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Performance of Blood-Based Biomarkers for Human Circadian Pacemaker Phase: Training Sets Matter As Much As

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Developing reliable circadian biomarkers requires careful consideration of training data and experimental conditions. Current methods show variability and may not generalize to real-world scenarios like shift work.

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Area of Science:

  • Chronobiology
  • Molecular Biology
  • Biomarker Discovery

Background:

  • Circadian medicine utilizes biomarkers to assess human health.
  • Low-burden, multivariate molecular approaches are promising for real-world circadian phase assessment.
  • Lack of established standards hinders the development and evaluation of circadian biomarkers.

Purpose of the Study:

  • To explore determinants and confounds in developing blood-based biomarkers for suprachiasmatic nucleus (SCN) phase.
  • To compare feature-selection methods (Partial Least Squares Regression, ZeitZeiger, Elastic Net) and clock genes.
  • To investigate the impact of training sample size and experimental protocols on biomarker performance.

Main Methods:

  • Reanalysis of publicly available datasets.
  • Comparison of three feature-selection methods and a standard set of clock genes.
  • Exploration of training sample size and experimental protocol effects.

Main Results:

  • Small training sample sizes lead to overfitting and poor performance.
  • Biomarker performance depends on both feature-selection method and training data's experimental conditions.
  • Biomarkers trained under baseline conditions may not perform well in real-world scenarios (e.g., shiftwork).
  • Selected molecular features show little overlap across methods but relate to steroid hormone response (e.g., cortisol).

Conclusions:

  • Circadian biomarker development must adhere to established biomarker concepts and circadian biology principles.
  • Current approaches show limitations in generalizability, especially for real-world applications.
  • Further research is needed to establish robust standards for circadian biomarker development and validation.