Search

Abstract Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out‐of‐pocket costs, perceived risks, and the lack of AD‐specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non‐invasive and cost‐effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.

Abstract Blood-based biomarkers of Alzheimer disease (AD) may facilitate testing of historically under-represented groups. The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to compare AD biomarkers in participants who identify their race as either Black or white. Plasma samples from 324 Black and 1,547 white participants underwent analysis with C2N Diagnostics’ PrecivityAD test for Aβ42 and Aβ40. Compared to white individuals, Black individuals had higher average plasma Aβ42/40 levels at baseline, consistent with a lower average level of amyloid pathology. Interestingly, this difference resulted from lower average levels of plasma Aβ40 in Black participants. Despite the differences, Black and white individuals had similar longitudinal rates of change in Aβ42/40, consistent with a similar rate of amyloid accumulation. Our results agree with multiple recent studies demonstrating a lower prevalence of amyloid pathology in Black individuals, and additionally suggest that amyloid accumulates consistently across both groups.

Abstract The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.

Abstract Background Structural and functional changes of the choroid plexus (ChP) have been reported in Alzheimer’s disease (AD). Nonetheless, the role of the ChP in the pathogenesis of AD remains largely unknown. We aim to unravel the relation between ChP functioning and core AD pathogenesis using a unique proteomic approach in mice and humans. Methods We used an APP knock-in mouse model, APPNL-G-F, exhibiting amyloid pathology, to study the association between AD brain pathology and protein changes in mouse ChP tissue and CSF using liquid chromatography mass spectrometry. Mouse proteomes were investigated at the age of 7 weeks (n = 5) and 40 weeks (n = 5). Results were compared with previously published human AD CSF proteomic data (n = 496) to identify key proteins and pathways associated with ChP changes in AD. Results ChP tissue proteome was dysregulated in APPNL-G-F mice relative to wild-type mice at both 7 and 40 weeks. At both ages, ChP tissue proteomic changes were associated with epithelial cells, mitochondria, protein modification, extracellular matrix and lipids. Nonetheless, some ChP tissue proteomic changes were different across the disease trajectory; pathways related to lysosomal function, endocytosis, protein formation, actin and complement were uniquely dysregulated at 7 weeks, while pathways associated with nervous system, immune system, protein degradation and vascular system were uniquely dysregulated at 40 weeks. CSF proteomics in both mice and humans showed similar ChP-related dysregulated pathways. Conclusions Together, our findings support the hypothesis of ChP dysfunction in AD. These ChP changes were related to amyloid pathology. Therefore, the ChP could become a novel promising therapeutic target for AD.

Rainbow trout (Oncorhynchus mykiss) is a dominant aquaculture species of the Salmonidae family, native only to the North Pacific. Recently, the gut microbiome has been shown to reflect the health status and responses to environmental changes in farmed fish. In this analysis we investigated the microbiome composition of the intestinal tract in 20 wild-caught rainbow trout specimens sampled in Alaska, USA. The targeted 16S rRNA gene (V3-V4 region) was sequenced on the Illumina NovaSeq 6000 platform. After quality control, demultiplexing and adapter trimming reads were analyzed using the DADA2 pipeline to obtain Amplicon Sequencing Variants (ASVs) which were subsequently taxonomically assigned. We found two phyla dominating the gut ecosystem present in every sample, Firmicutes and Fusobacteria, followed by lower abundances of Cyanobacteria, Proteobacteria and Bacteroidetes. At the genus level, we found high relative abundances of Cetobacterium and Clostridium sensu stricto 1. Interestingly, we did not identify often dominant genera Mycoplasma, Pseudomonas or Weisella which were prevalent in numerous studies previously, in cultured rainbow trout. Wild fish are exposed to a plethora of unpredictable environmental challenges, ranging from fluctuating water temperatures to variable food availability, as opposed to controlled conditions in production facilities. Examining and comparing the gut ecosystem of wild and reared individuals holds great potential in optimizing management practices for commercially important species. Microbiome studies can provide novel ways to enhance the overall welfare of fish, strengthen disease prevention and increase sustainability in aquaculture production.

Abstract Common and rare variants in the LRRK2 locus are associated with Parkinson’s disease (PD) risk, but the downstream effects of these variants on protein levels remain unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7006 aptamers (6138 unique proteins) in 3107 individuals). The dataset comprised six different and independent cohorts (five using the SomaScan7K (ADNI, DIAN, MAP, Barcelona-1 (Pau), and Fundació ACE (Ruiz)) and the PPMI cohort using the SomaScan5K panel). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 25 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk, and seven were validated in the PPMI cohort. Mendelian randomization analyses identified GPNMB, LCT, and CD68 causal for PD and nominate one more (ITGB2). These 25 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

The use of biomarkers for the early detection of Alzheimer’s disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect β-amyloid (Aβ) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aβ-PET may have additional value for predicting time to cognitive impairment in AD. The current study aims to evaluate the ability of baseline- versus longitudinal rate of change in-11C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

Abstract The extracellular buildup of amyloid beta (Aβ) plaques in the brain is a hallmark of Alzheimer’s disease (AD). Detection of Aβ pathology is essential for AD diagnosis and for identifying and recruiting research participants for clinical trials evaluating disease-modifying therapies. Currently, AD diagnoses are usually made by clinical assessments, although detection of AD pathology with positron emission tomography (PET) scans or cerebrospinal fluid (CSF) analysis can be used by specialty clinics. These measures of Aβ aggregation, e.g. plaques, protofibrils, and oligomers, are medically invasive and often only available at specialized medical centers or not covered by medical insurance, and PET scans are costly. Therefore, a major goal in recent years has been to identify blood-based biomarkers that can accurately detect AD pathology with cost-effective, minimally invasive procedures. To assess the performance of plasma Aβ assays in predicting amyloid burden in the central nervous system (CNS), this review compares twenty-one different manuscripts that used measurements of 42 and 40 amino acid-long Aβ (Aβ42 and Aβ40) in plasma to predict CNS amyloid status. Methodologies that quantitate Aβ42 and 40 peptides in blood via immunoassay or immunoprecipitation-mass spectrometry (IP-MS) were considered, and their ability to distinguish participants with amyloidosis compared to amyloid PET and CSF Aβ measures as reference standards was evaluated. Recent studies indicate that some IP-MS assays perform well in accurately and precisely measuring Aβ and detecting brain amyloid aggregates.

Abstract Objectives Concentrations of amyloid‐β peptides (Aβ42/Aβ40) and neurofilament light (NfL) can be measured in plasma or cerebrospinal fluid (CSF) and are associated with Alzheimer’s disease brain pathology and cognitive impairment. This study directly compared plasma and CSF measures of Aβ42/Aβ40 and NfL as predictors of cognitive decline. Methods Participants were 65 years or older and cognitively normal at baseline with at least one follow‐up cognitive assessment. Analytes were measured with the following types of assays: plasma Aβ42/Aβ40, immunoprecipitation‐mass spectrometry; plasma NfL, Simoa; CSF Aβ42/Aβ40, automated immunoassay; CSF NfL plate‐based immunoassay. Mixed effects models evaluated the global cognitive composite score over a maximum of 6 years as predicted by the fluid biomarkers. Results Analyses included 371 cognitively normal participants, aged 72.7 ± 5.2 years (mean ± standard deviation) with an average length of follow‐up of 3.9 ± 1.6 years. Standardized concentrations of biomarkers were associated with annualized cognitive change: plasma Aβ42/Aβ40, 0.014 standard deviations (95% confidence intervals 0.002 to 0.026); CSF Aβ42/Aβ40, 0.020 (0.008 to 0.032); plasma Nfl, −0.018 (−0.030 to −0.005); and CSF NfL, −0.024 (−0.036 to −0.012). Power analyses estimated that 266 individuals in each treatment arm would be needed to detect a 50% slowing of decline if identified by abnormal plasma measures versus 229 for CSF measures. Interpretation Both plasma and CSF measures of Aβ42/Aβ40 and NfL predicted cognitive decline. A clinical trial that enrolled individuals based on abnormal plasma Aβ42/Aβ40 and NfL levels would require only a marginally larger cohort than if CSF measures were used.

Background: Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).

Abstract Introduction Continuous measures of amyloid burden as measured by positron emission tomography (PET) are being used increasingly to stage Alzheimer's disease (AD). This study examined whether cerebrospinal fluid (CSF) and plasma amyloid beta (Aβ)42/Aβ40 could predict continuous values for amyloid PET. Methods CSF Aβ42 and Aβ40 were measured with automated immunoassays. Plasma Aβ42 and Aβ40 were measured with an immunoprecipitation–mass spectrometry assay. Amyloid PET was performed with Pittsburgh compound B (PiB). The continuous relationships of CSF and plasma Aβ42/Aβ40 with amyloid PET burden were modeled. Results Most participants were cognitively normal (427 of 491 [87%]) and the mean age was 69.0 ± 8.8 years. CSF Aβ42/Aβ40 predicted amyloid PET burden until a relatively high level of amyloid accumulation (69.8 Centiloids), whereas plasma Aβ42/Aβ40 predicted amyloid PET burden until a lower level (33.4 Centiloids). Discussion CSF Aβ42/Aβ40 predicts the continuous level of amyloid plaque burden over a wider range than plasma Aβ42/Aβ40 and may be useful in AD staging. Highlights Cerebrospinal fluid (CSF) amyloid beta (Aβ)42/Aβ40 predicts continuous amyloid positron emission tomography (PET) values up to a relatively high burden. Plasma Aβ42/Aβ40 is a comparatively dichotomous measure of brain amyloidosis. Models can predict regional amyloid PET burden based on CSF Aβ42/Aβ40. CSF Aβ42/Aβ40 may be useful in staging AD.

Potential gains in future salmon habitat associated with glacier loss have yet to be quantified. This study projects future gains in Pacific salmon freshwater habitat within western North America by linking a model of glacier mass change for 315 glaciers, forced by five different Global Climate Models, with a simple model of salmon stream habitat potential.

Measurement(s) temperature of water • temperature profile Technology Type(s) digital curation Factor Type(s) lake location • temporal interval Sample Characteristic - Environment lake • reservoir Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14619009

Abstract Background Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. Methods We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. Results The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. Conclusion The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

“Brain-predicted age” quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18–89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Abstract Variation in size and age at maturity is an important component of life history that is influenced by both environmental and genetic factors. In salmonids, large size confers a direct reproductive advantage through increased fecundity and egg quality in females, while larger males gain a reproductive advantage by monopolizing access to females. In addition, variation in size and age at maturity in males can be associated with different reproductive strategies; younger smaller males may gain reproductive success by sneaking among mating pairs. In both sexes, there is a trade‐off between older age and increased reproductive success and increased risk of mortality by delaying reproduction. We identified four Y‐chromosome haplogroups that showed regional‐ and population‐specific variation in frequency using RADseq data for 21 populations of Alaska Chinook salmon. We then characterized the range‐wide distribution of these haplogroups using GT‐seq assays. These haplogroups exhibited associations with size at maturity in multiple populations, suggesting that lack of recombination between X and Y‐chromosomes has allowed Y‐chromosome haplogroups to capture different alleles that influence size at maturity. Ultimately, conservation of life history diversity in Chinook salmon may require conservation of Y‐chromosome haplotype diversity.

Abstract Globally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade−1, comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m−3 decade−1). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade−1), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade−1 to + 0.65 °C decade−1. The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences.

Abstract Spatial and temporal variation in thermal conditions are important dimensions of aquatic landscapes, yet we do not understand how this heterogeneity will respond to climate change. Snowpack in many regions is declining but impacts on aquatic thermal regimes remain poorly understood. Our analyses of summer stream temperatures across a complex river basin in southwest Alaska show that loss of snowpack has disproportionate effects on water temperatures in streams that are cold under typical conditions, thereby homogenizing stream temperatures across the landscape during low snow conditions. Streams draining steep watersheds warmed from a summer average of 4–8°C between high and low snow years and became more responsive to variation in regional air temperature (as proxy for solar radiation), while streams draining flat watersheds changed negligibly. Our results suggest that conservation strategies for aquatic landscapes that focus on snow‐dominated cold‐water refugia may not be robust to future climate warming and changes in snowpack.

Background: Cerebrospinal fluid (CSF) neurofilament light chain (NfL) reflects neuro-axonal damage and is increasingly used to evaluate disease progression across neurological conditions including Alzheimer disease (AD). However, it is unknown how NfL relates to specific types of brain tissue. We sought to determine whether CSF NfL is more strongly associated with total gray matter, white matter, or white matter hyperintensity (WMH) volume, and to quantify the relative importance of brain tissue volume, age, and AD marker status (i.e., APOE genotype, brain amyloidosis, tauopathy, and cognitive status) in predicting CSF NfL. Methods: 419 participants (Clinical Dementia Rating [CDR] Scale > 0, N = 71) had CSF, magnetic resonance imaging (MRI), and neuropsychological data. A subset had amyloid positron emission tomography (PET) and tau PET. Pearson correlation analysis was used to determine the association between CSF NfL and age. Multiple regression was used to determine which brain volume (i.e., gray, white, or WMH volume) most strongly associated with CSF NfL. Stepwise regression and dominance analyses were used to determine the individual contributions and relative importance of brain volume, age, and AD marker status in predicting CSF NfL. Results: CSF NfL increased with age (r = 0.59, p 0.05) when considered simultaneously. Age and WMH volume were consistently more important (i.e., have greater R2 values) than AD markers when predicting CSF NfL. Conclusions: CSF NfL is a non-specific marker of aging and white matter integrity with limited sensitivity to specific markers of AD. CSF NfL likely reflects processes associated with cerebrovascular disease.

OBJECTIVES/GOALS: The preclinical stage of Alzheimer disease (AD) is a clinically silent period that can be detected through neuroimaging and biofluid biomarkers. The goal of this study was to determine whether performance of complex daily tasks is associated with plasma biomarkers of brain amyloidosis or neuroaxonal injury in cognitively normal (CN) older adults. METHODS/STUDY POPULATION: This is a cross-sectional analysis of an ongoing longitudinal cohort study. CN older adults performed three complex daily tasks (shopping, checkbook balancing, medication management) from the Performance Assessment of Self-Care Skills in their home. Tasks were scored for independence, with more assistance required indicating worse performance. Participants had a plasma sample obtained within two years of completing the tasks. Plasma amyloid (Aβ42 and Aβ40) were evaluated by high precision immunoprecipitation mass spectrometry assays and neurofilament light (NfL) was measured with single molecule array (Simoa) assays. Nonparametric partial correlations were used to quantify the associations between task performance and plasma AD biomarkers, controlling for age and gender. RESULTS/ANTICIPATED RESULTS: 105 CN participants (mean age 74.7 years, 55% female, 88% white) were included. After controlling for age and gender, worse performance of complex daily tasks (more assistance required) was associated with increased plasma NfL (Spearman’s: 0.23, p=0.04) but not plasma Aβ42/Aβ40. DISCUSSION/SIGNIFICANCE: This study suggests that worse performance of complex daily tasks in CN older adults may be associated with increased plasma NfL a marker of neuroaxonal injury, but not with plasma amyloid. These findings could lead to a better understanding of clinical changes that may occur prior to the onset of noticeable memory symptoms in AD or related dementias.

Introduction Progression to symptomatic Alzheimer disease (AD) occurs slowly over a series of preclinical stages. Declining functional mobility may be an early indicator of loss of brain network integration and may lead to an increased risk of experiencing falls. It is unknown whether measures of functional mobility and falls are preclinical markers of AD. The purpose of this study is to examine (1) the relationship between falls and functional mobility with AD biomarkers to determine when falls occur within the temporal progression to symptomatic Alzheimer disease, and (2) the attentional compared with perceptual/motor systems that underlie falls and functional mobility changes seen with AD.Methods and analysis This longitudinal cohort study will be conducted at the Knight Alzheimer Disease Research Center. Approximately 350 cognitively normal participants (with and without preclinical AD) will complete an in-home visit every year for 4 years. During each yearly assessment, functional mobility will be assessed using the Performance Oriented Mobility Assessment, Timed Up and Go, and Timed Up and Go dual task. Data regarding falls (including number and severity) will be collected monthly by self-report and confirmed through interviews. This study will leverage ongoing neuropsychological assessments and neuroimaging (including molecular imaging using positron emission tomography and MRI) performed by the Knight Alzheimer Disease Research Center. Relationships between falls and biomarkers of amyloid, tau and neurodegeneration will be evaluated.Ethics and dissemination This study was approved by the Washington University in St. Louis Institutional Review Board (reference number 201807135). Written informed consent will be obtained in the home prior to the collection of any study data. Results will be published in peer-reviewed publications and presented at national and international conferences.Trial registration number NCT04949529; Pre-results.

Despite growing recognition that complex population portfolios are important sources of ecosystem stability and resilience, the structure of such stock complexes is often not considered in monitoring schemes to inform environmental impact assessments. The use of “index” or “indicator” sites, and of short assessment windows, to make broad claims about the relative importance of specific habitats or populations within a portfolio, assume that regional populations are highly synchronous and that abundance is stable through time. Asynchrony among populations and changes in population productivity over time render these assumptions fundamentally flawed. We used 57 years of abundance data for sockeye salmon (Oncorhynchus nerka) spawning in a set of eight streams in the Wood River watershed, southwest Alaska, to demonstrate how natural patterns of variability affect the ability of fixed assessment windows to characterize the contribution of individual populations to the entire portfolio. Additionally, simulations were used to demonstrate how different levels of synchrony and autocorrelation affect the ability of monitoring schemes to estimate the contributions of individual populations to a portfolio over the long-term. We found that fixed assessment windows were distinctly limited in their ability to characterize a population’s contribution to a portfolio over the long-tem. Asynchronous dynamics among populations, and the presence of autocorrelation that creates slow changes in populations, weaken the ability to characterize a stream's potential contribution to a portfolio from short-term assessments. These results suggest that the structure of population portfolios, and the presence of directional changes in productivity within individual populations, need to be taken into account when carrying out environmental risk assessments that aim to measure the contribution of an individual population or habitat to system wide dynamics. Typical risk assessments that depend on short monitoring periods are likely to vastly underestimate the potential long-term value of any specific habitat, and the population it supports, as a component of a multi-population portfolio.

Abstract Opportunities for growth and survival of aquatic organisms are spatially and temporally variable as habitat conditions across watersheds respond to interacting climatic, geomorphic, and hydrologic conditions. As conservation efforts often focus on identifying and protecting critical habitats, it is important to understand how this spatial and temporal variation in habitat quality affects the production dynamics of populations. Here, we use microchemical records preserved in otoliths to reconstruct juvenile habitat‐use by sockeye salmon that survived to spawn in a single population on the Alaska Peninsula. Successful individuals demonstrated a diverse array of juvenile behavioral strategies both within and among years. Importantly, the dominant juvenile behavioral strategy used by successful individuals changed among years, suggesting shifts in the relative benefits of different rearing habitats. The growth benefits of remaining in a more productive rearing lake were greatest in warm years indicating environmental influence on relative habitat quality. However, we found no strong relationship between the amount of growth accumulated in the productive rearing lake and overall population productivity across years. These results highlight the dynamic nature of habitat conditions and the beneficial effect of maintaining connectivity between diverse habitats for population productivity. When short‐term studies are used to demonstrate the relative values of different habitats to species of conservation concern, there is a distinct risk of under‐valuing habitats that may be critically important under alternative environmental conditions. In particular, land‐use decisions that reduce the range of habitat options available to species may erode a population’s ability to withstand environmental change over the long term.

Abstract Males of many fish species exhibit alternative reproductive tactics, which can influence the maturation schedules, fishery productivity, and resilience to harvest of exploited populations. While alternative mating phenotypes can persist in stable equilibria through frequency‐dependent selection, shifts in tactic frequencies have been observed and can have substantial consequences for fisheries. Here, we examine the dynamics of precocious sneaker males called “jacks” in a population of sockeye salmon (Oncorhynchus nerka) from Frazer Lake, Alaska. Jacks, which are of little commercial value due to their small body sizes, have recently been observed at unusually high levels in this stock, degrading the value of regional fisheries. To inform future strategies for managing the prevalence of jacks, we used long‐term monitoring data to identify what regulates the frequencies of alternative male phenotypes in the population over time. Expression of the jack life history could not be explained by environmental factors expected to influence juvenile body condition and maturation probability. Instead, we found a strong positive association between the proportion of individuals maturing as jacks within a cohort and the prevalence of jacks among the males that sired that cohort. Moreover, due to differences in age‐at‐maturity between male phenotypes, and large interannual variability in recruitment strength, jacks from strong year‐classes often spawn among older males from the weaker recruitments of earlier cohorts. Through such “cohort mismatches,” which are amplified by size‐selective harvest on older males, jacks frequently achieve substantial representation in the breeding population, and likely high total fertilizations. The repeated occurrence of these cohort mismatches appears to disrupt the stabilizing influence of frequency‐dependent selection, allowing the prevalence of jacks to exceed what might be expected under equilibrium conditions. These results emphasize that the dynamics of alternative life histories can profoundly influence fishery performance and should be explicitly considered in the management of exploited populations.

Abstract Habitat alteration and species exploitation are fundamental issues in conservation, yet their interacting effects on food webs are rarely considered. We used a foraging model based on the Wood River basin (Alaska, USA) to explore how watershed development and commercial fisheries affect energy flow from sockeye salmon to brown bears. We found that, where salmon are abundant, fisheries can harvest large fractions of runs without substantially reducing bear consumption of salmon, but that watershed development could strongly reduce bear consumption if it shortens the duration of foraging opportunities by reducing population‐level variation in salmon spawn timing. Habitats with the lowest resource abundance (small streams) were particularly profitable for bear foraging because they offer salmon at unique times of the season. This result challenges environmental impact assessments that assume ecological effects respond solely to changes in resource abundance.

Economies dependent on natural resources could gain resilience to abrupt ecosystem and market shifts through proactive risk-buffering approaches. Using data from Alaskan fisheries, Clineet al. show that communities relying on diverse fisheries were more resilient to major ocean and market regime shifts in 1989.