Abstract
Background:
Serum uric acid (SUA) has been implicated in various inflammatory conditions, yet its association with inflammatory proteins and treatment outcomes in psoriasis remains unclear. This study aimed to investigate the association of SUA levels with specific circulating inflammatory proteins and treatment response to ixekizumab, a monoclonal antibody against interleukin-17A, in patients with moderate-to-severe psoriasis.
Methods:
Patients attending 26 dermatology centers across China were recruited into this real-world prospective cohort study conducted between August 2020 and May 2022. The SUA levels of the participants were measured to assess hyperuricemia, and treatment response was evaluated using the Psoriasis Area and Severity Index (PASI). All participants received ixekizumab at a dose of 160 mg at week 0, followed by 80 mg every 2 weeks through week 12. Adjusted logistic regression analysis was used to explore differences in treatment response between patients with and without hyperuricemia. The restricted cubic spline (RCS) was employed to explore the continuous relationship between SUA and the response outcomes. Specifically, we measured 92 circulating plasma inflammatory markers using the Olink Proteomics.
Results:
A total of 390 patients with psoriasis were included in the study. The psoriasis patients with hyperuricemia demonstrated a higher prevalence of obesity, hyperlipidemia, and hyperglycemia compared with those without hyperuricemia. After 12 weeks of ixekizumab treatment, patients with hyperuricemia exhibited a lower likelihood of achieving PASI 75 (adjusted odds ratio [aOR] 0.84, 95% confidence interval [CI] 0.69–0.99), PASI 90 (aOR 0.37, 95% CI 0.15–0.62), and PASI 100 (aOR 0.57, 95% CI 0.37–0.91). Olink Proteomics identified significant correlations between SUA concentration and the levels of 26 circulating inflammatory proteins, and pathway analysis showed enrichment of pathways such as response to tumor necrosis factor (TNF), lymphocyte activation, and cytokine activity.
Conclusion:
Elevated SUA levels can hinder the effectiveness of ixekizumab and positively correlate with specific circulating inflammatory proteins in patients with psoriasis, emphasizing the need for further investigation and consideration of metabolic factors in psoriasis management strategies.
Trial registeration:
Chinese Clinical Trial Registry (ChiCTR2000036186)
Keywords: Psoriasis, Ixekizumab, Hyperuricemia, Serum uric acid, Circulating inflammatory proteins
Introduction
Psoriasis is a chronic inflammatory skin disorder affecting approximately 2–3% of the global population, characterized by the development of erythematous plaques with silvery scales, often associated with significant physical and psychological burden on affected individuals.[1] The etiology of psoriasis is believed to involve complex interactions between genetic, environmental, and immunological factors, but despite extensive research efforts, the precise etiology of psoriasis remains incompletely understood.[2] Emerging evidence suggests a potential link between psoriasis and metabolic abnormalities, including obesity, dyslipidemia, and insulin resistance, which may contribute to the chronic inflammatory state observed in affected individuals.[3]
Moreover, recent epidemiological studies have demonstrated a higher prevalence of hyperuricemia among patients with psoriasis compared to the general population, suggesting a potential interplay between uric acid metabolism and psoriatic pathogenesis.[4] Among the various inflammatory mediators implicated in psoriasis pathogenesis, serum uric acid (SUA) has recently emerged as a potential modulator of inflammation beyond its well-established role in gout. SUA, a metabolic end product of purine metabolism, has been linked to inflammatory processes through its ability to induce cytokine production and activate immune cells.[5,6] However, the clinical significance of SUA in patients with psoriasis remains poorly elucidated, particularly regarding its impact on treatment outcomes.
Ixekizumab, a high-affinity monoclonal antibody targeting interleukin (IL)-17A, has shown remarkable efficacy in achieving skin clearance and enhancing quality of life in patients with psoriasis and is currently one of the most widely used biologics in China.[7] Despite the therapeutic advancements, variability in treatment response among patients remains a clinical challenge; thus, there is a clear need to gain a deeper understanding of the factors influencing treatment outcomes.[8] Therefore, this study aimed to address the gap in knowledge regarding the influence of SUA on treatment response in psoriasis patients receiving ixekizumab therapy. Furthermore, our study identifies specific inflammatory proteins whose levels correlate significantly with SUA concentrations, thereby shedding light on potential mechanistic links between SUA and psoriatic inflammation.[9] Insights gleaned from this study may inform personalized treatment strategies and highlight the importance of considering metabolic comorbidities in the management of psoriasis.
Methods
Study design
This study employed a prospective cohort design to assess the influence of SUA levels on treatment response to ixekizumab in patients with psoriasis. In order to ensure geographic diversity and representation of various clinical practices, we recruited patients from 26 dermatology centers across China. The centers were chosen based on their patient volume and experience in managing psoriasis. All participating centers followed a standardized protocol for patient enrollment, aiming to minimize selection bias. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the ethics committee of Shanghai Skin Disease Hospital (Approval No. 2020-11), the coordinating center for this multicenter study. Details of all participating centers are presented in Supplementary Table 1, http://links.lww.com/CM9/C996. Informed consent was obtained from all participants before enrollment, and measures were taken to ensure patient confidentiality and data security throughout the study period. The dataset will be available upon reasonable request for further research purposes. The study was registered at Chinese Clinical Trial Registry (ChiCTR2000036186).
Participants
Patients were recruited into this real-world prospective cohort study conducted between August 2020 and May 2022. Inclusion criteria were as follows: age at least 18 years; diagnosis of moderate-to-severe plaque psoriasis; and being prescribed ixekizumab in a routine clinical care setting according to the approved label by the investigator. Patients were excluded based on the following: they were administered ixekizumab in the past; those with a history of severe systemic diseases; and those receiving concomitant treatment with medications known to influence uric acid levels; and absence of baseline information and venous blood samples. Patients received ixekizumab 160 mg at week 0, then 80 mg every 2 weeks up to and including week 12. Treatment response was assessed using the Psoriasis Area and Severity Index (PASI) at baseline and after 12 weeks of therapy.
Measurement of serum uric acid levels
SUA levels were determined for all participants at baseline using standardized laboratory methods. Blood samples were collected after an overnight fast to minimize potential fluctuations due to recent dietary intake. The analysis of SUA levels was conducted using validated enzymatic assays performed on automated clinical chemistry analyzers. Hyperuricemia was defined as SUA levels exceeding the upper limit of the normal range, as per established clinical guidelines. Specifically, SUA levels above 7.0 mg/dL (420 µmol/L) for males and 6.0 mg/dL (360 µmol/L) for females were considered indicative of hyperuricemia in this study.
Olink Proteomics and bioinformatics analysis
We randomly selected serum samples from 15 patients for analysis using the Olink® Target 96 Inflammation panel, which enables the simultaneous quantification of 92 inflammatory proteins. The assay was performed by Olink Proteomics (Shanghai, China), using the Olink Target 96 Inflammation panel based on the proximity extension assay (PEA) technology. This high-throughput proteomic technology employs proximity extension assays to achieve precise and sensitive measurements of protein levels.[10] Spearman correlation coefficients were computed to evaluate the relationship between SUA levels and the circulating inflammatory proteins. Additionally, pathway enrichment analysis was performed using the GeneMANIA web server (University of Toronto, Toronto, Ontario, Canada)[11] to identify biological pathways enriched among the inflammatory proteins associated with SUA levels.
Statistical analysis
Continuous variables were reported as means ± standard deviation (SD) if assumed to follow a normal distribution and as medians (interquartile range [IQR]) if non-normally distributed. Student’s t-test and Mann–Whitney U test were used for comparison, respectively. Categorical variables were presented as frequencies (%) and analyzed using chi-squared or Fisher’s exact tests. We employed adjusted logistic regression analysis to assess the association between hyperuricemia and treatment response, as this method allows us to account for potential confounders while estimating odds ratios. We chose this model due to its suitability for binary outcome variables. Variables included in the adjusted models were selected based on clinical relevance and earlier research, and included age, gender, age at onset, comorbidities (psoriatic arthritis, obesity, hypertension, hyperlipidemia, and hyperglycemia), baseline PASI score, baseline Dermatology Life Quality Index (DLQI) score, previous biologic treatments, and specific areas involvement. Subgroup analysis was conducted to investigate potential interactions and influencing factors. We adopted proactive measures to minimize the occurrence of missing data by standardizing data collection procedures across all participating centers. For instance, of missing data, we used multiple imputation techniques to handle the gaps effectively. All covariates except the subgroup variables were adjusted in the analysis. R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses. A P-value of <0.05 was considered to indicate statistically significant associations or differences.
Results
Baseline characteristics
Based on SUA levels, 390 patients were divided into patients with hyperuricemia (n = 162) and without hyperuricemia (n = 228). Comparing the baseline characteristics of the two groups, it was observed that individuals with hyperuricemia had higher body weight (75 kg vs. 68 kg) and higher body mass index (BMI) (26.1 kg/m2vs. 23.7 kg/m2). Moreover, the hyperuricemia group had a greater proportion of male patients (86.4% [140/162] vs. 67.5% [154/228]). Furthermore, patients with hyperuricemia demonstrated a higher prevalence of obesity (27.2% [44/162] vs. 11.0% [25/228]), hyperlipidemia (28.4% [46/162] vs. 16.7% [38/228]), and hyperglycemia (30.2% [49/162] vs. 20.6% [47/228]) [Table 1].
Table 1.
Comparison of baseline characteristics between patients with and without hyperuricemia.
| Characteristics | Total (n = 390) | Psoriasis patients without hyperuricemia (n = 228) | Psoriasis patients with hyperuricemia (n = 162) | Statistics | P-value |
|---|---|---|---|---|---|
| Age (years) | 40 ± 13 | 41 ± 14 | 39 ± 12 | 0.501* | 0.620 |
| Male | 294 (75.4) | 154 (67.5) | 140 (86.4) | 17.335‡ | <0.001 |
| Age at onset of psoriasis (years) | 30 ± 15 | 30 ± 16 | 29 ± 12 | 0.837* | 0.370 |
| Duration of psoriasis (years) | 10 ± 8 | 11 ± 7 | 10 ± 4 | 1.502* | 0.130 |
| Body weight (kg) | 71 (63–80) | 68 (60–78) | 75 (66–85) | –6.728† | <0.001 |
| BMI (kg/m2) | 24.7 ± 3.9 | 23.7 ± 3.6 | 26.1 ± 3.9 | –6.006* | <0.001 |
| Baseline PASI score | 20 ± 14 | 19 ± 13 | 21 ± 14 | –1.131* | 0.260 |
| Baseline BSA score | 31 ± 23 | 30 ± 23 | 31 ± 23 | –0.295* | 0.670 |
| Baseline DLQI score | 13 ± 7 | 12 ± 7 | 14 ± 7 | –1.690* | 0.091 |
| Comorbidities | |||||
| Psoriatic arthritis | 158 (40.5) | 91 (39.9) | 67 (41.4) | 0.011‡ | 0.770 |
| Obesity | 69 (17.7) | 25 (11.0) | 44 (27.2) | 13.513‡ | <0.001 |
| Hypertension | 43 (11.0) | 26 (11.4) | 17 (10.5) | 0.165‡ | 0.780 |
| Hyperlipidemia | 84 (21.5) | 38 (16.7) | 46 (28.4) | 8.188‡ | 0.005 |
| Hyperglycemia | 96 (24.6) | 47 (20.6) | 49 (30.2) | 6.087‡ | 0.014 |
| Earlier psoriasis treatments | |||||
| Previous traditional systemic treatments | 182 (46.7) | 97 (42.5) | 85 (52.5) | 0.230‡ | 0.629 |
| Previous phototherapy | 75 (19.2) | 41 (18.0) | 34 (21.0) | 0.574‡ | 0.460 |
| Previous biologic treatments | 29 (7.4) | 20 (8.8) | 9 (5.6) | 1.438‡ | 0.230 |
| Psoriasis involvement | |||||
| Nails affected | 142 (36.4) | 76 (33.3) | 66 (40.7) | 1.121‡ | 0.130 |
| Scalp affected | 328 (84.1) | 193 (84.6) | 135 (83.3) | 0.050‡ | 0.730 |
| Genital area affected | 41 (10.5) | 23 (10.1) | 18 (11.1) | 0.101‡ | 0.750 |
| Palmoplantar area affected | 114 (29.2) | 62 (27.2) | 52 (32.1) | 1.222‡ | 0.290 |
Data are presented as n (%), mean ± standard deviation (SD), or median (IQR), as appropriate. *Student’s t-test, †Mann–Whitney U test, ‡chi-squared test. BMI: Body mass index; BSA: Body surface area; DLQI: Dermatology Life Quality Index; IQR: Interquartile range; PASI: Psoriasis Area and Severity Index.
Comparison of treatment efficacy
At 3 months, compared with patients without hyperuricemia, a lower proportion of patients with hyperuricemia achieved PASI 75 (80% [99/123] vs. 92% [165/180]), PASI 90 (61% [75/123] vs. 72% [130/180]), and PASI 100 (35% [43/123] vs. 46% [83/180]). After adjusting for potential confounders, patients with hyperuricemia were less likely than those without hyperuricemia to achieve a PASI 75 (adjusted odds ratio [aOR] 0.84, 95% confidence interval [CI] 0.69–0.99), PASI 90 (aOR 0.37, 95% CI 0.15–0.62), and PASI 100 (aOR 0.57, 95% CI 0.37–0.91) [Table 2]. The results of subgroup analyses were similar to those of the primary analysis, and the baseline variables did not interact significantly with the grouping based on the presence of hyperuricemia (Pfor interaction >0.05) [Table 3]. Furthermore, we have redefined our inclusion, SUA levels above 420 μmol/L are classified as hyperuricemia regardless of gender. Our reanalysis shows that the results remain consistent with our initial findings [Supplementary Table 2, http://links.lww.com/CM9/C996].
Table 2.
Treatment efficacy comparison between psoriasis patients with and without hyperuricemia.
| Characteristics | Without hyperuricemia | With hyperuricemia | P-value |
|---|---|---|---|
| PASI 75 (n [%]) | 165 (92) | 99 (80) | |
| Crude OR with 95% CI | Ref. | 0.73 (0.59–0.94) | 0.002 |
| Adjusted OR with 95% CI | Ref. | 0.84 (0.69–0.99) | 0.037 |
| PASI 90 (n [%]) | 130 (72) | 75 (61) | |
| Crude OR with 95% CI | Ref. | 0.48 (0.24–0.72) | 0.042 |
| Adjusted OR with 95% CI | Ref. | 0.37 (0.15–0.62) | 0.022 |
| PASI 100 (n [%]) | 83 (46) | 43 (35) | |
| Crude OR with 95% CI | Ref. | 0.61 (0.33–0.92) | 0.042 |
| Adjusted OR with 95% CI | Ref. | 0.57 (0.37–0.91) | 0.039 |
CI: Confidence interval; OR: Odds ratio; PASI: Psoriasis Area and Severity Index; Ref.: Reference.
Table 3.
Effect of hyperuricemia on treatment outcomes—subgroup analysis.
| Subgroups | PASI 100 | PASI 90 | PASI 75 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Adjusted OR (95% CI) | P-value | Pfor interaction | Adjusted OR (95% CI) | P-value | Pfor interaction | Adjusted OR (95% CI) | P-value | Pfor interaction | |
| Overall | 0.60 (0.36–0.95) | 0.041 | 0.62 (0.37–0.95) | 0.037 | 0.34 (0.11–0.64) | 0.012 | |||
| Age | 0.650 | 0.614 | 0.542 | ||||||
| <40 years | 0.71 (0.36–0.98) | 0.048 | 0.61 (0.30–0.95) | 0.048 | 0.32 (0.10–0.87) | 0.032 | |||
| ≥40 years | 0.52 (0.25–0.96) | 0.036 | 0.54 (0.22–1.04) | 0.075 | 0.17 (0.04–0.59) | 0.025 | |||
| Gender | 0.496 | 0.720 | 0.842 | ||||||
| Female | 0.67 (0.37–0.89) | 0.045 | 0.57 (0.28–0.92) | 0.029 | 0.26 (0.05–0.50) | 0.010 | |||
| Male | 0.43 (0.15–0.77) | 0.005 | 0.64 (0.28–1.88) | 0.450 | 0.15 (0.04–0.84) | 0.004 | |||
| BMI (kg/m2) | 0.072 | 0.918 | 0.399 | ||||||
| <24 | 0.29 (0.13–0.65) | 0.003 | 0.47 (0.22–1.06) | 0.070 | 0.19 (0.04–0.53) | 0.012 | |||
| ≥24 | 0.87 (0.45–1.02) | 0.069 | 0.50 (0.25–0.99) | 0.049 | 0.20 (0.06–0.66) | 0.027 | |||
| Duration of psoriasis | 0.543 | 0.348 | 0.367 | ||||||
| <2 years | 0.62 (0.24–0.95) | 0.014 | 0.77 (0.29–2.02) | 0.462 | 0.33 (0.05–1.72) | 0.315 | |||
| ≥2 years | 0.56 (0.31–1.01) | 0.059 | 0.45(0.26–0.84) | 0.012 | 0.11 (0.09–0.44) | 0.002 | |||
| Diabetes | 0.110 | 0.242 | 0.776 | ||||||
| No | 0.57 (0.34–0.94) | 0.029 | 0.55 (0.32–0.89) | 0.016 | 0.28 (0.10–0.52) | 0.021 | |||
| Yes | 0.95 (0.53–0.93) | 0.045 | 0.88 (0.26–1.00) | 0.051 | 0.25 (0.12–0.67) | 0.002 | |||
| Hypertension | 0.709 | 0.309 | 0.528 | ||||||
| No | 0.62 (0.37–1.03) | 0.069 | 0.66 (0.35–1.06) | 0.090 | 0.20 (0.10–0.55) | 0.021 | |||
| Yes | 0.47 (0.04–0.85) | 0.029 | 0.05 (0.00–0.80) | 0.069 | 0.75 (0.38–1.95) | 0.089 | |||
| Psoriatic arthritis | 0.296 | 0.612 | 0.643 | ||||||
| No | 0.72 (0.39–1.03) | 0.066 | 0.62 (0.34–0.98) | 0.047 | 0.27 (0.06–0.52) | 0.002 | |||
| Yes | 0.39 (0.16–0.93) | 0.038 | 0.39 (0.12–0.96) | 0.042 | 0.23 (0.04–1.53) | 0.210 | |||
| Baseline PASI score | 0.459 | 0.745 | 0.798 | ||||||
| <15 | 0.53 (0.28–0.99) | 0.048 | 0.56 (0.39–1.03) | 0.092 | 0.29 (0.08–0.68) | 0.001 | |||
| ≥15 | 0.79 (0.34–1.09) | 0.095 | 0.42 (0.12–1.15) | 0.081 | 0.17 (0.07–0.58) | 0.017 | |||
BMI: Body mass index; CI: Confidence interval; OR: Odds ratio; PASI: Psoriasis Area and Severity Index.
The SUA levels were further analyzed in patients with psoriasis to compare changes before and after treatment with ixekizumab. We observed a significant decrease in mean SUA levels, from 6.44 ± 1.11 mg/dL to 6.20 ± 1.19 mg/dL at 12 week (t = 2.99, P = 0.003) [Supplementary Figure 1, http://links.lww.com/CM9/C996]. However, it is important to note that this reduction, while statistically significant, may not be clinically meaningful due to its relatively small magnitude.
Restricted cubic spline (RCS) analysis
RCS models were used to examine the dose–response relationships between SUA levels and the probabilities of achieving PASI 75, PASI 90, and PASI 100 responses. Overall, we found no significant evidence of a nonlinear relationship between SUA levels and the efficacy index (Pnonlinear >0.05). As SUA levels increased, the odds of achieving PASI 75, PASI 90, and PASI 100 responses decreased correspondingly [Figure 1].
Figure 1.
Higher serum uric acid levels were associated with reduced treatment response to ixekizumab in patients with psoriasis. (A) Restricted cubic spline fitting for the association between serum uric acid level and PASI 75 response; (B) Restricted cubic spline fitting for the association between serum uric acid level and PASI 90 response; and (C) Restricted cubic spline fitting for the association between serum uric acid level and PASI 100 response. PASI: Psoriasis Area and Severity Index.
Circulating inflammatory proteins analysis
Using Olink Proteomics technology, we quantified 92 circulating inflammatory proteins in the serum samples of patients with psoriasis. Among these, 26 proteins showed significant correlations with SUA levels (P <0.05) [Figure 2A and B]. Pathway enrichment analysis using GeneMANIA tool revealed that the identified proteins were significantly enriched in several biological pathways relevant to immune response and inflammation, such as in response to tumor necrosis factor (TNF), positive regulation of lymphocyte activation, cytokine activity, cytokine receptor binding, and cellular response to chemokine [Figure 2C]. Analysis of circulating proteins before and after ixekizumab treatment revealed significant reductions in eight proteins, including interleukin (IL)-17C, IL-17A, IL-20, monocyte chemoattractant protein-3 (MCP-3), IL-24, C-C motif chemokine ligand 20 (CCL20), TNF-related activation-induced cytokine (TRANCE), and C-X-C motif chemokine ligand 10 (CXCL10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated significant enrichment of these proteins in pathways such as IL-17 signaling, cytokine-cytokine receptor interaction, TNF signaling, and chemokine signaling [Supplementary Figure 2, http://links.lww.com/CM9/C996]. Interestingly, no significant correlation was observed between SUA levels and IL-17A, the direct target of ixekizumab [Supplementary Figure 3, http://links.lww.com/CM9/C996].
Figure 2.
Elevated serum uric acid levels were associated with a distinct proinflammatory protein signature and enrichment of cytokine-related pathways. (A) The coexpression heatmap showed the levels of 92 circulating inflammatory proteins with increasing serum uric acid concentration. (B) Spearman correlation showed that 26 circulating inflammatory proteins were significantly positively correlated with serum uric acid concentration. (C) Predicttion of the pathways and functions of the above 26 circulating inflammatory proteins using GeneMANIA.
Discussion
Psoriasis is a complex, multifactorial inflammatory skin disorder characterized by abnormal immune responses and systemic inflammation.[12] Although the pathogenesis of psoriasis involves various genetic, environmental, and immunological factors, emerging evidence suggests a potential link between psoriasis and metabolic abnormalities, including hyperuricemia.[13] This study contributes to our understanding of the impact of hyperuricemia on treatment response in patients with psoriasis receiving ixekizumab therapy, shedding light on the intricate interplay between metabolic factors and disease management based on the significant correlations observed between various inflammatory proteins and SUA. Given the high prevalence of metabolic abnormalities in psoriasis populations, these results underscore the importance of comprehensive metabolic screening and management in guiding treatment decisions and optimizing therapeutic outcomes.
The mechanisms underlying the association between hyperuricemia and treatment resistance in psoriasis are likely multifactorial and complex. Hyperuricemia has been implicated in systemic inflammation and oxidative stress, both of which play crucial roles in the pathogenesis of psoriasis. Elevated serum uric acid levels have been shown to activate the NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, leading to increased production of proinflammatory cytokines such as IL-1β and IL-18.[14,15] These inflammatory mediators contribute to the perpetuation of psoriatic inflammation and may interfere with the efficacy of biologic therapies. Moreover, hyperuricemia is often associated with insulin resistance and dyslipidemia, which can further exacerbate systemic inflammation and undermine treatment responses in patients with psoriasis.[16–18] Future studies exploring the mechanistic links between hyperuricemia, inflammation, and treatment resistance are warranted to elucidate the underlying pathophysiological pathways and identify potential therapeutic targets.
Chronic low-grade inflammation, characteristic of metabolic syndrome, may synergize with the underlying inflammatory processes in psoriasis, creating a milieu conducive to disease progression and treatment resistance.[19] Previous research has established an association between metabolic syndrome, including obesity, hypertension, diabetes, and insulin resistance, with poor response to biologic therapies in patients with psoriasis.[20] Our study adds to the growing body of evidence by demonstrating an association between hyperuricemia and poorer treatment response in patients with psoriasis. Some known effective strategies for managing elevated SUA levels include lifestyle changes, such as a low-purine diet, increased hydration, and weight loss, which can effectively reduce SUA. For patients with persistent hyperuricemia, pharmacological options such as xanthine oxidase inhibitors may be necessary, particularly when dietary measures fail. Integrating these strategies into clinical practice may mitigate the effects of high SUA on treatment resistance, improving therapeutic outcomes for patients with psoriasis. Furthermore, future studies should investigate the associations between various subtypes of hyperuricemia and renal function in patients with psoriasis in order to enhance our understanding of their interrelated impacts on treatment outcomes.
Elevated SUA levels were correlated with specific inflammatory proteins implicated in pathways such as response to TNF, positive regulation of lymphocyte activation, and cytokine activity. These findings underscore the potential role of SUA as a biomarker of systemic inflammation in psoriasis and suggest its involvement in regulating inflammatory processes beyond its traditional role in gout.[21] The identification of specific inflammatory proteins that correlate with SUA levels may provide valuable insights for clinicians. For example, monitoring levels of these proteins may help predict which patients are at higher risk of treatment resistance due to elevated SUA. Furthermore, targeting SUA-mediated pathways may offer new avenues for therapeutic development aimed at modulating immune dysregulation and improving outcomes for patients with psoriasis. Although our findings suggest significant correlations between SUA levels and circulating inflammatory proteins, the sample size of patients may limit the generalizability of these results. Further independent validation in larger cohorts or through complementary methodologies is essential for robust conclusions.
In this study, hyperuricemia was defined using standard clinical thresholds (≥420 μmol/L for males and ≥360 μmol/L for females). Although these thresholds are widely used in the general population, their applicability in patients with psoriasis may be questionable. The high prevalence of metabolic syndrome among these patients could impact uric acid metabolism, potentially altering the clinical interpretation of uric acid levels. Future research should investigate whether it is necessary to adjust the diagnostic criteria for hyperuricemia specifically for the population with psoriasis, to more accurately assess its impact on treatment response.
The implications of our findings extend beyond the realm of psoriasis management to encompass broader considerations in clinical practice and research.[22] Firstly, the identification of hyperuricemia as a potential predictor of treatment response highlights the importance of personalized medicine approaches in psoriasis care. Based on the findings, it is suggested that clinicians screen for hyperuricemia in psoriasis cases before initiating biologic therapy. Those identified with elevated SUA levels might benefit from tailored treatment approaches, potentially including lifestyle modifications or adjunctive therapies to manage uric acid levels, alongside standard psoriasis treatment.[23] Furthermore, our findings underscore the need for integrated, multidisciplinary care models that address both the dermatological manifestations of psoriasis and its associated metabolic comorbidities. Collaborative efforts among dermatologists, rheumatologists, endocrinologists, and other healthcare providers are essential for optimizing patient outcomes and reducing the burden of disease in populations with psoriasis.
The strengths of our study include its multicenter, real-world design, which enhances the generalizability of our findings to diverse patient populations. However, several limitations warrant consideration. As an observational study, we are unable to establish causal relationships between SUA levels and treatment outcomes in psoriasis. The associations observed may be influenced by various confounding factors that we may not have fully accounted for, despite our efforts to control for known variables. Although our study was conducted across 26 dermatology centers in China, the findings may not be directly applicable to all populations or regions due to variations in genetic, environmental, and healthcare factors. Therefore, further research is needed to validate these findings in diverse cohorts and settings to enhance the generalizability of our results. Finally, the 12-week follow-up limits our evaluation of long-term outcomes, highlighting the need for further research to understand the sustained impact of SUA levels on treatment response.
In conclusion, this study provides valuable insights into the influence of SUA levels on ixekizumab treatment outcomes and the relationship of SUA levels with inflammatory proteins in psoriasis. The findings underscore the clinical significance of considering metabolic comorbidities in psoriasis management and highlight the need for personalized, multidisciplinary approaches to care. Further research is warranted to elucidate the underlying mechanisms linking SUA, inflammatory proteins, and treatment response and to explore novel therapeutic interventions aimed at optimizing outcomes in psoriasis patients with metabolic dysfunction.
Funding
This work was supported by the National Key Research and Development Program of China (No. 2023YFC2508106), National Natural Science Foundation of China (Nos. 82073429 and 82273510), Innovation Program of Shanghai Municipal Education Commission (No. 2019-01-07-00-07-E00046), Clinical Research Plan of SHDC (Nos. 2020CR1014B and 22022302), and Clinical Research Plan of Shanghai Municipal Health Commission (No. 202340006).
Conflicts of interest
None.
Supplementary Material
Footnotes
Yuxiong Jiang and Zhuolin Guo contributed equally to this work.
How to cite this article: Jiang YX, Guo ZL, Yao LL, Huang DW, Ma R, Zhong XY, Wang Y, Lu JJ, Li Y, Shi YL. Association of serum uric acid with circulating inflammatory proteins and treatment response to ixekizumab in patients with psoriasis: A multicenter prospective cohort study in China. Chin Med J 2026;139:2930–2937. doi: 10.1097/CM9.0000000000004261
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