Abstract
Background
The assessment of disease activity in rheumatoid arthritis (RA) is crucial for clinical management. In recent years, composite inflammatory markers, such as the leukocyte-to-albumin ratio (LAR), have demonstrated prognostic value in various diseases; however, its relevance in RA remains unclear. This study aims to investigate the association between LAR and disease activity in RA, as measured by the DAS28-ESR and DAS28-CRP scores.
Methods
This retrospective study enrolled 1070 patients with RA, who were categorized into three groups based on LAR tertiles: T1 (0.06–0.14), T2 (0.14–0.19), and T3 (0.19–0.88). Demographic characteristics, laboratory parameters (including inflammatory markers, complete blood count, and biochemical indicators), and disease activity scores were collected. Univariate analysis, multivariate linear regression, and piecewise linear regression models were employed to evaluate the association between LAR and DAS28 scores, with adjustments for confounding factors such as sex, age, and medication use.
Results
Patients in the high-LAR group (T3) exhibited significantly elevated levels of inflammatory markers (ESR, CRP, and fibrinogen; all P < 0.001) and lower albumin levels (P < 0.001). Univariate analysis revealed a significant positive correlation between LAR and both DAS28-ESR (β = 3.949, P < 0.001) and DAS28-CRP (β = 4.804, P < 0.001). After multivariate adjustment, LAR remained independently associated with DAS28-ESR (β = 1.846, P < 0.001) and DAS28-CRP (β = 2.450, P < 0.001). Piecewise regression analysis identified an inflection point in the LAR–DAS28 relationship at LAR = 0.20. Below this threshold, the association was stronger (DAS28-ESR β = 6.33, P < 0.001), whereas above 0.20, the association was attenuated yet remained significant (β = 2.03, P < 0.001).
Conclusion
LAR is an independent factor associated with disease activity in RA, with a particularly pronounced correlation observed in the lower LAR range. These findings suggest that LAR may serve as a simple and cost-effective auxiliary indicator for clinical assessment of inflammatory status and disease activity in RA.
Keywords: rheumatoid arthritis, leukocyte to albumin ratio, disease activity, inflammatory markers, nonlinear relationship
Introduction
Rheumatoid Arthritis (RA) is an autoimmune disease characterized by chronic synovitis, joint destruction, and systemic inflammation, with a global prevalence rate of approximately 0.5–1%. If not effectively controlled, it can lead to joint deformities, loss of function, and extra-articular lesions, severely affecting patients’ quality of life.1 Currently, commonly used clinical assessment tools for RA, such as DAS28-ESR and DAS28-CRP, integrate clinical symptoms and inflammatory markers, but they still rely on clinician assessment and are costly, limiting their widespread application.2
In recent years, multiple studies have indicated that inflammation-related ratios in blood routine and biochemical indicators3–5 exhibit potential value in assessing the disease activity of rheumatoid arthritis (RA). These indicators are easy to obtain, low in cost, and can systematically reflect the body’s inflammatory and immune status. The high disease activity state of rheumatoid arthritis often involves significant immune cell activation and accompanying nutrient depletion, therefore, a comprehensive indicator that can simultaneously reflect these two dimensions may provide a more comprehensive evaluation. Among them, the Leukocyte to Albumin Ratio (LAR), as an emerging composite indicator, encompasses both inflammatory response (increased white blood cell count) and nutritional status/chronic disease burden (decreased albumin). Inflammation has been proven to be a triggering factor and complication of RA, and vice versa. An increased white blood cell count indicates inflammation and physiological stress, suggesting that an elevated white blood cell count can be considered a factor of disease deterioration.6 Similarly, albumin, the main protein found in serum, is considered an acute-phase reactant protein with osmotic and anti-inflammatory properties. Meanwhile, hypoalbuminemia often indicates malnutrition and chronic consumption due to disease.7 Therefore, decreased albumin reflects the severity of RA, including high disease activity, malnutrition, and poor prognosis risks (such as increased cardiovascular events and mortality).8 LAR uniquely combines a direct marker of innate immune cell mobilization (leukocytes) with a key indicator of nutritional status and chronic disease burden (albumin). While the C-reactive protein-to-albumin ratio (CAR) also incorporates albumin, LAR reflects cellular immune activation more directly than the acute-phase protein CRP. Studies have shown that LAR has significant prognostic value in infections,9 tumors,10 and cardiovascular diseases,11 but its significance in RA has not been confirmed. This study aims to evaluate the association between LAR and disease activity of RA (based on DAS28-ESR and DAS28-CRP) through a large-sample cross-sectional study. Although LAR has shown prognostic value in other diseases, its role in RA remains unelucidated. This study aims to investigate the association between LAR and disease activity in a large, heterogeneous cohort of RA patients, with a particular focus on exploring potential nonlinear relationships.
Methods
Study Subjects
Building upon our previous investigation into the Platelet-to-Albumin Ratio (PAR) in early RA,12 this study extends the exploration to LAR in a broader RA population. From March 1, 2022, to December 31, 2024, 1199 patients with rheumatoid arthritis (RA) were admitted to Xingtai People’s Hospital. Inclusion criteria: 1. Age ≥ 18 years; 2. Meet the classification criteria for RA proposed by the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR).13 Exclusion criteria: 1. Age less than 18 years (n=6); 2. History of malignant tumor (n=18); 3. Patients with severe hepatic and renal insufficiency (n=34) and acute or severe infections (n=30); 4. Lack of white blood cell count (n=12) and albumin data (n=29). Finally, data from 1070 patients were included in the analysis. (Figure 1. Patient screening flowchart). This study adheres to the Declaration of Helsinki and has been approved by the Ethics Committee of Xingtai People’s Hospital. All subjects provided informed consent before enrollment. The patient cohort for this LAR study is entirely distinct and non-overlapping with the cohort from our previous PAR study.12 Given the retrospective cross-sectional design, a formal prospective sample size calculation was not performed. The achieved sample size (N=1070) provided >99% power for the primary analysis, substantially reducing the risk of Type II error.
Figure 1.
Flowchart depicting the participant’s screening.
Data Collection
By accessing the electronic medical record system, the following data were collected: demographic information: age, gender. Clinical characteristics: duration of disease (months), height and weight (used to calculate body mass index, BMI, kg/m2). Treatment regimen: whether glucocorticoids were used, the number and type of traditional disease-modifying antirheumatic drugs (cDMARDs), and whether biological agents (bDMARDs) were used. Laboratory indicators: all blood samples were collected in the morning before the patient received emergency treatment, and were collected on an empty stomach. Complete blood count analysis was performed using a Sysmex XN-series automated hematology analyzer; serum biochemical indices (including albumin) were measured using a Roche Cobas c702 automated clinical chemistry analyzer. All assays were conducted in accordance with the manufacturers’ standardized operating procedures and routine internal quality control protocols. Inflammation indicators: erythrocyte sedimentation rate (ESR, mm/H), C-reactive protein (CRP, mg/L). Blood routine: white blood cell count (WBC, ×109/L), neutrophil count (Neut, ×109/L), lymphocyte count (Lymph, ×109/L), monocyte count (Mono, ×109/L), red blood cell count (RBC, ×1012/L), hemoglobin (HGB, g/L), red blood cell distribution width (RDW, fL), platelet count (PLT, ×109/L), platelet distribution width (PDW, fL). Biochemical indicators: albumin (Albumin, g/L), fibrinogen (Fibrinogen, g/L). Disease activity assessment: DAS28-ESR and DAS28-CRP were calculated based on the Disease Activity Score in 28 joints formula. The disease activity scores and laboratory parameters used in this analysis represent a single baseline assessment obtained prior to treatment initiation or adjustment. All participants were hospitalized patients who specifically went to the rheumatology clinic for RA treatment or routine follow-up. The formula for calculating the leukocyte-to-albumin ratio (LAR) is: LAR = [white blood cell count (×109/L)] / [albumin (g/L)].
Statistical Analysis
This study utilized R language (version 4.2.2) and SPSS statistical software (version 26.0) for data analysis. Continuous variables, after normality testing, were expressed as mean ± standard deviation (Mean ± SD) for those conforming to a normal distribution, and single-factor analysis of variance (ANOVA) was used for inter-group comparison; for those not conforming to a normal distribution, they were expressed as median (interquartile range) [M (IQR)], and Kruskal–Wallis H-test was used for inter-group comparison. Categorical variables were expressed as case count (percentage) [n (%)], and chi-square test (χ2-test) was used for inter-group comparison. Univariate and multivariate linear regression models were employed for analysis. Multivariate linear regression models were constructed to assess the relationship between LAR and RA disease activity in three distinct models. In addition to unadjusted model, Model 1 was adjusted for sociodemographic variables, specifically sex; age, along with medical history; BMI. Building Model 2 introduced further adjustments for blood test variables including platelet Red blood cell count; fibrinogen; monocytes. Albumin, and the medication profile, which encompassed the use of glucocorticoids, the types of DMARDs and bDMARDs prescribed. A generalized additive model was applied to explore the nonlinear relationship between LAR and DAS28-ESR and DAS28-CRP. If a nonlinear relationship was detected, a two-piecewise linear regression model was employed to estimate the threshold effect of LAR on DAS28-ESR and DAS28-CRP based on the smoothing plot, using a smooth plot and a recursive method to identify the inflection point with the highest model likelihood. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. Multicollinearity among independent variables was assessed using variance inflation factors (VIF), with all VIF values < 5, indicating no substantial multicollinearity. Residual diagnostics were performed to verify the assumptions of linear regression models.
Results
Baseline Characteristics of the Study Population
A total of 1070 patients were included in this study for analysis. All patients were divided into three groups based on the LAR quartiles: Group T1 (0.06–0.14, n=356), Group T2 (0.14–0.19, n=357), and Group T3 (0.19–0.88, n=357). The results showed significant differences (P < 0.05) in multiple baseline characteristics among the three groups. Patients in Group T3 (high LAR group) exhibited more aggressive disease characteristics: significantly higher levels of inflammatory markers, including ESR (75.00 vs 41.00 mm/H, P < 0.001), CRP (43.93 vs 8.23 mg/L, P < 0.001), fibrinogen (4.45 vs 3.45 g/L, P < 0.001), as well as white blood cell count, neutrophil count, monocyte count, and platelet count (all P < 0.001). Simultaneously, the albumin level in this group was significantly lower (34.90 vs 38.80 g/L, P < 0.001). Correspondingly, the disease activity scores were also highest in Group T3, with both DAS28-ESR (4.89 vs 4.19, P < 0.001) and DAS28-CRP (4.23 vs 3.39, P < 0.001) significantly higher than those in Group T1 (Table 1) In addition, patients in the higher LAR group had a higher proportion of males (T3: 35.29% vs T1: 9.83%, P < 0.001), were older (62.00 vs 54.00 years, P < 0.001), and had a higher proportion of glucocorticoid users (14.01% vs 7.02%, P = 0.010), while the proportion of those treated with DMARDs was lower (38.94% vs 57.87%, P < 0.001). There were no significant differences in disease duration (MH), BMI, red blood cell count (RBC), and hemoglobin (HGB) among the groups (P > 0.05).
Table 1.
General Feature Description According to Tertiles of LAR
| Characteristics | Total | Tertiles of LAR | |||
|---|---|---|---|---|---|
| T1 (0.06–0.14) | T2 (0.14–0.19) | T3 (0.19–0.88) | P value | ||
| N | 1099 | 356 | 357 | 357 | |
| Sex, % | <0.001 | ||||
| Female | 843 (78.80%) | 321 (90.17%) | 291 (81.51%) | 231 (64.71%) | |
| Male | 227 (21.20%) | 35 (9.83%) | 66 (18.49%) | 126 (35.29%) | |
| Age (year) | 59.00 (50.00, 67.00) | 54.00 (45.00, 62.75) | 59.00 (49.00, 67.00) | 62.00 (54.00, 70.00) | <0.001 |
| MH (month) | 60.00 (12.00, 120.00) | 70.00 (17.00, 120.00) | 60.00 (12.00, 120.00) | 60.00 (12.00, 132.00) | 0.576 |
| BMI (kg/m2) | 23.63 (21.26, 26.03) | 23.58±3.36 | 23.75 (21.47–26.27) | 23.93±3.68 | 0.336 |
| Glucocorticoids, % | 0.010 | ||||
| No | 956 (89.30%) | 331 (92.98%) | 318 (89.08%) | 307 (85.99%) | |
| Yes | 114 (10.70%) | 25 (7.02%) | 39 (10.92%) | 50 (14.01%) | |
| DMARDs, % | <0.001 | ||||
| No | 566 (52.90%) | 150 (42.13%) | 198 (55.46%) | 218 (61.06%) | |
| Yes | 504 (47.10%) | 206 (57.87%) | 159 (44.54%) | 139 (38.94%) | |
| Type of DMARDs, % | <0.001 | ||||
| 0 | 566 (52.90%) | 150 (42.13%) | 198 (55.46%) | 218 (61.06%) | |
| 1 | 371 (34.70%) | 145 (40.73%) | 118 (33.05%) | 108 (30.25%) | |
| ≥2 | 133 (12.40%) | 61 (17.14%) | 41 (11.49%) | 31 (8.68%) | |
| BDMARDs, % | 0.052 | ||||
| No | 1001 (93.60%) | 324 (91.01%) | 340 (95.24%) | 337 (94.40%) | |
| Yes | 69 (6.40%) | 32 (9.99%) | 17 (4.76%) | 20 (5.60%) | |
| ESR (mm/H) | 60.00 (35.00, 86.25) | 41.00 (25.00, 66.00) | 61.00 (37.00, 83.00) | 75.00 (51.00, 99.00) | <0.001 |
| CRP (mg/L) | 28.43 (7.45, 47.08) | 8.23 (2.46, 30.86) | 25.30 (8.92, 40.45) | 43.93 (27.53, 72.08) | <0.001 |
| WBC (109/L) | 6.12 (4.89, 7.63) | 4.47±0.90 | 6.15 (5.66, 6.57) | 8.32 (7.39, 9.65) | <0.001 |
| Neut (109/L) | 3.94 (2.84, 5.14) | 2.52±0.76 | 3.99 (3.41, 4.46) | 5.73 (4.84, 6.83) | <0.001 |
| Lymph (109/L) | 1.56 (1.20, 1.90) | 1.31 (0.09, 1.63) | 1.62 (1.27, 1.85) | 1.79 (1.41, 2.25) | <0.001 |
| Mono (109/L) | 0.44 (0.34, 0.56) | 0.35 (0.27, 0.43) | 0.45 (0.37, 0.52) | 0.57 (0.44, 0.71) | <0.001 |
| RBC (1012/L) | 3.86 (3.54, 4.81) | 3.86 (3.50, 4.14) | 3.86 (3.56, 4.20) | 3.81±0.50 | 0.133 |
| HGB (g/L) | 110.15 (100.00, 122.00) | 111.00 (99.25, 121.00) | 110.15 (102.00, 122.00) | 111.20±17.64 | 0.706 |
| RDW (fL) | 43.80 (41.20, 46.90) | 43.75 (41.03, 47.40) | 42.82 (41.05, 45.35) | 45.00 (41.85, 47.80) | <0.001 |
| PLT (109/L) | 279.00 (225.00, 342.00) | 233.00 (189.25, 277.75) | 287.13 (241.50, 337.50) | 327.00 (270.50, 400.00) | <0.001 |
| PDW (fL) | 10.40 (9.40, 11.50) | 10.70 (9.70, 11.80) | 10.60 (9.40, 11.50) | 9.90 (9.00, 11.10) | <0.001 |
| Albumin (g/L) | 36.90 (33.80, 40.10) | 38.80 (36.30, 41.65) | 36.78 (34.60, 40.10) | 34.90 (31.40, 37.90) | <0.001 |
| Fibrinogen (g/L) | 4.00 (3.28, 4.60) | 3.45 (2.96, 4.05) | 4.02 (3.35, 4.47) | 4.45 (3.89, 5.08) | <0.001 |
| DAS28-ESR | 4.53 (3.93, 5.05) | 4.19 (3.53, 4.73) | 4.53 (3.96, 4.99) | 4.89 (4.37, 5.26) | <0.001 |
| DAS28-CRP | 3.81 (3.22, 4.31) | 3.39 (2.72, 3.92) | 3.81 (3.23, 4.20) | 4.23 (3.71, 4.59) | <0.001 |
Abbreviations: LAR, Leukocyte to Albumin ratio; MH, medical history (RA disease duration); BMI, body mass index; DMARDs, disease-modifying antirheumatic drugs; BDMARDs, biologics; ESR, erythrocyte sedimentation rate; CRP, high-sensitivity C-reactive protein; WBC, white blood cell count; Neut, neutrophil count; Lymph, lymphocyte count; Mono, monocyte count; RBC, red blood cell; HGB, hemoglobin; RDW, red blood cell distribution width; PLT, platelet; PDW, platelet distribution width.
Univariate Analysis
The univariate linear regression analysis (Table 2) revealed a significant positive association between LAR and disease activity. Specifically, each unit increase in LAR was strongly correlated with higher DAS28-ESR (β = 3.949, 95% CI: 3.300–4.599, P < 0.001) and DAS28-CRP (β = 4.804, 95% CI: 4.203–5.406, P < 0.001).Several other variables were also identified as significant factors influencing disease activity (P < 0.05). ESR, CRP, neutrophil count, monocyte count, platelet count, and fibrinogen showed positive correlations with DAS28 scores. In contrast, the use of DMARDs (particularly combination therapy with ≥2 agents), biologic agent use, male sex, older age, higher red blood cell count, and higher hemoglobin level were negatively correlated with DAS28 scores. No significant associations were observed between DAS28 scores and lymphocyte count, RDW, disease duration, or BMI (P > 0.05).
Table 2.
The Results of Univariate Analysis
| Characteristics | Statistics | DAS28-ESR β (95% CI) |
PERS value |
DAS28-CRP β (95% CI) |
PCRP value |
|---|---|---|---|---|---|
| Sex, N (%) | |||||
| Female | 843 (78.80%) | Ref | Ref | ||
| Male | 227 (21.20%) | −0.104 (−0.224, 0.017) | 0.091 | −0.270 (−0.385, −0.155) | <0.001 |
| Age (year) | 59.00 (50.00–67.00) | 0.013 (0.009, 0.017) | <0.001 | 0.013 (0.009, 0.017) | <0.001 |
| MH (month) | 60.00 (12.00–120.00) | 0.000 (0.000, 0.001) | 0.701 | 0.000 (0.000, 0.001) | 0.382 |
| BMI (kg/m2) | 23.63 (21.26–26.03) | −0.008 (−0.022, 0.006) | 0.242 | −0.004 (−0.017, 0.010) | 0.609 |
| Glucocorticoids, N (%) | |||||
| No | 956 (89.30%) | Ref | Ref | ||
| Yes | 114 (10.70%) | −0.116 (−0.276, 0.043) | 0.152 | −0.109 (−0.262, 0.045) | 0.166 |
| DMARDs, N (%) | |||||
| No | 566 (52.90%) | Ref | Ref | ||
| Yes | 504 (47.10%) | −0.285 (−0.382, −0.188) | <0.001 | −0.298 (−0.391, −0.205) | <0.001 |
| Type of DMARDs | |||||
| 0 | 566 (52.90%) | Ref | Ref | ||
| 1 | 371 (34.70%) | −0.168 (−0.271, −0.065) | 0.001 | −0.215 (−0.314, −0.116) | <0.001 |
| ≥2 | 133 (12.40%) | −0.302 (−0.450, −0.154) | <0.001 | −0.235 (−0.378, −0.092) | <0.001 |
| BDMARDs | |||||
| No | 1001 (93.60%) | Ref | Ref | ||
| Yes | 69 (6.40%) | −0.363 (−0.562, −0.164) | <0.001 | −0.335 (−0.528, −0.143) | 0.001 |
| ESR (mm/H) | 60.00 (35.00–86.25) | 0.019 (0.018, 0.020) | <0.001 | 0.015 (0.014, 0.016) | <0.001 |
| CRP (mg/L) | 28.43 (7.45–47.08) | 0.011 (0.009, 0.012) | <0.001 | 0.013 (0.013, 0.014) | <0.001 |
| WBC (109/L) | 6.12 (4.89–7.63) | 0.087 (0.065, 0.108) | <0.001 | 0.118 (0.097, 0.138) | <0.001 |
| Neut (109/L) | 3.94 (2.84–5.14) | 0.119 (0.094, 0.144) | <0.001 | 0.158 (0.135, 0.182) | <0.001 |
| Lymph (109/L) | 1.56 (1.20–1.90) | −0.055 (−0.139, 0.029) | 0.201 | −0.046 (−0.127, 0.035) | 0.267 |
| Mono (109/L) | 0.44 (0.34–0.56) | 0.428 (0.163, 0.694) | 0.002 | 0.743 (0.489, 0.997) | <0.001 |
| RBC (1012/L) | 3.86 (3.54–4.81) | −0.388 (−0.486, −0.290) | <0.001 | −0.208 (−0.304, −0.111) | <0.001 |
| HGB (g/L) | 110.15 (100.00–122.00) | −0.014 (−0.017, −0.011) | <0.001 | −0.009 (−0.012, −0.006) | <0.001 |
| RDW (fL) | 43.80 (41.20–46.90) | 0.002 (−0.003, 0.007) | 0.482 | 0.004 (−0.001, 0.009) | 0.091 |
| PLT (109/L) | 279.00 (225.00–342.00) | 0.003 (0.002, 0.003) | <0.001 | 0.003 (0.002, 0.003) | <0.001 |
| PDW (fL) | 10.40 (9.40–11.50) | −0.097 (−0.122, −0.073) | <0.001 | −0.081 (−0.105, −0.057) | <0.001 |
| Albumin (g/L) | 36.90 (33.80–40.10) | −0.067 (−0.076, −0.057) | <0.001 | −0.068 (−0.077, −0.059) | <0.001 |
| Fibrinogen (g/L) | 4.00 (3.28–4.60) | 0.383 (0.340, 0.427) | <0.001 | 0.384 (0.343, 0.425) | <0.001 |
| LAR | 0.17 (0.13–0.21) | 3.949 (3.300, 4.599) | <0.001 | 4.804 (4.203, 5.406) | <0.001 |
Abbreviations: MH, medical history (RA disease duration); BMI, body mass index; DMARDs, disease-modifying antirheumatic drugs; BDMARDs, biologics; ESR, erythrocyte sedimentation rate; CRP, high-sensitivity C-reactive protein; WBC, white blood cell count; Neut, neutrophil count; Lymph, lymphocyte count; Mono, monocyte count; RBC, red blood cell; HGB, hemoglobin; RDW, red blood cell distribution width; PLT, platelet; PDW, platelet distribution width; LAR, Leukocyte to Albumin ratio.
Multivariate Linear Regression Analysis of LAR and Disease Activity
To further validate the independent association between LAR and disease activity, we constructed multivariate linear regression models with incremental adjustments (Table 3). In the crude model (Model 1), LAR as a continuous variable showed strong associations with both DAS28-ESR (β = 3.949, P < 0.001) and DAS28-CRP (β = 4.804, P < 0.001). After full adjustment in Model 3—which included sex, age, platelet count, red blood cell count, fibrinogen, monocyte count, albumin, and treatment regimens (glucocorticoids, biologics, and number of DMARD types)—LAR remained significantly and independently associated with disease activity, although the effect sizes were attenuated. Each unit increase in LAR was associated with an increase of 1.846 points in DAS28-ESR (95% CI: 1.043–2.649, P < 0.001) and 2.450 points in DAS28-CRP (95% CI: 1.686–3.213, P < 0.001). Similarly, when analyzed by LAR tertiles, both the T2 and T3 groups showed significantly higher DAS28 scores compared to the T1 group, with a significant dose-response trend (P for trend < 0.001).
Table 3.
Multivariate Linear Regression Results of Association Between LAR and DAS28
| Exposure | Crude Model β (95% CI) |
Adjust I β (95% CI) |
Adjust II β (95% CI) |
|---|---|---|---|
| DAS28-ESR | |||
| LAR | 3.949 (3.300, 4.599) | 3.798 (3.123, 4.473) | 1.846 (1.043, 2.649) |
| LAR (Tertiles) | |||
| T1 | Ref | Ref | Ref |
| T2 | 0.321 (0.208, 0.435) | 0.309 (0.195, 0.422) | 0.117 (0.013, 0.221) |
| T3 | 0.661 (0.547, 0.775) | 0.634 (0.516, 0.752) | 0.269 (0.139, 0.398) |
| P for trend | <0.001 | <0.001 | <0.001 |
| DAS28-CRP | |||
| LAR | 4.804 (4.203, 5.406) | 4.498 (3.871, 5.125) | 2.450 (1.686, 3.213) |
| LAR (Tertiles) | |||
| T1 | Ref | Ref | Ref |
| T2 | 0.383 (0.277, 0.489) | 0.361 (0.255, 0.467) | 0.162 (0.063, 0.262) |
| T3 | 0.801 (0.695, 0.907) | 0.745 (0.635, 0.856) | 0.355 (0.232, 0.478) |
| P for trend | <0.001 | <0.001 | <0.001 |
Notes: Crude model model adjust for: None. Adjust I model adjust for: sex; age; MH medical history (RA disease duration); BMI. Adjust II model adjust for: sex; age; platelet; Red blood cell count; fibrinogen; monocytes, albumin Glucocorticoids; Biologics; type of DMARDs.
Abbreviation: LAR, leukocyte to Albumin ratio.
Analysis of the Nonlinear Relationship Between LAR and Disease Activity
To further explore the complex association between LAR and disease activity, a piecewise linear regression model was applied (Table 4). The results revealed a significant nonlinear relationship between LAR and DAS28 scores, with a distinct inflection point identified at LAR = 0.20 (log-likelihood ratio test, P < 0.001). When LAR was below 0.20, the association with DAS28 scores was substantially stronger (β = 6.33 for DAS28-ESR; β = 7.86 for DAS28-CRP). Above the threshold of 0.20, the association remained statistically significant but was noticeably attenuated (β = 2.03 for DAS28-ESR; β = 2.58 for DAS28-CRP). The nonlinear relationships are visually represented in Figure 2a and b.
Table 4.
The Result of Two-Piecewise Linear Regression Model of LAR with DAS28-ESR and DAS28-CRP
| DAS28-ESR β (95% CI) |
DAS28-CRP β (95% CI) |
|
|---|---|---|
| Fitting model by standard linear regression | 4.43 (4.39, 4.48) | 3.72 (3.30, 4.14) |
| Fitting model by two-piecewise linear regression | ||
| Inflection point of LAR | 0.20 | 0.20 |
| <0.20 | 6.33 (4.70, 7.96) | 7.86 (6.29, 9.44) |
| >0.20 | 2.03 (0.76, 3.30) | 2.58 (1.45, 3.72) |
| P for log likelihood ratio test | <0.001 | <0.001 |
Notes: The models were adjusted for sex; age; neutrophil count; lymphocyte count; platelet distribution width; glucocorticoids; biologics; type of DMARDs.
Abbreviation: LAR, Leukocyte to Albumin ratio.
Figure 2.
(a) Nonlinear relationship of LAR with DAS28-ESR. The red curve represents the fitted nonlinear association from a two-piecewise linear regression model (inflection point at LAR = 0.20). The blue dashed line represents the linear regression fit for reference. (b) A nonlinear relationship of LAR with DAS28-CRP.The red curve represents the fitted nonlinear association from a two-piecewise linear regression model (inflection point at LAR = 0.20). The blue dashed line represents the linear regression fit for reference.
Notes: The model was adjusted for sex, age, BMI, medical history, glucocorticoids, type of DMARDs, bDMARDs, lymphocyte count, neutrophils count, and PDW.
Discussion
This study conducted a large-sample cross-sectional analysis to deeply explore the association between a novel inflammatory composite index, the leukocyte-to-albumin ratio (LAR), and disease activity in rheumatoid arthritis (RA). Our research revealed a significant correlation between high LAR levels and disease activity. Furthermore, in a multivariate regression model, LAR emerged as an independent influencing factor for both DAS28-ESR and DAS28-CRP, and this association remained robust even after excluding multiple potential confounding factors. There is a clear nonlinear relationship between LAR and disease activity, with an inflection point at LAR=0.20. When LAR is below this inflection point (LAR<0.20), the association with disease activity becomes stronger. As a simple index integrating inflammation and nutritional status, LAR holds significant potential application value in the disease assessment of RA.
LAR simultaneously encompasses information on both white blood cell count (representing the activation of the innate immune system and inflammatory response) and serum albumin level (reflecting systemic nutrition and inflammatory status). An increase in white blood cells is a core characteristic of synovitis in RA.14 On the other hand, hypoalbuminemia is very common in RA patients, and its mechanism is not solely malnutrition; rather, it is primarily “inflammatory hypoalbuminemia”. Under the influence of cytokines, vascular permeability increases, albumin catabolism intensifies, and the liver preferentially synthesizes acute-phase proteins rather than albumin.15 Therefore, LAR cleverly captures the dual processes of “enhanced pro-inflammatory forces” and “depletion of anti-inflammatory/nutritional reserves”, providing a solid theoretical basis for its use as an indicator of RA activity.16 Our results are consistent with previous research exploring the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR),3,4 but LAR introduces the crucial dimension of albumin, offering a more comprehensive evaluation.
RA is a systemic autoimmune disease, characterized by a chronic inflammatory state that continuously activates the innate immune system.17 Pro-inflammatory cytokines (such as TNF-α, IL-1, IL-6) are released in large amounts,18,19 acting on the bone marrow to stimulate the production of granulocyte colony-stimulating factor (G-CSF), leading to the proliferation, differentiation, and release of neutrophils into the bloodstream. This is the main mechanism underlying the elevation of peripheral white blood cell (WBC) count.20 Leukocytes and other immune cells can release various inflammatory cytokines, playing a crucial role in the inflammatory response.21 Leukocytes are capable of releasing degradative enzymes and reactive oxygen species, exhibiting strong cytotoxic potential. Their extracellular traps (NETs) can not only kill microorganisms but also provide self-antigens, assisting in the production of autoantibodies.22 Leukocytes are a core component of the innate immune system, playing a pivotal role in the synovial inflammation and joint destruction of RA. Furthermore, the prognostic value of WBC in various diseases has been widely validated. Multiple studies have confirmed that an elevated WBC count upon admission in patients with acute myocardial infarction (AMI) is an independent risk factor for poor prognosis.21 Within 24 hours of AMI onset, WBC levels significantly increase, predominantly neutrophils, and are correlated with the severity of coronary lesions (such as multivessel disease), serving as an independent risk factor for major adverse cardiovascular and cerebrovascular events (MACCE).23 For patients with acute ischemic stroke (AIS), an elevated WBC count is an independent risk factor for poor short-term prognosis,24 and the risk increases with higher WBC counts.25 Additionally, numerous cross-sectional studies have shown that patients with active RA have significantly higher WBC and neutrophil counts compared to patients in remission and healthy controls.26,27
Albumin is synthesized by the liver and is one of the most important proteins in human plasma.28 In RA, its reduced levels are not only due to malnutrition but primarily result from metabolic reprogramming under inflammatory conditions. In acute or chronic inflammatory states, the liver’s synthetic function shifts from producing albumin (a negative acute-phase response protein) to the substantial synthesis of positive acute-phase response proteins (such as C-reactive protein and fibrinogen).29 Pro-inflammatory cytokines, especially IL-6, inhibit the expression of albumin mRNA, reducing its synthesis.30 Simultaneously, increased vascular permeability and hypermetabolic states induced by inflammation also accelerate the breakdown and extracorporeal loss of albumin. Albumin levels also reflect the overall nutritional status of patients.31 Patients with long-term active RA often suffer from malnutrition due to chronic wasting, poor appetite, and potential gastrointestinal dysfunction.32 Low albumin levels are associated with fatigue, weakness, anemia, and other symptoms in RA patients, indicating poorer overall health and potential extra-articular manifestations.5 Studies show that serum albumin (ALB) levels in patients with active RA are significantly lower than those in normal controls and are negatively correlated with the Disease Activity Score-28 (DAS-28).33 This means that higher disease activity is often associated with lower serum albumin levels. Furthermore, serum albumin levels are inversely correlated with the severity of diabetic nephropathy, ranging from normal albuminuria to macroalbuminuria, and are also independently associated with diabetic peripheral vascular disease.34 Hypoalbuminemia is an independent risk factor for the development of chronic heart failure and a predictor of poor outcomes, such as all-cause mortality or readmission.35 Additionally, hypoalbuminemia is a marker of severe and poor prognosis in infectious diseases such as sepsis,36 as well as in chronic wasting diseases like malignancies.11
In recent years, a plethora of inflammatory composite indicators have emerged, solely to provide a more comprehensive reflection of the severity of diseases. Inflammatory composite indicators related to rheumatoid arthritis (RA) are particularly prevalent. Studies have indicated3,4 that the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in patients with active RA are typically significantly higher than those in healthy controls or patients in disease remission. These ratios are positively correlated with traditional inflammatory indicators such as Disease Activity Score 28 (DAS28), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR), as well as with other systemic involvement, such as the hematological and cardiovascular systems. Furthermore, the C-reactive protein (CRP) and albumin (Alb) ratio (CAR) integrates information from the inflammatory marker CRP and the nutritional/negative acute-phase protein albumin.5 The CAR in patients with active RA is significantly higher than that in remission patients and healthy controls, and it is positively correlated with DAS28 scores, CRP, ESR, and negatively correlated with albumin levels themselves, indicating disease activity. Like NLR and PLR, CAR demonstrates good value in assessing RA disease activity. Our research indicator, LAR, cleverly integrates the two dimensions of “inflammation drive” and “consumption inhibition”. It can more comprehensively reflect the pathophysiological essence of RA disease activity than a single indicator. Our research results demonstrate the strong correlation, independence, and nonlinear relationship between LAR and disease activity. Lessomo et al found11 that LAR can be used to assess the risk of thrombosis and disease severity in patients with atrial fibrillation. This is consistent with our research trend and provides new supporting evidence for LAR as an effective tool for clinical evaluation of RA. LAR may be used to identify high-risk individuals: those with high inflammatory burden, nutritional deviation, and who may require more intensive treatment or close follow-up. Monitoring the dynamic changes of LAR may serve as an auxiliary reference for assessing treatment response and guiding treatment strategy adjustments. To situate our findings within the broader context of our research program, we note that the present results regarding LAR extend the work of our group on inflammatory-nutritional composite indices in RA. Previously, we reported a nonlinear relationship between PAR and disease activity in early RA.11 The current study demonstrates that LAR, a related yet distinct index, also exhibits a significant and independent nonlinear association with disease activity in a general RA population. The inflection point for LAR (0.20) identified in this study differs from that previously reported for PAR, suggesting that different composite indices may capture unique aspects of the inflammatory and nutritional imbalance in RA. The stronger association of LAR in its lower range parallels our earlier observation with PAR, reinforcing the concept that these ratios are particularly sensitive in reflecting disease activity at lower levels of inflammation or nutritional compromise. This consistency across different indices strengthens the rationale for their clinical application, while their distinct inflection points highlight the need for index-specific interpretation.
Limitations
This study has several limitations. First, as a retrospective investigation relying on electronic medical records, it is susceptible to information bias or missing data due to unrecorded or non-standardized measurements. Second, being a single-center study with all participants recruited from the same hospital, the sample may lack representativeness and generalizability, potentially introducing selection bias. Third, as a single-center study, our findings may have limited generalizability to other clinical settings. Although we adjusted for multiple confounding factors—such as age, sex, and treatment modalities—unmeasured or unrecorded variables (eg, comorbidities, lifestyle factors, specific medication dosages) may remain, potentially affecting the robustness of the findings. Future prospective, multi-center studies are warranted to validate the clinical utility of LAR and elucidate its underlying biological mechanisms.
Conclusion
In summary, this cross-sectional study establishes that the Leukocyte-to-Albumin Ratio (LAR) exhibits a significant and independent nonlinear association with disease activity in rheumatoid arthritis. The identified inflection point (LAR=0.20) should be considered exploratory and requires validation in independent cohorts. As a cost-effective and readily available parameter, LAR holds promise as a complementary tool for disease assessment. However, its prognostic utility and potential for clinical integration depend entirely on future prospective, multi-center studies designed to determine whether it adds value to existing strategies for risk stratification, treatment response prediction, or early intervention.
Funding Statement
This study was supported by Key R&D Projects in Xingtai City (No. 2025ZC074).
Use of Artificial Intelligence Tools (To Be Included Only When AI Tools are Used)
No artificial intelligence tools were used in the preparation of this paper.
Data Sharing Statement
The datasets used and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author (Xiaoli Li, drlixiaoli86@163.com) upon reasonable request. Requests will be evaluated based on scientific merit and purpose of use, and may require a formal data sharing agreement to ensure compliance with ethical and legal standards.
Ethics Approval and Consent to Participate
This study was approved by the Research Ethics Committee of Xingtai People’s Hospital (Approval No. 2025[031]). All procedures complied with relevant local regulations and institutional policies. Written informed consent was obtained from all participants prior to enrollment.
Author Contributions
LL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review & editing. YL: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing-original draft. QT: Conceptualization, Investigation, Methodology, Software, Visualization, Writing-original draft. JZ: Conceptualization, Investigation, Methodology, Software, Visualization,Writing-original draft. YH: Conceptualization, Investigation, Methodology, Software, Visualization, Writing-original draft. XL: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing-review & editing. All authors reviewed and approved the final manuscript submitted. All authors have agreed on the journal to which the article has been submitted, and agree to be accountable for all aspects of the work. Lina Leng and Ying Li contributed equally to this work and share first authorship.
Disclosure
The authors declare that they have no competing interests.
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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
The datasets used and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author (Xiaoli Li, drlixiaoli86@163.com) upon reasonable request. Requests will be evaluated based on scientific merit and purpose of use, and may require a formal data sharing agreement to ensure compliance with ethical and legal standards.