Circadian Imbalance, Genetic Susceptibility, and Risk of Psoriasis

Abstract

Background: Circadian rhythm disruption is increasingly recognized as a driver of systemic inflammation, yet the combined impact of lifestyle-related circadian imbalance and genetic predisposition on psoriasis risk remains poorly understood.

Objective: This study aimed to investigate the prospective association between a composite circadian imbalance index (CII) and incident psoriasis, and to evaluate its interaction with genetic risk.

Methods: The present prospective cohort included 260,118 participants from the UK Biobank. The primary study outcome was the incidence of psoriasis. Cox proportional hazards models were used to examine the association between CII and the risk of psoriasis. Sensitivity analyses, interaction analyses, and mediation analysis were performed.

Results: During a mean follow-up of 16.49 years, 2,519 incident psoriasis cases were identified. Higher CII was significantly associated with increased psoriasis risk. A significant additive interaction was observed between high CII and high polygenic risk score (PRS). Mediation analyses identified several blood biomarkers as partial mediators of this association.

Conclusions: Circadian imbalance represents a robust independent risk factor for psoriasis, especially among those with high genetic susceptibility. Given that this association is partially mediated by inflammatory and metabolic pathways, our findings suggest that optimizing circadian-related behaviors may serve as a critical preventive strategy.

Graphical abstract

Keywords

circadian imbalance / psoriasis / genetic susceptibility / UK Biobank

Introduction

Psoriasis is a chronic immune-mediated inflammatory disease affecting approximately 2.8% of the UK population and 2%–3% of the global population[1–5]. Beyond cutaneous manifestations, psoriasis represents a systemic condition associated with significant comorbidities including psoriatic arthritis, cardiovascular disease, metabolic syndrome, and reduced life expectancy[6]. Despite therapeutic advances, current management strategies focus primarily on treating established disease rather than preventing onset in high-risk individuals.

Circadian rhythms govern immune function, cell proliferation, energy metabolism, and skin barrier homeostasis[7]. Circadian misalignment disrupts the synchrony between peripheral oscillators and central cues, promoting keratinocyte proliferation and interleukin-23/interleukin-17 (IL-23/IL-17) axis dysregulation, which accelerates psoriatic plaque progression[8]. Previous prospective evidence links rotating night shift work to a 19% increased psoriasis risk[9]. Previous studies have largely focused on single circadian-related factors, whereas circadian imbalance in real-life settings often involves the co-occurrence of multiple related abnormalities[10]. To quantify cumulative circadian dysregulation, the circadian imbalance index (CII) was developed as a composite proxy. This metric incorporates chronotype, sleep timing, neuroticism, caffeine intake, and vitamin D levels. These components were selected based on their biological associations with circadian rhythms in large-scale epidemiological cohorts[11,12].

Psoriasis demonstrates substantial genetic predisposition, with its onset and clinical progression driven by the complex interplay between genomic susceptibility and cumulative environmental exposures across the life course[13]. The polygenic risk score (PRS) effectively captures cumulative genetic susceptibility, facilitating the stratification of patients by predicted disease severity[14]. However, whether a composite CII modifies the genetic susceptibility to psoriasis remains to be elucidated.

Therefore, using the UK Biobank database, this study investigates the association between psoriasis risk and a composite CII, which integrates major circadian imbalance factors.

Materials and methods

Data source and study participants

The present study utilized data obtained from the UK Biobank database, a large-scale, nationally representative, prospective cohort study in which more than 500,000 participants aged 37–73 years were recruited from more than twenty assessment centers in England, Scotland, and Wales during 2006–2010[15].

In the present study, 501,936 participants were included in the longitudinal cohort. We excluded participants who were diagnosed with psoriasis at or before baseline (n = 11,211), those missing data for CII calculation (n = 172,241), and those lacking values of covariates (n = 58,366) (Fig. S1 and Table S1).

Assessment of circadian imbalance related factors and construction of circadian imbalance index (CII)

The CII was calculated by summing the points across 5 key circadian imbalance-related factors and ranged from 0 to 5 (Fig. S2), with a higher index indicating a greater propensity to circadian imbalance, which is detailed in Text S1 and Table S2[11,16–21]. For further analyses, we categorized participants into circadian imbalance groups: CII low group (0 ≤ CII ≤ 1), CII middle group (2 ≤ CII ≤ 3), and CII high group (4 ≤ CII ≤ 5). This composite index was derived and previously applied in several studies, but has not yet been validated in psoriasis.

Outcomes

The primary outcome in this study was incident psoriasis, which was defined and ascertained using the International Statistical Classification of Diseases, Tenth Revision (ICD-10) code for psoriasis (ICD-10: L40) in primary care data, hospital admissions, self-report, or death register records, as was done in previous studies[22–24].

Polygenic risk score

The UK Biobank has released a systematically validated standard PRS set which includes the specific score for psoriasis (Field ID: 26269). We further classified participants with high (the highest PRS quartile), moderate (the middle two PRS quartiles), or low (the lowest PRS quartile) genetic risk.

Covariates

Potential confounders, identified via literature review[25–28], encompassed a range of demographic, socioeconomic, and health-related variables, including age, gender, race/ethnicity, body mass index (BMI), educational level, Townsend deprivation index (TDI), smoking status, alcohol drinking status, physical activity group, and history of hypertension/diabetes (Text S2). Additionally, we calculated the variance inflation factor (VIF) for all covariates in our fully adjusted model to prevent multicollinearity (Table S3).

Statistical analysis

The characteristics of the participants at baseline are described as the means ± standard deviations (SD) for continuous variables and as the frequency and percentage (N [%]) for categorical variables. Statistical differences between groups were assessed using the Kruskal–Wallis test for continuous data and the chi-square test for categorical data.

For the following analysis, we examined the association of the CII with psoriasis via multivariable Cox proportional hazards regression analysis models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The models were adjusted for potential confounders. Model 1 was a crude model (without adjustment). Model 2 was adjusted for age, gender, and race/ethnicity. Model 3 was adjusted for all covariates. In addition, for analysis related to PRS, we also adjusted for genotyping batch and the first 10 genetic principal components based on Model 3[29]. Considering the context of multiple testing, we applied the Benjamini–Hochberg procedure to control the false discovery rate (FDR) (Text S4)[30].

Furthermore, the Kaplan–Meier method was used to compare the cumulative risk of psoriasis events among participants categorized by CII groups and genetic risk groups, and the log-rank test was used for statistical evaluation. Moreover, we conducted separate analyses for each single circadian imbalance factor. Afterward, restricted cubic splines (RCS) regression with 3 and 5 knots was applied to model the potential nonlinear dose-response relationship between PRS and psoriasis risk.

To assess potential interaction effects between PRS groups and CII, we also performed interaction analysis on the additive scale using two distinct indices: relative excess risk due to interaction (RERI), which measures the additional risk attributable to interaction; attributable proportion due to interaction (AP), indicating the proportion of the outcome attributable to the interaction between both exposures[31–33].

Next, we performed subgroup analysis to investigate the robustness of the results, and detailed methodologies for these analyses are provided in Text S3[34–38].

To further explore the mechanism underlying the association between the CII and the incidence of psoriasis, we conducted a series of mediation analyses to investigate the potential blood biomarkers (all the mediators were measured at baseline) linked to the CII and psoriasis. This mediation method estimated total effects (TE), direct effects (DE), and indirect effects (IE) as well as the proportion of mediation (%) calculated using the 1,000-iteration bootstrap method based on Model 3.

The data analysis of the present study was performed between February 06, 2026, and April 13, 2026. All analyses were performed using R version 4.4.3. A two-sided P value of < 0.05 was set as the threshold for statistical significance.

Results

Basic characteristics of the participants

A total of 260,118 participants in the longitudinal cohort from the UK Biobank were enrolled (Fig. S1), with an average age of 56.1 ± 8.1 years, and 135,717 (52.2%) participants were female. Table 1 displays the characteristic indicators of the participants stratified by the CII groups. The results also showed that participants in the high CII group exhibited remarkable differences from those in the low CII group. Briefly, individuals with high CII levels were younger, had higher BMI, and included a greater proportion of females, non-White participants, not current drinkers, current smokers, participants with lower education levels, those with low physical activity levels, higher TDI scores, and a higher prevalence of diabetes and hypertension.

Additional analyses provided further insight into the baseline characteristics of the included and excluded participants (Table S1).

Association between CII and the risk of psoriasis

At first, we explored the associations between each single circadian imbalance factor and risk of psoriasis. The results showed that evening chronotype, short or long sleep duration, high neuroticism score, and low serum vitamin D concentration were significantly associated with increased risk of psoriasis (Table S4).

Subsequently, we investigated the association between CII and risk of incident psoriasis (Table 2). In the present study, 2,519 incident cases of psoriasis were documented. Figure S3A shows the cumulative risk of incident psoriasis during a mean follow-up of 16.49 years across different CII groups (P_log-rank < 0.001).

As shown in Table 2, each one-point increase in CII corresponded to an HR of 1.10 after adjusting for all potential confounders (HR = 1.10, 95% CI: 1.06–1.14, P < 0.001). For categorized CII groups, the participants in the high CII group showed remarkably increased risk of psoriasis (HR = 1.37, 95% CI: 1.19–1.58, P < 0.001), with a distinct dose-response relationship (P_{for trend} < 0.001).

Joint association and interaction of CII and PRS with the risk of psoriasis

In Table S5, we found that the risk of psoriasis increased with each SD increase in PRS as anticipated (HR = 1.29, 95% CI: 1.25–1.34, P < 0.001). The cumulative incidence curves based on genetic risk groups also showed that cumulative risk of psoriasis events was higher among the participants in the high PRS group (P_log-rank < 0.001) (Fig. S3B). Next, the RCS analysis also showed that the relationship between PRS and risk of incident psoriasis was notably non-linear (P_{for non-linear} < 0.05; P_{for overall}< 0.001) (Fig. S4A and Fig. S4B).

The joint effect of CII and genetic predisposition with regard to the risk of incident psoriasis is presented in Figure 1. In comparison with the reference level (low genetic risk and low CII group), the group with high genetic risk and high CII suffered the highest risk of developing psoriasis (HR = 2.63, 95% CI: 2.04–3.39, P < 0.001) (Fig. 1). Meanwhile, this study observed positive additive interactions both in the strata of high CII/high genetic risk and middle CII/moderate genetic risk. Significant additive interactions were observed for middle CII/moderate genetic risk (RERI, 0.28) and high CII/high genetic risk (RERI, 0.69) (Fig. 1). The additive interaction accounted for 19% and 26% AP of the total risk observed in the middle CII/moderate genetic risk and high CII/high genetic risk, respectively, indicating that the combined effect was greater than the sum of their individual effects (Fig. 1). Detailed measures of additive and multiplicative interactions are summarized in Table 3.

Subgroup analysis

To delve deeper into the correlation between CII and the likelihood of psoriasis, we performed subgroup analysis between CII and psoriasis (Table S6). The subgroup analyses revealed consistent associations across all sub-groups (P_{for interaction} > 0.05), suggesting the robustness of the association.

Sensitivity analysis

To further evaluate the robustness of our findings regarding the association between CII and incident psoriasis, several sensitivity analyses were performed. The results remained stable across multiple sensitivity analyses (Tables S7–S20).

Mediation analysis

Then, we performed mediation analysis to further investigate the potential mechanism underlying the above association (Fig. 2 and Table S21). These blood biomarkers, reflecting systemic inflammation (C-reactive protein [CRP], white blood cell count [WBC], neutrophil count [NEUT], and monocyte count [MONO]), liver function (alkaline phosphatase [ALP], gamma glutamyl-transferase [GGT], alanine aminotransferase [ALT], aspartate aminotransferase [AST], and albumin [ALB]), renal function (Cystatin C), and lipid/glucose metabolism (triglyceride-glucose [TyG] index) demonstrated varying degrees of statistical significance (all P  < 2 × 10^–16) (Fig. 2). The proportions of these biomarkers ranged from 0.07% (0.01%–0.68%) for MONO to 2.61% (1.34%–4.81%) for ALP (Table S21).

Discussion

This prospective cohort study of 260,118 UK Biobank participants demonstrates that the composite CII was independently associated with the risk of incident psoriasis, and this association remained robust across multiple additional analyses. Importantly, we identified a significant synergistic interaction between high CII and high genetic risk. Mediation analyses suggested that several circulating biomarkers contributed modestly to this association.

Our findings extend previous epidemiological evidence linking circadian disruption to psoriasis. Unlike previous studies that have largely focused on a single sleep behavior or an individual circadian-related factor, the CII employed in our study provides a multi-dimensional assessment by integrating diverse factors, including evening chronotype, abnormal sleep duration, high neuroticism score, low vitamin D status, and atypical caffeine intake. This aligns with experimental evidence showing that CLOCK mutation relieves psoriasiform inflammation via interleukin-23 receptor (IL-23R) downregulation in γ/δ⁺ T cells, while period circadian regulator 2 (PER2) deletion exacerbates disease[8]. Moreover, psoriatic patients exhibit disrupted expression of core clock genes (cryptochrome circadian regulator 2 [CRY2] , reverse erythroblastosis virus α [REV-ERBα], cryptochrome circadian regulator 1 [CRY1], RAR-related orphan receptor α/γ [RORα/γ]) in lesional and perilesional skin compared with healthy controls[39,40]. It should be noted that the CII represents a pragmatic composite proxy of circadian disruption rather than a fully validated biological measure, and further validation using objective circadian indicators is warranted.

Furthermore, this study provides the evidence of a synergistic interaction between high CII levels and high PRS in psoriasis risk. From a precision prevention perspective, individuals with high PRS may achieve the greatest absolute risk reduction through optimization of circadian health, consistent with evidence that environmental triggers modify gene expression in autoimmune diseases[41]. This provides clinically interpretable evidence of joint effects between circadian imbalance and genetic susceptibility.

The mediation analysis revealed modest contributions from measured biomarkers, with ALP showing the strongest effect at 2.61%. This suggests that circulating factors only partially explain the association between circadian imbalance and psoriasis. The liver acts as a central circadian organ in which CLOCK gene coordinates metabolic and inflammatory homeostasis. Circadian disruption can reprogram hepatic gene expression and impair macronutrient metabolism, potentially leading to dysregulated ALP production through CLOCK-related pathways[42,43]. Inflammatory biomarkers (e.g., 2.05% neutrophils) underscore systemic inflammation as a core pathway. Accordingly, circadian disruption has been shown to promote cytokine release, while REV-ERB agonism has been reported to suppress IL-17 in γ/δ⁺ T cells and improve psoriatic-like skin inflammation. Complementarily, sleep loss has been associated with T helper 17/regulatory T (Th17/Treg) imbalance, which may further drive psoriasis pathogenesis[44]. It is noteworthy that although systemic biomarkers such as ALP and CRP were identified as significant mediators, they collectively explained only a small fraction of the association between CII and psoriasis. This suggests that the pathogenic impact of circadian dysregulation may be largely mediated through other pathways. Beyond the above mechanisms, the direct uncoupling of the peripheral molecular clock within keratinocytes may drive disease progression by altering local cellular homeostasis[45–47]. Furthermore, the role of the neuro-immune axis and localized cytokine release in the skin microenvironment, which was not directly measured by the systemic biomarkers in the present study, warrants further investigation in future prospective cohorts[48,49].

Several limitations should nevertheless be acknowledged. First, the observational nature of the data does not permit definitive conclusions about causality. Second, circadian imbalance was assessed at a single time point, yet individuals’ circadian and behavioral patterns may shift considerably over the life course. Third, the UK Biobank cohort is characterized by a degree of “healthy volunteer” bias, which means that participants generally report better health and lower levels of residential deprivation than the broader UK population. Fourth, the reliance on ICD codes limits the ability to differentiate between mild, moderate, and severe psoriasis. Fifth, residual confounding cannot be excluded, as several potentially important confounders were unavailable, including shift work history, social jetlag, sunlight exposure, dietary quality, family history of psoriasis, and immunosuppressive medications, which could influence both circadian behaviors and psoriasis risk. Furthermore, as UK Biobank participants are predominantly of European ancestry, caution is warranted when generalizing these findings to other ethnic populations. Finally, the CII has not been validated against objective circadian measures, which are unavailable in the UK Biobank. Therefore, further validation in independent cohorts is warranted.

Conclusion

In summary, our prospective cohort study based on the UK Biobank provides robust evidence that CII is significantly positively associated with an increased risk of incident psoriasis. This association exhibits a synergistic interaction with genetic susceptibility, particularly among individuals with a high PRS. Furthermore, we identified that several blood biomarkers play partial mediating roles.

References

[1] Springate DA, Parisi R, Kontopantelis E, et al. Incidence, prevalence and mortality of patients with psoriasis: a U.K. population-based cohort study. Br J Dermatol. 2017;176:650-658.

[2] Zhang J, Shi X, Bai G, Chen J, Fu Y, Chen A. Association between the exposure to brominated flame retardants and psoriasis risk among U.S. adults: a population-based study. Ecotoxicol Environ Saf. 2025;291:117836.

[3] Bai G, Peng Y, Liu Q, et al. Association between body roundness index and psoriasis among US adults: a nationwide population-based study. Lipids Health Dis. 2024;23(1):373.

[4] Lu J, Zhang N, Jiang X, Diao ZY, Chen DD, Yin ZQ. Global burden of psoriasis from 1990 to 2021: a population-based analysis. J Am Acad Dermatol. 2025;93:532-533.

[5] Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet. 2021;397(10281):1301-1315.

[6] Armstrong AW, Read C. Pathophysiology, clinical presentation, and treatment of psoriasis: a review. JAMA. 2020;323(19):1945-1960.

[7] Ndiaye MA, Nihal M, Wood GS, Ahmad N. Skin, reactive oxygen species, and circadian clocks. Antioxid Redox Signal. 2014;20(18):2982-2996.

[8] Ando N, Nakamura Y, Aoki R, et al. Circadian gene clock regulates psoriasis-like skin inflammation in mice. J Invest Dermatol. 2015;135(12):3001-3008.

[9] Li WQ, Qureshi AA, Schernhammer ES, Han J. Rotating night-shift work and risk of psoriasis in US women. J Invest Dermatol. 2013;133(2):565-567.

[10] Zhang S, Zhang M, Yuan Y, Li Z, Li X, Li X. Health risks and genetic architecture of objectively measured multidimensional sleep health. Nat Commun. 2025;16(1):7026.

[11] Zhang J, Tran DT, El Ghoul T, et al. A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK Biobank. Eur J Epidemiol. 2026;41(4):445-458.

[12] Żebrowska M, Wielscher M, Zhang J, et al. Genome wide association study of a Circadian Imbalance Index in 312,935 European ancestry UK Biobank participants identifies loci related to diabetes, mood and myocardial infarction. Res Sq [Preprint]. 2025:rs.3.rs-8329341.

[13] Armstrong AW, Blauvelt A, Callis Duffin K, et al. Psoriasis. Nat Rev Dis Primers. 2025;11(1):45.

[14] Maamari DJ, Abou-Karam R, Fahed AC. Polygenic risk scores in human disease. Clin Chem. 2025;71(1):69-76.

[15] Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.

[16] Jones SE, Lane JM, Wood AR, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10(1):343.

[17] Svensson AK, Svensson T, Kitlinski M, et al. Incident diabetes mellitus may explain the association between sleep duration and incident coronary heart disease. Diabetologia. 2018;61(2):331-341.

[18] Vivarelli S, Formica T, Puliatti Y, Spatari G, Fenga C. Night shift work and breast cancer: from etiopathology to precision risk analysis. NPJ Breast Cancer. 2025;12(1):7.

[19] Wikoff D, Welsh BT, Henderson R, et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. Food Chem Toxicol. 2017;109(Pt 1):585-648.

[20] Heckman MA, Weil J, Gonzalez de Mejia E. Caffeine (1, 3, 7-trimethylxanthine) in foods: a comprehensive review on consumption, functionality, safety, and regulatory matters. J Food Sci. 2010;75(3):R77-R87.

[21] Wang X, Hoffstaedter F, Kasper J, Eickhoff SB, Patil KR, Dukart J. Lifetime exposure to depression and neuroimaging measures of brain structure and function. JAMA Netw Open. 2024;7(2):e2356787.

[22] Zhang Y, Huang Y, Jing D, et al. Associations of sleep pattern and genetic risk with late-onset psoriasis: a large prospective cohort study. J Invest Dermatol. 2026;146(5):1236-1243.e2.

[23] Chattopadhyay A, Ye M, Chiang B, et al. Increased sodium consumption is associated with psoriasis: a population-based cohort study. J Eur Acad Dermatol Venereol. 2025;39(9):1622-1630.

[24] Xiong Y, Xia Y, Zhang X, et al. Joint exposure to multiple air pollutants, genetic risk and incident psoriasis: a large-scale prospective cohort study. Br J Dermatol. 2025;192(3):420-429.

[25] Chiang BM, Ye M, Chattopadhyay A, Halezeroglu Y, Van Blarigan EL, Abuabara K. Sodium intake and atopic dermatitis. JAMA Dermatol. 2024;160(7):725-731.

[26] Shen M, Xiao Y, Jing D, et al. Associations of combined lifestyle and genetic risks with incident psoriasis: a prospective cohort study among UK Biobank participants of European ancestry. J Am Acad Dermatol. 2022;87(2):343-350.

[27] Shao X, Yu J, Liu Q, et al. Systemic inflammation response index mediates the association between relative fat mass and psoriasis risk: a population-based study. Lipids Health Dis. 2025;24(1):119.

[28] Shao X, Ou Y, Liu Q, et al. Association between metabolic score for visceral fat and psoriasis: findings from NHANES. Eur J Med Res. 2025;30(1):739.

[29] Lin BD, Pries LK, van Os J, Luykx JJ, Rutten BPF, Guloksuz S. Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank. J Hum Genet. 2023;68(9):653-656.

[30] Ma Y, Wang J, Tang L, et al. Association of air pollution exposure and genetic susceptibility with increased risk of thoracic aortic aneurysm and dissection. Nat Cardiovasc Res. 2025;4(10):1397-1408.

[31] Assmann SF, Hosmer DW, Lemeshow S, Mundt KA. Confidence intervals for measures of interaction. Epidemiology. 1996;7(3):286-290.

[32] Ma Y, Li D, Cui F, et al. Air pollutants, genetic susceptibility, and abdominal aortic aneurysm risk: a prospective study. Eur Heart J. 2024;45(12):1030-1039.

[33] Wang Y, Luo J, Tan Z, et al. Interactive and joint associations of C-reactive protein-triglyceride glucose index and body roundness index on incident cardiovascular diseases: a nationwide prospective cohort study. Nutr Metab Cardiovasc Dis. 2026;36(6):104609.

[34] Meng M, Wei R, Wu Y, et al. Long-term risks of respiratory diseases in patients infected with SARS-CoV-2: a longitudinal, population-based cohort study. E Clinical Medicine. 2024;69:102500.

[35] Qiao Y, Yang H, Lin R, Yu Y, Zhao M, Xi B. Accelerometer-derived “weekend warrior” physical activity pattern and cardiovascular disease in individuals with hypertension: a prospective cohort study. BMC Med. 2025;23(1):506.

[36] Liu X, Zhan H, Ou Y, Shangguan Y, Zhang J. Normalized creatinine-to-cystatin C ratio and atherosclerotic cardiovascular disease risk in the UK Biobank. Eur J Prev Cardiol. 2026:zwag113.

[37] Münte E, Zhang X, Khurana A, Hartmann P. Prevalence of extremely severe obesity and metabolic dysfunction among US children and adolescents. JAMA Netw Open. 2025;8(7):e2521170.

[38] Luo S, Chen W, Hu W, et al. Parental education, own education, and cognitive function in middle-aged and older adults. JAMA Netw Open. 2025;8(5):e2513036.

[39] Pasquali L, Srivastava A, Meisgen F, et al. The keratinocyte transcriptome in psoriasis: pathways related to immune responses, cell cycle and keratinization. Acta Derm Venereol. 2019;99(2):196-205.

[40] Németh V, Horváth S, Kinyó Á, Gyulai R, Lengyel Z. Expression patterns of clock gene mRNAs and clock proteins in human psoriatic skin samples. Int J Mol Sci. 2021;23(1):121.

[41] Gupta R, Debbaneh MG, Liao W. Genetic epidemiology of psoriasis. Curr Dermatol Rep. 2014;3(1):61-78.

[42] Mukherji A, Bailey SM, Staels B, Baumert TF. The circadian clock and liver function in health and disease. J Hepatol. 2019;71(1):200-211.

[43] Liu S, Zhang F, Wang Y, Zhuo K, Zhao Y. Synchronizing the liver clock: time-restricted feeding aligns rhythmic gene expression in key metabolic pathways. Cells. 2026;15(2):193.

[44] Zhu Z, Yin Q, Duan X. Xiaoyin-anshen formula alleviates psoriasis complicated by sleep disturbances by regulating melatonin, antioxidant enzymes, and pro-inflammatory cytokines in mice. Front Pharmacol. 2024;15:1427985.

[45] Hardman JA, Tobin DJ, Haslam IS, et al. The peripheral clock regulates human pigmentation. J Invest Dermatol. 2015;135(4):1053-1064.

[46] Kirchner SJ, Lei V, Kim PT, et al. An aging-susceptible circadian rhythm controls cutaneous antiviral immunity. JCI Insight. 2023;8(20):e171548.

[47] Addison R, Weatherhead SC, Pawitri A, et al. Therapeutic wavelengths of ultraviolet B radiation activate apoptotic, circadian rhythm, redox signalling and key canonical pathways in psoriatic epidermis. Redox Biol. 2021;41:101924.

[48] Yuan X, Ou C, Li X, Zhuang Z, Chen Y. The skin circadian clock gene F3 as a potential marker for psoriasis severity and its bidirectional relationship with IL-17 signaling in keratinocytes. Int Immunopharmacol. 2024;132:111993.

[49] Prema SS, Shanmugamprema D. Systemic psoriasis: from molecular mechanisms to global management strategies. Clin Rev Allergy Immunol. 2025;68(1):79.

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