Description
On September 28 - 29, 2023, the National Heart, Lung, and Blood Institute (NHLBI), National Cancer Institute (NCI), National Institute on Aging (NIA), National Institute of Neurological Disorders and Stroke (NINDS), and the Office of Dietary Supplements (ODS) convened a 2-day virtual workshop open to the public on the topic: “Health and Aging Trajectories: Shared and Competing Risks & Resiliencies for Chronic Diseases Associated with Aging”.
The conference assembled experts from multiple disciplines to discuss our current understanding of risk factors and processes associated with distinct aging trajectories driven by different major chronic diseases, which hallmarks may predict onset of disease versus disease outcomes, and how this knowledge may help identify strategies for intercepting, preventing, and reducing the risk of age-related chronic diseases and extending the healthspan.
Four scientific sessions were prefaced by introductory remarks by National Institutes of Health (NIH) Leadership from the NHLBI Division of Cardiovascular Sciences, NCI Division of Cancer Prevention, NINDS Division of Neuroscience, and NIA Division of Aging Biology and invited keynote lectures.
The scientific presentations and panel discussions were centered around the following major topics:
- Do we age differently? How the aging process can increase the risk for diseases such as cancer, cardiovascular, and neurodegenerative diseases.
- Molecular and cellular mechanisms of chronic age-related diseases.
- Identifying and managing shared and competing disease risks.
- Quo Vadis: increasing resilience, preventing, and intercepting disease outcomes.
All the sessions recordings are available via the NIH Videocast:
Day 1 - Sept 28th, 2023: https://videocast.nih.gov/watch=51101
Day 2 - Sept 29th, 2023: https://videocast.nih.gov/watch=51103
Program Booklet
Background
By the year 2050, approximately 16% of the world’s population will be over 65 years, and life expectancy for men and women in developed countries is expected to average 80 and 83 years, respectively. These gains in longevity have not been followed by enhancements in healthspan, especially for those from diverse and disadvantaged backgrounds.
The reasons for the uncoupling of life- and healthspan are not fully understood and represent an opportunity to identify factors driving divergent aging trajectories. Each person’s biological aging path is unique and shaped by multiple factors: genetics, the internal and external (i.e., exposome) environment, epigenetics, lifestyle choices, and social and psychosocial determinants. Identifying features shared by aging processes and chronic age-related diseases such as cardiovascular disease (CVD), cancer, and neurocognitive disorder, including Alzheimer’s disease (AD) may identify strategies for early detection and intervention that intercept, delay the onset, reduce disease severity, and promote healthier aging.
Summary of Discussions
Do we age differently?
Age is the major risk factor for chronic diseases such as cancer, CDV, or AD, that may lead to divergent individual aging trajectories or can also coexist in individuals with multiple morbidities. As opposed to chronological aging, biological aging is influenced by genetics and the exposome, including lifetime exposures to environmental influences (pollution, climate change), and personal lifestyle factors (diet, exercise, sleep, and stress).
Body-wide molecular and cellular mechanisms of chronic age-related diseases
Aging processes are inextricably linked to the emergence of age-related chronic diseases.
Senescence: Cellular senescence is a cardinal feature of aging whereby anti-apoptotic mutations drive cells to survive but not divide. Senescent cells contribute to inflammation, cellular dysfunction, and disease in all body systems. In the central nervous system, cellular senescence can contribute to structural (brain) and functional (cognitive) aging, while in other tissues senescence can shift the balance toward carcinogenesis. Cellular senescence is also associated with decreased resilience and shorter lifespan in individuals, as exemplified by decreased survival following COVID-19 infection. Senolytics, compounds that selectively clear senescent cells, are increasingly being tested as interventions for age-related chronic diseases.
Resilience: Resilience is an important concept in health and aging trajectories that comprises three interconnected domains: psychosocial, physical, and cognitive, and relates to the ability to withstand corresponding stressors. Resilience declines with age, but it is modifiable through lifestyle modifications (diet, exercise, sleep, social connection), making it an attractive intervention target.
Gut microbiome: The gut microbiome mediates body’s interaction with its environment influencing various bodily functions, from metabolism and digestion to immunity, mood, and cognition, and has emerged as a key modulator of health and lifespan. Microbiome composition changes in response to diet, medication, illness, and environmental stimuli. Microbiome stability and signaling efficiency diminish with age, contributing to organ dysfunction, loss of gut integrity, inflammation, and neurocognitive deficits. The microbiome responds to diet, environment, and medication, and could be targeted in age-related diseases.
Stress and Immunity: The cellular responses to acute and chronic stress often initiate in the brain, where neurotransmitters and chemokines activate the release of immune cells from the bone marrow. Immune cells are mediators of homeostasis, inflammation, or atherosclerosis. The age-related waning of immunity and morbidity are mechanistically linked. Clonal hematopoiesis, where somatic mutations accumulate in hematopoietic stem cells, can drive cancer, CVD, stroke, chronic obstructive pulmonary disease (COPD), or AD.
Psychome: Brain-body circuits also dictate many of the interactions between lifestyle and biological aging. The psychome (all psychological traits, characteristics, cognitive processes, motivations, and emotional responses) can potentially influence multiple biological aging processes. Identifying psychome-associated biomarkers may unveil the substantial impact of depression and loneliness on age-associated morbidity and inform our understanding of the role of resilience in longevity and healthspan.
Endothelial dysfunction: Endothelial dysfunction is a prominent feature of age-related metabolic changes. Arterial stiffness, fibrosis, smooth muscle cell proliferation, inflammation and oxidative stress are major contributors to body-wide vascular and organ dysfunction associated with CVD, cancer, and degenerative diseases, including vascular dementia, that alter aging trajectories.
Cerebrovascular changes: Age-related vascular changes in the brain can contribute to decreased cognitive function and concomitant brain volume losses, and appear to affect women preferentially. Cerebral hemodynamics may be an early marker of brain aging. Early diagnosis and intervention in mild cognitive impairment (MCI) may prevent further age-related cognitive changes and dementia. Combining biomarkers such as plasma amyloid and tau with neuroimaging may offer insights into age-related neurocognitive trajectories and the effects of risk factors such as hypertension, APOEe4 genotype, sex, physical activity, stress, and inflammation on age-related health outcomes.
Malignant transformation: The transition from a normal to a malignant cellular phenotype, or somatic evolution theory, offers a mechanistic bridge between age-associated chronic diseases and carcinogenesis. With age, DNA damage response pathways become less efficient and permissible of somatic mutations. Together with age-associated immune dysregulation, mutation-driven clonal expansion can manifest as cancer, atherosclerosis, pulmonary fibrosis, or other chronic diseases. Aging and cancer are heterogeneous processes that share several features, including genomic instability, metabolic derangements, telomere alterations, and senescence. Understanding these manifestations will support development of tailored prevention, diagnosis, and treatment strategies.
Identifying and managing shared and competing disease risks
Prevalence of concurrent age-related conditions in relation to social disparities: Many age-related diseases share common risks and can be present as multimorbidity (MM) needing to be addressed in the same individual. MM is more prevalent in minorities and disadvantaged populations. African Americans and Hispanics are more likely to develop MMs and pass away from them. The high rates of severe disease and death among older adults with COVID-19 further exemplified the increased vulnerabilities associated with MM and health disparities. African Americans also experience higher rates of neurodegenerative diseases that are diagnosed earlier and are further accelerated when both the sickle cell trait (SCT) and the APOEe4 allele are present. Effect of lifestyle interventions found to be protective for cognitive impairment as well as other age-related conditions were addressed in the final session.
Quo Vadis: increasing resilience, preventing, and intercepting disease outcomes
Resilience relates to a system’s ability to withstand stressors, broadly studied as molecular/cellular, physiologic, psychosocial and spiritual, and environmental/community resilience. Resilience declines with age, but numerous experimental and clinical studies show that lifestyle modifications, including integrating information from using wearable devices, show that lifestyle modifications such as exercise, increased sleep, weight loss, and mindfulness meditation improve measures of resilience and resilience and health outcomes for age-related chronic conditions. Lesser studied interventions, such as providing stable health insurance and social safety nets, also have shown the potential benefit of pleiotropic interventions. Such interventions might be especially valuable in disadvantaged communities.
Advances in early detection and personalized care are fueled by new health technologies including wearable devices that can continuously monitor multiple individual health parameters, integrating each person’s unique combination of genetic, epigenetic, environmental and lifestyle exposures. Training machine learning (ML), artificial intelligence (AI), and large language models (LLMs) tools across large longitudinal population data sets can optimize their potential to predict health outcomes. By linking genes to diseases and identifying drugs and drug targets, these advances hold the promise to enable prediction of individual disease risks and aging trajectories, and the possibility to intercept and intervene in age-related diseases.
Knowledge Gaps and Opportunities
Workshop participants identified the most pressing knowledge gaps and opportunities and considered ways to stimulate collaborations among researchers studying aging and age-associated disorders in order to advance the field and increase healthspan.
- Aging research demands proactive interdisciplinary strategies to collectively address the multiple aspects and concomitant risks of age-related diseases, including data collection from clinical longitudinal studies.
- Development of consensus definitions of biological aging, senescence, and resilience, in addition to identification of reliable biomarkers, shared aging models, approaches and standard measures that capture relevant diagnostic data (including data from wearables and mobile apps) is necessary to generate reliable and comparable data to inform individual and population-level aging interventions.
- Provide longitudinal data and a lifespan perspective that captures the temporal and temporospatial features of sex and age-related changes and stressors at the cellular, organ, and system-wide level to increase healthspan.
- Include the psychome and exposome as demographic characteristics in aging research.
- Determine optimal testing time and frequency to maintain optimal health trajectories.
- Develop consensus on best practices on how to measure, contextualize, and manage multimorbidities to improve health outcomes and reduce cardiovascular, neurodegenerative, and cancer/cancer-related health disparities.
- Focus on key cell types responsible for driving several age-related diseases.
- Elucidate how inflammation and immune dysfunction act as primary mediators of various aging trajectories and age-related diseases.
- Assess the intestinal barrier as a potential driver of chronic inflammation.
- Define the contribution of microbiome balance on homeostasis, in addition to the role of dysbiosis in aging, frailty, and chronic age-related diseases, and assess the microbiome as a modifiable therapeutic target
- Promote research to understand how the biology of aging influences the biology of cancer and carcinogenesis pathways. Develop biomarkers with clinical significance to evaluate and stratify cancer risk and develop new methods for cancer prevention and interception.
- Fine-tune the diagnosis of mild cognitive impairment to enable interventions that can potentially alter or slow the progression of dementia.
- Distinguish to what extent does accumulating pathology account for “age-related changes” that become risk factors for neurodegenerative disorders, cancer, and CVD.
- Assess the impact of stress and the resultant cumulative damages it induces by identifying relevant biomarkers and signaling pathways to advance preventive strategies that target age-related diseases.
- Identify safe and effective senotherapeutics/senolytics and their combination with lifestyle interventions as well as optimal intervention timing that improve resilience, reduce inflammation, improve stem cell function, extend healthspan, and overall delay, prevent, alleviate, or treat age-related diseases.
- Develop novel safe and effective interventions that enhance resilience.
- Explore effects of lifestyle changes at different life stages (e.g., changing metabolic needs, different sleep needs/patterns, capacity to exercise) on the development of age-related diseases, which populations may best benefit, and ascertain the most favorable time to intervene and promote sustainable behavior modification for lasting health benefits and disease prevention.
- Enhance representation of groups previously underrepresented or excluded in aging clinical trials such as minorities, marginalized groups, old and very old adults, and individuals with multiple morbidities, including cancer, heart disease, and AD. Explore population diversity, as well as social determinants of health (SDOH) factors influencing age-related disease trajectories.
- Leverage the use of advanced technologies such as more accessible wearables to collect longitudinal data that helps develop personalized strategies to prevent age-related diseases.
- Apply machine learning (ML), artificial intelligence (AI), and large language models (LLMs) on large, representative data sets to fill knowledge gaps and accelerate translational research to support optimal health trajectories.
- Develop precision medicine methods specific to an individual’s health profile that provide personalized approaches and include early identification, disease modulation, and intervention in age-related disorders.
Conclusion
As the global population ages, closing the gap between lifespan and healthspan will require development of a paradigm in which the various aging trajectories will be predictable, and thus become preventable or intercepted during early stages of disease development. Understanding the fundamental factors that govern homeostasis along the lifespan and the internal and external factors that perturb it may uncover new diagnostic biomarkers and therapeutic targets. Incorporating a longitudinal, temporal context in clinical investigations is likely to reveal windows of opportunity for prevention. Leveraging advanced technologies to develop richer, more representative datasets across the lifespan will accelerate discovery and clinical translation. Finally, accomplishing the rigorous research agenda proposed in this workshop will require collaboration across multiple disciplines to address the complex problem of managing age-related diseases with early interception of predicted aging trajectories. Ensuring that the voices of patients and previously underrepresented groups are heard at all stages of the research process must take priority, as must committing to translating actionable discoveries from bench to bedside.
Workshop Participants
Workshop Organizing Committee:
Zorina S. Galis, Ph.D., Co-chair, NHLBI
Gabriela Riscuta, M.D., C.N.S., Co-chair, NCI
Julia Berzhanskaya, Ph.D., NHLBI
Arya Biragyn, Ph.D., NIA
LaVerne Brown, Ph.D., ODS
Svetlana Kotliarova, Ph.D., NINDS
Klauzinska Malgorzata, Ph.D., NCI
Ilsa I. Rovira, M.S., NHLBI
Janine Simmons, M.D., Ph.D., NIA
Anil Wali, Ph.D., NCI
Dan Xi, Ph.D., NCI
Ronit Yarden, Ph.D., M.H.S.A., NHLBI
Opening Remarks (Day 1):
David C. Goff, Jr. M.D., Ph.D., F.A.C.P., F.A.H.A. (NHLBI) & Philip E. Castle, Ph.D., M.P.H. (NCI)
Opening Remarks (Day 2):
Lyn Jakeman, Ph.D. (NINDS) & Ronald A. Kohanski, Ph.D. (NIA)
Keynote Speakers:
Luigi Ferrucci, M.D., Ph.D., NIA
Michael Snyder, Ph.D., Stanford University
Christoph Kaleta, Ph.D., Kiel University
Session co-chairs:
Session 1: Malgorzata Klauzinska, Ph.D. (NCI) & Arya Biragyn, Ph.D. (NIA)
Session 2: Svetlana Kotliarova, Ph.D. (NINDS) & Anil Wali, Ph.D. (NCI)
Session 3: Janine Simmons, M.D., Ph.D. (NIA) & Ilsa I. Rovira M.S. (NHLBI)
Session 4: La Verne Brown, Ph.D. (ODS) & Gabriela Riscuta, M.D., C.N.S. (NCI)
Session Moderators:
Session 1: Michael Snyder, Ph.D.
Session 2: Louise McCullough, M.D., Ph.D.
Session 3: Karina Davidson, Ph.D.
Session 4: James DeGregori, Ph.D.
Speakers/Presenters:
Peggy Goodell, Ph.D., Baylor College of Medicine
Ashani T. Weeraratna, Ph.D., Johns Hopkins School of Medicine
Miranda E. Orr, Ph.D., Wake Forest University School of Medicine
Filip Swirski, Ph.D., Icahn School of Medicine at Mount Sinai
Tudor Oprea, M.D., Ph.D., University of New Mexico School of Medicine
James Kirkland, M.D., Ph.D., Mayo Clinic
Paul Robbins, Ph.D., University of Minnesota
Laura Niedernhofer, M.D., Ph.D., University of Minnesota
Sheila A. Stewart, Ph.D., Washington University in St. Louis
Edward G. Lakatta, M.D., NIA
Lisa Lesniewski, Ph.D., University of Utah
Christopher Hine, Ph.D., Cleveland Clinic
Patty J. Lee, M.D., Icahn School of Medicine at Mount Sinai
Louise D. McCullough, M.D., Ph.D., McGovern Medical School
Susan M. Resnick, Ph.D., NIA
Hyacinth I. Hyacinth, M.B.B.S., M.P.H., Ph.D., University of Cincinnati College of Medicine
Anda Botoseneanu, M.D., Ph.D., M.B.A., University of Michigan
Hannah Davis, Patient-Led Research Collaborative
Yonas E. Geda, M.D., M.Sc., Barrow Neurological Institute
Karina Davidson, Ph.D., Zucker School of Medicine & Northwell Health
Wendy Demark-Wahnefried, Ph.D., R.D.,
Peter M. Abadir, M.D., Johns Hopkins University School of Medicine
James DeGregori, Ph.D., University of Colorado Cancer Center
Jill N. Barnes, Ph.D., University of Alabama at Birmingham
Jennifer A. Schrack, Ph.D., Wisconsin School of Medicine and Public Health
Disclaimer: The findings, knowledge gaps, and opportunities described here represent a summary of individual opinions and ideas expressed during the workshop. The summary does not represent a consensus opinion or directive made to or by NHLBI or NIH.