Intelligence and life expectancy in late adulthood: A meta-analysis- Macarena Sánchez-Izquierdo et al. Intelligence, Volume 98, May–June 2023, 101738. Mar 2023. https://doi.org/10.1016/j.intell.2023.101738
Highlights
• There is a positive relationship between intelligence and survival.
• The most robust moderator is years of follow-up.
• Intelligence is a protective factor for reaching upper-middle age, thereafter survival depends less on intelligence and more on other factors.
Abstract: In an aging society, it is crucial to understand why some people live long and others do not. There has been a proliferation of studies in recent years that highlight the importance of psycho-behavioural factors in the ways of aging, one of those psychological components is intelligence. In this meta-analysis, the association between intelligence and life expectancy in late adulthood is analysed through the Hazard Ratio (HR). Our objectives are: (i) to update Calvin's meta-analysis, especially the estimate of the association between survival and intelligence; and (ii) to evaluate the role of some moderators, especially the age of the participants, to explore intelligence–mortality throughout adulthood and old age. The results show a positive relationship between intelligence and survival (HR•: 0.79; 95% CI: 0.81–0.76). This association is significantly moderated by the years of follow-up, the effect size being smaller the more years elapse between the intelligence assessment and the recording of the outcome. Intelligence is a protective factor to reach middle-high age, but from then on survival depends less and less on intelligence and more on other factors.
Keywords: IntelligenceMortalitymeta-analysisSystematic review
4. Discussion
>20 longitudinal studies from several countries (Australia, Denmark, Luxembourg, Sweden, United Kingdom, and USA) have demonstrated the link between higher intelligence and longer life. This gave rise to the field of cognitive epidemiology, which focuses on understanding the relationship between cognitive functioning and health.
This study aimed to update Calvin's meta-analysis, confirm the quantification of this association and also analyse whether the intelligence–mortality association varies across adulthood and old age. We found evidence that having intelligence of at least 1-SD above the mean seems to reduce the mortality rate, although our rate was a little lower (21.6%) than that of Calvin's meta-analysis (24%).
Another objective of the study was to analyse the influence of several factors as possible moderators, specifically bias, age, and sex. Our results showed that recent studies tend to find a weaker association between intelligence and mortality than older studies. Along this line, Calvin and colleagues have shown a trend for larger cohorts accumulating in more recent years (Calvin et al., 2011).
The average life expectancy of women exceeds that of men, however, sex does not moderate the association between intelligence and mortality, being the same for men and women, as previously reported in twin longitudinal studies (Arden et al., 2016) and Calvin et al. (2011).
The question “What causes the relationship between intelligence and longevity/ mortality?” remains unsolved and crucial. Factors such as childhood environment, family income, schooling, and healthy/unhealthy lifestyle habits (diet, exercise, tobacco use, alcohol, illnesses), have been studied (Deary, Weiss, & Batty, 2010; Whalley & Deary, 2001). As Deary et al. (2021) suggested, there seems to be a reciprocal dynamic association between intelligence and health throughout life, and although there are several constructs associated with health/ illness and death (e.g., parental social class, intelligence in youth, more education, higher health literacy, healthy behaviors, and more affluent social class) shared genetic differences are likely to account for only a small proportion of these associations.
In this study we wanted to re-evaluate the influence of Socio-economic status as a predictor of mortality; our results showed that childhood SES did not moderate the potential of intelligence for predicting mortality. Although several studies (Batty et al., 2007; Hemmingsson, Melin, Allebeck, & Lundberg, 2009) suggested that intelligence had effects on the risk of mortality independent from those of early socio-economic influences, other studies suggested that SES was not a confounder of the intelligence–mortality association (Calvin et al., 2011, Calvin et al., 2017). Furthermore, a study of over 900 Scottish participants (Hart et al., 2003) found that statistically controlling for economic class and a measure of “deprivation” reflecting unemployment, overcrowding, and other adverse living conditions accounted for only about 30% of the IQ-mortality correlation.
Along this line, Gottfredson (Gottfredson, 2004) argued that underlying IQ differences explained social inequalities in health and that these were not necessarily mediated via adult/person's-own SES. This idea was tested by Batty, Der, Macintyre, and Deary (2006) who found that IQ does not completely explain socioeconomic inequalities in health, however, it might contribute to them through a variety of processes.
Another line of research suggested that genes may contribute to the link between IQ and mortality. Arden and colleagues (Arden et al., 2016) analysed three twin studies (from the U.S., Denmark, and Sweden) and found a small positive phenotypic correlation between intelligence and lifespan, furthermore, in the combined sample, the genetic contribution to covariance was 95%; in the US study, 84%; in the Swedish study, 86%, and in the Danish study, 85%. As the authors highlighted, any genetic factors that contribute to intelligence and mortality may operate indirectly via good health choices or higher income which leads to better healthcare. Deary, Harris, and Hill (2019); Deary et al. (2021)) reviewed the genetics through genome-wide association studies (GWASs), Genome-wide complex trait analysis (GREML), and LD regression studies, which allowed them to estimate genetic correlations between phenotypes (intelligence and health).
The second question of this study: “Does the relationship between intelligence and mortality change in the older adults?” Yes, that relationship changes when the most long-lived studies were compared with the youngest studies. As several studies have suggested (Arden et al., 2016; Hart et al., 2005), the causes of the association between intelligence and lifespan may vary between ages. Childhood IQ has been related to mortality in Scottish populations: Hart et al., (2005) showed that childhood IQ was significantly related to deaths occurring up to age 65, but not to deaths occurring after age 65, whereas the Aberdeen study found that people with a lower IQ were less likely to be alive at age 76 (Whalley & Deary, 2001).
Analysing whether the relationship between intelligence and mortality changes in the older adults, our results showed a small but significant positive slope for FUY, which reflected that the association was slightly smaller as more years elapsed between time 1 (intelligence assessment) and time 2 (check for survival). This means that the relationship between intelligence and survival is dampened. Our findings confirm results from the Midspan studies (Hart et al., 2005). As suggested by several studies, one possible reason might be that higher IQ might be associated with better healthcare and engaging in healthier behaviors (Deary et al., 2019; Hart et al., 2005; Wraw, Der, Gale, & Deary, 2018), which is associated to a lower mortality risk (Gottfredson, 2004; Gottfredson & Deary, 2004). IQ might also predispose to conditions of adult life (Marmot & Kivimäki, 2009), quitting smoking in later life (Batty et al., 2007; Daly & Egan, 2017), and entry into safer environments (Whalley & Deary, 2001), which promote staying healthier and living longer.
One way of discovering why intelligence and mortality are related and why this association seems to be smaller at higher ages might be to review the specific causes of death to which intelligence relates from childhood and adulthood. Along this line, several studies have shown its association with most of the major causes of death. The main literature has reported inverse patterns of the association between childhood intelligence and respiratory disease (Batty, Deary, & Zaninotto, 2016; Calvin et al., 2017), coronary heart disease (e.g., Calvin et al., 2017; Christensen, Mortensen, Christensen, & Osler, 2016; Hart et al., 2004; Lawlor, Ronalds, Clark, Davey Smith, & Leon, 2005;), stroke (Calvin et al., 2017), total cardiovascular disease (Batty et al., 2016; Calvin et al., 2017; Christensen et al., 2016; Hart et al., 2003, Hart et al., 2004; Hemmingsson, Melin, Allebeck, & Lundberg, 2006; Leon, Lawlor, Clark, Batty, & Macintyre, 2009), digestive disease (Calvin et al., 2017), cancer (Batty et al., 2009, Batty et al., 2016; Leon et al., 2009), specifically with smoking-related diseases (Calvin et al., 2017), dementia (Calvin et al., 2017; Russ et al., 2013), and suicide (Hemmingsson et al., 2006,
Deary et al. (2021) presented consistent results showing intelligence associated with several causes of death (cardiovascular disease, coronary heart disease, stroke, respiratory disease, diabetes, digestive disease, dementia, non-smoking-related cancers, accidents and suicide), illnesses (hypertension, metabolic syndrome, diabetes, schizophrenia, and major depression), health biomarkers (e.g. systolic and diastolic blood pressure, heart rate, triglycerides and cholesterol, body mass index), and health behaviors (smoking and physical inactivity). As the authors highlight, intelligence's long-term association with health is mediated via adult social factors and health behaviors.
5. Limitations
The present meta-analysis includes large published studies representing in total >47,000 average sample size. However, it includes 22 studies: 16 studies were already included in the previous meta-analysis (Calvin et al., 2011) and six new articles.
Although all studies were adjusted for multiple potential moderators, there are likely to remain different factors, such as SES in adulthood, cause of death in the intelligence–mortality association, etc., that could substantially affect the results.
For those reasons, the combined models are not strictly comparable, since other moderators are frequently added to childhood SES and it is not possible to disentangle their effects in the meta-analysis. Although we are aware of this potential weakness, we have preferred to perform and report this analysis. If we had found an effect, it would have been difficult to interpret, but we did not any effect of this moderator. As might be expected, the inclusion of moderators leads to a significant increase in heterogeneity, which we may interpret to mean that some of the moderators increase the effect and others reduce it, but we cannot identify the role each of them plays at the meta-analytic level.
Also, future research should explore mediating effects on a pathway from premorbid intelligence to the risk of mortality, taking into account common genetic effects (e.g. with GWAS) and the role of socioeconomic status, health literacy, and adult environments and behaviors.
It should also be important to include other countries and cultures in the studies.
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