March 15, 2023
None
National Institute on Aging (NIA)
This Notice is a time-sensitive Request for Information (RFI) inviting comments and suggestions to be considered during the development of a challenge prize for early prediction of Alzheimer's disease and related dementias (AD/ADRD).
Background
Section 2002 "Eureka Prize Competitions" of the 21st Century Cures Act, enacted on December 13, 2016 (P.L. 114-255), requires the National Institutes of Health (NIH) to support and report on prize competitions in areas of biomedical science that could: 1) realize significant advancements and 2) improve health outcomes in human diseases and conditions that have a disproportionately small research investment relative to expenses for prevention and treatment, that represent a serious and significant disease burden, or for which there is potential for significant return on investment.
Section 2002 prize competitions, like other NIH prize competitions, must be carried out pursuant to NIH’s existing prize authority, i.e., the America COMPETES Act (P.L. 111-358), as revised by the American Innovation and Competitiveness Act (P.L. 114-326).
The National Institute on Aging (NIA), part of NIH, leads a broad scientific effort to understand the nature of aging and to extend the healthy, active years of life. NIA is the primary federal agency supporting and conducting AD/ADRD research. NIA is developing a challenge prize competition to discover the best data, methods, and strategies for the early prediction of these diseases. This includes an emphasis on building teams from varied backgrounds and solutions that generalize to groups that historically have been excluded from participation in AD/ADRD research, despite being disproportionately impacted by these conditions.
Information Requested
This RFI seeks input from stakeholders throughout the scientific research community and the general public regarding the proposed challenge for the early prediction of AD/ADRD. Individuals wishing to comment may provide input on the following topics:
1) Specific data types and datasets that could be used for discovery
2) Specific data sources that could be used for validation of dementia diagnosis
3) Stage of AD/ADRD to target for prediction models
Additional commentary on other topics not listed here are also welcome.
Note: We are interested in early prediction of cognitive decline and AD/ADRD for detection and diagnosis in all stages of disease from preclinical (asymptomatic) to clinical (e.g., mild cognitive impairment and dementia), measurement types (e.g., biomarker to cognition to function), and settings (e.g., clinic, community, workplace, home). This should not be limited to prediction for clinical purposes.
Proposed Project:
Early intervention may be important for successful disease modification in age-related cognitive decline and AD/ADRD, but we have significant limitations with early prediction of cognitive decline and dementia (along with the differential diagnoses needed to distinguish among the AD/ADRDs) using standard research and clinical tools. Standard clinical approaches to detecting changes in cognition in aging and AD/ADRD are not sensitive enough for early prediction of AD/ADRD onset. Potentially more sensitive approaches (e.g., neuroimaging, fluid biomarkers, neuropsychological tasks, digital and passive measures) can be expensive, difficult to interpret or have unclear performance in some individuals and groups, and may require access to academic medical centers, protected databases, or industry partners to ascertain data. Data sources, analytical algorithms, interpretations and applications of test results have known (and unknown) biases, methodological limitations, and questionable predictive validity, especially in groups that have historically been excluded from participation in AD/ADRD research.
We seek to stimulate use of data resources, especially those with appropriate sample diversity, including data inclusive of low-resourced, underserved communities disproportionately burdened by AD/ADRD. Appropriate population representation is an important priority for NIA and the field because the use and accuracy of AD/ADRD biomarkers may vary across and within the broader population. For example, for Asian, Black, or Hispanic older adults, the protein amyloid – which has long been considered a biomarker for AD – might play a smaller role in the development of cognitive impairment than other factors such as co-occurring chronic medical conditions (hypertension, diabetes) and sociodemographic and systemic factors, each of which has been found to contribute to racial and ethnic disparities in dementia diagnoses (Wilkins et al., 2022). This highlights the importance of identifying novel (non-amyloid, non-tau) biomarkers and non-biological (e.g., social determinants of health) predictors in adults from underrepresented racial and ethnic groups (Dark and Walker, 2022). The goal is to inform novel approaches to early detection that might ultimately lead to better tests, tools, and methodologies for clinical and research purposes.
Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for prediction of early biological, behavioral, psychological, functional, and clinical changes related to AD/ADRD.
To make progress, we need (1) data from a wider set of sources and types, including data relevant to low-resourced, underserved communities disproportionately burdened by AD/ADRD, so that we can better understand and address biases in existing data sources; (2) open, shareable data, stored in trusted repositories to determine “distributional robustness” of predictive algorithms; and (3) algorithms that meet “right to explanation” mandates (i.e., if an AI algorithm impacts people, people have a right to an explanation of how AI conclusions were reached). We need to find, access, and use data from sources that NIA supports (e.g., population-representative longitudinal studies like the Health and Retirement Study, Alzheimer’s Disease Sequencing Project, Alzheimer’s Disease Neuroimaging Initiative, and open datasets supported by NIA), real-world data from sources like electronic health records, Centers for Medicare and Medicaid Services claims, or from users themselves (e.g., through direct-to-consumer blood-based biomarkers and online cognitive testing), derived data from social media or device use, and/or combined data from different sources.
How to Submit a Response
All comments must be submitted electronically by email to NIAPrizeInput@nih.gov.
Responses must be received by 11:59:59 pm (ET) on April 14, 2023.
Responses to this RFI are voluntary. Do not include any proprietary, classified, confidential, trade secret, or sensitive information in your response. The responses will be reviewed by NIH staff, and individual feedback will not be provided to any responder. The Government will use the information submitted in response to this RFI at its discretion. The Government reserves the right to use any submitted information on public NIH websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future notices of funding opportunities.
This RFI is for information and planning purposes only and shall not be construed as a solicitation, grant, or cooperative agreement, or as an obligation on the part of the Federal Government, NIH, or individual NIH Institutes and Centers to provide support for any ideas identified in response to it. The Government will not pay for the preparation of any information submitted or for the Government’s use of such information. No basis for claims against the U.S. Government shall arise as a result of a response to this request for information or from the Government’s use of such information.
We look forward to your input and hope that you will share this RFI document with your colleagues.
National Institute on Aging (NIA)
Email: NIAPrizeInput@nih.gov
and Human Services (HHS)
