Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses

Elisa Borroni; Gianfranco Frigerio; Elisa Polledri; Rosa Mercadante; Cristina Maggioni; Luca Fedrizzi; Angela Cecilia Pesatori; Silvia Fustinoni; Michele Carugno

doi:10.3389/fpubh.2023.1082074

Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses

Front Public Health. 2023 Feb 23:11:1082074. doi: 10.3389/fpubh.2023.1082074. eCollection 2023.

Authors

Elisa Borroni¹, Gianfranco Frigerio^{1

2

3}, Elisa Polledri¹, Rosa Mercadante¹, Cristina Maggioni¹, Luca Fedrizzi³, Angela Cecilia Pesatori^{1

3}, Silvia Fustinoni^{1

3}, Michele Carugno^{1

3}

Affiliations

¹ Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
² Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
³ Occupational Health Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.

Abstract

Background and aim: Shift work, especially including night shifts, has been found associated with several diseases, including obesity, diabetes, cancers, and cardiovascular, mental, gastrointestinal and sleep disorders. Metabolomics (an omics-based methodology) may shed light on early biological alterations underlying these associations. We thus aimed to evaluate the effect of night shift work (NSW) on serum metabolites in a sample of hospital female nurses.

Methods: We recruited 46 nurses currently working in NSW in Milan (Italy), matched to 51 colleagues not employed in night shifts. Participants filled in a questionnaire on demographics, lifestyle habits, personal and family health history and work, and donated a blood sample. The metabolome was evaluated through a validated targeted approach measuring 188 metabolites. Only metabolites with at least 50% observations above the detection limit were considered, after standardization and log-transformation. Associations between each metabolite and NSW were assessed applying Tobit regression models and Random Forest, a machine-learning algorithm.

Results: When comparing current vs. never night shifters, we observed lower levels of 21 glycerophospholipids and 6 sphingolipids, and higher levels of serotonin (+171.0%, 95%CI: 49.1-392.7), aspartic acid (+155.8%, 95%CI: 40.8-364.7), and taurine (+182.1%, 95%CI: 67.6-374.9). The latter was higher in former vs. never night shifters too (+208.8%, 95%CI: 69.2-463.3). Tobit regression comparing ever (i.e., current + former) and never night shifters returned similar results. Years worked in night shifts did not seem to affect metabolite levels. The Random-Forest algorithm confirmed taurine and aspartic acid among the most important variables in discriminating current vs. never night shifters.

Conclusions: This study, although based on a small sample size, shows altered levels of some metabolites in night shift workers. If confirmed, our results may shed light on early biological alterations that might be related to adverse health effects of NSW.

Keywords: Random Forest; Tobit regression; machine-learning; night shift work; nurses; occupational health; targeted metabolomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aspartic Acid
Cross-Sectional Studies
Female
Hospitals
Humans
Sleep*
Work Schedule Tolerance*

Substances

Aspartic Acid

Grants and funding

This research was funded by the University of Milan (UNIMI - Piano Sostegno alla Ricerca Anno 2019 - Linea 2). GF was currently supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. The APC was supported by the Luxembourg National Research Fund (FNR).