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Air pollution is increasingly discussed as a risk factor for dementia, but the biological mechanisms are not yet fully understood. Biological markers like telomere length are relevant to study with air pollution, as they are associated with aging and dementia. The study aimed to investigate the relationship between source-specific air pollution exposure and telomere length in a low-level air pollution area, and whether this potential relationship depended on future dementia status. The data originated from the Betula study in Northern Sweden, where 509 participants recruited between 1988 and 1995 were included to investigate the association between annual mean air pollution concentrations at the participants’ residences and relative leukocyte telomere length using a linear regression model. No association was observed between air pollution and telomere length, with regression slope estimates close to zero and p-values > 0.10 (e.g. PM2.5_total: β = 0.01 (-0.011, 0.025) and BC_total: β = 0.03 (95% CI: -0.046, 0.114). There were indications of a positive association between longer telomere length and higher exposure to air pollution among individuals later diagnosed with dementia (N = 74), but these findings were not conclusive (p-values > 0.10) (PM2.5_total: β = 0.03, p-value = 0.12; BC_total: β = 0.11, p-value = 0.17). Although not statistically significant, our findings contribute to the evidence from low-exposure settings, and it is important to report these types of findings for a balanced understanding of potential health effects.

Various studies have reported an association between physical activity and grey matter volumes. Some studies have suggested that this relationship may be moderated by sex, yet the direction is still under debate. Focusing on hippocampus and dorsolateral prefrontal cortex (dlPFC), we tested whether the association between regional grey matter volumes and self-reported physical activity differs between women and men. We examined this interaction in five European cohorts from the Lifebrain consortium (n = 1809; age range: 18–88 years). Effect sizes were first determined by linear models run separately for each cohort, then pooled across datasets in a random-effects meta-analysis. Contrary to our hypotheses, there was no evidence of a relationship between physical activity and hippocampal or dlPFC volumes, nor was there a moderation by sex. Our null findings raise the question of whether self-report questionnaires of physical activity, which commonly feature in big datasets, are sufficiently sensitive to capture a—presumably modest—association between physical activity levels and grey matter outcomes. We conclude that the reliance on self-report questionnaires of physical activity is sub-optimal for brain-behaviour analyses.

BACKGROUND: Various studies have reported an association between physical activity and grey matter volumes. Some studies have suggested that this relationship may be moderated by sex, yet the direction is still under debate.

METHOD: Focusing on hippocampus and dorsolateral prefrontal cortex (dlPFC), we tested whether the association between regional grey matter volumes and self-reported physical activity differs between women and men. We examined this interaction in five European cohorts from the Lifebrain consortium (n = 1,809; age range: 18 - 88 years). Effect sizes were first determined by linear models run separately for each cohort, then pooled across datasets in a random-effects meta-analysis.

RESULT: Contrary to our hypotheses, there was no evidence of a relationship between physical activity and hippocampal or dlPFC volumes, nor was there a moderation by sex.

CONCLUSION: Our null findings raise the question of whether self-report questionnaires of physical activity, which commonly feature in big datasets, are sufficiently sensitive to capture a - presumably modest - association between physical activity levels and grey matter outcomes. We conclude that the reliance on self-report questionnaires of physical activity is sub-optimal for brain-behaviour analyses.

Throughout adulthood and ageing our brains undergo structural loss in an average pattern resembling faster atrophy in Alzheimer’s disease (AD). Using a longitudinal adult lifespan sample (aged 30-89; 2–7 timepoints) and four polygenic scores for AD, we show that change in AD-sensitive brain features correlates with genetic AD-risk and memory decline in healthy adults. We first show genetic risk links with more brain loss than expected for age in early Braak regions, and find this extends beyond APOE genotype. Next, we run machine learning on AD-control data from the Alzheimer’s Disease Neuroimaging Initiative using brain change trajectories conditioned on age, to identify AD-sensitive features and model their change in healthy adults. Genetic AD-risk linked with multivariate change across many AD-sensitive features, and we show most individuals over age ~50 are on an accelerated trajectory of brain loss in AD-sensitive regions. Finally, high genetic risk adults with elevated brain change showed more memory decline through adulthood, compared to high genetic risk adults with less brain change. Our findings suggest quantitative AD risk factors are detectable in healthy individuals, via a shared pattern of ageing- and AD-related neurodegeneration that occurs along a continuum and tracks memory decline through adulthood.

Introduction: Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification.

Methods: Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level.

Results: Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations.

Discussion: Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.

Introduction: Personality traits and neuropsychiatric symptoms such as neuroticism and depression share genetic overlap and have both been identified as risks factors for development of aging-related neurocognitive decline and Alzheimer’s disease (AD). This study aimed to examine revised personality factors derived from the Temperament and Character Inventory, previously shown to be associated with psychiatric disorders, as predictors of neuropsychiatric, cognitive, and brain trajectories of participants from a population-based aging study.

Methods: Mixed-effect linear regression analyses were conducted on data for the full sample (Nmax = 1,286), and a healthy subsample not converting to AD-dementia during 25-year follow-up (Nmax = 1,145), complemented with Cox proportional regression models to determine risk factors for conversion to clinical AD.

Results: Two personality factors, Closeness to Experience (CE: avoidance of new stimuli, high anxiety, pessimistic anticipation, low reward seeking) and Tendence to Liabilities (TL: inability to change, low autonomy, unaware of the value of their existence) were associated with higher levels of depressive symptoms, stress (CE), sleep disturbance (TL), as well as greater decline in memory, vocabulary and verbal fluency in the full sample. Higher CE was additionally associated with greater memory decline across 25 years in the healthy subsample, and faster right hippocampal volume reduction across 8 years in a neuroimaging subsample (N = 216). Most, but not all, personality-cognition associations persisted after controlling for diabetes, hypertension and cardiovascular disease. Concerning risks for conversion to AD, higher age, and APOE-ε4, but none of the personality measures, were significant predictors.

Conclusion: The results indicate that personality traits associated with psychiatric symptoms predict accelerated age-related neurocognitive declines even in the absence of neurodegenerative disease. The attenuation of some personality effects on cognition after adjustment for health indicators suggests that those effects may be partly mediated by somatic health. Taken together, the results further emphasize the importance of personality traits in neurocognitive aging and underscore the need for an integrative (biopsychosocial) perspective of normal and pathological age-related cognitive decline.

Studies of memory trajectories using longitudinal data often result in highly non-representative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semi-parametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.

It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

Objectives: Prospective studies suggest that memory deficits are detectable decades before clinical symptoms of dementia emerge. However, individual differences in long-term memory trajectories prior to diagnosis need to be further elucidated. The aim of the current study was to investigate long-term dementia and mortality risk for individuals with different memory trajectory profiles in a well-characterized population-based sample.

Methods: 1062 adults (aged 45–80 years) who were non-demented at baseline were followed over 23–28 years. Dementia and mortality risk were studied for three previously classified episodic memory trajectory groups: maintained high performance (Maintainers; 26%), average decline (Averages; 64%), and accelerated decline (Decliners; 12%), using multistate modeling to characterize individuals’ transitions from an initial non-demented state, possibly to a state of dementia and/or death.

Results: The memory groups showed considerable intergroup variability in memory profiles, starting 10–15 years prior to dementia diagnosis, and prior to death. A strong relationship between memory trajectory group and dementia risk was found. Specifically, Decliners had more than a fourfold risk of developing dementia compared to Averages. In contrast, Maintainers had a 2.6 times decreased dementia risk compared to Averages, and in addition showed no detectable memory decline prior to dementia diagnosis. A similar pattern of association was found for the memory groups and mortality risk, although only among non-demented.

Conclusion: There was a strong relationship between accelerated memory decline and dementia, further supporting the prognostic value of memory decline. The intergroup differences, however, suggest that mechanisms involved in successful memory aging may delay symptom onset.

Background: DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, hasshown promise for Alzheimer’s disease (AD) prediction.Objective: Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA)measures and identifying novel early blood-based DNAm AD-prediction biomarkers.

Methods: EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models(LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16years before clinical onset, and post-onset follow-up. NovelDNAmbiomarkers were generated with epigenome-wide LMMs,and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10–16 years), and post-AD-onset time-points.

Results: EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkersshowed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions(p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOE 4-carriership(OR = 1.38 per 1 SD DNAmscore increase versus OR= 13.58 for 4-allele carriage; AUCs = 77.2% versus 87.0%). Literaturereview showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with ouridentified CpGs.

Conclusion: The limited predictive value of EAA for AD extends prior findings by considering a longer follow-up time, andwith appropriate control for age, sex, APOE, and blood-cell proportions. Results also highlight challenges with replicatingdiscriminatory or predictive CpGs across studies.