Systemic inflammatory indices SIRI, AISI, and PWR are associated with sarcopenic obesity in middle-aged and older chinese adults: a cross-sectional study

Abstract

Background & aims

Chronic low-grade inflammation is implicated in sarcopenic obesity (SO); however, the relevance of routinely available blood cell inflammatory indices in SO remains unclear. This study aimed to examine the association between these indices and SO.

Methods

This cross-sectional study included 1,009 participants aged ≥ 50 years. SO was defined by the presence of both sarcopenia (defined based on sex-specific muscle mass percentage cutoffs: muscle mass < 39.3% for men or < 33.9% for women) and obesity [defined as body mass index (BMI) ≥ 28 kg/m², body fat percentage (PBF) ≥ 30% for men or ≥ 40% for women, visceral fat area (VFA) ≥ 100 cm², or waist circumference (WC) ≥ 80 cm for women and ≥ 90 cm for men]. Inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-white blood cell ratio (PWR), systemic immune-inflammatory index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI), were calculated from routine blood tests. Multivariable logistic regression models were used to examine the associations between these indices and SO, with adjustments for potential confounders.

Results

When the WC classification was used, risk of SO was significantly associated with SIRI (OR = 1.361, 95% CI: 1.057–1.753; P = 0.017) and AISI (OR = 1.248, 95% CI: 1.022–1.524; P = 0.029), but inversely associated with PWR (OR = 0.621, 95% CI: 0.390–0.988; P = 0.040). Similar modest associations were observed under the VFA classification. Under the BMI ≥ 28 kg/m² definition, SIRI (OR = 1.539, 95% CI: 1.133–2.092; P = 0.006) and AISI (OR = 1.374, 95% CI: 1.066–1.771; P = 0.014) remained significantly associated with SO, with effect sizes in the modest-to-moderate range. However, no significant associations were observed when SO was defined using PBF, suggesting that inflammatory indices may be more closely related to central rather than generalized adiposity.

Conclusions

The association between systemic immune inflammation indices and SO varied according to the obesity classification method. Given the cross-sectional design, these findings reflect associations rather than causality. SIRI, AISI, and PWR may have potential value in risk stratification, particularly under WC, VFA, or BMI ≥ 28 kg/m² definitions. Prospective studies with external validation are needed to confirm their clinical utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-026-01117-0.

Background

Sarcopenic obesity (SO), first defined by Baumgartner et al., is the coexistence of sarcopenia and obesity [1]. A meta-analysis reported that more than 10% of the older population worldwide is affected by SO [2]. Another meta-analysis showed that the prevalence of SO among community-dwelling older individuals (≥ 65 years) varies between 8% and 29%, depending on the diagnostic criteria used. Additionally, in the presence of other conditions such as dementia, cerebrovascular diseases, and heart disease, the prevalence of SO may fluctuate between 9% and 35% [3]. Compared with sarcopenia or obesity alone, SO may have a mutually reinforcing effect, resulting in stronger synergistic impacts on health outcomes in the older population, such as physical disability [4], metabolic disorders [5], cognitive impairment [6], cardiovascular diseases [7], and even an increased risk of mortality [8]. With the concurrent rise in obesity rates and aging populations, the prevalence of SO continues to increase, making it a critical public health issue in aging societies. By 2050, approximately 21% of the global population is expected to be 60 years or older, and the impact of SO on public health will become even more severe [9].

Contemporary consensus definitions of sarcopenia, including those proposed by the European Working Group on Sarcopenia in Older People (EWGSOP2) and the Asian Working Group for Sarcopenia (AWGS2019), emphasize muscle strength as the primary diagnostic criterion and underscore the importance of standardized assessment frameworks [10–11]. However, variability in operational definitions, particularly when combined with different obesity criteria, continues to contribute to heterogeneity in reported SO prevalence and challenges in consistent case identification [10–12]. This heterogeneity underscores the need for complementary and objective indicators to improve risk stratification and comparability across studies.

Oxidative stress and inflammation are the two hallmarks of age-related muscle atrophy [13]. An increasing number of studies have highlighted inflammation as a crucial regulator of skeletal muscle homeostasis, ultimately leading to sarcopenia [14–15]. The term “inflammaging” refers to the systemic, chronic, sterile, and low-grade inflammation commonly observed in many older individuals, which is associated with an increased risk of various diseases [16]. During aging, proinflammatory factors such as interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α) can induce muscle atrophy, accelerate protein catabolism, and inhibit muscle synthesis, thereby promoting the development of sarcopenia [13, 17]. Additionally, obesity, particularly the accumulation of visceral fat, results in the production of additional proinflammatory adipokines, further contributing to low-grade inflammation [18]. Feng et al. showed that the levels of systemic inflammatory markers were significantly higher in an SO group than in the control group [19]. These findings further support the involvement of chronic low-grade inflammation in SO and underscore the potential value of identifying accessible inflammatory markers for early risk stratification and prevention strategies in older adults.

As a result of chronic systemic inflammation, there are measurable changes in circulating immune cell populations, including neutrophils, lymphocytes, monocytes, and platelets. These changes provide a biological basis for using routine blood cell–derived indices as markers of systemic inflammation in SO. In complete blood cell analysis, the counts and ratios of white blood cells (WBCs), neutrophils (NEUTs), lymphocytes (LYMs), monocytes (MONOs), and platelets (PLTs) reflect the body’s immune response and inflammatory status. Compared to individual cell counts, the ratios are less susceptible to short-term fluctuations in disease conditions [20] and can provide a more comprehensive view of the balance between innate and adaptive immunity as inflammatory markers [21]. In comparison with traditional inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate, blood cell-derived inflammatory indices are cost-effective, readily accessible, and widely used as indicators of poor prognosis in diseases like malignancies, cardiovascular diseases, cognitive impairment, and autoimmune diseases [22–23].

Composite indices such as the platelet-to-white blood cell ratio (PWR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) were originally proposed as integrative markers of systemic immune–inflammatory balance [24]. These indices combine multiple leukocyte subtypes and, in the case of AISI, platelet counts, thereby capturing the interplay between innate immune activation and adaptive immune regulation. Previous studies have investigated these indices in metabolic disorders, cardiovascular diseases, and geriatric populations; however, their relevance to sarcopenic obesity remains insufficiently explored [25–26]. Current gold-standard diagnostic methods, such as bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), require specialized equipment and trained personnel, which may limit their feasibility in primary care and resource-constrained settings, reducing their practicality for routine monitoring. In this context, blood cell inflammatory indices derived from complete blood counts (CBC), a universally available and inexpensive test, may offer a practical adjunctive approach for identifying individuals at higher risk of SO. While not intended to replace BIA or DXA, these indices may complement these tools by identifying high-risk individuals who warrant further confirmatory assessment, thereby improving resource efficiency.

The associations between composite hematological inflammatory indices and sarcopenic obesity have not been comprehensively evaluated across multiple diagnostic definitions within the same population, and the consistency of these associations across different adiposity phenotypes remains unclear. Therefore, this study aimed to investigate the associations between blood cell–derived inflammatory indices and SO in middle-aged and older Chinese adults, and to evaluate whether these readily available and cost-effective indices could serve as complementary markers in clinical and community settings, particularly in light of diagnostic heterogeneity and practical limitations of existing assessment tools. Specifically, the primary objective was to assess the associations between systemic inflammatory indices, including SIRI, AISI, and PWR, and the presence of SO, while the exploratory objective was to determine whether these associations varied according to the criteria used to define SO, thereby enabling evaluation of their consistency and potential biological relevance across distinct adiposity phenotypes.

Methods

Study design and participants

This retrospective cross-sectional observational study included individuals aged ≥ 50 years who underwent routine health checkups at Xuanwu Hospital (Capital Medical University, Beijing, China) between September 1, 2024, and September 30, 2025 (Fig. 1). Participants were consecutively identified from the hospital health examination database during the study period. Of the 2,021 body composition analyses conducted during this period, 1,045 involved individuals meeting the age criterion. Participants were included if they had completed routine blood tests, liver function tests, kidney function tests, and body composition assessments during the same visit. Exclusion criteria comprised acute or chronic infections, severe hepatic or renal disease, malignancies, autoimmune disorders, acute cardiovascular or cerebrovascular events, implanted metal devices interfering with body composition assessment, recent use of weight-modifying medications, or incomplete clinical data. Individuals with missing key variables required for SO definition or inflammatory index calculation were also excluded. Based on these criteria, 1,009 participants were included in the final analysis. Because participants were drawn from individuals attending routine health examinations at a single tertiary hospital, the study population may not fully represent the general community, and some degree of selection bias cannot be excluded. This study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval No. KS2024309) and was conducted in accordance with the Declaration of Helsinki. Given the retrospective design and use of anonymized data, the requirement for informed consent was waived.

Fig. 1.

Study flow chart of participants

Demographic data and laboratory indicators

Demographic data, including age, sex, systolic blood pressure, diastolic blood pressure, and history of chronic diseases, were collected. Laboratory indicators were collected in the morning after an 8 h fast, and included a complete blood cell count including WBC, NEUT, LYM, MONO, and PLT counts. Levels of renal function markers such as urea and creatinine, lipid-related markers such as total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose were measured.

Anthropometric measurements

Body weight and height were measured using a digital scale, and BMI was calculated as weight (kg) divided by height squared (m²). Waist circumference (WC) was measured at the midpoint between the lower costal margin and anterior superior iliac crest by trained personnel. Body composition was assessed using bioelectrical impedance analysis (BIA) with the InBody 720 device (InBody Co., Ltd., Seoul, Korea), which has been previously validated against dual-energy X-ray absorptiometry in adult populations [27]. During the assessment, participants stood upright with arms and legs slightly abducted, ensuring proper electrode contact. Measurements were performed in the morning after an overnight fast under standardized conditions in accordance with the manufacturer’s recommendations. Participants were instructed to avoid vigorous physical activity and alcohol consumption for at least 24 h prior to testing and to empty their bladder before assessment. Parameters obtained included percentage body fat (PBF), BMI, visceral fat area (VFA), appendicular skeletal muscle mass (ASM), fat-free mass index (FFMI), and fat mass index (FMI).

Definitions of sarcopenia and obesity

According to Janssen et al., sarcopenia was defined as a muscle mass percentage more than one standard deviation below the mean value of a young reference population aged 18–39 years [28–29]. In this study, reference values were derived from young participants, with mean muscle mass percentages of 42.28% ± 2.95% for men and 36.71% ± 2.72% for women. Thus, sarcopenia was defined as < 39.3% in men and < 33.9% in women. Obesity was defined based on either whole-body or abdominal fat accumulation. Whole-body obesity was identified by two criteria: BMI ≥ 28 kg/m² (Chinese Working Group on Obesity) [30] or PBF ≥ 30% in men and ≥ 40% in women (WHO) [31]. Abdominal obesity was defined as VFA ≥ 100 cm² (Chinese Medical Association) [32] or WC ≥ 90 cm for men and ≥ 80 cm for women [33]. Participants were categorized into four phenotypes for each obesity definition: non-sarcopenic non-obese, non-sarcopenic obese, sarcopenic non-obese, and SO. These cut-off values were selected based on nationally or internationally recognized guidelines applicable to Chinese populations. Accordingly, SO was defined with four ways: sarcopenia combined with BMI-defined obesity, PBF-defined obesity, VFA-defined obesity, and WC-defined obesity.

Calculation of blood cell inflammatory indices

The seven inflammatory indices included in this study were selected based on their widespread use in prior research on aging, metabolic disorders, and systemic inflammation [34–35]. These indices represent commonly applied composite measures derived from routine blood cell counts and allow comparability with existing literature. Collectively, they capture different dimensions of immune and inflammatory balance, including neutrophil and lymphocyte interactions, monocyte involvement, and platelet-associated inflammatory responses [36]. Given the established role of chronic low-grade inflammation in sarcopenic obesity, inclusion of these commonly used indices allowed for a more comprehensive and comparable assessment of systemic inflammatory status.

Fasting venous blood samples were collected in the morning after an overnight fast of at least 8 h as part of routine health examinations. Complete blood cell counts were measured using an automated hematology analyzer (XN-1000, Sysmex Corporation, Kobe, Japan). All laboratory measurements were performed in the hospital’s central laboratory according to standardized operating procedures, and internal quality control was conducted daily to ensure analytical accuracy and precision. The following formulas were used to calculate inflammatory index values: neutrophil-to-lymphocyte ratio (NLR) = NEUT/LYM; platelet-to-lymphocyte ratio (PLR) = PLT/LYM; lymphocyte-to-monocyte ratio (LMR) = LYM/MONO; platelet-to-white blood cell ratio (PWR) = PLT/WBC; systemic immune-inflammatory index (SII) = PLT × NEUT / LYM; systemic inflammation response index (SIRI) = NEUT × MONO / LYM; and aggregate inflammation systemic index (AISI) = NEUT × MONO × PLT / LYM.

Statistical analysis

The Shapiro–Wilk test was used to assess the normality of continuous variables. Natural logarithm transformation was applied to variables that did not follow a normal distribution to approximate normality before parametric analyses. Continuous variables with a normal distribution were expressed as mean ± standard deviation (SD), whereas non-normally distributed variables were presented as median (interquartile range). Group comparisons for continuous variables were performed using one-way analysis of variance (ANOVA) when distributional assumptions were satisfied. Categorical variables were expressed as frequencies (percentages) and compared using the chi-square test. To assess the independent association between inflammatory indices and SO, multivariable logistic regression analysis was performed, given the binary nature of the outcome variable. Three models were constructed for adjustment: Model 1 included age and sex; Model 2 additionally included diabetes and hypertension; and Model 3 further included HDL-C, LDL-C, triglycerides (TG), and creatinine. These covariates were selected based on their established associations with metabolic status, systemic inflammation, and SO. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to estimate effect sizes. Statistical analyses were performed using SPSS software (version 26.1; IBM Corp., Armonk, NY, USA), and statistical significance was defined as a two-sided P < 0.05.

Results

Baseline characteristics

A total of 1,009 participants were included in the analysis (mean age, 60.30 ± 7.79 years; 50.9% men). When WC was used to define obesity, 43.3% of participants were classified as having SO. Detailed baseline characteristics are presented in Additional file 1. Compared with those without SO, participants with SO were older and had a higher prevalence of hypertension, diabetes, and non-alcoholic fatty liver disease. They also exhibited a more adverse metabolic profile and less favorable body composition parameters. No significant differences were observed in sex distribution or osteoporosis prevalence. The prevalence of SO varied substantially according to the obesity definition applied, ranging from 18.0% when defined by BMI to 48.8% when defined by VFA. Baseline characteristics according to alternative obesity definitions are presented in Additional files 2–4.

Blood inflammatory indices differed significantly across groups (Table 1). Participants with SO had higher WBC counts compared with the non-sarcopenic non-obesity group (6.34 ± 1.57 vs. 5.69 ± 1.43 × 10⁹/L, p < 0.001) and higher lymphocyte counts (2.09 ± 0.59 vs. 1.91 ± 0.56 × 10⁹/L, p = 0.004). Moreover, composite inflammatory indices, including SIRI and AISI, were significantly elevated in the SO group. Median SIRI was higher in the SO group (0.56 [0.39–0.77]) than in the non-sarcopenic non-obesity group (0.49 [0.33–0.68], p = 0.002). Similarly, median AISI was higher in the SO group (121.78 [82.17–178.87]) than in the non-sarcopenic non-obesity group (101.16 [65.86–153.88], p = 0.001). In contrast, median PWR was lower in the SO group (35.47 [29.32–42.60]) than in the non-sarcopenic non-obesity group (38.68 [32.53–46.27]), and the overall group difference was statistically significant (p = 0.005). No significant differences were observed in PLT (p = 0.592) or NEUT counts (p = 0.109). Similar patterns were observed when alternative obesity definitions (VFA, BMI, and PBF) were applied, although the magnitude of differences varied across definitions (Additional files 5–7). Overall, the SO group demonstrated a more pro-inflammatory profile across different obesity definitions.

Table 1.

Blood cell counts and blood cell inflammatory indices of patients classified using WC [X ± s, M (Q1, Q3)]

WBC (109/L)	Non-sarcopenic non-obesity N = 235, 23.3%	Non-sarcopenic obesity N = 209, 20.7%	Sarcopenic non-obesity N = 128, 12.7%	Sarcopenic obesity N = 437, 43.3%	P value
	5.69 ± 1.43d	5.97 ± 1.58d	5.96 ± 1.43d	6.34 ± 1.57abc	0.000
NEUT (109/L)	3.30 ± 1.09	3.67 ± 3.49	3.77 ± 3.78	3.72 ± 1.28	0.109
LYM (109/L)	1.91 ± 0.56bd	2.03 ± 0.56a	2.01 ± 0.59	2.09 ± 0.59a	0.004
MONO (109/L)	0.42 ± 1.87	0.31 ± 0.11	0.32 ± 0.12	0.33 ± 0.10	0.596
PLT (109/L)	218.00 ± 53.10	226.57 ± 131.48	218.52 ± 51.23	224.32 ± 59.93	0.592
PLR	114.51 (95.62, 140.10)	109.52 (87.43, 137.89)	109.41 (88.43, 135.94)	107.22 (87.77, 133.24)	0.058
NLR	1.67 (1.35, 2.17)	1.65 (1.34, 2.18)	1.72 (1.27, 2.19)	1.73 (1.35, 2.16)	0.696
LMR	6.47 (5.19, 7.92)	6.76 (5.34, 8.10)	6.53 (4.83, 8.42)	6.35 (4.97, 8.00)	0.507
PWR	38.68 (32.53, 46.27)	37.87 (30.27, 44.65)	36.43 (30.21, 43.99)	35.47 (29.32, 42.6)a	0.005
SII	360.00 (265.33, 474.59)	359.84 (287.85, 473.52)	362.10 (255.89, 504.78)	376.57 (289.17, 498.11)	0.298
SIRI	0.49 (0.33, 0.68)	0.47 (0.35, 0.69)	0.50 (0.35, 0.71)	0.56 (0.39, 0.77)ab	0.002
AISI	101.16 (65.86, 153.88)	109.08 (71.42, 150.04)	105.20 (72.88, 156.89)	121.78 (82.17, 178.87)ab	0.001

WBC: white blood cells; NEUT: neutrophils; LYM: lymphocytes; MONO: monocytes; PLT: platelets; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; PWR: platelet-to-white blood cell ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; AISI: aggregate inflammation systemic index

a Significant difference compared with non-sarcopenic non-obesity

b Significant difference compared with non-sarcopenic obesity

c Significant difference compared with sarcopenic non-obesity

d Significant difference compared with sarcopenic obesity (LSD post hoc tests)

Correlation Analysis Between Hematological Inflammatory Indices and SO

To provide a comprehensive comparison across definitions, associations between hematological inflammatory indices and SO in the fully adjusted model are summarized in Table 2 and illustrated in Fig. 2.

Table 2.

Associations between inflammatory indices and sarcopenic obesity defined by WC, VFA, BMI, and PBF

Index	WC		VFA		BMI		PBF
	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
NLR	1.154 (0.814–1.637)	0.421	1.067(0.753–1.512)	0.714	1.459(0.948–2.247)	0.086	1.046(0.730–1.501)	0.805
PLR	0.857 (0.599–1.225)	0.397	0.771(0.528–1.126)	0.178	1.031(0.693–1.532)	0.881	0.830(0.582–1.181)	0.300
LMR	0.799 (0.555–1.149)	0.225	0.920(0.642–1.320)	0.652	0.659(0.426–1.020)	0.061	0.875(0.603–1.270)	0.482
PWR	0.621 (0.390–0.988)	0.040	0.609(0.380–0.976)	0.039	0.791(0.520–1.203)	0.274	0.747(0.487–1.148)	0.184
SII	1.133(0.878–1.462)	0.336	1.113(0.860–1.439)	0.416	1.310(0.937–1.830)	0.114	1.064(0.820–1.381)	0.642
AISI	1.248 (1.022–1.524)	0.029	1.230(1.009–1.500)	0.040	1.374(1.066–1.771)	0.014	1.183(0.963–1.453)	0.109
SIRI	1.361 (1.057–1.753)	0.017	1.299(1.010–1.669)	0.041	1.539(1.133–2.092)	0.006	1.262(0.974–1.637)	0.079

NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; PWR, platelet-to-white blood cell ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index; AISI, aggregate index of systemic inflammation

Adjusted for age, sex, hypertension, diabetes, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, total cholesterol, and creatinine

Fig. 2.

Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between sarcopenic obesity (SO) and hematological inflammatory indices. ORs were derived from multivariable logistic regression models adjusted for age, sex, hypertension, diabetes, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, total cholesterol, and creatinine. (A) SO defined by WC. (B) SO defined by VFA. (C) SO defined by PBF. (D) SO defined by BMI ≥ 28 kg/m². WC, waist circumference; VFA, visceral fat area; PBF, percentage body fat; BMI, body mass index

Among the indices examined, SIRI and AISI were positively associated with SO under multiple obesity definitions. In the fully adjusted model, SIRI was significantly associated with higher odds of SO defined by WC (OR 1.361, 95% CI 1.057–1.753, p = 0.017), VFA (OR 1.299, 95% CI 1.010–1.669, p = 0.041), and BMI (OR 1.539, 95% CI 1.133–2.092, p = 0.006). Similarly, AISI was significantly associated with WC-defined SO (OR 1.248, 95% CI 1.022–1.524, p = 0.029), VFA-defined SO (OR 1.230, 95% CI 1.009–1.500, p = 0.040), and BMI-defined SO (OR 1.374, 95% CI 1.066–1.771, p = 0.014). However, neither SIRI nor AISI was statistically significant under the PBF definition in the fully adjusted model. In contrast, PWR showed a consistent inverse association with SO under the WC definition (OR 0.621, 95% CI 0.390–0.988, p = 0.040) and VFA definition (OR 0.609, 95% CI 0.380–0.976, p = 0.039), but these associations were not observed under BMI or PBF criteria. The remaining indices (NLR, PLR, LMR, and SII) did not exhibit consistent or reproducible associations across the four SO definitions in the fully adjusted model. Detailed estimates for Models 1 and 2 are provided in Additional files 8–11, and the direction of associations was generally consistent with that observed in the fully adjusted model.

Although some indices were significant in unadjusted analyses, these associations were largely attenuated after covariate adjustment. Notably, adjustment for age, sex, metabolic factors, and renal function parameters reduced the number of statistically significant associations, indicating that part of the crude associations may be attributable to confounding effects. Overall, the reproducibility of SIRI and AISI across multiple SO definitions and the consistency of their associations suggest that they may represent relatively stable inflammatory correlates of SO.

Discussion

This cross-sectional study explored the relationship between blood cell inflammatory indices and SO in middle-aged and older individuals. We found that AISI, SIRI, and PWR were independently associated with SO across multiple adjusted models, whereas SII was not significantly associated with SO after multivariable adjustment. These findings suggest that certain composite inflammatory indices may better reflect the systemic inflammatory phenotype underlying SO compared with traditional single-ratio indices.

From a clinical perspective, the magnitude of the observed associations warrants careful interpretation. For example, an OR of 1.361 for SIRI indicates a 36% increase in the odds of SO per unit increase in SIRI after multivariable adjustment. Although this effect size may be considered modest at the individual level, it is comparable to that reported for other inflammatory markers in epidemiological studies and may be meaningful at the population level, particularly in aging populations where both systemic inflammation and sarcopenic obesity are highly prevalent [37]. Therefore, even moderate relative increases in risk could translate into a substantial number of affected individuals in clinical practice. At the same time, some confidence intervals were relatively wide and approached the null value (e.g., PWR), suggesting limited precision of certain estimates. This imprecision may reflect sample size constraints or variability inherent in inflammatory biomarkers. Accordingly, these findings should be interpreted cautiously and require confirmation in larger prospective cohorts; nevertheless, the overall consistency in the direction of associations provides preliminary support for a potential link between systemic inflammation and SO.

Research on the relationship between blood cell inflammatory indices and SO is limited. Feng et al. reported elevated systemic inflammation in individuals with SO but did not identify specific inflammatory indices associated with SO [19]. Wan et al. used appendicular skeletal muscle mass index combined with PBF to diagnose SO and demonstrated that higher SII levels were significantly associated with increased SO risk, particularly in older adults [38]. In contrast, our study did not observe a significant association between SII and SO, but identified significant associations with AISI, SIRI, and PWR. Notably, the association between PWR and SO has been rarely examined, suggesting a possible role of platelet-related inflammatory balance in SO. This discrepancy may partly reflect differences in sarcopenia definitions, study populations, and covariate adjustment strategies [39]. Unlike Wan et al. [38], we defined sarcopenia based on muscle mass percentage relative to body weight, emphasizing muscle–adiposity imbalance rather than absolute muscle reduction. Because this relative index inherently accounts for adiposity burden, it may better capture the metabolic stress associated with excess fat accumulation and its link to chronic low-grade systemic inflammation [40]. Such inflammation may be more effectively reflected by composite indices incorporating multiple leukocyte subtypes (AISI and SIRI), whereas SII alone may be less sensitive to this metabolic-inflammatory context. Collectively, these findings suggest that composite inflammatory indices incorporating multiple leukocyte subtypes may reflect aspects of the inflammatory phenotype of SO and warrant further investigation for potential use in risk identification.

AISI and SIRI are composite indices derived from peripheral blood cell counts that are thought to reflect the balance between innate immune activation and adaptive immune regulation [41]. Compared with SIRI, AISI additionally incorporates platelet counts, potentially capturing not only leukocyte-predominant inflammatory responses but also thrombopoietic activity, which may have relevance in metabolic disorders. In the context of SO, visceral adiposity promotes chronic low-grade inflammation characterized by macrophage M1 polarization, increased secretion of proinflammatory cytokines such as IL-6 and TNF-α, and enhanced myelopoiesis [42]. These processes lead to elevated circulating NEUT and MONO levels, accompanied by relative LYM suppression under sustained inflammatory stress [43]. Such lymphocyte alterations may reflect an imbalance in adaptive immunity, including increased Th1/Th17 polarization and reduced regulatory T-cell activity, potentially driven by adipokine dysregulation (e.g., elevated leptin and decreased adiponectin) [44–45]. Consequently, the increase in NEUTs and MONOs combined with reduced LYMs would be expected to increase SIRI and AISI values. Rather than merely reflecting local adipose–muscle interactions, AISI and SIRI may serve as systemic markers of chronic low-grade inflammation in SO. Elevated NEUT and MONO levels are closely linked to metabolic stress, endothelial dysfunction, and insulin resistance, all of which contribute to muscle catabolism [46]. Persistent inflammatory signaling may impair muscle protein synthesis and promote proteolysis [46]. Therefore, these composite indices may reflect the integrated effects of visceral fat–associated metabolic inflammation on progressive muscle decline in SO.

The novel association observed between PWR and SO further extends this inflammatory framework. PWR represents the ratio of platelet count to total WBC count and may reflect the interplay between thrombopoiesis and leukocyte-mediated inflammation [47]. In obesity, adipokine imbalance and endothelial dysfunction may contribute to platelet activation and increased thrombopoietic activity [48]. Simultaneously, visceral fat–induced immune cell recruitment increases circulating WBC counts, which may consequently alter PWR values [49]. When SO was defined by WC and VFA, which better reflect central adiposity, the association between PWR and SO became significant. This pattern suggests that the inflammatory relevance of PWR may be more pronounced in the context of central fat accumulation, potentially reflecting coordinated platelet activation and leukocyte-mediated responses [50]. Overall, these findings imply that platelet–leukocyte–based inflammatory indices may be particularly sensitive to central obesity, highlighting the phenotypic heterogeneity of obesity and its differential effects on immune activation and thrombopoietic processes.

Importantly, no significant associations between SO and blood inflammatory indices were observed when SO was defined using PBF, which differed from the findings based on other diagnostic criteria. One possible explanation is that individuals classified as having SO according to PBF may differ in their metabolic and inflammatory profiles from those identified using WC or VFA criteria. Because PBF reflects overall adiposity without distinguishing between visceral and subcutaneous fat compartments, it may not adequately capture the metabolic abnormalities associated with central fat accumulation [51]. In contrast, visceral adipose tissue is considered more metabolically active and more strongly linked to systemic inflammation and insulin resistance [52]. Consistent with this possibility, in our study, individuals classified as having SO according to PBF appeared to have less pronounced central adiposity compared with those defined by WC or VFA, despite similar total body fat percentages, which may have reduced the detectable inflammatory association. Taken together, this discrepancy may reflect phenotypic heterogeneity within SO and differences in underlying metabolic risk profiles rather than the absence of inflammatory involvement. It also suggests that the choice of diagnostic criteria may influence the interpretation of study findings.

This study has several limitations. First, its cross-sectional design precludes causal inference between inflammatory indices and sarcopenic obesity. Prospective studies are needed to confirm these associations. Second, a major methodological limitation is that sarcopenia was defined solely based on reduced muscle mass, without incorporating measures of muscle strength or physical performance. This approach does not fully align with current consensus definitions (e.g., EWGSOP2 and AWGS2019), which prioritize low muscle strength. The absence of handgrip strength and gait speed data in the dataset prevented the application of updated criteria and may have resulted in misclassification. Nevertheless, muscle mass–based definitions have been widely used in epidemiological studies, particularly in large-scale datasets where functional measures are unavailable. Future studies incorporating comprehensive assessments of muscle strength and performance are warranted. Third, residual confounding and statistical considerations should be acknowledged. Although multiple covariates were adjusted for, information on certain lifestyle and clinical factors, such as physical activity, dietary protein intake, smoking, alcohol consumption, and the use of anti-inflammatory or lipid-lowering medications, was not available. These factors may influence both systemic inflammatory markers and body composition, and their absence from the models may have affected the observed associations. In addition, multiple statistical tests were conducted across several inflammatory indices and operational definitions of sarcopenic obesity, which may increase the risk of Type I error. Although the associations were generally consistent across adjusted models, false-positive findings cannot be entirely excluded. Finally, this was a single-center study without external validation, which may affect the broader applicability of the findings. Future studies in multi-center cohorts with more comprehensive covariate data would help further strengthen and extend these observations.

Conclusions

Our findings suggest that blood cell inflammatory indices, particularly SIRI and AISI, are positively associated with SO risk, while PWR is negatively associated. These associations were observed across different SO classification methods, supporting the potential relevance of these markers in identifying individuals with an unfavorable inflammatory profile. Given their low cost and routine availability, these indices may provide practical support for early screening in clinical settings. Prospective longitudinal studies are needed to establish temporal relationships and clarify potential causal pathways between systemic inflammation and SO development. In addition, future research should aim to determine clinically actionable cut-off values for these indices and evaluate their incremental predictive value beyond traditional anthropometric measures. Integrating inflammatory indices with muscle strength and physical performance assessments may facilitate the development of a comprehensive SO risk stratification model. Such efforts will help define the clinical applicability of these markers and inform targeted prevention and intervention strategies.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AISI

Aggregate inflammation systemic index

ASM

Appendicular skeletal muscle mass

BMI

Body mass index

FFMI

Fat-free mass index

FMI

Fat mass index

LMR

lymphocyte-to-monocyte ratio

LYM

Iymphocyte

MONO

Monocyte

NEUT

Neutrophil

NLR

Neutrophil-to-lymphocyte ratio

PBF

Body fat percentage

PLR

Platelet-to-lymphocyte ratio

PLT

Platelet

PWR

Platelet-to-white blood cell ratio

SII

Systemic immune-inflammatory index

SIRI

Systemic inflammation response index

SO

Sarcopenic obesity

VFA

Visceral fat area

WBC

White blood cell

WC

Waist circumference

Author contributions

YL and SL contributed equally to the work. They contributed to the conceptualization and investigation and wrote the original draft. CX and YX contributed to data collection. YLiu contributed to investigation. BH contributed to conceptualization and writing the original draft. All authors reviewed and approved the final version of the manuscript.

Funding

The work was supported by the Capital Clinical Diagnosis and Treatment Technology Research and Translational Application Project (Grant No. Z201100005520006) and the Nursing Research Project of the Affiliated Hospital of Guizhou Medical University (Grant No. gyfyhl-2024-A20).

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Declarations

Ethics approval and consent to participate

All procedures contributing to this work comply with the ethical standards of relevant national and institutional committees on human experimentation and with the Helsinki Declaration. This study received approval from the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval No. KS2024309).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.