Use of an Early Screening Tool for Identification of Modifiable Risk Factors to Delay the Progression of Alzheimer’s Disease
Fairfield University
Mary Gilbertson
Introduction
Alzheimer’s disease (AD), the most common cause of dementia, affects over 5 million people in the US, and over 46 million individuals worldwide. As the current population ages, this number is estimated to increase to 131.5 million people by year 2050. The global economic burden of dementia is currently 818 billion dollars (Prince et al, 2015). This figure is greater than the costs of cancer and heart disease combined, making it the most costly disease of our generation. Alzheimer’s Disease International (ADI) foresees an 85% increase in cost by 2030, with the developing countries bearing an increasing share of the disease burden (Prince, 2015). Many AD patients are cared for at home, with little financial, emotional, or physical support, leaving an increasing burden on care-givers, families, and resources.
Currently, the only available treatment for AD are acetylcholinesterase inhibitors (tacrine, donepezil, rivastigmine and galantamine) and one glutamate receptor antagonist (memantine). These medications offer modest benefit, but are not curative. Observational studies have identified a wide range of potentially modifiable risk factors for AD and dementia, including cardiovascular risk factors (hypertension, diabetes, obesity), psychosocial factors (depression, chronic stress) and health behaviors (low level of physical or mental activity/low education, smoking, poor nutrition). A 10%–25% reduction in risk factors could potentially prevent as many as 1.1–3.0 million cases worldwide and 184,000–492,000 cases in the US (Barnes & Yaffe, 2011). Reduction in risk factors have been shown to delay the onset and severity of disease, while improving health status and quality of life.
The purpose of this research was to investigate whether use of an early screening tool for identification of modifiable risk factors in Alzheimer’s Disease (AD) could delay the progression of AD.
Background/Significance
Currently the most cutting-edge diagnostic testing for Alzheimer’s Disease involves expensive (neuroimaging), invasive (cerebrospinal fluid analysis), and time consuming (neuropsychological) assessment, limiting the ability of primary care practitioners who are on the frontline of care, to screen and diagnose patients for Alzheimer’s in a timely and accurate manner. Therefore, there is an increasing need for additional noninvasive, cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD, who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD (Laske, 2014).
To decrease or delay the risk of developing Alzheimer's disease, it is critical to identify its risk factors, so as to determine how best to modify them. In order to understand which behaviors are modifiable, we must understand the many different forms of AD. The two most prevalent forms of AD are Familial and Sporadic. Familial AD is caused by an inherited genetic mutation. This mutation plays a role in the breakdown of a protein called amyloid precursor protein (APP). The breakdown of APP is part of a process that generates harmful forms of amyloid plaques that create neurofibrillary (tau) tangles in the brain. The amyloid plaques are a hallmark sign of familial AD. The neurofibrillary tangles are ultimately responsible for neuronal death.
Sporadic AD is caused by genetic, environmental and lifestyle factors. These patients may be able to retard the progression of their AD through proper lifestyle interventions. Furthermore, use of an early screening tool may identify those patients at risk for AD, years before the onset of symptom occurrence. Once risk factors in these patients have been identified we can use these variables to create a model for risk reduction.
A meta-analysis of more than 16,000 studies, published in the Journal of Neurology, Neurosurgery and Psychiatry (Xu et al, 2015), found 323 studies describing 93 risk factors that met their very strict criteria for ranking risk. They found “grade 1” evidence that the top modifiable risk factors for non-familial or “sporadic AD” were: high body mass index (BMI) in mid-life, type 2 diabetes, hypertension , depression, frailty, low education attainment (decreased cognitive activity), physical inactivity, current smoking, stress, and poor diet.
High BMI in Mid-life
A high BMI means that at least two modifiable risk factors are being ignored; exercise and diet. Furthermore, when a person engages in physical activity they produce a substance called brain-derived neurotrophic factor (BDNF). BDNF supports brain cells function with regard to memory storage and communication. (Szuhany KL, Bugatti M, Otto MW, 2015)
Type 2 Diabetes (DM)
According to the National Diabetes Statistics Report (2014), published by the Center for Disease Control (CDC), the number of cases of Type 2 diabetes is rapidly increasing in the US population. Type 2 diabetes is often associated with a high BMI, a risk factor for AD. Metabolic dysfunction (obesity, diabetes, and some cancers) has been shown to be a significant risk factor for cognitive decline, development of vascular dementia, and AD. (Jayaraman, A & Pike, C., 2014) Type 2 diabetics have problems with insulin resistance, brought on by lifestyles with poor diets composed of too much simple carbohydrates and sugars. These simple carbohydrates are metabolized into sugars which cause hyper insulin states in the individual. These hyper insulin states create a cascade of inflammatory cytokines that cross the blood brain barrier and ultimately cause brain cell death.
Hypertension (HTN)
Having hypertension increases the risk of vascular dementia. Microvascular changes first appear in the periphery when organs are affected by arterial narrowing from atherosclerosis. Eventually these vascular changes affect the brain, diminishing blood supply and increasing plaque formation. Plaque formation interferes with neuronal signaling, where brain messaging is often affected. The HUNT 1 and HUNT 2 studies, conducted over a period of 27 years in Norway, concluded that there is an inverse association between dementia and systolic blood pressure (SBP) in individuals over age 60. Among individuals less than 60 years of age, elevated SBP was associated with progression to AD. (Gabin, Tambs, Saltvedt, Sund & Holmen, 2017).
Stress
Chronic stress has been linked to systemic inflammation, which causes oxidative damage to cells. It has also been associated with increases in brain tissue alteration found in AD, the formations of tangles of cellular components from neurons. Stress increases the expression of APP and amyloid plaque formation. This has been demonstrated in both acute and chronic stress environments. These stress induced physiological changes can persist throughout the lifetime of the organism. Neurofibrillary tangles which are responsible for neuronal death, are also exacerbated by the stress response. (Justice, N., 2018)
Depression
Causative factors for depression are numerous and can overlap with other risk factors for AD. A new concept to describe the relationship of depression to AD was recently presented in the Journal of Alzheimer’s Disease. This concept was termed “cognitive debt”, and it is the result of repetitive negative thinking (RNT) ( Marchant, NL, Howard RJ, 2015). When treating depression and RNT, a combined approach of drugs, exercise, healthy diet and counseling have proven the most beneficial.
Frailty
Just as high BMI in mid-life is a risk factor for AD, so too is low body mass. Frailty with aging can be defined as loss of bone mineral density, loss of muscle strength, and a decline in physical mobility. Risk factors that co-exist with frailty include physical inactivity, inadequate nutrition including hydration, and smoking, which depletes nutrients, decreases appetite, and makes one more susceptible to disease.
A 7 year prospective cohort study involving 2,788 participants (Wang, Ji, & Wu, et al. 2017), analyzed the relationship between frailty and risk of dementia and AD, using the deficit accumulation-based frailty index (FI). When diagnosis of frailty was substantiated among participants, it was significantly associated with AD, dementia and death.
Low Educational Attainment
Decreased cognitive activity has been identified as the second greatest risk factor for AD behind advancing age, and is correlated with one’s educational status. A study on cognitive reserve presented at the Alzheimer's Association International Conference (AAIC, 2015) followed 7,574 volunteers for 21 years. The research team measured the participants' education and occupational accomplishments. Study participants with the lowest 20 percent of childhood school grades had a 21 percent higher risk for dementia. According to the research, people with the greatest protection against dementia, had both good grades during childhood and demanding jobs as adults. The significance behind these findings is that memory is muscle (motor memory), and like muscles, if they are not used, functional capacity will be lost.
Smoking
The cumulative body of research in regards to smoking and AD shows association of a significantly increased risk of AD from smoking. Smoking is also associated with earlier onset of symptoms in AD (Durrazo, Mattson & Weiner, 2014). Smoking decreases oxygen in the blood which causes cerebral oxidative stress, a potential mechanism for promoting pathogenesis of AD. (Barnes and Yaffe, 2011), estimated the prevalence of several modifiable risk factors on AD. Smoking was projected to account for 574,000 (11%) of AD cases in the US and 4.7 million (14%) cases worldwide.
Nutritional Status
The field of Nutrigenomics substantiates the belief that food is not just life-sustaining, but is information for our cells. Most of the chronic diseases we suffer today are from poor lifestyles. Many different dietary theories have been postulated over the years, but perhaps the one that has held up to the most scrutiny is the Mediterranean (MeDi) diet. In a 2011 study by Solfrizzi, et al, higher adherence to a Mediterranean-type diet was associated with slower cognitive decline, reduced risk of progression from mild cognitive impairment (MCI) to AD, reduced risk of AD, and a decreased all-cause mortality in AD patients. These findings suggested that adherence to the MeDi may affect not only the risk of AD, but also of predementia syndromes and their progression to overt dementia.
The MeDi diet was discovered in Greece and Italy in the 1960’s when researcher Ancel Keyes was investigating dietary patterns to decrease risk of cardiovascular disease. It consists of high levels of consumption of fats from fish, extra virgin olive oil, nonstarchy vegetables, low glycemic load fruits and a diet low in foods with added sugars (which includes simple carbohydrates)(Solfrizzi, 2011).
Physical Inactivity
During periods of exercise, the body excretes metabolic wastes through perspiration and respired air. Regular exercise reduces oxidative stress which helps to increase resiliency in the cell. Exercise also increases vascularization, induces neurogenesis, improves memory and brain plasticity. Studies have substantiated that walking improves cognition in AD while strength training is particularly more effective for improving postural and motor function, and reducing the risk of developing AD (Chen, Zhang, and Huang, 2016). Thus, a combined regimen of strength training and cardiovascular exercise should be employed most days of the week.
Methods
In 2014, per request of the World Dementia Council (WDC), the Alzheimer’s Association was asked to evaluate and report the current state of evidence in relation to modifiable risk factors for AD and dementia, since no cure has yet to be found. From a population based perspective, sufficient evidence exists to support a link between the following modifiable risk factors and reduced risk of AD. These include: regular physical activity, reduction of cardiovascular risk factors (obesity, DM, smoking and HTN), healthy diet, lifelong learning, and stress reduction. The research from the Alzheimer’s Association correlates strongly with other findings from researchers presented in this paper.
Based on these findings we developed an early screening tool for identification of modifiable risk factors which could delay the progression of AD. The screening tool consisted of a simple 12 question “questionnaire”, which could be employed in the primary care office setting during a routine 15 minute visit. Questions were developed according to the data set which substantiated through the literature, the ten risk factors that if modified, would best decrease an individual’s incidence of Alzheimer’s. A literature review was conducted to find all available “Gold Standard” evidence based criteria for each data measurement to construct the questions.
Questions were answered using a #2 pencil provided by office staff, and the patient was given five minutes to complete all questions. Most questions require a simple yes or no answer. They are formatted like a standardized test, so they can be scored by hand or through a machine. The hope is to standardize the screening tool, with results extrapolated out to the general population and then throughout the US.
[Screening Tool- See Appendix A]
To conduct the research we randomly picked 2,000 primary care offices throughout the US, representing all regions equally through designated centers (North, South, East and West). Inclusion criteria was that offices chosen for the study maintain an EHR throughout the 12 month period. We conducted the research using the screening tool (questionnaire) for a specific 12 month period. A HIPPA form was also provided. If the patient consented to the research it was checked off in the EHR , dated , and once collected, a completion status was obtained via computer. Office staff were instructed to hand out the tool to all patients aged 40 or older once in the 12 month period.. The age of 40 was chosen based on the scientific evidence that vascular changes develop years before symptomatology. Therefore, we want to identify those at risk as early as possible, in the hopes that they will modify their behaviors. Inclusion in the study was voluntary. The screening tool is simple, so no modifications are allowed. At the end of the visit the questionnaire was placed in a secure, locked box. Once a week on the day of their choice, managers scanned the data into an EHR based program which scored and tabulated the data. Computers in the participating offices are linked through networking software to promote communication, information exchange, work sharing and collaboration. Such networks can be joined locally, encompass a metropolitan area, or the entire United States (Mastrian & McGonigle, 2017).
Results
At the end of the 12 month period, teams of researchers, proficient with the use of EHR software, analyzed the data from the four research centers. The results proved statistically significant, where P = 0.05. Since we already knew what the modifiable risk factors were based on the most current, evidence-based research, all we needed to do was identify through the questionnaire who was at risk. For each modifiable risk factor tabulated, an educational handout was provided. If all ten risk factors needed to be addressed, the handouts were bound into a booklet. A follow-up appointment was made with each study participant to ensure that they understood the educational materials which explained risk reduction behaviors. The initial follow up was done by the primary care practitioner (NP or MD), but patients could request an additional visit for more instruction, with the RN. The teach-back method was employed. Results of the visit were recorded in the EHR.
At the end of the 12 month period, after all results were entered and tabulated, the research tool was checked for validity and reliability.
Discussion/Implications for Clinical Practice & Future Research
Once the results were analyzed, we were able to develop an Alzheimer Risk Algorithm. Our new algorithm can be added to clinical software systems and a practice could, for example, run this risk model on all eligible people and offer those at risk more detailed testing or specific preventive management. This is unique, because before the use of this screening tool, and then algorithm, we had no standardization for identifying those at risk for AD. There was also no specific research identifying “Gold Standards” of practice for Alzheimer’s. Since we know that early identification is the best way to retard progression of the disease, it is imperative to begin this screening early. Typically, a person is not given information to address their risk of Alzheimer’s until they are already displaying symptoms. We now know this is too late.
Aside from addressing risk reduction, these tools are important in identifying who might require more specific testing, so as to quantify the disease and its progression.
Educational materials and handouts can now be developed with regards to specific risk reduction modalities. Gold standard protocols can be employed in the algorithms, and educational materials developed to prevent further disease progression or to avert the onset of AD. These educational materials, screening tools and algorithms all have the ability to be downloadable for use by patients and practitioners nationwide and within multiple treatment settings. Family members can be alerted and screened, and tested for the 4 APOE alleles if necessary.
Strengths/Limitations
The primary strength of this study is that the risk determinants were based on the best available prevalence and relative risk estimates from recent systematic reviews and meta-analyses. However, there are some limitations. Since AD is a multifactorial disease, it is not known whether removal of a single risk factor will actually lower total incidence of AD. Many of the risk factors examined were interrelated. For example, hypertension, diabetes and obesity often occur simultaneously, and can be affected by physical activity. Since analysis of risk factors shows a correlation between multiple factors, risk reduction strategies that target multiple risk factors may have a better outcome in lowering AD risk. Also, scoring of the screening tool needs to be streamlined.
Finally, a literature review of multiple meta analyses and systematic reviews, as well as individual evidence-based studies showed some degree of variation in regards to the ten most pertinent risk factors. For instance, stress as a causative factor in AD progression was noted on some studies but not others. Frailty was often grouped with BMI but as low BMI instead of high. Some studies also included sleep, because in patients with AD, the only way they can clear amyloid plaque is during sleep. This made sense, but since sleep was not included in the large randomized trials, we left it out so as not to confound the data.
Conclusion
Further research needs to be conducted to refine the performance and validity of the Risk Assessment Tool, as well as the Alzheimer Risk Algorithm. Testing should be carried out in different settings and populations, in areas where the prevalence, detection, and recording of dementia/Alzheimer’s by PCP’s is variable. We also need to further understand how the tool might be used in practice, the ethical implications, how to extrapolate the data to different populations (ethnicities), and to identify the potential costs for health services. If much of the increase in AD is to occur in low and middle income countries, we need to be able to carry this research forward to identify these “at risk” populations and provide resources and education to decrease the devastating personal and economic burden of Alzheimer’s Disease.
APPENDIX A: SCREENING TOOL FOR EARLY RISK REDUCTION OF MODIFIABLE RISK FACTORS IN ALZHEIMER’S DISEASE
Directions: Please fill in the circle completely when marking your answer.
1. Have you been diagnosed with pre-diabetes or diabetes? O Yes O No
Has anyone in your immediate family been diagnosed with diabetes? O Yes O No
2. Have you been diagnosed with high blood pressure? O Yes O No
Do either of your parents have high blood pressure? O Yes O No
3. Do you smoke? O Yes O No
If you smoked in the past, has it been 5 years or longer since you quit smoking?
O Yes O No
4. Have you been diagnosed with depression? O Yes O No
Are you on medications for depression? O Yes O No
5. How stressful would you say your life is?
O Not stressful O Somewhat Stressful O Very Stressful
6. What is the highest grade you have attained? O 12th grade O some college O college graduate (undergrad) O graduate school O doctorate O post doctorate
7. How many hours of exercise do you do per week? O less than 90 min; O 90-150 min,
O More than 150 min per week
8. How many servings of vegetables do you eat per day (1/2 C = 1 serving)? O 6-9 servings; O 3-6 servings; O Less than 3 servings
9. How many servings of fruits (1/2 C = 1 serving) do you eat per day? O Less than or equal to 1 serving, O 1-3 servings, O greater than 3 servings
What types of fruits do you typically eat? (You may choose more than one answer).
O berries O banana, mango, pineapple, orange O apples, pears O dried fruits
10. Do you eat fatty fish (salmon, mackerel, sardines, trout, tuna) once or twice a week Or supplement with an Omega-3 fatty acid at 2 grams or more daily? O Yes O No
11. Do you eat out at fast food restaurants more than once per week? O Yes O No
12. *To be answered by practitioner or assistant:
What is your current weight and height? Height____________Weight_______________
(The practitioner will have the medical technician weigh and measure waist circumference at beginning of visit and calculate BMI- (calculated as weight in kilograms divided by height in meters squared).
O BMI <= 25 O BMI 25-29 O BMI >=30
Scoring:
Questions 1-4 0 points for each “NO” answer. 1 point for each “YES” answer.
Questions 5 0 points-“not stressful”, 1 point -“somewhat stressful”, 2 points-“very stressful”
Question 6 College grad or higher- 0 points, Some college- 1 point.
High school or less-score 2 points
Question 7 Active- 0 points. Moderately active- 1 point. Sedentary-2 points.
*Active is defined as 150 min or more of exercise per week.
Moderately active is defined as 90 minutes to < 150 minutes per week.
Sedentary is defined as < 90 minutes per week.
Question 8 6-9 servings-0 points, 3-6 servings- 1 point, less than 3 servings- 2 points
Question 9 1-3 servings-0 points, all other answers- 1 point
Question 10 Berries, apples/pears-0 points, banana group- 1 point, dried fruits- 2 points
Question 11 0 points for “Yes”, 1 point for “No”
Question 12 0 points for “No”, 1 point for “Yes”
Question 13 BMI<=25- 0 points, BMI 26-29- 1 point, BMI >=30 -2 points
Score: 0-5 Low risk
6-10 Moderate Risk
11-19 High risk
References
Prince, M., Wimo, A., Geurchet, M, et al. (2015) World Alzheimer’s report. The Global Impact
Of Dementia. Alzheimer’s Disease International.
Barnes, D. E., & Yaffe, K. (2011). The projected impact of risk factor reduction on Alzheimer's
disease prevalence. The Lancet. Neurology, 10(9), 819-28.
Xu W, Tan L, Wang H, et al. Meta-analysis of modifiable risk factors for Alzheimer's disease
J Neurol Neurosurg Psychiatry 2015;86:1299-1306.
Szuhany KL, Bugatti M, Otto MW (2015) A meta-analytic review of the effects of exercise on
brain-derived neurotrophic factor. J Psychiatr Res 60, 56-64.
Jayaraman, A., & Pike, C. J. (2014). Alzheimer's disease and type 2 diabetes: multiple
mechanisms contribute to interactions. Current diabetes reports, 14(4), 476.
Gabin, J. M., Tambs, K., Saltvedt, I., Sund, E., & Holmen, J. (2017). Association between blood
pressure and Alzheimer disease measured up to 27 years prior to diagnosis: the HUNT
Study. Alzheimer's research & therapy, 9(1), 37. doi:10.1186/s13195-017-0262-x.
Justice, Nicholas (2018) The relationship between stress and Alzheimer's disease. Neurobiology
of stress, 8, 127-133. doi:10.1016/j.ynstr.2018.04.002.
Marchant NL, Howard RJ (2015) Cognitive debt and Alzheimer's disease. J Alzheimers Dis 44,
755-770.
Wang, C., Ji, X., Wu, X., Tang, Z., Zhang, X., Guan, S., Liu, H., Fang, X (2017) Frailty in
Relation to the Risk of Alzheimer's Disease, Dementia, and Death in Older Chinese Adults: A
Seven-Year Prospective Study. Jnl Nutr Health Aging, 21(6):648-654. doi: 10.1007/s12603-
016-0798-7.
Durazzo, T. C., Mattsson, N., Weiner, M. W., Alzheimer's Disease Neuroimaging Initiative
(2014). Smoking and increased Alzheimer's disease risk: a review of potential
mechanisms. Alzheimer's & dementia : the journal of the Alzheimer's Association, 10(3
Suppl), S122-45.
Barnes, D. E., & Yaffe, K. (2011). The projected impact of risk factor reduction on Alzheimer's
disease prevalence. The Lancet. Neurology, 10(9), 819-28.
Solfrizzi, V., Panza, F., Frisardi, V., Seripa, D., Logroscino,G., Imbimbo, BP., Pilottto, A.,
(2011). Diet and Alzheimer’s disease risk factors or prevention: the current evidence. Expert
Rev Neurother, 11(5):677-708. doi: 10.1586/ern.11.56.
Chen, W. W., Zhang, X., & Huang, W. J. (2016). Role of physical exercise in Alzheimer's
disease. Biomedical reports, 4(4), 403-407.
Mastrian, K.G. & McGonigle, D. (2017). Informatics for Health Professionals. Burlington,
MA: Jones & Bartlett Learning.