Tuesday, April 23, 2024
4/23/2024
About 6M Americans =65 years suffer from Alzheimer's disease and related dementias (AD/ADRD) which poses significant emotional, physical, and financial burden on patients and their families, and costs the US economy over $250 billion annually. AD/ADRD is a growing national public health crisis as the number of Americans =65 years is projected to double by 2050. There are no approved disease-modifying drugs for AD/ADRD, and no new symptom-modifying drug has been approved since 2003. This highlights the need for novel research approaches aimed at preventing AD/ADRD. Physical activity (PA) has been identified as the lifestyle intervention with the strongest potential to prevent AD/ADRD. However, most of the randomized controlled trials (RCTs) of PA including the 23 ongoing RCTs currently sponsored by the NIA are small with short follow-up. Larger RCTs with longer follow-up are needed to provide definitive evidence of benefit. Identification of relevant biomarkers and quantification of future risk can mitigate long duration and cost of such RCTs. Epidemiologic studies with large database and long follow-up are limited by imprecise and unreliable self-reports of PA. We propose the novel concept of using cardiorespiratory fitness (CRF) as a reliable biomarker of PA for AD/ADRD risk reduction. Like body composition, blood pressure and heart rate, CRF is considered a biomarker of PA. Just as well-controlled blood glucose leads to optimal HbA1C, PA of adequate duration, frequency and intensity also leads to optimal CRF. The current application has been designed to test the hypotheses that higher CRF is independently linked to a lower risk of incident AD/ADRD and to determine optimal levels of CRF assessed by a standardized exercise tolerance test (ETT), for minimizing AD/ADRD risk at both population and individual levels. Our Specific Aims are (1) to determine the relationship between CRF and incident AD/ADRD, taking into consideration a non-linear relationship and potential interactions of CRF with other risk factors and (2) to define incremental CRF levels that are linked to progressively lower risk of AD/ADRD, overall, and in subgroups by age, sex, and race. Our Aim 3 is to develop and validate a deep learning-based risk prediction model to determine the optimal CRF level for individuals to achieve the lowest risk of AD/ADRD. Recent advances in deep (machine) learning, a key artificial intelligence (AI) technology, allow modeling of complex non-linear relationships necessary for more precise risk prediction. These aims will be achieved by using ETT data on 911,698 Veterans free of baseline AD/ADRD (mean age, 61 years, 54,411 women, 202,000 African American, 50,107 Hispanic Americans) with up to 19 years of follow-up data. Explainable deep learning risk prediction model will be developed in the VA data, and validated with a Medicare population to develop a “precision medicine” approach that will personalize CRF recommendations for individuals to lower their AD/ADRD risk. Future studies will incorporate molecular biomarkers to validate AD diagnosis and ascertain effect of CRF and CRF modification on AD risk.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
