# Statistical Models and Mechanisms Linking Biomarkers of Aging to Cognitive-Physical Decline and Dementia > **NIH NIH RF1** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $2,224,522 ## Abstract Dementia affects over 44 million adults worldwide, and Alzheimer’s disease (AD) and related dementias (ADRD) account for 60%-80% of all cases among older adults. Physical disability is often the final consequence of dementia before death. One-third of dementia cases may be attributable to modifiable factors, and due to unclear benefit of AD treatments, there is a need to identify intervention targets to prevent dementia and physical disability. Since both conditions may be preceded by poor cognitive and physical performance by over a decade, shared biological determinants of dual cognitive-physical decline that impact neurological and musculoskeletal systems may inform therapeutic targets to prevent dementia and physical disability. The geroscience hypothesis posits that targeting the biology of aging may better impact human health, including prevention of dementia and physical disability, than targeting specific diseases. Indeed, separate lines of research on cognitive and physical endpoints indicate that biomarkers reflecting the underlying biology of aging are related to both cognitive and physical decline. This work includes biomarkers of inflammation and hallmarks of aging such as cell senescence, altered cell communication, epigenetic changes, telomere attrition, nutrient signaling, and loss of proteostasis. However, epidemiologic studies have not rigorously investigated whether biological mechanisms of aging affect relations and dynamics between cognitive and physical decline or dementia and physical disability onset. Thus, identifying early biomarkers of biological aging mechanisms that are related to dual cognitive-physical decline and joint dementia-disability onset in initially health older adults is a key step toward geroscience-guided prevention trials. However, studies of longitudinal cognitive and physical endpoints are vulnerable to survival bias and unmeasured confounding. Limitations of extant statistical methods are a key barrier to accurately identifying biomarkers of shared biological mechanisms that may affect or predict cognitive and physical endpoints. Thus, new computational models are needed to overcome these barriers. Specific aims of this proposal are to: 1) test relations of biomarkers of aging with longitudinal dual cognitive-physical decline; 2) test relations of biomarkers of aging with time to incident joint dementia-disability onset; and 3) develop/validate a biomarker of aging risk score to predict joint dementia-disability. To this end, we propose a biological aging index and novel computational models for multivariate longitudinal and time-to- event outcomes and to apply them to harmonized data from 8 cohort studies of >11,000 community-dwelling adults aged at least 65 years with measured biomarkers. We hypothesize that biomarkers of aging predict and explain, in part, relations between cognitive and physical endpoints beyond known risk factors. New computa- tional models developed as essential tools to jointl... ## Key facts - **NIH application ID:** 10513438 - **Project number:** 1RF1NS128360-01 - **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE - **Principal Investigator:** Michelle Denise Shardell - **Activity code:** RF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $2,224,522 - **Award type:** 1 - **Project period:** 2022-08-18 → 2025-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10513438 ## Citation > US National Institutes of Health, RePORTER application 10513438, Statistical Models and Mechanisms Linking Biomarkers of Aging to Cognitive-Physical Decline and Dementia (1RF1NS128360-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10513438. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*