Association between systemic inflammation indicators and psoriasis: a cross-sectional study from NHANES

Huizi Xiong¹Zengyang Yu^2,3*

• ¹Department of Dermatology, The Fourth Affiliated Hospital of Soochow University,Suzhou Medical College, Soochow University, Suzhou 215000, China
• ²Department of Dermatology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
• ³Institute of Psoriasis, Tongji University School of Medicine, Shanghai 200072, China

1. Introduction Psoriasis is a complex and multifaceted chronic inflammatory skin disorder that affects a substantial portion of the global population. The characteristic scaly plaques not only cause physical discomfort but also impose a substantial burden on individuals and society at large. This disease is frequently associated with numerous comorbidities, including psoriatic arthritis, cardiovascular problems, metabolic syndrome, obesity, gastrointestinal disorders such as inflammatory bowel disease, and mental health disorders[1]. The immune system plays a crucial role in both the initiation and progression of psoriasis. In particular, Th17 cells and dendritic cells (DCs) are central to the dysregulated inflammatory response. These cells, along with other immune components such as mast cells, monocytes, neutrophils, innate lymphoid cells (ILCs), and macrophages, form an interconnected network that collaboratively drives the chronic inflammation characteristic of psoriasis[2]. Recent research has further elucidated the chronic inflammatory underpinnings of psoriasis, underscoring the immune system's central role in its pathophysiology[3]. The distinct leukocyte profile observed in the peripheral blood of psoriasis patients is characterized by a marked increase in neutrophil counts[4]. The recognition of the platelet-to-lymphocyte ratio (PLR) as a rapid and reliable indicator for identifying subclinical inflammatory diseases like systemic lupus erythematosus and heart failure is also of significant clinical relevance[5]. This suggests that PLR may have potential diagnostic value in the context of psoriasis as well. The neutrophil-percentage-to-albumin ratio (NPAR) is a compelling inflammatory biomarker that integrates the quantities of albumin and neutrophils. This composite index has demonstrated significant prognostic value, particularly in predicting mortality in critical conditions including acute myocardial infarction (AMI)[6], cardiogenic shock[7], coronary artery disease[8], and heart failure in the intensive care unit[9]. Similarly, other novel hematological indices such as the neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), and platelet-to-high-density lipoprotein cholesterol ratio (PHR) have also emerged as promising inflammatory markers. These ratios are increasingly recognized for their critical roles in the pathogenesis and progression of a wide range of diseases[10-11]. High-density lipoprotein cholesterol (HDL-C) is known for its anti-inflammatory properties. However, both its levels and function are compromised during inflammatory states[11]. Furthermore, HDL-C engages in complex regulatory interactions with various immune cell types[11]. Recent findings indicate that psoriasis patients have higher levels of C-reactive protein (CRP), MHR, neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR), all of which show a positive correlation with disease severity[12]. Interestingly, CRP--a well-known inflammatory biomarker--was found to be uniquely associated with MHR[13]. Although numerous inflammatory biomarkers have been established as correlates of psoriasis, the relationship between psoriasis and NPAR or NHR remains unexplored. This study is the first to investigate the association of NPAR, NHR, and other related inflammatory markers with psoriasis pathogenesis, thereby offering new perspectives for predicting disease progression. 2. Methods 2.1 Population under investigation The survey collected comprehensive data on demographic characteristics, economic conditions, nutritional intake, and general health, creating a valuable dataset for examining public health trends and addressing emerging health concerns. In this study, we utilized publicly available data from five two-year cycles of the National Health and Nutrition Examination Survey (NHANES), covering the periods 2003-2006 and 2009-2014, with a focus on adults aged 20 to 59. A total of 16,575 individuals participated in both the laboratory examinations, which included complete blood count, high-density lipoprotein cholesterol and albumin examination, and completed the Psoriasis questionnaire. Participants were excluded if they had missing psoriasis assessment data, insufficient information to calculate inflammation indices (namely NPAR, NLR, NHR, LHR, PHR, and MHR), or fell outside the designated age range. Ethical clearance for the NHANES project was granted by the National Center for Health Statistics, and all participants provided written informed consent. The final analysis included 432 participants with psoriasis and 16,143 without psoriasis (Figure 1). Figure 1. Participant selection flowchart for this study. 2.2 Measurement of Systemic Inflammatory Indicators: NPAR, NHR, LHR, PHR, and MHR The relevant laboratory measures were obtained from the NHANES database. HDL-C levels were assessed using an automatic device, with venous blood samples collected after an 8-hour fast. The standard reference range for HDL-C is 1.3–1.5 mmol/L for females and 1.0–1.5 mmol/L for males. White blood cell classification was performed using the Coulter VCS system, which employs automated mixing and dilution for sample preparation, as well as cell counting and molecular weight analysis. The ratios (NPAR, NHR, LHR, PHR, MHR and NLR) were calculated using the following formulas: NPAR: (Percent neutrophils of total leukocyte count [%]) × 100 / Albumin (g/dL); NHR: Neutrophil counts (10^9/L) / HDL-C (mmol/L); LHR: Lymphocyte counts (10^9/L) / HDL-C (mmol/L); PHR: Platelet counts (10^9/L) / HDL-C (mmol/L); MHR: Monocyte counts (10^9/L) / HDL-C (mmol/L); NLR: Neutrophil counts (10^9/L)/Lymphocyte counts (10^9/L). Participants were stratified into four groups--Quantile 1, Quantile 2, Quantile 3, and Quantile 4--based on the quartile ranges of NPAR, NHR, LHR, PHR, MHR, and NLR. 2.3 Psoriasis definition The diagnosis of psoriasis was based on self-reported data from the "medical conditions" section of the questionnaire. Participants were classified as having psoriasis if they answered "yes" to the question: "Has a doctor ever informed you that you have psoriasis?" and confirmed that the diagnosis was given by a healthcare provider. 2.4 Covariates In this study, a comprehensive set of covariates was carefully selected, covering demographic, clinical, and lifestyle factors. Demographic variables included age, gender (male or female), marital status, educational level (categorized as less than high school, high school graduate, some college or associate's degree, and college graduate or above), race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, and other racial/ethnic groups), and the poverty-to-income ratio (PIR). Medical comorbidities consisted of hypertension, hyperlipidemia, diabetes, cardiovascular disease (CVD), and cancer. Lifestyle and health-related factors included body mass index (BMI), smoking status (defined as having smoked at least 100 cigarettes in one's lifetime), and alcohol use (defined as consumption of at least 12 alcoholic beverages in the past year). Diagnoses of diabetes and CVD were based on self-reported physician-confirmed diagnoses. Specifically, diabetes was defined as a previous medical diagnosis of the condition. CVD was considered present if participants reported a history of heart failure, coronary artery disease (CAD), angina, myocardial infarction, or stroke. Hyperlipidemia was defined according to the National Cholesterol Education Program (NCEP) guidelines, incorporating any of the following: LDL cholesterol ≥ 4.1 mmol/L, HDL-C ≤ 1.0 mmol/L in men or ≤ 1.3 mmol/L in women, triglycerides ≥ 2.3 mmol/L, total cholesterol ≥ 6.2 mmol/L, or current use of cholesterol-lowering medication. Hypertension was defined as an average systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 80 mmHg, and/or current use of antihypertensive medication. Laboratory measures included neutrophil count, lymphocyte count, albumin, monocyte count, total cholesterol, and triglycerides. 2.5 Statistical analysis The statistical analyses in this study were conducted in accordance with NHANES guidelines, incorporating appropriate sample weights and accounting for the complex survey design, including clustering and stratification. Inflammatory biomarkers were categorized into quartiles, and baseline demographic and clinical characteristics across these quartiles were compared using Chi-square tests for categorical variables and t-tests for continuous variables. To evaluate the association between inflammatory biomarkers and psoriasis, three multivariable logistic regression models were constructed: Model 1: Unadjusted, examining the crude association; Model 2: Adjusted for demographic variables (age, gender, and race/ethnicity); Model 3: Further adjusted for socioeconomic factors (PIR and education level), lifestyle variables (smoking status and alcohol use), marital status, and comorbid conditions (diabetes, hypertension, hyperlipidemia, cancer, and cardiovascular disease). A trend test was performed across quartiles of each inflammatory biomarker to assess dose-response relationships. Subgroup analyses were conducted to evaluate potential effect modification by demographic, socioeconomic, and clinical factors. To investigate potential nonlinear associations, we applied smoothing curves fitted by generalized additive models (GAMs). Threshold effects were assessed using segmented regression models, which were compared against linear models via log-likelihood ratio tests. Breakpoints were identified through an iterative recursive algorithm. The discriminative ability of each inflammatory marker for psoriasis was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). A two-sided p-value < 0.05 was considered statistically significant. All analyses were performed using R software (version 4.2) and the EmpowerStats package (version 5.0) to ensure reproducibility and methodological rigor. 3. Results 3.1 Baseline characteristics Of the 16,575 participants included in the analysis, 432 (2.61%) reported a prior diagnosis of psoriasis. The study population comprised 52% females and 48% males, with a mean age of 38.76 ± 11.37 years. The racial/ethnic composition was as follows: 43% non-Hispanic White, 21% non-Hispanic Black, 18% Mexican American, and 7.7% other Hispanic individuals. The mean values for inflammatory biomarkers across the entire cohort were as follows: NPAR, 13.73 ± 2.60; NLR, 2.16 ± 1.05; and NHR, 3.51 ± 1.86. Participants with psoriasis were more likely to have attained higher education levels and were more frequently classified as smokers. They also exhibited a higher prevalence of comorbidities, including hypertension, hyperlipidemia, and cancer, with all differences being statistically significant (p < 0.05; see Table 1). Table 1: The baseline characteristics of participants enrolled in the NHANES cycles spanning from 2003-2006 and 2009-2014. Variables Overall Psoriasis P-value (n=16575) NO (n=16143) YES (n=432) Gender, n (%) 0.037 Male 7,945 (48%) 7,714 (48%) 231 (55%) Female 8,630 (52%) 8,429 (52%) 201 (45%) Age, Mean ± SD 38.76± (11.37) 38.75± (11.38) 39.20± (11.31) 0.495 Age strata, n (%) 0.134 20-39 8,626 (52%) 8,418 (52%) 208 (48%) 40-59 7,949 (48%) 7,725 (48%) 224 (52%) Race, n (%) 0.613 Mexican American 2,911 (18%) 2,827 (18%) 84 (19%) Other Hispanic 1,283 (7.7%) 1,255 (7.8%) 28 (7.2%) Non-Hispanic White 7,127 (43%) 6,948 (43%) 179 (40%) Non-Hispanic Black 3,524 (21%) 3,429 (21%) 95 (20%) Other Race 1,730 (10%) 1,684 (10%) 46 (13%) Education level, n (%) 0.003 Less than 9th grade 1,294 (8.2%) 1,247 (8.1%) 47 (12%) 9-11th grade 2,400 (15%) 2,339 (15%) 61 (13%) High school graduate 3,732 (23%) 3,650 (23%) 82 (17%) Some college or associate degree 5,237 (31%) 5,109 (31%) 128 (28%) College graduate or above 3,912 (23%) 3,798 (23%) 114 (30%) Marital status, n (%) 0.639 Married/Living with a partner 10,126 (61%) 9,855 (61%) 271 (63%) Divorced/Separated/Widowed 2,409 (15%) 2,339 (15%) 70 (15%) Never married 4,040 (25%) 3,949 (25%) 91 (22%) Income-to-poverty ratio, n (%) 0.070 ≤1.3 5,327 (33%) 5,197 (33%) 130 (31%) 1.3-3.5 6,086 (37%) 5,933 (37%) 153 (33%) >3.5 5,162 (30%) 5,013 (29%) 149 (35%) BMI, n (%) 0.502 <25 5,197 (35%) 5,079 (35%) 118 (32%) 25-30 5,342 (34%) 5,199 (34%) 143 (34%) ≥30 6,036 (31%) 5,865 (31%) 171 (34%) Smoking, n (%) <0.001 Yes 7,086 (44%) 6,860 (44%) 226 (54%) No 9,489 (56%) 9,283 (56%) 206 (46%) Alcohol use, n (%) 0.984 Yes 12,351 (79%) 12,030 (79%) 321 (79%) No 4,224 (21%) 4,113 (21%) 111 (21%) Hypertension, n (%) 0.003 Yes 3,683 (22%) 3,550 (22%) 133 (30%) No 12,892 (78%) 12,593 (78%) 299 (70%) Hyperlipidemia, n (%) <0.001 Yes 4,670 (30%) 4,505 (29%) 165 (40%) No 11,905 (70%) 11,638 (71%) 267 (60%) Cardiovascular disease, n (%) 0.514 Yes 187 (1.1%) 176 (1.1%) 11 (1.4%) No 16,388 (99%) 15,967 (99%) 421 (99%) Cancer, n (%) 0.016 Yes 638 (5.0%) 607 (4.9%) 31 (8.3%) No 15,937 (95%) 15,536 (95%) 401 (92%) Diabetes, n (%) 0.594 Yes 1,075 (5.5%) 1,041 (5.5%) 34 (6.2%) No 15,500 (94%) 15,102 (94%) 398 (94%) C 7.32± (2.20) 7.33± (2.21) 7.11± (1.98) 0.144 Platelet (103 cells/μL) 255.92± (65.20) 255.79± (65.13) 260.29± (67.21) 0.310 Lymphocyte (103 cells/μL) 2.17± (0.81) 2.17± (0.82) 2.10± (0.66) 0.211 Monocyte (103 cells/μL) 0.55± (0.18) 0.54± (0.18) 0.55± (0.16) 0.171 Neutrophil (103 cells/μL) 4.37± (1.74) 4.36± (1.74) 4.52± (1.65) 0.059 Neutrophils percent (%) 58.52± (8.97) 58.48± (8.97) 59.80± (8.63) 0.004 Albumin (g/L) 42.98± (3.50) 42.99± (3.50) 42.75± (3.34) 0.147 HDL-C (mmol/L) 1.37± (0.41) 1.37± (0.41) 1.34± (0.41) 0.179 NPAR (dL/g) 13.73± (2.60) 13.72± (2.60) 14.11± (2.56) 0.003 NLR (109/mmol) 2.16± (1.05) 2.16± (1.05) 2.29± (0.94) <0.001 NHR (109/mmol) 3.51± (1.86) 3.51± (1.86) 3.70± (1.77) 0.014 LHR (109/mmol) 1.75± (0.93) 1.75± (0.94) 1.72± (0.82) 0.800 PHR (109/mmol) 202.85± (79.16) 202.65± (79.20) 209.33± (77.83) 0.063 MHR (109/mmol) 0.44± (0.22) 0.44± (0.22) 0.45± (0.20) 0.098 p < 0.05 was deemed statistically significant; NHANES: National Health and Nutrition Examination Survey; Abbreviations: BMI: body mass index; HDL-C: high-density lipoprotein cholesterol; NPAR: neutrophil-percentage-to-albumin ratio; NLR: neutrophil-to-lymphocyte ratio; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; LHR: lymphocyte-to-high-density lipoprotein cholesterol ratio; PHR: platelet-to-high-density lipoprotein cholesterol ratio; MHR: Monocyte-to-high-density lipoprotein cholesterol ratio. 3.2 Psoriasis exhibits a correlation with inflammation indicators. This analysis evaluated the association between psoriasis and systemic inflammatory biomarkers using three progressively adjusted models (Table 2). In the unadjusted model (Model 1), each 10-unit increase in NPAR was associated with a 73% higher odds of psoriasis (OR = 1.73, 95% CI: 1.10– 2.72). This association remained significant after adjustment for demographic factors in Model 2 (OR = 1.75, 95% CI: 1.12–2.74) and further adjustment for socioeconomic, behavioral, and clinical covariates in Model 3 (OR = 1.90, 95% CI: 1.11–3.26). Quartile-based analysis of NPAR also demonstrated a consistent, statistically significant dose-response relationship with psoriasis across all models. For NHR, each one-unit increase was associated with a 5% increase in the odds of psoriasis in Model 1 (OR = 1.05, 95% CI: 1.01–1.10). The association remained significant after demographic adjustment in Model 2 (OR = 1.05, 95% CI: 1.01–1.10) but was attenuated and no longer significant in the fully adjusted Model 3 (OR = 1.03, 95% CI: 0.98–1.08). No significant associations were observed between psoriasis and LHR, PHR, or MHR in any of the models. Generalized additive models revealed nonlinear relationships between NPAR, NLR, NHR, and psoriasis. Threshold analysis identified breakpoints at NPAR = 16.386, NLR = 3.269, and NHR = 4.286 (Table 3). Below the breakpoint of 16.386, NPAR was positively associated with psoriasis risk (OR = 1.119, 95% CI: 1.062–1.181 per unit increase). Similarly, below a value of 3.269, NLR showed a positive association with psoriasis (OR = 1.415, 95% CI: 0.998–1.161 per unit increase) (Figure 2). Table 2: Associations between systemic inflammatory indicators and psoriasis: Multivariable logistic regression analysis of NHANES 2003-2006 and 2009-2014. Model 1 OR1 (95% CI2) Model 2 OR1 (95% CI2) Model 3 OR1 (95% CI2) NPAR Continuous 1.06 (1.01, 1.11) 1.06 (1.01, 1.11) 1.07 (1.01, 1.13) Categories Q1 Reference Reference Reference Q2 0.99 (0.69, 1.42) 1.00 (0.70, 1.43) 0.99 (0.69, 1.41) Q3 1.26 (0.94, 1.69) 1.26 (0.94, 1.70) 1.25 (0.92, 1.70) Q4 1.53 (1.09, 2.14) 1.54 (1.11, 2.15) 1.56 (1.11, 2.19) P for trend 0.005 0.004 0.006 NPAR/10 Continuous 1.73 (1.10, 2.72) 1.75 (1.12, 2.74) 1.90 (1.11, 3.26) Categories Q1 Reference Reference Reference Q2 0.99 (0.69, 1.42) 1.00 (0.70, 1.43) 0.99 (0.69, 1.41) Q3 1.26 (0.94, 1.69) 1.26 (0.94, 1.70) 1.25 (0.92, 1.70) Q4 1.53 (1.09, 2.14) 1.54 (1.11, 2.15) 1.56 (1.11, 2.19) P for trend 0.005 0.004 0.006 NLR Continuous 1.11 (1.03, 1.19) 1.11 (1.04, 1.20) 1.10 (1.02, 1.18) Categories Q1 Reference Reference Reference Q2 0.98 (0.68, 1.42) 0.99 (0.69, 1.43) 0.98 (0.68, 1.41) Q3 1.24 (0.88, 1.73) 1.24 (0.88, 1.74) 1.22 (0.87, 1.71) Q4 1.66 (1.19, 2.32) 1.67 (1.20, 2.33) 1.61 (1.15, 2.25) P for trend 0.001 0.001 0.003 NHR Continuous 1.05 (1.01, 1.10) 1.05 (1.01, 1.10) 1.03 (0.98, 1.08) Categories Q1 Reference Reference Reference Q2 1.09 (0.79, 1.50) 1.09 (0.79, 1.50) 1.04 (0.75, 1.45) Q3 1.44 (1.00, 2.07) 1.44 (1.00, 2.07) 1.36 (0.94, 1.96) Q4 1.50 (1.05, 2.13) 1.50 (1.06, 2.12) 1.33 (0.93, 1.91) P for trend 0.015 0.013 0.066 LHR Continuous 0.97 (0.85, 1.09) 0.97 (0.85, 1.09) 0.92 (0.80, 1.06) Categories Q1 Reference Reference Reference Q2 1.26 (0.93, 1.71) 1.25 (0.92, 1.69) 1.23 (0.90, 1.67) Q3 1.09 (0.78, 1.52) 1.09 (0.78, 1.52) 1.03 (0.73, 1.45) Q4 1.00 (0.71, 1.42) 1.00 (0.71, 1.42) 0.89 (0.62, 1.28) P for trend 0.820 0.824 0.391 PHR Continuous 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) Categories Q1 Reference Reference Reference Q2 0.97 (0.73, 1.29) 0.97 (0.73, 1.28) 0.96 (0.72, 1.27) Q3 1.05 (0.76, 1.46) 1.06 (0.76, 1.47) 1.00 (0.72, 1.39) Q4 1.33 (0.99, 1.79) 1.34 (1.00, 1.79) 1.22 (0.90, 1.67) P for trend 0.083 0.075 0.245 MHR Continuous 1.34 (0.85, 2.12) 1.35 (0.86, 2.11) 1.13 (0.70, 1.82) Categories Q1 Reference Reference Reference Q2 1.10 (0.74, 1.64) 1.10 (0.74, 1.63) 1.07 (0.72, 1.60) Q3 1.25 (0.90, 1.73) 1.25 (0.90, 1.73) 1.16 (0.83, 1.63) Q4 1.24 (0.86, 1.79) 1.24 (0.86, 1.78) 1.12 (0.77, 1.63) P for trend 0.175 0.165 0.476 Model 1: No covariates were adjusted. Model 2: Age, gender, and race were adjusted. Model 3: Age, gender, race, marital status, education level, income-to-poverty ratio, BMI, smoking, alcohol use, diabetes, cardiovascular disease, hypertension, hyperlipidemia, and cancer were adjusted. p < 0.05 was deemed statistically significant. OR1: Odd ratio. 95% CI2: 95% confidence interval. Abbreviations: NPAR: neutrophil-percentage-to-albumin ratio; NPAR/10: NPAR divided by 10; NLR: neutrophil-to-lymphocyte ratio; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; LHR: lymphocyte-to-high-density lipoprotein cholesterol ratio; PHR: platelet-to-high-density lipoprotein cholesterol ratio; MHR: Monocyte-to-high-density lipoprotein cholesterol ratio. Table 3: Threshold effect analysis of systemic inflammatory indicators on psoriasis using a two-stage linear regression model in Model 3. Threshold effect analysis Psoriasis OR1 (95% CI2) P-value NPAR Linear effect 1.005 (1.012, 1.100) 0.013 Inflection points of NPAR (K) 16.386 < K slope 1.119 (1.062, 1.181) <0.001 > K slope 0.885 (0.785, 0.983) 0.032 Log-likelihood ratio test <0.001 NPAR/10 Linear effect 1.702 (1.123, 2.591) 0.013 Inflection points of NPAR/10 (K) 1.639 < K slope 3.073 (1.816, 5.265) <0.001 > K slope 0.293 (0.089, 0.843) 0.032 Log-likelihood ratio test <0.001 NLR Linear effect 1.081 (0.998, 1.161) 0.043 Inflection points of NLR (K) 3.269 < K slope 1.415 (1.225, 1.635) <0.001 > K slope 0.711 (0.529, 0.906) 0.013 Log-likelihood ratio test <0.001 NHR Linear effect 1.049 (1.001, 1.098) 0.042 Inflection points of NHR (K) 4.286 < K slope 1.164 (1.048, 1.295) 0.005 > K slope 0.976 (0.891, 1.059) 0.583 Log-likelihood ratio test 0.030 Note: Adjusted for age, gender, race, marital status, education level, income-to-poverty ratio, BMI, smoking, alcohol use, diabetes, cardiovascular disease, hypertension, hyperlipidemia, and cancer. p < 0.05 was deemed statistically significant. OR1: Odd ratio. 95% CI2: 95% confidence interval. Abbreviations: NPAR: neutrophil-percentage-to-albumin ratio; NPAR/10: NPAR divided by 10; NLR: neutrophil-to-lymphocyte ratio; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio. Figure 2. Smooth curve fitting for the relationships between systemic inflammatory indicators and psoriasis. (A) Neutrophil-percentage-to-albumin ratio (NPAR); (B) neutrophil-to-lymphocyte ratio (NLR); (C) neutrophil-to-high-density lipoprotein cholesterol ratio (NHR); (D) lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR); (E) platelet-to-high-density lipoprotein cholesterol ratio (PHR); (F) monocyte-to-high-density lipoprotein cholesterol ratio (MHR). 3.3 Subgroup analysis Subgroup analyses were conducted to evaluate whether the associations between inflammatory markers and psoriasis varied by demographic and clinical factors, including age, gender, race, education level, marital status, PIR, BMI, smoking status, alcohol use, hypertension, hyperlipidemia, CVD, cancer, and diabetes (Figure 3). No significant interaction effects were observed between any of these covariates and NPAR in relation to psoriasis risk. However, stratified analyses revealed that elevated NHR was associated with higher odds of psoriasis among certain subgroups. Specifically, females (OR = 1.08, 95% CI: 1.03–1.15) and individuals with higher BMI (OR = 1.06, 95% CI: 1.00–1.12) in the highest quartile of NHR showed significantly increased risk. Similarly, within the NLR cohort, smokers (OR = 1.00, 95% CI: 0.92–1.08) and alcohol users (OR = 1.05, 95% CI: 0.98–1.13) showed a trend toward an elevated risk of psoriasis, although these associations did not reach statistical significance. Figure 3. Subgroup analysis of the association between quartile 4 of the three ratios and the prevalence of psoriasis. (A) Neutrophil-percentage-to-albumin ratio (NPAR). (B) neutrophil-to-lymphocyte ratio (NLR). (C) Neutrophil-to-high-density lipoprotein cholesterol ratio (NHR). All models were adjusted for age, gender, race, marital status, education level, income-to-poverty ratio, BMI, smoking, alcohol usage, diabetes, cardiovascular disease, hypertension, hyperlipidemia, and cancer, with the stratifying variable itself excluded from adjustment within each respective subgroup. Results are presented as odds ratios (OR), indicated by squares, with corresponding 95% confidence intervals (CIs) shown as horizontal lines. 3.4 ROC analysis We calculated the AUC values to evaluate the predictive capacity of systemic inflammatory indicators for psoriasis (Figure 4). The results indicated that the AUC values for NHR, NLR, and NPAR exhibited superior discriminative ability compared to the other inflammatory markers assessed. Additionally, as presented in Table 4, the AUC values for NPAR, NHR, and NLR were all statistically significant (p < 0.05). Figure 4. ROC curve analysis of systemic inflammatory biomarkers for the diagnosis of psoriasis. Table 4. AUC values of systemic inflammatory indicators. Test AUC1 95%Cl2 low 95%Cl2 up Best threshol d Specific ity Sensitivity P for the differenc e in AUC PHR 0.537 0.510 0.564 45.137 0.001 1 0.010 NHR 0.553 0.526 0.580 0.600 0.002 0.998 <0.001 MHR 0.548 0.520 0.575 0.274 0.232 0.822 0.001 LHR 0.511 0.484 0.539 0.418 0.003 0.998 0.463 NLR 0.556 0.529 0.583 2.119 0.595 0.506 <0.001 NPAR 0.553 0.527 0.580 91.801 0.019 0.986 <0.001 NPAR/10 0.553 0.527 0.580 91.801 0.019 0.986 <0.001 p < 0.05 was deemed statistically significant. AUC1, area beneath the curve. 95% Cl2, 95% confidence interval. Abbreviations: PHR: platelet-to-high-density lipoprotein cholesterol ratio; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; MHR: Monocyte-to-high-density lipoprotein cholesterol ratio; LHR: lymphocyte-to-high-density lipoprotein cholesterol ratio; NLR: neutrophil-to-lymphocyte ratio; NPAR: neutrophil-percentage-to-albumin ratio; NPAR/10: NPAR divided by 10. 4. Discussion This cross-sectional study identified significant associations between elevated levels of NPAR, NLR, and NHR and increased odds of psoriasis among U.S. adults. In contrast, no significant associations were observed for LHR, PHR, or MHR. After full adjustment for demographic, socioeconomic, behavioral, and clinical covariates—including age, gender, race, marital status, education, PIR, BMI, smoking, alcohol use, diabetes, CVD, hypertension, hyperlipidemia, and cancer—the association between NHR and psoriasis was no longer statistically significant. These findings are consistent with previous studies indicating a nonlinear relationship between NLR and both the prevalence and severity of psoriasis[14]. In this study, we report for the first time a correlation between elevated NPAR, NHR and an increased incidence of psoriasis. Our study supports the role of NPAR as a robust and versatile biomarker of systemic inflammation. Previous research has demonstrated its predictive value in a range of pathological conditions, including acute kidney injury, cardiogenic shock, myocardial infarction, diabetic retinopathy, metabolic dysfunction-associated steatotic liver disease, depression, and cancer[15-17]. Neutrophils, a critical component of human white blood cells, are instrumental in orchestrating the inflammatory response. Albumin, on the other hand, exhibits anti-inflammatory and antioxidant properties that are modulated by inflammatory states, resulting in concentration variations[18]. In individuals with dietary deficits and inflammation, hypoalbuminemia frequently indicates a poor prognosis[11]. Notably, our findings identify NPAR as a strong and independent predictor of psoriasis incidence. The simplicity of measuring neutrophil percentage and albumin concentration renders them valuable clinical indicators. NPAR outperforms other inflammatory blood biomarkers, such as the NLR and eosinophil-to-lymphocyte ratio, in predicting 5-year all-cause mortality, as evidenced by data from the NHANES database[19]. The NLR serves as a valuable and cost-effective indicator of systemic inflammation. Derived from routine laboratory tests, it provides an accessible measure of inflammatory status, particularly in patients exhibiting pronounced symptoms following stressful events[7, 20]. The NLR integrates two fundamental and complementary immune pathways: innate immunity, reflected by neutrophil levels, and adaptive immunity, represented by lymphocytes. This easily measurable parameter offers considerable clinical relevance, as elevated NLR values have been associated with increased levels of pro-inflammatory cytokines, underscoring its utility as a reliable marker of systemic inflammation[21]. Furthermore, subgroup analyses revealed that individuals in the highest quartile of NLR (Q4), as well as those who smoke or consume alcohol, faced an elevated risk of developing psoriasis. These findings reinforce the role of smoking and alcohol consumption as significant risk factors for psoriasis incidence. Neutrophils play a pivotal role in chronic inflammatory and autoimmune diseases, and their involvement in psoriasis is well-documented by histopathological features like neutrophil-filled Munro microabscesses[22]. In generalized pustular psoriasis, a pronounced neutrophil-rich infiltrate is a characteristic histopathological feature[23]. Previous studies have reported elevated NLR and platelet-to-lymphocyte ratio in psoriasis patients[24], although these markers do not always reflect disease severity[25]. Nonetheless, a decrease in NLR levels following psoriasis treatment highlights the significance of NLR levels in monitoring disease progression[26]. NPAR is a composite inflammatory marker that integrates neutrophil percentage and albumin levels[27]. As a negative acute-phase protein, albumin decreases under chronic inflammatory conditions[28], which may partly explain the elevated NPAR values observed in patients with psoriasis. Furthermore, albumin levels are less affected by acute fluctuations and thus reflect long-term inflammation more consistently. This stability makes NPAR a potentially more accurate indicator of psoriasis disease activity compared to NLR[29]. HDL-C possesses immunomodulatory, anti-inflammatory, antithrombotic, and antioxidant properties, spurring interest in HDL-C– based inflammatory biomarkers[11]. Four emerging ratios—LHR, MHR, NHR, and PHR—are derived by dividing lymphocyte, monocyte, neutrophil, or platelet counts by HDL-C levels, reflecting crosstalk between lipid metabolism and inflammation[7]. NHR, in particular, has emerged as a promising biomarker that integrates inflammatory and lipid metabolic pathways, offering valuable insight into their interplay[19]. Research showed that the NHR has high predictive accuracy for a range of systemic conditions, including acute biliary pancreatitis, schizophrenia, bipolar disorder, hypertension, cardiovascular risk, and hepatocellular carcinoma[11]. However, after adjusting for potential confounders, the association between NHR and psoriasis prevalence was not statistically significant. Our findings indicate that NPAR and NLR may serve as clinically useful biomarkers for psoriasis and its comorbidities—particularly cardiovascular disease (CVD) and metabolic syndrome—by reflecting shared pathways of systemic inflammation[30-31]. For instance, elevated NLR has been linked to increased cardiovascular risk and insulin resistance[32-33], while NPAR, integrating neutrophil percentage and albumin levels, may signal chronic inflammatory states and metabolic dysregulation, such as hypoalbuminemia commonly seen in persistent disease[34]. These markers could aid in identifying psoriasis patients at elevated risk for CVD or metabolic syndrome, facilitating earlier intervention and more personalized management. Future studies should validate their utility in risk stratification and treatment guidance within real-world clinical settings. This study has several strengths and limitations. A key strength is the substantial sample size, which enhances the reliability of the findings and aligns with previous research. In addition, the thorough adjustment for a wide range of covariates improves the accuracy of the estimated associations. Importantly, this is the first study to investigate the relationship between psoriasis and novel inflammatory indicators such as NPAR and NHR. However, several limitations should be noted. The cross-sectional design precludes causal inference, and the temporal sequence between elevated inflammatory markers and psoriasis remains unclear. Future prospective or interventional studies are needed to establish causality and elucidate underlying mechanisms. Additionally, some subgroup analyses featured small sample sizes—such as the analysis of smokers in the NLR group—which may have resulted in unstable estimates and reduced statistical power. These findings should therefore be interpreted with caution and validated in larger cohorts. Another limitation is the reliance on self-reported psoriasis diagnoses, which may introduce misclassification bias. Finally, the lack of data on psoriasis severity limited our ability to evaluate associations between inflammatory markers and disease progression, reducing the clinical depth of the analysis. 5. Conclusion NPAR and NLR show a significant positive correlation with psoriasis prevalence and outperform other inflammatory markers—such as PHR, MHR, and LHR—in both accuracy and discriminative ability, indicating their potential utility in the clinical identification and differentiation of psoriasis.