Is shift work associated with a higher risk of overweight or obesity? A systematic review of observational studies with meta-analysis

Is shift work associated with a higher risk of overweight or obesity? A systematic review of... Abstract Background An increasing number of original studies suggest that exposure to shift work could be associated with the risk of overweight and obesity, but the results remain conflicted and inconclusive. This study aimed to quantitatively synthesize available epidemiological evidence on the association between shift work and the risk of overweight and obesity by a meta-analysis. Methods The authors searched PubMed, Embase and the reference lists of all included studies up to April 2017, with a verification search in December 2017. Inclusion criteria were original studies that reported odds ratios, relative risks or hazard ratios (ORs, RRs or HRs, respectively) of at least one outcome of overweight or obesity. Summary risk estimates were calculated by random-effect models. Results Twenty-six studies (7 cohort studies, 18 cross-sectional studies and 1 case–control study) involving 311 334 participants were identified. Among these studies, the cut-off points of overweight and obesity varied greatly, so the heterogeneity was substantial; however, the results were stable. Shift work was found to be positively associated with the risk of overweight [RR: 1.25; 95% confidence interval (95% CI): 1.08–1.44] and obesity (RR: 1.17; 95% CI: 1.12–1.22). Conclusions Individuals involved in shift work are more likely to become overweight or obese. Appropriate preventive interventions in the organization of shift schedules according to ergonomic criteria would allow shift workers to avoid potential health impairment. shift work, overweight, obesity, meta-analysis, prevention Key Messages This meta-analysis indicated an association between shift work and increased overweight/obesity risk. The risk of becoming overweight on night-shift work is much higher than for rotating-shift work. We suggest that optimized interventions are needed in the organization of shift schedules to protect workers from overweight/obesity. Introduction Shift work (SW) refers to a job schedule in which employees work hours other than the normal working schedule of 9 a.m. to 5 p.m.1 The full spectrum of SW comprises regular evening or night shifts, split shifts, rotating shifts, on-call or casual shifts, irregular shifts, 24-hour shifts and other non-day shifts. It has been reported that 36.1% of employees undertake SW in China and Korea.2 A study on working conditions showed that nearly 20% of employees in industrialized countries were involved in SW.3 Previous studies have showed that SW is associated with negative health outcomes.4,5 SW has been implicated in disrupting the circadian rhythm,6 which may impair glucose metabolism and lipid homeostasis.7 An irregular sleep–wake cycle could influence hormones related to appetite regulation.7,8 SW has also been found to interrupt workers’ recovery from fatigue and to prevent performance of regular exercise.9 Metabolic disorders, altered eating behaviour10,11 and less exercise4 may result in overweight and obesity among shift workers. Persisting stereotypes describe overweight and obese people as lazy, less self-disciplined and incompetent.12 In the workplace, obesity is an important driver of costs associated with absenteeism, healthcare claims, sick leave, injuries and disability.13,14 The high prevalence of overweight and obesity is particularly concerning because the links between overweight/obesity, poor health outcomes and all-cause mortality are well established. Overweight and obesity increase the likelihood of diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis.15 Overweight and obesity were estimated to account for 3.4 million deaths per year and 93.6 million disability-adjusted life years (DALYs) in 2010.16 Growing attention has been paid by the public to the influence of SW on the risk of overweight and obesity,17–27 whereas the findings have remained inconsistent or even conflicting. The majority of studies report a positive relationship,17,18,20–22,24,27–32 whereas other studies show no obvious association.19,23,25,30,33–41 The influence of SW on overweight and obesity is still unclear. A meta-analysis in 2014 showed that night SW was associated with the risk of metabolic syndrome (MetS).42 However, the evidence was limited by retrieving only 13 studies on MetS; since then, a number of additional studies have been published. Recently, a published meta-analysis that considered overweight and obesity as a whole reported that night SW was positively related to the risk of obesity and overweight.43 However, there are some differences between overweight and obesity, such as the definitions and health consequences. The relationship between SW and overweight and obesity has not been systematically evaluated. Hence, we conducted a comprehensive meta-analysis of 27 observational studies to quantify the association between SW and obesity and overweight, respectively, and to provide evidence-based information for managers in charge of working time organization to effectively protect workers from overweight/obesity. Methods No protocol exists for this meta-analysis. Ethics Ethical approval was not required for this systematic review. Literature search strategy We conducted the meta-analysis according to the MOOSE guideline.44 We sought studies that reported risk estimates for the association between SW and at least one outcome of overweight or obesity. Outcomes were typically defined by primary study authors using country-specific iterations of the International Classification of Disease coding system. We imposed no limitation by study design, regional origin or nature of the control group, which could include day workers or the general population. We systematically searched PubMed and Embase from inception until December 2017. We used database-specific subject terms and keywords to generate an initial list of articles for scrutiny. We also scrutinized the reference lists of all eligible articles. Two health information specialists (Q.L. and J.S.) designed and implemented the search in consultation with the rest of the team. Two reviewers (P.D. and T.Y.) screened citations and assessed articles independently for inclusion; disagreements were settled through consultation with a third reviewer (Z.L.). Study selection criteria Studies meeting the following criteria were included in the meta-analysis: (i) the research design was cohort, case–control or cross-sectional; (ii) any type of SW was an exposure variable; (iii) the endpoint of interest was the prevalence or incidence of overweight or obesity; and (iv) the study reported the risk estimates and corresponding 95% CIs for the association between SW and overweight or obesity. Reviews, animal studies, clinical trials, commentaries and letters were excluded. Studies involving non-work-related or involuntary night-time light exposure were also excluded. Data extraction from studies with aggregate data We extracted details on the name of the first author, year of publication, region, study design, definition of shift workers and references, characteristics of participants, outcome measurements, number of participants and cases, risk estimates with 95% CIs and covariates adjusted in the statistical analysis. We classified SW schedules according to the primary study methodological descriptions as night, rotating and mixed. Two reviewers (Q.L. and T.Y.) extracted all outcome data independently after reformulation of study citation information. Disagreement among reviewers was discussed and agreement was reached by consensus. Quality appraisal We appraised all the included studies using the Newcastle-Ottawa Scale 10,45 which is a nine-point scoring system used to assess the quality of non-randomized studies included in a systematic review and/or meta-analysis. A high-quality study was defined as a study with at least seven points. Disagreements on quality assessment were resolved by discussion among the authors. Statistical analysis The relative risks (RRs) and 95% confidence intervals (CIs) were considered as the effect size in this meta-analysis. We calculated summary RRs by synthesizing across the shift schedules categorized in each study. A random-effects model was used to pool the effect estimates. RRs for overweight and obesity were calculated with 95% CIs. Subgroup analyses, hypothesized a priori, were conducted using a statistical test of interaction.46 Studied subgroups included study design (cohort, cross-sectional study and case–control study), type of SW, region of study, gender of participants and adjusted variables. We used the I2 statistic to measure heterogeneity. Values of 0–30% represented minimal heterogeneity, 31–50% represented moderate heterogeneity and >50% represented substantial heterogeneity.47 We assessed our results with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.48 Potential publication bias was assessed with visual inspection of the funnel plot, the Begg correlation test and the Egger linear regression test. All statistical analyses were performed with STATA.11.0 (Stata Corp, College Station, Texas, USA). All tests were two-sided, with a significance level of 0.05. Results Literature search The literature search identified 1493 citations from PubMed and 3559 from Embase, of which 553 were selected for further review (see Figure 1). Of these, we retrieved 56 articles in full and rated 26 as eligible for the review; one paper included a cohort and a case–control study. In total, there were 27 eligible published studies with aggregate data, of which 7 were cohort studies, 19 were cross-sectional studies and 1 was a case–control study. Figure 1. View largeDownload slide Flow diagram of identification of relevant observational studies of shift work in relation to the risk of overweight and obesity. Figure 1. View largeDownload slide Flow diagram of identification of relevant observational studies of shift work in relation to the risk of overweight and obesity. Characteristics and quality of the included studies The studies included 311 334 participants, with 10 473 overweight cases and 51 024 obesity cases. The study publication years ranged from 1999 to 2017. Briefly, we identified 27 primary studies of SW and risk of overweight/obesity. Among the studies included, shift schedules were classified as night shifts (n = 5), rotating shifts (n = 18) and mixed schedules (n = 4). All studies (n = 27) used non-shift workers as the reference category. Eight studies were conducted in Europe, eight in Asia, six in North America and three in South America; the remaining two were multinational. Five and 9 studies only reported separate outcomes of males and females, respectively, and 13 studies reported the outcomes of both genders. The quality scores of these studies appraised with the Newcastle-Ottawa Scale 10 ranged from 6 to 10. Overall, 7 studies had a score of 9, 11 had a score of 8, 6 had a score of 7 and 3 had a score of 6, which led to an average score of 7.81 points. Detailed characteristics and quality assessments of these studies are presented in Table 1. Table 1. Characteristics of studies included in the meta-analysis of shift work in relation to risk of overweight and obesity Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  BMI, body mass index; NA, not available; WC, waist circumstance. Table 1. Characteristics of studies included in the meta-analysis of shift work in relation to risk of overweight and obesity Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  BMI, body mass index; NA, not available; WC, waist circumstance. Summary of the definitions of SW, overweight and obesity Most studies reported the relationship between rotating SW and obesity,17–20,24,25,27–29,31,32,34–37,39,40,49 whereas five studies showed outcomes of night-only SW,22,23,30,38,41 two studies reported results of rotating shift and night shift separately21,33 and one study reported outcomes of night shift and day shift separately.50 Eight studies defined overweight as a body mass index (BMI) ≥ 25 kg/m2.21,27,30,32,37–41 Barbadoro, Kim and Balieiro defined overweight as BMI > 24.9 kg/m2, BMI ≥23 kg/m2 and waist circumstance ≥ 94 cm, respectively.22,29,36 Eleven studies defined obesity as BMI ≥30 kg/m2.18,21,27,30–33,36–39,49 Six defined the presence of obesity as BMI ≥ 25 kg/m217,22,29,34,50,51 and the remaining four studies defined obesity as BMI > 30 kg/m2,23 BMI >25 kg/m2,25 BMI ≥ 26.4 kg/m2,19 and by total body fat percentage,35 respectively. Only one study defined obesity according to the tenth revision of the International Classification of Diseases (ICD-10).20 SW increases risk of overweight and obesity Figure 2 shows the combined RR from the random-effect model for overweight in relation to SW. Twelve studies described the association between SW and the risk of overweight. Of the 12 studies, 6 showed no obvious relationship or an inverse association between SW and the risk of overweight, whereas 6 other studies reported a positive relationship. The pooled RR was 1.25 (95% CI: 1.08–1.44, I2 = 80.7%). A meta-analysis was performed to estimate the association between SW and the risk of obesity by combining results from all eligible studies, giving a summary RR of 1.17 (95% CI: 1.12–1.22; I2 = 92.2%) (see Figure 3). Only three studies reported the dose–response relationship between SW and obesity27,30,31 and the information is inadequate for further dose–response analysis, but we could indicate that a higher combined RR was associated with a higher frequency of monthly night shifts (at least eight nights/month) compared with the lower one. Peplonska and Kim indicated a positive dose–response relationship between years of SW and obesity risk.27,29,30 Figure 2. View largeDownload slide Forest plot of the association between shift work and overweight. Figure 2. View largeDownload slide Forest plot of the association between shift work and overweight. Figure 3. View largeDownload slide Forest plot of the association between shift work and obesity. Figure 3. View largeDownload slide Forest plot of the association between shift work and obesity. Subgroup analyses and sources of heterogeneity We performed subgroup analyses to assess whether specific study characteristics influenced the association between SW and overweight/obesity. As shown in Table 2, the increased risk for overweight was more evident in the night-shift group (RR: 1.38; 95% CI: 1.06–1.80). SW was associated with increased risks of obesity in most subgroups. Table 3 presents a higher pooled RR for obesity in the rotating-shift group (RR: 1.18; 95% CI: 1.08–1.29). Additionally, subgroup analyses suggested a stronger association between SW and obesity for cohort studies (RR: 1.16; 95% CI: 1.03–1.31) than for cross-sectional studies (RR: 1.11; 95% CI: 1.07–1.15). Table 2. Subgroup analyses of relative risks of overweight according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Stratified meta-analysis of shift work and the risk of overweight. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available. Table 2. Subgroup analyses of relative risks of overweight according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Stratified meta-analysis of shift work and the risk of overweight. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available. Table 3. Subgroup analyses of relative risks of obesity according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Stratified meta-analysis of shift work and the risk of obesity. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available; BMI, body mass index. Table 3. Subgroup analyses of relative risks of obesity according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Stratified meta-analysis of shift work and the risk of obesity. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available; BMI, body mass index. As substantial heterogeneity was found among the included studies, we conducted subgroup analyses and identified the potential cause of heterogeneity by various factors, such as gender, region of study, type of SW, study design and number of cases. Subgroup analyses suggested that different types of SW might be the main cause of heterogeneity for overweight. For night SW, no obvious heterogeneity was found. For rotating SW involving night shift, the heterogeneity was substantial. It is self-evident that the specific schedules of rotating SW differ greatly among different occupations and positions. The endpoints of obesity were different between studies, which might contribute to the heterogeneity of obesity under the influence of SW. For example, for three studies with cut-offs of obesity other than BMI, the combined RR was 1.17 (95% CI: 0.78–1.74), whereas the pooled RR of studies with cut-offs of BMI > 25 kg/m2 and BMI ≥ 25 kg/m2 was 1.23 (95% CI: 1.08–1.39), and the accumulated RR of studies with endpoints of BMI > 30 kg/m2 and BMI ≥ 30 kg/m2 was 1.12 (95% CI: 1.08–1.16). Sensitivity analyses When we excluded the studies of Oberlinner et al.20 and Son et al.,35 in which the ICD-10 and total body fat percentage definitions were used, the pooled RR of SW and obesity was 1.16 (95% CI: 1.11–1.21). We then excluded any single study in turn and combined the results of the remaining included studies, and the overall summary RRs for overweight and obesity were not substantially altered. The summary estimates derived from subsequent sensitivity analyses for overweight ranged from 1.19 (95% CI: 1.02–1.38) to 1.35 (95% CI: 1.13–1.62) and for obesity they ranged from 1.16 (95% CI: 1.11–1.21) to 1.17 (95% CI: 1.12–1.22). Publication bias There was no obvious publication bias detected by Begg’s and Egger’s tests (P = 0.047 for overweight, P = 0.004 for obesity) (Figures 4 and 5). Figure 4. View largeDownload slide Funnel plot for studies of shift work in relation to overweight risk. Figure 4. View largeDownload slide Funnel plot for studies of shift work in relation to overweight risk. Figure 5. View largeDownload slide Funnel plot for studies of shift work in relation to obesity risk. Figure 5. View largeDownload slide Funnel plot for studies of shift work in relation to obesity risk. Discussion In the current systematic review and meta-analysis, a total of 27 independent studies involving 311 334 participants (10 473 overweight cases and 51 024 obesity cases) were identified for examination. Evidence from these studies suggests that shift workers might have an increased risk of overweight and obesity, by 25% and 17%, respectively. This result for obesity is consistent with a previous meta-analysis by Wang et al.,42 which reported an increased risk of obesity of 1.66 (95% CI: 1.02–2.71) based on six studies. However, our meta-analysis included 16 additional studies on obesity with a much larger sample size. Additionally, Wang et al. did not do subgroup analysis of obesity, nor did they quantitatively analyse the association between SW and overweight. Recently, another published meta-analysis found the pooled odds ratio of night SW was 1.23 (95% CI: 1.17–1.29) for risk of obesity and overweight.43 Although the meta-analysis included original studies about abdominal obesity, unfortunately it omitted seven studies due to the searching strategy and the definitions of outcomes. The present meta-analysis analysed the association between overweight as well as obesity and SW patterns. We found that night SW was positively associated with overweight 1.38 (95% CI: 1.06–1.80), whereas rotating SW increased the risk of obesity 1.18 (95% CI: 1.08–1.29). Taken together, our meta-analysis may be the latest evidence of association of SW with overweight/obesity to date. Circadian disruption can be the biological explanation for the pathogenesis of obesity. Long-term exposure to SW might disturb the normal circadian rhythm, which might impair glucose metabolism and lipid homeostasis.7 Although the mechanism linking obesity to circadian disruption has not yet been fully understood, both animal experiments and human studies have indicated that disruption of the circadian rhythm might be causal for obesity.52–54 Melatonin is an important mediator of circadian disruption resulting in SW and obesity. Melatonin production is promoted in darkness but suppressed by light in the night, so SW including night shift could inhibit the secretion of melatonin. An animal study reported that the reduced production of melatonin was associated with metabolic disturbance.55 Leptin acts as a natural appetite suppressor, aiding in the regulation of energy intake and expenditure by inhibiting appetite and speeding up metabolism.56 Additionally, Qin et al.57 indicated that the disturbance of circadian rhythms leads to the impairment of insulin response to glucose, and the changes of melatonin and leptin were highly consistent with those of night-eating syndrome, which would result in excess weight. The main findings of our study suggested that rotating-shift workers had an increased risk of obesity of 16% compared with the reference groups. Weight gain can provide an ‘infrastructure’ for health disorders. Therefore, shift schedules should be arranged according to ergonomic criteria to limit negative effects on hormone secretion and health. Sleep deprivation—another plague of the modern world—is recognized to be involved in the pathogenesis of obesity.58 Epidemic studies have revealed an association between chronic sleep deprivation and long-term weight gain.59,60 Sleep disturbance, which adversely influences hormonal rhythms and metabolism, also contributes to overweight and obesity.7 Several pathways could link sleep duration to obesity, including increased food intake, reduced energy expenditure and alterations in levels of appetite-regulating hormones, such as leptin and ghrelin.61 Sleep deprivation has been associated with decreased leptin levels and increasing ghrelin, resulting in increased appetite leading to weight gain.62 The mechanisms linking SW with overweight/obesity are shown in Figure 6. Naps taken before and during night shifts have been shown to yield beneficial effects on sleepiness and fatigue. The hypothesis that napping during night shifts could help to decrease melatonin suppression and reduce the possibility of negative consequences has been tested.63,64 Hence, shift schedules should allow workers sufficient opportunities to sleep during protected intervals between shifts. Alternatively, partial rather than complete arrangement to day-oriented schedules (e.g. day shifts and day off) might yield sufficient positive performance and improve sleep outcomes. Figure 6. View largeDownload slide The hypothesized adverse health effects of shift work and potential mechanisms. The risk of overweight/obesity increases with circadian disruption and sleep deprivation in shift workers. Consequently, the likelihood of chronic diseases (e.g. diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis) increases. Figure 6. View largeDownload slide The hypothesized adverse health effects of shift work and potential mechanisms. The risk of overweight/obesity increases with circadian disruption and sleep deprivation in shift workers. Consequently, the likelihood of chronic diseases (e.g. diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis) increases. From a nutritional perspective, it has been shown that sleep deprivation could induce physiological adaptations that change the individual nutritional status.65 Other studies have shown that a short sleep duration seems to not only enhance appetite,7 but also to generate a preference for fat-containing foods.66,67 Itani et al.34 reported that a short sleep duration is associated with the onset of obesity. Our findings showed that night-shift workers were more likely to be overweight (RR: 1.24). In view of the fact that SW deregulates sleep and wakefulness, efforts should be made towards either minimizing the unfavourable effects on sleep or managing sleep and naps. Additionally, eating behaviour might be changed by working schedules, especially when night work is involved,11 because of a diverse range of biological, social and cultural factors. Also, shift workers experience the most job-related stress,68 which may act as a potential risk factor for weight gain by affecting eating behaviours and food choices and by establishing barriers to healthy eating.69 Rotating-shift workers showed a much higher pooled effect on obesity. Rotating shifts involving night work may disturb the circadian rhythm, affect eating habits and reduce physical activities.21 Coffey et al.70 reported the most job-related stress and fatigue in rotating-shift nurses, which might establish barriers to healthy eating and increase risk of obesity.69,71 A recent study suggested that shift workers had similar diet quality but higher energy intake compared with day workers.72 The higher energy intake might be one of the causes of SW-induced overweight, obesity and other adverse health outcomes. Guidance on a healthy eating style is required for this at-risk population group. Overweight and obesity not only impair work performance, but also increase the morbidity of chronic diseases (e.g. type 2 diabetes mellitus, cancers, hypertension, cardiovascular diseases, osteoarthritis, obstructive sleep apnea).73 The involvement of both behavioural factors, such as eating late, a calorie-rich diet, sleep deprivation and insufficient physical activity, and intrinsic aspects, including circadian disruption and adipose deregulation, has been identified.11,74 Many studies have shown that shift workers are more prone to the development of overweight and obesity. Our meta-analysis provided support for a possible association of SW and overweight/obesity. We are aware that these results might be biased on the basis of our searching strategies, initially designed for studying SW and the risk of overweight/obesity. As a relationship between overweight/obesity and risk of chronic diseases has been confirmed, avoiding overweight and obesity is an important strategy in managing risk for chronic diseases. Strengths and weaknesses Our study has several strengths. First, it is the largest synthesis of SW and obesity, as well as overweight, reported to date. We have overcome these limitations and provide in-depth analyses of the relationship between SW and overweight/obesity. Second, sensitivity analysis and consistent results from various subgroup analyses indicate that our findings are reliable and robust, although heterogeneity existed among the included studies. On the other hand, several caveats in our study need to be noted. First, there are several methodological limitations (including overweight/obesity assessment and analytical methodology) worth consideration. Second, due to the limitations of eligible data, the subgroups based on type of SW were crudely classified into day SW, night SW and rotating SW subgroups, regardless of the SW duration, cumulative SW exposure and frequency of SW. Third, high heterogeneity across studies was present for the SW–overweight and SW–obesity links, which throws some doubt on the reliability of the summary RR estimates. However, the results were stable. Fourth, most included studies used the cross-sectional study design, and the relationship between SW and the risk of overweight/obesity might be underestimated for the potential healthy worker survival effect in the cross-sectional studies.75 Fifth, only studies published in the electronic databases in English and Chinese were reviewed for pertinence, and studies in other languages were omitted. Despite these limitations, the meta-analysis included a substantial number of cohort studies, which greatly strengthened the statistical power of the analysis and provided adequate evidence for the authors to draw a reliable conclusion. For further studies based on our findings, we suggest that investigations need to improve the standardization of different shift schedules and outcome definitions, which would provide research evidence. Additionally, research should be conducted to confirm our findings and establish the potential biological mechanisms. Finally, more interventional studies are needed to prevent overweight and obesity in the workplace. Conclusion In conclusion, the present meta-analysis indicates that SW might be associated with an increased risk of overweight and obesity. Owing to the substantial heterogeneity, further high-quality studies are required to address the existing limitations in the literature. Given the worldwide increasing prevalence of SW and the potential side effects of overweight/obesity on health, the results of our study suggest that both the managers in charge of working time organization and the involved workers must be sufficiently informed about the possible adverse effects of SW. Furthermore, shift schedules should be designed according to ergonomic criteria recommended to be suitable to limit negative effects on health by avoiding or minimizing circadian disruption and accumulation of sleep deficiency. Funding This work was supported by the Fundamental Research Funds for the Central Universities, Huazhong University of Science and Technology, Wuhan, China. (2016YXMS215). Acknowledgements Q.L. developed the idea and prepared a draft review protocol. Q.L. and J.S. conducted the literature search. B.L., P.D., T.L. and C.W. collected the identified articles. Q.L. and T.Y. extracted data from the included studies. Y.G., H.L. and X.W. checked the extracted data. 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Is shift work associated with a higher risk of overweight or obesity? A systematic review of observational studies with meta-analysis

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Oxford University Press
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© The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
ISSN
0300-5771
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1464-3685
D.O.I.
10.1093/ije/dyy079
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Abstract

Abstract Background An increasing number of original studies suggest that exposure to shift work could be associated with the risk of overweight and obesity, but the results remain conflicted and inconclusive. This study aimed to quantitatively synthesize available epidemiological evidence on the association between shift work and the risk of overweight and obesity by a meta-analysis. Methods The authors searched PubMed, Embase and the reference lists of all included studies up to April 2017, with a verification search in December 2017. Inclusion criteria were original studies that reported odds ratios, relative risks or hazard ratios (ORs, RRs or HRs, respectively) of at least one outcome of overweight or obesity. Summary risk estimates were calculated by random-effect models. Results Twenty-six studies (7 cohort studies, 18 cross-sectional studies and 1 case–control study) involving 311 334 participants were identified. Among these studies, the cut-off points of overweight and obesity varied greatly, so the heterogeneity was substantial; however, the results were stable. Shift work was found to be positively associated with the risk of overweight [RR: 1.25; 95% confidence interval (95% CI): 1.08–1.44] and obesity (RR: 1.17; 95% CI: 1.12–1.22). Conclusions Individuals involved in shift work are more likely to become overweight or obese. Appropriate preventive interventions in the organization of shift schedules according to ergonomic criteria would allow shift workers to avoid potential health impairment. shift work, overweight, obesity, meta-analysis, prevention Key Messages This meta-analysis indicated an association between shift work and increased overweight/obesity risk. The risk of becoming overweight on night-shift work is much higher than for rotating-shift work. We suggest that optimized interventions are needed in the organization of shift schedules to protect workers from overweight/obesity. Introduction Shift work (SW) refers to a job schedule in which employees work hours other than the normal working schedule of 9 a.m. to 5 p.m.1 The full spectrum of SW comprises regular evening or night shifts, split shifts, rotating shifts, on-call or casual shifts, irregular shifts, 24-hour shifts and other non-day shifts. It has been reported that 36.1% of employees undertake SW in China and Korea.2 A study on working conditions showed that nearly 20% of employees in industrialized countries were involved in SW.3 Previous studies have showed that SW is associated with negative health outcomes.4,5 SW has been implicated in disrupting the circadian rhythm,6 which may impair glucose metabolism and lipid homeostasis.7 An irregular sleep–wake cycle could influence hormones related to appetite regulation.7,8 SW has also been found to interrupt workers’ recovery from fatigue and to prevent performance of regular exercise.9 Metabolic disorders, altered eating behaviour10,11 and less exercise4 may result in overweight and obesity among shift workers. Persisting stereotypes describe overweight and obese people as lazy, less self-disciplined and incompetent.12 In the workplace, obesity is an important driver of costs associated with absenteeism, healthcare claims, sick leave, injuries and disability.13,14 The high prevalence of overweight and obesity is particularly concerning because the links between overweight/obesity, poor health outcomes and all-cause mortality are well established. Overweight and obesity increase the likelihood of diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis.15 Overweight and obesity were estimated to account for 3.4 million deaths per year and 93.6 million disability-adjusted life years (DALYs) in 2010.16 Growing attention has been paid by the public to the influence of SW on the risk of overweight and obesity,17–27 whereas the findings have remained inconsistent or even conflicting. The majority of studies report a positive relationship,17,18,20–22,24,27–32 whereas other studies show no obvious association.19,23,25,30,33–41 The influence of SW on overweight and obesity is still unclear. A meta-analysis in 2014 showed that night SW was associated with the risk of metabolic syndrome (MetS).42 However, the evidence was limited by retrieving only 13 studies on MetS; since then, a number of additional studies have been published. Recently, a published meta-analysis that considered overweight and obesity as a whole reported that night SW was positively related to the risk of obesity and overweight.43 However, there are some differences between overweight and obesity, such as the definitions and health consequences. The relationship between SW and overweight and obesity has not been systematically evaluated. Hence, we conducted a comprehensive meta-analysis of 27 observational studies to quantify the association between SW and obesity and overweight, respectively, and to provide evidence-based information for managers in charge of working time organization to effectively protect workers from overweight/obesity. Methods No protocol exists for this meta-analysis. Ethics Ethical approval was not required for this systematic review. Literature search strategy We conducted the meta-analysis according to the MOOSE guideline.44 We sought studies that reported risk estimates for the association between SW and at least one outcome of overweight or obesity. Outcomes were typically defined by primary study authors using country-specific iterations of the International Classification of Disease coding system. We imposed no limitation by study design, regional origin or nature of the control group, which could include day workers or the general population. We systematically searched PubMed and Embase from inception until December 2017. We used database-specific subject terms and keywords to generate an initial list of articles for scrutiny. We also scrutinized the reference lists of all eligible articles. Two health information specialists (Q.L. and J.S.) designed and implemented the search in consultation with the rest of the team. Two reviewers (P.D. and T.Y.) screened citations and assessed articles independently for inclusion; disagreements were settled through consultation with a third reviewer (Z.L.). Study selection criteria Studies meeting the following criteria were included in the meta-analysis: (i) the research design was cohort, case–control or cross-sectional; (ii) any type of SW was an exposure variable; (iii) the endpoint of interest was the prevalence or incidence of overweight or obesity; and (iv) the study reported the risk estimates and corresponding 95% CIs for the association between SW and overweight or obesity. Reviews, animal studies, clinical trials, commentaries and letters were excluded. Studies involving non-work-related or involuntary night-time light exposure were also excluded. Data extraction from studies with aggregate data We extracted details on the name of the first author, year of publication, region, study design, definition of shift workers and references, characteristics of participants, outcome measurements, number of participants and cases, risk estimates with 95% CIs and covariates adjusted in the statistical analysis. We classified SW schedules according to the primary study methodological descriptions as night, rotating and mixed. Two reviewers (Q.L. and T.Y.) extracted all outcome data independently after reformulation of study citation information. Disagreement among reviewers was discussed and agreement was reached by consensus. Quality appraisal We appraised all the included studies using the Newcastle-Ottawa Scale 10,45 which is a nine-point scoring system used to assess the quality of non-randomized studies included in a systematic review and/or meta-analysis. A high-quality study was defined as a study with at least seven points. Disagreements on quality assessment were resolved by discussion among the authors. Statistical analysis The relative risks (RRs) and 95% confidence intervals (CIs) were considered as the effect size in this meta-analysis. We calculated summary RRs by synthesizing across the shift schedules categorized in each study. A random-effects model was used to pool the effect estimates. RRs for overweight and obesity were calculated with 95% CIs. Subgroup analyses, hypothesized a priori, were conducted using a statistical test of interaction.46 Studied subgroups included study design (cohort, cross-sectional study and case–control study), type of SW, region of study, gender of participants and adjusted variables. We used the I2 statistic to measure heterogeneity. Values of 0–30% represented minimal heterogeneity, 31–50% represented moderate heterogeneity and >50% represented substantial heterogeneity.47 We assessed our results with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.48 Potential publication bias was assessed with visual inspection of the funnel plot, the Begg correlation test and the Egger linear regression test. All statistical analyses were performed with STATA.11.0 (Stata Corp, College Station, Texas, USA). All tests were two-sided, with a significance level of 0.05. Results Literature search The literature search identified 1493 citations from PubMed and 3559 from Embase, of which 553 were selected for further review (see Figure 1). Of these, we retrieved 56 articles in full and rated 26 as eligible for the review; one paper included a cohort and a case–control study. In total, there were 27 eligible published studies with aggregate data, of which 7 were cohort studies, 19 were cross-sectional studies and 1 was a case–control study. Figure 1. View largeDownload slide Flow diagram of identification of relevant observational studies of shift work in relation to the risk of overweight and obesity. Figure 1. View largeDownload slide Flow diagram of identification of relevant observational studies of shift work in relation to the risk of overweight and obesity. Characteristics and quality of the included studies The studies included 311 334 participants, with 10 473 overweight cases and 51 024 obesity cases. The study publication years ranged from 1999 to 2017. Briefly, we identified 27 primary studies of SW and risk of overweight/obesity. Among the studies included, shift schedules were classified as night shifts (n = 5), rotating shifts (n = 18) and mixed schedules (n = 4). All studies (n = 27) used non-shift workers as the reference category. Eight studies were conducted in Europe, eight in Asia, six in North America and three in South America; the remaining two were multinational. Five and 9 studies only reported separate outcomes of males and females, respectively, and 13 studies reported the outcomes of both genders. The quality scores of these studies appraised with the Newcastle-Ottawa Scale 10 ranged from 6 to 10. Overall, 7 studies had a score of 9, 11 had a score of 8, 6 had a score of 7 and 3 had a score of 6, which led to an average score of 7.81 points. Detailed characteristics and quality assessments of these studies are presented in Table 1. Table 1. Characteristics of studies included in the meta-analysis of shift work in relation to risk of overweight and obesity Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  BMI, body mass index; NA, not available; WC, waist circumstance. Table 1. Characteristics of studies included in the meta-analysis of shift work in relation to risk of overweight and obesity Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  Study  Year  Study design  Country  Gender  Participants  Cases of obesity  Cases of overweight  Type of shift work  Assessment of obesity  Assessment of overweight  Adjustment of covariates  Grundy et al.  2017  Cross-sectional  Canada  male  1561  248  854  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, education, marital status, family income, smoking status, physical activity, energy consumption  Barbadoro et al.  2016  Cross-sectional  Italy  Male and female  36 814  3427  NA  Rotating  BMI ≥ 30 kg/m2  NA  Gender, age, education, household BMI, leisure time physical activity, smoking habit, nutritional habit, drugs/chronic conditions  Gomez-Parra et al.  2016  Cross-sectional  Colombia  Male and female  200  14  160  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Gender, age, work related stress, current smoking status, occupation, educational level, years working in shifts  Yoon et al.  2016  Cross-sectional  Korea  Female  42 234  7249  NA  Rotating  BMI > 25 kg/m2  NA  Age, marital status, education level, household income, smoking history, alcohol intake, physical activity and sleep duration  Ramin et al.  2015  Cohort  USA  Female  54 724  15 983  NA  Rotating  BMI ≥ 30 kg/m2  NA  Age, education level of the nurse’s spouse/partner, physical activity, chronotype, body mass index at age 18  McGlynn et al.  2015  Cohort  Canada  Female  1097  90  268  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Buchvold et al.  2015  Cross-sectional  Norway  Male and female  2059  2038  NA  Night  BMI > 30 kg/m2  NA  Age, sex, exercise  Givens et al.  2015  Cross-sectional  USA  Male and female  1593  497  464  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age (continuous), gender, education, and race/ethnicity  McGlynn et al.  2015  Case–control  Canada  Female  1611  306  477  Rotating  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Population-based sample was adjusted for education and age; alumni cohort sample was adjusted for smoking, parity and age  Neilsztramko  2015  Cross-sectional  Canada  Male and female  4323  1050  NA  Mixed  BMI ≥ 30 kg/m3  NA  Age, sex and children in the household  Peplonska et al.  2015  Cross-sectional  Poland  Female  724  168  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, smoking, pack years, marital status, body silhouette at age 20  Son et al.  2015  Cross-sectional  Korea  Male and female  2952  742  NA  Rotating  TBF% males: ≥25.7%, females: ≥36.0%  NA  Age, education, income, marital status, alcohol intake, smoking, energy intake, physical activity, sleep time, stress, menopausal status, work hours, stability of work  Balieiro et al.  2014  Cross-sectional  Brazil  Male  150  NA  80  Night  BMI ≥ 25 kg/m2  WC ≥ 94 cm  Age  Peplonska et al.  2014  Cross-sectional  Poland  Male and female  605  114  289  Night  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Age, marital status and education  Barbadoro et al.  2013  Cross-sectional  Italy  Male  339  NA  241  Rotating  NA  BMI >  24.9 kg/m2  Age, family history of obesity, alcohol consumption and physical activity  Kim et al.  2013  Cross-sectional  Korean  Female  9989  736  5287  Rotating  BMI ≥ 25 kg/m2  BMI ≥  23 kg/m2  Age, current smoking status, drinking habit, marital status, family income, education, dietary habits, regular exercise, sleep problems, self-perceived health status  Macagnan et al.  2012  Cross-sectional  Brazil  Male and female  1206  NA  800  Night  NA  BMI ≥  25 kg/m2  Demographic variables, socio-economic variables and parental overweight, behavioural variables (number of meals) and sleep characteristics  Zhao et al.  2012  Cross-sectional  Multinational  Female  2086  1132  661  Mixed  BMI ≥ 30 kg/m2  BMI ≥  25 kg/m2  Diet quality, physical activity, smoking status, alcohol consumption, work pattern, general physical and mental health  Itani et al.  2011  Cohort  Japan  Male and female  22 743  10 420  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age class, eating habits, alcohol consumption, smoking habit, exercise habit, mental complaints, hypertension, hyperglycemia, hypertriglyceridemia and hypo-HDL cholesterolemia  Kubo et al.  2011  Cohort  Japan  Male  9912  3319  NA  Rotating  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking and physical activity during leisure time  Bushnell et al.  2010  Cross-sectional  Multinational  Male and female  26 442  NA  NA  Mixed  BMI ≥ 30 kg/m2  NA  Age group, sex, marital/living status, job tenure and occupational group  Chen et al.  2010  Cross-sectional  China  Female  1838  NA  NA  Mixed  BMI ≥ 25 kg/m2  NA  Age, smoking, drinking, education and duration of work  Oberlinner et al.  2009  Cohort  Germany  Male  31 346  2431  NA  Rotating  ICD-10  NA  Age, job level, cigarette smoking and alcohol intake  Watari et al.  2006  Cohort  Japan  Male and female  25 312  1060  NA  Rotating  BMI ≥  26.4 kg/m2  NA  Smoking, age, work style, one-way commuting time, consumption of alcohol, sleeping hours, exercise, eating style  Chee et al.  2004  Cross- sectional  Malaysia  Female  1612  NA  603  Rotating  NA  BMI ≥  25 kg/m2  Age  Karlsson et al.  2001  Cross- sectional  Sweden  Male and female  27 485  NA  NA  Rotating  BMI ≥  30 kg/m2  NA  Age, education  van Amelsvoort et al.  1999  Cohort  Netherlands  Male and female  377  NA  NA  Rotating  BMI ≥  25 kg/m2  NA  Smoking  BMI, body mass index; NA, not available; WC, waist circumstance. Summary of the definitions of SW, overweight and obesity Most studies reported the relationship between rotating SW and obesity,17–20,24,25,27–29,31,32,34–37,39,40,49 whereas five studies showed outcomes of night-only SW,22,23,30,38,41 two studies reported results of rotating shift and night shift separately21,33 and one study reported outcomes of night shift and day shift separately.50 Eight studies defined overweight as a body mass index (BMI) ≥ 25 kg/m2.21,27,30,32,37–41 Barbadoro, Kim and Balieiro defined overweight as BMI > 24.9 kg/m2, BMI ≥23 kg/m2 and waist circumstance ≥ 94 cm, respectively.22,29,36 Eleven studies defined obesity as BMI ≥30 kg/m2.18,21,27,30–33,36–39,49 Six defined the presence of obesity as BMI ≥ 25 kg/m217,22,29,34,50,51 and the remaining four studies defined obesity as BMI > 30 kg/m2,23 BMI >25 kg/m2,25 BMI ≥ 26.4 kg/m2,19 and by total body fat percentage,35 respectively. Only one study defined obesity according to the tenth revision of the International Classification of Diseases (ICD-10).20 SW increases risk of overweight and obesity Figure 2 shows the combined RR from the random-effect model for overweight in relation to SW. Twelve studies described the association between SW and the risk of overweight. Of the 12 studies, 6 showed no obvious relationship or an inverse association between SW and the risk of overweight, whereas 6 other studies reported a positive relationship. The pooled RR was 1.25 (95% CI: 1.08–1.44, I2 = 80.7%). A meta-analysis was performed to estimate the association between SW and the risk of obesity by combining results from all eligible studies, giving a summary RR of 1.17 (95% CI: 1.12–1.22; I2 = 92.2%) (see Figure 3). Only three studies reported the dose–response relationship between SW and obesity27,30,31 and the information is inadequate for further dose–response analysis, but we could indicate that a higher combined RR was associated with a higher frequency of monthly night shifts (at least eight nights/month) compared with the lower one. Peplonska and Kim indicated a positive dose–response relationship between years of SW and obesity risk.27,29,30 Figure 2. View largeDownload slide Forest plot of the association between shift work and overweight. Figure 2. View largeDownload slide Forest plot of the association between shift work and overweight. Figure 3. View largeDownload slide Forest plot of the association between shift work and obesity. Figure 3. View largeDownload slide Forest plot of the association between shift work and obesity. Subgroup analyses and sources of heterogeneity We performed subgroup analyses to assess whether specific study characteristics influenced the association between SW and overweight/obesity. As shown in Table 2, the increased risk for overweight was more evident in the night-shift group (RR: 1.38; 95% CI: 1.06–1.80). SW was associated with increased risks of obesity in most subgroups. Table 3 presents a higher pooled RR for obesity in the rotating-shift group (RR: 1.18; 95% CI: 1.08–1.29). Additionally, subgroup analyses suggested a stronger association between SW and obesity for cohort studies (RR: 1.16; 95% CI: 1.03–1.31) than for cross-sectional studies (RR: 1.11; 95% CI: 1.07–1.15). Table 2. Subgroup analyses of relative risks of overweight according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Stratified meta-analysis of shift work and the risk of overweight. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available. Table 2. Subgroup analyses of relative risks of overweight according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All  13  1.25 (1.08, 1.44)  80.7  0  NA  Type of shift work   Rotating-shift work  8  1.21 (1.02, 1.43)  73.2  0  0.860   Night-shift work  5  1.38 (1.06, 1.80)  28.5  0.231  Gender   Female  6  1.14 (0.97, 1.35)  84.3  0  0.913   Male  5  1.46 (0.98, 2.15)  51.2  1.104  Region   Europe  4  1.32 (0.98, 1.77)  0  0.432  0.873   North America  3  1.27 (0.79, 2.06)  90.5  0   South America  3  1.35 (0.80, 2.28)  57.2  0.097   Asia  2  1.38 (1.08, 1.77)  68.9  0.073  Stratified meta-analysis of shift work and the risk of overweight. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available. Table 3. Subgroup analyses of relative risks of obesity according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Stratified meta-analysis of shift work and the risk of obesity. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available; BMI, body mass index. Table 3. Subgroup analyses of relative risks of obesity according to shift-work status Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Study groups  N  Relative risk (95% CI)  Heterogeneity   P-value for interaction  I2  P  All studies  23  1.17 (1.12, 1.22)  92.2  0  NA  Type of shift work   Day-shift work  2  1.14 (0.79, 1.64)  61.3  1.108  0.82   Night-shift work  7  1.05 (1.00, 1.10)  81  0   Rotating-shift work  17  1.18 (1.08, 1.29)  91.7  0  Study design   Cohort  7  1.16 (1.03, 1.31)  81.2  0  0.87   Cross-sectional  15  1.11 (1.07, 1.15)  87.7  0   Case–control  1  1.20 (0.92, 1.57)  NA  NA  Gender   Female  13  1.19 (1.06, 1.34)  90.8  0  0.938   Male  9  1.27 (1.10, 1.46)  81.9  0  Geographical location   Asia  7  1.14 (1.02, 1.28)  48.9  0.068  0.87   North America  5  1.23 (1.05, 1.44)  67.6  0.015   Europe  7  1.23 (1.02, 1.48)  96  0   South America  2  2.27 (1.07, 4.81)  0  0.389  Participants   Medical staff  6  1.09 (1.04, 1.03)  94.7  0  0.317   Other workers  16  1.21 (1.10, 1.33)  59.8  1.115  Number of cases   <1000  7  1.27 (1.09, 1.48)  47.5  0.168  0.783   1000–5000  6  1.08 (1.04, 1.12)  94.2  0   >5000  3  1.18 (1.06, 1.31)  63  0  Cut-off of obesity   BMI > 25 kg/m2 or ≥ 25 kg/m2  7  1.23 (1.08, 1.39)  49.7  0.064  0.73   BMI > 30 kg/m2 or ≥ 30 kg/m2  12  1.12 (1.08, 1.16)  74.6  0.047   Other cut-offs  3  1.17 (0.78, 1.74)  84.3  0.002  Adjustment for physical exercise   Yes  11  1.08 (1.05, 1.13)  78.3  0.032  0.183   No  10  1.30 (1.13, 1.50)  79.8  0  Adjustment for energy intake   Yes  8  1.10 (1.02, 1.20)  77.2  0.036  0.364   No  13  1.23 (1.09, 1.39)  95.1  0  Stratified meta-analysis of shift work and the risk of obesity. All groups were compared with 8-hour day-shift employees as the reference category. CI, confidence interval; N, number of studies; P, P-value for heterogeneity; NA, not available; BMI, body mass index. As substantial heterogeneity was found among the included studies, we conducted subgroup analyses and identified the potential cause of heterogeneity by various factors, such as gender, region of study, type of SW, study design and number of cases. Subgroup analyses suggested that different types of SW might be the main cause of heterogeneity for overweight. For night SW, no obvious heterogeneity was found. For rotating SW involving night shift, the heterogeneity was substantial. It is self-evident that the specific schedules of rotating SW differ greatly among different occupations and positions. The endpoints of obesity were different between studies, which might contribute to the heterogeneity of obesity under the influence of SW. For example, for three studies with cut-offs of obesity other than BMI, the combined RR was 1.17 (95% CI: 0.78–1.74), whereas the pooled RR of studies with cut-offs of BMI > 25 kg/m2 and BMI ≥ 25 kg/m2 was 1.23 (95% CI: 1.08–1.39), and the accumulated RR of studies with endpoints of BMI > 30 kg/m2 and BMI ≥ 30 kg/m2 was 1.12 (95% CI: 1.08–1.16). Sensitivity analyses When we excluded the studies of Oberlinner et al.20 and Son et al.,35 in which the ICD-10 and total body fat percentage definitions were used, the pooled RR of SW and obesity was 1.16 (95% CI: 1.11–1.21). We then excluded any single study in turn and combined the results of the remaining included studies, and the overall summary RRs for overweight and obesity were not substantially altered. The summary estimates derived from subsequent sensitivity analyses for overweight ranged from 1.19 (95% CI: 1.02–1.38) to 1.35 (95% CI: 1.13–1.62) and for obesity they ranged from 1.16 (95% CI: 1.11–1.21) to 1.17 (95% CI: 1.12–1.22). Publication bias There was no obvious publication bias detected by Begg’s and Egger’s tests (P = 0.047 for overweight, P = 0.004 for obesity) (Figures 4 and 5). Figure 4. View largeDownload slide Funnel plot for studies of shift work in relation to overweight risk. Figure 4. View largeDownload slide Funnel plot for studies of shift work in relation to overweight risk. Figure 5. View largeDownload slide Funnel plot for studies of shift work in relation to obesity risk. Figure 5. View largeDownload slide Funnel plot for studies of shift work in relation to obesity risk. Discussion In the current systematic review and meta-analysis, a total of 27 independent studies involving 311 334 participants (10 473 overweight cases and 51 024 obesity cases) were identified for examination. Evidence from these studies suggests that shift workers might have an increased risk of overweight and obesity, by 25% and 17%, respectively. This result for obesity is consistent with a previous meta-analysis by Wang et al.,42 which reported an increased risk of obesity of 1.66 (95% CI: 1.02–2.71) based on six studies. However, our meta-analysis included 16 additional studies on obesity with a much larger sample size. Additionally, Wang et al. did not do subgroup analysis of obesity, nor did they quantitatively analyse the association between SW and overweight. Recently, another published meta-analysis found the pooled odds ratio of night SW was 1.23 (95% CI: 1.17–1.29) for risk of obesity and overweight.43 Although the meta-analysis included original studies about abdominal obesity, unfortunately it omitted seven studies due to the searching strategy and the definitions of outcomes. The present meta-analysis analysed the association between overweight as well as obesity and SW patterns. We found that night SW was positively associated with overweight 1.38 (95% CI: 1.06–1.80), whereas rotating SW increased the risk of obesity 1.18 (95% CI: 1.08–1.29). Taken together, our meta-analysis may be the latest evidence of association of SW with overweight/obesity to date. Circadian disruption can be the biological explanation for the pathogenesis of obesity. Long-term exposure to SW might disturb the normal circadian rhythm, which might impair glucose metabolism and lipid homeostasis.7 Although the mechanism linking obesity to circadian disruption has not yet been fully understood, both animal experiments and human studies have indicated that disruption of the circadian rhythm might be causal for obesity.52–54 Melatonin is an important mediator of circadian disruption resulting in SW and obesity. Melatonin production is promoted in darkness but suppressed by light in the night, so SW including night shift could inhibit the secretion of melatonin. An animal study reported that the reduced production of melatonin was associated with metabolic disturbance.55 Leptin acts as a natural appetite suppressor, aiding in the regulation of energy intake and expenditure by inhibiting appetite and speeding up metabolism.56 Additionally, Qin et al.57 indicated that the disturbance of circadian rhythms leads to the impairment of insulin response to glucose, and the changes of melatonin and leptin were highly consistent with those of night-eating syndrome, which would result in excess weight. The main findings of our study suggested that rotating-shift workers had an increased risk of obesity of 16% compared with the reference groups. Weight gain can provide an ‘infrastructure’ for health disorders. Therefore, shift schedules should be arranged according to ergonomic criteria to limit negative effects on hormone secretion and health. Sleep deprivation—another plague of the modern world—is recognized to be involved in the pathogenesis of obesity.58 Epidemic studies have revealed an association between chronic sleep deprivation and long-term weight gain.59,60 Sleep disturbance, which adversely influences hormonal rhythms and metabolism, also contributes to overweight and obesity.7 Several pathways could link sleep duration to obesity, including increased food intake, reduced energy expenditure and alterations in levels of appetite-regulating hormones, such as leptin and ghrelin.61 Sleep deprivation has been associated with decreased leptin levels and increasing ghrelin, resulting in increased appetite leading to weight gain.62 The mechanisms linking SW with overweight/obesity are shown in Figure 6. Naps taken before and during night shifts have been shown to yield beneficial effects on sleepiness and fatigue. The hypothesis that napping during night shifts could help to decrease melatonin suppression and reduce the possibility of negative consequences has been tested.63,64 Hence, shift schedules should allow workers sufficient opportunities to sleep during protected intervals between shifts. Alternatively, partial rather than complete arrangement to day-oriented schedules (e.g. day shifts and day off) might yield sufficient positive performance and improve sleep outcomes. Figure 6. View largeDownload slide The hypothesized adverse health effects of shift work and potential mechanisms. The risk of overweight/obesity increases with circadian disruption and sleep deprivation in shift workers. Consequently, the likelihood of chronic diseases (e.g. diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis) increases. Figure 6. View largeDownload slide The hypothesized adverse health effects of shift work and potential mechanisms. The risk of overweight/obesity increases with circadian disruption and sleep deprivation in shift workers. Consequently, the likelihood of chronic diseases (e.g. diabetes, hypertension, coronary heart disease, stroke, obstructive sleep apnea, certain cancers and osteoarthritis) increases. From a nutritional perspective, it has been shown that sleep deprivation could induce physiological adaptations that change the individual nutritional status.65 Other studies have shown that a short sleep duration seems to not only enhance appetite,7 but also to generate a preference for fat-containing foods.66,67 Itani et al.34 reported that a short sleep duration is associated with the onset of obesity. Our findings showed that night-shift workers were more likely to be overweight (RR: 1.24). In view of the fact that SW deregulates sleep and wakefulness, efforts should be made towards either minimizing the unfavourable effects on sleep or managing sleep and naps. Additionally, eating behaviour might be changed by working schedules, especially when night work is involved,11 because of a diverse range of biological, social and cultural factors. Also, shift workers experience the most job-related stress,68 which may act as a potential risk factor for weight gain by affecting eating behaviours and food choices and by establishing barriers to healthy eating.69 Rotating-shift workers showed a much higher pooled effect on obesity. Rotating shifts involving night work may disturb the circadian rhythm, affect eating habits and reduce physical activities.21 Coffey et al.70 reported the most job-related stress and fatigue in rotating-shift nurses, which might establish barriers to healthy eating and increase risk of obesity.69,71 A recent study suggested that shift workers had similar diet quality but higher energy intake compared with day workers.72 The higher energy intake might be one of the causes of SW-induced overweight, obesity and other adverse health outcomes. Guidance on a healthy eating style is required for this at-risk population group. Overweight and obesity not only impair work performance, but also increase the morbidity of chronic diseases (e.g. type 2 diabetes mellitus, cancers, hypertension, cardiovascular diseases, osteoarthritis, obstructive sleep apnea).73 The involvement of both behavioural factors, such as eating late, a calorie-rich diet, sleep deprivation and insufficient physical activity, and intrinsic aspects, including circadian disruption and adipose deregulation, has been identified.11,74 Many studies have shown that shift workers are more prone to the development of overweight and obesity. Our meta-analysis provided support for a possible association of SW and overweight/obesity. We are aware that these results might be biased on the basis of our searching strategies, initially designed for studying SW and the risk of overweight/obesity. As a relationship between overweight/obesity and risk of chronic diseases has been confirmed, avoiding overweight and obesity is an important strategy in managing risk for chronic diseases. Strengths and weaknesses Our study has several strengths. First, it is the largest synthesis of SW and obesity, as well as overweight, reported to date. We have overcome these limitations and provide in-depth analyses of the relationship between SW and overweight/obesity. Second, sensitivity analysis and consistent results from various subgroup analyses indicate that our findings are reliable and robust, although heterogeneity existed among the included studies. On the other hand, several caveats in our study need to be noted. First, there are several methodological limitations (including overweight/obesity assessment and analytical methodology) worth consideration. Second, due to the limitations of eligible data, the subgroups based on type of SW were crudely classified into day SW, night SW and rotating SW subgroups, regardless of the SW duration, cumulative SW exposure and frequency of SW. Third, high heterogeneity across studies was present for the SW–overweight and SW–obesity links, which throws some doubt on the reliability of the summary RR estimates. However, the results were stable. Fourth, most included studies used the cross-sectional study design, and the relationship between SW and the risk of overweight/obesity might be underestimated for the potential healthy worker survival effect in the cross-sectional studies.75 Fifth, only studies published in the electronic databases in English and Chinese were reviewed for pertinence, and studies in other languages were omitted. Despite these limitations, the meta-analysis included a substantial number of cohort studies, which greatly strengthened the statistical power of the analysis and provided adequate evidence for the authors to draw a reliable conclusion. For further studies based on our findings, we suggest that investigations need to improve the standardization of different shift schedules and outcome definitions, which would provide research evidence. Additionally, research should be conducted to confirm our findings and establish the potential biological mechanisms. Finally, more interventional studies are needed to prevent overweight and obesity in the workplace. Conclusion In conclusion, the present meta-analysis indicates that SW might be associated with an increased risk of overweight and obesity. Owing to the substantial heterogeneity, further high-quality studies are required to address the existing limitations in the literature. Given the worldwide increasing prevalence of SW and the potential side effects of overweight/obesity on health, the results of our study suggest that both the managers in charge of working time organization and the involved workers must be sufficiently informed about the possible adverse effects of SW. Furthermore, shift schedules should be designed according to ergonomic criteria recommended to be suitable to limit negative effects on health by avoiding or minimizing circadian disruption and accumulation of sleep deficiency. Funding This work was supported by the Fundamental Research Funds for the Central Universities, Huazhong University of Science and Technology, Wuhan, China. (2016YXMS215). Acknowledgements Q.L. developed the idea and prepared a draft review protocol. Q.L. and J.S. conducted the literature search. B.L., P.D., T.L. and C.W. collected the identified articles. Q.L. and T.Y. extracted data from the included studies. Y.G., H.L. and X.W. checked the extracted data. 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International Journal of EpidemiologyOxford University Press

Published: May 29, 2018

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