Systemic immune-inflammation index as a novel biomarker for metabolic syndrome: A systematic review and meta-analysis

Abstract

Background:

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that increases the likelihood of cardiovascular disease and type 2 diabetes mellitus. Given the increasing prevalence of MetS, early detection and risk assessment are crucial for mitigating long-term complications. The Systemic Immune-Inflammation Index (SII) has emerged as a potential biomarker for inflammatory conditions; however, its role in MetS remains unclear. This systematic review and meta-analysis aimed to evaluate the association between the SII and MetS.

Methods:

A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science databases until June 1, 2026. Eligible studies that assessed the relationship between SII and MetS were included. Data extraction was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and study quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist.

Results:

The meta-analysis revealed that individuals with MetS had significantly higher SII values as a continuous variable than non-MetS individuals (SMD, 0.34; 95% CI: 0.05–0.64; P = .01). As a categorical variable, an elevated SII was associated with increased odds of MetS (odds ratio = 1.34, 95% CI: 1.08–1.67, P < .01). The diagnostic accuracy of SII showed a pooled area under the receiver operating characteristic curve of 0.69 (95% CI: 0.41–0.98). The pooled sensitivity of the SII for identifying MetS was 0.69 (95% CI: 0.41–0.96), while the pooled specificity was 0.60 (95% CI: 0.55–0.65).

Conclusion:

The SII score was significantly associated with MetS. However, its diagnostic accuracy was limited (pooled area under the receiver operating characteristic curve = 0.69, based on only 2 studies). The SII may serve as a supplementary marker or risk stratification tool, but more studies are needed to obtain a precise estimate of its diagnostic performance. The observed heterogeneity and very low Grading of Recommendations Assessment, Development and Evaluation evidence further underscore the need for future prospective research to establish standardized cutoff values and clarify its clinical role.

1. Introduction

Metabolic syndrome (MetS) is a collection of interconnected cardiometabolic risk factors, including central obesity, dyslipidemia, hypertension, and insulin resistance, which together increase the risk of cardiovascular diseases (CVDs) and type 2 diabetes mellitus.^[1-3] With the global rise in the prevalence of MetS, early detection and risk assessment have become critical for preventing long-term complications.^[4-6] Although conventional diagnostic tools such as fasting glucose levels, lipid profiles, and blood pressure measurements are widely used, there is increasing interest in identifying novel, cost-effective, and readily available biomarkers to enhance early diagnosis and risk prediction.^[3,5,7,8] Among these, systemic inflammatory markers have gained traction because of the well-recognized role of chronic low-grade inflammation in the development of MetS.^[9-12]

The systemic immune-inflammation index (SII), a newly emerging hematological biomarker derived from platelet, neutrophil, and lymphocyte counts, has shown promise as an indicator of immune and inflammatory status.^[13-15] Although the SII has been widely studied in oncology and CVDs, where it has demonstrated prognostic significance, its potential role in metabolic disorders, particularly MetS, remains insufficiently explored.^[16-20] As inflammation plays a key role in the pathophysiology of MetS, incorporating SII as a screening or predictive marker may strengthen existing diagnostic models and provide further insights into disease mechanisms.^[16,17]

Recent studies examining the link between the SII and MetS have reported inconsistent findings regarding its diagnostic and prognostic significance. While some studies suggest that elevated SII levels are associated with a greater prevalence and severity of MetS, others indicate a weaker relationship, raising concerns about its reliability as an independent marker.^[16,17,21] These conflicting results highlight the need for a systematic review and meta-analysis to synthesize current evidence, clarify the role of the SII in MetS, and assess its clinical potential for early detection and risk evaluation. Therefore, this study aimed to evaluate the association between the SII and MetS and provide a comprehensive analysis of its potential as a valid biomarker for MetS.

2. Materials and methods

This systematic review and meta-analysis was registered in PROSPERO (Registration ID: CRD420251002267). The methodology followed the guidelines set out in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and adhered to the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions.^[22,23]

2.1. Search strategy

A thorough search was performed across multiple electronic databases, including PubMed, Scopus, Embase, and Web of Science, up to June 1, 2026. Keywords related to the Systemic Immune-Inflammation Index and Metabolic Syndrome, such as “Systemic Immune-Inflammation Index,” “SII,” “Metabolic Syndrome,” and “Metabolic X Syndrome,” were used. No restrictions on language or publication date were applied. A detailed search strategy for each database is provided in the Supplementary Material (Table S1, Supplemental Digital Content 1).

2.2. Study selection and eligibility criteria

Studies were included if they met the following criteria: assessed the relationship between SII and MetS, were published in peer-reviewed journals, and provided sufficient data on the association between SII and the components of MetS. We excluded non-observational studies, including case reports, reviews, and studies without relevant data.

2.3. Quality assessment and certainty of evidence

The quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. This tool was used to evaluate the methodological rigor and transparency of the studies.^[24-26] Any disagreements in quality assessment between the reviewers were resolved through discussion or with the involvement of a third reviewer. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation approach.

2.4. Data extraction

Data were systematically extracted using a standardized form that captured key details such as author names, publication year, sample size, participant demographics, levels of SII, and diagnostic criteria for MetS. Any disagreements that arose during the data extraction process were resolved by consensus or, if necessary, by consulting a third reviewer. When SII values were reported as medians with interquartile ranges, we converted them into estimated means and standard deviations using the validated methods proposed by Luo et al^[27] and Wan et al.^[28]

2.5. Statistical analysis

Statistical analyses were conducted using Comprehensive Meta-Analysis (CMA) software, version 4.0 (Biostat, Inc.). A random-effects model was used to compute pooled estimates of the association between SII and MetS, adjusting for variations across studies. The results were reported with 95% confidence intervals (CIs). To assess heterogeneity, we applied the I² statistic, with values greater than 50% and a P-value of<.1 from Cochran’s Q test considered indicative of significant heterogeneity. Sensitivity analyses were performed by removing individual studies to test the stability of the results. Funnel plots were used to assess publication bias, and Egger’s and Begg’s tests were used to check for asymmetry. A P-value of<.1 was deemed indicative of significant publication bias.

3. Results

3.1. Study selection

A total of 162 records were identified through the database searches. After 78 duplicate records were removed, 84 studies were screened. Among these, 62 were excluded based on the initial screening criteria, leaving 22 studies for full-text review. Upon detailed evaluation, an additional 9 studies were excluded (Table S2, Supplemental Digital Content 2). Ultimately, 13 studies were included in the final systematic review (Fig. 1).

Figure 1.

Study selection process.

3.2. Study characteristics

This meta-analysis included 13 eligible studies with diverse study designs, primarily cross-sectional (n = 10), retrospective (n = 1), and prospective (n = 2) studies. These studies were conducted across various geographical locations, including the USA (n = 8),^{[16,17,29-34]} Italy (n = 1),^[35]Romania (n = 2),^[21,36] Turkey (n = 1),^[37] and China (n = 1).^[38] The study population varied widely, including the general adult population,^{[16,17,29-32,34,38]} children and adolescents with obesity,^[21,36,37] adult shift workers^[33] and adults with severe obesity.^[35] The sample sizes were heterogeneous, ranging from small groups with 62 participants^[37] to large datasets involving over 17,000 participants.^[31] The criteria used to define MetS included the National Cholesterol Education Program Adult Treatment Panel III and International Diabetes Federation criteria (Table 1 and Fig. 2). Overall, all 13 studies demonstrated high methodological quality. The included studies fulfilled the majority of the Joanna Briggs Institute criteria, including appropriate study designs, clearly defined study populations, valid measurements of exposure and outcomes, and appropriate statistical analyses. None of the studies were considered to have low methodological quality. Given the consistently high quality of the included studies, a sensitivity analysis based on methodological quality was not applicable (Table S3, Supplemental Digital Content 3).

Table 1.

Characteristic of included studies.

First author	Publication year	Type of study	Duration of study	Data source	Country	Population	Definition of MetS	Overall					MetS+					MetS−
								No. participants	Male/female	Age	SII	BMI	No. patients	Male/female	Age	SII	BMI	No. participants	Male/female	Age	SII	BMI
Bao and Wang[33]	2025	Cross-sectional	2005–2010	NHANES	USA	Shift workworn older than 20 years old	Harmonized criteria	3079	NR	NR	NR	NR	529	NR	45† (38–56)	431† (312–645)	NR	2550	NR	38† (29-48)	421† (312–645)	NR
Liang et al.[29]	2024	Cross-sectional	2007–2016	NHANES	USA	General population aged 40-79 years	NCEP-ATP III criteria	13,119	6381/6738	56.06* (55.76, 56.37)	543.80* (534.67, 552.94)	29.46* (29.26, 29.65)	5742	2625/3117	57.69* (57.30, 58.07)	555.79* (544.50, 567.07)	32.88* (32.63, 33.13)	7377	3756/3621	54.93* (54.54, 55.31)	535.40* (523.85, 546.95)	27.06* (26.87, 27.24)
Marra et al[35]	2024	Retrospective	NR	Istituto Auxologico Italiano	Italy	Adults diagnosed with severe obesity‡	IDF cirteria	231	88/143	52.3† (36.4-63.3)	477.5† (344.1–665.3)	43.6† (40.5–48.3)	163	71/92	52.3† (41.5–63.7)	478.6† (355.2–668.7)	44.2† (40.8–48.9)	68	17/51	44.9† (27.3–62.4)	471.5† (338.3–628.3)	42.7† (40.4–46.1)
Nicoară et al[21]	2023	Cross-sectional	2015–2023	Pediatric Emergency Hospital in Timisoara	Romania	Children aged 10-18 years diagnosed with obesity§	IDF cirteria	191	111/80	13† (11–15)	NR	NR	66	38/28	13† (11–15)	715.87† (472.98-909.15)	32.9† (29.3, 37.9)	125	73/52	12† (11–15)	363.4† (277.9–477.01)	29.9† (27.9, 32.2)
Öztürk et al[37]	2019	Prospective	2016–2019	İzmir Tepecik Training and Research Hospital	Turkey	Cases: obese children/ controls: normal population	IDF cirteria	62 cases/ 48 controls	52/58	NR	NR	NR	28	15/13	14.1* (3.3)	513.5	NR	34 cases/48 controls	37/45	13.8* (2.9)	Cases: 467.2 Controls: 377.9	NR
Podeanu et al[36]	2024	Cross-sectional	2021–2024	Clinical Emergency County Hospital of Craiova	Romania	Children aged 6-16 diagnosed with obesity and overweight‖	NCEP-ATP III criteria	71	48/23	10.96† (2.653)	NR	28.15* (3.81)	32	NR	NR	510.985*	28.445*	39	NR	NR	465.032*	27.910*
Ramezankhani et al[17]	2025	Cross-sectional	2010–2012	BioLINCC	USA	General population	NCEP-ATP III	2755	1305/1450	69.2* (9.3)	513.0* (331.9)	29.2* (5.5)	1082	463/619	69.6* (9.1)	522.5* (345.8)	32.4* (5.2)	1673	842/831	69.2* (9.3)	507.1* (322.6)	27.3* (4.9)
Sun et al[34]	2026	Prospective	2013–2018	NHANES	USA	Adults	AHA/NHLBI	6140	2976/3164	48.40 (SE = 0.41)	505.56 (SE = 5.35)	NR	2295	1075/1220	54.90 (SE = 0.52)	554.08 (SE = 8.17)	NR	3845	1901/1944	44.82 (SE = 0.49)	478.77 (SE = 5.93)	NR
Wang et al[38]	2022	Cross-sectional	2012–2013	NCRCHS	China	General population	IDF cirteria	7420	3359/4061	53.7*	390.19* (227.63)	NR	2317	680/1637	NR	391.40* (238.51)	NR	5963	2397/3566	NR	379.64* (201.63)	NR
Wei et al[30]	2025	Cross-sectional	2001–2018	NHANES	USA	General population	NCEP-ATP III criteria	15,959	8188/7771	47.25* (0.24)	495.74* (2.79)	NR	6739	NR	NR	NR	NR	9220	NR	NR	NR	NR
Yuguang et al[31]	2024	Cross-sectional	2011–2018	NHANES	USA	General population	NCEP-ATP III criteria	17,582	8269/9259	NR	NR	NR	5901	2810/3091	NR	497.62‖ (332.28)	NR	11,951	5974/5977	NR	539.48‖ (325.84)	NR
Zeng et al[32]	2024	Cross-sectional	2015–2018	NHANES	USA	General population	NCEP-ATP III criteria	6999	3425/3574	47.32* (16.68)	448.84† (323.77, 626.20)	33.67* (6.75)	2539	1206/1333	53.63* (14.99)	483.97† (345.54, 665.60)	33.67* (6.75)	4460	2166/2294	44.15* (16.59)	431.21† (312.97, 601.08)	27.55* (6.30)
Zhao et al[16]	2023	Cross-sectional	2011–2016	NHANES	USA	General population	NCEP-ATP III criteria	12,402	6198/6204	47.69* (0.72)	NR	NR	3489	NR	NR	NR	NR	8913	NR	NR	NR	NR

BioLINCC = biologic Specimen and Data Repository Information Coordinating Center, BMI = body mass index, CDC = Centers for Disease Control and Prevention, IDF criteria = International Diabetes Federation criteria, NCEP-ATP III criteria = The National Cholesterol Education Program Adult Treatment Panel III, NCRCHS = Northeast China Rural Cardiovascular Health Study, NHANES = The National Health and Nutrition Examination Survey, NR = not reported, SE = standard error, WHO = World Health Organization.

^*Mean (standard deviation).

^†Median (interquartile range).

^‡Obesity was defined in adulthood as the presence of a BMI > 30 kg/m².

^§Obesity was diagnosed according to the WHO guidelines (WHO Reference 2007 for older children and adolescents) as having a BMI-for-age greater than 2 standard deviations above the WHO growth reference median. Determined by a body mass index (BMI) ≥ 85th percentile, calculated in accordance with the CDC criteria in relation to age and gender.

^‖Mean (standard error).

Figure 2.

Comparison of SII as a continuous variable between individuals with MetS and non-MetS (A) forest plot, (B) sensitivity analysis, (C) prediction interval analysis, and (D) publication bias assessment. CI = confidence interval, SII = Systemic Immune-Inflammation Index.

3.3. Results of meta-analysis

3.3.1. Comparison of SII as a continuous variable between MetS and non-MetS groups

The meta-analysis revealed that individuals with MetS had significantly higher SII values than healthy individuals (standardized mean difference [SMD]: 0.34, 95% CI: 0.05–0.64, P = .01; Fig. 2A). The results showed significant heterogeneity (I² = 95.04%, P < .01). Sensitivity analysis showed that the difference between MetS and normal individuals became non-significant when the studies by Nicoară et al,^[21] Wang et al,^[38] and Ramezankhani et al^[17] were removed (Fig. 2B). The prediction interval for the effect size was calculated as −1.01 to 1.70 (Fig. 2C). Publication bias was not significant based on the Egger and Begg tests, with P-values of .25 and .30, respectively. Furthermore, the trim-and-fill method was applied to adjust for potential bias, adding 1 study to the right side of the funnel plot. After adjustment, the pooled SMD was 0.50 (95% CI: 0.28–0.98; Fig. 2D).

3.3.2. Comparison of SII as a categorical variable between MetS and non-MetS group

The meta-analysis revealed that individuals with MetS had significantly higher odds of elevated SII as a categorical variable than non-MetS individuals (odds ratio [OR] = 1.34, 95% CI: 1.08–1.67, P < .01; Fig. 3A). The analysis demonstrated significant heterogeneity among the included studies (I² = 79.30%, P < .01). Sensitivity analysis showed that the difference between MetS and normal individuals became non-significant after the removal of Wang et al^[38] (Fig. 3B). The prediction interval for the effect size was calculated as 0.1 to 17.31 (Fig. 3C). Publication bias was significant based on the Egger test (P = .05); however, the Begg test did not show significant publication bias (P = .29). The trim-and-fill analysis did not impute any studies (Fig. 3D).

Figure 3.

Comparison of SII as a categorical variable between individuals with MetS and non-MetS (A) forest plot, (B) sensitivity analysis, (C) prediction interval analysis, and (D) publication bias assessment. CI = confidence interval, SII = Systemic Immune-Inflammation Index.

3.4. The AUC, sensitivity, and specificity of SII in individuals with MetS

This meta-analysis assessed the diagnostic performance of the SII in individuals with MetS. The pooled area under the receiver operating characteristic curve (AUC) for SII was 0.69 (95% CI: 0.41–0.98), with significant heterogeneity (I² = 96.94%, P < .01; Fig. 4A). The pooled sensitivity of the SII was 0.69 (95% CI: 0.41–0.96), also showing significant heterogeneity (I² = 97.79%, P < .01; Fig. 4B). The pooled specificity was 0.60 (95% CI: 0.55–0.65), with low heterogeneity (I² = 13.03%, P = .28; Fig. 4C). Owing to the small number of included studies (only 2), sensitivity analysis, publication bias assessment, and prediction interval analysis were not feasible in this case.

Figure 4.

Pooled AUC (A), pooled sensitivity (B), and pooled specificity of SII in individuals with metabolic syndrome (C). CI = confidence interval, SII = Systemic Immune-Inflammation Index.

3.5. Assessment of the epidemiological strength of SII in MetS

The epidemiological strength of the SII in MetS was reflected in the overall quality of the evidence as “very low,” primarily due to serious inconsistency in the results and the observational nature of the studies. The risk of bias was not a significant concern, and there was no serious indirectness or imprecision in the data (Table 2).

Table 2.

Results of GRADE evaluation.

Quality assessment							Quality
No of studies	Design	Risk of bias	Inconsistency	Indirectness	Imprecision	Other considerations
4	observational studies	no serious risk of bias	serious	no serious indirectness	no serious imprecision	none	Very low

GRADE = Grading of Recommendations Assessment, Development and Evaluation.

4. Discussion

The results of this systematic review and meta-analysis demonstrate a significant association between SII and MetS, reinforcing the potential role of inflammation in the pathophysiology of this condition. Individuals with MetS exhibited significantly higher SII values than non-MetS individuals, both as a continuous variable (SMD: 0.34, 95% CI: 0.05–0.64, P = .01) and as a categorical variable (odds ratio [OR] = 1.34, 95% CI: 1.08–1.67, P < .01). The diagnostic accuracy of the SII, as reflected by an AUC of 0.69, further suggests that it is a biomarker for the identification of MetS. However, the observed heterogeneity across studies underscores the need for standardized cutoff values and further validation in diverse populations.

MetS is a complex metabolic disorder characterized by the coexistence of central obesity, dyslipidemia, hypertension, and insulin resistance, which together increase the risk of CVDs and type 2 diabetes mellitus.^[39] Chronic low-grade inflammation plays a pivotal role in the pathogenesis of MetS and acts as a central mediator linking metabolic dysfunction to cardiovascular complications.

Given the crucial role of inflammation in the progression of MetS, there is a growing interest in identifying inflammatory biomarkers that facilitate early detection and risk assessment.^[9] Several biomarkers have been identified as valuable tools for screening MetS. For instance, the triglyceride-glucose index is a sensitive and specific marker for MetS.^[40] Elevated C-reactive protein levels have been strongly associated with metabolic disorders, including dyslipidemia, diabetes, and MetS.^[41] More recently, SII has emerged as a promising biomarker across various diseases, as it integrates neutrophil, platelet, and lymphocyte counts, providing insights into immune system activity.^[42,43] Previous studies have documented significant immune alterations in individuals with MetS, including lymphocyte counts, neutrophil function, and platelet levels, compared with healthy controls.^[42,44,45] Additionally, the SII has been associated with cardiovascular risk factors, further highlighting its potential clinical relevance.^[19,46]

For instance, Gao et al found a significant association between elevated SII levels and Cardiovascular–Kidney-Metabolic Syndrome, reporting a 1.48-fold increased risk per 1000-unit increase in SII (95% CI: 1.20–1.81, P < .001).^[47] A study by Zhao et al, utilizing data from the National Health and Nutrition Examination Survey (2011–2016), investigated the association between the SII and MetS in a cohort of 12,402 adults. Their findings demonstrated a significant positive correlation, with individuals in the highest SII quartile exhibiting a 33% greater risk of MetS than those in the lowest quartile even after adjusting for potential confounders. Additionally, the SII was strongly associated with increased waist circumference (OR = 2.17, 95% CI: 1.65–2.87, P < .001) and elevated blood pressure (OR = 1.65, 95% CI: 1.20–2.27, P = .003). These results suggest that the SII may serve as a reliable biomarker for the early identification of individuals at risk for MetS.^[16]

Similarly, a study by Ramezankhani et al using data from the Multi-Ethnic Study of Atherosclerosis (n = 2755) identified a significant association between the SII and MetS, particularly with key metabolic components such as hyperglycemia (OR = 1.23, 95% CI: 1.05–1.44) and elevated blood pressure (OR = 1.47, 95% CI: 1.14–1.89). Moreover, obesity status was found to modify the relationship between the SII and abdominal obesity, suggesting a potential role of systemic inflammation in metabolic dysregulation. These findings further reinforce the clinical relevance of SII as a potential biomarker for MetS, underscoring the need for prospective studies to validate its prognostic value.^[17]

Studies conducted among pediatric populations have also yielded findings consistent with those observed in adults. For instance, Nicoară et al investigated a cohort of 191 obese children and adolescents aged 10–18 years and reported significantly elevated SII levels in participants diagnosed with metabolic syndrome compared with those without the syndrome.^[21] Similarly, Podeanu et al observed comparable associations in a study of children aged 6–16 years who were obese or overweight, indicating that higher SII values were significantly associated with the presence of metabolic syndrome.^[36]

The findings of the present study are consistent with those of previous reports, demonstrating a significant association between elevated SII levels and the presence of MetS, further supporting its utility in metabolic risk stratification.

The pathophysiological mechanisms underlying the elevation of the SII in MetS include a complex network of chronic low-grade inflammation, immune cell dysregulation, oxidative stress, and endothelial dysfunction^[48] (Fig. 5). As a composite biomarker integrating neutrophil, platelet, and lymphocyte counts, the SII provides insight into the immune-inflammatory alterations characteristic of MetS, which contribute to disease progression and cardiovascular risk.^[42-46] Critically, because SII is mathematically defined as (Platelets × Neutrophils)/Lymphocytes, any increase in platelet or neutrophil counts or a relative decrease in lymphocyte count will directly amplify the index value. Thus, understanding how MetS independently affects each of these three cell lineages is essential for interpreting the SII as a disease biomarker.

Figure 5.

Pathophysiological mechanisms underlying elevated SII in metabolic syndrome. IL-6 = interleukin-6, LPS = lipopolysaccharides, ROS = reactive oxygen species, SII = Systemic Immune-Inflammation Index, TLR = Toll-like receptor, VAT = visceral adipose tissue.

A central driver of elevated SII in MetS is adipose tissue dysfunction and sustained proinflammatory signaling. Visceral adipose tissue functions as an immuno-metabolic organ, actively secreting proinflammatory cytokines, including interleukin-6 and tumor necrosis factor-alpha, as well as dysregulated adipokines, such as leptin and resistin.^[48,49] This inflammatory milieu promotes insulin resistance by impairing insulin receptor signaling in the skeletal muscle, hepatic, and adipose tissues, exacerbating hyperglycemia and metabolic dysregulation.^[48] The persistent activation of inflammatory pathways results in altered leukocyte homeostasis, leading to increased neutrophil and platelet counts along with disturbances in lymphocyte subsets, hallmarks of systemic immune activation, as captured by an elevated SII^[42-45] (Fig. 5). From an SII perspective, the simultaneous increase in both neutrophil and platelet counts exerts a multiplicative effect on the numerator, driving the index upward more potently than either abnormality alone.

Neutrophil proliferation and oxidative stress are additional contributors to SII elevation in MetS. Chronic hyperglycemia, dyslipidemia, and adipose-derived inflammatory mediators promote neutrophil activation and prolonged survival.^[50] Furthermore, hyperglycemia-induced oxidative stress leads to excessive production of reactive oxygen species (ROS), which further drives neutrophil recruitment and degranulation. Neutrophil-derived ROS and proteolytic enzymes promote endothelial dysfunction and insulin resistance, establishing a cycle of inflammation and tissue injury.^[50] Given that the neutrophil count is a primary component of the SII calculation,^[51] the increased neutrophil burden in MetS^[52] substantially elevates the index, reflecting heightened innate immune system activation (Fig. 5). Notably, even modest increases in neutrophil counts are mathematically amplified in SII due to concurrent platelet elevation, making the index a sensitive readout of innate immune activation in MetS.

Platelet hyperreactivity and the prothrombotic state further contribute to SII elevation in MetS. Systemic inflammation, oxidative stress, and endothelial dysfunction induce platelet activation, thereby increasing platelet-leukocyte aggregates, thromboxane A2 release, and pro-coagulant signaling.^[53] This hypercoagulable state accelerates atherogenesis and significantly increases the risk of thrombotic complications, including myocardial infarction and stroke.^[54] Because the platelet count is multiplied by the neutrophil count in the SII formula, platelet-driven inflammatory responses markedly amplify the index, making it a surrogate marker for inflammation-mediated thrombogenic risk in MetS^[51] (Fig. 5). Thus, the SII captures not only inflammation but also the prothrombotic nature of MetS, as both components contribute to the numerator.

Beyond alterations in innate immunity, lymphocyte dysregulation and adaptive immune imbalance play crucial roles in modulating the SII. While some individuals with MetS exhibit lymphopenia due to chronic immune activation and lymphocyte exhaustion,^[55] others demonstrate selective expansion of proinflammatory lymphocyte subsets such as Th1 and Th17 cells, which sustain inflammation.^[56,57] Since the SII formula inversely incorporates lymphocyte count, a relative decrease in lymphocyte numbers exacerbates the upward trajectory of the SII, reinforcing the predominance of innate immune responses over adaptive immune regulation in MetS^[51] (Fig. 5). This inverse relationship means that even stable neutrophil and platelet counts will yield a higher SII if lymphocyte counts fall, making the SII uniquely sensitive to shifts in adaptive immunity.

Emerging evidence highlights the gut–adipose axis and Toll-like receptor (TLR) activation as key mediators linking immune dysregulation to metabolic inflammation. Increased gut permeability, driven by dysbiosis and dietary excess, facilitates bacterial endotoxin translocation into the circulation, particularly lipopolysaccharides, which activate TLR2 and TLR4 in immune and endothelial cells.^[48] This activation triggers downstream inflammatory cascades that amplify neutrophil activation and platelet aggregation. Over time, sustained TLR signaling perpetuates immune dysregulation, exacerbating insulin resistance, vascular inflammation, and ultimately increasing SII values^[48] (Fig. 5). From a mechanistic standpoint, TLR activation serves as an upstream common pathway that simultaneously increases neutrophil and platelet counts while altering lymphocyte subsets, thereby affecting all three components of the SII.

Further compounding these mechanisms, oxidative stress and endothelial dysfunction serve as critical amplifiers for inflammatory and thrombotic pathways in MetS. Persistent hyperglycemia and dyslipidemia drive excessive ROS generation, which damages endothelial cells and disrupts NO homeostasis.^[58] This dysfunction enhances platelet adhesion and leukocyte infiltration, creating a self-reinforcing cycle of vascular injury and chronic inflammation.^[58] As these processes progress, the SII emerges as a robust marker of systemic immune activation and endothelial dysfunction, correlating with disease severity and cardiovascular risk in MetS^[46] (Fig. 5). Because oxidative stress independently promotes neutrophil mobilization and platelet reactivity, while contributing to lymphopenia, its effects are mathematically consolidated within the SII formula.

Taken together, the elevation of the SII in MetS reflects a multidimensional interplay between chronic adipose-driven inflammation, neutrophil and platelet hyperactivity, oxidative stress, immune imbalance, and endothelial dysfunction.^[44,48] By integrating multiple immune-inflammatory parameters into a single index, SII serves as a promising biomarker for stratifying inflammatory burden^[51] and predicting adverse outcomes in individuals with MetS.^[43] The unique mathematical structure of SII, which multiplies neutrophils and platelets while dividing by lymphocytes, distinguishes it from simpler markers, such as neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio, as it captures synergistic interactions between innate and adaptive immune compartments. Its clinical utility warrants further investigation in prospective studies to elucidate its prognostic value and potential integration into risk assessment models for metabolic diseases and CVDs.

The findings of this study suggest that the SII could serve as a supplementary biomarker for MetS screening and risk stratification. Its accessibility and cost-effectiveness make it a practical tool for routine clinical assessment, particularly in resource-limited settings, where advanced inflammatory markers may not be readily available.^[43] However, given the moderate diagnostic accuracy observed in this study, the SII should not be used as a standalone diagnostic marker for MetS. Instead, it can be incorporated into a multi-marker panel alongside established metabolic and inflammatory markers to improve early detection and risk prediction. Future research should focus on defining optimal SII cutoff values and evaluating their predictive value in longitudinal studies.

4.1. Limitations and future directions

Despite these promising results, there are notable limitations to our study. Substantial heterogeneity across the included studies restricts the generalizability of the findings. Variations in the study population, methodologies, and diagnostic criteria for MetS contribute to this variability. Furthermore, owing to the limited number of included studies (fewer than 10 in each analysis), meta-regression analyses could not be performed to explore the sources of heterogeneity. Subgroup analyses based on factors such as sex, age, geographic region, or MetS diagnostic criteria were also not feasible due to insufficiently reported data across studies. Future research with a larger number of studies and more complete data reporting is necessary to perform such analyses and better understand the sources of heterogeneity. Although publication bias was not consistently significant, its potential cannot be entirely ruled out, particularly in categorical analyses. Furthermore, the relatively small number of studies assessing the diagnostic performance of the SII limits the ability to draw robust conclusions regarding its clinical applicability. Specifically, the pooled estimates of AUC, sensitivity, and specificity were based on only 2 studies. A diagnostic meta-analysis of this size lacks sufficient statistical power and generalizability, and the resulting estimates are prone to substantial imprecision and bias. Therefore, these results must be interpreted with considerable caution, and the current evidence does not permit any definitive conclusions regarding the diagnostic accuracy of the SII for identifying MetS. Future research should focus on prospective cohort studies to validate the association between the SII and MetS while addressing potential confounders. Large-scale multicenter trials are essential to establish standardized reference ranges and evaluate the diagnostic performance of the SII across diverse populations. Additionally, mechanistic studies are needed to elucidate the biological pathways linking the SII to metabolic dysfunction to provide deeper insight into its role in disease progression. There was variability among the included studies regarding the definition and categorization of the SII, with studies applying different classification methods, such as tertile- or quartile-based categories, which may have contributed to heterogeneity and affected the comparability of the pooled estimates. However, subgroup analyses based on different SII categorization approaches were not performed because of the limited number of eligible studies available for each subgroup, which could have resulted in unreliable estimates and insufficient statistical power. Finally, integrating the SII into predictive models alongside genetic and metabolic markers could enhance its utility in personalized medicine approaches for MetS management.

5. Conclusion

In conclusion, this systematic review and meta-analysis demonstrated that SII is significantly elevated in individuals with MetS. However, its diagnostic accuracy is limited (pooled AUC = 0.69, based on only two studies), and it should not be used as an independent diagnostic test for MetS. Instead, the SII may serve as a supplementary marker or risk stratification tool when combined with other clinical and laboratory parameters. Given the substantial heterogeneity across studies and the very low Grading of Recommendations Assessment, Development and Evaluation quality of evidence, future prospective studies are needed to establish standardized cutoff values, obtain a more precise estimate of diagnostic performance, and clarify the incremental value of SII beyond existing inflammatory markers, such as C-reactive protein or the triglyceride-glucose index. By enhancing our understanding of the inflammatory basis of MetS, these efforts may contribute to more effective prevention and management strategies for individuals at risk for metabolic diseases and CVDs.

Author contributions

Conceptualization: Shahriar Ghodous, Shika M. Jain, Ehsan Amini-Salehi.

Data curation: Abinash Mahapatro, Saisree Reddy Adla Jala, Elan Mohanty.

Formal analysis: Abinash Mahapatro, Shahriar Ghodous, Ehsan Amini-Salehi.

Investigation: Saisree Reddy Adla Jala, Herby Jeanty, Bahar Darouei, Seyedsina Moghimnejadhosseini, Amirmahdi Mojtahedzadeh.

Methodology: Shahriar Ghodous, Seyyed Mohammad Hashemi, Ehsan Amini-Salehi.

Project administration: Pegah Rashidian.

Supervision: Ehsan Amini-Salehi, Abinash Mahapatro.

Validation: Saisree Reddy Adla Jala, Reza Amani-Beni, Ehsan Amini-Salehi.

Visualization: Shahriar Ghodous, Pegah Rashidian.

Writing – original draft: Abinash Mahapatro, Saisree Reddy Adla Jala, Shahriar Ghodous, Pegah Rashidian, Mohit Mirchandani, Herby Jeanty, Satabdi Sahu, Reza Amani-Beni, Bahar Darouei, Shika M. Jain, Seyedsina Moghimnejadhosseini, Amirmahdi Mojtahedzadeh, Seyyed Mohammad Hashemi, Ehsan Amini-Salehi.

Writing – review & editing: Abinash Mahapatro, Saisree Reddy Adla Jala, Shahriar Ghodous, Pegah Rashidian, Mohit Mirchandani, Herby Jeanty, Satabdi Sahu, Reza Amani-Beni, Bahar Darouei, Shika M. Jain, Seyedsina Moghimnejadhosseini, Amirmahdi Mojtahedzadeh, Seyyed Mohammad Hashemi, Ehsan Amini-Salehi.