R01AG080670
Project Grant
Overview
Grant Description
Social and Behavioral Determinants of Health and Alzheimer's Disease: Cohort Study of the US Military Veteran Population - Social and Behavioral Determinants of Health and Alzheimer's Disease: Cohort Study of the US Military Veteran Population
Alzheimer's Disease (AD) affects an estimated 5.8 million US adults. Veterans are particularly susceptible to AD due to demographic, clinical, and economic factors. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job loss and financial and food insecurity. Together with behavioral health factors (e.g., smoking and substance use) and mental health, adverse social and behavioral determinants of health (SBDH) contribute to adverse health outcomes.
Associations between SBDH and AD have been noted, but most studies used structured electronic health record (EHR) or survey data. SBDH are not routinely added to structured EHR. Natural language processing (NLP) approaches can be developed to automatically extract SBDH and their attributes. This application responds to PAR-22-093 and NOT-AG-18-047.
The specific aims are:
Aim 1: Establish NLP-enriched case definitions of adverse SBDH and AD-related information (e.g., signs and symptoms of cognitive decline), and examine their incidences by first chart-reviewing ~10,000 EHR notes (e.g., primary care, neurology, psychiatric, and social work notes) and then developing and evaluating sophisticated NLP systems for automatically capturing SBDH and AD-related information.
Aim 2: Using NLP-enriched SBDH as independent variables from a nested case-control design, we will analyze the associations between adverse SBDH and incident AD. We will also evaluate how the associations vary by age, sex, race/ethnicity. We will compare results using NLP-enriched SBDH vs. using structured data (only) SBDH.
Hypothesis 1: Patients with adverse SBDH have substantially higher AD risk, after adjusting for potential covariates (e.g., patient-specific demographic and clinical factors).
Hypothesis 2: The effects of adverse SBDH on AD risk vary by age, sex, and race/ethnicity, after adjusting for covariates (e.g., patient-specific clinical factors).
Hypothesis 3: The effects of adverse SBDH on incident AD are likely cumulative and duration-dependent, with more and longer adverse SBDH leading to higher AD risk.
Aim 3: Early AD diagnosis may prevent or delay AD development through intervention efforts on SBDH. Cognitive decline occurs 4-8 years prior to AD diagnosis. We will study whether inclusion of NLP-enriched adverse SBDH and AD-related information helps early AD diagnosis. We will use three types of predictive models: statistical regression, traditional machine learning, and innovative deep learning models.
Alzheimer's Disease (AD) affects an estimated 5.8 million US adults. Veterans are particularly susceptible to AD due to demographic, clinical, and economic factors. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job loss and financial and food insecurity. Together with behavioral health factors (e.g., smoking and substance use) and mental health, adverse social and behavioral determinants of health (SBDH) contribute to adverse health outcomes.
Associations between SBDH and AD have been noted, but most studies used structured electronic health record (EHR) or survey data. SBDH are not routinely added to structured EHR. Natural language processing (NLP) approaches can be developed to automatically extract SBDH and their attributes. This application responds to PAR-22-093 and NOT-AG-18-047.
The specific aims are:
Aim 1: Establish NLP-enriched case definitions of adverse SBDH and AD-related information (e.g., signs and symptoms of cognitive decline), and examine their incidences by first chart-reviewing ~10,000 EHR notes (e.g., primary care, neurology, psychiatric, and social work notes) and then developing and evaluating sophisticated NLP systems for automatically capturing SBDH and AD-related information.
Aim 2: Using NLP-enriched SBDH as independent variables from a nested case-control design, we will analyze the associations between adverse SBDH and incident AD. We will also evaluate how the associations vary by age, sex, race/ethnicity. We will compare results using NLP-enriched SBDH vs. using structured data (only) SBDH.
Hypothesis 1: Patients with adverse SBDH have substantially higher AD risk, after adjusting for potential covariates (e.g., patient-specific demographic and clinical factors).
Hypothesis 2: The effects of adverse SBDH on AD risk vary by age, sex, and race/ethnicity, after adjusting for covariates (e.g., patient-specific clinical factors).
Hypothesis 3: The effects of adverse SBDH on incident AD are likely cumulative and duration-dependent, with more and longer adverse SBDH leading to higher AD risk.
Aim 3: Early AD diagnosis may prevent or delay AD development through intervention efforts on SBDH. Cognitive decline occurs 4-8 years prior to AD diagnosis. We will study whether inclusion of NLP-enriched adverse SBDH and AD-related information helps early AD diagnosis. We will use three types of predictive models: statistical regression, traditional machine learning, and innovative deep learning models.
Funding Goals
TO ENCOURAGE BIOMEDICAL, SOCIAL, AND BEHAVIORAL RESEARCH AND RESEARCH TRAINING DIRECTED TOWARD GREATER UNDERSTANDING OF THE AGING PROCESS AND THE DISEASES, SPECIAL PROBLEMS, AND NEEDS OF PEOPLE AS THEY AGE. THE NATIONAL INSTITUTE ON AGING HAS ESTABLISHED PROGRAMS TO PURSUE THESE GOALS. THE DIVISION OF AGING BIOLOGY EMPHASIZES UNDERSTANDING THE BASIC BIOLOGICAL PROCESSES OF AGING. THE DIVISION OF GERIATRICS AND CLINICAL GERONTOLOGY SUPPORTS RESEARCH TO IMPROVE THE ABILITIES OF HEALTH CARE PRACTITIONERS TO RESPOND TO THE DISEASES AND OTHER CLINICAL PROBLEMS OF OLDER PEOPLE. THE DIVISION OF BEHAVIORAL AND SOCIAL RESEARCH SUPPORTS RESEARCH THAT WILL LEAD TO GREATER UNDERSTANDING OF THE SOCIAL, CULTURAL, ECONOMIC AND PSYCHOLOGICAL FACTORS THAT AFFECT BOTH THE PROCESS OF GROWING OLD AND THE PLACE OF OLDER PEOPLE IN SOCIETY. THE DIVISION OF NEUROSCIENCE FOSTERS RESEARCH CONCERNED WITH THE AGE-RELATED CHANGES IN THE NERVOUS SYSTEM AS WELL AS THE RELATED SENSORY, PERCEPTUAL, AND COGNITIVE PROCESSES ASSOCIATED WITH AGING AND HAS A SPECIAL EMPHASIS ON ALZHEIMER'S DISEASE. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Massachusetts
United States
Geographic Scope
State-Wide
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 91% from $1,592,630 to $3,049,168.
University Of Massachusetts Lowell was awarded
Veteran Cohort Study: Social Determinants of Health Alzheimer's Disease
Project Grant R01AG080670
worth $3,049,168
from National Institute on Aging in March 2023 with work to be completed primarily in Massachusetts United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.866 Aging Research.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 9/24/25
Period of Performance
3/1/23
Start Date
2/29/28
End Date
Funding Split
$3.0M
Federal Obligation
$0.0
Non-Federal Obligation
$3.0M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01AG080670
Transaction History
Modifications to R01AG080670
Additional Detail
Award ID FAIN
R01AG080670
SAI Number
R01AG080670-4016992394
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
75NN00 NIH National Insitute on Aging
Funding Office
75NN00 NIH National Insitute on Aging
Awardee UEI
LTNVSTJ3R6D5
Awardee CAGE
1QW17
Performance District
MA-90
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Institute on Aging, National Institutes of Health, Health and Human Services (075-0843) | Health research and training | Grants, subsidies, and contributions (41.0) | $796,315 | 100% |
Modified: 9/24/25