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Cox, S. R.

Publications and source records attributed to Cox, S. R..

At least 37 records · Page 2Linked to original sources

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

The prevalence of clonal haematopoiesis of indeterminate potential (CHIP) in healthy individuals increases rapidly from age 60 onwards and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in stem cells that are also drivers of myeloid malignancies. Since mutations in stem cells often drive leukaemia, we hypothesised that stem cell fitness substantially contributes to transformation from CHIP to leukaemia. Stem cell fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. It is currently unknown whether mutations in different CHIP genes lead to distinct fitness advantages that could form the basis for patient stratification. We set out to quantify the fitness effects of CHIP drivers over a 12 year timespan in older age, using longitudinal error-corrected sequencing data. We developed a new method based on drift-induced fluctuation (DIF) filtering to extract fitness effects from longitudinal data, and thus quantify the growth potential of variants within each individual. Our approach discriminates naturally drifting populations of cells and faster growing clones, while taking into account individual mutational context. We show that gene-specific fitness differences can outweigh inter-individual variation and therefore could form the basis for personalised clinical management.

cell biology↗

DNA methylation proxies for 16 plasma proteins predict the incidence of 7 leading causes of morbidity

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore - disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

genomics↗

A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens

Inflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain, and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post-mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers ( epigenetic clocks), and two inflammatory biomarkers (DNA methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent correlations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus ({beta} range=0.83-1.14, p[≤]0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus ({beta}=1.32, p=5x10-4); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions ({beta}=0.40, p=0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

neuroscience↗

Birth weight is associated with brain tissue volumes seven decades later, but not with age-associated changes to brain structure

Birth weight, an indicator of fetal growth, is associated with cognitive outcomes in early life and risk of metabolic and cardiovascular disease across the life course. Cognitive ability in early life is predictive of cognitive ability in later life. Brain health in older age, defined by MRI features, is associated with cognitive performance. However, little is known about how variation in normal birth weight impacts on brain structure in later life. In a community dwelling cohort of participants in their early seventies we tested the hypothesis that birthweight is associated with the following MRI features: total brain (TB), grey matter (GM) and normal appearing white matter (NAWM) volumes; whiter matter hyperintensity (WMH) volume; a general factor of fractional anisotropy (gFA) and peak width skeletonised mean diffusivity (PSMD) across the white matter skeleton. We also investigated the associations of birthweight with cortical surface area, volume and thickness. Birthweight was positively associated with TB, GM and NAWM volumes in later life ({beta} [≥] 0.194), and with regional cortical surface area but not gFA, PSMD, WMH volume, or cortical volume or thickness. These positive relationships appear to be explained by larger intracranial volume rather than by age-related tissue atrophy, and are independent of body height and weight in adulthood. This suggests that larger birthweight is linked to increased brain tissue reserve in older life, rather than a resilience to age-related changes in brain structure, such as tissue atrophy or WMH volume. Significance StatementCognitive brain ageing carries a high personal, societal and financial cost and understanding its developmental origins is important for identifying possible preventative strategies. In a sample of older participants from the Lothian Birth Cohort 1936 we were able to explore the neurobiological correlates of birth weight, which is indicative of the fetal experience. We find that higher birth weight is related to larger brain tissue volumes in later life, but does not modify the trajectory of age-related change. This suggests that early life growth confers preserved differentiation, rather than differential preservation with regards to brain reserve. That these effects are detectable into later life indicates that this variable may be valuable biomarker in the epidemiology of ageing.

neuroscience↗

Variation in VKORC1 is associated with vascular dementia

Genetic variation in VKORC1 is associated with differences in the coagulation of blood and consequentially with sensitivity to the drug warfarin. Variation in VKORC1 has also been linked to parental dementia. However, it is unclear whether the relationship persists for the diagnosis in patients themselves, whether the association holds only for certain forms of dementia, and if those taking warfarin are at greater risk. Here, we use data from 211,423 participants from UK Biobank to examine the relationship between VKORC1, risk of dementia, and the interplay with warfarin use. We find that the T-allele in rs9923231 confers a greater risk for vascular dementia (OR=1.28, p=0.0069), but not for general dementia (OR=1.04, p=0.21) or Alzheimer dementia (OR=1.05, p=0.35), and that the risk of vascular dementia is not affected by warfarin use in carriers of the T-allele. Our study reports for the first time an association between rs9923231 and vascular dementia, but further research is warranted to explore potential mechanisms and specify the relationship between rs9923231 and features of vascular dementia.

genetics↗

Generation of twenty four induced pluripotent stem cell lines from twenty four members of the Lothian Birth Cohort 1936

Cognitive decline is among the most feared aspects of ageing. We have generated induced pluripotent stem cells (iPSCs) from 24 people from the Lothian Birth Cohort 1936, whose cognitive ability was tested in childhood and in older age. Peripheral blood mononuclear cells (PBMCs) were reprogrammed using non-integrating oriP/EBNA1 backbone plasmids expressing six iPSC reprogramming factors (OCT3/4 (POU5F1), SOX2, KLF4, L-Myc, shp53, Lin28, SV40LT). All lines demonstrated STR matched karyotype and pluripotency was validated by multiple methods. These iPSC lines are a valuable resource to study molecular mechanisms underlying individual differences in cognitive ageing and resilience to age-related neurodegenerative diseases.

cell biology↗

Three major dimensions of human brain cortical ageing in relation to cognitive decline across the 8th decade of life.

Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflates information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the 8th decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1,376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions, that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.

neuroscience↗

Hierarchical complexity of the macro-scale neonatal brain

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period; assess correspondences with hierarchical complexity in adulthood; and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers, and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

neuroscience↗

Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction

Educational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However little is known about the genetic architecture of these "non-cognitive" contributions to EA. Here, we use Genomic Structural Equation Modelling and results of prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.

genetics↗

Enhanced prediction of cognitive function using aging-sensitive networks within the human structural connectome

Using raw structural and diffusion brain MRI data from the UK Biobank (UKB; N = 3,155, ages 45-75 years) and the Lothian Birth Cohort 1936 (LBC1936; N = 534, all age 73 years), we examine aging of regional grey matter volumes (nodes) and white matter structural connectivity (edges) within networks-of-interest in the human brain connectome. In UKB, the magnitude of age-differences in individual node volumes and edge weights corresponds closely with their loadings on their respective principal components of connectome-wide integrity (|rnodes| = 0.459; |redges| = 0.595). In LBC1936, connectome-wide and subnetwork-specific composite indices of node integrity were predictive of processing speed, visuospatial ability, and memory, whereas composite indices of edge integrity were associated specifically with processing speed. Childhood IQ was associated with greater node integrity at age 73. However, node and edge integrity remained associated with age 73 cognitive function after controlling for childhood IQ. Adult connectome integrity is therefore both a marker of early-life cognitive function and a substrate of late-life cognitive aging.

neuroscience↗

Psychotic-like experiences, polygenic risk scores for schizophrenia and structural properties of the salience, default mode and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank) including information on PLEs, polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. These results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and predisposes to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

neuroscience↗

An epigenetic predictor of death captures multi-modal measures of brain health

Individuals of the same chronological age exhibit disparate rates of biological ageing. Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm) - a commonly studied epigenetic mechanism. A novel epigenetic clock, termed DNAm GrimAge has outperformed its predecessors in predicting the risk of mortality as well as a number of age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n=709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over twelve years of follow-up (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 x 10-16). Higher DNAm GrimAge was associated with lower age 11 IQ ({beta}=-0.11), lower age 73 general cognitive ability ({beta}=-0.18), decreased brain volume ({beta}=-0.25) and increased brain white matter hyperintensities ({beta}=0.17). Sixty-eight of 137 health- and brain-related phenotypes tested were significantly associated with DNAm GrimAge. Adjusting all models for childhood cognitive ability attenuated to non-significance a small number of associations (12/68 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge cross-sectionally associates with lower cognitive ability and brain vascular lesions in older age, independently of early life cognitive ability. Thus, this epigenetic predictor of mortality is also associated with multiple different measures of brain health and may aid in the prediction of age-related cognitive decline.

neuroscience↗

Neurology-related protein biomarkers are associated with general fluid cognitive ability and brain volume in older age

Identifying the biological correlates of late life cognitive function is important if we are to ascertain biomarkers for, and develop treatments to help reduce, age-related cognitive decline. This study investigated the associations between plasma levels of 91 neurology-related proteins (Olink(R) Proteomics) and general fluid cognitive ability in the Lothian Birth Cohort 1936 (LBC1936, N=798), the Lothian Birth Cohort 1921 (LBC1921, N=165), and the INTERVAL BioResource, (N=4,451). In LBC1936, we also examined mediation of protein-cognitive ability associations by MRI-derived indices of brain structure. In the LBC1936, 22 of the proteins and the first principal component (PC) created from a PC analysis of the 91 proteins, were associated with general fluid cognitive ability ({beta} between -0.11 and -0.17, p<0.0029). Total brain volume partially mediated the association between 10 of these proteins and general fluid cognitive ability. Effect sizes for the 22 proteins, although smaller, were all in the same direction as in LBC1936 in an age-matched subsample of INTERVAL. Similar effect sizes were found for the majority of these 22 proteins in the older LBC1921. The associations were not replicated in a younger subset of INTERVAL. In conclusion, we identified plasma levels of a number of neurology-related proteins that were associated with general fluid cognitive ability in later life, some of which were mediated by brain volume.

neuroscience↗

Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures

BackgroundSubstantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ.\n\nMethodsThe present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2,370 male and 2,574 female UK Biobank participants.\n\nResultsThere was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness ({beta}=.191, q=.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases ({beta}=.098, p=.026), compared to a negative association among controls ({beta}=-.087, p=.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons.\n\nConclusionsOur significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.

neuroscience↗

The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3,153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44--77 years), we constructed 85 x 85 node whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 [&le;] |{beta}| [&le;] 0.406) than the consistency-based approach which retained only 30% of connections (0.140 [&le;] |{beta}| [&le;] 0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC = 0.84) when matched at a plausible level of network sparsity (70%). Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean {beta} [&le;] |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean {beta} [&le;] |0.219|, p < 0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

neuroscience↗

White matter microstructure and its relation to longitudinal measures of depressive symptoms in mid-late life

BackgroundStudies of white matter microstructure in depression typically show alterations in depressed individuals, but they are frequently limited by small sample sizes and the absence of longitudinal measures of depressive symptoms. Depressive symptoms are however dynamic, and understanding the neurobiology of different trajectories could have important clinical implications.\n\nMethodsWe examined associations between current and longitudinal measures of depressive symptoms and white matter microstructure (Fractional Anisotropy, FA; Mean Diffusivity; MD) in the UK Biobank Imaging study. Depressive symptoms were assessed on 2-4 occasions over 5.9 to 10.7 years (on N=18,959 individuals on at least two occasions, N=4,444 on four occasions) from which we derived four measures of depressive symptomatology; (i) cross-sectional measure at the time of scan (imaging was conducted at a single time point), and three longitudinal measures, (ii) trajectory (iii) mean and (iv) intra-subject variance over time.\n\nResultsDecreased white matter microstructure in the anterior thalamic radiation demonstrated significant associations across all four measures of depressive symptoms (for MD: {beta}=0.020 to 0.029, pcorr<0.030). The greatest effect sizes were however seen between decreasing white matter integrity and increasing longitudinal progression of symptoms (for MD: {beta}=0.030 to 0.040, pcorr<0.049). Cross-sectional symptom severity was particularly associated with decreased white matter integrity in association fibres and thalamic radiations (MD: {beta}=0.015 to 0.039, pcorr<0.041). While greater mean and within subject variance of depressive symptoms were mainly associated with decreased white matter microstructure within projection fibres (MD: {beta}=0.019 to 0.029, pcorr<0.044).\n\nConclusionsThese findings indicate shared and differential neurobiological associations with severity, course and intra-subject variability of depressive symptoms. This enriches our understanding of the neurobiology underlying dynamic features of the disorder.

neuroscience↗

Brain imaging correlates of general intelligence in UK Biobank

The associations between indices of brain structure and measured intelligence are not clear. In part, this is because the evidence to date comes from mostly small and heterogenous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,363 participants providing both brain MRI and cognitive data, and a minimum N = 7318 providing the MRI data alongside a complete four-test battery. Participants age range was 44-81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was extracted from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age-and sex-corrected) total brain volume and a latent factor of general intelligence is r = 0.275, 95% C.I. = [0.252, 0.299]. A model that incorporated multiple global measures of grey and white matter macro-and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.4% and 5.9%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor.

neuroscience↗

Polygenic architecture of human neuroanatomical diversity

We analysed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26,000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r[~]0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes ([~]51%) captured [~]1.5 times more genetic variance than the rest; and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF<5% captured <1/4th of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG[~]0.45. Genetic correlations were similar to phenotypic and environmental correlations, however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available at https://github.com/neuroanatomy/genomic-architecture.

neuroscience↗