Association of circadian rhythms, CLOCK, MTNR1A, and MTNR1B gene polymorphisms and their interactions with type 2 diabetes in coal miners

Association of circadian rhythms, CLOCK, MTNR1A, and MTNR1B gene polymorphisms and their interactions with type 2 diabetes in coal miners | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of circadian rhythms, CLOCK, MTNR1A, and MTNR1B gene polymorphisms and their interactions with type 2 diabetes in coal miners Haoyue Cao, Qinglin Li, Juxiang Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5321076/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : To construct comprehensive indicators of circadian rhythm disorder (CICRD) and explore the interaction effects between CICRD and circadian rhythm-related gene polymorphisms (SNPs) on the risk of type 2 diabetes mellitus (T2DM). Methods : Baseline data were collected from the Xingtai coal site of the Occupational Cohort Study on Health Effects. A cross-sectional study was initially conducted, involving 4,070 coal miners who underwent occupational health examinations during 2017 and 2018. We performed factor analysis to construct the CICRD and logistic regression models to estimate the association between CICRD and T2DM. Restricted cubic spline (RCS) function was used to determine the exposure-response association. In the subsequent case-control analysis, 424 cases and 464 controls were randomly selected from 3,878 male coal miners. Logistic regression model was employed to examine the association between selected SNPs and T2DM. Gene-gene and gene-environment interactions were evaluated using log-linear models and the generalized multifactor dimensionality reduction (GMDR) method. Results: The CICRD constructed by factor analysis explained 79.771% of the original variance. After adjusting for confounding factors, CICRD was associated with the increased risk of T2DM. Variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene were associated with the increased risk of T2DM. Interactions between rs10830963 in the MTNR1B gene and rs11605924 in the CRY2 gene ( RERI : 0.2; AP : 0.46), as well as between rs7958822 in the BMAL2 gene and rs11605924 in the CRY2 gene ( RERI : 1.55; AP : 0.56), were associated with increased risk of T2DM. A CICRD score ≥ 0.2782 combined with high-risk genotypes at four SNPs (rs10830963 and rs1387153 in MTNR1B, rs7958822 in BMAL2, and rs11605924 in CRY2) was associated with increased risk of T2DM ( P < 0.05). The complex intersection of four-factor interaction model (rs10830963-rs1387153-rs7958822-rs11605924) and five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) based on GMDR method interactions increased the risk of T2DM in the full data set ( P < 0.05). Conclusion: An increase in CICRD, along with variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene, was associated with an increased risk of T2DM among coal miners. The four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) exhibited significant high-order interactions associated with an increased risk of T2DM among coal miners. Circadian rhythm Gene polymorphism type 2 diabetes mellitus Interaction effect Background Diabetes is a metabolic disorder caused by defects in insulin secretion, insulin action, or both, primarily characterized by chronic hyperglycemia[1]. The International Diabetes Federation (IDF) reported that in 2021, there were 536.6 million (10.5%) people aged 20 to 79 with diabetes globally, and this number is projected to increase to 783.2 million (12.2%) by 2045. Global health expenditure on diabetes was estimated at 966 billion dollars in 2021, with projections reaching 1,054 billion dollars by 2045 [2]. Diabetes can cause severe complications and reduce life expectancy[3]. As of 2021,there were 140.9 million diabetes cases in China [2], with type 2 diabetes mellitus (T2DM) accounting for over 90% of them[4]. The development of T2DM is a complex process involving interactions among multiple factors[5]. With the advance in modern biopsychosocial medical models and the diathesis-stress model [6], recent researches have increasingly focused on the complex interactions of various factors on T2DM. Previous studies have shown that shift work is associated with the onset and progression of T2DM[5, 7–10]. Shift work is a specific type of work schedule, such as rotating shifts, evening shifts, or night shifts[11]. This work schedule requires different workers or teams to accomplish daily tasks lasting 8 to 24 hours through shift handovers. In China, shift work is widely employed in industries such as mining, manufacturing, metallurgy, and services. In 2007, the International Agency for Research on Cancer classified shift work as a Group 2A carcinogen[12]. Shift work disrupts traditional sleep-wake patterns, causing circadian rhythm disturbances that result in various physiological and psychological disorders, which severely affects health[13–17]. Besides shift work, other factors contributing to circadian rhythm disruption include sleep disorders and insufficient sleep. When sleep and wakefulness occur at inappropriate circadian times, such as waking when the biological clock promotes sleep or sleeping when it promotes wakefulness, it can disrupt circadian rhythms[18, 19]. Moreover, artificial lighting has significantly altered nighttime environments[20]. Exposure to artificial light at night disrupts sleep/wake cycles and circadian functions, affecting nighttime hormone production and secretion, which may induce various diseases[21–24]. Therefore, in addition to shift work, factors like non-work-related light exposure at night, sleep disorders, and insufficient sleep are also key in studying circadian rhythm disruption. Circadian rhythms exert great influence on many aspects of mammalian life, including behavior, hormone secretion, temperature regulation, and the molecular control of gene transcription and translation[25–27]. The circadian timing system in mammals consists of a central clock situated in the suprachiasmatic nucleus (SCN) of the hypothalamus and peripheral clocks situated in various brain regions and tissues throughout the body. The SCN receives direct input from the retina, which allows environmental light to synchronize its rhythm with the 24-hour environmental cycle[28]. Timing signals from the SCN are transmitted to peripheral clocks via neural, endocrine, and temperature cues[29]. The molecular mechanisms of central and peripheral clocks rely on transcription-translation feedback loops, present in nearly every cell in the body[30–32]. The core of the positive feedback loop in circadian regulation consists of CLOCK (circadian locomotor output cycles kaput), BMAL1 (brain and muscle ARNT-like protein 1), and NPAS2 (neuronal PAS domain protein 2). The dimers formed by these proteins, BMAL1/CLOCK and BMAL1/NPAS2, rhythmically drive the expression of the period genes Period 1 (PER1), Period 2 (PER2), and Period 3 (PER3), as well as the cryptochrome genes Cryptochrome 1 (CRY1) and Cryptochrome 2 (CRY2). IIn contrast, PER and CRY proteins form heterodimers that inhibit their own transcription by interacting with the CLOCK/BMAL complex[33, 34]. Therefore, circadian rhythm-related genes are essential for maintaining the roughly 24-hour oscillatory rhythm. In China, the coal industry is one of the major economic pillars and a key source of national energy, holding a significant position. Consequently, the quality of life of coal miners is a major concern in China's occupational health field. With a large workforce and widespread shift work, circadian rhythm disruption is common among coal miners. This study focuses on circadian rhythm disruption, investigating workers from the Xingtai coal mining site in the Beijing-Tianjin-Hebei region as part of a health effects cohort study. This study will explore the roles of environmental and genetic factors related to circadian rhythms, using biotechnological methods from both "macro" and "micro" perspectives, with the aim to provide scientific evidence for T2DM prevention in coal miners. Method Study population Participants in this analysis were drawn from the baseline cohort of the Beijing-Tianjin-Hebei Region Occupational Health Effects Cohort Study, initiated by the Ministry of Science and Technology of China. The core aim of Beijing-Tianjin-Hebei Region Occupational Health Effects Cohort Study was to explore impact of occupational hazards on human health. This study was based on an epidemiological survey of 4,440 workers who underwent occupational health examinations at the Xingtai coal mining site between August 2017 and August 2018. Data were collected from questionnaires, physical examinations, laboratory tests, and assessments of occupational hazards. After excluding individuals who did not complete the questionnaire (246), had less than one year of work experience (22), lacked blood biochemical data (12), lacked physical examination data (27), or had severely incorrect or missing questionnaire information (63), a total of 4,070 Han Chinese coal miners were included in this study. This cross-sectional study revealed a significant association between comprehensive indicators of circadian rhythm disorder (CICRD) and T2DM risk in male workers, whereas no significant association was observed in female participants, potentially due to the limited sample size (Table S2). Therefore, based on the inclusion and exclusion criteria, SAS 9.4 software was used to randomly select study subjects from 3,878 male coal miners for the case-control study. Due to limited project funding for genotyping all participants in the cross-sectional survey, we employed a case-control study based on baseline data to explore the interaction between CICRD and circadian rhythm-related gene polymorphisms (SNPs) and their association with T2DM. A total of 424 cases and 464 controls were selected as study participants. Inclusion criteria for the case group: ① Han Chinese male coal miners diagnosed with T2DM during the survey; ② a minimum of 3 years of work experience; ③ signed informed consent. Exclusion criteria for the case group: ① Individuals with severe missing information on shift work, covariates, or blood biochemical data; ② Those taking antiretroviral drugs or diagnosed with cancer or thyroid disease; ③ Individuals with other diseases related to the genes selected in this study. Inclusion criteria for the control group were: ① Han Chinese male coal miners without T2DM, diagnosed using the same criteria as the case group; ② Age-matched to the case group within ± 5 years; ③ A minimum of 3 years of work experience; ④ Comparable residential conditions; ⑤ Signed informed consent. Exclusion criteria for the control group: ① Individuals with severe missing information on shift work, covariates, or blood biochemical data; ② Those taking antiretroviral medications or diagnosed with cancer or thyroid disorders; ③ Individuals with other diseases related to the genes selected in this study. $$\:\begin{array}{c}n=\frac{{\left[{z}_{\propto\:}\sqrt{2\stackrel{-}{p}\left(1-\stackrel{-}{p}\right)}+{z}_{\beta\:}\sqrt{{p}_{1}\left(1-{p}_{1}\right)+{p}_{0}\left(1-{p}_{0}\right)}\right]}^{2}}{{\left({p}_{1}-{p}_{0}\right)}^{2}}\end{array}$$ 6 $$\:\begin{array}{c}n=\frac{{\left[{z}_{\propto\:}\sqrt{2\stackrel{-}{p}\left(1-\stackrel{-}{p}\right)}+{z}_{\beta\:}\sqrt{{p}_{1}\left(1-{p}_{1}\right)+{p}_{0}\left(1-{p}_{0}\right)}\right]}^{2}}{{\left({p}_{1}-{p}_{0}\right)}^{2}}\end{array}$$ 7 $$\:\begin{array}{c}\stackrel{-}{p}=\frac{\left({p}_{1}+{p}_{0}\right)}{2}\end{array}$$ 8 $$\:\begin{array}{c}{p}_{1}=\frac{\left(OR\times\:{p}_{0}\right)}{\left(1-{p}_{0}+OR\times\:{p}_{0}\right)}\end{array}$$ 9 In the formula: P 0 —Exposure proportion of the research factor in the control group P 1 —Exposure proportion of the research factor in the case group. The hypothesis test was conducted with a Type I error rate α = 0.05. In this study, P 0 represents the minor allele frequency (MAF) set at 0.101. The expected odds ratio (OR) ranges from 1 to 2. The test power is denoted as Power = 1- β. A total of 424 cases and 464 controls were included in the study. A sample size and power relationship plot was generated using the PS-Power and Sample Size Calculation software. As shown in Figure S1 , the power increases with the sample size. When the number of participants in both the case and control groups reaches at least 424, the power reaches 0.932, exceeding 0.90. Evaluation of T2DM and CICRD According to the China T2DM Prevention and Control Guideline (2020 Edition)[2], T2DM is defined as fasting blood glucose ≥ 7.0 mmol/L, random blood glucose ≥ 11.1 mmol/L, or a previous hospital diagnosis of T2DM. In our study, seven basic evaluation indicators from three categories—shift work, light exposure, and sleep—were selected to construct the CICRD. These include the duration of shift work, cumulative number of night shifts, cumulative duration of night shifts, average frequency of night shifts, nighttime light exposure, insomnia status, and average sleep duration. The data was collected through face-to-face interviews and then checked against the company's records. Detailed definitions are presented in the supplementary materials. Definition and classification of covariates This study included basic demographic characteristics (gender, age, education level, marital status, and family income), lifestyle behaviors (smoking, drinking, physical activity, and diet), medical history (central obesity, hypertension, liver dysfunction, dyslipidemia, and renal dysfunction), and occupational exposures (dust, heat, CO, and noise). Detailed definitions are presented in the supplementary materials. Genetic testing of MTNR1B, BMAL1, and BMAL2 Genomic DNA was extracted from whole blood using a genomic DNA extraction kit (Genesky). DNA concentration and purity were determined using a spectrophotometer after it was fully dissolved. Tag SNPs related to circadian rhythm genes were selected if they had a minor allele frequency (MAF) ≥ 10% in the Chinese population and a linkage disequilibrium (LD) coefficient greater than 0.8 with other SNPs in the region. Tag SNPs were selected using the Tagger algorithm in the genetic haplotype analysis software HaploView 4.2. If multiple SNPs met the criteria, priority was given to those identified by genome-wide association studies (GWAS) as linked to T2DM. Subsequently, SNPs linked to elevated FG levels and insulin resistance were selected. After comprehensive analysis, a total of 6 SNPs were selected: rs10830963 and rs1387153 in the melatonin receptor 1B (MTNR1B) gene; rs11022775 and rs7950226 in the BMAL1 gene; rs7958822 in the brain and muscle ARNT-like protein 2 (BMAL2) gene; and rs11605924 in the CRY2 gene. The specific detection process is detailed in the Supplementary materials. Establishment of CICRD We confirmed the data’s suitability for factor analysis using the KMO test (0.774) and Bartlett’s sphericity test ( P < 0.001). The CICRD was developed based on seven indicators, including shift duration, night light exposure, and sleep status. Principal component analysis identified three factors: F1 (shift Work Factor), F2 (sleep factor), and F3 (light exposure factor). Each factor’s weight was assigned according to its explanatory variance, and the final CICRD score was calculated through normalization. The score explained 79.711% of the original data and provided a quantitative tool for exploring the relationship between circadian rhythm disruption and type 2 diabetes. The development process is described in the supplementary materials. Statistical Analysis Continuous variables were described as mean ± standard deviation (SD) or as median with interquartile range (IQR). The Kolmogorov-Smirnov test was used to assess the normality. If data met the criteria for parametric tests, a t-test was used for comparisons between groups; otherwise, the Mann-Whitney U test was applied. Categorical data were described as percentage, and comparisons between groups were conducted using chi-square test, Fisher’s exact test, or Cochran-Armitage trend test. Logistic regression model was performed to explore the association between influencing factors and T2DM. Factor analysis was used to construct the CICRD, and a restricted cubic spline (RCS) function was applied to fit the dose-response relationship between CICRD and T2DM among coal miners, with four knots at the 5th, 35th, 65th, and 95th percentiles. The Hardy-Weinberg equilibrium (HWE) test was used to determine whether the control group was a random sample from the target population. Logistic regression combined with SNPStats software was used to analyze the association of target SNPs with T2DM under codominant, dominant, recessive, over dominant, and additive models among male coal miners. The optimal model was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The median was used to classify CICRD into "<0.2782" and "≥0.2782". The CICRD and the dominant model of target SNPs were cross-classified, and logistic regression with multiplicative interaction and the Andersson additive interaction model were used to analyze gene-gene and gene-environment interactions. GMDR 0.9 was used for higher-order interaction analysis to construct optimal gene-gene and gene-environment interaction models. A two-tailed test with a significance level of α = 0.05 was applied. Result Demographic characteristics of participants in cross-sectional and case-control studies As shown in Table 1 , a total of 4070 participants were included in this study, comprising 3878 males (95.28%) and 192 females (4.72%). The mean age of male workers (39.41 ± 8.62 years) was higher than that of female workers (36.52 ± 8.81 years). The overall prevalence of T2DM was 16.5%, with a higher prevalence among male workers than female workers (16.7% vs. 12.5%). The demographic characteristics of the case and control groups are presented in Table S1 . The CICRD score of the case group (0.33 ± 0.15) was higher than that of the control group (0.29 ± 0.14). Significant differences were observed in terms of age, income, smoking status, dyslipidemia, liver dysfunction, hypertension, central obesity, family history of diabetes, and noise exposure between the two groups ( P < 0.05). Table 1 Sociodemographic characteristics of the subjects in the cross-sectional study Variable Total population (n = 4070) Male (n = 3878) Female (n = 192) P Age (years) 39.27 ± 8.65 39.41 ± 8.62 36.52 ± 8.81 < 0.001 a DASH score 23.29 ± 2.77 23.28 ± 2.69 23.56 ± 3.98 < 0.001 a Per capita monthly household income (Yuan/person) 1887.00 (1509.60,2516.00) 1887.00 (1509.60,2516.00) 1943.50 (1687.50,2516.00) < 0.001 a average sleep duration (h/d) 7.37 ± 1.29 7.36 ± 1.30 7.56 ± 1.19 0.020 a duration of shift work (year) 9.34 (0.37, 15.59) 9.42 (0.00, 14.93) 7.51 (2.08, 24.44) 0.002 a cumulative number of night shifts (night) 494 (21, 1171) 494 (0, 1134) 772 (253, 2229) < 0.001 a cumulative duration of night shifts (h) 3621 (131,8643) 3595 (0,8080) 6178 (2029,18033) < 0.001 a average frequency of night shifts (nights/month) 3.80 (3.04, 7.60) 3.80 (0.00, 7.60) 7.60 (6.08, 10.14) < 0.001 a Marital status, n (%) < 0.001 a Unmarried 165 (4.1) 139 (3.6) 26 (13.5) Married 3905 (95.9) 3739 (96.4) 166 (86.5) Education level, n (%) < 0.001 c Primary level 49 (1.2) 48 (1.2) 1 (0.5) Intermediate level 2922 (71.8) 2822 (72.8) 100 (52.1) Advanced level 1099 (27.0) 1008 (26.0) 91 (47.4) Smoking status, n (%) < 0.001 c Never smoking 1607 (39.5) 1440 (37.1) 167 (87.0) Ever smoking 298 (7.3) 297 (7.7) 1 (0.5) Current smoking 2165 (53.2) 2141 (55.2) 24 (12.5) Drinking status, n (%) < 0.001 c Never drinking 953 (23.4) 775 (20.0) 178 (92.7) Ever drinking 192 (4.7) 192 (5.0) 0 (0.0) Current drinking 2925 (71.9) 2911 (75.0) 14 (7.3) Salt taste preference, n (%) 0.575 b Light 711 (17.5) 677 (17.5) 34 (17.7) Moderate 1955 (48.0) 1869 (48.2) 86 (44.8) Salty 1404 (34.5) 1332 (34.3) 72 (37.5) insomnia status, n (%) 0.064 b No sleep disorder 2776 (68.2) 2655 (68.5) 121 (63.0) Suspected insomnia 861 (21.2) 820 (21.1) 41 (21.4) Insomnia 433 (10.6) 403 (10.4) 30 (15.6) Physical activity level, n (%) < 0.001 b Low 557 (13.7) 414 (10.7) 143 (74.5) Moderate 1647 (40.5) 1631 (42.0) 16 (8.3) High 1866 (45.8) 1833 (47.3) 33 (17.2) Family History of Diabetes, n (%) 0.006 No 3565 (87.6) 3409 (87.9) 156 (81.2) Yes 505 (12.4) 469 (12.1) 36 (18.8) Central Obesity, n (%) < 0.001 No 3015 (74.1) 2849 (73.5) 166 (86.5) Yes 1055 (25.9) 1029 (26.5) 26 (13.5) Dyslipidemia, n (%) < 0.001 No 3038 (74.6) 2872 (74.1) 166 (86.5) Yes 1032 (25.4) 1006 (25.9) 26 (13.5) Abnormal liver function, n (%) < 0.001 No 3268 (80.3) 3089 (79.7) 179 (93.2) Yes 802 (19.7) 789 (20.3) 13 (6.8) Abnormal Kidney Function, n (%) < 0.001 No 3869 (95.1) 3725 (96.1) 144 (75.0) Yes 201 (4.9) 153 (3.9) 48 (25.0) Hypertension, n (%) < 0.001 No 2627 (64.5) 2457 (63.4) 170 (88.5) Yes 1443 (35.5) 1421 (36.6) 22 (11.5) T2DM, n (%) < 0.001 No 3397 (83.5) 3229 (83.3) 168 (87.5) Yes 673 (16.5) 649 (16.7) 24 (12.5) Heat exposure, n (%) < 0.001 No 377 (9.3) 317 (8.2) 60 (31.2) Yes 3693 (90.7) 3561 (91.8) 132 (68.8) Noise exposure, n (%) < 0.001 No 1878 (46.1) 1849 (47.7) 29 (15.1) Yes 2192 (53.9) 2029 (52.3) 163 (84.9) Dust exposure, n (%) 0.008 No 1238 (30.4) 1196 (30.8) 42 (21.9) Yes 2832 (69.6) 2682 (69.2) 150 (78.1) CO exposure, n (%) < 0.001 No 939 (23.1) 861 (22.2) 78 (40.6) Yes 3131 (76.9) 3017 (77.8) 114 (59.4) nighttime light exposure, n (%) 0.017 b Darkest 1259 (30.9) 1203 (31.0) 56 (29.2) Moderate 2047 (50.3) 1962 (50.6) 85 (44.3) Brightest 764 (18.8) 713 (18.4) 51 (26.5) Shift work, n (%) < 0.001 No 1006 (24.7) 986 (25.4) 20 (10.4) Yes 3064 (75.3) 2892 (74.6) 172 (89.6) Note: a indicates that the Mann-Whitney U test was used due to non-compliance with parametric test assumptions; b denotes the result obtained by the Cochran-Armitage trend test; c refers to the Fisher’s exact test. Continuous variables are presented as mean ± SD or median (lower quartile, upper quartile). Analysis of association between CICRD and T2DM Figure S2 shows a positive linear relationship between CICRD and the risk of T2DM among coal miners ( P for overall association <0.001; P for non−linearity = 0.524). Table 2 shows that, after adjusting for confounding factors (Model 3), coal miners with CICRD in the ranges " 0.2782– " and " ≥0.3848 " have 1.43 folds (95% CI : 1.07–1.90) and 2.43 folds (95% CI : 1.80–3.21) higher T2DM risks, respectively, compared to the CICRD group less than "0.1839". The trend test results showed that for each one-level increase in CICRD, the risk of T2DM increased by 35% ( OR = 1.35, 95% CI : 1.23–1.48). In addition, for per one standard deviation (0.1468) increase in CICRD, the risk of T2DM among coal miners increases by 41% ( OR = 1.41, 95% CI : 1.28–1.57). In the sensitivity analysis, we examined the association between CICRD and T2DM across different population characteristics. The results and trends in Table S2 are consistent with the main findings. Table 2 Logistic regression analysis of CICRD and T2DM in coal miners CICRD n , (%) OR (95% CI ) Model 1 Model 2 Model 3 < 0.1839 1017 (24.99) 1.00 1.00 1.00 0.1839~ 1017 (24.99) 1.07 (0.82 ~ 1.40) 1.15 (0.85 ~ 1.54) 1.06 (0.78 ~ 1.43) 0.2782~ 1019 (25.03) 1.42 (1.10 ~ 1.82) 1.51 (1.14 ~ 2.02) 1.43 (1.07 ~ 1.90) ≥ 0.3848 1017 (24.99) 2.26 (1.77 ~ 2.89) 2.38 (1.79 ~ 3.17) 2.43 (1.80 ~ 3.21) Test for trend 1.33 (1.22 ~ 1.44) 1.34 (1.22 ~ 1.47) 1.35 (1.23 ~ 1.48) Per SD increase 1.40 (1.28 ~ 1.52) 1.39 (1.26 ~ 1.54) 1.41 (1.28 ~ 1.57) Model 1: adjusted for age and gender; Model 2: further adjusted for marital status, family income per capita, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, abnormal liver function, abnormal renal function, dyslipidemia, hypertension, and family history of diabetes; Model 3: further adjusted for occupational hazards (CO, noise, dust and heat); SD: standard deviation Correlation and interaction between targeted SNPs and T2DM in coal miners Table S3 shows that, among the five genetic models, the dominant model is the optimal one for rs10830963 (AIC = 1225.8, BIC = 1235.4), rs7958822 (AIC = 1227.5, BIC = 1237.1), and rs11605924 (AIC = 1229.5, BIC = 1239.1). After adjusting for confounding factors, coal miners with CG or GG genotypes at rs10830963 have a 1.50 times higher risk of T2DM compared to those with the CC genotype (95% CI: 1.14 ~ 1.98); coal miners with GA or AA genotypes at rs7958822 have a 1.43 times higher risk of T2DM compared to those with the GG genotype (95% CI: 1.09–1.86); and coal miners with AC or CC genotypes at rs11605924 have a 1.35 times higher risk of T2DM compared to those with the AA genotype (95% CI: 1.03–1.76), under the dominant model. Additionally, the association of rs1387153 in the MTNR1B gene, rs11022775 in the BMAL1 gene, and rs7950226 in the BMAL1 gene with T2DM in coal miners is presented in Table S4. Based on the prior results, three susceptibility gene loci for T2DM in coal miners were selected. Cross-classification was performed according to their respective dominant models to further analyze the interactions between these loci and their association with T2DM in coal miners. Table S5 shows that the risk for individuals with CG + GG genotypes at rs10830963 combined with the GA + AA genotypes at rs7958822 is 2.71 times higher (95% CI : 1.77–4.15) compared to those with the CC genotype at rs10830963 combined with the GG genotype at rs7958822. Additionally, the risk of disease for individuals with the CG + GG genotypes at rs10830963 combined with AC + CC genotypes at rs11605924 is 2.64 times higher (95% CI : 1.70–4.11) compared to those with the CC genotype at rs10830963 combined with the AA genotype at rs11605924. The risk of T2DM for coal miners with the GA + AA genotypes at rs7958822 combined with AC + CC genotypes at rs11605924 is 2.77 times higher (95% CI : 1.80–4.27) compared to those with the GG genotypes at rs7958822 combined with the AA genotype at rs11605924. The relative excess risk due to interaction (RERI) for the additive interaction between rs10830963 and rs11605924 is 0.21 (95% CI : 0.11–0.31), and the attributable proportion due to interaction (AP) is 0.46 (95% CI : 0.16–0.75). For the additive interaction between rs7958822 and rs11605924, the RERI is 1.55 (95% CI : 0.51–2.60), and the AP is 0.56 (95% CI : 0.32–0.80). The multiplicative interaction between rs7958822 and rs11605924 is also statistically significant ( P < 0.05) (Table S5). Additionally, no statistically significant multiplicative or additive interactions were found between rs10830963, rs7958822, rs11605924, and other genetic loci (Table S6). Furthermore, gene-gene interactions were analyzed using GMDR. Table 3 shows that only the four-factor (rs10830963-rs1387153-rs7958822-rs11605924) and two-factor (rs7958822-rs11605924) interaction effects are statistically significant ( P < 0.05). Compared to the two-factor model, the four-factor model has higher accuracy in both the training and validation sets, with a 12-fold cross-validation consistency of 91.67% (11/12). Therefore, the four-factor model was selected as the best gene-gene higher-order interaction model. In this model, the high-risk group is indicated in dark gray. For example, the combination of GG genotype at rs7958822 with CG genotype at rs10830963, AA genotype at rs11605924, and CC genotype at rs1387153 is classified as the high-risk group (Figure S3). The four-factor model was not statistically significant in the validation set. However, in the full dataset, coal miners with homozygous mutations in the gene had a 3.10 times higher risk of T2DM compared to those with the wild-type genotype (95% CI : 2.01–4.77) (Table S7). Table 3 Gene-Gene interaction models identified by GMDR Model Training set accuracy Validation set accuracy P Cross-validation consistency rs10830963 0.5607 0.5189 9 (0.0730) 8/12 rs7958822- rs11605924 0.5780 0.5413 10 (0.0193) 10/12 rs10830963- rs7958822- rs11605924 0.6066 0.5444 9 (0.0730) 10/12 rs10830963- rs1387153- rs7958822- rs11605924 0.6351 0.5804 11 (0.0032) 11/12 rs10830963- rs1387153- rs79588226- rs7958822- rs11605924 0.6683 0.5157 8 (0.1938) 12/12 rs10830963- rs1387153- rs11022775- rs79588226- rs7958822- rs11605924 0.6954 0.5305 9 (0.0730) 12/12 Analysis of interaction between CICRD and various gene loci We divided the constructed CICRD into two categories based on the median: “<0.2782” and “≥0.2782,” and performed cross-classification with the target SNPs. Table 4 shows that compared to the low-risk genotypes of the four SNPs (rs10830963, rs1387153, rs7958822, rs11605924) combined with CICRD < 0.2782, the high-risk genotypes of these SNPs combined with CICRD ≥ 0.2782 increased the risk of T2DM in coal miners. Specifically, the risk of T2DM is 2.67 times higher among the group with CICRD ≥ 0.2782 combined with the rs10830963 CG + GG genotypes, compared to the group with CICRD < 0.2782 combined with the CC genotype. For CICRD ≥ 0.2782 combined with the rs1387153 CT + TT genotypes, the risk is 1.93 times higher compared to CICRD < 0.2782 combined with the CC genotype. For CICRD ≥ 0.2782 combined with the rs7958822 GA + AA genotypes, the risk is 2.29 times higher compared to CICRD < 0.2782 combined with the GG genotype. Lastly, for CICRD ≥ 0.2782 combined with the rs11605924 AC + CC genotypes, the risk is 2.30 times higher compared to CICRD < 0.2782 combined with the AA genotype. Additionally, with each one-level increase in the combined categories of CICRD and the five SNPs (rs10830963, rs1387153, rs11022775, rs7958822, rs11605924), the risk of T2DM increases by 29%, 22%, 18%, 28%, and 28%, respectively. In this study, no statistically significant additive or multiplicative interactions between CICRD and the individual gene loci were found. Table 4 Multiplicative and additive interactions of shift work and genes on T2DM Risk SNPs CICRD Genotype Cases Controls OR (95% CI ) P Multiplicative Interaction Model 1 Model 2 Model 1 Model 2 rs10830963 < 0.2782 CC 51 103 1.00 1.00 0.719 0.614 CG + GG 121 135 1.81 (1.19 ~ 2.74) 2.16 (1.37 ~ 3.46) ≥ 0.2782 CC 89 88 2.04 (1.31 ~ 3.19) 2.00 (1.22 ~ 3.30) CG + GG 171 130 2.66 (1.77 ~ 3.99) 2.67 (1.69 ~ 4.22) Trend test 1.32 (1.17 ~ 1.50) 1.29 (1.13 ~ 1.48) RERI -0.20 (-1.25 ~ 0.86) -0.50 (-1.76 ~ 0.75) AP -0.07 (-0.47 ~ 0.33) -0.19 (-0.66 ~ 0.28) rs1387153 < 0.2782 CC 63 95 1.00 1.00 0.968 0.743 CT + TT 109 143 1.15 (0.77 ~ 1.72) 1.48 (0.94 ~ 2.33) ≥ 0.2782 CC 96 86 1.68 (1.09 ~ 2.59) 1.76 (1.08 ~ 2.84) CT + TT 164 132 1.87 (1.27 ~ 2.77) 1.93 (1.24 ~ 3.00) Trend test 1.25 (1.11 ~ 1.41) 1.22 (1.06 ~ 1.40) RERI 0.04 (-0.74 ~ 0.82) -0.30 (-1.27 ~ 0.66) AP -0.02 (-0.39 ~ 0.44) -0.16 (-0.65 ~ 0.34) rs11022775 < 0.2782 CC 137 195 1.00 1.00 0.930 0.852 CT + TT 35 43 1.16 (0.71 ~ 1.90) 0.98 (0.57 ~ 1.68) ≥ 0.2782 CC 205 178 1.64 (1.22 ~ 2.21) 1.44 (1.03 ~ 2.03) CT + TT 55 40 1.96 (1.23 ~ 3.11) 1.52 (0.91 ~ 2.53) Trend test 1.27 (1.12 ~ 1.43) 1.18 (1.03 ~ 1.36) RERI 0.16 (-0.87 ~ 1.19) 0.09 (-0.83 ~ 1.01) AP 0.08 (-0.42 ~ 0.59) 0.06 (-0.53 ~ 0.65) rs7950226 < 0.2782 AA 66 75 1.00 1.00 0.326 0.095 GA + GG 106 163 0.74 (0.49 ~ 1.12) 0.66 (0.42 ~ 1.04) ≥ 0.2782 AA 92 76 1.38 (0.88 ~ 2.16) 1.04 (0.63 ~ 1.72) GA + GG 168 142 1.34 (0.90 ~ 2.00) 1.17 (0.75 ~ 1.82) Trend test 1.19 (1.06 ~ 1.35) 1.14 (0.99 ~ 1.34) RERI 0.23 (-0.38 ~ 0.84) 0.46 (-0.06 ~ 0.98) AP 0.17 (-0.29 ~ 0.63) 0.40 (-0.09 ~ 0.88) rs7958822 < 0.2782 GG 66 75 1.00 1.00 0.937 0.838 GA + AA 106 163 1.44 (0.97 ~ 2.14) 1.77 (1.14 ~ 2.75) ≥ 0.2782 GG 92 76 1.67 (1.16 ~ 2.40) 1.54 (1.02 ~ 2.33) GA + AA 168 142 2.25 (1.55 ~ 3.25) 2.29 (1.52 ~ 3.48) Trend test 1.30 (1.15 ~ 1.46) 1.28 (1.12 ~ 1.46) RERI 0.14 (-0.73 ~ 1.01) -0.02 (-1.04 ~ 1.00) AP 0.06 (-0.32 ~ 0.44) -0.01 (-0.45 ~ 0.44) rs11605924 < 0.2782 AA 86 136 1.00 1.00 0.838 0.781 AC + CC 86 102 1.33 (0.90 ~ 1.98) 1.47 (0.96 ~ 2.27) ≥ 0.2782 AA 138 134 1.63 (1.14 ~ 2.33) 1.44 (0.96 ~ 2.16) AC + CC 122 84 2.30 (1.56 ~ 3.38) 2.30 (1.49 ~ 3.56) Trend test 1.31 (1.16 ~ 1.48) 1.28 (1.12 ~ 1.47) RERI 0.34 (-0.52 ~ 1.19) 0.39 (-0.54 ~ 1.33) AP 0.15 (-0.21 ~ 0.50) 0.17 (-0.21 ~ 0.55) Note: Adjusted for age, marital status, per capita monthly household income, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, liver function abnormalities, kidney function abnormalities, dyslipidemia, hypertension, family history of diabetes, CICRD, and occupational hazards (CO, noise, dust, and heat) Additionally, we analyzed the interactions between CICRD and genes using GMDR (Table 5 ), which indicated only the four-factor (rs10830963-rs7958822-rs11605924-CICRD) and five-factor (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) interaction models are statistically significant ( P < 0.05). Compared to the four-factor model, the five-factor model showed higher accuracy in both the training and validation sets, with 12-fold cross-validation consistency reaching 100% (12/12). Therefore, the five-factor model was selected as the best gene-gene higher-order interaction model, with high-risk combinations shown in Figure S4. In the five-factor interaction model, the risk of T2DM for coal miners with homozygous mutant genotypes combined with CICRD ≥ 0.2782 is 7.38 times (95% CI : 4.84–11.25) higher than for those with homozygous wild-type genotypes combined with CICRD < 0.2782 (Table S8). Table 5 CICRD-Target SNPs interaction models identified by GMDR Model Training set accuracy Validation set accuracy P Cross-validation consistency CICRD 0.5620 0.5438 10 (0.0193) 11/12 rs7950226- CICRD 0.5703 0.5421 8 (0.1938) 10/12 rs7958822- rs11605924- CICRD 0.5953 0.5271 7 (0.3872) 6/12 rs10830963- rs7958822- rs11605924- CICRD 0.6260 0.5357 10 (0.0193) 5/12 rs10830963- rs7950226- rs7958822- rs11605924- CICRD 0.6768 0.5759 11 (0.0032) 12/12 rs10830963- rs1387153- rs7950226- rs7958822- rs11605924- CICRD 0.7073 0.5363 8 (0.1938) 7/12 rs10830963- rs1387153- rs11022775- rs7950226- rs7958822- rs11605924- CICRD 0.7353 0.5354 7 (0.3872) 12/12 Note: Adjusted for age, marital status, per capita monthly household income, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, liver function abnormalities, kidney function abnormalities, dyslipidemia, hypertension, family history of diabetes, CICRD, and occupational hazards (CO, noise, dust, and heat). Cross-Validation: Refers to randomly dividing the data into 12 parts, using one part as the validation set and the remaining 11 parts as the training set, followed by training and validating the model to ensure balanced testing. Discussion In our study, the CICRD constructed using factor analysis based on seven indicators captured 79.771% of the information from the original data. The CICRD is significantly associated with the risk of T2DM in coal miners. In addition, the MTNR1B gene rs10830963, BMAL2 gene rs7958822, and CRY2 gene rs11605924 are also associated with the risk of T2DM in coal miners. Notably, both the four-factor interaction model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) are significantly associated with the risk of T2DM in coal miners. In our study, the assessment indicators related to circadian rhythm disorder include number of years working night shifts, cumulative number of night shifts, total duration of night shifts, average frequency of night shifts, nighttime light exposure, insomnia status, and average sleep duration. However, constructing new indicators involves key challenges with weight allocation. To preserve the original information and minimize subjectivity, this process must be approached carefully. This study used factor analysis to extract common factors from numerous original variables and condense them. Based on the field database, we selected seven fundamental indicators related to shift work, night-time light exposure, and sleep to construct the CICRD. We aimed to explore the association between circadian rhythm disorder and T2DM in coal miners from a comprehensive perspective. The results of this study indicate a positive linear association between CICRD and T2DM in coal miners. The results suggest that reducing the intensity and frequency of shift work, improving sleep quality, and avoiding sleep deprivation and night-time light exposure can help reduce circadian rhythm disorder, which is beneficial for preventing T2DM in coal miners. CICRD provides a basis and standard for assessing circadian rhythm disruption in the coal industry and potentially other industries, while also offering scientific evidence for screening high-risk T2DM populations. Thus, the development, application, and extrapolation of CICRD have significant scientific and public health value. Our study suggests that the G allele of the MTNR1B gene at rs10830963 is a risk allele for type 2 diabetes mellitus (T2DM) in coal miners, while rs1387153 showed no statistically significant association with T2DM in this population. MTNR1B belongs to the G protein-coupled receptor family involved in insulin secretion and encodes the melatonin receptor 1B[35]. Previous studies have reported several loci within the MTNR1B gene that are associated with fasting glucose (FG). In our study, we selected two loci of interest to both domestic and international researchers. Some studies have indicated that these two loci in the MTNR1B gene (rs1387153 and rs10830963) are associated with T2DM, elevated FG levels, and impaired insulin secretion[35–39]. These loci may increase the risk of T2DM by disrupting G protein activation[40]. A study based on a European population found that rs1387153 is associated with elevated FG, with the T allele being a risk factor for elevated FG levels and T2DM[37]. However, a study based on a Chinese Han population found that the T allele was associated only with FG and had no significant association with T2DM[38]. Our results also show that the T allele is not significantly associated with T2DM. A meta-analysis confirmed this finding, with stratified analysis by ethnicity showing a marginal association between rs1387153 and T2DM only in Caucasian populations, while no association was observed in Southeast Asian and South Asian populations[41]. The rs10830963 locus is located in the only intron of MTNR1B. A meta-analysis based on European populations showed that the G allele of rs10830963 is associated with elevated fasting glucose (FG) levels and decreased pancreatic β-cell function[36]. Additionally, the G allele of rs10830963 has been identified as a risk factor for elevated fasting glucose (FG) and type 2 diabetes mellitus (T2DM) in various populations, including Swedes[35], European biobank cohorts[39], Bosnians and Herzegovinians[42], American Whites[43], and Han Chinese[38, 44]. These findings are consistent with our study's conclusions. Melatonin, a cyclic hormone primarily secreted by the pineal gland, regulates circadian rhythms with elevated levels at night and decreased levels during the day[45]. Some researchers have suggested that melatonin is associated with T2DM and insulin levels[35]. Extensive animal studies have demonstrated that melatonin reduces insulin levels[46–48] and impairs glucose tolerance[49, 50] in rats. These biological mechanisms may help explain the observed associations. At the molecular level, circadian rhythms are regulated by oscillatory circuits of transcription factor expression, with BMAL1 being a key transcriptional regulator in this system [51]. BMAL1 forms heterodimers with CLOCK, rhythmically inducing the transcription of other circadian clock genes[27]. Studies have shown that pancreatic islets exhibit circadian oscillations of CLOCK and BMAL1 transcription factors. Disruption of these clock components in mouse islets leads to hypoinsulinemia and diabetes[52]. Similar studies have indicated that variants in the BMAL1 gene are associated with β-cell dysfunction, glucose intolerance, and T2DM in humans[53–55]. However, previous research on the association between BMAL1 gene variants rs11022775 and rs7950226 and T2DM is limited, with inconsistent findings. A study based on a UK family cohort indicated that haplotypes formed by BMAL1 gene variants rs11022775 and rs7950226 are associated with T2DM, with rs11022775 showing a stronger independent effect[55]. Another study found that haplotypes containing the T allele of rs11022775 and the A allele of rs7950226 are associated with increased T2DM risk, largely driven by rs11022775[56]. A meta-analysis of 13 independent studies involving 13,781 participants found that the BMAL1 gene polymorphism rs7950226 is associated with a reduced risk of metabolic syndrome in the general population, suggesting the A allele is a risk allele[57]. However, our study found no statistically significant association between BMAL1 gene polymorphisms rs11022775 and rs7950226 and T2DM in coal miners across all genetic models, consistent with a cross-sectional study conducted in an obese Japanese population.[58]. Additionally, research on the association between BMAL1 gene polymorphisms rs11022775 and rs7950226 and gestational diabetes provides valuable insights. One study found a significant association between the A allele of rs7950226 and the C allele of rs11022775 and increased risk of gestational diabetes. Furthermore, two haplotypes—comprising the G allele of rs7950226 and the C allele of rs11022775, as well as the A allele of rs7950226 and the C allele of rs11022775—were linked to increased susceptibility to gestational diabetes[53]. This suggests that the potential association between the polymorphisms rs11022775 and rs7950226 and T2DM, as well as related traits, cannot be excluded. Further studies with larger sample sizes across diverse populations are needed to explore these relationships more comprehensively. Our findings suggest that the A allele of the BMAL2 gene rs7958822 variant is a risk factor for T2DM. BMAL2, another key transcriptional regulator[59], plays a crucial role in the core feedback loop of the circadian timing system[60]. Animal studies suggest that BMAL2 gene expression may influence glucose and insulin levels[61]. Previous research on the association between BMAL2 and T2DM is limited. Several SNPs of this gene have been linked to psychiatric disorders [62, 63], and in European populations, BMAL2 has shown no association with metabolic syndrome or its components [64]. However, a study based on an Asian population found a significant association between the BMAL2 gene rs7958822 variant and T2DM in both obese men and women, indicating that the A allele may be a risk factor for T2DM[58]. This aligns with the T2DM risk alleles identified in our study; however, the observed association is not limited to obese individuals. Genetic susceptibility may be related to ethnicity. This association study, conducted in a Chinese Han population, indicates that the A allele of rs7958822 is a risk allele for T2DM, even after adjusting for confounding factors like obesity. Future research should focus on the association between the BMAL2 gene and T2DM across different ethnic groups, rather than limiting studies to psychiatric disorders. The core clock is driven by two interlinked sets of genes. These gene sets encode transcription factors with either activating or repressing effects. BMAL1 and CLOCK genes encode transcriptional activators, while PER1, PER2, PER3, CRY1, and CRY2 genes encode transcriptional repressors. The results of this study indicate that the rs11605924 locus of the CRY2 gene is associated with an increased risk of T2DM in coal miners, suggesting that the C allele is a risk allele for T2DM. A large-scale genome-wide meta-analysis of data from european origin populations identified nine new loci affecting FG, including CRY2[65]. A later study in a general Chinese population found that an increase in the A allele at the rs11605924 locus of the CRY2 gene is significantly associated with impaired FG and T2DM [66], which contrasts with the conclusions of our study. However, some evidence supports our study's conclusions. A study based on a Saudi Arabian population reported that the A allele at the rs11605924 locus is protective against T2DM[67]. Another study based on a Chinese population found that the C allele at the rs11605924 locus is a risk allele for T2DM[68]. Studies in North Indian and American populations, the A allele of rs11605924 is associated with a reduced risk of gestational diabetes, which further supports the conclusions of our study to some extent[69, 70]. Our study used log-linear models and GMDR to explore gene-gene interactions related to circadian rhythm. The combination of the CG + GG genotypes at rs10830963 with the AC + CC genotypes at rs11605924, as well as the GA + AA genotypes at rs7958822 with the AC + CC genotypes at rs11605924, represent high-risk combinations for T2DM. The combination of the CG + GG genotypes at the rs10830963 locus with the AC + CC genotypes at the rs11605924 locus, as well as the GA + AA genotypes at the rs7958822 locus with the AC + CC genotypes at the rs11605924 locus, represent high-risk combinations for T2DM. Additionally, this study is the first to report a four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) with significant interactions for T2DM, with certain genotype combinations being associated with a higher risk of developing T2DM. Therefore, it is important to focus not only on individual genes but also on genotype combinations, as they play a significant role in screening high-risk populations. The results of this study suggest that workers with these genotype combinations are not suited for shift work. Previous studies have reported an association between high-dimensional interactions among rs6850524, rs10830963, and rs1387153 loci in the CLOCK gene and metabolic syndrome[71]. Additionally, an interaction between the rs2119882 locus in the MTNR1A gene and the rs1801260 locus in the CLOCK gene has been identified in steelworkers, contributing to T2DM risk[72]. Lin E et al [73] found that the interaction between the ARNTL and RORB genes is associated with elevated FG levels. Circadian rhythm is generated by the combined action of a transcription-translation feedback loop involving interacting clock proteins and external environmental factors. Our study identified a five-factor optimal interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) for T2DM in coal miners. In this model, the combination of homozygous mutant genotypes with CICRD ≥ 0.2782 identifies a high-risk group for T2DM among coal miners. A study suggests that in steelworkers, a four-factor combination model (MTNR1A-MTNR1B-CLOCK-shift work) increases the risk of T2DM through complex interactions[72]. Another study based on the U.S. Biobank found that the interaction between morning preference and rs10830963 is associated with the risk of T2DM[39]. This study is the first to construct CICRD using factor analysis, explore the interaction between circadian rhythm-related genes and CICRD, and propose a five-factor interaction model. Gene-environment interactions are complex, and current evidence is insufficient to fully elucidate their mechanisms. However, the results indicate that individuals with high-risk combinations need protective measures, such as reducing shift work intensity, improving sleep quality, and avoiding night-time light exposure, to mitigate circadian rhythm disruptions and aid in T2DM prevention. When selecting shift workers, relevant departments should prioritize individuals with low-risk combinations. Advantages and limitations Research on the combined effects of circadian rhythm-related genes and environmental factors on T2DM is relatively limited both in China and internationally. Our study is the first to construct CICRD based on seven circadian rhythm disorder assessment indicators and propose four- and five-factor interaction models. We explored their interactions with circadian rhythm-related SNPs and their impact on T2DM, providing preliminary population-based evidence of gene-environment interaction mechanisms. Our study has some limitations. First, the study includes a limited number of females, and the association between CICRD and T2DM in coal miners is primarily driven by correlations observed in males. Stratified analysis did not reveal significant associations in females, highlighting the need for future large-scale epidemiological studies focusing on female populations. Second, information on sleep duration, sleep disorders, and nighttime light exposure was self-reported, which may introduce misclassification bias. Third, due to the healthy worker effect, the study population consists of relatively healthy Chinese coal miners, which may limit the generalizability of the conclusions. Fourth, as a cross-sectional study, it cannot establish causal relationships between exposures and outcomes. Finally, the development of T2DM is a prolonged process, and individuals on the verge of developing T2DM might be misclassified as non-cases. This selection bias could distort the results. Conclusion The CICRD, constructed using seven circadian rhythm disorder assessment indicators, captures 79.771% of the original data. An increase in CICRD, along with variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene, was associated with an increased risk of T2DM among coal miners. The high-order interactions in the four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) are significantly associated with an increased risk of T2DM in coal miners. CICRD provides a standard and theoretical basis for assessing circadian rhythm disorder and screening high-risk T2DM populations in coal miners. Additionally, it identifies T2DM susceptibility genes and interactions, showing that high-risk combinations are unsuitable for shift work, providing scientific evidence for the precise prevention of T2DM in coal miners. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. All participants gave informed consent before taking part in this study. This study was conducted by the Declaration of Helsinki and was approved by the Ethics Committee of North China University of Science and Technology (No. 15006). All the methods in this study were carried out in accordance with relevant guidelines and regulations. Clinical trial number Not applicable Consent for publication All the authors approved the manuscript for publication. Funding This work was supported by the National Key Projects of Research and Development of China (No.2016YFC0900605). Author Contribution HY.C: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; the creation of new software used in the workQL.L: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; the creation of new software used in the work; drafted the workJX. Y: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; substantively revised Acknowledgements Not applicable Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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Diabetes, obesity & metabolism. 2015;17 Suppl 1:123-33. doi: 10.1111/dom.12524. Kelly MA, Rees SD, Hydrie MZI, Shera AS, Bellary S, O'Hare JP, et al. Circadian gene variants and susceptibility to type 2 diabetes: a pilot study. PloS one. 2012;7(4):e32670. doi: 10.1371/journal.pone.0032670. Škrlec I, Talapko J, Džijan S, Cesar V, Lazić N, Biology HLJ. The Association between Circadian Clock Gene Polymorphisms and Metabolic Syndrome: A Systematic Review and Meta-Analysis. Biology. 2021;11(1). doi: 10.3390/biology11010020. Yamaguchi M, Uemura H, Arisawa K, Katsuura-Kamano S, Hamajima N, Hishida A, et al. Association between brain-muscle-ARNT-like protein-2 (BMAL2) gene polymorphism and type 2 diabetes mellitus in obese Japanese individuals: A cross-sectional analysis of the Japan Multi-institutional Collaborative Cohort Study. Diabetes research and clinical practice. 2015;110(3):301-8. doi: 10.1016/j.diabres.2015.10.009. Ikeda M, Yu W, Hirai M, Ebisawa T, Honma S, Yoshimura K, et al. cDNA cloning of a novel bHLH-PAS transcription factor superfamily gene, BMAL2: its mRNA expression, subcellular distribution, and chromosomal localization. Biochemical and biophysical research communications. 2000;275(2):493-502. doi: 10.1006/bbrc.2000.3248. Sasaki M, Yoshitane H, Du N-H, Okano T, chemistry YFJTJob. Preferential inhibition of BMAL2-CLOCK activity by PER2 reemphasizes its negative role and a positive role of BMAL2 in the circadian transcription. The Journal of biological chemistry. 2009;284(37):25149-59. doi: 10.1074/jbc.M109.040758. Shi S-q, Ansari TS, McGuinness OP, Wasserman DH, CB CHJJCb. Circadian disruption leads to insulin resistance and obesity. Current biology. 2013;23(5):372-81. doi: 10.1016/j.cub.2013.01.048. Kovanen L, Saarikoski ST, Haukka J, Pirkola S, Aromaa A, Lönnqvist J, et al. Circadian clock gene polymorphisms in alcohol use disorders and alcohol consumption. 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Li-Gao R, Wakil SM, Meyer BF, Dzimiri N, genomics DOM-KJP. Replication of Type 2 diabetes-associated variants in a Saudi Arabian population. Physiological genomics. 2018;50(4):296-7. doi: 10.1152/physiolgenomics.00100.2017. Hu C, Zhang R, Wang C, Wang J, Ma X, Hou X, et al. Variants from GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1 are associated with glucose metabolism in the Chinese. PloS one. 2010;5(11):e15542. doi: 10.1371/journal.pone.0015542. Ramos-Levi A, Barabash A, Valerio J, Torre NGdl, Mendizabal L, Zulueta M, et al. Genetic variants for prediction of gestational diabetes mellitus and modulation of susceptibility by a nutritional intervention based on a Mediterranean diet. Frontiers in endocrinology. 2022;13:1036088. doi: 10.3389/fendo.2022.1036088. Arora GP, Almgren P, Brøns C, Thaman RG, Vaag AA, Groop L, et al. Association between genetic risk variants and glucose intolerance during pregnancy in north Indian women. BMC medical genomics. 2018;11(1):64. doi: 10.1186/s12920-018-0380-8. Wang Y. Association of Occupational Hazard Exposome,Circadian Rhythm-related Genes and TheirInteractions with MetS in ron and Steel Workers. North China University of Science and Technology. 2020. Li Q, Zhang S, Wang H, Wang Z, Zhang X, Wang Y, et al. Association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers: a case-control study. BMC genomics. 2023;24(1):232. doi: 10.1186/s12864-023-09328-y. Lin E, Kuo P-H, Liu Y-L, Yang AC, Kao C-F, one S-JTJP. Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: Evidence from association and interaction analysis. PloS one. 2017;12(3):e0173861. doi: 10.1371/journal.pone.0173861. Additional Declarations No competing interests reported. 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The International Diabetes Federation (IDF) reported that in 2021, there were 536.6\u0026nbsp;million (10.5%) people aged 20 to 79 with diabetes globally, and this number is projected to increase to 783.2\u0026nbsp;million (12.2%) by 2045. Global health expenditure on diabetes was estimated at 966\u0026nbsp;billion dollars in 2021, with projections reaching 1,054\u0026nbsp;billion dollars by 2045 [2]. Diabetes can cause severe complications and reduce life expectancy[3]. As of 2021,there were 140.9\u0026nbsp;million diabetes cases in China [2], with type 2 diabetes mellitus (T2DM) accounting for over 90% of them[4]. The development of T2DM is a complex process involving interactions among multiple factors[5]. With the advance in modern biopsychosocial medical models and the diathesis-stress model [6], recent researches have increasingly focused on the complex interactions of various factors on T2DM.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that shift work is associated with the onset and progression of T2DM[5, 7\u0026ndash;10]. Shift work is a specific type of work schedule, such as rotating shifts, evening shifts, or night shifts[11]. This work schedule requires different workers or teams to accomplish daily tasks lasting 8 to 24 hours through shift handovers. In China, shift work is widely employed in industries such as mining, manufacturing, metallurgy, and services. In 2007, the International Agency for Research on Cancer classified shift work as a Group 2A carcinogen[12]. Shift work disrupts traditional sleep-wake patterns, causing circadian rhythm disturbances that result in various physiological and psychological disorders, which severely affects health[13\u0026ndash;17]. Besides shift work, other factors contributing to circadian rhythm disruption include sleep disorders and insufficient sleep. When sleep and wakefulness occur at inappropriate circadian times, such as waking when the biological clock promotes sleep or sleeping when it promotes wakefulness, it can disrupt circadian rhythms[18, 19]. Moreover, artificial lighting has significantly altered nighttime environments[20]. Exposure to artificial light at night disrupts sleep/wake cycles and circadian functions, affecting nighttime hormone production and secretion, which may induce various diseases[21\u0026ndash;24]. Therefore, in addition to shift work, factors like non-work-related light exposure at night, sleep disorders, and insufficient sleep are also key in studying circadian rhythm disruption.\u003c/p\u003e \u003cp\u003eCircadian rhythms exert great influence on many aspects of mammalian life, including behavior, hormone secretion, temperature regulation, and the molecular control of gene transcription and translation[25\u0026ndash;27]. The circadian timing system in mammals consists of a central clock situated in the suprachiasmatic nucleus (SCN) of the hypothalamus and peripheral clocks situated in various brain regions and tissues throughout the body. The SCN receives direct input from the retina, which allows environmental light to synchronize its rhythm with the 24-hour environmental cycle[28]. Timing signals from the SCN are transmitted to peripheral clocks via neural, endocrine, and temperature cues[29]. The molecular mechanisms of central and peripheral clocks rely on transcription-translation feedback loops, present in nearly every cell in the body[30\u0026ndash;32]. The core of the positive feedback loop in circadian regulation consists of CLOCK (circadian locomotor output cycles kaput), BMAL1 (brain and muscle ARNT-like protein 1), and NPAS2 (neuronal PAS domain protein 2). The dimers formed by these proteins, BMAL1/CLOCK and BMAL1/NPAS2, rhythmically drive the expression of the period genes Period 1 (PER1), Period 2 (PER2), and Period 3 (PER3), as well as the cryptochrome genes Cryptochrome 1 (CRY1) and Cryptochrome 2 (CRY2). IIn contrast, PER and CRY proteins form heterodimers that inhibit their own transcription by interacting with the CLOCK/BMAL complex[33, 34]. Therefore, circadian rhythm-related genes are essential for maintaining the roughly 24-hour oscillatory rhythm.\u003c/p\u003e \u003cp\u003eIn China, the coal industry is one of the major economic pillars and a key source of national energy, holding a significant position. Consequently, the quality of life of coal miners is a major concern in China's occupational health field. With a large workforce and widespread shift work, circadian rhythm disruption is common among coal miners. This study focuses on circadian rhythm disruption, investigating workers from the Xingtai coal mining site in the Beijing-Tianjin-Hebei region as part of a health effects cohort study. This study will explore the roles of environmental and genetic factors related to circadian rhythms, using biotechnological methods from both \"macro\" and \"micro\" perspectives, with the aim to provide scientific evidence for T2DM prevention in coal miners.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eParticipants in this analysis were drawn from the baseline cohort of the Beijing-Tianjin-Hebei Region Occupational Health Effects Cohort Study, initiated by the Ministry of Science and Technology of China. The core aim of Beijing-Tianjin-Hebei Region Occupational Health Effects Cohort Study was to explore impact of occupational hazards on human health. This study was based on an epidemiological survey of 4,440 workers who underwent occupational health examinations at the Xingtai coal mining site between August 2017 and August 2018. Data were collected from questionnaires, physical examinations, laboratory tests, and assessments of occupational hazards. After excluding individuals who did not complete the questionnaire (246), had less than one year of work experience (22), lacked blood biochemical data (12), lacked physical examination data (27), or had severely incorrect or missing questionnaire information (63), a total of 4,070 Han Chinese coal miners were included in this study. This cross-sectional study revealed a significant association between comprehensive indicators of circadian rhythm disorder (CICRD) and T2DM risk in male workers, whereas no significant association was observed in female participants, potentially due to the limited sample size (Table S2). Therefore, based on the inclusion and exclusion criteria, SAS 9.4 software was used to randomly select study subjects from 3,878 male coal miners for the case-control study. Due to limited project funding for genotyping all participants in the cross-sectional survey, we employed a case-control study based on baseline data to explore the interaction between CICRD and circadian rhythm-related gene polymorphisms (SNPs) and their association with T2DM. A total of 424 cases and 464 controls were selected as study participants.\u003c/p\u003e \u003cp\u003eInclusion criteria for the case group: ① Han Chinese male coal miners diagnosed with T2DM during the survey; ② a minimum of 3 years of work experience; ③ signed informed consent.\u003c/p\u003e \u003cp\u003eExclusion criteria for the case group: ① Individuals with severe missing information on shift work, covariates, or blood biochemical data; ② Those taking antiretroviral drugs or diagnosed with cancer or thyroid disease; ③ Individuals with other diseases related to the genes selected in this study.\u003c/p\u003e \u003cp\u003eInclusion criteria for the control group were: ① Han Chinese male coal miners without T2DM, diagnosed using the same criteria as the case group; ② Age-matched to the case group within ± 5 years; ③ A minimum of 3 years of work experience; ④ Comparable residential conditions; ⑤ Signed informed consent.\u003c/p\u003e \u003cp\u003eExclusion criteria for the control group: ① Individuals with severe missing information on shift work, covariates, or blood biochemical data; ② Those taking antiretroviral medications or diagnosed with cancer or thyroid disorders; ③ Individuals with other diseases related to the genes selected in this study.\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}n=\\frac{{\\left[{z}_{\\propto\\:}\\sqrt{2\\stackrel{-}{p}\\left(1-\\stackrel{-}{p}\\right)}+{z}_{\\beta\\:}\\sqrt{{p}_{1}\\left(1-{p}_{1}\\right)+{p}_{0}\\left(1-{p}_{0}\\right)}\\right]}^{2}}{{\\left({p}_{1}-{p}_{0}\\right)}^{2}}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}n=\\frac{{\\left[{z}_{\\propto\\:}\\sqrt{2\\stackrel{-}{p}\\left(1-\\stackrel{-}{p}\\right)}+{z}_{\\beta\\:}\\sqrt{{p}_{1}\\left(1-{p}_{1}\\right)+{p}_{0}\\left(1-{p}_{0}\\right)}\\right]}^{2}}{{\\left({p}_{1}-{p}_{0}\\right)}^{2}}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\stackrel{-}{p}=\\frac{\\left({p}_{1}+{p}_{0}\\right)}{2}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{p}_{1}=\\frac{\\left(OR\\times\\:{p}_{0}\\right)}{\\left(1-{p}_{0}+OR\\times\\:{p}_{0}\\right)}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eIn the formula:\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e—Exposure proportion of the research factor in the control group\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e—Exposure proportion of the research factor in the case group.\u003c/p\u003e \u003cp\u003eThe hypothesis test was conducted with a Type I error rate \u003cem\u003eα\u003c/em\u003e = 0.05. In this study, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e represents the minor allele frequency (MAF) set at 0.101. The expected odds ratio (OR) ranges from 1 to 2. The test power is denoted as Power = 1-\u003cem\u003eβ.\u003c/em\u003e A total of 424 cases and 464 controls were included in the study. A sample size and power relationship plot was generated using the PS-Power and Sample Size Calculation software. As shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, the power increases with the sample size. When the number of participants in both the case and control groups reaches at least 424, the power reaches 0.932, exceeding 0.90.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of T2DM and CICRD\u003c/h2\u003e \u003cp\u003e According to the China T2DM Prevention and Control Guideline (2020 Edition)[2], T2DM is defined as fasting blood glucose ≥ 7.0 mmol/L, random blood glucose ≥ 11.1 mmol/L, or a previous hospital diagnosis of T2DM. In our study, seven basic evaluation indicators from three categories—shift work, light exposure, and sleep—were selected to construct the CICRD. These include the duration of shift work, cumulative number of night shifts, cumulative duration of night shifts, average frequency of night shifts, nighttime light exposure, insomnia status, and average sleep duration. The data was collected through face-to-face interviews and then checked against the company's records. Detailed definitions are presented in the supplementary materials.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition and classification of covariates\u003c/h3\u003e\n\u003cp\u003eThis study included basic demographic characteristics (gender, age, education level, marital status, and family income), lifestyle behaviors (smoking, drinking, physical activity, and diet), medical history (central obesity, hypertension, liver dysfunction, dyslipidemia, and renal dysfunction), and occupational exposures (dust, heat, CO, and noise). Detailed definitions are presented in the supplementary materials.\u003c/p\u003e\n\u003ch3\u003eGenetic testing of MTNR1B, BMAL1, and BMAL2\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from whole blood using a genomic DNA extraction kit (Genesky). DNA concentration and purity were determined using a spectrophotometer after it was fully dissolved. Tag SNPs related to circadian rhythm genes were selected if they had a minor allele frequency (MAF) ≥ 10% in the Chinese population and a linkage disequilibrium (LD) coefficient greater than 0.8 with other SNPs in the region. Tag SNPs were selected using the Tagger algorithm in the genetic haplotype analysis software HaploView 4.2. If multiple SNPs met the criteria, priority was given to those identified by genome-wide association studies (GWAS) as linked to T2DM. Subsequently, SNPs linked to elevated FG levels and insulin resistance were selected. After comprehensive analysis, a total of 6 SNPs were selected: rs10830963 and rs1387153 in the melatonin receptor 1B (MTNR1B) gene; rs11022775 and rs7950226 in the BMAL1 gene; rs7958822 in the brain and muscle ARNT-like protein 2 (BMAL2) gene; and rs11605924 in the CRY2 gene. The specific detection process is detailed in the Supplementary materials.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of CICRD\u003c/h2\u003e \u003cp\u003eWe confirmed the data’s suitability for factor analysis using the KMO test (0.774) and Bartlett’s sphericity test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). The CICRD was developed based on seven indicators, including shift duration, night light exposure, and sleep status. Principal component analysis identified three factors: F1 (shift Work Factor), F2 (sleep factor), and F3 (light exposure factor). Each factor’s weight was assigned according to its explanatory variance, and the final CICRD score was calculated through normalization. The score explained 79.711% of the original data and provided a quantitative tool for exploring the relationship between circadian rhythm disruption and type 2 diabetes. The development process is described in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were described as mean ± standard deviation (SD) or as median with interquartile range (IQR). The Kolmogorov-Smirnov test was used to assess the normality. If data met the criteria for parametric tests, a t-test was used for comparisons between groups; otherwise, the Mann-Whitney U test was applied. Categorical data were described as percentage, and comparisons between groups were conducted using chi-square test, Fisher’s exact test, or Cochran-Armitage trend test. Logistic regression model was performed to explore the association between influencing factors and T2DM. Factor analysis was used to construct the CICRD, and a restricted cubic spline (RCS) function was applied to fit the dose-response relationship between CICRD and T2DM among coal miners, with four knots at the 5th, 35th, 65th, and 95th percentiles. The Hardy-Weinberg equilibrium (HWE) test was used to determine whether the control group was a random sample from the target population. Logistic regression combined with SNPStats software was used to analyze the association of target SNPs with T2DM under codominant, dominant, recessive, over dominant, and additive models among male coal miners. The optimal model was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The median was used to classify CICRD into \"\u0026lt;0.2782\" and \"≥0.2782\". The CICRD and the dominant model of target SNPs were cross-classified, and logistic regression with multiplicative interaction and the Andersson additive interaction model were used to analyze gene-gene and gene-environment interactions. GMDR 0.9 was used for higher-order interaction analysis to construct optimal gene-gene and gene-environment interaction models. A two-tailed test with a significance level of α = 0.05 was applied.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eDemographic characteristics of participants in cross-sectional and case-control studies\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 4070 participants were included in this study, comprising 3878 males (95.28%) and 192 females (4.72%). The mean age of male workers (39.41 ± 8.62 years) was higher than that of female workers (36.52 ± 8.81 years). The overall prevalence of T2DM was 16.5%, with a higher prevalence among male workers than female workers (16.7% vs. 12.5%). The demographic characteristics of the case and control groups are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The CICRD score of the case group (0.33 ± 0.15) was higher than that of the control group (0.29 ± 0.14). Significant differences were observed in terms of age, income, smoking status, dyslipidemia, liver dysfunction, hypertension, central obesity, family history of diabetes, and noise exposure between the two groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the subjects in the cross-sectional study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population (n = 4070)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale (n = 3878)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (n = 192)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.27 ± 8.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.41 ± 8.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.52 ± 8.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASH score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.29 ± 2.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.28 ± 2.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.56 ± 3.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer capita monthly household income (Yuan/person)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1887.00 (1509.60,2516.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1887.00 (1509.60,2516.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1943.50 (1687.50,2516.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaverage sleep duration (h/d)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.37 ± 1.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.36 ± 1.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.56 ± 1.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.020\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eduration of shift work (year)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.34 (0.37, 15.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.42 (0.00, 14.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.51 (2.08, 24.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecumulative number of night shifts (night)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e494 (21, 1171)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e494 (0, 1134)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772 (253, 2229)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecumulative duration of night shifts (h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3621 (131,8643)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3595 (0,8080)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6178 (2029,18033)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaverage frequency of night shifts (nights/month)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.80 (3.04, 7.60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80 (0.00, 7.60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.60 (6.08, 10.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (3.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (13.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3905 (95.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3739 (96.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (86.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (1.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (1.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2922 (71.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2822 (72.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (52.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1099 (27.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1008 (26.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (47.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1607 (39.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1440 (37.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (87.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver smoking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (7.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (7.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2165 (53.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2141 (55.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (12.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e953 (23.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e775 (20.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (92.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver drinking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (5.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2925 (71.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2911 (75.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (7.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt taste preference, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.575\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e711 (17.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e677 (17.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (17.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1955 (48.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1869 (48.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (44.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1404 (34.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1332 (34.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (37.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einsomnia status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.064\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo sleep disorder\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2776 (68.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2655 (68.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (63.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected insomnia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861 (21.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e820 (21.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (21.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433 (10.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403 (10.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (15.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity level, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e557 (13.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e414 (10.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (74.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1647 (40.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1631 (42.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (8.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1866 (45.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1833 (47.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (17.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily History of Diabetes, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3565 (87.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3409 (87.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (81.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (12.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (12.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (18.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Obesity, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3015 (74.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2849 (73.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (86.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1055 (25.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1029 (26.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (13.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3038 (74.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2872 (74.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (86.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1032 (25.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1006 (25.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (13.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal liver function, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3268 (80.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3089 (79.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (93.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e802 (19.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e789 (20.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (6.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal Kidney Function, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3869 (95.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3725 (96.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (75.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (4.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (3.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (25.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2627 (64.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2457 (63.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (88.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1443 (35.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1421 (36.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (11.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3397 (83.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3229 (83.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (87.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e673 (16.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e649 (16.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (12.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeat exposure, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (9.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317 (8.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (31.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3693 (90.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3561 (91.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (68.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoise exposure, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1878 (46.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1849 (47.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (15.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2192 (53.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2029 (52.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (84.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDust exposure, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1238 (30.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1196 (30.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (21.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2832 (69.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2682 (69.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (78.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO exposure, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e939 (23.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e861 (22.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (40.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3131 (76.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3017 (77.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (59.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enighttime light exposure, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.017\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDarkest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1259 (30.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1203 (31.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (29.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2047 (50.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1962 (50.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (44.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e764 (18.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e713 (18.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (26.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift work, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1006 (24.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e986 (25.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (10.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3064 (75.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2892 (74.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (89.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e indicates that the Mann-Whitney U test was used due to non-compliance with parametric test assumptions; \u003csup\u003eb\u003c/sup\u003e denotes the result obtained by the Cochran-Armitage trend test; \u003csup\u003ec\u003c/sup\u003e refers to the Fisher’s exact test. Continuous variables are presented as mean ± SD or median (lower quartile, upper quartile).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eAnalysis of association between CICRD and T2DM\u003c/h2\u003e\u003cp\u003eFigure S2 shows a positive linear relationship between CICRD and the risk of T2DM among coal miners (\u003cem\u003eP\u003c/em\u003e \u003csub\u003efor overall association\u003c/sub\u003e \u0026lt;0.001; \u003cem\u003eP\u003c/em\u003e \u003csub\u003efor non−linearity\u003c/sub\u003e = 0.524). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that, after adjusting for confounding factors (Model 3), coal miners with CICRD in the ranges \" 0.2782– \" and \" ≥0.3848 \" have 1.43 folds (95% \u003cem\u003eCI\u003c/em\u003e: 1.07–1.90) and 2.43 folds (95% \u003cem\u003eCI\u003c/em\u003e: 1.80–3.21) higher T2DM risks, respectively, compared to the CICRD group less than \"0.1839\". The trend test results showed that for each one-level increase in CICRD, the risk of T2DM increased by 35% (\u003cem\u003eOR\u003c/em\u003e = 1.35, 95% \u003cem\u003eCI\u003c/em\u003e: 1.23–1.48). In addition, for per one standard deviation (0.1468) increase in CICRD, the risk of T2DM among coal miners increases by 41% (\u003cem\u003eOR\u003c/em\u003e = 1.41, 95% \u003cem\u003eCI\u003c/em\u003e: 1.28–1.57). In the sensitivity analysis, we examined the association between CICRD and T2DM across different population characteristics. The results and trends in Table S2 are consistent with the main findings.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of CICRD and T2DM in coal miners\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCICRD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e, (%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 0.1839\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1017 (24.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1839~\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1017 (24.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.82 ~ 1.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.85 ~ 1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.78 ~ 1.43)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.2782~\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1019 (25.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.10 ~ 1.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51 (1.14 ~ 2.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43 (1.07 ~ 1.90)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 0.3848\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1017 (24.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26 (1.77 ~ 2.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38 (1.79 ~ 3.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43 (1.80 ~ 3.21)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest for trend\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.22 ~ 1.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.22 ~ 1.47)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35 (1.23 ~ 1.48)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer \u003cem\u003eSD\u003c/em\u003e increase\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.28 ~ 1.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.26 ~ 1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 (1.28 ~ 1.57)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: adjusted for age and gender;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: further adjusted for marital status, family income per capita, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, abnormal liver function, abnormal renal function, dyslipidemia, hypertension, and family history of diabetes;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3: further adjusted for occupational hazards (CO, noise, dust and heat);\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD: standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eCorrelation and interaction between targeted SNPs and T2DM in coal miners\u003c/h2\u003e\u003cp\u003eTable S3 shows that, among the five genetic models, the dominant model is the optimal one for rs10830963 (AIC = 1225.8, BIC = 1235.4), rs7958822 (AIC = 1227.5, BIC = 1237.1), and rs11605924 (AIC = 1229.5, BIC = 1239.1). After adjusting for confounding factors, coal miners with CG or GG genotypes at rs10830963 have a 1.50 times higher risk of T2DM compared to those with the CC genotype (95% CI: 1.14 ~ 1.98); coal miners with GA or AA genotypes at rs7958822 have a 1.43 times higher risk of T2DM compared to those with the GG genotype (95% CI: 1.09–1.86); and coal miners with AC or CC genotypes at rs11605924 have a 1.35 times higher risk of T2DM compared to those with the AA genotype (95% CI: 1.03–1.76), under the dominant model. Additionally, the association of rs1387153 in the MTNR1B gene, rs11022775 in the BMAL1 gene, and rs7950226 in the BMAL1 gene with T2DM in coal miners is presented in Table S4.\u003c/p\u003e\u003cp\u003eBased on the prior results, three susceptibility gene loci for T2DM in coal miners were selected. Cross-classification was performed according to their respective dominant models to further analyze the interactions between these loci and their association with T2DM in coal miners. Table S5 shows that the risk for individuals with CG + GG genotypes at rs10830963 combined with the GA + AA genotypes at rs7958822 is 2.71 times higher (95% \u003cem\u003eCI\u003c/em\u003e: 1.77–4.15) compared to those with the CC genotype at rs10830963 combined with the GG genotype at rs7958822. Additionally, the risk of disease for individuals with the CG + GG genotypes at rs10830963 combined with AC + CC genotypes at rs11605924 is 2.64 times higher (95% \u003cem\u003eCI\u003c/em\u003e: 1.70–4.11) compared to those with the CC genotype at rs10830963 combined with the AA genotype at rs11605924. The risk of T2DM for coal miners with the GA + AA genotypes at rs7958822 combined with AC + CC genotypes at rs11605924 is 2.77 times higher (95% \u003cem\u003eCI\u003c/em\u003e: 1.80–4.27) compared to those with the GG genotypes at rs7958822 combined with the AA genotype at rs11605924.\u003c/p\u003e\u003cp\u003eThe relative excess risk due to interaction (RERI) for the additive interaction between rs10830963 and rs11605924 is 0.21 (95% \u003cem\u003eCI\u003c/em\u003e: 0.11–0.31), and the attributable proportion due to interaction (AP) is 0.46 (95% \u003cem\u003eCI\u003c/em\u003e: 0.16–0.75). For the additive interaction between rs7958822 and rs11605924, the RERI is 1.55 (95% \u003cem\u003eCI\u003c/em\u003e: 0.51–2.60), and the AP is 0.56 (95% \u003cem\u003eCI\u003c/em\u003e: 0.32–0.80). The multiplicative interaction between rs7958822 and rs11605924 is also statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table S5). Additionally, no statistically significant multiplicative or additive interactions were found between rs10830963, rs7958822, rs11605924, and other genetic loci (Table S6).\u003c/p\u003e\u003cp\u003eFurthermore, gene-gene interactions were analyzed using GMDR. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that only the four-factor (rs10830963-rs1387153-rs7958822-rs11605924) and two-factor (rs7958822-rs11605924) interaction effects are statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Compared to the two-factor model, the four-factor model has higher accuracy in both the training and validation sets, with a 12-fold cross-validation consistency of 91.67% (11/12). Therefore, the four-factor model was selected as the best gene-gene higher-order interaction model. In this model, the high-risk group is indicated in dark gray. For example, the combination of GG genotype at rs7958822 with CG genotype at rs10830963, AA genotype at rs11605924, and CC genotype at rs1387153 is classified as the high-risk group (Figure S3). The four-factor model was not statistically significant in the validation set. However, in the full dataset, coal miners with homozygous mutations in the gene had a 3.10 times higher risk of T2DM compared to those with the wild-type genotype (95% \u003cem\u003eCI\u003c/em\u003e: 2.01–4.77) (Table S7).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene-Gene interaction models identified by GMDR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set accuracy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set accuracy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-validation consistency\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5607\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5189\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.0730)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7958822- rs11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5780\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5413\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.0193)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs7958822- rs11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.0730)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs1387153- rs7958822- rs11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6351\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5804\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (0.0032)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs1387153- rs79588226- rs7958822- rs11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6683\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5157\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0.1938)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs1387153- rs11022775- rs79588226- rs7958822- rs11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6954\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5305\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.0730)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eAnalysis of interaction between CICRD and various gene loci\u003c/h2\u003e\u003cp\u003eWe divided the constructed CICRD into two categories based on the median: “\u0026lt;0.2782” and “≥0.2782,” and performed cross-classification with the target SNPs. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that compared to the low-risk genotypes of the four SNPs (rs10830963, rs1387153, rs7958822, rs11605924) combined with CICRD \u0026lt; 0.2782, the high-risk genotypes of these SNPs combined with CICRD ≥ 0.2782 increased the risk of T2DM in coal miners. Specifically, the risk of T2DM is 2.67 times higher among the group with CICRD ≥ 0.2782 combined with the rs10830963 CG + GG genotypes, compared to the group with CICRD \u0026lt; 0.2782 combined with the CC genotype. For CICRD ≥ 0.2782 combined with the rs1387153 CT + TT genotypes, the risk is 1.93 times higher compared to CICRD \u0026lt; 0.2782 combined with the CC genotype. For CICRD ≥ 0.2782 combined with the rs7958822 GA + AA genotypes, the risk is 2.29 times higher compared to CICRD \u0026lt; 0.2782 combined with the GG genotype. Lastly, for CICRD ≥ 0.2782 combined with the rs11605924 AC + CC genotypes, the risk is 2.30 times higher compared to CICRD \u0026lt; 0.2782 combined with the AA genotype. Additionally, with each one-level increase in the combined categories of CICRD and the five SNPs (rs10830963, rs1387153, rs11022775, rs7958822, rs11605924), the risk of T2DM increases by 29%, 22%, 18%, 28%, and 28%, respectively. In this study, no statistically significant additive or multiplicative interactions between CICRD and the individual gene loci were found.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiplicative and additive interactions of shift work and genes on T2DM Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003eMultiplicative Interaction\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers10830963\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCG + GG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.81 (1.19 ~ 2.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.16 (1.37 ~ 3.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.04 (1.31 ~ 3.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00 (1.22 ~ 3.30)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCG + GG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.66 (1.77 ~ 3.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67 (1.69 ~ 4.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32 (1.17 ~ 1.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29 (1.13 ~ 1.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20 (-1.25 ~ 0.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.50 (-1.76 ~ 0.75)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07 (-0.47 ~ 0.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.19 (-0.66 ~ 0.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers1387153\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT + TT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15 (0.77 ~ 1.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48 (0.94 ~ 2.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68 (1.09 ~ 2.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76 (1.08 ~ 2.84)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT + TT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.27 ~ 2.77)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.93 (1.24 ~ 3.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25 (1.11 ~ 1.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22 (1.06 ~ 1.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04 (-0.74 ~ 0.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30 (-1.27 ~ 0.66)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02 (-0.39 ~ 0.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.16 (-0.65 ~ 0.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers11022775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT + TT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16 (0.71 ~ 1.90)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.57 ~ 1.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64 (1.22 ~ 2.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44 (1.03 ~ 2.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT + TT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.96 (1.23 ~ 3.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.52 (0.91 ~ 2.53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27 (1.12 ~ 1.43)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18 (1.03 ~ 1.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16 (-0.87 ~ 1.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09 (-0.83 ~ 1.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08 (-0.42 ~ 0.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06 (-0.53 ~ 0.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers7950226\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA + GG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74 (0.49 ~ 1.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.42 ~ 1.04)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38 (0.88 ~ 2.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (0.63 ~ 1.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA + GG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34 (0.90 ~ 2.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17 (0.75 ~ 1.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (1.06 ~ 1.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14 (0.99 ~ 1.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23 (-0.38 ~ 0.84)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46 (-0.06 ~ 0.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17 (-0.29 ~ 0.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40 (-0.09 ~ 0.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers7958822\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA + AA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44 (0.97 ~ 2.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.77 (1.14 ~ 2.75)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67 (1.16 ~ 2.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54 (1.02 ~ 2.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA + AA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.25 (1.55 ~ 3.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.29 (1.52 ~ 3.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30 (1.15 ~ 1.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (1.12 ~ 1.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 (-0.73 ~ 1.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (-1.04 ~ 1.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06 (-0.32 ~ 0.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01 (-0.45 ~ 0.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ers11605924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt; 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAC + CC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33 (0.90 ~ 1.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47 (0.96 ~ 2.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e≥ 0.2782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63 (1.14 ~ 2.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44 (0.96 ~ 2.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAC + CC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.30 (1.56 ~ 3.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.30 (1.49 ~ 3.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31 (1.16 ~ 1.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (1.12 ~ 1.47)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34 (-0.52 ~ 1.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39 (-0.54 ~ 1.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15 (-0.21 ~ 0.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17 (-0.21 ~ 0.55)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Adjusted for age, marital status, per capita monthly household income, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, liver function abnormalities, kidney function abnormalities, dyslipidemia, hypertension, family history of diabetes, CICRD, and occupational hazards (CO, noise, dust, and heat)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAdditionally, we analyzed the interactions between CICRD and genes using GMDR (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which indicated only the four-factor (rs10830963-rs7958822-rs11605924-CICRD) and five-factor (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) interaction models are statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Compared to the four-factor model, the five-factor model showed higher accuracy in both the training and validation sets, with 12-fold cross-validation consistency reaching 100% (12/12). Therefore, the five-factor model was selected as the best gene-gene higher-order interaction model, with high-risk combinations shown in Figure S4. In the five-factor interaction model, the risk of T2DM for coal miners with homozygous mutant genotypes combined with CICRD ≥ 0.2782 is 7.38 times (95% \u003cem\u003eCI\u003c/em\u003e: 4.84–11.25) higher than for those with homozygous wild-type genotypes combined with CICRD \u0026lt; 0.2782 (Table S8).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCICRD-Target SNPs interaction models identified by GMDR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set accuracy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set accuracy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-validation consistency\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5620\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5438\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.0193)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7950226- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5703\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5421\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0.1938)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7958822- rs11605924- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5953\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5271\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.3872)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs7958822- rs11605924- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6260\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.0193)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs7950226- rs7958822- rs11605924- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6768\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (0.0032)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs1387153- rs7950226- rs7958822- rs11605924- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7073\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5363\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0.1938)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10830963- rs1387153- rs11022775- rs7950226- rs7958822- rs11605924- CICRD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7353\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5354\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.3872)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Adjusted for age, marital status, per capita monthly household income, education level, smoking status, drinking status, salt taste preference, physical activity level, DASH score, liver function abnormalities, kidney function abnormalities, dyslipidemia, hypertension, family history of diabetes, CICRD, and occupational hazards (CO, noise, dust, and heat). Cross-Validation: Refers to randomly dividing the data into 12 parts, using one part as the validation set and the remaining 11 parts as the training set, followed by training and validating the model to ensure balanced testing.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, the CICRD constructed using factor analysis based on seven indicators captured 79.771% of the information from the original data. The CICRD is significantly associated with the risk of T2DM in coal miners. In addition, the MTNR1B gene rs10830963, BMAL2 gene rs7958822, and CRY2 gene rs11605924 are also associated with the risk of T2DM in coal miners. Notably, both the four-factor interaction model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) are significantly associated with the risk of T2DM in coal miners.\u003c/p\u003e \u003cp\u003eIn our study, the assessment indicators related to circadian rhythm disorder include number of years working night shifts, cumulative number of night shifts, total duration of night shifts, average frequency of night shifts, nighttime light exposure, insomnia status, and average sleep duration. However, constructing new indicators involves key challenges with weight allocation. To preserve the original information and minimize subjectivity, this process must be approached carefully. This study used factor analysis to extract common factors from numerous original variables and condense them. Based on the field database, we selected seven fundamental indicators related to shift work, night-time light exposure, and sleep to construct the CICRD. We aimed to explore the association between circadian rhythm disorder and T2DM in coal miners from a comprehensive perspective. The results of this study indicate a positive linear association between CICRD and T2DM in coal miners. The results suggest that reducing the intensity and frequency of shift work, improving sleep quality, and avoiding sleep deprivation and night-time light exposure can help reduce circadian rhythm disorder, which is beneficial for preventing T2DM in coal miners. CICRD provides a basis and standard for assessing circadian rhythm disruption in the coal industry and potentially other industries, while also offering scientific evidence for screening high-risk T2DM populations. Thus, the development, application, and extrapolation of CICRD have significant scientific and public health value.\u003c/p\u003e \u003cp\u003eOur study suggests that the G allele of the MTNR1B gene at rs10830963 is a risk allele for type 2 diabetes mellitus (T2DM) in coal miners, while rs1387153 showed no statistically significant association with T2DM in this population. MTNR1B belongs to the G protein-coupled receptor family involved in insulin secretion and encodes the melatonin receptor 1B[35]. Previous studies have reported several loci within the MTNR1B gene that are associated with fasting glucose (FG). In our study, we selected two loci of interest to both domestic and international researchers. Some studies have indicated that these two loci in the MTNR1B gene (rs1387153 and rs10830963) are associated with T2DM, elevated FG levels, and impaired insulin secretion[35\u0026ndash;39]. These loci may increase the risk of T2DM by disrupting G protein activation[40]. A study based on a European population found that rs1387153 is associated with elevated FG, with the T allele being a risk factor for elevated FG levels and T2DM[37]. However, a study based on a Chinese Han population found that the T allele was associated only with FG and had no significant association with T2DM[38]. Our results also show that the T allele is not significantly associated with T2DM. A meta-analysis confirmed this finding, with stratified analysis by ethnicity showing a marginal association between rs1387153 and T2DM only in Caucasian populations, while no association was observed in Southeast Asian and South Asian populations[41]. The rs10830963 locus is located in the only intron of MTNR1B. A meta-analysis based on European populations showed that the G allele of rs10830963 is associated with elevated fasting glucose (FG) levels and decreased pancreatic β-cell function[36]. Additionally, the G allele of rs10830963 has been identified as a risk factor for elevated fasting glucose (FG) and type 2 diabetes mellitus (T2DM) in various populations, including Swedes[35], European biobank cohorts[39], Bosnians and Herzegovinians[42], American Whites[43], and Han Chinese[38, 44]. These findings are consistent with our study's conclusions. Melatonin, a cyclic hormone primarily secreted by the pineal gland, regulates circadian rhythms with elevated levels at night and decreased levels during the day[45]. Some researchers have suggested that melatonin is associated with T2DM and insulin levels[35]. Extensive animal studies have demonstrated that melatonin reduces insulin levels[46\u0026ndash;48] and impairs glucose tolerance[49, 50] in rats. These biological mechanisms may help explain the observed associations.\u003c/p\u003e \u003cp\u003eAt the molecular level, circadian rhythms are regulated by oscillatory circuits of transcription factor expression, with BMAL1 being a key transcriptional regulator in this system [51]. BMAL1 forms heterodimers with CLOCK, rhythmically inducing the transcription of other circadian clock genes[27]. Studies have shown that pancreatic islets exhibit circadian oscillations of CLOCK and BMAL1 transcription factors. Disruption of these clock components in mouse islets leads to hypoinsulinemia and diabetes[52]. Similar studies have indicated that variants in the BMAL1 gene are associated with β-cell dysfunction, glucose intolerance, and T2DM in humans[53\u0026ndash;55]. However, previous research on the association between BMAL1 gene variants rs11022775 and rs7950226 and T2DM is limited, with inconsistent findings. A study based on a UK family cohort indicated that haplotypes formed by BMAL1 gene variants rs11022775 and rs7950226 are associated with T2DM, with rs11022775 showing a stronger independent effect[55]. Another study found that haplotypes containing the T allele of rs11022775 and the A allele of rs7950226 are associated with increased T2DM risk, largely driven by rs11022775[56]. A meta-analysis of 13 independent studies involving 13,781 participants found that the BMAL1 gene polymorphism rs7950226 is associated with a reduced risk of metabolic syndrome in the general population, suggesting the A allele is a risk allele[57]. However, our study found no statistically significant association between BMAL1 gene polymorphisms rs11022775 and rs7950226 and T2DM in coal miners across all genetic models, consistent with a cross-sectional study conducted in an obese Japanese population.[58]. Additionally, research on the association between BMAL1 gene polymorphisms rs11022775 and rs7950226 and gestational diabetes provides valuable insights. One study found a significant association between the A allele of rs7950226 and the C allele of rs11022775 and increased risk of gestational diabetes. Furthermore, two haplotypes\u0026mdash;comprising the G allele of rs7950226 and the C allele of rs11022775, as well as the A allele of rs7950226 and the C allele of rs11022775\u0026mdash;were linked to increased susceptibility to gestational diabetes[53]. This suggests that the potential association between the polymorphisms rs11022775 and rs7950226 and T2DM, as well as related traits, cannot be excluded. Further studies with larger sample sizes across diverse populations are needed to explore these relationships more comprehensively.\u003c/p\u003e \u003cp\u003eOur findings suggest that the A allele of the BMAL2 gene rs7958822 variant is a risk factor for T2DM. BMAL2, another key transcriptional regulator[59], plays a crucial role in the core feedback loop of the circadian timing system[60]. Animal studies suggest that BMAL2 gene expression may influence glucose and insulin levels[61]. Previous research on the association between BMAL2 and T2DM is limited. Several SNPs of this gene have been linked to psychiatric disorders [62, 63], and in European populations, BMAL2 has shown no association with metabolic syndrome or its components [64]. However, a study based on an Asian population found a significant association between the BMAL2 gene rs7958822 variant and T2DM in both obese men and women, indicating that the A allele may be a risk factor for T2DM[58]. This aligns with the T2DM risk alleles identified in our study; however, the observed association is not limited to obese individuals. Genetic susceptibility may be related to ethnicity. This association study, conducted in a Chinese Han population, indicates that the A allele of rs7958822 is a risk allele for T2DM, even after adjusting for confounding factors like obesity. Future research should focus on the association between the BMAL2 gene and T2DM across different ethnic groups, rather than limiting studies to psychiatric disorders.\u003c/p\u003e \u003cp\u003eThe core clock is driven by two interlinked sets of genes. These gene sets encode transcription factors with either activating or repressing effects. BMAL1 and CLOCK genes encode transcriptional activators, while PER1, PER2, PER3, CRY1, and CRY2 genes encode transcriptional repressors. The results of this study indicate that the rs11605924 locus of the CRY2 gene is associated with an increased risk of T2DM in coal miners, suggesting that the C allele is a risk allele for T2DM. A large-scale genome-wide meta-analysis of data from european origin populations identified nine new loci affecting FG, including CRY2[65]. A later study in a general Chinese population found that an increase in the A allele at the rs11605924 locus of the CRY2 gene is significantly associated with impaired FG and T2DM [66], which contrasts with the conclusions of our study. However, some evidence supports our study's conclusions. A study based on a Saudi Arabian population reported that the A allele at the rs11605924 locus is protective against T2DM[67]. Another study based on a Chinese population found that the C allele at the rs11605924 locus is a risk allele for T2DM[68]. Studies in North Indian and American populations, the A allele of rs11605924 is associated with a reduced risk of gestational diabetes, which further supports the conclusions of our study to some extent[69, 70].\u003c/p\u003e \u003cp\u003eOur study used log-linear models and GMDR to explore gene-gene interactions related to circadian rhythm. The combination of the CG\u0026thinsp;+\u0026thinsp;GG genotypes at rs10830963 with the AC\u0026thinsp;+\u0026thinsp;CC genotypes at rs11605924, as well as the GA\u0026thinsp;+\u0026thinsp;AA genotypes at rs7958822 with the AC\u0026thinsp;+\u0026thinsp;CC genotypes at rs11605924, represent high-risk combinations for T2DM. The combination of the CG\u0026thinsp;+\u0026thinsp;GG genotypes at the rs10830963 locus with the AC\u0026thinsp;+\u0026thinsp;CC genotypes at the rs11605924 locus, as well as the GA\u0026thinsp;+\u0026thinsp;AA genotypes at the rs7958822 locus with the AC\u0026thinsp;+\u0026thinsp;CC genotypes at the rs11605924 locus, represent high-risk combinations for T2DM. Additionally, this study is the first to report a four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) with significant interactions for T2DM, with certain genotype combinations being associated with a higher risk of developing T2DM. Therefore, it is important to focus not only on individual genes but also on genotype combinations, as they play a significant role in screening high-risk populations. The results of this study suggest that workers with these genotype combinations are not suited for shift work. Previous studies have reported an association between high-dimensional interactions among rs6850524, rs10830963, and rs1387153 loci in the CLOCK gene and metabolic syndrome[71]. Additionally, an interaction between the rs2119882 locus in the MTNR1A gene and the rs1801260 locus in the CLOCK gene has been identified in steelworkers, contributing to T2DM risk[72]. Lin E et al [73] found that the interaction between the ARNTL and RORB genes is associated with elevated FG levels.\u003c/p\u003e \u003cp\u003eCircadian rhythm is generated by the combined action of a transcription-translation feedback loop involving interacting clock proteins and external environmental factors. Our study identified a five-factor optimal interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) for T2DM in coal miners. In this model, the combination of homozygous mutant genotypes with CICRD\u0026thinsp;\u0026ge;\u0026thinsp;0.2782 identifies a high-risk group for T2DM among coal miners. A study suggests that in steelworkers, a four-factor combination model (MTNR1A-MTNR1B-CLOCK-shift work) increases the risk of T2DM through complex interactions[72]. Another study based on the U.S. Biobank found that the interaction between morning preference and rs10830963 is associated with the risk of T2DM[39]. This study is the first to construct CICRD using factor analysis, explore the interaction between circadian rhythm-related genes and CICRD, and propose a five-factor interaction model. Gene-environment interactions are complex, and current evidence is insufficient to fully elucidate their mechanisms. However, the results indicate that individuals with high-risk combinations need protective measures, such as reducing shift work intensity, improving sleep quality, and avoiding night-time light exposure, to mitigate circadian rhythm disruptions and aid in T2DM prevention. When selecting shift workers, relevant departments should prioritize individuals with low-risk combinations.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAdvantages and limitations\u003c/h2\u003e \u003cp\u003eResearch on the combined effects of circadian rhythm-related genes and environmental factors on T2DM is relatively limited both in China and internationally. Our study is the first to construct CICRD based on seven circadian rhythm disorder assessment indicators and propose four- and five-factor interaction models. We explored their interactions with circadian rhythm-related SNPs and their impact on T2DM, providing preliminary population-based evidence of gene-environment interaction mechanisms. Our study has some limitations. First, the study includes a limited number of females, and the association between CICRD and T2DM in coal miners is primarily driven by correlations observed in males. Stratified analysis did not reveal significant associations in females, highlighting the need for future large-scale epidemiological studies focusing on female populations. Second, information on sleep duration, sleep disorders, and nighttime light exposure was self-reported, which may introduce misclassification bias. Third, due to the healthy worker effect, the study population consists of relatively healthy Chinese coal miners, which may limit the generalizability of the conclusions. Fourth, as a cross-sectional study, it cannot establish causal relationships between exposures and outcomes. Finally, the development of T2DM is a prolonged process, and individuals on the verge of developing T2DM might be misclassified as non-cases. This selection bias could distort the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe CICRD, constructed using seven circadian rhythm disorder assessment indicators, captures 79.771% of the original data. An increase in CICRD, along with variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene, was associated with an increased risk of T2DM among coal miners. The high-order interactions in the four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) are significantly associated with an increased risk of T2DM in coal miners. CICRD provides a standard and theoretical basis for assessing circadian rhythm disorder and screening high-risk T2DM populations in coal miners. Additionally, it identifies T2DM susceptibility genes and interactions, showing that high-risk combinations are unsuitable for shift work, providing scientific evidence for the precise prevention of T2DM in coal miners.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki. All participants gave informed consent before taking part in this study. This study was conducted by the Declaration of Helsinki and was approved by the Ethics Committee of North China University of Science and Technology (No. 15006). All the methods in this study were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e All the authors approved the manuscript for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key Projects of Research and Development of China (No.2016YFC0900605).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHY.C: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; the creation of new software used in the workQL.L: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; the creation of new software used in the work; drafted the workJX. Y: conception OR design of the work; design of the work; the acquisition, analysis; interpretation of data; substantively revised\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTuomi T, Santoro N, Caprio S, Cai M, Weng J, Lancet LGJ. The many faces of diabetes: a disease with increasing heterogeneity. Lancet. 2014;383(9922):1084-94. doi: 10.1016/s0140-6736(13)62219-9.\u003c/li\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. 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North China University of Science and Technology. 2020.\u003c/li\u003e\n\u003cli\u003eLi Q, Zhang S, Wang H, Wang Z, Zhang X, Wang Y, et al. Association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers: a case-control study. BMC genomics. 2023;24(1):232. doi: 10.1186/s12864-023-09328-y.\u003c/li\u003e\n\u003cli\u003eLin E, Kuo P-H, Liu Y-L, Yang AC, Kao C-F, one S-JTJP. Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: Evidence from association and interaction analysis. PloS one. 2017;12(3):e0173861. doi: 10.1371/journal.pone.0173861.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Circadian rhythm, Gene polymorphism, type 2 diabetes mellitus, Interaction effect","lastPublishedDoi":"10.21203/rs.3.rs-5321076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5321076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To construct comprehensive indicators of circadian rhythm disorder (CICRD) and explore the interaction effects between CICRD and circadian rhythm-related gene polymorphisms (SNPs) on the risk of type 2 diabetes mellitus (T2DM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Baseline data were collected from the Xingtai coal site of the Occupational Cohort Study on Health Effects. A cross-sectional study was initially conducted, involving 4,070 coal miners who underwent occupational health examinations during 2017 and 2018. We performed factor analysis to construct the CICRD and logistic regression models to estimate the association between CICRD and T2DM. Restricted cubic spline (RCS) function was used to determine the exposure-response association. In the subsequent case-control analysis, 424 cases and 464 controls were randomly selected from 3,878 male coal miners. Logistic regression model was employed to examine the association between selected SNPs and T2DM. Gene-gene and gene-environment interactions were evaluated using log-linear models and the generalized multifactor dimensionality reduction (GMDR) method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The CICRD constructed by factor analysis explained 79.771% of the original variance. After adjusting for confounding factors, CICRD was associated with the increased risk of T2DM. Variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene were associated with the increased risk of T2DM. Interactions between rs10830963 in the MTNR1B gene and rs11605924 in the CRY2 gene (\u003cem\u003eRERI\u003c/em\u003e: 0.2; \u003cem\u003eAP\u003c/em\u003e: 0.46), as well as between rs7958822 in the BMAL2 gene and rs11605924 in the CRY2 gene (\u003cem\u003eRERI\u003c/em\u003e: 1.55; \u003cem\u003eAP\u003c/em\u003e: 0.56), were associated with increased risk of T2DM. A CICRD score ≥ 0.2782 combined with high-risk genotypes at four SNPs (rs10830963 and rs1387153 in MTNR1B, rs7958822 in BMAL2, and rs11605924 in CRY2) was associated with increased risk of T2DM (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The complex intersection of four-factor interaction model (rs10830963-rs1387153-rs7958822-rs11605924) and five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) based on GMDR method interactions increased the risk of T2DM in the full data set (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAn increase in CICRD, along with variants at rs10830963 in the MTNR1B gene, rs7958822 in the BMAL2 gene, and rs11605924 in the CRY2 gene, was associated with an increased risk of T2DM among coal miners. The four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) and the five-factor model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) exhibited significant high-order interactions associated with an increased risk of T2DM among coal miners.\u003c/p\u003e","manuscriptTitle":"Association of circadian rhythms, CLOCK, MTNR1A, and MTNR1B gene polymorphisms and their interactions with type 2 diabetes in coal miners","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 13:21:37","doi":"10.21203/rs.3.rs-5321076/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f188b23e-2522-4627-88e9-dcb787b0a8eb","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-07T08:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 13:21:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5321076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5321076","identity":"rs-5321076","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}