The past decade has seen an explosion of research on social determinants of health (SDoH), defined by the World Health Organization as the nonmedical factors that influence health outcomes and include the conditions in which people are born, grow up, and live and the wider set of forces and systems that shape the conditions of daily life (Jeste, Reference Jeste2022). Factors such as housing, employment, family income, education, food insecurity, rural versus urban setting, neighborhood characteristics, childhood adversity, racial discrimination, and access to health services have been studied to their contributions to occurrence of health conditions and to a variety of health outcomes. While considerable research on SDoH has focused on nongeriatric populations, recent research has examined how social factors affect health outcomes of older adults. For example, ageism as a social determinant has become a focus of research, with one study finding that ageism was associated with a marked increase in prevalence and cost (estimated at $63 billion) of the eight most expensive diseases in the United States (Levy et al., Reference Levy, Slade, Chang, Kannoth and Wang2020).
A recent issue of International Psychogeriatrics focused on ageism and other nonmedical SDoH. In a study of 1,570 participants, Ayalon and Cohn-Schwartz (Reference Ayalon and Cohn-Schwartz2022) examined self-perceptions of aging (SPA) and age-based discrimination among three groups: Veteran Israelis, Israeli Arabs, and immigrants from the Former Soviet Union. Among Israeli Arabs, being older was related to better SPA, whereas among the immigrants being older was related to worse SPA. As immigrants became older, they were more likely to report ageist experiences. Israeli Arabs reported higher levels of ageist experiences. In another study focused on awareness of positive and negative age-related changes, Sabatini et al. (Reference Sabatini2022) administered questionnaires to 609 United Kingdom participants and reported responses that were expected, (e.g. experiencing negative changes and attitudes toward aging) and unexpected (e.g. engagement in purposeful activities or in activities that distract from age-related thoughts). Other papers in the issue focused on governmental policies during the COVID pandemic in Uganda and effects on access to food and adequate nutrition (Giebel et al., Reference Giebel, Ivan, Burger and Ddumba2022) and the importance of improving problem-solving skills and providing support to family caregivers of people with dementia in Canada (Sadavoy et al., Reference Sadavoy, Sajedinejad and Chiu2022).
One extension of research on SDoH in the elderly to consider is in the area of healthy versus unhealthy brain aging. A recent study focused on developing the construct of social health and connected it to brain health (Vernooij-Dassen et al., Reference Vernooij-Dassen2022). Drawing from theoretical, conceptual and epidemiological research, the authors defined social health as well-being that relies on capacities both of the individual and the social environment. Thus, both individual (e.g. social participation) and social (e.g. social network) environmental elements (e.g. social network) are key for preserving social health. The authors suggested that maintenance of social health taps into cognitive reserve (CR), which can then slow cognitive impairment or maintain cognitive functioning. In addition, social health factors such as lower loneliness and volunteering, along with higher education and greater physical activity, have been associated with higher optimism and lower pessimism, while low social support was associated with higher pessimism (Craig et al., Reference Craig2023). In turn, higher optimism and lower pessimism support healthy aging.
Definitions of healthy brain aging often rely on neuroimaging findings. A review of 2,246 papers related to MRI of healthy brain aging examined expected MRI-based brain changes with normal aging (MacDonald and Pike, Reference MacDonald and Pike2021). They reported that the brain loses approximately 7% of its volume between the 20s and 60s and continues to decline thereafter. Brain volume is easy to obtain from MRI analysis and is important because it correlates with cognitive performance. As brain volume decreases with age, there is a concomitant increase in ventricular size and cerebrospinal fluid volume. Similar volumetric changes with aging affect the cerebral cortices and white matter. Interestingly, subcortical gray matter volume in totality appears to shrink at a slower rate than cortical gray matter. The authors also reviewed studies examining white matter hyperintensities (WMH) associated with aging, noting that while total volume of WMH increases with age, WMH in normal aging are generally confined to the periventricular region. They conclude that all of these measures can be combined to estimate brain age and compare with chronological age to get a sense of healthy versus “premature” aging. Similarly, Blinkouskaya et al. (Reference Blinkouskaya, Cacoilo, Gollamudi, Jalalian and Weickenmeier2021) reviewed the literature on structural studies of brain aging (10). They identified and quantified global brain volume changes as well as changes in volumes of gray matter, cortex, white matter, ventricles, and WMH.
Functional imaging modalities have also been used to examine healthy brain aging including both functional MRI and positron emission tomography (PET) studies. For example, fMRI has been employed to show resting-state functional connectivity (rsFC) differences between cognitive normal older adults and patients with mild cognitive impairment (MCI) and Alzheimer’s disease (Behfar et al., Reference Behfar2020). The investigators found increased rsFC among healthy aging and MCI in cognition-related brain regions of the middle frontal gyrus, precentral gyrus, and superior parietal lobe. This increase in rsFC occurred despite local atrophy among these groups, indicating a possible compensatory mechanism in MCI and healthy aging. Thus, healthy brain aging may be associated with a preserved ability to compensate for brain aging with functionally adaptive changes to brain activity. In a PET study, the investigators assessed healthy brain aging using [18F]FDG as a measure of cerebral glucose consumption and [11C]UCB-J PET as an indicator of synaptic density (Andersen et al., Reference Andersen2022). They confirmed earlier reports of synaptic density stability with normal aging.
Various imaging modalities have been used to characterize unhealthy or pathological brain aging. As noted above, some imaging studies define pathological aging in relation to brain changes beyond what is expected in the normal aging process (MacDonald and Pike, Reference MacDonald and Pike2021). Other studies highlight the constructs of “cognitive reserve” and “brain reserve” in both normal aging and in conditions associated with cognitive decline (Stern et al., Reference Stern2020). CR refers to the breadth of memory and thinking abilities, including flexibility and adaptability of attentional processes (conscious and subconscious) commonly subserving daily function (i.e. work, play, and leisure). Brain reserve (BR) refers to physical and neuroanatomical structures of the brain. In terms of measurement, CR is challenging to measure directly, but rather relies on methods that incorporate SDoH, e.g. education, intelligence, occupational complexity, leisure, and physical activity. On the other hand, measurement of BR relies on imaging-based measures such as gray matter volume, cortical surface area, cortical thickness, PET measures of synaptic integrity, and white matter microstructural properties.
The literature on social factors and unhealthy brain aging has mainly focused on cognitive decline as a proxy for pathological brain aging. For example, neighborhood environmental variation has been linked to dementia risk and may be related to extent of physical inactivity, depression, obesity, hypertension, diabetes, and social isolation (Finlay et al., Reference Finlay, Esposito, Langa, Judd and Clarke2022). Geographic areas with limited walkability and the presence of polluting sites had a significant negative association with cognitive function after controlling for individual and neighborhood factors (Yu et al., Reference Yu, Esposito, Li, Clarke, Judd and Finlay2023). In the United States, neighborhood characteristics may explain some racial differences in cognitive decline and dementia risk. Compared to Black participants living in higher segregation-lower neighborhood socioeconomic status (NSES) areas, Black participants living in lower segregation-lower NSES areas or higher segregation-higher NSES areas experienced slower decline in episodic memory over time (Meyer et al., Reference Meyer2023).
Recent literature has also focused on cognitive consequences of presence of personality factors, especially neuroticism, the tendency to experience negative emotions, such as anger, anxiety, and depressed mood. The link between neuroticism and cognitive decline is consistent with the notion that not only is stress bad for the brain, but that it is especially harmful in older individuals with neuroticism, whose brains may be poorly equipped to deal with stress. In a large community-based longitudinal study, compared with older adults scoring in the lowest quartile of neuroticism, those scoring in the highest quartile had a threefold increased risk of incident Alzheimer’s disease (Terracciano et al., Reference Terracciano2014). A meta-analysis found that neuroticism increased risk of both dementia and mild cognitive impairment (Low et al., Reference Low, Harrison and Lackersteen2013). Among depressed older adults, one-year neuroticism change with depression treatment was inversely associated with three-year change in cognitive performance such that, compared with individuals with decreased neuroticism scores, those with lack of improvement in neuroticism scores experienced greater cognitive decline (Steffens et al., Reference Steffens, Manning, Wu and Grady2022). Improvement in stress may have cognitive benefits. Dickinson et al. (Reference Dickinson, Potter, Hybels, McQuoid and Steffens2011) found the among older adults with and without depression, a decline in the total number of stressors was associated with a subsequent improvement on a global measure of cognition.
With this background in social factors and cognitive aging in mind, we can turn to a nascent but growing literature on SDoH and brain aging. In this issue of International Psychogeriatrics, Rodriguez et al. (Reference Rodriguez2023) extend the research on SDoH-related conditions to examine social determinants of white matter disease, a vascular pathology associated with unhealthy brain aging. Specifically, the authors sought to better understand the observation that socioeconomically deprived groups experience a higher burden of white-matter hyperintensities (WMH), a brain imaging marker of vascular disease, compared with other socioeconomic groups. They focused on two key drivers of socioeconomic status: education and income. Using data from the population-based Adult Study of the Leipzig Research Centre for Civilization Diseases (LIFE) based in Leipzig, Germany, the investigators identified 1,185 dementia-free individuals aged 40 to 80 years enrolled in a population registry. The study relied on both interview data and neuroimaging data. Through standard interviews, they obtained clinical and social data, specifically information on a variety of physical health conditions as well as reported education and income. For imaging, they employed a 3T magnetic resonance imaging (MRI) scanner, using sequences to obtain WMH data, which were then examined to determine severity scores using the Fazekas 0–3 scoring system. While the authors initially found an association between income and WMH in unadjusted analyses, subsequent analyses adjusted for age, gender, arterial hypertension, heart disease, and apolipoprotein E (APOE) e4 allele status showed no association between income and WMH. With statistical correction, education was not associated with either nor with WMH. Subsequent analyses revealed significant associations between higher distress and more WMH as well as between obesity and greater deep WMH. The authors concluded that differences in health-related risk factors might explain previously observed differences in WMH in socioeconomically deprived groups.
Other studies have examined stress as a SDoH and cerebrovascular disease. One such factor is stress associated with socioeconomic circumstances. For example, early life socioeconomic circumstance (as measured by paternal occupation) was associated with burden of WMH in late life (Murray et al., Reference Murray, McNeil, Salarirad, Whalley and Staff2014). Stress related to trauma and to racial discrimination have also been examined as cerebrovascular risk factors. Everyday racial discrimination was associated with faster accumulation of WMH over time among older Black adults (Zahodne et al., Reference Zahodne2023). A trauma history, particularly sexual assault, was associated with greater WMH volume controlling for covariates, including depressive and post-traumatic symptoms. Sexual assault may place women at risk for poor brain health (Thurston et al., Reference Thurston2022). Research has also focused on examining links between accumulated recent stressors and cerebrovascular disease. In one study, increased stressor exposure was associated with greater increases in volume of WMH, while reductions in stressor exposure were associated with less increase in hyperintensity volume (Johnson et al., Reference Johnson, McQuoid, Steffens, Payne, Beyer and Taylor2017). In a study quantifying stress using the Perceived Stress Scale (PSS), the investigators found that while increases in PSS score were associated with lower total brain volume and with increased odds of infarction, PSS scores were unrelated to WMH volume (Aggarwal et al., Reference Aggarwal2014).
Investigators have sought to elucidate putative mechanism linking stress and WMH. One group used laboratory-based mental stressors to determine degree of stress-induced blood pressure reactivity. After adjustment for age, gender, resting clinic blood pressure, and fasting glucose levels, higher systolic blood pressure reactivity was associated with an increased number of small silent infarcts and greater severity ratings of periventricular and deep WMH. Findings were similar for diastolic blood pressure reactivity (Waldstein et al., Reference Waldstein2004). This study provides a mechanistic framework for understanding links between stress and vascular-related structural brain changes.
In sum, the paper by Jacob et al. highlights a burgeoning area of research that focuses on social factors and biological measures of brain aging. As in this study, any investigation on this topic will need to incorporate the complex mix of biological, psychological, and social factors inherent with geriatric research. Medical conditions, distress, educational background, and social circumstances were shown to be salient in determining WMH volumes, and it is likely that these and other seemingly unconnected, but actually quite interrelated, factors will inform future studies of healthy and unhealthy brain aging.
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The past decade has seen an explosion of research on social determinants of health (SDoH), defined by the World Health Organization as the nonmedical factors that influence health outcomes and include the conditions in which people are born, grow up, and live and the wider set of forces and systems that shape the conditions of daily life (Jeste, Reference Jeste2022). Factors such as housing, employment, family income, education, food insecurity, rural versus urban setting, neighborhood characteristics, childhood adversity, racial discrimination, and access to health services have been studied to their contributions to occurrence of health conditions and to a variety of health outcomes. While considerable research on SDoH has focused on nongeriatric populations, recent research has examined how social factors affect health outcomes of older adults. For example, ageism as a social determinant has become a focus of research, with one study finding that ageism was associated with a marked increase in prevalence and cost (estimated at $63 billion) of the eight most expensive diseases in the United States (Levy et al., Reference Levy, Slade, Chang, Kannoth and Wang2020).
A recent issue of International Psychogeriatrics focused on ageism and other nonmedical SDoH. In a study of 1,570 participants, Ayalon and Cohn-Schwartz (Reference Ayalon and Cohn-Schwartz2022) examined self-perceptions of aging (SPA) and age-based discrimination among three groups: Veteran Israelis, Israeli Arabs, and immigrants from the Former Soviet Union. Among Israeli Arabs, being older was related to better SPA, whereas among the immigrants being older was related to worse SPA. As immigrants became older, they were more likely to report ageist experiences. Israeli Arabs reported higher levels of ageist experiences. In another study focused on awareness of positive and negative age-related changes, Sabatini et al. (Reference Sabatini2022) administered questionnaires to 609 United Kingdom participants and reported responses that were expected, (e.g. experiencing negative changes and attitudes toward aging) and unexpected (e.g. engagement in purposeful activities or in activities that distract from age-related thoughts). Other papers in the issue focused on governmental policies during the COVID pandemic in Uganda and effects on access to food and adequate nutrition (Giebel et al., Reference Giebel, Ivan, Burger and Ddumba2022) and the importance of improving problem-solving skills and providing support to family caregivers of people with dementia in Canada (Sadavoy et al., Reference Sadavoy, Sajedinejad and Chiu2022).
One extension of research on SDoH in the elderly to consider is in the area of healthy versus unhealthy brain aging. A recent study focused on developing the construct of social health and connected it to brain health (Vernooij-Dassen et al., Reference Vernooij-Dassen2022). Drawing from theoretical, conceptual and epidemiological research, the authors defined social health as well-being that relies on capacities both of the individual and the social environment. Thus, both individual (e.g. social participation) and social (e.g. social network) environmental elements (e.g. social network) are key for preserving social health. The authors suggested that maintenance of social health taps into cognitive reserve (CR), which can then slow cognitive impairment or maintain cognitive functioning. In addition, social health factors such as lower loneliness and volunteering, along with higher education and greater physical activity, have been associated with higher optimism and lower pessimism, while low social support was associated with higher pessimism (Craig et al., Reference Craig2023). In turn, higher optimism and lower pessimism support healthy aging.
Definitions of healthy brain aging often rely on neuroimaging findings. A review of 2,246 papers related to MRI of healthy brain aging examined expected MRI-based brain changes with normal aging (MacDonald and Pike, Reference MacDonald and Pike2021). They reported that the brain loses approximately 7% of its volume between the 20s and 60s and continues to decline thereafter. Brain volume is easy to obtain from MRI analysis and is important because it correlates with cognitive performance. As brain volume decreases with age, there is a concomitant increase in ventricular size and cerebrospinal fluid volume. Similar volumetric changes with aging affect the cerebral cortices and white matter. Interestingly, subcortical gray matter volume in totality appears to shrink at a slower rate than cortical gray matter. The authors also reviewed studies examining white matter hyperintensities (WMH) associated with aging, noting that while total volume of WMH increases with age, WMH in normal aging are generally confined to the periventricular region. They conclude that all of these measures can be combined to estimate brain age and compare with chronological age to get a sense of healthy versus “premature” aging. Similarly, Blinkouskaya et al. (Reference Blinkouskaya, Cacoilo, Gollamudi, Jalalian and Weickenmeier2021) reviewed the literature on structural studies of brain aging (10). They identified and quantified global brain volume changes as well as changes in volumes of gray matter, cortex, white matter, ventricles, and WMH.
Functional imaging modalities have also been used to examine healthy brain aging including both functional MRI and positron emission tomography (PET) studies. For example, fMRI has been employed to show resting-state functional connectivity (rsFC) differences between cognitive normal older adults and patients with mild cognitive impairment (MCI) and Alzheimer’s disease (Behfar et al., Reference Behfar2020). The investigators found increased rsFC among healthy aging and MCI in cognition-related brain regions of the middle frontal gyrus, precentral gyrus, and superior parietal lobe. This increase in rsFC occurred despite local atrophy among these groups, indicating a possible compensatory mechanism in MCI and healthy aging. Thus, healthy brain aging may be associated with a preserved ability to compensate for brain aging with functionally adaptive changes to brain activity. In a PET study, the investigators assessed healthy brain aging using [18F]FDG as a measure of cerebral glucose consumption and [11C]UCB-J PET as an indicator of synaptic density (Andersen et al., Reference Andersen2022). They confirmed earlier reports of synaptic density stability with normal aging.
Various imaging modalities have been used to characterize unhealthy or pathological brain aging. As noted above, some imaging studies define pathological aging in relation to brain changes beyond what is expected in the normal aging process (MacDonald and Pike, Reference MacDonald and Pike2021). Other studies highlight the constructs of “cognitive reserve” and “brain reserve” in both normal aging and in conditions associated with cognitive decline (Stern et al., Reference Stern2020). CR refers to the breadth of memory and thinking abilities, including flexibility and adaptability of attentional processes (conscious and subconscious) commonly subserving daily function (i.e. work, play, and leisure). Brain reserve (BR) refers to physical and neuroanatomical structures of the brain. In terms of measurement, CR is challenging to measure directly, but rather relies on methods that incorporate SDoH, e.g. education, intelligence, occupational complexity, leisure, and physical activity. On the other hand, measurement of BR relies on imaging-based measures such as gray matter volume, cortical surface area, cortical thickness, PET measures of synaptic integrity, and white matter microstructural properties.
The literature on social factors and unhealthy brain aging has mainly focused on cognitive decline as a proxy for pathological brain aging. For example, neighborhood environmental variation has been linked to dementia risk and may be related to extent of physical inactivity, depression, obesity, hypertension, diabetes, and social isolation (Finlay et al., Reference Finlay, Esposito, Langa, Judd and Clarke2022). Geographic areas with limited walkability and the presence of polluting sites had a significant negative association with cognitive function after controlling for individual and neighborhood factors (Yu et al., Reference Yu, Esposito, Li, Clarke, Judd and Finlay2023). In the United States, neighborhood characteristics may explain some racial differences in cognitive decline and dementia risk. Compared to Black participants living in higher segregation-lower neighborhood socioeconomic status (NSES) areas, Black participants living in lower segregation-lower NSES areas or higher segregation-higher NSES areas experienced slower decline in episodic memory over time (Meyer et al., Reference Meyer2023).
Recent literature has also focused on cognitive consequences of presence of personality factors, especially neuroticism, the tendency to experience negative emotions, such as anger, anxiety, and depressed mood. The link between neuroticism and cognitive decline is consistent with the notion that not only is stress bad for the brain, but that it is especially harmful in older individuals with neuroticism, whose brains may be poorly equipped to deal with stress. In a large community-based longitudinal study, compared with older adults scoring in the lowest quartile of neuroticism, those scoring in the highest quartile had a threefold increased risk of incident Alzheimer’s disease (Terracciano et al., Reference Terracciano2014). A meta-analysis found that neuroticism increased risk of both dementia and mild cognitive impairment (Low et al., Reference Low, Harrison and Lackersteen2013). Among depressed older adults, one-year neuroticism change with depression treatment was inversely associated with three-year change in cognitive performance such that, compared with individuals with decreased neuroticism scores, those with lack of improvement in neuroticism scores experienced greater cognitive decline (Steffens et al., Reference Steffens, Manning, Wu and Grady2022). Improvement in stress may have cognitive benefits. Dickinson et al. (Reference Dickinson, Potter, Hybels, McQuoid and Steffens2011) found the among older adults with and without depression, a decline in the total number of stressors was associated with a subsequent improvement on a global measure of cognition.
With this background in social factors and cognitive aging in mind, we can turn to a nascent but growing literature on SDoH and brain aging. In this issue of International Psychogeriatrics, Rodriguez et al. (Reference Rodriguez2023) extend the research on SDoH-related conditions to examine social determinants of white matter disease, a vascular pathology associated with unhealthy brain aging. Specifically, the authors sought to better understand the observation that socioeconomically deprived groups experience a higher burden of white-matter hyperintensities (WMH), a brain imaging marker of vascular disease, compared with other socioeconomic groups. They focused on two key drivers of socioeconomic status: education and income. Using data from the population-based Adult Study of the Leipzig Research Centre for Civilization Diseases (LIFE) based in Leipzig, Germany, the investigators identified 1,185 dementia-free individuals aged 40 to 80 years enrolled in a population registry. The study relied on both interview data and neuroimaging data. Through standard interviews, they obtained clinical and social data, specifically information on a variety of physical health conditions as well as reported education and income. For imaging, they employed a 3T magnetic resonance imaging (MRI) scanner, using sequences to obtain WMH data, which were then examined to determine severity scores using the Fazekas 0–3 scoring system. While the authors initially found an association between income and WMH in unadjusted analyses, subsequent analyses adjusted for age, gender, arterial hypertension, heart disease, and apolipoprotein E (APOE) e4 allele status showed no association between income and WMH. With statistical correction, education was not associated with either nor with WMH. Subsequent analyses revealed significant associations between higher distress and more WMH as well as between obesity and greater deep WMH. The authors concluded that differences in health-related risk factors might explain previously observed differences in WMH in socioeconomically deprived groups.
Other studies have examined stress as a SDoH and cerebrovascular disease. One such factor is stress associated with socioeconomic circumstances. For example, early life socioeconomic circumstance (as measured by paternal occupation) was associated with burden of WMH in late life (Murray et al., Reference Murray, McNeil, Salarirad, Whalley and Staff2014). Stress related to trauma and to racial discrimination have also been examined as cerebrovascular risk factors. Everyday racial discrimination was associated with faster accumulation of WMH over time among older Black adults (Zahodne et al., Reference Zahodne2023). A trauma history, particularly sexual assault, was associated with greater WMH volume controlling for covariates, including depressive and post-traumatic symptoms. Sexual assault may place women at risk for poor brain health (Thurston et al., Reference Thurston2022). Research has also focused on examining links between accumulated recent stressors and cerebrovascular disease. In one study, increased stressor exposure was associated with greater increases in volume of WMH, while reductions in stressor exposure were associated with less increase in hyperintensity volume (Johnson et al., Reference Johnson, McQuoid, Steffens, Payne, Beyer and Taylor2017). In a study quantifying stress using the Perceived Stress Scale (PSS), the investigators found that while increases in PSS score were associated with lower total brain volume and with increased odds of infarction, PSS scores were unrelated to WMH volume (Aggarwal et al., Reference Aggarwal2014).
Investigators have sought to elucidate putative mechanism linking stress and WMH. One group used laboratory-based mental stressors to determine degree of stress-induced blood pressure reactivity. After adjustment for age, gender, resting clinic blood pressure, and fasting glucose levels, higher systolic blood pressure reactivity was associated with an increased number of small silent infarcts and greater severity ratings of periventricular and deep WMH. Findings were similar for diastolic blood pressure reactivity (Waldstein et al., Reference Waldstein2004). This study provides a mechanistic framework for understanding links between stress and vascular-related structural brain changes.
In sum, the paper by Jacob et al. highlights a burgeoning area of research that focuses on social factors and biological measures of brain aging. As in this study, any investigation on this topic will need to incorporate the complex mix of biological, psychological, and social factors inherent with geriatric research. Medical conditions, distress, educational background, and social circumstances were shown to be salient in determining WMH volumes, and it is likely that these and other seemingly unconnected, but actually quite interrelated, factors will inform future studies of healthy and unhealthy brain aging.
Conflict of interest
The author declares no conflict of interest.
Acknowledgements
This work is supported by grants R01 MH108578 and P30 AG067988 from the U.S. National Institutes of Health.