Expired NOT-AG-23-020: Notice of Special Interest (NOSI): Maximizing the Scientific Value of Secondary Analyses of Existing Cohorts and Datasets in Order to Address Research Gaps and Foster Additional Opportunities in Aging Research

Notice of Special Interest (NOSI): Maximizing the Scientific Value of Secondary Analyses of Existing Cohorts and Datasets in Order to Address Research Gaps and Foster Additional Opportunities in Aging Research

Notice Number:

NOT-AG-23-020

Key Dates

Release Date:

May 9, 2023

First Available Due Date:

June 05, 2023

Expiration Date:

May 08, 2024

Related Announcements

August 23, 2021 - Notice of Special Interest (NOSI): Maximizing the Scientific Value of Secondary Analyses of Existing Cohorts and Datasets in Order to Address Research Gaps and Foster Additional Opportunities in Aging Research. See Notice NOT-AG-21-020.
May 07, 2020 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed). See NOFO PA-20-195.
May 05, 2020 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed). See NOFO PA-20-185.

Issued by

National Institute on Aging (NIA)

Purpose

The goal of this Notice of Special Interest (NOSI) is to encourage the use of existing cohorts and datasets for well-focused secondary analyses to investigate novel scientific ideas and/or address clinically related issues on: (1) aging changes influencing health across the lifespan (e.g., Alzheimer’s disease and Alzheimer's disease-related dementias (AD/ADRD)), (2) diseases and disabilities in older persons, and/or (3) the changes in basic biology of aging that underlie these impacts on health (the hallmarks of aging). Activities of high priority include those addressing specific hypotheses in basic biological research, clinical aging research, behavioral or social research, and/or translational geroscience to inform: the design and implementation of future epidemiologic or human intervention studies; interventions in animal models of aging; research on behavioral and social factors over the life course that influence health (e.g., early life adversity); current geriatric practice in maintenance of health, disease management, and prevention of disability; or research testing of possible causal relationships between rates of aging and findings extracted by secondary analysis of the existing data. Existing datasets may also be used to develop and test new mathematical modeling and statistical analytical approaches. Analyses of sex and/or gender differences across health disparity groups (e.g., racial and ethnic groups, socioeconomic status, and sexual and gender minorities) are of high relevance. Use of cohorts that are linked to electronic health record systems and/or Centers for Medicare and Medicaid Services (CMS) administrative data are especially welcome.

Applicants responding to this NOSI are strongly encouraged to describe plans for rapid sharing of data and results as well as innovative data analytics approaches (see Goal 3, NIH Strategic Plan For Data Science). Such information should be included in the Data Management and Sharing Plan.

Please note that applications proposing exploratory or developmental projects should consider using PA-20-195, whereas projects that already have sufficient preliminary data or a very strong and well-developed scientific premise should use PA-20-185.

Background

Biomedical research projects typically generate data with potential utility beyond the specific hypotheses and questions for which they were originally designed. Considerable potential exists in using secondary analyses of available clinical datasets and collections of biospecimens and related study data to explore and test hypotheses about how biological changes affect health across the life span, including disease and disability in old age. These types of analyses are also a cost-effective means to inform the design of future studies in clinical aging research.

Information collected through health administration activities (e.g., electronic health records, health insurance claims data, etc.) may also provide a rich source of data relating to health maintenance and disease prevention practices; the incidence and prevalence of chronic conditions and diseases (including multiple morbidities); patterns of geriatric care and medication use; health outcomes (including disabilities); resource utilization (including comparative effectiveness research); and economic impact. Administrative datasets have potential advantages in terms of availability; large size and representativeness; breadth of medical and demographic information; localization of care (e.g., community, hospital, and long-term facilities); and suitability for linkage with other datasets. In some settings, such as disease prevention or screening programs, results of clinical studies may also be available.

This NOSI will support secondary analyses focused on the following:

Addressing specific hypotheses in clinical aging research and/or informing the design and implementation of future epidemiologic or human intervention studies, or current geriatric practice in maintenance of health, management of disease, and prevention of disability.
Investigating behavioral and social factors that influence morbidity, mortality, and AD/ADRD over the life course, such as early life adversity, health systems, institutional factors, macro-social change (e.g., economic shocks, demographic shifts, etc.), behavioral resilience, and mechanisms of behavior change and behavioral interventions.
Using datasets, biospecimens, and related data associated with epidemiological, family-based, case-control, and other types of investigations for genetic and genomic studies.
The molecular, genetic, cellular, and physiological mechanisms underlying aging and age-related changes in humans and other organisms across numerous phyla.

This NOSI encourages applicants to use datasets as well as biospecimens and related data from a variety of sources, including:

Those supported through investigator-initiated research activities, cooperative agreements, and contracts from public or private sources.
Datasets whose aims were not intentionally aging-focused, but contain aging-relevant phenotypes.
Longstanding observational cohorts, with periodic assessments and/or biospecimens, investigating a broad range of risk/protective factors or pathophysiological mechanisms related to chronic diseases of the elderly (e.g., AD/ADRD) or to functional and cognitive decline in aging.
Observational cohorts assembled to address a specific question and followed prospectively over time with periodic assessments.
Genome-wide association data, large-scale sequencing data, and functional genomics data with associated clinical and endophenotypic data, including analyses of harmonized phenotypic data (e.g., electronic health record and clinical data associated with Alzheimer’s disease genetic data); approaches using artificial intelligence, machine learning, and deep learning approaches are encouraged.
Family-based studies (e.g., observational studies of siblings, parent-offspring, and twin pairs).
Cohorts from large clinical trials testing disease prevention or screening interventions (e.g., WHI), behavioral or medical interventions targeting disease management, or multi-component interventions that aim to ameliorate geriatric conditions or disability trajectory.
Data from clinical trials.
Groups identified by disease or non-disease status for comparison analyses (e.g., hip fracture patients) either retrospectively for risk factors (e.g., case-control studies) or prospectively for health-related outcomes.
Groups defined by administrative databases to explore specific hypotheses regarding aging changes across the lifespan or diagnosis and management of medical conditions common among older people (e.g., CMS data, managed and/or accountable care organization data, health insurance databases, electronic health records, etc.).
Specialized datasets collected to address specific questions (e.g., FDA Critical Path Initiative drug databases to evaluate analgesic efficacy).

Investigators are also encouraged to augment existing datasets as appropriate and feasible to address their study hypotheses. For example, new analyses performed on existing electronic linkage with or among administrative databases could increase the utility of the data for answering relevant clinical questions. Comparison between existing databases or within merged datasets (e.g., young adults with mid-life, mid-life with older adults, etc.) is also encouraged as appropriate to address the study question, especially to address specific research hypotheses related to aging changes across the lifespan.

Costs for archiving of data to be made publicly available may be included in the budget as long as the archival activities are related to the proposed secondary analyses. Plans for creation of a publicly archived database must include adequate dataset documentation and instructions for use by investigators not affiliated with the original study. Provision for easy accessibility of archived datasets is required. Applications requesting inclusion/archiving of datasets in the AgingResearchBiobank are welcome and should follow the process outlined at https://agingresearchbiobank.nia.nih.gov/.

Potential applicants may also wish to review the following links to identify observational cohorts or clinical trials from which valuable data and/or biospecimens may be analyzed to address clinical aging research questions:

Applicants are responsible for adhering to the individual study policies governing ancillary projects and access to clinical trials data and/or biorepository samples. The application must include a letter of support from the relevant study committee granting access to the datasets and/or repository (if the application involves analysis of stored samples) and approval of the proposed secondary analysis study.

Research Objectives:

Secondary analyses of existing cohorts, datasets, or collections of biospecimens and related study data may include, but are not limited to, the following:

Genetic, physiological, biological, psychological, behavioral, social, and environmental factors that affect aging changes (e.g., endocrine, musculoskeletal health, reproductive aging) at different points in the lifespan, including factors contributing to healthy aging and prevention or slowing of common adverse age-related changes.
Factors that affect the rate of aging across the lifespan, including those contributing to healthy or accelerated aging and/or prevention or slowing of common adverse age-related changes. Whether in the periphery or the central nervous system, this may include: (1) psychological, behavioral, social or environmental factors; (2) endocrine and/or exocrine factors; (3) microbiomes; and (4) other molecular targets (e.g., alleles, epigenomics, metabolomics, etc.)
Research on social, cultural and behavioral factors over the life course, such as early life adversity, education, institutional factors, employment, and health behaviors, using longitudinal models to explain how these factors influence cognitive aging and dementia, the aging process, and morbidity and mortality.
Research to elucidate the social and behavioral mechanisms (e.g., stress, socioeconomic status, race/ethnic status) that produce disparate life expectancy, AD/ADRD, and morbidity or disability.
The development of integrative methodologies to discover complex predictive profiles incorporating multiple data types, such as biological, clinical, social, and behavioral data.
Combinations of data that might be linked to the hallmarks of aging or reveal interactions among the hallmarks of aging.
Research on the biological processes that determine rates and heterogeneity of aging, and how external factors might interact with these mechanisms.
Genomic studies in diverse populations to better define criteria for diagnosis; polygenic risk scores; gene clusters; pathway analysis; endophenotypes; patterns of genetic variation; disease risk and progression; disease heterogeneity, and genetic architecture.
Identify features in the data that would allow for stratification of populations in order to reveal differential rates of aging or that could be used as biomarkers for rates of aging.
Find trends within the data that could suggest specific causes of faster versus slower rates of aging.
Topics especially relevant to uncovering potential causes of health disparities or female/male differences in rates of aging.
Differences in risk factors for age-related conditions at different ages, at different stages of disease progression, and in the presence or absence of co-existing conditions.
Complex effects of sustained reduction in caloric intake and/or related dietary practices in humans on protective and risk factors for aging processes and diseases associated with aging.
Physiologic, cellular, and molecular mechanisms (e.g., genetic, etc.) of sustained caloric restriction in humans.
Long-term health effects of interventions that are administered over a large part of the life span (e.g., antihypertensives, lipid lowering agents, analgesics).
Long-term effects of physical activity and lifestyle on health and functional outcomes throughout the life span.
Factors contributing to decline in functional status, development of disability, and loss of independence that are potential targets for interventions (1) in different care settings (e.g., community/outpatient, acute care/hospital, long-term care facilities) and transitions between settings, and (2) in sub-groups of older adults with special needs (e.g., cognitive impairment, vision or hearing loss, economic hardship).
Effects of specific combinations of two or more comorbid conditions or combinations of medications on risks for specific beneficial and/or adverse health outcomes. Examples include, but are not limited to, issues related to public health and cost impacting specific disease combinations; differences in health outcomes as they relate to alternative treatments and/or health care management strategies; and medication interactions, disease progression, and health outcomes in complex multimorbid older adults.
Research on social and behavioral resilience and protective factors (e.g., social support, spirituality) that produce increased life expectancy, successful cognitive aging, and lower morbidity or disability.
Research with a broad scientific scope on psychological, behavioral, and interpersonal processes of relevance to disparate life expectancy, AD/ADRD, and morbidity and disability.
For translational geroscience, evidence that might connect specific hallmarks of aging to late-life functional decline or increase of comorbidities.
Validation, revision, or development of new phenotypes for geriatric syndromes and conditions in older populations and population subgroups.
Occurrence and management of geriatric complaints, syndromes, or multi-factorial problems (e.g., fatigue, pain, frailty, unexplained anemia, urinary incontinence, falls, mobility disorders).
Potential differential effects of interventions due to age, race, gender, or other factors through subgroup analyses of data from completed clinical trials.
Exploratory analyses, including assays on stored biospecimens, to explore effects of interventions on additional outcomes.
Analysis and meta-analysis of existing data sets to inform designs of future clinical trials (e.g., to determine prevalence of a disease or condition, such as Alzheimer’s disease and related dementias, or a combination of conditions in a population of interest; to estimate effect size of an intervention and duration of treatment in a population of interest, etc.)
Analysis or meta-analysis of existing datasets and other data resources to address risk and progression of Alzheimer’s disease and related dementias from the preclinical to the clinical state.
Research on mechanisms of behavior change and behavioral interventions, cognitive and emotional functioning, behavior genetics and sociogenomics, technology and human factors, family and interpersonal relationships, and integrative biobehavioral research on the mechanistic pathways linking social and behavioral factors to health in midlife and older age.
Research on the impact of health care services, the health care system, and long-term supports and services (LTSS), including organizational influences, on the health and well-being of older persons with chronic disease, disability, and AD/ADRD, and on their care providers.

Methodology development. Single or multiple data sets may be used to develop and test new analytic approaches for any of the above topics. Additionally, methodological studies may also address inferential issues in observational data on treatment effects, such as confounding by indication, including the following:

Pooling and harmonizing data across multiple NIA-supported studies to identify socioeconomic mediators of health disparities in racial/ethnic minority populations, including analyses by gender, socioeconomic status, and age.
Research addressing the unique challenges related to access to health services and utilization among older adults.
Estimation of the economic burden of diseases affecting older adults including their formal or informal caregivers, especially with respect to disparities.

Application and Submission Information

This Notice applies to due dates on or after June 5, 2023 and subsequent receipt dates through May 08, 2024.

Submit applications for this initiative using one of the following notices of funding opportunities (NOFOs) or any reissues of these announcements through the expiration date of this Notice.

PA-20-185 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)
PA-20-195 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed)

All instructions in the SF424 (R&R) Application Guide and the funding opportunity announcement used for submission must be followed, with the following additions:

Applicants must include “NOT-AG-23-020" (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered responsive and will not be reviewed.
Applications will not be considered responsive and will not be reviewed if they do not include an explanation of the proposed research's relevance to the field and a description of the potential impact of research results, particularly regarding affects to clinical practice.
Applications will not be considered responsive and will not be reviewed if dissemination plans for study results are not addressed.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

Inquiries

Please direct all inquiries to the contacts in Section VII of the listed notice of funding opportunity with the following additions/substitutions:

Scientific/Research Contact(s)

General inquiries and questions about clinical research related to health and disease in the aged, research on aging over the human lifespan including its relationships to health outcomes, and research on health disparities, as well as inquiries related to availability of resources from the AgingResearchBiobank on specific study collections funded by NIA or related to inclusion/archival of datasets resultant from studies conducted under this NOSI should be directed to:

Rosaly Correa-de-Araujo, MD, MSc, Ph.D.
AgingResearchBiobank
Division of Geriatrics and Clinical Gerontology (DGCG)
National Institute on Aging
Telephone 301-496-6762
Email: [email protected]

Inquiries related to research defining cellular and molecular pathways that impact rates of human aging or age-related processes that contribute to healthy aging or disease should be directed to:

Yi-Ping Fu, Ph.D.
Division of Aging Biology (DAB)
National Institute on Aging (NIA)
Telephone: 301-496-6009
Email: [email protected]

Questions related to research on neuroscience and aging, cognitive changes and Alzheimer's disease and related dementias should be directed to:

Dallas W. Anderson, Ph.D.
Division of Neuroscience (DN)
National Institute on Aging (NIA)
Telephone: 301-402-6693
Email: [email protected]

Inquiries concerning proposed work in behavioral and social research on aging, including cognition, psychosocial and sociodemographic factors, long-term care, caregiving, behavioral medicine, retirement, economic status, and well-being over the life course, should be directed to:

Frank Bandiera, Ph.D.
Division of Behavioral and Social Research (DBSR)
National Institute on Aging (NIA)
Telephone: 301-496-3131
Email: [email protected]

Financial/Grants Management Contact(s)

Ryan Blakeney
National Institute on Aging (NIA)
Phone: 301-451-9802
E-mail: [email protected]