Introduction
Promoting successful cognitive ageing is a topic of major importance to individuals and the field of public health. For most, losing one's cognitive abilities, especially memory, is feared more than physical disability (Martin, Reference Martin2004). Cognitive decline in older age is associated with poorer health and quality of life (Plassman et al. Reference Plassman, Williams, Burke, Holsinger and Benjamin2010), impairments in functional activities, decision-making and independence (Tucker-Drob, Reference Tucker-Drob2011; Boyle et al. Reference Boyle, Yu, Wilson, Gamble, Buchman and Bennett2012; Jekel et al. Reference Jekel, Damian, Wattmo, Hausner, Bullock, Connelly, Dubois, Eriksdotter, Ewers, Graessel and Kramberger2015), and increased health care costs (Brayne, Reference Brayne2007). In the face of shifting demographics and an increase in human longevity, there is a pressing need to evaluate the potential contributions to cognitive function in later life. People differ in their cognitive abilities, both in terms of their overall level, and the rate at which they experience decline in older age (Gow et al. Reference *Gow, Johnson, Pattie, Brett, Roberts, Starr and Deary2011). A key question is: why do some people have a better cognitive trajectory than others? Identifying factors that predispose individuals to a faster rate of cognitive decline is an important step for developing intervention and treatment strategies aimed at maintaining cognitive and brain health into older age.
The main focus of the Lothian Birth Cohort (LBC) studies is people's differences in cognitive and brain ageing. The idea for these studies came about following the discovery of ledgers containing the results of the Scottish Mental Surveys (SMS) (Deary et al. Reference *Deary, Whalley and Starr2009c ). The surveys had tested the intelligence of almost a whole year-of-birth, twice. On 1 June 1932, almost every child born in 1921 and attending a Scottish school took the same general mental ability test, known as the Moray House Test (MHT) No. 12. The exercise was repeated on 4 June 1947 for almost every Scottish school pupil born in 1936. These were the SMS of 1932 and 1947. Most schools in Scotland participated, yielding test scores on 87 498 (SMS1932) and 70 805 (SMS1947) 11-year olds. From 1999, the LBC studies, based at the University of Edinburgh, recruited men and women in the Lothian region of Scotland who were surviving participants of the SMS of 1932 (to the LBC1921 study) and 1947 (to the LBC1936 study). Other SMS follow-up studies were conducted in Aberdeen, known as the Aberdeen Birth Cohort of 1921 (ABC1921) and 1936 (ABC1936) (Deary et al. Reference *Deary, Whalley and Starr2009c ; Reference *Deary, Whiteman, Starr, Whalley and Fox2004b ; Whalley et al. Reference †Whalley, Murray, Staff, Starr, Deary, Fox, Lemmon, Duthie, Collins and Crawford2011).
The original aim of the LBC studies was to seek the determinants of normal (non-pathological) cognitive ageing from childhood to older age; they were extended to study cognitive and brain ageing within older age. Both cohorts are richly phenotyped, with many data types in common: socio-demographic, medical, cognitive (including the same test as at age 11), magnetic resonance imaging (MRI), carotid ultrasound, retinal imaging, blood biomarkers, physical function and fitness, genetic and epigenetic, lifestyle, psychosocial, personality, and many others (see Supplementary tables 1 and 2). The LBC1921 study has completed five waves of testing since baseline (n = 550, mean age 79 years); most recently 54 participants were tested at age 92. No further testing of this cohort is planned. The LBC1936 study has completed four waves of testing since baseline (n = 1091, mean age 70 years); most recently 550 participants were tested at age 79. A fifth wave of data collection is planned to begin in the second half of 2017. Follow-up assessments for both cohorts were conducted at ~3-yearly intervals. For further details, see the cohort profile paper (Deary et al. Reference *Deary, Gow, Pattie and Starr2012a ) and the LBC studies website (http://www.lothianbirthcohort.ed.ac.uk/). Detailed structural brain MRI was performed at mean ages 73, 76, 79 in the LBC1936 (Wardlaw et al. Reference *Wardlaw, Bastin, Valdés Hernández, Maniega, Royle, Morris, Clayden, Sandeman, Eadie, Murray, Starr and Deary2011), and at age 90 in the LBC1921.
This overview summarises key results from among the 300+ LBC1921 and LBC1936 peer-reviewed publications, focussing on those addressing cognitive and brain ageing, and placing these findings within the context of the wider, relevant literature. Among the key ageing-relevant factors considered here will be genetic, social, health, biomedical and lifestyles. We introduce the concept of marginal gains to encapsulate the probably many small influences that appear to contribute to differences in people's brain and cognitive health in older age. The marginal gains idea is often applied to performance in elite sports and business. However, based on the work reviewed here, we suggest that the idea could provide a useful framework for understanding and promoting the process by which a possibly large range of potentially malleable risk and protective factors (each of which might show a small association) may lead to an aggregate benefit for cognitive and brain ageing in later life. We stress strongly, though, that the LBC studies are observational and not intervention studies.
Stability and change in intelligence
If we wish to understand the contributions to people's cognitive differences in older age, then arguably the first question one should ask is how much of the cognitive variation in older age is due to long-standing cognitive trait differences. The LBC studies showed that the biggest factor in explaining why people's cognitive skills differ in older age is childhood intelligence differences. When the same validated intelligence test (MHT) is administered at age 11 years and again to individuals when they are in their late 70s, the raw correlations are between 0.6 and 0.7 (Deary et al. Reference *Deary, Whiteman, Starr, Whalley and Fox2004b ) and are still above 0.5 when the individuals are in their late 80s (Gow et al. Reference *Gow, Johnson, Pattie, Brett, Roberts, Starr and Deary2011) and into their 90s (Deary et al. Reference *Deary, Pattie and Starr2013). Furthermore, MHT intelligence scores in childhood and older age correlate significantly with scores on well-validated cognitive tests, even at the age of 90 (Deary et al. Reference *Deary, Pattie and Starr2013). These correlations imply that about half or more of the variance in intelligence is stable across most of the human life course (see Deary, Reference *Deary2014). The cohorts also provide clear evidence that change in a general cognitive factor accounts for ~50% of the variance in age-related changes across multiple cognitive domains (see Tucker-Drob et al. Reference *Tucker-Drob, Briley, Starr and Deary2014; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016).
These results show that individuals who are cognitively more able in youth show a tendency to still show a higher level of cognitive function in older age (Deary et al. Reference *Deary, Whiteman, Starr, Whalley and Fox2004b ; Royle et al. Reference *Royle, Booth, Hernández, Penke, Murray, Gow, Maniega, Starr, Bastin, Deary and Wardlaw2013). But what about the rate of change in cognitive ageing? That is, in the field of cognitive ageing, it is often asked whether ‘ageing is kinder to the initially more able’ (Deary et al. Reference *Deary, Starr and MacLennan1999; Gow et al. Reference *Gow, Johnson, Mishra, Richards, Kuh and Deary2012c ). The results are mixed. Some suggest that those with a higher early-life cognitive ability decline in cognitive functioning at a slower rate in later life (Bourne et al. Reference †Bourne, Fox, Deary and Whalley2007), whereas others report no association (Gow et al. Reference *Gow, Johnson, Pattie, Whiteman, Starr and Deary2008, Reference *Gow, Johnson, Mishra, Richards, Kuh and Deary2012c ). In the LBC1936, those with higher childhood ability tended to decline more with age in visuospatial ability, but there was no statistically significant association with any of the other cognitive measures (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). Overall, results from the LBC studies suggest that cognitive ability in early life, although it has a strong association with cognitive level in older age, does not confer an advantage with respect to cognitive ageing trajectory (Gow et al. Reference *Gow, Johnson, Pattie, Brett, Roberts, Starr and Deary2011; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016).
Knowing the long-term stability of individual differences in cognitive abilities is a valuable foundation for estimating which factors account for the other variance in cognitive function in older age. Therefore, if about 50% of the variance in cognitive function in older age is traceable back to childhood, we should seek reliable sources of the other 50%, some of which will, of course, be measurement error. The non-error sources will probably include factors that are outside of our immediate and practical control, and those that might be amenable to change.
Brain correlates of cognitive ageing
Evidence derived from brain imaging studies of the LBCs (Wardlaw et al. Reference *Wardlaw, Bastin, Valdés Hernández, Maniega, Royle, Morris, Clayden, Sandeman, Eadie, Murray, Starr and Deary2011) supports a lifelong ‘trait’ of intelligence and its association with brain structure. In the LBC1936, intelligence at age 11 not only predicts cortical thickness in later life but accounts for over two-thirds of the cross-sectional association between cognitive ability and cortical thickness in later life. Adjusting for MHT scores at age 11 attenuates this association to non-significance (Karama et al. Reference *Karama, Bastin, Murray, Royle, Penke, Maniega, Gow, Corley, Valdés Hernández, Lewis, Rousseau, Lepage, Fonov, Collins, Booth, Rioux, Sherif, Adalat, Starr, Evans, Wardlaw and Deary2014). Individuals from the LBC studies who showed less relative decline in cognitive function between age 11 and later life also show better white matter microstructure (Deary et al. Reference *Deary, Bastin, Pattie, Clayden, Whalley, Starr and Wardlaw2006a ; Penke et al. Reference *Penke, Maniega, Bastin, Hernández, Murray, Royle, Starr, Wardlaw and Deary2012b ), fewer white matter hyperintensities (WMH) (Valdés Hernández et al. Reference *Valdés Hernández, Booth, Murray, Gow, Penke, Morris, Maniega, Royle, Aribisala, Bastin, Starr, Deary and Wardlaw2013), slower progression of WMH (Ritchie et al. Reference *Ritchie, Booth, Valdés Hernandez, Corley, Munoz Maniega, Gow, Royle, Pattie, Karama, Starr, Bastin, Wardlaw and Deary2015c , Reference *Ritchie, Dickie, Cox, Valdés Hernández, Corley, Royle, Pattie, Aribisala, Redmond, Munoz Maniega, Taylor, Sibbett, Gow, Starr, Bastin, Wardlaw and Deary d ), less brain atrophy and a larger intra-cranial volume (Royle et al. Reference *Royle, Booth, Hernández, Penke, Murray, Gow, Maniega, Starr, Bastin, Deary and Wardlaw2013), a bigger brain (Shenkin et al. Reference *Shenkin, Rivers, Deary, Starr and Wardlaw2009c ), less small vessel disease (Staals et al. Reference *Staals, Booth, Morris, Bastin, Gow, Corley, Redmond, Starr, Deary and Wardlaw2015), and fewer iron deposits (Penke et al. Reference *Penke, Hernandéz, Maniega, Gow, Murray, Starr, Bastin, Deary and Wardlaw2012a ; Valdés Hernández et al. Reference *Valdés Hernández, Ritchie, Glatz, Allerhand, Maniega, Gow, Royle, Bastin, Starr, Deary and Wardlaw2015b ). Individuals with better cognitive abilities at age 73 showed less brain volume loss and less WMH growth over a 3-year follow-up period (Ritchie et al. Reference *Ritchie, Bastin, Tucker-Drob, Munoz Maniega, Engelhardt, Cox, Royle, Gow, Corley, Pattie, Taylor, Valdés Hernández, Starr, Wardlaw and Deary2015a ). Coupled changes in white matter microstructure and fluid intelligence are consistent with a longitudinal link between brain ‘disconnection’ and cognitive ageing. The brain correlates of better cognitive ageing point to less shrinkage of the brain tissue generally, better white matter connections in the brain, and fewer hyperintensities in the brain's white matter (Ritchie et al. Reference *Ritchie, Booth, Valdés Hernandez, Corley, Munoz Maniega, Gow, Royle, Pattie, Karama, Starr, Bastin, Wardlaw and Deary2015c , Reference *Ritchie, Dickie, Cox, Valdés Hernández, Corley, Royle, Pattie, Aribisala, Redmond, Munoz Maniega, Taylor, Sibbett, Gow, Starr, Bastin, Wardlaw and Deary d ), and confirm that neuroimaging biomarkers are informative about cognitive changes. The state of the brain's structure in older age has significance beyond cognitive functioning; the LBC1936 study showed that older brain age (the deviation of the brain's structure from that expected for a given chronological age) is associated with earlier death (Cole et al. Reference *Cole, Ritchie, Bastin, Valdés-Hernández, Maniega, Royle, Corley, Pattie, Harris, Zhang, Wray, Redmond, Marioni, Starr, Cox, Wardlaw, Sharp and Deary2017).
The determinants of brain changes from age 73 to 76 have been investigated in the LBC1936. Relatively greater deterioration in MRI measures of brain macro and microstructure was associated with lower physical fitness and possession of APOE e4. Though other potential risk and protective (physical, health, cognitive, allostatic and genetic) variables were associated with baseline brain structure, they did not predict subsequent brain change over the short (3-year) follow-up period (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Dickie, Valdés Hernández, Corley, Royle, Redmond, Munoz Maniega, Pattie, Aribisala, Taylor, Clarke, Gow, Starr, Bastin, Wardlaw and Deary2017).
Genetic influences on cognitive level and cognitive change
In adulthood, twin studies estimate that between 50% and 80% of the variation in general cognitive function is caused by genetic differences (Deary et al. Reference *Deary, Johnson and Houlihan2009b ). The LBC studies contributed to a consortium that was the first to use single-nucleotide polymorphism (SNP) data to estimate the SNP-based heritability of individual differences in human intelligence (Davies et al. Reference *Davies, Tenesa, Payton, Yang, Harris, Liewald, Ke, Le Hellard, Chistoforou, Luciano, McGhee, Lopez, Gow, Corley, Redmond, Fox, Haggarty, Whalley, McNeill, Goddard, Espeseth, Lundervold, Reinvang, Pickles, Steen, Ollier, Portoeus, Horan, Starr, Pendleton, Visscher and Deary2011), which was subsequently expanded (Davies et al. Reference *Davies, Armstrong, Bis, Bressler, Chouraki, Giddaluru, Hofer, Ibrahim-Verbaas, Kirin, Lahti, van der Lee, Le Hellard, Liu, Marioni, Oldmeadow, Postmus, Smith, Smith, Thalamuthu, Thomson, Vitart, Wang, Yu, Zgaga, Zhao, Boxall, Harris, Hill, Liewald, Luciano, Adams, Ames, Amin, Amouyel, Assareh, Au, Becker, Beiser, Berr, Bertram, Boerwinkle, Buckley, Campbell, Corley, De Jager, Dufouil, Eriksson, Espeseth, Faul, Ford, Gottesman, Griswold, Gudnason, Harris, Heiss, Hofman, Holliday, Huffman, Kardia, Kochan, Knopman, Kwok, Lambert, Lee, Li, Li, Loitfelder, Lopez, Lundervold, Lundqvist, Mather, Mirza, Nyberg, Oostra, Palotie, Papenberg, Pattie, Petrovic, Polasek, Psaty, Redmond, Reppermund, Rotter, Schmidt, Schuur, Schofield, Scott, Steen, Stott, van Swieten, Taylor, Trollor, Trompet, Uitterlinden, Weinstein, Widen, Windham, Jukema, Wright, Wright, Yang, Amieva, Attia, Bennett, Brodaty, de Craen, Hayward, Ikram, Lindenberger, Nilsson, Porteous, Räikkönen, Reinvang, Rudan, Sachdev, Schmidt, Schofield, Srikanth, Starr, Turner, Weir, Wilson, van Duijn, Launer, Fitzpatrick, Seshadri, Mosley and Deary2015). The current estimate is that common SNPs in many genes, each having a very small effect, account for about 30% of the variation in human intelligence differences. This largely refers to contributions to the stable trait of human general intelligence. The emerging view of genetic influences on intelligence, confirmed by work on the cohorts, is that it is likely that a very large number of genetic variants have small effects (Deary et al. Reference Deary, Penke and Johnson2010; Plomin & Deary, Reference Plomin and Deary2015).
The LBC studies contributed to a study in which common genetic variants (SNPs) were estimated to account for about 24% (but with a relatively large standard error) of the variability in lifetime cognitive change, i.e. from childhood to older age (Deary et al. Reference *Deary, Yang, Davies, Harris, Tenesa, Liewald, Luciano, Lopez, Gow, Corley, Redmond, Fox, Rowe, Haggarty, McNeill, Goddard, Porteous, Whalley, Starr and Visscher2012b ). The same study found that the genetic factors contributed the majority influence on the lifetime stability of intelligence. Within that contribution, various candidate genes have been tested. However, other than possession of the ‘risk’ APOE e4 allele, which explains around 1–2% of the variance in cognitive change from youth to older age and within older age (Deary et al. Reference *Deary, Whiteman, Pattie, Starr, Hayward, Wright, Carothers and Whalley2002, Reference *Deary, Whiteman, Pattie, Starr, Hayward, Wright, Visscher, Tynan and Whalley2004a ; Luciano et al. Reference *Luciano, Gow, Harris, Hayward, Allerhand, Starr, Visscher and Deary2009a , Reference *Luciano, Gow, Taylor, Hayward, Harris, Campbell, Porteous, Starr, Visscher and Deary b ; Schiepers et al. Reference *Schiepers, Harris, Gow, Pattie, Brett, Starr and Deary2012; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016; also see Daviglus et al. Reference Daviglus, Bell, Berrettini, Bowen, Connolly, Cox, Dunbar-Jacob, Granieri, Hunt, McGarry, Patel, Potosky, Sanders-Bush, Silberberg and Trevisan2010) and is associated with age-related brain structural changes (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Dickie, Valdés Hernández, Corley, Royle, Redmond, Munoz Maniega, Pattie, Aribisala, Taylor, Clarke, Gow, Starr, Bastin, Wardlaw and Deary2017), no other candidate genes have been consistently linked to variation in cognitive function or with age-related cognitive decline (Visscher et al. Reference *Visscher, Tynan, Whiteman, Pattie, White, Hayward, Wright, Starr, Whalley and Deary2003; Deary et al. Reference *Deary, Harris, Fox, Hayward, Wright, Starr and Whalley2005b ; Kachiwala et al. Reference *Kachiwala, Harris, Wright, Hayward, Starr, Whalley and Deary2005; Houlihan et al. Reference *Houlihan, Harris, Luciano, Gow, Starr, Visscher and Deary2009; Marioni et al. Reference *Marioni, Penke, Davies, Huffman, Hayward and Deary2014b ).
A different approach is to ask whether people's polygenic risk for disorders related to cognitive decline contribute to cognitive change, even in individuals without the disorders. In the cohort studies, increased polygenic risk of certain diseases, such as coronary artery disease (Hagenaars et al. Reference *Hagenaars, Harris, Clarke, Hall, Luciano, Fernandez-Pujals, Davies, Hayward, Starr, Porteous, McIntosh and Deary2016), ischaemic stroke (Harris et al. Reference *Harris, Malik, Marioni, Campbell, Seshadri, Worrall, Sudlow, Hayward, Bastin, Starr, Porteous, Wardlaw and Deary2016a ) and schizophrenia (McIntosh et al. Reference *McIntosh, Gow, Luciano, Davies, Liewald, Harris, Corley, Hall, Starr, Porteous, Tenesa, Visscher and Deary2013), is associated with lower cognitive ability, and greater relative cognitive decline in the case of polygenic risk for schizophrenia (McIntosh et al. Reference *McIntosh, Gow, Luciano, Davies, Liewald, Harris, Corley, Hall, Starr, Porteous, Tenesa, Visscher and Deary2013). Lower cognitive ability in older age was not associated with polygenic risk for diabetes (Luciano et al. Reference *Luciano, Mõttus, Harris, Davies, Payton, Ollier, Horan, Starr, Porteous, Pendleton and Deary2014) or Alzheimer's disease (Lyall et al. Reference *Lyall, Lopez, Bastin, Maniega, Penke, Hernández, Royle, Starr, Porteous, Wardlaw and Deary2013a , Reference *Lyall, Royle, Harris, Bastin, Maniega, Murray, Lutz, Saunders, Roses, del Valdés Hernández, Starr, Porteous, Wardlaw and Deary b , Reference *Lyall, Harris, Bastin, Maniega, Murray, Lutz, Saunders, Roses, Hernández, Royle, Starr, Porteous, Wardlaw and Deary2014; Harris et al. Reference *Harris, Davies, Luciano, Payton, Fox, Haggarty, Ollier, Horan, Porteous, Starr, Whalley, Pendleton and Deary2014). DNA methylation, which can be used to form an epigenetic biomarker of age acceleration, was associated with cognitive function in the LBC1936, but not cognitive decline over 3 years within old age (Marioni et al. Reference *Marioni, Shah, McRae, Ritchie, Muniz-Terrera, Harris, Gibson, Redmond, Cox, Pattie, Corley, Taylor, Murphy, Starr, Horvath, Visscher, Wray and Deary2015a ). Apart from cognitive function, the LBC studies showed that a faster running epigenetic clock is associated with earlier death (Marioni et al. Reference Marioni, Shah, McRae, Chen, Colicino, Harris, Gibson, Henders, Redmond, Cox, Pattie, Corley, Murphy, Martin, Montgomery, Feinberg, Fallin, Multhaup, Jaffe, Joehanes, Schwartz, Just, Lunetta, Murabito, Starr, Horvath, Visscher, Wray and Deary2015b ).
Thus, the LBC studies contribute to evidence that intelligence level from adolescence to older age is highly heritable and highly polygenic, and is substantially stable over time, with genetic factors contributing much to the lifetime stability. The existence of some genetic contributions to lifetime cognitive change does not mean that these are not amenable to intervention; with a greater understanding of genes’ systems and gene expression pathways (e.g. Johnson et al. Reference *Johnson, Shkura, Langley, Delahaye-Duriez, Srivastava, Hill, Rackham, Davies, Harris, Moreno-Moral, Rotival, Speed, Petrovski, Katz, Hayward, Porteous, Smith, Padmanabhan, Hocking, Starr, Liewald, Visconti, Falchi, Bottolo, Rossetti, Danis, Mazzuferi, Foerch, Grote, Helmstaedter, Becker, Kaminski, Deary and Petretto2016), genetic contributions might well be modifiable. Establishing the heritability of intelligence and the genetic contributions to cognitive change is important for many reasons, not least because it also helps to elucidate the extent to which environmental influences contribute to lifetime cognitive change.
Early-life and demographic factors
Positive early-life factors, including birth parameters (Shenkin et al. Reference ‡Shenkin, Starr, Pattie, Rush, Whalley and Deary2001, Reference Shenkin, Starr and Deary2004; Grove et al. Reference Grove, Lim, Gale and Shenkin2017), education (Stern, Reference Stern2002; Banks & Mazzonna, Reference Banks and Mazzonna2012; Clouston et al. Reference Clouston, Kuh, Herd, Elliott, Richards and Hofer2012; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016), and childhood environment (Johnson et al. Reference *Johnson, Gow, Corley, Starr and Deary2010; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016), appear to have modest associations with better cognitive capacities in later life and better brain health according to only some MRI indices (Shenkin et al. Reference Shenkin, Bastin, MacGillivray, Deary, Starr and Wardlaw2009a ; Cox et al. Reference *Cox, Bak, Allerhand, Redmond, Starr, Deary and MacPherson2016; Field et al. Reference *Field, Doubal, Johnson, Backhouse, McHutchison, Cox, Corley, Pattie, Gow, Shenkin and Cvoro2016). However, we have not found evidence that early-life factors offer protection against cognitive decline in the LBCs or other studies (Shenkin et al. Reference Shenkin, Deary and Starr2009b ; Tucker-Drob et al. Reference Tucker-Drob, Johnson and Jones2009; Zahodne et al. Reference Zahodne, Glymour, Sparks, Bontempo, Dixon, MacDonald and Manly2011; Gottesman et al. Reference Gottesman, Rawlings, Sharrett, Albert, Alonso, Bandeen-Roche, Coker, Coresh, Couper, Griswold, Heiss, Knopman, Patel, Penman, Power, Selnes, Schneider, Wagenknecht, Windham, Wruck and Mosely2014; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). That is, they might be associated with long-standing cognitive level, but possibly not with cognitive trajectory.
Education is hypothesised to boost so-called cognitive reserve (Tucker & Stern, Reference Tucker and Stern2011). However, we acknowledge that some users of this term make it ambiguous, because sometimes it refers to higher premorbid cognitive level, and sometimes to less steep decline. Longer schooling in the LBC1936 was a significant predictor of higher scores on a latent general cognitive factor at age 70, independently of childhood IQ score (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016), and of greater cross-sectional cortical thickness in areas linked to the flexible integration of semantic knowledge (but not to other brain MRI markers; Cox et al. Reference *Cox, Bak, Allerhand, Redmond, Starr, Deary and MacPherson2016). However, there was no evidence of an association between education and a latent factor of cognitive change from age 70 to 76 (Ritchie et al. Reference *Ritchie, Bates and Deary2015b , Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). Though more education associates with better general cognitive abilities in later life, it appears, after adjustment for childhood intelligence, to be associated with some specific cognitive skills (Ritchie et al. Reference *Ritchie, Bates and Deary2015b ), and does not appear to improve more fundamental aspects of cognitive processing, such as reaction time and inspection time (Ritchie et al. Reference *Ritchie, Bates, Der, Starr and Deary2013). Education's effects might therefore be limited to specific aspects of intelligence tests, such as knowledge and perhaps reasoning, rather than a general factor of intelligence, and might offer no protection against cognitive decline (Ritchie et al. Reference *Ritchie, Bates and Deary2015b ).
Some established correlates of lower intelligence test scores in youth, including low parental socio-economic status (SES), poor maternal nutrition, maternal smoking, and poor perinatal nutrition, are related to maternal intelligence, suggesting that these associations might in part be accounted for by the genetic link between mother and child (Shenkin et al. Reference Shenkin, Starr and Deary2004; Deary et al. Reference *Deary, Der and Shenkin2005a ; Räikkönen et al. Reference Räikkönen, Pesonen, Heinonen, Lahti, Komsi, Eriksson, Seckl, Järvenpää and Strandberg2009). Indeed, parents pass on genetic variants associated with both intelligence and aspects of the socio-economic environment to their children (Deary et al. Reference Deary, Penke and Johnson2010; Marioni et al. Reference *Marioni, Davies, Hayward, Liewald, Kerr, Campbell, Luciano, Smith, Padmanabhan, Hocking, Hastie, Wright, Porteous, Visscher and Deary2014a ; Hill et al. Reference Hill, Hagenaars, Marioni, Harris, Liewald, Davies, Okbay, McIntosh, Gale and Deary2016). Yet, though social background (parental or environmental circumstances) may provide opportunities for educational and occupational attainment, results from the LBC studies suggest that childhood SES alone does not appear to have strong associations with cognitive decline in later life (Johnson et al. Reference *Johnson, Gow, Corley, Starr and Deary2010; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). It may be that the choices that individuals make (personal life history variables), rather than early-life social milieu, have the greatest effects on cognitive abilities in later life (Johnson et al. Reference *Johnson, Gow, Corley, Starr and Deary2010). Not only is childhood intelligence the strongest direct contributor to later life intelligence, it may contribute directly to one's ability to obtain education, and a safe and well-remunerated job, the typical indicators of SES (Deary et al. Reference *Deary, Taylor, Hart, Wilson, Davey Smith, Blane and Starr2005c ).
Consistent with the ‘use it or lose it’ hypothesis, researchers often ask whether a more intellectually demanding job in adulthood contributes to better cognitive function (Hultsch et al. Reference Hultsch, Hertzog, Small and Dixon1999). Occupational complexity was associated with better cognitive performance in the LBC1936 at age 70 (after adjusting for age 11 IQ, education and social deprivation), accounting for 1–2% of the variance (Smart et al. Reference *Smart, Gow and Deary2014). Gender differences identified in the 1936 cohort, in relative IQ change between youth and age 70 (with an effect size of 0.19) favouring men (Johnson et al. Reference *Johnson, Corley, Starr and Deary2011), an effect also reported in the LBC1921 (Deary et al. Reference *Deary, Whiteman, Starr, Whalley and Fox2004b ), might reflect men's greater involvement in the workforce in these samples and its potential to retard cognitive decline with age. However, not all studies report beneficial long-term effects of occupational characteristics on cognitive ageing (Finkel et al. Reference Finkel, Andel, Gatz and Pederson2009; Gow et al. Reference Gow, Avlund and Mortensen2014). More stimulating environments may help preserve cognitive ability in later life, but engagement in complex and intellectually stimulating activities may also be a consequence of individual differences in prior cognitive ability (Gow et al. Reference *Gow, Corley, Starr and Deary2012b ); a dynamic longitudinal association seems likely.
Lifestyle and psychosocial factors
A number of factors potentially under an individual's control might improve cognitive ageing prospects and reduce the risk of cognitive decline and impairment (Lee et al. Reference Lee, Back, Kim, Kim, Na, Cheong, Hong and Kim2010; Baumgart et al. Reference Baumgart, Snyder, Carrillo, Fazio, Kim and Johns2015). The LBC team have been investigating what these factors are and estimating the size of their effects. We preface this by stating that effect sizes are typically small, and so, like many potential behavioural changes for health, they would possibly have a detectable effect at population level rather than a manifest result for any individual.
Smoking
It is well documented that smoking is a risk factor for cognitive decline and dementia (Anstey et al. Reference Anstey, von Sanden, Salim and O'kearney2007; Peters et al. Reference Peters, Poulter, Warner, Beckett, Burch and Bulpitt2008). Evidence for past smoking is less consistent (Daviglus et al. Reference Daviglus, Bell, Berrettini, Bowen, Connolly, Cox, Dunbar-Jacob, Granieri, Hunt, McGarry, Patel, Potosky, Sanders-Bush, Silberberg and Trevisan2010). In the LBC1936, those still smoking at the age of 70 performed more poorly than ex- and never-smokers on most of the concurrently performed cognitive tasks (Corley et al. Reference *Corley, Gow, Starr and Deary2012). Cigarette smoking in older age was found to be associated with slightly lower scores in general cognitive abilities and in speed of information processing, accounting for 0.7% and 0.6% of the variance, respectively. Past smoking was not associated with significantly poorer performance when compared with never-smokers in any cognitive domain. Lower childhood IQ predicted more likely onset and less likely cessation of smoking in the LBC1936. In the LBC1921 at age 80, current smokers had significantly lower MHT scores in older age than never- and ex-smokers after adjusting for childhood IQ (Deary et al. Reference *Deary, Pattie, Taylor, Whiteman, Starr and Whalley2003; Starr et al. Reference †Starr, Deary, Fox and Whalley2007). A lower childhood IQ predicted cessation but not uptake of smoking, perhaps reflecting social attitudes to tobacco over this particular historical period (Taylor et al. Reference ‡Taylor, Hart, Davey Smith, Starr, Hole, Whalley, Wilson and Deary2003).
Our results suggest that cessation of smoking in adulthood may ‘buffer’ the cognitive ageing experienced by those who continue to smoke, perhaps via decreased progression of WMH volume (Dickie et al. Reference *Dickie, Ritchie, Cox, Sakka, Royle, Aribisala, Dickie, Ritchie, Cox, Sakka, Royle, Aribisala, Hernández, Maniega, Pattie, Corley, Starr, Bastin, Deary and Wardlaw2016), or decreased cortical thinning (Karama et al. Reference *Karama, Ducharme, Corley, Choinard-Decorte Starr, Wardlaw, Bastin and Deary2015). When accounting for the amount of lifetime smoking, Karama et al. found that the cortex of subjects who stopped smoking appeared to have partially ‘recovered’ (the study was cross-sectional, so this was inferred) for each year without smoking. Although complete cortical recovery in affected areas was estimated to take on average 25 years in this sample, these important findings suggest that partial recovery is possible. In terms of public health implications, findings which hint at underlying neurobiological mechanisms such as these are valuable. Here we suggest that there might be both cognitive and cerebral benefits to quitting smoking, in addition to the better known health benefits, even for older adults who have been smoking for many years.
Physical activity
A higher level of participation in physical activity in later life was associated with better general cognitive abilities and processing speed in the LBC1936 at baseline, accounting for 0.7% and 1% of the variance, respectively (Gow et al. Reference *Gow, Corley, Starr and Deary2012b ), and less cognitive decline over 11 years of follow-up (from age 79 to 90) in the LBC1921 (Gow et al. Reference *Gow, Pattie and Deary2017). The lack of association between a cumulative activity score (spanning the ages of 20–75) and cognitive ability indicates, there may be specific periods in which engagement in physical activities may be particularly beneficial. Our findings of better cognitive functioning with greater physical engagement supports the wider evidence that physical activity has a significant role in determining healthy cognitive ageing (Lee et al. Reference Lee, Back, Kim, Kim, Na, Cheong, Hong and Kim2010; Blondell et al. Reference Blondell, Hammersley-Mather and Veerman2014; Carvalho et al. Reference Carvalho, Rea, Parimon and Cusack2014; McKee & Schüz, Reference McKee and Schüz2015). Other studies have demonstrated that even mild activities, such as walking, were found to be protective in later life (Weuve et al. Reference Weuve, Kang, Manson, Breteler, Ware and Grodstein2004). The results of a meta-analysis of prospective studies suggest that all levels of physical activity offer significant and consistent protection against cognitive decline (Sofi et al. Reference Sofi, Valecchi, Bacci, Abbate, Gensini, Casini and Macchi2011). Yet, despite this promise, randomised controlled trials of physical activity are equivocal; some report positive effects on cognitive function (Lautenschlager et al. Reference Lautenschlager, Cox, Flicker, Foster, van Bockxmeer, Xiao, Greenop and Almeida2008), whereas others find no improvements in global or even domain-specific cognitive abilities (Sink et al. Reference Sink, Espeland, Castro, Church, Cohen, Dodson, Guralnik, Hendrie, Jennings, Katula and Lopez2015). However, a meta-analysis of 29 studies reported modest improvements in attention, processing speed, executive function and memory, with exercise training among non-demented adults (Smith et al. Reference Smith, Blumenthal, Hoffman, Cooper, Strauman, Welsh-Bohmer, Browndyke and Sherwood2010). Type and intensity of exercise may be a factor; aerobic training programmes in older people lead to significant improvements not observed in those doing strength and flexibility exercises or in controls (see Bherer et al. Reference Bherer, Erickson and Liu-Ambrose2013), suggesting a role for cardiorespiratory fitness in healthy cognitive ageing.
The biological mechanisms by which cognitive function might be enhanced through physical exercise training remain to be completely elucidated. Physical exercise in later life may exert a cognitively protective effect via preservation of brain microstructural integrity; more physical activity at age 73 was associated with less brain atrophy and fewer white matter lesions in the LBC1936 (Gow et al. Reference *Gow, Bastin, Maniega, Hernández, Morris, Murray, Royle, Starr, Deary and Wardlaw2012a ). Associations with higher fractional anisotropy and higher normal-appearing white matter volume became non-significant in the fully adjusted model. Another study showed that exercise training increases hippocampal volume and improves memory (Erickson et al. Reference Erickson, Voss, Prakash, Basak, Szabo, Chaddock, Kim, Heo, Alves, White and Wojcicki2011). These results, and others (Marks et al. Reference Marks, Madden, Bucur, Provenzale, White, Cabeza and Huettel2007; Voss et al. Reference Voss, Heo, Prakash, Erickson, Alves, Chaddock, Szabo, Mailey, Wójcicki, White, Gothe, McAuley, Sutton and Kramer2013; Tian et al. Reference Tian, Erickson, Simonsick, Aizenstein, Glynn, Boudreau, Newman, Kritchevsky, Yaffe, Harris and Rosano2014), offer evidence for neurotrophic effects of physical activity on brain structure. Exercise may, of course, enhance cognition indirectly by improving psychological wellbeing (Herring et al. Reference Herring, Jacob, Suveg, Dishman and O'Connor2012a , Reference Herring, Puetz, O'Connor and Dishman b ), improving sleep quality and minimising pain (Reid et al. Reference Reid, Baron, Lu, Naylor, Wolfe and Zee2010), all of which may secondarily impact neurocognitive functioning (Reppermund et al. Reference Reppermund, Brodaty, Crawford, Kochan, Slavin, Trollor, Draper and Sachdev2011). It remains to be seen whether these factors in fact mediate any positive effects that exercise has on cognitive and brain health.
Alcohol
Findings on the association between alcohol use and cognition were less clear, consistent with other research (Daviglus et al. Reference Daviglus, Bell, Berrettini, Bowen, Connolly, Cox, Dunbar-Jacob, Granieri, Hunt, McGarry, Patel, Potosky, Sanders-Bush, Silberberg and Trevisan2010). In the LBC1936, a higher intake of alcohol, particularly of wine, was associated with better memory function at age 70 after adjusting for age 11 IQ, accounting for around 1% of the variance in scores (Corley et al. Reference *Corley, Jia, Brett, Gow, Starr, Kyle, McNeill and Deary2011). An almost exclusive preference for wine among women alluded to a potentially beneficial effect of wine or of its components. However, memory performance was better in men with a higher overall alcohol intake and not necessarily due to wine intake per se. In the same sample, the use of Mendelian randomisation demonstrated that individuals with a higher genetic ability to process alcohol showed relative improvements in cognitive ability with more consumption, whereas those with low processing capacity showed a negative relationship between cognitive change and alcohol consumption with more consumption (Ritchie et al. Reference *Ritchie, Bates, Corley, McNeill, Davies, Liewald, Starr and Deary2014). This study indicates that any protective effects of alcohol consumption on cognitive change may be contingent on an individual's genetically influenced capacity to metabolise alcohol.
Diet and nutrition
Certain dietary components (Loef & Walach, Reference Loef and Walach2012; Morris, Reference Morris2012) and dietary patterns (Féart et al. Reference Féart, Samieri and Barberger-Gateau2010; Tangney et al. Reference Tangney, Kwasny, Li, Wilson, Evans and Morris2011; Allès et al. Reference Allès, Samieri, Féart, Jutand, Laurin and Barberger-Gateau2012) have been linked with better cognitive ageing in the literature, yet the results from the LBC studies have been mixed. In the LBC1936, the proportion of total variance in cognitive function at age 70 years accounted for by the age 70-reported intake of the nutrients B2, B12, folate, vitamin C was <1% (McNeill et al. Reference *McNeill, Jia, Whalley, Fox, Corley, Gow, Brett, Starr and Deary2011). In the same study, supplement use was associated with better cognitive function, but this was accounted for by a higher IQ in youth. Individuals adhering to a Mediterranean-type dietary pattern (Corley et al. Reference *Corley, Starr, McNeill and Deary2013) and consuming a greater intake of dietary flavonoids (Butchart et al. Reference *Butchart, Kyle, McNeill, Corley, Gow, Starr and Deary2011) and more caffeine (Corley et al. Reference *Corley, Jia, Kyle, Gow, Brett, Starr, McNeill and Deary2010b ) had better cognitive skills at age 70, but these associations largely disappeared upon adjustment for cognitive ability in youth. Individuals with a higher intake of these dietary components were more likely to have a higher IQ in both childhood and old age. In the same sample, those with a higher Mediterranean diet index score showed less total brain atrophy over a 3-year period (Luciano et al. Reference *Luciano, Corley, Cox, Hernández, Craig, Dickie, Karama, McNeill, Bastin, Wardlaw and Deary2017). However, the effect size was small (0.5%) and not corrected for multiple comparisons, and so replication studies are needed. In other analyses, healthy (nutrient-dense) dietary patterns were associated with significantly lower levels of circulating inflammatory markers (Corley et al. Reference *Corley, Starr and Deary2015). Given that chronic low-grade inflammation is a putative predictor of cognitive decline, a relationship between diet and cognitive decline via pro-inflammatory processes may be a promising avenue for future research. Dietary intake of iron was assessed in relation to brain ageing in the LBC1936, since brain iron accumulation is involved in neurodegenerative diseases. However, neither iron nor calorie or dietary cholesterol intake, at the levels found in normal western diets, was directly associated with iron deposition load assessed on structural MRI scans (Valdés Hernández et al. Reference *Valdés Hernández, Allan, Glatz, Kyle, Corley, Brett, Maniega, Royle, Bastin, Starr, Deary and Wardlaw2015a ). It appears that at least some aspects of diet that are associated with cognitive function in older age are confounded with prior cognitive ability, and probably the lifestyle changes associated with cognitive differences.
Dietary assessment was not included in the LBC1921 study, but lower serum B12 at age 79 was associated with greater cognitive decline between ages 11 and 79. By contrast, serum folate at age 79 correlated with age 11 IQ, and controlling for this reduced the correlation with IQ in old age to almost zero (Starr et al. Reference *Starr, Pattie, Whiteman, Deary and Whalley2005).
Intellectual activity
Performing socio-intellectual activities (examined using a latent factor) was no longer associated with general cognitive ability, processing speed or memory in the LBC1936, following adjustment for childhood IQ (Gow et al. Reference *Gow, Corley, Starr and Deary2012b ). Intellectual activities were not associated with any of the structural brain parameters assessed (Gow et al. Reference *Gow, Bastin, Maniega, Hernández, Morris, Murray, Royle, Starr, Deary and Wardlaw2012a ). However, we reported a positive effect of being bilingual on later-life cognition, even in those who acquired their second language in adulthood (Bak et al. Reference *Bak, Nissan, Allerhand and Deary2014). These effects could not be explained by other variables, such as childhood IQ, SES, immigration or gender. Early v. late acquisition showed differential effects, depending on childhood IQ. Overall, individuals with higher intelligence seem to benefit more from early acquisition and those with low intelligence from late acquisition, but neither group showed negative effects. Furthermore, learning a second language was related to better conflict processing, irrespective of initial childhood ability or social class (Cox et al. Reference *Cox, Bak, Allerhand, Redmond, Starr, Deary and MacPherson2016).
Social factors and psychological wellbeing
In analyses including social networks, social support and loneliness, loneliness was the only factor which contributed to the prediction of poorer old age cognitive abilities, explaining about 2% of the variance at age 79 in the LBC1921 (Gow et al. Reference *Gow, Pattie, Whiteman, Whalley and Deary2007). In the LBC1936, less loneliness, more social support and shared living arrangements were most consistently associated with aspects of cognitive ability, though these associations appeared to be partly accounted for by fewer symptoms of depression (Gow et al. Reference *Gow, Corley, Starr and Deary2013a ). In the same sample, lower levels of anxiety were associated with more favourable relative change in cognitive function between ages 11 and 70 (Johnson et al. Reference *Johnson, Gow, Corley, Starr and Deary2010). However, a large multicohort study (including the LBC1921) of mental wellbeing in relation to cognitive function found that associations in older people are small and may be confounded by personality trait differences (Gale et al. Reference *Gale, Cooper, Craig, Elliott, Kuh, Richards, Starr, Whalley and Deary2012).
Health
Intelligence in youth and older age is associated with important health outcomes (Deary et al. Reference Deary, Penke and Johnson2010), and age-related disease increases the risk of cognitive decline (Deary et al. Reference *Deary, Corley, Gow, Harris, Houlihan, Marioni, Penke, Rafnsson and Starr2009a ). An individual's cognitive trajectory is the result of a combination of shared influences with the rest of the body. However, some health conditions are modifiable via lifestyle changes and medication.
Physical fitness
In the LBC1921, grip strength and reasoning were correlated at each wave of testing in the ninth decade, but their trajectories of decline were not (Deary et al. Reference *Deary, Johnson, Gow, Pattie, Brett, Bates and Starr2011). A latent trait of physical fitness – derived from lung function, grip strength and walking speed – accounted for over 3% of the variance in cognitive change between ages 11 and 79 years in the LBC1921, after adjusting for childhood IQ (Deary et al. Reference *Deary, Whalley, Batty and Starr2006b ). The same physical fitness trait was associated with better 6-year cognitive change in the LBC1936, yet when physical measures were assessed individually, they showed few associations (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). Declining physical fitness over a 3-year period was associated with less brain volume at baseline in the LBC1936 sample, and did not diminish when covarying for education, social class, and health status (Aribisala et al. Reference *Aribisala, Gow, Bastin, Hernández, Murray, Royle, Maniega, Starr, Deary and Wardlaw2013), and longitudinally with age-related changes in brain structure (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Dickie, Valdés Hernández, Corley, Royle, Redmond, Munoz Maniega, Pattie, Aribisala, Taylor, Clarke, Gow, Starr, Bastin, Wardlaw and Deary2017). These results suggest that higher general fitness is protective against cognitive decline and brain ageing; this is important given that it is potentially modifiable.
Cardiovascular risk factors
The LBC studies have shown us that people's vascular health in later life are in part associated with early-life intelligence in addition to being associated with age-related cognitive change (McGurn et al. Reference *McGurn, Deary and Starr2008). In the LBC cohorts, lifestyle-related risk factors for cardiovascular disease (CVD) – diabetes, low high-density lipoprotein (HDL) cholesterol, and being overweight/obese – were (independently) associated with poorer cognitive function at age 70, but statistical significance was lost following adjustment for age 11 IQ in each of these analyses (Corley et al. Reference *Corley, Gow, Starr and Deary2010a , Reference *Corley, Starr and Deary2015; Mõttus et al. Reference *Mõttus, Luciano, Starr and Deary2013; Aslan et al. Reference *Aslan, Starr, Pattie and Deary2015).
A higher childhood IQ was found to predict lower hypertension in adulthood in a sample comprising the LBC1921 and MIDSPAN study such that there was a 3.15 mmHg decrease in systolic blood pressure and a 1.5 mmHg decrease in diastolic blood pressure for each standard deviation increase in childhood IQ (Starr et al. Reference ‡Starr, Taylor, Hart, Smith, Whalley, Hole, Wilson and Deary2004b ). However, individual differences in childhood IQ only partly accounted for the association between hypertension and lower adult cognitive function, suggesting that lifestyle factors have a part to play. Lower ankle-brachial index, a frequently used measure of generalised atherosclerosis, was associated with worse cognitive performance in older age, independently of prior cognitive ability, especially in the oldest old (>85 years), possibly because of long-term exposure to atherosclerotic disease (Laukka et al. Reference *Laukka, Starr and Deary2014). Multivariate analyses of the LBC1936 data suggest that diagnoses of CVD, hypertension or diabetes are not uniquely associated with cognitive performance (with the small exception of CVD and slower processing speed), or with cognitive decline, beyond the other predictors in the model (Tucker-Drob et al. Reference *Tucker-Drob, Briley, Starr and Deary2014). Following adjustment for age 11 IQ, Ritchie et al. (Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016) reported that the associations between CVD history and cognitive level in later life were attenuated by 90%.
In contrast, brain MRI studies in the LBC1936 indicate a small negative association between vascular risk factors (VRFs) and brain health, independently of prior ability. VRFs combined explained 1.4–2% of WMH variance, of which hypertension explained the most (Wardlaw et al. Reference *Wardlaw, Allerhand, Doubal, Hernández, Morris, Gow, Bastin, Starr, Dennis and Deary2014). A lower HDL cholesterol level (Dickie et al. Reference *Dickie, Ritchie, Cox, Sakka, Royle, Aribisala, Dickie, Ritchie, Cox, Sakka, Royle, Aribisala, Hernández, Maniega, Pattie, Corley, Starr, Bastin, Deary and Wardlaw2016) and poorer glycaemic control (in those who carry the APOE risk e4 allele) (Cox et al. Reference *Cox, Hernández, Kim, Royle, MacPherson, Ferguson, Maniega, Anblagan, Aribisala, Bastin, Park, Starr, Deary, MacLullich and Wardlaw2017), were identified as important independent CVD risk predictors for the progression of WMH from age 73 to 76. Hypertension is associated with increased WMH but research carried out by the LBC team has indicated that they may be associated indirectly, via increased arterial stiffness (Aribisala et al. Reference *Aribisala, Morris, Eadie, Thomas, Gow, Hernández, Royle, Bastin, Starr, Deary and Wardlaw2014).
The current prevailing view is that VRFs are potential aetiological factors for cognitive decline but that the association is age-dependent. For some VRFs, such as obesity, hypertension and hypercholesterolaemia, it is mid-life levels that seem to be more important than those measured at older ages for cognitive outcomes (Ballard et al. Reference Ballard, Gauthier, Corbett, Brayne, Aarsland and Jones2011; Qiu & Fratiglioni, Reference Qiu and Fratiglioni2015). Many clinical trials and epidemiological studies, including the LBCs, have been conducted among older adults >65 years when VRFs probably no longer act as risk factors and might be more likely to be modified by age-related concomitant disease.
Medications
Taking a greater number of medications is associated with a relative worsening of cognitive function from childhood to old age, explaining about 2.2% of the variance in cognitive change in the LBC1921 (Starr et al. Reference *Starr, McGurn, Whiteman, Pattie, Whalley and Deary2004a ). The total number of medications prescribed is a proxy indicator of disease burden, and, therefore, it is unclear whether it is the drugs themselves or the underlying disease for which they are prescribed that is causing the relative decline in IQ. In the same sample, however, taking statins, as indicated for CVD, was associated with a relative improvement in cognitive change across the life span, explaining about 2.8% of the variance (Starr et al. Reference *Starr, McGurn, Whiteman, Pattie, Whalley and Deary2004a ). Statin users in both LBC cohorts had lower childhood IQs, but the cross-sectional associations with cognitive function at age 70 in the LBC1936 were not robust in the final models, which adjusted for total cholesterol levels (Corley et al. Reference *Corley, Starr and Deary2015). In other literature, there is no consistent epidemiological evidence that exists for an association of cognitive decline with statins, anti-hypertensive medications or anti-inflammatory drugs (Daviglus et al. Reference Daviglus, Bell, Berrettini, Bowen, Connolly, Cox, Dunbar-Jacob, Granieri, Hunt, McGarry, Patel, Potosky, Sanders-Bush, Silberberg and Trevisan2010).
Health literacy
Health literacy was related, in the LBC1936, to childhood IQ and to IQ change from age 11 to 70, independently of SES, education, personality, and with worse general fitness, greater body mass index (BMI) and fewer natural teeth (Murray et al. Reference *Murray, Johnson, Wolf and Deary2011; Mõttus et al. Reference *Mõttus, Johnson, Murray, Wolf, Starr and Deary2014). Both studies found that health literacy is related to general (not health-specific) cognitive differences, and therefore raise the possibility that ‘health literacy’ measures are little more than cognitive ability measures. The results of Mõttus et al. suggest that health literacy measures do not add additional variance to the already present cognitive–health associations. Lifelong health may be associated with health literacy via the effect of general cognitive abilities on health knowledge and health management, of the sort an individual may require when diagnosed with an illness.
Biomedical factors
Allostatic load (AL)
AL has been proposed as a general framework for understanding the cumulative effects of life stress on individuals. Greater AL, which we and others have tried to capture as a compendium measure of a range of inflammatory, cardiovascular, and metabolic measures (e.g. Booth et al. Reference *Booth, Starr and Deary2013b ), was associated in LBC1936 with poorer general cognitive ability, processing speed and knowledge, but not memory or nonverbal reasoning, and with brain volume measures (especially lower white matter volume) (Booth et al. Reference *Booth, Royle, Corley, Gow, Hernández, Maniega, Ritchie, Bastin, Starr, Wardlaw and Deary2015). The associations of AL with cognitive abilities were not mediated by the brain volume measures. AL at age 73 was associated with IQ scores at age 11 but did not predict cognitive change from age 11 to 73. In this first study to consider AL, cognitive ability and neuroimaging measures of brain volume, the results suggest that the cumulative wear and tear on the body from a lifetime of stress responsivity is associated with both brain structure and cognitive ability in early- and later life but not with cognitive change from childhood to the early 70s.
Elevated levels of salivary cortisol (often considered an important component of AL) were related in a subsample of the LBC1936 to poorer lifetime cognitive change, but only for levels taken in response to a mild psychological stressor, and not for diurnal levels (Cox et al. Reference *Cox, Bastin, Ferguson, Maniega, MacPherson, Deary, Wardlaw and MacLullich2015a , Reference *Cox, MacPherson, Ferguson, Royle, Maniega, Hernández, Bastin, MacLullich, Wardlaw and Deary b , Reference *Cox, Hernández, Kim, Royle, MacPherson, Ferguson, Maniega, Anblagan, Aribisala, Bastin, Park, Starr, Deary, MacLullich and Wardlaw2017). This effect was significantly mediated via poorer white matter microstructure, but was unrelated to differences in hippocampal volume or shape.
A potential contributor to AL is infection from the cytomegalovirus (CMV). The LBC team reported that significant inverse CMV infection and cognitive ability associations were confounded by early-life cognitive, demographic and environmental factors (Gow et al. Reference *Gow, Firth, Harrison, Starr, Moss and Deary2013b ). In those who were CMV-infected, however, higher CMV antibody level was significantly associated with lower general cognitive ability and processing speed, accounting for around 1–2% of the variance, even after controlling for potential confounds. This indicates a potentially detrimental effect (via lifelong wear and tear) of increased antibody response rather than CMV infection per se.
Other biomedical factors
Few other biomedical factors examined in the cohorts have yielded robust associations with cognitive ageing. Research covering inflammation (Luciano et al. Reference *Luciano, Marioni, Gow, Starr and Deary2009c ; Aribisala et al. Reference *Aribisala, Morris, Eadie, Thomas, Gow, Hernández, Royle, Bastin, Starr, Deary and Wardlaw2014), thyroid function (Booth et al. Reference *Booth, Deary and Starr2013a ), renal function (Munang et al. Reference *Munang, Starr, Whalley and Deary2007), telomere length (Harris et al. Reference *Harris, Deary, MacIntyre, Lamb, Radhakrishnan, Starr, Whalley and Shiels2006, Reference *Harris, Martin-Ruiz, von Zglinicki, Starr and Deary2012, Reference *Harris, Marioni, Martin-Ruiz, Pattie, Gow, Cox, Corley, von Zglinicki, Starr and Deary2016b ), and retinal blood vessel parameters (Patton et al. Reference *Patton, Pattie, MacGillivray, Aslam, Dhillon, Gow, Starr, Whalley and Deary2007; Henderson et al. Reference *Henderson, Allerhand, Patton, Pattie, Gow, Dhillon, Starr and Deary2011; Laude et al. Reference *Laude, Lascaratos, Henderson, Starr, Deary and Dhillon2013; McGrory et al. Reference *McGrory, Taylor, Kirin, Corley, Pattie, Cox, Dhillon, Wardlaw, Doubal, Starr, Trucco, MacGillivray and Deary2016) all show weak-to-null associations with the level and change of cognitive domains or a variety of brain measures. Of those that were initially significant, many became non-significant or were markedly attenuated following adjustment for childhood IQ scores, i.e. they are examples of confounding or reverse causation. The association between inflammatory markers (particularly fibrinogen) and processing speed was an exception; significance was maintained in the presence of childhood IQ and/or CVD risk factor adjustments. This might reflect variation in physiological integrity (Luciano et al. Reference *Luciano, Marioni, Gow, Starr and Deary2009c ). However, inflammatory processes, long implicated in cognitive decline and the development of mild cognitive impairment (MCI) and dementia, were only weakly associated with markers of cerebral small vessel disease (Aribisala et al. Reference *Aribisala, Morris, Eadie, Thomas, Gow, Hernández, Royle, Bastin, Starr, Deary and Wardlaw2014).
Genetics and environment: a lifelong interaction
Accepting the importance of both genetic and environmental contributions to people's differences in cognitive ageing acknowledges their interplay across the life course and constitutes a new challenge for future research. Indeed, there is moderate-to-strong heritability of lifestyle factors that is stable over age (McGue et al. Reference McGue, Skytthe and Christensen2014), unsurprising, given that most behavioural characteristics are (partly) inherited. However, genetic influences can be modified by physiological and environmental influences, and these may play a larger role in the expression of cognitive impairments (Mortimer et al. Reference Mortimer, Borenstein, Gosche and Snowdon2005; Stern, Reference Stern2012). Alcohol (Ritchie et al. Reference *Ritchie, Bates, Corley, McNeill, Davies, Liewald, Starr and Deary2014) and glycaemic control (Cox et al. Reference *Cox, Hernández, Kim, Royle, MacPherson, Ferguson, Maniega, Anblagan, Aribisala, Bastin, Park, Starr, Deary, MacLullich and Wardlaw2017) have been highlighted here as potential targets for mitigating cognitive and brain ageing in those who fall into a risk group with a greater genetic predisposition towards such deleterious effects. Heritability should not be erroneously interpreted as evidence for unalterable genetic determination of behaviour. An illustration of the variable determinacy of genetic factors was the LBC1936 study in which polygenic scores for type 2 diabetes were more strongly associated with glycated haemoglobin in those with lower childhood IQ scores when compared with higher IQ scorers (Mõttus et al. Reference *Mõttus, Luciano, Starr, McCarthy and Deary2015). Behavioural change may be challenging, but it is possible either by individuals or by clinicians, as part of a delivered health-care intervention.
A multivariate approach to cognitive and brain ageing
Many of the studies reported above, take in essence, a univariate approach; that is, given that they do have some appropriate covariates, they focus mostly on a single potential determinant of cognitive level or change. Of course, some of these potential determinants will themselves be associated, and so we cannot simply make a list of determinants and assume they will have additive associations. Therefore, the LBC studies have recently taken a more complex multivariate approach in which important predictors were modelled simultaneously on cognitive level (age 70) and change (between ages 70 and 76 in LBC1936) using latent growth curve models (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). In these analyses, univariate correlates of age 70 cognitive ability level (at the same time measuring lifetime cognitive change from age 11 to age 70) were many; those individuals with better general cognitive function at age 70 were younger when tested, had higher childhood intelligence, were more educated, were from more professional occupational classes, lived in more affluent areas, were fitter (on all three performance indicators), had lower BMI, were less likely to smoke, and were less likely to have cardio-metabolic illness. Carriers of the APOE e4 allele also performed less well on the visuospatial and speed domains. Following multivariate adjustment, however, only age, sex (female), higher age 11 IQ, more education, and better forced expiratory volume remained significant correlates of better general cognitive ability level. Importantly, none of the social or health variables remained significantly associated with cognitive ability level when modelled together with other covariates (and correcting for multiple comparisons).
Likewise, few predictors of less cognitive decline between the ages 70 and 76 in LBC1936 survived multivariate modelling, with the exception of APOE e4 non-carrier status, sex (female) and better grip strength. The predictors included in these analyses together accounted for 80.5% of the variance in cognitive level, and 16.1% of the variance in general cognitive decline.
Predictors of longitudinal changes in brain structure have rarely been examined using multiple heterogeneous variables simultaneously. In a recent multivariate investigation of neurostructural changes in the LBC1936 (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Dickie, Valdés Hernández, Corley, Royle, Redmond, Munoz Maniega, Pattie, Aribisala, Taylor, Clarke, Gow, Starr, Bastin, Wardlaw and Deary2017), many variables significantly correlated with baseline (age 73) brain structure, but few could account for significant heterogeneity in subsequent brain change (between 73 and 76). Better physical fitness and APOE e4 non-carrier status were the most consistent predictors of differential rates of brain ageing, though effect sizes were small. Education and prior intelligence were correlates of brain structure, but not related longitudinally to ageing-related changes in brain structure. The distinction between cross-sectional and longitudinal analyses of brain ageing is important: in cross-sectional studies, it is not possible to differentiate between developmental processes that occur in earlier periods of life from effects that are specifically ageing-related (Tucker-Drob & Salthouse, Reference Tucker-Drob and Salthouse2011). Subsequent waves of the LBC1936 will bring more occasions of brain imaging over longer periods, providing a larger target for our predictors of differential brain ageing.
Collectively, the LBC studies suggest that a number of environmental factors may have small associations with cognitive abilities in later life. It is likely that many of these factors covary. If there are multiple univariate predictors, and few that survive the multivariate model, then one possibility is that some might mediate others via testable pathways. To understand the data more fully requires techniques such as structural equation modelling, which can explicitly explore mediation effects, latent traits and multiple outcome variables; there are many examples of these in the LBC reports. Multivariate techniques – considering many predictors together – may provide a more realistic consideration of the predictors of cognitive and brain ageing.
Discussion
In this paper, we have described some of the follow-up studies of people who took part in the SMS of 1932 and 1947, and given overviews of ~100 LBC papers that pertain to cognitive ageing across the life course, and to brain ageing, in the context of some existing literature. Broadly, the LBC studies have found two things from the cognitive ability test scores at age 11 years: first, those with higher childhood intelligence tend to be healthier and more cognitively able in old age; second, that some people got to older age with better or worse cognitive function that one would predict given their ability at age 11. Observations by the LBC studies over the past 15 or so years have helped to identify which candidate determinants are associated with these differences in lifetime cognitive resilience and brain and general health, across genetic, socio-demographic, health, and lifestyle domains.
Overall, the findings support the link suggested by Juvenal (first to second century AD) between a healthy body and a healthy mind. An older body, which is physiologically fitter and engages in regular physical activity, is associated with a higher childhood IQ score, tends to experience less cognitive change over the life course, and less cognitive decline and deleterious brain changes within old age. VRFs, such as smoking, hypertension and cholesterol, and greater cumulative AL, may have important associations with cortical thinning, brain white matter integrity, and brain atrophy in later life. All of this is in line with research, which provides evidence that the ageing brain retains a considerable functional plasticity which is very much dependent on the interaction of individuals with their environment (see Mora, Reference Mora2013).
The multivariate results to emerge from work on the cohorts suggest that when many potential predictors are modelled simultaneously, only a subset of correlates of cognitive level and brain structure are predictive of differences in cognitive and brain ageing, at statistically significant levels (Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016; Reference *Ritchie, Tucker-Drob, Cox, Dickie, Valdés Hernández, Corley, Royle, Redmond, Munoz Maniega, Pattie, Aribisala, Taylor, Clarke, Gow, Starr, Bastin, Wardlaw and Deary2017). One possible reason for stronger results in the cross-sectional data is that determinants have had most of the life course to contribute to brain and cognitive variance. In the change analyses within older age, we report only 3-year change in MRI data, and 6-year change in cognitive markers. Even though it is during a period of time when change over time is greater, it is still hard reliably to detect very subtle changes over such a short period. Nonetheless, our findings are complementary to those of systematic reviews and meta-analyses (see Daviglus et al. Reference Daviglus, Bell, Berrettini, Bowen, Connolly, Cox, Dunbar-Jacob, Granieri, Hunt, McGarry, Patel, Potosky, Sanders-Bush, Silberberg and Trevisan2010; Plassman et al. Reference Plassman, Williams, Burke, Holsinger and Benjamin2010), which report that few observational studies had sufficient evidence from which to draw conclusions about particular behavioural, social and economic factors, and their association with cognitive decline. Where there are few reports of significant associations with cognitive change, then interventions may be less likely to succeed. However, where there are more consistent significant associations with cognitive change, given that observational studies cannot address causality, then even this does not necessarily mean we have identified a target for intervention.
Strengths and limitations
As birth cohort samples (who share the same year of birth), there is a natural control for much of the confounding effects of chronological age. The LBCs are unusually well-phenotyped samples, which benefit from multiple later-life waves of follow-up. The use of multimodal neuroimaging in concert with these other data can help elucidate some potential underlying mechanisms. A major strength of these studies is the availability of cognitive data at distinct time points (age 11 and later life) creating a rare opportunity to distinguish factors that might have a true effect on later-life cognitive ability – possible causal factors – from the potentially confounded factors (i.e. confounded by childhood IQ). Ideally, the influence of premorbid cognitive ability on cognitive ability in later life is best assessed by using the earliest possible direct measure of cognitive ability to avoid the ‘contamination’ by lifestyle, adult SES, and health factors that may affect cognitive abilities, even by early adulthood. Childhood cognitive ability scores are rare in studies of cognitive ageing and shine fresh light on aspects rarely addressed by other observational investigations.
The LBC studies have limitations. Given that our samples are self-selecting, they are biased towards high-functioning, well-educated, motivated volunteers, as is the case in many studies of cognitive ageing. Nonetheless, potential incipient cognitive impairment among participants, as yet clinically undetected, must be acknowledged. Although the LBC studies can measure relative cognitive change from childhood to older age, they have not documented the cognitive changes that occurred from childhood to early adulthood and from there to age 70 (LBC1936) or age 79 (LBC1921). It is possible that changes over these periods of the life course are associated more strongly with some of the proposed predictors. Some lifestyle behaviours including those that influence cardiovascular and metabolic risk may be most influential in midlife compared with late life (Kuh & Cooper, Reference Kuh and Cooper1992; Carlson et al. Reference Carlson, Helms, Steffens, Burke, Potter and Plassman2008; Lee et al. Reference Lee, Back, Kim, Kim, Na, Cheong, Hong and Kim2010; Rovio et al. Reference Rovio, Spulber, Nieminen, Niskanen, Winblad, Tuomilehto, Nissinen, Soininen and Kivipelto2010). A particularly salient example of this is cholesterol (van Vliet et al. Reference van Vliet, van de Water, de Craen and Westendorp2009). Finally, due to the synergistic effect of lifestyle factors, the extent to which the apparent effects of one health behaviour is attributable to (i.e. confounded or mediated by) another is unclear. For example, other studies show that physical activity and exercise are increased by active social networks (Leroux et al. Reference Leroux, Moore, Richard and Gauvin2012), and that smokers tend to have poorer dietary choices than non-smokers (Woodward et al. Reference Woodward, Bolton–Smith and Tunstall–Pedoe1994). The focus on single associates/interventions may underestimate the effect of multimodal or combined approaches.
Confounding and reverse causation
One of the most consistent observations to emerge from the LBC studies is that the apparent causes of cognitive ageing may not be causes at all. Some of the putative health and lifestyle determinants of cognitive ageing differences are themselves predicted by long-ago measured childhood differences in intelligence (Whalley et al. Reference Whalley, Dick and McNeill2006; Deary et al. Reference *Deary, Corley, Gow, Harris, Houlihan, Marioni, Penke, Rafnsson and Starr2009a ; Deary, Reference *Deary2010). Therefore, a cross-sectional association between a variable such as diet and cognitive ability in older age might – in part or whole – be the result of childhood IQ predicting both. Physical function and disease states may in part be acting as proxy markers of lower childhood IQ, and this might account for portions of the variance in cognitive ability years later. The repeated demonstration of confounding by prior ability across multiple areas in the LBC studies covering lifestyle, health and biomedical markers, and MRI indexes of brain health, is relevant to the debate on what is called ‘differential preservation’ v. ‘preserved differentiation’ (Salthouse, Reference Salthouse2006; Bielak, Reference Bielak2010; Gow et al. Reference Gow, Mortensen and Avlund2012d ; Bielak et al. Reference Bielak, Cherbuin, Bunce and Anstey2014). Central to this theory is the critical question of whether certain factors alter the trajectory of age-related cognitive decline (differential preservation) or are associated with enhanced baseline cognitive ability (preserved differentiation). In searching for determinants of cognitive ageing, researchers aim to identify evidence of differential preservation. This important distinction highlights the need to design studies that will shed light on directionality when empirically feasible.
However, does childhood IQ represent a true causal link in some of the reported associations? One could argue that confounding by prior IQ does not necessarily rule out a protective or adverse effect of a predictor. Twin studies provide some reassurance for this (e.g. Crowe et al. Reference Crowe, Andel, Pedersen, Johansson and Gatz2003; Gatz et al. Reference Gatz, Mortimer, Fratiglioni, Johansson, Berg, Reynolds and Pedersen2006). The role of lifetime IQ might, more realistically, be one of substantive causation; that is, higher IQ might have a direct impact on the uptake of healthy behaviours via better health literacy, better decision making, and a greater understanding of the consequences of one's behaviour. In essence, higher IQ individuals may be more likely to engage in a lifestyle that is protective against cognitive decline. With these theoretical considerations in mind, it seems most plausible that there is a dynamic cycle involving IQ, self-management of health, and ultimate cognitive outcomes.
Another possibility is that associations between candidate determinants and cognitive health may be caused by some third confounding variable or set of variables and may be the result of a more basic factor(s) affecting both the apparent cause and effect. Thus, many researchers are interested in whether there are general ageing effects, known as the ‘common cause’ theory of ageing that occur across cognitive and physical modalities attributable to core biological processes that deteriorate with age (Schaie, Reference Schaie2005). However, recent evidence from the LBC studies has cast doubt on this idea. Not only did physical functions appear to age separately, there was also no compelling evidence for coupled change across cognitive and physical functions in later life (Deary et al. Reference *Deary, Johnson, Gow, Pattie, Brett, Bates and Starr2011; Ritchie et al. Reference *Ritchie, Tucker-Drob, Cox, Corley, Dykiert, Redmond, Pattie, Taylor, Sibbett, Starr and Deary2016). Contrary to the ‘common cause’ hypothesis, these findings suggest multiple largely independent causes of ageing across bodily systems.
Marginal gains: a modest multivariate recipe for healthy cognitive ageing
From our LBC results overall, it seems likely that, other than intelligence scores from youth, a large number of genetic and other predictors have small associations with cognitive efficiency in later life. The most consistently cited factors linked to cognitive ability or ageing – smoking, physical activity, and variation in the APOE gene – have effect sizes of the same magnitude, often accounting for 1–2% or less of the variance in cognitive outcomes (Whalley et al. Reference †Whalley, Fox, Deary and Starr2005; Deary et al. Reference *Deary, Corley, Gow, Harris, Houlihan, Marioni, Penke, Rafnsson and Starr2009a , Reference *Deary, Yang, Davies, Harris, Tenesa, Liewald, Luciano, Lopez, Gow, Corley, Redmond, Fox, Rowe, Haggarty, McNeill, Goddard, Porteous, Whalley, Starr and Visscher b ). Though small in statistical terms, modifying some of such factors or their downstream effects could have substantial benefits at population levels. It is important to know what these effects are, so we can discover the combination of factors that might help people age better.
In terms of preserving mental abilities or delaying cognitive decline, the available evidence suggests that there is no magic bullet. Thus, the small effects we find, even if replicable and causal, might be useful at the population level, but are not necessarily good predictors at an individual level. Perhaps a helpful way of thinking about successful cognitive ageing can be gained from the theory of marginal gains, a concept that has become commonplace in the world of elite sport (see Clear, Reference Clear2015, http://jamesclear.com/marginal-gains). The principle behind the marginal gains idea is that if you improve in every variable (or lifestyle factor) underpinning or influencing your performance (in our case, cognitive abilities) by just 1% or so, then, cumulatively, you get a significant improvement, or an ‘aggregate of marginal gains’. In terms of Team Sky (GB's professional cycling team), every aspect of cycling was decomposed and improved. A programme of small changes was implemented from addressing nutrition, to the ergonomics of the bike seat, clothing, bedding, sleeping position, and even handwashing techniques to prevent infections. Within 3 years, the team had won three Tour de France competitions and 70% of all track cycling gold medals at the 2012 Olympic Games.
Mutatis mutandis, an approach, which recognises the complexity of the factors influencing brain and cognitive health in later life might provide a useful framework for promoting healthy cognitive ageing. At an individual level, it encourages a proactive approach, in finding and exploiting small margins for improvement at every stage. Small changes, as suggested by marginal gains theory, may help individuals to overcome the perceived barriers to behaviour change (e.g. self-confidence and self-efficacy), which often prevent people from embarking on a new and often overwhelming regime. Clearly, some contributions to cognitive ageing are more open to interventions than others. Over time, whereas it may not be apparent, each small positive lifestyle change, such as going out for a walk every day, could add up to a significant advantage in terms of improving physical health (reducing risk of hypertension, dyslipidemia and metabolic syndrome, obesity), mental health (loneliness and social isolation, depression) and cognitive health, and this is likely to have a cumulative population-level effect. Tailoring interventions that take into account individual differences in risk genotypes might help to target an optimal set of gains. The ‘magic’ may lie in the accumulation of many such gains over time to put individuals, in elite sporting terms, ‘ahead of the opposition’. Let us not fail to mention that ‘marginal gains’ would be a life-course approach; e.g. given that childhood cognitive ability accounts for about half of the variance in cognitive function in older age, then any effective boost to cognitive level in youth will be some insurance against descending to lower cognitive levels in older age.
More to life than cognitive function
Cognitive function is critical for mental and physical health. However, happiness and satisfaction with life are also key indices of successful ageing. Life satisfaction in the cohorts was unrelated to IQ in either childhood or late adulthood or to cognitive change over the intervening period. It may be that an individual's subjective wellbeing comes from having sufficient cognitive ability for the important aspects of one's life (Gow et al. Reference *Gow, Whiteman, Pattie, Whalley, Starr and Deary2005). Life satisfaction is also partly a result of personality factors, mood states (Brett et al. Reference *Brett, Gow, Corley, Pattie, Starr and Deary2012), and social factors, such as having a strong social network (Gow et al. Reference *Gow, Pattie, Whiteman, Whalley and Deary2007). Although space prohibits more detailed discussion, some of our reports in the LBCs have focussed on health and happiness/wellbeing in older age, both quantitatively (Zammit et al. Reference *Zammit, Starr, Johnson and Deary2012, Reference *Zammit, Starr, Johnson and Deary2014) and qualitatively (Carpentieri et al. Reference ‡Carpentieri, Elliot, Brett and Deary2016, Reference ‡Carpentieri, Elliot, Brett and Deary2017; Lapsley et al. Reference *Lapsley, Pattie, Starr and Deary2016).
Conclusions
People start off at different cognitive levels and vary in how much their cognitive functions change with age, even in those who do not have dementia or other neurodegenerative changes. The LBC studies suggest that cognitive and brain ageing are most likely the result of a multivariate accumulation of disparate influences. Potential risk and protective factors include contributions from genetic, medical, lifestyle and psychosocial domains (see Box 1). Identifying these factors is a key priority for research aiming to address the challenges associated with demographic ageing. Lifestyle factors can be promoted or discouraged, as appropriate, via interventions aimed at delaying, ameliorating or even reversing age-related cognitive decline. We must hope that even genetic contributions, linked to cognitive change and decline, have discoverable mechanisms which might afford interventions. But, whereas predictors of cognitive level in old age are numerous, predictors of cognitive decline's slope – that is, correlates of differential preservation – have, as yet, been few and far between, often with very small effect sizes.
Further longitudinal investigations of potentially malleable factors and cognitive decline in the LBC are in progress, affording greater power, reliably to detect these subtle associations due to longer follow-up periods and a greater number of sampling points. We also continue to expand our information sources on the participants, which now includes whole-genome sequencing on almost all participants, DNA methylation testing on most participants on most waves, gene expression on LBC1936 at 70 and 76, post-mortem brain tissue (Henstridge et al. Reference *Henstridge, Jackson, Kim, Herrmann, Wright, Harris, Bastin, Starr, Wardlaw, Gillingwater, Smith, McKenzie, Cox, Deary and Spires-Jones2015), stem cells, lifetime addresses on LBC1936 to assess environmental exposures, and National Health Service medical records linkage.
Future research should continue to examine predictors of actual cognitive changes rather than simple levels of performance, because the latter are ambiguous with respect to the temporality and direction of causation. At the present time, in order to enhance one's chances of healthy cognitive ageing, the stronger cases may be made for being physically active and fit, keeping one's health in check (and keeping AL low), and avoiding smoking; and perhaps less strong cases may be also made for learning a new language, increasing one's social network, and eating healthy. Small and manageable improvements across a broad range of behaviours have potential for improving long-term cognitive and brain outcomes.
Box 1 Key points
-
• A Good Start – Intelligence differences in youth are the largest contributor to cognitive ability differences in older age.
-
• Stable Minds – Intelligence differences measured at age 11 are relatively stable across the life course, even into the ninth decade.
-
• Genetic Contributions – Common genetic variants account for about 24% of change in general cognitive ability between youth and old age. Variation in APOE is part of this.
-
• Few Clear Determinants of Change – There are multiple correlates of cognitive ability level in later life but as-yet few reliable genetic, lifestyle, health and psychosocial predictors of cognitive change.
-
• Clearest Results – The most consistently-cited lifestyle and health factors linked to brain and cognitive ability or cognitive ageing are smoking, physical activity and fitness, and allostatic load.
-
• Social Factors – Education and occupational complexity might also contribute to healthy cognitive ageing in terms of level but not of slope.
-
• Confounding/reverse causation – Some factors, such as body mass index, diet, and inflammation, are associated with cognitive function in older age but these associations largely disappear after adjusting for childhood IQ, implying that the latter might be a confounder that is associated to the supposed exposure.
-
• Cortical Disconnection – Declines in brain white matter microstructure are coupled with declines in cognitive ability in some, but not all, cognitive domains.
-
• Gene × Environment – Some people may be more predisposed to the possible negative cognitive effects of bio-behavioural factors (such as alcohol and poor glycaemic control).
-
• Marginal Gains – Successful cognitive and brain ageing is most probably achieved by optimising a number factors linked to brain and cognitive measures, which each only account for a small % of the variance.
-
• Last Cautions – These points are largely based on observational and not intervention studies, and also require independent replication.
Supplementary Material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291717001489.
Acknowledgements
We thank the LBC1921 and LBC1936 participants. We also thank: the members of the LBC1921 and LBC1936 research team who collected and collated the data analysed; the other LBC study investigators and reports of the authors; the nurses, radiographers, and other staff at the Wellcome Trust Clinical Research Facility and the Brain Research Imaging Centre in Edinburgh; the staff at Lothian Health Board; and the staff at the Scottish Council for Research in Education (SCRE) Centre at the University of Glasgow.
The Lothian Birth Cohort 1921 data collection was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; 15/SAG09977) and by a Royal Society-Wolfson Award to author I.J.D. The LBC1936 is supported by Age UK (Disconnected Mind programme grant), which also supports author J.C. Brain imaging acquisition and analysis (which took place at the Brain Imaging Research Centre, University of Edinburgh) was supported by the Medical Research Council (MRC; G1001245, G0701120), which also supports author S.R.C. (MR/M013111/1). The work was undertaken by the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the UK Medical Research Council (MRC) is gratefully acknowledged.
Declaration of Interest
None.
Ethical Standards
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
Open access
