medRxiv : the preprint server for health sciencesJul 30, 2026

Estimating Human Body Clock Timing from Blood Markers in Large Populations

Circadian Biology Weekly Brief ↗PubMed ↗DOI ↗

Updated Aug 3, 2026

Abstract

Among 3,228 plasma biomarkers, 58% exhibit significant diurnal variation.

Circadian phase can be inferred from blood biomarkers, demonstrating a connection between biological rhythms and physiology.
Machine-learning models can predict blood sampling time with an accuracy of approximately R² ≈ 0.68 using around 60 proteins.
A new concept called circadian acceleration (CA) is introduced, reflecting individual deviation from the average circadian phase.
CA shows temporal stability and is associated with personal chronotype and work shift patterns.
The heritability of CA is estimated at h² SNP ≈ 0.10, indicating a genetic link to chronotype and sleep-related traits.
These findings highlight the potential of plasma proteomics for large-scale molecular studies of circadian rhythms.

Simplified

Circadian timing influences human physiology and disease risk, yet scalable measures of molecular circadian phase are lacking. Here we infer circadian phase from circulating blood biomarkers in UK Biobank. Among 3,228 plasma biomarkers, 58% exhibit significant diurnal variation, with harmonic modeling identifying acrophase clustering consistent with canonical circadian patterns and independent constant-routine datasets. Machine-learning models trained on plasma proteomics predict sampling time (R²≈0.68) and retain substantial accuracy with ∼60 proteins. We define a novel construct, circadian acceleration (CA), as deviation from the population-average phase; CA is temporally stable, associates with chronotype and shift work, and responds to environmental perturbation. CA is heritable (h² _SNP ≈0.10) and genetically correlated with chronotype and accelerometry-derived sleep traits. These results establish plasma proteomics as a scalable approach for population-level molecular circadian phenotyping.