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Plasma sCD36 as non-circadian marker of chronic circadian disturbance in shift workers

Author

Listed:
  • Daniella van de Langenberg
  • Jelle J Vlaanderen
  • Martijn E T Dolle
  • Aase Handberg
  • Roel C H Vermeulen
  • Linda W M van Kerkhof

Abstract

Shift work induces chronic circadian disturbance, which might result in increased health risks, including cardio-metabolic diseases. Previously, we identified sCD36 as a potential non-circadian biomarker of chronic circadian disturbance in mice. The aim of the current study (n = 232 individuals) was to identify whether sCD36 measured in plasma can be used as a non-circadian marker of chronic circadian disturbance in humans, which would allow its use to measure the effects of interventions and monitoring in large-scale studies. We compared levels of plasma sCD36 of day workers with recent ( 5 years) night-shift workers within the Klokwerk study. We detected no differences in sCD36 levels between day workers and recent or experienced night-shift workers, measured during a day or afternoon shift. In addition, sCD36 levels measured directly after a night shift were not different from sCD36 levels measured during day or afternoon shifts, indicating no acute effect of night shifts on sCD36 levels in our study. In summary, our study does not show a relation between night-shift work experience (recent or long-term) and plasma levels of sCD36. Since we do not know if and for which time span night-shift work is associated with changes in sCD36 levels, and our study was relatively small and cross-sectional, further evidence for an association between chronic circadian disruption and this candidate biomarker sCD36 should be gathered from large cohort studies.

Suggested Citation

  • Daniella van de Langenberg & Jelle J Vlaanderen & Martijn E T Dolle & Aase Handberg & Roel C H Vermeulen & Linda W M van Kerkhof, 2019. "Plasma sCD36 as non-circadian marker of chronic circadian disturbance in shift workers," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-14, October.
    • Handle: RePEc:plo:pone00:0223522
    DOI: 10.1371/journal.pone.0223522
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
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