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"https://publications-affiliated.scilifelab.se/researcher/a7bfd631ded14746be0b94b8f1cac968.json"}}, {"family": "Zhang", "given": "Cheng", "initials": "C", "orcid": "0000-0002-3721-8586", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f46e4161be08458994efddb75ed75874.json"}}, {"family": "Zhong", "given": "Wen", "initials": "W", "orcid": "0000-0002-7422-6104", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d6ffb1140aaa42c5ac63fe76653fc63b.json"}}, {"family": "Zwahlen", "given": "Martin", "initials": "M", "orcid": "0000-0002-0064-4776", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/456805fa3f4f47328f3923b78bf66996.json"}}, {"family": "von Feilitzen", "given": "Kalle", "initials": "K", "orcid": "0000-0002-0257-7554", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f4c4545bd2324b7d9b6696dab5bcb7dd.json"}}, {"family": "Nilsson", "given": "Peter", "initials": "P", "orcid": "0000-0002-4657-8532", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3db33fa3edb94febb8be7927a16838d0.json"}}, {"family": "Edfors", "given": "Fredrik", "initials": "F", "orcid": "0000-0002-0017-7987", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9e213b04f8984c8eb00c68f5bc2f6a36.json"}}, {"family": "Uhl\u00e9n", "given": "Mathias", "initials": "M", "orcid": "0000-0002-4858-8056", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9ea446aa574042a295d5f69437402f76.json"}}], "type": "journal article", "published": "2025-12-18", "journal": {"title": "Science (New York, N.Y.)", "issn": "1095-9203", "volume": "390", "issue": "6779", "pages": "eadx2678", "issn-l": "0036-8075"}, "abstract": "The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.", "doi": "10.1126/science.adx2678", "pmid": "41066540", "labels": {"SciLifeLab Fellow": "", "Adil Mardinoglu": ""}, "xrefs": [], "notes": [], "created": "2026-09-23T08:47:54.949Z", "modified": "2026-09-23T08:47:57.223Z"}]}