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Human Observatory Study (HOS)

9 giugno 2026 aggiornato da: William Brandenburg, MD, Longevity Metrics, Inc.

The Human Observatory: A Prospective Individual and Population-Level Study of Aging, Health, and Longevity

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.

Panoramica dello studio

Descrizione dettagliata

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.

Tipo di studio

Osservativo

Iscrizione (Stimato)

1000000

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Contatto studio

Luoghi di studio

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

  • Adulto
  • Adulto più anziano

Accetta volontari sani

Metodo di campionamento

Campione non probabilistico

Popolazione di studio

Participants of all ages, health statuses, and demographic backgrounds enrolled in the 100-Year Human Aging Study at any fixed or mobile clinical site, plus participants completing the online health screener. No exclusions based on health status, geographic location, language, or population group. The ecological surveillance layer requires no individual enrollment.

Descrizione

Inclusion Criteria:

  • Enrolled in the 100-Year Human Aging Study at any fixed or mobile clinical site; OR completion of online health screener with provision of geographic anchor data and consent.

Exclusion Criteria:

  • Age under 18 years (current protocol; pediatric amendment planned).

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Life Expectancy Estimates by Geography
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
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.
From enrollment until death, assessed periodically, up to 100 years
Geographic Disease Cluster and Outbreak Detection
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Statistically anomalous concentrations of incident disease, mortality spikes, or shared symptom patterns at neighborhood and community resolution.
From enrollment until death, assessed periodically, up to 100 years

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Individual Screening Recommendation Accuracy
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Concordance between population-level causal estimates used to generate individualized screening recommendations and actual individual health outcomes at longitudinal follow-up, assessed periodically as outcomes accrue.
From enrollment until death, assessed periodically, up to 100 years
Causal Effect Estimates for Modifiable Exposures
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Estimated attributable life-years gained or lost per unit change in modifiable environmental, occupational, and social exposures.
From enrollment until death, assessed periodically, up to 100 years
Geographic Variation in Disability-Free Life Expectancy
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Disability-free life expectancy stratified by geography, ascertained via the functional independence and disability survey instrument used across all three associated protocols.
From enrollment until death, assessed periodically, up to 100 years
Health Equity Characterization
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Life expectancy gaps and chronic disease disparities stratified by geography, income, race and ethnicity, educational attainment, and rural-urban classification.
From enrollment until death, assessed periodically, up to 100 years
Human Tree of Life Growth
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Total participants linked to genealogical record; multigenerational depth achieved; proportion of enrolled participants with identified biological relatives in the registry; total historical individuals linked across all genealogical databases.
From enrollment until death, assessed periodically, up to 100 years
Population Biological Age Acceleration
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Mean difference between chronological age and biological age estimate for repeat-visit participants, stratified by geographic and demographic characteristics.
From enrollment until death, assessed periodically, up to 100 years
Multi-Domain Predictor Modeling
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
Assessment of individual and composite clinical, biological, behavioral, environmental, social, occupational, genealogical, and geographic measurements as predictors of all-cause mortality, life expectancy, and incident serious disease at population scale; analyses evaluate which domains are independently predictive, which are redundant, and which combinations provide additive or synergistic predictive value.
From enrollment until death, assessed periodically, up to 100 years
Incident Serious Health Events and Chronic Disease
Lasso di tempo: From enrollment until death, assessed periodically, up to 100 years
New diagnosis of myocardial infarction, stroke, cancer, dementia, heart failure, atrial fibrillation, sepsis, venous thromboembolism, COPD, chronic hypoxia, major fracture, type 2 diabetes, hypertension, COPD, chronic kidney disease, metabolic syndrome, or osteoporosis ascertained via periodic follow-up contact and health data network linkage.
From enrollment until death, assessed periodically, up to 100 years

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Investigatori

  • Investigatore principale: William Brandenburg, MD, Longevity Metrics

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Effettivo)

25 aprile 2026

Completamento primario (Stimato)

31 dicembre 2099

Completamento dello studio (Stimato)

31 dicembre 2099

Date di iscrizione allo studio

Primo inviato

9 giugno 2026

Primo inviato che soddisfa i criteri di controllo qualità

9 giugno 2026

Primo Inserito (Effettivo)

15 giugno 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

15 giugno 2026

Ultimo aggiornamento inviato che soddisfa i criteri QC

9 giugno 2026

Ultimo verificato

1 giugno 2026

Maggiori informazioni

Termini relativi a questo studio

Piano per i dati dei singoli partecipanti (IPD)

Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?

NO

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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