What this is
- This research investigates the association between gene in MTNR1A and CLOCK and acne susceptibility among gas station workers.
- The study focuses on how circadian rhythm disruptions, particularly in night shift workers, may influence acne risk.
- Findings indicate that specific genetic variants are linked to increased acne susceptibility, especially under conditions of circadian misalignment.
Essence
- CLOCK rs1801260 are associated with increased acne risk, particularly among night shift workers. MTNR1A rs2119882 shows a significant association only in this subgroup.
Key takeaways
- Participants with AG/GG genotypes of CLOCK rs1801260 had a higher risk of developing acne, with an adjusted () of 5.08. This indicates a strong genetic influence on acne susceptibility.
- In night shift workers, the CC genotype of MTNR1A rs2119882 was associated with increased acne risk (adjusted = 3.97). This suggests that circadian disruptions exacerbate genetic predispositions.
Caveats
- The study's sample size of 90 may limit the generalizability of the findings. Larger cohorts are needed for more definitive conclusions.
- The cross-sectional design prevents establishing causality between gene and acne risk, indicating the need for longitudinal studies.
Definitions
- Polymorphism: A genetic variation that can lead to different traits or susceptibility to diseases among individuals.
- Odds Ratio (OR): A measure of association between exposure (in this case, genetic variants) and an outcome (acne), indicating the odds of the outcome occurring in the exposed group compared to the unexposed group.
AI simplified
Introduction
Acne is a widespread chronic inflammatory disease, ranked second among the skin diseases according to the Global Burden of Disease study [1], with a global prevalence of approximately 9.4% [2] predominantly seen in adolescents [1,3]. Acne affects 85% of adolescents and may persist beyond adolescent years into adulthood [4]. Genetic, environmental, and lifestyle factors are hypothesized to cause acne [5], such as excess sebum production by androgens, altered keratinization, propionibacterium acnes, inflammation, diet (high intake of sugary and fatty foods), sleep deprivation, and obstruction of the sebaceous follicles [5,6].
Circadian rhythm plays an important role in hormone synthesis, glucose and lipid metabolism, and chronic inflammation [7]. Disruption of circadian rhythms directly or indirectly influences a variety of physiological activities, including the sleep-wake cycle, hormone secretion, periodic changes in body temperature, and blood glucose regulation [8]. Shift work, including night shifts or rotating schedules, represents an irregular work pattern that may disrupt circadian rhythm [9,10]. Such circadian misalignment in night shift workers may contribute to the development of skin conditions, including acne [10].
Circadian-related genes, such as melatonin receptor genes (MTNR) and clock circadian regulator (CLOCK), participate in transcription-translation feedback loops that drive circadian oscillations, with expression levels cycling over a 24-hour period [11]. Melatonin, a major circadian hormone secreted by the pineal gland under the control of the suprachiasmatic nucleus (SCN), mediates its physiological effects primarily through membrane receptors MTNR1A and MTNR1B [12]. Among these, MTNR1A is more broadly expressed in the skin, including keratinocytes and dermal fibroblasts [13], and plays important roles in antioxidative and anti-inflammatory pathways [14,15]. The rs2119882 polymorphism, located in the promoter region of MTNR1A, is considered a key functional SNP, influencing MTNR1A expression [9,16,17].
Notably, MTNR1A appears to play a more direct role in acne pathogenesis compared to MTNR1B. It exhibits higher expression in skin and reproductive axis tissues [18], and is more closely linked to androgen-regulated sebum production and follicular keratinization—key features of acne [19–21]. Additionally, MTNR1A plays a stronger role in local anti-inflammatory responses, including suppression of IL-6 and IL-1β and modulation of macrophage activity [22,23]. Under stress conditions, it regulates the expression of steroidogenic genes and testosterone synthesis, and its promoter methylation is altered in inflammatory diseases and severe acne—underscoring its multifaceted role in acne pathogenesis [24–27]. These features collectively support the prioritization of MTNR1A in this study.
The CLOCK gene, located on chromosome 4q12 and comprising 20 exons, is a central component of the circadian system, regulating energy metabolism [28]. The rs1801260 polymorphism in CLOCK, located in the 3’ untranslated region (UTR), affects the binding of microRNAs and alters mRNA stability or translation efficiency [29]. It has been linked to metabolic syndrome [30], sleep disorders and obesity [31], which are known risk factors for acne development [32]. In human skin, CLOCK is rhythmically expressed and may modulate keratinocyte proliferation, lipid secretion, and cutaneous inflammation [33]. Circadian disruption due to night-shift work may be aggravated in individuals carrying specific CLOCK variants, a condition linked to impaired skin barrier repair and heightened inflammation—both relevant to acne [34].
In China, the overall prevalence of acne is approximately 8.1%, based on a large-scale, community-based epidemiological study involving 17,345 individuals across six cities, which found 1,399 confirmed cases of acne [35]. Occupational populations account for 50% to 70% of China’s total workforce, and Shenzhen hosts a substantial proportion of these workers. In 2018 alone, over 190,000 workers in Shenzhen were exposed to occupational hazards [36], which may increase their vulnerability to various health conditions, including acne [37].
The high prevalence of acne and its related complications in occupational populations— particularly those with irregular work schedules—underscores the importance of targeted investigations [31]. Circadian rhythm disruptions, often experienced by shift workers, have been strongly linked to sleep disorders, which are recognized as significant risk factors for acne [31]. Both genetic predisposition and environmental exposures (e.g., artificial light at night, volatile chemicals) may act synergistically in shift workers [38]. Polymorphisms in circadian genes, such as MTNR1A rs2119882 and CLOCK s1801260, may amplify the physiological effects of circadian misalignment, leading to increased sebum production, follicular inflammation, and oxidative stress—all hallmarks of acne development [29,31].
However, the direct role of these SNPs in acne susceptibility has not yet been investigated, particularly in environmentally stressed occupational groups such as gas station workers. This study aims to explore the relationship between MTNR1A rs2119882 and CLOCK rs1801260 variants and acne risk in Chinese occupational populations, with a focus on shift-working gas station employees. The findings are expected to provide crucial insights into the genetic predisposition to acne, guiding primary prevention strategies, screening of high-risk populations, and the development of targeted interventions for acne management.
Materials and methods
Study design and sample size
This case-control study aimed to investigate the association between genetic variations in MTNR1A and CLOCK genes (in terms of candidate single nucleotide polymorphisms, SNPs) and acne susceptibility. The study population consisted of fuel station workers and healthy individuals undergoing occupational health examinations at the Shenzhen Prevention and Treatment Center for Occupational Disease between July 2023 and July 2024.
The sample size was calculated using standard methods for case-control studies [39]:
Assuming an exposure rate of 10% for the minor allele of CLOCK rs1801260 in the control group (P0 = 0.10) [40], and 40% in the case group (P1= 0.40), with a 1:2 case-control ratio (r = 1:2), α = 0.05, and 80% power (β=0.2), the required sample size was estimated at 78 (26 cases and 52 controls). To account for a 15% dropout rate, at least 30 cases and 60 controls were recruited in this study.
Study subjects
A total of 30 fuel station workers with acne were randomly assigned to the acne-affected group (AAG), and 30 acne-free fuel station workers were selected for the acne-free group (AFG). Additionally, 30 healthy individuals undergoing routine health examinations during the same period were randomly selected as the healthy control group (HCG).
Inclusion criteria for study participants were as follows: (1) aged 18 years or older; (2) no history of hypertension, diabetes, cardiovascular disease, or malignancy, and no long-term use of medication for chronic diseases; (3) willingness to complete a structured questionnaire and undergo a facial skin examination; and (4) availability of peripheral blood samples for genetic analysis.
Acne is generally diagnosed through physical examination rather than laboratory tests [41]. Additionally, patient self-reporting has been validated as a reliable method in dermatology for identifying dermatological conditions and symptoms of acne [42]. In this study, acne diagnosis was based on the International Consensus on Acne Classification, which defines acne by the presence of comedones, papules, pustules, nodules, or cysts on the face and/or trunk as diagnostic criteria for acne [43]. Participants who did not exhibit any of these features were classified into the control group.
Ethics
The study protocol was approved by the Ethics Committee of the Shenzhen Prevention and Treatment Center for Occupational Disease (Approval No. LL-2023019, approval date: June 29, 2023). All participants provided written informed consent before enrollment.
DNA isolation and genotyping
Peripheral venous blood samples (5 mL) were collected from each participant into EDTA-K2 anticoagulant tubes by trained nurses during routine occupational health examinations. The tubes were gently inverted to ensure proper mixing with the anticoagulant and temporarily stored at −20°C. Subsequently, samples were then transported under cold chain conditions to the laboratory and stored at −80°C for future analyses.
Genomic DNA was extracted from whole blood using the Ezup column-based genomic DNA extraction kit (Sangon Biotech, China), according to the manufacturer’s protocols (https://www.sangon.com/productImage/DOC/B518253/B518253_ZH_P.pdf↗). Genotyping was performed for two SNPs associated with circadian regulation: MTNR1A gene rs2119882 and CLOCK gene rs1801260. The quality of DNA was assessed using a UV-Vis spectrophotometer (SMA4000; Merinton, China) to measure concentration and purity, and 1% agarose gel electrophoresis (AGE) (FR-980A; Shanghai Furi Technology Co., LTD, China) to evaluate DNA integrity [36].
Primer sequences for the MTNR1A rs2119882 and CLOCK rs1801260 (Table 1) were designed using Primer Premier 5 software. Polymerase chain reaction (PCR) amplification of MTNR1A and CLOCK SNPs was conducted under the following thermal cycling conditions: an initial denaturation at 95°C for 5 minutes; 10 cycles of denaturation at 94°C for 30 seconds, annealing starting at 63°C with a 0.5°C decrement per cycle for 30 seconds, and extension at 72°C for 30 seconds. This was followed by 30 cycles of denaturation at 95°C for 30 seconds, annealing at 58°C for 30 seconds, and extension at 72°C for 30 seconds. A final extension was performed at 72°C for 10 minutes to ensure complete elongation of PCR products. The amplified samples were then held at 4°C prior to downstream analysis.
A 5 μl aliquot of the PCR product was analyzed via 1% AGE to assess the presence and integrity of the amplified DNA. The target PCR bands were isolated and purified using the SanPrep Column DNA Gel Extraction Kit (Sangon Biotech, China), following the manufacturer’s protocol. Genotyping of the SNPs rs2119882 and rs1801260 was performed using the 3730XL DNA Analyzer (Applied Biosystems, USA). The same primer pairs used in the PCR amplification were also used for bidirectional Sanger sequencing of the amplicons. The resulting data were processed and analyzed using sequence analysis software to confirm the SNP genotypes. As a quality control measure, 5% of randomly selected samples were genotyped in blinded duplicates, and the results were 100% concordant.
| Gene | SNP | Sequence (5’-3’) | L (bp) |
|---|---|---|---|
| MTNR1A | rs2119882 | Forward: CCGTTCATTGTGTTTCCTCTCT | 255 |
| Reverse: ATTATCGGAAACATCTAGCACCATC | |||
| CLOCK | rs1801260 | Forward: CCCTGGAGGTCATTTCATAGCT | 230 |
| Reverse: AAGTTCCAGCAGTTTCATGAGAT |
Covariate assessment
A standardized questionnaire was administered via face-to-face interviews by trained medical staff to collect potential risk factors for acne [44,45]. The questionnaire encompassed three domains: (1) sociodemographic data, including age, gender, height, weight, marital status, education level, skin type, and family history of acne; (2) lifestyle factors, such as smoking and alcohol consumption, physical activity frequency, daily water intake, and dietary preferences; and (3) occupational factors, including rotating night shift work and sleep duration.
Smoking was defined as having smoked at least one cigarette per day for a minimum of one year, while alcohol consumption was defined as drinking 50 grams of wine or one bottle of beer per day for at least one year [46]. Individuals who had alternated between day and night shifts for more than one year and were still working rotating night shifts at the time of the survey were classified as “night shift workers” [47].
Statistical analysis
The Shapiro–Wilk test was used to assess the normality of continuous variables, which were presented as mean ± standard deviation (SD) for normally distributed data. Group comparisons were performed using one-way analysis of variance (ANOVA) for continuous variables, while categorical variables were summarized as frequencies and percentages and compared using the Chi-square test. The Hardy-Weinberg equilibrium (HWE) for each SNP was also tested using the Chi-square test.
Univariate logistic regression was used to explore the associations between MTNR1A and CLOCK polymorphisms and acne risk. Multivariate logistic regression was then conducted to adjust for potential confounders, with results expressed as crude and adjusted odds ratios (ORs) along with 95% confidence intervals (CIs). Variables with a p-value < 0.100 in univariate analysis were included in the multivariate model to avoid excluding potentially important predictors [48]. This threshold was used solely for variable inclusion and not for determining statistical significance.
Genetic associations were further examined using five inheritance models: codominant, dominant, recessive, overdominant, and additive. In the codominant model, each genotype (e.g., TT, TC, CC) was analyzed as a separate category, with the major homozygous genotype used as the reference. The dominant model compared individuals carrying at least one minor allele (e.g., TC + CC) against major allele homozygotes (e.g., TT). The recessive model compared minor allele homozygotes (e.g., CC) to the combined group of heterozygotes and major homozygotes (TT + TC). In the overdominant model, heterozygotes (e.g., TC) were compared to both homozygous groups (TT + CC). The additive model assumed a linear effect of allele dosage, coded as 0, 1, or 2, corresponding to the number of minor alleles carried. To estimate the ORs and 95% CI, both univariate and multivariate logistic regression analyses were applied to each genetic model. This approach is consistent with established practices in genetic epidemiology [48].
To evaluate the robustness of these associations, additional stratified univariate and multivariate logistic regression analyses were performed within the fuel station population. Sensitivity analyses were also conducted using two alternative reference groups—healthy individuals and acne-free fuel station workers—to assess the stability of the gene-acne associations across different reference populations.
Statistical significance was set at p-value < 0.05 and all analyses were performed with R software (version 4.3.3).
Results
Comparison of characteristics among the three groups
A total of 90 participants, aged 18–46 years (mean age: 27.52 ± 6.67 years), were included in the study, of whom 77 (85.56%) were male. The proportion of male participants was higher in the HCG (100%) and AFG (83.33%) groups compared to the AAG group (73.33%). Additionally, participants in the AAG group had the shortest average sleep duration, and 93.33% of them involved in rotating night shift work (p < 0.05). No statistically significant differences were observed in variables such as family history of acne, skin type, or dietary habits (p > 0.05), as shown in Table 2.
| Variables | Total(= 90)n | HCG(= 30)n | AFG(= 30)n | AAG(= 30)n | -valuep |
|---|---|---|---|---|---|
| Age, years | 27.52 ± 6.67 | 28.97 ± 6.95 | 27.87 ± 7.10 | 25.73 ± 5.68 | 0.162 |
| BMI, kg/m2 | 22.04 ± 2.71 | 22.12 ± 2.60 | 22.08 ± 3.00 | 21.93 ± 2.59 | 0.961 |
| Sex,(%)n | 0.007 | ||||
| Male | 77 (85.56) | 30 (100.00) | 25 (83.33) | 22 (73.33) | |
| Female | 13 (14.44) | 0 (0.00) | 5 (16.67) | 8 (26.67) | |
| Marital status,(%)n | 0.565 | ||||
| Single | 5 (5.56) | 3 (10.00) | 0 (0.00) | 2 (6.67) | |
| Married | 78 (86.67) | 25 (83.33) | 27 (90.00) | 26 (86.67) | |
| Divorced/Widowed | 7 (7.78) | 2 (6.67) | 3 (10.00) | 2 (6.67) | |
| Education,(%)n | 0.231 | ||||
| Middle school or below | 16 (17.78) | 3 (10.00) | 5 (16.67) | 8 (26.67) | |
| High school | 59 (65.56) | 21 (70.00) | 18 (60.00) | 20 (66.67) | |
| University or above | 15 (16.67) | 6 (20.00) | 7 (23.33) | 2 (6.67) | |
| Sleep duration (Hours) | 6.62 ± 1.22 | 6.97 ± 1.19 | 6.70 ± 1.26 | 6.20 ± 1.13 | 0.046 |
| Skin types,(%)n | 0.691 | ||||
| Oily skin | 42 (46.67) | 12 (40.00) | 12 (40.00) | 18 (60.00) | |
| Dry skin | 11 (12.22) | 4 (13.33) | 5 (16.67) | 2 (6.67) | |
| Normal skin | 10 (11.11) | 4 (13.33) | 4 (13.33) | 2 (6.67) | |
| Combination skin | 27 (30.00) | 10 (33.33) | 9 (30.00) | 8 (26.67) | |
| Family history of acne,(%)n | 0.201 | ||||
| No | 35 (38.89) | 12 (40.00) | 15 (50.00) | 8 (26.67) | |
| Yes | 55 (61.11) | 18 (60.00) | 15 (50.00) | 22 (73.33) | |
| Smoking status,(%)n | 0.782 | ||||
| No | 61 (67.78) | 19 (63.33) | 22 (73.33) | 20 (66.67) | |
| Yes | 29 (32.22) | 11 (36.67) | 8 (26.67) | 10 (33.33) | |
| Drinking status,(%)n | 0.734 | ||||
| No | 70 (77.78) | 25 (83.33) | 22 (73.33) | 23 (76.67) | |
| Yes | 20 (22.22) | 5 (16.67) | 8 (26.67) | 7 (23.33) | |
| Rotating night shift work,(%)n | 0.009 | ||||
| No | 20 (22.22) | 12 (40.00) | 6 (20.00) | 2 (6.67) | |
| Yes | 70 (77.78) | 18 (60.00) | 24 (80.00) | 28 (93.33) | |
| Physical activity frequency,(%)n | 0.956 | ||||
| No | 35 (38.89) | 12 (40.00) | 10 (33.33) | 13 (43.33) | |
| 1-2 times per week | 33 (36.67) | 11 (36.67) | 12 (40.00) | 10 (33.33) | |
| ≥3 times per week | 22 (24.44) | 7 (23.33) | 8 (26.67) | 7 (23.33) | |
| Water intake per day,(%)n | 0.061 | ||||
| ≤ 600 mL | 24 (26.67) | 5 (16.67) | 8 (26.67) | 11 (36.67) | |
| 601-1500 mL | 44 (48.89) | 12 (40.00) | 17 (56.67) | 15 (50.00) | |
| >1500 mL | 22 (24.44) | 13 (43.33) | 5 (16.67) | 4 (13.33) | |
| Dietary habits,(%)n | 0.8 | ||||
| Spicy food | 13 (14.44) | 5 (16.67) | 5 (16.67) | 3 (10.00) | |
| Sweet food | 25 (27.78) | 7 (23.33) | 8 (26.67) | 10 (33.33) | |
| Fatty food | 33 (36.67) | 10 (33.33) | 10 (33.33) | 13 (43.33) | |
| Light food | 19 (21.11) | 8 (26.67) | 7 (23.33) | 4 (13.33) |
The fragment of PCR amplification
The PCR amplification fragments for the MTNR1A and CLOCK gene SNPs (rs2119882 and rs1801260) are displayed in Fig 1. The electrophoresis results demonstrated that the amplified products yielded single, well-defined bands at the expected positions, with a clear background, indicating successful amplification.
The electrophoresis image of fragments of PCR amplification for rs2119882 and rs1801260. Agarose gel electrophoresis was used to assess the quality and specificity of primers forrs2119882 andrs1801260. Lanes A-1 to A-8 represent DNA samples from acne-free gas station workers (AFG group). (A) (B) MTNR1A CLOCK
HWE analysis for MTNR1A and CLOCK genes
At the MTNR1A rs2119882 locus, three genotypes (TT, TC, and CC) were detected, while the CLOCK rs1801260 locus exhibited the AA, AG, and GG genotypes, as shown in S1 Fig. The most common alleles were rs2119882-T and rs1801260-A, present in 57.22% and 88.33% of participants, respectively. HWE testing confirmed that both the MTNR1A rs2119882 and CLOCK rs1801260 loci were in equilibrium across all three groups, suggesting the representativeness and genetic stability of the study population (p > 0.05, S1 Table).
Association betweenandgene polymorphisms and acne risk MTNR1A CLOCK
Table 3 presents the genotype distributions of MTNR1A rs2119882 (TT, TC, CC) and CLOCK rs1801260 (AA, AG, GG) under codominant, dominant, recessive, and overdominant genetic inheritance models. No statistically significant differences in genotype frequencies were observed among the HCG, AFG, and AAG groups (p-value > 0.05, S2 Table).
However, when analyzing the distribution of acne cases across different genotypes of the MTNR1A rs2119882 and CLOCK rs1801260 polymorphisms, individuals with the GG genotype exhibited a significantly higher acne prevalence (75%) compared to those with the AA genotype (27.4%) (p = 0.034; Fig 2B). This suggests a potential association between the GG genotype of CLOCK rs1801260 and increased acne susceptibility, as shown in Fig 2.
Association between acne risk andandgene polymorphisms. MTNR1A CLOCK (A) wasrs2119882; (B) wasrs1801260. Each bar represents the proportion of acne cases within each genotype group. The exact acne prevalence (%) is labeled above each bar. Asterisks (*) indicate statistically significant differences (< 0.05). MTNR1A CLOCK p
| Gene | Gene model | Genotype | Crude Model | Adjusted Model | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | -valuep | OR (95% CI) | -valuep | |||
| gene rs2119882 locusMTNR1A | Codominant | TT | Ref | Ref | ||
| TC | 1.93 (0.59–6.26) | 0.276 | 2.29 (0.61–8.61) | 0.22 | ||
| CC | 3.00 (0.79–11.42) | 0.107 | 3.97 (1.01–15.69) | 0.049 | ||
| Dominant | TT | Ref | Ref | |||
| TC + CC | 2.26 (0.75–6.76) | 0.146 | 2.81 (0.82–9.61) | 0.1 | ||
| Recessive | TT + TC | Ref | Ref | |||
| CC | 2.01 (0.67–6.08) | 0.215 | 2.41 (0.71–8.13) | 0.157 | ||
| Overdominant | TT + CC | Ref | Ref | |||
| TC | 1.16 (0.44–3.02) | 0.768 | 1.19 (0.41–3.46) | 0.747 | ||
| Additive | – | 1.73 (0.89–3.37) | 0.105 | 1.99 (1.01–3.93) | 0.047 | |
| gene rs1801260 locusCLOCK | Codominant | AA | Ref | Ref | ||
| AG | 2.92 (0.73–11.62) | 0.128 | 4.08 (0.85–19.53) | 0.079 | ||
| GG | 5.84 (0.57–60.04) | 0.138 | 8.69 (0.66–115.04) | 0.101 | ||
| Dominant | AA | Ref | Ref | |||
| AG + GG | 3.51 (1.03–11.94) | 0.045 | 4.96 (1.22–20.14) | 0.025 | ||
| Recessive | AA + AG | Ref | Ref | |||
| GG | 4.92 (0.48–49.93) | 0.178 | 6.69 (0.51–87.16) | 0.147 | ||
| Overdominant | AA + GG | Ref | Ref | |||
| AG | 2.59 (0.66–10.19) | 0.173 | 3.37 (0.73–15.61) | 0.12 | ||
| Additive | – | 2.62 (1.01–6.77) | 0.047 | 3.37 (1.14–9.95) | 0.028 | |
CLOCK rs1801260 polymorphisms increase acne risk
To evaluate the association between gene polymorphisms and acne risk, the HCG and AFG groups were combined as a unified control group, while the AAG group was designated as the case group. Binary logistic regression was used to explore the associations of different genetic models of MTNR1A rs2119882 and CLOCK rs1801260 with acne susceptibility. The multivariate model was adjusted for potential confounders identified in the univariate analysis (p < 0.100).
The results indicated a significant allelic association at the CLOCK rs1801260 locus. Univariate and multivariate logistic regression analyses under the dominant model (AG + GG vs AA) revealed that workers carrying the AG/GG genotypes had a markedly higher risk of developing acne compared to those with the AA genotype (unadjusted OR = 3.79, 95% CI: 1.27–11.31; adjusted OR = 5.08, 95% CI: 1.41–18.33). Under the additive model (per G allele), acne risk increased progressively with each additional G allele (unadjusted OR = 2.95, 95% CI: 1.22–7.13; adjusted OR = 3.51, 95% CI: 1.25–9.81), as shown in Fig 3.
Association analysis ofrs2119882 andrs1801260 polymorphisms with acne risk under five genetic models. MTNR1A CLOCK Odds ratios (ORs) were calculated using logistic regression, with reference genotypes defined by the major allele homozygotes. For the additive model, genotypes were coded based on the number of minor alleles (0, 1, 2). Error bars represent 95% confidence intervals. Bolded values indicate statistically significant results (< 0.05). p
Subgroup analysis in rotating night shift workers
Due to the limited number of non-night shift workers (n = 20, with only 2 cases of acne), subgroup analysis was restricted to participants engaged in rotating night shifts. The multivariate model revealed that the rs2119882 variant of the MTNR1A gene in homozygosis (CC) was most strongly associated with acne risk (OR = 3.97, 95% CI: 1.01–15.69; p = 0.049). Under the additive model, the risk of acne increased with each additional C allele (OR = 1.99, 95% CI: 1.01–3.93). Similarly, for the CLOCK rs1801260 variant, carriers of the minor genotype (AG/GG) showed significant associations with acne among rotating night shift workers under both dominant and additive models (Table 3).
Sensitivity analysis
The sensitivity analysis revealed that, when using the HCG group as the control, the homozygous AA genotype of CLOCK rs1801260 was associated with the lowest acne risk, consistent with the results presented in Fig 3. However, when the AFG group was used as the control, a significant association was observed only in the multivariate analysis under the dominant model, where workers carrying the AG/GG genotypes had a higher risk of acne compared to those with the AA genotype. No significant association was identified under the additive model, as detailed in S3 and S4 Tables.
Discussion
This case-control study presents a novel exploration of the relationship between circadian rhythm gene polymorphisms and acne risk in an occupational population. While MTNR1A rs2119882 was not significantly associated with acne susceptibility in the overall population, night shift workers carrying the CC genotype showed a markedly increased risk of (adjusted OR = 3.97, 95% CI: 1.01–15.69) and the C allele remained a risk factor under the additive model. In contrast, the CLOCK rs1801260 G allele was consistently associated with increased acne susceptibility across models, with higher risk observed in night shift workers carrying AA/AG genotypes. These findings highlight the influence of circadian gene variants, particularly in the context of circadian disruption, may contribute to pathogenesis through gene-environment interactions.
MTNR1A, a key G protein-coupled receptor, mediates melatonin’s physiological effects and is involved in steroid hormone synthesis, metabolic regulation, and the apoptosis of granulosa cells [49]. It also regulates the antioxidative and anti-inflammatory function of melatonin, which are crucial for maintaining skin health [50]. The rs2119882 polymorphism, located in the promoter region of the MTNR1A gene, may influence gene transcription by altering transcription factor binding, thereby modulating receptor expression.
Although the CC genotype of MTNR1A rs2119882 did not reach statistical significance in the overall population, the observed trend toward increased acne risk aligns with findings in the night shift worker subgroup, where a significant association was detected (adjusted OR = 3.97, p = 0.049). This suggests that limited sample size in the general analysis may have underpowered detection of a real effect. Additionally, a similar upward trend in acne prevalence among C allele carriers was observed in the night shift subgroup, indicating a potential biological effect that warrants further validation in larger cohorts.
Rotating night shift workers are particularly vulnerable to circadian rhythm disruption, often due to prolonged exposure to artificial light at night. Blue-enriched and high-intensity light can suppress melatonin release, especially when exposure occurs during biologically sensitive windows [51,52]. In individuals with the C allele, potential downregulation of MTNR1A expression may further compromise melatonin signaling, diminishing its protective effects against oxidative stress and inflammation [53]. However, it is also important to note that appropriately timed exposure to light—particularly in the early biological night—can support circadian entrainment in night shift workers, potentially mitigating melatonin suppression and improving physiological adaptation.
This gene–environment interaction may promote a proinflammatory cutaneous microenvironment, thereby increasing acne susceptibility. MTNR1A has previously been implicated in several inflammatory and endocrine-related disorders, such as polycystic ovary syndrome [54] and sleep disorders [55], both of which share mechanistic pathways with acne, including hormonal imbalance and immune dysregulation. Given the overlap, it is biologically plausible that dysfunction of MTNR1A under circadian disruption contributes to acne pathogenesis. These findings highlight the importance of integrating both genetic predisposition and environmental light exposure in acne risk evaluation.
Our findings revealed a robust association between the CLOCK rs1801260 polymorphism and acne susceptibility. Under the dominant model, individuals carrying the AG/ GG genotypes exhibited a significantly higher risk of developing acne compared to those with the AA genotype (OR = 5.08, 95% CI = 1.41–18.33). This risk was further elevated among rotating night shift workers (OR = 4.96, 95% CI = 1.22–20.14). Sensitivity analyses confirmed the consistency of this association, indicating the GG genotype may amplify acne risk especially in populations vulnerable to circadian disruption. Previous studies have established a link between the rs1801260 variant and sleep disorders [32,56,57], which are recognized contributors to acne development. Moreover, CLOCK gene variants are known to influence sleep quality and circadian regulation [58], reinforcing the biological plausibility of our findings.
The CLOCK gene, as a central regulator of circadian rhythms, coordinates key biological processes including skin barrier maintenance, inflammation control, and cellular repair [53]. The rs1801260 polymorphism, located in the 3′UTR of the CLOCK gene, may disrupt microRNA binding, thereby altering post-transcriptional regulation and affecting mRNA stability or translation efficiency [29]. The G allele, present in AG and GG genotypes, is thought to reduce miRNA binding affinity (particularly for miR-182 and miR-107), leading to decreased CLOCK protein expression. This downregulation may weaken the molecular circadian feedback loop and impair the rhythmic expression of CLOCK-regulated targets such as PPARα and Rev-Erbα, which are crucial for controlling lipid metabolism, sebocyte activity, and cutaneous inflammation [59].
CLOCK also influences insulin sensitivity and energy balance through its interaction with Sirt1, a key NAD + -dependent deacetylase that modulates fat and glucose metabolism [60,61]. Polymorphisms in CLOCK and other core circadian genes have been associated with metabolic syndrome, a condition strongly associated with acne pathogenesis [62]. In individuals carrying the AG or GG genotypes, particularly under circadian disruption such as night shifts or artificial light exposure, these molecular disturbances may promote excessive sebum production, increased inflammatory cytokine expression (e.g.,: IL-1β, IL-17, IL-6, TNF-α, IFN-γ) [63], and skin barrier dysfunction, ultimately contributing to acne susceptibility [63,64].
In modern society, circadian rhythm disruption is increasingly common due to factors such as night shifts, rotating work schedules, and trans-meridian travel [47]. Such disruption is often exacerbated by continuous exposure to artificial light at night, which induces neuroendocrine stress, particularly in shift workers. Prolonged stress leads to the activation of the hypothalamic-pituitary-adrenal (HPA) axis [12]. This process initiates the secretion of corticotropin-releasing hormone (CRH), followed by adrenocorticotrophic hormone (ACTH), which stimulates the adrenal cortex to release glucocorticoids [65,66]. Glucocorticoids, crucial in metabolic regulation, also affect circadian rhythms with peak secretion early in the morning [67]. Disrupted circadian alignment, as often seen in shift workers, can elevate glucocorticoid levels and exacerbate stress responses, thereby potentially increasing acne susceptibility [64,68].
In the sensitivity analysis, the study confirmed the robustness of the rs1801260 association, particularly among night shift workers, who are more prone to circadian rhythm disruptions. These findings support a gene–environment interaction model, in which the rs1801260 G allele, under conditions of circadian disruption, may exacerbate metabolic and inflammatory dysregulation, thereby increasing susceptibility to acne.
This study has several limitations that must be considered. The relatively small sample size (n = 90) may reduce the statistical power and limit the generalizability of the findings. Moreover, the study population was limited to gas station workers engaged in rotating night shifts, which may not fully be representative of the general population, particularly those without circadian rhythm disruption. The cross-sectional design precludes any inference of causality between gene polymorphisms and acne susceptibility. Although adjustments were made for confounders such as age and family history, residual confounding may persist due to unmeasured variables like diet, skincare habits, and workplace chemical exposures. Furthermore, the absence of direct circadian biomarkers, such as melatonin levels, limits the ability to definitively link between CLOCK gene variation and acne pathogenesis. Further studies with larger, more diverse cohorts and longitudinal designs are warranted to confirm and extend these findings.
Conclusions
Our findings suggest that the CLOCK rs1801260 polymorphism, particularly the GG genotype, may increase acne susceptibility—like through circadian rhythm disruption and its downstream effects on metabolic and inflammatory pathways, particularly in shift-working populations. Future research is needed to elucidate the mechanistic links between circadian genes variants and dermatological disorders such as acne, and to evaluate circadian-targeted strategies for prevention and treatment.
Supporting information
Acknowledgments
We extend our appreciation to the editor and the reviewers for their invaluable and constructive comments. We thank all workers for participating in the study. Minimal data set and R code used in this study have been deposited in a public repository: https://zenodo.org/records/15266039↗.
Data Availability
All data and R-code files are available from the Zenodo database (accession number 15266039).
Funding Statement
This work was supported by Research and Cultivation Program Project of Shenzhen Prevention and Treatment Center for Occupational Diseases (SZF-PY-2023-011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
Associated Data
Supplementary Materials
Data Availability Statement
All data and R-code files are available from the Zenodo database (accession number 15266039).