Paper 025: Continuity Across Scales — A Framework for Comparing Circadian Disruption and Organ Aging

This paper documents our current thinking at the time of writing. It is a position paper and conceptual framework, not a clinical study. Some observations discussed here are established in the scientific literature; others are interpretations or hypotheses developed by the ZKOMI Research Team.

The framework has informed prototype development and commercial software, but product behavior is not presented as scientific validation. As evidence, standards, technology, and our understanding evolve, this paper may be revised, superseded, or withdrawn.

Note to readers: This paper does not propose a new biological mechanism. It proposes a framework for comparing established observations from different fields and asks whether a recurring systems-level pattern — biological systems changing, recovering, or aging on partially independent timelines — can provide a useful way of thinking about health continuity across different timescales.

The distinction matters throughout this paper: the evidence is established; the organizational framework is ours; the relationship between the two is a hypothesis.

Chronological time is one of the simplest measurements in healthcare. It is also one of the least complete descriptions of biological time.

In July 2026, Tony Wyss-Coray and Eric Topol published a review in Nature Medicine examining the rapidly developing field of biological aging clocks. The review describes biological clocks that can estimate aspects of aging across individuals, organs, tissues and cells, with potential applications in risk assessment, prevention, early detection and evaluation of interventions.

One of the important implications of this work is that aging is not uniform.

Research has demonstrated differences in biological aging across tissues and cell types within the same individual. Horvath's multi-tissue epigenetic clock, for example, demonstrated that DNA methylation patterns could be used to estimate age across a wide range of human tissues and cell types.

Proteomic research has added another dimension. Lehallier and colleagues analyzed 2,925 plasma proteins in 4,263 people aged 18–95 and identified marked nonlinear changes in the plasma proteome across the lifespan, including distinct waves of change in the fourth, seventh and eighth decades.

More recent work has extended the concept from whole-body measures toward organ- and cell-specific aging. The 2026 Wyss-Coray and Topol review highlights substantial heterogeneity in organ aging and notes associations between organ aging patterns and health outcomes.

The important point for this paper is not that one particular clock is correct.

It is that chronological age is not sufficient to describe biological time.

A calendar tells us how much time has passed.

It does not necessarily tell us what the body has done with that time.

The Continuity Project began with a different problem.

What happens when the body's timing systems are disrupted by movement?

Jet lag is not simply the experience of being tired after a flight. It reflects a mismatch between the internal circadian timing system and the new external environment. The literature on jet lag describes disruption of sleep, performance and other physiological functions following rapid movement across time zones.

Circadian biology also shows that the body does not operate according to a single clock.

The mammalian circadian system includes a central pacemaker in the suprachiasmatic nuclei and subsidiary clocks throughout the body. The central system coordinates timing through neural and hormonal signals, while peripheral tissues maintain their own oscillatory processes.

This distributed architecture creates the possibility of temporal asynchrony.

Following disruption, different physiological processes do not necessarily return to their previous state simultaneously. Sleep, alertness, hormonal rhythms, immune activity and autonomic regulation can each respond to changes in timing through different mechanisms and on different trajectories.

Research on circadian disruption has demonstrated effects on immune and inflammatory regulation, while work on night-shift adaptation has shown changes across sleep, performance, mood and autonomic modulation.

This is where our research question begins.

At the scale of aging, biological systems can appear to move at different rates across years and decades.

At the scale of circadian disruption, physiological systems can respond and recover on different trajectories across hours and days.

We are interested in whether the organizational resemblance between these phenomena is useful.

We do not claim that they share the same mechanism.

They do not.

We are asking whether they share a systems-level pattern:

Biological systems do not necessarily change, recover, or adapt in lockstep.

We use the term organizational principle deliberately.

We are not proposing that organ aging and circadian disruption are biologically equivalent. They involve different mechanisms, different timescales and different clinical questions.

Instead, we are exploring whether asynchronous biological timing can serve as a useful abstraction across scales.

At one scale, an individual's organs may exhibit different aging trajectories.

At another, different physiological processes may respond differently to a disruption in environmental timing.

In both cases, the body is better represented as a collection of interacting systems than as a single number moving along a single line.

This leads to a question that sits at the center of the Continuity Project:

If biological systems operate on multiple timelines, how should health information represent those timelines over time?

The Continuity Project has developed a framework for representing biological context during disruption.

It currently consists of four related components.

The Three-Clock System.

The Three-Clock System distinguishes between T_bio, T_local and T_utc: biological time, local civil time and universal time.

The purpose is not to claim that T_bio is a directly measured physiological clock.

It is a computational representation of biological timing context during movement.

The system asks a simple question:

Where is the person's biological timing relative to the time shown by the clock around them?

This provides a conceptual parallel to the distinction between biological and chronological age, while operating on a much shorter timescale.

BIO / UTC Anchor Logic.

Not every health-related event should be anchored to the same concept of time.

Some timing decisions are related to biological rhythms. Others depend on elapsed intervals or an absolute reference.

The framework therefore distinguishes between biological and universal-time anchors rather than treating all time-dependent events as equivalent.

This is a software design principle informed by chronobiology, not a claim that the system can directly measure every underlying biological process.

Narrative Correlation.

Health data becomes more meaningful when it can be interpreted in relation to time and circumstances.

The Continuity Project therefore explores a form of narrative correlation: connecting observations with the person's position in a journey, previous patterns and known physiological context.

The objective is not diagnosis.

It is to preserve the relationship between what happened, when it happened, and what happened next.

Second Sense.

The Second Sense concept explores the same principle from the user's perspective.

When someone records an experience such as fatigue, digestive disruption or discomfort, the system can place that observation within the person's existing context rather than treating it as an isolated event.

Again, this is not intended to diagnose a condition.

It is an exploration of whether context accumulated over time can make individual observations more intelligible.

The distinction we are exploring is continuity rather than measurement alone.

Biological aging clocks can provide valuable information about biological state. Circadian measurements can provide information about biological timing. Wearables and smartphones can generate longitudinal observations.

But a measurement taken at one point in time is still a point in time.

Digital phenotyping research has demonstrated the potential of personal digital devices to capture human behavior and physiology longitudinally and in naturalistic settings. It has also emphasized that data must be interpreted in relation to meaningful scientific questions and context if it is to become useful information.

This distinction matters to the Continuity Project.

We are not proposing that continuous data is automatically better than episodic data.

We are asking whether preserving the trajectory around a measurement can change what that measurement means.

A value may look different because something fundamental has changed.

Or because the person is traveling.

Or recovering from an illness.

Or experiencing a temporary disruption.

Or simply because biological measurements vary.

Without the surrounding timeline, those possibilities can be difficult to distinguish.

Continuity therefore means preserving enough of the surrounding history to understand a measurement as part of a trajectory rather than as an isolated event.

Biological clocks estimate aspects of biological state.

The Continuity framework asks what happens when those estimates are placed inside a person's timeline.

At the scale of aging, this might eventually mean knowing not only an organ's estimated biological age, but how that estimate has changed, what else was happening during the same period, and whether the observed change persists.

At the scale of circadian disruption, it might mean knowing not only that someone feels fatigued, but when the disruption began, how their sleep and timing changed, what happened during previous disruptions, and how recovery unfolded.

The organizing logic is similar:

measure → locate in time → compare with prior state → interpret in context → observe what happens next.

This is not a diagnostic framework.

It is a framework for preserving context around change.

And this distinction is important.

Continuity does not replace diagnosis. It does not replace biomarkers. It does not replace clinical judgment.

It is intended to preserve the information that exists between those events.

The Continuity Project is a research program, but it is also informing the development of commercial software.

That relationship needs to be stated carefully.

The product does not prove the research hypothesis.

Instead, the hypothesis informs what we build, and the product gives us a practical environment in which to explore whether the underlying ideas are useful.

Compass by ZKOMI is an early implementation of this approach.

It applies the Three-Clock System and related continuity concepts to people whose health context can be disrupted by travel, changing time zones, fragmented care and movement between providers.

Health Context Tokens explore a related problem: whether people can carry and selectively share relevant health context without surrendering custody of their complete medical history.

The Council project explores whether multiple clinical perspectives can be represented as part of one longitudinal patient timeline rather than as disconnected episodes.

These are product implementations of research ideas, not scientific validation of them.

The scientific questions remain open.

The development of biological aging clocks makes the Continuity question more important, not less.

If biological measurement continues moving from research laboratories toward routine individual use, people may increasingly receive measurements of biological age, organ age, cellular age or biological pace.

The scientific challenge then changes.

It is no longer only:

Can we measure biological time?

It becomes:

How should a person understand a biological measurement in the context of their own history?

This is where the research paths may eventually intersect.

The Continuity Project does not currently measure organ age.

It does not claim to predict organ aging.

It does not claim that its circadian framework can reproduce biological aging clocks.

But the emergence of increasingly sophisticated biological clocks creates a future research question:

Could a continuity framework eventually provide longitudinal context around biological measurements that today are primarily interpreted as individual data points?

That question is not answered by this paper.

It is one reason to keep investigating it.

This paper is deliberately limited in its claims.

The comparison between organ aging and circadian disruption is conceptual.

The mechanisms are different.

The timescales are different.

The available measurements are different.

The Continuity framework has not been validated against proteomic or epigenetic measures of biological aging.

The ZKOMI application is not a clinical trial and should not be interpreted as evidence that the framework improves health outcomes.

Several questions remain open:

• Can asynchronous biological timing provide a useful abstraction across different timescales?
• What can continuous longitudinal context reveal that episodic measurements cannot?
• Can personal baselines improve interpretation of biological measurements?
• What is the relationship between accumulated circadian disruption and biological aging?
• Could organ-specific biological age measurements eventually be incorporated into a continuity framework?
• How should biological measurements and their uncertainty be communicated to individuals without encouraging overinterpretation?
• Which parts of continuity belong on the device, and which — if any — should be shared with clinicians or researchers?
• Can researchers independently test whether continuity context changes understanding, behavior or clinical decision-making?

These are research questions, not product claims.

Biological aging research is showing that chronological time is an incomplete representation of biological time.

Circadian research shows something related at another scale: biological timing is distributed, and different physiological processes can respond to disruption through different trajectories.

The Continuity Project is exploring whether these observations can be connected by a broader systems-level idea:

The body does not move in lockstep.

If that is true, then health information may need to represent more than a single number or a single moment.

It may need to preserve where a person was, what changed, what came before, what followed, and how different biological processes moved through time.

Calendars measure elapsed time.

Biological clocks attempt to measure biological time.

Continuity asks what happens between the measurements.

That is the question we are building around.

We publish this paper as a contribution to an open research conversation, not as a definitive conclusion.

We are particularly interested in connecting with researchers working across biological clocks, aging, circadian biology, digital phenotyping, longitudinal health data, human-computer interaction and patient-controlled health information.

The goal is not to claim that one framework explains all of these fields.

The goal is to find out whether there is something worth investigating at their intersection.

We welcome criticism, collaboration and alternative interpretations.

Contact: hello@zkomi.com

Biological Aging

1. Wyss-Coray, T. & Topol, E. J. (2026). Biological aging clocks in health and disease. Nature Medicine, 32, 2383–2394.
2. Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14, R115.
3. Lehallier, B., Gate, D., Schaum, N., et al. (2019). Undulating changes in human plasma proteome profiles across the lifespan. Nature Medicine, 25, 1843–1850.
4. Oh, H. S., et al. (2023). Organ aging signatures in the plasma proteome track health and disease. Nature, 624, 164–172.
5. Oh, H. S., et al. (2025). Plasma proteomics links brain and immune system aging with healthspan and longevity. Nature Medicine, 31, 2703–2711.

Circadian Timing and Disruption

6. Waterhouse, J., Reilly, T., Atkinson, G. & Edwards, B. (2007). Jet lag: trends and coping strategies. The Lancet, 369(9567), 1117–1129.
7. Dibner, C., Schibler, U. & Albrecht, U. (2010). The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annual Review of Physiology, 72, 517–549.
8. Saper, C. B., Scammell, T. E. & Lu, J. (2005). Hypothalamic regulation of sleep and circadian rhythms. Nature, 437, 1257–1263.
9. Castanon-Cervantes, O., Wu, M., Ehlen, J. C., et al. (2010). Dysregulation of inflammatory responses by chronic circadian disruption. Journal of Immunology, 185(10), 5796–5805.
10. Boudreau, P., Dumont, G. A. & Boivin, D. B. (2013). Circadian adaptation to night shift work influences sleep, performance, mood and the autonomic modulation of the heart. PLoS ONE, 8(7), e70813.

Longitudinal and Digital Health

11. Steinhubl, S. R., Muse, E. D. & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), 283rv3.
12. Torous, J., Onnela, J.-P. & Keshavan, M. (2017). New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Translational Psychiatry, 7, e1053.
13. Huckvale, K., Venkatesh, S. & Christensen, H. (2019). Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine, 2, 88.

Adaptation

14. McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences, 840, 33–44.

ZKOMI Research

15. ZKOMI Research Team. (2026). Paper 002: The Three-Clock System. The Continuity Project.
16. ZKOMI Research Team. (2026). Paper 006: The Cortisol–Peptide Interaction Map. The Continuity Project.
17. ZKOMI Research Team. (2026). Paper 011: Settle Before You Sync. The Continuity Project.
18. ZKOMI Research Team. (2026). Paper 014: Second Sense — Narrative Correlation as the Foundation of Health Continuity. The Continuity Project.
19. ZKOMI Research Team. (2026). Paper 015: The AHA Engine. The Continuity Project.
20. ZKOMI Research Team. (2026). Paper 019: Health Context Tokens. The Continuity Project.