tool for MCI and dementia. The longitudinal studies are currently undergoing in China (Xiangya Hospital) and in Australia (AIBL).

Background: Mid‐life obesity is linked with increased risk for late‐life dementia, however relationships between later‐life body composition and dementia are less clear.Method: Using a combined cohort from two major observational studies (AIBL and ADNI) and categorising Body Mass Index (BMI) according to WHO criteria, we computed multivariable linear mixed‐effects models to determine association between BMI category and outcomes: brain beta‐amyloid PET quantification (centiloid); MRI (global, hippocampal and white matter hyperintensity volume); and cognition (PACC [preclinical Alzheimer cognitive composite], composite scores for attention, executive function and episodic memory). All models were adjusted by covariables age, gender, education, APOE ε4, and study (AIBL/ADNI).Results: The cohort included 4,668 observations from 2,828 individuals (49.4% male, mean age 73.8 years, 38.2% APOE ε4 carriage, 61.8% normal cognition [NC], 25.6% Mild Cognitive Impairment [MCI], 12.6% dementia due to Alzheimer’s disease). The average BMI was 26.8 kg/m² (0.6% underweight, 37.7% normal‐range, 41.7% overweight, 20.0% obese [BMI 30 kg/m², respectively]).Relative to normal‐range BMI, those with overweight‐or‐obese range BMI had a significant negative association with PET centiloid level, beta = ‐0.150 (0.037 se) and ‐0.309 (0.042), respectively (i.e. greater BMI had lower amyloid level). This remained significant for obese‐range BMI in fully‐adjusted models when restricted to only NC participants. Also, obesity was associated with greater hippocampal volume, beta = 0.192 (0.046), but this was not significant when restricted to NC only. Associations with total grey matter/cortical volume and white matter hyperintensities were not significant. Obese‐range BMI was associated with stronger performance on attention and episodic memory composites, but not for PACC and executive function. For NC‐only, there was only significant negative association between obesity and attention performance. In multivariate analyses, neither obesity category nor BMI were associated with longitudinal change in cognitive scores.Conclusion: Presence of obesity in later life was associated with lower brain amyloid levels and less hippocampal atrophy in a mixed cohort including older adults with dementia, mild cognitive impairment and normal cognition. While mid‐life obesity is associated greater risk for AD, later life associations appear influenced by weight loss with emerging dementia.

Background: Lipid dyshomeostasis is associated with various disorders including Alzheimer’s disease (AD). The work from our laboratory (Su et al, JEV 2021), and others, suggests that extracellular vesicles (EVs) contain lipid biomarkers that could aid in the diagnosis of AD and diseases associated with impaired lipid metabolism.Knowing the lipid content of EVs is a key first step to discovering EV‐based lipid biomarkers in blood. Some studies have reported the lipid content of ‘’EVs’’ in blood. However, used EV isolation techniques that are known to co‐isolate free lipid and lipoproteins.Here we: 1) demonstrate the importance of size exclusion resin size to isolating highly enriched EVs for lipidomic analysis, 2) have developed a reference EV plasma lipidome for the research community and 3) determining if plasma EVs report on lipid dyshomeostasis in Alzheimer’s disease.Method: Human plasma EVs were isolated using an assortment of commercially available kits or by density fractionation and size exclusion chromatography and characterised by western blot, transmission electron microscopy, and quantitative mass spectrometry based proteomic and lipidome analysis.Result: Of the high‐throughput EV isolation kits tested, the majority were unable to separate lipoproteins from EVs for the purposes of EV lipidomics. We then relied on comparing labor intensive methods (density gradient and size exclusion) to identify a combination of techniques that isolate the analytes of interest, EV proteins and lipids. Quantitative proteome and lipidome analysis strategies are being applied to highly enriched EVs with the goal of providing the reference lipidome of EVs in plasma and in Alzheimer’s disease.Conclusion: To enhance the confidence of EV lipid identification and reveal the lipidome of EVs in plasma, our study compared and identified techniques to enrich EVs and deplete non‐EV associated lipid and protein from human plasma. These findings serve as a reference resource and the foundation for research investigating plasma EV lipids as Alzheimer’s disease biomarkers.

Background: Tau PET imaging enables prospective longitudinal observation of the rate and location of tau accumulation in Alzheimer’s disease (AD). ¹⁸F‐MK6240 is a newer, high affinity tracer for the paired helical filaments of tau in AD. It is widely used in clinical trials, despite sparse longitudinal natural history data. We aimed to evaluate the impact of two reference regions on the magnitude and effect size of regional change.Method: One hundred and fifty‐eight participants: 83 Aβ‐ cognitively unimpaired (CU), 37 Aβ+ CU, 19 Aβ+ MCI, 19 Aβ+ AD had annual ¹⁸F‐MK6240 PET imaging for a mean of 1.6 years. Standardized uptake value ratios (SUVR) were generated for three in‐house composite ROI: mesial temporal (Me), temporoparietal (Te), and rest of neocortex (R), and a Free‐Surfer derived meta‐temporal (MT) ROI. Two reference regions were examined: cerebellar cortex (SUVRcb) and eroded subcortical white matter (SUVRwm).Result: Figure 1 shows the annual rate of tau SUVR change in composite ROI. In Aβ+ CU, tau accumulation was greatest in Me (2.8‐3.2%/ year) (vs Aβ‐ CU, Cohen’s d = 0.30 SUVRcb, 0.24 SUVRwm) and MT (3.2‐3.6%/year) (vs Aβ‐ CU, d = 0.36 SUVRcb, 0.31 SUVRwm). In Aβ+ MCI, tau accumulation was greatest in Te (2.3‐3.7%/year) (vs Aβ‐ CU, d = 0.34 SUVRcb, 0.41 SUVRwm). In Aβ+ AD, greatest increase was in Te and R, with a decrease in Me. In Aβ+ AD, SUVRcb showed marginally greater increase in these regions (e.g. 3.8%/year compared to 2.68%/year SUVRwm in R) (vs Aβ‐ CU, d = 0.60 SUVRcb, 0.27 SUVRwm). In the young Aβ+ AD dementia group, spill‐over from adjacent high cortical binding or an increase in white matter binding elevates signal in the reference region.Conclusion: The rate of tau accumulation varies according to brain region and baseline tau. Change is greatest in the MTL in preclinical AD, in the MTL and lateral temporal cortex in MCI, and Braak V‐VI regions in AD dementia. The PET measured change is greater and less variable using an eroded white matter reference region, except in AD dementia, where spill‐over from adjacent high cortical binding or increase in white matter binding elevates the reference region.

Background: Plasma amyloid‐β (Aβ) 1‐42/Aβ1‐40 ratio, phosphorylated‐tau181 (p‐tau181), glial fibrillary acidic protein (GFAP) and neurofilament light (NFL) are putative blood‐based biomarkers for Alzheimer’s disease (AD). However, head‐to‐head comparisons of the afore‐mentioned biomarkers across the AD continuum are lacking.Method: Plasma Aβ1‐42, Aβ1‐40, p‐tau181, GFAP and NFL were measured using the Single Molecule Array platform and compared cross‐sectionally in models with and without AD risk factors (age, sex and APOE ε4 status) across the AD continuum in Aβ PET positive confirmed participants (CU Aβ+, n = 39; MCI Aβ+, n = 33; AD Aβ+, n = 46) and Aβ PET negative confirmed participants (CU Aβ‐, n = 81; MCI Aβ‐, n = 26) from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) cohort. Area under the receiver operating characteristic curves (AUCs) for predicting Aβ PET‐/+ status were compared using DeLong test. Associations between plasma biomarker levels at baseline and prospective cognitive decline (CDR‐SOB) and brain Aβ load were also assessed.Result: Lower plasma Aβ1‐42/Aβ1‐40 ratio and higher p‐tau181 and GFAP were observed in preclinical AD (Aβ1‐42/Aβ1‐40 ratio: ∼19%; p‐tau181: ∼70%; GFAP: ∼52%), prodromal AD (Aβ1‐42/Aβ1‐40 ratio: ∼14%; p‐tau181: ∼69%; GFAP: ∼45%) and AD (Aβ1‐42/Aβ1‐40 ratio: ∼16%; p‐tau181: ∼91%; GFAP: ∼85%), and higher plasma NFL was observed in prodromal AD (∼27%) and AD (∼45%) (Figure 1). Based on the AUCs, p‐tau181 performed equivalent to or better than (≥) other biomarkers, in predicting Aβ PET‐/+ status across the AD continuum (p‐tau AUCs for CU Aβ‐ vs CU Aβ+ ∼81%/ MCI Aβ+ ∼86%/ AD ∼92%). A biomarker panel of Aβ1‐42/Aβ1‐40 ratio, p‐tau181 and GFAP performed ≥ the biomarkers alone in predicting Aβ PET‐/+ status across the AD continuum (biomarker panel AUCs for CU Aβ‐ vs CU Aβ+ 90%/ MCI Aβ+ 89%/ AD 96%). Higher p‐tau181 (β = 0.531, p = 3.18E‐08), GFAP (β = 0.530, p = 5.07E‐08) and NFL (β = 0.487, p = 4.76E‐06) were associated with cognitive decline prospectively and lower plasma Aβ1‐42/Aβ1‐40 ratio (β = ‐6.035, p = 1.56E‐04) and higher p‐tau181 (β = 2.823, p = 4.72E‐05) and GFAP (β = 2.075, p = 0.003) were associated with increased Aβ PET load prospectively.Conclusion: These findings suggest that plasma biomarkers are altered across the AD continuum and are associated with cognitive decline and increased brain Aβ load prospectively.

Background and Objectives This prospective study sought to determine the association of modifiable/nonmodifiable components included in the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score with hippocampal volume (HV) loss and episodic memory (EM) decline in cognitively normal (CN) older adults classified as brain β-amyloid (Aβ) negative (Aβ−) or positive (Aβ+). Methods Australian Imaging, Biomarkers and Lifestyle study participants (age 58–91 years) who completed ≥2 neuropsychological assessments and a brain Aβ PET scan (n = 592) were included in this study. We computed the CAIDE risk score (age, sex, APOE ε4 status, education, hypertension, body mass index [BMI], hypercholesterolemia, physical inactivity) and a modifiable CAIDE risk score (CAIDE-MR; education, hypertension, BMI, hypercholesterolemia, physical inactivity) for each participant. Aβ+ was classified using Centiloid >25. Linear mixed models assessed interactions between each CAIDE score, Aβ group, and time on HV loss and EM decline. Age, sex, and APOE ε4 were included as separate predictors in CAIDE-MR models to assess differential associations. Exploratory analyses examined relationships between individual modifiable risk factors and outcomes in Aβ− cognitively normal (CN) adults. Results We observed a significant Aβ group × CAIDE × time interaction on HV loss (β [SE] = –0.04 [0.01]; p < 0.000) but not EM decline (β [SE] = –2.33 [9.96]; p = 0.98). Decomposition revealed a significant CAIDE × time interaction in Aβ+ participants only. When modifiable/nonmodifiable CAIDE components were considered separately, we observed a significant Aβ group × CAIDE-MR × time interaction on EM decline only (β [SE] = 3.03 [1.18]; p = 0.01). A significant CAIDE-MR score × time interaction was observed in Aβ− participants only. Significant interactions between APOE ε4 and age × time on HV loss and EM decline were observed in both groups. Exploratory analyses in Aβ− CN participants revealed a significant interaction between BMI × time on EM decline (β [SE] = –3.30 [1.43]; p = 0.02). Discussion These results are consistent with studies showing that increasing age and APOE ε4 are associated with increased rates of HV loss and EM decline. In Aβ− CN adults, lower prevalence of modifiable cardiovascular risk factors was associated with less HV loss and EM decline over ∼10 years, suggesting interventions to reduce modifiable cardiovascular risk factors could be beneficial in this group.