Journal of Occupational & Environmental Medicine:
Dietary Inflammatory Index Scores Differ by Shift Work Status: NHANES 2005 to 2010
Wirth, Michael D. MSPH, PhD; Burch, James MS, PhD; Shivappa, Nitin MBBS, MPH; Steck, Susan E. PhD, MPH, RD; Hurley, Thomas G. MSc; Vena, John E. PhD; Hébert, James R. ScD
Continued Medical Education
From the Cancer Prevention and Control Program (Drs Wirth, Burch, Shivappa, Steck, and Hébert and Mr Hurley), University of South Carolina, Columbia, SC; Department of Epidemiology and Biostatistics (Drs Burch, Shivappa, Steck, and Hébert), University of South Carolina, Columbia, SC; WJB Dorn VA Medical Center (Dr Burch), Columbia, SC; and Department of Epidemiology and Biostatistics (Dr Vena), College of Public Health, University of Georgia, Athens, Ga.
Address correspondence to: Michael Wirth, MSPH, PhD, Cancer Prevention and Control Program, University of South Carolina, 915 Greene St, Suite 200, Columbia, SC 29208 (email@example.com).
This work was supported by the South Carolina Statewide Cancer Prevention and Control Program and by grant number 1U54 CA153461-01 (Dr Hébert (PI)) from the National Cancer Institute, Center to Reduce Cancer Health Disparities (Community Networks Program) to the South Carolina Cancer Disparities Community Network-II (SCCDCN-II). Dr Wirth's participation was supported through an ASPIRE-II Grant from the University of South Carolina Office of Research and by the South Carolina Cancer Prevention and Control Research Network funded under Cooperative Agreement Number 3U48DP001936-01 from the Centers for Disease Control and Prevention and the National Cancer Institute. Dr Hébert was supported by an Established Investigator Award in Cancer Prevention and Control from the Cancer Training Branch of the National Cancer Institute (K05 CA136975).
Authors Wirth, Burch, Shivappa, Steck, Hurley, Vena, and Hébert have no relationships/conditions/circumstances that present potential conflict of interest.
The JOEM editorial board and planners have no financial interest related to this research.
Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site (www.joem.org).
Objective: Shift workers are affected by diet- and inflammation-related diseases, including cardiovascular disease, diabetes, and cancer. We examined a dietary inflammatory index (DII) in relation to shift work from the National Health and Nutrition Examination Survey data (2005 to 2010).
Methods: The DII was calculated using data from a 24-hour dietary recall. Shift work categories included day workers, evening/night shift workers, or rotating shift workers. General linear models were fit to examine the relationship between shift work and adjusted mean DII values.
Results: Among all shift workers and specifically rotating shift workers, higher (ie, more pro-inflammatory) mean DII scores (1.01 and 1.07 vs 0.86; both P ≤ 0.01) were observed compared with day workers. Women tended to express strong evening/night shift effects.
Conclusions: More proinflammatory diets observed among shift workers may partially explain increased inflammation-related chronic disease risk observed in other studies among shift workers compared with their day-working counterparts.
* Review previous research on disease associations with dietary inflammatory index (DII) and on associations between DII and shift work.
* Outline the new findings on the association between DII and shift work, including potential differences between groups of shift workers.
* Discuss potential contributors to elevated DII scores in shift workers and implications for interventions to reduce the health impacts of shift work.
Shift work may contribute to the growing obesity and diabetes epidemics in the United States, with poor diet being one of the primary culprits.1 An increased prevalence of poor eating habits and gastrointestinal distress among shift workers likely contributes to diseases associated with shift work, such as hypertension, obesity, metabolic syndrome, cardiovascular disease, diabetes, and cancer.2–7 A recent review describing the relative paucity and inconsistencies of research findings from dietary studies focusing on shift workers highlights the potential etiologic role of increased consumption of calories, fats, proteins, carbohydrates, and sweets among shift workers compared with non–shift workers.8 Western-style diets have been associated with increased chronic, systemic inflammation, whereas Mediterranean diets (ie, high in fruit and vegetable consumption) have been associated with lower levels of systemic inflammation.9–11 Chronic inflammation is an underlying pathophysiological process contributing to the risk of metabolic syndrome, cardiovascular disease, diabetes, and cancer.12
A population-based dietary inflammatory index (DII) was developed to characterize an individual's diet on a continuum from maximally anti- to proinflammatory.13,14 Using two different sources of dietary intake information—the 24-hour dietary recall and a structured assessment instrument, the 7-day dietary recall—the DII predicted C-reactive protein in the Seasonal Variation in Blood Lipids (SEASONS) study.15 Data from the Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) study indicate that higher (ie, more proinflammatory) DII scores are associated with perturbations of the glucose intolerance component of metabolic syndrome and C-reactive protein in circulation. Police officers from BCOPS working shifts tended to have elevated DII scores compared with day workers (0.99 vs 0.67; P = 0.32; manuscript submitted). We hypothesized that among a large population of workers in the National Health and Nutrition Examination Survey (NHANES) shift workers would have similarly elevated DII scores compared with those individuals working only during the day.
This cross-sectional study used data (2005 to 2010) from adult (≥20 years of age) NHANES participants. Information was collected in 2-year cycles using a complex, multistage, probability design to select participants from various locations and minority populations to ensure a nationally representative sample of the US population.16 Data included self-report work status, demographics, anthropometrics, lifestyle factors, and a 24-hour dietary recall. Other than excluding those younger than 20 years, no other exclusion criteria were applied. The NHANES truncated the age at 85 years for the 2005 to 2006 cycle and 80 years for the 2007 to 2008 and 2009 to 2010 cycles. To maintain consistency between cycles, age from each cycle was truncated to 80 years.
Micronutrient and macronutrient values (known as food parameters) derived from the 24-hour dietary recall were assigned scores on the basis of research summarizing findings from 1943 articles.13 DII calculation is linked to a regionally representative world database (food consumption from 11 populations around the world) that provided a mean and standard deviation for each parameter. The “standard mean” is subtracted from the actual exposure and divided by its standard deviation. This z score is then converted to a percentile (to minimize the effect of outliers or right-skewing) and centered by doubling the value and subtracting 1. The product for each food parameter and adjusted article score was calculated and then summed across all food parameters to create the overall DII score for each “participant.” The greater the DII score the more proinflammatory the diet; more negative values are more anti-inflammatory.13 The food parameters included in the calculation of the DII for this study included total calories; carbohydrates; proteins; fats; grams of alcohol; fiber; cholesterol; saturated, monounsaturated, and polyunsaturated fatty acids; omega-3 and omega-6 polyunsaturated fatty acids; niacin; vitamins A, B1, B2, B6, B12, C, D, and E; iron; magnesium; zinc; selenium; folic acid; β-carotene; and caffeine.
The primary exposure for this analysis was shift work status. Participants who were unemployed (ie, “looking for work” or “not working at a job or business”) were excluded from the analyses. Those who responded with “working at a job or business” or “with a job or business but not at work” were then asked what type of shift they typically work. Options included (1) regular daytime, (2) evening shifts, (3) night shifts, (4) rotating shifts, or (5) another schedule (this category was removed for the lack of additional information; n = 714). Interviewers were instructed to continue probing to determine the exact type of shift work. Night (2.5%) and evening (3%) shift workers were combined. Duration of work in their main occupation was categorized on the basis of a median split (ie, ≤4 years vs >4 years). Night/evening shift workers and rotating shift workers were analyzed separately and in a combined group (“any shift work”).
Analyses were performed using survey design procedures in SAS® (version 9.3, Cary, NC), which took into account the design effects of stratification and clustering inherent in the NHANES sampling procedure. Six-year sampling weights were created by multiplying each of the 2-year sampling weights by one third.16 Chi-squared or t tests were used to compare population characteristics by shift work status. Variables selected as potential confounders were identified in a series of bivariate analyses (shift work and covariate). Variables with a P value of ≤0.20 were added to a “full” model. A backward elimination procedure was then used to develop final models that included all variables that were statistically significant (P < 0.05) or, when removed from the model, changed the β-coefficient of the primary independent variable (ie, shift work) by at least 10%. General linear models were used to compute least square means and 95% confidence intervals for the DII among categories of shift work, after adjustment for selected confounders (age, race, education, income, marital status, perceived health, and amount of moderate–intense recreational physical activity per week; classification levels for each confounder are presented in Table 1). Sex and current work duration were examined as effect modifiers.
Those participating in any shift work (n = 1,445), as compared with day workers (n = 6198), were more likely to be single (30% vs 14%; P < 0.01), uninsured (29% vs 17%; P < 0.01), non-Hispanic black (17% vs 9%; P < 0.01), to report their health as fair or poor (14% vs 10%; P < 0.01), smoke tobacco (29% vs 20%; P < 0.01), and live in a household with smokers (23% vs 15%; P < 0.01). In addition, shift workers were less likely to have a college degree (16% vs 34%; P < 0.01) or an income of more than $65,000 (36% vs 50%; P < 0.01). Shift workers also were younger than day workers (mean age, 37.7 ± 0.43 years vs 42.8 ± 0.24 years; P < 0.01), had fewer rooms in their household (6.0 ± 0.08 vs 6.4 ± 0.08; P < 0.01), and reported fewer hours of sleep per night (6.7 ± 0.05 hours vs 6.8 + 0.02 hours; P = 0.01).
Any shift work was associated with higher adjusted mean DII values compared with day workers (1.01 vs 0.86; P ≤ 0.01). When shift workers were separated by subgroup, rotating shift workers had higher adjusted mean DII values compared with day workers (1.07 vs 0.86; P < 0.01), whereas no statistically significant difference in the DII was observed between evening/night shift workers and day workers (Table 2). Crude DII values were substantially lower among males than among females (0.49 vs 1.23; P < 0.01), and therefore analyses were stratified by sex. Among men, rotating shift workers had higher adjusted mean DII values compared with day workers (0.93 vs 0.63; P < 0.01). Among women, evening/night shift workers had higher mean DII values compared with day workers (1.48 vs 1.17; P = 0.01) (Table 2). A difference in mean DII scores was observed among workers stratified by the median duration of current occupation (0.69 vs 0.90, respectively; P < 0.01), although this effect did not modify the relationship between shift work and the DII (data not presented). Mean DII values for each potential confounder included in the main analysis are presented in a supplemental table (see Supplemental Digital Content 1, http://links.lww.com/JOM/A147, which shows the mean DII by categories of all confounders).
We compared the inflammatory potential of diet in the NHANES, a nationally representative sample of shift workers and day workers using a novel DII. The mean DII value in this NHANES population was 0.87 ± 1.08, similar to other populations (ie, SEASONS study, 0.84 ± 1.9915), but higher (ie, more proinflammatory) than a working-only population of police officers (ie, BCOPS, 0.59 ± 2.55; manuscript submitted). Findings from this study indicate that shift workers have more proinflammatory diets than day workers; however, it is not clear what such a magnitude of difference actually means biologically or how it would translate into health outcomes. However, it is probably not trivial as this difference represents about 10% of the interquartile range of possible DII values.13 Inflammation plays an etiologic role in the development of several chronic disorders associated with shift work, including metabolic syndrome, cancer, cardiovascular disease, stroke, and diabetes.17–19 Associations between the DII and health outcomes in future longitudinal studies will provide more definitive inferences regarding the relationship between the DII and adverse health outcomes. Results from this study show that shift workers' diets have greater proinflammatory potential compared with their day-working counterparts.
Stress, fatigue, sleep loss, and familial disruption that occur among shift workers likely contribute to poor dietary habits, potentially increasing the inflammatory nature of their diet. Shift workers snack more often than day workers.20 They may eat a meal with their family before work, and another meal during work, which could result in overeating.8 Healthy and nutritious food options may not be available during night work hours.8 Nighttime consumption of food may disrupt circadian processes, which could affect appetite and metabolism.21 This is supported by studies among shift workers, indicating abnormal levels or dysregulated circadian rhythms of several hormones involved in the regulation of appetite or metabolism, including leptin and ghrelin.19,22 Night shift workers have more gastrointestinal symptoms and antacid use relative to day workers,3,23 as well as increased risks for central adiposity, ulcers, hypertension, and coronary heart disease.2,4,24,25 However, the relative contributions of diet to these outcomes have not been fully characterized.
This study had several noteworthy strengths, limitations, and uncertainties. Although the DII difference between shift workers and day workers in the current analysis was similar to that observed in the BCOPS study (manuscript submitted), statistical significance in this study was achieved, most likely because of precision conferred by its larger sample size. Another strength is the representation of minorities in proportion to their representation in the general US population. Limitations include the use of self-report questions for shift work ascertainment, as opposed to the use of electronic payroll records (eg, BCOPS).26,27 However, we identified a shift work prevalence of 19%, which is similar to other reports.28 Recently, a similar definition of shift work was used to examine the relationship between shift work and prostate-specific antigen using NHANES data,29 or depression using the Korean NHANES.30 The use of “years at current occupation” to quantify shift work most likely led to misclassification as individuals may not have spent the entire duration on one specific shift type. It is unclear why DII values among men on rotating shifts were higher than among day workers, whereas DII values among women working night/evening shifts were elevated relative to day workers. Given the cross-sectional nature of this study and relative paucity of shift work–related information, it was not possible to determine whether the differences reflect dietary habits that may be elicited by different shift schedules, whether these differences are more representative of sex-related differences in dietary patterns, or whether they relate to job-specific or occupational differences among shift-working men and women. Lastly, only one 24-hour dietary recall was used to calculate the DII. Estimates of dietary intake are subject to day-to-day variability, so a single day of information would provide a relatively imprecise estimate of usual intake that adds to overall variability and, thereby, imprecision.31
Shift workers suffer from an increased risk of numerous chronic disorders.17 This most likely occurs through a complex causal model involving numerous behavioral, psychosocial, and biological perturbations. Inflammatory diets represent a target for behavioral interventions to reduce the health impacts of shift work.1 The DII may serve as a useful instrument to evaluate the impacts of diet and dietary interventions among shift workers. However, chronic disease risk among shift-working populations is most likely multifaceted. Interventions to reduce chronic disease risk among shift workers should incorporate several important lifestyle changes (eg, healthy diet, physical activity, proper sleep, and light exposure). Dietary habits and barriers are poorly understood among shift workers,8 and future research should elaborate on the interplay between diet and shift work as they relate to chronic disease prevention to develop effective lifestyle inventions among shift workers.
1. PLOS Medicine Editors Poor diet in shift workers: a new occupational health hazard? PLoS Med. 2011;8:e1001152.
2. Costa G. Shift work and occupational medicine: an overview. Occup Med (Oxf). 2003;53:83–88.
3. Burch JB, Tom J, Zhai Y, Criswell L, Leo E, Ogoussan K. Shiftwork impacts and adaptation among health care workers. Occup Med. 2009;59:159–166.
4. Knutsson A. Health disorders of shift workers. Occup Med. 2003;53:103–108.
5. Zhao I, Bogossian F, Turner C. Does maintaining or changing shift types affect BMI? A longitudinal study. J Occup Environ Med 2012;54:525–531.
6. Barbadoro P, Santarelli L, Croce N, et al. Rotating shift-work as an independent risk factor for overweight Italian workers: a cross-sectional study. PLoS One. 2013;8:e63289.
7. Sookoian S, Gemma C, Fernandez Gianotti T, et al. Effects of rotating shift work on biomarkers of metabolic syndrome and inflammation. J Intern Med. 2007;261:285–292.
8. Lowden A, Moreno C, Holmback U, Lennernas M, Tucker P. Eating and shift work—effects on habits, metabolism and performance. Scand J Work Environ Health. 2010;36:150–162.
9. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Dietary patterns and markers of systemic inflammation among Iranian women. J Nutr. 2007;137:992–998.
10. Ahluwalia N, Andreeva VA, Kesse-Guyot E, Hercberg S. Dietary patterns, inflammation and the metabolic syndrome. Diabetes Metab. 2013;39:99–110.
11. Nettleton JA, Steffen LM, Mayer-Davis EJ, et al. Dietary patterns are associated with biochemical markers of inflammation and endothelial activation in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr. 2006;83:1369–1379.
12. Libby P. Inflammatory mechanisms: the molecular basis of inflammation and disease. Nutr Rev. 2007;65:S140–146.
13. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hebert JR. Designing and developing a literature-derived population-based dietary inflammatory index [published online ahead of print August 14, 2013]. Public Health Nutr. 2013.
14. Cavicchia PP, Steck SE, Hurley TG, et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr. 2009;139:2365–2372.
15. Shivappa N, Steck SE, Hurley TG, et al. A Population-Based Dietary Inflammatory Index Predicts Levels of C-Reactive Protein (CRP) in the SEASONS Study [published online ahead of print October 10, 2013]. Public Health Nutr. 2013.
16. CDC. Centers for Disease Control and Prevention. National Health and Nutrition Examinations Survey data. Hyattsville, MD: National Center for Health Statistics; 2013.
17. Kantermann T, Juda M, Vetter C, Roenneberg T. Shift-work research: where do we stand, where should we go? Sleep Biol Rhythms. 2010;8:95–105.
18. Karlsson BH, Knutsson AK, Lindahl BO, Alfredsson LS. Metabolic disturbances in male workers with rotating three-shift work. Results of the WOLF study. Int Arch Occup Environ Health 2003;76:424–430.
19. Schiavo-Cardozo D, Lima MM, Pareja JC, Geloneze B. Appetite-regulating hormones from the upper gut: disrupted control of xenin and ghrelin in night workers [published online ahead of print December 1, 2012]. Clin Endocrinol (Oxf). 2012. doi:10.1111/cen.12114.
20. Waterhouse J, Buckley P, Edwards B, Reilly T. Measurement of, and some reasons for, differences in eating habits between night and day workers. Chronobiol Int. 2003;20:1075–1092.
21. Escobar C, Salgado R, Rodriguez K, Blancas Vazquez AS, Angeles-Castellanos M, Buijs RM. Scheduled meals and scheduled palatable snacks synchronize circadian rhythms: consequences for ingestive behavior. Physiol Behav. 2011;104:555–561.
22. Crispim CA, Waterhouse J, Damaso AR, et al. Hormonal appetite control is altered by shift work: a preliminary study. Metabolism. 2011;60:1726–1735.
23. Barak Y, Achiron A, Kimh R, et al. Health risks among shift workers: a survey of female nurses. Health Care Women Int. 1996;17:527–533.
24. Knutsson A. Shift work and coronary heart disease. Scand J Soc Med Suppl. 1989;44:1–36.
25. Pickering TG. Could hypertension be a consequence of the 24/7 society? The effects of sleep deprivation and shift work. J Clin Hypertens (Greenwich). 2006;8:819–822.
26. Wirth M, Burch J, Violanti J, et al. Shiftwork duration and the awakening cortisol response among police officers. Chronobiol Int. 2011;28:446–457.
27. Violanti JM, Burchfiel CM, Miller DB, et al. The Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) pilot study: methods and participant characteristics. Ann Epidemiol. 2006;16:148–156.
28. Bureau of Labor Statistics. Workers on Flexible and Shift Schedules in May 2004. United States Department of Labor. Available at http://www.bls.gov/cps/
. Published 2005.
29. Flynn-Evans EE, Mucci L, Stevens RG, Lockley SW. Shiftwork and prostate-specific antigen in the National Health and Nutrition Examination Survey. J Natl Cancer Inst. 2013;105:1292–1297.
30. Kim I, Kim H, Lim S, et al. Working hours and depressive symptomatology among full-time employees: results from the fourth Korean National Health and Nutrition Examination Survey (2007–2009). Scand J Work Environ Health. 2013;39:515–520.
31. Basiotis PP, Welsh SO, Cronin FJ, Kelsay JL, Mertz W. Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J Nutr. 1987;117:1638–1641.
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