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Biomarker Research

Before biomarker tests were developed in the early 2000s, the only sure way to know whether a person had Alzheimer’s was via autopsy. Today, thanks to multiple, recent scientific advances, researchers can now use either brain imaging methods, such as PET scans with radioactive tracers, or lab tests of substances in spinal fluid to diagnose people living with the disease. Biomarkers allow for a more accurate diagnosis by helping to discern between the different diseases that cause dementia. For example, detecting beta-amyloid plaques and tau tangles with either brain scans or spinal fluid tests helps researchers differentiate between Alzheimer’s and Lewy body dementias.

NIH funding has enabled significant recent progress in developing, testing, and validating biomarkers for diagnosing Alzheimer’s and related dementias. These technological advances have helped scientists discover that changes in the brain that occur during Alzheimer’s are evident long before a person shows outward signs of cognitive impairment or dementia. Scientists have early results that show that beta-amyloid plaques, tau proteins, and other biomarkers not only are present in the brain and spinal fluid but also circulate in the bloodstream. In 2019 and early 2020, NIH-supported scientists reported advances in the development of blood-based tests that could enable rapid screening of volunteers who wish to enroll in studies. Using a blood test to screen instead would reduce the number of research volunteers who undergo brain PET imaging or spinal taps, which are expensive and invasive.

Some of the progress reported recently from diverse biomarker projects underway includes:

For now, these blood tests can be used only by researchers in clinical studies. It is likely that eventually, FDA-approved tests will be made available to physicians, enabling them to screen their patients for Alzheimer’s and related dementias before symptoms appear.

In addition to blood tests, other NIH-supported research projects are designed to look beyond current measures to detect people with dementia. These include changes in vision and pupil responses that may signal Alzheimer’s, or a combined decline in memory and walking speed as a sign of dementia.

Some examples of NIH-funded programs to develop biomarkers include:

  • Alzheimer’s Disease Neuroimaging Initiative. The NIH-supported Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a long- standing public-private partnership to help researchers develop tools for clinical trials by tracking how brain imaging and fluid biomarkers change as the disease progresses from cognitively normal to mild dementia to Alzheimer’s. Investigators developing drugs in clinical trials use ADNI-developed tools in their studies. Researchers can access ADNI brain scans, biomarker data, and DNA and fluid samples. Now in its 16th year, the three phases of ADNI (ADNI1/GO, ADNI2, and ADNI3) have developed biomarkers for use in selecting clinical trial participants and for assessing treatment outcomes. When ADNI3 was launched in 2016, ADNI data had already been downloaded for research purposes more than 11 million times, and scientists had used ADNI data to publish more than 1,200 scientific papers. Because of its open-access policy of sharing data, ADNI has enabled scientists to develop a better understanding of biomarkers and the progression of Alzheimer’s.
  • Accelerating Medicines Partnership- Alzheimer’s Disease. The Accelerating Medicines Partnership Alzheimer’s Disease (AMP AD) Biomarkers Project is a consortium of two NIA-supported Phase 2/Phase 3 secondary prevention trials, the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study, and the Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU). Through these, scientists are testing several anti-amyloid therapies. The goal is to explore tau brain imaging for tracking responsiveness to treatment and disease progression. Data and biosamples from both studies will be made broadly available to the research community. In fact, the screening/prerandomization data and biosamples from the A4 study are already available via the Laboratory of Neuroimaging (LONI) Image and Data Archive. The A4 study is the first pivotal Phase 3 study to release data prior to the completion of the trial in accordance with Collaboration for Alzheimer’s Prevention: Principles to guide data and sample sharing in preclinical Alzheimer’s disease trials.
  • The Accelerating Medicines Partnership- Parkinson’s Disease. Complementing AMP- AD, NIH has also launched AMP-Parkinson’s Disease (AMP-PD) to develop and test treatments for Parkinson’s disease and related disorders like Lewy body dementia. One of the hallmarks of Parkinson’s is clusters of Lewy bodies in the brain. In 2020, AMP-PD will be adding whole genome sequences from more than 2,000 people with Lewy body dementia to its data platform. These will be made available for analysis by the general research community and are intended to further the goal of providing a better understanding of the disease and potential treatment targets.
  • ALLFTD Research Consortium. In 2019, a new NIH-supported consortium integrated two ongoing studies of frontotemporal lobar degeneration. The goal is to advance the development of treatments by helping researchers better understand the disease process by finding improved brain imaging and other methods for accurately identifying participants and for measuring disease progression.
  • The Lewy Body Disease Center Without Walls. A key finding in the brain tissue of people with Lewy body disease (LBD) is an accumulation of abnormal alpha-synuclein protein, similar to what is observed in Parkinson’s disease. However, abnormal deposits of the Alzheimer’s- related beta-amyloid protein are also commonly observed. The Lewy Body Disease Center Without Walls (LBD CWOW) is a collaboration between six research institutions to determine why both proteins accumulate in LBD brain tissue, whether the protein structures are unique to LBD (versus Alzheimer’s or Parkinson’s disease), and how these proteins lead to tissue damage and dementia. Using samples from hundreds of brains from donors, the investigators will identify the exact protein structures, genes, and proteins that are unique to LBD brain cells and circuits. They will then use cell models to learn how these components interact with each other to impair brain function.
  • MarkVCID. Although researchers have conducted many clinical studies to find ways to prevent cognitive impairment and dementia from certain blood vessel diseases, they have been hampered by the limited availability of biomarkers. MarkVCID involves seven research sites and a coordinating center to develop biomarkers for the small vessel diseases of the brain. The consortium has developed and is currently testing 11 different biomarker kits — which include several types of vascular imaging and fluid-based biomarkers — across several clinical research sites, including within the SPRINT MIND research consortium.

New tools and technologies will be critical for advancing biomarker research or extending its use beyond the measurement of blood or spinal fluid. Some recent examples of tools and technologies under development include:

  • EHR Risk of Alzheimer’s and Dementia Assessment Rule. By analyzing Kaiser Permanente electronic health records (EHRs) of more than 16,000 visits by more than 4,000 older adults over two decades, a research team developed the first dementia risk prediction model, which they called EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR). Some predictors of dementia included missing appointments, having diabetes, and exhibiting symptoms such as psychosis. Their findings suggest that a tool like this could be used with EHRs to accurately detect people who may have undiagnosed dementia. When they tested the tool, they found that 498 of the 1,015 researcher-confirmed dementia cases had not been recognized by clinicians before study investigators independently identified these cases from predictors recorded in the EHR.
  • Advances in cognitive testing. The very earliest signs of cognitive decline can be very subtle and may require data collected over days, weeks, or months to detect. As it happens, current digital devices are quite good at capturing large amounts of behavioral and physiological data without requiring people to make multiple, in-person visits to research sites. NIH has made significant investments in improving the sensitivity of cognitive measures for use on mobile devices. These measures may help with detection of the very earliest signs of cognitive decline in an individual and also enable the collection of data from many users to ultimately correlate cognitive change with other health, behavioral, and social variables. The NIH-supported Mobile Toolbox is developing smartphone-based cognitive tests, which would move testing from the lab to people’s daily lives, and also a testing infrastructure for widespread use by researchers in population studies of cognitive change.

For a more in-depth look at the research implementation milestones in this area, including progress and accomplishments, visit

Find a list of references for this section in the PDF version.

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