By combining multi-omics, deep phenotyping, and AI, we uncover the biological patterns that shape healthspan—so interventions can be personalized, predictive, and effective.
At Healthspan Horizons, we study how human biology changes over time—and why people age differently. Our scientific vision is to build a new foundation for proactive health by connecting multi-omics, physiology, digital biomarkers, and real-world behavior into a coherent, predictive picture of individual healthspan.
We capture a full picture of human biology, integrating molecules, systems, behaviors, and environment.
We model how biological networks shift across the lifespan to identify actionable, modifiable patterns.
We turn scientific discovery into practical guidance and interventions that improve everyday healthspan.
We combine multi-omics, physiological data, digital biomarkers, and AI-driven modeling to reveal early signals, modifiable pathways, and long-term health trajectories.
We build individualized models that track how a person’s biology changes over time.
By using “twin logic”—comparing people to their own predicted trajectory or to highly matched biological peers—we identify meaningful deviations from healthy patterns long before traditional methods can.
These digital twin systems allow us to test scenarios, quantify potential improvements, and personalize recommendations without requiring clinical interventions.
Our GAP engine compares a person’s genetic potential with their current biological reality to pinpoint the most modifiable opportunities for improving healthspan.
Using multi-omics, deep phenotyping, and AI, we identify where molecular, metabolic, or physiological markers diverge from expected levels—and which changes can yield the greatest impact.
This helps translate complex biology into clear priorities for proactive health.
Resilience reflects the body’s ability to respond to stress, recover, and return to equilibrium.
We quantify resilience across major systems—metabolic, cardiovascular, inflammatory, cognitive, and more—using dynamic biomarkers and longitudinal tracking.
These scores help identify hidden vulnerabilities, early declines, and the specific levers that can strengthen physiological reserve.
We estimate biological age using multi-omics signatures, molecular clocks, and system-level aging markers.
Beyond whole-body age, we model organ- and pathway-specific trajectories to understand how different systems age at different rates.
These insights reveal where a person is aging faster or slower—and how interventions can shift aging trajectories toward healthier patterns.
Our research powers two core systems at Healthspan Horizons. These engines translate deep biological insight into guidance, predictions, and intervention pathways that evolve with each individual.
Your personal guidance engine.
The Compass integrates multi-omics, biomarkers, and real-world signals to identify early priorities, surface emerging risks, and quantify potential improvements in healthspan.
Its N-of-1 modeling and digital twin simulations help map your best paths forward.
Our AI-powered scientific knowledge system.
The Synthesizer connects research, biological mechanisms, molecular signatures, and real-world outcomes into a unified framework.
It continuously learns from new data, helping discover modifiable pathways and generate evidence-based interventions at scale.
The Price Lab conducts team-driven research programs that apply systems biology and AI to understand—and ultimately extend—human healthspan.
This project addresses a foundational question in geroscience: when does inherited disease risk become biologically visible, and how does aging shape that expression? While polygenic risk scores (PRS) are typically treated as static predictors, we hypothesize that genetic risk is an age-dependent property that emerges through specific molecular pathways only at certain stages of life.
To test this, we integrate polygenic risk with high-dimensional multi-omic data using age-aware deep learning models. These models explicitly incorporate age alongside genetic risk, enabling counterfactual simulations in which PRS is increased and predicted downstream effects are read out across metabolites, proteins, and clinical biomarkers. By comparing alternative age-modeling strategies, we identify which genetic effects are age-independent versus those that emerge through aging biology.
The scientific aim is to disentangle inherited liability from aging-driven physiology and to determine when genetic risk becomes actionable. This work supports earlier, mechanism-informed detection of disease vulnerability and timing-specific prevention strategies for age-related disease.
Aging varies widely across individuals, yet clinical biomarkers—such as lipids, glycemic markers, and inflammatory proteins—are often interpreted as if they reflect the same underlying process in everyone. In reality, these markers conflate two distinct components: genetically anchored physiology and acquired aging burden driven by environment and lifestyle. The GAP Analysis aims to separate these components and shift geroscience from reactive interpretation toward proactive, genetically contextualized forecasting.
We are developing a genomics-calibrated framework that integrates polygenic information with multi-omic and longitudinal clinical data from multiple human cohorts. Using genetically informed residual analysis, we quantify the modifiable component of aging physiology by comparing measured values to genetically expected baselines. Preliminary analyses show that this modifiable gap strongly associates with individual response to lifestyle interventions.
The scientific goal is to identify where aging biology is most modifiable and actionable. This work enables personalized prioritization of interventions and earlier detection of aging trajectories that can still be meaningfully altered.
Aging is accompanied by progressive functional decline, underscoring the need for interventions that improve healthspan rather than lifespan alone. This project tests whether common dietary phytochemicals—curcumin and sulforaphane—can improve healthspan in C. elegans, and whether combinations of compounds provide additional benefit.
We conducted a high-throughput longitudinal screen of nine natural products using adult mobility as a primary healthspan readout. Curcumin, sulforaphane, and their combination were selected for replicated follow-up studies, where each improved late-adult mobility. To understand underlying mechanisms, we performed RNA-seq in early adulthood and applied pathway, network, and transcription-factor activity analyses. Curcumin primarily altered lipid remodeling and innate immune programs, while sulforaphane induced glutathione-linked detoxification. The combination preserved features of both, expanding detoxification signatures even when functional gains were not additive.
This work provides a mechanistic framework for rational, multi-target natural product combinations to support healthier aging. Ongoing studies examine flavonoid kinase targets to connect chemical structure with biological selectivity.
This project investigates how aging unfolds across organs and biological systems, and whether blood can serve as a minimally invasive window into tissue-specific aging processes. A major challenge in aging science is determining how molecular changes observed in circulation relate to biological aging within organs where disease emerges.
We apply large-scale plasma and tissue proteomics, integrating thousands of protein measurements with systems-biology and AI-based analyses. By pairing blood with matched tissues and modeling coordinated versus disorganized protein programs across age, we characterize how immune, inflammatory, metabolic, and structural pathways evolve in a sex- and tissue-specific manner.
The goal is to define context-aware biomarkers and biological signatures that enable earlier detection of aging-related decline, guide precision interventions, and support blood-based monitoring of healthspan and therapeutic response.
This research program focuses on understanding how environmental exposures shape healthspan and identifying strategies to mitigate environmental health risks. A central goal is to integrate environmental context into personalized healthspan assessment and intervention.
Dr. Ward-Caviness leads the development of an environmental module for the Healthspan Compass, enabling factors such as air quality, extreme weather, and natural disasters to be incorporated into individual healthspan profiles. This module translates complex environmental data into actionable insights and integrates them with clinical and molecular information to better characterize health trajectories.
In parallel, this work advances fundamental understanding of environmental biology. Ongoing projects identify biomarkers of environmental sensitivity using metabolomics and DNA methylation, and investigate gene–environment interactions that modulate aging and disease risk. Together, these efforts establish a foundation for incorporating environmental risk into precision healthspan decision-making.
This project examines how inherited genetic risk shapes the trajectory of aging physiology over time, rather than influencing disease at a single point. While polygenic risk scores are often used as static predictors, we hypothesize that genetics primarily defines a baseline that conditions how physiological changes unfold across decades.
We develop interpretable deep learning models that jointly analyze polygenic risk, static clinical features, and longitudinal laboratory data. These models learn distinct latent representations for genetic baseline and time-varying physiology, allowing us to identify early divergence in aging trajectories long before clinical diagnosis. We also perform latent-space perturbations to explore counterfactual scenarios, such as how disease risk might evolve under different genetic or physiological conditions.
The scientific aim is to disentangle inherited risk from modifiable aging dynamics and enable earlier, trajectory-based forecasting of disease vulnerability. This work supports precision prevention strategies that are timed and tailored to an individual’s genetic and physiological context.
Our work spans peer-reviewed research, translational reviews, and public-facing thought leadership—advancing the science of healthspan and its real-world impact.
2023
Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention
Nature Medicine
2021
Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19
Nature Biotechnology
2021
Gut microbiome pattern reflects healthy aging and predicts survival in humans
Nature Metabolism
2021
The geometry of clinical labs and wellness states from deeply phenotyped humans
Nature Communications
2020
Multiomic blood correlates of genetic risk identify presymptomatic disease alterations
Proceedings of the National Academy of Sciences
2019
Blood metabolome signature predicts gut microbiome alpha-diversity in
health and disease
Nature Biotechnology
April 4, 2023
The Age of Scientific Wellness: Why the Future of Medicine is Personalized, Predictive, Data-rich, and In Your Hands
Harvard University Press/Belknap.
2024
The transition from genomics to phenomics in personalized population health
Nature Reviews Genetics
2024
A data-driven approach to improve wellness and reduce recurrence in cancer survivors
Frontiers in Oncology
2023
Healthy aging and the human gut microbiome: why we cannot just turn back the clock
Nature Aging
2021
Systems modeling of metabolic dysregulation in neurodegenerative diseases
Current Opinion in Pharmacology
2021
From taxonomy to metabolic output: what factors define gut microbiome health?
Gut Microbes
I lead the Price Lab’s scientific vision, integrating AI, multi-omics, and deep phenotyping to advance predictive, proactive healthspan science and transform longevity research into real-world impact.
President and CEO of the Buck Institute, Dr. Eric Verdin is a world-leading aging scientist advancing metabolic and epigenetic mechanisms to translate longevity science into transformative human health impact.
