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BACKGROUND: We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors. METHODS: Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrels C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots. RESULTS: Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65). CONCLUSIONS: The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

INTRODUCTION: The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis. RESULTS: A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. HIGHLIGHTS: A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.

Abstract Background Patient data contain a wealth of information that could aid in understanding the onset and progression of disease. However, the task of modelling clinical data, which consist of multiple heterogeneous time series of different lengths, measured at different time intervals, is a complex one. A growing body of research has applied temporal pattern mining to this problem to identify common patterns in clinical attributes over time. However, the vast majority of these algorithms use techniques that are not ideally suited to clinical data. We present an efficient and scalable framework designed specifically for temporal pattern mining of real-world clinical data. Our framework combines temporal abstraction, an extended version of the efficient pattern-growth algorithm, TPMiner, the concepts of relative risk and the odds ratio to identify interesting and high-risk patterns and multiprocessing to improve computational efficiency. A complete set of cut-off values for discretisation and interpretation of the data is provided and is applicable to studies on ageing populations in general. We name this framework Clinical Temporal Pattern Mining or C-TPM. Results The framework is applied to data from two real-world studies of Alzheimer’s disease (AD). The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.87 and contain clinically relevant variables. A visualisation module provides a clear picture of the discovered patterns for ease of interpretability. Conclusions The framework provides an effective and scalable method of modelling multivariate, longitudinal clinical data and can identify patterns in uncommon diseases and those that progress slowly over time. It is generalisable to clinical data from other medical domains as well as non-clinical data.

BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individuals regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.

Abstract Beneficial associations between higher fruit and vegetable intakes and risk of depression appear to exist but few studies have focused on adults aged 45 + years and the potential that associations are due to residual confounding has not been tested. This longitudinal study of twins (n = 3483, age 45–90 years) from Australia, Denmark, Sweden and USA, assessed the associations between baseline fruit/vegetable intake and depressive symptoms over 5–11 years using linear mixed effects models. Intakes from food frequency questionnaires were trichotomized. Depressive symptoms were assessed using validated measures. The co-twin method was used to examine familial confounding. Compared with low intakes, both high fruit and high vegetable intakes were associated with lower depressive symptoms (fruit: β -.007 [95%CI − .014,

Abstract Background We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors. Methods Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel’s C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer–Lemeshow goodness of fit test along calibration plots. Results Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65). Conclusions The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.

Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population. We examined a cohort of near-centenarians and centenarians without dementia (aged 95-103 years, n=44). We constructed a structural disconnection matrix based on the disruption of white matter pathways caused by white matter hyperintensities (WMHs), aiming to explore the relationship between functional connections, cognitive preservation and white matter damage. Our results revealed that structural damage can reliably explain the variations of functional connections or cognitive maintenance. Notably, we found significant correlations between the weights in the functional connectivity model and the weights in the cognition model. We observed positive correlations between models for brain disconnections and cognitive function in near-centenarians and centenarians. The strongest effects were found between attention and somatomotor network (SMN) (r=0.397, p

INTRODUCTION: Sex differences in dementia risk, and risk factor (RF) associations with dementia, remain uncertain across diverse ethno-regional groups. METHODS: A total of 29,850 participants (58% women) from 21 cohorts across six continents were included in an individual participant data meta-analysis. Sex-specific hazard ratios (HRs), and women-to-men ratio of hazard ratios (RHRs) for associations between RFs and all-cause dementia were derived from mixed-effect Cox models. RESULTS: Incident dementia occurred in 2089 (66% women) participants over 4.6 years (median). Women had higher dementia risk (HR, 1.12 [1.02, 1.23]) than men, particularly in low- and lower-middle-income economies. Associations between longer education and former alcohol use with dementia risk (RHR, 1.01 [1.00, 1.03] per year, and 0.55 [0.38, 0.79], respectively) were stronger for men than women; otherwise, there were no discernible sex differences in other RFs. DISCUSSION: Dementia risk was higher in women than men, with possible variations by country-level income settings, but most RFs appear to work similarly in women and men.

IntroductionThere are limited data on prevalence of dementia in centenarians and near-centenarians (C/NC), its determinants, and whether the risk of dementia continues to rise beyond 100.MethodsParticipant-level data were obtained from 18 community-based studies (N = 4427) in 11 countries that included individuals ≥95 years. A harmonization protocol was applied to cognitive and functional impairments, and a meta-analysis was performed.ResultsThe mean age was 98.3 years (SD = 2.67); 79% were women. After adjusting for age, sex, and education, dementia prevalence was 53.2% in women and 45.5% in men, with risk continuing to increase with age. Education (OR 0.95;0.92-0.98) was protective, as was hypertension (odds ratio [OR] 0.51;0.35-0.74) in five studies. Dementia was not associated with diabetes, vision and hearing impairments, smoking, and body mass index (BMI).DiscussionAmong the exceptional old, dementia prevalence remains higher in the older participants. Education was protective against dementia, but other factors for dementia-free survival in C/NC remain to be understood.

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

Alzheimer’s disease (AD) is a devastating neurodegenerative condition that affects memory and cognition, characterized by neuronal loss and currently lacking a cure. Mutations in PSEN1 (Presenilin 1) are among the most common causes of early-onset familial AD (fAD). While changes in neuronal excitability are believed to be early indicators of AD progression, the link between PSEN1 mutations and neuronal excitability remains to be fully elucidated. This study examined iPSC-derived neurons (iNs) from fAD patients with PSEN1 mutations S290C or A246E, alongside CRISPR-corrected isogenic cell lines, to investigate early changes in excitability. Electrophysiological profiling revealed reduced excitability in both PSEN1 mutant iNs compared to their isogenic controls. Neurons bearing S290C and A246E mutations exhibited divergent passive membrane properties compared to isogenic controls, suggesting distinct effects of PSEN1 mutations on neuronal excitability. Additionally, both PSEN1 backgrounds exhibited higher current density of voltage-gated potassium (Kv) channels relative to their isogenic iNs, while displaying comparable voltage-gated sodium (Nav) channel current density. This suggests that the Nav/Kv imbalance contributes to impaired neuronal firing in fAD iNs. Deciphering these early cellular and molecular changes in AD is crucial for understanding disease pathogenesis.

AIM: To synthesize international findings on the alcohol-dementia relationship, including representation from low- and middle-income countries. METHODS: Individual participant data meta-analysis of 15 prospective epidemiological cohort studies from countries situated in six continents. Cox regression investigated the dementia risk associated with alcohol use in older adults aged over 60 years. Additional analyses assessed the alcohol-dementia relationship in the sample stratified by sex and by continent. Participants included 24 478 community dwelling individuals without a history of dementia at baseline and at least one follow-up dementia assessment. The main outcome measure was all-cause dementia as determined by clinical interview. RESULTS: At baseline, the mean age across studies was 71.8 (standard deviation = 7.5, range = 60-102 years), 14 260 (58.3%) were female and 13 269 (54.2%) were current drinkers. During 151 636 person-years of follow-up, there were 2124 incident cases of dementia (14.0 per 1000 person-years). When compared with abstainers, the risk for dementia was lower in occasional [hazard ratio (HR) = 0.78; 95% confidence interval (CI) = 0.68-0.89], light-moderate (HR = 0.78; 95% CI = 0.70-0.87) and moderate-heavy drinkers (HR = 0.62; 95% CI = 0.51-0.77). There was no evidence of differences between life-time abstainers and former drinkers in terms of dementia risk (HR = 0.98; 95% CI = 0.81-1.18). In dose-response analyses, moderate drinking up to 40 g/day was associated with a lower risk of dementia when compared with lif-time abstaining. Among current drinkers, there was no consistent evidence for differences in terms of dementia risk. Results were similar when the sample was stratified by sex. When analysed at the continent level, there was considerable heterogeneity in the alcohol-dementia relationship. CONCLUSIONS: Abstinence from alcohol appears to be associated with an increased risk for all-cause dementia. Among current drinkers, there appears to be no consistent evidence to suggest that the amount of alcohol consumed in later life is associated with dementia risk.

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

IntroductionThough consistent evidence suggests that physical activity may delay dementia onset, the duration and amount of activity required remains unclear.MethodsWe harmonized longitudinal data of 11,988 participants from 10 cohorts in eight countries to examine the dose-response relationship between late-life physical activity and incident dementia among older adults.ResultsUsing no physical activity as a reference, dementia risk decreased with duration of physical activity up to 3.1 to 6.0 hours/week (hazard ratio [HR] 0.88, 95% confidence interval [CI] 0.67 to 1.15 for 0.1 to 3.0 hours/week; HR 0.68, 95% CI 0.52 to 0.89 for 3.1 to 6.0 hours/week), but plateaued with higher duration. For the amount of physical activity, a similar pattern of dose-response curve was observed, with an inflection point of 9.1 to 18.0 metabolic equivalent value (MET)-hours/week (HR 0.92, 95% CI 0.70 to 1.22 for 0.1 to 9.0 MET-hours/week; HR 0.70, 95% CI 0.53 to 0.93 for 9.1 to 18.0 MET-hours/week).DiscussionThis cross-national analysis suggests that performing 3.1 to 6.0 hours of physical activity and expending 9.1 to 18.0/MET-hours of energy per week may reduce dementia risk.

Background: Urban neighbourhood environments may impact older adults’ cognitive health. However, longitudinal studies examining key environmental correlates of cognitive health are lacking. We estimated cross-sectional and longitudinal associations of neighbourhood built and natural environments and ambient air pollution with multiple cognitive health outcomes in Australian urban dwellers aged 60+ years. Methods: The study included 1160 participants of the PATH Through Life study (60+ cohort) who were followed up for 12 years (four assessments; 2001/02 to 2013/15) and with data on socio-demographics, health, cognitive functions and diagnoses, and full residential address. Neighbourhood environmental features encompassed population and street-intersection densities, non-commercial land use mix, transit points, presence of blue space, percentages of commercial land, parkland and tree cover, and annual average PM2.5 and NO2 concentrations. All exposures except for tree cover were assessed at two time points. Generalised additive mixed models estimated associations of person-level average, and within-person changes in, exposures with cognitive functions. Multi-state hidden Markov models estimated the associations of neighbourhood attributes with transitions to/from mild cognitive impairment (MCI). Results: Dense, destination-rich neighbourhoods were associated with a lower likelihood of transition to MCI and reversal to no MCI. Positive cross-sectional and longitudinal associations of non-commercial land use mix, street intersection density and percentage of commercial land were observed especially with global cognition and processing speed. While access to parkland and blue spaces were associated with a lower risk of transition to MCI, the findings related to cognitive functions were mixed and supportive of an effect of parkland on verbal memory only. Higher levels of PM2.5 and NO2 were consistently associated with steeper declines and/or decreases in cognitive functions and worse cognitive states across time. Conclusion: To support cognitive health in ageing populations, neighbourhoods need to provide an optimal mix of environmental complexity, destinations and access to the natural environment and, at the same time, minimise ambient air pollution.

Down syndrome regression disorder (DSRD) is a rare condition involving subacute cognitive decline, loss of previously acquired developmental skills, and prominent neuropsychiatric symptoms, particularly catatonia, in people with Down syndrome. It is thought to involve both autoimmune and neuropsychiatric mechanisms. Research, however, is largely restricted to case studies and retrospective case series and is particularly limited in terms of prospective longitudinal follow-up. We report a case study of a person with DSRD who received both immunomodulatory (intravenous immunoglobulin; IVIG) and psychiatric interventions (electroconvulsive therapy, ECT) over two years with regular assessments using caregiver and clinician ratings. This revealed a small, unsustained response to IVIG and a rapid, sustained response once ECT was introduced. The case highlights the importance of multimodal assessment involving multiple medical specialties, the need to trial different therapies due to the condition’s complexity, and the significant barriers that patients and their families face in accessing care.

Background: The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated. This study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI), and dementia (1) and to cross-validate the new norms for detecting MCI and dementia in a community sample of older adults with clinical diagnoses (2). Methods: The validation sample included 143 older adults (mean age = 87.7, SD = 3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia solely based on a consensus diagnosis; in addition, three tests (SKT, Mini-Mental State Examination (MMSE), and Addenbrooke’s Cognitive Examination III (ACE-III)) to measure cognitive impairment were applied. Sensitivity and specificity for all three tests, as well as bivariate correlations, were calculated. Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 70.3% for all three diagnostic groups. All correlations between the three tests and the consensus diagnosis were significant (p < 0.01). Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

IntroductionCoronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.MethodsThis article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.ResultsSuccessful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.DiscussionThe Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.Key pointsThe following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

BackgroundEpidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available.ObjectiveWe apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results.MethodsUsing 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available.ResultsResults indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis.ConclusionThis latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.

The Alzheimer's Association International Conference held its sixth Satellite Symposium in Sydney, Australia in 2019, highlighting the leadership of Australian researchers in advancing the understanding of and treatment developments for Alzheimer's disease (AD) and other dementias. This leadership includes the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), which has fueled the identification and development of many biomarkers and novel therapeutics. Two multimodal lifestyle intervention studies have been launched in Australia; and Australian researchers have played leadership roles in other global studies in diverse populations. Australian researchers have also played an instrumental role in efforts to understand mechanisms underlying vascular contributions to cognitive impairment and dementia; and through the Women's Healthy Aging Project have elucidated hormonal and other factors that contribute to the increased risk of AD in women. Alleviating the behavioral and psychological symptoms of dementia has also been a strong research and clinical focus in Australia.

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.