Human Observatory Study

Activities of Daily Living, Aging, All-cause Mortality, Cardiovascular Diseases, Cognitive Dysfunction, Dementia, Environmental Exposure, Frailty, Health Equity, Health Related Quality of Life, Life Expectancy, Metabolic Syndrome, Mortality, Musculoskeletal Disease, Neoplasms, Neurodegenerative Disease, Occupational Diseases, Physical Disability, Social Determinants of Health

longevity, biological aging, causal inference, life expectancy, exposome, Environmental Health, Social Determinants, Genealogy, Family History, Human Family Tree, Population Health, Neighborhood Health, Geographic Health Disparities, Health Equity, Mortality Prediction, Biomarker Validation, Cardiopulmonary Exercise Testing, Body Composition, Preventive Medicine, Healthspan, Functional Decline, Centenarian, Space Medicine, Aerospace Medicine, World Model

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. This linkage creates a feedback architecture designed to improve both simultaneously.

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. The Human Observatory Study is conducted across all Longevity Metrics participation pathways, current and future: the Boulder fixed laboratory; all current and future fixed clinical sites; mobile screening units including the Health Ahead Bus; and an online participation pathway through which participants enroll and contribute structured data without in-person screening. Ecological data are ingested continuously from public and private registries independent of individual enrollment. All pathways operate under a single protocol with identical procedures, data management, informed consent, and safety standards. This study is one of four that compound into one system. The 100-Year Human Aging Study (NCT07563777) supplies the clinical data and validates what it means for health, disease, disability, and death. The Health Ahead Comparative Effectiveness Study (NCT07669168) moves the screening toward increasing automation and mobility while maintaining quality. The Longevity Metrics AI/ML Development Study (NCT pending approval) builds the models that make automation, prediction, and broad utilization possible, and returns each model's geographic residuals here. This study does with sociodemographic and environmental data what the 100-Year study does with clinical data, and defines the validated envelope within which each model's output is labeled.

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