Sage Journals: Discover world-class research

Abstract

Objective

To investigate the correlation between systemic immune-inflammation index (SII) values and the risk of arthritis among adult participants.

Methods

Cross-sectional data from adult participants (aged ≥18 years) in the National Health and Nutrition Examination Survey 2011–2018 dataset were utilized. The association between systemic immune-inflammation index values and arthritis risk was explored through multivariate regression and restricted cubic spline model-based analyses.

Results

An analysis of categorical variables indicated that the risk of arthritis was significantly higher in the highest systemic immune-inflammation index quartile (Q4) compared with the lowest quartile (Q1; odds ratio = 1.36, 95% confidence interval = 1.23–1.51). Continuous variable and spline model analyses suggested that there was a significant and positive correlation between high systemic immune-inflammation index values and an increased risk of arthritis (odds ratio = 1.12, 95% confidence interval = 1.08–1.16, p < 0.001). A subgroup analysis revealed that this correlation was stronger in the older population than in the younger population (p interaction = 0.041). A sensitivity analysis suggested that the association between systemic immune-inflammation index values and arthritis risk was stable.

Conclusions

Our research suggests a correlation between high systemic immune-inflammation index values and an increased risk of arthritis within the examined sample. Consequently, tracking systemic immune-inflammation index values could potentially facilitate the early detection of arthritis.

Keywords

Systemic immune-inflammation index arthritis cross-sectional study risk assessment National Health and Nutrition Examination Survey

Introduction

Data from the Registration dataset at the Centers for Disease Control and Prevention in the United States indicate that approximately 23% of the population will receive a diagnosis of arthritis at least once during their lifetime.¹ The two most prevalent forms of arthritis are rheumatoid arthritis (RA) and osteoarthritis (OA). RA is an autoimmune disease, whereas OA is degenerative in nature. Other forms of arthritis may be attributed to trauma or less frequently to bacterial infection, leading to suppurative arthritis.² Genetics, lifestyle, occupation, age, and sex significantly influence the likelihood and location of arthritis development within the body. Neglecting to address arthritis within its optimal treatment window can result in irreversible joint damage or even total loss of function. Consequently, early detection, precise diagnosis, and timely intervention are imperative for both preventing and managing arthritis.

Several cellular inflammatory agents, including tumor necrosis factor-alpha (TNF-α), interleukin (IL)-17, IL8, and IL6, play significant roles in the pathogenesis of arthritis.^3,4 Furthermore, the prognosis of patients with malignant tumors has been associated with the systemic immune-inflammation index (SII), a comprehensive measure of the systemic inflammatory response.^5–8 In addition, a growing body of research has demonstrated that SII values are a reliable tool for predicting the severity of various illnesses and monitoring the effectiveness of treatment strategies.^9,10 Recent research has shown that SII values can be beneficial in predicting the onset and severity of conditions such as psoriatic arthritis and RA.^11,12 Currently, the correlation between SII values and the risk of arthritis is not well-established.

The objective of this study was to examine the correlation between SII values and the comprehensive risk of arthritis while also considering its potential interaction with diverse population characteristics. It is anticipated that these findings will provide valuable insights for the early prevention and therapeutic intervention of arthritis.

Material and methods

Study population

We integrated and conducted a robust analysis of data derived from the National Health and Nutrition Examination Survey (NHANES) dataset in the United States, spanning over a continuous 8-year period,¹³ i.e. 2011–2012, 2013–2014, 2015–2016, and 2017–2018. This study is a cross-sectional secondary data analysis using NHANES survey data. Adult participants from the NHANES dataset were included consecutively based on the availability of complete laboratory and demographic data. The NHANES uses a stratified, multistage probability sampling method to survey the noninstitutionalized civilian population in the United States and assesses their dietary, health, and laboratory characteristics biennially.

For the present analysis, we included adult male participants with complete laboratory data required to calculate the SII, including platelet, neutrophil, and lymphocyte counts. Participants were excluded if they had missing SII-related data, were female, or were under 18 years of age. All study protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and informed consent was obtained from all participants. Further methodological details are available on the official NHANES website (https://www.cdc.gov/nchs/nhanes/).

SII calculation

The SII is determined by the following formula: (platelet count × neutrophil count)/lymphocyte count.¹⁰ These hematological parameters were measured using the Beckman Coulter DxH 800 automated hematology analyzer in the NHANES central laboratory. All laboratory tests were conducted following standardized quality assurance and control protocols outlined in the NHANES Laboratory Procedures Manual. SII = (platelet count × neutrophil count)/lymphocyte count (the obtained value is a unitless ratio).

Arthritis assessment

The NHANES participants completed a questionnaire survey on arthritis at a mobile testing center. Arthritis was defined based on participants’ responses to the multiple choice question 160a: “Have you ever been informed that you have arthritis by a doctor or other health professional?” Participants who answered “yes” were considered to have arthritis. It should be noted that NHANES does not further specify the type of arthritis (e.g. OA, RA, or other forms), and our analysis therefore pertains to general arthritis status without subtype differentiation.

Evaluation of covariates

The choice of covariates was predicated upon factors identified in the existing literature as being significantly associated with arthritis.^14–16 The covariates included in the study encompassed demographic information such as sex, age, ethnicity, educational level, marital status, and family income; individual health metrics such as body mass index (BMI), metabolic equivalent of task (MET), and systolic and diastolic blood pressure as well as smoking and alcohol consumption statuses; and health conditions such as hypertension, hyperlipidemia, diabetes, coronary heart disease, and chronic heart failure. Laboratory values such as total cholesterol, triglycerides, and fasting glucose were measured using enzymatic or colorimetric assays, depending on the variable, following NHANES standardized laboratory procedures, which are publicly accessible from the NHANES website (https://www.cdc.gov/nchs/nhanes/). Hypertension was defined as a systolic blood pressure of ≥130 mmHg, a diastolic blood pressure of ≥80 mmHg, or the current use of antihypertensive medication. Hyperlipidemia was defined as a total cholesterol level exceeding 5.72 mmol/L, a triglyceride level above 1.70 mmol/L, or the use of lipid-lowering agents. Diagnoses of diabetes, coronary heart disease, and chronic heart failure were based on self-reported medical evaluations.

Statistical analysis

R software (version 4.2.1) was utilized for all statistical analyses. The NHANES adopts a stratified multistage sampling design and incorporates sample weights to ensure the collection of nationally representative data. The data in the present study were weighted using the sample weight calculation method as recommended by the NHANES. Specifically, data from 2011 to 2018 were amalgamated, with the 8-year weight being equivalent to one-quarter of the 2-year weight. To impute missing data for the covariates and enhance the statistical efficiency of the analysis, multiple imputation was employed.¹¹ Continuous variables that were normally distributed were presented as means ± standard deviations (means ± SDs) and were analyzed using t-tests. Continuous variables with a non-normal distribution were reported as medians with quartiles and were evaluated using Mann–Whitney U tests. Categorical variables, represented by the number of cases, are displayed as percentages (%) and were assessed using Rao–Scott likelihood ratio chi-square tests. The population was divided into four categories based on the quartile (Q) of SII values: Q1: <P25, Q2: P25–P50, Q3: P50–P75, and Q4: ≥P75.

The study employed a weighted logistic regression model to examine the relationship of SII values, treated as both a categorical variable (with Q1 serving as the control group) and a continuous variable (incremented per standard deviation), with the risk of arthritis. The results were reported in terms of odds ratios (ORs), 95% confidence intervals (CIs), and p values. Model 1 offered an approximate analysis, whereas model 2 included adjustments for sex, age, ethnicity, education level, marital status, and family income. Model 3 further incorporated adjustments for smoking and alcohol consumption status. The dose–response relationship between SII values and arthritis risk was scrutinized using a restricted cubic spline (RCS) model. The RCS model was fitted with four knots, selected based on standard practice to balance model flexibility and stability, ensuring reproducibility. We examined the differences in associations among various covariate groupings by stratifying participants based on sex, age (<60 and ≥60 years), BMI (<25 and ≥25 kg/m²), ethnicity (non-Hispanic white, non-Hispanic black, and other ethnicities), education level (below high school, high school, and above high school), smoking status (yes or no), alcohol consumption status (yes or no), hypertension (yes or no), hyperlipidemia (yes or no), and diabetes (yes or no). Subsequently, we compared the results within each stratum. A sensitivity analysis was conducted to assess the robustness of the association, encompassing a complete case analysis and a multivariate regression analysis with additional covariate adjustments. Bilateral p values below 0.05 were considered to indicate statistical significance.

The study was reviewed and approved by the Beilun District People’s Hospital in Zhejiang, China. Informed consent was obtained from all participants involved in the study.

Ethics approval and participant consent

This study was approved by the NCHS Research Ethics Review Board, which confirmed that all participants had given informed consent. The protocol and data from the NHANES dataset can be accessed at the official website https://www.cdc.gov/nchs/nhanes/ (https://www.cdc.gov/nchs/nhanes/). The NHANES program is reviewed and approved annually by the NCHS Research Ethics Review Board. As this study was a secondary analysis of publicly available, deidentified data, additional institutional review board approval was not required. All participants or their legal representatives provided written informed consent during recruitment. This study adhered to the Declaration of Helsinki of 1975 as revised in 2013. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁷

Results

General demographic characteristics of the participants

Among the 23,646 participants included in this study, 6031 (25.5%) had a diagnosis of arthritis (Figure 1). Participants with arthritis were younger, more likely to be female, had a lower BMI, and a higher MET compared with those without arthritis (p < 0.001). A high proportion of individuals with arthritis also suffered from hypertension, hyperlipidemia, diabetes, coronary heart disease, or chronic heart failure (p < 0.001; refer to Table 1 for details). The mean SII value of participants with arthritis (550.4 ± 393.7) was significantly higher than that of participants without arthritis (499.6 ± 304.0; p < 0.001).

Figure 1.

Flowchart of the sample selection from NHANES 2011–2018 dataset. NHANES: National Health and Nutrition Examination Survey.

Table 1.

Basic characteristics of participants with arthritis in the NHANES 2011–2018 dataset.

	All	Nonarthritis	Arthritis	p value
Number	23,646 (100.0)	17,615 (100.0)	6031 (100.0)	NA
Age (years)	48.3 ± 18.6	43.6 ± 17.7	62.3 ± 13.7	<0.001
Sex, %				<0.001
Male	11,540 (48.8)	9121 (51.8)	2419 (40.1)
Female	12,106 (51.2)	8494 (48.2)	3612 (59.9)
Ethnicity, %				<0.001
Non-Hispanic White	8567 (36.2)	5737 (32.6)	2830 (46.9)
Non-Hispanic Black	5376 (22.7)	3954 (22.4)	1422 (23.6)
Others	9703 (41.0)	7924 (45.0)	1779 (29.5)
Education, %				0.396
Below high school level	2686 (11.4)	1825 (10.4)	861 (14.3)
High school	14,742 (62.3)	11,273 (64.0)	3469 (57.5)
Above high school	6218 (26.3)	4517 (25.6)	1701 (28.2)
Marital status, %				<0.001
Married/Living with partner	13,461 (56.9)	10,181 (57.8)	3280 (54.4)
Widowed/divorced/separated	5072 (21.4)	2899 (16.5)	2173 (36.0)
Never married	5113 (21.6)	4535 (25.7)	578 (9.6)
Poverty income ratio	2.4 ± 1.6	2.4 ± 1.6	2.4 ± 1.6	0.0068
Smoking status, %				<0.001
No	13877 (58.7)	11,008 (62.5)	2869 (47.6)
Yes	9769 (41.3)	6607 (37.5)	3162 (52.4)
Alcohol consumption status, %				0.7692
Never	6582 (27.8)	5036 (28.6)	1546 (25.6)
Former	1084 (4.6)	628 (3.6)	456 (7.6)
Current	15,980 (67.6)	11,951 (67.8)	4029 (66.8)
Metabolic equivalent minutes (MET)	1091.7 ± 1331.9	1155.5 ± 1359.4	905.3 ± 1229.2	<0.001
BMI (kg/m²)	29.1 ± 7.2	28.4 ± 6.8	31.2 ± 7.7	<0.001
SBP (mmHg)	124.3 ± 18.7	122.1 ± 17.9	130.7 ± 19.7	<0.001
DBP (mmHg)	70.6 ± 13.2	70.6 ± 12.9	70.3 ± 13.9	0.0586
Platelet	238.5 ± 62.1	239.9 ± 61.1	234.5 ± 64.6	<0.001
Neutrophils	4.2 ± 1.7	4.2 ± 1.7	4.4 ± 1.8	<0.001
Lymphocyte	2.2 ± 2.8	2.2 ± 1.5	2.2 ± 4.8	0.5517
SII	512.7 ± 330.2	499.6 ± 304.0	550.4 ± 393.7	<0.001
Creatinine(mg/dL)	11182.7 ± 7412.4	11501.8 ± 7610.3	10242.7 ± 6709.1	<0.001
Serum uric acid (μmol/L)	323.4 ± 85.9	320.3 ± 83.6	332.3 ± 91.5	<0.001
Blood urea nitrogen (mmol/L)	5.0 ± 2.2	4.7 ± 1.9	5.7 ± 2.7	<0.001
ALT (IU/L)	24.3 ± 20.3	24.7 ± 19.1	23.3 ± 23.5	<0.001
AST(IU/L)	24.8 ± 16.8	24.8 ± 16.0	24.8 ± 18.8	0.7803
HDL (mmol/L)	1.4 ± 0.4	1.4 ± 0.4	1.4 ± 0.4	<0.001
LDL (mmol/L)	2.8 ± 0.9	2.8 ± 0.9	2.8 ± 1.0	0.9457
TC (mmol/L)	4.9 ± 1.1	4.9 ± 1.1	4.9 ± 1.1	<0.001
TG (mmol/L)	1.7 ± 1.5	1.7 ± 1.5	1.8 ± 1.3	<0.001
Hypertension				<0.001
No	15,273 (64.6)	12,967 (73.6)	2306 (38.2)
Yes	8373 (35.4)	4648 (26.4)	3725 (61.8)
Hyperlipidemia				<0.001
No	15,903 (67.3)	13,101 (74.4)	2802 (46.5)
Yes	7743 (32.7)	4514 (25.6)	3229 (53.5)
Diabetes				<0.001
No	20,495 (86.7)	15,951 (90.6)	4544 (75.3)
Yes	3151 (13.3)	1664 (9.4)	1487 (24.7)
CHD				<0.001
No	22,710 (96.0)	17,219 (97.8)	5491 (91.0)
Yes	936 (4.0)	396 (2.2)	540 (9.0)
CHF				<0.001
No	22,863 (96.7)	17,306 (98.2)	5557 (92.1)
Yes	783 (3.3)	309 (1.8)	474 (7.9)

Continuous variables were presented as mean ± SD. Categorical variables were presented as percentage (95% confidence interval).

BMI: body mass index; SII: systemic immune-inflammation index; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALT: alanine aminotransferase; AST: aspartate aminotransferase; TC: total cholesterol; TG: triglycerides; CHD: coronary heart disease; CHF: chronic heart failure; HDL: high-density lipoprotein; LDL: low-density lipoprotein.

Association between SII values and arthritis risk

The relationship between SII values and the risk of arthritis was investigated using multivariate regression analysis (Table 2). All models indicated a significant correlation between high SII values and increased arthritis risk, as evidenced by a statistically significant trend test (p for all trends <0.001). Model 3, which was adjusted for sex, age, ethnicity, education level, marital status, family income, smoking status, and alcohol consumption status, showed that relative to Q1, the OR for Q4 (comprising individuals with the highest SII values) was 1.36 (95% CI: 1.23–1.51). The OR for a 1 standard deviation increase in SII value was 1.12 (95% CI: 1.08–1.16). The RCS analysis confirmed the association between SII values and arthritis risk as a statistically significant linear dose–response relationship (p for linear relationship < 0.001, p for nonlinear relationship = 0.780), with the critical SII value (inflection point) identified at 2.4 (Figure 2). This SII threshold of 2.4, a unitless ratio derived from platelet, neutrophil, and lymphocyte counts, indicates a point above which the risk of arthritis significantly increases, potentially guiding clinical risk stratification and screening.

Table 2.

Association between systemic immune-inflammation index and arthritis risk.

	Q1	Q2	Q3	Q4	Per 1 SD increment	p for trend
Expose	2.50 (0.10, 3.10)	3.60 (3.10, 4.00)	4.50 (4.10, 5.10)	6.10 (5.10, 35.20)
Cases/person-years	1250/5394	1492/5851	1325/5087	1496/5184
Model 1	Reference	1.09 (0.99–1.20)	1.13 (1.02–1.24)	1.48 (1.34–1.63)	1.16 (1.12–1.20)	<0.001
Model 2	Reference	1.12 (1.01–1.23)	1.13 (1.02–1.25)	1.45 (1.31–1.60)	1.15 (1.11–1.19)	<0.001
Model 3	Reference	1.11 (1.00–1.22)	1.09 (0.98–1.21)	1.36 (1.23–1.51)	1.12 (1.08–1.16)	<0.001

Model 1: no covariates were adjusted. Model 2: adjusted for sex, age, race, education level, marital status, and poverty income ratio. Model 3: further adjusted for smoking and alcohol consumption based on model 2.

Figure 2.

Restricted cubic spline analysis of the association between systemic immune-inflammation index and arthritis risk.

Subgroup analysis

We investigated the potential impact of covariates on the relationship between SII values and arthritis risk by performing a subgroup analysis. This analysis was categorized based on sex, age, BMI, ethnicity, education level, smoking status, alcohol consumption status, hypertension status, hyperlipidemia status, and diabetes status. The findings from this analysis are presented in Table 3. The results of the multivariate regression analysis and interaction tests indicated a significant interaction between age and the causal link between polyunsaturated fatty acid deficiency and ischemic stroke-related disability measured through Disability-Adjusted Life Years (DALYs) (p for interaction = 0.041). Furthermore, a notable positive correlation was observed between SII values and arthritis risk in the older group (age ≥60 years; OR = 1.21, 95% CI: 1.05–1.40). Conversely, no significant association was found between SII values and arthritis risk in the younger group (age <60 years; OR = 1.32, 95% CI: 0.85–2.07).

Table 3.

Subgroup analysis of the association between SII and arthritis risk based on model 3.

Subgroup	Q1	Q2	Q3	Q4	p for interaction
Sex
Male	Ref	1.07 (0.92–1.24)	1.05 (0.90–1.23)	1.38 (1.18–1.61)	0.760
Female	Ref	1.14 (1.00–1.31)	1.14 (0.99–1.31)	1.38 (1.20–1.59)
Age, years
<60	Ref	1.48 (0.95–2.31)	1.33 (0.84–2.11)	1.32 (0.85–2.07)	0.041
≥60	Ref	1.12 (0.97–1.3)	1.12 (0.97–1.29)	1.21 (1.05–1.4)
BMI, kg/m²
<25	Ref	1.13 (0.92–1.37)	1.03 (0.83–1.28)	1.40 (1.12–1.75)	0.492
≥25	Ref	1.04 (0.93–1.18)	1.01 (0.89–1.13)	1.21 (1.07–1.36)
Ethnicity
Non-Hispanic White	Ref	1.22 (1.04–1.44)	1.19 (1.01–1.4)	1.50 (1.27–1.76)	0.579
Non-Hispanic Black	Ref	1.08 (0.89–1.30)	0.99 (0.80–1.22)	1.31 (1.05–1.62)
Others	Ref	1.05 (0.88–1.25)	1.09 (0.91–1.31)	1.27 (1.06–1.53)
Education
Below high school level	Ref	1.21 (0.92–1.61)	1.22 (0.91–1.62)	1.15 (0.85–1.56)	0.283
High school	Ref	1.06 (0.93–1.20)	1.06 (0.93–1.21)	1.37 (1.2–1.57)
Above high school	Ref	1.18 (0.97–1.44)	1.11 (0.91–1.37)	1.47 (1.20–1.8)
Smoking
No	Ref	1.14 (0.99–1.30)	1.13 (0.98–1.30)	1.44 (1.24–1.68)	0.947
Yes	Ref	1.06 (0.91–1.23)	1.04 (0.90–1.21)	1.28 (1.10–1.48)
Alcohol consumption
No	Ref	1.15 (0.96–1.36)	1.05 (0.87–1.26)	1.41 (1.17–1.69)	0.786
Yes	Ref	1.09 (0.97–1.23)	1.11 (0.98–1.26)	1.34 (1.18–1.52)
Hypertension
No	Ref	1.07 (0.93–1.24)	1.01 (0.87–1.18)	1.34 (1.15–1.56)	0.620
Yes	Ref	1.07 (0.93–1.23)	1.05 (0.91–1.21)	1.17 (1.01–1.35)
Hyperlipidemia
No	Ref	1.19 (1.03–1.36)	1.11 (0.96–1.28)	1.40 (1.21–1.61)	0.352
Yes	Ref	1.00 (0.86–1.15)	1.04 (0.89–1.20)	1.27 (1.09–1.48)
Diabetes
No	Ref	1.11 (1.00–1.24)	1.08 (0.97–1.22)	1.26 (1.12–1.42)	0.141
Yes	Ref	0.95 (0.74–1.21)	0.90 (0.70–1.15)	1.26 (0.98–1.60)

Model 3 was adjusted for sex, age, race, education level, marital status, poverty income ratio, smoking, and alcohol consumption.

Ref: reference; SII; systemic immune-inflammation index.

Sensitivity analysis

A sensitivity analysis was performed among participants with comprehensive data, revealing that the positive correlation between SII values and arthritis risk remained consistent (Supplemental Table 1). The three statistical models consistently showed a heightened risk of arthritis associated with elevated SII values, with a statistically significant trend test (p for trend <0.001). In the most extensively adjusted model (model 3), relative to Q1, the OR for the group with the highest SII level (Q4) was 1.34 (95% CI: 1.20–1.51). Furthermore, each 1 standard deviation increase in SII value corresponded to an OR of 1.12 (95% CI: 1.07–1.16).

Utilizing model 3 as a foundation, we adjusted for additional covariates—including hypertension, hyperlipidemia, diabetes, coronary heart disease, chronic heart failure, MET, blood glucose concentration, and systolic blood pressure—to ascertain the stability of the association. After comprehensive adjustment for these covariates, there remained a positive association between SII values and the risk of developing arthritis, with the trend test demonstrating statistical significance (p for trend = 0.001). When comparing the group with the highest SII values (Q4) with Q1, the OR was 1.22 (95% CI: 1.10–1.36), and for each 1 standard deviation increase in SII value, the corresponding OR was 1.08 (95% CI: 1.04–1.12). These findings are presented in Supplemental Table 2.

Discussion

This study is the first exploration of the relationship between SII values and arthritis risk, employing data from the NHANES dataset for the period 2011–2018. The results derived from both categorical and continuous variable analyses suggest a significant correlation between elevated SII values and heightened arthritis risk. The RCS model demonstrates a marked and linear dose–response relationship between SII values and arthritis risk, indicating a critical inflection point at an SII value of 2.4. Subgroup analysis revealed an interaction between this positive correlation and age, with the association proving significant solely in the older population. Sensitivity analysis results corroborate that the relationship between SII values and arthritis risk remains robust, unaffected by imputed datasets or potential confounding factors.

Our findings align with those of previous research suggesting that SII values serve as an effective biomarker for arthritis. A case–control study involving 257 women with RA and 71 age-matched women without RA demonstrated that SII values accurately reflected RA status, thereby enhancing diagnostic accuracy.¹⁸ A subsequent case–control study, involving 109 patients with RA and 31 without RA, corroborated the previous findings. It was determined that an SII value of 574.20 represented the most critical point for active RA.¹⁹ Kelesoglu et al. conducted a study on 106 patients with psoriatic arthritis and 103 age- and sex-matched patients without the condition. They discovered that²⁰ individuals suffering from moderate-to-severe psoriatic arthritis exhibited significantly elevated SII values compared with those in remission or with mild symptoms (p < 0.001). Furthermore, Yorulmaz et al. established that SII values could function as an independent prognostic indicator for individuals with psoriatic arthritis.²¹ Moreover, a 2023 study utilizing the NHANES dataset identified a positive correlation between elevated SII values and increased RA risk, with a designated cutoff SII value of 578.25.¹² Although the positive correlation identified in the present study mirrors those reported in previous research, our critical SII value notably differs, suggesting that SII cutoff values are contingent on the specific type of arthritis. This variability may stem from differences in the underlying inflammatory mechanisms of various arthritis types, such as RA, which is autoimmune-driven, versus OA, which is primarily degenerative. For instance, RA involves systemic inflammation that may elevate SII values more significantly than the localized inflammation typical of OA. Consequently, establishing type-specific SII thresholds could enhance the clinical utility of SII as a diagnostic or prognostic tool. Future studies should prioritize differentiating clinical cutoff SII values for various arthritis subtypes to improve predictive accuracy and guide targeted interventions. To the best of our knowledge, this is the first study to explore the association between SII values and arthritis risk in a large, population-based sample encompassing all types of arthritis, rather than focusing on a single subtype.

In the subgroup analysis, we discovered that only age influenced the relationship between SII values and arthritis risk. Furthermore, when the data were stratified by age, it revealed a positive correlation between high SII values and an elevated risk of arthritis exclusively in the older age group (≥60 years). Previous research has established that RA with onset in old age exhibits a distinct clinical presentation and may possess differing biological characteristics compared with RA with younger onset.²² With advancing age, there is a noted nonspecific activation of the innate immune system, resulting in elevated levels of chronic inflammation and its corresponding complications.^23,24 The incidence of complications among older RA patients surpasses that in younger RA patients, potentially indicating the confluence of RA and aging in the immune-related aging process.²⁵ Compared with young-onset RA, older onset RA usually progresses more swiftly, involves more systemic elements, and results in less favorable functional outcomes. Consequently, the indicative efficacy and subsequent clinical applicability of SII values might be more pronounced in the older population than in the younger one. The SII threshold of 2.4 provides a practical benchmark for risk stratification. However, its clinical adoption requires validation in prospective cohorts. Given that arthritis subtypes differ in inflammation patterns (e.g. systemic vs. localized), future studies should prioritize subtype-specific SII thresholds to optimize predictive accuracy.¹²

Arthritis can present in acute or chronic forms, with common characteristics including monocyte infiltration, inflammation, synovial swelling, fascia formation, joint stiffness, and progressive joint injury. RA is a systemic autoimmune disease that can affect multiple joints and organ systems, including the skin, eyes, lungs, and cardiovascular system. Although the mechanisms underlying different types of arthritis may vary significantly, our understanding of their pathogeneses remains limited. Despite being a prevalent condition, arthritis often does not receive the attention it deserves from patients, leading to missed opportunities for optimal treatment and potentially adverse impacts on quality of life. Consequently, early detection, diagnosis, and intervention are crucial in preventing severe arthritis-related disabilities.

The SII is a comprehensive hematological marker derived from platelet (PLT), neutrophil (N), and lymphocyte counts (L), calculated using the formula PLT × N/L. This index can be readily obtained from routine blood test data, making it both simple and cost-effective. Consequently, incorporating SII values into clinical practice for identifying arthritis could enhance current evaluation methods. Our findings indicate that SII may be a valuable adjunct in the screening and management of arthritis.

This study had several strengths. First, this study was based on an analysis of data derived from the NHANES dataset, which features a substantial sample size. Consequently, the study exhibited robust statistical power in identifying potential associations.²⁶ Second, we verified that SII values were normally distributed by implementing a natural-log transformation prior to analysis. Third, we deployed an RCS model to examine and delineate the dose–response relationship between SII values and the risk of arthritis while also pinpointing the inflection point.

This study also had certain limitations. First, although the NHANES dataset comprises a substantial sample size, it exclusively represents the civilian population of the United States. Therefore, the generalizability of our findings may be limited. Future research endeavors should aim to bridge this gap and explore the applicability of our results to the Chinese population. Second, the NHANES dataset omits certain species associated with inflammation, such as TNF-α, IL6, and IL10. Consequently, these indicators could not be integrated into our analysis, potentially affecting the comprehensiveness of our outcomes. Third, this study lacked arthritis subtype classification data in the NHANES dataset. Because different forms of arthritis may involve distinct inflammatory pathways, the association between SII and arthritis risk observed in our study may reflect an averaged effect across heterogeneous disease entities. Finally, it is important to note that data regarding arthritis diagnoses derived from the NHANES dataset were collected through interview methods. Consequently, the potential for recall bias may have influenced the precision of the reported diagnoses and the subsequent classifications of arthritis. Moreover, even physician-confirmed self-reported diagnoses may lead to misclassification of arthritis types, as diagnostic accuracy can vary due to differences in clinical assessment or patient recall, potentially affecting the reliability of our findings. As this was a cross-sectional study, no causal relationship could be established between SII values and arthritis occurrence. In the future, a prospective and refined cohort is needed to validate our results. Notably, the exclusion of females may obscure sex-specific associations. For instance, estrogen suppresses neutrophil activation via nuclear factor-kappa B (NF-κB) inhibition, potentially altering the SII–arthritis risk relationship. Future studies should explore sex-stratified models to validate our findings.

Conclusions

We observed a positive relationship between high SII values and the risk of arthritis, but this was only significant in older participants. Furthermore, we identified a critical SII value of 2.4, which holds evident clinical relevance. Overall, this research suggests that the SII can be beneficial for predicting arthritis among the older population and for assessing arthritis prognosis and status. Our conclusions pertain to adult males in the US population; further research is needed to assess applicability to females and other ethnic groups.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251353037 - Supplemental material for Correlation analysis of systemic immune-inflammation index values and arthritis risk in adults

Supplemental material, sj-pdf-1-imr-10.1177_03000605251353037 for Correlation analysis of systemic immune-inflammation index values and arthritis risk in adults by Xuefei Xia, Yongyong Lou, Yu Zhou, Haoming Ling and Tingting Ge in Journal of International Medical Research

Footnotes

Acknowledgments

We thank all authors for their contributions to the article.

Author contributions

Xuefei Xia, Yongyong Lou, and Peng Shu were responsible for analysis and interpretation of data and writing of the article. Chao Xin and Yu Zhou were responsible for data collection and statistical analysis. Haoming Ling was responsible for revising the manuscript and obtaining funding. All authors have read and agreed to the published version of the manuscript.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Data availability statement

The datasets generated and/or analyzed during the current study are available in the [zenodo] repository at .

Declaration of conflicting interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical considerations

The NCHS Research Ethics Review Board authorized this study and verified that informed consent had been provided by all of the participants.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Tingting Ge

Supplemental material

Supplemental material for this article is available online.

References

Barbour

Helmick

Boring

, et al. Vital signs: prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation – United States, 2013-2015. MMWR Morb Mortal Wkly Rep 2017; 66: 246–253.

Tang

CH.

Research of pathogenesis and novel therapeutics in arthritis. Int J Mol Sci 2019; 20: 1646.

Blüml

Redlich

Smolen

JS.

Mechanisms of tissue damage in arthritis. Semin Immunopathol 2014; 36: 531–540.

Grillet

Pereira

RVS

Van Damme

, et al. Matrix metalloproteinases in arthritis: towards precision medicine. Nat Rev Rheumatol 2023; 19: 363–377.

Nikiphorou

Philippou

Nutrition and its role in prevention and management of rheumatoid arthritis. Autoimmun Rev 2023; 22: 103333.

Giannini

Antonucci

Petrelli

, et al. One year in review 2020: pathogenesis of rheumatoid arthritis. Clin Exp Rheumatol 2020; 38: 387–397.

Jang

Kwon

Lee

JJ.

Rheumatoid arthritis: pathogenic roles of diverse immune cells. Int J Mol Sci 2022; 23: 905.

Xie

Xue

, et al. Association of the systemic immune-inflammation index with all-cause mortality in patients with arteriosclerotic cardiovascular disease. Front Cardiovasc Med 2022; 9: 952953.

Okutan

Aci

Keskin

, et al. New inflammatory markers associated with disease activity in rheumatoid arthritis: pan-immune-inflammation value, systemic immune-inflammation index, and systemic inflammation response index. Reumatologia 2024; 62: 439–446.

10.

Keskin

Karakaya

Aci

, et al. Systemic immune inflammation index and pan-immune inflammation value in predicting human leukocyte antigen-b27 positivity: a study on ankylosing spondylitis patients. Meandros Medical And Dental Journal 2024; 25: 244–252.

11.

Kelesoglu Dincer

Sezer

Systemic immune inflammation index as a reliable disease activity marker in psoriatic arthritis. J Coll Physicians Surg Pak 2022; 32: 773–778.

12.

Yorulmaz

Hayran

Akpinar

, et al. Systemic immune-inflammation index (SII) predicts increased severity in psoriasis and psoriatic arthritis. Curr Health Sci J 2020; 46: 352–357.

13.

Tang

Peng

Liu

, et al. Systemic immune-inflammation index and bone mineral density in postmenopausal women: a cross-sectional study of the national health and nutrition examination survey (NHANES) 2007–2018. Front Immunol 2022; 13: 975400.

14.

Qin

Wang

, et al. Systemic immune-inflammation index is associated with increased urinary albumin excretion: a population-based study. Front Immunol 2022; 13: 863640.

15.

Xie

Xiao

, et al. Association between SII and hepatic steatosis and liver fibrosis: a population-based study. Front Immunol 2022; 13: 925690.

16.

Liu

Wang

, et al. The association between systemic immune-inflammation index and rheumatoid arthritis: evidence from NHANES 1999–2018. Arthritis Res Ther 2023; 25: 34.

17.

Von Elm

Altman

Egger

; STROBE Initiativeet al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147: 573–577.

18.

Johnson

Paulose-Ram

Ogden

, et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2 2013: 1–24.

19.

von Hippel

PT.

Regression with missing YS: an improved strategy for analyzing multiply imputed data. Sociological Methodology 2007; 37: 83–117.

20.

Choe

Lee

Kim

SK.

Association between novel hematological indices and measures of disease activity in patients with rheumatoid arthritis. Medicina (Kaunas) 2023; 59: 117.

21.

Satis

New inflammatory marker associated with disease activity in rheumatoid arthritis: the systemic immune-inflammation index. Curr Health Sci J 2021; 47: 553–557.

22.

Serhal

Lwin

Holroyd

, et al. Rheumatoid arthritis in the elderly: characteristics and treatment considerations. Autoimmun Rev 2020; 19: 102528.

23.

Chalan

Van den Berg

Kroesen

, et al. Rheumatoid arthritis, immunosenescence and the hallmarks of aging. Curr Aging Sci 2015; 8: 131–146.

24.

Franceschi

Garagnani

Parini

, et al. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol 2018; 14: 576–590.

25.

Ruban

Jacob

Pope

, et al. The influence of age at disease onset on disease activity and disability: results from the Ontario Best Practices Research Initiative. Clin Rheumatol 2016; 35: 759–763.

26.

Andres

Rothstein

Elser

, et al. Trends in the prevalence of stroke among community-dwelling individuals in the US, 1999–2018. JAMA Neurol 2023; 80: 646–648.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB