R01AG072582
Project Grant
Overview
Grant Description
Using Informatics to Evaluate and Predict Cataract Surgery Impact on Alzheimer's Disease and Related Dementias and Mild Cognitive Impairment Outcomes - Project Summary/Abstract
Background
Visual impairment has been strongly associated with Alzheimer's Disease and Related Dementias (ADRD) in numerous cross-sectional and longitudinal studies, and we have found that worse baseline vision is tied to increasingly higher risk of subsequent dementia. Neurosensory deprivation from visual impairment may place greater demands on cognitive resources, accelerating cognitive decline and increasing the incidence of cognitive impairment. Conversely, improving vision could improve cognitive outcomes by increasing neurosensory input and reducing cognitive demand for processing visual information. Cataracts are the most common cause of visual impairment—fortunately reversible with surgery. However, we have found that ADRD patients are only half as likely to undergo cataract surgery as those without ADRD. This may reflect concerns regarding less potential benefit and greater perceived risks.
Objectives
Our long-term goal is to evaluate cataract surgery as a potential intervention to "bend the curve" for risk of ADRD onset and progression, including optimizing patient selection and timing for surgery. The objective of this proposal is to investigate how cataract surgery may affect the incidence and progression of Mild Cognitive Impairment (MCI) and ADRD, develop models to predict individual patients' ADRD/MCI outcomes following cataract surgery, and identify key confounders, mediators, and effect modifiers. We hypothesize that cataract surgery is associated with (1) reduction in incidence of new MCI and ADRD and (2) reduced cognitive decline and impairment progression among patients with baseline MCI or ADRD, and that (3) we will be able to predict individual patient outcomes. We propose to use methods our group has developed to archive and analyze electronic health record (EHR) data, to develop a curated data set and achieve three aims: (1) determine impact of cataract surgery on ADRD and MCI incidence; (2) determine impact of cataract surgery on cognitive decline and impairment among patients with baseline ADRD or MCI, and (3) develop patient-level predictive models for ADRD and MCI outcomes after cataract surgery.
Impact
EHR-based machine learning analysis has not been applied to ADRD research to date, and the influence of cataract surgery on cognitive outcomes is not yet known. Finding that a widely-available cataract surgery intervention improves cognitive outcomes would be transformative. We estimate a potential unmet need for cataract surgery affecting almost 350,000 patients annually—just among the subset of patients with existing Alzheimer's Disease. Results from this work will directly inform discussion of cataract surgery risks and benefits and will also facilitate future research, including pragmatic clinical trial design. By developing and disseminating open-source EHR-based algorithms to identify and classify cognitive and visual impairment, this proposal will enable investigation of other ADRD risk factors and interventions, eye disease research, and a more precise approach to managing individual patients.
Background
Visual impairment has been strongly associated with Alzheimer's Disease and Related Dementias (ADRD) in numerous cross-sectional and longitudinal studies, and we have found that worse baseline vision is tied to increasingly higher risk of subsequent dementia. Neurosensory deprivation from visual impairment may place greater demands on cognitive resources, accelerating cognitive decline and increasing the incidence of cognitive impairment. Conversely, improving vision could improve cognitive outcomes by increasing neurosensory input and reducing cognitive demand for processing visual information. Cataracts are the most common cause of visual impairment—fortunately reversible with surgery. However, we have found that ADRD patients are only half as likely to undergo cataract surgery as those without ADRD. This may reflect concerns regarding less potential benefit and greater perceived risks.
Objectives
Our long-term goal is to evaluate cataract surgery as a potential intervention to "bend the curve" for risk of ADRD onset and progression, including optimizing patient selection and timing for surgery. The objective of this proposal is to investigate how cataract surgery may affect the incidence and progression of Mild Cognitive Impairment (MCI) and ADRD, develop models to predict individual patients' ADRD/MCI outcomes following cataract surgery, and identify key confounders, mediators, and effect modifiers. We hypothesize that cataract surgery is associated with (1) reduction in incidence of new MCI and ADRD and (2) reduced cognitive decline and impairment progression among patients with baseline MCI or ADRD, and that (3) we will be able to predict individual patient outcomes. We propose to use methods our group has developed to archive and analyze electronic health record (EHR) data, to develop a curated data set and achieve three aims: (1) determine impact of cataract surgery on ADRD and MCI incidence; (2) determine impact of cataract surgery on cognitive decline and impairment among patients with baseline ADRD or MCI, and (3) develop patient-level predictive models for ADRD and MCI outcomes after cataract surgery.
Impact
EHR-based machine learning analysis has not been applied to ADRD research to date, and the influence of cataract surgery on cognitive outcomes is not yet known. Finding that a widely-available cataract surgery intervention improves cognitive outcomes would be transformative. We estimate a potential unmet need for cataract surgery affecting almost 350,000 patients annually—just among the subset of patients with existing Alzheimer's Disease. Results from this work will directly inform discussion of cataract surgery risks and benefits and will also facilitate future research, including pragmatic clinical trial design. By developing and disseminating open-source EHR-based algorithms to identify and classify cognitive and visual impairment, this proposal will enable investigation of other ADRD risk factors and interventions, eye disease research, and a more precise approach to managing individual patients.
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
Palo Alto,
California
943033225
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 283% from $793,778 to $3,042,048.
The Leland Stanford Junior University was awarded
Predicting Cataract Surgery Impact on Alzheimer's Disease Cognitive Decline
Project Grant R01AG072582
worth $3,042,048
from National Institute on Aging in September 2022 with work to be completed primarily in Palo Alto California United States.
The grant
has a duration of 4 years 8 months and
was awarded through assistance program 93.866 Aging Research.
The Project Grant was awarded through grant opportunity Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01 Clinical Trial Optional).
Status
(Ongoing)
Last Modified 8/20/25
Period of Performance
9/1/22
Start Date
5/31/27
End Date
Funding Split
$3.0M
Federal Obligation
$0.0
Non-Federal Obligation
$3.0M
Total Obligated
Activity Timeline
Transaction History
Modifications to R01AG072582
Additional Detail
Award ID FAIN
R01AG072582
SAI Number
R01AG072582-2810065290
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NN00 NIH National Insitute on Aging
Funding Office
75NN00 NIH National Insitute on Aging
Awardee UEI
HJD6G4D6TJY5
Awardee CAGE
1KN27
Performance District
CA-16
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
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) | $1,563,104 | 100% |
Modified: 8/20/25