There is consistent evidence for a positive association between education and a wide range of cognitive abilities. In particular, spatial abilities have been shown to be strongly related to academic achievement. However, studying this association is complex as both education and spatial abilities are modulated by multivariate sociodemographic factors, likely to vary across countries. Most previous studies relied on small sample sizes or were restricted to a limited number of countries, thus were unable to control for these covariates. To overcome these limitations, we used a spatial navigation task embedded in a mobile video game. We quantified the wayfinding ability of 397,162 people across 38 countries and showed that on average, education level was positively associated with wayfinding ability. This difference was stronger in older participants and increased with task difficulty. However, the effect of education was different across countries, from near-zero and non-significant in India (Bayes' factor = 0.08, Hedge's g = -0.03, 95%CI = [-0.15, 0.08]), to modest and significant in Romania (Bayes' factor = 345.44, Hedge's g = 0.15, 95%CI = [0.08, 0.22]). We did not find any relationship between the education effect size of countries and economic indicators such as GDP per capita. Using the 1972 reform increasing the minimum school leaving age in the UK as a natural experiment, we used a regression discontinuity design to show that education has a causal effect on wayfinding ability.

We attempted to replicate previous findings of rising heritability and declining shared environmental influence on intelligence across development using the Twins Early Development Study—a large, longitudinal sample of British twins. Two longitudinal twin models (a Cholesky and Simplex model) were fitted to 9 waves of cognitive test data for 10,535 twin pairs. Heritability rose significantly from 24% at age 2 to 48-52% at age 16. Concurrently, shared environmental influence declined significantly from 65% to 9-13%. Moreover, nonshared environmental influences also increased significantly, rising from 11% to 39-40%. In early childhood, developmental gains in heritability were explained by the emergence of new genetic influences. In later childhood and adolescence, gains in heritability were explained by the continued emergence of new genetic influences but also the amplification of pre-existing genetic influences. This evidence for genetic amplification suggests that active gene-environment correlation is partly responsible for gains in heritability later in development. Shared environmental influence declined across development because pre-existing environmental influences decayed between waves faster than new environmental influences accumulated. Lastly, the rise in nonshared environmental influence was principally explained by age-specific environmental effects that increased in magnitude across development.

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.

The rapidly evolving field of closed loop technology promises to revolutionise the management of type 1 diabetes.1 Several systems are now commercially available in the United Kingdom and are supported by a robust evidence base.2,3 Currently only 3.8% (190/4952) of adults with type 1 diabetes within our health board (NHS Lothian) are on hybrid closed loop (HCL) systems. Of current HCL users, only 6.3% belong to the most deprived Scottish Index of Multiple Deprivation (SIMD) quintile whilst 37.4% belong to the most affluent quintile. 70% of HCL users are female (explained, in part, by use commenced in pregnancy). To help gauge interest in diabetes technologies (and HCL in particular), we sent a questionnaire to all 2210 people with type 1 diabetes attending Royal Infirmary of Edinburgh clinics (the largest diabetes clinic within NHS Lothian). As the primary purpose of this exercise was service planning and delivery, ethical approval was not required. There were 782 respondents (35.4% response rate). Responders were older (51 years [36– 63] vs. 43 [29– 58], p < 0.001), had lower HbA1c (60 mmol/mol [52– 69] vs. 66 [57– 79], p < 0.001), were more likely to be CSII users (30.5% vs. 16.3%, p < 0.001) and were more affluent (SIMD 5 response 43.5% vs. SIMD 1 response 24.9%, p < 0.001). Of those not currently using continuous subcutaneous insulin infusion (CSII), 48.2% (274/568) expressed an interest in using this technology. Of those not currently using HCL, 66.5% (485/729) expressed an interest in HCL use. Lower age was strongly associated with interest in HCL (47 years [33– 59] vs. 58 [46– 68], p < 0.001). Current CSII users and Freestyle Libre users were significantly more likely to express an interest in using HCL as were those with positive anxiety (GAD2) and depression (PHQ2) scores (Table 1). The only significant difference in CGM

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

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.

Background Understanding the neurodegenerative mechanisms underlying cognitive declines in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates biological pathways shared between brain morphometry, ageing, and cognitive ability. Methods We develop Genomic Principal Components Analysis (genomic PCA) to model general dimensions of variance in brain morphometry within brain networks at the level of their underlying genetic architecture. With genomic PCA we extract genetic principal components (PCs) that index global dimensions of genetic variance across phenotypes (unlike ancestral PCs that index genetic similarity between participants). Genomic PCA is applied to genome-wide association data for 83 brain regions which we calculated in 36,778 participants of the UK Biobank cohort. Using linkage disequilibrium score regression, we estimate genetic overlap between brain networks and indices of cognitive ability and brain ageing. Results A genomic principal component (PC) representing brain-wide dimensions of shared genetic architecture accounted for 40% of the genetic variance across 83 individual brain regions. Genomic PCs corresponding to canonical brain networks accounted for 47-65% of the genetic variance in the corresponding brain regions. These genomic PCs were negatively associated with brain age ( r g = −0.34). Loadings of individual brain regions on the whole-brain genomic PC corresponded to sensitivity of a corresponding region to age ( r = - 0.27). We identified positive genetic associations between genomic PCs of brain morphometry and general cognitive ability ( r g = 0.17-0.21). Conclusion These results demonstrate substantial shared genetic etiology between connectome-wide dimensions of brain morphometry, ageing, and cognitive ability, which will help guide investigations into risk factors and potential interventions of ageing-related cognitive decline.

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, Ju spacing diaeresis lich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcel-lation 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 similar to 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 reproduc-ibility in ROI-based studies. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ObjectiveDepression 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. MethodData 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. ResultsDepressive 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. ConclusionsOur 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.

Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses---where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.

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.

We highlight a particular type of publication bias unique to secondary data analysis, and particularly common in epidemiologic research. We begin by setting a reminder of the scientific method of inquiry, and—by analogy with the movement for full transparency in clinical trials—present arguments for reporting all results of secondary data analysis. We then describe the ways in which data dredging—a subtle form a p-hacking—can lead to a distorted scientific literature; we highlight prior research that has empirically demonstrated this. We conclude by arguing that in order to combat this bias, epidemiologists should move toward preregistering analyses, and epidemiologic journals should encourage this through the implementation of Registered Reports. Finally, we respond to some common criticisms of preregistration.

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.

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 conflate information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the eighth decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1376 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.

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.

Psychological scientists have attempted to speak to the COVID-19 crisis. Psychology research on COVID-19, we argue, is unsuitable for making policy decisions. We offer a taxonomy that lets our science advance in Evidence Readiness Levels to be suitable for policy; we caution practitioners to take extreme care translating our findings to applications.

Identifying predictors of cognitive ability and brain structure in later life is an important step towards understanding the mechanisms leading to cognitive decline and dementia. This study used ultra-performance liquid chromatography mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) to measure targeted and untargeted metabolites, mainly lipids and lipoproteins, in ∼600 members of the Lothian Birth Cohort 1936 (LBC1936) at aged ∼73 years. Penalized regression models (LASSO) were then used to identify sets of metabolites that predict variation in general cognitive ability and structural brain variables. UPLC-MS-POS measured lipids, together predicted 19% of the variance in total brain volume and 17% of the variance in both grey matter and normal appearing white matter volumes. Multiple subclasses of lipids were included in the predictor, but the best performing lipid was the sphingomyelin SM(d18:2/14:0) which occurred in 100% of iterations of all three significant models. No metabolite set predicted cognitive ability, or white matter hyperintensities or connectivity. Future studies should concentrate on identifying specific lipids as potential cognitive and brain-structural biomarkers in older individuals.### Competing Interest StatementThe authors have declared no competing interest.

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.

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.

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 × 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 ≤ |β| ≤ 0.406) than the consistency-based approach which retained only 30% of connections (0.140 ≤ |β| ≤ 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 β ≤ |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ≤ |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.