Physiology & Behavior 142 (2015) 121–125
Contents lists available at ScienceDirect
Physiology & Behavior journal homepage: www.elsevier.com/locate/phb
The effect of shift work on red blood cell distribution width Paul D. Loprinzi ⁎ The University of Mississippi, Center for Health Behavior Research, University, MS, United States
H I G H L I G H T S • Elevated red blood cell distribution width (RDW) predicts premature mortality. • No study has examined the effect of shift work on RDW. • Women working a rotating shift had a 46% increased odds of having elevated RDW.
a r t i c l e
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Article history: Received 2 December 2014 Received in revised form 18 January 2015 Accepted 19 January 2015 Available online 19 January 2015 Keywords: Cardiovascular disease Epidemiology Work schedule
a b s t r a c t Limited research demonstrates that shift work (e.g., evening shift, night shift, rotating shift) increases the risk of certain health outcomes, such as hypertriglyceridemia and metabolic syndrome. Red blood cell distribution width (RDW), which is commonly assessed and reported by physicians, is a novel biomarker of cardiovascular disease. However, no study has examined the association of shift work on RDW, which was the purpose of this study. Data from the 2005–2010 NHANES were used. RDW was assessed from a blood sample; shift work was assessed from a questionnaire, and various demographic, behavioral/psychological, occupational, and biological parameters were included as covariates. The fully adjusted model showed that the odds of having an elevated RDW for women on rotating shift vs. day shift increased by 46% (OR = 1.46; 95% CI: 1.03–2.08). Women on a rotating shift had increased odds of having an elevated RDW, which is concerning as elevated RDW increases the risk of cardiovascular disease and mortality. Health care professionals are encouraged to include questions about organization of work schedules and their tolerance of such schedules during the patient's consultation. © 2015 Elsevier Inc. All rights reserved.
1. Introduction In the working world, shift work is necessary for obvious economic and social goals, with different shift schedules including: regular daytime shift, regular evening shift, regular night shift, rotating shift, or another schedule. Previous systematic reviews have examined the effect of shift work on various health outcomes [1,2]. Based on these review papers published in 1999 and 2011, few studies have examined the effect of shift work on diabetes or inflammation. Non-daytime shift work (e.g., night shift) was associated with an increased risk of hypertriglyceridemia, but the effects of non-daytime shift work on body habitus (e.g., body mass index, overweight, obese) and cholesterol were not consistently associated with each other, which is consistent with a recent (2014) epidemiological study using data from the National Health and Nutrition Examination Survey (NHANES) [3]. Recently (2014), Wang et al. performed a meta-analysis among 13 studies, and ⁎ Center for Health Behavior Research, The University of Mississippi, 229 Turner Center, University, MS 38677, United States. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.physbeh.2015.01.020 0031-9384/© 2015 Elsevier Inc. All rights reserved.
concluded that night shift work is associated with an increased risk of metabolic syndrome [4], which is consistent with their other recent (2014) meta-analysis demonstrating an increased risk of breast cancer among night-shift workers [5]. Relatedly, emerging research demonstrates that shift work may also have a negative effect on cognition [6]. Consequently, and although further studies are needed on this topic, there is evidence to suggest that non-daytime shift work may increase an individual's risk for chronic disease, with hypothesized pathophysiological mechanisms including shift work-induced changes in desynchronized circadian rhythms, sleep disturbances, modulation of food intake, psychosocial stress, and social inequalities [7,8]. To date, no study has examined the effect of shift work on red blood cell distribution width (RDW). RDW, an indicator of anisocytosis, is a novel biomarker indicative of cardiovascular disease (CVD) and all-cause mortality [9–12]. Although inconclusive, the pathophysiology linking RDW with morbidity and mortality may be a result of inflammation-induced anisocytosis and/or disordered iron homeostasis [13–16]. To further our knowledge of the potential shift work–CVD link, the purpose of this study was to examine the
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P.D. Loprinzi / Physiology & Behavior 142 (2015) 121–125
association between shift work and RDW. It was hypothesized that non-daytime shift work would be associated with increased RDW when compared to regular daytime shift work. 2. Methods 2.1. Design and participants Data from 2005–2010 NHANES were used. NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage, stratified, clustered probability design. The continuous NHANES collects data each year on a representative sample of Americans, with data released in two year cycles (e.g., 2005–2006, 2007–2008, and 2009–2010). Participants are interviewed in their home and subsequently examined in a mobile examination center (MEC). The 2005–2010 NHANES study procedures were approved by the National Center for Health Statistics ethics review board. Consent was obtained from all participants prior to any data collection. In the 2005–2010 NHANES, 7130 (n = 3861 for males; n = 3269 for females) adult participants (≥20 yrs) indicated that they currently work and provided data on all study variables, with these 7130 participants constituting the analytic sample. In the initial sample, 118 participants had cardiovascular disease, but these participants were excluded and not included in the final analytic sample (N = 7130) as those with cardiovascular disease have elevated RDW [17–19] and are generally not assigned to non-daytime shifts [2]. Notably, analyses were computed with and without the 118 cardiovascular disease participants in the analytic models, and results were similar. 2.2. Assessment of work shift Among adults indicating that they are currently working at a job or business, they were asked: “Which of the following best describes the hours you usually work at your main job or business: a regular daytime schedule, a regular evening shift, a regular night shift, a rotating shift, or another schedule.” 2.3. Assessment of red blood cell distribution width The complete blood count was from a blood sample using the Beckman Coulter MAXM analyzer. RDW was derived from the coefficient of variation of the red cell volume distribution histogram and reported as a %. Previous prospective research demonstrates that RDW ≥ 13.0% increases mortality risk by 20% in the general population [11]. Therefore, elevated RDW was defined as an RDW value of ≥13.0%. In the present sample, a RDW of 13.0% was equal to the 75th percentile of the RDW distribution. 2.4. Assessment of covariates Covariates in the analytic models consisted of demographic, behavioral, psychological, occupational, and biological parameters. The demographic parameters included: age (yrs), gender (male/ female), race–ethnicity (white/non-white), education (high school or less/some college or more), and survey year (i.e., 2005–2006, 2007–2008, or 2009–2010). The behavioral/psychological parameters included: physical activity, self-reported sleep duration (h/night), energy intake (kcal), saturated fat intake (g), depression, and cotinine (ng/mL; smoking biomarker). Physical activity was assessed from the Global Physical Activity Questionnaire, which has demonstrated evidence of reliability and validity [20]. Participants were asked to report whether they engaged in moderate-to-vigorous physical activity (yes/no) in the past 30 days. Depression (expressed as a continuous variable) was assessed using the Patient Health Questionnaire-9 (PHQ-9) [21]. Energy intake and saturated fat intake were assessed from the mobile examination
center dietary interview. Serum cotinine was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. The occupational parameters included: self-reported hours worked per week, # months at current job, and description of job work. Description of job work was defined into 6 categories: 1) an employee of a private company, business, or individual for wages, salary, or commission; 2) a federal government employee; 3) a state government employee; 4) a local government employee; 5) self-employed in own business, professional practice or farm; and 6) working without pay in family business or farm. The biological parameters included: measured body mass index (kg/m2), C-reactive protein (mg/dL), iron (μg/dL), measured mean arterial pressure (mm Hg; ([diastolic blood pressure × 2) + systolic blood pressure] / 3)), physician-diagnosed diabetes (yes/no), and total cholesterol (mg/dL). Details on the data collection protocol and procedures for these biological parameters have been published elsewhere [22].
2.5. Data analysis Multivariable logistic regression analysis was used to examine the association between shift work schedule and elevated (≥ 13.0%; ≥75th percentile) red blood cell distribution width (RDW), with separate analyses computed for males and females; regular daytime shift served as the referent group for the analytic models. Five a-priori analytical models were computed, including: Model 1: unadjusted model; Model 2: controlling for demographic variables; Model 3: controlling for demographic plus behavioral and psychological variables; Model 4: controlling for demographic, behavioral/psychological, and occupational variables; and Model 5: controlling for all variables, including demographic, behavioral/psychological, occupational, and biological variables. All analyses (Stata, v. 12; analyzed in 2014) accounted for the NHANES complex survey design by the use of survey sample weights, stratum and primary sampling units. Sample weights were created for the combined NHANES cycles following analytical guidelines for the continuous NHANES. Statistical significance was established as p b 0.05.
3. Results Table 1 displays the weighted characteristics of the analyzed sample, with results stratified by gender. Men and women, respectively, were, on average, 41.7 yrs and 42.3 yrs. The majority of men (75.0%) and women (76.7%) worked a regular daytime schedule. The proportion of men and women, respectively, with an elevated RDW (≥ 13%) was 17.6% and 27.0%. Men and women, respectively, worked an average of 44.9 and 38.1 h/wk and worked at their current job for 102.8 and 92.6 months. Table 2 displays the weighted multivariable regression analyses examining the association between shift work schedule and elevated (≥ 13.0%) RDW. No results were statistically significant for men, but the results were in the expected direction (i.e., non-daytime shifts had a non-statistically significant increased odds of having elevated RDW). However, compared to women working a regular daytime schedule, women working a rotating shift had increased odds of having an elevated RDW; results were significant for each analytic model. For example, the fully adjusted model showed that the odds of having an elevated RDW for women on rotating shift vs. day shift increased by 46% (OR = 1.46; 95% CI: 1.03–2.08; p = 0.03). There was no evidence of multicollinearity in the regression models; for example, in the fully adjusted model, the mean variance inflation factor was 1.3, the highest individual variance inflation factor was 2.72, and all tolerance statistics were N0.36.
P.D. Loprinzi / Physiology & Behavior 142 (2015) 121–125
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Table 1 Characteristics of the analyzed sample, 2005–2010 NHANES (N = 7130). Demographics
Males (n = 3861) N
Age, yrs Race–ethnicity, % White Other Education, % High school or less Some college or more Survey year, % 2005–2006 2007–2008 2009–2010 Behavioral and psychological % Physically active a Sleep duration, h/night Cotinine, ng/mL Energy intake, kcal Total saturated fat, g Occupational Hours worked per week # Months working at job Description of job work, % Private company/business Federal employee State employee Local employee Self-employed Family business Shift work, % Regular daytime schedule Regular evening shift Regular night shift Regular rotating shift Another schedule Biological Body mass index (kg/m2) C-reactive protein (mg/dL) Iron, μg/dL Mean arterial pressure, mm Hg % Diabetes RDW, % % Elevated RDW (≥13%)
Females (n = 3269)
Weighted mean/%
95% CI
N
Weighted mean/%
95% CI
3861
41.7
41.0–42.3
3269
42.3
41.7–42.8
1804 2057
70.3 29.6
66.5–74.1 25.8–33.4
1563 1706
71.9 28.1
67.8–75.9 24.0–32.1
1908 1953
40.3 59.6
37.2–43.4 56.5–62.7
1296 1973
32.4 67.5
30.3–34.6 65.3–69.8
1716 1342 1343
34.1 33.5 32.4
30.5–37.6 29.9–37.1 28.7–35.9
995 1107 1167
33.9 33.6 32.5
29.2–38.5 28.8–38.4 28.1–36.7
2994 3861 3861 3861 3861
79.2 6.71 75.4 2727.5 34.2
77.0–81.4 6.66–6.75 67.7–83.2 2678–2776 33.3–35.2
2214 3269 3269 3269 3269
72.6 6.90 43.6 1896.1 24.1
70.3–75.0 6.84–6.96 37.8–49.3 1866–1926 23.5–24.6
3861 3861
44.9 102.8
44.3–45.6 96.9–108.7
3269 3269
38.1 92.6
37.6–38.6 87.3–98.0
2902 88 153 220 486 12
74.3 2.3 4.0 5.9 13.1 0.1
72.2–76.4 1.7–2.8 3.3–4.7 5.0–6.8 11.3–14.9 0.0–0.2
2346 96 288 240 288 11
71.8 2.5 8.3 8.0 8.7 0.3
69.5–74.2 1.8–3.3 7.1–9.5 6.6–9.4 7.4–10.1 0.0–0.5
2851 204 169 300 337
75.0 4.8 4.1 6.9 9.0
73.2–76.7 3.9–5.7 3.3–4.8 5.8–7.9 8.0–10.0
2478 152 142 262 235
76.7 4.3 3.3 7.6 7.7
74.7–78.8 3.4–5.3 2.4–4.3 6.4–8.9 6.5–9.0
3861 3861 3861 3861 250 3861 830
28.6 0.29 96.7 88.8 4.9 12.48 17.6
28.3–28.8 0.26–0.33 94.8–98.6 88.3–89.3 4.0–5.8 12.4–12.5 15.8–19.3
3269 3269 3269 3269 196 3269 1055
28.3 0.41 80.7 85.1 4.9 12.72 27.0
27.9–28.7 0.38–0.44 79.1–82.3 84.6–85.7 3.9–6.0 12.6–12.8 24.4–29.6
RDW = red blood cell distribution width. a Physically active defined as those self-reporting engaging in moderate-to-vigorous physical activity in the past 30 days.
4. Discussion In this national sample of U.S. adults, a statistically significant association was not observed for each non-daytime shift pattern (i.e., evening, night, another schedule), but in partial support of the stated hypothesis, a rotating shift schedule increased the odds of having an elevated RDW (≥13.0%) by 46% for women, independent of various demographic, behavioral, occupational, and biological parameters. This is concerning as elevated RDW predicts premature mortality (e.g., RDW ≥ 13.3% increased mortality risk by 19% among critically ill patients; [23] RDW ≥ 13.0% increased mortality risk by 20% in general population ≥ 45 years [11]). Previous research demonstrates that regular, long-term non-daytime shift work increases the risk of metabolic syndrome [4], breast cancer [5], cognitive dysfunction [6] and select CVD risk factors [1,2]. Other studies, particularly non-U.S. populations, have also reported negative effects of rotating shifts on CVD risk factors. In a prospective study among Japanese male adults, a rotating shift increased the progression from mild to severe hypertension [24], which is similar to findings among Belgium adults [25]. Among Japanese adults, Dochi et al. [26,27] showed that a rotating shift increased the risk of dyslipidemia, which was also supported by findings in Belgium [25] and Swedish samples [28].
To my knowledge, this is the first study to examine the association of shift work on RDW. RDW is hypothesized to influence health via inflammation-induced anisocytosis and/or disordered iron homeostasis [13–16], and shift work is hypothesized to influence health through desynchronized circadian rhythms, sleep disturbances, modulation of food intake, psychosocial stress, and social inequalities [7,8]. Given that a rotating shift was the only shift work pattern associated with RDW, coupled with the fact that indices of sleep disturbances (e.g., sleep duration), modulation of food intake (caloric intake, saturated fat intake), psychosocial stress (e.g., depression), and social inequalities (e.g., education) were statistically controlled for in the analytic models, it is plausible to suggest that a rotating shift may increase RDW via desynchronized circadian rhythms. Non-work days from other shift work patterns (e.g., night shifts, evening shifts) may allow the individual to resynchronize their circadian rhythms, but this may be more difficult with a rotating shift given the constant changing schedule. A limitation of this study is the cross-sectional study design; thus, cause-and-effect determination is not possible given the inability to ascertain temporality between shift work and RDW. However, given the previous research demonstrating an effect of shift work schedule on
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Table 2 Multivariable logistic regression analysis examining the association between shift work schedule and elevated (≥13.0%) red blood cell distribution width (RDW) among working Americans, 2005–2010 NHANES (N = 7130). Analytical model
Odds (95% CI) of having elevated RDW a (≥13.0%; ≥75th percentile) Males (n = 3861) Evening shift
Night shift
Females (n = 3269) Rotating shift
Another schedule
Evening shift
Night shift
Rotating shift
Another schedule
Model 1: unadjusted Model 2: demographics Model 3: demographics +
1.08 (.72–1.61) 1.07 (.68–1.70) 1.12 (.84–1.49) 1.32 (.96–1.81) 1.21 (.80–1.8) .99 (.59–1.65) 1.43 (1.06–1.94) .82 (.55–1.22) 1.19 (.78–1.81) 1.29 (.80–2.1) 1.25 (.93–1.68) 1.34 (.96–1.87) 1.26 (.80–1.97) .99 (.60–1.63) 1.53 (1.11–2.10) .85 (.55–1.30) 1.08 (.68–1.73) 1.12 (.70–1.78) 1.22 (.90–1.64) 1.31 (.93–1.83) 1.26 (.80–1.99) .96 (.58–1.57) 1.52 (1.11–2.09) .86 (.56–1.31)
behavioral/psychological Model 4: demographics +
1.10 (.70–1.73) 1.14 (.71–1.81) 1.21 (.89–1.64) 1.36 (.98–1.89) 1.27 (.81–1.99) .95 (.58–1.57) 1.53 (1.12–2.10) .87 (.57–1.33)
behavioral/psychological + occupational Model 5: demographics +
1.12 (.71–1.77) 1.04 (.65–1.65) 1.25 (.92–1.68) 1.33 (.96–1.86) 1.18 (.72–1.92) .93 (.57–1.51) 1.46 (1.03–2.08) .83 (.55–1.28)
behavioral/psychological + occupational + biological 5 analytical models were computed for each gender: Model 1: unadjusted. Model 2: demographics: covariates included age, race–ethnicity, education, and survey year. Model 3: demographics + behavioral/psychological: same covariates in Model 2 plus physical activity, sleep duration, energy intake, saturated fat intake, depression, and cotinine (smoking). Model 4: demographics + behavioral/psychological + occupational: same covariates in Model 3 plus hours worked per week, # months at current job, and description of job work. Model 5: demographics + behavioral/psychological + occupational + biological: same covariates in Model 4 plus body mass index, C-reactive protein, iron, mean arterial pressure, diabetes, and total cholesterol. a Referent group is regular daytime schedule.
other CVD risk factors and chronic disease, it is plausible to suggest a causal relationship between shift work and RDW. Further, I chose not to statistically control for triglycerides (known to be influenced by work shift) as only a subsample of participants were tested for fasting triglycerides. And although employed in other NHANES studies [3,29], another limitation is the self-report nature of the shift work information. Major strengths of this study include the novel investigation (shift work on RDW), employing a national sample of U.S. adults, and including a comprehensive set of covariates in the analytical models. Future research is needed to better understand the gender-specific findings demonstrated herein. Although speculative, it is possible that the non-significant findings for men were a result of men, compared to women, being less likely to work a rotating shift and having an elevated RDW. In conclusion, in this national sample of U.S. adults, men working a non-daytime shift had non-statistically significant increased odds of having an elevated RDW; however, women working a rotating shift had statistically significant increased odds of having an elevated RDW. Given that an assessment of complete blood count is relatively inexpensive and one that is routinely assessed and reported by physicians, RDW may serve as a useful diagnostic tool for quantifying risk for all-cause mortality and cardiovascular disease. If future research supports these findings, health care professionals may also find it useful to include questions about organization of work schedules and their tolerance of such schedules during the patient's consultation. Further, annual health screenings through workplace health programs may also wish to assess RDW to better monitor the health effects induced from the employee's shift work schedule. Acknowledgments No funding was used to prepare this manuscript. References [1] H. Boggild, A. Knutsson, Shift work, risk factors and cardiovascular disease, Scand. J. Work Environ. Health 25 (1999) 85–99. [2] Y. Esquirol, B. Perret, J.B. Ruidavets, et al., Shift work and cardiovascular risk factors: new knowledge from the past decade, Arch. Cardiovasc. Dis. 104 (2011) 636–668.
[3] P. Santhanam, H.K. Driscoll, T.W. Gress, R. Khthir, Metabolic disease and shift work: is there an association? An analysis of NHANES data for 2007–2008, Occup. Environ. Med. 71 (2014) 661–662. [4] F. Wang, L. Zhang, Y. Zhang, et al., Meta-analysis on night shift work and risk of metabolic syndrome, Obes. Rev. 15 (2014) 709–720. [5] F. Wang, K.L. Yeung, W.C. Chan, et al., A meta-analysis on dose–response relationship between night shift work and the risk of breast cancer, Ann. Oncol. 24 (2013) 2724–2732. [6] J.C. Marquie, P. Tucker, S. Folkard, C. Gentil, D. Ansiau, Chronic effects of shift work on cognition: findings from the VISAT longitudinal study, Occup. Environ. Med. (2014). [7] S. Puttonen, M. Harma, C. Hublin, Shift work and cardiovascular disease — pathways from circadian stress to morbidity, Scand. J. Work Environ. Health 36 (2010) 96–108. [8] T. Mosendane, F.J. Raal, Shift work and its effects on the cardiovascular system, Cardiovasc. J. Afr. 19 (2008) 210–215. [9] T.S. Perlstein, J. Weuve, M.A. Pfeffer, J.A. Beckman, Red blood cell distribution width and mortality risk in a community-based prospective cohort, Arch. Intern. Med. 169 (2009) 588–594. [10] Y. Borne, J.G. Smith, O. Melander, B. Hedblad, G. Engstrom, Red cell distribution width and risk for first hospitalization due to heart failure: a population-based cohort study, Eur. J. Heart Fail. 13 (2011) 1355–1361. [11] K.V. Patel, L. Ferrucci, W.B. Ershler, D.L. Longo, J.M. Guralnik, Red blood cell distribution width and the risk of death in middle-aged and older adults, Arch. Intern. Med. 169 (2009) 515–523. [12] K.V. Patel, R.D. Semba, L. Ferrucci, et al., Red cell distribution width and mortality in older adults: a meta-analysis, J. Gerontol. A Biol. Sci. Med. Sci. 65 (2010) 258–265. [13] M.E. Emans, K. van der Putten, K.L. van Rooijen, et al., Determinants of red cell distribution width (RDW) in cardiorenal patients: RDW is not related to erythropoietin resistance, J. Card. Fail. 17 (2011) 626–633. [14] Z. Forhecz, T. Gombos, G. Borgulya, Z. Pozsonyi, Z. Prohaszka, L. Janoskuti, Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state, Am. Heart J. 158 (2009) 659–666. [15] G. Lippi, G. Targher, M. Montagnana, G.L. Salvagno, G. Zoppini, G.C. Guidi, Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients, Arch. Pathol. Lab. Med. 133 (2009) 628–632. [16] L.A. Allen, G.M. Felker, M.R. Mehra, et al., Validation and potential mechanisms of red cell distribution width as a prognostic marker in heart failure, J. Card. Fail. 16 (2010) 230–238. [17] G.M. Felker, L.A. Allen, S.J. Pocock, et al., Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank, J. Am. Coll. Cardiol. 50 (2007) 40–47. [18] R.R. van Kimmenade, A.A. Mohammed, S. Uthamalingam, P. van der Meer, G.M. Felker, J.L. Januzzi Jr., Red blood cell distribution width and 1-year mortality in acute heart failure, Eur. J. Heart Fail. 12 (2010) 129–136. [19] C. Jung, B. Fujita, A. Lauten, et al., Red blood cell distribution width as useful tool to predict long-term mortality in patients with chronic heart failure, Int. J. Cardiol. 152 (2011) 417–418. [20] F.C. Bull, T.S. Maslin, T. Armstrong, Global physical activity questionnaire (GPAQ): nine country reliability and validity study, J. Phys. Act. Health 6 (2009) 790–804.
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Contents lists available at ScienceDirect
Physiology & Behavior journal homepage: www.elsevier.com/locate/phb
The effect of shift work on red blood cell distribution width Paul D. Loprinzi ⁎ The University of Mississippi, Center for Health Behavior Research, University, MS, United States
H I G H L I G H T S • Elevated red blood cell distribution width (RDW) predicts premature mortality. • No study has examined the effect of shift work on RDW. • Women working a rotating shift had a 46% increased odds of having elevated RDW.
a r t i c l e
i n f o
Article history: Received 2 December 2014 Received in revised form 18 January 2015 Accepted 19 January 2015 Available online 19 January 2015 Keywords: Cardiovascular disease Epidemiology Work schedule
a b s t r a c t Limited research demonstrates that shift work (e.g., evening shift, night shift, rotating shift) increases the risk of certain health outcomes, such as hypertriglyceridemia and metabolic syndrome. Red blood cell distribution width (RDW), which is commonly assessed and reported by physicians, is a novel biomarker of cardiovascular disease. However, no study has examined the association of shift work on RDW, which was the purpose of this study. Data from the 2005–2010 NHANES were used. RDW was assessed from a blood sample; shift work was assessed from a questionnaire, and various demographic, behavioral/psychological, occupational, and biological parameters were included as covariates. The fully adjusted model showed that the odds of having an elevated RDW for women on rotating shift vs. day shift increased by 46% (OR = 1.46; 95% CI: 1.03–2.08). Women on a rotating shift had increased odds of having an elevated RDW, which is concerning as elevated RDW increases the risk of cardiovascular disease and mortality. Health care professionals are encouraged to include questions about organization of work schedules and their tolerance of such schedules during the patient's consultation. © 2015 Elsevier Inc. All rights reserved.
1. Introduction In the working world, shift work is necessary for obvious economic and social goals, with different shift schedules including: regular daytime shift, regular evening shift, regular night shift, rotating shift, or another schedule. Previous systematic reviews have examined the effect of shift work on various health outcomes [1,2]. Based on these review papers published in 1999 and 2011, few studies have examined the effect of shift work on diabetes or inflammation. Non-daytime shift work (e.g., night shift) was associated with an increased risk of hypertriglyceridemia, but the effects of non-daytime shift work on body habitus (e.g., body mass index, overweight, obese) and cholesterol were not consistently associated with each other, which is consistent with a recent (2014) epidemiological study using data from the National Health and Nutrition Examination Survey (NHANES) [3]. Recently (2014), Wang et al. performed a meta-analysis among 13 studies, and ⁎ Center for Health Behavior Research, The University of Mississippi, 229 Turner Center, University, MS 38677, United States. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.physbeh.2015.01.020 0031-9384/© 2015 Elsevier Inc. All rights reserved.
concluded that night shift work is associated with an increased risk of metabolic syndrome [4], which is consistent with their other recent (2014) meta-analysis demonstrating an increased risk of breast cancer among night-shift workers [5]. Relatedly, emerging research demonstrates that shift work may also have a negative effect on cognition [6]. Consequently, and although further studies are needed on this topic, there is evidence to suggest that non-daytime shift work may increase an individual's risk for chronic disease, with hypothesized pathophysiological mechanisms including shift work-induced changes in desynchronized circadian rhythms, sleep disturbances, modulation of food intake, psychosocial stress, and social inequalities [7,8]. To date, no study has examined the effect of shift work on red blood cell distribution width (RDW). RDW, an indicator of anisocytosis, is a novel biomarker indicative of cardiovascular disease (CVD) and all-cause mortality [9–12]. Although inconclusive, the pathophysiology linking RDW with morbidity and mortality may be a result of inflammation-induced anisocytosis and/or disordered iron homeostasis [13–16]. To further our knowledge of the potential shift work–CVD link, the purpose of this study was to examine the
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association between shift work and RDW. It was hypothesized that non-daytime shift work would be associated with increased RDW when compared to regular daytime shift work. 2. Methods 2.1. Design and participants Data from 2005–2010 NHANES were used. NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage, stratified, clustered probability design. The continuous NHANES collects data each year on a representative sample of Americans, with data released in two year cycles (e.g., 2005–2006, 2007–2008, and 2009–2010). Participants are interviewed in their home and subsequently examined in a mobile examination center (MEC). The 2005–2010 NHANES study procedures were approved by the National Center for Health Statistics ethics review board. Consent was obtained from all participants prior to any data collection. In the 2005–2010 NHANES, 7130 (n = 3861 for males; n = 3269 for females) adult participants (≥20 yrs) indicated that they currently work and provided data on all study variables, with these 7130 participants constituting the analytic sample. In the initial sample, 118 participants had cardiovascular disease, but these participants were excluded and not included in the final analytic sample (N = 7130) as those with cardiovascular disease have elevated RDW [17–19] and are generally not assigned to non-daytime shifts [2]. Notably, analyses were computed with and without the 118 cardiovascular disease participants in the analytic models, and results were similar. 2.2. Assessment of work shift Among adults indicating that they are currently working at a job or business, they were asked: “Which of the following best describes the hours you usually work at your main job or business: a regular daytime schedule, a regular evening shift, a regular night shift, a rotating shift, or another schedule.” 2.3. Assessment of red blood cell distribution width The complete blood count was from a blood sample using the Beckman Coulter MAXM analyzer. RDW was derived from the coefficient of variation of the red cell volume distribution histogram and reported as a %. Previous prospective research demonstrates that RDW ≥ 13.0% increases mortality risk by 20% in the general population [11]. Therefore, elevated RDW was defined as an RDW value of ≥13.0%. In the present sample, a RDW of 13.0% was equal to the 75th percentile of the RDW distribution. 2.4. Assessment of covariates Covariates in the analytic models consisted of demographic, behavioral, psychological, occupational, and biological parameters. The demographic parameters included: age (yrs), gender (male/ female), race–ethnicity (white/non-white), education (high school or less/some college or more), and survey year (i.e., 2005–2006, 2007–2008, or 2009–2010). The behavioral/psychological parameters included: physical activity, self-reported sleep duration (h/night), energy intake (kcal), saturated fat intake (g), depression, and cotinine (ng/mL; smoking biomarker). Physical activity was assessed from the Global Physical Activity Questionnaire, which has demonstrated evidence of reliability and validity [20]. Participants were asked to report whether they engaged in moderate-to-vigorous physical activity (yes/no) in the past 30 days. Depression (expressed as a continuous variable) was assessed using the Patient Health Questionnaire-9 (PHQ-9) [21]. Energy intake and saturated fat intake were assessed from the mobile examination
center dietary interview. Serum cotinine was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. The occupational parameters included: self-reported hours worked per week, # months at current job, and description of job work. Description of job work was defined into 6 categories: 1) an employee of a private company, business, or individual for wages, salary, or commission; 2) a federal government employee; 3) a state government employee; 4) a local government employee; 5) self-employed in own business, professional practice or farm; and 6) working without pay in family business or farm. The biological parameters included: measured body mass index (kg/m2), C-reactive protein (mg/dL), iron (μg/dL), measured mean arterial pressure (mm Hg; ([diastolic blood pressure × 2) + systolic blood pressure] / 3)), physician-diagnosed diabetes (yes/no), and total cholesterol (mg/dL). Details on the data collection protocol and procedures for these biological parameters have been published elsewhere [22].
2.5. Data analysis Multivariable logistic regression analysis was used to examine the association between shift work schedule and elevated (≥ 13.0%; ≥75th percentile) red blood cell distribution width (RDW), with separate analyses computed for males and females; regular daytime shift served as the referent group for the analytic models. Five a-priori analytical models were computed, including: Model 1: unadjusted model; Model 2: controlling for demographic variables; Model 3: controlling for demographic plus behavioral and psychological variables; Model 4: controlling for demographic, behavioral/psychological, and occupational variables; and Model 5: controlling for all variables, including demographic, behavioral/psychological, occupational, and biological variables. All analyses (Stata, v. 12; analyzed in 2014) accounted for the NHANES complex survey design by the use of survey sample weights, stratum and primary sampling units. Sample weights were created for the combined NHANES cycles following analytical guidelines for the continuous NHANES. Statistical significance was established as p b 0.05.
3. Results Table 1 displays the weighted characteristics of the analyzed sample, with results stratified by gender. Men and women, respectively, were, on average, 41.7 yrs and 42.3 yrs. The majority of men (75.0%) and women (76.7%) worked a regular daytime schedule. The proportion of men and women, respectively, with an elevated RDW (≥ 13%) was 17.6% and 27.0%. Men and women, respectively, worked an average of 44.9 and 38.1 h/wk and worked at their current job for 102.8 and 92.6 months. Table 2 displays the weighted multivariable regression analyses examining the association between shift work schedule and elevated (≥ 13.0%) RDW. No results were statistically significant for men, but the results were in the expected direction (i.e., non-daytime shifts had a non-statistically significant increased odds of having elevated RDW). However, compared to women working a regular daytime schedule, women working a rotating shift had increased odds of having an elevated RDW; results were significant for each analytic model. For example, the fully adjusted model showed that the odds of having an elevated RDW for women on rotating shift vs. day shift increased by 46% (OR = 1.46; 95% CI: 1.03–2.08; p = 0.03). There was no evidence of multicollinearity in the regression models; for example, in the fully adjusted model, the mean variance inflation factor was 1.3, the highest individual variance inflation factor was 2.72, and all tolerance statistics were N0.36.
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Table 1 Characteristics of the analyzed sample, 2005–2010 NHANES (N = 7130). Demographics
Males (n = 3861) N
Age, yrs Race–ethnicity, % White Other Education, % High school or less Some college or more Survey year, % 2005–2006 2007–2008 2009–2010 Behavioral and psychological % Physically active a Sleep duration, h/night Cotinine, ng/mL Energy intake, kcal Total saturated fat, g Occupational Hours worked per week # Months working at job Description of job work, % Private company/business Federal employee State employee Local employee Self-employed Family business Shift work, % Regular daytime schedule Regular evening shift Regular night shift Regular rotating shift Another schedule Biological Body mass index (kg/m2) C-reactive protein (mg/dL) Iron, μg/dL Mean arterial pressure, mm Hg % Diabetes RDW, % % Elevated RDW (≥13%)
Females (n = 3269)
Weighted mean/%
95% CI
N
Weighted mean/%
95% CI
3861
41.7
41.0–42.3
3269
42.3
41.7–42.8
1804 2057
70.3 29.6
66.5–74.1 25.8–33.4
1563 1706
71.9 28.1
67.8–75.9 24.0–32.1
1908 1953
40.3 59.6
37.2–43.4 56.5–62.7
1296 1973
32.4 67.5
30.3–34.6 65.3–69.8
1716 1342 1343
34.1 33.5 32.4
30.5–37.6 29.9–37.1 28.7–35.9
995 1107 1167
33.9 33.6 32.5
29.2–38.5 28.8–38.4 28.1–36.7
2994 3861 3861 3861 3861
79.2 6.71 75.4 2727.5 34.2
77.0–81.4 6.66–6.75 67.7–83.2 2678–2776 33.3–35.2
2214 3269 3269 3269 3269
72.6 6.90 43.6 1896.1 24.1
70.3–75.0 6.84–6.96 37.8–49.3 1866–1926 23.5–24.6
3861 3861
44.9 102.8
44.3–45.6 96.9–108.7
3269 3269
38.1 92.6
37.6–38.6 87.3–98.0
2902 88 153 220 486 12
74.3 2.3 4.0 5.9 13.1 0.1
72.2–76.4 1.7–2.8 3.3–4.7 5.0–6.8 11.3–14.9 0.0–0.2
2346 96 288 240 288 11
71.8 2.5 8.3 8.0 8.7 0.3
69.5–74.2 1.8–3.3 7.1–9.5 6.6–9.4 7.4–10.1 0.0–0.5
2851 204 169 300 337
75.0 4.8 4.1 6.9 9.0
73.2–76.7 3.9–5.7 3.3–4.8 5.8–7.9 8.0–10.0
2478 152 142 262 235
76.7 4.3 3.3 7.6 7.7
74.7–78.8 3.4–5.3 2.4–4.3 6.4–8.9 6.5–9.0
3861 3861 3861 3861 250 3861 830
28.6 0.29 96.7 88.8 4.9 12.48 17.6
28.3–28.8 0.26–0.33 94.8–98.6 88.3–89.3 4.0–5.8 12.4–12.5 15.8–19.3
3269 3269 3269 3269 196 3269 1055
28.3 0.41 80.7 85.1 4.9 12.72 27.0
27.9–28.7 0.38–0.44 79.1–82.3 84.6–85.7 3.9–6.0 12.6–12.8 24.4–29.6
RDW = red blood cell distribution width. a Physically active defined as those self-reporting engaging in moderate-to-vigorous physical activity in the past 30 days.
4. Discussion In this national sample of U.S. adults, a statistically significant association was not observed for each non-daytime shift pattern (i.e., evening, night, another schedule), but in partial support of the stated hypothesis, a rotating shift schedule increased the odds of having an elevated RDW (≥13.0%) by 46% for women, independent of various demographic, behavioral, occupational, and biological parameters. This is concerning as elevated RDW predicts premature mortality (e.g., RDW ≥ 13.3% increased mortality risk by 19% among critically ill patients; [23] RDW ≥ 13.0% increased mortality risk by 20% in general population ≥ 45 years [11]). Previous research demonstrates that regular, long-term non-daytime shift work increases the risk of metabolic syndrome [4], breast cancer [5], cognitive dysfunction [6] and select CVD risk factors [1,2]. Other studies, particularly non-U.S. populations, have also reported negative effects of rotating shifts on CVD risk factors. In a prospective study among Japanese male adults, a rotating shift increased the progression from mild to severe hypertension [24], which is similar to findings among Belgium adults [25]. Among Japanese adults, Dochi et al. [26,27] showed that a rotating shift increased the risk of dyslipidemia, which was also supported by findings in Belgium [25] and Swedish samples [28].
To my knowledge, this is the first study to examine the association of shift work on RDW. RDW is hypothesized to influence health via inflammation-induced anisocytosis and/or disordered iron homeostasis [13–16], and shift work is hypothesized to influence health through desynchronized circadian rhythms, sleep disturbances, modulation of food intake, psychosocial stress, and social inequalities [7,8]. Given that a rotating shift was the only shift work pattern associated with RDW, coupled with the fact that indices of sleep disturbances (e.g., sleep duration), modulation of food intake (caloric intake, saturated fat intake), psychosocial stress (e.g., depression), and social inequalities (e.g., education) were statistically controlled for in the analytic models, it is plausible to suggest that a rotating shift may increase RDW via desynchronized circadian rhythms. Non-work days from other shift work patterns (e.g., night shifts, evening shifts) may allow the individual to resynchronize their circadian rhythms, but this may be more difficult with a rotating shift given the constant changing schedule. A limitation of this study is the cross-sectional study design; thus, cause-and-effect determination is not possible given the inability to ascertain temporality between shift work and RDW. However, given the previous research demonstrating an effect of shift work schedule on
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Table 2 Multivariable logistic regression analysis examining the association between shift work schedule and elevated (≥13.0%) red blood cell distribution width (RDW) among working Americans, 2005–2010 NHANES (N = 7130). Analytical model
Odds (95% CI) of having elevated RDW a (≥13.0%; ≥75th percentile) Males (n = 3861) Evening shift
Night shift
Females (n = 3269) Rotating shift
Another schedule
Evening shift
Night shift
Rotating shift
Another schedule
Model 1: unadjusted Model 2: demographics Model 3: demographics +
1.08 (.72–1.61) 1.07 (.68–1.70) 1.12 (.84–1.49) 1.32 (.96–1.81) 1.21 (.80–1.8) .99 (.59–1.65) 1.43 (1.06–1.94) .82 (.55–1.22) 1.19 (.78–1.81) 1.29 (.80–2.1) 1.25 (.93–1.68) 1.34 (.96–1.87) 1.26 (.80–1.97) .99 (.60–1.63) 1.53 (1.11–2.10) .85 (.55–1.30) 1.08 (.68–1.73) 1.12 (.70–1.78) 1.22 (.90–1.64) 1.31 (.93–1.83) 1.26 (.80–1.99) .96 (.58–1.57) 1.52 (1.11–2.09) .86 (.56–1.31)
behavioral/psychological Model 4: demographics +
1.10 (.70–1.73) 1.14 (.71–1.81) 1.21 (.89–1.64) 1.36 (.98–1.89) 1.27 (.81–1.99) .95 (.58–1.57) 1.53 (1.12–2.10) .87 (.57–1.33)
behavioral/psychological + occupational Model 5: demographics +
1.12 (.71–1.77) 1.04 (.65–1.65) 1.25 (.92–1.68) 1.33 (.96–1.86) 1.18 (.72–1.92) .93 (.57–1.51) 1.46 (1.03–2.08) .83 (.55–1.28)
behavioral/psychological + occupational + biological 5 analytical models were computed for each gender: Model 1: unadjusted. Model 2: demographics: covariates included age, race–ethnicity, education, and survey year. Model 3: demographics + behavioral/psychological: same covariates in Model 2 plus physical activity, sleep duration, energy intake, saturated fat intake, depression, and cotinine (smoking). Model 4: demographics + behavioral/psychological + occupational: same covariates in Model 3 plus hours worked per week, # months at current job, and description of job work. Model 5: demographics + behavioral/psychological + occupational + biological: same covariates in Model 4 plus body mass index, C-reactive protein, iron, mean arterial pressure, diabetes, and total cholesterol. a Referent group is regular daytime schedule.
other CVD risk factors and chronic disease, it is plausible to suggest a causal relationship between shift work and RDW. Further, I chose not to statistically control for triglycerides (known to be influenced by work shift) as only a subsample of participants were tested for fasting triglycerides. And although employed in other NHANES studies [3,29], another limitation is the self-report nature of the shift work information. Major strengths of this study include the novel investigation (shift work on RDW), employing a national sample of U.S. adults, and including a comprehensive set of covariates in the analytical models. Future research is needed to better understand the gender-specific findings demonstrated herein. Although speculative, it is possible that the non-significant findings for men were a result of men, compared to women, being less likely to work a rotating shift and having an elevated RDW. In conclusion, in this national sample of U.S. adults, men working a non-daytime shift had non-statistically significant increased odds of having an elevated RDW; however, women working a rotating shift had statistically significant increased odds of having an elevated RDW. Given that an assessment of complete blood count is relatively inexpensive and one that is routinely assessed and reported by physicians, RDW may serve as a useful diagnostic tool for quantifying risk for all-cause mortality and cardiovascular disease. If future research supports these findings, health care professionals may also find it useful to include questions about organization of work schedules and their tolerance of such schedules during the patient's consultation. Further, annual health screenings through workplace health programs may also wish to assess RDW to better monitor the health effects induced from the employee's shift work schedule. Acknowledgments No funding was used to prepare this manuscript. References [1] H. Boggild, A. Knutsson, Shift work, risk factors and cardiovascular disease, Scand. J. Work Environ. Health 25 (1999) 85–99. [2] Y. Esquirol, B. Perret, J.B. Ruidavets, et al., Shift work and cardiovascular risk factors: new knowledge from the past decade, Arch. Cardiovasc. Dis. 104 (2011) 636–668.
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