Tractable limitations of current polygenic scores do not excuse genetically confounded social science.

Burt's critique of using polygenic scores in social science conflates the "scientific costs" of sociogenomics with "sociopolitical and ethical" concerns. Furthermore, she paradoxically enlists recent advances in controlling for environmental confounding to argue such confounding is scientifically "intractable." Disinterested social scientists should support ongoing efforts to improve this technology rather than obstructing progress and excusing genetically confounded research.

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.

General dimensions of human brain morphometry inferred from genome-wide association data.

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

A quantified comparison of cortical atlases on the basis of trait morphometricity.

BACKGROUND: Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences. METHODS: Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas. RESULTS: Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated. DISCUSSION: Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

Socioeconomic deprivation is associated with reduced efficacy of an insulin adjustment education program for people with type 1 diabetes.

BACKGROUND: The Dose Adjustment for Normal Eating (DAFNE) course teaches insulin dose adjustment to match dietary carbohydrates and improve glycaemic control in participants with type 1 diabetes mellitus (T1DM). We investigated the association between socioeconomic deprivation and reduction in HbA1c as a marker of sustained glycaemic control, after attending DAFNE education. METHODS: This retrospective observational study identified adults with T1DM who attended DAFNE training in NHS Lothian, South East Scotland. We extracted age, sex, postcode-based Scottish Index of Multiple Deprivation (SIMD) quintiles and annual HbA1c measurements available four years before and after course attendance. We calculated mean HbA1c before (baseline) and after attendance at DAFNE, across four annual measurements. Change in mean HbA1c (mmol/mol) was categorised into three groups: decrease (≥ - 2.5), no change (<±2.5), increase (≥ + 2.5). We used multivariable ordinal logistic regression, with baseline mean HbA1c as a covariate, to investigate the association of SIMD quintile with reduction in mean HbA1c. RESULTS: 335 participants were included. Age and sex distribution were similar across SIMD quintiles (Mean age = 45, range 21-91, 59% women). Lower SIMD quintiles (greater deprivation) had higher baseline mean HbA1c (SIMD 1: 76.0, SIMD 5: 69.0). Higher SIMD quintiles (lower deprivation) were associated with lower odds of no change/increase in mean HbA1c (SIMD 5, odds ratio = 0.25, 95% confidence interval 0.10, 0.58, p = 0.001, multivariable analysis). CONCLUSION: Socioeconomic deprivation was associated with higher baseline mean HbA1c and lower reduction in HbA1c following DAFNE education. Future research could explore causes and how best to support participants from deprived areas. PREVIOUS SUBMISSIONS: This work has not been previously submitted to a journal. This work was presented as a poster at The ABCD Conference 2021 and the abstract (of no more than 300 words) from the meeting has been published: Innes CWD, Henshall DE, Wilson B, Poon M, Morley SD, Ritchie SA. Socioeconomic deprivation is associated with reduced efficacy of an insulin adjustment education programme for people with type 1 diabetes. Br J Diabetes. 2021; 21: 293-296.

The aetiological relationship between depressive symptoms and health-related quality of life: A population-based twin study in Sri Lanka.

OBJECTIVE: Depression often co-occurs with poor health-related quality of life (HRQL). Twin studies report genetic and individual-level environmental underpinnings in the aetiology of both depression and HRQL, but there is limited twin research exploring this association further. There is also little evidence on sex differences and non-Western populations are underrepresented. In this paper we explored the phenotypic and aetiological relationship between depressive symptoms and HRQL and possible sex differences in a low-middle-income Sri Lankan population. METHOD: Data for 3,948 participants came from the Colombo Twin and Singleton Follow-up Study (CoTaSS-2). Using self-report measures of depressive symptoms and HRQL, we conducted univariate and bivariate sex-limitation twin analyses. RESULTS: Depressive symptoms showed moderate genetic (33%) and strong nonshared environmental influences (67%). Nonshared environment accounted for the majority of variance in all the subscales of HRQL (ranging from 68 to 93%), alongside small genetic influences (ranging from 0 to 23%) and shared environmental influences (ranging from 0 to 28%). Genetic influences were significant for emotional wellbeing (23%). Shared environmental influences were significant for four out of the eight HRQL variables (ranging from 22-28%), and they were more prominent in females than males. Depressive symptoms were significantly associated with lower HRQL scores. These correlations were mostly explained by overlapping nonshared environmental effects. For traits related to emotional functioning, we also detected substantial overlapping genetic influences with depressive symptoms. CONCLUSIONS: Our study confirmed previous findings of a negative association between depressive symptoms and HRQL. However, some of the aetiological factors of HRQL differed from Western studies, particularly regarding the effects of shared environment. Our findings highlight the importance of cross-cultural research in understanding associations between psychological wellbeing and HRQL.

Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences.

Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier to finding more of this 'missing heritability' is assessment--the use of diverse measures across GWA studies as well as time and the cost of assessment. In a series of four studies, we created a 15-min (40-item), online, gamified measure of g that is highly reliable (alpha = 0.78; two-week test-retest reliability = 0.88), psychometrically valid and scalable; we called this new measure Pathfinder. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields reliable verbal and nonverbal scores, correlated substantially with standard measures of g collected at previous ages (r ranging from 0.42 at age 7 to 0.57 at age 16). Pathfinder showed substantial twin heritability (0.57, 95% CIs = 0.43, 0.68) and SNP heritability (0.37, 95% CIs = 0.04, 0.70). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical, and behavioural sciences.

Etiological pathways of depressive and anxiety symptoms linked to personality traits: A genetically-informative longitudinal study.

BACKGROUND: The comorbidity of depression and anxiety is associated with an increased risk of prolonged adverse mental health status. However, little is currently known about their genetic and environmental influences that help to explain both the comorbidity and distinctiveness. Using longitudinal twin data, the present study investigated both the overlapping and distinct relationships between depression and anxiety viewed from the perspective of Gray's Reinforcement Sensitivity Theory (RST): two personality traits of the Behavioral Inhibition and Activation Systems (BIS and BAS). METHODS: A total of 422 twin pairs (298 monozygotic and 124 dizygotic pairs) participated by completing a personality questionnaire at wave 1, and mood symptoms questionnaires at wave 2. The waves were on average 2.23 years apart. RESULTS: Multivariate Cholesky decomposition indicated that the genetic variance of the personality traits (BIS and BAS) explained all of the genetic variance in depressive and anxiety symptoms. Additionally, genetic factors related to the BIS positively explained depressive and anxiety symptoms, whereas genetic factors related to the BAS negatively explained only depressive symptoms. LIMITATIONS: Limitations include shorter time interval and the reliance on self-reported data. CONCLUSIONS: The present study provided evidence explaining the overlap and differentiation of depressive and anxiety symptoms by using data on personality traits in a longitudinal, genetically-informative design. The findings suggested the personality traits from Gray's RST model played an important role in the prediction, and clarified the description, of both depressive and anxiety symptoms.

Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines.

BACKGROUND: Aging-related cognitive decline is a primary risk factor for Alzheimer's disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. METHODS: Using structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45-78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability. RESULTS: In the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network's small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. CONCLUSIONS: These results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

Impact of routine clinic measurement of serum C-peptide in people with a clinician-diagnosis of type 1 diabetes.

AIMS/HYPOTHESIS: The aim of this study was to determine the impact of the routine use of serum C-peptide in an out-patient clinic setting on individuals with a clinician-diagnosis of type 1 diabetes. METHODS: In this single-centre study, individuals with type 1 diabetes of at least 3 years duration were offered random serum C-peptide testing at routine clinic review. A C-peptide ≥200 pmol/L prompted further evaluation of the individual using a diagnostic algorithm that included measurement of islet cell antibodies and genetic testing. Where appropriate, a trial of anti-diabetic co-therapies was considered. RESULTS: Serum C-peptide testing was performed in 859 individuals (90% of the eligible cohort), of whom 114 (13.2%) had C-peptide ≥200 pmol/L. The cause of diabetes was reclassified in 58 individuals (6.8% of the tested cohort). The majority of reclassifications were to type 2 diabetes (44 individuals; 5.1%), with a smaller proportion of monogenic diabetes (14 individuals; 1.6%). Overall, 13 individuals (1.5%) successfully discontinued insulin, while a further 16 individuals (1.9%) had improved glycaemic control following the addition of co-therapies. The estimated total cost of the testing programme was £23,262 (~€26,053), that is, £27 (~€30) per individual tested. In current terms, the cost of prior insulin therapy in the individuals with monogenic diabetes who successfully stopped insulin was approximately £57,000 (~€64,000). CONCLUSIONS/INTERPRETATION: Serum C-peptide testing can easily be incorporated into an out-patient clinic setting and could be a cost-effective intervention. C-peptide testing should be strongly considered in individuals with a clinician-diagnosis of type 1 diabetes of at least 3 years duration.

HbA1c response and hospital admissions following commencement of flash glucose monitoring in adults with type 1 diabetes.

INTRODUCTION: Our aim was to assess the effect of introducing flash monitoring in adults with type 1 diabetes with respect to change in hemoglobin A1c (HbA1c) and frequency of hospital admissions. RESEARCH DESIGN AND METHODS: Prospective observational study of adults with type 1 diabetes in our center, in whom a prescription for a flash monitoring sensor was collected. Primary outcome was change in HbA1c between 2016 and after flash monitoring. Rates of hospital admission were compared between the first year after flash monitoring and the corresponding 12-month period 2 years earlier. RESULTS: Approximately half of all adults with type 1 diabetes, attending our center, collected prescriptions for flash monitoring sensors (n=2216). Median fall in HbA1c was -1 (-0.1) mmol/mol (%) (p<0.001) and was greatest in those with baseline HbA1c >75 (9.0) mmol/mol (%): -10 (-0.9) mmol/mol (%), p<0.001. 43% of those with a baseline HbA1c >53 mmol/mol (7%) experienced a ≥5 mmol/mol (0.5%) fall in HbA1c. In addition to higher HbA1c, early commencement within 1 month of NHS-funded flash monitoring (p<0.001), and male gender (p=0.013) were associated with a fall in HbA1c of ≥5 (0.5) mmol/mol (%). Socioeconomic deprivation (p=0.009) and collecting fewer than 2 sensors per month (p=0.002) were associated with lack of response. Overall, hospital admissions did not change but an increase in admissions for hypoglycemia was observed (1.1% vs 0.3%, p=0.026). CONCLUSIONS: Flash monitoring is associated with reduction in HbA1c in individuals with HbA1c >58 mmol/mol. Numerous clinical features are independently associated with HbA1c response. An increase in hypoglycemia admissions occurred following flash monitoring.

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.

Cognitive ability and education: How behavioural genetic research has advanced our knowledge and understanding of their association.

Cognitive ability and educational success predict positive outcomes across the lifespan, from higher earnings to better health and longevity. The shared positive outcomes associated with cognitive ability and education are emblematic of the strong interconnections between them. Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. The current review evaluates the contribution of decades of behavioural genetic research to our knowledge and understanding of the biological and environmental basis of the association between cognitive ability and education. The evidence reviewed points to a strong genetic basis in their association, observed from middle childhood to old age, which is amplified by environmental experiences. In addition, the strong stability and heritability of educational success are not driven entirely by cognitive ability. This highlights the contribution of other educationally relevant noncognitive characteristics. Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.

Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds.

Fluctuating body asymmetry is theorized to indicate developmental instability, and to have small positive associations with low socioeconomic status (SES). Previous studies have reported small negative associations between fluctuating body asymmetry and cognitive functioning, but relationships between fluctuating brain asymmetry and cognitive functioning remain unclear. The present study investigated the association between general intelligence (a latent factor derived from a factor analysis on 13 cognitive tests) and the fluctuating asymmetry of four structural measures of brain hemispheric asymmetry: cortical surface area, cortical volume, cortical thickness, and white matter fractional anisotropy. The sample comprised members of the Lothian Birth Cohort 1936 (LBC1936, N = 636, mean age = 72.9 years). Two methods were used to calculate structural hemispheric asymmetry: in the first method, regions contributed equally to the overall asymmetry score; in the second method, regions contributed proportionally to their size. When regions contributed equally, cortical thickness asymmetry was negatively associated with general intelligence (ß = -0.18,p < .001). There was no association between cortical thickness asymmetry and childhood SES, suggesting that other mechanisms are involved in the thickness asymmetry-intelligence association. Across all cortical metrics, asymmetry of regions identified by the parieto-frontal integration theory (P-FIT) was not more strongly associated with general intelligence than non-P-FIT asymmetry. When regions contributed proportionally, there were no associations between general intelligence and any of the asymmetry measures. The implications of these findings, and of different methods of calculating structural hemispheric asymmetry, are discussed.

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 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed 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: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 â€‹≤ â€‹|ß| â€‹≤ â€‹0.409); and 2) 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 â€‹≤ â€‹|ß| â€‹≤ â€‹0.406) than the consistency-based approach which retained only 30% of connections (0.140 â€‹≤ â€‹|ß| â€‹≤ â€‹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 â€‹= â€‹0.84), when matched at 70% network sparsity. 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 ߠ​≤ â€‹|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean ߠ​≤ â€‹|0.219|, p â€‹< â€‹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.

Polygenic predictors of age-related decline in cognitive ability.

Polygenic scores can be used to distil the knowledge gained in genome-wide association studies for prediction of health, lifestyle, and psychological factors in independent samples. In this preregistered study, we used fourteen polygenic scores to predict variation in cognitive ability level at age 70, and cognitive change from age 70 to age 79, in the longitudinal Lothian Birth Cohort 1936 study. The polygenic scores were created for phenotypes that have been suggested as risk or protective factors for cognitive ageing. Cognitive abilities within older age were indexed using a latent general factor estimated from thirteen varied cognitive tests taken at four waves, each three years apart (initial n = 1091 age 70; final n = 550 age 79). The general factor indexed over two-thirds of the variance in longitudinal cognitive change. We ran additional analyses using an age-11 intelligence test to index cognitive change from age 11 to age 70. Several polygenic scores were associated with the level of cognitive ability at age-70 baseline (range of standardized ß-values = -0.178 to 0.302), and the polygenic score for education was associated with cognitive change from childhood to age 70 (standardized ß = 0.100). No polygenic scores were statistically significantly associated with variation in cognitive change between ages 70 and 79, and effect sizes were small. However, APOE e4 status made a significant prediction of the rate of cognitive decline from age 70 to 79 (standardized ß = -0.319 for carriers vs. non-carriers). The results suggest that the predictive validity for cognitive ageing of polygenic scores derived from genome-wide association study summary statistics is not yet on a par with APOE e4, a better-established predictor.

Physical frailty and decline in general and specific cognitive abilities: the Lothian Birth Cohort 1936.

BACKGROUND: Physical frailty is associated with many adverse outcomes including disability, chronic disease, hospitalisation, institutionalisation and death. It is unclear what impact it might have on the rate of normal cognitive ageing. We investigated whether physical frailty was related to initial level of, and change in, cognitive abilities from age 70 to 79 years. METHOD: Participants were 950 members of the Lothian Birth Cohort 1936. Physical frailty was assessed at age 70 years using the Fried criteria. Cognitive function was assessed at ages 70, 73, 76 and 79 years. We used linear regression to examine cross-sectional and prospective associations between physical frailty status at age 70 years and factor score estimates for baseline level of and change in four cognitive domains (visuospatial ability, memory, processing speed and crystallised ability) and in general cognitive ability. RESULTS: Physical frailty, but not prefrailty, was associated with lower baseline levels of visuospatial ability, memory, processing speed and general cognitive ability after control for age, sex, education, depressive symptoms, smoking and number of chronic illnesses. Physical frailty was associated with greater decline in each cognitive domain: age-adjusted and sex-adjusted standardised regression coefficients (95% CIs) were: -0.45 (-0.70 to -0.20) for visuospatial ability, -0.32 (-0.56 to -0.07) for memory, -0.47 (-0.72 to -0.22) for processing speed, -0.43 (-0.68 to -0.18) for crystallised ability and -0.45 (-0.70 to -0.21) for general cognitive ability. These associations were only slightly attenuated after additional control for other covariates. CONCLUSION: Physical frailty may be an important indicator of age-related decline across multiple cognitive domains.