Alzheimer’s disease (AD) clinical trials have fallen into a pattern. The recent announcement of another disappointing outcome (1) is the latest in a succession of treatments that have failed to produce much patient benefit. Additional trials are in progress, perhaps one or more of them will successfully cure or arrest dementia.
One consistent issue with failed AD interventions is the nagging worry that they were provided too late to reverse or halt dementia (2, 3). Truth be told, scientists are still struggling to confidently discern when AD begins as well as how the various pathological changes linked to it all fit together and in what order (4). The search for AD biomarkers, objectively measurable characteristics reliably revealing the presence of the illness, has been intense. Several neuroimaging and biochemical attributes are currently used by AD researchers (2, 3), but these procedures require specialized facilities with expert personnel, produce patient discomfort and are expensive (5). In addition, it has been hard to figure out what these structural and biological indicators prove in the early phases of AD and accurately predict concerning the future of patients with them. Although the biomarkers currently employed in research are worthwhile, less costly methods that are easier to use would be a boon.
An impressive body of evidence suggests that the hallmark pathological brain changes found in AD patients emerge silently years before clinical signs and symptoms of dementia become obvious (3). Unfortunately, while the ‘preclinical’ phase of AD could be the best point to intervene against dementia, it is the least understood chapter in the natural history of this malady (3). Delineating this part of the AD story has just become more critical because there is some solid good news about AD. Studies have confirmed that dementia rates are dropping (6), a strong indication that AD might be preventable or its pace of attack delayed. The problem is no one can say why this decrease occurred. Given that we are probably years away from any direct medical cure it is vital to pursue this observation.
Identifying AD patients early offers the best prospect of doing something to help them. Studies have revealed some medical/behavioral attributes like obesity, high blood pressure, diabetes and physical inactivity are commonly present in dementia patients (2). These are potentially modifiable or treatable issues. Discovering persons at high risk for AD may make it possible to provide them with aggressive interventions or educate them as to how to do this on their own before dementia strikes. This is where Google might fit in.
How could Google possibly identify persons at risk for AD before they realize anything is wrong when even the best biomarkers can’t manage such a feat? The software engineers could take a careful look at what Google users have written and assess how it may have changed with time. The consensus is that AD dementia is the culmination of a slow process. The long evolution of losses leaves discoverable traces in the writings and works of its victims. Artist William Utermohlen documented the mounting thefts of AD in a series of self-portraits (7). The writings of author Iris Murdoch revealed the probable imprint of AD prior to her receiving a formal, ultimately pathology-confirmed diagnosis (8, 9). Perhaps Google engineers will consider extending previous observations in a large-scale research effort to identify and validate early signals of AD. A capacity to distinguish early stage disease may help scientists better comprehend how AD begins and unfolds.
Google engineers might be poised to build on previous work to achieve something far beyond the current capabilities of medical science – predict subjects at high risk for AD decades before they exhibit dementia. A detailed study of women in religious orders revealed that the autobiographical essays they had written as they entered their vocation predicted which of them were more likely to end up afflicted with AD at the end of their lives (10, 11, 12). Sisters whose writings expressed a greater density of ideas and positive emotions seemed to evade becoming demented in old age, while those producing less complex works were at greater risk of AD. In effect, an analysis of writing style and content in documents produced decades earlier revealed the relative risk for future AD development. If additional research extends these findings and new tools to evaluate large groups of subjects quickly and reliably are developed, AD research may benefit enormously. The prospect that sophisticated software screens might be able to group persons by relative risk and recognize – at the earliest possible stage – those most able to benefit from interventions could change the threat posed by AD.
Work on early detection of AD by software-based mass screening methods is immensely important, but also poses some difficult ethical challenges. The same software tool that provides insights to help someone avoid AD could be used for less altruistic purposes. The scientific and engineering communities have faced such dual use dilemmas before. Hopefully, it will be possible to exploit the tremendous opportunities ahead and ensure they yield the maximum benefit to public well being. It may be time to literally ‘Google’ AD.
(1) P. Belluck. 2016. Eli Lilly’s Experimental Alzheimer’s Drug Failed in Large Trial. The New York Times, 23 November 2016. http://nyti.ms/2f6v69u
(2) E. Cavedo et al. 2014. The Road Ahead to Cure Alzheimer’s Disease: Development of Biological Markers and Neuroimaging Methods for Prevention Trials Across All Stages and Target Populations. Journal of Prevention of Alzheimer’s Disease 1(3): 181-202. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606938/
(3) B. Dubois et al. 2016. Preclinical Alzheimer’s Disease: Definition, Natural History and Diagnostic Criteria. Alzheimer’s & Dementia 12(3):292-323. http://www.sciencedirect.com/science/article/pii/S1552526016000509
(4) C. R. Jack et al. 2016. A/T/N: An Unbiased Descriptive Classification Scheme for Alzheimer Disease Biomarkers. Neurology 87(5):539-547. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970664/
(5) R. Williams. 2011. Biomarkers: Warning Signs. Nature 475:S5-S7, 13 July 2011. http://palgrave.nature.com/nature/journal/v475/n7355_supp/full/475S5a.html
(6) G. Kolata. 2016. U.S. Dementia Rates Are Dropping Even as Population Ages. The New York Times, 21 November 2016. http://nyti.ms/2eYS0zC
(7) D. Grady. 2006. Self-Portraits Chronicle a Descent Into Alzheimer’s. The New York Times, 24 October 2006. http://www.nytimes.com/2006/10/24/health/24alzh.html
(8) P. Garrad et al. 2004. The Effects of Very Early Alzheimer’s Disease on the Characteristics of Writing by a Renowned Author. Brain 128(2);250-260. http://brain.oxfordjournals.org/content/128/2/250
(9) R. Highfield. 2004. Decline of Iris Murdoch… in her own words. The Telegraph, 1 December 2004. http://www.telegraph.co.uk/news/uknews/1477906/Decline-of-Iris-Murdoch…-in-her-own-words.html
(10) D. A. Snowdon et al. 1996. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: Findings from the Nun Study. Journal of the American Medical Association 275:528–532.
(11) P. Belluck. 2001. Nuns Offer Clues to Alzheimer’s and Aging. The New York Times, 7 May 2001. http://www.nytimes.com/2001/05/07/us/nuns-offer-clues-to-alzheimer-s-and-aging.html
(12) D. A. Mortimer et al. 2005. Very Early Detection of Alzheimer Neuropathology and the Role of Brain Reserve in Modifying Its Clinical Expression. Journal of Geriatric Psychiatry and Neurology 18(44):218-223. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1405917/