What this is
- This research investigates the link between and type 2 diabetes among steelworkers.
- It examines how specific gene (CLOCK, MTNR1A, and MTNR1B) interact with shift work to influence diabetes risk.
- The study utilizes a case-control design with 251 cases and 451 controls from a steel company in China.
Essence
- and the MTNR1B gene variant rs1387153 are associated with an increased risk of type 2 diabetes among steelworkers. The interaction of these factors may significantly elevate this risk.
Key takeaways
- was linked to a higher risk of type 2 diabetes. Workers on rotating shifts showed increased diabetes risk compared to those who never worked shifts.
- The MTNR1B gene variant rs1387153 was associated with an increased diabetes risk. This variant's presence modified the relationship between night shift work and diabetes.
- Complex interactions among the CLOCK, MTNR1A, and MTNR1B genes, along with , may contribute to diabetes risk. This suggests a multifactorial model influencing diabetes susceptibility.
Caveats
- This study's case-control design limits causal inference. The findings cannot definitively establish that shift work causes diabetes.
- Diagnosis of diabetes was based on a single fasting blood glucose measurement, which may introduce bias.
- The study's participant pool is limited to steelworkers, which may restrict the generalizability of the findings to other populations.
Definitions
- Rotating night shift work: A work schedule that alternates between day and night shifts, typically for over one year.
- Polymorphism: A variation in the DNA sequence that may affect how genes function and contribute to disease risk.
AI simplified
Introduction
The development of type 2 diabetes is a challenging public health problem in many countries, and the incidence of diabetes worldwide is increasing rapidly [1]. It is estimated that by 2025, the number of diabetic patients will increase by 65% to 380 million, which has brought a great burden of disease to the world [2].
Shift work is an irregular or abnormal work system compared with daytime work, such as night work or rotating shifts [3]. This working system generally exists in the service industry, transportation industry, manufacturing industry, and so on, and has many working systems [4]. Disruption of circadian rhythms through shift work directly or indirectly increases the risk of many diseases, such as breast cancer, nonalcoholic fatty liver disease, and type 2 diabetes [5, 6].
Circadian rhythm plays an important role in controlling and maintaining internal balance and adapting to the changes in external environmental conditions [7]. It is reported that many human physiological activities (including the sleep–wake cycle, hormone secretion, periodic changes in body temperature, blood pressure, and blood glucose levels) are related to circadian rhythm [8]. A large number of studies have indicated that the risk of type 2 diabetes would be increased by shift work [9, 10]. Although the specific mechanisms were not yet clear, studies have shown that night shift exposure caused by shift work may disrupt endogenous circadian rhythm, especially the blood glucose levels, inflammatory biomarkers and decrease in insulin sensitivity, which may affect the risk of type 2 diabetes through some direct and indirect pathways [11, 12]. In addition to the external environmental factors such as shift work, CLOCK-controlled (CLOCK) genes, the key gene in the transcriptional translational feedback loops formed by the circadian rhythm system, and melatonin receptor (MTNR) 1A and 1B gene can affect this pathogenic process at the molecular level [13–16]. However, so far, the results of the association between shift work and type 2 diabetes remain inconsistent [17–19], which may be due to different shift evaluation indicators. Therefore, we evaluated rotating night shift work with four indicators, including rotating night shift work, current shift status, duration of night shifts (years) and average frequency of night shifts (night/month). In addition, there is little epidemiological evidence about the interaction between CLOCK, MTNR1A, MTNR1B genes polymorphisms and shift work on type 2 diabetes. Thus, from the perspective of population epidemiology, this study aims to explore the association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers through a case–control study.
Methods
Study population
Study participants for this analysis were from the baseline population of a cohort study conducted by the Ministry of science and technology of China. This cohort study was conducted in HBIS Group’s Tangsteel Company in Tangshan, China, and the purpose was to explore the health effects of occupational harmful factors on the human body. The subjects of this study were workers who participated in an occupational health examination in Tangshan Hongci hospital from February to June 2017. Because of the short follow-up period, the number of new cases of type 2 diabetes was far from a nested case–control study. In addition, the funding for our project did not support our testing of the genotypes of all research subjects, so we did not conduct a cross-sectional study. Taking into account human and financial resources as well as available information, we selected a case–control study design based on the baseline data. First, according to the diagnostic criteria of type 2 diabetes, we determined the preliminary case population (961) and control population (6264). The diagnosis of type 2 diabetes was based on the diagnostic principles of the World Health Organization (WHO) in 1999 and the American Diabetes Association’s criteria [20]. The second step was to select cases from the preliminary case population according to the inclusion and exclusion criteria of cases. In the third step, we randomly selected controls from the preliminary control population with SAS 9.4 software according to the inclusion and exclusion criteria of controls. Finally, according to the inclusion and exclusion criteria of cases and controls, 251 cases and 451 controls were selected from 7025 people. In the case group (251), 87 (34.6%) workers who were receiving hypoglycemic therapy or had been diagnosed by the hospital were defined as type 2 diabetes patients, and 164 (65.4%) workers with one or more classic symptoms (excessive thirst, polyuria or frequent urination, weight loss, hunger) plus fasting plasma glucose concentrations of at least 7.0 mmol/L were also defined as type 2 diabetes patients.
Inclusion criteria of cases
①Han (The most populous ethnic group in the baseline survey) in-service (At the time of the baseline survey, workers who participated in occupational health examinations included retired, in-service, and those who left for some reasons) workers with type 2 diabetes identified in the baseline data; ②The age is 30 ~ 60 years old; ③The subject must have worked in HBIS Group’s Tangsteel Company in Tangshan for at least three years; ④Those who signed the informed consent form.
Exclusion criteria of cases
①The workers with missing blood biochemical data(26), shift information (12), and covariates (18); ②The workers who took some antiretroviral drugs (57), and the workers with cancer (7), excess alcohol intake (63), history of the hepatobiliary disease (59), thyroid disease (76), and renal failure (1) were excluded; ③The workers with other diseases (3) related to circadian clock genes and hormone receptors selected in this study; ④Those who did not sign informed consent were also excluded (9).
Inclusion criteria of controls
①Han in-service workers without type 2 diabetes were diagnosed by the same diagnostic criteria in the source population of the cases; ②Matched with the case group according to age (± 5 years); ③The subject must have worked in HBIS Group’s Tangsteel Company in Tangshan for at least three years; ④ From the same workshop as the cases; ⑤ Similar living conditions; ⑥ Those who signed the informed consent form.
Exclusion criteria of controls
①The workers with missing blood biochemical data (738), shift information (97), and covariates (137); ②The workers who took some antiretroviral drugs (474), and the workers with cancer (18), excess alcohol intake (122), history of the hepatobiliary disease (452), thyroid disease (409), and renal failure (3) were excluded; ③The workers with other diseases (25) related to circadian clock genes and hormone receptors selected in this study; ④Those who did not sign informed consent were also excluded (56).\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}n=\frac{{\left[{z}_{\propto }\sqrt{2\overline{p }\left(1-\overline{p }\right)}+{z}_{\beta }\sqrt{{p}_{1}\left(1-{p}_{1}\right)+{p}_{0}\left(1-{p}_{0}\right)}\right]}^{2}}{{\left({p}_{1}-{p}_{0}\right)}^{2}}\end{array}$$\end{document}n=z∝2p¯1-p¯+zβp11-p1+p01-p02p1-p02\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}\overline{p }=\frac{\left({p}_{1}+{p}_{0}\right)}{2}\end{array}$$\end{document}p¯=p1+p02\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}{p}_{1}=\frac{\left(OR\times {p}_{0}\right)}{\left(1-{p}_{0}+OR\times {p}_{0}\right)}\end{array}$$\end{document}p1=OR×p01-p0+OR×p0p0: The exposure rate of control group;
p1: The exposure rate of case group.
In this study, we screened the data according to the National Biotechnology Information Center (NCBI) single nucleotide polymorphism (SNP) database (http://www.ncbi.nlm.nih.gov/SNP↗). The minimum allele frequency (MAF) of the selected SNP locus was 0.226, significance level α = 0.05, β = 0.10, and expected OR = 2.0. According to the above conditions, the sample size of the case group and the control group was 221. The sample sizes of the case group and control group were 251 and 451, respectively, which met the sample size required for this case–control study. All participants gave informed consent before taking part in this study. This research was approved by the Ethics Committee of the North China University of Science and Technology.
Assessment of rotating night shift work
In this study, the modern four-crew-three-shift system and the historical three-crew-two-shift system were mainly two kinds of rotating night shift work schedules. Rotating night shift work refers to the work schedule that continuously alternates day shift and night shift for more than 1 year. Workers who have continuously alternated day shift and night shift for more than one year and were still rotating night shift work as of the date of the survey were defined as “current” night shift workers. After more than one year of rotating night shift work, workers who were no longer on rotating night shift work as of the date of the survey were defined as ever-rotating night shift workers. Shift workers whose shift duration didn’t exceed one year and the workers who worked regular working hours at all times were defined as never rotating night shift workers. In the modern four-crew-three-shift system, each group had two-morning shifts (08:00–16:00), two-afternoon shifts (16:00–00:00), two-night shifts (00:00–08:00) and then had 2 days off. In the historical three-crew-two-shift system, each group had a morning shift (08:00–20:00), a night shift (20:00–08:00) and then took 1 day off. The data were collected from face-to-face interviews and then checked with the records of the Tangsteel company. Participants were asked whether they had worked on a shift schedule of alternating day and night shifts (3 or more hours of working time between 00:00 and 05:00) [21] during their employment. If yes, they were asked about their current shift status (ever, current), rotating night shift work schedules, schedule arrangement, start date and end date of each schedule. In this study, different exposure indicators were used to evaluate rotating night shift work, such as rotating night shift work, current shift status, duration of night shifts (years), and average frequency of night shifts (nights/month). The specific evaluation and calculation of shift indicators were shown in the supplementary file.
Blood tests and assessment of covariates
Subjects were asked to fast for 8 h before veinous blood from the anterior elbow can be drawn. Blood samples were collected and centrifuged immediately at room temperature (3000 r/min, 15 min). All blood samples were tested in the central laboratory of Tangshan Hongci Hospital using automatic biochemical analyzers (mindray, BS-800, China) within four hours.
After several pre-surveys, the questionnaire of the study has been revised repeatedly, and the survey was conducted face to face with participants by uniformly trained personnel. In the questionnaire, the data on sex, age, family disease history, smoking, drinking, physical exercise, diet, sleep and occupational exposure factors (including high temperature, noise, dust, and carbon monoxide (CO)) were also collected. In addition, we measured the height and weight of the participants. The specific assessment of these covariates was shown in the supplementary file.
Detection of CLOCK, MTNR1A and MTNR1B genes
DNA in blood was extracted by a blood genomic DNA extraction kit (Beijing Tiangen Biochemical Technology Co., Ltd). After genomic DNA extraction, its purity and concentration were measured by spectrophotometer. According to the gene information of the Han population in Ensembl, 1000 Genomes Project database and NCBI database, Tag SNPs were screened by the Tagger algorithm of haplotype analysis software Haploview 4.2. The conditions were that the MAF of tag SNPs in the Chinese population was ≥ 8% and the linkage disequilibrium coefficient (pairwise correlation coefficient, r2) with other SNPs in the region was greater than 0.8. When multiple SNPs met the above two points at the same time, the SNPs related to type 2 diabetes that have been identified by the genome-wide association study (GWAS) were preferred; Functional regions, such as promoter region, 3’ untranslated region, URT, 5’ UTR and exon region, were considered next; When multiple loci were in the functional region at the same time or all loci were in the nonfunctional region, the loci with the strongest linkage degree were preferred. After a comprehensive analysis, 3 SNPs were finally selected.
The primers in this study were designed by Shanghai Biotechnology Co., Ltd, and the appropriate primers were selected according to the Rolling circle replication (PCR) and enzyme digestion conditions. Thus, the primer information used in this study was shown in table S1. (Table S, Supplementary file). Before PCR amplification, the synthesized target SNPs primers were fully dissolved in pure water and stored in a refrigerator at—20℃. The PCR reaction system used in the experiment and the PCR reaction conditions of the target SNPs were shown in Table S2 and Table S3, respectively. (Tables S, and S, Supplementary file). 1 2 3
Statistical analyses
Continuous variables were represented by means ± standard deviation. If the continuous variables met the normality and homogeneity of variance, the differences between the case group and the control group were compared by the independent-sample T test. If not, the differences between groups were compared by Wilcoxon’s rank-sum test. Categorical variables were represented by the number of individuals (%), and the differences between groups were compared by chi-square tests. The Chi-square test was used to determine whether the survey object was a random sample in the target population, according to Hardy–Weinberg equilibrium (HWE). The logistic regression was used to explore the effect of CLOCK, MTNR1A, MTNR1B genes polymorphisms and rotating night shift work on the risk of type 2 diabetes. Previous studies [19, 22, 23] have shown that sleep and BMI were mediating roles between shift and diabetes, so these two factors were excluded when adjusting for confounding factors. The effect of gene–gene interactions and genes-rotating night shift work interactions based on the multiplicative and additive model on type 2 diabetes were explored by logistic regression, generalized multifactor dimensionality (GMDR), and additive interaction model improved by Andersson et al. [24]. Analyses were conducted with Statistical Package for the Social Sciences (SPSS for Windows, version 19.0, SPSS Inc, Chicago, IL, USA) software and Statistical Analysis System Version 9.4 (SAS, Institute, Raleigh, NC) with a two-sided significance threshold of P < 0.05. Bonferroni correction was used for the correction of multiple comparisons.
Results
Characterization of study population
| Characteristics | Control group | Case group | χ/2Z | valueP |
|---|---|---|---|---|
| = 451n | = 251n | |||
| Sex, n (%) | 1.276 | 0.259 | ||
| Male | 440 (97.6) | 248 (98.8) | ||
| Female | 11 (2.4) | 3 (1.2) | ||
| Smoking status, n (%) | 6.04 | 0.049 | ||
| Never | 175 (38.8) | 76 (30.2) | ||
| Ever | 24 (5.3) | 20 (8.0) | ||
| Current | 252 (55.9) | 155 (61.8) | ||
| Drinking status, n (%) | 3.145 | 0.207 | ||
| Never | 257 (57.0) | 130 (51.8) | ||
| Ever | 10 (2.2) | 3 (1.2) | ||
| Current | 184 (40.8) | 118 (47.0) | ||
| Physical activity (MET- hours/week), n (%) | 1.205 | 0.547 | ||
| Low | 11 (2.4) | 4 (1.6) | ||
| Middle | 30 (6.7) | 21 (8.4) | ||
| High | 410 (90.9) | 226 (90.0) | ||
| Hypertension, n (%) | 2.294 | 0.13 | ||
| No | 323 (71.6) | 166 (66.1) | ||
| Yes | 128 (28.4) | 85 (33.9) | ||
| Dyslipidaemia, n (%) | 0.409 | 0.523 | ||
| No | 261 (57.9) | 139 (55.4) | ||
| Yes | 190 (42.1) | 112 (44.6) | ||
| Liver dysfunction, n(%) | 3.429 | 0.064 | ||
| No | 376 (83.4) | 195 (77.7) | ||
| Yes | 75 (16.6) | 56 (22.3) | ||
| Family history of diabetes | 1.788 | 0.181 | ||
| No | 405 (89.8) | 217 (86.5) | ||
| Yes | 46 (10.2) | 34 (13.5) | ||
| Renal dysfunction, n (%) | 0.396 | 0.35 | ||
| No | 382 (84.7) | 217 (86.5) | ||
| Yes | 69 (15.3) | 34 (13.5) | ||
| BMI (kg/m), n (%)2 | 0.103 | 0.95 | ||
| < 24 | 172 (38.1) | 98 (39.0) | ||
| 24–27 | 220 (48.8) | 122 (48.6) | ||
| ≥ 28 | 59 (13.1) | 31 (12.4) | ||
| High temperature, n (%) | 2.075 | 0.013 | ||
| No | 225 (49.9) | 111 (44.2) | ||
| Yes | 226 (50.1) | 140 (55.8) | ||
| Noise, n (%) | 3.917 | 0.048 | ||
| No | 2 (0.4) | 5 (2.0) | ||
| Yes | 449 (99.6) | 246 (98.0) | ||
| Dust, n (%) | 2.517 | 0.113 | ||
| No | 80 (17.7) | 33 (13.1) | ||
| Yes | 371 (82.3) | 218 (86.9) | ||
| Rotating night shift work | 11.389 | 0.001 | ||
| Never | 105 (23.3) | 32 (12.7) | ||
| Yes | 346 (76.7) | 219 (87.3) | ||
| CO, n (%) | 1.546 | 0.214 | ||
| No | 207 (45.9) | 103 (41.0) | ||
| Yes | 244 (54.1) | 148 (59.0) | ||
| Age (years) | 45.1 ± 8.2 | 47.6 ± 7.6 | 3.831 | < 0.001 |
| Average sleep duration (h) | 6.8 ± 1.2 | 6.5 ± 1.4 | 2.582 | 0.01 |
| DASH Score | 21.4 ± 2.2 | 21.7 ± 2.3 | 1.727 | 0.084 |
Main effect of rotating night shift work
| Exposure metrics | Control group,n(%) | Case group, n(%) | Model 1 | Model 2 |
|---|---|---|---|---|
| (95% CI)OR | (95% CI)OR | |||
| Rotating night shift work | ||||
| Never | 105 (23.3) | 32 (12.7) | 1.00 (ref) | 1.00 (ref) |
| Yes | 346 (76.7) | 219 (87.3) | 2.08 (1.35–3.19) | 2.00 (1.28–3.11) |
| Current shift status | ||||
| Never | 105 (23.2) | 32 (12.7) | 1.00 (ref) | 1.00 (ref) |
| Ever | 124 (27.5) | 69 (27.5) | 1.83 (1.12–2.99) | 1.86 (1.12–3.08) |
| Current | 222 (49.3) | 150 (59.8) | 2.22 (1.42–3.47) | 2.08 (1.31–3.30) |
| Duration of night shifts (years) | ||||
| Never | 105 (23.2) | 32 (12.7) | 1.00 (ref) | 1.00 (ref) |
| 1–10 | 99 (22.0) | 49 (19.5) | 1.62 (0.96–2.74) | 1.80 (1.04–3.12) |
| 10–20 | 77 (17.1) | 47 (18.7) | 2.00 (1.17–3.43) | 2.30 (1.31–4.04) |
| 20–30 | 118 (26.2) | 87 (34.8) | 2.42 (1.49–3.92) | 2.23 (1.35–3.67) |
| > 30 | 52 (11.5) | 36 (14.3) | 2.27 (1.27–4.06) | 1.51(0.82–2.80) |
| (95% CI) for trendOR | 1.24 (1.10–1.39) | 1.15 (1.01–1.30) | ||
| Average frequency of night shifts (nights/month) | ||||
| Never | 105 (23.2) | 32 (12.7) | 1.00 (ref) | 1.00 (ref) |
| < 3 | 115 (25.5) | 62 (24.7) | 1.77(1.07–2.92) | 1.64 (0.97–2.76) |
| 3–8 | 206 (45.8) | 134 (53.4) | 2.13 (1.36–3.35) | 2.10 (1.32–3.34) |
| > 8 | 25 (5.5) | 23 (9.2) | 3.02 (1.51–6.02) | 2.72 (1.33–5.57) |
| (95% CI) for trendOR | 1.41 (1.17–1.69) | 1.39 (1.15–1.68) | ||
Main effect of CLOCK, MTNR1A, MTNR1B genes polymorphisms
In addition, the results of association analysis between MTNR1A gene rs2119882 locus, CLOCK gene rs1801260 locus and type 2 diabetes among steelworkers were shown in table S5. Under all models, no statistically significant association was found between MTNR1A gene rs2119882 locus, CLOCK gene rs1801260 locus and type 2 diabetes. (Table S, Supplementary file). 5
| Gene | Gene Model | Genotype | Control group, n (%) | Case group, n(%) | Model 1 | Model 2 |
|---|---|---|---|---|---|---|
| OR(95% CI) | OR(95% CI) | |||||
| MTNR1B gene rs1387153 locus | Codominant | C/C | 157 (34.8) | 72 (28.7) | 1.00 (ref) | 1.00 (ref) |
| C/T | 230 (51.0) | 118 (47.0) | 1.12 (0.78–1.60) | 1.18 (0.81–1.71) | ||
| T/T | 64 (14.2) | 61 (24.3) | 2.08 (1.33–3.25) | 1.96 (1.22–3.14) | ||
| Dominant | C/C | 157 (34.8) | 72 (28.7) | 1.00 (ref) | 1.00 (ref) | |
| C/T-T/T | 294 (65.2) | 179 (71.3) | 1.33 (0.95–1.86) | 1.36 (0.96–1.93) | ||
| Recessive | C/C–C/T | 387 (85.8) | 190 (75.7) | 1.00 (ref) | 1.00 (ref) | |
| T/T | 64 (14.2) | 61 (24.3) | 1.94 (1.31–2.87) | 1.78 (1.18–2.68) | ||
| Overdominant | C/C-T/T | 221 (49.0) | 133 (53.0) | 1.00 (ref) | 1.00 (ref) | |
| C/T | 230 (51.0) | 118 (47.0) | 0.85 (0.63–1.16) | 0.92 (0.66–1.27) | ||
| Log-additive | –- | –- | –- | 1.40 (1.12–1.75) | 1.37 (1.09–1.73) |
Gene–gene interactions and gene-environment interactions
Equally important, the gene–gene interactions were also shown in Table S9. The univariate and multivariate results suggested that the additives and multiplicative interactions between rs1801260 locus and rs2119882 locus on type 2 diabetes were statistically significant. However, the results of cross-classification analysis showed that their joint effects were not statistically significant. (Table S, Supplementary file) So, we further explored the association between their joint effect and type 2 diabetes among steelworkers stratified by rs2119882 and rs1801260. Compared with the population carrying the rs2119882 CT or CC genotype and the rs1801260 TT genotype, the population carrying the rs2119882 CT or CC genotype and the rs1801260 CT or CC genotype had an increased risk of type 2 diabetes, with a multivariable-adjusted OR, (95% CI) was 1.87, (95% CI, 1.07–3.28). Compared with the population carrying the rs2119882 TT genotype and the rs1801260 CT or CC genotype, the population carrying the rs2119882 CT or CC genotype and the rs1801260 CT or CC genotype had an increased risk of type 2 diabetes, with a multivariable-adjusted OR, (95% CI) was 3.89, (95% CI, 1.22–12.42). (Table S, Supplementary file) Additionally, other additive and multiplicative gene–gene interactions have not been observed. (Table S, Supplementary file). 9 10 9
Furthermore, Genetic and environmental factors with additive or multiplicative interactions between them were analyzed by stratification. Only in the population carrying the rs1387153 locus CT or TT genotype, the association between rotating night shift work and increased risk of type 2 diabetes was statistically significant. Similarly, we found that compared with workers carrying rs1801260 locus TT genotype, the risk of type 2 diabetes with rs1801260 locus CT or CC genotype workers increased among workers carrying rs2119882 locus CT or CC genotype. (Table S, Supplementary file). 11
In addition, although no statistical significance was found in the validation set, in the best interaction model, workers with homozygous mutations had a significantly higher risk of type 2 diabetes than those with homozygous wild-type in the total dataset. (Table S, Supplementary file). 12
| Genotype | Factors | Control group, n | Case group, n | (95% CI)OR | for multiplicative interactionP | ||
|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 1 | Model 2 | ||||
| rs2119882 | Rotating night shift work | 0.652 | 0.562 | ||||
| T/T | No | 38 | 12 | 1.00 (ref) | 1.00 (ref) | ||
| C/T-C/C | No | 67 | 20 | 0.95 (0.42–2.14) | 1.03 (0.44–2.39) | ||
| T/T | Yes | 115 | 86 | 2.37 (1.17–4.80) | 2.36 (1.14–4.87) | ||
| C/T-C/C | Yes | 231 | 133 | 1.82 (0.92–3.61) | 1.85 (0.92–3.75) | ||
| RERI | -0.49 (-1.81–0.83) | -0.54 (-1.92–0.85) | |||||
| AP | -0.27 (-0.89–0.36) | -0.29 (-0.94–0.36) | |||||
| rs1387153 | Rotating night shift work | 0.069 | 0.054 | ||||
| C/C | No | 27 | 11 | 1.00 (ref) | 1.00 (ref) | ||
| C/T-T/T | No | 78 | 21 | 0.66 (0.22–1.55) | 0.62 (0.26–1.47) | ||
| C/C | Yes | 130 | 61 | 1.15 (0.54–2.47) | 1.04 (0.48–2.29) | ||
| C/T-T/T | Yes | 216 | 158 | 1.80 (0.87–3.73) | 1.64 (0.77–3.45) | ||
| RERI | 1.15 (0.54–2.47) | 0.98 (0.40–1.55) | |||||
| AP | 0.66 (0.28–1.55) | 0.60 (0.07–1.12) | |||||
| rs1801260 | Rotating night shift work | 0.729 | 0.639 | ||||
| T/T | No | 92 | 26 | 1.00 (ref) | 1.00 (ref) | ||
| C/T-C/C | No | 13 | 6 | 1.63 (0.57–4.72) | 1.66 (0.56–4.93) | ||
| T/T | Yes | 295 | 178 | 2.14 (1.33–3.43) | 2.08 (1.27–3.38) | ||
| C/T-C/C | Yes | 51 | 41 | 2.85 (1.56–5.18) | 2.60 (1.40–4.84) | ||
| RERI | 0.07 (-1.97–2.12) | -0.14 (-2.23–1.95) | |||||
| AP | 0.03 (-0.69–0.74) | -0.05 (-0.87–0.76) | |||||
| Model | Cross-validation consistency | Training balanced accuracy | Test balanced accuracy | P |
|---|---|---|---|---|
| Shift | 7/10 | 0.5543 | 0.5319 | 0.172 |
| MTNR1B-Shift | 8/10 | 0.5777 | 0.5457 | 0.377 |
| MTNR1B-CLOCK-Shift | 5/10 | 0.5924 | 0.5171 | 0.172 |
| MTNR1A-MTNR1B-CLOCK-Shift | 10/10 | 0.6137 | 0.5574 | 0.011 |
Sensitivity analyses
The purpose of the stratified analysis was to explore whether the association between rotating night shift work and type 2 diabetes was still stable among workers who ever or currently rotated night shift work. Univariate and multivariate results showed that the association between duration of night shifts, the average frequency of night shifts and type 2 diabetes were both significant, no matter in which stratification. Compared with those who never night shift work, the risk of type 2 diabetes was higher with the longer duration of night shifts among ever night shift workers (P = 0.009). And compared with those who never night shift work, the risk of type 2 diabetes was higher with the higher average frequency of night shifts among current night shift workers (P = 0.001). (Table S13, Supplementary file).
In conclusion, compared with Table 2, the results of sensitivity analysis were still robust. Even after stratification, the relationship between the duration and average frequency of night shifts and type 2 diabetes were still statistically significant, which was consistent with the findings in Table 2 to a large extent.
Discussion
The present analysis showed that rotating night shift work and MTNR1B gene rs1387153 locus were associated with increased risk of type 2 diabetes, and the association between rotating night shift work and risk of type 2 diabetes appeared to be modified by MTNR1B gene rs1387153 locus. The results showed that there were significant gene-environment interactions among rotating night shift work, current shift status, the duration of night shifts and MTNR1B gene rs1387153 locus on type 2 diabetes. Moreover, there was a positive gene–gene interaction between CLOCK gene rs1801260 locus and MTNR1A gene rs2119882 locus on type 2 diabetes. At present, this study found that people who carry both the rs1801260 locus CT or CC genotype and the rs2119882 locus CT or CC genotype had a higher risk of type 2 diabetes. The complex interaction of the MTNR1A-MTNR1B-CLOCK-rotating night shift work model could significantly increase the risk of type 2 diabetes.
Although the results of the association between shift work and the risk of type 2 diabetes were inconsistent, the results generally agreed with existing evidence from some cohort studies [17, 25, 26]. There were several reasons for the inconsistent results of previous studies, such as different assessments of shift work and different study populations. In this study, we evaluated shift work through four indicators, such as rotating night shift work (yes or never), current shift status (never ever and current), duration of night shifts (years) and average frequency of night shifts (nights/month). Although the grouping of the duration of night shifts was different, this research was consistent with the findings of Pan A [27] and Vimalananda VG [28], which showed that the increase in the duration of night shifts may increase the risk of type 2 diabetes. An early paper found that the positive association between shift working years and type 2 diabetes was entirely mediated by weight [19]. In contrast, a multivariate study also showed that even after adjusting for BMI, the association between years of night-shift work and type 2 diabetes was still significant [29]. The results of this multivariate study showed that the association was significant even after adjusting for occupational hazards. A study based on the UK Biobank showed that working more night shifts per month was associated with a higher incidence rate of type 2 diabetes than never shift workers [30]. The results of this study suggested that working more night shifts per month increased the risk of type 2 diabetes, which was consistent with this study to some extent. This indicated that the circadian rhythm disorder caused by long duration and high-frequency night shift work should be taken into consideration in exploring the relationship between type 2 diabetes and shift work.
The CLOCK, MTNR1A and MTNR1B genotype frequencies of the subjects in this study were in accordance with the Hardy–Weinberg equilibrium. The findings of the study provide evidence that encoding the MTNR1B gene may play a role in type 2 diabetes etiology, as the MTNR1B gene was associated with type 2 diabetes risk in this data. Previous studies tend to focus more on the rs10830963 locus in the MTNR1B gene [31–33]. In contrast, this study suggested that the MTNR1B gene rs1387153 locus was associated with the increased risk of type 2 diabetes, which was supported by some studies [34, 35]. In addition, this study found a significant interaction between the MTNR1B gene rs1387153 locus and rotating night shift work on type 2 diabetes, which was basically consistent with the results of a laboratory study [11]. We also found a significant SNP-SNP interaction between rs2119882 and rs1801260. Furthermore, we found the association between high-dimensional interaction in the MTNR1A-MTNR1B-CLOCK-rotating night shift work model on type 2 diabetes through GMDR. To our knowledge, there are few studies on the high-dimensional interaction between circadian clock genes and melatonin receptor genes and rotating night shift work on type 2 diabetes based on GMDR. The results suggested that these interactions affect may contribute to the risk of type 2 diabetes in some complex biological mechanisms.
The potential complex biological mechanisms under the interaction model need to be further studied in the future, but some correlations seem to be explained. At the molecular level, a series of interacting clock proteins maintain the circadian rhythm of the body through transcriptional translational feedback loops [36, 37]. As a core clock gene, the CLOCK gene plays an important role in the initiation and maintenance of the circadian rhythm system [38, 39]. In fact, the existence of circadian clock genes in pancreatic β-cells has been proved by many studies [40, 41]. Melatonin inhibits insulin secretion by β-cells [14], which suggested a link between abnormal melatonin signaling and the risk of type 2 diabetes. MTNR1A and MTNR1B are the main melatonin receptors in humans [42], and abnormal variants of them would trigger aberrant changes in melatonin that may contribute to the pathogenesis of type 2 diabetes [43]. In addition to the biological mechanism at the molecular level, the balance of circadian rhythm can be broken through the external environment. For example, shift workers were exposed to artificial lights at night, which can also trigger aberrant changes in melatonin [44]. Thus, the risk of type 2 diabetes may be increased by their interaction.
Strengths and limitations of study
In this study, we have considered many variables, and we have checked some variables in Tangsteel company to ensure their accuracies, such as shift schedule (including current shift status, duration of night shifts and average frequency of night shifts) and occupational hazards (including high temperature, noise, dust and CO). To our knowledge, this study is the first study to investigate the interaction between circadian rhythm-related genes and shift work on type 2 diabetes among steelworkers using the log-linear model and GMDR method. The study also has certain limitations. First, this study was a case–control study, unable to determine the causal relationship between shift and type 2 diabetes. Secondly, part of the cases was defined according to the fasting blood glucose value only once, which may bring some bias to the results to a certain extent. Thirdly, some of the information obtained through face-to-face interviews may be inaccurate, such as family history of type 2 diabetes, smoking and drinking. Fourth, there were some selection biases in our research. We excluded the subjects with missing covariates and shift information when selecting cases and controls, which may cause some bias in the results; the occurrence of type 2 diabetes is a long process, and those who were about to have type 2 diabetes may be wrongly considered as non-cases; the participants selected for the study may differ from those not selected in some characteristics, such as those who were older and had a longer duration of night shifts were not selected for the study, while those who were relatively younger were selected for the study, which would cover the relationship between shift work and type 2 diabetes to some extent. Fifth, the information about the circulation level of other markers was missing in this study, which limited the interpretation of the results to a certain extent and it should be fully considered in future research. Finally, the subjects were from steel company, and there were some limitations in the extrapolation of the results.
Conclusions
Rotating night shift work and rs1387153 variants in MTNR1B were associated with an increased risk of type 2 diabetes even after multivariate adjustment. These findings were consistent with some previous studies, and provide support for these associations. The complex interaction of the MTNR1A-MTNR1B-CLOCK-rotating night shift work model could significantly increase the risk of type 2 diabetes. The findings of the study of the complex gene-environment interactions may be helpful in developing effective prevention strategies to prevent type 2 diabetes. Further research is needed to confirm our findings and clarify the specific biological mechanism.
Supplementary Information
Additional file 1.