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Clinical characterisation of super-responders and super-non-responders in psoriasis patients treated with interleukin-17 inhibitors: A real-world study
Corresponding author: Dr. Hong Liu, Dermatology Hospital of Shandong First Medical University, and Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Zhangzhuang, Jinan, Shandong, China. hongyue2519@hotmail.com
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Received: ,
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How to cite this article: Zhang L, Geng H, Li W, Liu H. Clinical characterisation of super-responders and super-non-responders in psoriasis patients treated with interleukin-17 inhibitors: A real-world study. Indian J Dermatol Venereol Leprol. 2025;91:712-8. doi: 10.25259/IJDVL_32_2025
Abstract
Background
In psoriasis patients treated with biologics, some achieve an ideal response to biologics and remain clear of lesions for several months, even years (super-responders-SRs), whereas others repeatedly fail to respond (super-non-responders-SNRs) to the biologic.
Objective
To investigate the clinical characteristics of SRs and SNRs in psoriasis patients treated with biologic interleukin-17 inhibitors.
Methods
Chronic plaque psoriasis or psoriasis vulgaris patients who received IL-17i treatment for more than 6 months were included.
Results
In total, 664 patients were included, with 120 (18.63%) categorised as SRs and 82 (12.73%) as SNRs. Compared to SNRs, SRs were mostly female, had lower body weight and BMI, and were less likely to have received previous systemic treatment. Lower weight and BMI and the absence of previous treatment may serve as predictors of SRs. Compared to controls, the SNRs had higher body weight and BMI, longer disease duration, were more likely to have received previous systemic treatment, and had a greater number of comorbidities. Higher BMI, longer disease duration, the presence of previous treatment, and abnormal C-reactive protein (CRP) value serve as predictors of SNRs.
Limitations
Single-centre study has a relatively small size.
Conclusion
Lower BMI and the absence of previous treatment may serve as predictors of SRs. In contrast, higher BMI, longer disease duration, the presence of previous treatment, and abnormal CRP value may serve as predictors of SNRs.
Keywords
Clinical characterisation
super-responders
super-non-responders
interleukin-17 inhibitors
Introduction
Psoriasis is an inflammatory disease induced by the interaction of genetic, immune, and environmental factors. It is characterised by scaly red patches or plaques, which can be localised or widely distributed. Its overall prevalence varies globally, ranging from 0.1% in East Asia to 1.5% in Western Europe.1 In China, the incidence has increased from 0.123% in 1984 to 0.47% in 2008.2 Psoriasis is prone to recurrence, affects multiple systems, and has a chronic course; therefore, it requires long-term drug therapy and management.
Recent genetic and immunological studies have identified the Th17 and interleukin-23 (IL-23) axis as key drivers of psoriasis pathogenesis.1,3 Over the past decade, several biologic agents targeting and inhibiting this axis have been widely used in clinical practice, including tumour necrosis factor (TNF)-a, IL-12/23, IL-17, and IL-23.1 Biologics are generally considered effective for the treatment of psoriasis and should be the first choice for psoriasis patients, particularly IL-17 antagonists.3 Among them, secukinumab and ixekizumab are widely used in China, with their efficacy and safety demonstrated in both clinical trials and real-life settings.4,5
As the proportion of patients treated with biologics in the real world increases, we observe that some patients experience repeated treatment failures; these patients are described as super-non-responders (SNRs).6,7 At the same time, Talamonti et al.8 observed a rapid and sustained complete response in psoriasis patients treated with biological therapy, who are often referred to as super-responders (SRs). Genetic factors might play a role in SRs and SNRs.8,9 Factors, such as obesity or a history of diabetes, have been associated with poorer treatment response to biologics.10 These results suggest that an analysis of the clinical characteristics of patients can facilitate more precise management. Currently, there is no consensus on the precise definition of SRs and SNRs concerning psoriasis. Most studies on SRs have primarily focused on guselkumab;11-16 however, Mastorino et al.17 observed that SRs are more likely to be treated with IL-17 inhibitors (IL-17i). In China, the definitions of SRs and SNRs may vary significantly due to factors such as the predominance of IL-17i as the most used biological agent, health insurance policies, and patient preferences. Notably, no cases of SRs and SNRs associated with IL-17i have been reported in Asian populations.
Our ultimate treatment goal is to achieve prolonged periods of psoriasis remission. In this context, SRs arguably represent an ideal treatment response.7 In this study, by analysing the clinical characteristics and laboratory data of SRs and SNRs, we aim to reveal potential factors influencing treatment response, accurately identify these individuals, and ultimately improve therapeutic management strategies for patients with psoriasis.
Methods
Study design
This is a monocentric, retrospective cohort study that included medical records from patients with chronic plaque psoriasis or psoriasis vulgaris who received IL-17i treatment for over 6 months between January 2019 and August 2024 at the Dermatology Hospital of, Shandong First Medical University.
Study population
Patients treated with IL-17i were included if they met the following inclusion criteria: (i) confirmed chronic plaque psoriasis or psoriasis vulgaris; (ii) IL-17i used for more than half a year; (iii) consent to participate in this study. The exclusion criteria were patients with other forms of psoriasis, biologic indication other than psoriasis, patients lost to follow-up before week 24, patients who discontinued treatment due to adverse events or other reasons, patients with incomplete information, or who were not willing to participate in this study.
Definition of SR, SNR, and controls
Based on our real-life experience, we categorised patients into three groups: SRs, SNRs, and controls (regular responders). SRs were patients who achieved Psoriasis Area and Severity Index (PASI) 0 before week 4 and maintained it during 24 weeks of treatment. Patients were categorised as being SNRs if they had PASI≥2 at week 24 or body surface area (BSA)≥3 at relapse during treatment. All patients who do not meet the criteria for SRs or SNRs were designated as controls. These patients either achieve a PASI>0 at week 4 and maintain a PASI<2 or BSA < 3 at week 24, or they achieve a PASI score of 0 at week 4 but experience a relapse (with PASI < 2 or BSA < 3) before completing 24 weeks of treatment.
Outcomes
Disease severity measures included PASI, and BSA. The effective endpoints were the improvement of 100% in PASI compared with the baseline score. The key independent variables included baseline demographic and clinical characteristics and pre- and post-treatment laboratory findings.
Statistical analysis
The normality of the continuous data distribution was assessed using the Shapiro-Wilk test and Kolmogorow-Smironov test. Means ± standard deviation (SD) were described for continuous data that was normally distributed, medians with 25th and 75th percentiles were reported for continuous data that was not normally distributed, and numbers with percentages were provided for categorical data. Percentages were based on the number of non-missing values. Difference analysis between two groups: For normally distributed data, the independent sample t-test was used to assess differences between groups, and for non-normally distributed data, the U test was conducted; For categorical data, the Chi-square was used to assess differences between groups. Comparative analysis among multiple groups: For continuous data with normal distributions and homogeneous variances, one-way analysis of variance (one-way ANOVA) followed by least significant difference (LSD), and adjusted according to the Bonferroni correction. For continuous data with non-normal distributions or heterogeneous variances, the Kruskal-Wallis test and multiple comparisons (Bonferroni correction) were conducted. For categorical data, the Chi-square test was followed by the pairwise comparison when the overall chi-square was significant. The pairwise p= 0.05/3=0.0167, adjusted according to Bonferroni correction.
We compared SRs with those who were SNRs and the rest of the patients from the comparators. Several subgroups were assessed: patients with BMI<25kg/m2 vs. patients with BMI ≥25kg/m2, previous treatment, and the number of comorbidities. To explore which factors were potentially independent predictors of SR, a multinomial logistic regression analysis was performed with the significance level set at 0.05. An α = 0.05 in the univariate analysis was chosen to enhance the addition of candidate factors into the model. To investigate which inflammation biomarkers were independently predictive of SRs and SNRs, a multivariable binary logistic regression analysis was performed with the significance level set at 0.05. To ensure a robust selection of candidate variables, we included all factors that showed significant associations in the preliminary biomarker comparisons between SRs and SNRs. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported.
A two-sided P-value <0.05 was considered statistically significant in all instances, and statistical comparisons across groups were conducted using P-values corrected for multiple comparisons. All analyses were performed using IBM SPSS Statistics 26.0.
Results
The study cohort comprised 664 patients receiving IL-17 inhibitor therapy with a mean age of 39.31±15.24 years and a male predominance of 436 (67.70%). Among the patients included, 120 (18.63%) were categorised as SRs, 82 (12.73%) were SNRs, and 442 (66.57%) were comparators [Table 1]. Further analysis of the SNRs subgroup revealed that 55 (67.07%) were primary non-responders (no initial response), while 27 (32.93%) were secondary non-responders (loss of response over time).
| Variables | SRs (n=120) | SNRs (n= 82) | Comparators (n=442) | SRs vs SNRs P-value 1 | SRs vs Comparators P-value 2 | SNRs vs Comparators P-value 3 |
|---|---|---|---|---|---|---|
| Age(M±SD) | 37.63±13.90 | 41.94±13.87 | 39.29±15.78 | 0.048 | 0.290 | 0.147 |
| Sex, n (%) | ||||||
| Female | 49(40.8%) | 19(23.2%) | 140(31.7%) | 0.009 | 0.060 | 0.124 |
| Male | 71(59.2%) | 63(76.8%) | 302(68.3%) | |||
| Weight (kg, M±SD) | 70.27±15.68 | 79.94±14.74 | 73.27±15.27 | <0.0001 | 0.057 | <0.001 |
| BMI (kg/m2,M±SD) | 24.62±3.99 | 26.86±3.67 | 25.37±3.97 | <0.0001 | 0.066 | 0.002 |
| <18.5, n (%) | 8(6.7%) | 1(1.2%) | 20(4.5%) | 0.004 | 0.138 | 0.021 |
| 18.5-<25, n (%) | 59(49.2%) | 24(29.6%) | 184(41.6%) | |||
| 25-<30, n (%) | 39(32.5%) | 41(50.6%) | 195(44.1%) | |||
| ≥30, n (%) | 14(11.7%) | 15(18.5%) | 43(9.7%) | |||
| Family history, n (%) | 16(13.3%) | 18(25.4%) | 84(19.1%) | 0.036 | 0.142 | 0.224 |
| Smoking, n (%) | 36(30.3%) | 31(38.3%) | 136(31.2%) | 0.238 | 0.844 | 0.211 |
| Previous treatment, n (%) | ||||||
| None | 73(60.8%) | 29(35.4%) | 244(55.3%) | 0.001 | 0.132 | 0.002 |
| Conventional | 35(29.2%) | 39(47.6%) | 163(37.0%) | |||
| Biological | 7(5.8%) | 3(3.7%) | 11(2.5%) | |||
| Conventional and Biological | 5(4.2%) | 11(13.4%) | 23(5.2%) | |||
| Age of onset (M±SD) | 22.92±10.94 | 24.68±13.68 | 24.72±13.36 | 0.349 | 0.181 | 0.979 |
|
Late-onset psoriasis (≥40 years), n (%) |
16(13.3%) | 12(14.65%) | 68(15.44%) | 0.793 | 0.576 | 0.682 |
| Disease duration (M±SD) | 14.75±11.34 | 17.96±14.15 | 14.48±10.73 | 0.049 | 0.819 | 0.011 |
| PsA/Nail, n (%) | 7(5.8%) | 4(4.9%) | 25(5.7%) | 0.769 | 0.941 | 0.777 |
| Baseline BSA (M±SD) | 17.48±15.81 | 18.59±14.04 | 17.19±16.33 | 0.656 | 0.875 | 0.502 |
| Comorbidities (n,%) | 37(30.8%) | 36(43.9%) | 150(33.9%) | 0.084 | 0.553 | 0.118 |
| Hypertension, n (%) | 6(5%) | 7(8.5%) | 19(4.3%) | 0.314 | 0.741 | 0.105 |
| Diabetes, n (%) | 5(4.2%) | 6(7.3%) | 13(2.9%) | 0.333 | 0.499 | 0.052 |
| Cardiovascular disease, n (%) | 0(0%) | 2(2.4%) | 9(2.0%) | 0.086 | 0.115 | 0.815 |
| Obesity, n (%) | 27(22.5%) | 26(31.7%) | 111(25.1%) | 0.144 | 0.555 | 0.212 |
| Stroke, n (%) | 0(0%) | 1(1.2%) | 3(0.7%) | 0.225 | 0.336 | 0.605 |
| Dyslipidemia, n (%) | 2(1.7%) | 3(3.7%) | 8(1.8%) | 0.372 | 0.916 | 0.284 |
| Thyroid, n (%) | 0(0%) | 3(3.7%) | 4(0.9%) | 0.035 | 0.296 | 0.046 |
| 0 | 83(69.2%) | 46(56.1%) | 294(66.5%) | 0.065 | 0.126 | 0.012 |
| 1 | 30(25.0%) | 25(30.5%) | 127(28.7%) | |||
| 2 | 7(5.8%) | 8(9.8%) | 12(2.7%) | |||
| ≥3 | 0(0%) | 3(3.7%) | 9(2.0%) | |||
| Type of biologics, n (%) | ||||||
| Secukinumab | 92(76.7%) | 62(75.6%) | 325(73.5%) | 0.862 | 0.486 | 0.694 |
| Ixekizumab | 28(23.3%) | 20(24.4%) | 117(26.5%) | |||
| Medication use, n (%) | ||||||
| Regular | 90(78.9%) | 58(85.3%) | 310(76.2%) | 0.288 | 0.534 | 0.095 |
| Irregular | 24(21.1%) | 10(14.7%) | 97(23.8%) |
SR: Super responder; SNR: Super-non-responder; M: Means; BMI: Body mass index; PsA: Psoriatic arthritis; BSA: Body surface area, SD: Standard deviation.
Demographics and disease characteristics
Compared to SNRs, SRs exhibited a higher proportion of females (40.8% vs 23.2%, P=0.009), lower body weight (mean: 70.27 kg vs. 79.94 kg, P<0.0001), lower BMI (mean: 24.62 kg/m2 vs. 26.86 kg/m2, P<0.0001), a higher proportion of patients with a BMI<25kg/m2 (55.83% vs. 30.87%, P<0.0001), a greater number of patients who had not previously undergone conventional or biological treatment (60.83% vs. 35.37%, P=0.001). No differences were observed for age, family history, smoking status, age of onset, proportion of late onset psoriasis, BSA, psoriatic arthritis (PsA) or nail psoriasis, disease duration, and comorbidities when comparing SRs to SNRs [Table 1]. Additionally, the multinomial logistic regression analysis revealed that lower BMI (OR, 0.854; 95%CI, 0.772-0.945, p=0.002) and the absence of previous treatment (OR, 2.822; 95%CI, 1.540-5.171, p=0.001) were significant independent predictors of SRs when compared to SNRs [Table 2].
| Variables | B | SE | Z | P | OR (95%CI) | |
|---|---|---|---|---|---|---|
| SRs | Female | 0.582 | 0.345 | 2.838 | 0.092 | 1.789 (0.909∼3.520) |
| BMI | -0.157 | 0.051 | 9.381 | 0.002 | 0.854(0.772∼0.945) | |
| Absence of previous treatment | 1.037 | 0.309 | 11.262 | 0.001 | 2.822 (1.540∼5.171) | |
| No comorbidities | -0.224 | 0.400 | 0.315 | 0.574 | 0.799 (0.365∼1.748) | |
| Disease duration | -0.018 | 0.013 | 2.051 | 0.152 | 0.982(0.958∼1.007) | |
| Controls | Female | 0.288 | 0.301 | 0.914 | 0.339 | 1.334 (0.739∼2.409) |
| BMI | -0.102 | 0.044 | 5.437 | 0.020 | 0.903 (0.828∼0.984) | |
| Absence of previous treatment | 0.783 | 0.260 | 9.107 | 0.003 | 2.189 (1.316∼3.641) | |
| No comorbidities | -0.061 | 0.332 | 0.034 | 0.854 | 0.941 (0.491∼1.802) | |
| Disease duration | -0.022 | 0.010 | 4.502 | 0.034 | 0.978 (0.959∼0.998) | |
B: Regression coefficient, SE: Standard error, Z: Wald chi-square, OR: Odds ratio, CI: Confidence interval, BMI: Body mass index.
There were no significant differences between SRs and controls in age, sex, weight, BMI, family history, smoking status, previous treatment, age of onset, proportion of late onset psoriasis, disease duration, proportion of PsA or nail psoriasis, baseline BSA, and comorbidities [Table 1].
Compared to controls, the SNRs exhibited higher body weight (mean: 79.94 kg vs. 73.27 kg, P<0.001), higher BMI (mean: 26.86 kg/m2 vs. 25.37 kg/m2, P=0.002), longer disease duration (mean: 17.96 years vs. 14.48 years, P=0.011), a higher proportion of patients with a BMI≥25kg/m2 (69.14% vs. 53.85%, P=0.009), a greater proportion of patients who had received previous conventional or biological treatment (64.63% vs. 44.67%, P=0.002) and more patients with two or more comorbidities (13.41% vs. 4.75%, P=0.012). No differences were observed for age, sex, family history, smoking status, age of onset, proportion of late onset psoriasis, proportion of PsA or nail psoriasis, or baseline BSA when comparing SNRs to controls [Table 1]. Additionally, the multinomial logistic regression analysis revealed that higher BMI (OR, 0.903; 95%CI, 0.828.-0.984, p=0.020), the presence of previous treatment history (OR, 2.189; 95%CI, 1.316.-3.641, p=0.003) and longer disease duration (OR, 0.978; 95%CI, 0.959-0.998, p=0.034) were significant independent predictors of SNRs when compared to controls [Table 2].
There were no significant differences in the use of secukinumab or ixekizumab among SRs, SNRs, and regular responders. Meanwhile, due to economic conditions and other constraints, some patients in China do not strictly adhere to medication instructions and may appropriately delay their dosing intervals. Irregular dosing refers to the gradual lengthening of the dosing interval after 3 months of treatment. Under medical guidance, patients extended the dosing interval from the instructed 4 weeks to 6 weeks. If no recurrence occurred, the interval was further extended to 8 weeks and so on. Our analysis revealed that whether medication is taken regularly did not influence their response to biological agents [Table 1]. Through statistical analysis of primary and secondary non-responders, we found significant differences between the two groups only in BMI (mean: 27.65 kg/m2 vs. 25.53 kg/m2, P=0.019) and comorbidities (52.7% vs. 22.2%, P=0.009).
Laboratory data and predictors of super-response status to IL-17
We enrolled 50 SRs and 13 SNRs with complete pre- and post-treatment laboratory data. The baseline characteristics of these groups did not show statistically significant differences except for BMI (P=0.004). Statistical analysis indicated significant differences in neutrophil count (P=0.019) and C-reactive protein (CRP) (P=0.008) between SRs and SNRs. No differences were observed for white blood cell count, platelet count, lymphocyte ratio, lymphocyte count, Neutrophil ratio, erythrocyte sedimentation rate (ESR), Immunoglobulin E (IgE), neutrophil-to-lymphocyte ratio (NLR), White blood cell count-neutrophil count (W-N), systemic inflammation index (SII) (SII=Platelet count × Neutrophil count/Lymphocyte count Count), white blood cell to platelet ratio, platelet-to-lymphocyte ratio (PLR) and blood lipids (including total cholesterol, triglycerides, high density lipoprotein (HDL), low density lipoprotein (LDL)) [Table 3]. In addition, by binary logistic regression analysis, we found that SNRs were 14.817 times more likely to have abnormal CRP values (normal CRP: CRP < 5mg/L) than SRs (p=0.002) [Table 4].
| Variables | SRs (n=50) | SNRs (n=13) | P |
|---|---|---|---|
| Age (M±SD) | 44.08±17.28 | 41.75±13.03 | 0.555 |
| Sex, n (%) | |||
| Female | 23(46.0%) | 4(30.8%) | 0.234 |
| Male | 27(54.0%) | 9(69.2%) | |
| BMI (M±SD) | 25.59±2.91 | 28.66±5.38 | 0.004 |
| White blood cell count (M±SD) | 6.42±1.35 | 8.34±1.55 | 0.091 |
| Platelet count, median (P25, P75) | 273(207-313) | 281(202-302) | 0.779 |
| Lymphocyte ratio (M±SD) | 33.21±7.50 | 25.41±7.60 | 0.124 |
| Lymphocyte count (M±SD) | 2.12±0.60 | 2.09±0.64 | 0.656 |
| Neutrophil ratio (M±SD) | 56.58±6.47 | 64.89±6.79 | 0.130 |
| Neutrophil count (M±SD) | 3.64±0.92 | 5.43±1.22 | 0.019 |
| ESR, median (P25, P75) | 12(6-15) | 13(6.5-28.5) | 0.168 |
| CRP, median (P25, P75) | 1.5(0.6-2.47) | 6.1(1.5-7.4) | 0.008 |
| IgE, median (P25, P75) | 22(12-65.93) | 33.5(5.7-585.2) | 0.188 |
| NLR (M±SD) | 1.82±0.57 | 2.88±1.28 | 0.135 |
| W-N (M±SD) | 2.77±0.65 | 2.92±0.77 | 0.620 |
| SII, median (P25, P75) | 431.37(354.01-634.32) | 539.97(464.29-902.25) | 0.169 |
| WBC/PLT, median (P25, P75) | 0.025(0.0231-0.0326) | 0.030(0.030-0.035) | 0.118 |
| PLR (M±SD) | 129.65±34.96 | 135.97±60.64 | 0.599 |
| Total cholesterol (M±SD) | 4.99±1.38 | 4.84±1.22 | 0.789 |
| Triglycerides (M±SD) | 1.53±0.89 | 2.71±2.86 | 0.255 |
| HDL (M±SD) | 1.31±0.29 | 1.22±0.43 | 0.282 |
| LDL (M±SD) | 3.07±1.06 | 2.94±0.87 | 0.941 |
M: Mean, SD: Standard deviation, P25: 25th percentile, P75: 75th percentile, ESR: Erythrocyte sedimentation rate, CRP: C-reactive protein, IgE: Immunoglobulin E, NLR: Neutrophil-to-lymphocyte ratio, W-N=White blood cell count-Neutrophil count, SII: Systemic inflammation index, WBC/PLT: White blood cell to platelet ratio, PLR: Platelet-to-lymphocyte ratio, HDL: High density lipoprotein, LDL: Low density lipoprotein, BMI: Body mass index.
| Variables | B | SE | Z | P | OR (95%CI) |
|---|---|---|---|---|---|
| Abnormal CRP value | 2.696 | 0.881 | 9.361 | 0.002 | 14.817 (2.635∼83.316) |
| Abnormal Neutrophil count | 0.610 | 1.322 | 0.213 | 0.645 | 1.840 (0.138∼24.549) |
CRP: C-reactive protein (normal CRP: CRP < 5mg/L), B: Regression coefficient, SE: Standard error, Z: Wald chi-square, OR: Odds ratio, CI: Confidence interval, Normal neutrophil count: 1.8∼6.3×109/L.
Discussion
In this study of patients with psoriasis treated with IL-17i, we observed approximately 18.63% of the patients being SRs and 12.73% of patients being SNRs. SRs tend to be younger, have not previously received systemic treatment and have shorter disease duration compared to SNRs. In contrast, SNRs typically have higher body weight, longer disease duration, a history of systemic treatment, and a greater number of comorbidities compared with the control group. These results enhance our understanding of SR and SNR in the context of IL-17i treatment and provide clinical evidence to improve the management and follow-up of patients with psoriasis.
There is currently no consensus regarding the impact of gender on SR status. Menéndez Sánchez et al.18 reported a higher prevalence of males among non-SRs receiving IL-23 inhibitors for psoriasis. In contrast, Mason et al.6 and Mercieca et al.19 suggested that female sex is associated with SNRs. Our univariate analysis revealed that women are more likely to be classified as SR compared to SNR. However, this gender difference was not statistically significant in the subsequent multinomial logistic regression analysis. We believe that the discrepancies in findings can be attributed not only to hormonal20 and genetic factors8 but also to another important factor: body weight. Aesthetic standards may lead Chinese women to maintain lower body weights. Most studies support the association between lower body weight and BMI with SR status.14,17 However, some studies indicate that BMI or weight may not be linked to super-responsiveness to biologics.18,21 In our study, we found that SRs exhibited a higher proportion of patients with a BMI < 25kg/m2 compared to SNRs. The primary factors contributing to SR status are dosing and the reduction of body weight in obese patients,10,22,23 which may enhance the efficacy of biologics. This suggests that individualised dosing based on body weight, along with drug level monitoring, could potentially improve treatment responses for these patients. Nevertheless, underdosing cannot fully account for the 11.7% of SRs who have a BMI ≥ 25kg/m2. Moreover, a greater number of SRs had not previously received conventional or biological treatment, a point noted in some literature.24 Here, we observed that lower BMI and the absence of previous treatment serve as positive independent predictors of SRs when compared to SNRs.
The baseline demographic and clinical characteristics showed no significant differences between the SRs group and the control group, a finding that has not been previously reported in the literature. We speculate that the lack of significant differences in our study may be attributed to an insufficiently comprehensive analysis of influencing factors, such as socioeconomic status, lifestyle or unmeasured confounders that could impact the outcomes. Additionally, the homogeneity of our study population or the relatively small sample size might have contributed to this result. Furthermore, whether there are differences between these two groups in terms of drug survival rates, laboratory test results and genetic factors remains unclear. Drug survival rates which reflect both efficacy and tolerability, are critical indicators of treatment success and may vary based on underlying genetic or metabolic profiles. Similarly, laboratory test results could provide insights into potential biomarkers or mechanistic pathways that differentiate the SRs group from the control group. These unresolved questions highlight the need for more robust and large-scale studies to explore these aspects in greater depth.
In line with previous studies,7 we believe that high BMI are associated with SNRs. Interestingly, we found that the SNRs had a higher proportion of patients with a BMI≥25kg/m2 compared to the control group. Some suggested that patients with short disease duration (SDD) achieve complete skin clearance more rapidly than those with long disease duration (LDD).15 Our study also revealed that SNRs have a longer disease duration and which is a positive independent predictor of SNRs when compared to controls. Some researchers argue that bio-naive patients are more likely to become SRs. Our findings indicated that a greater number of SRs had not previous conventional or biological treatment, whereas SNRs had a higher proportion of individuals who had received previous conventional or biological treatment. And the presence of previous treatment was a positive independent predictor of SNRs when compared to controls. This has led us to favour biologic agents as the preferred treatment option for patients with psoriasis. Previous studies have indicated that comorbidities negatively influence SR23 and that higher number of comorbidities are associated with SNRs.6 Similarly, our study found a greater number of comorbidities are associated with SNRs. This may be attributed to the fact that the absence of comorbidities is associated with longer drug survival. Additionally, the presence of high BMI and comorbidities was significantly associated with primary non-responders to SNRs, underscoring the potential negative impact of elevated BMI and comorbidities on biologic outcomes.
For patients treated with biologic agents, no inflammation biomarkers have been identified that can reliably predict treatment response. NLR, PLR, monocyte-to-lymphocyte ratio (MLR), and SII have been shown to be significantly increased in patients with psoriasis, and some studies suggested that these indicators correlate with disease severity.25-27 While these metrics are utilised to predict early responders in other conditions, such as atopic dermatitis (AD), they have not been thoroughly investigated in the context of psoriasis treatment. According to our experience, abnormal CRP may serve as a predictor of SRs. We acknowledge that the sample size may limit the statistical power for these analyses. These results should be validated in larger, independent cohorts in future studies to confirm their reliability and generalisability. Furthermore, the choice of secukinumab (SEC) or ixekizumab (IXE) does not appear to be associated with achieving super-response.
Limitations
The study is exposed to certain limitations. Our research relied on data collected in daily life which may contain inaccuracies. Additionally, we did not acquire information on crucial indicators, such as drug levels. Furthermore, for reasons such as constraints imposed by health insurance policies, we include only the most used biologic agents, specifically Sec and Ixe, thus excluding newer biologic agents. Finally, the sample size may limit the generality of the results of this study.
Conclusion
This is the first article to describe the clinical characteristics and laboratory data of SRs and SNRs in an Asian population treated with IL-17i. In conclusion, lower BMI and the absence of previous treatment may serve as predictors of SRs. In contrast, higher BMI, longer disease duration, the presence of previous treatment and abnormal CRP value may serve as predictors of SNRs.
Ethical approval
As a retrospective study involving no patient intervention and utilizing fully anonymized data, this research was exempted from ethical review in accordance with China’s Ethical Review Measures for Biomedical Research Involving Humans.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent.
Financial support and sponsorship
Joint Innovation Team for Clinical &; Basic Research (202410).
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
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