The Human Observatory: A Prospective Individual and Population-Level Study of Aging, Health, and Longevity
Summary
The Human Observatory Study is a prospective observational and ecological surveillance study building a continuously-updating world model for human health, disease, and death at the individual and population level. Individual multi-system clinical data from enrolled participants are linked to a continuously-ingested ecological data infrastructure spanning environmental exposures, social determinants, genealogical and family history records, mortality data, and population health databases at geographic resolutions from home address to global scale and beyond. The resulting model generates individual screening recommendations informed by population-level causal estimates, and population-level causal forecasts anchored by present-timepoint individual clinical biology. Thus creating a feedback architecture designed to improve both simultaneously.
Detailed description
Existing approaches to human health prediction face a structural limitation: individual clinical studies measure biology without capturing the environment, while population epidemiology captures the environment without individual biological ground truth. The Human Observatory Study resolves this by operating at both levels simultaneously through a linked dual-layer architecture. At the individual level, participants enrolled in the 100-Year Human Aging Study contribute comprehensive multi-system health measurements. This includes clinical, physiological, cognitive, behavioral, social, occupational, and environmental data collected at fixed and mobile clinical sites. These measurements provide the biological present timepoint that historical population data alone cannot supply. At the population level, the Observatory continuously ingests ecological data from public and private registries across multiple input domains. This includes air quality, water and chemical contaminants, wildfire and smoke exposure, altitude and terrain, climate, satellite earth observation, occupational and industrial exposure, mortality and vital statistics, demographics and social determinants, and clinical data networks at geographic resolutions from home address to global scale and beyond. This ecological layer captures the environmental and social causal structure of health and disease continuously and does not require individual enrollment. A foundational input domain is genealogy and family history. Health and disease run in families across generations. The Observatory is designed to build and continuously expand a linked genealogical database connecting living and historical individuals to their family health histories. Information is obtained from public genealogical records, death registries, family history self-report, and genetic data where available. The long-term vision is a genealogical infrastructure of sufficient depth and breadth to trace familial health patterns across the full recorded human family tree. Therefore connecting individual present-timepoint biology to multigenerational patterns of disease, longevity, and environmental exposure that no existing biobank or longitudinal study has attempted to capture at this scale. The linked architecture enables a feedback loop with two outputs: population-level causal estimates that inform individual screening recommendations, and individual clinical data that give population models a present biological anchor for prospective forecasting. The degree to which each input domain, alone and in combination, predicts health, disease, and death across geographic scales from neighborhood to global and beyond is the central scientific question the Observatory is designed to answer. The Observatory launches in Colorado, chosen as the founding site for its exceptional natural variation in altitude, wildfire smoke corridors, mining and industrial chemical geographies, and frontier-to-urban socioeconomic gradient all within a compact, well-characterized geography with established academic research infrastructure. Colorado proves the model. The architecture then replicates geographically, with each new location enriching the world model for every other. The long-term vision is global coverage and beyond. Every geography will contribute its environmental, social, and biological signal to a world model that gets more accurate with every geography studied, every participant enrolled, every dataset ingested, and every causal analysis conducted.
Arms & interventions
- OtherMulti-Domain Ecological and Clinical Data Linkage
Linkage of individual multi-system clinical health measurements to continuously-ingested ecological data from public and private registries spanning environmental, social, genealogical, and population health domains at geographic resolutions from home address to global scale and beyond. The linked dataset feeds a continuously-updating causal inference engine generating life expectancy estimates, disease cluster detection, individual screening recommendations, and population health intelligence.
Outcome measures
Primary
Life Expectancy Estimates by Geography
Continuously-updated life expectancy point estimates with credible intervals generated at individual, neighborhood, ZIP code, county, state, national, global, and beyond-earth scales using individual clinical data linked to population mortality records, environmental context, and ecological data.
Time frame: From enrollment until death, assessed periodically, up to 100 years
Geographic Disease Cluster and Outbreak Detection
Statistically anomalous concentrations of incident disease, mortality spikes, or shared symptom patterns at neighborhood and community resolution.
Time frame: From enrollment until death, assessed periodically, up to 100 years
Secondary
Individual Screening Recommendation Accuracy
Time frame: From enrollment until death, assessed periodically, up to 100 years
Causal Effect Estimates for Modifiable Exposures
Time frame: From enrollment until death, assessed periodically, up to 100 years
Geographic Variation in Disability-Free Life Expectancy
Time frame: From enrollment until death, assessed periodically, up to 100 years
Health Equity Characterization
Time frame: From enrollment until death, assessed periodically, up to 100 years
Human Tree of Life Growth
Time frame: From enrollment until death, assessed periodically, up to 100 years
Population Biological Age Acceleration
Time frame: From enrollment until death, assessed periodically, up to 100 years
Multi-Domain Predictor Modeling
Time frame: From enrollment until death, assessed periodically, up to 100 years
Incident Serious Health Events and Chronic Disease
Time frame: From enrollment until death, assessed periodically, up to 100 years
Eligibility criteria
Study locations (1)
Longevity Metrics
Boulder, Colorado, 80301