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FROM BRAIN DISEASE TO BRAIN HEALTH: PRIMARY PREVENTION OF ALZHEIMER’S DISEASE AND RELATED DISORDERS IN A HEALTH SYSTEM USING AN ELECTRONIC MEDICAL RECORD-BASED APPROACH

 

A.M. Fosnacht1, S. Patel1, C. Yucus1, A. Pham1, E. Rasmussen1, R. Frigerio1, S. Walters2, D. Maraganore1

 

1. NorthShore Neurological Institute, NorthShore University HealthSystem, Evanston, IL, USA; 2. Research Institute, NorthShore University HealthSystem, Evanston, IL, USA

Corresponding Author: Demetrius M. Maraganore, MD, Ruth Cain Ruggles Chairman, Department of Neurology, Medical Director, NorthShore Neurological Institute, NorthShore University Health System, 2650 Ridge Ave., Evanston, IL, 60201, USA, Tel: 1-847-570-1678, Fax: 1-847-733-5565, Email: dmaraganore@northshore.org

J Prev Alz Dis 2017;4(3):157-164
Published online January 31, 2017, http://dx.doi.org/10.14283/jpad.2017.3

 


Abstract

Background: Alzheimer’s disease and aging brain disorders are progressive, often fatal neurodegenerative diseases. Successful aging, modern lifestyles and behaviors have combined to result in an expected epidemic. Risks for these diseases include genetic, medical, and lifestyle factors; over 20 modifiable risks have been reported.
Objectives: We aim to primarily prevent Alzheimer’s disease and related disorders through electronic medical record (EMR)-based screening, risk assessments, interventions, and surveillance.
Design:  We identified modifiable risks; developed human, systems and infrastructural resources; developed interventions; and targeted at-risk groups for the intervention.
Setting:  A Community Based Health System.
Participants: In year one (June 2015 to May 2016), 133 at-risk patients received brain health services with the goal of delaying or preventing Alzheimer’s disease and related disorders.
Measurements: We created mechanisms to identify patients at high risk of neurodegenerative disease; EMR-based structured clinical documentation support tools to evaluate risk factors and history; evidence-based interventions to modify risk; and the capacity for annual surveillance, pragmatic trials, and practice-based and genomic research using the EMR.
Results:  This paper describes our Center for Brain Health, our EMR tools, and our first year of healthy but at-risk patients.
Conclusion: We are translating research into primary prevention of Alzheimer’s disease and related disorders in our health system and aim to shift the paradigm in Neurology from brain disease to brain health.

Key words: Neurodegeneration, brain health, primary prevention, risk assessments, surveillance, electronic medical record.


 

Introduction

Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder characterized by progressive accumulation of beta-amyloid protein plaques and tangles of the protein tau in and around neurons the brain. It is the most common type of dementia, accounting for well over half of cases (1).   Early presentation includes lapses in memory regarding conversations, recent events and names, followed by psychological manifestations and then by impaired behavioral and motor functions.  It is the 6th leading cause of death in the US (1).
Social phenomena such as the aging of Baby Boomers, “successful aging”, and growth of the oldest-old segment of the population have forecasted epidemics of aging-related disorders (1).  Alzheimer’s disease is of particular concern due to high prevalence, limitedly efficacious pharmaceuticals, and disappointing clinical trials (2).   Today, 5.4 million Americans live with AD and prevalence expectations for the year 2050 range from 13 to 16 million (1).   Prevalence patterns are mirrored in developed nations and AD has been identified as a priority by the G8 nation’s recently formed World Dementia Council (3).
Alzheimer’s disease is expensive. The combined costs of AD total $236 billion per year (1), fueled by a number of factors including extraordinary cost of nursing homes, which can exceed $92,378 per year (4), and hospital stays, which are longer for patients with AD regardless of reason for hospitalization (1).   By mid-century, costs are expected to exceed $1 trillion. Survey research reveals heavy burden to unpaid/family caregivers, who report serious work and career-related sacrifices, emotional stress, physical pain, and financial struggle (1).
The lifetime risk for AD is approximately 1 in 5 for women and 1 in 10 for men (1); however, knowledge of a variety of health and genetic factors allows personalization of risk estimates. In 1993, the Duke Alzheimer’s Disease Research Center Group published 3 papers describing their discovery that variations in the gene Apolipoprotein E (APOE) are associated with different risks of non-familial AD (5).   Depending on one’s APOE genotype, risk may be 40% less than referent or fifteen times more. In 2010, the US National Institutes of Health issued an Independent State-Of-The-Science Report that named diabetes, smoking, and depression as having reliable evidence for increasing risk for AD.  Evidence for other factors was lacking, and the authors called for more rigorous and higher quality research (6).    Five years later, a meta-analysis by the Alzheimer’s Association reported a robustly stronger body of literature supporting modifiable risk factors for cognitive decline and dementia (3). Research also supports a variety of APOE gene-environment interactions and highlights the particularly beneficial implications of risk mitigation for APOE ε4 carriers (7-9).
In 2015 the results of the first randomized-controlled trial of a multi-domain intervention among at-risk community-dwelling elderly were published, which demonstrated that multi-modal lifestyle intervention could improve or maintain cognitive function even in elderly at-risk adults (10). Accordingly, calls for action concerning models and initiatives in primary prevention of AD have been published.  Statistical estimates of what is possible for prevalence reduction in the US range from 30% to 50% (11, 12).
Based on the ability to risk-stratify, the weight of the evidence on modifiable risk factors, and available resources, the Neurology Department at NorthShore University HealthSystem aimed to build mechanisms to identify cognitively healthy but at-risk individuals years before a possible diagnosis of AD, Parkinson’s disease (PD) or chronic traumatic encephalopathy (CTE), to build practice-based interventions to manage risk factors, and surveillance mechanisms to monitor brain health with the goal of primary prevention of neurodegenerative disease.  Uniquely, we have built in the capacity for quality improvement and practice-based research using the electronic medical record (EMR), including creation of a biobank (“clinomics”), a Neurology Practice Based Research Network (NPBRN), and informatics tools to conduct pragmatic trials using subgroup based adaptive designs (13).   This paper describes how we built the Center for Brain Health, describes our EMR tools and first year of patients, and characterizes our opportunity for risk mitigation and prevention.

 

Methods

Step 1: Identification of risk and protective factors

We identified as having strong evidence in the literature the following factors that increase risk for AD and related disorders: genetic (family history, APOE ε4, other susceptibility genes) (14-17), metabolic (cholesterol, diabetes, midlife obesity) (3, 5, 6, 11, 12, 17), vascular (cardiovascular disease, midlife hypertension, stroke) (3, 12), infectious and inflammatory (periodontitis, others) (18), head trauma (3),  diet (homocysteine, standard American diet, nutrient deficiencies) (17, 19),  habits (smoking, alcohol abuse) (3,6,11,12,17,20),  sleep (poor quality, disorders) (3, 7, 8), depression (3, 6, 10, 16),  early menopause (natural or surgical) (21),  sedentary lifestyle (11, 12) and certain medications (22, 23) (Table 1). We identified as having strong evidence in the literature as decreasing risk for AD and related disorders the following factors: genetic (APOE ε2) (5, 15), social (education, income, engagement) (3, 24), lifestyle (physical, mental exercise) (3, 6, 17, 25),  diet (Mediterranean) (3, 17),  vitamins (B6/B12/Folate, A, C, D, E) (17, 26),  medications (NSAIDS, statins, early hormone replacement therapy, antihypertensives, antidiabetics) (17, 27, 28). (Table 1)

 

 

Table 1. Survey of the literature reveals at least 20 modifiable risk factors for Alzheimer’s disease and related disorders

Table 1. Survey of the literature reveals at least 20 modifiable risk factors for Alzheimer’s disease and related disorders

 

 

Step 2:  Resource mobilization

The Center for Brain Health exists within the NorthShore Neurological Institute of NorthShore University HealthSystem and utilizes its staff, offices, equipment and infrastructure.  NorthShore University HealthSystem is a comprehensive, fully integrated healthcare delivery system serving the North Chicago region and includes 4 hospitals and 2,100 affiliated/employed physicians (29). It was amongst the first health systems to adopt the EMR in the US and was amongst the first to demonstrate “meaningful use” of EMR technology in ways that translate to improved quality, safety and efficiency for patients.  Downstream from its EMR, NorthShore maintains an enterprise data warehouse (EDW) that fosters health-related data analytics and enables broad capacity for increasingly longitudinal health-related research and informatics.
Concept development and planning began via the invitation of physicians and internal professionals to join one of five working groups that met monthly for up to 24 months to develop the concept vis-à-vis their areas of expertise. All groups were lead by the Chairman of Neurology (Director, Center for Brain Health).
The Research and Development Working Group consisted of experts in biomedical research informatics, epidemiology, genomics, molecular medicine, neurology, neuropathology, neuroradiology, nutrition, primary care, and animal models of neurodegeneration. They were tasked with deliberating over gaps in knowledge regarding at-risk populations, risk assessments, interventions, and addressing these gaps through point-of-care research utilizing the EMR.
The Community Engagement Working Group consisted of experts in community relations, marketing, patient engagement, philanthropy, and public health. They were tasked with building strategic community partnerships, community resource leveraging, and identifying events for participation to expand the reach of screening.
The Targeted Populations Working Group consisted of experts in genomics, healthcare administration, health information technology (HIT), marketing, neurology (memory disorders and movement disorders), public health, primary care, reproductive endocrinology, and sports concussion. They were charged with describing high-risk populations and recruitment mechanisms.
The Risk Assessments and Surveillance Working Group consisted of experts in healthcare administration, neurology (memory disorders and movement disorders), neuropsychology, neuroradiology, nuclear medicine, primary care, and public health. Their goal was to identify reliable, valid and pragmatic measures of brain health for patient assessments, and mechanisms for surveillance and capturing outcomes.
The Interventions Working Group consisted of experts in cognitive and physical therapy, healthcare administration, HIT, integrative medicine, medical social work, neurology (memory disorders and movement disorders), nutrition, and public health.  Their goal was to develop a suite of evidence-based interventions for modifiable risk factors for AD, PD and CTE and strategize how to operationalize these within the framework of the health system.
Key practitioners recruited for Center for Brain Health include three neurologists (PD, memory, sleep and integrative), a registered and research dietitian, a medical social worker, a physician assistant, and therapists from cognitive and physical therapy.  Non-clinical staff includes a practice manager, a senior clinical research associate, research assistants, a statistician, and HIT programmer analysts.

Step 3: Build Structured Clinical Documentation Support (SCDS) Toolkits into the Electronic Medical Record

As part of an initiative funded by the Agency for Healthcare Research and Quality to improve the quality of neurology clinical practice and facilitate point-of-care practice-based research using the EMR, the Center for Brain Health utilizes a SCDS toolkit in the EMR for all patient encounters (13). Center for Brain Health neurologists met biweekly for three months to standardize Brain Health office visit types according to evidence-based medicine (toward Best Practices).  We developed consensus on: definitions of AD, PD and CTE; outcomes of interest to clinicians and patients; valid and feasible outcome measures for point-of-care assessments; and factors known to influence the outcomes and measures. The neurologists met with members of NorthShore’s EMR optimization team biweekly over three months to develop and test the SCDS toolkit which navigates care, writes progress notes, provides clinical decision support, and electronically captures structured data.
The brain health SCDS toolkit is utilized by the care team (medical assistant, nurse, neurologist, research assistant when appropriate).  The tools include a custom navigator (index of electronic forms), electronic forms (documentation flow sheets, including cascading data elements, auto-scoring and interpreting and other “smart form” features), Best Practice Advisories (pop-up alerts), and order sets. The content of the electronic forms includes several score test measures (Appendix A) and customized fields that discretely document: chief complaints, patient information (ancestry, special diets, caffeine use, exercise habits, health maintenance, toxin and medication exposures), past medical history (specific to aging brain disorders), family history (specific to aging brain disorders), traumatic brain injury, prior treatments (nootropic, dopaminergic, nutraceutical), prior diagnostics (brain imaging modalities, electroencephalography, polysomnography, neuropsychological, genetic, and cerebrospinal fluid testing), and blood tests.  These tools electronically capture 400+ fields of data.  Some examples of screenshots of the toolkit are shown in Figure 1, with additional screenshots shown in Appendix B.

Figure 1. Screenshots of structured clinical documentation support tools that we have built into the electronic medical record (EPIC) that assess known risks to Alzheimer’s and related disorders. These tools electronically capture large amounts of clinical and diagnostic data. Data capture includes: Mediterranean diet and Readiness Assessment (both shown below). Additional screenshots are shown in Appendix A. © 2015 EPIC Systems, used with permission

Figure 1. Screenshots of structured clinical documentation support tools that we have built into the electronic medical record (EPIC) that assess known risks to Alzheimer’s and related disorders. These tools electronically capture large amounts of clinical and diagnostic data. Data capture includes: Mediterranean diet and Readiness Assessment (both shown below). Additional screenshots are shown in Appendix A. © 2015 EPIC Systems, used with permission

 

Step 4: Identification of at-risk patients

To identify at-risk patients currently engaged with our health system, we sent mailings to Primary Care Providers (PCPs), posted articles to internal websites and newsletters, and gave presentations to NorthShore committees (Council of Chairmen, Medical Group Primary Care Committee, and Medical Group Specialty Care Committee) and departments (Internal Medicine, Neurology, Obstetrics and Gynecology).
To identify at-risk individuals in the community, we sponsored activities and gave presentations at community events. We authored blogs and webinars for local chapters of aging and dementia-related national organizations. The Center for Brain Health initiative has been represented at national scientific conferences and featured in media interviews. We advertised on television, radio, the internet, billboards, newspapers, and magazines.
We developed informatics tools to identify targeted populations: a web- and paper-based Brain Health Quiz (Appendix C), an EMR-based Alzheimer’s Risk Score Algorithm, and EMR-based flags.  The Brain Health Quiz is an un-scored self-screening tool of evidence-based risk factors for AD, PD, and CTE by which individuals can learn about their risk factors and self-refer.  We distribute the paper-based quiz at events and post it electronically on NorthShore intranet and internet sites.
The Alzheimer’s Risk Score is an algorithm-based clinical decision support tool that predicts mild cognitive impairment (MCI), dementia, or AD in the next five years for patients aged 60+.  It will be built into the EMR as a widget (on-demand score) and as a Best Practice Advisory (pop-up notification for high-risk patients).  To construct the algorithm: we utilized data stored in the EDW for patients ages 60+ who had visited their primary care physician in 2009 and again in 2014.  We excluded patients who in 2009 had a diagnosis of MCI, dementia, or AD.  We included as independent variables any of the 23 factors listed in the Brain Health Quiz that were captured by the EMR by 2009.  We included as dependent variables diagnosis of MCI, dementia, or AD by 2014.  Using stepwise statistical model building, we identified variables in the patients’electronic records that contributed to higher risk.  Details regarding the model will be published separately.  Additionally, we will flag patients in the EMR with a documented family history of AD or PD, multiple concussions or more-severe brain injuries, rapid eye movement sleep behavior disorder, or early-unopposed menopause, for primary care physicians to consider referral to the Center for Brain Health.

Step 5: Develop personalized medicine and interventions

We implement personalized, evidence-based interventions to mitigate risk factors and maximize protective factors, and evidence-based interventions with the potential to benefit all patients.  These interventions included lifestyle and behavioral changes, medications, and management of diseases associated with increased risk of AD.  We maintain compliance-focused follow-up via interval visits with a physician assistant.  Annual follow-up visits with the neurologist focus on updating risk profiles and modifying interventions according to proximal outcomes and the latest evidence.  In the event that patients develop aging brain disorders despite our best efforts, they are transitioned at the earliest point to relevant neurology subspecialty practices.
The diagnostic testing and interventions are defined by an order set built into the EMR.  The “smart set” includes frequently ordered labs tests (e.g., metabolic panel, complete blood count with differential, cardiac risk, and vitamin D-25, Vitamin B12, Folic acid blood levels), imaging tests (MRI, CT), additional lab tests (e.g., glucose test, homocysteine level), genetic tests (e.g., APOE, early onset Alzheimer’s evaluation), additional procedures (e.g., cerebrospinal fluid examination), frequently ordered medications (e.g., Folic Acid-Pyridoxine-Cyancobalamin), consults (e.g. dietitian, physical therapy), diagnoses, and billing codes.
We built into the EMR Best Practice Advisories that prompt neurologists to enroll patients into a DNA biobank (each is genotyped for one million single nucleotide polymorphism markers), or to complete a mental health order set when patients were severely anxious or depressed and not taking an anxiolytic or an antidepressant and without documentation of a visit with a mental health practitioner in the prior year.

Step 6: Community Engagement

To include our community in defining our clinical services, we created a Community Advisory Council whose membership consisted of patients with aging brain disorders, caregivers, municipal leaders, public health experts, and professionals representing our partnering organizations. We met bimonthly to provide progress updates and receive feedback.

Statistical Methods

We generated from the Center-for-Brain-Health-specific data mart in the enterprise data warehouse a descriptive cohort report (medians and ranges, means and standard deviations, frequencies, overall and in men and women separately), visualized the data using box plots, bar graphs, and normal Q-Q plots, and performed pairwise correlations (without and with adjustments) and principal component (PC) analysis of the scored tests (without and with Varimax rotations).  A statistician (SW) performed the analyses using SAS 9.3 (Cary, NC) and R software.

 

Results

We created mechanisms to identify patients at high risk of AD, PD, and CTE; the SCDS tools to evaluate risk factors and history; the evidence-based interventions to modify risk; and the capacity for annual surveillance, pragmatic trials, and practice-based and genomic research using the EMR.
In our first year (June 2015 to May 2016), we saw 133 patients. Fifteen were found to have MCI and were referred to the Memory Disorders Clinic for care.  The remaining 118 patients were cognitively normal; median age was 59.5 (range 31-81); 81 were women. The median number of risk factors reported via the Brain Health Quiz was 5 (range 1-10).  The median body mass index (BMI) for women was 26 and for men 27.  The median PREDIMED score (a validated 14-item Mediterranean diet questionnaire) was 7 for both genders (range, 1-12); only 3.4% of patients had “strongly adherent to the Mediterranean diet” scores.  Our patients were highly educated with 100% having finished high school, 83% completing 4 years college, 51% completing 2 years graduate school and 21% receiving a post-graduate education. A complete descriptive cohort report is provided in Appendix D.
Women reported with higher frequencies family histories of dementia, PD, depression, sleep apnea, prior head injuries, prior use of NSAIDs, exposure to pesticides, and alcohol use. Women scored more often in the clinical insomnia and depression ranges.  Men were more likely to report caffeine use and sedentary activity.  Men scored more often in the moderate to severe anxiety range. None of these gender differences reached statistical significance.  Men were more likely to report statin use, the only gender difference reaching statistical significance (p <0.05).
Table 2 provides results of pairwise correlations; Figure 2 illustrates results of the PC analysis.  The analyses included 118 patients with complete data for a Mediterranean diet questionnaire (PREDIMED); Center for Epidemiological Studies-Depression scale (CED-D); Generalized Anxiety Disorder 7-item scale (GAD-7); Insomnia Severity Index (ISI); 9-hole peg test, dominant hand (9-hole dom); 9-hole peg test, non-dominant hand (9-hole non-dom); 25-foot walking test (25ft walk); Short Test of Mental Status (STMS); Unified Parkinson’s Disease Rating Scale-Motor scale (UPDRS); The Eight-Item Informant Interview to Differentiate Aging and Dementia (AD8); a Parkinsonism screening questionnaire (PARK); Body-mass index (BMI); and a Brain Health Readiness Assessment (Readiness) which measures patients’ willingness to engage in behavior change specific to risk and protective factors.  Several pairwise correlations were statistically significant even accounting for multiple comparisons.  None of the measures were over-correlated (rho < 0.8 or > -0.8).  We performed PC analyses restricting to validated score test measures (9-hole peg tests dominant and non-dominant, 25-ft walk, AD-8, CES-D, GAD-7, ISI, PARK, PREDIMED, STMS, UPDRS), and also including additional continuous trait measures (age at study, BMI, Readiness).  For the PC analyses that included all measures, the 9-hole peg test (dominant hand) loaded to the first PC most heavily. Two-factor maps (PCs 1 and 2), without and with Varimax rotation (Figure 2), revealed clustering of ISI, BMI, AD-8, PREDIMED, and Readiness. Another cluster included STMS, 9-hole peg test (dominant, non-dominant), 25-ft walk, age at study, PARK, and UPDRS.  GAD-7 and CES-D formed a 3rd distal cluster.  Inspection of the PCs after Varimax rotation and restricting to factors with eigen values >1 revealed a three factor solution, where the score tests with component loadings >0.40 on PC1 included the 9-hole peg test non-dominant and the 9-hole peg test dominant and age at study, and the score tests with component loadings >0.40 on PC2 included the GAD-7 and CES-D, and the score tests with component loadings >0.40 on PC3 included AD-8 and Readiness.  See Appendix E for a complete score test analytic report.
56 patients were referred to the dietitian.  28 patients opted for APOE genotype analysis.  Of the 118 in our cohort, 101 were eligible for enrollment in our blood DNA and plasma biobank, and 88 (87.1%) of the eligible subjects participated.

Table 2. Pairwise correlations of score test measures at initial visits for 118 patients in the cohort. Table shows pairwise correlations adjusted for age and gender. Correlation coefficients (rho) are shown in the cells only for significant correlations, p-value < .05/n where n = 91, the number of tests. Thus significance is established for p-value < 5.5 x 10-4 (after Bonferroni correction for multiple testing). Note that score tests compared to themselves (x and y axis of the table) are perfectly correlated (rho = 1.0)

Table 2. Pairwise correlations of score test measures at initial visits for 118 patients in the cohort. Table shows pairwise correlations adjusted for age and gender. Correlation coefficients (rho) are shown in the cells only for significant correlations, p-value < .05/n where n = 91, the number of tests. Thus significance is established for p-value < 5.5 x 10-4 (after Bonferroni correction for multiple testing). Note that score tests compared to themselves (x and y axis of the table) are perfectly correlated (rho = 1.0)

PREDIMED (Mediterranean diet questionnaire); CES-D (Center for Epidemiological Studies-Depression); GAD-7 (Generalized Anxiety Disorder 7-item scale); ISI (Insomnia Severity Index); 9-hole dom (9-hole peg test, dominant hand); 9-hole non-dom (9-hole peg test, non-dominant hand); 25ft walk (25-foot walking test); STMS (Short Test of Mental Status); UPDRS (Unified Parkinson’s Disease Rating Scale-Motor); AD8 (The Eight-Item Informant Interview to Differentiate Aging and Dementia); PARK (Parkinsonism screening questionnaire); BMI (Body-mass index); Readiness (Brain Health Readiness Assessment).

Figure 2. Principal component analyses for score test measures at initial visits for 118 patients in the cohort

Figure 2. Principal component analyses for score test measures at initial visits for 118 patients in the cohort

A) Cascade figure, demonstrating the proportion of the variance between the measures explained stepwise by each of the principal components. B) Illustrates the principal component mappings of first principal component (PC1) versus second principle component (PC2) loadings for each of the score tests and their spatial relationships.  C) Illustrates the principal component mappings of PC1 and PC2 loadings for each of the score tests, after Varimax rotation (to maximize the distance between measures).  PREDIMED (Mediterranean diet questionnaire); CES-D (Center for Epidemiological Studies-Depression); GAD-7 (Generalized Anxiety Disorder 7-item scale); ISI (Insomnia Severity Index); 9-hole dom (9-hole peg test, dominant hand); 9-hole non-dom (9-hole peg test, non-dominant hand); 25ft walk (25-foot walking test); STMS (Short Test of Mental Status); UPDRS (Unified Parkinson’s Disease Rating Scale-Motor); AD8 (The Eight-Item Informant Interview to Differentiate Aging and Dementia); PARK (Parkinsonism screening questionnaire); BMI (Body-mass index); Readiness (Brain Health Readiness Assessment)

 

Discussion

In its first year, the Center for Brain Health initiative identified and engaged individuals at increased risk for Alzheimer’s disease and related disorders, explored gender differences, and identified opportunities for mitigation of risk at the individual, system, and community levels. The majority of our patients are women; they scored higher on our Readiness Questionnaire indicating that we may need to work harder to identify and engage men.  On the other hand, as the lifetime risk of Alzheimer’s disease is double in woman versus men (1), this gender bias in referrals may be appropriate.
Only four patients (3.4%) were strongly adherent to the Mediterranean diet, and the median BMI for both genders was above the threshold of normal weight-for-height. As midlife obesity increases risk for AD (3, 11) and strong adherence to the Mediterranean diet reduces risk (3), these data serve as examples that characterize and describe the opportunity we have for risk mitigation in our ever expanding cohort   While long term outcomes are beyond the scope of this descriptive paper, we aim to publish data regarding patient compliance and outcomes as the cohort matures and when change can be measured.
Identification of high-risk individuals within a population, risk mitigation, and surveillance are building blocks of primary prevention initiatives. We demonstrate that this is possible in a large health system and that leveraging the EMR and analytics can automate efforts and create learning opportunities.  Not long ago, the adage that Alzheimer’s disease cannot be prevented was ubiquitous.  But literature disputing that adage is growing, as are calls for action on primary prevention and public health approaches to the AD epidemic. In Baumgart’s 2015 meta-analysis (3), the authors state that it’s no longer acceptable to linger in academic discussion; that the evidence is too strong to warrant inaction. Similarly, Norton and others urge the undertaking of a population health approach (12). And, we may have proof that risk factor management will lower dementia prevalence in the real-world.  This year, Satizabal and others reported a decline of dementia incidence among Framingham Heart Study participants of 44% over 3 decades (30). While the factors responsible for this reduction remain unclear, it is noteworthy that during those 3 decades, the level of education rose and most vascular risk factors declined (30).
We are not alone in this venture; similar initiatives have been developed around the country (Weil Cornell Alzheimer’s Prevention Clinic by New York Presbyterian, the Alzheimer’s Prevention Program at Cedars-Sinai, the Alzheimer’s Risk Assessment and Intervention Program at the University of Alabama at Birmingham) but we are unique in many ways including 1) our scope: we aim to primarily prevent not only Alzheimer’s disease, but also Parkinson’s disease and chronic traumatic encephalopathy; 2) our use of informatics: we are utilizing SCDS tools built into the EMR, and are building into the EMR an Alzheimer’s Risk Score to assist patients and physicians to identify and define risk; 3) our commitment to genomics and clinomics: we are biobanking DNAs from consenting subjects, genotyping the samples for 1 million genomic markers, and associating the genotypes with electronically captured clinical data.  We anticipate referrals from NorthShore’s system-wide Genomic Health Initiative that will identify thousands of APOE E4 carriers; 4) our community engagement: through the Community Advisory Board, we partner to expand the scope of our services; 5) our collaborations: through the Neurology Practice Based Research Network (13) we are sharing our EMR tools and data, which will vastly increase our ability to improve the quality of the care we provide, to make new discoveries relating to brain health, to achieve better outcomes, and ultimately to reduce the burden of brain disorders in the communities we serve.

 

Acknowledgments: The authors acknowledge the generous support of the Auxiliary of NorthShore University HealthSystem with respect to the initial building of electronic medical record (EMR) toolkits, and thank the medical assistants, nurses, neurologists, EMR Optimization and Enterprise Data Warehouse programmers, administrators, and research personnel at NorthShore University HealthSystem who contributed to the quality improvement and practice-based research initiative using the EMR. The authors thank Vimal Patel, PhD for his assistance with editing, formatting, and submitting the manuscript for publication. Finally, the authors thank the neurology patients who inspire us to improve quality and to innovate our clinical practice every day.

Funding: The authors also acknowledge funding support from the Agency for Healthcare Research and Quality (R01HS024057).

Ethical standards: This is a cross-sectional description of historical patients seen in a clinical practice. There is no enrollment.

 

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21.    Rocca WA, Shuster LT, Grossardt BR, et al. Long-term effects of bilateral oophorectomy on brain aging: unanswered questions from the Mayo Clinic Cohort Study of Oophorectomy and Aging. Womens Health (Lond). 2009;5(1):39-48.
22.    Badiola N, Alcalde V, Pujol A, et al. The proton-pump inhibitor lansoprazole enhances amyloid beta production. PLoS One. 2013;8(3):e58837.
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APPENDIX

Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

Dementia Prevention: Optimizing the Use of Observational Data for Personal, Clinical, and Public Health Decision-Making

P.A. Dacks1, S. Andrieu2, D. Blacker3, A.J. Carman1, A.M. Green4, F. Grodstein5, V.W. Henderson6, B.D. James7, R.F. Lane1, J. Lau8, P.-J. Lin9, B.C. Reeves10, R.C. Shah7, B. Vellas2, K. Yaffe11, K. Yurko-Mauro12, D.W. Shineman1, D.A. Bennett7, H.M. Fillit1

1. Alzheimer’s Drug Discovery Foundation, New York, NY; 2. UMR 1027 INSERM-University Toulouse III, CHU Toulouse, Toulouse, France; 3. Gerontology Research Unit, Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, and Department of Epidemiology, Harvard School of Public Health, Boston, MA; 4. MD PhD JD, Attorney at Law, Cambridge, MA; 5. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; 6. Department of Health Research & Policy (Epidemiology); Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA; 7. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL; 8. Center for Evidence-based Medicine, School of Public Health, Brown University, Providence, RI; 9. Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA; 10. Clinical Trials and Evaluation Unit, School of Clinical Sciences, University of Bristol, UK; 11. School of Medicine, University of California San Francisco, San Francisco, CA; 12. DSM Nutritional Products

Corresponding Author: Penny A Dacks, Alzheimer’s Drug Discovery Foundation, New York, NY, pdacks@alzdiscovery.org

J Prev Alz Dis 2014;1(2):117-123

Published online November 5, 2014, http://dx.doi.org/10.14283/jpad.2014.35

 


Abstract

Worldwide, over 35 million people suffer from Alzheimer’s disease and related dementias. This number is expected to triple over the next 40 years. How can we improve the evidence supporting strategies to reduce the rate of dementia in future generations? The risk of dementia is likely influenced by modifiable factors such as exercise, cognitive activity, and the clinical management of diabetes and hypertension. However, the quality of evidence is limited and it remains unclear whether specific interventions to reduce these modifiable risk factors can, in turn, reduce the risk of dementia. Although randomized controlled trials are the gold-standard for causality, the majority of evidence for long-term dementia prevention derives from, and will likely continue to derive from, observational studies. Observational research has some unavoidable limitations, but its utility for dementia prevention might be improved by, for example, better distinction between confirmatory and exploratory research, higher reporting standards, investment in effectiveness research enabled by increased data-pooling, and standardized exposure and outcome measures. Informed decision-making by the general public on low-risk health choices that could have broad potential benefits could be enabled by internet-based tools and decision-aids to communicate the evidence, its quality, and the estimated magnitude of effect.

Key words: Alzheimer’s, primary prevention, non-randomized studies, low-risk, communication.


 

Introduction

Alzheimer’s disease and other age-related dementias (referred to here for simplicity as “dementia”) afflict over 35 million people world-wide. The societal cost of care in 2010 was estimated at over $600 billion, 1% of the world’s aggregated gross domestic product, with 89% of those costs incurred by high-income countries (1).

Mounting evidence suggests that modifiable factors in mid-life will alter an individual’s risk of dementia in later decades. For example, one analysis concluded that almost half the statistical probability of getting Alzheimer’s disease may be accounted for by seven modifiable risk factors – diabetes, midlife hypertension, midlife obesity, smoking, depression, cognitive inactivity or low educational attainment, and physical inactivity – and reducing the prevalence of these risk factors by 10% could prevent up to 1.1 million cases of the disease worldwide (2). Another analysis estimated that a 10% reduction in body mass index among overweight or obese beneficiaries would save Medicare and Medicaid $6 billion and $35 billion, respectively, from the cost of dementia over the lifetime of baby boomers (3).

Analyses like these provide a compelling rationale to invest in further research about preventing dementia through lifestyle changes, non-pharmaceutical interventions, and the management of other health risks particularly in midlife. However, the current evidence has major limitations that led the NIH State-of-Science Report on Preventing Alzheimer’s Disease and Cognitive Decline to conclude in 2010 that there are no preventive interventions currently available. First, most of the available evidence has focused on identifying risk factors rather than the effectiveness of specific actions to modify those risk factors. Second, the evidence derives almost entirely from observational research of quality that varies and is often difficult to assess.

How can we optimize the existing data particularly for low-risk long-term interventions on modifiable risk factors? Meta-analyses of randomized controlled trials currently are viewed to provide the highest level of evidence for causal inference and intervention efficacy. However, sole dependence on randomized controlled trials is not a feasible solution (4). While observational studies have inherent limitations, they can provide evidence complementary to RCTs. The goal of this paper is to recommend strategies to maximize the utility of observational data for low-risk health choices that may protect against dementia.

 

Why RCTs will not be the only source of evidence for dementia prevention interventions

Randomized controlled trials are the gold standard to quantify causal inference but they are few and far between for dementia prevention, particularly for primary prevention before the disease takes hold in the brain. Alzheimer’s and several related causes of dementia likely begin in the brain decades before the appearance of clinical symptoms. Although biomarkers have been proposed, none have yet been validated as diagnostic or as an efficacy surrogate, so proof of primary prevention requires studying a treatment that begins and continues for years if not decades before clinical symptoms manifest. For these reasons, randomized trials have typically been of insufficient duration and power to detect the effectiveness of primary prevention interventions (5).

In recent years, collaborative initiatives have improved the design and feasibility of randomized trials for Alzheimer’s prevention, particularly secondary prevention in asymptomatic but high-risk populations in whom the disease has likely initiated in the brain but has not yet manifested in clinical symptoms[5; 6; 7; 8; 9]. Additional outcomes relevant to dementia might be added into large RCTs designed to answer different research questions, as occurred with the PREADVISE trial that added memory loss and dementia prevention outcomes to a trial for cancer prevention with selenium and vitamin E (NCT00040378). Randomized trials of drugs, nutrition, vitamin supplementation, or exercise originally designed for cardiovascular or diabetes indications may be re-repurposed years later, linking participant IDs with routinely available data from medical records or other sources to obtain long-term follow-up and outcome measures relevant to dementia.

Despite these important advances, randomized trials are unlikely the sole and sufficient solution to answer many questions about low-risk and long-term prevention strategies. Randomized trials of sufficient duration often have restrictive eligibility criteria that prevent the trial results from being generalized with confidence to other groups of adults (4, 5). Non-pharmaceutical strategies to reduce dementia like behavior change, nutrition, and health management typically have limited commercial value, so the research must generally be funded by government agencies or philanthropy. RCTs are extremely expensive; however, trials necessary to test non-pharmaceutical prevention strategies will likely require long-term multi-domain interventions, increasing the time and money required to detect benefit. In the meantime, our population continues to age and the critical windows to intervene and prevent dementia may be closing for many individuals.

 

Maximize the utility of observational data

Evidence-based medicine has been defined as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients” (10). It requires careful analysis of the available high-quality research on population samples to inform decisions for individual patients. Major evidence-based medicine groups like GRADE, Cochrane, and AHRQ agree that although double-blind randomized controlled trials are the gold-standard for causality, observational studies can sometimes be considered as moderate or even strong evidence (11) that may be included in systematic reviews (12, 13). In practice, however, observational studies rarely achieve that status.

Observational studies have inherent limitations, particularly for causal inference, that cannot be easily overcome. The risk of confounding and other biases can be reduced but never eliminated by statistical adjustments. Because observational studies are typically less well-resourced, they can also be undermined to a greater extent than RCTs by other biases that may not be taken into account. However, sole dependence on randomized controlled trials is not a feasible solution [4], particularly for long-term and low-risk non- pharmaceutical interventions. The goal of this paper is to recommend six strategies to maximize the utility of observational data to support decision-making by various stakeholders on low-risk health choices that may protect against dementia.

 

Register all longitudinal observational studies:

Randomized controlled trials have strict requirements to publicly register their study design and hypotheses in advance, eg. in www.clinicaltrials.gov. No similar requirement exists for observational studies. Therefore, when appraising the results of such studies, it is generally unclear how many hypotheses were generated and tested to produce a single result reported in a publication. RCTs are also expected to analyze data according to a detailed pre-specified plan; analytic choices are usually much more numerous for observational studies and prespecified plans are rare, raising uncertainty about statistical design and the risk of selective reporting (14). The rationale for prospective registration of observational research was recently discussed in detail by Weiderpass and colleagues, including the importance for the International Committee of Medical Journal Editors to change their policy to require prospective registration of observational studies published in their member journals (15).

Validate exploratory results with confirmatory study designs

To raise the credibility and utility of observational studies for public health decisions, initial hypotheses generated by exploratory studies should be validated on independent observational data-sets. Validation studies should be held to confirmatory study design standards, required to register pre-specified hypotheses for precisely defined exposures or interventions and outcomes, with detailed protocols or analysis plans in advance of analyzing the data, and justification of why the proposed sample-size provides adequate power to test the hypothesis (12).

Raise reporting standards to enable independent evaluation of bias in study design

Both RCTs and observational studies tend to generate exaggerated estimates of the effectiveness of exposure or interventions (16), usually as a result of biased measurement of outcomes, attrition, or selective reporting. However, the risk of these biases affecting RCTs may be reduced through specific design features (e.g. blinding of outcome measurement) and more easily detected because of formal reporting standards including the requirement for prespecified protocols (12) for study design and data analysis. The recent SPIRIT checklist of trial characteristics and features to be described in RCT protocols seeks to improve their quality (17).

Most of the standards for RCTs set out in the CONSORT Statement (18) can and should be applied to confirmatory observational cohort studies designed to estimate the effects of an intervention. Other observational studies can follow the guidelines developed by STROBE (Strengthen The Reporting of Observational Studies in Epidemiology), a collaborative initiative with check-lists for reporting of cohort studies, case-control studies, and cross-sectional studies, as well as conference abstracts (19). Reporting standards should clearly identify studies as exploratory or confirmatory. Although reporting standards will not raise the quality of the analyses per se, adherence to the standards allows users of the evidence to appraise the risk of bias and the quality of study design. Empowering users to appraise research is critical to maximize the usefulness of observational data for dementia prevention.

Invest in effectiveness research from observational studies

Randomized controlled trials for prevention have often been criticized for testing interventions for too short a duration or too late in a disease process to detect a benefit that may accumulate slowly over decades. Conversely, the rigor of trial designs (eg, a very specific dose of treatment, a very specific age group, a very specific level of patient health) yield directly relevant evidence on the effectiveness of that intervention at that stage of life/health. Such specificity can directly inform patients’, doctors’, and policy makers’ decisions about future management for the kinds of people who took part – typically people are concerned about their cognitive health because they are older and already experiencing cognitive decline.

In contrast, the majority of observational studies have explored etiology and risk factor associations, i.e., the identification and quantification of the risk of a disease conferred by a broad ‘exposure,’ usually a lifestyle, health behavior, or circumstance of living. Such etiological research can suggest possible causes of the disease but it is not equivalent to rigorous evidence for the effectiveness of one specific, well-defined intervention (eg. 400IU of vitamin E daily for three years) to reduce exposure to the risk factor in one well-defined population (12). For example, broad evidence that low intake of fish and omega-3 fatty acids is associated with a higher risk of dementia (20) is not evidence that public health interventions to encourage fish intake will reduce dementia incidence. That is, unless major assumptions are made, it is unclear what dose of fish intake may give rise to an important benefit, what types of fish may be most beneficial, the age (or stage of development of disease) when a person needs to increase fish intake, or the number of years that high fish intake must be maintained. Similarly, diabetes is often associated with a higher risk of dementia (2) but whether specific treatments to prevent diabetes or improve glycemic control in diabetes can reduce the risk of dementia has been less studied, including the duration, time-frame, and treatment necessary. Thus, again, observational studies have not provided directly actionable interventions.

Some questions about the benefits or harms of specific interventions may be addressed with longitudinal observational data by analyzing in detail how changes in a given lifestyle or treatment variable are associated with subsequent cognitive outcomes, such as adoption of a Mediterranean diet, daily walking for one-half hour per day, or participating in programs that assist in weight loss. However, the data from individual cohorts may often lack the power needed to yield results which are sufficiently detailed to produce actionable public health recommendations. This limitation may sometimes be addressed by pooling data from different cohorts or databases, as described below; that is, by combining data from multiple cohorts, very fine exposure categories could be created which may then produce results to directly inform health guidelines. Data-sets for new large cohorts may also be built using cost-effective internet and smart-phone based technologies to gather highly detailed longitudinal information. Expanded investment in effectiveness research from observational data could yield more practical information for low-risk actionable health choices and dementia prevention.

 

Foster data-pooling and data-sharing

Individual observational studies often examine cohorts that are too small to provide sufficient statistical power to assess the effectiveness of specific interventions with information on treatment type, magnitude, duration, and the time-frame of use within the disease trajectory. They also typically lack the power to deal with heterogeneity of disease or intervention efficacy, such as the interaction of a given intervention or risk factor with genotype, comorbidities, and nutrient combinations. Data-pooling can optimize the use of existing data to cost-effectively raise power to address more complex and subtle questions although data pooling per se does not reduce the risk of bias. On the other hand, data-pooling may resolve some concerns from publication bias by unearthing data that has been gathered from large cohorts but not published because of negative findings, lack of publication impact, or time restrictions.

Data-pooling can be facilitated when researchers share their data on centralized and sometimes open-access databases such as the National Archive of Computerized Data on Aging (http://www.icpsr.umich.edu/icpsrweb/NACDA/), Synapse (http://sagebase. org/synapse-overview/), Figshare (http://figshare. com/), Dryad Digital Repository (http://datadryad. org/), and the Neuroscience Information Framework (http://www.neuinfo.org/). The data from some cohorts cannot legally be contributed into open-access repositories because of restrictions in the consent forms signed by participants. However, some web-based interface platforms can allow individual researchers to maintain control of their data while facilitating analyses that pool data among collaborators. For example, the Global Alzheimer’s Association Interactive Network (GAAIN; http://www.gaain.org/) has created a computational infrastructure along these lines, as has the Integrative Analysis of Longitudinal Studies of Aging in collaboration with Maelstrom Research.

For some recent cohorts, investigators have implemented innovative consent forms that enable data- sharing, such as Portable Legal Consent developed by Sage Bionetworks (21). The Alzheimer’s Disease Neuroimaging Initiative (http://www.adni-info.org/) and the Health and Retirement Study (http://hrsonline.isr.umich.edu/) are high-profile studies that share de- identified cohort data. Distinct types of observational data may be linked, such as electronic medical records and biobanks that have been linked in cost-effective

alternatives to traditional patient cohorts for pharmacogenomics (22). Further, data-sharing could be facilitated by cohort consortiums like the National Cancer Institute Cohort Consortium, the CHARGE consortium for genomic epidemiology of heart and aging research (23), and the Social Science Genetic Association Consortium (24).

While data-pooling and open-access data-sharing have substantial promise, they require resources, time, harmonization, and logistics. Clear standards for conduct, design, and reporting must be established to ensure quality and enable systematic reviewers to recognize when overlapping datasets have been used in distinct publications, so that specific data-sets do not exert a mistakenly large influence (25). Some data sources are expensive and pooling data across studies often requires substantial data management and complex analyses, as well as detailed prespecified analysis plans. Researchers need funding from granting agencies for this kind of work and wider recognition by academic institutions of its value. Publication credit can help, such as efforts like Figshare and the Scientific Data journal launched in 2014 by Nature Publishing Group. The Bioresource Research Impact Factor can give credit to researchers who create valuable databases (26). Overall, investment in data-pooling and data-sharing can pay off by expanding the utility of existing and future datasets.

 

Encourage standardized exposure and outcome measures

Combining different bodies of evidence to address a given research question is impaired by major differences in exposure and outcome measures. In cohort studies, the exposure variables for physical activity have been measured as categorical estimates of “low, middle or high” or “sedentary versus active” versus continuous variables of calories burned, distance traveled on foot, and time spent exercising (27). Outcomes also vary widely: some studies assay clinical diagnosis of Alzheimer’s, some all-cause dementia, some a prescription for acetylcholinesterase inhibitors. Other studies avoid clinical diagnoses and rely on diverse assays of cognitive decline that may or may not relate to incident diagnosis of dementia.

In this context, it is challenging and sometimes impossible to determine whether results across studies are truly in agreement or not or simply too diverse to compare. Although some diversity of exposure measurements may raise the ability to detect the “active ingredient” of an association, the use of standard exposure and outcome measures could improve the ability the interpret a body of evidence for a specific therapeutic question. Valuable lessons may be learned from other fields that have tackled similar concerns, such as the pioneering research of OMERACT, Outcome Measures in Rheumatology (http://www.omeract.org/), which has in turn led to the wider COMET initiative for Core Outcome Measures in Effectiveness Trials (www.comet-initiative.org/).

 

Communicate the evidence and its strength to the public

No intervention has been proven unequivocally to decrease the risk of dementia, but when is the evidence sufficient for action by individuals, doctors, or public health authorities? The answer depends on the person who makes the decision. Even with extensive population-based evidence, an individual patient’s choice should depend on his or her specific situation, including overall health and illnesses, lifestyle, and preferences for potential benefits, risks, and costs. To ensure that the existing science can be used as effectively as possible for diverse decisions, the evidence and its quality should be communicated in a clear and credible manner whether or not it is sufficient for a public health recommendation.

The choice of statins for primary prevention of cardiovascular disease in low-risk individuals is an example of individual decision-making in the setting of extensive population-based evidence. In people with a low risk of cardiovascular disease, statins may (28) or may not (29) significantly reduce overall mortality depending on the statistical analysis and included data. Statins reduce the risk of myocardial infarction and stroke with a need to treat 140 low-risk patients to prevent one event, but may also raise the risk of diabetes by 10 to 50 percent and the risk of musculoskeletal disorders with 1 harm in every 37 to 47 patients treated (29). Missing information, short trial duration, and other concerns suggest that the RCTs have not adequately characterized the risk-benefit profile for low-risk patients (29-31). Clinicians are advised to assist low-to-moderate risk patients in making the benefit-harm decision on an individual basis (29, 31).

For questions of dementia prevention, the quality of the evidence is substantially lower than that for statins and cardiovascular protection. The evidence that does exist is typically communicated to the public in a piece- meal fashion through popular media of varying quality and by advocacy groups. The evidence from a single in vitro or animal study may be portrayed in the same way as evidence from a large carefully conducted meta- analysis of prospective cohort studies. Individuals who are searching for information about dementia prevention are left in a fog.

Most of the potential strategies for dementia prevention will affect health and well-being beyond the brain. This risk/benefit profile is important when evaluating whether the evidence is sufficient for a given action. For example, moderate actions to reduce social isolation may protect against dementia with relatively few risks and potential benefits to quality of life, depression risk, and general health (32). On the other hand, observational evidence suggest that long-term use of ibuprofen might perhaps protect against dementia (33) but the rationale is weakened by other data that high- dose chronic ibuprofen raises the risk of hypertension (34), a risk factor for dementia, as well as major coronary events and gastrointestinal complications (35). Even “low-risk” strategies such as increasing exercise require time and money that will impact quality of life and be weighed differently by individuals with distinct risks and priorities.

How can the existing evidence be made as useful as possible for the choices made by individuals? The initial need is to communicate the science behind a given action, the strength of the evidence, and the potential risks and benefits. However, few individuals will have the knowledge or skills to use and understand this information no matter how clearly it is described. Individuals often entrust their health decisions to doctors as knowledgeable interpreters of the evidence. Unfortunately, most doctors lack the time to adequately discuss the risks and benefits for all their patients’ health choices. They also lack the time and sometimes the training to read and interpret the most current scientific evidence.

Complementary strategies to communicate information to individual citizens and doctors should be encouraged. Internet-based decision-aids can provide evidence-based summaries and a framework for individuals and clinicians to integrate diverse variables and preferences into an evidence-based personalized decision. Internet-based decision-aids are already available to help patients decide whether to take treatments for menopausal symptoms (Menopause Map from the Hormone Health Network; http://www.hormone.org/menopausemap/) and osteoporosis after menopause Agency for Healthcare Research and Quality (http://www.effectivehealthcare.ahrq.gov

http://www.alz.org/research/science/alzheimers_prevention_a/ehc/decisionaids/osteoporosis/index.cfm?module=LM 1&restartModule=1&urlName=LM1_welcome). For several other diseases, evidence for risk factors developed primarily from observational studies has been translated into risk calculators to help individuals understand their risk of various diseases based on age, body-mass index, ethnicity, lifestyle, family history, and other factors, with customized recommendations to reduce disease risk (eg. www.yourdiseaserisk.wustl.edu). Similar tools may be useful for dementia risk. Other sites aim to explain the quality of the scientific evidence available for dementia prevention strategies (eg. www.cognitivevitality.org; www.alz.org/research/science/alzheimers_prevention_and_risk.asp).

Tools like these can help people understand their risk of dementia and the potential effect of modifiable risk factors. They can also enable people make decisions on the basis of incomplete evidence if they explain the quality of the evidence and the strength of a potential association or effect. The resources required are considerable, particularly given the need to update tools regularly based on emerging research. If done carefully, however, such tools could maximize the usefulness of scientific research for the general public, whether that research derives from preclinical studies, observational studies, or randomized trials.

 

Summary of Recommendations to optimize the use of observational studies for decision- making on dementia prevention

  1. Register all longitudinal studies

  2. Validate initial exploratory results regarding dementia prevention with confirmatory studies of independent observational datasets that use established methodological and reporting standards to ensure that results meet the highest possible standards of quality and transparency.

  3. Raise reporting standards to improve the ability of independent experts applying evidence-based methods to evaluate the quality and risk of bias in non- randomized studies.

  4. Invest in effectiveness research using observational design in order to improve the evidence-base for specific decisions related to dementia prevention.

  5. Encourage and facilitate data-pooling and data-sharing when opportunities exist to maximize the utility of unpublished data and raise power for research on the effectiveness of dementia prevention strategies and the heterogeneity of dementia progression and risk.

  6. Establish standardized outcome and exposure metrics to improve the capacity to interpret a body of evidence regarding a specific question on dementia prevention.

  7. Communicate the evidence to the general public using accessible internet-based tools and decision-aids to facilitate better informed evidence-based personal decisions. These communications should clearly state the risk of bias of the research and the broad potential ramifications relevant to the public.

 

Conclusion

Scientific evidence suggests that choices today may reduce the number of dementia patients tomorrow. The evidence is not conclusive and comes primarily from observational studies. Over time, the evidence available from randomized trials will increase for many questions, particularly given the important efforts underway to improve the quality of the evidence through the design and funding of randomized trials and biomarker development. However, opportunities also exist to improve the quality and transparency of observational studies, and to communicate the available evidence to the public to facilitate informed decision-making. These opportunities are particularly important for dementia prevention strategies that are low-risk yet may require decades of use or multi-domain interventions in order to reduce the risk of dementia.

In 1965, Sir Austin Bradford Hill laid out nine criteria necessary to prove causality: strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy. Only one of these criteria requires randomization.

 

Acknowledgments: We thank Molly V. Wagster, PhD from the National Institute on Aging for contributing to the panel and providing feedback on the manuscript. We thank James McNally, PhD, National Archive of Computerized Data on Aging, University of Michigan, Ann Arbor, MI, for participating in the panel. We thank Ivan Oelrich, PhD, for astute editing.

Funding: The authors of this paper participated in an advisory panel convened by the Alzheimer’s Drug Discovery Foundation (ADDF) in April 2013 on Alzheimer’s Disease Prevention: improving the translation of diverse and incomplete scientific evidence into clinical and public health recommendations. The panel and manuscript were funded by the ADDF. The cost of authors’ time spent on this project was funded by NIH P30AG10161 for DAB, BDJ, and RCS, NIH K24AG031155 for KY, NIH P50 AG005134 and Fidelity Biosciences for DB and by the UK NIHR Bristol Cardiovascular Biomedical Research Unit for BCR

Conflicts of Interest: None of the authors have declared any financial interests, relationships or affiliations relevant to the subject of this manuscript.

Ethical standards: Not applicable.

 

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