Association between blood cell inflammatory indices and sarcopenic obesity in middle-aged and older Chinese adults: a cross-sectional study

Association between blood cell inflammatory indices and sarcopenic obesity in middle-aged and older Chinese adults: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between blood cell inflammatory indices and sarcopenic obesity in middle-aged and older Chinese adults: a cross-sectional study Yuhong Luo, Lingzhi Shu, Chen Xin, Yuhua Liu, Yan Xu, Binru Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8441209/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Nutrition & Metabolism → Version 1 posted 13 You are reading this latest preprint version Abstract Background & aims: Despite the known association between chronic inflammation and reduced muscle mass, the use of inflammatory indices in sarcopenic obesity (SO) remains unexplored. Thus, this study aimed to explore the relationship between blood cell inflammatory indices and SO and assess their potential role in disease evaluation and monitoring. Methods Methods: This cross-sectional study included 1,009 participants aged ≥ 50 years. SO was defined by the presence of both sarcopenia (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. ANOVA and regression analyses were used to examine the relationship between these indices and SO. Results When the WC classification was used, risk of SO was significantly associated with the 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 negatively correlated with PWR (OR = 0.621, 95% CI: 0.390–0.988, P = 0.040). The results were similar for the VFA classification. When the BMI ≥ 28 kg/m² classification was used, risk of SO was significantly associated with 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). However, when the PBF classification was used, blood cell inflammatory indices and risk of SO were not significantly correlated. Conclusions The correlation between systemic immune inflammation indices and SO may be influenced by the SO classification method. Owing to their advantages of being objective, low-cost, and easy-to-use markers, SIRI, AISI, and PWR may serve as biomarkers for the screening and management of SO when classified by WC, VFA, or BMI ≥ 28 kg/m². Sarcopenic obesity Blood cell inflammatory index Waist circumference Visceral fat Body mass index Figures Figure 1 Figure 2 Background Sarcopenic obesity (SO), first defined by Baumgartner et al., is the coexistence of sarcopenia and obesity [ 1 ]. A meta-analysis reported that > 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 effects 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 ]. Oxidative stress and inflammation are the two hallmarks of age-related muscle atrophy [ 10 ]. An increasing number of studies have highlighted inflammation as a crucial regulator skeletal muscle homeostasis, ultimately leading to sarcopenia [ 11 – 12 ]. The term “inflammaging” refers to the systemic, chronic, sterile, and low-grade inflammation observed in many older individuals, and is associated with an increased risk of various diseases [ 13 ]. 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 [ 10 , 14 ]. Additionally, obesity, particularly the accumulation of visceral fat, results in the production of additional proinflammatory adipokines, further contributing to low-grade inflammation [ 15 ]. Feng et al. showed that the levels of systemic inflammatory markers were significantly higher in an SO group than in the control group [ 16 ]. Therefore, the prevalence of sarcopenia and SO in this vulnerable population is a major concern. Recognizing the link between these conditions and inflammation is essential for improving healthcare practices and preventing muscle deterioration in older populations. In complete blood cell analysis, the counts and ratios of white blood cells (WBCs), neutrophils (NEUTs), lymphocytes (LYMs), monocytes (MONOs), and platelets (PLTs) are related to the body's immune response and inflammatory status. Compared with individual cell counts, the ratios are less susceptible to short-term changes in disease conditions [ 17 ] and can comprehensively reflect the balance between innate and adaptive immunity as inflammatory indices [ 18 ]. Compared with traditional inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate, blood cell inflammatory indices are cost-effective, readily accessible, and widely used as indicators of poor prognosis in diseases such as malignancies, cardiovascular diseases, cognitive impairment, and autoimmune diseases [ 19 – 20 ]. However, the application of inflammatory indices in SO remains unclear. Despite growing recognition of SO's clinical significance, its diagnosis remains hampered by reliance on costly and inaccessible tools. Current gold-standard methods like bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) require specialized equipment and trained operators—resources scarce in primary care and developing regions. Furthermore, these modalities are contraindicated in patients with metal implants and impractical for routine monitoring. In contrast, blood cell inflammatory indices (e.g., SIRI, AISI) derived from complete blood counts (CBC)—a universally available and inexpensive test—offer a potentially valuable triaging tool. While they are not intended to replace BIA or DXA, they may complement these tools by identifying high-risk individuals who merit further confirmatory assessment, thereby improving the efficiency of resource utilization. Thus, this study aimed to explore the relationship between blood cell inflammatory indices and SO, and to investigate whether these indices can be used to evaluate SO. Methods Study design and participants This retrospective 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 ). 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, liver, and kidney function tests. 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. Based on these criteria, 1,009 participants were included in the final analysis. This study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval No. KS2024309) and were conducted in accordance with the Declaration of Helsinki. Given the retrospective design and use of anonymized data, the requirement for informed consent was waived. 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). During the assessment, participants stood upright with arms and legs slightly abducted, ensuring proper electrode contact. 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 is defined as muscle mass percentage of more than one standard deviation below the sex-specific mean of healthy young adults [aged 18–39 years] [ 21 – 22 ]. 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) or PBF ≥ 30% in men and ≥ 40% in women (WHO). Abdominal obesity was defined as VFA ≥ 100 cm² (Chinese Medical Association) or WC ≥ 90 cm for men and ≥ 80 cm for women. Participants were categorized into four phenotypes for each obesity definition: non-sarcopenic non-obese, non-sarcopenic obese, sarcopenic non-obese, and SO. 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 following formula 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 for data that did not follow a normal distribution. Continuous variables that followed a normal distribution are expressed as X ± S, while those that did not are expressed as M (Q1, Q3). Continuous variables were compared using one-way analysis of variance (ANOVA). Categorical variables are expressed as frequencies (%) and were compared using the chi-square test. To assess the independent relationship between the inflammatory indices and SO, a multivariable logistic regression analysis was conducted. Three models were used for adjustment: Model 1, which included age and sex; Model 2, in which diabetes and hypertension were added to Model 1; and Model 3, in which HDL-C, LDL-C, TG, and creatinine were added to Model 2. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined. Statistical analyses were performed using the SPSS software (version 26.1), with significance set at P < 0.05. Results Baseline characteristics The study included 1009 participants, of which 514 (50.9%) were men and 495 (49.1%) were women, with a mean age of 60.30 ± 7.79 years. Table 1 presents the main baseline characteristics of participants when WC was used to classify obesity. Of the participants, 235 (23.3%) had non-sarcopenic non-obesity, 209 (20.7%) had non-sarcopenic obesity, 128 (12.7%) had sarcopenic non-obesity, and 437 (43.3%) had SO. Patients with SO were older and more likely to have hypertension, diabetes, and non-alcoholic fatty liver disease than were those in the reference group. In contrast, no significant differences in sex or osteoporosis were found between patients with SO and those in the reference group. Metabolic indicators in patients with SO showed significant differences in triglyceride, HDL-C, uric acid, and fasting blood glucose levels across the groups (P = 0.000), whereas total cholesterol, LDL-C, and creatinine levels were not significantly different (P > 0.05). Furthermore, anthropometric measurements revealed that the SO group had significantly higher weight, WC, BMI, blood pressure, PBF, WHR, VFA, FFMI, FMI, SMI, and SMI/weight ratio than did the non-SO group (P < 0.05), indicating a more adverse body fat distribution and muscle quality characteristics in the former, particularly in terms of VFA and FMI. Notably, the prevalence of SO varied by obesity definition, 43.3% under WC, 38.1% under PBF, 48.8% under VFA, and 18.0% under BMI. The main baseline characteristics of participants classified by BMI, PBF, and VFA-defined obesity were consistent with the above findings (additional files 1, 2, and 3). Blood analysis results indicated that the WBC count in the SO group was significantly higher than that in the other three groups ( P = 0.000) and that the LYM count was also elevated in the SO group ( P = 0.004). Although PLT and NEUT counts did not significantly differ between the groups, PWR was significantly lower in the SO group ( P = 0.005). Additionally, both SIRI and AISI were significantly higher in the SO group (P = 0.002 and P = 0.001, respectively), and although SII was not significantly different, it was also elevated compared with that in the other three groups, further suggesting a heightened inflammatory state in the SO group (Table 2). Compared with the SO group defined by WC, the SO groups classified by VFA, BMI, and PBF also showed notable increases in WBC and LYM counts, as well as in SIRI, SII and AISI, though the extent and direction of these increases varied across definitions (additional files 4, 5, and 6). However, the PLT level in the obesity group defined by BMI and PBF was higher than that in the groups defined by WC and VFA. Under the VFA, BMI, and PBF definitions, the SO group consistently showed the lowest PLR, with particularly marked differences compared with those in the normal and obesity groups. For all definitions, the PWR of the SO group was significantly lower compared with that of the normal group, with the difference being more pronounced in the definitions based on BMI and PBF. Under different definitions, LMR was highest in the obesity group and lowest in the SO or sarcopenia group, except under the PBF definition, where it was highest in the sarcopenia group and lowest in the obesity group, indicating that the relationship between obesity and immune function varies depending on the definition used. Table 1. The main Baseline characteristics of patients classified using WC 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 Demographics Age (years) 58.53 ± 7.18 c, d 58.38 ± 6.57 b, c, d 62.57 ± 9.06 a 61.506 ± 7.86 a,b < 0.001 Gender (% male) 127 (54.0%) 117 (56.0%) 63 (49.2%) 207 (47.3%) 0.144 Hypertension (%) 48 (20.4%) 69 (33.0%) 45 (35.1%) 206 (47.1%) < 0.001 Diabetes (%) 13 (5.5%) 34 (16.3%) 17 (13.3%) 70 (16.0%) < 0.001 Osteoporosis (%) 162 (68.9%) 148 (70.8%) 90 (70%) 303 (69.3%) 0.971 Non-alcoholic fatty liver (%) 30 (14.6%) 95 (83.3%) 32 (33.3%) 270 (61.7%) < 0.001 Metabolic panel Triglycerides (mmol/l) 1.37 ± 1.25 b, d 1.95 ± 1.81 a, c 1.55 ± 0.88 b, d 1.97 ± 1.50 a, c < 0.001 Total cholesterol (mmol/l) 5.16 ± 1.14 5.22 ± 1.19 5.19 ± 1.06 5.16 ± 1.11 0.924 HDL-C (mmol/l) 1.54 ± 0.39 b, c, d 1.29 ± 0.29 a, c 1.45 ± 0.40 a, b, d 1.29 ± 0.36 a, c < 0.001 LDL-C (mmol/l) 3.15 ± 1.04 3.19 ± 0.94 3.10 ± 0.86 3.19 ± 0.99 0.785 Creatinine (μmol/L) 64.37 ± 15.79 68.28 ± 47.88 62.07 ± 15.22 64.85 ± 40.02 0.435 Uric acid (μmol/L) 329.35 ± 82.39 b, d 360.37 ± 82.63 a, b, c 330.59 ± 88.26 d 364.44 ± 94.82 a, c < 0.001 Fasting blood glucose (mmol/L) 5.45 ± 1.42 b, c 6.00 ± 1.64 a 5.85 ± 1.89 a 6.07 ± 1.90 a, d < 0.001 Anthropometric measurements Height (cm) 163.82 ± 8.00 b, c, d 166.79 ± 7.81 a, c, d 158.75 ± 7.79 a, b, d 161.51 ± 8.73 a, b, c 0.025 Weight (kg) 60.72 ± 9.22 b, d 72.31 ± 10.65 a c 60.48 ± 7.81 b d 72.94 ± 11.65 a c < 0.001 WC (cm) 78.01 ± 7.10 b, d 90.62 ± 6.88 a, c, d 79.86 ± 6.25 b, d 96.61 ± 7.56 a, b, c < 0.001 BMI (kg/m²) 22.52 ± 2.22 b c, d 25.84 ± 2.33 a, c, d 23.93 ± 1.97 a, b, d 27.84 ± 2.80 a, b, c < 0.001 SBP (mmHg) 124.72 ± 15.71 b, c, d 133.01 ± 16.42 a, d 131.65 ± 19.19 a, d 137.62 ± 18.73 a, b, c < 0.001 DBP (mmHg) 75.16 ± 10.12 b, d 80.89 ± 10.61 a 77.69 ± 10.04 d 82.75 ± 33.25 a, c < 0.001 PBF (%) 26.70 ± 5.48 b, c, d 29.88 ± 4.63 a, c, d 34.93 ± 4.33 a, b, d 38.01 ± 5.25 a, b, c 0.014 WHR 0.87 ± 0.04 b, c, d 0.92 ± 0.06 a, c, d 0.91 ± 0.04 a, b, d 0.96 ± 0.05 a, b, c < 0.001 VFA (cm 2 ) 74.29 ± 17.53 b, c, d 100.49 ± 18.88 a, c, d 108.39 ± 22.24 a, b, d 144.21 ± 29.88 a, b, c < 0.001 FFMI (kg/m²) 16.5 ± 1.95 b 19.14 ± 15.23 a, c, d 15.56 ± 1.57 b, d 17.16 ± 1.96 b, c < 0.001 FMI (kg/m²) 6.02 ± 1.39 b, c, d 7.74 ± 1.23 a, c, d 8.36 ± 1.35 a, b, d 10.62 ± 2.11 a, b, c < 0.001 SMI (kg/m²) 6.84 ± 1.01 b, c, d 7.61 ± 1.33 a, c, d 6.31 ± 0.87 a, b, d 7.13 ± 0.98 a, b, c < 0.001 Muscle percent (%) 39.87 ± 3.56 b, c, d 38.70 ± 3.63 a, c, d 34.82 ± 2.89 a, b, d 33.59 ± 3.20 a, b, c < 0.001 BMI: body mass index; PBF: percent body fat; WHR: waist–hip ratio; VFA: visceral fat area; FFMI: fat-free mass index; FMI: fat mass index; SMI: skeletal muscle index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-C: low-density lipoprotein-cholesterol; LDL-C: high-density lipoprotein-cholesterol. 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). Table 2. Blood cell counts and blood cell inflammatory indices of patients classified using WC [X ± s, M (Q1, Q3)] 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 WBC (10 9 /L) 5.69 ± 1.43 d 5.97 ± 1.58 d 5.96 ± 1.43 d 6.34 ± 1.57 a, b, c 0.000 NEUT (10 9 /L) 3.30 ± 1.09 3.67 ± 3.49 3.77 ± 3.78 3.72 ± 1.28 0.109 LYM (10 9 /L) 1.91 ± 0.56 b, d 2.03 ± 0.56 a 2.01 ± 0.59 2.09 ± 0.59 a 0.004 MONO (10 9 /L) 0.42 ± 1.87 0.31 ± 0.11 0.32 ± 0.12 0.33 ± 0.10 0.596 PLT (10 9 /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) a, b 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) a, b 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 explore the association between hematological inflammatory markers and SO, we conducted unadjusted and adjusted logistic regression analyses (Table 3). In the unadjusted model, increased AISI and SIRI, along with decreased PWR, were significantly associated with an increased risk of SO ( P < 0.05). After adjusting for confounding factors, such as age, sex, hypertension, diabetes, and levels of HDL-C, LDL-C, triglycerides, total cholesterol, and creatinine, AISI (OR = 1.233, 95% CI = 1.009 − 1.506, P = 0.040), SIRI (OR = 1.335, 95% CI = 1.034 − 1.724, P = 0.017), and PWR (OR = 0.609, 95% CI = 0.380 − 0.977, P = 0.040) remained significantly associated with SO risk (Fig. 2). An increased WC typically reflects an increase in visceral fat. Therefore, we also used VFA instead of WC to classify obesity. In all adjusted models, PWR was significantly associated with a lower risk of SO, even after adjusting for multiple confounding factors (OR = 0.609, 95% CI = 0.380 − 0.976, P = 0.039). Meanwhile, AISI was significantly associated with a higher risk of SO in the adjusted models (OR = 1.230, 95% CI = 1.009 – 1.500, P = 0.040), whereas the association was not significant in the unadjusted model (P = 0.057). In contrast, SIRI was significantly associated with an increased risk of SO in the adjusted models (OR = 1.299, 95% CI = 1.010 − 1.669, P = 0.041) but not in the unadjusted model. Therefore, similar to SO defined by WC, SO defined by VFA was significantly associated with PWR, AISI, and SIRI (Additional file 7 and Fig. 2). Given that BMI is the most widely used simple anthropometric measure for determining obesity in clinical settings, we further analyzed whether blood cell inflammatory indices are associated with SO defined by sarcopenia combined with obesity (BMI ≥ 28 kg/m²). In the unadjusted model, the risk of SO significantly increased with increased NLR, SII, AISI, and SIRI and decreased LMR and PWR ( P < 0.05). However, in the adjusted model, only AISI (OR = 1.374, 95% CI = 1.066 − 1.771, P = 0.014) and SIRI (OR = 1.539, 95% CI = 1.133 − 2.092, P = 0.006) were significantly associated (Additional file 8 and Fig. 2). PBF, a key indicator for assessing obesity levels and muscle status, is of significant value in defining SO. Our study showed that in the PBF definition, AISI and SIRI were significantly associated with the risk of SO in some models (models 1 and 2) whereas other indices did not show significant associations in the adjusted models (Additional file 9 and Fig. 2). Table 3 Association between sarcopenic obesity defined by WC and blood cell inflammatory indices Unadjusted model OR (95% CI) p Model 1 OR (95% CI) P Model 2 OR (95% CI) P Model 3 OR (95% CI) P NLR 1.243 (0.901–1.715) 0.184 1.285 (0.924–1.786) 0.136 1.227 (0.875–1.722) 0.236 1.154 (0.814–1.637) 0.421 PLR 0.727 (0.521–1.015) 0.061 0.748 (0.529–1.058) 0.100 0.807 (0.568–1.145) 0.229 0.857 (0.599–1.225) 0.397 LMR 0.851 (0.611–1.185) 0.338 0.811 (0.573–1.147) 0.237 0.787 (0.553–1.120) 0.183 0.799 (0.555–1.149) 0.225 PWR 0.523 (0.349–0.785) 0.002 0.480 (0.306–0.754) 0.001 0.541 (0.342–0.856) 0.009 0.621 (0.390–0.988) 0.040 SII 1.159 (0.906–1.482) 0.240 1.223 (0.953–1.571) 0.114 1.184 (0.921–1.522) 0.188 1.133（0.878–1.462） 0.336 AISI 1.300 (1.074–1.572) 0.007 1.376 (1.131–1.673) 0.001 1.324 (1.087–1.614) 0.005 1.248 (1.022–1.524) 0.029 SIRI 1.427 (1.139–1.788) 0.002 1.539 (1.211–1.955) 0.000 1.469 (1.149–1.877) 0.002 1.361 (1.057–1.753) 0.017 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 inflammation systemic index. Model 1 = age, gender; Model 2 = Model 1 + hypertension, diabetes; Model 3 = Model 2 + high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, total cholesterol, creatinine. Discussion This cross-sectional study explored the relationship between blood cell inflammatory indices and SO in middle-aged and older individuals. The results showed that AISI, SIRI, and PWR were significantly associated with SO across multiple models. These findings suggest that systemic inflammation significantly increases the risk of SO. SO is characterized by concurrent muscle loss and fat accumulation, with chronic low-grade inflammation playing a central role in its development [ 23 ]. Excessive adipose tissue releases free fatty acids into ectopic sites such as the muscles, liver, and heart, leading to intramuscular fat deposition and intracellular lipid accumulation. These lipids impair mitochondrial function and muscle contraction, causing muscle weakness and promoting insulin resistance by deactivating insulin receptors and disrupting glucose transport [ 24 – 27 ]. Mitochondrial dysfunction and reactive oxygen species (ROS) further exacerbate muscle protein dysfunction and weakness [ 28 ]. Beyond local effects, lipids disrupt muscle homeostasis through inflammatory signaling. Obesity-induced inflammation is largely driven by adipose tissue macrophages (ATMs), which shift from anti-inflammatory M2 to proinflammatory M1 phenotypes in obese individuals owing to hypoxia and low perfusion [ 29 – 30 ]. This transition correlates with increased insulin resistance. Concurrently, obesity promotes proinflammatory T cell responses (Th1 and Th17) and suppresses regulatory T cells, while B lymphocytes in visceral fat secrete IgG2c and TNF-α, further contributing to insulin resistance [ 31 ]. The resulting inflammatory milieu activates adipokines, induces muscle apoptosis, and modulates atrophy-related proteins [ 32 ]. Thus, ectopic lipid deposition and systemic inflammation are interlinked mechanisms driving the progression of SO [ 33 ]. Research on the relationship between blood cell inflammatory indices and SO is limited. Feng et al. found that systemic inflammation variables were significantly higher in an SO group than they were in a control group. However, they did not determine which specific systemic inflammation indices were associated with SO [ 16 ]. Wan et al. used the appendicular skeletal mass index combined with PBF to diagnose SO and studied the relationship between SII and SO. Their results showed that increased SII was significantly associated with increased risk of SO in middle-aged and older individuals, particularly in the latter [ 34 ]. In contrast, we did not find a significant association between the SII and SO in the present study; however, we found significant correlations between AISI, SIRI, or PWR and SO. These discrepancies could stem from the variation in sarcopenia diagnostic criteria used across studies. Our study defined sarcopenia based on muscle mass percentage, which focuses more on the ratio of muscle mass to body weight than it does on the absolute reduction in muscle mass. This ratio might have a stronger association with chronic systemic inflammation, as reflected by AISI and SIRI, whereas SII might not be sensitive enough to capture this relationship. Additionally, our findings reveal a significant and novel association between the PLT/WBC ratio (PWR) and SO, highlighting PWR as a previously unrecognized regulator of chronic metabolic dysregulation in SO, a relationship not previously reported in the context of muscle–fat crosstalk. Given these results, PWR holds promise as a potential inflammatory marker for SO and warrants further investigation. Future studies should consider integrating multiple inflammatory indices, such as AISI, SIRI, and PWR, to comprehensively assess the inflammatory profile of SO and explore their roles in its underlying pathophysiological mechanisms. This integrative approach may enhance the sensitivity and predictive accuracy of inflammatory indices in identifying individuals at risk for SO. AISI and SIRI are indices of systemic inflammation based on complete blood cell counts, and may be closely related to the pathophysiological processes of SO. AISI evaluates the systemic inflammatory response through the ratios of NEUTs, PLTs, MONOs, and LYMs, and has been widely used in diseases such as idiopathic pulmonary fibrosis and hypertension [ 35 – 36 ]. SIRI measures the ratio of NEUTs, MONOs, and LYMs to assess systemic inflammation and immune system function, and has demonstrated significant prognostic value in various diseases, including pneumonia, rheumatoid arthritis, acute pancreatitis, and cardiovascular diseases [ 37 – 40 ]. As core components of SIRI, NEUTs and MONOs play a role in atherosclerosis and inflammatory responses [ 41 ]. This study found that AISI and SIRI were significantly associated with SO (defined by WC, VFA, and BMI). AISI and SIRI effectively assessed the systemic inflammatory response by capturing variations in peripheral blood cell components such as NEUTs, MONOs, and LYMs. Unlike local inflammatory mechanisms described in the progression of SO, these indices offer a peripheral, quantifiable perspective on systemic immune activation. For instance, elevated NEUT and MONO levels—core components of AISI and SIRI—are known contributors to chronic inflammatory conditions and metabolic dysfunctions [ 42 ]. In this study, both indices showed significant correlations with SO, likely reflecting underlying immune dysregulation and chronic inflammation characteristic of obesity. Notably, shifts in immune cell populations, including increased Th1/Th17 activity and decreased regulatory T cell presence, were consistent with the proinflammatory profiles captured by AISI and SIRI. These immune alterations may be driven, at least in part, by adipokine imbalances—such as elevated leptin and reduced adiponectin—which are known to promote proinflammatory Th1/Th17 responses while suppressing Treg-mediated immune regulation. Together, these findings suggest a potential link between adipose tissue dysfunction, systemic immune activation, and SO severity [ 43 ]. In addition, we found a significant association between PWR and SO when defined by WC and VFA. Wan et al. suggested that the association between systemic inflammation and SO varies depending on WC. Compared with individuals with normal WC, the association between the SII and SO is stronger in those with elevated WC, suggesting that abnormal visceral fat accumulation exacerbates inflammatory responses [ 34 ]. Visceral fat, more so than overall body fat, strongly stimulates inflammatory cells such as NEUTs and MONOs, which increase systemic inflammation and influence WBC levels, thereby indirectly affecting the PWR [ 44 ]. This again highlights the mechanistic relevance of fat distribution—particularly central adiposity—as a modulator of systemic immune activation via innate immune cell recruitment and platelet priming. Conversely, no significant association of PWR, AISI, or SIRI with SO was observed when defined by PBF after multivariable adjustment. As an indicator of total body fat, PBF does not fully reflect the metabolic and inflammatory burden represented by visceral adiposity [ 45 – 47 ], which may explain the relatively weaker correlations observed. Notably, PLT levels were found to be elevated in the obesity groups classified according to the BMI and PBF criteria, as compared with those defined by WC or VFA. While BMI and PBF reflect general or proportional adiposity, which can enhance systemic metabolic stress and hematopoietic activation, WC and VFA focus more specifically on central fat accumulation. This may not uniformly influence platelet production or activation. The elevated platelet levels may reflect compensatory thrombopoiesis driven by chronic inflammation and endothelial dysfunction, mechanisms often observed in metabolic syndrome and linked to adipokine dysregulation. These findings suggest that platelet-related inflammatory responses may be more closely linked to overall adiposity than they are to the visceral distribution of fat alone. Taken together, these results highlight the phenotypic heterogeneity of obesity and its differential effects on inflammation-related hematologic parameters. This study has several limitations. First, its cross-sectional design limits the ability to infer causal relationships between exposures and outcomes. Prospective studies are warranted to confirm these findings and assess their applicability across diverse clinical settings and populations. Second, the lack of correlation analyses between inflammatory indices and key cytokines such as IL-6 and TNF-α hinders a deeper understanding of the mechanisms underlying chronic inflammation. Third, sarcopenia was defined solely based on reduced muscle mass, without incorporating assessments of muscle strength or physical performance, which does not fully align with EWGSOP2 and AWGS diagnostic criteria. Lastly, we did not account for several potential confounding variables, including physical activity, dietary protein intake, and glucocorticoid use, which may have influenced the observed associations. 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 relationships were consistent across different SO classification methods, indicating the potential relevance of these markers in screening practices. Given their low cost and accessibility, these indices may offer a practical tool to support early detection and management of SO in clinical settings. However, further studies in larger and more diverse populations are needed to validate these associations, evaluate their predictive value over time, and clarify the underlying inflammatory mechanisms. Such research will help determine the clinical applicability of these markers and guide future prevention and treatment strategies. 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, lymphocyte 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 Declarations Acknowledgements Not applicable Authors' 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). Availability of data and materials The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. 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. References Baumgartner RN. 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The blood cell counts and blood cell inflammatory indices of patients classified using VAF [X±s, M(Q1, Q3)]. Additionalfile5.docx Additional file 5. The blood cell counts and blood cell inflammatory indices of patients classified using BMI [X±s, M(Q1, Q3)]. Additionalfile6.docx Additional file 6. The blood cell counts and blood cell inflammatory indices of patients classified using PBF [X±s, M(Q1, Q3)]. Additionalfile7.docx Additional file 7. Association between sarcopenic obesity defined by VFA and blood cell inflammatory indices. Additionalfile8.docx Additional file 8. Association between sarcopenic obesity defined by BMI and blood cell inflammatory indices. Additionalfile9.docx Additional file 9. Association between sarcopenic obesity defined by PBF and blood cell inflammatory indices. 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D: Association between sarcopenic obesity defined by BMI ≥ 28 kg/m\u003csup\u003e2\u003c/sup\u003e and the systemic immune inflammation index.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/0c5d409ccce682246dee7ffc.png"},{"id":105755647,"identity":"af3b278f-5c99-4ac7-aff4-43a73d7b47ee","added_by":"auto","created_at":"2026-03-30 16:28:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1058939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/ee3e2277-b9fc-4505-b799-baefad07911a.pdf"},{"id":101273240,"identity":"f5ace905-49ea-4230-84db-a7e3d3396925","added_by":"auto","created_at":"2026-01-28 03:04:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this cross-sectional study are included within the article and its supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1.\u003c/strong\u003e The main baseline characteristics of patients classified using BMI.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/0bd84af7365734f56a5195ec.docx"},{"id":101297883,"identity":"5a5269ae-791e-4714-9a94-e71aa6deae6d","added_by":"auto","created_at":"2026-01-28 09:29:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2.\u003c/strong\u003e The main baseline characteristics of patients classified using PBF.\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/8b53d4591221e6bea2f115ff.docx"},{"id":101297880,"identity":"65250ec1-fa58-4f31-9d28-825ec7249ee5","added_by":"auto","created_at":"2026-01-28 09:29:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3.\u003c/strong\u003e The main baseline characteristics of patients classified using VFA.\u003c/p\u003e","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/a99bded3c35b09cbe4657436.docx"},{"id":101298015,"identity":"66dbb5ba-d439-47be-9a94-b46173675e01","added_by":"auto","created_at":"2026-01-28 09:29:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4. \u003c/strong\u003eThe blood cell counts and blood cell inflammatory indices of patients classified using VAF [X±s, M(Q1, Q3)].\u003c/p\u003e","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/3d21a61bf44de1fd4bd5bd49.docx"},{"id":101298580,"identity":"c978c949-9dba-413b-ac24-0f4a6ea06e47","added_by":"auto","created_at":"2026-01-28 09:35:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 5. \u003c/strong\u003eThe blood cell counts and blood cell inflammatory indices of patients classified using BMI [X±s, M(Q1, Q3)].\u003c/p\u003e","description":"","filename":"Additionalfile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/a03d22c4d0459d5abd88a7a3.docx"},{"id":101297560,"identity":"0b25fea4-658b-41af-8348-4eaec8b66b89","added_by":"auto","created_at":"2026-01-28 09:27:55","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 6. \u003c/strong\u003eThe blood cell counts and blood cell inflammatory indices of patients classified using PBF [X±s, M(Q1, Q3)].\u003c/p\u003e","description":"","filename":"Additionalfile6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/f4e581fee9ff6472bcda491e.docx"},{"id":101297869,"identity":"cb22e216-dd7e-4768-996c-9401aa290431","added_by":"auto","created_at":"2026-01-28 09:29:09","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 7.\u003c/strong\u003e Association between sarcopenic obesity defined by VFA and blood cell inflammatory indices.\u003c/p\u003e","description":"","filename":"Additionalfile7.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/c48ea4c4af4ec1e1b976e4af.docx"},{"id":101298101,"identity":"86bd8f40-5285-498f-ae0c-878bf16d9e13","added_by":"auto","created_at":"2026-01-28 09:30:21","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":17874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 8. \u003c/strong\u003eAssociation between sarcopenic obesity defined by BMI and blood cell inflammatory indices.\u003c/p\u003e","description":"","filename":"Additionalfile8.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/8689ed4fc5006d4ded22cf82.docx"},{"id":101297550,"identity":"253d4a2c-5bca-4f61-bdb7-9e67c8a2d5db","added_by":"auto","created_at":"2026-01-28 09:27:52","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 9. \u003c/strong\u003eAssociation between sarcopenic obesity defined by PBF and blood cell inflammatory indices.\u003c/p\u003e","description":"","filename":"Additionalfile9.docx","url":"https://assets-eu.researchsquare.com/files/rs-8441209/v1/8bcfc4304cc596c607ec843d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between blood cell inflammatory indices and sarcopenic obesity in middle-aged and older Chinese adults: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eSarcopenic obesity (SO), first defined by Baumgartner et al., is the coexistence of sarcopenia and obesity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A meta-analysis reported that \u0026gt;\u0026thinsp;10% of the older population worldwide is affected by SO [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another meta-analysis showed that the prevalence of SO among community-dwelling older individuals (\u0026ge;\u0026thinsp;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% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compared with sarcopenia or obesity alone, SO may have a mutually reinforcing effect, resulting in stronger synergistic effects on health outcomes in the older population, such as physical disability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], metabolic disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], cognitive impairment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], cardiovascular diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and even an increased risk of mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOxidative stress and inflammation are the two hallmarks of age-related muscle atrophy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. An increasing number of studies have highlighted inflammation as a crucial regulator skeletal muscle homeostasis, ultimately leading to sarcopenia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The term \u0026ldquo;inflammaging\u0026rdquo; refers to the systemic, chronic, sterile, and low-grade inflammation observed in many older individuals, and is associated with an increased risk of various diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, obesity, particularly the accumulation of visceral fat, results in the production of additional proinflammatory adipokines, further contributing to low-grade inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Feng et al. showed that the levels of systemic inflammatory markers were significantly higher in an SO group than in the control group [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, the prevalence of sarcopenia and SO in this vulnerable population is a major concern. Recognizing the link between these conditions and inflammation is essential for improving healthcare practices and preventing muscle deterioration in older populations.\u003c/p\u003e \u003cp\u003eIn complete blood cell analysis, the counts and ratios of white blood cells (WBCs), neutrophils (NEUTs), lymphocytes (LYMs), monocytes (MONOs), and platelets (PLTs) are related to the body's immune response and inflammatory status. Compared with individual cell counts, the ratios are less susceptible to short-term changes in disease conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and can comprehensively reflect the balance between innate and adaptive immunity as inflammatory indices [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Compared with traditional inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate, blood cell inflammatory indices are cost-effective, readily accessible, and widely used as indicators of poor prognosis in diseases such as malignancies, cardiovascular diseases, cognitive impairment, and autoimmune diseases [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the application of inflammatory indices in SO remains unclear.\u003c/p\u003e \u003cp\u003eDespite growing recognition of SO's clinical significance, its diagnosis remains hampered by reliance on costly and inaccessible tools. Current gold-standard methods like bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) require specialized equipment and trained operators\u0026mdash;resources scarce in primary care and developing regions. Furthermore, these modalities are contraindicated in patients with metal implants and impractical for routine monitoring. In contrast, blood cell inflammatory indices (e.g., SIRI, AISI) derived from complete blood counts (CBC)\u0026mdash;a universally available and inexpensive test\u0026mdash;offer a potentially valuable triaging tool. While they are not intended to replace BIA or DXA, they may complement these tools by identifying high-risk individuals who merit further confirmatory assessment, thereby improving the efficiency of resource utilization.\u003c/p\u003e \u003cp\u003eThus, this study aimed to explore the relationship between blood cell inflammatory indices and SO, and to investigate whether these indices can be used to evaluate SO.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective observational study included individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years who underwent routine health checkups at Xuanwu Hospital (Capital Medical University, Beijing, China) between September 1, 2024, and September 30, 2025 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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, liver, and kidney function tests. 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. Based on these criteria, 1,009 participants were included in the final analysis. This study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval No. KS2024309) and were conducted in accordance with the Declaration of Helsinki. Given the retrospective design and use of anonymized data, the requirement for informed consent was waived.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDemographic data and laboratory indicators\u003c/h3\u003e\n\u003cp\u003eDemographic 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.\u003c/p\u003e\n\u003ch3\u003eAnthropometric measurements\u003c/h3\u003e\n\u003cp\u003eBody weight and height were measured using a digital scale, and BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;). 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). During the assessment, participants stood upright with arms and legs slightly abducted, ensuring proper electrode contact. 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).\u003c/p\u003e\n\u003ch3\u003eDefinitions of sarcopenia and obesity\u003c/h3\u003e\n\u003cp\u003eAccording to Janssen et al., sarcopenia is defined as muscle mass percentage of more than one standard deviation below the sex-specific mean of healthy young adults [aged 18\u0026ndash;39 years] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, reference values were derived from young participants, with mean muscle mass percentages of 42.28% \u0026plusmn; 2.95% for men and 36.71% \u0026plusmn; 2.72% for women. Thus, sarcopenia was defined as \u0026lt;\u0026thinsp;39.3% in men and \u0026lt;\u0026thinsp;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\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2; (Chinese Working Group on Obesity) or PBF\u0026thinsp;\u0026ge;\u0026thinsp;30% in men and \u0026ge;\u0026thinsp;40% in women (WHO). Abdominal obesity was defined as VFA\u0026thinsp;\u0026ge;\u0026thinsp;100 cm\u0026sup2; (Chinese Medical Association) or WC\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for men and \u0026ge;\u0026thinsp;80 cm for women. Participants were categorized into four phenotypes for each obesity definition: non-sarcopenic non-obese, non-sarcopenic obese, sarcopenic non-obese, and SO. Accordingly, SO was defined with four ways: sarcopenia combined with BMI-defined obesity, PBF-defined obesity, VFA-defined obesity, and WC-defined obesity.\u003c/p\u003e\n\u003ch3\u003eCalculation of blood cell inflammatory indices\u003c/h3\u003e\n\u003cp\u003eThe following formula were used to calculate inflammatory index values: neutrophil-to-lymphocyte ratio (NLR)\u0026thinsp;=\u0026thinsp;NEUT/LYM; platelet-to-lymphocyte ratio (PLR)\u0026thinsp;=\u0026thinsp;PLT/LYM; lymphocyte-to-monocyte ratio (LMR)\u0026thinsp;=\u0026thinsp;LYM/MONO; platelet-to-white blood cell ratio (PWR)\u0026thinsp;=\u0026thinsp;PLT/WBC; systemic immune-inflammatory index (SII)\u0026thinsp;=\u0026thinsp;PLT \u0026times; NEUT/LYM; systemic inflammation response index (SIRI)\u0026thinsp;=\u0026thinsp;NEUT \u0026times; MONO/LYM; and aggregate inflammation systemic index (AISI)\u0026thinsp;=\u0026thinsp;NEUT \u0026times; MONO \u0026times; PLT/LYM.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test was used to assess the normality of continuous variables. Natural logarithm transformation was applied for data that did not follow a normal distribution. Continuous variables that followed a normal distribution are expressed as X\u0026thinsp;\u0026plusmn;\u0026thinsp;S, while those that did not are expressed as M (Q1, Q3). Continuous variables were compared using one-way analysis of variance (ANOVA). Categorical variables are expressed as frequencies (%) and were compared using the chi-square test. To assess the independent relationship between the inflammatory indices and SO, a multivariable logistic regression analysis was conducted. Three models were used for adjustment: Model 1, which included age and sex; Model 2, in which diabetes and hypertension were added to Model 1; and Model 3, in which HDL-C, LDL-C, TG, and creatinine were added to Model 2. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined. Statistical analyses were performed using the SPSS software (version 26.1), with significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 1009 participants, of which 514 (50.9%) were men and 495 (49.1%) were women, with a mean age of 60.30 \u0026nbsp;\u0026plusmn;\u0026nbsp; 7.79 years. Table 1 presents the main baseline characteristics of participants when WC was used to classify obesity. Of the participants, 235 (23.3%) had non-sarcopenic non-obesity, 209 (20.7%) had non-sarcopenic obesity, 128 (12.7%) had sarcopenic non-obesity, and 437 (43.3%) had SO. Patients with SO were older and more likely to have hypertension, diabetes, and non-alcoholic fatty liver disease than were those in the reference group. In contrast, no significant differences in sex or osteoporosis were found between patients with SO and those in the reference group. Metabolic indicators in patients with SO showed significant differences in triglyceride, HDL-C, uric acid, and fasting blood glucose levels across the groups (P = 0.000), whereas total cholesterol, LDL-C, and creatinine levels were not significantly different (P \u0026gt; 0.05). Furthermore, anthropometric measurements revealed that the SO group had significantly higher weight, WC, BMI, blood pressure, PBF, WHR, VFA, FFMI, FMI, SMI, and SMI/weight ratio than did the non-SO group (P \u0026lt; 0.05), indicating a more adverse body fat distribution and muscle quality characteristics in the former, particularly in terms of VFA and FMI. Notably, the prevalence of SO varied by obesity definition, 43.3% under WC, 38.1% under PBF, 48.8% under VFA, and 18.0% under BMI. The main baseline characteristics of participants classified by BMI, PBF, and VFA-defined obesity were consistent with the above findings (additional files 1, 2, and 3).\u003c/p\u003e\n\u003cp\u003eBlood analysis results indicated that the WBC count in the SO group was significantly higher than that in the other three groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.000) and that the LYM count was also elevated in the SO group (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.004). Although PLT and NEUT counts did not significantly differ between the groups, PWR was significantly lower in the SO group (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.005). Additionally, both SIRI and AISI were significantly higher in the SO group (P = 0.002 and P = 0.001, respectively), and although SII was not significantly different, it was also elevated compared with that in the other three groups, further suggesting a heightened inflammatory state in the SO group (Table 2). Compared with the SO group defined by WC, the SO groups classified by VFA, BMI, and PBF also showed notable increases in WBC and LYM counts, as well as in SIRI, SII and AISI, though the extent and direction of these increases varied across definitions (additional files 4, 5, and 6). However, the PLT level in the obesity group defined by BMI and PBF was higher than that in the groups defined by WC and VFA. Under the VFA, BMI, and PBF definitions, the SO group consistently showed the lowest PLR, with particularly marked differences compared with those in the normal and obesity groups. For all definitions, the PWR of the SO group was significantly lower compared with that of the normal group, with the difference being more pronounced in the definitions based on BMI and PBF. Under different definitions, LMR was highest in the obesity group and lowest in the SO or sarcopenia group, except under the PBF definition, where it was highest in the sarcopenia group and lowest in the obesity group, indicating that the relationship between obesity and immune function varies depending on the definition used.\u003c/p\u003e\n\u003cp\u003eTable 1. The main Baseline characteristics of patients classified using WC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"930\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNon-sarcopenic non-obesity N = 235, 23.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNon-sarcopenic obesity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN = 209, 20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSarcopenic non-obesity N = 128, 12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSarcopenic obesity N = 437, 43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e58.53 \u0026plusmn; 7.18 \u003csup\u003ec, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e58.38 \u0026plusmn; 6.57 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e62.57 \u0026plusmn; 9.06 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e61.506 \u0026plusmn; 7.86 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eGender (% male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e127 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e117 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e63 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e207 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e48 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e69 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e45 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e206 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e13 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e34 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e17 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e70 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOsteoporosis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e162 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e148 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e90 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e303 (69.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNon-alcoholic fatty liver (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e30 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e95 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e32 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e270 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic panel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTriglycerides (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 1.25 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 1.81\u003csup\u003e\u0026nbsp;a, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.55 \u0026plusmn; 0.88 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.97 \u0026plusmn; 1.50 \u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTotal cholesterol (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.16 \u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5.22 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.19 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.16 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.54 \u0026plusmn; 0.39 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.29 \u0026plusmn; 0.29 \u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.45 \u0026plusmn; 0.40 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.29 \u0026plusmn; 0.36 \u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3.15 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.19 \u0026plusmn; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.10 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.19 \u0026plusmn; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCreatinine (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e64.37 \u0026plusmn; 15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e68.28 \u0026plusmn; 47.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e62.07 \u0026plusmn; 15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e64.85 \u0026plusmn; 40.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eUric acid (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e329.35 \u0026plusmn; 82.39\u003csup\u003e\u0026nbsp;b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e360.37 \u0026plusmn; 82.63 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e330.59 \u0026plusmn; 88.26 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e364.44 \u0026plusmn; 94.82\u003csup\u003e\u0026nbsp;a, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFasting blood glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.45 \u0026plusmn; 1.42 \u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e6.00 \u0026plusmn; 1.64 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.85 \u0026plusmn; 1.89\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6.07 \u0026plusmn; 1.90 \u003csup\u003ea, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnthropometric measurements\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e163.82 \u0026plusmn; 8.00 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e166.79 \u0026plusmn; 7.81 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e158.75 \u0026plusmn; 7.79 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e161.51 \u0026plusmn; 8.73 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e60.72 \u0026plusmn; 9.22\u003csup\u003e\u0026nbsp;b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e72.31 \u0026plusmn; 10.65\u003csup\u003e\u0026nbsp;a c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e60.48 \u0026plusmn; 7.81\u003csup\u003e\u0026nbsp;b d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e72.94 \u0026plusmn; 11.65 \u003csup\u003ea c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e78.01 \u0026plusmn; 7.10 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e90.62 \u0026plusmn; 6.88 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e79.86 \u0026plusmn; 6.25 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e96.61 \u0026plusmn; 7.56\u003csup\u003e\u0026nbsp;a, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e22.52 \u0026plusmn; 2.22\u003csup\u003e\u0026nbsp;b c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e25.84 \u0026plusmn; 2.33 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e23.93 \u0026plusmn; 1.97 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e27.84 \u0026plusmn; 2.80 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e124.72 \u0026plusmn; 15.71\u003csup\u003e\u0026nbsp;b, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e133.01 \u0026plusmn; 16.42 \u003csup\u003ea, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e131.65 \u0026plusmn; 19.19\u003csup\u003e\u0026nbsp;a, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e137.62 \u0026plusmn; 18.73 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e75.16 \u0026plusmn; 10.12 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e80.89 \u0026plusmn; 10.61 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e77.69 \u0026plusmn; 10.04 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e82.75 \u0026plusmn; 33.25\u003csup\u003e\u0026nbsp;a, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePBF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e26.70 \u0026plusmn; 5.48 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e29.88 \u0026plusmn; 4.63 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e34.93 \u0026plusmn; 4.33 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e38.01 \u0026plusmn; 5.25 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.04 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.92 \u0026plusmn; 0.06\u003csup\u003e\u0026nbsp;a, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.91 \u0026plusmn; 0.04 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.96 \u0026plusmn; 0.05 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVFA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e74.29 \u0026plusmn; 17.53 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e100.49 \u0026plusmn; 18.88 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e108.39 \u0026plusmn; 22.24 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e144.21 \u0026plusmn; 29.88 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFFMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e16.5 \u0026plusmn; 1.95 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e19.14 \u0026plusmn; 15.23 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e15.56 \u0026plusmn; 1.57 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17.16 \u0026plusmn; 1.96 \u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6.02 \u0026plusmn; 1.39 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7.74 \u0026plusmn; 1.23 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.36 \u0026plusmn; 1.35\u003csup\u003e\u0026nbsp;a, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e10.62 \u0026plusmn; 2.11 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6.84 \u0026plusmn; 1.01 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7.61 \u0026plusmn; 1.33\u003csup\u003e\u0026nbsp;a, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6.31 \u0026plusmn; 0.87 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7.13 \u0026plusmn; 0.98 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMuscle percent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e39.87 \u0026plusmn; 3.56 \u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e38.70 \u0026plusmn; 3.63 \u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e34.82 \u0026plusmn; 2.89 \u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e33.59 \u0026plusmn; 3.20\u003csup\u003e\u0026nbsp;a, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: body mass index; PBF: percent body fat; WHR: waist\u0026ndash;hip ratio; VFA: visceral fat area; FFMI: fat-free mass index; FMI: fat mass index; SMI: skeletal muscle index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-C: low-density lipoprotein-cholesterol; LDL-C: high-density lipoprotein-cholesterol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea Significant difference compared with non-sarcopenic non-obesity.\u003c/p\u003e\n\u003cp\u003eb Significant difference compared with non-sarcopenic obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ec Significant difference compared with sarcopenic non-obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed Significant difference compared with sarcopenic obesity (LSD post hoc tests).\u003c/p\u003e\n\u003cp\u003eTable 2. Blood cell counts and blood cell inflammatory indices of patients classified using WC [X\u0026nbsp;\u0026plusmn;\u0026nbsp;s, M (Q1, Q3)]\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNon-sarcopenic non-obesity N = 235, 23.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNon-sarcopenic obesity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN = 209, 20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eSarcopenic non-obesity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN = 128, 12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eSarcopenic obesity N = 437, 43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e5.69 \u0026plusmn; 1.43 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e5.97 \u0026plusmn; 1.58\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.96 \u0026plusmn; 1.43 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e6.34 \u0026plusmn; 1.57 \u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNEUT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e3.30 \u0026plusmn; 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.67 \u0026plusmn; 3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.77 \u0026plusmn; 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e3.72 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLYM (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.91 \u0026plusmn; 0.56 \u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.03 \u0026plusmn; 0.56 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.01 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e2.09 \u0026plusmn; 0.59 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMONO (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.42 \u0026plusmn; 1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.31 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.32 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e0.33 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e218.00 \u0026plusmn; 53.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e226.57 \u0026plusmn; 131.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e218.52 \u0026plusmn; 51.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e224.32 \u0026plusmn; 59.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e114.51 (95.62, 140.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e109.52 (87.43, 137.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e109.41 (88.43, 135.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e107.22 (87.77, 133.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.67 (1.35, 2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.65 (1.34, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.72 (1.27, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e1.73 (1.35, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e6.47 (5.19, 7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6.76 (5.34, 8.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.53 (4.83, 8.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e6.35 (4.97, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e38.68 (32.53, 46.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e37.87 (30.27, 44.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e36.43 (30.21, 43.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e35.47 (29.32, 42.6) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e360.00 (265.33, 474.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e359.84 (287.85, 473.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e362.10 (255.89, 504.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e376.57 (289.17, 498.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.49 (0.33, 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.47 (0.35, 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.50 (0.35, 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e0.56 (0.39, 0.77) \u003csup\u003ea, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e101.16 (65.86, 153.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e109.08 (71.42, 150.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e105.20 (72.88, 156.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e121.78 (82.17, 178.87) \u003csup\u003ea, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWBC: 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.\u003c/p\u003e\n\u003cp\u003ea Significant difference compared with non-sarcopenic non-obesity.\u003c/p\u003e\n\u003cp\u003eb Significant difference compared with non-sarcopenic obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ec Significant difference compared with sarcopenic non-obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed Significant difference compared with sarcopenic obesity (LSD post hoc tests).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation analysis between hematological inflammatory indices and SO\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the association between hematological inflammatory markers and SO, we conducted unadjusted and adjusted logistic regression analyses (Table 3). In the unadjusted model, increased AISI and SIRI, along with decreased PWR, were significantly associated with an increased risk of SO (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). After adjusting for confounding factors, such as age, sex, hypertension, diabetes, and levels of HDL-C, LDL-C, triglycerides, total cholesterol, and creatinine, AISI (OR = 1.233, 95% CI = 1.009 \u0026minus; 1.506, \u003cem\u003eP\u003c/em\u003e = 0.040), SIRI (OR = 1.335, 95% CI = 1.034 \u0026minus; 1.724, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.017), and PWR (OR = 0.609, 95% CI = 0.380 \u0026minus; 0.977, \u003cem\u003eP\u003c/em\u003e = 0.040) remained significantly associated with SO risk (Fig. 2).\u003c/p\u003e\n\u003cp\u003eAn increased WC typically reflects an increase in visceral fat. Therefore, we also used VFA instead of WC to classify obesity. In all adjusted models, PWR was significantly associated with a lower risk of SO, even after adjusting for multiple confounding factors (OR = 0.609, 95% CI = 0.380 \u0026minus; 0.976, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.039). Meanwhile, AISI was significantly associated with a higher risk of SO in the adjusted models (OR = 1.230, 95% CI = 1.009 \u0026ndash; 1.500, P = 0.040), whereas the association was not significant in the unadjusted model (P = 0.057). In contrast, SIRI was significantly associated with an increased risk of SO in the adjusted models (OR = 1.299, 95% CI = 1.010 \u0026minus; 1.669, \u003cem\u003eP\u003c/em\u003e = 0.041) but not in the unadjusted model. Therefore, similar to SO defined by WC, SO defined by VFA was significantly associated with PWR, AISI, and SIRI (Additional file 7 and Fig. 2).\u003c/p\u003e\n\u003cp\u003eGiven that BMI is the most widely used simple anthropometric measure for determining obesity in clinical settings, we further analyzed whether blood cell inflammatory indices are associated with SO defined by sarcopenia combined with obesity (BMI\u0026nbsp;\u0026ge;\u0026nbsp;28 kg/m\u0026sup2;). In the unadjusted model, the risk of SO significantly increased with increased NLR, SII, AISI, and SIRI and decreased LMR and PWR (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). However, in the adjusted model, only AISI (OR = 1.374, 95% CI = 1.066 \u0026minus; 1.771, \u003cem\u003eP\u003c/em\u003e = 0.014) and SIRI (OR = 1.539, 95% CI = 1.133 \u0026minus; 2.092, \u003cem\u003eP\u003c/em\u003e = 0.006) were significantly associated (Additional file 8 and Fig. 2).\u003c/p\u003e\n\u003cp\u003ePBF, a key indicator for assessing obesity levels and muscle status, is of significant value in defining SO. Our study showed that in the PBF definition, AISI and SIRI were significantly associated with the risk of SO in some models (models 1 and 2) whereas other indices did not show significant associations in the adjusted models (Additional file 9 and Fig. 2).\u003c/p\u003e\n\u003cp\u003eTable 3 Association between sarcopenic obesity defined by WC and blood cell inflammatory indices\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eUnadjusted model\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.243 (0.901\u0026ndash;1.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.285 (0.924\u0026ndash;1.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.227 (0.875\u0026ndash;1.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.154 (0.814\u0026ndash;1.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.727 (0.521\u0026ndash;1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.748 (0.529\u0026ndash;1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.807 (0.568\u0026ndash;1.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.857 (0.599\u0026ndash;1.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.851 (0.611\u0026ndash;1.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.811 (0.573\u0026ndash;1.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.787 (0.553\u0026ndash;1.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.799 (0.555\u0026ndash;1.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003ePWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.523 (0.349\u0026ndash;0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.480 (0.306\u0026ndash;0.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.541 (0.342\u0026ndash;0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.621 (0.390\u0026ndash;0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.159 (0.906\u0026ndash;1.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.223 (0.953\u0026ndash;1.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.184 (0.921\u0026ndash;1.522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.133（0.878\u0026ndash;1.462）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eAISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.300 (1.074\u0026ndash;1.572)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.376 (1.131\u0026ndash;1.673)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.324 (1.087\u0026ndash;1.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.248 (1.022\u0026ndash;1.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.427 (1.139\u0026ndash;1.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.539 (1.211\u0026ndash;1.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.469 (1.149\u0026ndash;1.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.361 (1.057\u0026ndash;1.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNLR: 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 inflammation systemic index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1 = age, gender; Model 2 = Model 1 + hypertension, diabetes; Model 3 = Model 2 + high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, total cholesterol, creatinine.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study explored the relationship between blood cell inflammatory indices and SO in middle-aged and older individuals. The results showed that AISI, SIRI, and PWR were significantly associated with SO across multiple models. These findings suggest that systemic inflammation significantly increases the risk of SO.\u003c/p\u003e \u003cp\u003eSO is characterized by concurrent muscle loss and fat accumulation, with chronic low-grade inflammation playing a central role in its development [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Excessive adipose tissue releases free fatty acids into ectopic sites such as the muscles, liver, and heart, leading to intramuscular fat deposition and intracellular lipid accumulation. These lipids impair mitochondrial function and muscle contraction, causing muscle weakness and promoting insulin resistance by deactivating insulin receptors and disrupting glucose transport [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mitochondrial dysfunction and reactive oxygen species (ROS) further exacerbate muscle protein dysfunction and weakness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Beyond local effects, lipids disrupt muscle homeostasis through inflammatory signaling. Obesity-induced inflammation is largely driven by adipose tissue macrophages (ATMs), which shift from anti-inflammatory M2 to proinflammatory M1 phenotypes in obese individuals owing to hypoxia and low perfusion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This transition correlates with increased insulin resistance. Concurrently, obesity promotes proinflammatory T cell responses (Th1 and Th17) and suppresses regulatory T cells, while B lymphocytes in visceral fat secrete IgG2c and TNF-α, further contributing to insulin resistance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The resulting inflammatory milieu activates adipokines, induces muscle apoptosis, and modulates atrophy-related proteins [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, ectopic lipid deposition and systemic inflammation are interlinked mechanisms driving the progression of SO [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on the relationship between blood cell inflammatory indices and SO is limited. Feng et al. found that systemic inflammation variables were significantly higher in an SO group than they were in a control group. However, they did not determine which specific systemic inflammation indices were associated with SO [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Wan et al. used the appendicular skeletal mass index combined with PBF to diagnose SO and studied the relationship between SII and SO. Their results showed that increased SII was significantly associated with increased risk of SO in middle-aged and older individuals, particularly in the latter [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, we did not find a significant association between the SII and SO in the present study; however, we found significant correlations between AISI, SIRI, or PWR and SO. These discrepancies could stem from the variation in sarcopenia diagnostic criteria used across studies. Our study defined sarcopenia based on muscle mass percentage, which focuses more on the ratio of muscle mass to body weight than it does on the absolute reduction in muscle mass. This ratio might have a stronger association with chronic systemic inflammation, as reflected by AISI and SIRI, whereas SII might not be sensitive enough to capture this relationship. Additionally, our findings reveal a significant and novel association between the PLT/WBC ratio (PWR) and SO, highlighting PWR as a previously unrecognized regulator of chronic metabolic dysregulation in SO, a relationship not previously reported in the context of muscle\u0026ndash;fat crosstalk. Given these results, PWR holds promise as a potential inflammatory marker for SO and warrants further investigation. Future studies should consider integrating multiple inflammatory indices, such as AISI, SIRI, and PWR, to comprehensively assess the inflammatory profile of SO and explore their roles in its underlying pathophysiological mechanisms. This integrative approach may enhance the sensitivity and predictive accuracy of inflammatory indices in identifying individuals at risk for SO.\u003c/p\u003e \u003cp\u003eAISI and SIRI are indices of systemic inflammation based on complete blood cell counts, and may be closely related to the pathophysiological processes of SO. AISI evaluates the systemic inflammatory response through the ratios of NEUTs, PLTs, MONOs, and LYMs, and has been widely used in diseases such as idiopathic pulmonary fibrosis and hypertension [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. SIRI measures the ratio of NEUTs, MONOs, and LYMs to assess systemic inflammation and immune system function, and has demonstrated significant prognostic value in various diseases, including pneumonia, rheumatoid arthritis, acute pancreatitis, and cardiovascular diseases [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As core components of SIRI, NEUTs and MONOs play a role in atherosclerosis and inflammatory responses [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This study found that AISI and SIRI were significantly associated with SO (defined by WC, VFA, and BMI). AISI and SIRI effectively assessed the systemic inflammatory response by capturing variations in peripheral blood cell components such as NEUTs, MONOs, and LYMs. Unlike local inflammatory mechanisms described in the progression of SO, these indices offer a peripheral, quantifiable perspective on systemic immune activation. For instance, elevated NEUT and MONO levels\u0026mdash;core components of AISI and SIRI\u0026mdash;are known contributors to chronic inflammatory conditions and metabolic dysfunctions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, both indices showed significant correlations with SO, likely reflecting underlying immune dysregulation and chronic inflammation characteristic of obesity. Notably, shifts in immune cell populations, including increased Th1/Th17 activity and decreased regulatory T cell presence, were consistent with the proinflammatory profiles captured by AISI and SIRI. These immune alterations may be driven, at least in part, by adipokine imbalances\u0026mdash;such as elevated leptin and reduced adiponectin\u0026mdash;which are known to promote proinflammatory Th1/Th17 responses while suppressing Treg-mediated immune regulation. Together, these findings suggest a potential link between adipose tissue dysfunction, systemic immune activation, and SO severity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, we found a significant association between PWR and SO when defined by WC and VFA. Wan et al. suggested that the association between systemic inflammation and SO varies depending on WC. Compared with individuals with normal WC, the association between the SII and SO is stronger in those with elevated WC, suggesting that abnormal visceral fat accumulation exacerbates inflammatory responses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Visceral fat, more so than overall body fat, strongly stimulates inflammatory cells such as NEUTs and MONOs, which increase systemic inflammation and influence WBC levels, thereby indirectly affecting the PWR [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This again highlights the mechanistic relevance of fat distribution\u0026mdash;particularly central adiposity\u0026mdash;as a modulator of systemic immune activation via innate immune cell recruitment and platelet priming. Conversely, no significant association of PWR, AISI, or SIRI with SO was observed when defined by PBF after multivariable adjustment. As an indicator of total body fat, PBF does not fully reflect the metabolic and inflammatory burden represented by visceral adiposity [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which may explain the relatively weaker correlations observed.\u003c/p\u003e \u003cp\u003eNotably, PLT levels were found to be elevated in the obesity groups classified according to the BMI and PBF criteria, as compared with those defined by WC or VFA. While BMI and PBF reflect general or proportional adiposity, which can enhance systemic metabolic stress and hematopoietic activation, WC and VFA focus more specifically on central fat accumulation. This may not uniformly influence platelet production or activation. The elevated platelet levels may reflect compensatory thrombopoiesis driven by chronic inflammation and endothelial dysfunction, mechanisms often observed in metabolic syndrome and linked to adipokine dysregulation. These findings suggest that platelet-related inflammatory responses may be more closely linked to overall adiposity than they are to the visceral distribution of fat alone. Taken together, these results highlight the phenotypic heterogeneity of obesity and its differential effects on inflammation-related hematologic parameters.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its cross-sectional design limits the ability to infer causal relationships between exposures and outcomes. Prospective studies are warranted to confirm these findings and assess their applicability across diverse clinical settings and populations. Second, the lack of correlation analyses between inflammatory indices and key cytokines such as IL-6 and TNF-α hinders a deeper understanding of the mechanisms underlying chronic inflammation. Third, sarcopenia was defined solely based on reduced muscle mass, without incorporating assessments of muscle strength or physical performance, which does not fully align with EWGSOP2 and AWGS diagnostic criteria. Lastly, we did not account for several potential confounding variables, including physical activity, dietary protein intake, and glucocorticoid use, which may have influenced the observed associations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings suggest that blood cell inflammatory indices, particularly SIRI and AISI, are positively associated with SO risk, while PWR is negatively associated. These relationships were consistent across different SO classification methods, indicating the potential relevance of these markers in screening practices. Given their low cost and accessibility, these indices may offer a practical tool to support early detection and management of SO in clinical settings. However, further studies in larger and more diverse populations are needed to validate these associations, evaluate their predictive value over time, and clarify the underlying inflammatory mechanisms. Such research will help determine the clinical applicability of these markers and guide future prevention and treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAISI, aggregate inflammation systemic index\u003c/p\u003e\n\u003cp\u003eASM, appendicular skeletal muscle mass\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eFFMI, fat-free mass index\u003c/p\u003e\n\u003cp\u003eFMI, fat mass index\u003c/p\u003e\n\u003cp\u003eLMR, lymphocyte-to-monocyte ratio\u003c/p\u003e\n\u003cp\u003eLYM, lymphocyte\u003c/p\u003e\n\u003cp\u003eMONO, monocyte\u003c/p\u003e\n\u003cp\u003eNEUT, neutrophil\u003c/p\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003ePBF, body fat percentage\u003c/p\u003e\n\u003cp\u003ePLR, platelet-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003ePLT, platelet\u003c/p\u003e\n\u003cp\u003ePWR, platelet-to-white blood cell ratio\u003c/p\u003e\n\u003cp\u003eSII, systemic immune-inflammatory index\u003c/p\u003e\n\u003cp\u003eSIRI, systemic inflammation response index\u003c/p\u003e\n\u003cp\u003eSO, sarcopenic obesity\u003c/p\u003e\n\u003cp\u003eVFA, visceral fat area\u003c/p\u003e\n\u003cp\u003eWBC, white blood cell\u003c/p\u003e\n\u003cp\u003eWC, waist circumference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaumgartner RN. Body composition in healthy aging. Ann N Y Acad Sci. 2000;904:437\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1749-6632.2000.tb06498.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1749-6632.2000.tb06498.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Q, Mei F, Shang Y, et al. 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Adapting to obesity with adipose tissue inflammation. Nat Rev Endocrinol. 2017;13(11):633\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrendo.2017.90\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2017.90\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenic obesity, Blood cell inflammatory index, Waist circumference, Visceral fat, Body mass index","lastPublishedDoi":"10.21203/rs.3.rs-8441209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8441209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground \u0026amp; aims:\u003c/h2\u003e \u003cp\u003eDespite the known association between chronic inflammation and reduced muscle mass, the use of inflammatory indices in sarcopenic obesity (SO) remains unexplored. Thus, this study aimed to explore the relationship between blood cell inflammatory indices and SO and assess their potential role in disease evaluation and monitoring.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMethods: This cross-sectional study included 1,009 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years. SO was defined by the presence of both sarcopenia (muscle mass\u0026thinsp;\u0026lt;\u0026thinsp;39.3% for men or \u0026lt;\u0026thinsp;33.9% for women) and obesity [defined as body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;, body fat percentage (PBF)\u0026thinsp;\u0026ge;\u0026thinsp;30% for men or \u0026ge;\u0026thinsp;40% for women, visceral fat area (VFA)\u0026thinsp;\u0026ge;\u0026thinsp;100 cm\u0026sup2;, or waist circumference (WC)\u0026thinsp;\u0026ge;\u0026thinsp;80 cm for women and \u0026ge;\u0026thinsp;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. ANOVA and regression analyses were used to examine the relationship between these indices and SO.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWhen the WC classification was used, risk of SO was significantly associated with the SIRI (OR\u0026thinsp;=\u0026thinsp;1.361, 95% CI, 1.057\u0026ndash;1.753; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and AISI (OR\u0026thinsp;=\u0026thinsp;1.248, 95% CI, 1.022\u0026ndash;1.524; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), but negatively correlated with PWR (OR\u0026thinsp;=\u0026thinsp;0.621, 95% CI: 0.390\u0026ndash;0.988, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040). The results were similar for the VFA classification. When the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2; classification was used, risk of SO was significantly associated with SIRI (OR\u0026thinsp;=\u0026thinsp;1.539, 95% CI: 1.133\u0026ndash;2.092, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and AISI (OR\u0026thinsp;=\u0026thinsp;1.374, 95% CI: 1.066\u0026ndash;1.771, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). However, when the PBF classification was used, blood cell inflammatory indices and risk of SO were not significantly correlated.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe correlation between systemic immune inflammation indices and SO may be influenced by the SO classification method. Owing to their advantages of being objective, low-cost, and easy-to-use markers, SIRI, AISI, and PWR may serve as biomarkers for the screening and management of SO when classified by WC, VFA, or BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;.\u003c/p\u003e","manuscriptTitle":"Association between blood cell inflammatory indices and sarcopenic obesity in middle-aged and older Chinese adults: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:04:00","doi":"10.21203/rs.3.rs-8441209/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T12:54:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T08:06:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T11:54:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205618884505839169186904838079014369379","date":"2026-02-07T17:45:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T15:46:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316469248463752091124472145391763115943","date":"2026-02-04T15:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335161718260698967127222629839741539166","date":"2026-02-04T09:54:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T14:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49945954198960169196019209005898124928","date":"2026-02-02T13:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T09:51:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T03:16:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T03:16:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2025-12-24T09:09:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6df6e84c-9742-4264-8d71-659d5d72efec","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:23:41+00:00","versionOfRecord":{"articleIdentity":"rs-8441209","link":"https://doi.org/10.1186/s12986-026-01117-0","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2026-03-26 16:12:58","publishedOnDateReadable":"March 26th, 2026"},"versionCreatedAt":"2026-01-28 03:04:00","video":"","vorDoi":"10.1186/s12986-026-01117-0","vorDoiUrl":"https://doi.org/10.1186/s12986-026-01117-0","workflowStages":[]},"version":"v1","identity":"rs-8441209","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8441209","identity":"rs-8441209","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}