Abstract
Purpose
The neutrophil percentage-to-albumin ratio (NPAR) is a readily accessible yet underexplored inflammatory marker in rheumatoid arthritis (RA). This study aimed to explore the association of NPAR with disease activity and to evaluate its performance in identifying high disease activity.
Patients and Methods
A cross-sectional analysis was performed among 1,191 hospitalized RA patients admitted to Xingtai People’s Hospital between March 2022 and December 2024. NPAR was computed as (neutrophil percentage × 100) divided by serum albumin (g/dL). Disease activity was quantified using the 28-joint Disease Activity Score (DAS28) by erythrocyte sedimentation rate (DAS28-ESR) and C-reactive protein (DAS28-CRP). Multiple linear regression models were applied to determine the independent association between NPAR and disease activity, and receiver operating characteristic curve analyses were conducted to examine the ability of NPAR to identify high disease activity.
Results
989 patients met the inclusion criteria for final analysis (average age 57.6 years; 78.0% women), 95.6% exhibited moderate-to-high disease activity. After fully adjusting for variables, NPAR remained independently and positively associated with disease activity. Each standard deviation increase in NPAR was associated with an increase of 0.37 in DAS28-CRP and 0.34 in DAS28-ESR. A clear dose–response relationship was observed across increasing NPAR tertiles. In addition, NPAR demonstrated moderate discriminatory performance for distinguishing high from moderate disease activity, with areas under the ROC curve of 0.75 (95% CI, 0.72–0.79) for DAS28-CRP and 0.71 (95% CI, 0.68–0.75) for DAS28-ESR. Notably, NPAR showed better discriminatory ability than other conventional complete blood count-derived indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-albumin ratio (PAR), and neutrophil-to-albumin ratio (NAR).
Conclusion
In hospitalized patients with moderate-to-high disease activity, NPAR was positively associated with RA disease activity and showed meaningful discriminatory performance for identifying high disease states.
Keywords: biomarkers, disease progression, inflammation, leukocyte count
Introduction
Rheumatoid arthritis (RA) is a chronic immune-mediated inflammatory disorder characterized by persistent synovitis that leads to progressive joint swelling, pain, and ultimately structural destruction. Without timely diagnosis and intervention, RA can result in irreversible joint damage, functional disability, and systemic comorbidities,1 imposing a substantial global health burden and markedly impairing patients’ quality of life.2 Early identification of high-risk individuals and prompt initiation of appropriate therapy are therefore essential to prevent long-term disability and halt disease progression.1,2
Although the precise immunopathogenesis of RA remains incompletely understood, dysregulated activation of innate and adaptive immune cells drives autoimmunity and chronic inflammation within the synovium.3 Conventional inflammatory markers, such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), are widely used in clinical practice to evaluate disease activity; however, both are influenced by numerous non-inflammatory factors, exhibit limited specificity, and fail to fully capture the complexity of RA-related inflammatory processes.4 While cytokines central to RA pathogenesis, including interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), provide more direct insights into inflammatory pathways,3,5 their high cost and technical requirements limit their routine clinical application. These practical constraints highlight an unmet clinical need for cost-effective and readily available biomarkers to serve as a complement to existing indicators, thereby assisting in a more comprehensive reflection of the underlying inflammatory milieu in routine practice.
In contrast, peripheral blood leukocyte-derived parameters offer a convenient, inexpensive, and broadly accessible alternative for reflecting systemic inflammation.3,6 Complete blood count (CBC)-based inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) have been extensively studied and validated as correlates of RA disease activity.7–9 The neutrophil percentage-to-albumin ratio (NPAR) is an emerging inflammatory index that integrates neutrophil percentage, representing innate immune activation, with serum albumin, a marker of anti-inflammatory capacity, antioxidant status, and systemic nutritional reserve. Compared with conventional cell-based ratios (eg., NLR, PLR, MLR), which primarily reflect the balance among leukocyte subsets, NPAR provides a broader perspective by simultaneously incorporating inflammatory burden and host nutritional/inflammatory homeostasis. Neutrophil percentage, rather than absolute neutrophil count, is highly sensitive to stress-driven shifts in leukocyte distribution and may more accurately reflect innate immune activation. Albumin, a negative acute-phase reactant, declines in response to inflammatory catabolism and is further influenced by nutritional and metabolic status, thereby serving as an indicator of systemic health and inflammatory burden. Recent studies have highlighted NPAR as a promising indicator of disease risk and prognosis across various conditions.10–15 Elevated NPAR has been associated with metabolic syndrome,10 chronic kidney disease (CKD),11 periodontitis,12,13 asthma,14 breast cancer,15 and depression.16 Moreover, NPAR has demonstrated prognostic value in patients with CKD,17 myocardial infarction,18 and multiple malignancies.19 Its analytic simplicity, low cost, and wide availability render NPAR particularly attractive for routine clinical use, especially in primary care and resource-limited settings. However, these leukocyte-driven indices are susceptible to pharmacological confounding, as glucocorticoids can independently elevate neutrophil parameters irrespective of disease activity, necessitating rigorous statistical adjustment in clinical analyses.
Despite these emerging insights, the relevance of NPAR to RA remains poorly understood. To date, only one study has reported a positive association between elevated NPAR and increased RA risk (OR = 1.05).20 However, whether NPAR correlates with RA disease activity and how it performs relative to other CBC-derived inflammatory markers are important questions that remain unanswered. Therefore, the present study aimed to elucidate the association between NPAR and RA disease activity and to compare its discriminatory performance against several other established CBC-based inflammatory indices, including the neutrophil-to-albumin ratio (NAR), NLR, PLR, and platelet-to-albumin ratio (PAR).
Materials and Methods
Study Population
This retrospective cross-sectional analysis included a consecutive series of 1,191 hospitalized patients diagnosed with RA at Xingtai People’s Hospital between March 2022 and December 2024. Eligibility was determined according to the 2010 ACR/EULAR classification criteria for RA.21 Individuals were excluded if they were younger than 18 years old; had ongoing or chronic infectious diseases, coexisting autoimmune disorders, serious hepatic or renal dysfunction, or malignancies; lacked complete DAS28 data; or presented with extreme NPAR values (< mean − 3 standard deviation (SD) or > mean + 3 SD). After applying these criteria, 989 patients were ultimately included in the final dataset. The patient selection process is illustrated in Figure 1. The study adhered to the principles of the Declaration of Helsinki and obtained approval from the Ethics Committee of Xingtai People’s Hospital (approval number: 2025[031]). Owing to the retrospective design and the use of de-identified clinical information, the requirement for informed consent was waived, consistent with previous methodological practices.22,23 This study follows the RECORD guidelines.
Figure 1.
Flowchart of participant selection for the study cohort.
Clinical Data Collection
Demographic and general clinical characteristics were retrieved from the hospital’s electronic medical record system, including sex, age, body mass index (BMI), tobacco and alcohol use, and comorbidities such as hypertension, coronary heart disease, and diabetes mellitus. Information on RA duration and recent exposure to medications, including nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), biological DMARDs (bDMARDs), and targeted synthetic DMARDs (tsDMARDs), was also documented. Disease activity assessment was based on measures routinely obtained at admission, including the 28-joint tender joint count (TJC28), 28-joint swollen joint count (SJC28), the patient global assessment (PGA) score and evaluator’s global assessment (EGA) score on a 100-mm visual analogue scale. All clinical assessments were conducted by trained rheumatologists during face-to-face evaluations.
Laboratory variables collected at admission or the following morning included complete blood count parameters (white blood cell, neutrophil, lymphocyte, and red blood cell counts; hemoglobin; and platelet count), serum albumin, CRP, ESR, rheumatoid factor (RF), and autoantibodies. All laboratory measurements were performed in the hospital’s central laboratory following standardized testing procedures. Inflammatory composite indices were calculated as follows: NPAR = neutrophil percentage × 100 / albumin (g/dL);12–15 NAR = neutrophil count / albumin (g/L); PAR = platelet count / albumin (g/L); NLR = neutrophil count / lymphocyte count; and PLR = platelet count / lymphocyte count. Disease activity was initially evaluated using 28-joint Disease Activity Score by erythrocyte sedimentation rate (DAS28-ESR) and C-reactive protein (DAS28-CRP) based on established formulas,24 classifying patients into remission/low (< 3.2), moderate (≥ 3.2 to < 5.1), and high (≥ 5.1) activity groups. To validate these findings without laboratory parameters, the Clinical Disease Activity Index (CDAI) was also applied, stratifying patients into remission/low (< 10), moderate (≥ 10 to < 22), and high (≥ 22) activity categories.
Statistical Analysis
Continuous variables were summarized as mean ± SD or median (interquartile range, IQR), depending on distributional characteristics, and were compared using one-way ANOVA or the Kruskal–Wallis test. Categorical variables were expressed as frequencies (percentages) and compared using chi-square tests. Missing data were minimal (<1%) for variables such as BMI (n = 5) and RF (n = 9), and were replaced with corresponding mean or median values accordingly. Missing data for BMI and RF (<1% of the total dataset) were imputed using mean or median values due to the negligible amount of missingness. To ensure this imputation did not bias our results, we also performed a complete-case analysis excluding these patients, which yielded virtually identical results. A detailed description of missing patterns is provided in Supplementary Table S1.
The association between NPAR and DAS28 was evaluated using multivariable linear regression. Three incremental models were constructed: Model 1 was unadjusted; Model 2 was adjusted for sex, age, lymphocyte count, platelet count, and glucocorticoids; Model 3 was further adjusted for hypertension, methotrexate (MTX), leflunomide (LEF), types of csDMARDs, bDMARDs, tsDMARDs, glucocorticoids, monocyte count, and RF. Covariates were selected based on a >10% change in effect estimates (Model 2) or a regression coefficient p-value < 0.1 (Model 3). Disease duration and RF values were natural log–transformed to improve model fit. To examine the stability of the findings, NPAR was categorized into tertiles for sensitivity analyses, and linear trends were evaluated using the median value of each tertile as a continuous variable. To evaluate the consistency of the associations, we conducted subgroup analyses and tested for interactions using likelihood ratio tests. The discriminatory performance of inflammatory indices for identifying high disease activity was quantified by the area under the receiver operating characteristic curve (ROC).
All statistical procedures were performed using R (http://www.r-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). A two-sided p-value < 0.05 was considered statistically significant.
Results
Study Population Characteristics
The final analysis included 989 hospitalized individuals with RA, with a mean age of 57.62 ± 12.84 years and a predominance of female patients (77.96%). Reflecting the clinical profile of an inpatient population, most subjects presented with moderate-to-high disease activity, whereas only 44 patients (4.45%) met criteria for remission or low disease activity (DAS28-ESR < 3.2). A comprehensive summary of demographic and clinical features is provided in Table 1 and Table 2.
Table 1.
Clinical Characteristics of RA Patients Stratified by NPAR Tertiles (N=989)
| Characteristics | Total | Tertiles of NPAR | P value | ||
|---|---|---|---|---|---|
| T1 7.10–15.71 |
T2 15.72–18.68 |
T3 18.69–29.43 |
|||
| N | 989 | 330 | 329 | 330 | |
| Age (year) | 57.62 (12.84) | 55.33 (13.21) | 57.09 (12.46) | 60.44 (12.33) | <0.001 |
| Female, n (%) | 771 (77.96%) | 286 (86.67%) | 261 (79.33%) | 224 (67.88%) | <0.001 |
| BMI (kg/m2) | 23.99 (3.48) | 23.96 (3.56) | 24.21 (3.57) | 23.80 (3.31) | 0.303 |
| DD (month) | 48.00 (12.00–132.00) | 60.00 (12.00–138.00) | 48.00 (12.00–132.00) | 48.00 (12.00–132.00) | 0.846 |
| Smoking, n (%) | 54 (5.46%) | 8 (2.42%) | 20 (6.08%) | 26 (7.88%) | 0.007 |
| Drinking, n (%) | 18 (1.82%) | 4 (1.21%) | 7 (2.13%) | 7 (2.12%) | 0.599 |
| HTN, n (%) | 320 (32.36%) | 95 (28.79%) | 109 (33.13%) | 116 (35.15%) | 0.203 |
| DM, n (%) | 91 (9.20%) | 25 (7.58%) | 30 (9.12%) | 36 (10.91%) | 0.333 |
| CHD, n (%) | 69 (6.98%) | 22 (6.67%) | 25 (7.60%) | 22 (6.67%) | 0.863 |
| WBC (109/L) | 6.50 (2.15) | 5.62 (1.82) | 6.69 (2.16) | 7.18 (2.17) | <0.001 |
| Neut (109/L) | 4.20 (1.78) | 3.10 (1.30) | 4.39 (1.59) | 5.12 (1.78) | <0.001 |
| NP, % | 63.19 (9.85) | 53.97 (8.35) | 64.96 (6.09) | 70.66 (6.34) | <0.001 |
| Lymph (109/L) | 1.63 (0.59) | 1.87 (0.59) | 1.65 (0.60) | 1.38 (0.47) | <0.001 |
| Mon (109/L) | 0.47 (0.19) | 0.45 (0.17) | 0.47 (0.19) | 0.50 (0.20) | 0.004 |
| RBC (1012/L) | 3.89 (0.49) | 3.98 (0.46) | 3.93 (0.47) | 3.74 (0.52) | <0.001 |
| Hb (g/L) | 111.26 (17.05) | 115.22 (16.40) | 112.98 (15.66) | 105.58 (17.56) | <0.001 |
| PLT (109/L) | 291.51 (90.58) | 267.71 (73.69) | 289.76 (93.95) | 317.07 (95.83) | <0.001 |
| ALB, g/L | 37.10 (4.70) | 40.18 (3.84) | 37.89 (3.54) | 33.24 (3.74) | <0.001 |
| ESR (mm/h) | 55.00 (32.00–86.00) | 37.00 (23.00–61.00) | 56.00 (36.00–84.00) | 78.50 (52.00–104.75) | <0.001 |
| CRP (mg/L) | 25.29 (7.07–47.24) | 7.82 (2.31–25.01) | 23.65 (8.15–42.12) | 45.75 (27.46–75.30) | <0.001 |
| RF (IU/mL) | 160.90 (47.63–334.23) | 140.10 (37.82–276.90) | 168.60 (51.65–309.55) | 196.30 (52.00–432.50) | 0.003 |
| ACPA, n (%) | 892 (91.02%) | 292 (90.40%) | 304 (92.68%) | 296 (89.97%) | 0.426 |
| APF, n (%) | 777 (81.19%) | 246 (78.59%) | 270 (84.64%) | 261 (80.31%) | 0.133 |
| AKA, n (%) | 718 (75.10%) | 222 (70.93%) | 248 (77.74%) | 248 (76.54%) | 0.107 |
| DAS28-ESR | 5.08 (0.97) | 4.61 (0.94) | 5.09 (0.90) | 5.54 (0.82) | <0.001 |
| DAS28-CRP | 4.38 (0.92) | 3.88 (0.87) | 4.38 (0.82) | 4.87 (0.80) | <0.001 |
| NSAIDs, n (%) | 507 (51.26%) | 176 (53.33%) | 171 (51.98%) | 160 (48.48%) | 0.438 |
| GLU, n (%) | 106 (10.72%) | 29 (8.79%) | 28 (8.51%) | 49 (14.85%) | 0.012 |
| MTX, n (%) | 204 (20.63%) | 75 (22.73%) | 72 (21.88%) | 57 (17.27%) | 0.176 |
| LEF, n (%) | 194 (19.62%) | 81 (24.55%) | 58 (17.63%) | 55 (16.67%) | 0.021 |
| Types of cDMARDs, n (%) | 0.045 | ||||
| 0 | 513 (51.87%) | 148 (44.85%) | 174 (52.89%) | 191 (57.88%) | |
| 1 | 354 (35.79%) | 136 (41.21%) | 114 (34.65%) | 104 (31.52%) | |
| 2 | 116 (11.73%) | 45 (13.64%) | 38 (11.55%) | 33 (10.00%) | |
| 3 | 6 (0.61%) | 1 (0.30%) | 3 (0.91%) | 2 (0.61%) | |
| bDMARDs, n (%) | 42 (4.25%) | 22 (6.67%) | 8 (2.43%) | 12 (3.64%) | 0.021 |
| tsDMARDs, n (%) | 23 (2.33%) | 9 (2.73%) | 8 (2.43%) | 6 (1.82%) | 0.732 |
Notes: Values are shown as mean ± SD, median (interquartile range) or n (%).
Abbreviations: DAS28-ESR, disease activity score 28 using erythrocyte sedimentation rate; BMI, body mass index; DD, disease duration; DM, diabetes mellitus; CHD, coronary heart disease; WBC, white blood cell; Neut, neutrophil; NP, neutrophil percentage; Lymph, lymphocyte; RBC, red blood cell; Hb, hemoglobin; PLT, platelet; ALB, albumin; NPAR, neutrophil percentage to albumin ratio; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; RF, rheumatoid factor; ACPA, anti-citrullinated protein antibody; AKA, anti-keratin antibody; APF, anti-perinuclear factor; NSAIDs, non-steroidal anti-inflammatory drugs; cDMARDs, conventional disease-modifying antirheumatic drugs; bDMARDs, biological disease-modifying antirheumatic drugs; tsDMARDs, targeted synthetic disease-modifying antirheumatic drugs.
Table 2.
Clinical Characteristics of RA Patients Stratified by DAS28-ESR (N=989)
| Characteristics | Total | DAS28-ESR | P value | ||
|---|---|---|---|---|---|
| <3.2 | ≥3.2, <5.1 | ≥5.1 | |||
| N (%) | 989 (100%) | 44 (4.45%) | 413 (41.76%) | 532 (53.79%) | _ |
| Age (year) | 57.62 (12.84) | 54.36 (13.76) | 54.83 (13.02) | 60.05 (12.12) | <0.001 |
| Female, n (%) | 771 (77.96%) | 36 (81.82%) | 339 (82.08%) | 396 (74.44%) | 0.016 |
| BMI (kg/m2) | 23.99 (3.48) | 23.97 (3.40) | 24.10 (3.55) | 23.90 (3.44) | 0.684 |
| DD (month) | 48.00 (12.00–132.00) | 60.00 (24.00–126.50) | 58.00 (12.00–144.00) | 48.00 (9.00–132.00) | 0.306 |
| Smoking, n (%) | 54 (5.46%) | 4 (9.09%) | 17 (4.12%) | 33 (6.20%) | 0.208 |
| Drinking, n (%) | 18 (1.82%) | 1 (2.27%) | 7 (1.69%) | 10 (1.88%) | 0.953 |
| HTN, n (%) | 320 (32.36%) | 13 (29.55%) | 115 (27.85%) | 192 (36.09%) | 0.025 |
| DM, n (%) | 91 (9.20%) | 7 (15.91%) | 27 (6.54%) | 57 (10.71%) | 0.026 |
| CHD, n (%) | 69 (6.98%) | 2 (4.55%) | 33 (7.99%) | 34 (6.39%) | 0.513 |
| WBC (109/L) | 6.50 (2.15) | 5.65 (2.02) | 6.05 (2.02) | 6.91 (2.18) | <0.001 |
| Neut (109/L) | 4.20 (1.78) | 3.43 (1.64) | 3.77 (1.66) | 4.59 (1.78) | <0.001 |
| NP, % | 63.19 (9.85) | 59.25 (10.61) | 60.77 (10.10) | 65.40 (9.03) | <0.001 |
| Lymph (109/L) | 1.63 (0.59) | 1.62 (0.55) | 1.65 (0.59) | 1.62 (0.59) | 0.784 |
| Mon (109/L) | 0.47 (0.19) | 0.46 (0.17) | 0.45 (0.18) | 0.50 (0.19) | <0.001 |
| RBC (1012/L) | 3.89 (0.49) | 4.02 (0.63) | 3.97 (0.48) | 3.81 (0.47) | <0.001 |
| Hb (g/L) | 111.26 (17.05) | 119.84 (18.34) | 114.07 (17.43) | 108.36 (16.03) | <0.001 |
| PLT (109/L) | 291.51 (90.58) | 237.18 (54.59) | 269.05 (85.71) | 313.44 (90.59) | <0.001 |
| ALB, g/L | 37.10 (4.70) | 39.52 (4.16) | 38.45 (4.18) | 35.86 (4.76) | <0.001 |
| NPAR | 17.36 (3.75) | 15.12 (3.04) | 16.01 (3.29) | 18.59 (3.69) | <0.001 |
| ESR (mm/h) | 55.00 (32.00–86.00) | 15.00 (8.00–27.50) | 32.00 (23.00–50.00) | 81.00 (60.50–102.00) | <0.001 |
| CRP (mg/L) | 25.29 (7.07–47.24) | 3.43 (1.60–8.31) | 8.62 (2.94–24.63) | 40.50 (24.38–62.89) | <0.001 |
| RF (IU/mL) | 160.90 (47.63–334.23) | 60.25 (24.18–167.65) | 121.30 (37.75–278.60) | 206.60 (59.50–424.00) | <0.001 |
| ACPA, n (%) | 892 (91.02%) | 37 (86.05%) | 371 (90.93%) | 484 (91.49%) | 0.484 |
| APF, n (%) | 777 (81.19%) | 36 (83.72%) | 313 (79.64%) | 428 (82.15%) | 0.574 |
| AKA, n (%) | 718 (75.10%) | 31 (72.09%) | 287 (73.21%) | 400 (76.78%) | 0.420 |
| NSAIDs, n (%) | 507 (51.26%) | 20 (45.45%) | 217 (52.54%) | 270 (50.75%) | 0.631 |
| GLU, n (%) | 106 (10.72%) | 8 (18.18%) | 45 (10.90%) | 53 (9.96%) | 0.235 |
| MTX, n (%) | 204 (20.63%) | 12 (27.27%) | 116 (28.09%) | 76 (14.29%) | <0.001 |
| LEF, n (%) | 194 (19.62%) | 18 (40.91%) | 80 (19.37%) | 96 (18.05%) | 0.001 |
| Types of cDMARDs, n (%) | <0.001 | ||||
| 0 | 513 (51.87%) | 12 (27.27%) | 184 (44.55%) | 317 (59.59%) | |
| 1 | 354 (35.79%) | 18 (40.91%) | 169 (40.92%) | 167 (31.39%) | |
| 2 | 116 (11.73%) | 14 (31.82%) | 56 (13.56%) | 46 (8.65%) | |
| 3 | 6 (0.61%) | 0 (0.00%) | 4 (0.97%) | 2 (0.38%) | |
| bDMARDs, n (%) | 42 (4.25%) | 0 (0.00%) | 28 (6.78%) | 14 (2.63%) | 0.003 |
| tsDMARDs, n (%) | 23 (2.33%) | 2 (4.55%) | 16 (3.87%) | 5 (0.94%) | 0.007 |
Notes: Values are shown as mean ± SD, median (interquartile range) or n (%).
Abbreviations: DAS28-ESR, disease activity score 28 using erythrocyte sedimentation rate; BMI, body mass index; DD, disease duration; HTN, Hypertension; DM, diabetes mellitus; CHD, coronary heart disease; WBC, white blood cell counts; Neut, neutrophil counts; NP, neutrophil percentage; Lymph, lymphocyte counts; Mon, Monocyte counts; RBC, red blood cell counts; Hb, hemoglobin; PLT, platelet counts; ALB, albumin; NPAR, neutrophil percentage to albumin ratio; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; RF, rheumatoid factor; ACPA, anti-citrullinated protein antibody; AKA, anti-keratin antibody; APF, anti-perinuclear factor; NSAIDs, non-steroidal anti-inflammatory drugs; GLU, Glucocorticoids; MTX, Methotrexate; LEF, Leflunomide; cDMARDs, conventional disease-modifying antirheumatic drugs; bDMARDs, biological disease-modifying antirheumatic drugs; tsDMARDs, targeted synthetic disease-modifying antirheumatic drugs.
To investigate the clinical and inflammatory phenotype associated with NPAR, patients were categorized into tertiles. As shown in Table 1, individuals in the highest tertile (T3) demonstrated a markedly different clinical profile compared with those in the lower tertiles (T1 and T2). Patients in T3 tended to be older, had a higher proportion of males and smokers, and exhibited substantially elevated inflammatory parameters, including ESR, CRP, RF, WBC counts, neutrophil counts and percentages, monocyte counts, and platelet counts. Disease activity scores (DAS28-ESR and DAS28-CRP) were also highest in this group. In contrast, lymphocyte counts, serum albumin concentrations, and hemoglobin levels were significantly lower among T3 patients. Regarding treatment patterns, the T3 tertile was less frequently prescribed LEF and other csDMARDs, yet glucocorticoid use was considerably more common (all P < 0.05).
Further analyses based on DAS28 categories yielded results parallel to the NPAR tertile comparisons. As summarized in Table 2, patients with high disease activity (DAS28-ESR ≥ 5.1) were more often male, older, and more likely to have hypertension. They also exhibited markedly elevated inflammatory biomarkers and reduced albumin and hemoglobin levels relative to those with moderate or low activity/remission. Treatment profiles also differed across activity groups: MTX, LEF, tsDMARDs, and types of csDMARDs were used less frequently in highly active disease, whereas bDMARD utilization was relatively low in both the lowest and highest activity categories.
Factors Associated with Disease Activity in Univariate Analysis
As presented in Supplementary Table S2, the univariate analysis identified several clinical and laboratory variables that were significantly related to disease activity. Male patients, those of older age, and individuals with hypertension showed a tendency toward higher DAS28 scores. Multiple inflammatory parameters, including total WBC count, neutrophil count and proportion, platelet count, RF levels, and NPAR, were also positively associated with disease activity. In contrast, indicators reflecting erythropoietic status and nutritional/inflammatory balance, such as higher RBC counts, hemoglobin concentrations, and serum albumin levels, were inversely associated with disease activity. A similar negative association was observed for MTX, LEF, other csDMARDs, and tsDMARDs, suggesting that patients receiving these therapies were less likely to have highly active disease.
Association of NPAR with Disease Activity in Rheumatoid Arthritis
Multivariable regression analyses revealed a clear, graded association between NPAR levels and RA disease activity. As summarized in Table 3, higher NPAR values were consistently linked to increased DAS28 scores across all analytic models. In the fully adjusted Model 3, a one SD rise in NPAR corresponded to a 0.34-unit increase in DAS28-ESR (95% CI: 0.27–0.40) and a 0.37-unit increase in DAS28-CRP (95% CI: 0.31–0.43), even after adjustment for demographic characteristics, comorbidities, inflammatory cell counts, RF, and multiple medication classes including glucocorticoids, MTX, LEF, and csDMARD, bDMARD, and tsDMARD therapies. Sensitivity analyses using NPAR tertiles further reinforced these findings. Participants in the highest tertile exhibited substantially greater disease activity compared with those in the lowest tertile, with mean differences of 0.74 (95% CI: 0.58–0.89) for DAS28-ESR and 0.78 (95% CI: 0.64–0.92) for DAS28-CRP. Importantly, the positive gradient across tertiles remained stable across all regression models, indicating the robustness of the association.
Table 3.
Multivariate Linear Regression Results of Association Between NPAR and DAS28
| Exposure | Model 1 β (95% CI) |
Model 2 β (95% CI) |
Model 3 β (95% CI) |
|---|---|---|---|
| DAS28-ESR | |||
| NPAR-per unit increase | 0.11 (0.09, 0.12) | 0.10 (0.08, 0.11) | 0.09 (0.07, 0.11) |
| NPAR-per SD increase | 0.40 (0.35, 0.46) | 0.37 (0.30, 0.43) | 0.34 (0.27, 0.40) |
| NPAR (Tertiles) | |||
| T1 | Ref | Ref | Ref |
| T2 | 0.48 (0.35, 0.62) | 0.42 (0.29, 0.55) | 0.39 (0.26, 0.52) |
| T3 | 0.93 (0.79, 1.07) | 0.79 (0.64, 0.94) | 0.74 (0.58, 0.89) |
| P for trend | <0.0001 | <0.0001 | <0.0001 |
| DAS28-CRP | |||
| NPAR-per unit increase | 0.12 (0.10, 0.13) | 0.11 (0.09, 0.13) | 0.10 (0.08, 0.12) |
| NPAR-per SD increase | 0.44 (0.39, 0.49) | 0.41 (0.35, 0.47) | 0.37 (0.31, 0.43) |
| NPAR (Tertiles) | |||
| T1 | Ref | Ref | Ref |
| T2 | 0.51 (0.38, 0.63) | 0.44 (0.32, 0.57) | 0.40 (0.28, 0.52) |
| T3 | 0.99 (0.87, 1.12) | 0.85 (0.71, 0.99) | 0.78 (0.64, 0.92) |
| P for trend | <0.0001 | <0.0001 | <0.0001 |
Notes: Model 1 was adjusted for none; Model 2 was adjusted for sex, age, lymphocyte counts, platelet counts and glucocorticoids; Model 3 was adjusted for sex, age, hypertension, lymphocyte counts, monocyte counts, platelet counts, rheumatoid factor, glucocorticoids, methotrexate, leflunomide, types of conventional disease-modifying antirheumatic drugs, biological disease-modifying antirheumatic drugs, and targeted synthetic disease-modifying antirheumatic drugs.
Abbreviations: NPAR, neutrophil percentage to albumin ratio; DAS28-ESR, disease activity score 28 using erythrocyte sedimentation rate; DAS28-CRP, disease activity score 28 using C-reactive protein; SD, standard deviation; CI, confidence interval.
Evaluation of the Discriminative Ability of NPAR in Identifying High Disease Activity
The capacity of NPAR to differentiate RA patients with high disease activity from those with moderate activity was examined using ROC curve analysis. As illustrated in Table 4 and Figure 2, NPAR outperformed several other hematologic inflammatory indicators, such as neutrophil count, neutrophil percentage, albumin, NLR, PLR, PAR, and NAR, in distinguishing high disease activity. Using DAS28-ESR as the reference standard, NPAR yielded an area under the curve (AUC) of 0.71 (95% CI: 0.68–0.75). When disease activity was defined by DAS28-CRP, its discriminative performance improved further, with an AUC of 0.75 (95% CI: 0.72–0.79). Collectively, these findings indicate that NPAR possesses favorable discriminatory accuracy relative to other CBC-based inflammatory markers for identifying patients with highly active RA.
Table 4.
AUCs of Complete Blood Count-Derived Inflammatory Biomarkers in Predicting High Disease Activity
| Variables | AUC (95% CI) | Sensitivity | Specificity | Youden index | Cut-off |
|---|---|---|---|---|---|
| DAS28-ESR | |||||
| ALB | 0.66 (0.63, 0.69) | 0.59 | 0.65 | 0.24 | 37.05 |
| NC | 0.65 (0.62, 0.69) | 0.75 | 0.50 | 0.25 | 3.39 |
| NP | 0.64 (0.61, 0.68) | 0.80 | 0.43 | 0.23 | 59.45 |
| NLR | 0.64 (0.61, 0.68) | 0.58 | 0.65 | 0.23 | 2.66 |
| PLR | 0.63 (0.60, 0.67) | 0.71 | 0.50 | 0.21 | 160.05 |
| PAR | 0.70 (0.67, 0.73) | 0.67 | 0.65 | 0.32 | 7.30 |
| NAR | 0.69 (0.65, 0.72) | 0.76 | 0.54 | 0.30 | 0.09 |
| NPAR | 0.71 (0.68, 0.75) | 0.75 | 0.62 | 0.37 | 16.26 |
| DAS28-CRP | |||||
| ALB | 0.72 (0.68, 0.76) | 0.73 | 0.60 | 0.33 | 37.05 |
| NC | 0.68 (0.65, 0.72) | 0.77 | 0.52 | 0.29 | 3.77 |
| NP | 0.66 (0.62, 0.70) | 0.70 | 0.54 | 0.24 | 64.15 |
| NLR | 0.67 (0.64, 0.71) | 0.68 | 0.59 | 0.27 | 2.66 |
| PLR | 0.63 (0.59, 0.67) | 0.77 | 0.43 | 0.20 | 159.50 |
| PAR | 0.73 (0.69, 0.76) | 0.80 | 0.54 | 0.34 | 7.31 |
| NAR | 0.73 (0.70, 0.77) | 0.68 | 0.70 | 0.38 | 0.12 |
| NPAR | 0.75 (0.72, 0.79) | 0.72 | 0.67 | 0.39 | 17.80 |
Abbreviations: AUC, areas under the curve; ALB, albumin; NC, neutrophil counts; NP, neutrophil percentage; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; PAR, platelet-to-albumin ratio; NAR, neutrophil to albumin ratio; NPAR, neutrophil percentage-to-albumin ratio.
Figure 2.
Receiver operating characteristic (ROC) analysis for discriminating high disease activity in rheumatoid arthritis. (A) ROC curves using DAS28-ESR (Disease Activity Score 28 based on erythrocyte sedimentation rate) criteria; (B) ROC curves using DAS28-CRP (Disease Activity Score 28 based on C-reactive protein) criteria. Biomarkers evaluated include neutrophil count, neutrophil percentage, albumin. Diagnostic performance is expressed as the area under the curve (AUC).
Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PAR, platelet-to-albumin ratio; NAR, neutrophil-to-albumin ratio; NPAR, neutrophil percentage-to-albumin ratio.
Sensitivity and Subgroup Analyses
As shown in Supplementary Table S3, subgroup analyses demonstrated that the positive association between NPAR and disease activity remained consistent across diverse patient populations, including strata of sex, age, hypertension status, and treatment regimens (eg., glucocorticoids, methotrexate, and leflunomide), with no significant interaction effects identified (all P for interaction > 0.05).
To exclude pharmacological confounding, a sensitivity analysis was restricted to the 883 glucocorticoid-naïve patients. The associations remained entirely robust, and the discriminative ability for high disease activity was undiminished (Supplementary Tables S4 and S5). Second, utilizing the laboratory-parameter-free Clinical Disease Activity Index (CDAI) as an alternative outcome in an additional sensitivity analysis, a robust positive association was consistently observed between NPAR and CDAI. Furthermore, NPAR continued to demonstrate the highest discriminative accuracy compared to its individual components or other CBC-derived inflammatory indicators (Supplementary Tables S6 and S7).
Discussion
In this retrospective analysis of 989 hospitalized patients with RA, our findings reveal for the first time that NPAR is not only robustly and independently correlated with disease activity, reflected by per-SD increases of 0.34 in DAS28-ESR and 0.37 in DAS28-CRP, but also provides moderate discriminatory accuracy for identifying patients with highly active disease, with AUCs of 0.71 (DAS28-ESR) and 0.75 (DAS28-CRP), surpassing conventional CBC-derived inflammatory indicators including NLR, PLR, PAR, and NAR. These findings support its potential utility as an accessible and low-cost adjunctive indicator in routine RA evaluation. However, whether NPAR is sensitive enough to detect subtle fluctuations in mild, outpatient RA remains unknown and requires prospective validation.
The potential of CBC-derived biomarkers as adjunctive tools for evaluating RA has drawn considerable interest recently, largely due to their availability, low cost, and correlation with immune-mediated inflammation. Among these indices, NLR and PLR have been the most extensively investigated. A multicenter retrospective study including 1,009 RA patients identified NLR as a valuable complementary diagnostic marker, though its performance was inferior to CRP and RF.25 Taylor et al reported that a high baseline NLR was associated with superior clinical outcomes in patients receiving filgotinib, suggesting its potential for therapy stratification.26 Furthermore, both NLR and PLR correlate significantly with ultrasound-assessed and conventional disease activity measures,27 a finding supported by meta-analyses confirming their elevation in active versus inactive RA.8 Similarly, platelet-related markers such as PAR have emerged as promising indicators, and our previous work identified nonlinear associations between PAR and disease activity in early RA.28 However, despite growing attention toward CBC-derived biomarkers, clinical research specifically examining NPAR has remained sparse. Prior literature has been limited to its association with the risk of developing RA,20 without exploring its relevance in established disease or its potential for activity monitoring. Our study advances the field by providing the first evidence that NPAR not only correlates with disease activity but exhibits better discriminative accuracy than other CBC-derived inflammatory indices, particularly in hospitalized individuals with high disease activity.
The biological utility of NPAR as a dynamic monitoring tool is complicated by the divergent response kinetics of its components. The rapid, hours-long response of neutrophils to acute stress starkly contrasts with the 20-day half-life of serum albumin, a negative acute-phase reactant that declines sluggishly. This kinetic mismatch creates an asymmetric behavior: NPAR may spike sharply during acute RA flares driven by neutrophilia, but it is prone to lag during treatment-induced remission due to delayed albumin recovery. Therefore, NPAR lacks the kinetic sensitivity required for real-time, monthly tracking of minor fluctuations. Rather than serving as a short-term monitor, NPAR offers a dual-dimensional assessment, simultaneously capturing acute inflammatory insults (neutrophils) and chronic inflammatory consumption (albumin). This asymmetry suggests NPAR is more sensitive for detecting disease worsening than rapid improvement, ultimately making it a more robust tool for mapping long-term disease trajectories rather than short-term monthly assessments. Future longitudinal pharmacodynamic studies are warranted to validate this kinetic hypothesis.
A critical caveat in translating NPAR into routine clinical practice is its potential susceptibility to pharmacological confounding, particularly from glucocorticoids. By promoting neutrophil demargination and suppressing apoptosis, glucocorticoids can induce peripheral neutrophilia, thereby artificially inflating NPAR independent of the underlying disease state. Although we statistically adjusted for glucocorticoid use, such binary covariate adjustment is inherently insufficient to fully capture dose- and duration-dependent pharmacodynamic effects. To rigorously interrogate this residual confounding, we conducted a sensitivity analysis exclusively in glucocorticoid-naïve patients (n = 883). As detailed in Supplementary Tables S4 and S5, the associations remained entirely robust; both the linear predictive capacity for disease activity (eg., per-SD increase in NPAR corresponded to a β = 0.36 increase in DAS28-CRP; 95% CI: 0.30–0.42) and the discriminative ability for high disease activity were undiminished. This empirical evidence substantiates that the observed NPAR-disease activity relationship reflects genuine inflammatory pathophysiology rather than an iatrogenic artifact. Nevertheless, precisely disentangling treatment-mediated effects from intrinsic disease-driven inflammation warrants future prospective studies with longitudinal monitoring during glucocorticoid tapering.
From another clinical perspective, this unique composition endows NPAR with specific advantages in complex scenarios. For instance, NPAR might hold particular value in the follow-up of RA patients using IL-6 inhibitors, where traditional acute-phase reactants like CRP may be suppressed and insufficient to reflect true disease activity. However, it is crucial to acknowledge a critical clinical pitfall: NPAR can be significantly confounded by intervening infections, as both neutrophil percentages and serum albumin levels are highly sensitive to infectious processes. Therefore, systemic infections must be rigorously ruled out before utilizing NPAR as a surrogate marker for an RA flare.
A fundamental statistical consideration when evaluating CBC-derived indices is the inherent mathematical collinearity with composite disease activity scores. Because DAS28 heavily relies on acute-phase reactants (CRP/ESR), there is a theoretical risk of circular reasoning when correlating it with NPAR, which integrates albumin. To address this inherent limitation, we conducted a sensitivity analysis utilizing the Clinical Disease Activity Index (CDAI), a composite score strictly devoid of laboratory parameters, as an alternative outcome. Notably, the robust positive association between NPAR and disease activity persisted significantly in the CDAI model (Supplementary Table S6), with NPAR continuing to demonstrate the highest discriminative ability compared to individual components or other CBC-derived inflammatory indicators (Supplementary Table S7). This convergence of evidence effectively mitigates the concern of circular reasoning. Besides, as presented in our ROC analysis (Table 4), the composite NPAR index consistently yielded higher discriminatory accuracy for high disease activity compared to the measurement of albumin or neutrophil percentage alone. This empirically validates the rationale of combining these opposing acute-phase signals into a single index. While we acknowledge that some degree of inherent collinearity is unavoidable in this field of research, these findings support the incremental clinical value of NPAR over its isolated components.
The strong association between NPAR and disease activity is biologically plausible, given the central roles of neutrophils and albumin in systemic inflammation. Neutrophils are among the earliest responders in RA pathogenesis. Their rapid migration into inflamed synovium is driven by chemokines such as IL-8, IL-17, and TNF-α, and their activation results in the release of reactive oxygen species, proteolytic enzymes, and proinflammatory cytokines that directly contribute to synovial damage and cartilage degradation.29–31 Additionally, neutrophil extracellular traps (NETs) provide a rich source of citrullinated antigens that stimulate ACPA production, forming a critical mechanistic link between innate immune activation and adaptive autoimmunity.32,33 Elevated NET formation has been observed both in the circulation and within synovial tissue of RA patients and is strongly associated with high ACPA titers, supporting its role in disease perpetuation.34,35 Cytokines such as TNF-α, IL-6, IL-17 not only enhance neutrophil recruitment but also prolong their survival by suppressing apoptosis.36,37 Interactions between neutrophils and fibroblast-like synoviocytes subsequently promote the activation of T and B cells, establishing a pathogenic feedback circuit that perpetuates disease activity.38 Notably, the clinical efficacy of agents targeting these pathways, including TNF-α inhibitors, IL-6 receptor blockers, and Janus kinase inhibitors, can be partially attributed to their capacity to curb NET formation and normalize neutrophil turnover.35,38 Albumin, in contrast, acts as a negative acute-phase reactant with significant anti-inflammatory and antioxidant properties. Its concentration is inversely correlated with disease activity,39 a finding consistent with our own results. By demonstrating an inverse relationship between neutrophils and albumin, NPAR reinforces the biological plausibility of its role as a composite marker of systemic inflammation in RA, which likely explains its favorable discriminatory ability compared with single-dimension leukocyte ratios such as NLR or PLR. However, it should be noted that these mechanistic pathways are speculative in the absence of direct molecular evidence from our cohort, and further basic research is warranted to validate these hypotheses.
Several features strengthen the validity of our findings. Our sample size of 989 patients represents one of the largest cohorts to examine hematologic biomarkers in RA. The inclusion of exclusively hospitalized individuals resulted in a population enriched for moderate-to-high disease activity, enabling robust assessment of inflammatory indices across a wide clinical spectrum. Rigorous adjustment for demographic, clinical, laboratory, and pharmacologic variables minimized confounding influences, and sensitivity analyses using NPAR tertiles confirmed the stability of our results. Despite these strengths, several limitations must be acknowledged. First, our study exclusively enrolled hospitalized RA patients, leading to severe spectrum bias where over 95% of the cohort had moderate-to-high disease activity. This limits the generalizability of NPAR to routine outpatient populations where disease is often well-controlled. Validation in more diverse clinical settings and populations is needed. Second, as a cross-sectional study, we cannot infer causality or evaluate temporal relationships between changes in NPAR and fluctuations in disease activity. Third, while we adjusted for the use of glucocorticoids and DMARDs, the lack of specific dosage and duration data means we cannot entirely rule out residual confounding, given that high-dose steroids can cause leukocytosis and DMARDs may alter hepatic albumin synthesis. Fourth, patients with extreme NPAR values (< mean − 3 SD or > mean + 3 SD) were excluded to satisfy statistical assumptions; however, in clinical practice, these extreme values may represent severe acute flare-ups, meaning their exclusion might slightly alter the perceived clinical utility of NPAR in the most critical scenarios. Finally, molecular studies are needed to elucidate the biological pathways linking neutrophil activation, albumin metabolism, and disease activity. Future multicenter, prospective cohort studies with longitudinal follow-up are warranted to validate these findings and determine the clinical utility of NPAR as a dynamic biomarker in routine RA management.
Conclusion
In conclusion, this cross-sectional study provides evidence that NPAR is independently associated with disease activity in hospitalized patients with RA and moderate-to-high disease activity. As a simple, low-cost, and readily accessible biomarker, NPAR may serve as a valuable complement to current assessment tools, facilitating the rapid identification of high-risk patients. However, given the inherent constraints of a single-center, cross-sectional design, future prospective and multicenter studies are essential to validate these findings and to elucidate whether NPAR provides incremental clinical value beyond standard composite indices like DAS28.
Acknowledgments
The authors sincerely thank all the patients who participated in this study. We also acknowledge the clinical staff of the Department of Rheumatology and Immunology at Xingtai People’s Hospital for their assistance in data collection and patient care.
Funding Statement
This study was supported by Key R&D Projects in Xingtai City (No. 2025ZC074).
Data Sharing Statement
Based on considerations for protecting patient privacy and data confidentiality, the relevant datasets from this study are not publicly available. For legitimate research purposes, they can be provided by the co-corresponding authors (Xiaoli Li / Dengxiang Liu) upon request.
Ethics Approval and Consent to Participate
The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Research Ethics Committee of Xingtai People’s Hospital (approval number: 2025[031]). Given the retrospective design of the study and the anonymization of patient data, the Ethics Committee waived the requirement for informed consent.
Consent for Publication
All authors have consented to the publication of this paper. This paper does not contain any individual person’s data in any form (including individual details, images, or videos). Therefore, consent for publication is not applicable.
Author Contributions
Xiaoli Li: Conceptualization, Methodology, Formal analysis, Supervision, Funding acquisition, Writing – review & editing.
Dengxiang Liu: Methodology, Formal analysis, Supervision, Writing – review & editing.
Jingfang Shen: Investigation, Formal analysis, Writing – original draft, Writing – review & editing.
Jinfeng Zhang: Investigation, Formal analysis, Writing – original draft, Writing – review & editing.
Yaorong Han: Investigation, Writing – review & editing.
Lina Leng: Investigation, Writing – review & editing.
Shicong Ban: Investigation, Writing – review & editing.
Wenxia Liu: Investigation, Writing – review & editing.
Beibei Wang: Formal analysis, Writing – review & editing.
Fang Liang: Formal analysis, Writing – review & editing.
All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that they have no competing interests relevant to this work. The funder had no role in the design of the study, the collection, analysis, or interpretation of data, or the writing of the paper.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Based on considerations for protecting patient privacy and data confidentiality, the relevant datasets from this study are not publicly available. For legitimate research purposes, they can be provided by the co-corresponding authors (Xiaoli Li / Dengxiang Liu) upon request.