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Predicting Cognitive Decline: Advances in Pre-Clinical Diagnosis of Alzheimer’s Disease


Studies to date indicate the onset of Alzheimer’s disease (AD) begins many years before clinical symptoms are apparent. Early detection of AD and therapeutic intervention could reduce the progression and severity of disease, minimizing the time spent living with severe dementia.

An earlier diagnosis of AD means that individuals would have more time to prepare for the cost of future care, and insurers could more accurately price for the cost of resulting claims. Additionally, insurers should prepare for the potentially increased risk of anti-selection as time continues to lengthen between diagnosis and manifestation of symptoms. Most cases of AD are diagnosed in people aged 65 and over. However, diagnosing AD is difficult and a definitive diagnosis is usually only made on autopsy. The current diagnosis of AD is based on clinical assessment and exclusion criteria for other forms of dementia.

For further reading on Alzheimer’s disease, see:

Factors Influencing Cognitive Decline

Individuals with early-onset AD may not experience symptoms for years until a clinical diagnosis is made. For example, it has been found that people with normal brain volumes and neuronal structure could be suffering from AD and yet remain cognitively normal.1 It is already well-established that physical exercise, cognitive stimulation, social interactions, adequate sleep, and good air quality reduce the risk of frailty and cognitive decline in old age. Studies suggest that cognitive stimulation, particularly through performing complex tasks, can activate brain areas in patients who are already suffering initial cognitive decline.2

A study analyzing data from the UK Biobank showed that when participants followed six healthy lifestyle behaviors (healthy diet, physical activity, six to nine hours of sleep per day, moderate alcohol consumption, not smoking and a body mass index <30 kg/m), the risk of dementia was cut by nearly half compared to those who followed only two or fewer healthy behaviors.3

Cellular damage can be delayed or even prevented through dietary interventions such as calorie restriction (CR). See Calorie Restriction (CR) – What is it and what does the research tell us? CR protects the brain against aging and oxidative stress.2 Among the positive effects of CR is the reduction of growth hormone insulin-like factor (IGF-1) that may offer protective effects against AD. Circulating IGF-1 is a hormone mostly produced by the liver that regulates energy metabolism, cell proliferation and differentiation, body size and lifespan. IGF-1 is transported across the blood-brain barrier, causing changes in IGF-1 input to the brain. Low plasma IGF-1 concentrations have been shown to predict survival in long-lived people and reduce the risk of cancer and diabetes.4 A study of Ashkenazi Jewish centenarians found genetic mutations in the IGF-1 receptor resulted in reduced IGF-1 signaling compared to controls.5

Pre-Clinical Diagnosis of AD

Current tools used to diagnose AD include non-invasive tests such as the mini-mental state examination (MMSE), the cognitive abilities screening instrument (CASI), the clock-drawing task (CDT), and the clinical dementia rating (CDR) scale. However, all these tools have drawbacks, such as a limited ability to diagnose early dementia and the length of time required to complete an examination.6

The Ascertain Dementia 8 (AD8) questionnaire was developed in 2005 by researchers at Washington University in St. Louis to detect early signs of dementia in people who are asymptomatic. It can be completed in less than three minutes and uses a simple scoring system requiring minimal training for use.7 An AD8 score of less than two indicates a negative dementia screening test result, while a score of two or greater indicates a positive screen for dementia. The AD8 test has been shown to have improved sensitivity over the MMSE (sensitivity 72.6%, specificity 90.6%) and correlates highly (r= 0.75) with the CDR and neuropsychological tests to detect dementia. In a study of 257 participants, individuals with a positive AD8 score displayed significantly lower mean levels of CSF Aβ42, and greater CSF tau, ptau181 and the tau(s)/Aβ42 ratios. Those with positive cortical Pittsburgh Compound B (PiB) had the lowest levels of CSF Aβ42, the presence of which can be seen in ”pre-clinical” cognitive impairment.8

Screening for dementia can also be completed using magnetic resonance imaging (MRI) and amyloid imaging using PiB, a radioactive analog used in positron emission tomography (PET) scans to image Aβ plaques in the brain. Other tests include measuring temporal lobe volumes and mean cortical binding potential (MCBP), which is obtained by taking the mean of the binding potentials from areas of the brain known to have high uptake in individuals with AD.9

Growing evidence suggests that cerebrospinal fluid (CSF) biomarkers of AD in asymptomatic individuals can help predict cognitive impairment and dementia. CSF biomarkers include Aβ42, total tau, phosphorylated tau at serine/threonine 181 (p-tau181), tau(s) Aβ42/40 ratios, and cortical Aβ measured by PET scans. Soluble β-amyloid (Aβ) is a principle component of amyloid plaques, while tau is the principle component of neurofibrillary tangles. They can be objectively measured and evaluated as indicators of age-related normal or pathogenic processes. Carriers of the APOE4 gene have been found to have higher cortical Aβ burden compared to non-carriers and have greater rates of cognitive decline. Examining the relationship between these markers of disease may add to a likely diagnosis of AD.10, 11

In a study by Ingber et al, of 328 cognitively normal individuals aged 50 and older, 17.7% were found to be cognitively impaired at the end of follow-up. Abnormal biomarkers were observed in 32.9% of participants for Aβ42, 15.6% for tau, 15.9% for ptau181, 15.2% for tau/ Aβ42, and 18.6% for ptau181/ Aβ42. Participants with larger intracranial volumes and abnormal biomarker values had significantly increased functional impairment.1 A study by Roe et al found that abnormal levels of AD biomarkers in adults aged 45-88 years old were associated with a faster rate of cognitive impairment and signaled pathology at least several years before the onset of clinical symptoms. Individuals with abnormal levels of both tau and Aβ42 progressed the fastest to cognitive impairment, relative to other combinations of biomarkers.9

A new blood test is now helping to predict AD by identifying whether a person is likely to have amyloid plaques in the brain. C2N Diagnostics launched the PrecivityADTM test in October 2020, available in the U.S. at a cost of $1,250. A blood sample is tested for the ratio of Aβ42/40 and the APOE genotype and assessed in conjunction with a person’s age. An overall score, known as the Amyloid Probability Score (APS), indicates the likelihood of amyloid plaques in the brain. Low APS scores of 0-36 are consistent with negative PET scan results for amyloid plaques; APS scores of 37-57 do not distinguish between presence or absence of amyloid plaques; and APS scores of 58-100 indicate a high likelihood of amyloid plaques. Studies to date indicate that the test correctly identifies amyloid plaques in 86% of patients.12

While not yet approved by the Food and Drug Administration (FDA), the PrecivityADTM test is approved under the Clinical Laboratory Improvement Amendments (CLIA) regulations in the U.S. The FDA, however, granted the test a ”breakthrough device” designation in 2019, designed to accelerate the path to approval. The test is also approved for use in the European Union.13

Cytosine-phosphate guanine dinucleotide (CpG) methylation profiling of biomarkers in leukocyte DNA offers a tangible solution for investigation and monitoring of AD and is already in use for early detection of diseases such as cancer and heart disease. Leukocyte samples are easily obtained from patients and are minimally invasive and affordable. A recent study that analyzed blood leukocytes to identify polymorphisms, which increase the risk of AD by affecting the formation of neurofibrillary tangles, showed a sensitivity and specificity of 97% in predicting AD in cognitively healthy individuals.14


Given the rising incidence of dementia globally, there is a significant need to diagnose AD early so that interventions and treatments can be applied to delay the onset of clinical symptoms. The development of new diagnostics is helping to identify individuals with asymptomatic AD, who may then be able to seek treatment. Drugs such as aducanumab might be beneficial, as early diagnosis is associated with better response to existing therapies. Insurers may be able to provide cover for the cost of such tests or offer new products covering early dementia care prior to the onset of clinical symptoms. In carefully designed products, positive test results could be the trigger for an early payout of cover based on evidence of early dementia in asymptomatic individuals. Thus, for underwriting and product development, it is important that insurers are familiar with these new technologies becoming available in the market.


  1. Ingber, A.P. et al (2016). Cerebrospinal fluid biomarkers and reserve variables as predictors of future ‘non-cognitive’ outcomes of Alzheimer’s disease. Journal of Alzheimer’s Disease 2016; 52(3): 1055-1064. Available from: Cerebrospinal Fluid Biomarkers and Reserve Variables as Predictors of Future “Non-Cognitive” Outcomes of Alzheimer’s Disease ( [accessed July 2021]
  2. Bertozzi, B. et al (2017). Beyond calories: an integrated approach to promote health, longevity and well-being. Gerontology 2017; 63(1): 13-19. Available from: Beyond calories: an integrated approach to promote health, longevity and well-being ( [accessed July 2021]
  3. American Heart Association (2021). Healthy lifestyle behaviors reduced dementia risk despite family history of dementia. Medical Express 2021, May 20. Available from: Healthy lifestyle behaviors reduced dementia risk despite family history of dementia ( [accessed July 2021]
  4. Longo, V.D. et al (2015). Interventions to slow aging in humans: are we ready? Aging Cell, 2015; 14 (4): 497-510. Available from: Interventions to Slow Aging in Humans: Are We Ready? ( [accessed July 2021]
  5. Parrella, E. et al (2013). Protein restriction cycles reduce IGF-1 and phosphorylated tau, and improve behavioral performance in an Alzheimer’s disease mouse model. Aging Cell 2013 April; 12(2): 257-268. Available from: nihms566268.pdf [accessed July 2021]
  6. Yang, Y.H. et al (2011). Application of AD8 questionnaire to screen very mild dementia in Taiwanese. American Journal of Alzheimer’s Disease & Other Dementias; 26(2): 134-138. Available from: [accessed July 2021]
  7. Chen, H.H. et al (2017). The diagnostic accuracy of the Ascertain Dementia 8 questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: a systematic review and meta-analysis. Family Practice, 2018; 35(3): 239-246. Available from: The diagnostic accuracy of the Ascertain Dementia 8 questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: a systematic review and meta-analysis ( [accessed July 2021]
  8. Galvin, J.E. et al (2010). Relationship of dementia screening tests with biomarkers of Alzheimer’s disease. Brain 2010; 133: 3290-3300. Available from: Relationship of dementia screening tests with biomarkers of Alzheimer’s disease ( [accessed July 2021]
  9. Roe, C.M. et al (2013). Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology May 7 2013; 80(19): 1784-91. Available from: WNL205007 1784..1791 ( [accessed July 2021]
  10. Thirunavu, V. et al (2019). Higher body mass index is associated with lower cortical amyloid-β burden in cognitively normal individuals in late-life. Journal of Alzheimer’s Disease 2019; 69(3): 817-827. Available from: Higher Body Mass Index Is Associated with Lower Cortical Amyloid-β Burden in Cognitively Normal Individuals in Late-Life ( [accessed July 2021]
  11. Galvin, J.E. (2010). Dementia screening, biomarkers and protein misfolding. Prion 2011 Jan-Mar; 5(1): 16-21. Available from: [accessed July 2021]
  12. Meglio, M. (2020). First widely accessible Alzheimer Disease blood test reaches clinic. NeurologyLive Nov 6, 2020. Available from: First Widely Accessible Alzheimer Disease Blood Test Reaches Clinic ( [accessed July 2021]
  13. C2N Diagnostics (2020). The PrecivityADTM Test: Advanced diagnostics in Alzheimer’s Disease. Available from: PrecivityAD™ [accessed July 2021]
  14. Bahado-Singh, R.O. (2021). Artificial intelligence and leukocyte epigenomics: evaluation and prediction of late-onset Alzheimer’s disease. PloS ONE; 16(3): e0248375. Available from: Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer’s disease ( [accessed July 2021]

The Author

  • Hilary Henly
    Global Medical Researcher
    RGA International Reinsurance Company dac
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This article is the second in a three-part series, highlighting areas of research funded by the Longer Life Foundation (LLF), a not-for-profit organization that supports the study of scientific and public health factors predicting mortality, morbidity, longevity, and wellness. The LLF is a collaboration between Reinsurance Group of America, Incorporated (RGA) and Washington University School of Medicine in St. Louis.

RGA’s Hilary Henly, Global Medical Researcher, analyzes findings from studies funded by the LLF (references 1,2,7,8,9), looking at the different factors influencing cognitive decline and the new techniques available that are helping to predict an early diagnosis of Alzheimer’s disease before the onset of clinical symptoms.


  • AD
  • AD screening
  • AD8 score
  • aducanumab
  • alzheimers disease
  • alzheimers screening
  • behavioral science
  • consumer Engagement
  • customer engagement insurance
  • customer experience insurance
  • customer relationship insurance
  • dementia
  • dementia screening
  • demographic insurance
  • disability income
  • drug abuse
  • drug abuse morbidity
  • drug use
  • drug use trend
  • early dementia
  • early dementia care
  • e-cigarette underwriting
  • eldercare AI life insurance
  • eldercare life insurance
  • fitness tracker insurance
  • health. Lifestyle-related morbidity
  • Henly
  • henly hillary
  • Hilary
  • Hilary Henly
  • insurance application contestability
  • insurance application regulation exclusion
  • insurance bundle
  • insurance linked wellness program
  • internet of things data risk assessment
  • internet of things insurance
  • Iot data insurance
  • IoT insurance
  • lifestyle behavior drug abuse mortality
  • life-style related mortality
  • lifestyle-related morbidity
  • LLF
  • Longer Life Foundation
  • marijuana underwriting
  • mental health
  • mini-mental state examination
  • MMSE
  • morbidity improvement
  • opioid case management
  • opioid claim management
  • opioid underwriting
  • opioids
  • pain management insurance
  • pain management risk assessment
  • post level term lapse
  • post-term lapse
  • PrecivityAD
  • prescription drug abuse underwriting
  • return to work
  • rules engine
  • rules set
  • Schedule I underwriting regulation
  • SI life insurance
  • simplified issue
  • simplified underwriting
  • simplified underwriting life insurance
  • St Louis
  • substance abuse
  • underwriting automation
  • upselling insurance
  • vaping underwriting
  • Washington University
  • wearable
  • wearable data insurance
  • wearables
  • wearables insurance
  • wearables risk assessment
  • wellness
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  • wellness program Vitality
  • wellness program wearables
  • wellness: senior services life insurance