The data from the UK Biobank was applied in this prospective cohort study, which enrolled above 500,000 participants aged 37 to 73 years from April 2006 to December 2010, with follow-up concluding in July 2023. The participants were invited to take part in an assessment at one of 22 centers distributed across the UK, having sites in England, Scotland, and Wales. Detailed information regarding the UK Biobank was published previously (22). Participants were excluded under the condition that they (1) had psoriasis at baseline, (2) had data missing for any of the five components of metabolic syndrome or other relevant covariates, (3) lacked data on polygenic risk scores (PRS), or (4) had data missing for calculating biological aging. The sample choosing process is shown in Figure 1 .

The diagnosis of psoriasis was primarily confirmed through hospital admission records obtained from the England Hospital Episode Statistics, Scotland Incidence Records, and the Wales Patient Event Database. Confirmation criteria included events recorded for the first time (field numbers 131742 and 131743), including primary care data, ICD codes in hospital inpatient records, ICD codes in death registration records, and self-reported health status codes validated by nurses after a doctor’s diagnosis. The follow-up period was defined as starting from the day each participant was evaluated up to July 6, 2023. For full-scale details regarding psoriasis assessment in the UK Biobank, access the UK Biobank’s official site (https://www.biobank.c.k/).

As per the 2009 standards developed by the American Heart Association, the National Heart, Lung, and Blood Institute, and the International Diabetes Federation, MetS is typified by having at least three of these elements: high blood pressure (systolic ≥130 mm Hg or diastolic ≥85 mm Hg); raised triglycerides (≥1.7 mmol/L); HDL cholesterol undergoes a reduction (<1 To men, the value reads 0 mmol/L, <1.3 mmol/L in women); escalated blood - sugar quantity, demonstrated by a raised HbA1c (≥42 mmol/mol [6.0%]); and elevated waist size.

The research platform of the UK Biobank offers a uniform polygenic risk score (PRS), created by meta-analyzing data from various genome-wide association studies (GWAS). Prior research has enhanced the understanding of the PRS methodology and the standard PRS sets used in GWAS data (23). In brief, the UK Biobank employs standardized definitions for subgroups, diseases, and quantitative traits to assess PRS consistently. The PRS algorithm is based on a meta-analysis of specific traits, utilizing the Bastion method to integrate data across diverse ancestral populations and related traits. A higher PRS reflects increased genetic susceptibility to the disease. For psoriasis (Field ID, 26269), participants were categorized into low (quintile 1), medium (quintiles 2-4), and high (quintile 5) genetic risk categories according to the PRS quintiles (24).

Baseline sociodemographic and lifestyle data were collected via a touchscreen questionnaire. Sociodemographic variables comprised age (< 60 vs ≥ 60 years), race (white vs non-white), sex, Townsend Deprivation Index (divided into five quintiles), education level (degree, professional qualification, or secondary school), and household income (< £18,000, £18,000-£30,999, £31,000-£51,999, £52,000-£100,000, or > £100,000). Lifestyle factors included elements of smoking status (never, former, or current), alcohol consumption habit (never, former, or current), fasting time length (1–6 hours), activity (low, medium, high), and self-reported history of cardiovascular disease (CAD). A no-report category was included for participants who were uncertain or declined to respond.

Biological aging in this study was assessed using the PhenoAge algorithm, which estimates all-cause mortality risk based on a Gompertz proportional hazards model incorporating chronological age and nine clinical biomarkers: albumin, alkaline phosphatase, creatinine, C-reactive protein, glucose, mean corpuscular volume, red cell distribution width, white blood cell count, and lymphocyte percentage. These biomarkers were initially identified through a Cox penalized regression model using data from the NHANES III cohort (25). PhenoAge acceleration (PhenoAgeAccel), reflecting deviations in biological aging relative to chronological age, was calculated as the residual from regressing PhenoAge on chronological age (26). To address the skewed distribution of certain biomarkers, we applied percentile-based winsorization, setting values below the 1st percentile and above the 99th percentile to the corresponding cutoff points to minimize the influence of outliers (27). All calculations were performed using the “BioAge” package in R.

Descriptive statistical methods were employed to contrast the fundamental traits of participants who had Metabolic Syndrome (MetS) with those who did not. Variables of a categorical nature are denoted in percentage terms, whereas continuous variables are shown as average values with standard deviations (SD). The comparison of continuous and categorical variables across groups utilized T-tests, Wilcoxon rank-sum tests, and Chi-square (χ2) tests. Responses or missing data, such as “do not know/prefer not to answer,” were classified. The duration of follow-up was determined from the date of participant enrollment until either psoriasis diagnosis, death, dropout, or the study’s conclusion. The link between MetS, its elements, genetic vulnerability, and the risk of psoriasis was assessed using a Cox regression model, resulting in the computation of hazard ratios (HRs). The Schoenfeld residual-based test was used to confirm no significant deviations from proportional hazards in all models. Model 1 was adjusted for age, gender, household income, education level, Townsend deprivation index, and race. In Model 2, we further adjusted for polygenic risk score, cardiovascular disease history, smoking status, alcohol consumption, and fasting duration. To examine the joint exposure effects of MetS and genetic susceptibility on psoriasis risk, participants were categorized into six groups based on genetic risk (low, medium, high) and MetS (yes/no). Multicollinearity was assessed, and no significant issues were found. Additionally, the P-value for the trend was computed by coding joint exposure as a continuous variable. Stratified analyses were performed by gender (male, female) and age (< 60 years and ≥ 60 years).

Mediation analysis was conducted using the “mediation” package in R. First, a linear regression model was fitted to assess the association between the exposure (metabolic syndrome and its components) and the mediator (standardized PhenoAgeAccel), adjusting for potential confounders including age, sex, ethnicity, education level, TDI, income, smoking status, alcohol consumption, CAD history, fasting time, and physical activity. Next, a logistic regression model was constructed to evaluate the association between the exposure and the outcome (psoriasis), with both the exposure and mediator included in the model, adjusting for the same set of covariates. The average causal mediation effect (ACME) and the average direct effect (ADE) were estimated using the “mediate” function, with 1,000 bootstrap simulations to calculate confidence intervals.

Multiple sensitivity tests were performed to evaluate the solidity of the results, such as (1) omitting participants who had less than two years of follow-up to reduce differential bias; (2) excluding patients with fasting times less than 3 hours to reduce potential bias from serum metabolic biomarkers; (3) excluding patients with missing data of MetS components and covariates. Each P-value was bidirectional, establishing statistical significance at P<0.05. Every analysis was performed utilizing RStudio version 4.3.3.

This study included 319,263 participants, of whom 231,006 (72.4%) did not have metabolic syndrome and 88,257 (27.6%) had metabolic syndrome. Table 1 displays initial traits categorized according to MetS status. Compared to those without MetS, individuals with a MetS diagnosis were more likely to be older, male, of non-white background, have lower levels of education, live in socioeconomically disadvantaged areas, earn less income, have a history of smoking, abstain from alcohol or be former drinkers, have a medium activity and report a prior diagnosis of coronary artery disease. At baseline, patients with MetS also manifested higher prevalences of hypertension, hyperglycemia, elevated triglycerides, central obesity, and low HDL cholesterol.

Exploring the link between metabolic syndrome and the occurrence of psoriasis, we performed a multivariable-adjusted Cox regression analysis, controlling for demographic variables in Model 1. Results in Table 2 reveal a notable link between metabolic syndrome and a heightened likelihood of psoriasis. Taking Model 1 as a base, Model 2 was additionally adapted for factors like polygenic risk score, smoking status, alcohol status, fasting time, activity, and history of CAD, according to previous findings ( Table 2 ). People diagnosed with MetS showed a 30% increased likelihood of developing psoriasis in comparison to those free of the condition (HR 1.30; 95% CI, 1.20-1.40). Particularly speaking, the elements of metabolic syndrome—hypertriglyceridemia (HR 1.14 95% CI, 1.06-1.23), hyperglycemia (HR 1.19; 95% CI, 1.09-1.30), magnified waist size (HR 1.32; 95% CI, 1.23-1.42), and was accompanied by a drop in HDL cholesterol (HR 1.25; 95% CI, 1.15-1.36) — were respectively related to a greater possibility of psoriasis. Yet, there was no obvious link between hypertension (HR 1.03; 95% CI, 0.95-1.12) coupled with the risk of psoriasis. Besides, the likelihood of psoriasis grew in proportion to the number of metabolic abnormalities.

Table 3 shows how genetic risk affects psoriasis development. We measured genetic risk using polygenic risk scores. After adjustment, higher genetic risk groups had progressively higher psoriasis risk. The medium-risk group had 39% higher risk than the low-risk group (HR=1.39; 95%CI=1.25-1.54; p<0.001). The high-risk group had 2.22 times higher risk than the low-risk group (HR=2.22; 95%CI=1.98-2.49; p<0.001). The risk increased significantly with higher genetic risk scores (p-trend<0.001).

According to the data presented in Table 4 , the link between metabolic syndrome and the risk of psoriasis varies based on genetic vulnerability levels. In individuals with moderate genetic susceptibility, those with metabolic syndrome had a 27% increased risk of psoriasis (HR, 1.27; 95% CI, 1.15-1.41; P < 0.001). In individuals with high genetic susceptibility, the association was even stronger, with a 41% increased risk (HR, 1.41; 95% CI, 1.23-1.62; P < 0.001). No significant interaction between metabolic syndrome and genetic susceptibility was observed regarding psoriasis risk (P = 0.143). Further analysis of psoriasis risk revealed that all components of metabolic syndrome– such as increased waist circumference, lower HDL cholesterol, higher fasting blood glucose, and higher triglyceride levels– are associated with increased psoriasis risk. This association was more pronounced among individuals with high genetic susceptibility. However, elevated blood pressure did not demonstrate a significant association with psoriasis risk. Additionally, a significant interaction was found between elevated triglycerides and genetic risk (P = 0.021).

In Figure 2 , we examined the joint association of metabolic syndrome status and PRS with the risk of psoriasis development. In the fully adjusted model, participants with both metabolic syndrome and high genetic susceptibility exhibited a significantly elevated risk of psoriasis compared to those without metabolic syndrome and with low genetic susceptibility, with an HR of 2.93 (95% CI, 2.51-3.43). When evaluating specific metabolic components, the HRs were as follows: 2.68 (95% CI, 2.25-3.19) for elevated fasting glucose, 2.83 (95% CI, 2.39-3.36) for reduced high-density lipoprotein cholesterol, 2.52 (95% CI, 2.15-2.94) for elevated triglycerides, 2.21 (95% CI, 1.81-2.69) for elevated blood pressure, and 2.95 (95% CI, 2.52-3.45) for increased waist circumference. These findings suggest that the presence of components of metabolic syndrome, particularly associated with high genetic susceptibility, significantly increases the risk of developing psoriasis.

Sensitivity analyses were performed after excluding participants with missing MetS component and covariate data, less than 2 years of follow-up, or fasting times under 3 hours. As reported in Supplementary File 1 , the presence of MetS and its components was significantly associated with an increased risk of psoriasis, and the dose-response relationship was significant with an increase in the number of MetS components (P represents trend < 0.001). Furthermore, Supplementary File 2 revealed that the presence of MetS continued to be associated with a higher risk of psoriasis across different PRS categories. These results enhance the performance of MetS, its components, and the risk of psoriasis under varying analytical conditions.

The analysis categorized results based on age, gender, and genetic predisposition. Findings indicated that metabolic syndrome was linked to a 46% higher risk of psoriasis in individuals younger than 60 years (HR: 1.46, 95% CI: 1.31-1.62) and a 17% increase in those aged 60 and above (HR: 1.17, 95% CI: 1.05-1.30). Among participants with a high polygenic risk score, MetS further amplified the risk, with HRs of 2.48 (95% CI: 1.98-3.12) for individuals over 60 and 3.49 (95% CI: 2.81-4.34) for those under 60. Gender-based analysis showed that MetS raised psoriasis risk by 36% in women (HR: 1.36, 95% CI: 1.22-1.51) and 25% in men (HR: 1.25, 95% CI: 1.12-1.39). Additionally, individuals with both high PRS and MetS exhibited a more than threefold risk increase, with HRs of 2.93 (95% CI: 2.34-3.67) in women and 2.96 (95% CI: 2.37-3.70) in men. Additional findings are available in Supplementary Files 3 - 6 .

Mediation analysis indicated that PhenoAgeAccel acted as a mediator between metabolic syndrome and the risk of psoriasis (IE = 0.0007; DE = 0.0018; proportion mediated = 28.8%; P < 0.001) ( Figure 3A ).

When examining individual components of metabolic syndrome, PhenoAge acceleration mediated 28.8% of the association between increased waist circumference and psoriasis risk (IE = 0.0008; DE = 0.0019; P < 0.001) ( Figure 3B ) and 25.4% of the association between reduced HDL cholesterol and psoriasis risk (IE = 0.0005; DE = 0.0016) ( Figure 3C ). Additionally, PhenoAgeAccel mediated 40.2% of the association between elevated fasting glucose and psoriasis risk (IE = 0.0007; DE = 0.0010) ( Figure 3E ) and 24.8% of the association between elevated triglycerides and psoriasis risk (IE = 0.0003; DE = 0.0010) ( Figure 3F ). In the elevated blood pressure group, although the indirect effect was significant (IE = 0.0003; P < 0.001), the direct effect and the proportion mediated were not significant ( Figure 3D ).

To further verify the mediating role of PhenoAgeAccel, several sensitivity analyses were conducted, and the results remained largely consistent with the primary findings ( Supplementary Figures 1 - 3 ).