Sleep disturbance-related depressive symptom and brain volume reduction in shift-working nurses

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

BACKGROUND

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.

METHODS

Using three population-based datasets (N = 1813), we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N = 378, N = 334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association.

FINDINGS

Sleep quality could predict individual DSS (r = 0.43, R2 = 0.18, rMSE = 2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R2 = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS.

INTERPRETATION

Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS.

FUNDING

The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses

BACKGROUND

Shift work disrupts circadian rhythms and alters sleep patterns, resulting in various health problems. To quantitatively assess the impact of shift work on brain health, we evaluated the brain age index (BAI) derived from sleep electroencephalography (EEG) results in night-shift workers and compared it with that in daytime workers.

METHODS

We studied 45 female night shift nurses (mean age: 28.2 ± 3.3 years) and 44 female daytime workers (30.5 ± 4.7 years). Sleep EEG data were analyzed to calculate BAI. The BAI of night shift workers who were asleep during the daytime with those of daytime workers who were asleep at night were statistically compared to explore associations between BAI, duration of shift work, and sleep quality.

RESULTS

Night-shift workers exhibited significantly higher BAI (2.14 ± 6.04 vs. 0 ± 5.35), suggesting accelerated brain aging and altered sleep architecture, including reduced delta and sigma wave frequency activity during non-rapid eye movement sleep than daytime workers. Furthermore, poor deep sleep quality, indicated by a higher percentage of N1, lower percentage of N3, and higher arousal index, was associated with increased BAI among shift workers. Additionally, a longer duration of night-shift work was correlated with increased BAI, particularly in older shift workers.

CONCLUSION

Night-shift work, especially over extended periods, may be associated with accelerated brain aging, as indicated by higher BAI and alterations in sleep architecture. Interventions are necessary to mitigate the health impacts of shift work. Further research on the long-term effects and potential strategies for sleep improvement and mitigating brain aging in shift workers is warranted.

Poor sleep quality is associated with decreased regional brain glucose metabolism in healthy middle-aged adults

Sleep disturbance is associated with the development of neurodegenerative disease. We aimed to address the effects of sleep quality on brain glucose metabolism measured by 18F-Fl uorodeoxyglucose (18F-FDG) positron emission tomography (PET) in healthy middle-aged adults. A total of 378 healthy men (mean age: 42.8±3.6 years) were included in this study. Participants underwent brain 18F-FDG PET and completed the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Additionally, anthropometric measurements were obtained. PETs were spatially normalized to MNI space using PET templates from SPM5 with PMOD. The Automated Anatomical Labeling 2 atlas was used to define regions of interest (ROIs). The mean uptake of each ROI was scaled to the mean of the global cortical uptake of each individual and defined as the standardized uptake value ratio (SUVR). After the logarithmic transformation of the regional SUVR, the effects of the PSQI-K on the regional SUVR were investigated using Bayesian hierarchical modeling. Brain glucose metabolism of the posterior cingulate, precuneus, and thalamus showed a negative association with total PSQI-K scores in the Bayesian model ROI-based analysis. Voxel-based analysis using statistical parametric mapping revealed a negative association between the total PSQI-K scores and brain glucose metabolism of the precuneus, postcentral gyrus, posterior cingulate, and thalamus. Poor sleep quality is negatively associated with brain glucose metabolism in the precuneus, posterior cingulate, and thalamus. Therefore, the importance of sleep should not be overlooked, even in healthy middle-aged adults.

Cerebral blood flow in the paracentral lobule is associated with poor subjective sleep quality among patients with a history of methadone maintenance treatment

Introduction

Sleep disorders are prevalent and significant among individuals receiving methadone maintenance treatment (MMT), adversely affecting their quality of life and treatment adherence. While cerebral blood flow (CBF) plays a crucial role in the development of various diseases, its relationship with sleep disorders remains uncertain. This observational study focuses on possible correlations between CBF and poor subjective sleep quality in MMT patients.

Methods

A total of 75 participants with a history of MMT were recruited and assessed using pseudo-continuous arterial spin labeling magnetic resonance imaging to determine CBF. A LAASO regression model was employed to identify the region of interest (ROI) most associated with sleep disturbance. The association between the CBF of the ROI and the Pittsburgh Sleep Quality Index (PSQI) was examined using regression analyses. Age, gender, BMI, history of hypertension, diabetes, hyperlipidemia, and methadone withdrawal were included as covariates.

Results

Among MMT patients with poor subjective sleep quality, significantly higher CBF was observed in the right paracentral lobule (56.1057 ± 11.1624 ml/100 g/min, p = 0.044), right cerebelum_3 (56.6723 ± 15.3139 ml/100 g/min, p = 0.026), right caudate nucleus (48.9168 ± 6.9910 ml/100 g/min, p = 0.009), and left caudate nucleus (47.6207 ± 6.1374 ml/100 g/min, p = 0.006). Furthermore, a positive correlation was found between CBF in the right paracentral lobule and the total PSQI score (β = 0.1135, p = 0.0323), with the association remaining significant even after adjustment for covariates (β = 0.1276, p = 0.0405).

Conclusion

MMT patients with poor subjective sleep quality exhibited significantly altered CBF in multiple brain regions. The association between increased CBF in the right paracentral lobule and subjective sleep quality in MMT patients could be crucial in understanding sleep disorders in individuals undergoing MMT.

Clinical trial registration

https://www.chictr.org.cn/, identifier: ChiCTR2100051931.

Dysfunction of large-scale brain networks underlying cognitive impairments in shift work disorder

It has been demonstrated that shift work can affect cognitive functions. Several neuroimaging studies have revealed altered brain function and structure for patients with shift work disorder (SWD). However, knowledge on the dysfunction of large-scale brain networks underlying cognitive impairments in shift work disorder is limited. This study aims to identify altered functional networks associated with cognitive declines in shift work disorder, and to assess their potential diagnostic value. Thirty-four patients with shift work disorder and 36 healthy controls (HCs) were recruited to perform the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional scans. After surface-based preprocessing, we calculated within- and between-network functional connectivity (FC) using the Dosenbach atlas. Moreover, correlation analysis was done between altered functional connectivity of large-scale brain networks and scores of cognitive assessments in patients with shift work disorder. Finally, we established a classification model to provide features for patients with shift work disorder concerning the disrupted large-scale networks. Compared with healthy controls, increased functional connectivity within-networks across the seven brain networks, and between-networks involving ventral attention network (VAN)-subcortical network (SCN), SCN-frontoparietal network (FPN), and somatosensory network (SMN)-SCN were observed in shift work disorder. Decreased functional connectivity between brain networks was found in shift work disorder compared with healthy controls, including visual network (VN)-FPN, VN-default mode network (DMN), SMN-DMN, dorsal attention network (DAN)-DMN, VAN-DMN, and FPN-DMN. Furthermore, the altered functional connectivity of large-scale brain networks was significantly correlated with scores of immediate memory, visuospatial, and delayed memory in patients with shift work disorder, respectively. Abnormal functional connectivity of large-scale brain networks may play critical roles in cognitive dysfunction in shift work disorder. Our findings provide new evidence to interpret the underlying neural mechanisms of cognitive impairments in shift work disorder.

[Insomniac symptoms and suicidality-link and management]

BACKGROUND

A link between insomniac symptoms and suicidality has long been suspected and deserves specific attention.

OBJECTIVE

We examine the current evidence for this relationship from epidemiology and neurobiology in order to propose a targeted management.

MATERIAL AND METHOD

Clinical example and selective Medline-literature research for insomnia symptoms and suicidality.

RESULTS

Epidemiological data and statistical analysis show that symptoms of insomnia are independent risk factors for suicidality. Neurobiological factors associated with combined insomnia symptoms and suicidality are: serotonergic dysfunction and circadian rhythm disorder leading to hypofrontality with reduced problem solving capacity and impaired emotional and impulse-control. Social isolation, recurrent rumination, comorbid psychiatric disorders, access to potentially lethal drugs or weapons need urgent evaluation in patients with a combination of suicidality and symptoms of insomnia.

CONCLUSION

patients with insomnia and further risk factors for suicide need to be treated resolutely and at an early stage. Modern sleep-promoting antidepressants with low toxicity and antipsychotics must be preferred in the treatment of patients with insomniac sleep disorders and suicidality. Multimodal anti-insomnia and anti-depressive therapy adapted to the circadian rhythm can exert a favorable influence both on depressive-suicidal and insomnia symptoms and their inherent risks.

Sleep disturbance and internalizing symptoms in adolescents: a moderated mediation model of self-control and mindfulness

OBJECTIVES

Despite accumulating evidence regarding the impact of sleep disturbance on internalizing symptoms among adolescents, the underlying psychological mechanisms remain inadequately explored. This study aimed to investigate a conceptual framework elucidating how sleep disturbance influences internalizing symptoms in adolescents through the mediating role of self-control, with mindfulness as a moderator.

METHODS

In this cross-sectional study, 1876 Chinese adolescents (Mage = 14.88 years, SD = 1.47 years, range = 12-19 years, 44.7% boys) completed the Youth Self-Rating Insomnia Scale (YSIS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Brief Self-control Scale (BSCS), and Mindful Attention Awareness Scale-Children (MAAS-C) to provide data on sleep-related variables, internalizing symptoms (anxiety and depression), self-control, and mindfulness, respectively. The PROCESS macro for SPSS was applied to perform moderated mediation analysis.

RESULTS

Sleep disturbance demonstrated a significant positive correlation with internalizing symptoms in adolescents, including anxiety (β = 0.481, p < 0.001) and depression (β = 0.543, p < 0.001). Self-control served as a mediator between sleep disturbance and two forms of internalizing symptoms. Moreover, mindfulness moderated the pathways from self-control to internalizing symptoms (for anxiety symptoms: β = 0.007, p < 0.001; for depression symptoms: β = 0.006, p < 0.001), and the mediating relationships were weaker for adolescents exhibiting higher levels of mindfulness.

CONCLUSIONS

Our findings enhance understanding of the impact, pathways, and influencing factors of sleep disturbance on adolescent internalizing symptoms, suggesting the importance of enhancing mindfulness levels in addressing self-control deficits and subsequently reducing internalizing symptoms among adolescents.

Cortical thickness and sub-cortical volumes in post-H1N1 narcolepsy type 1: A brain-wide MRI case-control study

OBJECTIVE

There was more than a 10-fold increase in the incidence of narcolepsy type 1 (NT1) after the H1N1 mass vaccination in 2009/2010 in several countries. NT1 is associated with loss and increase of cell groups in the hypothalamus which may be associated with secondary affected sub-cortical and cortical gray matter. We performed a case-control comparison of MRI-based global and sub-cortical volume and cortical thickness in post-H1N1 NT1 patients compared with controls.

METHODS

We included 54 post-H1N1 NT1 patients (51 with confirmed hypocretin-deficiency; 48 H1N1-vaccinated with Pandemrix®; 39 females, mean age 21.8 ± 11.0 years) and 114 healthy controls (77 females, mean age 23.2 ± 9.0 years). 3T MRI brain scans were obtained, and the T1-weighted MRI data were processed using FreeSurfer. Group differences among three global, 10 sub-cortical volume measures and 34 cortical thickness measures for bilateral brain regions were tested using general linear models with permutation testing.

RESULTS

Patients had significantly thinner brain cortex bilaterally in the temporal poles (Cohen's d = 0.68, p = 0.00080), entorhinal cortex (d = 0.60, p = 0.0018) and superior temporal gyrus (d = 0.60, p = 0.0020) compared to healthy controls. The analysis revealed no significant group differences for sub-cortical volumes.

CONCLUSIONS

Post-H1N1(largely Pandemrix®-vaccinated) NT1 patients have significantly thinner cortex in temporal brain regions compared to controls. We speculate that this effect can be partly attributed to the hypothalamic neuronal change in NT1, including loss of function of the widely projecting hypocretin-producing neurons and secondary effects of the abnormal sleep-wake pattern in NT1 or could be specific for post-H1N1 (largely Pandemrix®-vaccinated) NT1 patients.

Brain structural correlates of subjective sleepiness and insomnia symptoms in shift workers

Background

Studies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers.

Methods

Shift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms).

Results

No differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers.

Conclusion

Left motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively.

Identifying major depressive disorder with associated sleep disturbances through fMRI regional homogeneity at rest

BACKGROUND

Anomalies in regional homogeneity (ReHo) have been documented in patients with major depressive disorder (MDD) and sleep disturbances (SDs). This investigation aimed to scrutinize changes in ReHo in MDD patients with comorbid SD, and to devise potential diagnostic biomarkers for detecting sleep-related conditions in patients with MDD.

METHODS

Patients with MDD and healthy controls underwent resting-state functional magnetic resonance imaging scans. SD severity was quantified using the 17-item Hamilton Rating Scale for Depression. Subsequent to the acquisition of imaging data, ReHo analysis was performed, and a support vector machine (SVM) method was employed to assess the utility of ReHo in discriminating MDD patients with SD.

RESULTS

Compared with MDD patients without SD, MDD patients with SD exhibited increased ReHo values in the right posterior cingulate cortex (PCC)/precuneus, right median cingulate cortex, left postcentral gyrus (postCG), and right inferior temporal gyrus (ITG). Furthermore, the ReHo values in the right PCC/precuneus and ITG displayed a positive correlation with clinical symptoms across all patients. SVM classification results showed that a combination of abnormal ReHo in the left postCG and right ITG achieved an overall accuracy of 84.21%, a sensitivity of 81.82%, and a specificity of 87.50% in identifying MDD patients with SD from those without SD.

CONCLUSION

We identified disrupted ReHo patterns in MDD patients with SD, and presented a prospective neuroimaging-based diagnostic biomarker for these patients.

Risk prediction of sleep disturbance in clinical nurses: a nomogram and artificial neural network model

BACKGROUND

Sleep disturbance occur among nurses at a high incidence.

AIM

To develop a Nomogram and a Artificial Neural Network (ANN) model to predict sleep disturbance in clinical nurses.

METHODS

A total of 434 clinical nurses participated in the questionnaire, a cross-sectional study conducted from August 2021 to June 2022.They were randomly distributed in a 7:3 ratio between training and validation cohorts.Nomogram and ANN model were developed using predictors of sleep disturbance identified by univariate and multivariate analyses in the training cohort; The 1000 bootstrap resampling and receiver operating characteristic curve (ROC) were used to evaluate the predictive accuracy in the training and validation cohorts.

RESULTS

Sleep disturbance was found in 180 of 304 nurses(59.2%) in the training cohort and 80 of 130 nurses (61.5%) in the validation cohort.Age, chronic diseases, anxiety, depression, burnout, and fatigue were identified as risk factors for sleep disturbance. The calibration curves of the two models are well-fitted. The sensitivity and specificity (95% CI) of the models were calculated, resulting in sensitivity of 83.9%(77.5-88.8%)and 88.8% (79.2-94.4%) and specificity of83.1% (75.0-89.0%) and 74.0% (59.4-84.9%) for the training and validation cohorts, respectively.

CONCLUSIONS

The sleep disturbance risk prediction models constructed in this study have good consistency and prediction efficiency, and can effectively predict the occurrence of sleep disturbance in clinical nurses.

The Relationship between Working Night Shifts and Depression among Nurses: A Systematic Review and Meta-Analysis

BACKGROUND

For many years, occupational physicians have debated whether there is a link between working the night shift and depression and other co-occurring mental health issues, with an emphasis on work-related, biological, individual, and environmental factors. We performed this systematic review and meta-analysis to estimate the overall association between sleep deprivation and depression among nurses working night shifts.

METHODS

A systematic search was carried out across the electronic databases PubMed, Scopus, and Web of Science from inception to 30 September 2022, for studies that reported a relationship between estimated night shift work and depression in nurses. The outcomes were measured using the odds ratio (OR) and matching 95% confidence interval (CI). The I² statistic was used to assess heterogeneity. The Grading of Recommendations Assessment, Development and Evaluation technique was used to evaluate the quality of the evidence, and the Newcastle-Ottawa Scale was utilized to assess the methodological quality of each of the included studies. We determined the overall relationship between working nights and the onset of depression.

RESULTS

A total of 20 studies were included in the systematic review. Furthermore, 8 studies were included in the meta-analysis due to their common use of the OR as an effect measure. The 8 studies gave an overall estimate indicating a statistically significant association between night shift work and depression among nurses (OR = 1.49 95% CI: 1.26, 1.76). The prediction interval for the overall estimate was (0.995, 2.231). This implies that the true OR in a future study would most likely fall within this range, with a 95% certainty.

CONCLUSIONS

The outcome of this systematic review and meta-analysis showed a significant association between night shift work, the circadian and sleep disruption it causes, and the risk of depression in nurses. This demonstrates that nurses who work night shifts are at risk of developing depression.

Keep Your Mask On: The Benefits of Masking for Behavior and the Contributions of Aging and Disease on Dysfunctional Masking Pathways

Environmental cues (e.g., light-dark cycle) have an immediate and direct effect on behavior, but these cues are also capable of “masking” the expression of the circadian pacemaker, depending on the type of cue presented, the time-of-day when they are presented, and the temporal niche of the organism. Masking is capable of complementing entrainment, the process by which an organism is synchronized to environmental cues, if the cues are presented at an expected or predictable time-of-day, but masking can also disrupt entrainment if the cues are presented at an inappropriate time-of-day. Therefore, masking is independent of but complementary to the biological circadian pacemaker that resides within the brain (i.e., suprachiasmatic nucleus) when exogenous stimuli are presented at predictable times of day. Importantly, environmental cues are capable of either inducing sleep or wakefulness depending on the organism’s temporal niche; therefore, the same presentation of a stimulus can affect behavior quite differently in diurnal vs. nocturnal organisms. There is a growing literature examining the neural mechanisms underlying masking behavior based on the temporal niche of the organism. However, the importance of these mechanisms in governing the daily behaviors of mammals and the possible implications on human health have been gravely overlooked even as modern society enables the manipulation of these environmental cues. Recent publications have demonstrated that the effects of masking weakens significantly with old age resulting in deleterious effects on many behaviors, including sleep and wakefulness. This review will clearly outline the history, definition, and importance of masking, the environmental cues that induce the behavior, the neural mechanisms that drive them, and the possible implications for human health and medicine. New insights about how masking is affected by intrinsically photosensitive retinal ganglion cells, temporal niche, and age will be discussed as each relates to human health. The overarching goals of this review include highlighting the importance of masking in the expression of daily rhythms, elucidating the impact of aging, discussing the relationship between dysfunctional masking behavior and the development of sleep-related disorders, and considering the use of masking as a non-invasive treatment to help treat humans suffering from sleep-related disorders.

Influence of rotating shifts and fixed night shifts on sleep quality of nurses of different ages: a systematic literature review and meta-analysis

The objective of this study was to understand the influence of rotating shifts and fixed night shifts on the sleep quality of nurses using a systematic literature review and meta-analysis and to determine whether the sleep quality of nurses working rotating shifts or fixed night shifts differs from that of nurses working fixed day shifts. We conducted a meta-analysis on literature that had used the Pittsburgh Sleep Quality Index (PSQI) to gauge sleep quality and had been published between 2000 and 2020 in the Cochrane Library, CINAHL, MEDLINE, and PubMed databases. This study was conducted in accordance with PRISMA, and the quality of the literature was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross Sectional Studies. CMA 3.0 was employed for data analysis, and meta-regression analysis was conducted to examine the differences between the PSQI scores of nurses working fixed day shifts and those working rotating shifts or fixed night shifts. Following assessment, a total of 29 papers (34 items of data) were analyzed. The papers included a total of 3,935 nurses who worked fixed day shifts, 3,777 nurses who worked rotating shifts, and 1,559 nurses who worked fixed night shifts. Our subgroup analysis indicated that the sleep quality of nurses working rotating shifts was poorer than the sleep quality of nurses working fixed day shifts; the standardized mean difference (SMD) was 0.38 (95% CI: 0.24-0.52, p< .001), and the meta-regression analysis found that the difference in sleep quality between nurses over 40 years old working rotating shifts and those working fixed day shifts was significantly smaller than the difference in sleep quality between nurses 40 years old or younger working rotating shifts and those working fixed day shifts (β = -0.368, p= .011). The sleep quality of nurses working fixed night shifts was also poorer than the sleep quality of nurses working fixed day shifts; the SMD was 0.58 (95% CI: 0.38-0.78, p< .001), and the meta-regression analysis revealed that the difference in sleep quality between nurses over 40 years old working fixed night shifts and those working fixed day shifts was not significantly different from the difference in sleep quality between nurses 40 years old or younger working fixed night shifts and those working fixed day shifts (β = -0.118, p= .608). This meta-analysis found that both nurses working rotating shifts and those working fixed night shifts had poorer sleep quality than those working fixed day shifts and that age influenced the difference in sleep quality only between nurses working rotating shifts and those working fixed day shifts; the difference in sleep quality between nurses 40 years old or younger working rotating shifts and those working fixed day shifts was greater than the difference in sleep quality between nurses over 40 years old working rotating shifts and those working fixed day shifts.

Association of industrial work schedules with development of metabolic syndrome, insulin resistance, and serum adipokine concentrations

Background

Association of work schedule in industrial workers with the progression of metabolic syndrome, insulin resistance, and serum adipokine concentrations is incompletely explored.

Objective

To determine the association of work schedule with the progression of metabolic syndrome, insulin resistance, and adipokine concentrations in industrial workers.

Methods

In a cross-sectional study design of industrial workers we compared metabolic syndrome, insulin resistance, and adipokines concentration between workers in the day shift (n = 52), rotational shift (n = 21), and night shift (n = 15). The international Diabetes Federation criteria were used to diagnose metabolic syndrome. We used a homeostatic model assessment of insulin resistance (HOMA-IR). Serum insulin, leptin, and adiponectin concentrations were measured using enzyme-linked immunosorbent assays. Serum glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) concentrations were monitored using Prietest clinical chemistry reagents.

Results

The proportional difference in metabolic syndrome (0.31, 95% confidence interval [CI] 0.036-0.587, P = 0.026), median difference of leptin (0.61, 95% CI 0.186-1.034, P = 0.005), and leptin-to-adiponectin ratio (LAR; 0.45, 95% CI 0.235-0.665, P < 0.001) was significantly higher, and serum adiponectin was lower (-2.00, 95% CI -4.197 to 0.197, P = 0.07) in the night-shift workers compared with that of day-shift workers. Among rotational-shift workers, the proportional difference between metabolic syndrome (0.14, 95% CI -0.098 to 0.378, P = 0.25), median difference of leptin (0.25, 95% CI -0.124 to 0.624, P = 0.19), and LAR (0.09, 95% CI -0.099 to 0.279, P = 0.35) was higher, and serum adiponectin concentration was lower (-0.73, 95% CI -2.660 to 1.208, P = 0.46) compared with that of day-shift workers; however, the altered differences were not significant. We observed a higher proportion of difference in HOMA-IR in shift workers (night and rotation) than in day-shift workers.

Conclusion

Night-shift workers are vulnerable to a higher risk of metabolic syndrome, HOMA-IR, and adipokine changes.