{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 19,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. Benacom"
    },
    {
      "name": "A. Specht"
    },
    {
      "name": "J. C. Nicholas"
    },
    {
      "name": "R. Guillard"
    },
    {
      "name": "M. Gillman"
    },
    {
      "name": "R. Dubin"
    },
    {
      "name": "P. Ganz"
    },
    {
      "name": "J. I. Rotter"
    },
    {
      "name": "K. D. Taylor"
    },
    {
      "name": "S. S. Rich"
    },
    {
      "name": "P. Y. Liu"
    },
    {
      "name": "A. C. Wood"
    },
    {
      "name": "M. Y. Mi"
    },
    {
      "name": "R. Deo"
    },
    {
      "name": "K.-M. Zitting"
    },
    {
      "name": "L. M. Raffield"
    },
    {
      "name": "C. A. Czeisler"
    },
    {
      "name": "J. F. Duffy"
    },
    {
      "name": "E. Mignot"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/005xqwskepcsq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.30.741635",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2.full",
  "id": "005xqwskepcsq",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ],
    [
      "openalex",
      "1607|Advanced Proteomics Techniques and Applications"
    ]
  ],
  "openalex": "W7202023459",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/005xqwskepcsq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:50Z",
      "native_id": "10.64898/2026.07.30.741635",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.07.30.741635v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Short-term, long-term, and genetic determinants of human plasma proteome variability",
  "title_text": "Short-term, long-term, and genetic determinants of human plasma proteome variability",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:50Z",
  "url": "https://research.draconitic.com/paper/005xqwskepcsq",
  "venue": "bioRxiv",
  "versions": []
}