Scientific publications, white papers and plain-language explainers on biomarkers, biological age, metabolomics and preventive health, from the BioTwin team.
Peer-reviewed open-access study in Digital Twin (Taylor & Francis) by Hauguel, Anctil, and Noel, showing that metabolomic profiles from dried blood spots are stable enough to identify individuals across 18,288 samples and 134 analytical batches.
Preprint by Hauguel, Anctil, and Noel showing that dried blood spot metabolomics in 1,784 adults captures the same carnitine, TCA-cycle, redox-thiol, and tryptophan-kynurenine pathways previously implicated in ME/CFS plasma studies, scaling with symptom severity.
Wearables show what life looks like from the outside. Biology helps show what may be changing on the inside. BioTwin brings those two layers together.
Repeated measurements add personal context while established clinical reference ranges remain useful.
What a molecular signal represents, and why a signal count is not a count of clinically validated tests.
A simple explanation of what a human virtual twin is, why it matters, and how BioTwin uses biology, behavior, and time to build a more useful picture of health.
Understand the wellness experience, the research and the scope of BioTwin medical applications.
A one-time result can be helpful, but it cannot fully explain how your biology is changing. BioTwin is built around trajectories, not snapshots.
Scientific overview of BioTwin's medical research methodology, validation approach, and clinical development pathway.
A blood test is a snapshot. A human body is a film. The founding idea behind BioTwin: a person should be understood as a trajectory, not a single measurement.
One word covers too many biological realities. BioTwin's fatigue work treats fatigue as a measurable state space, not a single symptom.
Cancer prevention is personal before it is scientific. Why preventive health needs to move from isolated detection events to continuous biological context.
An individual observational case explores how repeated biological measurements vary during a vegan diet transition.
How repeated measurements and lifestyle records can help explore individual nutritional responses.
The body keeps receipts. Why caffeine and alcohol are personal pharmacology hiding inside everyday habits, and what longitudinal data reveals.
A flight is not one event in the body. It is a sequence. Your itinerary says when you arrived. Your biology says when you recovered.
Wearables are useful. They are also incomplete. When devices disagree about the same body, which one should you believe?
How training load, sleep and delayed fatigue can help put recovery in context, without treating a wellness score as a safe exercise threshold.
The most useful comparison in health may not be you versus everyone else. It may be you versus you. The Me vs. Me thesis behind every BioTwin score.
What a biological-age estimate can tell you, why repeated measurements need context, and what the observational Vitoli analysis does and does not establish.
How an individual observational record generates research questions that must then be tested in appropriate cohorts.
The data, quality controls and validation needed to turn a longitudinal model into a useful tool, with a clear distinction between research and clinical use.
How metabolic signals complement other health information, and why an association is not yet a clinical application.
Why trends add context to a single result, and why a trend alone is not a diagnosis or proof that an intervention works.
Peer-reviewed open-access study in Digital Twin (Taylor & Francis) by Hauguel, Anctil, and Noel, showing that metabolomic profiles from dried blood spots are stable enough to identify individuals across 18,288 samples and 134 analytical batches.
Why it matters: Establishes the methodological foundation for personal-baseline interpretation of longitudinal biomarker data, the core mechanic behind BioTwin's virtual twin model. The same-lab DBS LC-MS protocol and the GroupKFold batch-aware validation standard introduced here underpin all of BioTwin's downstream disease classification work.
Preprint by Hauguel, Anctil, and Noel showing that dried blood spot metabolomics in 1,784 adults captures the same carnitine, TCA-cycle, redox-thiol, and tryptophan-kynurenine pathways previously implicated in ME/CFS plasma studies, scaling with symptom severity.
Why it matters: Reports fatigue-associated metabolic biology from BioTwin's single-laboratory dried blood spot LC-MS platform in a 1,784-participant community cohort, supporting further clinically adjudicated validation of at-home self-collected DBS.
Preprint by Anctil, Hauguel, Rhéaume, Grobmyer, and Noel reporting a retrospective case-control classification study of 114 biopsy-confirmed breast cancer cases and 2,620 non-cancer controls, evaluated across six classifier families.
Why it matters: Reports disease-classification signal from BioTwin's same-lab dried blood spot LC-MS protocol in this retrospective case-control cohort, with batch-aware validation designed to reduce the optimistically biased estimates common in the field.
Preprint by Hauguel, Noel, and Anctil quantifying how poorly four consumer wearables agree with each other across an N-of-1 dataset spanning more than 2,400 days, and how much a harmonization layer can recover.
Why it matters: Wearable signals are a core input to the virtual twin, so knowing exactly where two devices disagree, and by how much, is a prerequisite for combining them. This work shows that identical metric labels do not guarantee comparable measurements and that device-specific recalibration is often needed.
Peer-reviewed open-access study (Digital Twin, 2024) by Fradin, Noel and colleagues, the foundational BioTwin work showing that untargeted metabolomics from dried blood spots can profile and identify individuals across 277 volunteers.
Why it matters: This is BioTwin's foundational, peer-reviewed publication. It first demonstrated that a self-collected dried blood spot carries enough metabolomic signal to profile and identify an individual, the proof of concept that the large-scale 1,257-participant identification study and the disease-detection work were later built on.
Scientific documents explaining BioTwin's validation work, research methodology, and how the virtual twin technology is developed and evaluated.
Scientific overview of BioTwin's medical research methodology, validation approach, and clinical development pathway.
Scientific overview of BioTwin's non-medical virtual twin technology, including longitudinal Bio-Signature tracking, personal baseline modeling, and wellness-only interpretation.
Wearables show what life looks like from the outside. Biology helps show what may be changing on the inside. BioTwin brings those two layers together.
Repeated measurements add personal context while established clinical reference ranges remain useful.
What a molecular signal represents, and why a signal count is not a count of clinically validated tests.
A simple explanation of what a human virtual twin is, why it matters, and how BioTwin uses biology, behavior, and time to build a more useful picture of health.
Understand the wellness experience, the research and the scope of BioTwin medical applications.
A one-time result can be helpful, but it cannot fully explain how your biology is changing. BioTwin is built around trajectories, not snapshots.