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This longitudinal study modeled children's complex executive function (EF) development using the Groton Maze Learning Task (GMLT). Using a cohort-sequential design, 147 children (61 males, 5.5-11 years) were recruited from six multicultural primary schools in Melbourne and Perth, Australia. Race/ethnicity data were not available. Children were assessed on the GMLT at 6-month intervals over 2-years between 2010 and 2012. Growth curve models describe age-related change from 5.5 to 12.5 years old. Results showed a quadratic growth trajectory on each measure of error--that is, those that reflect visuospatial memory, executive control (or the ability to apply rules for action), and complex EF. The ability to apply rules for action, while a rate-limiting factor in complex EF, develops rapidly over early-to-mid childhood.

This correlational survey study examined professional development, participatory management, and teachers' job performance in public secondary schools in Ogun State, Nigeria. The sample size comprised 504 participants selected through a multi-stage sampling technique from 12,745 teachers in 217 public secondary schools. The study was guided by two null hypotheses, tested at 0.05 level of significance. Two researcher-designed instruments namely. Participatory Management and Professional Development Questionnaire (PM/PDQ) and Teachers' Job Performance Scale (TJPS), were used to collect data. The Pearson Product-Moment Correlation statistical tool was used for data analysis. Results showed that teachers' job performance was significantly and positively related to both professional development and participatory management respectively. The study concluded that teachers' job performance could be enhanced through these two management intervention strategies. It was therefore, recommended among other things, that concerned authorities should improve on existing teachers' professional development programmes and encourage participatory management among the teachers.

Job concern is a common element in any work organization. It is, no doubt, capable of determining the extent to which an employee is engaged with the job. This study therefore investigated job concern factors (namely workload, job hazard, and interpersonal discrimination) and academic staff engagement in public universities in Lagos State, Nigeria. The descriptive survey research design was used for the study. Five null-hypotheses, tested at .05 level of significance, guided the study. Participants were 250 randomly selected academic staff from two purposively selected public universities in Lagos State. A self-constructed questionnaire was used for data collection. Data were analyzed using inferential statistics, specifically One Sample t-test, Pearson Product-Moment Correlation, Pearson Chi Square, and Independent t-test respectively. Findings showed that in public universities in Lagos State, Nigeria, the level of academic staff engagement was low [t (249) = 230.15, p>0.05]. No significant relationship was found between workload and employee engagement [r (248) = -0.611; p>0.05]. Further finding showed that employee engagement was significantly related to job hazard [r (248) = 0.502, p<0.05]. There was also significant relationship between interpersonal discrimination and employee engagement [r (248) = 0.721; p<0.05]. Finally, there was no significant difference in employee engagement among academic staff in Federal and State Universities[t (249) = 3.122, p>0.05]. It was recommended that Government and institutional administrators should ensure that the working environment is made more conducive for scholarly works, employ more qualified staff, and avoid interpersonal discrimination among academic staff

There has been an increased global attention on the need to make education more flexible and accessible due to the advent of modern mobile technologies. These modern mobile technologies enable people including those with disabilities to receive education anywhere and anytime. This study therefore investigated the behavioural intentions of Nigerian Students With Disabilities (SWDs) to adopt Mobile Learning (ML). The descriptive survey design was used. Data were collected through a self-structured, validated and reliable (r = 0.78) questionnaire from a total of 104 purposively selected SWDs. Two research questions and one null hypothesis guided the study. Multiple Regression (MR) and Independent t-test were used for data analysis. Findings revealed that the selected five factors namely computer self-efficacy, perceived usefulness, computing skills, ease of use, and enabling environment jointly contributed 5.3% to the variance in behavioural intentions of SWDs to adopt mobile learning. Also, the relative contributions of each of the explanatory variables in order of importance were computer self-efficacy (ß = 0.331), perceived usefulness (ß = 0.201), computing skills (ß=0.111), perceived ease of use (ß = 0.086), and enabling environment (ß = 0.012). Significant difference in behavioural intentions to adopt mobile learning among SWDs was established based on gender (t-cal = 0.303; df = 102; p<0.05). Although, mobile learning is still considered as a new technological innovation worldwide, its usage has grown tremendously. It is therefore recommended among others that ML providers should develop more friendly and easy to use ML systems and devices for the SWDs. The SWDs should develop positive computer self-efficacy beliefs and improve upon their computing skills.

Background: Remote and unsupervised administration of neuropsychological tests may increase opportunities to understand cognition in everyday life compared to in‐clinic assessments. The technological sophistication and widespread use of smartphones now allows this platform to be used for remote neuropsychological testing. However, it is important to ensure that the validity of neuropsychological tests extends to remote and unsupervised administration. As the Cogstate Brief Battery (CBB) has been validated for remote use on computers, the aim of this study was to now examine whether the acceptability and validity holds when administered via smartphone to cognitively unimpaired (CU) middle‐aged and older adults. Method: CU adults (n = 173) aged 41‐75 years (M(SD) = 62.72 (7.18)) enrolled in the Healthy Brain Project completed the smartphone CBB remotely. The mean speed and accuracy of performance on the Detection (DET, psychomotor function), Identification (IDN, attention), One Back (OBK, working memory) and One Card Learning (OCL, visual learning) tests were used to define performance. Acceptability was determined by the percentage of participants completing the full CBB. Relationships between CBB performance with age and test difficulty were described to inform validity. Relationships between objective CBB performance and subjective ratings of how participants felt about their overall performance, obtained via survey, were also explored. Result: 100% of participants completed the smartphone CBB. Increasing age was associated with slower performance on all tests (Figure 1a‐1d), and reduced accuracy on the OBK (Figure 1h). Older adults (66+ years) performed slower than middle‐aged adults (40‐65 years) on all CBB tests, and with less accuracy on OBK (Table 1). Performance speed across all tests slowed with increasing test difficulty (Figure 2). Whilst a ceiling effect was observed for accuracy of simpler tests (DET, IDN, OBK), accuracy was lowest for the most difficult test (OCL) (Figure 2). Subjective ratings of overall CBB performance was significantly, albeit weakly, correlated with OCL accuracy (r = 0.23, p =.003). Conclusion: The results support the acceptability and validity of the remote, unsupervised administration of the smartphone CBB in CU middle‐aged and older adults. These results provide a foundation for gaining further understanding of the performance of the smartphone CBB in diverse and impaired populations. [ABSTRACT FROM AUTHOR]

Background: In cognitively unimpaired (CU) individuals, the PACC is widely used as a cognitive outcome measure and endpoint in observational studies and clinical trials. However, it has drawn criticism for being heavily weighted towards memory. Increasing evidence indicates a decline spanning multiple cognitive domains in CU individuals. Therefore, using principal component analysis (PCA), we derived data‐driven domain‐specific cognitive composites. And subsequently, compared them against their summed z‐score counterparts in predicting progression to mild cognitive impairment (MCI). Method: Baseline cognitive, demographic, and genotype data of 2,853 CU older adults (aged 41.6 to 98.3) was obtained from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) Consortium. Using varimax‐rotated PCA, tests significantly loading (≥ 0.5) onto each principal component were extracted to derive domain‐specific cognitive composites. The resulting domain scores were normalised to a mean of 0 and SD of 1, with a higher score indicating better cognition. Cox regression was used to assess the association between progression to MCI and baseline demographics, cognition, and APOE ε4 carriage. Akaike information criteria (AIC) was used to compare the fitness of PCA‐derived composites against the zPACC and z‐score domain‐specific composites. Result: Baseline cohort characteristics are described in Table 1. PCA explained 68% of the variance and resulted in four independent cognitive composites (Figure 1): memory; executive function; attention and processing speed; and global cognition. At 15 years from baseline, 309 participants progressed to MCI, while 2,544 remained CU. Cox regression showed that the four cognitive composites, age and APOE genotype significantly predicted progression to MCI (Concordance = 77%, p < 0.001, AIC = 4105, Table 1). Additionally, the PCA‐derived composites performed comparably, if not better than the summed z‐score counterparts, the PACC (Concordance = 74%, p < 0.001, AIC = 4163) and domain‐specific composites (Concordance = 75%, p < 0.001, AIC = 4142). Baseline older age, APOE ε4 carriage in a dose‐dependent manner (Figure 2) and poorer cognition for each PCA‐composite were independently associated with progression to MCI. Conclusion: Together with APOE ε4 carriage, our PCA‐derived domain‐specific composites performed better than their summed z‐score counterparts at predicting progression to MCI 15 years before symptom onset. [ABSTRACT FROM AUTHOR]

Background: Anxiety and stress have been identified as potential risk factors of cognitive impairment, but research examining this association in midlife has been limited. Method: The current study examined the associations between anxiety and stress symptoms, and cognition in middle‐aged cognitively unimpaired adults (n = 2,463) enrolled in the Healthy Brain Project. Exploratory factor analysis was conducted on items in the Anxiety Subscale of the Hospital Anxiety and Depression Scale, Anxiety and Stress Subscales of the Depression, Anxiety and Stress Scale, and Perceived Stress Scale. Cognition was measured using the Cogstate Brief Battery (CBB). Result: The solution that resulted in the best fit to the data comprised five factors which explained for 48% of the variance: Over‐Reactivity, Panic, Low‐Control, Tension and Intolerance. Panic‐related symptoms were significantly associated with both poorer attention and memory. Having clinically meaningful anxiety symptoms, or both anxiety and stress symptoms, was associated with poorer memory. Conclusion: Anxiety and stress symptoms may be linked to a heightened risk of developing cognitive impairment. Specifically, panic‐related symptoms may play a significant role in the relationship between anxiety symptoms and cognition. Limitations include: 1) cross‐sectional nature of the study limited ability to ascertain direction of causality; 2) individuals with major psychiatric conditions were excluded from the study, thus limiting external validity of the current findings; 3) HBP sample has a higher proportion of individuals with a family history of dementia compared to the general population, hence the sample presents with an increased risk of developing dementia and generalizability of our findings may be limited. [ABSTRACT FROM AUTHOR]

Learning is more personal and the responsibility rests more squarely on the shoulders of students in distance learning systems. Also, many distance learners have several other equally important commitments such as home demands, social engagements, and religious obligations that compete with their academic work. Balancing these responsibilities with academic pursuit in such a way that one does not affect the other may become problematic for the students, especially those who are not self-regulated. This may subsequently impact student academic performance. In view of this scenario, it is expected that distance learners will have good self-regulation skills in order to perform better in their academic endeavours. This study sought to discern a causal explanation of distance learners' academic performance vis-à-vis their self-regulation skills. The study utilized a descriptive, 'ex-post facto' research design. Simple random sampling technique was used to select 1,500 participants while the University of Ibadan's Distance Learning Centre was purposively selected. Data were collected through a questionnaire during the 2009 contact session. Three hypotheses were formulated and tested at the 0.05 level of significance. Pearson correlation, regression analysis, and t-tests were employed for data analysis. Students' self-regulation skills and academic performance are positively and significantly correlated. (R2 =016;P<0.5). The study however, revealed no significant difference in students' self-regulations skills on a gender basis. The need for students to have good self-regulation skills and monitor their academic progress was recommended.

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]

Background: The heterogeneity of neurodegenerative diseases, including Alzheimer's disease (AD), has hampered precision diagnosis and treatment. Machine learning methods enable the identification of genetically‐explained disease subtypes with distinct brain phenotypes. Method: Gene‐SGAN is a novel, multi‐view, weakly‐supervised deep clustering method that jointly considers phenotypic and genetic data, thereby deriving subtypes of disease‐related brain changes with genetic associations. We apply the method to derive subtypes related to AD. Analyses used structural MRIs and whole‐genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (1,533 participants, including controls and patients with Mild Cognitive Impairment (MCI) and AD). Result: Gene‐SGAN identified four distinct subtypes within MCI/AD: A1, normal anatomy; A2, focal medial temporal lobe (MTL) atrophy; A3, severe atrophy in neocortex and MTL; A4, severe cortical but relatively modest MTL atrophy (Figure 1). The four subtypes reveal significant differences in seven known AD‐related genetic variants (Figure 2). The identified subtype‐associated SNPs not only resemble previous findings, but also suggest genetic protective effects contributing to the observed resilience (A1), potential inflammatory mechanisms underlying subtypes (A2&A3), and the effect of SNPs on atypical atrophy patterns of AD (A4). Clinically, A1 and A3 participants showed the best and the worst cognitive performances (Figure 3). A3 participants had the most abnormal CSF Aβ (p<0.001 vs. A1&A4 and p = 0.039 vs. A2), and exhibited the highest white matter hyperintensity (WMH) volumes (p<0.001 vs. A1&A4 and p = 0.017 vs. A2). However, A2 participants showed significantly higher CSF phospho‐tau than A3 participants (p = 0.019). A4 participants were the youngest group (p<0.001 vs. all other groups), suggesting the inclusion of early onset AD participants, who have a higher prevalence of hippocampal‐sparing variants such as posterior cortical atrophy. The four genetically‐associated subtypes also possess significant differences in a large set of plasma/CSF biomarkers, which are related to distinct biological mechanisms contributing to the heterogeneity of AD. Conclusion: Deep learning revealed four morphologic/genetic MCI/AD subtypes. The derived subtypes display distinct neuroanatomical patterns and genetic determinants and also differ in many clinically interpretable biomarkers. These subtypes provide potential for drug discovery and repurposing, optimization of clinical trial recruitment, and personalized medicine based on an individual's genetic profile. [ABSTRACT FROM AUTHOR]

Background: The glymphatic system has been suggested as an important clearance mechanism for amyloid‐β (Aβ) during sleep. Animal and cellular models have suggested this clearance mechanism involves the water‐channel protein, Aquaporin‐4 (encoded by the AQP4 gene), located primarily in the astrocytic end‐feet. We have previously reported on the interaction between genetic variants within AQP4, sleep and cross‐sectional cortical amyloid‐β (Aβ) burden. This study investigated how the relationship between AQP4 genetic variants and sleep may further influence changes in cortical Aβ levels. Methods: The current study assessed cognitively unimpaired individuals enrolled in the Australian Imaging, Biomarker and Lifestyle (AIBL) Study of Ageing, who were considered Aβ accumulators (n = 319). Aβ accumulators were defined as those who had a high baseline level of cortical Aβ (centiloid ³20), or a positive rate of change in cortical Aβ, calculated using a minimum of 3 study time‐points. 13 AQP4 gene variants were selected for analysis based on fine mapping of the gene region, and sleep was assessed using measures calculated from the Pittsburgh Sleep Quality Index (PSQI) questionnaire. The relationship between AQP4 gene variants and rate of Aβ accumulation was investigated using linear regressions. Additional linear regression models with AQP4 variant x sleep measure interaction terms and post‐hoc simple slopes were analysed to assess how these relationships may influence Aβ accumulation. Results: AQP4‐rs68006382 was associated with Aβ accumulation, where minor allele carriers/homozygotes were observed to have a higher rate of accumulation when compared to major allele homozygotes. Linear regressions with AQP4 variant x sleep measure interaction terms revealed that in AQP4‐rs68006382 minor allele carriers there was a significant relationship between Aβ accumulation and sleep quality. Additionally, interaction analysis revealed a relationship between Aβ accumulation and sleep latency in AQP4‐rs3875089 minor allele carriers. Conclusion: The results of this study confirm our previous findings and suggests that interactions between AQP4 genotypes and sleep measures are also associated with the rate of Aβ accumulation. [ABSTRACT FROM AUTHOR]

Background: The International Shopping List Test (ISLT) is a rater‐administered verbal list learning test, sensitive to memory dysfunction in early Alzheimer's disease (AD). This study examined the acceptability of a self‐administered ISLT (called LILA) in cognitively unimpaired (CU) middle‐aged and older adults. The validity of LILA was determined by the nature of ISLT learning curves and from examination of the effects of age. Method: CU adults (n = 113) completed LILA on their own smartphone device in a remote, unsupervised setting (n = 113). The LILA app administered a novel ISLT, where a list of 12 shopping list items was read aurally through the speaker to the participant. Participants were required to recall, into the smartphone, as many words as they could remember. This immediate recall trial was repeated three times (T1‐3). After a 10‐minute delay, participants recalled as many of the 12 words as they could. LILA performance measures included, number of words recalled on each trial, and on the delayed recall trial. Result: 10% of participants (n = 11) failed to provide a response on at least one learning trial (T1 = 3, T2 = 2, T3 = 4, DR = 2), with this data excluded from subsequent analyses. All participants reported feeling highly (74%) or moderately (26%) confident about using a smartphone, and reported using their phone daily. Improvement in immediate recall, known to occur for the ISLT was observed for LILA (Fig 1). Lower LILA immediate recall scores were associated with older age and higher depressive symptomatology (Table 1), but not sex, education or anxiety symptoms. Lower scores on delayed recall was associated with male sex and lower years of education (Table 1), but not age or mood symptoms. Conclusion: The very low rate of data loss indicates high acceptability of LILA in CU middle‐ and older‐aged adults. Validity of performance was indicated by the learning curves, which were qualitatively similar to those of the rater‐administered ISLT, and the effect of age on the immediate recall outcome. These data provide a strong foundation for future studies of the sensitivity of LILA to memory impairment in AD. [ABSTRACT FROM AUTHOR]

Background: The success of therapeutic options for treatment of Alzheimer's disease (AD) and the growing emphasis for such treatment to commence in the pre‐clinical phase makes it necessary to have robust empirical models of clinical disease progression to understand findings from clinical trials, allow clinicians to evaluate effects of new drugs, and to select individuals for future trials. Such models have been developed from relatively small samples, with incomplete data/substantial loss to follow‐up. The ADOPIC consortium provides the largest complete AD natural history sample to date. We applied our sigmoid models of disease progression to cognitive data from ADOPIC, and developed an algorithm to predict cognitive change. Method: We developed an online application (Prediction of Alzheimer's Disease Progression Tool, [PADPT]) to estimate decline in cognition over the course of AD within 2,861 ADOPIC participants (1,434 Cognitively unimpaired [CU], 342 MCI [Mild Cognitive Impairment], 211 AD, and 847 progressed from CU→MCI or MCI→AD), with ≥1 PET Amyloid (Aβ) scan and ≥36 months of cognitive assessment with the pre‐clinical Alzheimer's cognitive composite (PACC). Individual participant slopes were defined according to age and gender using linear mixed effects models. Aβ+ (≥50CL) participant rates of decline vs their mean PACC score were investigated using quantile polynomial regression. Within PADPT, single visit information on age, clinical classification and APOE ε4 allele status is entered to compute the estimated annual rate of decline. Candidate results are mapped to the sigmoid curve to determine their predicted rate of cognitive decline over the chosen time period. Result: Quantile regression bands (QRB's) aligned with participant cognitive decline. Individuals with stable cognition clustered to the top right of the plot (Figure 1). Mean slopes for APOE ε4 allele status separated QRB's for CU and AD, but not for MCI groups. All participants allocated to cognitive decline groups (past the first sigmoid inflection point) demonstrated decline in PACC score that remained in the QRB's, regardless of initial clinical classification. Conclusion: Used in conjunction with baseline profile information, the PADPT application can assist with clinical decision making both pre and post‐treatment and also identify suitable clinical trial candidates who, untreated, will show subtle decline in cognition. [ABSTRACT FROM AUTHOR]

Background: Recent studies suggest learning deficits are the earliest cognitive abnormality to emerge in Alzheimer's disease (AD). Using the Online Repeatable Cognitive Assessment‐Language Learning Test (ORCA‐LLT), cognitively unimpaired (CU) Aβ+ older adults showed a substantial (d∼2) learning deficit compared to Aβ‐ controls. This deficit was six times greater than that observed from reduced practice effects after 9+ years of assessment with conventional neuropsychological memory tests (d∼0.3). A similar but smaller learning deficit (d∼1.2) was also observed in CU Aβ‐ ε4 carriers compared to matched non‐carriers, consistent with observations that Aβ accumulation has begun in this group. We aimed to determine the development of AD‐related learning deficits by examining the effect of age and APOE ε4 on the ORCA‐LLT in CU middle‐aged adults. Method: CU adults (n = 513) provided saliva samples for APOE genotyping, and completed the ORCA‐LLT, which required participants to learn associations between 50 Chinese characters and their English language equivalents over 5 days. Learning curves were computed for ORCA‐LLT performance accuracy across days, with learning rate defined using the exponent estimated from each power curve. The Cogstate Brief Battery was also completed and provided the Cogstate Learning/Working‐Memory Composite. All assessments were completed remotely and unsupervised on an online platform. Result: Learning curves were evident for all groups, though learning rates slowed with increasing age (Fig 1). Learning rates were slower in ε4 carriers compared to non‐carriers in the 65‐70 (d∼0.9) and 60‐64‐year age groups (d∼0.4). Learning rates were equivalent between ε4 carriers and non‐carriers in all other age groups (Fig 1, Table 1). No differences were observed between ε4 carriers and non‐carriers on the Cogstate Learning/Working‐Memory Composite for any age group (Table 1). Conclusion: In late middle‐aged adults, ε4 was associated with a large reduction in ability to learn novel information. This impairment was not evident in performance on conventional cognitive tests. It is likely that Aβ levels in this group are increasing abnormally or have reached threshold for abnormality. Placed in the context of our previous observations, these data support the hypothesis that the earliest cognitive abnormality to reflect AD pathophysiological processes is a reduced ability to benefit from new information. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition, with considerable variation in disease progression from the mild cognitive impairment (MCI) stage. Predicting disease progression will support prognostic decisions and patient management. Here we designed a machine learning (ML) stack model, where a classifier was used to differentiate MCI progressors from non‐progressors (i.e. whether an individual with MCI progress to AD), and different regression models were then applied to each category to forecast their Mini‐Mental State Examination (MMSE) score after 3 years. Method: Demographic and neuropsychological data from 2428 participants of the AIBL study was used for model construction and validation. The model was validated via a stratified 3‐fold cross‐validation approach. Support vector machine (SVM), gradian boosting (GB), random forest (RF) was experimented, and their performance were compared. MMSE score was a predicted variable indicating cognition. Result: The performance of RF‐stack model was superior to SVM‐ and GB‐stack models. Largest area under the receiver operating characteristic curve (0.85) when RF‐stack model was employed (compared with SVM 0.79 and GB 0.70), signifying the highest accuracy in classify MCI progressors and non‐progressors. RF‐stack model achieved the best performance in predicting MMSE score after 3 years), evidenced by the smallest median mean average error (MAE) (RF 1.51, SVM 1.52, GB 2.51). The MAE indicated how much the predicted MMSE score deviated from the AIBL record. Representative prediction results from RF‐stack model for AIBL participants were presented in Table 1. Conclusion: Our RF‐stack model relies on neuropsychological test scores and demographic data to predict the probability of an individual progressing from MCI to AD and accurately forecast the MMSE score after 3 years. This model achieved is comparable if not better prediction accuracy than the existing ML models using neuroimaging data. Further model optimization and validation using data collected from other longitudinal AD cohort studies are required. [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]