More intelligent individuals reported lower symptom counts, less chronic conditions, lower rates of depression, less frequent visits to the doctor, and fewer limitations in their daily lives

An intelligent mind in a healthy body? Predicting health by cognitive ability in a large European sample. Jonathan Fries Jakob Pietschnig. Intelligence, Volume 93, July–August 2022, 101666. https://doi.org/10.1016/j.intell.2022.101666

Highlights

• We demonstrate that cognitive ability predicts various aspects of health in adults over 55 years.

• Effect sizes are modest, but may have a considerable impact on population level.

• The most closely g-related construct (mathematical reasoning) predicted indicators of health most consistently.

• Environmental and behavioral risk factors do not play a meaningful role for the intelligence-health association.

Abstract: Intelligence has been consistently demonstrated to be a predictor of health outcomes. However, the exact mechanisms are subject of debate. Environmental and behavioral risk factors have been suggested to affect the intelligence-health association, but the available literature has mostly focused on children and young adults. Here, we aimed to investigate the intelligence-health association in older adults. We analyzed data from the Study of Health and Retirement in Europe (SHARE), a representative longitudinal survey in which participants above 50 years of age (N range = 10,000-30,000+) were interviewed in seven waves from 2004 to 2017. Indicators of physical and mental health (e.g., number of symptoms; self-reported depression) were associated with cognitive function variables (mathematical reasoning, word recall, verbal fluency) which were used as proxy measures for intelligence. Behavioral and environmental risk factors (e.g., legal drug consumption, physical inactivity, work environment) were examined as potential moderator variables for the intelligence-health association. More favorable health outcomes were modestly, but consistently associated with higher cognitive ability across variables (r range = |0.13|-|0.29|). Mixed-model Poisson regression analyses showed a reduction of 11% in self-reported symptom numbers with each unit increase in mathematical reasoning. Environmental and behavioral risk factors exhibited mostly trivial moderating effects on the intelligence-health association. Our findings reveal a positive association of intelligence and health in a representative longitudinal European sample. Environmental and behavioral risk factors offered little explanatory value for this association, suggesting a different underlying mechanism such as a general fitness factor that affects both intelligence and health.

4. Discussion

Here, we investigated the associations of cognitive ability and health in a representative longitudinal sample of EU residents above the age of 50. Specifically, we examined various indicators of physical and mental health, along with behavioral and environmental risk factors as well as cognitive functioning. In our analyses, a consistent pattern emerged: cognitive ability was associated with the rate of chronic illnesses, symptoms, limitations in daily activities, and other indicators of health. This was true both for individual cognitive ability indicators and the g-factor approximation we calculated from these indicators. Environmental and behavioral risk factors, such as smoking, alcohol consumption, BMI, or work environment showed little moderating effects. Physical inactivity was the only covariate that exhibited moderating effects on the intelligence-health association.

The strength of the associations was small to moderate in size. Such effects have practical meaning in the short term but can be considered even more meaningful on a larger scale and in the long term. Intelligence and health are relevant to every single human. Thus, even small differences cumulate over time and can have tremendous consequences not only on an individual but perhaps more importantly, a societal level (Funder & Ozer, 2019). Previous research has reported effect sizes of similar strength (for an overview, see Deary et al., 2021). The direction of effects was as expected: higher cognitive ability was positively related to more favorable health outcomes. More intelligent individuals reported lower symptom counts, less chronic conditions, lower rates of depression, less frequent visits to the doctor, and fewer limitations in their daily lives.

Notably, the largest effects were observed for self-perceived health. This variable encompasses a broad spectrum of health and may paint a more comprehensive picture than the other – more specific – indicators in the SHARE dataset. This makes sense because self-perceived or self-rated health is considered a reliable and robust predictor of health and mortality and has often been used in many studies on aging (e.g., French, Sargent-Cox, & Luszcz, 2012; Machón, Vergara, Dorronsoro, Vrotsou, & Larrañaga, 2016). This can be attributed to the multi-dimensional and dynamic properties of this variable. Interviewees may not be aware of every detrimental condition from which they suffer, because some conditions may remain undiagnosed. However, self-perceived health assesses a wide range of sensations and symptoms that may indicate countless physical and mental health conditions in clinical and pre-clinical stages (Benyamini, 2011). Because self-perceived health arguably captures a larger proportion of variance in overall health than any other available variable, this variable may yield therefore the best representation of the true effect in terms of the intelligence-health association in the SHARE dataset.

It is important to consider the meaning of different directionalities of the observed effects; specifically, whether higher cognitive ability is the consequence of better health or whether higher cognitive ability leads to better health over the course of life. Though the longitudinal design of this study encompassed only a later part in participants' lives and does not allow for causal inferences, our results indicate that cognitive ability has an age-independent effect on health. If the intelligence-health association were a mere consequence of deteriorating health, this correlation would be expected to be substantially attenuated when controlling for age, because age is a robust predictor of self-perceived as well as objectively measured health (e.g., Cullati, Rousseaux, Gabadinho, Courvoisier, & Burton-Jeangros, 2014; Rockwood, Song, & Mitnitski, 2011). However, this was not the case in our analyses, thus indicating that cognitive ability may be more likely to affect health instead of the other way round. Nonetheless, it seems plausible that good health also exerts positive effects on intelligence, thus representing a positive feedback loop in which higher cognitive ability facilitates better health which in turn helps maintaining high abilities.

Cancer on a general level did not correlate with any indicator of cognitive ability. This makes sense, because in contrast to many other health conditions (such as cardiovascular disease), a large number of cancer types does not depend on lifestyle factors and can therefore be expected not to be influenced by deliberated lifestyle choices. This observation is consistent with previous findings on this topic suggesting that lifestyle choices may not cause most types of cancer, with the notable exception of lung cancer (Calvin et al., 2017).

Across analyses, participants' numeracy scores proved to be the most robust predictor out of individual cognitive ability indicators: correlations decreased only to a minor degree when controlled for participant age, thus indicating that these associations were relatively unaffected by expectable age-related ability declines. In regression analyses, numeracy most reliably predicted health outcomes. This was expected because mathematical reasoning can be assumed to be more highly g-loaded than the other cognitive function indices in the SHARE dataset and is likely to estimate general cognitive ability more reliably (Peng et al., 2019). When controlled for age and education, the associations between cognitive ability and health were attenuated but remained meaningful and invariably maintained their respective directions which indicates the remarkable robustness of this effect. This was the case for correlations, as well as regression analyses.

Intelligence exhibited some associations with environmental and behavioral risk factors, but the directions of effects were not as clear-cut as with health. Higher cognitive ability was associated with lower rates of physical inactivity, which is consistent with previous findings (e.g., Wraw et al., 2018) that are suggestive of higher-intelligence individuals exhibiting higher ability and motivation to engage in vigorous physical activities (but see Kumpulainen et al., 2017, for contrasting results). Physical inactivity may be related to health literacy which is defined as the ability to gain access to information about health topics, as well as to interpret it and communicate about it. Health literacy is considered to be a prerequisite of informed health-related decision making (Berkman, Davis, & McCormack, 2010). Health literacy has been proposed as an important factor in the intelligence-health relationship because more intelligent individuals are assumed to obtain and process relevant health information more easily than less intelligent persons. Some research even suggests that health literacy is simply a context-specific component of general intelligence (Reeve & Basalik, 2014).

We had hypothesized BMI, smoking, and alcohol consumption to be negatively associated with cognitive ability. However, BMI was not meaningfully associated with cognitive ability, and neither was smoking. In previous accounts, smoking cessation has often been found to be linked with intelligence, but uptake of smoking has not (Taylor et al., 2003). Here, we only considered whether participants had ever smoked, but not if or when they quit which may have masked a potential association with cognitive ability.

Among risk factors, consumption of alcoholic beverages exhibited small, but meaningful positive correlations with cognitive ability, indicating that individuals with higher cognitive ability in fact consumed more alcohol than lower-ability persons. Previous studies have found similar correlations indicating more frequent overall alcohol consumption in subjects with higher childhood intelligence, but lower rates of problematic drinking behavior (Cheng & Furnham, 2013; Kanazawa & Hellberg, 2010). These findings may be attributed to several different causes. It has been suggested that more intelligent individuals might be better equipped to avoid adverse health effects of drinking (e.g., by reducing their intake when they become aware about an onset of problematic drinking behavior). Considering that our analyses indicated no correlation with unfavorable health outcomes, our results are generally in line with this interpretation. Others have speculated that the success in certain (particularly white-collar) professions may depend to some extent on the willingness to drink alcohol in social settings that are typically related to more cognitively challenging jobs (Batty et al., 2008). Another possibility is that more intelligent individuals are better able to veil their problematic consumption from others or even physicians tend to misattribute problematic behaviors in more intelligent persons to less socially undesirable causes (Just-Ostergaard et al., 2019).

Unsurprisingly, participants who worked in higher-risk work environments scored lower on the cognitive function variables. This could indicate that low-risk, white-collar jobs are selected for via intelligence, which would be in line with previous research (Strenze, 2007). However, because in the present study cognitive ability was assessed at an average age of 64 years, one could argue that lower cognitive function may reflect overall poor health as a consequence of environmental factors such as work environment. Nevertheless, work environment exhibited only trivial bivariate associations with health which contrasts this interpretation.

Among environmental and behavioral risk factors, physical inactivity was the only one that showed consistent associations with health, especially regarding the number of limitations in activities of daily life faced by participants. Importantly, physical inactivity can be considered both a risk factor and an outcome because it may be the result of prolonged illness (Watson et al., 2016). The SHARE participants that were included in our analyses were assessed six to seven times over the course of the study. If one assumes that declining health is associated with increasing physical inactivity, including the repeated measures as a random-effects variable would have attenuated the influence of deteriorating health. In fact, our mixed-effects regression analyses showed that physical inactivity meaningfully predicted various health outcomes, thus indicating that physical inactivity may be at least in part accountable for worse health outcomes. Nevertheless, the SHARE data do not allow for causal inferences on whether physical inactivity was the cause of illness or caused by deteriorating health. Gerontological literature has established that physical activity is a major protective factor in preventing or delaying chronic illness. Therefore, physicians recommend that physical activity is resumed or picked up even in the presence of chronic health conditions (Watson et al., 2016).

Smoking did not appear to meaningfully correlate with any unfavorable health outcome and neither did alcohol consumption. Other studies on the SHARE data came to similar conclusions (e.g., Abuladze, Kunder, Lang, & Vaask, 2017). Importantly, we did not examine the amount of smoking here, because we only included a binary item that assessed whether participants had ever smoked. The group that answered “Yes” also encompassed individuals that had quit smoking in the past. It is well-documented that quitting smoking has a positive impact on many aspects of physical and mental health (Critchley & Capewell, 2003; Taylor et al., 2014). Thus, the adverse health effects of smoking might have been obscured by the inclusion of these contrasting groups. Alcohol consumption, on the other hand, was measured by current consumption levels. Our findings align with studies that demonstrate no adverse or suggest even beneficial health effects of moderate alcohol consumption in elderly individuals (Balsa et al., 2008).

Participants with higher BMI's exhibited slightly elevated rates of chronic conditions and reported slightly worse self-perceived health. It is important to note that BMI has long been subject to criticism because it does not account for body fat percentage and body fat distribution which are the main drivers of morbidity and mortality due to obesity (Nuttall, 2015). The BMI's questionable reliability negatively affects its capacity to predict health outcomes which might explain the low correlations we found in the SHARE sample. The fact that BMI showed associations with health despite its methodological issues suggests that the correlations we found in the SHARE sample can be considered to represent a bottom threshold of the true association.

Contrary to our expectations, health behaviors did not moderate the relationship between cognitive ability and health. The only notable exception was physical inactivity, but as discussed above, the direction of effect is ambiguous and unfavorable health effects cannot be causally attributed to physical inactivity here because they arguably exacerbate one another. BMI, smoking, alcohol consumption, and work environment risk did not meaningfully interact with cognitive ability. These results suggest that the intelligence-health association cannot be sufficiently explained by environmental and behavioral risk factors (i.e., at least by those that were assessed in the SHARE interviews). Thus, a different mechanism is required to understand the relationship.

In the literature, education has often been found to explain a substantial proportion of the variance in the intelligence-health association (e.g., Ariansen et al., 2015). In our analyses, controlling for education attenuated the association, indicating a moderating influence. Nevertheless, effect magnitudes remained meaningful, and the directions of effects were unchanged. This suggests that education did exert influence on the relationship between cognitive ability and health outcomes but was not sufficient to explain the effect in its entirety.

The intelligence-health association did not decrease when participants' country of residence was included in exploratory regression analyses. This indicates that the relationship is not meaningfully impacted by regional disparities.

An alternative explanation for this remarkably robust association could be found in a genetic factor that influences health as well as cognitive functioning. The existence of a general fitness factor has been suggested before based on phenotypical findings (Arden, Gottfredson, & Miller, 2009; Prokosch et al., 2009), but in recent years more evidence from genome-wide association studies has emerged that directly supports this theory. These studies suggest that a substantial proportion of variance in the intelligence-health association can be explained by genetic variation (Hill et al., 2019). High intelligence and favorable health often coincide because the biological bases of these features are located on the same genes. One of the challenges in this line of research is to deal with the question of causality: do genetic variants affect intelligence which subsequently affects health, or vice versa – or are both intelligence and health affected by the same genetic locations (Deary, Harris, & Hill, 2019)? The results we present here lend some phenotypic support for the latter interpretation. However, more research is necessary to clarify the direction of causality.

4.1. Limitations and future directions

In the current study, our goal was to shed light on the association of cognitive ability and health as well as its underpinnings. The SHARE-dataset represents an invaluable source to investigate these questions goal due to its representativeness, comprehensiveness, and longitudinal nature, but only comprises a limited number of cognitive measures. Cognitive function was assessed using four subtests measuring mathematical reasoning, immediate and delayed word recall, as well as verbal fluency. Despite being considered important components of intelligence in most contemporary established models (such as the CHC-model of intelligence, McGrew, 1997), these four indices can be only considered to be proxies of general cognitive ability. Therefore, we were unable to provide results of more fine-grained domain-specific associations of intelligence with health. In future community surveys, it would be beneficial to include more cognitive ability subtests, especially in terms of highly g-loaded tests such as Raven-typed matrices or figural analogies.

So far, in many longitudinal studies investigating the effects of intelligence on health (e.g., the Lothian Birth Cohort studies, Deary et al., 2007), intelligence was assessed at a young age and subsequently used as a predictor of health outcomes in later life. Here, we are limited by the SHARE study design which does not provide a measure of childhood cognitive ability; the earliest point of assessment was at a participant age of 50 years. Therefore, our analyses potentially carry an inherent bias: reduced cognitive ability at an older age may be the result of poor health, not its cause. To reduce the risk of reverse causation, we controlled for age in correlation analyses. Moreover, if low cognitive ability was the result of certain health conditions, cognitive decline can be expected to progress over the course of the multiple SHARE interview waves. In regression analyses, the cognitive development over the course of the study was held constant which at the very least attenuates this effect.

Death or severe illness may have caused participants to drop out of the SHARE study in later waves. Thus, it is possible that health is overestimated in later waves because the participants exhibiting the worst health are likely to have exited the study. This may have had an attenuating effect on the intelligence-health association in our analyses, thus leading to a conservative effect estimation.

In our analyses, we included risk factors hypothesized to moderate the intelligence-health association. Although the SHARE interviews cover some aspects, other potentially relevant factors are missing. For instance, managing individual health needs is a crucial skill that gains relevance over the human lifespan (Gottfredson, 2004). How well one takes care of one's own health includes how diligently treatment regimens are being observed. Thus far, few studies have tracked patients' adherence to treatment plans in relation to their cognitive ability (e.g., Deary, Corley, et al., 2009; Deary, Gale, et al., 2009). These are only some examples of many conceivably influential variables on this association which future researchers may wish to consider.

The intelligence-health association is not merely a matter of psychometric interest, but highly relevant to society at large. In plain words, more intelligent individuals have a higher chance of good health and a long life compared to less intelligent individuals. Advances in healthcare equality seem to have little mitigating effects (Gottfredson, 2004). Consequently, this association has been causing disparities in health outcomes and life expectancies along the lines of the intelligence distribution. Scientific inquiry is a necessary first step in the process of addressing this issue, but large-scale public policy solutions are needed to ensure that all members of society have equal chances of a long and healthy life, regardless of their respective genetic makeup. Unfortunately, public health interventions are often limited to targeting health behaviors, such as smoking or obesity, and ignore the deeper nature of the intelligence-health connection that the evidence is pointing to.