Abstract
Objective
To investigate the interaction between circadian rhythm disorders and related gene polymorphisms (SNPs) in the risk of developing type 2 diabetes (T2DM).
Methods
Cross-sectional study included 4,070 coal miners who underwent occupational health examinations between 2017 and 2018. We constructed comprehensive indicators of circadian rhythm disorder (CICRD) using factor analysis. In case-control analysis, 424 cases and 464 controls were randomly selected from 3,878 male coal miners. Logistic regression models were used to examine the relationship between CICRD, 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 captures 79.771% of seven circadian rhythm disorder assessment indicators. Higher CICRD and variants at rs10830963 (MTNR1B), rs7958822 (BMAL2), and rs11605924 (CRY2) were associated with an increased risk of T2DM (P < 0.05).A CICRD score ≥ 0.2782 with each high-risk SNP (rs10830963, rs1387153, rs7958822, rs11605924) significantly increased T2DM risk (P < 0.05).The five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) based on the GMDR method significantly increased T2DM risk in the full dataset (P < 0.05).
Conclusion
The interaction between circadian rhythm disruption and high-risk SNP genotypes further amplifies the risk of T2DM among coal miners. Notably, the five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) provides a standardized basis for assessing circadian rhythm disruption, screening high-risk populations, and identifying that high-risk genetic combinations are unsuitable for shift work, offering scientific evidence for the precision prevention of T2DM in coal miners.
Keywords: circadian rhythm, gene polymorphism, interaction effect, MINER, type 2 diabetes mellitus
Background
Diabetes is marked by chronic hyperglycemia from impaired insulin secretion or action (1). In 2021, 536.6 million (10.5%) people aged 20–79 had diabetes globally, projected to rise to 783.2 million (12.2%) by 2045 (2). With the advance in modern biopsychosocial medical models (3), recent researches have focused on the complex interactions of factors in T2DM.
Studies show shift work is linked to T2DM onset and progression (4, 5). Shift work, including rotating, evening, and night shifts, is widely used in China’s mining and manufacturing industries to cover 8–24 hours tasks. In 2007, the International Agency for Research on Cancer classified shift work as a Group 2A carcinogen (6). Shift work disrupts sleep-wake patterns, causing circadian rhythm disturbances and health-impacting disorders (7, 8). In addition to shift work, sleep disorders and insufficient sleep disrupt circadian rhythms, especially when sleep and wakefulness occur at inappropriate biological times (9, 10). Moreover, artificial light at night disrupts sleep cycles and circadian functions, altering hormone production and potentially causing diseases (11, 12). Therefore, in addition to shift work, factors like nighttime light exposure, sleep disorders, and insufficient sleep are crucial in studying circadian rhythm disruption.
In recent years, advancements in genomic technologies have facilitated the discovery of new genes and variants associated with T2DM and its traits. The association between circadian rhythm gene polymorphisms and T2DM has become a research focus. Kan MY et al. (13) found that the G allele of rs10830963 and the T allele of rs1387153 in the MTNR1B gene are linked to higher T2DM risk and elevated FG in Han Chinese and European populations. A Japanese study first identified a significant association between the AG+AA genotype of the BMAL2 gene rs7958822 and T2DM risk in obese individuals (14). Although circadian rhythm gene polymorphisms are significantly associated with T2DM, the risk alleles of certain variants remain controversial. A study in the general Chinese population found that the A allele of the CRY2 gene rs11605924 was significantly positively associated with impaired FG and T2DM (15). Another study in the Saudi Arabian population reported that the A allele of rs11605924 is a protective factor against T2DM risk (16). Genetic backgrounds in different populations may influence the association between circadian rhythm gene polymorphisms and T2DM risk, warranting further investigation across multiple populations to elucidate the underlying mechanisms.
The coal industry is a key pillar of China’s economy and energy supply. Coal miners’ quality of life is a important concern in China’s occupational health. Widespread shift work makes circadian rhythm disruption common among coal miners. This study examines circadian rhythm disruption among Xingtai coal miners in the Beijing-Tianjin-Hebei region as part of a health cohort study. Our study examines environmental and genetic impacts on circadian rhythm, offering evidence for T2DM prevention in coal miners.
Methods
Study population
Participants were drawn from the baseline data of the coal miner cohort in the Beijing-Tianjin-Hebei Occupational Health Effects Cohort Study, initiated by China’s Ministry of Science and Technology to investigate the impact of occupational hazards on human health. The study included 4,440 workers from the Xingtai coal mining site who underwent health exams between August 2017 and August 2018. All fasting venous blood samples were collected in the morning within a standardized time window (8:00-10:00 a.m.) to minimize circadian variability. Data were collected from questionnaires, physical examinations, laboratory tests, and assessments of occupational hazards. After excluding individuals with incomplete questionnaires (246), less than one year of work experience (22), missing biochemical (12) or physical exam data (27), or invalid questionnaire information (63), 4,070 Han Chinese coal miners were included in the study. Cross-sectional study found CICRD significantly associated with T2DM risk in male workers, but not in females, likely due to the small sample size (Supplementary Table 1). Therefore, based on inclusion and exclusion criteria (Supplementary Materials), 3,878 male coal miners were randomly sampled as participants for the case-control study. Due to limited funding for genotyping all participants in the cross-sectional survey, we conducted a case-control study using baseline data to explore the association and interaction between CICRD and circadian rhythm-related gene polymorphisms (SNPs) with T2DM. A total of 424 cases and 464 controls were selected as study participants (Supplementary Figure 1).
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. Our study constructed CICRD using seven indicators across shift work, light exposure, and sleep: shift duration, cumulative night shifts, night shift frequency and duration, nighttime light exposure, insomnia status, and average sleep duration. Data was collected via interviews and verified with company records. Detailed definitions are presented in the Supplementary Materials.
Definition and classification of covariates
The study included demographic data (gender, age, education, marital status, family income), lifestyle behaviors (smoking, drinking, physical activity, diet), medical history (central obesity, hypertension, liver dysfunction, dyslipidemia, renal dysfunction), and occupational exposures (dust, heat, CO, noise), with detailed definitions in the Supplementary Materials.
Genetic testing of MTNR1B, BMAL1, and BMAL2
Genomic DNA was extracted from whole blood using a Genesky kit, and its concentration and purity were assessed with a spectrophotometer. Tag SNPs related to circadian rhythm genes were selected based on a minor allele frequency (MAF) ≥10% in the Chinese population and a linkage disequilibrium (LD) coefficient >0.8. The Tagger algorithm in HaploView 4.2 was used for selection, prioritizing SNPs identified by genome-wide association studies (GWAS) as linked to T2DM. Six SNPs were selected: rs10830963 and rs1387153 (MTNR1B), rs11022775 and rs7950226 (BMAL1), rs7958822 (BMAL2), and rs11605924 (CRY2). Detailed detection methods are provided in the Supplementary Tables S2-S8.
Establishment of CICRD
The suitability of the data for factor analysis was confirmed using the KMO test (0.774) and Bartlett’s sphericity test (P < 0.001). CICRD was developed based on seven indicators, including shift duration, nighttime light exposure, and sleep status. Principal component analysis identified three factors: F1 (Shift Work Factor), F2 (Sleep Factor), and F3 (Light Exposure Factor). Factor weights were assigned based on explanatory variance, and the final CICRD score was normalized, explaining 79.711% of the data. This score serves as a quantitative tool to explore the link between circadian rhythm disruption and T2DM. Details are provided in the Supplementary Tables S9-S12.
Statistical analysis
Continuous variables were presented as mean ± standard deviation (SD) or median with interquartile range (IQR) and analyzed using t-tests or Mann-Whitney U tests for group comparisons. Categorical variables were presented as frequency (n) and percentage (%) and analyzed using chi-square tests, Fisher’s exact tests, or Cochran-Armitage trend tests for group comparisons. Logistic regression model was performed to explore the association between influencing factors and T2DM. Factor analysis constructed the CICRD, and a restricted cubic spline (RCS) function modeled the dose-response relationship between CICRD and T2DM, with 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
Cross-sectional study included 4,070 participants (3,878 males, 192 females). Males were older (39.41 ± 8.62 years) than females (36.52 ± 8.81 years), and type 2 diabetes prevalence was higher in males (16.7%) than in females (12.5%) (Table 1). Case-control study showed a higher CICRD score in case group (0.33 ± 0.15) than control group (0.29 ± 0.14), with significant differences in age, income, smoking, dyslipidemia, liver dysfunction, hypertension, obesity, diabetes family history, and noise exposure (P < 0.05) (Supplementary Table 13).
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 | <0.001a |
| DASH score | 23.29 ± 2.77 | 23.28 ± 2.69 | 23.56 ± 3.98 | <0.001a |
| 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) | <0.001a |
| average sleep duration (h/d) | 7.37 ± 1.29 | 7.36 ± 1.30 | 7.56 ± 1.19 | 0.020a |
| duration of shift work (year) | 9.34 (0.37, 15.59) | 9.42 (0.00, 14.93) | 7.51 (2.08, 24.44) | 0.002a |
| cumulative number of night shifts (night) | 494 (21, 1171) | 494 (0, 1134) | 772 (253, 2229) | <0.001a |
| cumulative duration of night shifts (h) | 3621 (131,8643) | 3595 (0,8080) | 6178 (2029,18033) | <0.001a |
| 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) | <0.001a |
| Marital status, n (%) | <0.001a | |||
| Unmarried | 165 (4.1) | 139 (3.6) | 26 (13.5) | |
| Married | 3905 (95.9) | 3739 (96.4) | 166 (86.5) | |
| Education level, n (%) | <0.001c | |||
| 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 (%) | <0.001c | |||
| 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 (%) | <0.001c | |||
| 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 (%) | 0.575b | |||
| 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 (%) | 0.064b | |||
| 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 (%) | <0.001b | |||
| 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 (%) | 0.017b | |||
| 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) |
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
Supplementary Figure 2 shows a positive linear relationship between CICRD and the risk of T2DM among coal miners (Pfor overall association<0.001; Pfor non-linearity=0.524).Coal miners with CICRD scores of “0.2782–” and “≥0.3848” have 1.43-fold (95% CI: 1.07–1.90) and 2.43-fold (95% CI: 1.80–3.21) higher T2DM risks, respectively, compared to those with scores below “0.1839” (Table 2). Sensitivity analysis confirmed the association between CICRD and T2DM across subgroups, consistent with the main findings (Supplementary Table 1).
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
Supplementary Table 14 indicates that the dominant model is optimal for rs10830963, rs7958822, and rs11605924, with CG/GG (OR = 1.50, 95% CI: 1.14–1.98), GA/AA (OR = 1.43, 95% CI: 1.09–1.86), and AC/CC (OR = 1.35, 95% CI: 1.03–1.76) genotypes, respectively, showing a higher T2DM risk compared to their reference genotypes. However, rs1387153 (MTNR1B), rs11022775 (BMAL1), and rs7950226 (BMAL1) showed no statistically significant association with T2DM under any genetic model. Based on above findings, three T2DM susceptibility loci were identified, and their interactions were analyzed using cross-classification under dominant models. Supplementary Table 15 shows that T2DM risk significantly increases when rs10830963 CG+GG is combined with rs7958822 GA+AA (OR = 2.71, 95% CI: 1.77–4.15), rs11605924 AC+CC (OR = 2.64, 95% CI: 1.70–4.11), or when rs7958822 GA+AA is combined with rs11605924 AC+CC (OR = 2.77, 95% CI: 1.80–4.27), compared to their respective reference genotypes. In addition, there were positive interactions between rs10830963 and rs11605924, rs7958822 and rs11605924 on T2DM (P < 0.05).
GMDR analysis showed that the four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) was statistically significant (P = 0.003) and achieved a cross-validation consistency of 91.67% (11/12), making it the optimal high-order interaction model (Supplementary Table 16). In this model, the high-risk group is marked in dark gray (Supplementary Figure 3). In the full dataset, coal miners with homozygous mutations had a 3.10-fold (95% CI: 2.01–4.77) higher risk of T2DM compared to those with wild-type genotypes (Supplementary Table 17).
On the left side of the Sankey diagram were the different levels of indicators related to circadian rhythm disorder, among which were the genotypes of three biological clock genes, and on the right side were the dichotomous outcomes (T2DM/non-T2DM); The line width corresponds to the number of people in the combination of “environmental factors+genotype”, which intuitively showed the impact of environmental genetic interaction on T2DM. The combination of long-term, high-frequency night shifts, severe insomnia, and short sleep environments with high exposure levels, combined with the G/G genotype of MTNR1B, the A/A genotype of BMAL2, and the C/C genotype of CRY2, corresponds to a thicker line flowing towards “T2DM”, which is a high-risk combination of T2DM (Figure 1).
Figure 1.
Sankey diagram of the interaction between environmental and genetic factors.
Analysis of interaction between CICRD and various gene loci
CICRD was categorized as “<0.2782” or “≥0.2782” and analyzed for SNP interactions. The risk of T2DM is significantly higher for CICRD ≥ 0.2782 combined with specific genotypes: rs10830963 CG+GG (OR = 2.67, 95% CI: 1.77–4.15), rs1387153 CT+TT (OR = 1.93, 95% CI: 1.30–2.87), rs7958822 GA+AA (OR = 2.29, 95% CI: 1.60–3.27), and rs11605924 AC+CC (OR = 2.30, 95% CI: 1.63–3.25), compared to CICRD < 0.2782 combined with their respective reference genotypes. However, our study found no significant additive or multiplicative interactions between CICRD and individual gene loci (Table 3).
Table 3.
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) | ||||||
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).
GMDR analysis showed that the five-factor model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) was statistically significant (P = 0.003) with a cross-validation consistency of 100% (12/12), making it the optimal high-order interaction model (Table 4). Five-factor model was selected as the best gene-gene higher-order interaction model, with high-risk combinations shown in Supplementary Figure 4. In the full dataset, coal miners with homozygous mutant genotypes combined with CICRD ≥ 0.2782 is 7.38 times (95% CI: 4.84–11.25) higher risk of T2DM compared to those with homozygous wild-type genotypes combined with CICRD < 0.2782 (Supplementary Table 18).
Table 4.
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 |
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 and the gene variants rs10830963 (MTNR1B), rs7958822 (BMAL2), and rs11605924 (CRY2) are significantly associated with the risk of T2DM in coal miners. Notably, both the gene-gene four-factor interaction model (rs10830963-rs1387153-rs7958822-rs11605924) and the CICRD-gene 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.
MTNR1B belongs to the G protein-coupled receptor family involved in insulin secretion and encodes the melatonin receptor 1B (17). Previous studies identified MTNR1B loci associated with fasting glucose (FG). 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 (18). 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 (13). Our results also show that the T allele is not significantly associated with T2DM. The rs10830963 locus is located in the only intron of MTNR1B. A meta-analysis found the rs10830963 G allele linked to higher fasting glucose and reduced β-cell function (19). Additionally, the rs10830963 G allele is a risk factor for elevated FG and T2DM in different populations such as American white people (20), and Han Chinese (21). These findings are consistent with our study results. Melatonin, a hormone from the pineal gland, regulates circadian rhythms (22). Animal studies have demonstrated that melatonin reduces insulin levels (23) and impairs glucose tolerance (24) in rats. These biological mechanisms may help explain the observed associations.
Studies show pancreatic islets have circadian CLOCK and BMAL1 oscillations, and disrupting these components in mice causes hypoinsulinemia and diabetes (25). However, studies on BMAL1 variants rs11022775 and rs7950226 and T2DM are scarce and inconsistent. A study found that haplotypes with the rs11022775 T allele and rs7950226 A allele increase T2DM risk, largely driven by rs11022775 (26). A meta-analysis of 13,781 participants found the BMAL1 rs7950226 A allele associated with reduced metabolic syndrome risk (27). Our study found no significant association between BMAL1 rs11022775 and rs7950226 and T2DM in coal miners, consistent with a study in obese Japanese individuals (14). This indicates the need for larger studies in diverse populations to further explore these relationships.
Animal study suggests that BMAL2 gene expression may influence glucose and insulin levels (28). Previous research on the association between BMAL2 and T2DM is limited. BMAL2 SNPs are linked to psychiatric disorders but show no association with metabolic syndrome in European populations (29, 30). However, a study in an Asian population found the BMAL2 rs7958822 A allele significantly associated with T2DM in obese individuals (14). This aligns with the T2DM risk alleles identified in our study. Genetic susceptibility may vary by ethnicity, warranting further research on BMAL2’s link to T2DM across diverse populations.
The core clock is regulated by transcription factors, with CRY2 playing a key role as a transcriptional repressor. A study in a Chinese population found the CRY2 rs11605924 A allele linked to impaired FG and T2DM (15), contrasting with our findings. However, some evidence supports our findings. A study in a Saudi Arabian population found the rs11605924 A allele protective against T2DM (16). A Chinese study found the rs11605924 C allele to be a T2DM risk allele, also supporting our findings (31).
Beyond their established roles in circadian regulation and glucose metabolism, these genes are embedded in neural networks that govern feeding behavior. Recent reviews have shown that circadian clocks regulate both homeostatic and hedonic food intake via hypothalamic and mesolimbic reward circuits, providing a mechanistic link between circadian misalignment, overeating, and metabolic disease (32, 33). Furthermore, variants in circadian rhythm genes may also influence T2DM risk through their effects on direct physiological markers of circadian rhythmicity. Those genes participate in the regulation of both central and peripheral clocks, and their functional alterations may disrupt 24-hour patterns of blood pressure, core body temperature, and nocturnal melatonin secretion—key physiological markers of internal circadian organization (34–36). Indeed, circadian misalignment has been shown to impair glucose metabolism and insulin sensitivity (37). Studies of circadian rhythm physiology and pancreatic endocrine function further support that clock gene dysregulation can impair insulin secretion and glucose homeostasis (38). Thus, gene-related circadian misalignment may represent an additional mechanistic pathway linking these polymorphisms to metabolic dysfunction and T2DM.
Our study is the first to report a four-factor model (rs10830963-rs1387153-rs7958822-rs11605924) with significant interaction, linking specific genotype combinations to increased T2DM risk. Focusing on genotype combinations, not just individual genes, is crucial for identifying high-risk populations. Previous studies have reported an association between high-dimensional interactions among rs6850524, rs10830963, and rs1387153 loci in the CLOCK gene and metabolic syndrome (39). Lin E et al. (40) 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 interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD), where homozygous mutant genotypes combined with CICRD ≥ 0.2782 indicate high T2DM risk in 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 (41). Another study based on the U.S. Biobank found that the interaction between morning preference and rs10830963 is associated with the risk of T2DM (42). However, the interaction between genes and the environment is complex, and current evidence is insufficient to fully elucidate its mechanisms.
Our study has some limitations. First, the study’s limited female participants showed no significant CICRD-T2DM associations, with findings driven by males, underscoring the need for future large-scale studies on females. Second, self-reported data may cause misclassification bias. Third, the healthy worker effect in this relatively healthy population of Chinese coal miners may limit the generalizability of the conclusions. Fourth, as a cross-sectional study, it cannot establish causal relationships between exposures and outcomes. Finally, the prolonged T2DM development may misclassify pre-T2DM individuals as non-cases, causing selection bias.
Conclusion
The CICRD captures 79.771% of seven circadian rhythm disorder assessment indicators. Higher CICRD and variants at rs10830963 (MTNR1B), rs7958822 (BMAL2), and rs11605924 (CRY2) were associated with an increased risk of T2DM. The four-factor gene-gene model (rs10830963-rs1387153-rs7958822-rs11605924) and five-factor CICRD-gene model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) are significantly linked to increased T2DM risk in coal miners. CICRD provides a standard and theoretical basis for evaluating circadian rhythm disruption and identifying high-risk T2DM individuals among coal miners. The identification of T2DM susceptibility genes and their interactions suggests that high-risk combinations are unsuitable for shift work, offering scientific evidence for the precise prevention of T2DM in coal miners.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Key Projects of Research and Development of China (No.2016YFC0900605), Tianjin Health Science and Technology Projects (No. TJWJ2025QN052) and the Tianjin Union Medical Center (First Affiliated Hospital of Nankai University) hospital level project (2024YJ010) and the National Science and Technology Major Project (No. 2024ZD0521200, 2024ZD0521203).
Footnotes
Edited by: Åke Sjöholm, Gävle Hospital, Sweden
Reviewed by: Pamela Senesi, MultiMedica (IRCCS), Italy
Olesia Kalmukova, Taras Shevchenko National University of Kyiv, Ukraine
Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: Development of the dataset continues. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to HC 1113551219@qq.com.
Ethics statement
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). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
QL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft. HC: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft. XZ: Writing – review & editing. JY: Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review & editing. CZ: Writing – review & editing, Funding acquisition.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2026.1567465/full#supplementary-material
References
- 1. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Lancet LGJ. The many faces of diabetes: a disease with increasing heterogeneity. Lancet. (2014) 383:1084–94. doi: 10.1016/s0140-6736(13)62219-9, PMID: [DOI] [PubMed] [Google Scholar]
- 2. Sun 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. Diabetes Res Clin practice. (2022) 183:109119. doi: 10.1016/j.diabres.2021.109119, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Colodro-Conde L, Couvy-Duchesne B, Zhu G, Coventry WL, Byrne EM, Gordon S, et al. A direct test of the diathesis-stress model for depression. Mol Psychiatry. (2018) 23:1590–6. doi: 10.1038/mp.2017.130, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Pietroiusti A, Neri A, Somma G, Coppeta L, Iavicoli I, Bergamaschi A, et al. Incidence of metabolic syndrome among night-shift healthcare workers. Occup Environ Med. (2010) 67:54–7. doi: 10.1136/oem.2009.046797, PMID: [DOI] [PubMed] [Google Scholar]
- 5. Morikawa Y, Nakagawa H, Miura K, Soyama Y, Ishizaki M, Kido T, et al. Shift work and the risk of diabetes mellitus among Japanese male factory workers. Scandinavian J work Environ Health. (2005) 31:179–83. doi: 10.5271/sjweh.867, PMID: [DOI] [PubMed] [Google Scholar]
- 6. Straif K, Baan R, Grosse Y, Secretan B, Ghissassi FE, Bouvard V, et al. Carcinogenicity of shift-work, painting, and fire-fighting. Lancet. (2007) 8:1065–6. doi: 10.1016/s1470-2045(07)70373-x, PMID: [DOI] [PubMed] [Google Scholar]
- 7. Thomas C, Epidemiology Cpjejo . Shift work and risk factors for cardiovascular disease: a study at age 45 years in the 1958 British birth cohort. Eur J Epidemiol. (2010) 25:305–14. doi: 10.1007/s10654-010-9438-4, PMID: [DOI] [PubMed] [Google Scholar]
- 8. Song L, Shen J, Wang J, Zhang Y, Zhou Z, Sang L, et al. Shift patterns, physical exercise, and Type 2 diabetes mellitus (T2DM): a prospective cohort study in China. Trans Behav Med. (2023) 13:183–91. doi: 10.1093/tbm/ibac089, PMID: [DOI] [PubMed] [Google Scholar]
- 9. Baron KG, Psychiatry Kjrjiro . Circadian misalignment and health. Int Rev Psychiatry. (2014) 26:139–54. doi: 10.3109/09540261.2014.911149, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Gronfier C, Wright KP, Kronauer RE, America CACJPotNAoSotUSo . Entrainment of the human circadian pacemaker to longer-than-24-h days. Proc Natl Acad Sci United States America. (2007) 104:9081–6. doi: 10.1073/pnas.0702835104, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Meléndez-Fernández OH, Liu JA, Sciences Rjnjijom . Circadian rhythms disrupted by light at night and mistimed food intake alter hormonal rhythms and metabolism. Int J Mol Sci. (2023) 24(4):3392. doi: 10.3390/ijms24043392, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Obayashi K, Saeki K, Iwamoto J, Okamoto N, Tomioka K, Nezu S, et al. Exposure to light at night, nocturnal urinary melatonin excretion, and obesity/dyslipidemia in the elderly: a cross-sectional analysis of the HEIJO-KYO study. J Clin Endocrinol Metab. (2013) 98:337–44. doi: 10.1210/jc.2012-2874, PMID: [DOI] [PubMed] [Google Scholar]
- 13. Kan MY, Zhou DZ, Zhang D, Zhang Z, Chen Z, Yang YF, et al. Two susceptible diabetogenic variants near/in MTNR1B are associated with fasting plasma glucose in a Han Chinese cohort. Diabetic Med. (2010) 27:598–602. doi: 10.1111/j.1464-5491.2010.02975.x, PMID: [DOI] [PubMed] [Google Scholar]
- 14. 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 Res Clin Pract. (2015) 110:301–8. doi: 10.1016/j.diabres.2015.10.009, PMID: [DOI] [PubMed] [Google Scholar]
- 15. Liu C, Li H, Qi L, Loos RJ, Qi Q, Lu L, et al. Variants in GLIS3 and CRY2 are associated with type 2 diabetes and impaired fasting glucose in Chinese Hans. PloS One. (2011) 6:e21464. doi: 10.1371/journal.pone.0021464, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Li-Gao R, Wakil SM, Meyer BF, Dzimiri N, Mook-Kanamori DO. Replication of Type 2 diabetes-associated variants in a Saudi Arabian population. Physiol Genomics. (2018) 50:296–7. doi: 10.1152/physiolgenomics.00100.2017, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lyssenko V, Nagorny CLF, Erdos MR, Wierup N, Jonsson A, Spégel P, et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet. (2009) 41:82–8. doi: 10.1038/ng.288, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Bouatia-Naji N, Bonnefond A, Cavalcanti-Proença C, Sparsø T, Holmkvist J, Marchand M, et al. A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet. (2009) 41:89–94. doi: 10.1038/ng.277, PMID: [DOI] [PubMed] [Google Scholar]
- 19. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet. (2009) 41:77–81. doi: 10.1038/ng.290, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Day F, Karaderi T, Jones MR, Meun C, He C, Drong A, et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PloS Genet. (2018) 14:e1007813. doi: 10.1371/journal.pgen.1007813, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Rönn T, Wen J, Yang Z, Lu B, Du Y, Groop L, et al. A common variant in MTNR1B, encoding melatonin receptor 1B, is associated with type 2 diabetes and fasting plasma glucose in Han Chinese individuals. Diabetologia. (2009) 52:830–3. doi: 10.1007/s00125-009-1297-8, PMID: [DOI] [PubMed] [Google Scholar]
- 22. Research Epjjop . Melatonin, endocrine pancreas and diabetes. J pineal Res. (2008) 44:26–40. doi: 10.1111/j.1600-079X.2007.00519.x, PMID: [DOI] [PubMed] [Google Scholar]
- 23. Rasmussen DD, Mitton DR, Larsen SA, Research Smyjjop . Aging-dependent changes in the effect of daily melatonin supplementation on rat metabolic and behavioral responses. J pineal Res. (2001) 31:89–94. doi: 10.1034/j.1600-079x.2001.310113.x, PMID: [DOI] [PubMed] [Google Scholar]
- 24. Nehme PA, Amaral F, Lowden A, Skene DJ, Cipolla-Neto J, Hypotheses CRCMJM . Reduced melatonin synthesis in pregnant night workers: Metabolic implications for offspring. Med hypotheses. (2019) 132:109353. doi: 10.1016/j.mehy.2019.109353, PMID: [DOI] [PubMed] [Google Scholar]
- 25. Marcheva B, Ramsey KM, Buhr ED, Kobayashi Y, Su H, Ko CH, et al. Disruption of the clock components CLOCK and BMAL1 leads to hypoinsulinaemia and diabetes. Nat Genet. (2010) 466:627–31. doi: 10.1038/nature09253, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. 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:e32670. doi: 10.1371/journal.pone.0032670, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Škrlec I, Talapko J, 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):20. doi: 10.3390/biology11010020, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Shi S-Q, Ansari TS, McGuinness OP, Wasserman DH. Circadian disruption leads to insulin resistance and obesity. Curr Biol. (2013) 23:372–81. doi: 10.1016/j.cub.2013.01.048. CB CHJJCb. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Sipilä T, Kananen L, Greco D, Donner J, Silander K, Terwilliger JD, et al. An association analysis of circadian genes in anxiety disorders. Biol Psychiatry. (2010) 67:1163–70. doi: 10.1016/j.biopsych.2009.12.011 [DOI] [PubMed] [Google Scholar]
- 30. Englund A, Kovanen L, Saarikoski ST, Haukka J, Reunanen A, Aromaa A, et al. NPAS2 and PER2 are linked to risk factors of the metabolic syndrome. J circadian rhythms. (2009) 7:5. doi: 10.1186/1740-3391-7-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. 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:e15542. doi: 10.1371/journal.pone.0015542, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Chamorro R, Jouffe C, Oster H, Uhlenhaut NH, Meyhofer SM. When should I eat: A circadian view on food intake and metabolic regulation. Acta Physiol (Oxf). (2023) 237:e13936. doi: 10.1111/apha.13936, PMID: [DOI] [PubMed] [Google Scholar]
- 33. la, Blancas-Velazquez AS, Stenvers DJ, Kalsbeek A. Circadian influences on feeding behavior. Neuropharmacology. (2024) 256:110007. doi: 10.1016/j.neuropharm.2024.110007, PMID: [DOI] [PubMed] [Google Scholar]
- 34. Costello HM, Gumz ML. Circadian rhythm, clock genes, and hypertension: recent advances in hypertension. Hypertension. (2021) 78:1185–96. doi: 10.1161/HYPERTENSIONAHA.121.14519, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Brown SA, Zumbrunn G, Fleury-Olela F, Preitner N, Schibler U. Rhythms of mammalian body temperature can sustain peripheral circadian clocks. Curr Biol. (2002) 12:1574–83. doi: 10.1016/s0960-9822(02)01145-4, PMID: [DOI] [PubMed] [Google Scholar]
- 36. Takimoto M, Hamada A, Tomoda A, Ohdo S, Ohmura T, Sakato H, et al. Daily expression of clock genes in whole blood cells in healthy subjects and a patient with circadian rhythm sleep disorder. Am J Physiol Regul Integr Comp Physiol. (2005) 289:R1273–9. doi: 10.1152/ajpregu.00126.2005, PMID: [DOI] [PubMed] [Google Scholar]
- 37. Speksnijder EM, Bisschop PH, Siegelaar SE, Stenvers DJ, Kalsbeek A. Circadian desynchrony and glucose metabolism. J Pineal Res. (2024) 76:e12956. doi: 10.1111/jpi.12956, PMID: [DOI] [PubMed] [Google Scholar]
- 38. Chan K, Wong FS, Pearson JA. Circadian rhythms and pancreas physiology: A review. Front Endocrinol (Lausanne). (2022) 13:920261. doi: 10.3389/fendo.2022.920261, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Wang Y. Association of occupational hazard exposome,Circadian rhythm-related genes and theirInteractions with metS in ron and steel workers. Tangshan, China: North China University of Science and Technology; (2020). [Google Scholar]
- 40. Lin E, Kuo PH, Liu YL, Yang AC, Kao CF, One SJTJP. 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:e0173861. doi: 10.1371/journal.pone.0173861, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. 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) 24:232. doi: 10.1186/s12864-023-09328-y, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Dashti HS, Vetter C, Lane JM, Smith MC, Wood AR, Weedon MN, et al. Assessment of MTNR1B type 2 diabetes genetic risk modification by shift work and morningness-eveningness preference in the UK biobank. Diabetes. (2020) 69:259–66. doi: 10.2337/db19-0606, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data analyzed in this study is subject to the following licenses/restrictions: Development of the dataset continues. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to HC 1113551219@qq.com.