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INTRODUCTION: The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aβ) over 15 years in a cohort of cognitively unimpaired older adults. METHODS: PA and Aβ measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross‐sectional and longitudinal relationships between PA and brain Aβ. Moderation analyses examined apolipoprotein E (APOE) ε4 carriage impact on the PA‐Aβ relationship. RESULTS: PA was not associated with brain Aβ at baseline (β = –0.001, p = 0.72) or over time (β = –0.26, p = 0.24). APOE ε4 status did not moderate the PA‐Aβ relationship over time (β = 0.12, p = 0.73). Brain Aβ levels did not predict PA trajectory (β = –54.26, p = 0.59). DISCUSSION: Our study did not identify a relationship between habitual PA and brain Aβ levels. Highlights: Physical activity levels did not predict brain amyloid beta (Aβ) levels over time in cognitively unimpaired older adults (≥60 years of age).Apolipoprotein E (APOE) ε4 carrier status did not moderate the physical activity–brain Aβ relationship over time.Physical activity trajectories were not impacted by brain Aβ levels. [ABSTRACT FROM AUTHOR]

Dietary nitrate improves cardiovascular health via a nitric oxide (NO) pathway. NO is key to both cardiovascular and brain health. There is also a strong association between vascular risk factors and brain health. Dietary nitrate intake could therefore be associated with better cognitive function and reduced risk of cognitive decline. This is yet to be investigated. The aim of this study was to investigate the association between habitual intake of dietary nitrate from sources where nitrate is naturally present, and cognitive function, and cognitive decline, in the presence or absence of the apolipoprotein E (APOE) ε4 allele. The study included 1254 older adult participants of the Australian Imaging, Biomarkers and Lifestyle Study of Ageing who were cognitively normal at baseline. Plant-derived, vegetable-derived, animal derived nitrate (not including meat where nitrate is an allowed additive), and total nitrate intakes were calculated from baseline food frequency questionnaires using comprehensive nitrate databases. Cognition was assessed at baseline and every 18 months over a follow-up period of 126 months using a comprehensive neuropsychological test battery. Multivariable-adjusted linear mixed effect models were used to examine the association between baseline nitrate intake and cognition over the 126 months (median [IQR] follow-up time of 36 [18–72] months), stratified by APOE ε4 carrier status. In non APOE ε4 carriers, for every 60 mg/day higher intake of plant-derived nitrate at baseline there was an associated higher language score [β (95% CI): 0.10 (0.01, 0.19)] over 126 months, after multivariable adjustments. In APOE ε4 carriers, there was an associated better episodic recall memory [0.24 (0.08, 0.41)] and recognition memory [0.15 (0.01, 0.30)] scores. Similar associations were seen for the intakes of vegetable-derived and total nitrate. Additionally, in APOE ε4 carriers, for every 6 mg/day higher intake of animal-derived nitrate (excluding meat with nitrate as an allowed additive) at baseline there was an associated higher executive function score [β (95% CI): 1.41 (0.42, 2.39)]. We did not find any evidence of an association between dietary nitrate intake and rate of cognitive decline. Our results suggest that habitual intake of dietary nitrate from sources where nitrate is naturally present impacts cognitive performance in an APOE genotype contingent manner. Further work is needed to validate our findings and understand potential mechanisms underlying the observed effects. [ABSTRACT FROM AUTHOR]

Introduction: The current study investigated the association between objectively measured physical activity and cognition in older adults over approximately 8 years. Methods: We utilized data from 199 cognitively unimpaired individuals from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, aged ≥60. Actigraphy was used to measure physical activity (intensity, total activity, and energy expenditure) at baseline. Cognition was assessed using a comprehensive cognitive battery every 18‐months. Results: Higher baseline energy expenditure predicted better episodic recall memory and global cognition over the follow‐up period (p = 0.031; p = 0.047, respectively). Those with higher physical activity intensity and greater total activity also had better global cognition over time (both p = 0.005). Finally, higher total physical activity predicted improved episodic recall memory over time (p = 0.022). Discussion: These results suggest that physical activity can preserve cognition and that activity intensity may play an important role in this association. Highlights: Greater total physical activity predicts preserved episodic memory and global cognition.Moderate intensity physical activity (>3.7 metabolic equivalents of task [MET]) predicts preserved global cognition.Expending > 373 kilocalories per day may benefit episodic memory and global cognition. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's disease (AD), a long preclinical and prodromal period precedes dementia onset in which cognition declines slowly, but at variable rates for different domains. Positive AD biomarkers, (e.g., amyloid, Tau, or APOE ε4 carriage), increase rates of cognitive decline and transition to dementia. However, the extent to which cognitive decline, identified in some older adults, reflects neurodegenerative disease or normal aging in the absence of knowledge about biomarkers or genes is not well understood. We applied a novel approach to classify cognitive decline in individuals using six cognitive composite scores (AIBL Pre‐clinical Alzheimer's Cognitive Composite (AIBL‐PACC), episodic recall, attention, executive function, language and recognition). We examine group‐wise differences across cognitive composite scores and assessed frequencies for AD biomarker positivity amongst the novel groups. Method: Prospective data from 1,389 Cognitively unimpaired (CU) participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing were analysed using an unsupervised multivariate mixture modelling framework, accounting for age, gender and APOE ε4 allele status. Participants cognitive status was classified for their final visit. The cognitive profile and biomarker characteristics of adults with abnormal cognitive decline was compared. Result: Differences in composite score values between cognitive decline and no decline groups were strongest for episodic recall (Cohen's d = 1.49) and the AIBL‐PACC score (Cohen's d = 1.45, Table 1, Figure 1A). By comparison, differences in composite scores between Aβ‐PET groups, or APOE ε4 allele carriers/non‐carriers were of less magnitude (Table 1, Figure 1). Participants with cognitive decline were more likely to be amyloid positive, (decline: 42% vs no decline: 28%, p<0.0001), but not ε4 allele carriers (decline: 28% VS no decline: 27%, p>0.05). Conclusion: A measure of objective cognitive decline, created using flexible mixture models with six cognitive composite scores from AIBL, has utility for discerning participants with early changes in cognition. Based upon using the cognitive data alone, this method was able to detect amyloid positivity in those CU participants with cognitive decline. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study is a prospective study collecting extensive cognitive, clinical, fluid, and imaging biomarkers data from older adults living in Australia. Integration of outcomes between large prospective studies of AD will provide greater precision in models of AD brain‐behavior relationships, so it is important to align composite scores for cognitive domains between such studies. Methods: Detailed methods for AIBL, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Alzheimer's Coordinating Center (NACC) have been published. Briefly, AIBL participants had cognition assessed with an extensive neuropsychological test battery alongside health and biomarker assessments at entry and each 18‐months thereafter. Granular‐level cognitive data were obtained and an expert panel of two neuropsychologists and a behavioral neurologist categorized each element as assessing memory, executive functioning, language, visuospatial, or none of these, exactly as we have done previously. We also identified elements we had previously calibrated from other studies; after careful quality control and confirmation these served as anchors enabling co‐calibration. We used confirmatory factor analysis bi‐factor models to calibrate the AIBL battery with other studies. We used those calibrations to obtain co‐calibrated scores for all AIBL participants at every study visit. Here we show descriptive statistics for baseline visits, separately by diagnosis (normal cognition, mild cognitive impairment (MCI), dementia) for two enrollment waves for AIBL as well as for each phase of ADNI and across the Uniform Data Set (UDS) 1 & 2 (UDS1/2) and UDS3 time periods for NACC. Results: Box plots for memory, executive functioning, language, and visuospatial for people with normal cognition are in Figure 1, MCI in Figure 2, and dementia in Figure 3. These figures show there is substantial cognitive variation across waves within these disease stage groups and across studies. Conclusion: Co‐calibrated neuropsychological domain scores provide a common metric for integrating cognitive data across studies. Co‐calibrated scores aggregated across large prospective AD studies such as AIBL, ADNI, and NACC provide a foundation for large‐scale models of the development of AD and can serve as phenotypes for genetics studies. Co‐calibrated scores are available from AIBL, ADNI, and from NACC. [ABSTRACT FROM AUTHOR]

Background: Plasma biomarkers now show an accuracy in detecting Amyloid Beta (Aβ) similar to AD biomarkers derived from cerebral spinal fluid (CSF). However, the ability of plasma AD biomarkers, alone or in combination, to predict cognitive decline has not yet been compared to that of CSF AD biomarkers. Method: Plasma biomarker data from 233 participants' first visit in the Australian Imaging, Biomarkers and Lifestyle study (AIBL) was submitted to linear mixed effects models (LME) to quantify the relationship with change in cognition (measured using the AIBL PACC) and in clinical disease stage (CDR SoB) in both PET Aβ‐ (Centiloid value <20CL) and Aβ+ (Centiloid value ≥20CL) participant subgroups. Separate models were used to assess CSF (Elecsys) and plasma (ADx NeuroSciences) data for Aβ42, pTau181 and the pTau181/Aβ42 ratio. Biomarker values were classified into low vs high levels based on ROC‐derived thresholds optimizing separation of PET Aβ status (low vs high at 20 CL). Changes in cognitive and clinical symptoms were then compared between the low/high plasma biomarker groups. Result: In Aβ‐ participants, no significant interactions between binary biomarker classification and time were observed for AIBL PACC or CDR SoB, for either CSF or plasma biomarkers. In the Aβ+ participants, interactions between the binary plasma biomarker classification and change in cognition were greater in magnitude that those detected for CSF biomarker classification. For plasma, abnormally high values of both pTau181 and the pTau181/Aβ42 ratio predicted a significant increase over time in CDR SoB (Figure 1H & 1L) and a significant decrease over time in the AIBL PACC score (Figure 1F & 1J), compared the group with low values on the same biomarkers. In cognitively unimpaired Aβ‐ participants, the AIBL PACC score declined in those with abnormally high values of the pTau181 and the pTau181/Aβ42 ratio (Figure 1F & 1J). Conclusion: Assays to measure pTau181 and Aβ42 in the plasma possess an accuracy equivalent to those derived from CSF. In particular, abnormally high levels of plasma pTau181 or the ratio of pTau181 to Aβ42 ratio provide a strong prediction of early cognitive changes, even in those with normal PET Aβ status. [ABSTRACT FROM AUTHOR]

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

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

Background: Animal studies have consistently shown that physical activity reduces brain levels of beta‐amyloid (Aβ). However, human studies of physical activity and Alzheimer's disease (AD)‐related brain biomarkers have produced somewhat inconsistent findings. To date, most human studies examining physical activity and Aβ have utilised cross‐sectional analyses and relatively small sample sizes. As brain Aβ is detectable decades prior to the onset of clinical symptoms, examination using longitudinal brain imaging is vital to provide insight into how ongoing physical activity levels impact the progression of AD. In addition, the causal direction of these relationships requires further investigation; some research suggests physical activity levels may decline prior to dementia diagnosis. Method: Habitual physical activity and Aβ measures were collected over 15 years from 731 cognitively normal older adults (60 years and over) participating in the Australian Imaging, Biomarkers and Lifestyle Study of Ageing. Physical activity was assessed via the self‐reported International Physical Activity Questionnaire and brain Aβ measures obtained via PET imaging. Regression modelling examined the cross‐sectional and longitudinal relationships between physical activity and brain Aβ. Moderation analyses examined the impact of apolipoprotein (APOE) ε4 carriage on the physical activity‐Aβ relationship. To further examine the causal nature of this relationship, the trajectory of physical activity levels for individuals as a predictor of either high or low Aβ brain load was also examined. Result: Physical activity was not associated with brain Aβ at baseline (β = ‐0.001, p = 0.131) or over time (β = ‐0.101, p = 0.571). APOE ε4 status did not moderate the relationship between physical activity and brain Aβ over time (β = 0.04, p = 0.917). Brain Aβ levels were not associated with physical activity trajectory (β = ‐54.26, p = 0.590). Conclusion: Our study did not identify a relationship between habitual physical activity and brain Aβ levels. Future longitudinal studies should examine middle‐aged participants to assess whether physical activity influences Aβ accumulation earlier in life. [ABSTRACT FROM AUTHOR]

Background: Observational studies consistently demonstrate that physical activity is associated with enhanced cognitive function and reduced risk for dementia. However, there remains significant heterogeneity in study findings regarding the effect of exercise interventions on cognitive function. Individual variation in sleep behaviours are hypothesized to be a source of this variability in the effectiveness of exercise to influence cognition, however this has not yet been investigated. Thus, the current study aimed to 1) investigate the influence of a 6‐month exercise intervention on sleep pre‐ and post‐intervention and 2) investigate whether baseline sleep measures moderate exercise‐induced cognitive changes. Method: We utilised data from the Intense Physical Activity and Cognition (IPAC) study (n = 89), a 6‐month moderate intensity and high intensity exercise intervention, in cognitively normal community‐dwelling older adults aged 60‐80 years (68.76 ± 5.32). Exercise was supervised and completed on a stationary exercise bicycle, and cognitive function was measured using a comprehensive neuropsychological battery administered pre‐ and post‐intervention. Sleep was measured using the Pittsburgh sleep quality index (PSQI) to yield measures of sleep duration, efficiency, and sleep latency. Result: There was no effect of the exercise intervention on any sleep outcomes from pre‐ to post‐intervention. There was a significant moderating effect of baseline sleep efficiency on both episodic memory and global cognition within the moderate intensity exercise group (β = ‐0.024, SE = 0.008, p =.004; β = ‐0.011, SE = 0.005, p =.036), such that those with poorer sleep efficiency at baseline showed greater exercise‐induced cognitive improvements. Conclusion: These results indicate that those with poorer sleep may have the greatest exercise‐induced cognitive benefits and that baseline sleep behaviours may be an important source of heterogeneity in previous exercise interventions targeting cognitive outcomes. [ABSTRACT FROM AUTHOR]

Background: Cognitive reserve (CR) is partially responsible for inter‐individual differences in developing dementia due to Alzheimer's disease (AD). However, education years, as the primary proxy of CR, does not capture various life experiences, vocational background, and resources available to those who did not have an opportunity to complete formal education. We compared education years against Hollingshead Four‐Factor Index of Socioeconomic Status (SES‐Adults) to examine the latter's utility as a better proxy of cognitive reserve. The SES‐Adults is a survey collecting information on marital status, retired/employed status, educational attainment, and occupational prestige for participants and their partners. The survey results are then converted into one of the 5 social class categories including low, working, middle, upper‐middle, and upper socioeconomic status. Method: A cross‐sectional study was conducted on a cohort of adults and older adults (n = 264) with mean age of 67.96 years. Participants were recruited from a longitudinal study investigating neuropsychological and biological correlates of dementia, namely the Western Australian Memory Study (WAMS). We examined the relationships between education years and the SES‐Adults scores, with cognitive function and amyloid load while controlling for age, gender (male/female), and APOE ε4 carriage. Amyloid load was measured using 18F‐NAV‐469 tracer. Result: The cohort included 190 females and 74 males with 80 APOE ε4 carriers (available genotyped individuals = 203). Linear regression results showed education years was significantly correlated with both general cognitive function (MoCA) and amyloid load (R2 = 0.2935, Adjusted‐R2 = 0.2513; p<0.026) while controlling for the effects of age, gender, and APOE ε4 carriage. The SES‐Adults was not significantly related to cognition or amyloid load (p =.914). When SES was included in regression model, the relationships between education years and outcome measures turned non‐significant. Conclusion: We found education years to be significantly related to both cognition and amyloid load. However, no significant results was noted for the SES‐Adults. This cross‐sectional study showed that education is a reliable CR proxy as compared to a comprehensive measure of socioeconomic status(SES‐ Adults). [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a multifactorial disease characterised by a long period of neuronal loss which manifests clinically as cognitive decline and ultimately dementia. Understanding the nature and magnitude of cognitive decline prior to dementia is critical to understanding the disease course. Here we quantify change in six cognitive composite scores to compare their temporal sequence and rates of change early in the AD disease course. Method: Amyloid‐beta positive (Aβ+) cognitively unimpaired (CU), MCI and AD participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing with ≥three study visits (N = 857), were used to derive composite scores for executive function, language, attention and processing, recognition, visuospatial, and a general composite used in clinical trials (AIBL‐PACC). A multivariate method for combining cognitive change trajectory curves was used to place each composite score trajectory in a common numerical space. Using the mean AIBL‐PACC scores for the prodromal group (MCI‐Aβ+) as a reference, change over a three‐year period for each composite was estimated. Given the AIBL requirement for a minimum 1.5 SD change in two or more cognitive domains to justify progression to MCI/AD, disease time was set with the zero point at a ‐1.5 AIBL‐PACC score, representing a 1.5 SD decline in AIBL‐PACC from the mean CU Ab‐ population at baseline. Result: The AIBL‐PACC, language and visuospatial functioning scores showed the steepest trajectories indicating AD related decline in these scores was fastest (Figure 1). AIBL‐PACC reached the ‐1.5 point on the y‐axis first followed closely by memory recognition, with steeper trajectory of decline for the AIBL‐PACC across the age range. Focusing on AIBL‐PACC, it took 2.15 years to transition from ‐1.5 to the mean of the prodromal group (‐1.87). At five years past the start of the prodromal stage, the data suggests a drop in the AIBL‐PACC to ‐2.54, an approximate decline of ∼0.22 points per year. Conclusion: This is the first presentation of a novel multivariate disease trajectory method examining expected change across the AD continuum among Aβ+ persons. A 3‐year change in the PACC composite demonstrated here will be relevant to measure disease progression during clinical trials. [ABSTRACT FROM AUTHOR]

Background: Mid‐life obesity is linked with increased risk for late‐life dementia, however relationships between later‐life body composition and dementia are less clear. Method: Using a combined cohort from two major observational studies (AIBL and ADNI) and categorising Body Mass Index (BMI) according to WHO criteria, we computed multivariable linear mixed‐effects models to determine association between BMI category and outcomes: brain beta‐amyloid PET quantification (centiloid); MRI (global, hippocampal and white matter hyperintensity volume); and cognition (PACC [preclinical Alzheimer cognitive composite], composite scores for attention, executive function and episodic memory). All models were adjusted by covariables age, gender, education, APOE ε4, and study (AIBL/ADNI). Results: The cohort included 4,668 observations from 2,828 individuals (49.4% male, mean age 73.8 years, 38.2% APOE ε4 carriage, 61.8% normal cognition [NC], 25.6% Mild Cognitive Impairment [MCI], 12.6% dementia due to Alzheimer's disease). The average BMI was 26.8 kg/m2 (0.6% underweight, 37.7% normal‐range, 41.7% overweight, 20.0% obese [BMI <18, 18‐25, 25‐30 and >30 kg/m2, respectively]). Relative to normal‐range BMI, those with overweight‐or‐obese range BMI had a significant negative association with PET centiloid level, beta = ‐0.150 (0.037 se) and ‐0.309 (0.042), respectively (i.e. greater BMI had lower amyloid level). This remained significant for obese‐range BMI in fully‐adjusted models when restricted to only NC participants. Also, obesity was associated with greater hippocampal volume, beta = 0.192 (0.046), but this was not significant when restricted to NC only. Associations with total grey matter/cortical volume and white matter hyperintensities were not significant. Obese‐range BMI was associated with stronger performance on attention and episodic memory composites, but not for PACC and executive function. For NC‐only, there was only significant negative association between obesity and attention performance. In multivariate analyses, neither obesity category nor BMI were associated with longitudinal change in cognitive scores. Conclusion: Presence of obesity in later life was associated with lower brain amyloid levels and less hippocampal atrophy in a mixed cohort including older adults with dementia, mild cognitive impairment and normal cognition. While mid‐life obesity is associated greater risk for AD, later life associations appear influenced by weight loss with emerging dementia. [ABSTRACT FROM AUTHOR]

Background: There is a paucity of interventional research that systematically assesses the role of exercise intensity and cardiorespiratory fitness, and their relationship with executive function in older adults. To address this limitation, we have examined the effect of a systematically manipulated exercise intervention on executive function.Methods: Ninety-nine cognitively normal participants (age = 69.10 ± 5.2 years; n = 54 female) were randomized into either a high-intensity cycle-based exercise, moderate-intensity cycle-based exercise, or no-intervention control group. All participants underwent neuropsychological testing and fitness assessment at baseline (preintervention), 6-month follow-up (postintervention), and 12-month postintervention. Executive function was measured comprehensively, including measures of each subdomain: Shifting, Updating/ Working Memory, Inhibition, Verbal Generativity, and Nonverbal Reasoning. Cardiorespiratory fitness was measured by analysis of peak aerobic capacity; VO2peak.Results: First, the exercise intervention was found to increase cardiorespiratory fitness (VO2peak) in the intervention groups, in comparison to the control group (F =10.40, p≤0.01). However, the authors failed to find mean differences in executive function scores between the high-intensity, moderate intensity, or inactive control group. On the basis of change scores, cardiorespiratory fitness was found to associate positively with the executive function (EF) subdomains of Updating/Working Memory (β = 0.37, p = 0.01, r = 0.34) and Verbal Generativity (β = 0.30, p = 0.03, r = 0.28) for intervention, but not control participants.Conclusion: At the aggregate level, the authors failed to find evidence that 6-months of high-intensity aerobic exercise improves EF in older adults. However, it remains possible that individual differences in experimentally induced changes in cardiorespiratory fitness may be associated with changes in Updating/ Working Memory and Verbal Generativity. [ABSTRACT FROM AUTHOR]

Background: Beta‐amyloid (Aß) PET assists diagnosis of Alzheimer's disease (AD) and has an important role in selection for trials. Aß PET is costly. Reports suggest that plasma measures of phospho‐tau have high concordance with both Aß and tau PET. We compared a new assay, plasma p217+tau to Aß and tau PET. Method: 181 MCI/mild AD and 222 cognitively unimpaired (CU) participants in AIBL and the Australian Dementia Network (ADNeT) trial screening program were studied. Aß PET threshold was set at 25 centiloids (CL) as the standard for defining a positive scan and also at 50 CL which best correlates with NIA/AA AD neuropathologic change criteria. In CU we also evaluated a 20 CL PET threshold as this may be used in preclinical trial selection. Tau PET threshold was set at the SUVR (normalised to cerebellar cortex) for a meta‐temporal ROI (entorhinal cortex, amygdala, parahippocampus, and fusiform, inferior and middle temporal gyri) at the 95th percentile of Aß PET negative CU. Plasma p217+tau was measured with SIMOA using a Janssen in‐house assay. ROC area under the curve (AUC) was generated with Youden Index defined sensitivity (sens) and specificity (spec). Result: Rank‐order correlation between p217+tau and Aß CL was significant in MCI/AD: ρ=0.65, p<10‐22 and CU: ρ=0.45, p<10‐12 and with tau SUVR for MCI/AD:ρ=0.68, p<10‐25 and CU:ρ=0.32, p<10‐7. In MCI/AD the AUC for p217+tau to predict Aß >25CL was 0.91 (sens 83%, spec 85%) and at >50CL was 0.90 (sens 73%, spec 93%). For tau+ PET the AUC was 0.86 (sens 73%, spec 90%). In the CU, the AUC vs Aß >25CL was 0.84 (sens 81%, spec 81%) dropping to 0.79 (sens 72%, spec 82%) for the 20CL PET threshold. The AUC vs tau+ PET in CU was 0.87 (sens 90%, spec 76%). Conclusion: Plasma p217+tau showed good concordance with PET defined Aß and tau positivity in both MCI/AD and CU indicating potential to support the diagnosis of AD and to screen for preclinical AD trials. Early triage with p217+tau may reduce the number of screening Aß PET needed to identify Aß+ PET CU for preclinical trials by 50%. [ABSTRACT FROM AUTHOR]

Objectives: High-intensity exercise is a potential therapeutic tool to postpone or prevent the onset of cognitive decline. However, there is a lack of sufficient evidence regarding the longitudinal effects of structured resistance training on cognitive function in healthy adults. The purpose of this study was to investigate the effect of two ecologically valid, intense 12-week resistance training programs on cognitive function in late middle-aged adults.Design: Single-site parallel randomised controlled trial at the Department of Exercise Science strength and conditioning laboratory. Groups allocated by minimisation randomisation.Methods: Forty-five healthy adults (age range=41-69 years) were enrolled and randomised into (A) high-load, long rest resistance training (n=14), or (B) moderate-load, short rest resistance training (n=15) twice per week for 12 weeks, or a non-exercising control (n=16). Follow-up within seven days. Data were collected September 2016-December 2017. Cognitive function assessed using the CogState computerised battery. Assessors were blinded to participant group allocation. Secondary outcomes were maximal muscle strength and body composition.Results: Forty-four participants were analysed in 2018. Delayed verbal memory performance was improved (p=0.02) in resistance training groups (g=0.67-0.79) when compared to the control group, with no differences between training groups. Likewise, increases in maximal muscle strength were observed (p<0.01) in resistance training groups when compared to the control group, with no differences between training groups. No differences in body composition were observed. There were no adverse events or side-effects of the intervention.Conclusions: 12 weeks of intense resistance training improves delayed verbal memory irrespective of training design (i.e., high-load vs. moderate-load).Trial Registration: This study is registered at www.anzctr.org.au ACTRN12616000690459. [ABSTRACT FROM AUTHOR]

Background: Findings from animal studies report links between obstructive sleep apnoea (OSA) models and elevated glial fibrillary acidic protein (GFAP). In humans, elevated plasma GFAP, reflecting reactive astrogliosis, is considered an early event in the Alzheimer's disease (AD) continuum. The current study investigated whether OSA risk predicts the slope of plasma GFAP in cognitively unimpaired older adults. Method: Linear mixed effect model analyses were conducted on 70 cognitively unimpaired older adult (M = 73.1, SD = 5.2, 57.1% female) participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing. Individuals completed the well‐validated STOP‐Bang questionnaire to assess OSA risk (Low; Intermediate; High), and underwent positron emission tomography (PET) to quantify brain Aβ burden. Plasma GFAP was measured using the Simoa® platform over three timepoints (range 30.06 ‐ 99.45 months). The model included, a random intercept, a three‐way interaction between OSA risk, baseline brain Aβ status and time, adjusting for Apolipoprotein E (APOE) ε4 status, sex, baseline age, and baseline body mass index. Result: High risk of OSA and high brain Aβ (>25 Centiloids) were independently associated with higher baseline plasma GFAP and forecast a negative longitudinal plasma GFAP trajectory. Conclusion: These novel findings suggest that OSA mechanisms, independent of prior brain Aβ accumulation, may contribute to early reactive astrogliosis in cognitively unimpaired older adults. Additionally, a decline in plasma GFAP was observed during the preclinical period, both in those at high risk of OSA and those at greater risk of AD due to high brain Aβ. [ABSTRACT FROM AUTHOR]

Background: Research indicates a role for suboptimal sleep, in cognitive ageing. An increasing, though inconsistent body of evidence has associated aspects of suboptimal self‐reported sleep quality with poorer cognitive performance. The current study investigated whether self‐reported sleep quality parameters predict the slope of domain specific cognitive performance. Method: Participants were drawn from the Australian Imaging, Biomarkers and Lifestyle study of ageing. Linear mixed effect model (LMM) analyses were conducted on 525 cognitively unimpaired, older adults (M = 74.1, SD = 5.5, 56.4% female), characterised by apolipoprotein E (APOE) ε4 status, with baseline self‐reported sleep characteristics. Comprehensive cognitive assessments were conducted over a minimum 3 timepoints (range 29.64 ‐ 98.16 months). All models included random slopes, random intercepts, interaction effects for time, and APOE ε4 carriage, with each model adjusted based on the model of best fit for each cognitive outcome variable of interest. Result: Following Bonferroni correction, sleep duration <6 hours and sleep efficiency <65% in the whole cohort, and in APOE ε4 non‐carriers, forecasted accelerated decline in attention and speed of processing. In the same groups, greater sleep efficiency (%) forecasted greater preservation within the attention and speed of processing domain. No associations were observed for other sleep variables or cognitive domains of interest. Conclusion: These findings suggest a role for self‐reported suboptimal sleep efficiency and duration in the early stages of cognitive decline, represented by declining attention and speed of processing performance, in cognitively unimpaired individuals. Additionally, short sleep duration and poor sleep efficiency may increase future risk of cognitive decline independent of APOE ε4 carriage. Our findings also suggest the potential utility of self‐report screening for sleep efficiency and duration as a marker for increased risk of declining attention and speed of processing performance. Individuals with suboptimal sleep may benefit from an intervention to improve sleep efficiency and duration to lessen future risk of cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Given the long preclinical disease course of Alzheimer disease (AD) pathology, novel treatments may be more efficacious if administered before the emergence of dementia. Thus, accurate prediction of who will develop AD dementia is of key importance in selecting individuals for trials of treatment and may become crucial for future selection of patients for therapy.Methods: As part of the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing, 901 individuals who did not have dementia were recruited. We assigned individuals according to Petersen criteria and Winblad criteria for Mild Cognitive Impairment (MCI) at baseline. We then stratified individuals with amnestic MCI into 2 groups according to the severity of their memory impairment on baseline neuropsychological assessment. Incident diagnosis of AD dementia was established by consensus of an expert panel at 36 months.Results: At 36 months, 725 (80.5%) participants were followed up, 54 (7.4%) of whom developed AD dementia. Subjects with amnestic MCI according to Petersen criteria were more likely to develop AD dementia [positive predictive value; PPV, 24.1%; 95% confidence interval (CI), 18.4-30.6] than healthy controls (PPV, 1.0%; 95% CI, 0.3-2.3). Winblad criteria were also effective, with multiple domain amnestic MCI being most accurate at predicting AD dementia (PPV, 47.3%; 95% CI, 33.7-61.2). Finally, more severe amnestic impairment below the median was useful for predicting the development of AD dementia in single domain amnestic MCI (PPV, 28.1%; 95% CI, 17.0-41.5) and in multiple domain amnestic MCI (PPV, 65.7%; 95% CI, 47.8-80.9).Conclusions: Memory impairment per se, impairment in multiple cognitive domains and severity of memory impairment were all associated with greater risk of developing AD dementia in this sample. Characterizing the severity of memory impairment may provide prognostic stratification within Petersen or Winblad taxonomies of amnestic MCI. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) neuropathology begins 20 to 30 years before clinical diagnosis. Thus, there is an opportunity to implement strategies to prevent the progression of AD. Insulin resistance in the brain causes a disruption in glucose utilisation. Medium‐chain fatty acids (MCFA) are readily available substrates for the synthesis of ketone bodies (KB), which are an alternative brain fuel not controlled by insulin. A constant supply of KB may close the energy gap in the AD‐prone brain, and in doing so prevent or delay symptom onset, or protect the brain from further degeneration. There is increasing evidence that multidomain lifestyle interventions with dietary components significantly impact cognitive decline. Yet, these interventions have not considered the effects MCFA supplementation, to ensure sufficient energy for the brain. Method: This is a phase II placebo‐controlled, randomised double‐blind 24‐month intervention trial of MCFA supplementation within the AUstralian‐multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention (AU‐ARROW) study, to investigate the additive effect of MCFA supplementation in older adults at increased risk of AD. A total of 124 participants will be recruited aged 60‐79 years, with subjective cognitive decline. Participants will be randomised 1:1 into either the intervention or placebo groups and will consume 3 daily doses at 15mL per dose of either MCFA or placebo supplement whilst continuing the AU‐ARROW protocol, which includes dietary education, physical activity, brain training, and health education. Result: The primary outcomes involve statistical assessment of cognition, blood ketone body levels, and brain glucose utilisation. At baseline, and every 6 months, participants will have fasting blood samples collected. At baseline, 12, and 24 months, participants will undergo cognitive assessments. At baseline and 24 months, participants will undergo fluorodeoxyglucose positron emission tomography scans to determine brain glucose utilisation. Conclusion: The final protocol is expected to maximise data collection and analyses. Combined with AU‐ARROW, it will allow for a) the implementation of MCFA supplementation guidelines in a plan to reduce cognitive decline and risk of AD, b) the determination of effects of MCFA consumption on neuroimaging and blood‐based biomarkers, and c) the comparison of between subject differences. [ABSTRACT FROM AUTHOR]

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

Background: Cognitive reserve is the term used to describe the apparent ability of one's cognitive processes or neural networks to adapt in response to neuropathology and brain injury. This protective ability is proposed to account for individual differences in age‐associated cognitive decline and Alzheimer's disease (AD) risk, where higher levels of cognitive reserve are associated with more preserved cognitive functioning and delayed onset of AD dementia. Women have a higher prevalence of AD than men, and account for two‐thirds of AD diagnoses. Sex differences in the protective ability of cognitive reserve may account for differences in Alzheimer's disease prevalence between men and women, but few studies have explored this possibility. This study evaluates sex as a moderator of the relationship between cognitive reserve and brain volume effects on executive function. Method: The study used archival data from 997 older adult participants in the Australian Imaging, Biomarkers, and Lifestyle(AIBL) Flagship Study of Ageing (see Table 1 for descriptives). Structural equation modelling was used to estimate a proxy for cognitive reserve and to run the analytical model. Cognitive reserve was defined as the variance in episodic memory not explained by demographics (e.g., education) and brain integrity (i.e., the residual reserve index; Reed et al., 2010). Executive functioning intercept and slope – derived from up to 7 study visits (18‐month follow‐up intervals) – were regressed onto the main and interaction effects of the residual reserve index, brain integrity (i.e., the combination of grey matter, hippocampal, and white matter hyperintensity volumes), and sex, plus covariates (e.g., number of apolipoprotein alleles; age). Result: A significant three‐way interaction was observed between cognitive reserve, brain integrity, and sex; Women benefitted more than men from the protective effects of cognitive reserve at comparably low levels of brain integrity. Further analyses also showed that women, on average, have higher levels of cognitive reserve as reflected by the residual reserve index. Conclusion: Sex differences in cognitive reserve highlight a potential sex‐related mechanism by which cognitive reserve confers protection against cognitive decline in the face of low brain integrity. These findings further our understanding of potential factors that maximise the protective effect of cognitive reserve. [ABSTRACT FROM AUTHOR]

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

Introduction The objective of this study was to evaluate the relationship between self-reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers. Methods In 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self-reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ 42, and CSF tau levels was evaluated using linear regression. Results No differences in brain amyloid load, CSF Aβ 42 , or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels. Discussion Our findings indicate a relationship exists between self-reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers. [ABSTRACT FROM AUTHOR]

Objectives: Brain-derived neurotrophic factor (BDNF) has been shown to increase in an intensity dependent manner in response to aerobic exercise. However, previous research investigating the use of resistance exercise to increase BDNF levels has been less conclusive, likely due to the low intensity nature of traditional resistance exercise programs. This study examined the influence of acute resistance exercise to-fatigue on serum BDNF levels and blood lactate.Design: Acute crossover study.Methods: Eleven untrained to intermediately trained males (age: 25.0±1.3 year) and five untrained females (age: 23.2±1.1 year) were recruited to undertake two bouts of resistance exercise. Strength (five sets of five repetitions, 180s recovery) and hypertrophy (three sets of ten repetitions, 60s recovery) based resistance exercise was implemented to-fatigue to examine the effect on serum BDNF and blood lactate levels immediately post-, and 30min post-exercise.Results: An interaction (p<0.01; ES=0.52) was observed between conditions immediately post-exercise, with hypertrophy resulting in significantly greater BDNF levels when compared with strength exercise. Changes in lactate and BDNF from baseline to post- exercise were positively correlated following hypertrophy exercise (r=0.70; p<0.01), but not correlated following strength exercise (r=0.18; p=0.56).Conclusions: The use of a to-fatigue hypertrophy based resistance exercise protocol provides the necessary stimulus to increase peripheral serum BDNF. Mechanistically, the presence of lactate does not appear to drive the BDNF response during resistance exercise. [ABSTRACT FROM AUTHOR]

Introduction “Walkable” neighborhoods offer older adults opportunities for activities that may benefit cognition-related biological mechanisms. These have not previously been examined in this context. Methods We objectively assessed neighborhood walkability for participants (n = 146) from the Australian Imaging, Biomarkers and Lifestyle study with apolipoprotein E ( APOE ) genotype and two 18-month-apart brain volumetric and/or amyloid β burden assessments. Linear mixed models estimated associations of neighborhood walkability with levels and changes in brain imaging outcomes, the moderating effect of APOE ε4 status, and the extent to which associations were explained by physical activity. Results Cross-sectionally, neighborhood walkability was predictive of better neuroimaging outcomes except for left hippocampal volume. These associations were to a small extent explained by physical activity. APOE ε4 carriers showed slower worsening of outcomes if living in walkable neighborhoods. Discussion These findings indicate associations between neighborhood walkability and brain imaging measures (especially in APOE ε4 carriers) minimally attributable to physical activity. [ABSTRACT FROM AUTHOR]

Objective: To examine how β-amyloid (Aβ), APOE and BDNF genotypes, and cortisol relate to depressive and anxiety symptoms in cognitively normal older women and men.Methods: Cross-sectional data were analyzed from 423 older adults from the Australian Imaging Biomarkers and Lifestyle study. Analyses of covariance evaluated associations between Aβ, APOE and BDNF genotype, and cortisol in relation to severity of depressive and anxiety symptoms.Results: Among Aβ+ older adults, APOE ε4 carriage was associated with greater severity of anxiety symptoms (d = 0.55); and in the full sample, APOE ε4 carriage was linked to greater severity of depressive (d = 0.26) and anxiety (d = 0.21) symptoms. Among Aβ+ women, ε4 carriers reported greater anxiety symptoms than non-ε4 carriers (d = 0.83), and female BDNF rs6265 Val66 Met allele carriers reported greater depressive symptoms (d = 0.29).Conclusion: Sex moderated the relationship between Aβ, APOE genotype, and BDNF genotype in predicting severity of anxiety and depressive symptoms in cognitively normal older adults. [ABSTRACT FROM AUTHOR]

Introduction The objective of this study was to determine the utility of subjective memory decline (SMD) to predict episodic memory change and rates of clinical progression in cognitively normal older adults with evidence of high β-amyloid burden (CN Aβ+). Methods Fifty-eight CN Aβ+ participants from the Australian Imaging, Biomarkers, and Lifestyle study responded to an SMD questionnaire and underwent comprehensive neuropsychological assessments. Participant data for three follow-up assessments were analyzed. Results In CN Aβ+, subjects with high SMD did not exhibit significantly greater episodic memory decline than those with low SMD. High SMD was related to greater rates of progression to mild cognitive impairment or Alzheimer's disease (AD) dementia (hazard ratio = 5.1; 95% confidence interval, 1.4–20.0, P = .02) compared with low SMD. High SMD was associated with greater depressive symptomatology and smaller left hippocampal volume. Discussion High SMD is a harbinger of greater rates of clinical progression in preclinical AD. Although SMD reflects broader diagnostic implications for CN Aβ+, more sensitive measures may be required to detect early subtle cognitive change. [ABSTRACT FROM AUTHOR]