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Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.

An inverse association between cancer and Alzheimer's disease (AD) has been demonstrated; however, the association between cancer and mild cognitive impairment (MCI), and the association between cancer and cognitive decline are yet to be clarified. The AIBL dataset was used to address these knowledge gaps. The crude and adjusted odds ratios for MCI/AD and cognitive decline were compared between participants with/without cancer (referred to as C+ and C- participants). A 37% reduction in odds for AD was observed in C+ participants compared to C- participants after adjusting for all confounders. The overall risk for MCI and AD in C+ participants was reduced by 27% and 31%, respectively. The odds of cognitive decline from MCI to AD was reduced by 59% in C+ participants after adjusting for all confounders. The risk of cognitive decline from MCI to AD was halved in C+ participants. The estimated mean change in Clinical Dementia Rating-Sum of boxes (CDR-SOB) score per year was 0.23 units/year higher in C- participants than in C+ participants. Overall, an inverse association between cancer and MCI/AD was observed in AIBL, which is in line with previous reports. Importantly, an inverse association between cancer and cognitive decline has also been identified.

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend

In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.

INTRODUCTION: As knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD. METHODS: Frequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers. RESULTS: AD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross-sectionally, mutation carriers with AD neurological examination findings showed a more than two-fold faster cognitive decline and had greater parieto-temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time. DISCUSSION: ADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs.

BACKGROUND AND AIMS: Neurodegeneration underpins the pathological processes of younger-onset dementia (YOD) and has been implicated in primary psychiatric disorders (PSYs). Cerebrospinal fluid (CSF) neurofilament light (NfL) has been used to investigate neurodegeneration severity through correlation with structural brain changes in various conditions, but has seldom been evaluated in YOD and PSYs. METHODS: This retrospective study included patients with YOD or PSYs with magnetic resonance imaging (MRI) of the brain and CSF NfL analysis. Findings from brain MRI were analysed using automated volumetry (volBrain) to measure white matter (WM), grey matter (GM) and whole brain (WB) volumes expressed as percentages of total intracranial volume. Correlations between NfL and brain volume measurements were computed whilst adjusting for age. RESULTS: Seventy patients (47 with YOD and 23 with PSY) were identified. YOD types included Alzheimer disease and behavioural variant frontotemporal dementia. PSY included schizophrenia and major depressive disorder. MRI brain sequences were either fast spoiler gradient-echo (FSPGR) or magnetization-prepared rapid acquisition gradient-echo (MPRAGE). In the total cohort, higher NfL was associated with reduced WB in the FSPGR and MPRAGE sequences (r = -0.402 [95% confidence interval (CI), -0.593 to -0.147], P = 0.008 and r = -0.625 [95% CI, -0.828 to -0.395], P < 0.001, respectively). Higher NfL was related to reduced GM in FSPGR (r = 0.385 [95% CI, -0.649 to -0.014], P = 0.017) and reduced WM in MPRAGE (r = -0.650 [95% CI, -0.777 to -0.307], P < 0.001). Similar relationships were seen in YOD, but not in PSY. CONCLUSION: Higher CSF NfL is related to brain atrophy in YOD, further supporting its use as a nonspecific marker of neurodegeneration severity.

INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

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

Background Alzheimer's Disease (AD) is a heterogeneous condition that can be viewed as a continuum involving multiple stages from cognitively normal to mild cognitive impairment and finally dementia. Recent works in Disease Progression Modelling (DPM) have attempted to use biomarkers to construct representations of progression that are more informative of patients' status than clinical diagnosis alone. However, there is no clear consensus (Figure 1) regarding the choice of evaluation methods or metrics to facilitate model comparison. We propose the use a simple, universally applicable biomarker‐based baseline to estimate patient‐realigning time‐shifts and subsequently evaluate it against multiple released DPM methods. Method We computed an abnormality score for 2439 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) by averaging the normalised values collected for 12 biomarkers representative of amyloid, tau, neurodegeneration, and cognition. Abnormality ranges were determined from 251 amyloid‐positive patients. These scores were compared to values from published DPM models (Figure 3). Additionally, all methods were benchmarked in different ways, including diagnosis classification and cluster analysis. Result We found that, despite its simplicity, our abnormality score reasonably separated patients according to their diagnosis (Figure 2). Computed scores also correlated significantly with those of other methods (Figure 3, Pearson correlations up to 0.891). While our approach only required cross‐sectional information, other methods rely on longitudinal data. Our method performed similarly to others in the cutoff‐based disease classification task, with test F1‐scores of 0.734 and 0.890 in the harder tasks of CN/MCI and AD/MCI, respectively (Table 1). In a three‐class setting, all methods including our baseline showed a good level of agreement, with Rand indices all above 0.6 (Figure 4). Conclusion A significant challenge in DPM for AD lies in estimat

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+/T

Background Distinguishing behavioural variant frontotemporal dementia (bvFTD) from non‐neurodegenerative ‘non‐progressor’, ‘phenocopy’ mimics of frontal lobe dysfunction, can be one of the most challenging clinical dilemmas. A biomarker of neuronal injury, neurofilament light chain (NfL), could reduce misdiagnosis and delay. Method Cerebrospinal fluid (CSF) NfL, amyloid beta 1‐42 (AB42), total and phosphorylated tau (T‐tau, P‐tau) levels were examined in patients with an initial diagnosis of bvFTD. Based on follow up information, patients were categorised as Progressors. Non‐Progressors were subtyped in to Phenocopy Non‐Progressors (non‐neurological/neurodegenerative final diagnosis), and Static Non‐Progressors (static deficits, not fully explained by non‐neurological/neurodegenerative causes). Result Forty‐three patients were included: 20 Progressors, 23 Non‐Progressors (15 Phenocopy, 8 Static), 20 controls. NfL concentrations were lower in Non‐Progressors (Non‐Progressors Mean, M=554pg/mL, 95%CI:[461, 675], Phenocopy Non‐Progressors M=459pg/mL, 95%CI:[385, 539], Static Non‐Progressors M=730pg/mL, 95%CI:[516, 940]), compared to bvFTD Progressors (M=2397pg/mL, 95%CI:[1607, 3332]). NfL distinguished Progressors from Non‐Progressors with the highest accuracy (area under the curve 0.92, 90%/87% sensitivity/specificity, 86%/91% positive/negative predictive value, 88% accuracy). Static Non‐Progressors tended to have higher T‐tau and P‐tau levels compared to Phenocopy Non‐Progressors. Conclusion This study demonstrated strong diagnostic utility of CSF NfL in distinguishing bvFTD from phenocopy non‐progressor variants, at baseline, with high accuracy, in a real‐world clinical setting. This has important clinical implications to improve outcomes for patients and clinicians facing this challenging clinical dilemma, as well as for healthcare services, and clinical trials. Further research is required to investigate heterogeneity within the non‐progressor group and potential d

Longitudinal observational cohort studies are being conducted worldwide to understand cognition, biomarkers, and the health of the aging population better. Cross-cohort comparisons and networks of registries in Alzheimer's disease (AD) foster scientific exchange, generate insights, and contribute to the evolving clinical science in AD. A scientific working group was convened with invited investigators from established cohort studies in AD, in order to form a research collaboration network as a resource to address important research questions. The Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD) collaboration network was created to bring together global resources and expertise, to generate insights and improve understanding of the natural history of AD, to inform design of clinical trials in all disease stages, and to plan for optimal patient access to disease-modifying therapies once they become available. The network brings together expertise and data insights from 7 cohorts across Australia, Europe, and North America. Notably, the network includes populations recruited through memory clinics as well as population-based cohorts, representing observations from individuals across the AD spectrum. This report aims to introduce the CONCORD-AD network, providing an overview of the cohorts involved, reporting the common assessments used, and describing the key characteristics of the cohort populations. Cohort study designs and baseline population characteristics are compared, and available cognitive, functional, and neuropsychiatric symptom data, as well as the frequency of biomarker assessments, are summarized. Finally, the challenges and opportunities of cross-cohort studies in AD are discussed.

INTRODUCTION: The current practice of quantifying cerebrospinal fluid (CSF) biomarkers as an aid in the diagnosis of Alzheimer's disease (AD) varies from center to center. For a same biochemical profile, interpretation and reporting of results may differ, which can lead to misunderstandings and raises questions about the commutability of tests. METHODS: We obtained a description of (pre-)analytical protocols and sample reports from 40 centers worldwide. A consensus approach allowed us to propose harmonized comments corresponding to the different CSF biomarker profiles observed in patients. RESULTS: The (pre-)analytical procedures were similar between centers. There was considerable heterogeneity in cutoff definitions and report comments. We therefore identified and selected by consensus the most accurate and informative comments regarding the interpretation of CSF biomarkers in the context of AD diagnosis. DISCUSSION: This is the first time that harmonized reports are proposed across worldwide specialized laboratories involved in the biochemical diagnosis of AD.

In this article the affiliation details for Ulman Lindenberger were incorrectly given as ‘Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany’ but should have been ‘Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany’, ‘Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany’. The original article has been corrected.

BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While

The Alzheimer's Association International Conference held its sixth Satellite Symposium in Sydney, Australia in 2019, highlighting the leadership of Australian researchers in advancing the understanding of and treatment developments for Alzheimer's disease (AD) and other dementias. This leadership includes the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), which has fueled the identification and development of many biomarkers and novel therapeutics. Two multimodal lifestyle intervention studies have been launched in Australia; and Australian researchers have played leadership roles in other global studies in diverse populations. Australian researchers have also played an instrumental role in efforts to understand mechanisms underlying vascular contributions to cognitive impairment and dementia; and through the Women's Healthy Aging Project have elucidated hormonal and other factors that contribute to the increased risk of AD in women. Alleviating the behavioral and psychological symptoms of dementia has also been a strong research and clinical focus in Australia.

OBJECTIVES: An increasing number of people are undergoing lumbar puncture (LP) for the purposes of research. Performing LP for research purposes introduces considerations that differ from LP performed for clinical, diagnostic or therapeutic reasons. The demand for research LP will greatly increase as biomarkers are used to both diagnose and monitor disease progression in clinical trials. Minimising adverse events is paramount because research participants receive no clinical benefit and often need repeat procedures. We describe the experience of performing LP for research by anaesthetists. METHODS: We reviewed the clinical protocol and incidence of adverse events in 326 research LP in an anaesthesia department. RESULTS: There was a lower incidence of adverse events compared with previous reports when LP was undertaken for clinical reasons. The incidence of severe post-LP headache was 1.3% when an atraumatic spinal needle with a 27 gauge tip and a 22 gauge shaft was used. CONCLUSIONS: We describe the practice to sample cerebrospinal fluid (CSF) by LP for research purposes. Specific practices include the sitting position of the participant, aspiration rather than passive CSF withdrawal, attention to the sterility of the procedure, monitoring of vital signs and importantly the use of 22/27 gauge microtip spinal needle. TRIAL REGISTRATION NUMBERS: ACTRN12612000493842, NCT04623242.

Demonstrating a slowing in the rate of cognitive decline is a common outcome measure in clinical trials in Alzheimer's disease (AD). Selection of cognitive endpoints typically includes modeling candidate outcome measures in the many, richly phenotyped observational cohort studies available. An important part of choosing cognitive endpoints is a consideration of improvements in performance due to repeated cognitive testing (termed "practice effects"). As primary and secondary AD prevention trials are comprised predominantly of cognitively unimpaired participants, practice effects may be substantial and may have considerable impact on detecting cognitive change. The extent to which practice effects in AD prevention trials are similar to those from observational studies and how these potential differences impact trials is unknown. In the current study, we analyzed data from the recently completed DIAN-TU-001 clinical trial (TU) and the associated DIAN-Observational (OBS) study. Results indicated that asymptomatic mutation carriers in the TU exhibited persistent practice effects on several key outcomes spanning the entire trial duration. Critically, these practice related improvements were larger on certain tests in the TU relative to matched participants from the OBS study. Our results suggest that the magnitude of practice effects may not be captured by modeling potential endpoints in observational studies where assessments are typically less frequent and drug expectancy effects are absent. Using alternate instrument forms (represented in our study by computerized tasks) may partly mitigate practice effects in clinical trials but incorporating practice effects as outcomes may also be viable. Thus, investigators must carefully consider practice effects (either by minimizing them or modeling them directly) when designing cognitive endpoint AD prevention trials by utilizing trial data with similar assessment frequencies.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

As prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and “sporadic” late-onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy-confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy-confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.

INTRODUCTION: Many patients with cognitive and neuropsychiatric symptoms face diagnostic delay and misdiagnosis. We investigated whether cerebrospinal fluid (CSF) neurofilament light (NfL) and total-tau (t-tau) could assist in the clinical scenario of differentiating neurodegenerative (ND) from psychiatric disorders (PSY), and rapidly progressive disorders. METHODS: Biomarkers were examined in patients from specialist services (ND and PSY) and a national Creutzfeldt-Jakob registry (Creutzfeldt-Jakob disease [CJD] and rapidly progressive dementias/atypically rapid variants of common ND, RapidND). RESULTS: A total of 498 participants were included: 197 ND, 67 PSY, 161 CJD, 48 RapidND, and 20 controls. NfL was elevated in ND compared to PSY and controls, with highest levels in CJD and RapidND. NfL distinguished ND from PSY with 95%/78% positive/negative predictive value, 92%/87% sensitivity/specificity, 91% accuracy. NfL outperformed t-tau in most real-life clinical diagnostic dilemma scenarios, except distinguishing CJD from RapidND. DISCUSSION: We demonstrated strong generalizable evidence for the diagnostic utility of CSF NfL in differentiating ND from psychiatric disorders, with high accuracy.

Prior to the usual clinical symptoms of dementia, there can be subtle changes in cognitive function that differ from the normal age-related cognitive decline, which has been termed mild cognitive impairment (MCI). The increase in the numbers of individuals with possible MCI presenting to health care professionals, notably, General Practitioners (GPs), is going to rise dramatically in the coming years. With ever increasing demands on GPs, it is therefore timely to provide information that can be accessed by health care professionals to assist them in making appropriate diagnoses and to provide the most relevant, evidence-based treatment options. We have provided a comprehensive list of recommendations that aim to address key aspects of MCI in primary care. Specifically, these relate to detection and diagnosis; sharing the diagnosis, monitoring, and follow up; practical interventions to potentially delay progression; and personalizing care-planning, engagement, and patient motivation for the long term.

BACKGROUND: Distinguishing behavioural variant frontotemporal dementia (bvFTD) from non-neurodegenerative 'non-progressor' mimics of frontal lobe dysfunction, can be one of the most challenging clinical dilemmas. A biomarker of neuronal injury, neurofilament light chain (NfL), could reduce misdiagnosis and delay. METHODS: Cerebrospinal fluid (CSF) NfL, amyloid beta 1-42 (AB42), total and phosphorylated tau (T-tau, P-tau) levels were examined in patients with an initial diagnosis of bvFTD. Based on follow-up information, patients were categorised as Progressors or Non-Progressors: further subtyped into Non-Progressor Revised (non-neurological/neurodegenerative final diagnosis), and Non-Progressor Static (static deficits, not fully explained by non-neurological/neurodegenerative causes). RESULTS: Forty-three patients were included: 20 Progressors, 23 Non-Progressors (15 Non-Progressor Revised, 8 Non-Progressor Static), and 20 controls. NfL concentrations were lower in Non-Progressors (Non-Progressors Mean, M = 554 pg/mL, 95%CI:[461, 675], Non-Progressor Revised M = 459 pg/mL, 95%CI:[385, 539], and Non-Progressor Static M = 730 pg/mL, 95%CI:[516, 940]), compared to Progressors (M = 2397 pg/mL, 95%CI:[1607, 3332]). NfL distinguished Progressors from Non-Progressors with the highest accuracy (area under the curve 0.92, 90%/87% sensitivity/specificity, 86%/91% positive/negative predictive value, 88% accuracy). Non-Progressor Static tended to have higher T-tau and P-tau levels compared to Non-Progressor Revised Diagnoses. CONCLUSION: This study demonstrated strong diagnostic utility of CSF NfL to distinguish bvFTD from non-progressor variants, at baseline, with high accuracy, in a real-world clinical setting. This has important clinical implications, to improve outcomes for patients and clinicians facing this challenging clinical dilemma, healthcare services, and clinical trials. Further research is required to investigate heterogeneity within the non-progressor group and

BACKGROUND: In the clinical setting, identification of the genetic cause in patients with early-onset dementia (EOD) is challenging due to multiple types of genetic tests required to arrive at a diagnosis. Whole-genome sequencing (WGS) has the potential to serve as a single diagnostic platform, due to its superior ability to detect common, rare and structural genetic variation. METHODS: WGS analysis was performed in 50 patients with EOD. Point mutations, small insertions/deletions, as well as structural variants (SVs) and short tandem repeats (STRs), were analysed. An Alzheimer's disease (AD)-related polygenic risk score (PRS) was calculated in patients with AD. RESULTS: Clinical genetic diagnosis was achieved in 7 of 50 (14%) of the patients, with a further 8 patients (16%) found to have established risk factors which may have contributed to their EOD. Two pathogenic variants were identified through SV analysis. No expanded STRs were found in this study cohort, but a blinded analysis with a positive control identified a C9orf72 expansion accurately. Approximately 37% (7 of 19) of patients with AD had a PRS equivalent to >90th percentile risk. DISCUSSION: WGS acts as a single genetic test to identify different types of clinically relevant genetic variations in patients with EOD. WGS, if used as a first-line clinical diagnostic test, has the potential to increase the diagnostic yield and reduce time to diagnosis for EOD.

An increasing number of studies have revealed that dysregulated lipid homeostasis is associated with the pathological processes that lead to Alzheimer’s disease (AD). If changes in key lipid species could be detected in the periphery, it would advance our understanding of the disease and facilitate biomarker discovery. Global lipidomic profiling of sera/blood however has proved challenging with limited disease or tissue specificity. Small extracellular vesicles (EV) in the central nervous system, can pass the blood-brain barrier and enter the periphery, carrying a subset of lipids that could reflect lipid homeostasis in brain. This makes EVs uniquely suited for peripheral biomarker exploration.

Background The quantification of cerebrospinal fluid (CSF) biomarkers (Amyloid beta peptides [Aß1‐40 and Aß1‐42], t‐tau and p‐tau(181)) is progressively implemented in specialized laboratories as an aid for the multidisciplinary diagnosis of Alzheimer’s disease (AD). There is however a diversity of practices between centers related to pre‐analytical and analytical conditions, the calculation of ratios between analytes, the applied cut‐off, or the use of interpretation scales. Finally, for the same biochemical profile, the interpretation and reporting of results may differ from one center to another, which may raise questions about the commutability of the tests. So far, no consensus has been reached between the different laboratories involved to define the most appropriate conclusions/comments based on the profile of the quantified biomarkers. This work is an essential step towards a consensual harmonization of clinical reporting after CSF analysis in the context of AD diagnosis, as advocated by the "Biofluid Based Biomarkers PIA" working group of the Alzheimer's Association. Method We obtained, by means of a questionnaire, a description of the pre‐analytical and analytical protocols and examples of reporting from 40 centers located in 15 countries, i.e. in the majority of countries that have implemented clinical CSF tests for the diagnosis of AD. We then adopted a consensus approach to propose harmonized comments corresponding to different AD CSF biomarker profiles observed in patients. Result Pre‐analytical procedures were very similar, among the centers. Regarding the analytical part, more than 88% of the laboratories use automatized immunoassays and more than 83% measure Aß1‐40 and compute the Aß1‐42/Aß1‐40 ratio, in addition to the three core biomarkers (Aß1‐42, t‐tau and p‐tau(181)). The cut‐off values of biomarkers used by the different laboratories are widely dispersed. Delay before sending back the results is lower than 1 week in more than 34% of t

BACKGROUND: Indigenous Australians are at increased risk of developing dementia - Alzheimer disease and mixed dementia diagnoses are the most common. While prion diseases have been reported in Indigenous peoples of Papua New Guinea and the United States, the occurrence and phenotype of prion disease in Indigenous Australians is hitherto unreported. AIM: To report the incidence rate and clinical phenotype of Creutzfeldt-Jakob disease (CJD) in Indigenous Australians. METHOD: Crude sporadic CJD (sCJD) incidence rates and indirect age standardisation of all CJD were assessed to calculate the standardised mortality ratio (SMR) of the Indigenous Australian population in comparison to the all-resident Australian population, along with analysis of clinical phenotypes. RESULTS: We report an illustrative case of an Indigenous Australian from regionally remote Western Australia dying from typical 'probable' sCJD 2 months after disease onset, with Australian National CJD Registry (ANCJDR) surveillance overall demonstrating eight Indigenous Australians dying from sCJD (five post-mortem confirmed, three classified as 'probable') with a clinical phenotype similar to non-indigenous people, including median age at death of 61 years (interquartile range IQR = 16 years) and median duration of illness of 3 months (IQR = 1.6 months). Indigenous Australians with sCJD were geographically dispersed throughout Australia. The calculated overall crude annual rate of sCJD in Indigenous Australians compared to the remainder of the Australian population was not significantly different (0-3.87/million for Indigenous Australians; 0.94-1.83/million for non-indigenous). The overall indirect age-standardised CJD mortality ratio for the indigenous population for the years 2006-2018 was 1.49 (95% CI, 0.75-2.98), also not significantly different to the all-resident Australian population. CONCLUSION: CJD occurs in Indigenous Australians with clinical phenotype and occurrence rates similar to non-Indigeno

Background Accurate, timely diagnosis of neurodegenerative disorders, in particular distinguishing primary psychiatric from neurological disorders and in younger people, can be challenging. There is a need for biomarkers to reduce the diagnostic odyssey and improve outcomes. Neurofilament light (NfL) has shown promise as a diagnostic biomarker in a wide range of disorders. Our Markers in Neuropsychiatric Disorders (MiND) Study builds on our pilot (Eratne et al, ANZJP, 2020), to explore the diagnostic and broader utility of plasma and cerebrospinal fluid (CSF) NfL and other novel markers such as phosphorylated tau 181 (p‐tau181), in a broad range of psychiatric and neurodegenerative/neurological disorders, with a view of translation into routine clinical practice. Methods We assessed plasma and/or CSF NfL and p‐tau181 concentrations in broad cohorts, including: patients assessed for neurocognitive/psychiatric symptoms at Neuropsychiatry and Melbourne Young‐Onset Dementia services and other services, in a wide range of disorders including Alzheimer disease, frontotemporal dementia, schizophrenia, bipolar disorder, depression, Niemann‐Pick Type C, epilepsy, functional neurological disorders. The most recent primary consensus diagnosis informed by established diagnostic criteria was categorised: primary psychiatric disorder (PPD), neurodegenerative/neurological disorder (ND), or healthy controls (HC). Results Findings from over 500 patients/participants will be presented, which indicate that CSF and plasma NfL levels are significantly elevated in a broad range of ND compared to a broad range of PPD, and HC, and bvFTD progressors from phenocopy syndromes, differentiating with areas under the curve of >0.90, sensitivity and specificity >90%. Plasma P‐tau181 levels distinguished Alzheimer disease (mainly younger sporadic), compared to other neurodegenerative disorders, with AUC 0.90, 90% sensitivity and specificity. As recruitment, sample analysis, data coll

Background Patients presenting with neuropsychiatric symptoms often face significant diagnostic odyssey. Our recent research (Eratne et al, ANZJP, 2020) found neurofilament light (NfL) differentiated between neurodegenerative and psychiatric disorders, with high accuracy. Yet the clinical utility of NfL, as to whether it can aid clinicians in avoiding misdiagnosis in a real‐world clinical setting, is unknown. Our primary aim was to measure the rates of diagnostic change in patients with neuropsychiatric symptoms assessed at a tertiary multidisciplinary service, and determine whether baseline cerebrospinal (CSF) NfL level could have prevented misdiagnoses, by predicting the final diagnosis after follow up. Methods We conducted a retrospective file review of patients assessed at an Australian neuropsychiatry and young‐onset dementia service between 2009‐2020. NfL levels were measured from CSF collected at their baseline assessment. Blinded investigators (MK, HD, DE) extracted clinical data including diagnoses from discharge summaries and outpatient letters from the initial assessment and re‐assessment. Baseline and final diagnoses were categorised as neurodegenerative disorder [ND], or, other non‐neurodegenerative conditions including primary psychiatric disorder [Other/PPD]. We also obtained follow‐up information on patients that were subsequently seen at external services where available. Results From a preliminary analysis of those with follow‐up information for at least a year (N=32), six patients’ diagnostic categories (19%) were revised (ND to Other/PPD=5; Other/PPD to ND=1). In all six cases (figure 2), baseline CSF NfL levels, using our previously established cut‐off, would have predicted the final revised diagnosis. As this study is underway, findings for over 200 patients will be presented for the Conference. Conclusions In a real‐world tertiary clinical setting, baseline CSF NfL would have accurately predicted diagnostic change, showing promise to

Background Alzheimer’s disease (AD) clinical trials require cognitive test scores that assess change in cognitive function accurately. Here, we propose new composite cognitive test scores to detect earlier stages of AD accurately by using the full neuropsychological testing battery (in ADNI) and a manifold learning dimension reduction technique namely UMAP. Method Data for this study included N=1585 ADNI participants ([492 cognitively normal (CN), 804 mild cognitively impaired (MCI), 289 AD; aged 73.8±7.1; 708 females]; Table 1). Subjects with 3 or more follow‐up sessions were included. Cognitive test scores with more than 60% missing data were excluded. Missing data within included test scores were imputed using the MissForest algorithm. A linear mixed model using all follow‐up data was applied to calculate the random slope (rate of change) and random intercept for each cognitive score and for each subject. The scores and demographic measurements: age, gender, years of education and APOE‐ɛ4 status were used to inform the UMAP. Levels for the output variable were defined as: 1) stable CN, 2) CN who progressed to MCI or probable dementia due to AD, 3) stable MCI, 4) MCI who progressed to dementia AD and 5) dementia due to AD. The model calculated two composite scores. These cognitive stages were predicted using Support Vector Machine (SVM) analysis of both the new composite scores and the traditional clinical rating measures of Clinical Dementia Rating (CDR) and Mini‐Mental State Examination (MMSE). Result Predicting cognitive stages using the proposed composite scores show a highly significant improvement with a 0.981 accuracy and 0.976 reliability (evaluated by Cohen's kappa coefficient), compared to using the combination of CDR and MMSE scores covaried for demographics, which had 0.660 accuracy and 0.567 reliability. Individuals’ clinical and preclinical stages with regards to UMAP two‐dimensional embedding and the clinical rating measures, CDR and MMSE

Background The development of cognitive endpoints that can accurately assess changes in cognition over short time frames is crucial for clinical trials and research of Alzheimer’s disease (AD). Understanding the changing influence of contributing test scores on composites throughout the disease course provides the opportunity to optimise cognitive composite scores for different stages of AD. Method AIBL participants with declining cognitive performance were included in this study N=1275 [688 cognitively unimpaired (CU), 277 mild cognitively impaired (MCI), 310 AD; aged 73±9; 718 females]). Two cognitive composite scores (Episodic Memory (EM) and PACC) and their component test scores (California Verbal Learning Test‐II Delayed Recall (CVLT‐II DR), Logical Memory Delayed Recall (LMII), Rey Complex Figure Test 30 minute delayed recall (RCFT‐DR) and CVLT‐II DR, LMII, Digit Symbol Substitution Test (DS), MMSE, respectively) were evaluated. We first examined the relationship between each of component tests score for each composite. We then compared the extent to which longitudinal trajectories of each component test score and each cognitive composite score differed at each disease stage. Result CVLT‐II DR contributed the most to the EM composite followed by RCFT‐DR and LMII with the influence remaining unchanged across each disease stage. For PACC, CVLT‐II DR contributed the most to the initial decline, with MMSE and LMII contributing similar amounts and DS contributing the least. CVLT‐II DR contributed substantially to changes in PACC earlier in the disease course but MMSE drove the PACC change in later stages of disease. Initially, both composites follow similar longitudinal trajectories. However, the EM composite reaches a floor not observed for the PACC. Conclusion Understanding the temporal contribution of component tests scores on cognitive composites could provide improved cognitive endpoints tailored to use. For instance, MMSE is sensitive to change later

Background Previous studies have shown that Aß‐amyloid (Aß) likely promotes tau to spread beyond the medial temporal lobe. However, the Aß levels necessary for tau to spread in the neocortex is still unclear. Method 466 participants underwent tau imaging with [18F]MK6420 and Aß imaging with [18F]NAV4694 (Fig. 1). Aß scans were quantified on the Centiloid (CL) scale with a cut‐off of 25CL for abnormal levels of Aß (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus and parahippocampus) using the cerebellar cortex as reference region. Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A‐ subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Result The plots of prevalence of T+ (Fig. 2) show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aß level between 10‐40 CL reaching 23% in Me, 15% in Te and 11% in R. Between 40‐70 CL, the prevalence of T+ subjects per CL increased four‐fold faster and at 70 CL was 64% in Me, 51% in Te and 37% in R. In cognitively unimpaired (Fig. 3), there were no T+ in R below 50 CL. The highest prevalence of T+ was found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aß levels below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aß levels are required before neocortical tau becomes detectable.

In Alzheimer’s disease (AD), a single-nucleotide polymorphism in the gene encoding brain-derived neurotrophic factor (BDNFVal66Met) is associated with worse impact of primary AD pathology (beta-amyloid, Aβ) on neurodegeneration and cognitive decline, rendering BDNFVal66Met an important modulating factor of cognitive impairment in AD. However, the effect of BDNFVal66Met on functional networks that may underlie cognitive impairment in AD is poorly understood. Using a cross-validation approach, we first explored in subjects with autosomal dominant AD (ADAD) from the Dominantly Inherited Alzheimer Network (DIAN) the effect of BDNFVal66Met on resting-state fMRI assessed functional networks. In seed-based connectivity analysis of six major large-scale networks, we found a stronger decrease of hippocampus (seed) to medial-frontal connectivity in the BDNFVal66Met carriers compared to BDNFVal homozogytes. BDNFVal66Met was not associated with connectivity in any other networks. Next, we tested whether the finding of more pronounced decrease in hippocampal-medial-frontal connectivity in BDNFVal66Met could be also found in elderly subjects with sporadically occurring Aβ, including a group with subjective cognitive decline (N = 149, FACEHBI study) and a group ranging from preclinical to AD dementia (N = 114, DELCODE study). In both of these independently recruited groups, BDNFVal66Met was associated with a stronger effect of more abnormal Aβ-levels (assessed by biofluid-assay or amyloid-PET) on hippocampal-medial-frontal connectivity decreases, controlled for hippocampus volume and other confounds. Lower hippocampal-medial-frontal connectivity was associated with lower global cognitive performance in the DIAN and DELCODE studies. Together these results suggest that BDNFVal66Met is selectively associated with a higher vulnerability of hippocampus-frontal connectivity to primary AD pathology, resulting in greater AD-related cognitive impairment.

The Alzheimer's Association International Conference held its sixth Satellite Symposium in Sydney, Australia in 2019, highlighting the leadership of Australian researchers in advancing the understanding of and treatment developments for Alzheimer's disease (AD) and other dementias. This leadership includes the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), which has fueled the identification and development of many biomarkers and novel therapeutics. Two multimodal lifestyle intervention studies have been launched in Australia; and Australian researchers have played leadership roles in other global studies in diverse populations. Australian researchers have also played an instrumental role in efforts to understand mechanisms underlying vascular contributions to cognitive impairment and dementia; and through the Women's Healthy Aging Project have elucidated hormonal and other factors that contribute to the increased risk of AD in women. Alleviating the behavioral and psychological symptoms of dementia has also been a strong research and clinical focus in Australia.

Nationwide surveillance of Creutzfeldt-Jakob disease and other human prion diseases is performed by the Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR). National surveillance encompasses the period since 1 January 1970, with prospective surveillance occurring from 1 October 1993. Over this prospective surveillance period, considerable developments have occurred in pre-mortem diagnostics; in the delineation of new disease subtypes; and in a heightened awareness of prion diseases in healthcare settings. Surveillance practices of the ANCJDR have evolved and adapted accordingly. This report summarises the activities of the ANCJDR during 2020. Since the ANCJDR began offering diagnostic cerebrospinal fluid (CSF) 14-3-3 protein testing in Australia in September 1997, the annual number of referrals has steadily increased. In 2020, 510 domestic CSF specimens were referred for 14-3-3 protein testing and 85 persons with suspected human prion disease were formally added to the national register. As of 31 December 2020, just over half (44 cases) of the 85 suspect case notifications remain classified as 'incomplete'; 27 cases were excluded through either detailed clinical follow-up (9 cases) or neuropathological examination (18 cases); 18 cases were classified as 'definite' and eleven as 'probable' prion disease. For 2020, sixty percent of all suspected human-prion-disease-related deaths in Australia underwent neuropathological examination. No cases of variant or iatrogenic CJD were identified. The SARS-CoV-2 pandemic did not affect prion disease surveillance outcomes in Australia.

Erratum to Commun Dis Intell (2018) 2021;45 (https://doi.org/10.33321/cdi.2021.45.38).