Of the many outstanding mysteries of neuroscience, the pathogenic origins of Alzheimer’s disease (AD) remain one of the most perplexing neurological puzzles. An estimated 5.7 million Americans are presently afflicted with the disease, which gradually ravages the aging brain, resulting in progressive loss of memory followed by other cognitive abilities. Due to this accelerating pressing public health concern, funding agencies have grasped the urgency of supporting research aimed at detecting and treating this debilitating neurodegenerative disease, and continue to increase their funding allocations for AD research. Yet, despite decades of investigation, our knowledge of the basic biological underpinnings of AD remains remarkably inconclusive.
No lab should be an island
Two significant obstacles to unraveling the mystery of AD are the complexity of this heterogeneous disease, and the challenge of unifying disparate findings from independent research groups using vastly different techniques and samples. For instance, how does the research community integrate reports on structural brain changes in AD from two labs each using different brain imaging techniques, and studying small groups of demographically and clinically distinct samples of patients? This problem, of researchers working in isolated microcosms to solve enormously complex puzzles, is not unique to AD research. An evolution in how scientists conduct research–from working in protected research bubbles to collaboration and openly sharing data across lab boundaries–is rapidly gaining traction across disciplines. The solution to cracking the AD puzzle just might be found by a continued shift in this direction.
Collaboration and data-sharing in AD research
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been a shining example of how collaborative work among experts in a given field can generate progress leaps and bounds faster than individual groups plugging away independently. ADNI was established in 2004 with the intent to collect a large set of brain imaging (MRI and PET), cognitive, genetic, blood and cerebrospinal fluid data on individuals with AD, mild cognitive impairment (considered a precursor to AD), and healthy controls. Across the US and Canada, over 2,000 individuals at 63 locations have participated to date, with many returning for annual assessments. By adhering to a standardized protocol across research sites, study data can easily be integrated for large-scale analysis. Perhaps what has made the project so fruitful, besides producing a goldmine of multimodal data from well-characterized AD participants, is the open availability of ADNI data to any qualified researcher who requests access. With the brightest minds in the field granted free access to this rich dataset, the opportunities for critical and novel interrogation are boundless.
The fruits of ADNI
A recently published review by Dallas Veitch and collaborators on the growing findings from ADNI highlights its prolific yield. Since its inception 14 years ago, 1739 papers utilizing the ADNI dataset have been published to date. Because of the range of measures collected, this vast literature covers novel findings in how brain structure, neuropathology, cognition, and genetics contribute to AD development and progression. Here are just a few highlights of the emerging advances from ADNI that Veitch and colleagues discuss in their comprehensive review.
Disease time-course. Data-driven disease models of the temporal order of pathological brain changes and their casual relationships suggest that multiple mechanisms are likely at play in AD. Although preliminary, these models suggest that vascular changes and the appearance of amyloid-ß (a pathological protein that accumulates in the AD brain) occur early. These changes are soon followed by altered brain metabolism, disrupted structural and functional connectivity, atrophy and cognitive decline.
Genetic risk. Studies have confirmed that the APOE ε4 allele is a major genetic risk factor for AD, but many other genes have also been implicated. Candidate genes include those involved in oxidative stress, amyloid and tau formation, vascular health, and calcium signaling. APOE ε4 may elevate risk through cerebrovascular mechanisms, via both amyloid-dependent and -independent pathways.
AD subtypes. What we commonly consider AD may in fact represent multiple subtypes of AD-like dementia. Distinct subgroups have been identified that vary in their patterns of neuropathology and brain atrophy, cognitive deficits, and vascular and metabolic changes. An emerging theme is that AD is a heterogeneous disease with many causal pathways and manifestations.
Amyloid-tau relationship. Levels of neurofibrillary tau tangles–the other pathological protein implicated in AD–are greater in individuals with high levels of amyloid-ß, but only tau (and not amyloid-ß) correlates with cognitive function. Accumulating evidence suggests a synergistic relationship between amyloid-ß and tau, whereby tau spread accelerates once amyloid burden reaches a critical threshold, collectively triggering a cascade of metabolic and degenerative changes that ultimately impair cognitive function.
Vascular dysfunction. Vascular risk factors, such as hypertension, high cholesterol or diabetes, may contribute to brain atrophy and cognitive decline through both amyloid-dependent and amyloid-independent pathways. Vascular dysfunction is thought to be one of the earliest precipitating factors to AD onset, suggesting that addressing vascular risk may be critical to preventing or slowing AD progression.
Sex differences. Women are twice as likely to develop AD than men, which may be due in part to their increased longevity. But even after accounting for their longer lifespan, women appear to have greater vulnerability to amyloid-ß and tau, and demonstrate differences in genetic risk factors for AD.
Although the mystery of AD has yet to be solved, our understanding of the disease is rapidly progressing, thanks in large part to the integration of data and resources across the AD research community. Open, collaborative science is becoming increasingly valued for its ability to unify research efforts, and will no doubt be crucial to future progress in many areas of research.
Veitch DP et al. (2018). Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s & Dementia.
Image credit Hansson et al. (2017) Front Neurosci
Any views expressed are those of the author, and do not necessarily reflect those of PLOS.
Emilie Reas received her PhD in Neuroscience from UC San Diego, where she used fMRI to study memory. As a postdoc at UCSD, she currently studies how the brain changes with aging and disease. In addition to her tweets for @PLOSNeuro she is @etreas.