Short-term, long-term, and genetic determinants of human plasma proteome variability

Abstract

Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data.

Longitudinal plasma proteome profiling after allogeneic hematopoietic stem cell transplantation: heterogeneous development and associations with acute GvHD
Iris C. Kreft, Myrddin W. Verheij, Maartje van den Biggelaar +8
Frontiers in immunology16 Oct 2026Peer-reviewedCC BYFull textImmunologyEurope PMC
Variability in Clinical Performance of the FDA-Cleared Lumipulse P-Tau217/β-Amyloid 1-42 Plasma Ratio
Alicia Algeciras‐Schimnich, Joshua A. Bornhorst, Daniel J. Figdore +14
JAMA Neurology1 Jun 2026Peer-reviewedLicence not stated6 citedNeuroscienceOpenAlex
Genetic variability and multi-trait analysis for enhanced yield stability under acid soil stress in common bean (Phaseolus vulgaris L.)
Abebe Hinkossa Wirtu, Wosene Gebreselassie Abtew, Berhanu Amsalu Fenta +4
Scientific Reports28 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Plant biologyCrossref
Genetic structure and demographic decline in the endangered short-tailed chinchilla (Chinchilla chinchilla) of the Atacama Region
Moisés A. Valladares, Paola A. Sáez, Paulo S. Zepeda +2
Scientific Reports20 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textGenetics & genomicsCrossref
Duration-Dependent Bias and Variability in Short-Interval Time Reproduction: Evidence for Separable Timing and Control-Related Mechanisms
Aliza Naseer, Elena Gorbunova
PsyArXiv19 Sep 2026PreprintCC BY 4.0Full textNeuroscienceOSF

Cite

BibTeX RIS

BibTeX

@misc{benacom2026shortterm,
  title = {{Short-term, long-term, and genetic determinants of human plasma proteome variability}},
  author = {D. Benacom and A. Specht and J. C. Nicholas and R. Guillard and M. Gillman and R. Dubin and P. Ganz and J. I. Rotter and K. D. Taylor and S. S. Rich and P. Y. Liu and A. C. Wood and M. Y. Mi and R. Deo and K.-M. Zitting and L. M. Raffield and C. A. Czeisler and J. F. Duffy and E. Mignot},
  howpublished = {bioRxiv},
  year = {2026},
  month = jul,
  doi = {10.64898/2026.07.30.741635},
  url = {https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Short-term, long-term, and genetic determinants of human plasma proteome variability
AU  - D. Benacom
AU  - A. Specht
AU  - J. C. Nicholas
AU  - R. Guillard
AU  - M. Gillman
AU  - R. Dubin
AU  - P. Ganz
AU  - J. I. Rotter
AU  - K. D. Taylor
AU  - S. S. Rich
AU  - P. Y. Liu
AU  - A. C. Wood
AU  - M. Y. Mi
AU  - R. Deo
AU  - K.-M. Zitting
AU  - L. M. Raffield
AU  - C. A. Czeisler
AU  - J. F. Duffy
AU  - E. Mignot
PY  - 2026
DA  - 2026/07/30
PB  - bioRxiv
DO  - 10.64898/2026.07.30.741635
UR  - https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2
AB  - Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data.
ER  -

Abstract

Related

Cite