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. 2021 Oct 14;36(7):1257–1265. doi: 10.1093/arclin/acab052

Reducing Your Risk of Alzheimer’s Dementia: Building a Better Brain as We Age

David A Bennett 1,✉
PMCID: PMC8517621  PMID: 34651647

Abstract

Alzheimer’ dementia is a large and growing public health problem. Of utmost importance for limiting the impact of the disease on society is the prevention of dementia, that is, delay onset either by years whereby death ensues prior to dementia onset. The Religious Orders Study and the Rush Memory and Aging Project are two harmonized cohort studies of aging and dementia that include organ donation at death. Ongoing since 1994 and 1997, respectively, we published on the association of numerous experiential, psychological, and medical risk factors for dementia, many of which are potentially modifiable. Here, selected findings are reviewed based on a presentation at the 2020 National Academy of Neuropsychology given virtually in Chicago in October of 2020.

Keywords: Dementia, Alzheimer’s disease

Introduction

Dementia is a large and growing public health problem. The public health imperative is disease prevention (Brookmeyer et al., 2016; Sloane et al., 2002). This requires identifying dementia risk factors followed by a means to delay dementia onset. Pathologic Alzheimer’s disease (AD) is most common in persons with advanced age. However, it rarely occurs in isolation. Rather, AD develops in the brains of persons with a variety of comorbid conditions that can also contribute to cognitive decline with a clinical phenotype, Alzheimer’s dementia, that is not easily distinguished from AD alone (Brenowitz et al., 2017; Rahimi & Kovacs, 2014). Further, the brain, like other physiologic systems, have mechanisms to protect itself and maintain cognition in the event of injury or disease (Rentz et al., 2017; Yao, Sweeney, Nagorski, Shulman, & Allen, 2020).

The Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) are harmonized cohort studies of aging and dementia (Bennett et al., 2012; Bennett et al., 2018; Bennett, Schneider, Arvanitakis, & Wilson, 2012). By design, they can address a wide range of issues that confront the study of dementia. Other than age, the only other entry criteria is that subjects be able to sign a consent and an Anatomic Gift Act for organ donation. Thus, it is a community-based study of risk factors for aging and dementia. This part of the study has all of the features essential to a well-designed prospective, analytic epidemiologic cohort study. Further, it enjoys a clinical follow-up rate of more than 90% since the studies launched in 1994 and 1997. With the addition of the Anatomic Gift Act as a requirement for entry, the autopsy rate is about 85%. These latter features ensure strong internal validity of findings.

This paper summarizes the relation of mixed pathologies to cognitive decline and dementia. Then, several risk factors for cognitive decline and risk of dementia are discussed. Finally, two illustrative case reports are provided to highlight the potential impact of risk profiles and mixed pathologies on cognitive aging. Findings use data exclusively from ROSMAP.

Relation of Mixed Pathologies to Change in Cognition

Here, the terms recommended by the most recent National Institute on Aging-Alzheimer’s Association Research Framework are used (Jack Jr. et al., 2018). AD refers to a pathologic diagnosis of AD by histo- and immunopathology at autopsy, or with biomarkers during life, regardless of clinical symptoms. The term Alzheimer’s dementia refers to the clinical syndrome long referred to as “AD” in the literature, that is, a slowly progressive loss of episodic memory and other cognitive domains of sufficient severity to interfere with usual life activities.

Relation of Alzheimer’s Dementia and Mild Cognitive Impairment to Cognitive Decline

As clinicians, we often bin patients into diagnoses. There are many good and important reasons to do this. For example, they guide clinical decision making, they are convenient summary terms for use with patients and one another, they are linked to billing codes, and counting cases is useful for public health projections and future health care service needs, and many other reasons. However, from a biologic point of view they can at times be a hindrance and interfere with understanding the factors that lead to clinical disease. This is certainly the case for the most common complex disease, Alzheimer’s dementia.

Alzheimer’s dementia and mild cognitive impairment (MCI) are diagnoses based on cut points along a continuum. This continuum is built into their criteria which require loss of cognition. Figure 1a illustrates the continuum of Alzheimer’s dementia and Fig. 1b the continuum of MCI (Bennett, Yu, & De Jager, 2014). The bottom line (Fig. 1a), persons who developed Alzheimer’s dementia, illustrates that about 8 years after baseline, the rate of cognitive decline increases markedly using a model that allows a random change point in the rate of change. However, these people are clearly experiencing substantial decline from baseline until the change point. Further, the top line illustrates that persons not developing dementia are also declining. Figure 1b illustrates the same thing for incident MCI. Here the change point is at 6 years and changes are more subtle but still obvious.

Fig. 1.

Fig. 1

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(a) Random change point model allowed for those who developed incident Alzheimer’s dementia (bottom) and but for those who did not (top). (b) Random change point model allowed for those who developed incident MCI (bottom) and but for those who did not (top).

Relation of Neuropathology to Cognitive Decline

Pathologically, like Alzheimer’s dementia, AD is based on a cut point along a continuum (Hyman et al., 2012). In this case, it is based on the level and distribution of neuritic plaques and neurofibrillary tangles. Persons without AD are not necessarily without any AD pathology much like persons without dementia may still experience modest cognitive decline. At older ages, the most common cause of Alzheimer’s dementia is mixed pathologies (Boyle et al., 2019). These are mostly AD, by far the most common, mixed with one or more coexisting pathologies. These pathologies have additive effects on the odds of dementia (Arvanitakis, Capuano, Leurgans, Bennett, & Schneider, 2016; Boyle et al., 2019; Farfel et al., 2019).

Figure 2 shows a random sample of cognitive slopes over multiple years prior to death (Boyle et al., 2021). The thick line is the mean. Wide person-specific differences are clearly evident. We then calculated the variance of cognitive change explained by the pathologies (Boyle et al., 2018; Boyle et al., 2021). This is illustrated in Fig. 3. Remarkably, with more than 10 different brain pathologies, we explain less than half of the person-specific variance in the slopes. These pathologies include other neurodegenerative disease pathologies including cortical Lewy bodies, TDP-43, and hippocampal sclerosis, as well as cerebrovascular disease including cerebral amyloid angiopathy, macro- and microscopic infarctions, and athero- and arteriolo-sclerosis. Although some of this variance can be captured with soluble β-amyloid, and white matter changes best quantified with neuroimaging, watershed microinfarcts, and interactions between pathologies, substantial variance cannot be explained (Dawe et al., 2018; Dawe et al., 2020; Kapasi et al., 2018; Kapasi et al., 2021; Yu et al., 2019). To be clear, some of this variance is from modest improvements in cognition from practice effects and learning, and some is from subtle decline. Thus, the pathologies explain about two thirds of Alzheimer’s dementia cases (Boyle et al., 2019). In the future, better measures of pathologies and new pathologies will be discovered which may improve these numbers.

Fig. 2.

Fig. 2

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Random sample of cognitive change over multiple years prior to death. A 0 denotes time of death. Dark line is mean.

Fig. 3.

Fig. 3

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Variance of cognitive change explained by common neurodegenerative and cerebrovascular diseases.

The brain, however, is a physiologic system. All physiologic systems have mechanisms to maintain homeostasis and optimal function in the face of physiologic challenges from injury or disease. Many systems are simply redundant, for example, two lungs, two kidneys, and a liver that can regenerate. The brain, by contrast is plastic. It is constantly learning and remodeling. Thus, loss of cognitive is a complex function of mixed pathologies with additive and interactive effects, along with neural reserve and resilience mechanisms that help maintain cognition despite brain pathologies (Bennett, 2017). Evidence comes indirectly via a variety of risk factors associated with cognitive decline that are agnostic to brain pathologies (Yu et al., 2015). We also have direct evidence from discovering structural elements, for example, neurons, and a variety of genes and proteins associated with cognitive decline agnostic to brain pathologies (Mostafavi et al., 2018; Ramos-Miguel et al., 2021; White et al., 2017; Wilson et al., 2013; Yu et al., 2018; Yu et al., 2020).

Selected Risk Factors for Cognitive Decline

Here are discussed six experiential and six psychological risk factors.

Experiential risk factors: The relation of years of education to Alzheimer’s dementia is complicated as education is strongly associated with level of cognition across the adult life span. We have examined it in a number of papers, and like the field overall, the results have been mixed. In the most recent paper with the largest sample size, education was associated with level of but not change in cognition (Wilson et al., 2019). Nor was it associated with the acceleration of decline that began about 2 years prior to dementia diagnosis. Finally, education was associated with a lower odds of both macro- and microscopic infarctions.

We first reported the inverse association of cognitive activity with Alzheimer’s dementia risk in ROS nearly two decades ago (Wilson et al., 2002). We followed a few years later in MAP (Wilson, Scherr, Schneider, Tang, & Bennett, 2007). There are always concerns about inverse causality such that loss of cognition leads loss of cognitive activity. Using a cross-lagged model, we found that loss of cognitive activity was the leading indicator rather than vice versa (Wilson, Segawa, Boyle, & Bennett, 2012). Further, both early and late-life cognitive activity were related to cognitive decline controlling for common brain pathologies suggesting that cognitive activity is not a consequence of brain pathologies (Wilson et al., 2013).

Social activity is also inversely associated with cognitive decline (James, Wilson, Barnes, & Bennett, 2011). Loneliness, or self-perceived social isolation is also associated with cognitive decline and risk of dementia (Wilson et al., 2007). This is different from a small social network. Although we did not find an association of social networks with cognitive decline, we did find that it modified the relation of AD pathology to cognition (Bennett, Schneider, Tang, Arnold, & Wilson, 2006). In other words, there was little benefit from social networks in the absence of pathology but as AD pathology increased the beneficial effects of a larger social network increased. Finally, a larger life space which is the spatial extent with which one interacts in the world, from confined to ones’ room to out of the neighborhood, is associated with a lower risk of Alzheimer’s dementia as well as MCI (James, Boyle, Buchman, Barnes, & Bennett, 2011).

Psychological risk factors: Purpose in life comes from the positive psychology literature and is a measure of psychological well-being. It refers to the tendency to derive meaning from ones’ experiences in life. We used a measure adapted from Ryff’s Scales of Psychological Well-Being (Ryff & Keyes, 1995). Greater purpose in life was associated with a lower risk of Alzheimer’s dementia and MCI (Boyle, Buchman, Barnes, & Bennett, 2010). Like social networks, it modified the relation of AD pathology to cognitive decline (Boyle et al., 2012). Further, it was associated with a lower odds of macroscopic infarctions (Yu, Boyle, Segawa, et al., 2015).

Depressive symptoms, not depression, is a trait-like measure that rarely interferes with usual function. We found it was associated with an increased risk of Alzheimer’s dementia (Wilson, Barnes, et al., 2002). Because depressive symptoms are common in dementia, there is the concern about reverse causality similar to cognitive activities. However, depressive symptoms do not change during prodromal phase of Alzheimer’s dementia suggesting it is a risk factor (Wilson, Arnold, Beck, Bienias, & Bennett, 2008). In fact, following the onset of incident Alzheimer’s dementia, depressive symptoms improve slightly (Wilson et al., 2014). Finally, depressive symptoms are not related to measures of common neuropathologies (Wilson, Capuano, et al., 2014).

Neuroticism and conscientiousness are two of the Big Five personality traits. Neuroticism, or proneness to psychological distress, refers to how one responds to stress rather than to actual stress. Greater neuroticism is associated with a higher risk of Alzheimer’s dementia and MCI (Wilson et al., 2006; Wilson et al., 2007). Further, it is not related to neuropathologies (Wilson, Arnold, Schneider, Li, & Bennett, 2007). Conscientiousness, by contrast, is associated with a lower risk of Alzheimer’s dementia and MCI (Wilson, Schneider, Arnold, Bienias, & Bennett, 2007). Interestingly, it modifies the relation of terminal decline, the marked increase rate of cognitive decline that starts 3–4 years prior to death, and cortical Lewy bodies (Wilson et al., 2015). Neuroticism has six subscales including anxiety and vulnerability to stress both of which were associated with a greater risk of Alzheimer’s dementia and faster rate of cognitive decline, but neither was associated with neuropathologies (Wilson, Begeny, Boyle, Schneider, & Bennett, 2011).

Finally, harm avoidance, is a personality trait that refers to behavioral inhibition. We found that harm avoidance was associated with an elevated risk of Alzheimer’s dementia (Wilson et al., 2011). Further, it was associated with macroscopic infarctions (Wilson et al., 2014).

A Tale of Two Women

Next, we illustrate the potential effects of these experiential and psychological factors and mixed pathologies with a comparison of two MAP participants. They were matched for age at study entry, sex, baseline diagnosis, years of follow-up, age at death, and the presence of pathologic diagnosis of AD at autopsy. Specifically, both enrolled at age 79, were women, had eight annual evaluations, and died at age 87. Both were without cognitive impairment at study entry, and both met pathologic criteria for AD at autopsy. Case 1 had an MMSE of 30 at baseline whereas Case 2 had an MMSE of 28. The risk profiles of the two cases are listed in Table 1 and illustrated in Fig. 4 (Case 1 solid line on bottom, and Case 2 dotted line on top. Neuropathologic findings are summarized in Table 2.

Table 1.

Risk factor profile of two matched cases

Risk Factor Mean Range Case 1 Case 2
Education 14.4 0–28 22 12
Cognitive activity 3.1 1–4.7 4.2 2.3
Social activity 2.6 1–4.2 3.5 2.7
Social isolation 2.3 1–4.6 2 2.4
Social network 6.8 0–66 3 8
Conscientiousness 33.5 12–48 42 NA
Purpose in life 3.6 2–5 4 3
Harm avoidance 10.5 0–34 4.2 22
Anxiety 12.2 0–32 8 16
Neuroticism 15.7 0–45 11 29
Depressive symptoms 1.2 0–10 1 3
Life space 5.3 0–6 6 5
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Fig. 4.

Fig. 4

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Risk profile of two cases with better profile associated with lower risk above (Case 1, dotted line) and worse profile associated with higher risk below (Case 2, solid line).

Table 2.

Postmortem indices of two matched cases

Pathology Case 1 Case 2
Brain weight 1,246 g 1,088 g
Atrophy Mild diffuse Mild diffuse
Neuritic plaques Moderate-frequent Moderate-frequent
Neurofibrillary tangles Moderate-frequent Moderate-frequent
NIA-Reagan diagnosis Intermediate likelihood Intermediate likelihood
Β-amyloid load 11% 3.9%
Neocortical PHFtau tangle density 10.4/mm2 10.0/mm2
Mesial temporal PHFtau tangle density 0.66/mm2 0.1/mm2
Cerebral amyloid angiopathy No Significant
Cortical Lewy bodies 0 0
Cerebral infarctions 0 three small chronic infarctions
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Case 1

She was highly educated and engaged in a lot of cognitive activity (Table 1). She was very socially active and was below average on social isolation; she was also below average on social networks, overall suggesting that she had highly meaningful relationships. She was very conscientious and had a lot of purpose in life. She was low on harm avoidance, anxiety, neuroticism, and depressive symptoms. She engaged in the full extent of life space. Overall, she had an outstanding risk profile with many factors associated with a lower risk of Alzheimer’s dementia (Fig. 4).

Although a crude measure, her brain weight was above average for an adult female, let alone someone of her advanced age, with mild diffuse atrophy (Table 2). She met pathologic criteria for AD. Her β-amyloid load and PHFtau tangle density were on the high side. She did not have cerebral amyloid angiopathy, cortical Lewy bodies or cerebral infarctions. Thus, other than AD her brain looked good, that is, she did not have mixed pathologies.

Case 2

She had only a high school education and was below average on cognitive activity (Table 1). She was average on social activity and social isolation despite a greater than average social network, suggesting less meaningful relationships. She was below average on purpose in life. She was very high on harm avoidance, anxiety, neuroticism, and depressive symptoms. Her life space was slightly constricted. Overall, she had a poor risk profile with many factors associate with higher risk of Alzheimer’s dementia, as well as risk of cerebrovascular disease (Fig. 4).

Although a crude measure, her brain weight was below average for an adult female, with mild diffuse atrophy (Table 2). She met pathologic criteria for AD. Her β-amyloid load was average, and mesial temporal PHFtau tangle density was quite low. She had significant cerebral amyloid angiopathy and three chronic cerebral infarctions, but no cortical Lewy bodies. Thus, she had two pathologies in addition to AD that are known to add to the likelihood of dementia.

Result

The consequences of combination of a better risk profile and the absence of mixed pathologies in Case 1 is striking in comparison to Case 2. First, we created a composite measure of global cognition. This is the average z-score of 19 tests of cognition where 0 is the mean for all participants at baseline (Wilson, Barnes, & Bennett, 2003). What we find are marked differences in the cognitive trajectory for these two women (Fig. 5).

Fig. 5.

Fig. 5

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Case 1is solid line and Case 2 is dotted line. Trajectories of global cognition for the two cases.

Case 1 was almost one standard unit above the mean at baseline. Her cognition fluctuated slightly over time, but when last tested prior to death, she was slightly better than one standard unit suggesting a small amount of learning or practice effects over time (Fig. 5, solid line).

Case 2 was above the mean at baseline but about a half standard unit lower than Case 1. These are crude data and much of that may have been the result of the 10-year difference in education. In marked contrast to Case 1, Case 2 steadily declined over the years and was about 2 units below 0 prior to death, well within the dementia range which is, on average, about −1.5 standard units.

Summary

Dementia is a consequence of multiple pathologies that add to and interact with one another, in combination with a variety of resilience markers that can maintain or degrade cognition regardless of brain pathologies. Many experiential and psychological risk factors for cognitive decline and Alzheimer’s dementia have little direct relationship with AD pathology. Some are associated with cerebrovascular disease. Overall, one’s risk profile has the potential to have a large impact on late-life cognitive performance over time as seen with two illustrious case reports.

Acknowledgments

I thank the study participants and the faculty and staff of the Rush Alzheimer’s Disease Center. More information on ROSMAP can be found at https://www.radc.rush.edu.

Funding

The work was supported by National Institute on Aging grants P30AG10161, R01AG15819, and R01AG17917 (D.A.B.).

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