Beyond Biomarkers: How the Exposome Is Changing Longevity Science

Supplements. Epigenetic tests. Red light masks. Wearables. Saunas and cold plunges.

Biohacking has moved from a niche interest into a highly visible culture of constant tracking, sophisticated testing and increasingly elaborate optimisation protocols. New gadgets and wellness trends appear constantly, accompanied by a dizzying amount of advice about the “best” way to live longer. The strength of the evidence varies, as does the price. Some approaches are widely accessible, while others remain available only to a small and affluent group. 

For all the excitement, the biohacking boom has a glaring blind spot: most people cannot realistically participate in the version of longevity it promotes.

The dominant image still assumes considerable control over one’s time, money, environment and healthcare. Advice to buy organic food, undergo regular testing, optimise sleep and use the latest wearable may be useful for someone with the resources to follow it. For someone working multiple jobs, caring for relatives, living with chronic pain or struggling to pay rent, it can feel detached from reality, or even downright insulting.

Global figures put this divide into perspective.

In 2023, an estimated 4.6 billion people lacked full coverage for essential health services, while 2.1 billion experienced financial hardship from out-of-pocket healthcare costs in 2022 [1]. In 2025, approximately 2.7 billion people could not afford a healthy diet [2]. The World Bank estimates that 847 million people lived in extreme poverty in 2024 [3].

Against that backdrop, a $300 wearable, private laboratory panel or tightly controlled “longevity diet” can feel less like personalised healthcare and more like something entirely out of reach. 

Most people want to live long and remain healthy, but enthusiasm for extreme longevity is often conditional on what those additional years are expected to look like. In a Finnish survey of 1,405 adults aged 75 to 96, only 32.9% wanted to live to 100, and that preference was often conditional on remaining healthy. Those who did not want to reach 100 cited anticipated disability, pain, loneliness, loss of autonomy, meaninglessness and becoming a burden to others [4]. A study of 1,631 younger and middle-aged adults in the United States similarly found that pessimistic expectations about old age were associated with preferring a shorter lifespan, while fewer negative expectations were associated with wanting to live beyond current life expectancy [5].

This helps explain why radical life extension is often met with resistance. The objection is not necessarily to living longer, but to doing so without health, autonomy, meaningful relationships, purpose or enjoyment.

This is a key reason geroscience is beginning to look beyond conventional biomarkers. People with similar genetics, laboratory results and reported behaviours can still experience very different health trajectories depending on factors such as housing quality, safety, access to nutritious food, social support, prenatal exposures and birth complications.

The exposome provides a framework for studying these differences [6].

What Is the Exposome?

To understand the exposome, it helps to first clarify how biomarkers fit within it.

The FDA and NIH define a biomarker as a measurable characteristic that indicates a normal biological process, a disease process, or a biological response to an exposure or intervention [7]. Blood glucose, blood pressure, inflammatory proteins and certain genetic or epigenetic measurements can all function as biomarkers in the appropriate context.

Biomarkers are central to exposome research because they can reveal how environmental, behavioural, social and psychological exposures are reflected within the body. However, a biomarker generally captures a particular biological state or response. The exposome is broader, encompassing the cumulative pattern of exposures across a person’s life, when they occurred, how they interacted and how they contributed to biological change over time [6].

In 2005, epidemiologist Christopher Wild introduced the term exposome as a complement to the genome. He initially described it as encompassing environmental exposures, including lifestyle factors, across the life course from the prenatal period onward [8].

Wild later organised the exposome into three broad domains: the general external environment, the specific external environment and the internal environment [9]. Subsequent frameworks have expanded the concept to incorporate external exposures, social conditions, molecular responses and the functional consequences of exposure [6,10].

In practice, this can include physical and chemical exposures such as pollution, water quality, toxins, temperature, radiation, light and noise; biological exposures such as pathogens and microbial environments; and behavioural factors such as diet, sleep, movement, smoking, alcohol and medication use. It also includes social and structural conditions such as income, housing, education, occupation and healthcare access, alongside experiences such as stress, trauma, belonging, purpose and connection. Internal responses including inflammation, metabolism, hormonal activity, epigenetic change and the microbiome can help reveal how these influences are reflected within the body [6,10].

Exposure Depends on Timing and Context 

The exposome is not simply a collection of biological and environmental variables. It also considers when an exposure occurred, how long it lasted, what other exposures occurred alongside it, and how the body responded over time [6,10].

Pregnancy offers a clear example. The same food, medication, pollutant, infection or level of physical exertion may have a different significance during pregnancy than at another stage of life. Early childhood, puberty, illness, recovery and older age can also represent periods in which particular exposures have different effects [10].

Exposures also interact. Shift work may disrupt sleep and eating patterns. Housing insecurity may increase stress while limiting food choice and continuity of care. Chronic pain may reduce movement, worsen sleep, affect relationships and contribute to depression.

What makes the exposome distinct is not simply the number of factors considered, but the effort to understand their timing, interaction and biological consequences across the life course.

Exposomics is the emerging scientific field that develops methods to measure and analyse this complexity, connecting exposures with molecular responses, physiological changes and health outcomes [6].

The suffix -omics, as used in genomics, proteomics and metabolomics, refers to fields that study large, interconnected sets of biological information rather than examining one molecule at a time [11]. Exposomics applies a similar systems-level approach to the complex exposures shaping human health.

When Similar Biology Leads to Different Outcomes 

Many of the most consequential exposome factors fall within what are known as social determinants of health.

The World Health Organization defines these as the conditions in which people are born, grow, work, live and age, including their access to money and resources and their ability to influence decisions that shape daily life. WHO reports a gap of approximately 33 years in average life expectancy between countries with the highest and lowest life expectancies. Inequality also exists within national borders: life expectancy can differ by decades depending on where people live and the social groups to which they belong [12].

Two people may appear similar in their clinical data yet live in materially different circumstances. One may work multiple jobs or rotating night shifts, care for children or relatives, live in an area without safe outdoor space or reliable access to fresh food, or be unable to afford preventive care. Their health may also be shaped by unsafe housing, pollution, extreme heat, chronic noise, discrimination, social exclusion, trauma, abuse or ongoing financial pressure.

Blanket advice such as “eat healthier,” “reduce stress” or “exercise more” may be reasonable and evidence-based. But without a realistic pathway for putting it into practice, that advice can feel meaningless or even condescending.

The scale of this problem is global. In 2022, 64.8% of people in Africa, 35.1% in Asia, 27.7% in Latin America and the Caribbean, and 20.1% in Oceania were unable to afford a healthy diet. These inequalities are not confined to lower-income countries. Even in Northern America and Europe, 4.8% of people faced the same barrier [13]. In the United States, millions also continue to live in poverty or without health insurance [14]. 

Early-life adversity can compound these conditions. A major systematic review linked multiple adverse childhood experiences with higher risks of mental illness, substance use, interpersonal violence and poor physical health [15]. These effects can reinforce one another by influencing relationships, sleep, perceived safety, health behaviours and the ability to seek or maintain care, creating cycles that are difficult to escape through willpower alone. 

Accounting for this context is essential if personalised health guidance is to be meaningful and globally accessible.

From Lived Experience to Scientific Evidence 

Some of the influences that may matter most for health are also among the hardest to measure. Joy, purpose, optimism, faith, belonging, hope and a sense of agency are experienced subjectively rather than captured directly by a blood test or wearable. That does not make them scientifically irrelevant. It means researchers need better ways to define, quantify and connect them with biological change.

A meta-analysis of 148 studies involving more than 300,000 participants found that stronger social relationships were associated with a 50% higher likelihood of survival during the periods studied [16]. Higher optimism has been associated with an 11% to 15% longer lifespan and a greater likelihood of exceptional longevity in two large cohorts [17]. Greater purpose in life has also been associated with lower mortality among adults over 50 [18].

Patterns Among the Oldest Old

Research involving centenarians, who live to at least 100, and supercentenarians, who live to at least 110, has helped bring these less tangible influences into clearer focus.

A 2026 synthesis of 28 qualitative studies involving 359 centenarians identified connection to people and place, autonomy, adaptation to loss and continued meaning-making as recurring themes in their lived experience [19].

Blue Zone research has popularised related observations across long-lived populations, particularly the potential roles of diet, everyday movement, social support, cultural belonging and environmental context. A 2025 demographic review also noted continuing questions about age validation and the strength of evidence across proposed regions. Even so, patterns reported in long-lived populations such as Sardinia, Okinawa, Nicoya and Ikaria offer useful hypotheses for more rigorous investigation [20].

Individual stories illustrate the questions behind the data. Jamaican supercentenarian Violet Brown credited hard work, Christian faith and respect for others, while Sister André of France lived through two world wars and the 1918 influenza pandemic and later became the oldest verified survivor of COVID-19 [21,22]. Such accounts combine biology, adversity, adaptation, relationships and meaning, but cannot reveal which factors influenced longevity or through which pathways.

Taken together, centenarian accounts and smaller observational studies reveal recurring and scientifically interesting patterns. Larger, more diverse studies following people over time could help determine whether good health supports optimism, purpose and connection, whether these experiences contribute to better health, or whether both reinforce one another.

From Anecdotes to Empirical Evidence 

The challenge of quantifying intangible experiences extends far beyond exceptional longevity.

People regularly report that a particular diet, supplement, spiritual practice, exercise routine or unconventional intervention improved their health, relieved symptoms or even altered the course of a chronic condition. These accounts may contain valuable signals, but they rarely isolate a single variable. The outcome may reflect the intervention itself, its dose and timing, individual biology, simultaneous lifestyle changes, prior expectations and the wider social or therapeutic context.

The scientific task is therefore not simply to declare an anecdote “true” or “false.” It is to reconstruct what happened, identify which factors may have contributed to the outcome, determine for whom and under what conditions they may apply, and test whether the effect can be reproduced.

This was the central question behind Rejuve.AI CEO Jasmine Smith’s 2025 RAADfest presentation, From Anecdotes to Algorithms: Scaling N-of-1 for Longevity. Observations should not be dismissed simply because they begin as anecdotes, but they must be structured, measured and tested without being mistaken for proof [23]. 

Expectations and treatment context can also influence outcomes. Placebo effects can produce measurable improvements through positive expectations and learned responses, while nocebo effects can worsen symptoms or perceived side effects through negative expectations [24]. This does not mean symptoms are imagined or that mindset can override disease. It means that expectation, prior experience and the context surrounding an intervention can influence how it is experienced and, in some cases, how the body responds.

These effects can complicate anecdotal reports because an improvement or decline may not result solely from the intervention being credited. Rather than dismissing them as noise, researchers can treat expectation and context as additional factors to measure.

Measuring the Exposome

Wearables and blood tests capture only part of the exposome. Measuring it meaningfully requires combining different kinds of information to understand what a person encountered, how their biology responded and how their health changed over time [6,10].

Public and geospatial data  can provide information about pollution, temperature, housing, transport, food access and socioeconomic conditions. Wearables can track movement, sleep, heart rate and daily routines, while small portable or home-based devices can measure conditions such as air quality, noise, light and temperature in the places a person actually spends time.

These measurements can be combined with clinical records, blood or saliva samples, multi-omics data, medical history and measures of physical and cognitive function. Just as importantly, participants can provide context that may not exist in any clinical or public dataset: what they eat, where and when they work, which medications they take, whether they feel safe or supported, and whether they are experiencing pregnancy, illness, bereavement, relocation, financial pressure or another major life change.

Researchers sometimes refer to these as bottom-up and top-down approaches. Bottom-up measurement begins with exposures in a person’s surroundings, while top-down measurement looks for evidence of exposure and biological response within the body [25].

Modern exposomics increasingly combines the two. Environmental data may show that someone lives near heavy traffic without revealing how their body responded. A molecular signature may indicate exposure or inflammation without identifying its precise source.

Consider a disease that occurs more frequently within a particular population or ethnic group. That association alone cannot show how much of the difference reflects inherited susceptibility and how much is related to diet, food quality and affordability, income, environmental conditions, healthcare access, cultural practices or chronic stress. These influences may also interact.

Exposomics can help researchers examine those relationships more carefully. For an individual, the aim is not simply to repeat their group’s average risk. It is to identify which factors appear most relevant to their health, what may be driving changes over time, and which interventions are realistic within their circumstances.

The same applies to health optimisation. Two people can follow the same diet, supplement protocol or exercise programme and experience very different results. Their response may depend on their starting biology, medication use, sleep, stress, environment, previous exposures and how consistently the intervention can realistically be followed.

Repeated measurements help connect these pieces: what changed, what else was happening at the time, how the body responded and whether an intervention produced a meaningful result [6,10].

Communities as Exposome Testbeds 

Communities naturally bring together people with different genetic backgrounds, biological baselines and life histories within a shared environment. With thoughtful study design, repeated measurement and informed consent, both permanent settings such as neighbourhoods and clinics, and temporary communities such as festivals, raves and pop-up cities, can become valuable exposome testbeds where researchers study how different people respond to shared surroundings or experiences.

Community-based research has already demonstrated the value of this model. The Green Heart Louisville Project treated neighbourhood greening as a controlled community intervention. Researchers combined environmental monitoring with biological samples, clinical measures and information about mental wellbeing and social experience before and after more than 8,000 trees and shrubs were planted in selected areas, comparing the results with nearby communities. Preliminary findings showed that residents in the greened areas had 13% to 20% lower levels of the inflammatory biomarker hsCRP than residents in comparison areas [26]. 

In 2025, researcher Tina Woods and collaborators advanced this idea directly within longevity science. Writing in Nature Medicine, they argued that cities, communities and clinics could serve as real-world testbeds for identifying the conditions that support healthspan, or the years spent in good health, resilience and human flourishing [27].

Woods has also helped translate that framework into practice through the JoyScore Experiment, an ongoing open-science study series investigating whether joy, synchrony and human connection leave measurable physiological or biological signatures. Longevity Rave is the participatory format used within the series, creating a structured shared experience through music, rhythm and movement [28].

Rejuve.AI collaborated with Woods and other partners on early JoyScore studies at Frontier Tower in San Francisco and within the broader Roatán Longevity Biomarkers Competition. Across the two settings, researchers combined participant-reported experience with physiological, environmental, functional and biological data. Roatán also established the first official IRLDB research cohort [29,31].

The larger opportunity is to make this approach repeatable across communities, events and locations by combining longitudinal data collection, shared research protocols and AI-assisted analysis. 

Real-world exposome research still requires careful design. People may struggle to record past experiences accurately, biological changes can have more than one possible cause, and combining health, behavioural, environmental and location data raises important privacy and consent concerns [6,10]. The goal is to preserve the context of real life while collecting enough consistent, repeatable information to make the findings scientifically useful. 

From Better Measurement to Practical Personalisation

 

Even well-supported health advice is only useful when there is a realistic way to apply it within someone’s actual circumstances.

A health platform may correctly identify that a person would benefit most from more sleep, movement or nutritious food. Exposome-aware personalisation goes further by asking what is preventing that change, whether the barrier is structural, financial or medical, and whether it is temporary or ongoing. It can also consider whether pregnancy, illness, recovery or a major life event has changed the person’s baseline, what alternatives are realistically available, and what support could make the recommendation actionable.

A night-shift worker may not be able to follow conventional sleep advice. Someone who is pregnant or planning to become pregnant may require different nutritional, exercise or medication guidance. A person experiencing grief may temporarily lose routines that previously supported their health, while someone living in an unsafe neighbourhood may need an alternative to outdoor walking rather than another reminder to reach 10,000 steps. 

The recommendation may be scientifically correct while its implementation is completely wrong for the individual. 

This is where health technology must move beyond telling people what to do and begin helping them determine how to make it work in real life. True personalisation requires not only identifying the most effective intervention, but adapting it to the person’s environment, resources, responsibilities and current stage of life.

The Rejuve Longevity App already allows users to record special life events, including travel, relocation and major relationship changes that may affect routines, stress, sleep and wellbeing [30]. This context can help connect otherwise isolated measurements and reveal why a health trend or response to an intervention may have changed over time.

By combining biological data with behaviour, environment and participant-contributed context, health technology can move from generic recommendations towards guidance that is both scientifically informed and realistically actionable. This is how better measurement can support more meaningful personalisation and make longevity optimisation useful to a far broader population. 

AI and the Exposome: Modelling Health as a Living System  

The scale and complexity of exposome data make artificial intelligence especially valuable for connecting information that would otherwise be difficult to interpret together. AI has already been proposed as a core enabling technology for a broader Human Exposome Project capable of connecting molecular, environmental and population-level information at scale [31]. 

A single person’s exposomic profile may contain thousands of environmental measurements, molecular features, behaviours, locations, life events and subjective reports. These data are collected at different times, may be incomplete, and often influence one another in ways that are neither simple nor linear [6,32]. 

Exposome researchers already use statistical and machine-learning methods to reduce noise, manage uncertainty and connect information across biological systems [32]. Modern AI expands this toolkit. Large language models can help structure symptoms, routines and life events described in everyday language, while multimodal models can analyse text alongside wearable, clinical, environmental and molecular data. 

A more ambitious emerging direction is the development of world models. Rather than analysing a static snapshot, a world model learns how a system changes over time and uses that representation to anticipate possible future states or the likely consequences of different actions [33].

Most current world-model research focuses on physical environments, robotics and autonomous systems. Applying the same general principle to exposomics could support dynamic models of health that connect biology, behaviour, environment, life events and responses to interventions. At the community level, these models could represent a health ecosystem in which biological susceptibility interacts with food access, housing, pollution, income, working conditions, social connection and healthcare availability to shape patterns of disease and healthy ageing. 

With diverse longitudinal data and real-world validation, such models could help predict which changes are likely to produce the greatest healthspan gains within a particular community. Cleaner air may offer the greatest benefit in one location, while affordable nutritious food, preventive healthcare, safer opportunities for movement or working conditions that support adequate sleep may have more impact elsewhere. Researchers could then test these priorities through community-based studies, compare predicted and observed results, and improve the models over time.

The quality of these models will still depend on the data used to build them. If exposome datasets mainly represent affluent biohackers with continuous wearable data, private testing and highly controlled routines, the resulting models may perform poorly for people living under very different conditions. Diverse participation and repeated measurements are therefore essential, along with testing models against new populations and clearly distinguishing association, prediction and evidence that an intervention works. 

The opportunity is to use AI to reveal how biology, environment, behaviour and lived experience interact, why people respond differently to the same intervention, and which factors offer the most useful opportunities for action. 

When Longevity Becomes Healthcare 

When people hear the word “longevity,” they often picture elite medical care, expensive products and enough time and money to optimise every detail of life. Yet its central goal should already be the goal of healthcare: preventing avoidable illness, preserving function and helping people remain healthy for as long as possible.

The goal is not simply longer life, but more years spent healthy, independent and able to enjoy the things that make life worthwhile. Exposomics, longitudinal research and AI create a pathway towards prevention that reflects both biology and the conditions in which people actually live.

Together, this creates the potential for a virtuous cycle. Better evidence supports earlier and more targeted prevention. Effective prevention reduces avoidable disease and its social and economic costs. Those gains create greater capacity to invest in research, healthier environments, public infrastructure and wider access to preventive services, producing more evidence and further improving outcomes.

The benefits extend beyond individual health. Populations that remain healthier for longer reduce avoidable pressure on hospitals, long-term care and family caregivers, while more people remain independent and able to participate in work, education, family and community life. 

This broader return is often described as the longevity dividend: the health, social and economic value created by extending healthy life and delaying age-related disease [34]. 

OECD analysis similarly finds that preventing non-communicable disease can ease pressure on public health budgets while protecting productivity and generating broader economic gains [35].

The cost of sequencing a human genome has fallen from millions of dollars to hundreds [36]. Alongside this, the cost of running capable AI models is also falling rapidly. Stanford’s 2025 AI Index reported that the inference cost of a model performing at approximately GPT-3.5 level declined more than 280-fold between November 2022 and October 2024 [37]. 

Lower costs alone will not guarantee equal access, but they make it increasingly possible to move tools that began in specialist laboratories and premium wellness markets into ordinary clinics, community programmes and consumer technology. As the evidence improves, governments and health systems can increasingly direct investment towards the changes most likely to improve healthspan within their own populations. 

A healthier population is both a moral imperative and a national advantage. It strengthens economic resilience, social participation and national wellbeing. Making healthspan a public priority is therefore an ethical and sensible direction for any nation seeking long-term prosperity. 

Rejuve.AI: Advancing Longevity Intelligence 

Rejuve.AI is advancing this transition by building a longevity intelligence layer that connects biological measurements with behaviour, environment, life events and participant-contributed experience over time. By applying AI to longitudinal individual and community data, the aim is to turn fragmented health information into more practical guidance and stronger evidence for accessible, preventive longevity care [38].

Longevity-focused healthcare is not an added luxury. It is the logical evolution of healthcare itself. 

---

References

[1] World Health Organization, & World Bank. (2025). Tracking universal health coverage: 2025 global monitoring report. World Health Organization. https://www.who.int/teams/health-financing-and-economics/global-monitoring-report/2025

[2] Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, UNICEF, World Food Programme, & World Health Organization. (2026). The State of Food Security and Nutrition in the World 2026: Understanding and addressing the high cost of a healthy diet. FAO. https://doi.org/10.4060/cd8306en

[3] Lønborg, J. H., Viveros, M., Castaneda Aguilar, R. A., Lakner, C., Lara Ibarra, G., & Nguyen, M. C. (2026, March 26). March 2026 global poverty update from the World Bank: New data and updated poverty numbers. World Bank Data Blog. https://blogs.worldbank.org/en/opendata/march-2026-global-poverty-update-from-the-world-bank--new-data-a

[4] Karppinen, H., Laakkonen, M.-L., Strandberg, T. E., Huohvanainen, E. A., & Pitkälä, K. H. (2016). Do you want to live to be 100? Answers from older people. Age and Ageing, 45(4), 543–549. https://doi.org/10.1093/ageing/afw059

[5] Bowen, C. E., & Skirbekk, V. (2017). Old age expectations are related to how long people want to live. Ageing & Society, 37(9), 1898–1923. https://doi.org/10.1017/S0144686X16000726

[6] Wan, M., Simonin, E. M., Johnson, M. M., Zhang, X., Lin, X., Gao, P., Patel, C. J., Yousuf, A., Snyder, M. P., Hong, X., Wang, X., Sampath, V., & Nadeau, K. C. (2025). Exposomics: A review of methodologies, applications, and future directions in molecular medicine. EMBO Molecular Medicine, 17(4), 599–608. https://doi.org/10.1038/s44321-025-00191-w

[7] FDA-NIH Biomarker Working Group. (2016–). BEST: Biomarkers, EndpointS, and other Tools resource. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK326791/

[8] Wild, C. P. (2005). Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology, Biomarkers & Prevention, 14(8), 1847–1850. https://doi.org/10.1158/1055-9965.EPI-05-0456

[9] Wild, C. P. (2012). The exposome: From concept to utility. International Journal of Epidemiology, 41(1), 24–32. https://doi.org/10.1093/ije/dyr236

[10] Zhang, P., Carlsten, C., Chaleckis, R., Hanhineva, K., Huang, M., Isobe, T., et al. (2021). Defining the scope of exposome studies and research needs from a multidisciplinary perspective. Environmental Science & Technology Letters, 8(10), 839–852. https://doi.org/10.1021/acs.estlett.1c00648

[11] Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials. (2012). Summary. In C. M. Micheel, S. J. Nass, & G. S. Omenn (Eds.), Evolution of translational omics: Lessons learned and the path forward. National Academies Press. https://www.ncbi.nlm.nih.gov/books/NBK202158/ 

[12] World Health Organization. (2025). World report on social determinants of health equity. https://www.who.int/publications/i/item/9789240107588

[13] Food and Agriculture Organization of the United Nations. (2024, August 1). Healthy diets remain unaffordable for a third of the world’s population. https://www.fao.org/newsroom/detail/healthy-diets-remain-unaffordable-for-a-third-of-the-world-s-population/

[14] U.S. Census Bureau. (2025, September 9). Income, poverty and health insurance coverage in the United States: 2024. https://www.census.gov/newsroom/press-releases/2025/income-poverty-health-insurance-coverage.html

[15] Hughes, K., Bellis, M. A., Hardcastle, K. A., Sethi, D., Butchart, A., Mikton, C., Jones, L., & Dunne, M. P. (2017). The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. The Lancet Public Health, 2(8), e356–e366. https://doi.org/10.1016/S2468-2667(17)30118-4

[16] Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta-analytic review. PLOS Medicine, 7(7), e1000316. https://doi.org/10.1371/journal.pmed.1000316

[17] Lee, L. O., James, P., Zevon, E. S., Kim, E. S., Trudel-Fitzgerald, C., Spiro, A., Grodstein, F., & Kubzansky, L. D. (2019). Optimism is associated with exceptional longevity in two epidemiologic cohorts of men and women. Proceedings of the National Academy of Sciences, 116(37), 18357–18362. https://doi.org/10.1073/pnas.1900712116

[18] Alimujiang, A., Wiensch, A., Boss, J., Fleischer, N. L., Mondul, A. M., McLean, K., Mukherjee, B., & Pearce, C. L. (2019). Association between life purpose and mortality among US adults older than 50 years. JAMA Network Open, 2(5), e194270. https://doi.org/10.1001/jamanetworkopen.2019.4270

[19] Miyahara, M., Moebs, I., Madden, T., & Konno, R. (2026). Centenarians’ views and experiences of longevity: A meta-ethnographic systematic review. The Gerontologist, 66(4), gnag002. https://doi.org/10.1093/geront/gnag002

[20] Poulain, M., & Herm, A. (2025). Blue Zone, a demographic concept and beyond. American Journal of Lifestyle Medicine. Advance online publication. https://doi.org/10.1177/15598276251342502

[21] LongeviQuest. (n.d.). Violet Brown. Retrieved July 29, 2026, from https://longeviquest.com/supercentenarian/violet-brown/

[22] Guinness World Records. (2022, April 25). World’s oldest person confirmed as 118-year-old Sister André. https://www.guinnessworldrecords.com/news/2022/4/worlds-oldest-person-confirmed-as-118-year-old-sister-andre-699611

[23] RejuveAI. (2025). From Anecdotes to Algorithms: Scaling N-of-1 for Longevity | Jasmine Smith at RAADfest 2025  [Video]. YouTube. https://www.youtube.com/watch?v=FJmKvZpX4gg

[24] Colloca, L., & Barsky, A. J. (2020). Placebo and nocebo effects. The New England Journal of Medicine, 382(6), 554–561. https://doi.org/10.1056/NEJMra1907805

[25] Rappaport, S. M. (2011). Implications of the exposome for exposure science. Journal of Exposure Science & Environmental Epidemiology, 21(1), 5–9. https://doi.org/10.1038/jes.2010.50

[26] University of Louisville. (2024, August 27). UofL Green Heart Project: Residents’ inflammation lower after trees added to neighborhoods. https://news.louisville.edu/news/uofl-green-heart-project-residents-inflammation-lower-after-trees-added-neighborhoods 

[27] Woods, T., Palmarini, N., Corner, L., & Siow, R. (2025). Cities, communities and clinics can be testbeds for human exposome and aging research. Nature Medicine, 31, 1066–1068. https://doi.org/10.1038/s41591-025-03519-8

[28] Longevity Rave. (n.d.). The JoyScore Experiment. Retrieved July 29, 2026, from https://www.longevityrave.world/the-joyscore-experiment

[29] Smith, J. (2025, December 4). Rejuve.AI joins the Joyspan Experiment at Frontier Tower. Rejuve.AI. https://www.rejuve.ai/post/rejuve-ai-joins-the-joyspan-experiment-at-frontier-tower

[30] Smith, J. (2026, February 26). IRLDB goes live in Roatán: A new model for evidence-driven longevity. Rejuve.AI. https://www.rejuve.ai/post/irldb-goes-live-in-roat%C3%A1n-a-new-model-for-evidence-driven-longevity

[31] Hartung, T. (2025). How AI can deliver the Human Exposome Project. Nature Medicine, 31, 1738. https://doi.org/10.1038/s41591-025-03749-w

[32] Maitre, L., Guimbaud, J.-B., Warembourg, C., Güil-Oumrait, N., Petrone, P. M., Chadeau-Hyam, M., Vrijheid, M., Basagaña, X., & Gonzalez, J. R. (2022). State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event. Environment International, 168, 107422. https://doi.org/10.1016/j.envint.2022.107422

[33] Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025). Mastering diverse control tasks through world models. Nature, 640, 647–653. https://doi.org/10.1038/s41586-025-08744-2 

[34] Olshansky, S. J. (2013). Articulating the case for the longevity dividend. Public Policy & Aging Report, 23(4), 3–6. https://doi.org/10.1093/ppar/23.4.3 

[35] OECD. (2026). The health and economic benefits of tackling non-communicable diseases. OECD Publishing. https://doi.org/10.1787/e20cbbc3-en

[36] National Human Genome Research Institute. (n.d.). DNA sequencing costs: Data. Retrieved August 6, 2026, from https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data

[37] Stanford Institute for Human-Centered Artificial Intelligence. (2025). The 2025 AI Index Report. Stanford University. https://hai.stanford.edu/ai-index/2025-ai-index-report

[38] Smith, J. (2026, April 17). Rejuve’s next evolution: From research network to longevity intelligence layer. Rejuve.AI. https://www.rejuve.ai/post/rejuve-s-next-evolution