Evidence Roadmap
Development status by stage
Where the science stands today, what is active, and what milestones are required before clinical or consumer efficacy claims can be substantiated.
Stage 1
Complete
Observational Foundation
Population-scale protein–disease associations established in UKB-PPP (54,306 participants, 2,923 proteins via Olink Explore 3072) and EPIC (520,000 participants). Organ-specific proteomic aging signatures validated across three independent publications in Nature and Nature Medicine (Oh et al. 2023, Oh et al. 2025, Wang et al. 2025). AUC 0.75–0.88 for cardiometabolic risk prediction from proteomics alone (Carrasco-Zanini et al. 2024). Exposome–proteome contributions to aging quantified (Argentieri et al. 2025; Tsuo et al. 2025).
Published · Peer-reviewed
Stage 2
In Progress
Platform Analytical Validation
Nomic Omni 1000 analytical validation: precision (<10% CV), reproducibility, and cross-platform concordance with Olink PEA (r>0.95). Organ-specific aging clock construction on Nomic discovery data. DPS/VAMS matrix equivalence studies to validate at-home capillary collection against venipuncture reference standard.
Active · 2025–2026
Stage 3
Planned
Independent Prospective Replication
External validation of SYN-WAVE organ aging scores in a demographically distinct, prospective cohort (target: US-based, 2,000–5,000 participants). Head-to-head comparison of proteomic risk models vs. established clinical risk scores (Framingham, Pooled Cohort Equations). Required before any clinical or consumer efficacy claim can be made.
Planned · 2026–2027
Stage 4
Future
Clinical Utility Demonstration
Prospective decision-impact study demonstrating that proteomic risk scores change physician or patient behaviour and improve downstream clinical outcomes. Required for MolDX reimbursement consideration for SYN-DROME LDT. Timeline: 2027–2028 at earliest, contingent on Series A funding and regulatory pre-submission.
Future · Series A milestone
Evidence architecture: Stages 1–2 leverage existing UKB-PPP and EPIC observational data to validate the platform and substantiate wellness-level claims. Stage 3 independent replication is required before clinical positioning. This is the same evidence pathway used by GRAIL, Biodesix, and reimbursed MolDX products.
Key Publications
Foundational peer-reviewed literature
Ten papers from Nature, Nature Medicine, and Nature Aging — grouped by scientific theme. Papers marked Synoptic Team were authored by current Synoptic leadership.
Synoptic Team Papers
2 papers
Plasma proteomic associations with genetics and health in the UK Biobank
Nature, 622, 329–338 · DOI: 10.1038/s41586-023-06592-6
The UKB Pharma Proteomics Project flagship paper. Established the largest plasma proteomics resource in the world: 2,923 proteins measured in 54,306 participants using Olink Explore 3072, linked to genome-wide genetic variants (10,000+ pQTLs), clinical phenotypes, and longitudinal disease endpoints. Causal protein–disease relationships mapped via Mendelian randomisation at unprecedented scale. Synoptic's CEO (Chris Whelan) was senior corresponding author, directing the project during his J&J tenure. The primary reference dataset and analytical benchmark for SYN-DROME's disease risk panel design.
Personal aging markers and ageotypes revealed by deep longitudinal profiling
Nature Medicine, 26, 83–90 · DOI: 10.1038/s41591-019-0719-5
First-authored by Synoptic's CTO (Sara Ahadi). Introduced the ageotype concept: individuals age along distinct molecular trajectories that are organ-system-specific — metabolic, immune, hepatic, nephrotic. Deep longitudinal multi-omic profiling revealed that proteomic changes during aging are non-linear, person-specific, and meaningfully distinct between organ systems. The conceptual framework underlying SYN-WAVE's organ-by-organ scoring architecture and Synoptic's longitudinal repeat-testing thesis.
Organ-Specific Proteomic Aging
3 papers
Organ aging signatures in the plasma proteome track health and disease
Nature, 624, 164–172 · DOI: 10.1038/s41586-023-06802-1
Established that organ-specific biological age is detectable from plasma proteins alone, without tissue biopsies or imaging. Defined the original 11-organ clock architecture (heart, adipose, liver, kidney, lung, brain, immune, musculoskeletal, endocrine, gut, vasculature) and demonstrated that accelerated organ aging predicts incident disease, mortality, and cognitive decline independently of chronological age. Cornerstone reference for SYN-WAVE's organ scoring methodology.
Plasma proteomics links brain and immune system aging with healthspan and longevity
Nature Medicine, 31 · DOI: 10.1038/s41591-025-03798-1
Follow-up to Oh et al. 2023, demonstrating that brain and immune system aging scores are the two strongest predictors of healthspan and exceptional longevity across multiple independent cohorts. Validates SYN-WAVE's neuro and immune sub-panels as the highest-signal organ systems for consumer health monitoring and longevity-oriented product positioning.
Organ-specific proteomic aging clocks predict disease and longevity across diverse populations
Nature Aging, 5 · DOI: 10.1038/s43587-025-01016-8
Demonstrated that organ-specific proteomic aging clocks trained in UK Biobank generalise across ethnically diverse cohorts, including a Chinese population cohort, removing the key concern about European-ancestry-only training data. Strengthens the case for a universally applicable scoring approach and addresses a central diligence question about generalisability of the underlying models.
Aging Clocks & Proteomic Trajectories
2 papers
Undulating changes in human plasma proteome profiles across the lifespan
Nature Medicine, 25, 1843–1850 · DOI: 10.1038/s41591-019-0673-2
Seminal paper establishing that proteomic aging is non-linear: plasma protein levels change in three distinct waves at approximately ages 34, 60, and 78. Reframed the field from a linear model of cumulative protein change toward the plasma proteome as a dynamic, time-indexed readout of biological state. Establishes the scientific rationale for repeat-sample monitoring rather than single-timepoint risk assessment.
An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging
Nature Aging, 1, 598–615 · DOI: 10.1038/s43587-021-00082-y
Derived from the 1000 Immunomes Project. Demonstrated that a deep-learning protein clock trained on inflammatory markers (iAge) predicts multimorbidity, frailty, and cardiovascular disease independently of chronological age. CXCL9 identified as the dominant contributor. Inflammation quantified via the proteome is measurable, modifiable, and predictive — directly informing SYN-WAVE's inflammation-axis sub-score design.
Proteomic Disease Prediction & Exposome
3 papers
Proteomic signatures improve risk prediction for common and rare diseases
Nature Medicine, 30, 2288–2299 · DOI: 10.1038/s41591-024-03142-z
In UK Biobank, small panels of 5–20 proteins outperformed established clinical risk scores (Framingham, PCE) for type 2 diabetes, cardiovascular disease, and dementia across multiple time horizons. Net reclassification improvement demonstrated for all three conditions. The direct methodological blueprint for SYN-DROME's multi-disease proteomic risk panel design and clinical claim architecture.
Integrating the environmental and genetic architectures of aging and mortality
Nature Medicine, 31 · DOI: 10.1038/s41591-024-03483-9
Using twin-design studies across multiple cohorts, demonstrated that modifiable environmental factors explain substantially more variance in biological aging and mortality than genetic factors. Proteomic biomarkers integrate environmental exposure signals at least as well as genomic measures. Provides the causal rationale for measuring the exposome via plasma proteomics as the highest-leverage target for personalisable aging intervention — the central scientific premise of SYN-WAVE.
Proteomic prediction of disease largely reflects environmental risk exposure
Preprint · 2025 · PDF ↗
Companion analysis to Argentieri et al. Demonstrated that predictive power in proteomic disease models derives primarily from environment-driven (and therefore modifiable) protein variation, not fixed genetic background. Shared genetic architecture explains little of the protein–disease predictive signal once environmental factors are accounted for. Directly validates the interventional and actionable framing of SYN-WAVE results: biological age scores reflect lifestyle inputs, not immutable genetics.
Cohort Infrastructure
Population cohort resources available to the programme
Five population cohorts are available to Synoptic's scientific programme — through existing team relationships, scientific advisory board connections, and open-access researcher programmes. Access to each cohort is contingent on approval of a scientific application submitted after company incorporation.
UK Biobank Pharma Proteomics Project
UKB-PPP · 13-consortium pharmaceutical collaboration
PlatformOlink Explore 3072
Proteins measured2,923
pQTLs identified10,000+
Follow-upLongitudinal (10+ years)
AncestryPrimarily European, multi-ethnic
Access modelUK Biobank Trusted Research Environment (TRE); scientific application required
Synoptic access pathway
Chris Whelan (CEO) was senior corresponding author on the UKB-PPP flagship Nature 2023 paper and has direct familiarity with the data structure, analytical pipelines, and TRE access protocols. Daniel McCartney (Principal Scientist, contracted) holds independent UKB access for proteomics workstreams. Formal access requires a scientific application to UK Biobank after Synoptic's incorporation.
Relevance to SYN-DROME: Primary reference dataset for proteomic disease-risk associations. Protein–disease association library and pQTL resource provide the discovery foundation for multi-disease risk score development and MR-based causal inference.
European Prospective Investigation into Cancer & Nutrition
EPIC · 23-country prospective cohort
Countries23 (European)
DesignProspective (enrolled 1992–2000)
BiobankPlasma samples archived
Key endpointsCancer, CVD, metabolic disease
Dietary dataValidated questionnaires + biomarkers
Access modelCollaborative application to EPIC steering committee; scientific review required
Synoptic access pathway
Karl Smith-Byrne (confirmed Synoptic SAB) is Chair of the EPIC Genetics Working Group and a Steering Committee member of the NCI Cohort Consortium, providing direct collaborative standing within EPIC. Access to specific EPIC data components requires approval of a scientific application through the EPIC steering committee after incorporation.
Relevance to SYN-WAVE: Primary cohort for exposome–proteome associations. EPIC's unparalleled dietary, lifestyle, and environmental exposure data — combined with a large plasma biobank — makes it the principal resource for SYN-WAVE's environmental exposure modelling and protein mediator analysis.
Generation Scotland
GS · Scottish multi-omic family cohort
LocationScotland (multi-centre)
DesignFamily-based + general population
ProteomicsOlink + SomaScan sub-studies
DNAmOne of world's largest single-cohort datasets
GenomicsGWAS-level array + imputation
Access modelCollaboration agreement with University of Edinburgh; scientific application required
Synoptic access pathway
Daniel McCartney, PhD (Synoptic Principal Scientist, contracted) led and managed the Generation Scotland multi-omics resource and developed one of the world's largest single-cohort DNA methylation datasets within GS. Access for Synoptic's incorporated entity requires a formal scientific application and data sharing agreement with the University of Edinburgh.
Relevance to SYN-WAVE: Critical for DNAm–proteome integration and aging clock cross-validation. Generation Scotland's multi-omic depth enables mediation analyses linking exposome → methylome → proteome → disease — supporting the biological interpretation layer of SYN-WAVE reports.
All of Us Research Program
AoU · NIH national precision medicine initiative
FunderNIH (US national initiative)
DesignProspective; rolling enrolment (2018–present)
Diversity focus>50% from racial/ethnic minority groups; US-representative
Data layersWhole genome sequencing, EHR linkage, wearables, surveys, biosamples
ProteomicsOlink sub-studies ongoing; expanding
Access modelNIH Researcher Workbench; registration, training, and data use agreement required
Synoptic access pathway
All of Us operates an open researcher access model via the NIH Researcher Workbench. Access requires researcher registration, completion of NIH data security training, and a data use agreement. No prior collaboration required; application is open to incorporated research entities. Provides the US-based, demographically diverse validation cohort needed for Stage 3 of Synoptic's evidence roadmap.
Relevance to Stage 3 validation: The primary candidate for independent prospective replication of SYN-WAVE organ aging scores in a US, demographically diverse population — directly addressing the European-ancestry limitation of UKB-PPP training data and the evidence gap between Stage 2 and Stage 3 of the roadmap.
Melbourne Collaborative Cohort Study
MCCS · Cancer Council Victoria
LocationMelbourne, Australia
DesignProspective (enrolled 1990–1994; follow-up ongoing)
AncestryEuropean; includes substantial Italian and Greek immigrant sub-cohorts
BiobankBlood samples archived at multiple timepoints
LinkageCancer registry, national death index, hospital admissions
Access modelCollaboration agreement with Cancer Council Victoria; scientific application required
Synoptic access pathway
Access is managed by Cancer Council Victoria through a formal scientific application and data sharing agreement. Repeat blood sampling across multiple timepoints makes MCCS particularly valuable for longitudinal proteomic aging analyses. Application is open to external incorporated research partners following scientific review.
Relevance to SYN-WAVE & SYN-DROME: Longitudinal multi-timepoint blood biobank enables proteomic aging trajectory analysis and cancer-endpoint validation in an independent Southern Hemisphere cohort — complementing the European UKB-PPP and EPIC datasets and strengthening cross-population generalisability of SYN-DROME's cancer risk panels.