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Some people appear to be smarter than others, but how do we measure intelligence? Why do some people have better thinking powers than others? What does intelligence predict about people's health and social outcomes? This \"Very Short Introduction\" uses the best, large-scale psychological data to answer important questions about intelligence, such as how environment, genes, brain structure, gender, and age affect people's thinking skills. It asks whether intelligence increased over the 20th century. Ian Deary also considers the new field of cognitive epidemiology, which discovers links between higher intelligence and better health, lower rates of illness, and longer life. -- From publisher's description.

UK Biobank is a health resource with data from over 500,000 adults. The cognitive assessment in UK Biobank is brief and bespoke, and is administered without supervision on a touchscreen computer. Psychometric information on the UK Biobank cognitive tests are limited. Despite the non-standard nature of these tests and the limited psychometric information, the UK Biobank cognitive data have been used in numerous scientific publications. The present study examined the validity and short-term test-retest reliability of the UK Biobank cognitive tests. A sample of 160 participants (mean age = 62.59, SD = 10.24) was recruited who completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests ('reference tests'). Fifty-two participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and reference tests. Two measures of general cognitive ability were created by entering scores on the UK Biobank cognitive tests, and scores on the reference tests, respectively, into separate principal component analyses and saving scores on the first principal component. Four-week test-retest correlations were calculated for UK Biobank tests. UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r = 0.53, range = 0.22 to 0.83, p≤.005). The measure of general cognitive ability based on the UK Biobank cognitive tests correlated at r = 0.83 (p < .001) with a measure of general cognitive ability created using the reference tests. Four-week test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r = 0.55, range = 0.40 to 0.89, p≤.003). Despite the brief, non-standard nature of the UK Biobank cognitive tests, some tests showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.

Epidemiological studies have shown that weaker grip strength in later life is associated with disability, morbidity, and mortality. Grip strength is a key component of the sarcopenia and frailty phenotypes and yet it is unclear how individual measurements should be interpreted. Our objective was to produce cross-sectional centile values for grip strength across the life course. A secondary objective was to examine the impact of different aspects of measurement protocol. We combined 60,803 observations from 49,964 participants (26,687 female) of 12 general population studies in Great Britain. We produced centile curves for ages 4 to 90 and investigated the prevalence of weak grip, defined as strength at least 2.5 SDs below the gender-specific peak mean. We carried out a series of sensitivity analyses to assess the impact of dynamometer type and measurement position (seated or standing). Our results suggested three overall periods: an increase to peak in early adult life, maintenance through to midlife, and decline from midlife onwards. Males were on average stronger than females from adolescence onwards: males' peak median grip was 51 kg between ages 29 and 39, compared to 31 kg in females between ages 26 and 42. Weak grip strength, defined as strength at least 2.5 SDs below the gender-specific peak mean, increased sharply with age, reaching a prevalence of 23% in males and 27% in females by age 80. Sensitivity analyses suggested our findings were robust to differences in dynamometer type and measurement position. This is the first study to provide normative data for grip strength across the life course. These centile values have the potential to inform the clinical assessment of grip strength which is recognised as an important part of the identification of people with sarcopenia and frailty.

Key Points More than 100 years of empirical research provide conclusive evidence that a general factor of intelligence (also known as g , general cognitive ability, mental ability and IQ (intelligence quotient)) exists, despite some claims to the contrary. Intelligence can be reliably measured, is stable in rank-order across the lifespan, and is predictive of many important life outcomes, including educational and occupational success, health and longevity. Intelligence shows high heritability in quantitative genetic studies; this heritability increases across the lifespan to mid-adulthood and partly overlaps with genetic variance that influences brain structure. As with many other highly heritable complex traits, the genetic polymorphisms underlying normal-range intelligence differences remain elusive. One possible explanation is that many mildly harmful, lineage-specific, rare genetic variants ('mutation load') might be responsible for the heritability of intelligence. The most robust finding in the neuroscience of intelligence is that larger brains, and a greater volume of grey matter in various regions in the brain, are associated with higher intelligence. Intelligence does not reside in a single localized area in the brain. The available evidence suggests a widely distributed network of parieto-frontal brain areas underlies intelligence. The distributed nature of intelligence in the brain suggests a crucial role of white matter integrity and an efficient neurological network structure. Both hypotheses have initial empirical support. Functional efficiency (that is, low energy consumption in task-relevant brain areas) is also related to higher intelligence, especially when task difficulty is neither particularly high nor particularly low. Various lines of evidence suggest that men and women might use their brains differently to achieve similar levels of cognitive performance. These sex differences might extend to individual differences: people might differ in how they use their brains to solve the same cognitive tasks. The biological bases of individual differences in intelligence are largely unknown. Deary and colleagues discuss why, despite its high heritability, the molecular underpinnings of intelligence remain elusive, and show that variations in the structure and efficiency of brain pathways might contribute to intelligence differences. Neuroscience is contributing to an understanding of the biological bases of human intelligence differences. This work is principally being conducted along two empirical fronts: genetics — quantitative and molecular — and brain imaging. Quantitative genetic studies have established that there are additive genetic contributions to different aspects of cognitive ability — especially general intelligence — and how they change through the lifespan. Molecular genetic studies have yet to identify reliably reproducible contributions from individual genes. Structural and functional brain-imaging studies have identified differences in brain pathways, especially parieto-frontal pathways, that contribute to intelligence differences. There is also evidence that brain efficiency correlates positively with intelligence.

UK Biobank includes 502,649 middle- and older-aged adults from the general population who have undergone detailed phenotypic assessment. The majority of participants completed tests of cognitive functioning, and on average four years later a sub-group of N = 20,346 participants repeated most of the assessment. These measures will be used in a range of future studies of health outcomes in this cohort. The format and content of the cognitive tasks were partly novel. The aim of the present study was to validate and characterize the cognitive data: to describe the inter-correlational structure of the cognitive variables at baseline assessment, and the degree of stability in scores across longitudinal assessment. Baseline cognitive data were used to examine the inter-correlational/factor-structure, using principal components analysis (PCA). We also assessed the degree of stability in cognitive scores in the subsample of participants with repeat data. The different tests of cognitive ability showed significant raw inter-correlations in the expected directions. PCA suggested a one-factor solution (eigenvalue = 1.60), which accounted for around 40% of the variance. Scores showed varying levels of stability across time-points (intraclass correlation range = 0.16 to 0.65). UK Biobank cognitive data has the potential to be a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population.

Intra-individual variability in reaction time (RT IIV) is considered to be an index of central nervous system functioning. Such variability is elevated in neurodegenerative diseases or following traumatic brain injury. It has also been suggested to increase with age in healthy ageing. To investigate and quantify age differences in RT IIV in healthy ageing; to examine the effect of different tasks and procedures; to compare raw and mean-adjusted measures of RT IIV. Four electronic databases: PsycINFO, Medline, Web of Science and EMBASE, and hand searching of reference lists of relevant studies. English language journal articles, books or book chapters, containing quantitative empirical data on simple and/or choice RT IIV. Samples had to include younger (under 60 years) and older (60 years and above) human adults. Studies were evaluated in terms of sample representativeness and data treatment. Relevant data were extracted, using a specially-designed form, from the published report or obtained directly from the study authors. Age-group differences in raw and RT-mean-adjusted measures of simple and choice RT IIV were quantified using random effects meta-analyses. Older adults (60+ years) had greater RT IIV than younger (20-39) and middle-aged (40-59) adults. Age effects were larger in choice RT tasks than in simple RT tasks. For all measures of RT IIV, effect sizes were larger for the comparisons between older and younger adults than between older and middle-aged adults, indicating that the age-related increases in RT IIV are not limited to old age. Effect sizes were also larger for raw than for RT-mean-adjusted RT IIV measures. RT IIV is greater among older adults. Some (but not all) of the age-related increases in RT IIV are accounted for by the increased RT means.

AbstractObjectiveTo compare established associations between risk factors and mortality in UK Biobank, a study with an exceptionally low rate of response to its baseline survey, against those from representative studies that have conventional response rates.DesignProspective cohort study alongside individual participant meta-analysis of other cohort studies.SettingUnited Kingdom.ParticipantsAnalytical sample of 499 701 people (response rate 5.5%) in analyses in UK Biobank; pooled data from the Health Surveys for England (HSE) and the Scottish Health Surveys (SHS), including 18 studies and 89 895 people (mean response rate 68%). Both study populations were linked to the same nationwide mortality registries, and the baseline age range was aligned at 40-69 years.Main outcome measureDeath from cardiovascular disease, selected malignancies, and suicide. To quantify the difference between hazard ratios in the two studies, a ratio of the hazard ratios was used with HSE-SHS as the referent.ResultsRisk factor levels and mortality rates were typically more favourable in UK Biobank participants relative to the HSE-SHS consortium. For the associations between risk factors and mortality endpoints, however, close agreement was seen between studies. Based on 14 288 deaths during an average of 7.0 years of follow-up in UK Biobank and 7861 deaths over 10 years of mortality surveillance in HSE-SHS, for cardiovascular disease mortality, for instance, the age and sex adjusted hazard ratio for ever having smoked cigarettes (versus never) was 2.04 (95% confidence interval 1.87 to 2.24) in UK Biobank and 1.99 (1.78 to 2.23) in HSE-SHS, yielding a ratio of hazard ratios close to unity (1.02, 0.88 to 1.19). The overall pattern of agreement between studies was essentially unchanged when results were compared separately by sex and when baseline years and censoring dates were aligned.ConclusionDespite a very low response rate, risk factor associations in the UK Biobank seem to be generalisable.

Intelligence is an important human trait on which people differ. Few studies have examined the stability of intelligence differences from childhood or youth to older age using the same test. The longest such studies are those that have followed up on some of the participants of the Scottish Mental Surveys of 1932 and 1947. Their results suggest that around half of the individual differences in intelligence are stable across most of the human life course. This is valuable information because it can be used as a guide to how much of people's cognitive-aging differences might be amenable to alleviation.

BackgroundMaintaining cognitive function is an important aspect of healthy ageing. In this study, we examined age trajectories of cognitive decline in a large nationally representative sample of older people in England. We explored the factors that influence such decline and whether these differed by gender.MethodsLatent growth curve modelling was used to explore age-specific changes, and influences on them, in an 8-year period in memory, executive function, processing speed and global cognitive function among 10 626 participants in the English Longitudinal Study of Ageing. We run gender-specific models with the following exposures: age, education, wealth, childhood socioeconomic status, cardiovascular disease, diabetes, physical function, body mass index, physical activity, alcohol, smoking, depression and dementia.ResultsAfter adjustment, women had significantly less decline than men in memory (0.011, SE 0.006), executive function (0.012, SE 0.006) and global cognitive function (0.016, SE 0.004). Increasing age and dementia predicted faster rates of decline in all cognitive function domains. Depression and alcohol consumption predicted decline in some cognitive function domains in men only. Poor physical function, physical inactivity and smoking were associated with faster rates of decline in specific cognitive domains in both men and women. For example, relative to study members who were physically active, the sedentary experienced greater declines in memory (women −0.018, SE 0.009) and global cognitive function (men −0.015, SE 0.007 and women −0.016, SE 0.007).ConclusionsThe potential determinants of cognitive decline identified in this study, in particular modifiable risk factors, should be tested in the context of randomised controlled trials.

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.