Genotypes and phenotypes of motor neuron disease: an update of the genetic landscape in Scotland.

BACKGROUND: Using the Clinical Audit Research and Evaluation of Motor Neuron Disease (CARE-MND) database and the Scottish Regenerative Neurology Tissue Bank, we aimed to outline the genetic epidemiology and phenotypes of an incident cohort of people with MND (pwMND) to gain a realistic impression of the genetic landscape and genotype-phenotype associations. METHODS: Phenotypic markers were identified from the CARE-MND platform. Sequence analysis of 48 genes was undertaken. Variants were classified using a structured evidence-based approach. Samples were also tested for C9orf72 hexanucleotide expansions using repeat-prime PCR methodology. RESULTS: 339 pwMND donated a DNA sample: 44 (13.0%) fulfilled criteria for having a pathogenic variant/repeat expansion, 53.5% of those with a family history of MND and 9.3% of those without. The majority (30 (8.8%)) had a pathogenic C9orf72 repeat expansion, including two with intermediate expansions. Having a C9orf72 expansion was associated with a significantly lower Edinburgh Cognitive and Behavioural ALS Screen ALS-Specific score (p = 0.0005). The known pathogenic SOD1 variant p.(Ile114Thr), frequently observed in the Scottish population, was detected in 9 (2.7%) of total cases but in 17.9% of familial cases. Rare variants were detected in FUS and NEK1. One individual carried both a C9orf72 expansion and SOD1 variant. CONCLUSIONS: Our results provide an accurate summary of MND demographics and genetic epidemiology. We recommend early genetic testing of people with cognitive impairment to ensure that C9orf72 carriers are given the best opportunity for informed treatment planning. Scotland is enriched for the SOD1 p.(Ile114Thr) variant and this has significant implications with regards to future genetically-targeted treatments.

Life-course pathways to exceptional longevity: Evidence from the Lothian Birth Cohort of 1921.

BACKGROUND: Longevity, a hallmark of successful ageing, is a multifactorial trait with influences from birth onwards. However, limited evidence exists on the pathways linking diverse life-course exposures to longevity, especially within a single cohort. METHODS: We investigated associations between life-course factors and longevity among community-dwelling adults aged 79 (N=547) from the Lothian Birth Cohort 1921 with a mortality follow-up of 24 years. Cox proportional hazards and structural equation (path) models were used to explore how factors from early-life (social class, childhood IQ, education), mid-life (social class), later-life (health, lifestyle, psychosocial well-being), as well as sex, personality and APOE e4 status, influence survival time in days. RESULTS: During follow-up (1999-2023), 538 participants (98%) died (mean age of death=89.3 years) and 9 survived (mean age=101.6 years). Factors associated with lower mortality risk in the multivariable Cox model were higher cognitive function (HR=0.72; 95% CI:0.59-0.88), better physical function (HR=0.61; 95% CI:0.44-0.85), and greater physical activity (HR=0.81; 95% CI 0.71-0.92), while history of cancer was associated with higher mortality risk (HR=1.84; 95% CI:1.22-2.77). The life-course path model identified the same direct predictors, with additional contributions from female sex and non-smoking status, to greater longevity. Early- and mid-life factors (IQ, education, social class), and emotional stability, conscientiousness, and female sex, were indirectly and positively associated with survival trajectories via multiple dimensions of adult health. CONCLUSIONS: In understanding why people live to very old ages it is necessary to consider factors from throughout the life course, and to include demographic, psychosocial, and health variables.

Methylome-wide studies of six metabolic traits.

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised ßrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.

Life-course neighbourhood deprivation and brain structure in older adults: the Lothian Birth Cohort 1936.

Neighbourhood disadvantage may be associated with brain health but the importance of exposure at different stages of the life course is poorly understood. Utilising the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and local neuroimaging measures at age 73. A total of 689 participants had at least one valid brain measures (53% male); to maximise the sample size structural equation models with full information maximum likelihood were conducted. Residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (ß = -0.06; SE = 0.02; sample size[N] = 658; number of pairwise complete observations[n]=390), grey matter (ß = -0.11; SE = 0.03; N = 658; n = 390), and normal-appearing white matter volumes (ß = -0.07; SE = 0.03; N = 658; n = 390), thinner cortex (ß = -0.14; SE = 0.06; N = 636; n = 379), and lower general white matter fractional anisotropy (ß = -0.19; SE = 0.06; N = 665; n = 388). We also found some evidence on the accumulating impact of neighbourhood deprivation from birth to late adulthood on age 73 total brain (ß = -0.06; SE = 0.02; N = 658; n = 276) and grey matter volumes (ß = -0.10; SE = 0.04; N = 658; n = 276). Local analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower social classes, the brain-neighbourhood associations were particularly strong, with the impact of neighbourhood deprivation on total brain and grey matter volumes, and general white matter fractional anisotropy accumulating across the life course. Our findings suggest that living in deprived neighbourhoods across the life course, but especially in mid- to late adulthood, is associated with adverse brain morphologies, with lower social class amplifying the vulnerability.

Copy-number variants and polygenic risk for intelligence confer risk for autism spectrum disorder irrespective of their effects on cognitive ability.

Introduction: Rare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer susceptibility for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD susceptibility, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability. Methods: In a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability. Results: Bottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that susceptibility for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD. Discussion: Our findings suggest that cognitive ability itself may not be the factor driving the underlying liability for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|ß| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Epigenetic scores of blood-based proteins as biomarkers of general cognitive function and brain health.

BACKGROUND: Epigenetic Scores (EpiScores) for blood protein levels have been associated with disease outcomes and measures of brain health, highlighting their potential usefulness as clinical biomarkers. They are typically derived via penalised regression, whereby a linear weighted sum of DNA methylation (DNAm) levels at CpG sites are predictive of protein levels. Here, we examine 84 previously published protein EpiScores as possible biomarkers of cross-sectional and longitudinal measures of general cognitive function and brain health, and incident dementia across three independent cohorts. RESULTS: Using 84 protein EpiScores as candidate biomarkers, associations with general cognitive function (both cross-sectionally and longitudinally) were tested in three independent cohorts: Generation Scotland (GS), and the Lothian Birth Cohorts of 1921 and 1936 (LBC1921 and LBC1936, respectively). A meta-analysis of general cognitive functioning results in all three cohorts identified 18 EpiScore associations (absolute meta-analytic standardised estimates ranged from 0.03 to 0.14, median of 0.04, PFDR < 0.05). Several associations were also observed between EpiScores and global brain volumetric measures in the LBC1936. An EpiScore for the S100A9 protein (a known Alzheimer disease biomarker) was associated with general cognitive functioning (meta-analytic standardised beta: - 0.06, P = 1.3 × 10-9), and with time-to-dementia in GS (Hazard ratio 1.24, 95% confidence interval 1.08-1.44, P = 0.003), but not in LBC1936 (Hazard ratio 1.11, P = 0.32). CONCLUSIONS: EpiScores might make a contribution to the risk profile of poor general cognitive function and global brain health, and risk of dementia, however these scores require replication in further studies.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples.

BACKGROUND: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS: We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS: In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION: Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Childhood cognitive ability and self-harm and suicide in later life.

Background: Self-harm and suicide remain prevalent in later life. For younger adults, higher early-life cognitive ability appears to predict lower self-harm and suicide risk. Comparatively little is known about these associations among middle-aged and older adults. Methods: This study examined the association between childhood (age 11) cognitive ability and self-harm and suicide risk among a Scotland-wide cohort (N = 53037), using hospital admission and mortality records to follow individuals from age 34 to 85. Multistate models examined the association between childhood cognitive ability and transitions between unaffected, self-harm, and then suicide or non-suicide death. Results: After adjusting for childhood and adulthood socioeconomic conditions, higher childhood cognitive ability was significantly associated with reduced risk of self-harm among both males (451 events; HR = 0.90, 95% CI [0.82, 0.99]) and females (516 events; HR = 0.89, 95% CI [0.81, 0.98]). Childhood cognitive ability was not significantly associated with suicide risk among those with (Male: 16 events, HR = 1.05, 95% CI [0.61, 1.80]; Female: 13 events, HR = 1.08, 95% CI [0.55, 2.15]) or without self-harm events (Male: 118 events, HR = 1.17, 95% CI [0.84, 1.63]; Female: 31 events, HR = 1.30, 95% CI [0.70, 2.41]). Limitations: The study only includes self-harm events that result in a hospital admission and does not account for self-harm prior to follow-up. Conclusions: This extends work on cognitive ability and mental health, demonstrating that these associations can span the life course and into middle and older age.

Childhood intelligence and risk of depression in later-life: A longitudinal data-linkage study.

Background: Lower childhood intelligence test scores are reported in some studies to be associated with higher risk of depression in adulthood. The reasons for the association are unclear. This longitudinal data-linkage study explored the relationship between childhood intelligence (at age ∼11) and risk of depression in later-life (up to age ∼85), and whether childhood family structure and adulthood socio-economic and geographical factors accounted for some of this association. Methods: Intelligence test scores collected in the Scottish Mental Survey 1947 were linked to electronic health records (hospital admissions and prescribing data) between 1980 and 2020 (n = 53,037), to identify diagnoses of depression. Mixed-effect Cox regression models were used to explore the relationship between childhood intelligence test scores and risk of depression in later-life. Analyses were also adjusted for childhood family structure (size of family) and adulthood socio-economic and geographical factors (Carstairs index, urban/rural). Results: Twenty-seven percent of participants were diagnosed with depression during follow-up (n = 14,063/53,037). Greater childhood intelligence test scores were associated with a reduced risk of depression in an unadjusted analysis (HR = 0.95, 95% CI = 0.93 to 0.97, P < 0.001), and after adjustment for factors experienced in childhood and adulthood (HR = 0.95, 95% CI = 0.91 to 1.00, P = 0.032). When identifying depression using only hospital admissions data, greater childhood intelligence test scores were associated with a reduced risk of depression following unadjusted analysis (HR = 0.86, 95% CI = 0.82 to 0.90, P < 0.001), and after adjusting for risk factors in childhood and adulthood (HR = 0.94, 95% CI = 0.89 to 0.99, P = 0.026). There was no association between childhood cognitive test scores and depression when identifying cases of depression using only prescribed drugs data. Conclusions: This study provides additional evidence suggesting that higher childhood intelligence predicts reduced risk of later-life depression only when depression is assessed based on hospital admission records. Childhood family structure and adulthood socio-economic and geographical factors did not seem to be substantial confounders.

Copy-number variants and polygenic risk for intelligence confer risk for autism spectrum disorder irrespective of their effects on cognitive ability.

Rare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer risk for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD risk, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability. In a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability. Bottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that risk for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD. Our findings suggest that cognitive ability itself may not be the factor driving the underlying risk for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits.

Cognitive aging and experience of playing a musical instrument.

Musical instrument training has been found to be associated with higher cognitive performance in older age. However, it is not clear whether this association reflects a reduced rate of cognitive decline in older age (differential preservation), and/or the persistence of cognitive advantages associated with childhood musical training (preserved differentiation). It is also unclear whether this association is consistent across different cognitive domains. Our sample included 420 participants from the Lothian Birth Cohort 1936. Between ages 70 and 82, participants had completed the same 13 cognitive tests (every 3 years), measuring the cognitive domains of verbal ability, verbal memory, processing speed, and visuospatial ability. At age 82, participants reported their lifetime musical experiences; 40% had played a musical instrument, mostly in childhood and adolescence. In minimally adjusted models, participants with greater experience playing a musical instrument tended to perform better across each cognitive domain at age 70 and this association persisted at subsequent waves up to age 82. After controlling for additional covariates (childhood cognitive ability, years of education, socioeconomic status, and health variables), only associations with processing speed (ß = 0.131, p = .044) and visuospatial ability (ß = 0.154, p = .008) remained statistically significant. Participants with different amounts of experience playing a musical instrument showed similar rates of decline across each cognitive domain between ages 70 and 82. These results suggest a preserved differentiation effect: Cognitive advantages (in processing speed and visuospatial ability) associated with experience playing a musical instrument (mostly earlier in life) are preserved during older age. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy.

Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.

Identifying dementia using medical data linkage in a longitudinal cohort study: Lothian Birth Cohort 1936.

BACKGROUND: The Lothian Birth Cohort 1936 (LBC1936) is a longitudinal study of ageing with well-characterised assessments, but until now, it has relied on self-report or proxies for dementia such as cognitive tests. Our aims were twofold: a) to describe a framework for identifying dementia in a cohort study. b) to report the age-specific incidence and prevalence of all-cause dementia and dementia subtypes in 865 individuals in the LBC1936. METHODS: Electronic Health Records (EHR) of all participants were reviewed, and relevant information was extracted to form case vignettes for everyone with any record of cognitive dysfunction. The EHR data sources include hospital and clinic letters, general practitioner and hospital referrals, prescribed medications, imaging and laboratory results. Death certificate data were obtained separately. Clinician assessments were performed when there was concern about a participant's cognition. A diagnosis of probable dementia, possible dementia, or no dementia was agreed upon by a consensus diagnostic review board, comprised of a multidisciplinary team of clinical dementia experts who reviewed case vignettes and clinician assessment letters. For those with probable dementia, a subtype was also determined, where possible. We compared the agreement between our newly ascertained dementia diagnoses with the existing self-reported dementia diagnoses. RESULTS: Self-reported dementia diagnoses were positive in only 17.8% of ascertained dementia diagnoses. The EHR review identified 163/865 (18.8%) individuals as having cognitive dysfunction. At the consensus diagnostic review board, 118/163 were diagnosed with probable all-cause dementia, a prevalence of 13.6%. Age-specific dementia prevalence increased with age from 0.8% (65-74.9 years) to 9.93% (85-89.9 years). Prevalence rates for women were higher in nearly all age groups. The most common subtype was dementia due to Alzheimer disease (49.2%), followed by mixed Alzheimer and cerebrovascular disease (17.0%), dementia of unknown or unspecified cause (16.1%), and dementia due to vascular disease (8.5%). CONCLUSIONS: We present a robust systematic framework and guide for other cohort teams wanting to ascertain dementia diagnoses. The newly ascertained dementia diagnosis provides vital data for further analyses of LBC1936 to allow exploration of lifecourse predictors of dementia.

Predictive blood biomarkers and brain changes associated with age-related cognitive decline.

Growing evidence supports the use of plasma levels of tau phosphorylated at threonine 181, amyloid-ß, neurofilament light and glial fibrillary acidic protein as promising biomarkers for Alzheimer's disease. While these blood biomarkers are promising for distinguishing people with Alzheimer's disease from healthy controls, their predictive validity for age-related cognitive decline without dementia remains unclear. Further, while tau phosphorylated at threonine 181 is a promising biomarker, the distribution of this phospho-epitope of tau in the brain is unknown. Here, we tested whether plasma levels of tau phosphorylated at threonine 181, amyloid-ß, neurofilament light and fibrillary acidic protein predict cognitive decline between ages 72 and 82 in 195 participants in the Lothian birth cohorts 1936 study of cognitive ageing. We further examined post-mortem brain samples from temporal cortex to determine the distribution of tau phosphorylated at threonine 181 in the brain. Several forms of tau phosphorylated at threonine 181 have been shown to contribute to synapse degeneration in Alzheimer's disease, which correlates closely with cognitive decline in this form of dementia, but to date, there have not been investigations of whether tau phosphorylated at threonine 181 is found in synapses in Alzheimer's disease or healthy ageing brain. It was also previously unclear whether tau phosphorylated at threonine 181 accumulated in dystrophic neurites around plaques, which could contribute to tau leakage to the periphery due to impaired membrane integrity in dystrophies. Brain homogenate and biochemically enriched synaptic fractions were examined with western blot to examine tau phosphorylated at threonine 181 levels between groups (n = 10-12 per group), and synaptic and astrocytic localization of tau phosphorylated at threonine 181 were examined using array tomography (n = 6-15 per group), and localization of tau phosphorylated at threonine 181 in plaque-associated dystrophic neurites with associated gliosis were examined with standard immunofluorescence (n = 8-9 per group). Elevated baseline plasma tau phosphorylated at threonine 181, neurofilament light and fibrillary acidic protein predicted steeper general cognitive decline during ageing. Further, increasing tau phosphorylated at threonine 181 over time predicted general cognitive decline in females only. Change in plasma tau phosphorylated at threonine 181 remained a significant predictor of g factor decline when taking into account Alzheimer's disease polygenic risk score, indicating that the increase of blood tau phosphorylated at threonine 181 in this cohort was not only due to incipient Alzheimer's disease. Tau phosphorylated at threonine 181 was observed in synapses and astrocytes in both healthy ageing and Alzheimer's disease brain. We observed that a significantly higher proportion of synapses contain tau phosphorylated at threonine 181 in Alzheimer's disease relative to aged controls. Aged controls with pre-morbid lifetime cognitive resilience had significantly more tau phosphorylated at threonine 181 in fibrillary acidic protein-positive astrocytes than those with pre-morbid lifetime cognitive decline. Further, tau phosphorylated at threonine 181 was found in dystrophic neurites around plaques and in some neurofibrillary tangles. The presence of tau phosphorylated at threonine 181 in plaque-associated dystrophies may be a source of leakage of tau out of neurons that eventually enters the blood. Together, these data indicate that plasma tau phosphorylated at threonine 181, neurofilament light and fibrillary acidic protein may be useful biomarkers of age-related cognitive decline, and that efficient clearance of tau phosphorylated at threonine 181 by astrocytes may promote cognitive resilience.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain.

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Life-course neighbourhood deprivation and brain structure in older adults: The Lothian Birth Cohort 1936.

Neighbourhood disadvantage may be associated with brain health but the importance at different stages of the life course is poorly understood. Utilizing the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and regional neuroimaging measures at age 73. We found that residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (ß=-0.06; SE=0.02; n=390) and grey matter volume (ß=-0.11; SE=0.03; n=390), thinner cortex (ß=-0.15; SE=0.06; n=379), and lower general white matter fractional anisotropy (ß=-0.19; SE=0.06; n=388). Regional analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower occupational social classes, the brain-neighbourhood associations were stronger, with the impact of neighbourhood deprivation accumulating across the life course. Our findings suggest that living in deprived neighbourhoods is associated with adverse brain morphologies, with occupational social class adding to the vulnerability.

Unraveling Neuro-Proteogenomic Landscape and Therapeutic Implications for Human Behaviors and Psychiatric Disorders.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.