Developing Cognitive Markers of Alzheimer’s Disease for Primary Care: Implications for Behavioral and Global Prevention

Abstract

A comprehensive approach to the prevention of Alzheimer’s disease (AD) warrants a synergy across multiple domains and procedures. Whereas the study of biological markers has mobilized major activity in the field, the development of cognitive markers is largely ignored, despite the unique advantages they may offer. Cognitive markers essentially assess the core clinical feature that biological markers intend to predict. In this respect, cognitive markers expand the foundation of preclinical diagnostics and disease staging in a manner that integrates both physiological and psychological factors. In addition, the cost-effective implementation of cognitive markers makes them remarkably conducive to community-wide screenings, and thereby a vital component of any global blueprint for prevention. Specifically, in the primary care setting, cognitive markers may provide effective gate keeping for more invasive, labor intensive, and expensive procedures. From this perspective, cognitive markers may provide the first step for identifying preclinical treatment recipients in general public. Moreover, the detection of preclinical decline via cognitive markers can increase awareness of AD risk and the motivation for making protective lifestyle changes. The behavioral approach might be expedient for prevention in light of the compelling evidence of lifestyle amelioration of AD risk. In an integrative view, incorporating cognitive markers to primary care may facilitate a synergetic development in preventive interventions that carries epidemiological significance. This paper addresses the theoretical, methodological, and pragmatic aspects of this prospect.

Keywords

Alzheimer’s disease early diagnosis prevention screening

INTRODUCTION

In Western countries, the consistent rise in life expectancy steadily increases the prevalence of AD [1, 2]. On its current epidemiological trajectory, AD imposes an economic and psychosocial burden that grows substantially heavier from year to year. Although studies vary in their estimates of prevalence, several reviews have sized the progressive scope and impact of AD to epidemic proportions [3, 4].

The growing disease burden of AD has compelled a major investment in the development of remedial measures. However, this effort was largely hampered by a number of formidable challenges [5]. Most notably, restoring declined psychosocial abilities in AD patients requires an extensive regeneration of brain structures that have atrophied over the course of the illness. In this regard, all clinical trials of AD treatment to date have failed to demonstrate a remedial process with sustained benefits [5 –7], presumably due to major gaps in the knowledge and technologies necessary for achieving neuronal regeneration of this magnitude and complexity. At the moment, interventions that effectively slow down the pace of degeneration, in lieu of a corrective process, appear to be within closer reach [8]. However, the extent to which they would serve to contain the prospective toll of the AD epidemic might be limited. Under these circumstances, increasing the emphasis on the development of preventive interventions becomes paramount.

The current thrust of AD prevention aspires to shift the course of illness in a manner that yields epidemiological gains [5, 9]. Specifically, given that AD occurs at increasingly higher rates toward the end of life, delaying the manifestation of psychosocial impairments, even by a few years, will result in decreased prevalence [10]. This approach to prevention seeks to apply disease-modifying interventions many years before psychosocial functioning is impacted, while the brain is still relatively well preserved [11, 12]. Multiple studies support the feasibility of this general strategy with evidence that the neuropathological process of AD begins years and possibly decades before the psychosocial impairment reaches diagnostic criteria [13, 14]. The substantial lag between the inception of AD pathogenesis and the manifestation of psychological impairments provides interventions with a wide window of time to exert a preventative effect. In this paradigm, the effective identification of disease markers— temporally distant predictors of AD— is critical for achieving a favorable outcome.

Most of the research on AD markers has thus far focused on biological indicators of the illness. Disease markers of a biological nature may play an essential role in uncovering etiological factors, developing preclinical interventions, and providing physiological staging of illness progression [15]. However, they may not be sufficient for achieving wide scale prevention, as their integration into the routine practice of preventive healthcare may be complicated by practical considerations. Most importantly, obtaining biological marker data involves expensive and sometimes invasive procedures that require a great deal of professional expertise and specialized facilities. For example, detecting preclinical accumulation of amyloid-beta peptide (Aβ) requires positron emission tomography (PET) imaging, or the analysis of Aβ₄₂ concentration in cerebrospinal fluid (CSF). Similarly, procedures associated with biomarkers of synaptic dysfunction employ fluorodeoxyglucose (FDG) imaging and functional MRI (fMRI). These methods, albeit essential for the development of effective prevention, are not conducive to community-wide screenings. Assessing large portions of the population for preclinical AD requires highly cost-, time-, and labor-effective procedures. In this respect, cognitive markers can offer unique advantages by functioning as cost-effective gatekeepers for expensive, biologically based procedures.

From this perspective, the relevance of cognitive markers to the effective application of any preclinical intervention becomes evident. In the absence of practical methods for identifying the incipience of AD pathogenesis in the general public, the epidemiological significance of new pharmacological discoveries may be limited, as the new drugs will fail to reach the appropriate treatment recipients in the community. For this reason, in the practice of preventive healthcare, cognitive markers should lead the endeavor of preclinical diagnosis.

Previous research on cognitive markers supports this notion. Similar to the neuropathological process of AD, abnormal decline in cognitive functioning begins many years before functional impairments reach diagnostic criteria [16]. Longitudinal studies that applied cognitive testing showed evidence of preclinical departure from normal aging across several cognitive domains [17 –19]. These findings suggest that cognitive testing can serve as a disease marker for AD. However, it is important to note that in order to advance the mission of AD prevention within the constraints of primary healthcare systems, cognitive markers will need to acquire strong predictive properties with very brief or “weightless” procedures.

Beyond their pragmatic value, cognitive markers are essential for AD prevention because they assess the core clinical feature that biological markers only partially predict. In this regard, it is important to employ biological and cognitive markers in tandem. Obtaining their concomitant observations over the course of the preclinical period will provide an integrative foundation for disease staging and clinical care more generally. In a broader perspective, a comprehensive prevention strategy for AD needs to encompass both biological and cognitive markers in a synergistic manner. At this point, extensive cognitive data are collected within the context of biomarker research; however, cognitive models, valid instruments, and practical procedures for detecting preclinical AD in the community, as opposed to research settings, do not currently exist, and should be developed.

THEORY: THE NOTION OF PREVENTIVE COGNITIVE SCREENING

Preventive cognitive screening is fundamentally different in both concept and method from the type of community screening that is frequently discussed in the literature. Previous debates about cognitive screenings in community dwelling adults focused primarily on elderly or at risk individuals, with the general goal of detecting mild cognitive impairment (MCI) as an early sign of disease incidence. In contrast, preventive screening seeks to identify the preclinical phase of the illness; namely, pathology that develops before cognitive or psychosocial impairments can be formally diagnosed. To this end, the preventive paradigm embraces a repeated, non-selective, community-wide screening that begins in the premorbid (i.e., prior to the inception of pathology) phase of midlife. Figure 1 depicts a clinical protocol of annual, cognitive monitoring in primary care across the premorbid, preclinical, and post clinical phases of AD. In the figure, S represents the consecutive cognitive scores, starting at age 45.

The repeated premorbid assessment of the community is designed to quantify the cognitive decline associated with healthy aging. This procedure aims to establish a baseline comparison for detecting the preclinical shifts in the cognitive slopes of prospective AD patients. In general, this approach is deemed more conducive to prevention than at-risk assessment because it allows for a much earlier detection of AD pathogenesis, and the delivery of disease modifying interventions. These may integrate innovative pharmacology, effective management of midlife medical risk factors, and a behavioral shift toward a protective lifestyle.

The promotion of preventive screenings requires theoretical advances in neuropsychological measurement. Briefly, cognitive assessment for preventive purposes represents a shift from conventional practice. The field of neuropsychology evolved within a disease model that aimed to identify impairment. In contrast, the preventive paradigm is based on a model that focuses on wellness. As noted earlier, within this paradigm, cognitive testing would no longer seek to detect impairment, but instead capture subtle departures from normal aging that do not carry a perceptible impact on psychosocial functioning. Although an abnormal pace of cognitive decline implicates a pathological process, the absence of impairment at the time of evaluation affects the assessment procedure in fundamental ways.

Most importantly, preventive assessment introduces a new unit of measurement. A conventional neuropsychological evaluation assesses cognitive functioning with a test score that estimates absolute ability; whereas, preventive assessment applies a unit of measurement that reflects magnitude of change. In practical terms, it observes the difference among multiple test scores instead of the value of a single score. In its most basic form, the new unit of measurement is given by Δ = S₁ - S₂. For example, cognitive decline from age 65 to 66 will be assessed by the difference of consecutive annual test scores: Δ_65-66 = S₆₅ - S₆₆. At the moment, normative data for Δ do not exist for any particular measure. This absence limits the clinical interpretation of intrapersonal changes, which occur across the entire spectrum of cognitive functioning, independent ofimpairment.

Figure 2 provides a basic demonstration of how this notion applies to the detection of preclinical AD, and further illustrates the advantages of preventive assessment over models that selectively screen for impairment in the elderly. In this figure, Δ marks the annual difference in the test scores of an individual subject. Based on a hypothetical normative distribution of Δ, the annual screening data indicate a statistically abnormal decline between 2010 and 2011, despite the fact that the absolute values of both test scores remain well above the population mean. As the figure shows, in this case, conventional impairment based procedures that make comparisons to average normative performance will detect the problem about a decade later.

For the purpose of the current discussion, Fig. 2 simplifies the illustration of preventive screening with a single cognitive score; however, it is important to note that an effective cognitive marker will probably require multivariate predictors. A second issue deserving mention here is related to the unfavorable signal to noise ratio that affects the measurement of cognitive change. In theory, signal strength is a function of the temporal proximity to the inception of AD pathogenesis. Hence, the power to detect abnormal cognitive decline deflates disproportionally during the preclinical phase.

Both of these challenges warrant advances in test design, as well as the application of artificial intelligence or other analytic tools, hitherto ignored by the field. In this regard, it is important to recognize that extant neuropsychological tests have not yet demonstrated psychometric properties surrounding cognitive change and longitudinal outcome. For this reason, they may not necessarily be superior to newly developed tests that are designed specifically for this purpose. In fact, within the research on AD prevention, an academic effort to develop the psychometrics of cognitive change may prove fruitful. Hence, AD researchers who seek to incorporate cognitive markers to ongoing longitudinal studies should consider new academic developments alongside widely used measures.

METHODOLOGY: THE UNIQUE CHARACTERISTICS OF PREVENTIVE SCREENS

A conservative approach to cognitive screening in primary care settings would likely rely on the most commonly used and widely researched screening instruments; however, steering preventive screenings in this direction may be fraught with serious disadvantages. In general, conventional cognitive screens suffer from major limitations that render them inadequate for preventive assessment. Cognitive screens were developed to assess highly compromised states of mental status, rather than detect subtle changes in cognitive abilities. Applied to AD assessment, a screening test is thus more likely to uncover advanced pathology, or dementia, than flag preclinical decline. In other words, conventional screening instruments are designed to identify the irreversible condition that cognitive markers aim to prevent. For this reason, conventional cognitive screens are highly unsatisfactory for preventive purposes.

The most significant limitation of conventional screens is rooted in their alternative approach to measurement. Instead of assessing discrepancies from the average of a normative sample, they determine impairment with a cutoff score on a scale that essentially lacks gradation of difficulty. In fact, conventional screens, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), deliberately set the overall challenge of the test to a minimal level. This creates a significant ceiling effect that results in undifferentiated performance across age groups, and an exceptionally low variance in test scores. For example, on the MMSE or the MoCA, it is quite feasible to maintain the maximum score on the test between the age of 25 and 75, as most test items pose little challenge to people without dementia. In this regard, many patients may still be fully oriented to place and time, and capable of recalling a few words, or copying a simple design, despite abnormal decline in cognitive abilities. In other words, although failing minimal challenge may indicate serious neurological pathology, intact performance on conventional screens does not rule out milder forms of impairment. Hence, this type of impairment-based assessment is likely to mask preclinical deterioration, especially in high functioning individuals with ample cognitive reserve.

Studies on dementia screening in primary care settings reached the same conclusion. Physicians in the primary care setting fail to diagnose between 26% and 76% of probable dementia cases [20 –22]. Milder forms of dementia are even more likely to be missed, with rates of under-diagnosis estimated at 90% in some cases [23]. Consistent with these findings, other studies indicate that the commonly used measures representing best practices overlook milder forms of impairment. For example, the MMSE misses approximately 80% of patients with mild cognitive impairment [24]. These measures, which also include the Memory Impairment Screen, Abbreviated Mental Test, the 7-minute screen, and the Mini-Cog, among others, often succeed in detecting severe morbid decline but fail to flag preclinical presentations [25 –27]. These findings largely confirm the methodological limitations of minimal challenge for early or preclinical detection of AD.

Following this line of reasoning, preventive screening should present challenges that are designed to assess the cognitive limits of patients. During the preclinical period, subtle changes in cognitive functioning would be easier to discern when the test evaluates maximal ability as opposed to fixed clinical standards of impairment. In other words, the screening procedure is more likely to detect preclinical decline when the baseline comparison observes the maximum point. Figure 3 provides an example of a computerized test that assesses maximal abilities. On this test of visual attention, subjects are required to mouse-click on the numbers in ascending order as fast as possible. An executive variant of this test instructs subjects to alternate across even and odd numbers in an ascending sequence. If the test score is based on completion time, performance is capped only by maximal ability, without the ceiling effect associated with tests that measure output with an aggregation of points. On tests that assess maximal ability, performance will decline with age, even in the absence of pathology [28]. In this regard, tests that are designed to assess cognitive limits, especially in age sensitive domains, are more likely to distinguish between normal cognitive aging and the decline associated with preclinical AD. For this reason, it is essential that cognitive markers will embody the quality of limit testing.

Another methodological issue deserving attention is related to the training effects that may emerge over the course of repeated measurement. This problem is significantly reduced by a large number of equivalent test versions. For example, in the test that appears in Fig. 3, the computer generates a different set of numbers for each administration, so little or no training effects are expected to emerge on this task in the context of annual assessments.

Beyond methodological guidelines, pragmatic considerations will also shape the properties of cognitive markers. Specifically, cognitive markers should be designed to achieve their mark within the practical limitations of the primary care setting. Given the increasing time constraints imposed on physicians and other clinic staff, the screening procedure needs to be self— as opposed to clinician— administered. In addition, it needs to be very brief and easily embedded within the workflow a physical exam. As a rule of thumb, administration should not exceed several minutes for intact patients. To further increase the cost and labor effectiveness of the screening, all test related procedures should be electronic and automatic. In summary, an effective preventive cognitive screen needs to accommodate all of these practical factors without detracting from its validity.

PRACTICAL CONSIDERATIONS: DEMENTIA SCREENING IN PRIMARY CARE SETTINGS AS A CLINICAL VEHICLE FOR DEVELOPING COGNITIVE MARKERS

The development of cognitive markers presents a longitudinal challenge of remarkable scope. Early detection of preclinical decline requires data that extend well into the premorbid period. In many cases, the full gamut of repeated measurements, from the first premorbid screening to the eventual diagnosis of AD, may span several decades. The challenge that lies in the length of data collection is further compounded by its breadth. Specifically, the data should give rise to empirical distributions of longitudinal parameters that represent the community well with considerable demographic and clinical stratification. In addition, the validation of the cognitive marker warrants a sample large enough to capture a sizable number of conversions from the premorbid state to post-clinical AD. Taken together, these factors pose formidable practical barriers to the research on cognitive markers for AD.

An alternative avenue to advance is to rely on the analysis of natural clinical data. These data do not currently exist, but can come into being in healthcare systems that are willing to incorporate annual screenings for dementia to primary care practice as a standard of care. If the screening protocol replaces the conventional dementia screen with a self-administered instrument that possesses preventive properties, the necessary data for the development of a cognitive marker will begin to accrue with minimal expense. Within this strategy, the screening procedure will serve multiple functions. Assessing maximal ability, the preventive screen will detect dementia, when present, and quantify the magnitude of cognitive decline when dementia is absent. The resulting data can be used to identify preclinical AD and predict its duration. This strategy effectively narrows the role of research to the analytical task and further improvement in test design. In this respect, the clinical approach to the development of a cognitive marker for AD offers important practical advantages. However, its feasibility does remain largely dependent on the value that primary care policies ascribe to community-wide screening for dementia.

THE MERIT OF COMMUNITY-WIDE SCREENING FOR DEMENTIA

Community-wide screening for dementia has been fraught with controversy in recent decades [11 , 32]. Detracting opinions voiced a general objection to the proactive diagnosis of non-contagious conditions without remedy, due to the consequent emotional toll on patients and their families [27, 33]. However, arguments on the other side of the issue challenged this general view with evidence that early diagnosis tends to alleviate rather than increase the burden of AD [23 , 34]. Early diagnosis decreases family tension by diminishing relational stress, and providing access to specialized supportive services and informed medical care [23 , 35]. In addition, it facilitates effective life planning regarding work transitions, and decisions surrounding legal, financial, housing, and healthcare issues [26 , 36]. In this respect, early diagnosis typically leads to fewer social, financial, medical, and safety concerns [34, 36]. From an economic perspective, there is evidence that the cost of care decreases with early diagnoses [37 –39]. Thus, despite the absence of remedial interventions for AD, there is evidence to suggest that early diagnosis is associated with multiple benefits to patients, their family, public health, and society at large.

This conclusion led a number of influential legal, medical, and administrative bodies to support proactive screenings for dementia. The Affordable Care Act now requires cognitive screening for Medicare patients during the Welcome to Medicare and Annual Wellness Visits [40]. Similarly, recent national initiatives in the United States and Europe, including the U.S. National Alzheimer’s Project Act, the National Dementia Strategy for the UK, and the French Plan Alzheimer, launched strategic screenings for dementia on a large scale [36]. These developments indicate that the importance of community wide-screening for dementia is increasingly recognized, both locally and globally. In fact, certain programs have already moved into the preliminary phase of implementing preventive screening procedures. If these initiatives employ preventative screening instruments, in lieu of conventional screens of dementia, they will set the development of cognitive markers into motion through the routine operation of clinical practice. Over time, this transition may yield monumental preventative gains with minimal cost.

COGNITIVE MARKERS MAY EXPAND THE SCOPE OF COMMUNITY SCREENINGS

The research on early diagnosis of AD grounds dementia screening in empirically based practices. However, the implementation of screening initiatives should advance in a manner that considers their liabilities. Among these, emotional distress that results from the awareness of cognitive decline is the most critical. In some cases, learning about the diagnosis of AD, and the prospective loss of mental faculties, can be devastating [24, 41]. For this reason, it is conceivable that some people would wish to remain unaware of their condition and eschew the psychosocial ramifications of a formal diagnosis. The main risk in community-wide screening, therefore, lies in imposing disturbing news on patients who prefer to remain incognizant of their illness, without offering them remedial treatment. A meticulous procedure of informed consent to screening may satisfy one ethical aspect of the issue; yet, it still fails to address the more serious problem of leaving many AD patients and their families, in the absence of a diagnosis, to cope with degenerative dementia without appropriate care.

The development of cognitive markers through the process of preventive screenings may offer an avenue to ameliorate this plight. During the early phase of the preclinical period, a preventive screen does not provide a diagnosis, but rather observes an abnormal decline in cognitive functioning. In this respect, preventive screening may raise fewer objections than screening for dementia, as the outcome of the assessment carries minimal psychosocial implications for the near future; and, more importantly still, it does not preclude a favorable long-term outcome. For this reason, many patients who would otherwise avoid an assessment of dementia, when present, might comply more readily with a preventive screening procedure when their cognitive faculties are either intact or relatively well preserved. They may find preventive screening during midlife more appealing than dementia screening in older age because the former offers advance warning and thereby the potential for offsetting the progression of the illness. Although this potential is yet to be maximized by the development of specific medical interventions for AD, it may be realized to some extent on a shorter time horizon via the combination of preventive healthcare and midlife changes in lifestyle. For a more complete list of pros and cons for implementing preventive cognitive screening in primary care, see Table 1.

COGNITIVE MARKERS IN THE SERVICE OF PREVENTIVE MEDICAL HEALTHCARE

When appropriately anchored in the premorbid period, the starting post of preventive screenings aligns well with the major medical risk factors of midlife, such as cardiovascular disease, type 2 diabetes mellitus, hypertension, and hypercholesterolemia [9 , 42–44]. If the routine monitoring of these conditions includes repeated cognitive measurement, their prospective link to both pre and post-clinical AD can be observed in individual patients in a manner that may inform their treatment. Previous research on the medical management of risk factors has been inconclusive with respect to AD prevention [45 –49], potentially due to the limited time frame of investigations and the variance in the disease stages examined. In this respect, cognitive screenings that stretch across the full gamut of cognitive decline, from the premorbid stage of early midlife to the post-clinical deterioration of old age, may provide the type of longitudinal data that are difficult to collect through other types of research projects. On a systemic level, these data may provide insight into the methods and time frames that yield the most effective medical management of risk factors for AD. From the perspective of individual patients, repeated feedback about the pace of cognitive decline from midlife may increase the motivation for shifting health related behaviors in a manner that is more consistent with a protective lifestyle.

A CHANGE IN LIFESTYLE IS A DISEASE-MODIFYING INTERVENTION

Aside from medical management of risk factors, a healthy lifestyle is currently the only known method that may play an important role in preventing AD. The Okinawa Centenarian Study reveals a tight connection between a healthy lifestyle and preserved cognition well past the age of Western lifespan [50]. Several analyses of these data observed a substantially lower rate of AD prevalence in Okinawan communities in comparison to Western countries [51]. Further evidence for the effects of behavioral prevention comes from research that has linked an array of poor health habits in midlife to increased risk for AD in old age [10].

Among these habits, smoking has been implicated as one of the most detrimental threats to cognitive functioning. A comprehensive population-based study (N = 21,123) observed that midlife smoking increased the risk of AD and other dementias by more than 100% over the course of two decades, independent of other medical risk factors [52]. Additional studies highlighted similar findings in APOE ɛ4 non-carriers, as evidence for the indiscriminate connection between smoking and cognitive decline [53, 54].

A second factor that may have an independent contribution to dementia in general and AD in particular is obesity [10, 15]. Multiple meta-analyses indicate that a mid-life BMI greater than 30 significantly increases the prospective probability of AD [56, 57]. Beyond the independent effect of excessive weight on cognitive decline, the role of the typical diets that lead to obesity is also important to consider. Some may lack essential nutrients for maintaining neurological health, while others include ingredients that elevate medical risk factors for AD [58 –60]. The research on preventive nutrition indicates that dietary choices can protect against AD in multiple ways. For example, a diet rich in flavonoids and polyphenols may exert a preventative effect by promoting cardiovascular health [61], whereas, omega-(n)-3 polyunsaturated fatty acids may enhance neurological functioning more directly [62 –65]. Thus, studies on nutrition collectively suggest that both caloric intake and dietary choices affect the development of AD.

The literature on physical exercise provides further support for the potential of behavioral prevention. A substantial body of evidence indicates that physical activity and fitness are related to both improved cognitive performance and decreased risk for dementia [66 –72]. Although the underlying mechanism of this association has not yet been fully elucidated, studies that employed exercise interventions in the elderly revealed important neurological and cognitive gains. In healthy individuals, exercise was reported to increase hippocampal volume and perfusion, enhance neurogenesis, reduce cerebral Aβ deposits, and improve performance on neuropsychological tests [73, 74]. Exercise programs for adults with cognitive impairment also show cognitive enhancing effects, especially on measures of memory, attention, and global cognition [73 , 75–77]. Another preventive benefit of exercise is related to the management of medical risk factors, such as improvements in lipid and cardiovascular profiles [74]. Exercise may therefore exert multiple preventive effects, especially during preclinical or premorbid periods.

Another lifestyle factor that is related to cognitive aging is psychosocial stress. Multiple studies linked chronic psychosocial stress and elevated anxiety to increased risk of dementia and a more rapid age-related decline in global cognition [78, 79]. The research on the physiology of psychosocial stress reveals robust associations among chronic emotional distress, oxidative stress, and neurological impairment [80], with specific studies mounting compelling evidence for the role that oxidative stress plays in the pathogenesis of AD [81]. Related mechanisms considered the effects of stress on inflammatory processes and glucose metabolism as important etiological factors of neurodegeneration [82]. Although the research on psychosocial stress and dementia is still evolving, the bulk of the studies in this area suggest that chronic stress may exert neurotoxic effects that exacerbate cognitive decline in mid to late life.

Consistent with these findings, the literature on protective factors for AD highlights the importance of socializing [83 –85]. A number of studies linked social isolation to accelerated cognitive decline [86]; whereas, social engagement and supportive networks predicted lower rates of AD and better cognitive outcome [84 , 87–89], independent of various confounding factors, such as physical and intellectual activity [83, 90]. Social engagement and supportive networks may protect against AD by mitigating the ill effects of stress on the brain and cognition [91, 92]. In addition, there is evidence that stress-reducing activities that are not social in nature, such as mindfulness meditation, may further enhance cognitive and neurological health [93 –95]. Stress management may therefore be an important behavioral factor of AD prevention.

Finally, cognitive intervention programs have gained traction as possible preventive measures for AD. Although studies find that cognitive training has no positive effect on people with dementia [96], there is some variable evidence that it may slow down the pace of cognitive decline among patients with MCI [97 –101]. In healthy older adults, the protective effect of cognitive training emerged as strong and persistent on a systematic review of randomized clinical trials [102]. The extent to which these findings extrapolate to preclinical AD without MCI is unknown, yet this possibility appears plausible based on the balance of the data.

In summary, extensive research indicates that AD is related to various lifestyle factors, including smoking, body weight, diet, physical activity, cognitive enrichment, social connection, and stress. The fact that these factors are subject to behavioral change raises the hypothesis that people may have some control over the development of AD, even in the absence of medical interventions. Although the collective research on lifestyle factors is consistent with this hypothesis, it important to note that the precise mechanisms through which risk and protective factors are related to AD neuropathology remain unknown. For this reason, it would be inaccurate to assume that successful management of particular risk factors would necessarily reduce the risk of developing AD [103]. At the same time, the prospective correlations between such a wide array of lifestyle factors and AD outcome do suggest that a global shift in health related behaviors holds preventive potential. A healthy lifestyle clearly plays a substantial role in preventing illness across most physiological systems in the human body. Hence, it is unlikely that the brain presents an exception to this rule, even though the mediating mechanisms for preserving neurological health are complicated and remain largely obscure at this point. In this regard, it is important to stress that, beyond AD, the preventive effects of behavioral modification apply to other forms of dementia, and may be particularly relevant to cases that point to vascular pathology as an important etiological factor.

In this view, the potential of cognitive markers to inspire a protective shift in lifestyle among at risk patients might be important. Research on motivational interviewing in primary care settings indicates that medical feedback can lead to significant changes in health related behaviors, when delivered by trained clinicians and accompanied by programmatic supports [104, 105]. Thus, under certain conditions, cognitive markers employed by primary care clinics may play an important role in spurring the behavioral prevention of AD.

CONCLUSION

Epidemiological predictions estimate that the prevalence of AD will triple over the next four decades [10]. In the absence of remedial interventions, the effort to offset this trajectory warrants a major investment in preventive measures. Prevention research indicates that the neuropathological processes associated with AD begin to unfold many years prior to its clinical diagnosis. In consequence, investigative efforts have now shifted to the development of preclinical interventions. At this point, this endeavor focuses primarily on identifying the biological markers of AD; whereas, the development of cognitive markers for clinical use receives less attention. Although biological markers may lead to etiological discoveries that inform treatment, the development of cognitive markers offers unique advantages for implementing preventive programs on a large scale.

Specifically, applying cognitive markers in primary care settings, as a standard of care, will allow the identification of preclinical AD in the community. In the absence of such capabilities, AD prevention is difficult to envision, even with the advent of effective pharmacological innovations that can markedly decrease AD progression in preclinical stages. Stated differently, without cognitive markers, appropriate treatment recipients will have limited access to preclinical interventions, because preclinical AD in the general public will remain largely unidentified.

The detection of preclinical decline at the earliest possible point requires a clinical protocol that anchors the inception of annual cognitive screenings deep in the premorbid period of midlife. This starting post will allow physicians to quantify intrapersonal departures from normal cognitive aging before impairment arises, and integrate the feedback about the pace of midlife decline in the management of medical conditions that increase the probability of AD in older age. In addition, in the primary care setting, cognitive markers can be used to increase the awareness of risk in a manner that motivates significant shifts in health-related behaviors. These effects may be magnified by education about the protective effects of a healthy lifestyle and comprehensive programmatic supports. Within this framework, cognitive markers may play a major role in offsetting the epidemiological trajectory of AD, even in the absence of medical breakthroughs. In a broader perspective, there is reason to believe that a radical shift in behavior toward healthy practices may promote not only brain health, but salubrious senescence more globally. In this view, the development of cognitive markers for primary care may yield far-reaching public health benefits that exceed the delimited realm of dementia.

Disclosure Statement

Authors’ disclosures available online (http://j-alz.com/manuscript-disclosures/16-0309r1).

References

Duthey

(2013) Alzheimer disease and other demen-tias. WHO Background Paper, http://www.who.int/medicines/areas/priority_medicines/BP6_11Alzheimer.pdf, Last updated June 11, 2013, Accessed on May 10, 2015.

Johnson

, Davis

, Bosanquet

(2000) The epidemic of Alzheimer’s disease. Pharmacoeconomics 18, 215–223.

Alzheimer’s Association (2015) Alzheimer’s disease facts and figures. Alzheimers Dement 11, 332–384.

Hampel

, Prvulovic

, Teipel

, Jessen

, Luckhaus

, Frölich

, Riepe

, Dodel

, Leyhe

, Bertram

, Hoffmann

, Faltraco

, German

Task Force on Alzheimer’s Disease

(2011) The future of Alzheimer’s disease: The next 10 years. Prog Neurobiol 95, 718–728.

Daviglus

, Bell

, Berrettini

, Bowen

, Connolly

Jr , Cox

, Dunbar-Jacob

, Granieri

, Hunt

, McGarry

, Patel

, Potosky

, Sanders-Bush

, Silberberg

, Trevisan

(2010) National Institutes of Health State-of-the-Science Conference statement: Preventing Alzheimer disease and cognitive decline. Ann Intern Med 153, 176–181.

Birks

(2006) Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database Syst Rev 1, CD005593.

Carrillo

, Brashear

, Logovinsky

, Ryan

, Feldman

, Siemers

, Abushakra

, Hartley

, Petersen

, Khachaturian

, Sperling

(2013) Can weprevent Alzheimer’s disease? Secondary “prevention” trials in Alzheimer’s disease. Alzheimers Dement 9, 123–131.

Tayeb

, Murray

, Price

, Tarazi

(2013) Bapineuzumab and solanezumab for Alzheimer’s disease: Is the ‘amyloid cascade hypothesis’ still alive? Expert Opin Biol Ther 13, 1075–1084.

Lista

, Dubois

, Hampel

(2015) Paths to Alzheimer’s disease prevention: From modifiable risk factors to biomarker enrichment strategies. J Nutr Health Aging 19, 154–163.

10.

Barnes

, Yaffe

(2011) The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 10, 819–828.

11.

Kivipelto

, Solomon

, Ahtiluoto

, Ngandu

, Lehtisalo

, Antikainen

, Bäckman

, Hänninen

, Jula

, Laatikainen

, Lindström

, Mangialasche

, Nissinen

, Paajanen

, Pajala

, Peltonen

, Rauramaa

, Stigsdotter-Neely

, Strandberg

, Tuomilehto

, Soininen

(2013) The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER): Study design and progress. Alzheimers Dement 9, 657–665.

12.

Richard

, Andrieu

, Solomon

, Mangialasche

, Ahtiluoto

, Moll van Charante

, Coley

, Fratiglioni

, Neely

, Vellas

, van Gool

, Kivipelto

(2012) Methodological challenges in designing dementia prevention trials – the European Dementia Prevention Initiative (EDPI). J Neurol Sci 322, 64–70.

13.

Molinuevo

, Cummings

, Dubois

, Scheltens

(2013) Early diagnosis and intervention in predementia Alzheimer’s disease, an issue of medical clinics. Med Clin North Am 97, xiii–xv.

14.

Sperling

, Aisen

, Beckett

, Bennett

, Craft

, Fagan

, Iwatsubo

, Jack

Jr , Kaye

, Montine

, Park

, Reiman

, Rowe

, Siemers

, Stern

, Yaffe

, Carrillo

, Thies

, Morrison-Bogorad

, Wagster

, Phelps

(2011) Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292.

15.

Hampel

, Frank

, Broich

, Teipel

, Katz

, Hardy

, Herholz

, Bokde

, Jessen

, Hoessler

, Sanhai

, Zetterberg

, Woodcock

, Blennow

(2010) Biomarkers for Alzheimer’s disease: Academic, industry and regulatory perspectives. Nat Rev Drug Discov 9, 560–574.

16.

Amieva

, Le Goff

, Millet

, Orgogozo

, Pérès

, Barberger-Gateau

, Jacqmin-Gadda

, Dartigues

(2008) Prodromal Alzheimer’s disease: Successive emergence of the clinical symptoms. Ann Neurol 64, 492–498.

17.

Liang

, Crutch

, Nicholas

, Ryan

, Warrington

, Shakespeare

, Yeatman

, Rossor

, Fox

(2012) Longitudinal presymptomatic cognitive changes in individuals at risk of familial Alzheimer’s disease. Alzheimers Dement 8(Suppl), P438–P439.

18.

Mårdh

, Nägga

, Samuelsson

(2013) A longitudinal study of semantic memory impairment in patients with Alzheimer’s disease. Cortex 49, 528–533.

19.

Summers

, Saunders

(2012) Neuropsychological measures predict decline to Alzheimer’s dementia from mild cognitive impairment. Neuropsychology 26, 498–508.

20.

Chodosh

, Petitti

, Elliott

, Hays

, Crooks

, Reuben

, Galen Buckwalter

, Wenger

(2004) Physician recognition of cognitive impairment: Evaluating the need for improvement. J Am Geriatr Soc 52, 1051–1059.

21.

Olafsdóttir

, Skoog

, Marcusson

(2000) Detection of dementia in primary care: The Linköping study. Dement Geriatr Cogn Disord 11, 223–229.

22.

Valcour

, Masaki

, Curb

, Blanchette

(2000) The detection of dementia in the primary care setting. Arch Intern Med 160, 2964–2968.

23.

Clionsky

, Clionsky

(2014) Dementia screening: Saying no to the USPSTF and yes to brief cognitive evaluation. J Alzheimers Dis Parkinsonism 4, e132–.

24.

Fox

, Alessi

, Ahluwalia

, Hachinski

(2014) The use of wide-scale mental agility testing to identify people at risk of dementia: Crucial or harmful? J R Coll Physicians Edinb 44, 30–35.

25.

Harvan

, Cotter

(2006) An evaluation of dementia screening in the primary care setting. J Am Acad Nurse Pract 18, 351–660.

26.

Holsinger

, Deveau

, Boustani

, Williams

(2007) Does this patient have dementia? JAMA 297, 2391–2404.

27.

Moyer

, U.S. Preventive Services Task Force (2014) Screening for cognitive impairment in older adults: US Preventive Services Task Force recommendation statement. Ann Intern Med 160, 791–797.

28.

Ashendorf

, Jefferson

, O’Connor

, Chaisson

, Green

, Stern

(2008) Trail Making Test errors in normal aging, mild cognitive impairment, and dementia. Arch Clin Neuropsychol 23, 129–137.

29.

Lezak

, Howieson

, Bigler

, Tranel

(2012) Neuropsychological Assessment, Oxford University Press, Oxford.

30.

Cortes

, Vapnik

(1995) Support vector networks. Machine Learning 20, 273–297.

31.

Schmidhuber

(2015) Deep learning in neural networks: An overview. Neural Netw 61, 85–117.

32.

Geldmacher

, Kerwin

(2013) Practical diagnosis and management of dementia due to Alzheimer’s disease in the primary care setting: An evidence-based approach. Prim Care Companion CNS Disord 15, pcc. 12r01474–.

33.

Boustani

, Peterson

, Hanson

, Harris

, Lohr

, U.S. Preventive Services Task Force (2003) Screening for dementia in primary care: A summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 138, 927–937.

34.

Boustani

, Callahan

, Unverzagt

, Austrom

, Perkins

, Fultz

, Hui

, Hendrie

(2005) Implementing a screening and diagnosis program for dementia in primary care. J Gen Intern Med 20, 572–577.

35.

Belleville

, Fouquet

, Duchesne

, Collins

, Hudon

(2014) Detecting early preclinical Alzheimer’s disease via cognition, neuropsychiatry, and neuroimaging: Qualitative review and recommendations for testing. J Alzheimers Dis 42(Suppl 4), S375–S382.

36.

de Vugt

, Verhey

(2013) The impact of early dementia diagnosis and intervention on informal caregivers. Prog Neurobiol 110, 54–62.

37.

Barnett

, Lewis

, Blackwell

, Taylor

(2014) Early intervention in Alzheimer’s disease: A health economic study of the effects of diagnostic timing. BMC Neurol 14, 101–.

38.

Saito

, Nakamoto

, Mendez

, Mehta

, McMurtray

(2014) Cost effective community based dementia screening: A Markov model simulation. Int J Alzheimers Dis 2014, 103138.

39.

Søgaard

, Sørensen

, Waldorff

, Eckermann

, Buss

, Waldemar

(2014) Cost analysis of early psychosocial intervention in Alzheimer’s disease. Dement Geriatr Cogn Disord 37, 141–153.

40.

Center for Medicaid, Medicare Services, MedicareLearning Network (2016) Annual wellness visit (AWV),including personalized prevention plan services (PPPS). MLN Matters, http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/downloads/MM7079.pdf, Last updated March 2, 2016, Accessed on March 10, 2016

41.

Lee

, Roen

, Thornton

(2014) The psychological impact of a diagnosis of Alzheimer’s disease. Dementia (London) 13, 289–305.

42.

Diniz

, Butters

, Albert

, Dew

, Reynolds

(2013) Late-life depression and risk of vascular dementia and Alzheimer’s disease: Systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry 202, 329–335.

43.

Kloppenborg

, van den Berg

, Kappelle

, Biessels

(2008) Diabetes and other vascular risk factors for dementia: Which factor matters most? A systematic review. Eur J Pharmacol 585, 97–108.

44.

Ninomiya

(2014) Diabetes mellitus and dementia. Curr Diab Rep 14, 487–.

45.

Dufouil

, Brayne

(2014) The continuing challenge of turning promising observational evidence about risk for dementia to evidence supporting prevention. JAMA Intern Med 174, 333–335.

46.

Duron

, Hanon

(2010) Antihypertensive treatments, cognitive decline, and dementia. J Alzheimers Dis 20, 903–914.

47.

Exalto

, Whitmer

, Kappele

, Biessels

(2012) An update on type 2 diabetes, vascular dementia and Alzheimer’s disease. Exp Gerontol 47, 858–864.

48.

McGuinness

, Todd

, Passmore

, Bullock

(2009) Blood pressure lowering in patients without prior cerebrovascular disease for prevention of cognitive impairment and dementia. Cochrane Database Syst Rev 4, CD004034–.

49.

Rocca

, Petersen

, Knopman

, Hebert

, Evans

, Hall

, Gao

, Unverzagt

, Langa

, Larson

, White

(2011) Trends in the incidence and prevalence of Alzheimer’s disease, dementia, and cognitive impairment in the United States. Alzheimers Dement 7, 80–93.

50.

Ozaki

, Uchiyama

, Tagaya

, Ohida

, Ogihara

(2007) The Japanese Centenarian Study: Autonomy was associated with health practices as well as physical status. J Am Geriatr Soc 55, 95–101.

51.

Ogura

, Nakamoto

, Uema

, Yamamoto

, Yonemori

, Yoshimura

(1995) Prevalence of senile dementia in Okinawa, Japan. COSEPO Group. Study Group of Epidemiology for Psychiatry in Okinawa. Int J Epidemiol 24, 373–380.

52.

Rusanen

, Kivipelto

, Quesenberry

Jr , Zhou

, Whitmer

(2011) Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Arch Intern Med 171, 333–339.

53.

Qiu

, Kivipelto

, von Strauss

(2009) Epidemiology of Alzheimer’s disease: Occurrence, determinants, and strategies toward intervention. Dialogues Clin Neurosci 11, 111–128.

54.

Anstey

, von Sanden

, Salim

, O’Kearney

(2007) Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. Am J Epidemiol 166, 367–378.

55.

Fitzpatrick

, Kuller

, Lopez

, Diehr

, O’Meara

, Longstreth

Jr , Luchsinger

(2009) Midlife and late-life obesity and the risk of dementia: Cardiovascular health study. Arch Neurol 66, 336–342.

56.

Profenno

, Porsteinsson

, Faraone

(2010) Meta-analysis of Alzheimer’s disease risk with obesity, diabetes, and related disorders. Biol Psychiatry 67, 505–512.

57.

Beydoun

, Beason-Held

, Kitner-Triolo

, Beydoun

, Ferrucci

, Resnick

, Zonderman

(2011) Statins and serum cholesterol’s associations with incident dementia and mild cognitive impairment. J Epidemiol Community Health 65, 949–957.

58.

Luchsinger

, Noble

, Scarmeas

(2007) Diet and Alzheimer’s disease. Curr Neurol Neurosci Rep 7, 366–372.

59.

Seneff

, Wainwright

, Mascitelli

(2011) Nutrition and Alzheimer’s disease: The detrimental role of a high carbohydrate diet. Eur J Intern Med 22, 134–140.

60.

Solfrizzi

, Panza

, Frisardi

, Seripa

, Logroscino

, Imbimbo

, Pilotto

(2011) Diet and Alzheimer’s disease risk factors or prevention: The current evidence. Expert Rev Neurother 11, 677–708.

61.

Schaefer

, Bongard

, Beiser

, Lamon-Fava

, Robins

, Au

, Tucker

, Kyle

, Wilson

, Wolf

(2006) Plasma phosphatidylcholine docosahexaenoic acid content and risk of dementia and Alzheimer disease: The Framingham Heart Study. Arch Neurol 63, 1545–1550.

62.

Huang

, Zandi

, Tucker

, Fitzpatrick

, Kuller

, Fried

, Burke

, Carlson

(2005) Benefits of fatty fish on dementia risk are stronger for those without APOE epsilon4. Neurology 65, 1409–1414.

63.

Hughes

, Ganguli

(2009) Modifiable midlife risk factors for late-life cognitive impairment and dementia. Curr Psychiatry Rev 5, 73–92.

64.

Laitinen

, Ngandu

, Rovio

, Helkala

, Uusitalo

, Viitanen

, Nissinen

, Tuomilehto

, Soininen

, Kivipelto

(2006) Fat intake at midlife and risk of dementia and Alzheimer’s disease: A population-based study. Dement Geriatr Cogn Disord 22, 99–107.

65.

Luchtman

, Song

(2013) Cognitive enhancement by omega-3 fatty acids from child-hood to old age: Findings from animal and clinical studies. Neuropharmacology 64, 550–565.

66.

Abbott

, White

, Ross

, Masaki

, Curb

, Petrovitch

(2004) Walking and dementia in physically capable elderly men. JAMA 292, 1447–1453.

67.

Brown

, Liu-Ambrose

, Tate

, Lord

(2009) The effect of group-based exercise on cognitive performance and mood in seniors residing in intermediate care and self-care retirement facilities: A randomised controlled trial. Br J Sports Med 43, 608–614.

68.

Cassilhas

, Viana

, Grassmann

, Santos

, Tufik

, Mello

(2007) The impact of resistance exercise on the cognitive function of the elderly. Med Sci Sports Exerc 39, 1401–1407.

69.

Colcombe

, Kramer

(2003) Fitness effects on the cognitive function of older adults: A meta-analytic study. Psychol Sci 14, 125–130.

70.

Larson

, Wang

, Bowen

, McCormick

, Teri

, Crane

, Kukull

(2006) Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann Intern Med 144, 73–81.

71.

Verghese

, Lipton

, Katz

, Hall

, Derby

, Kuslansky

, Ambrose

, Sliwinski

, Buschke

(2003) Leisure activities and the risk of dementia in the elderly. N Engl J Med 348, 2508–2516.

72.

Weuve

, Kang

, Manson

, Breteler

, Ware

, Grodstein

(2004) Physical activity, including walking, and cognitive function in older women. JAMA 292, 1454–1461.

73.

Maass

, Düzel

, Goerke

, Becke

, Sobieray

, Neumann

, Lövden

, Lindenberger

, Bäckman

, Braun-Dullaeus

, Ahrens

, Heinze

, Müller

, Düzel

(2015) Vascular hippocampal plasticity after aerobic exercise in older adults. Mol Psychiatry 20, 585–593.

74.

Nelson

, Rejeski

, Blair

, Duncan

, Judge

, King

, Macera

, Castaneda-Sceppa

(2007) Physical activity and public health in older adults: Recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 39, 1435–1445.

75.

Intlekofer

, Cotman

(2013) Exercise counteracts declining hippocampal function in aging and Alzheimer’s disease. Neurobiol Dis 57, 47–55.

76.

García-Mesa

, López-Ramos

, Giménez-Llort

, Revilla

, Guerra

, Gruart

, Laferla

, Cristòfol

, Delgado-García

, Sanfeliu

(2011) Physical exercise protects against Alzheimer’s disease in 3xTg-AD mice. J Alzheimers Dis 24, 421–454.

77.

Winchester

, Dick

, Gillen

, Reed

, Miller

, Tinklenberg

, Mungas

, Chui

, Galasko

, Hewett

, Cotman

(2013) Walking stabilizes cognitive functioning in Alzheimer’s disease (AD) across one year. Arch Gerontol Geriatr 56, 96–103.

78.

Wang

, Wahlberg

, Karp

, Winblad

, Fratiglioni

(2012) Psychosocial stress at work is associated with increased dementia risk in late life. Alzheimers Dement 8, 114–120.

79.

Wilson

, Krueger

, Arnold

, Schneider

, Kelly

, Barnes

, Tang

, Bennett

(2007) Loneliness and risk of Alzheimer disease. Arch Gen Psychiatry 64, 234–240.

80.

Bouayed

, Rammal

, Soulimani

(2009) Oxidative stress and anxiety: Relationship and cellular pathways. Oxid Med Cell Longev 2, 63–67.

81.

Perry

, Cash

, Smith

(2002) Alzheimer disease and oxidative stress. J Biomed Biotechnol 2, 120–123.

82.

Machado

, Herrera

, de Pablos

, Espinosa-Oliva

, Sarmiento

, Ayala

, Venero

, Santiago

, Villarán

, Delgado-Cortés

, Argüelles

, Cano

(2014) Chronic stress as a risk factor for Alzheimer’s disease. Rev Neurosci 25, 785–804.

83.

Bennett

, Schneider

, Tang

, Arnold

, Wilson

(2006) The effect of social networks on the relation between Alzheimer’s disease pathology and level of cognitive function in old people: A longitudinal cohort study. Lancet Neurol 5, 406–412.

84.

Crooks

, Lubben

, Petitti

, Little

, Chiu

(2008) Social network, cognitive function, and dementia incidence among elderly women. Am J Public Health 98, 1221–1227.

85.

Pillai

, Verghese

(2009) Social networks and their role in preventing dementia. Indian J Psychiatry 51(Suppl 1), S22–S28.

86.

Berkman

, Glass

, Brissette

, Seeman

(2000) From social integration to health: Durkheim in the new millennium. Soc Sci Med 51, 843–857.

87.

Amieva

, Stoykova

, Matharan

, Helmer

, Antonucci

, Dartigues

(2010) What aspects of social network are protective for dementia? Not the quantity but the quality of social interactions is protective up to 15 years later. Psychosom Med 72, 905–911.

88.

James

, Wilson

, Barnes

, Bennett

(2011) Late-life social activity and cognitive decline in old age. J Int Neuropsychol Soc 17, 998–1005.

89.

Wang

, Karp

, Winblad

, Fratiglioni

(2002) Late-life engagement in social and leisure activities is associated with a decreased risk of dementia: A longitudinal study from the Kungsholmen project. Am J Epidemiol 155, 1081–1087.

90.

Fratiglioni

, Paillard-Borg

, Winblad

(2004) An active and socially integrated lifestyle in late life might protect against dementia. Lancet Neurol 3, 343–353.

91.

Kuiper

, Zuidersma

, Oude Voshaar

, Zuidema

, van den Heuvel

, Stolk

, Smidt

(2015) Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev 22, 39–57.

92.

Saczynski

, Pfeifer

, Masaki

, Korf

, Laurin

, White

, Launer

(2006) The effect of social engagement on incident dementia: The Honolulu-Asia Aging Study. Am J Epidemiol 163, 433–440.

93.

Banks

, Welhaf

, Srour

(2015) The protective effects of brief mindfulness meditation training. Conscious Cogn 33, 277–285.

94.

Chiesa

, Calati

, Serretti

(2011) Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin Psychol Rev 31, 449–464.

95.

Gard

, Hölzel

, Lazar

(2014) The potential effects of meditation on age-related cognitive decline: A systematic review. Ann N Y Acad Sci 1307, 89–103.

96.

Bahar-Fuchs

, Clare

, Woods

(2013) Cognitive training and cognitive rehabilitation for mild to moderate Alzheimer’s disease and vascular dementia. Cochrane Database Syst Rev 6, CD003260.

97.

Ball

, Berch

, Helmers

, Jobe

, Leveck

, Marsiske

, Morris

, Rebok

, Smith

, Tennstedt

, Unverzagt

, Willis

, Advanced Cognitive Training for Independent, Vital Elderly Study Group (2002) Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA 288, 2271–2281.

98.

Martin

, Clare

, Altgassen

, Cameron

, Zehnder

(2011) Cognition-based interventions for healthy older people and people with mild cognitive impairment. Cochrane Database Syst Rev 1, CD006220.

99.

Gates

, Sachdev

, Fiatarone Singh

, Valenzuela

(2011) Cognitive and memory training in adults at risk of dementia: A systematic review. BMC Geriatr 11, 55.

100.

Jean

, Bergeron

, Thivierge

, Simard

(2010) Cognitive intervention programs for individuals with mild cognitive impairment: Systematic review of the literature. Am J Geriatr Psychiatry 18, 281–296.

101.

Reijnders

, van Heugten

, van Boxtel

(2013) Cognitive interventions in healthy older adults and people with mild cognitive impairment: A systematic review. Ageing Res Rev 12, 263–275.

102.

Valenzuela

, Sachdev

(2009) Can cognitive exercise prevent the onset of dementia? Systematic review of randomized clinical trials with longitudinal follow-up. Am J Geriatr Psychiatry 17, 179–187.

103.

Solomon

, Mangialasche

, Richard

, Andrieu

, Bennett

, Breteler

, Fratiglioni

, Hooshmand

, Khachaturian

, Schneider

, Skoog

, Kivipelto

(2014) Advances in the prevention of Alzheimer’s disease and dementia. J Intern Med 275, 229–250.

104.

Lundahl

, Moleni

, Burke

, Butters

, Tollefson

, Butler

, Rollnick

(2013) Motivational interviewing in medical care settings: A systematic review and meta-analysis of randomized controlled trials. Patient Educ Couns 93, 157–168.

105.

Rubak

, Sandbaek

, Lauritzen

, Borch-Johnsen

, Christensen

(2009) General practitioners trained in motivational interviewing can positively affect the attitude to behaviour change in people with type 2 diabetes. One year follow-up of an RCT, ADDITION Denmark. Scand J Prim Health Care 27, 172–179.