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      Immune disruptions and night shift work in hospital healthcare professionals: The intricate effects of social jet-lag and sleep debt

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          Abstract

          Objectives

          We aimed to examine the effects of circadian and sleep rhythm disruptions on immune biomarkers among hospital healthcare professionals working night shifts and rotating day shifts.

          Methods

          Hospital nurses working either as permanent night shifters (n=95) or as day shifters rotating between morning and afternoon shifts (n=96) kept a daily diary on their sleep and work schedules over a full working week. Blood samples were collected at the beginning and end of the last shift during the week, and participants were categorized into three groups based on work shift: morning shift (39 day shifters sampled at 7:00 and 14:00), afternoon shift (57 day shifters sampled at 14:00 and 21:00), and night shift (95 night shifters sampled at 21:00 and 7:00). Circulating blood counts in immune cells, interleukin-6 and C-reactive protein concentrations as well as total sleep time per 24 hours during work days (TST24w) and free days (TST24f), sleep debt (TST24f — TST24w) and social jet-lag (a behavioral proxy of circadian misalignment) were assessed.

          Results

          Compared with day shifters, night shifters had shorter sleep duration (TST24w=5.4 ± 1.4h), greater sleep debt (3.2 ± 1.4 h) and social jet-lag (6.7 ± 2.4 h). Variations of immune biomarkers concentrations were consistent with the expected diurnal variations among day shifters (i.e., low level in the morning, increase during the day, peak value in the evening). By contrast, in night shifters, blood concentrations of total lymphocytes, T-helper cells, cytotoxic T-cells, memory B-cells and interleukin-6 were lower at 21:00, increased during the night, and reached higher values at 7:00. Multivariate analyses ruled out significant impact of TST24w, sleep debt, and social jet-lag on immune biomarkers concentrations among day shifters. In contrast, among night shifters, multivariate analyses indicated a combined effect of total sleep time (TST24w), sleep debt and social jet-lag for total lymphocytes and T-helper cells but only a social jet-lag effect for interleukin-6 and a single total sleep time effect for neutrophil and B-Cells.

          Conclusions

          Altogether, our results point to intricate response patterns of immune rhythms to circadian misalignment and sleep debt in night shifters. Specifically, these altered pattern expressions of immune cells may increase vulnerability to infections and reduce vaccination efficiency in night workers.

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          Most cited references73

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          Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies.

          Aims To assess the relationship between duration of sleep and morbidity and mortality from coronary heart disease (CHD), stroke, and total cardiovascular disease (CVD). Methods and results We performed a systematic search of publications using MEDLINE (1966-2009), EMBASE (from 1980), the Cochrane Library, and manual searches without language restrictions. Studies were included if they were prospective, follow-up >3 years, had duration of sleep at baseline, and incident cases of CHD, stroke, or CVD. Relative risks (RR) and 95% confidence interval (CI) were pooled using a random-effect model. Overall, 15 studies (24 cohort samples) included 474 684 male and female participants (follow-up 6.9-25 years), and 16 067 events (4169 for CHD, 3478 for stroke, and 8420 for total CVD). Sleep duration was assessed by questionnaire and incident cases through certification and event registers. Short duration of sleep was associated with a greater risk of developing or dying of CHD (RR 1.48, 95% CI 1.22-1.80, P < 0.0001), stroke (1.15, 1.00-1.31, P = 0.047), but not total CVD (1.03, 0.93-1.15, P = 0.52) with no evidence of publication bias (P = 0.95, P = 0.30, and P = 0.46, respectively). Long duration of sleep was also associated with a greater risk of CHD (1.38, 1.15-1.66, P = 0.0005), stroke (1.65, 1.45-1.87, P < 0.0001), and total CVD (1.41, 1.19-1.68, P < 0.0001) with no evidence of publication bias (P = 0.92, P = 0.96, and P = 0.79, respectively). Conclusion Both short and long duration of sleep are predictors, or markers, of cardiovascular outcomes.
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            A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms.

            An English language self-assessment Morningness-Eveningness questionnaire is presented and evaluated against individual differences in the circadian vatiation of oral temperature. 48 subjects falling into Morning, Evening and Intermediate type categories regularly took their temperature. Circadian peak time were identified from the smoothed temperature curves of each subject. Results showed that Morning types and a significantly earlier peak time than Evening types and tended to have a higher daytime temperature and lower post peak temperature. The Intermediate type had temperatures between those of the other groups. Although no significant differences in sleep lengths were found between the three types, Morning types retired and arose significantly earlier than Evening types. Whilst these time significatly correlated with peak time, the questionnaire showed a higher peak time correlation. Although sleep habits are an important déterminant of peak time there are other contibutory factors, and these appear to be partly covered by the questionnaire. Although the questionnaire appears to be valid, further evaluation using a wider subject population is required.
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              Quantity and Quality of Sleep and Incidence of Type 2 Diabetes

              OBJECTIVE To assess the relationship between habitual sleep disturbances and the incidence of type 2 diabetes and to obtain an estimate of the risk. RESEARCH DESIGN AND METHODS We conducted a systematic search of publications using MEDLINE (1955–April 2009), EMBASE, and the Cochrane Library and manual searches without language restrictions. We included studies if they were prospective with follow-up >3 years and had an assessment of sleep disturbances at baseline and incidence of type 2 diabetes. We recorded several characteristics for each study. We extracted quantity and quality of sleep, how they were assessed, and incident cases defined with different validated methods. We extracted relative risks (RRs) and 95% CI and pooled them using random-effects models. We performed sensitivity analysis and assessed heterogeneity and publication bias. RESULTS We included 10 studies (13 independent cohort samples; 107,756 male and female participants, follow-up range 4.2–32 years, and 3,586 incident cases of type 2 diabetes). In pooled analyses, quantity and quality of sleep predicted the risk of development of type 2 diabetes. For short duration of sleep (≤5–6 h/night), the RR was 1.28 (95% CI 1.03–1.60, P = 0.024, heterogeneity P = 0.015); for long duration of sleep (>8–9 h/night), the RR was 1.48 (1.13–1.96, P = 0.005); for difficulty in initiating sleep, the RR was 1.57 (1.25–1.97, P < 0.0001); and for difficulty in maintaining sleep, the RR was 1.84 (1.39–2.43, P < 0.0001). CONCLUSIONS Quantity and quality of sleep consistently and significantly predict the risk of the development of type 2 diabetes. The mechanisms underlying this relation may differ between short and long sleepers.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                09 September 2022
                2022
                : 13
                : 939829
                Affiliations
                [1] 1 Université Paris Cité, VIFASOM (UPR 7330 Vigilance Fatigue, Sommeil et Santé Publique) , Paris, France
                [2] 2 APHP, APHP-Centre Université de Paris, Hôtel Dieu, Centre du Sommeil et de La Vigilance , Paris, France
                [3] 3 Inserm, CESP (Center for research in Epidemiology and Population Health), Team Exposome and Heredity, University Paris-Saclay, Gustave-Roussy , Villejuif, France
                [4] 4 Institut de Recherche Biomédicale des Armées (IRBA), Unité Fatigue et Vigilance , Brétigny sur Orge, France
                [5] 5 UPR “Chronothérapie, Cancers, et Transplantation”, Faculté de Médecine, Université Paris-Saclay , Villejuif, France
                [6] 6 Hepato-Biliary Center, Hôpital Paul Brousse , Villejuif, France
                [7] 7 Cancer Chronotherapy Team, Cancer Research Centre, Division of Biomedical Sciences, Warwick Medical School , Coventry, United Kingdom
                Author notes

                Edited by: Yoshiro Kobayashi, Toho University, Japan

                Reviewed by: Christopher Depner, The University of Utah, United States; Tetsuo Nakajima, National Institute of Radiological Sciences (NIRS), Japan

                *Correspondence: Brice Faraut, brice.faraut@ 123456aphp.fr

                †These authors have contributed equally to this work

                This article was submitted to Molecular Innate Immunity, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.939829
                9509137
                36164341
                bfb4e1d6-5f4e-490e-86cd-ae8442328ae3
                Copyright © 2022 Faraut, Cordina-Duverger, Aristizabal, Drogou, Gauriau, Sauvet, Lévi, Léger and Guénel

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 May 2022
                : 29 July 2022
                Page count
                Figures: 2, Tables: 4, Equations: 0, References: 73, Pages: 15, Words: 7952
                Categories
                Immunology
                Original Research

                Immunology
                night shift work,sleep debt,social-jet lag,circulating leucocytes,interleukin-6,circadian disruption,hospital workers

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