Medicine (Austin & Northern Health) - Research Publications

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Head-to-Head comparison between Philips Gemini TF64 and Siemens Biograph Vision 600 for brain amyloid Centiloid quantitation

Li, S ; Bourgeat, P ; Bozinovski, S ; Huang, K ; Guzman, R ; Williams, R ; Fripp, J ; Villemagne, VL ; Rowe, C ; Dore, V (Wiley, 2022-12-01)

Abstract Background The Centiloid (CL) scale calibrates the beta‐amyloid (Aβ) deposition from different PET tracers to a standardised 0‐100 CL unit scale. As imaging sites update their PET cameras, most are switching to digital detector systems with superior resolution and sensitivity that may affect quantitation. This has significant implications for dementia clinical trials. In this study, we examine the impact on CL quantification between Philips Gemini TF64 and Siemens Biograph Vision 600. Method Seven subjects (76.4±2.2 yo) were imaged with 18F‐NAV4694 on both Gemini TF64 and Biograph Vision consecutively with an average scan interval of 25.1±11.2 weeks. The injected doses were 200MBq and 100MBq, respectively. On the Gemini TF64, the PET images were reconstructed by LOR‐RAMLA algorithm with smoothing parameter setup as ‘SHARP’. On Biograph Vision, the PET images were reconstructed by OSEM‐3D (8 iterations and 5 subsets, TOF enabled) with 3mm post Gaussian smoothing. A T1 MRI image was acquired for each subject. As per the standard Centiloid method the whole cerebellum was used as the reference in SUVR images, and all images were processed using CapAIBL to calculate the CL using both MR‐based and MR‐Less spatial normalisation. Result Figure 1 shows the CL images of a subject scanned on Gemini TF64 and Biograph Vision within sixteen weeks. The Biograph Vision images have higher contrast and higher spatial resolution despite using half of the dose. Figure 2 shows the linear regression plot of the scanner comparison. Biograph Vision CL are progressively higher than those obtained from the Gemini TF64 as the CL value rises (Table 1). There were no significant differences between the MR‐based and MR‐less results. Conclusion Biograph Vision yields higher SUVR and therefore CL values compared to Gemini TF64 in a head‐to‐head comparison. These results show that the selection of PET camera has a significant impact on CL quantification, which needs to be considered when merging cohorts from different studies or changing cameras during longitudinal studies or trials. These initial results indicate that the CL difference could be corrected by a linear transform.
CenTauRz: A standardized quantification of tau PET scans

Dore, V ; Bullich, S ; Bohorquez, SS ; Leuzy, A ; Shimada, H ; Rowe, C ; Bourgeat, P ; Lopresti, BJ ; Huang, K ; Krishnadas, N ; Fripp, J ; Takado, Y ; Stephens, AW ; Weimer, R ; Higuchi, M ; Hansson, O ; Villemagne, VL (Wiley, 2022-12-01)

Background: Over the past decade, several PET tracers were developed to visualise and quantify tau pathology in vivo. However, all these tracers have distinct off-target binding, different dynamic ranges and likely different levels of non-specific binding resulting in large variability in semiquantification. We propose to standardise the sampling and the quantification across all available tau tracers. Method: 549 participants underwent tau scans with either 18F-FTP (Cognitively Unimpaired (CU)=54/AD=14), 18F-MK6240 (CU=186/AD=89), 18F-PI2620 (CU=17/AD=21), 18F-PM-PBB3 (CU=30/AD=28), 18F-GTP1 (CU=7/AD=38) or 18F-RO948 (CU=35/AD=30). All CU individuals were Aβ- and all AD were Aβ+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. We constructed a “universal” tau mask from the intersection of all the specific tau tracer masks, after subtracting AD from CU. All tau PET studies were sampled with a Mesial Temporal (MTL) and a Meta Temporal (MetaT) composites constrained by the universal mask. For each tracer and in composite, the mean and standard deviation of the Aβ- CU SUVR for each tau tracer were used to generate z-scores (CenTauRz). Result: Using a threshold of 2 CenTauRz in the MetaT regions, all tracers highly discriminated Aβ+ AD from Aβ- CU (ACC=[0.94-1], sens=[0.84-1], spec=[0.96-1]) with mean CenTauRz for the different AD cohorts ranging from 8 to 14. Lower accuracy was observed in the MTL (ACC=[0.78-1]) due to lower sensitivity in some cohorts [0.65-1] however, the specificity was similar to that in the MetaT composite (spec=[0.94,1]). Conclusion: All tracers exhibited comparably high discriminative power to separate Aβ+ AD from Aβ- CU, where AD Aβ+ displayed a consistent range of CenTauRz across tracers. However, there were some differences between cohorts. For example, different PET scanners, with different sensitivities were used. For some cohorts, scans were selected as extreme representative cases, while for others the scans were more representative of clinical settings, with AD patients at early stages (with low or negative tau scans) or with suspected hippocampal sparing subtype that likely explains the lower accuracy in the MTL for some cohorts. Further studies with larger cohorts to validate the universal mask and CenTauRz scale are ongoing.
Cross‐sectional and longitudinal comparison of 18F‐MK6240 and 18F‐Flortaucipir in populations matched for centiloid, age and MMSE

Bourgeat, P ; Krishnadas, N ; Dore, V ; Mulligan, RS ; Tyrrell, R ; Bozinovski, S ; Huang, K ; Lamb, F ; Fripp, J ; Villemagne, VL ; Rowe, C (Wiley Open Access, 2022-12)

Background Longitudinal tau quantification may provide a useful outcome measure in disease‐specific therapeutic trials. Different tau PET tracers may have different sensitivity to longitudinal changes, but without a head‐to‐head comparison, equating results from different cohorts using different tracers can be biased. In this study, we aim to minimise this bias by matching participants in two cohorts imaged using 18F‐MK6240 and 18F‐Flortaucipir (FTP). Method A subset of 93 participants from AIBL and 93 from ADNI, imaged at baseline and 1 year later using 18F‐MK6240 and 18F‐FTP, respectively, were matched based on baseline clinical diagnosis, MMSE, age, and Centiloid value (CL). PET images were analysed with CapAIBL. Amyloid positivity (+/‐) was defined based on a threshold of 25CL. Subjects were grouped as 34 cognitively unimpaired amyloid negative (CU‐) and 24 positive (CU+), 18 mild cognitive impairment positive (MCI+) and 17 Alzheimer’s disease positive (AD+). Tracer retention was measured in the mesial temporal (Me), meta‐temporal (MT), temporoparietal (Te) and rest of the cortex (R). T‐tests were employed to assess group separation at baseline using SUVR and longitudinally using SUVR/Yr. Result As per selection criteria, there were no significant differences in age, MMSE or Centiloid between the cohorts using 18F‐MK6240 or 18F‐FTP in each subgroups. Baseline SUVR were significantly different between CU‐/CU+, CU+/MCI+ and CU+/AD+ in all regions for both tracers, except for CU‐/CU+ in R for 18F‐MK6240 (Figure 1). Using 18F‐MK6240, rate of change in CU+ was significantly higher than CU‐ in MT and Te, and both MCI+ and AD+ were higher than CU+ in R (Figure 2.Left). Using 18F‐FTP, rate of change in MCI+ was significantly higher than CU+ in Te, and AD+ higher than CU+ in MT, Te and R (Figure 2.Right). Conclusion In our matched cohorts using 18F‐MK6240 or 18F‐FTP, we found that, at baseline, both tracers can detect significant differences between clinical groups. However, 18F‐MK6240 was able to detect higher rates of accumulation at preclinical stages (CU+). These results in well‐matched cohorts indicate that 18F‐MK6240 might be a more sensitive tracer to detect early accumulation. Longitudinal head‐to‐head comparison will be required to confirm these results.
Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study

Sewell, KR ; Rainey‐Smith, S ; Villemagne, VL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, P ; Laws, SM ; Masters, CL ; Rowe, C ; Martins, RN ; Erickson, KI ; Brown, BM (Wiley Open Access, 2022-12)

Background Physical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required. Methods Participants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition. Results Higher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022). Conclusion These results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.
Plasma glial fibrillary acidic protein is associated with reactive astrogliosis assessed via 18F-SMBT-1 PET

Chatterjee, P ; Dore, V ; Pedrini, S ; Krishnadas, N ; Thota, RN ; Bourgeat, P ; Rainey‐Smith, S ; Burnham, SC ; Fowler, C ; Taddei, K ; Mulligan, RS ; Ames, D ; Masters, CL ; Fripp, J ; Rowe, C ; Martins, RN ; Villemagne, VL (Wiley, 2022-12)

Background Reactive astrogliosis is an early event along the Alzheimer’s disease (AD) continuum. We have shown that plasma glial fibrillary acidic protein (GFAP), reflecting reactive astrogliosis, is elevated in cognitively unimpaired individuals with preclinical AD (Chatterjee et al., 2021). We reported similar findings using 18F‐SMBT‐1, a PET tracer for monoamine oxidase B (MAO‐B) (Villemagne et al., 2022). To provide further evidence of their relationship with reactive astrogliosis we investigated the association between GFAP and 18F‐SMBT‐1 in the same participants. Method Plasma GFAP, Aβ42 and Aβ40 levels were measured using the Single Molecule Array platform in 71 participants comprising 54 healthy controls (12 Aβ+ and 42 Aβ‐), 11 MCI(3 Aβ+ and 8 Aβ‐) and 6 probable AD(5 Aβ+ and 1 Aβ‐) patients from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing cohort. These participants also underwent 18F‐SMBT‐1 and Aβ PET imaging. Aβ imaging results were expressed in Centiloids (CL; ≥20 CL classified as Aβ+). 18F‐SMBT‐1 Standard Uptake Value Ratio (SUVR) were generated using the subcortical white matter as reference region. Linear regression analyses were carried out using plasma GFAP levels as the dependent variable and regional 18F‐SMBT‐1 SUVR as the independent variable, before and after adjusting for age, sex, soluble Aβ (plasma Aβ1‐42/Aβ1‐40 ratio) and insoluble Aβ (Aβ PET). Result Plasma GFAP was significantly associated with 18F‐SMBT‐1 SUVR in brain regions of early Aβ deposition, such as the supramarginal gyrus (SG, β=.361, p=.002), posterior cingulate (PC, β=.308, p=.009), lateral temporal (LT, β=.299, p=.011), lateral occipital (LO, β=.313, p=.008) before adjusting for any covariates. After adjusting for covariates age, sex and soluble Aβ, GFAP was significantly associated with 18F‐SMBT‐1 PET signal in the SG (β=.333, p<.001), PC (β=.278, p=.005), LT (β=.256, p=.009), LO (β=.296, p=.004) and superior parietal (SP, β=.243, p=.016). On adjusting for age, sex and insoluble Aβ, GFAP was significantly associated with SMBT‐1 PET in the SG (β=.211, p=.037) however only a trend towards significance was observed in the PC (β=.186, p=.052) and LT (β=.171, p=.067) (Figure 1). Conclusion There is an association between plasma GFAP and regional SMBT‐1 PET that is primarily driven by brain Aβ load.
Alzheimer’s disease specific MRI brain regions are differentially associated with accelerated decline as defined using sigmoidal cognitive turning point methodology in amyloid‐positive AIBL participants

Gillis, C ; Cespedes, MI ; Maserejian, NN ; Dore, V ; Maruff, P ; Fowler, C ; Rainey‐Smith, S ; Villemagne, VL ; Rowe, C ; Martins, RN ; Vacher, M ; Masters, CL ; Doecke, JD (Wiley, 2022-12)

Background Variability in cognitive decline among adults with Alzheimer’s disease (AD) is seen across studies. While such variability is often modelled using linear models, in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, application of a sigmoidal methodology has shown excellent precision in modelling cognitive and biomarker changes. Here we expand these findings by examining associations of brain volumes in AD specific Regions of Interest (ROIs) with accelerated cognitive decline among amyloid‐beta positive (Ab+) AIBL participants. Method Longitudinal cognitive scores for the AIBL PACC, Language, Visuospatial functioning and CDR‐SB were mapped to sigmoidal trajectories, with a threshold defining the inflection point of accelerated cognitive decline. Participants to the left of the threshold were classified as having non‐accelerated decline (non‐accelerators), and participants beyond the threshold were classed as accelerators (Figure 1B). Using these classifications, we investigated differences in 16 ICV corrected ROI (left and right hemispheres pooled) for reductions in brain volume via generalised linear models adjusted for age, gender, and APOE‐e4 status. Three participant subgroups were tested: 1) Ab+/Tau unknown, 2) Ab+/Tau‐ and 3) Ab+/Tau+. Significant t‐values for the summed ROI volumes were mapped on a standard brain mesh for visualisation. Result Of regions tested, two stood out consistently amongst top markers in each of the participant subgroups and cognitive outcomes: 1) supramarginal volume and 2) middle temporal volume (Figure 1C). Largest volume differences between accelerators and non‐accelerators were seen in the Ab+/Tau+ group; whilst smallest p‐values were in the Ab+/Tau unknown group due to a larger sample size (Table 1). Brain mesh visualization showed most of the AD signature ROIs altered in accelerator groups as compared with non‐accelerator groups. Figure 1D shows the AD signature for each cognitive outcome amongst the Ab+/Tau participant group. Top ranked ROI for the left being middle temporal volume (T=7.10, PACC) and supramarginal volume (T=7.10, CDR‐SB). Conclusion Sigmoid analyses of MRI using binary cognitive scores show decreased ROI volumes in AIBL Ab+ participants with accelerated cognitive decline. This effect was mediated by known information on Tauopathy. Whilst effect sizes were high, smaller sample sizes in some groups affected p‐values and should therefore be replicated in larger samples.
Comparing the longitudinal progression of CSF biomarkers with PET Amyloid biomarkers for Alzheimer’s disease

Cox, T ; Bourgeat, P ; Dore, V ; Doecke, JD ; Fripp, J ; Chatterjee, P ; Schindler, EE ; Benzinger, TLS ; Rowe, C ; Villemagne, VL ; Weiner, MW ; Morris, JC ; Masters, CL (Wiley, 2022-12)

Background Cerebrospinal fluid (CSF) soluble biomarkers are useful at detecting pre‐clinical levels of Alzheimer’s disease (AD) biomarkers of b‐amyloid (Ab) and tau. Disease progression times for participants in longitudinal studies can be estimated for different biomarkers. Utilizing a new technique, this work compared the disease progression times between CSF and PET biomarkers. Methods Four hundred and ten participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) including participants form ACS/OASIS, ADNI and AIBL with three or more data points of longitudinal CSF Ab42 and pTau181 (pTau) and Ab PET were selected. PET results were expressed in Centiloid (CL), (299 cognitively unimpaired, 107 mild cognitively impaired, 4 AD dementia; aged 69±9; 216 females (NAIBL=30, NADNI=252, NOASIS=128). Disease trajectory curves for individual biomarkers and the pTau/Ab42 ratio were created by: 1) Fitting a function to the rates of change of the variable of interest versus its mean value), 2) integrating the fit to obtain longitudinal trajectory curves as a function of disease progression time for each of the variables. The participants’ disease progression time along each curve were estimated. Threshold values for Ab PET and pTau/Ab42 ratios were calculated using a gaussian mixture model. Estimates of age of onset were calculated using the progression times. The participants’ disease progression times for each of the different variables were compared using rank correlations. Results Rank correlations for the progression times were: r(Ab42, Ab PET) = 0.75, r(pTau, Ab PET)=0.62, and r(pTau/Ab42, Ab PET)=0.83. The estimated ages at which participants’ reach Ab PET and the pTau/Ab42 ratio thresholds are compared in Fig 1, the average age at which were estimated to reach the threshold values were 55 yr for pTau/Ab42 (threshold of 0.021) and 61 yr for Ab PET (threshold of 22 CL). Conclusions The high correlation between pTau/Ab42 and Ab PET, indicates that pTau/Ab42 captures the progression of AD pathology better than the individual CSF biomarkers. On average participants’ reach abnormal levels of pTau/Ab42 earlier than Ab PET. Further work is required to understand individual variations in progression times.
18F-MK6240 longitudinal tau PET in ageing and Alzheimer’s disease

Krishnadas, N ; Dore, V ; Mulligan, RS ; Tyrrell, R ; Bozinovski, S ; Huang, K ; Lamb, F ; Burnham, SC ; Villemagne, VL ; Rowe, CC (Wiley, 2021-12)

Background Longitudinal tau PET may prove useful for clinical trials, through its ability to detect patterns and rates of in vivo tau accumulation in ageing and Alzheimer’s disease (AD). Clinical trials are increasingly targeting the preclinical phase of AD. Flortaucipir studies estimate a 3% annual increase in global cortical tau SUVR in amyloid‐β positive (Aβ+ve) cognitively impaired (CI) cohorts, whereas either no change, or low rates of increase (0.5%), have been demonstrated in Aβ+ve cognitively unimpaired (CU) cohorts. F‐18 MK6240 is a novel tau tracer, with high target to background binding. We aimed to evaluate regional rates of 18F‐MK6240 accumulation in ageing and the AD continuum. Method We performed PET acquisition 90‐100 minutes post‐injection of 185MBq (±10%) 18F‐MK6240 at baseline and 12 months for 67 Aβ‐ve CU, 20 Aβ+ve CU and 19 Aβ+ve CI participants. SUVR (standardized uptake value ratio) for the entorhinal cortex, amygdala, hippocampus, parahippocampus and composite regions of interest (ROI) (Me, mesial temporal; Te, temporoparietal cortices) were generated using the cerebellar cortex as the reference region. Result Age did not significantly differ between the groups (mean age 74 ± 4.4 Aβ‐ve CU, 76.2 ± 5.7 Aβ+ve CU, 72.5 ± 6.4 Aβ+ve CI). Aβ+ve participants (CU and CI) had higher baseline tau SUVR and higher annual percentage increases in tau SUVR compared to Aβ‐ve participants in all regions examined (Table 1) (Figure 1). CU Aβ+ve participants had larger increases in Me vs Te (1.6% vs 0.7%), while CI Aβ+ve participants had larger increases in Te vs Me (4.3% vs 1.9%). Compared to Aβ‐ve CU participants, Aβ+ve CU participants had higher increases in the amygdala (2.9% vs 1.8%) and entorhinal cortex (1.9% vs 0.7%). Conclusion Longitudinal tau imaging using 18F‐MK6240 discriminates between ageing and stages of AD. Rate of accumulation in preclinical AD (Aβ+ve CU) was highest in mesial temporal regions, while in CI individuals, rates were highest in the temporoparietal cortex. The amygdala and entorhinal cortex may be early regions to discriminate tau accumulation between Aβ‐ve CU and Aβ+ve groups. However, as the variance is large, the precision of these estimates may be refined with a larger sample size. Recruitment is ongoing.
Examining the structural correlates of amyloid‐beta in people with DLB

Gajamange, S ; Yassi, N ; Chin, KS ; Desmond, PM ; Villemagne, VL ; Rowe, CC ; Watson, R (Wiley, 2021-12)

Background Dementia with Lewy bodies (DLB) is a neurodegenerative disorder characterized pathologically by the deposition of alpha synuclein. Many patients with DLB also have brain compatible with Alzheimer’s disease (namely Amyloid‐β and tau), which can lead to challenges with clinical diagnosis and management. In this study we aim to understand the influence of Aβ on brain atrophy in DLB patients. Method 19 participants with probable DLB underwent 3T MRI T1‐weighted (voxel size=0.8x0.8x0.8mm3, TR=2400ms, TE=2.31ms) and β‐amyloid (Aβ) PET (radiotracer 18F‐NAV4694) imaging. Participants were grouped into Aβ negative (n=10; age=71.6±5.8 years) and Aβ positive (n=9; age=75.1±4.3 years) with a threshold of 50 centiloid units to identify neuropathological change (Amadoru et al. 2020). Brain volume measures (regional subcortical grey matter and global white and grey matter) were segmented from T1‐weighted images with FreeSurfer (Fischl et al. 2002, Fischl 2012). Given previous literature suggesting prominence of thalamic structural changes in DLB, we also specifically analysed changes in the thalamus by segmenting the thalamus into 25 nuclei, which were then grouped into six regions (anterior, lateral, ventral, intralaminar, medial and posterior) (Watson et al. 2017, Iglesias et al. 2018). All brain volumes were expressed as fractions of intracranial volume to account for differences in head size. Group comparison analyses were not controlled for age and sex as both these covariates did not statistically differ between groups. Result Brain volume differed significantly between Aβ‐ and Aβ+ DLB patients in the left thalamus (Aβ‐:4.39±0.37x103, Aβ+:4.07±0.19x103, p=0.03) and right thalamus (Aβ‐:4.17±0.34x103, Aβ+:3.84±0.22 x103, p=0.03). Specifically, the ventral (LEFT; Aβ‐:1.78±0.15, Aβ+:1.63±0.14, p=0.03. RIGHT; Aβ‐:1.83±0.15, Aβ+:1.65±0.12, p=0.01) and posterior (LEFT; Aβ‐:1.30±0.12, Aβ+:1.17±0.10, p=0.04. RIGHT; Aβ‐:1.42±0.14, Aβ+:1.21±0.12, p=0.003) regions were significantly reduced in Aβ+ compared to Aβ‐ DLB patients. Conclusion We demonstrated significant thalamic atrophy in Aβ+ patients compared to Aβ‐ DLB patients. We did not observe significant differences in grey matter and hippocampal volume between patient groups. This study showed that AD‐related processes in DLB patients are associated with thalamic atrophy, specifically in the ventral and posterior regions. Future studies would benefit a larger DLB cohort to further understand the association between AD‐related pathology and the regional thalamic correlates of clinical function.
Lipidomic signatures for APOE genotypes provides new insights about mechanisms of resilience in Alzheimer’s disease

Wang, T ; Huynh, K ; Giles, C ; Lim, WLF ; Duong, T ; Mellett, NA ; Smith, A ; Olshansky, G ; Drew, BG ; Cadby, G ; Melton, PE ; Hung, J ; Beilby, J ; Watts, GF ; Chatterjee, P ; Martins, I ; Laws, SM ; Bush, AI ; Rowe, CC ; Villemagne, VL ; Ames, D ; Masters, CL ; Arnold, M ; Kastenmüller, G ; Nho, K ; Saykin, AJ ; Baillie, R ; Han, X ; Martins, RN ; Moses, E ; Kaddurah‐Daouk, RF ; Meikle, PJ (Wiley, 2021-12)

Background The apolipoprotein E gene (APOE) genotype is the first and strongest genetic risk factor for late‐onset Alzheimer’s disease and has emerged as a novel therapeutic target for AD. The encoded protein (Apolipoprotein E, APOE) is well‐known to be involved in lipoprotein transport and metabolism, but its effect on lipid metabolic pathways and the potential mediating effect of these on disease risk have not been fully defined. Method We performed lipidomic analysis on three independent cohorts (AIBL, n = 693; ADNI, n=207; BHS, n=4,384) and defined the association between APOE polymorphisms (ε4 and ε2) and plasma lipid species. To identify associations independent of lipoprotein metabolism, the analyses was performed with adjustment for clinical lipids (total cholesterol, HDL‐C and triglycerides). Causal mediation analysis was performed to estimate the proportion of risk in the outcome model explained by a direct effect of APOE genotype on prevalent AD — the average direct effect (ADE) — and the proportion that was mediated by lipid species or lipidomic risk models — the average causal mediation effect (ACME). Result We identified multiple associations of species from lipid classes such as ceramide, hexosylceramide, sphingomyelin, plasmalogens, alkyldiacylglycerol and cholesteryl esters with APOE polymorphisms (ε4 and ε2) that were independent of clinical lipoprotein measurements. There were 104 and 237 lipid species associated with APOE ε4 and ε2 respectively which were largely discordant. Of these 116 were also associated with Alzheimer’s disease. Individual lipid species (notably the alkyldiacylglycerol subspecies) or lipidomic risk models of APOE genotypes mediated up to 10% and 30% of APOE ε4 and ε2 treatment effect on AD risks respectively. Conclusion We demonstrate a strong relationship between APOE polymorphisms and peripheral lipid species. Lipids species mediate a proportion of the effects of APOE genotypes in risk of AD, particularly resilience with e2. Our results highlight the involvement of lipids in how APOE e2 mediates its resilience to AD and solidify their involvement with the disease pathway.

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