Platform-independent estimation of human physiological time from single blood samples

Significance The importance of circadian rhythm in health is evident in studies from metabolic disorders to Alzheimer’s disease. However, translating these observations to the clinic remains stymied due to the burden of measuring physiological time. Methods to assess physiological time using blood biomarkers address this issue, but they must be accurate and generalizable across protocols and platforms. Here, we present TimeMachine, an algorithm that can estimate the circadian phase from a single blood sample. Validation on four distinct datasets shows TimeMachine accurately recovers phase estimates from a single-timepoint gene expression profile of human peripheral blood mononuclear cells across varied protocols and technologies, an important advance over current methods. This algorithm offers a feasible approach for incorporating circadian biomarkers in research and clinical care.

• Publication year

  2024

• Venue

  Proceedings of the National Academy of Sciences of the United States of America

• Publication date

  2024-01-08

• Fields of study

  Medicine

• Identifiers
  DOI 10.1073/pnas.2308114120PMID 38190520PMCID 10801856
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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