Australian e-Health Research Centre

MOTIVATION: In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. RESULTS: Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. AVAILABILITY AND IMPLEMENTATION: DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters' choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren't typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state-of-the-art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

OBJECTIVE: Assess Aβ deposition longitudinally and explore its relationship with cognition and disease progression. METHODS: Clinical follow-up was obtained 20 ± 3 months after [¹¹C]Pittsburgh compound B (PiB)-positron emission tomography in 206 subjects: 35 with dementia of the Alzheimer type (DAT), 65 with mild cognitive impairment (MCI), and 106 age-matched healthy controls (HCs). A second PiB scan was obtained at follow-up in 185 subjects and a third scan after 3 years in 57. RESULTS: At baseline, 97% of DAT, 69% of MCI, and 31% of HC subjects showed high PiB retention. At 20-month follow-up, small but significant increases in PiB standardized uptake value ratios were observed in the DAT and MCI groups, and in HCs with high PiB retention at baseline (5.7%, 2.1%, and 1.5%, respectively). Increases were associated with the number of apolipoprotein E ε4 alleles. There was a weak correlation between PiB increases and decline in cognition when all groups were combined. Progression to DAT occurred in 67% of MCI with high PiB versus 5% of those with low PiB, but 20% of the low PiB MCI subjects progressed to other dementias. Of the high PiB HCs, 16% developed MCI or DAT by 20 months and 25% by 3 years. One low PiB HC developed MCI. INTERPRETATION: Aβ deposition increases slowly from cognitive normality to moderate severity DAT. Extensive Aβ deposition precedes cognitive impairment, and is associated with ApoE genotype and a higher risk of cognitive decline in HCs and progression from MCI to DAT over 1 to 2 years. However, cognitive decline is only weakly related to change in Aβ burden, suggesting that downstream factors have a more direct effect on symptom progression.

OBJECTIVE: Cardiac rehabilitation (CR) is pivotal in preventing recurring events of myocardial infarction (MI). This study aims to investigate the effect of a smartphone-based home service delivery (Care Assessment Platform) of CR (CAP-CR) on CR use and health outcomes compared with a traditional, centre-based programme (TCR) in post-MI patients. METHODS: In this unblinded randomised controlled trial, post-MI patients were randomised to TCR (n=60; 55.7±10.4 years) and CAP-CR (n=60; 55.5±9.6 years) for a 6-week CR and 6-month self-maintenance period. CAP-CR, delivered in participants' homes, included health and exercise monitoring, motivational and educational material delivery, and weekly mentoring consultations. CAP-CR uptake, adherence and completion rates were compared with TCR using intention-to-treat analyses. Changes in clinical outcomes (modifiable lifestyle factors, biomedical risk factors and health-related quality of life) across baseline, 6 weeks and 6 months were compared within, and between, groups using linear mixed model regression. RESULTS: CAP-CR had significantly higher uptake (80% vs 62%), adherence (94% vs 68%) and completion (80% vs 47%) rates than TCR (p<0.05). Both groups showed significant improvements in 6-minute walk test from baseline to 6 weeks (TCR: 537±86-584±99 m; CAP-CR: 510±77-570±80 m), which was maintained at 6 months. CAP-CR showed slight weight reduction (89±20-88±21 kg) and also demonstrated significant improvements in emotional state (K10: median (IQR) 14.6 (13.4-16.0) to 12.6 (11.5-13.8)), and quality of life (EQ5D-Index: median (IQR) 0.84 (0.8-0.9) to 0.92 (0.9-1.0)) at 6 weeks. CONCLUSIONS: This smartphone-based home care CR programme improved post-MI CR uptake, adherence and completion. The home-based CR programme was as effective in improving physiological and psychological health outcomes as traditional CR. CAP-CR is a viable option towards optimising use of CR services. TRIAL REGISTRATION NUMBER: ANZCTR12609000251224.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Abstract A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer’s disease neuropathological hallmarks (that is, amyloid-β plaques and tau neurofibrillary tangles) can preserve their cognition over time or are destined to decline. In this large multicenter amyloid and tau positron emission tomography (PET) study ( n = 1,325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid PET-positive (A + ) and tau PET-positive (T + ) in the medial temporal lobe (A + T MTL + ) and/or in the temporal neocortex (A + T NEO-T + ) and compared them with A + T − and A − T − groups. Cox proportional-hazards models showed a substantially increased risk for progression to mild cognitive impairment in the A + T NEO-T + (hazard ratio (HR) = 19.2, 95% confidence interval (CI) = 10.9–33.7), A + T MTL + (HR = 14.6, 95% CI = 8.1–26.4) and A + T − (HR = 2.4, 95% CI = 1.4–4.3) groups versus the A − T − (reference) group. Both A + T MTL + (HR = 6.0, 95% CI = 3.4–10.6) and A + T NEO-T + (HR = 7.9, 95% CI = 4.7–13.5) groups also showed faster clinical progression to mild cognitive impairment than the A + T − group. Linear mixed-effect models indicated that the A + T NEO-T + ( β = −0.056 ± 0.005, T = −11.55, P &lt; 0.001), A + T MTL + ( β = −0.024 ± 0.005, T = −4.72, P &lt; 0.001) and A + T − ( β = −0.008 ± 0.002, T = −3.46, P &lt; 0.001) groups showed significantly faster longitudinal global cognitive decline compared to the A − T − (reference) group (all P &lt; 0.001). Both A + T NEO-T + ( P &lt; 0.001) and A + T MTL + ( P = 0.002) groups also progressed faster than the A + T − group. In summary, evidence of advanced Alzheimer’s disease pathological changes provided by a combination of abnormal amyloid and tau PET examinations is strongly associated with short-term (that is, 3–5 years) cognitive decline in cognitively unimpaired individuals and is therefore of high clinical relevance.

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of selecting k skyline points so that the number of points, which are dominated by at least one of these k skyline points, is maximized. We first present an efficient dynamic programming based exact algorithm in a 2d-space. Then, we show that the problem is NP-hard when the dimensionality is 3 or more and it can be approximately solved by a polynomial time algorithm with the guaranteed approximation ratio 1-1/e. To speed-up the computation, an efficient, scalable, index-based randomized algorithm is developed by applying the FM probabilistic counting technique. A comprehensive performance evaluation demonstrates that our randomized technique is very efficient, highly accurate, and scalable.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

INTRODUCTION: To promote telehealth implementation and uptake, it is important to assess overall clinical effectiveness to ensure any changes will not adversely affect patient outcomes. The last systematic literature review examining telehealth effectiveness was conducted in 2010. Given the increasing use of telehealth and technological developments in the field, a more contemporary review has been carried out. The aim of this review was to synthesise recent evidence associated with the clinical effectiveness of telehealth services. METHODS: A systematic search of 'Pretty Darn Quick'-Evidence portal was carried out in November 2020 for systematic reviews on telehealth, where the primary outcome measure reported was clinical effectiveness. Due to the volume of telehealth articles, only systematic reviews with meta-analyses published between 2010 and 2019 were included in the analysis. RESULTS: = 2). The evidence showed that for all disciplines, telehealth across a range of modalities was as effective, if not more, than usual care. DISCUSSION: This review demonstrates that telehealth can be equivalent or more clinically effective when compared to usual care. However, the available evidence is very discipline specific, which highlights the need for more clinical effectiveness studies involving telehealth across a wider spectrum of clinical health services. The findings from this review support the view that in the right context, telehealth will not compromise the effectiveness of clinical care when compared with conventional forms of health service delivery.

OBJECTIVE: Elucidating the role of aggregated beta-amyloid in relation to gray matter atrophy is crucial to the understanding of the pathological mechanisms of Alzheimer disease and for the development of therapeutic trials. The present study aims to assess this relationship. METHODS: Brain magnetic resonance imaging and [(11)C]Pittsburgh compound B (PiB)-positron emission tomography scans were obtained from 94 healthy elderly subjects (49 with subjective cognitive impairment), 34 patients with mild cognitive impairment, and 35 patients with Alzheimer disease. The correlations between global and regional neocortical PiB retention and atrophy were analyzed in each clinical group. RESULTS: Global and regional atrophy were strongly related to beta-amyloid load in participants with subjective cognitive impairment but not in patients with mild cognitive impairment or Alzheimer disease. Global neocortical beta-amyloid deposition correlated to atrophy in a large brain network including the hippocampus, medial frontal and parietal areas, and lateral temporoparietal cortex, whereas regional beta-amyloid load was related to local atrophy in the areas of highest beta-amyloid load only, that is, medial orbitofrontal and anterior and posterior cingulate/precuneus areas. INTERPRETATION: There is a strong relationship between beta-amyloid deposition and atrophy very early in the disease process. As the disease progresses to mild cognitive impairment and Alzheimer disease clinical stages, pathological events other than, and probably downstream from, aggregated beta-amyloid deposition might be responsible for the ongoing atrophic process. These findings suggest that antiamyloid therapy should be administered very early in the disease evolution to minimize synaptic and neuronal loss.

Alzheimer's disease is increasingly considered a large-scale network disconnection syndrome, associated with progressive aggregation of pathological proteins, cortical atrophy, and functional disconnections between brain regions. These pathological changes are posited to arise in a stereotypical spatiotemporal manner, targeting intrinsic networks in the brain, most notably the default mode network. While this network-specific disruption has been thoroughly studied with functional neuroimaging, changes to specific white matter fibre pathways within the brain's structural networks have not been closely investigated, largely due to the challenges of modelling complex white matter structure. Here, we applied a novel technique known as 'fixel-based analysis' to comprehensively investigate fibre tract-specific differences at a within-voxel level (called 'fixels') to assess potential axonal loss in subjects with Alzheimer's disease and mild cognitive impairment. We hypothesized that patients with Alzheimer's disease would exhibit extensive degeneration across key fibre pathways connecting default network nodes, while patients with mild cognitive impairment would exhibit selective degeneration within fibre pathways connecting regions previously identified as functionally implicated early in Alzheimer's disease. Diffusion MRI data from Alzheimer's disease (n = 49), mild cognitive impairment (n = 33), and healthy elderly control subjects (n = 95) were obtained from the Australian Imaging, Biomarkers and Lifestyle study of ageing. We assessed microstructural differences in fibre density, and macrostructural differences in fibre bundle morphology using fixel-based analysis. Whole-brain analysis was performed to compare groups across all white matter fixels. Subsequently, we performed a tract of interest analysis comparing fibre density and cross-section across 11 selected white matter tracts, to investigate potentially subtle degeneration within fibre pathways in mild cognitive impairment, initially by clinical diagnosis alone, and then by including amyloid status (i.e. a positive or negative amyloid PET scan). Our whole-brain analysis revealed significant white matter loss manifesting both microstructurally and macrostructurally in Alzheimer's disease patients, evident in specific fibre pathways associated with default mode network nodes. Reductions in fibre density and cross-section in mild cognitive impairment patients were only exhibited within the posterior cingulum when statistical analyses were limited to tracts of interest. Interestingly, these degenerative changes did not appear to be associated with high amyloid accumulation, given that amyloid-negative, but not positive, mild cognitive impairment subjects exhibited subtle focal left posterior cingulum deficits. The findings of this study demonstrated a stereotypical distribution of white matter degeneration in patients with Alzheimer's disease, which was in line with canonical findings from other imaging modalities, and with a network-based conceptualization of the disease.awx355media15726254535001.

BACKGROUND: Many systematic reviews exist on the use of remote patient monitoring (RPM) interventions to improve clinical outcomes and psychological well-being of patients with heart failure. However, research is broadly distributed from simple telephone-based to complex technology-based interventions. The scope and focus of such evidence also vary widely, creating challenges for clinicians who seek information on the effect of RPM interventions. OBJECTIVE: The aim of this study was to investigate the effects of RPM interventions on the health outcomes of patients with heart failure by synthesizing review-level evidence. METHODS: We searched PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and the Cochrane Library from 2005 to 2015. We screened reviews based on relevance to RPM interventions using criteria developed for this overview. Independent authors screened, selected, and extracted information from systematic reviews. AMSTAR (Assessment of Multiple Systematic Reviews) was used to assess the methodological quality of individual reviews. We used standardized language to summarize results across reviews and to provide final statements about intervention effectiveness. RESULTS: A total of 19 systematic reviews met our inclusion criteria. Reviews consisted of RPM with diverse interventions such as telemonitoring, home telehealth, mobile phone-based monitoring, and videoconferencing. All-cause mortality and heart failure mortality were the most frequently reported outcomes, but others such as quality of life, rehospitalization, emergency department visits, and length of stay were also reported. Self-care and knowledge were less commonly identified. CONCLUSIONS: Telemonitoring and home telehealth appear generally effective in reducing heart failure rehospitalization and mortality. Other interventions, including the use of mobile phone-based monitoring and videoconferencing, require further investigation.

More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach.

Current state-of-the-art diagnostic measures of Alzheimer's disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time-consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.

The earliest detectable change in Alzheimer's disease (AD) is the buildup of amyloid plaque in the brain. Early detection of AD, prior to irreversible neurological damage, is important for the efficacy of current interventions as well as for the development of new treatments. Although PiB-PET imaging and CSF amyloid are the gold standards for early AD diagnosis, there are practical limitations for population screening. AD-related pathology occurs primarily in the brain, but some of the hallmarks of the disease have also been shown to occur in other tissues, including the retina, which is more accessible for imaging. Retinal vascular changes and degeneration have previously been reported in AD using optical coherence tomography and laser Doppler techniques. This report presents results from analysis of retinal photographs from AD and healthy control participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing. This is the first study to investigate retinal blood vessel changes with respect to amyloid plaque burden in the brain. We demonstrate relationships between retinal vascular parameters, neocortical brain amyloid plaque burden and AD. A number of RVPs were found to be different in AD. Two of these RVPs, venular branching asymmetry factor and arteriolar length-to-diameter ratio, were also higher in healthy individuals with high plaque burden (P = 0.01 and P = 0.02 respectively, after false discovery rate adjustment). Retinal photographic analysis shows potential as an adjunct for early detection of AD or monitoring of AD-progression or response to treatments.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

INTRODUCTION: Plasma amyloid beta (Aβ)1-42/Aβ1-40 ratio, phosphorylated-tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) are putative blood biomarkers for Alzheimer's disease (AD). However, head-to-head cross-sectional and longitudinal comparisons of the aforementioned biomarkers across the AD continuum are lacking. METHODS: Plasma Aβ1-42, Aβ1-40, p-tau181, GFAP, and NfL were measured utilizing the Single Molecule Array (Simoa) platform and compared cross-sectionally across the AD continuum, wherein Aβ-PET (positron emission tomography)-negative cognitively unimpaired (CU Aβ-, n = 81) and mild cognitive impairment (MCI Aβ-, n = 26) participants were compared with Aβ-PET-positive participants across the AD continuum (CU Aβ+, n = 39; MCI Aβ+, n = 33; AD Aβ+, n = 46) from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) cohort. Longitudinal plasma biomarker changes were also assessed in MCI (n = 27) and AD (n = 29) participants compared with CU (n = 120) participants. In addition, associations between baseline plasma biomarker levels and prospective cognitive decline and Aβ-PET load were assessed over a 7 to 10-year duration. RESULTS: Lower plasma Aβ1-42/Aβ1-40 ratio and elevated p-tau181 and GFAP were observed in CU Aβ+, MCI Aβ+, and AD Aβ+, whereas elevated plasma NfL was observed in MCI Aβ+ and AD Aβ+, compared with CU Aβ- and MCI Aβ-. Among the aforementioned plasma biomarkers, for models with and without AD risk factors (age, sex, and apolipoprotein E (APOE) ε4 carrier status), p-tau181 performed equivalent to or better than other biomarkers in predicting a brain Aβ-/+ status across the AD continuum. However, for models with and without the AD risk factors, a biomarker panel of Aβ1-42/Aβ1-40, p-tau181, and GFAP performed equivalent to or better than any of the biomarkers alone in predicting brain Aβ-/+ status across the AD continuum. Longitudinally, plasma Aβ1-42/Aβ1-40, p-tau181, and GFAP were altered in MCI compared with CU, and plasma GFAP and NfL were altered in AD compared with CU. In addition, lower plasma Aβ1-42/Aβ1-40 and higher p-tau181, GFAP, and NfL were associated with prospective cognitive decline and lower plasma Aβ1-42/Aβ1-40, and higher p-tau181 and GFAP were associated with increased Aβ-PET load prospectively. DISCUSSION: These findings suggest that plasma biomarkers are altered cross-sectionally and longitudinally, along the AD continuum, and are prospectively associated with cognitive decline and brain Aβ-PET load. In addition, although p-tau181 performed equivalent to or better than other biomarkers in predicting an Aβ-/+ status across the AD continuum, a panel of biomarkers may have superior Aβ-/+ status predictive capability across the AD continuum. HIGHLIGHTS: Area under the curve (AUC) of p-tau181 ≥ AUC of Aβ42/40, GFAP, NfL in predicting PET Aβ-/+ status (Aβ-/+). AUC of Aβ42/40+p-tau181+GFAP panel ≥ AUC of Aβ42/40/p-tau181/GFAP/NfL for Aβ-/+. Longitudinally, Aβ42/40, p-tau181, and GFAP were altered in MCI versus CU. Longitudinally, GFAP and NfL were altered in AD versus CU. Aβ42/40, p-tau181, GFAP, and NfL are associated with prospective cognitive decline. Aβ42/40, p-tau181, and GFAP are associated with increased PET Aβ load prospectively.

INTRODUCTION: Our objective was to investigate the effect of sex on cognitive decline within the context of amyloid β (Aβ) burden and apolipoprotein E genotype. METHODS: We analyzed sex-specific effects on Aβ-positron emission tomography, apolipoprotein, and rates of change on the Preclinical Alzheimer Cognitive Composite-5 across three cohorts, such as the Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker and Lifestyle, and Harvard Aging Brain Study (n = 755; clinical dementia rating = 0; age (standard deviation) = 73.6 (6.5); female = 55%). Mixed-effects models of cognitive change by sex, Aβ-positron emission tomography, and apolipoprotein ε4 were examined with quadratic time effects over a median of 4 years of follow-up. RESULTS: Apolipoprotein ε4 prevalence and Aβ burden did not differ by sex. Sex did not directly influence cognitive decline. Females with higher Aβ exhibited faster decline than males. Post hoc contrasts suggested that females who were Aβ and apolipoprotein ε4 positive declined faster than their male counterparts. DISCUSSION: Although Aβ did not differ by sex, cognitive decline was greater in females with higher Aβ. Our findings suggest that sex may play a modifying role on risk of Alzheimer's disease-related cognitive decline.

Soil carbon (C) stabilisation is known to depend in part on its distribution in structural aggregates, and upon soil microbial activity within the aggregates. However, the mechanisms and relative contributions of different microbial groups to C turnover in different aggregates under various management practices remain unclear. The aim of this study was to determine the role of soil aggregation and their associated microbial communities in driving the responses of soil organic matter (SOM) to multiple management practices. Our results demonstrate that higher amounts of C inputs coupled with greater soil aggregation in residue retention management practices has positive effects on soil C content. Our results provide evidence that different aggregate size classes support distinct microbial habitats which supports the colonisation of different microbial communities. Most importantly our results indicate that the effects of management practices on soil C is modulated by soil aggregate sizes and their associated microbial community and are more pronounced in macro-aggregate compared with micro-aggregate sizes. Based on our findings we recommend that differential response of management practices and microbial control on the C turnover in macro-aggregates and micro-aggregate should be explicitly considered when accounting for management impacts on soil C turnover.