The Relationship between Sarcopenia and the Systemic Immune-Inflammation Index in a Patient Age 80 and Above Short Title: Sarcopenia and Systemic Inflammatory Index

The Relationship between Sarcopenia and the Systemic Immune-Inflammation Index in a Patient Age 80 and Above Short Title: Sarcopenia and Systemic Inflammatory Index | 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 The Relationship between Sarcopenia and the Systemic Immune-Inflammation Index in a Patient Age 80 and Above Short Title: Sarcopenia and Systemic Inflammatory Index Handan Duman, Damla Tüfekçi² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7349098/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Sarcopenia, defined as the loss of muscle mass and function associated with aging, significantly impacts the quality of life of elderly individuals. This study investigated the relationship between the risk of sarcopenia and the systemic immune-inflammation index (SII) in elderly individuals aged 80 years and above. Methods This study involved 214 patients aged 80 years and above who presented at the Rize Recep Tayyip Erdoğan University Training and Research Hospital. The risk of sarcopenia was assessed via the Strength, Assistance with Walking, Rising from a Chair, Climbing Stairs, and Falls test, and laboratory tests were used to analyze inflammation levels. Results The findings of this study revealed significant differences in inflammation parameters among patients at risk of sarcopenia. SII and C-reactive protein/albumin ratios are particularly associated with the risk of sarcopenia. Age, sex, the serum ALB level, the neutrophil count, the platelet count, and the SII emerged as independent predictors of the risk of sarcopenia. Receiver operating characteristic analysis demonstrated that the SII may serve as a predictor of the risk of sarcopenia. Conclusion The integration of biomarkers such as the SII in the detection of sarcopenia in elderly individuals may play an important role in early diagnosis and treatment management. These findings further our understanding of the relationship between sarcopenia and inflammation while supporting the potential use of the SII in clinical practice. Trial Registration: This is not a clinical trial. Sarcopenia Systemic immune-inflammation index Elderly population Figures Figure 1 Introduction The world is experiencing a significant demographic shift, characterized by an aging population in most countries due to rising life expectancy and declining birth rates [ 1 , 2 ]. In 2006, individuals aged 60 and over accounted for 11% of the global population, a figure projected to reach 22% by 2050 [ 3 , 4 ]. In Türkiye, which is traditionally classified as a developing nation with a lower proportion of older individuals than younger individuals, the elderly population recently exceeded 10% for the first time. Projections indicate that this figure will rise to 23.1% by 2050 and to 31.7% by 2075 [ 5 ]. This demographic shift represents one of the most pressing challenges of modern times, necessitating the development of innovative approaches to aging. As the geriatric population grows, new classifications have emerged to define this group. Terms such as “young-elderly,” “middle-elderly,” and “very old” are commonly used, with the geriatric population being categorized into “young-elderly” (60–69 years), “middle-elderly” (70–79 years), and “very old” (80 + years) [ 6 – 9 ]. The increase in the elderly population has brought the issue of sarcopenia, a condition characterized by the loss of muscle mass and strength often associated with chronic diseases, to the forefront of medical research. Sarcopenia is defined as age-related muscle loss, and a decline in muscle strength, mass, and motor functions reduces individuals’ quality of life by making their activities of daily living difficult [ 10 ]. While it is not adequately addressed in routine health assessments, sarcopenia, with a prevalence ranging from 6–22% in the geriatric population, places a significant burden on healthcare spending [ 11 ]. According to the European Sarcopenia in Advanced Age Study Group, the diagnosis of sarcopenia is based on a decrease in muscle function and bone mass, and research into early diagnostic biomarkers is ongoing [ 12 ]. The Strength, Assistance with Walking, Rising from a Chair, Climbing Stairs, and Falls (SARC-F) score, a straightforward and pragmatic instrument, is employed for the screening of sarcopenia and is defined as a decline in muscle mass and function attributable to aging or disease. In the context of primary care, the Sarcopenia Assessment Risk Calculator SARC-F questionnaire has been documented as representing a pragmatic approach for sarcopenia screening and early diagnosis in elderly individuals under the supervision of family physicians [ 11 ]. Factors such as a decrease in anabolic hormones (insulin-like growth factor-1, estrogen, and testosterone) with increasing age, the induction of catabolism by inflammatory cytokines (tumor necrosis factor-α and interleukin-6), a sedentary lifestyle, and nutritional disorders have been implicated in the etiopathogenesis of sarcopenia [ 11 ]. Researchers have postulated that inflammation and oxidative stress, which increase with age, play pivotal roles in the pathogenesis of this condition [ 13 ]. This process is characterized by the development of chronic low-grade inflammation, known as ‘inflammaging’ [ 14 ]. Numerous studies have demonstrated that inflammatory biomarkers exhibit a high degree of efficacy in predicting morbidity and mortality in geriatric populations [ 15 , 16 ]. The systemic immune-inflammation index (SII) is an important marker of inflammation and the immune response and is calculated by means of platelet (PLT), neutrophil, and lymphocyte counts. Increased SII values have been correlated with severity and unfavorable prognoses in chronic diseases [ 17 , 18 ]. This study evaluated the relationship between the SII and the sarcopenia screening tool SARC-F score in the elderly population and examined the potential role of that score in the early diagnosis and management of sarcopenia. Material and methods Patient Population and Data Collection: This study included individuals aged 80 years and older who presented to the LIFE Unit and the endocrine outpatient clinic of the Recep Tayyip Erdoğan University Training and Research Hospital in Rize, Türkiye. This research was evaluated by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee and deemed to be ethically appropriate (decision number 2024/175). Written informed consent was obtained from all participants or their legally authorized representatives. All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The variables of age, sex, chronic diseases, medications, and sarcopenia status were evaluated. The exclusion criteria were age younger than 80 years; the presence of active malignancy, acute infection, immunodeficiency, or advanced chronic renal failure; and a history of cerebrovascular events, end-stage heart failure, trauma, and psychiatric disorders. The risk of sarcopenia was assessed via the SARC-F questionnaire, which is known to exhibit low to medium sensitivity and high specificity in determining muscle strength [ 19 , 20 ]. The SARC-F score evaluates five domains: strength, walking, climbing stairs, and falling. A score of four or above indicates a high probability of sarcopenia. Blood samples Hemoglobin (Hgb), C-reactive protein (CRP), and albumin levels (Abbott Architect c1600 Clinical Chemistry; Abbott Core Laboratory, Germany, normal range: 3.5–5.5 g/dL) were measured from blood samples via standard methods. Complete blood counts, including PLT and white blood cell (WBC) counts, were assessed via a CELL-DYN Ruby hematology analyzer (Abbott Laboratories). The CRP/albumin ratio (CAR) was calculated as the ratio of CRP to albumin multiplied by 100. The SII (neutrophil count × platelet count/lymphocyte count) was obtained from complete blood count values. Statistical analysis The normality of the distribution of variables was evaluated via both visual methods (such as histograms and probability plots) and the Kolmogorov–Smirnov and Shapiro–Wilk tests. The Levene test was used to assess the homogeneity of variance. Continuous variables are expressed as the means ± standard deviations, and categorical variables are expressed as percentages. Data for nonnormally distributed data are presented as medians and interquartile ranges. Categorical group comparisons were conducted via the chi-square test or Fisher's exact test, with the latter being employed when the chi-square assumption was violated due to low expected frequencies. Normally distributed continuous variables were analyzed via the independent samples t test, whereas the Mann‒Whitney U test was used for nonnormally distributed data. Univariate and backward stepwise multivariate logistic regression analyses were conducted. Multivariate logistic regression analysis was employed to identify independent factors associated with sarcopenia. The findings are reported as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided p value of less than 0.05 was considered statistically significant. All the statistical analyses were performed via SPSS version 21.0 software (IBM SPSS, Inc.). Multivariate logistic regression analysis was conducted to determine independent predictors of the risk of sarcopenia. Receiver operating characteristic (ROC) curve analysis was applied to establish CRP, albumin, and CAR cutoff values. Predictive performance was evaluated by calculating the area under the ROC curve (AUC) or c statistic, and comparisons of c statistics were carried out via MedCalc statistical software. Results Two hundred fourteen 214 patients, with a mean age of 86.78 ± 5.84 years, were included in the study. A risk of sarcopenia was detected in 61.2% of the patients. A statistically significant difference in terms of age was observed between the group with sarcopenia (87.81 ± 5.79) and the nonsarcopenia group (85.15 ± 5.57) (p < 0.001). Women represented 64.5% of the entire study group, 51.8% of the sarcopenia group and 48.2% of the nonsarcopenic group. The sex distributions differed significantly between the two groups (p = 0.002). No significant differences were detected in terms of diabetes mellitus (DM), hypertension (HT), or body mass index (BMI) between the participants with and without a risk of sarcopenia (36.6% vs 30.1%, p = 0.203; 68.7% vs 71.1%, p = 0.416; and 29.6 ± 6.00 vs 30.5 ± 5.07, p = 0.454, respectively). Analysis of laboratory values revealed no significant differences between the groups in terms of fasting blood glucose, lymphocyte, or CRP values. Albumin levels were 38 (35–40) in the sarcopenia group and 40 (37–45) in the nonsarcopenic group (p < 0.001). The CAR values were 0.12 (0.08–0.16) in the sarcopenia group and 0.09 (0.08–0.12) in the nonsarcopenic group (p = 0.021). The SII was 445.91 (280.66–558.84) in the sarcopenia group and 340.71 (218.03–505.88) in the nonsarcopenic group. These differences between the sarcopenia and nonsarcopenia groups were statistically significant (p = 0.029). The neutrophil count was 4.16 (3.41–5.06) in the sarcopenia group and 4.24 (3.52–5.88) in the nonsarcopenic group, and the difference was also significant (p = 0.002). The platelet counts also differed significantly between the sarcopenia group at 237.00 (209.00–281.00) and the nonsarcopenic group at 206.00 (181.00–268.00) (p = 0.008) (Table 1 ). The prevalence of polypharmacy was 27.6%. Polypharmacy was observed in 94.9% of patients with sarcopenia and 5.1% of nonsarcopenic patients. This difference was also statistically significant (p < 0.001). Table 1 Demographic and laboratory characteristics of the participants Variable All patients (n = 214) Sarcopenia (+) (n = 131) Sarcopenia (-) (n = 83) p Age, years 86.78 ± 5.84 87.81 ± 5.79 85.15 ± 5.57 0.014 Sex, male % 64.5 72.5 51.8 0.003 Diabetes, % 34.1 36.6 30.1 0.203 Hypertension, % 69.6 68.7 71.1 0.416 Polypharmacy 27.6 94.9 5.1 0.000 Hemoglobin, g/dL 12.27 ± 1.19 12.16 ± 1.18 12.43 ± 1.18 0.109 BMI 29.98 ± 5.66 29.6 ± 6.00 30.5 ± 5.07 0.454 Glucose mg/dL 99(94–103) 98(94–108) 103(92–120) 0.116 CRP mg/dL 4.29 (3.2–5.9) 4.3 (3.4–6.18) 4 (3.1–5.5) 0,212 Albumin g/dL 39 (35–42) 38 (35–40) 40 (37–45) < 0.001 CAR ×100 0.10 (0.08–0.16) 0,12(0.08–0.16) 0.09(0.08–0.12) 0.021 SII 370.88(259.35-556.28) 445.91(280.66-558.84) 340.71(218.03-505.88) 0.029 WBC count ×10 3 /µL 6.49(5.43–7.56) 6.57(5.37–7.87) 6.11(5.46–7.24) 0.028 Neutrophil ×10 3 /µL 4.00(3.28–4.88) 4.16(3.41–5.06) 4.24(3.52–5.88) 0.002 Lymphocyte % 2.30(1.75-3.0) 2.30(1.75–3.1) 2.40(1.75–2.80) 0.204 Platelet ×10 3 /µL 229,00(192.00-277.25) 237.00(209.00-281.00) 206.00(181.00-268.00) 0.008 Normally distributed parameters are shown as the median ± standard deviation. The median (IQR) values are given ​​for parameters not normally distributed. BMI – body mass index; WBC: white blood cell; SII: systemic inflammatory index; CAR: C-reactive protein albumin ratio In comprehensive geriatric evaluations, basic activities of daily living, instrumental activities of daily living, the Clinical Frailty Scale, the Mini Nutritional Assessment, and the Get Up and Walk test scores were significantly higher in participants at risk of sarcopenia (Table 2 ). Table 2 Comprehensive Clinical and Geriatric Characteristics of the Participants Variables Total (n = 214) Sarcopenia (+) (n = 131) Sarcopenia (-) (n = 83) p BADL – Dependent 58.4 74 33.7 0.000 IADL- Dependent 87.4 96.2 73.5 0.000 CFS- Frailty 54.2 61.1 21.7 0.000 MNA-Normal 51.4 61.8 27.7 0.000 Get Up and Go Test (-) 79 82.4 33.7 0.000 Abbreviations: Basic Activities of Daily Living (BADL), Instrumental Activities of Daily Living (IADL), Clinical Frailty Scale (CFS), Mini Nutritional Assessment (MNA) Those parameters that differed significantly between the two groups were subjected to univariate and multivariate logistic regression analysis (backward method). Age, sex, and the serum ALB concentration, SII, neutrophil count, and PLT were identified as independent predictors of the risk of sarcopenia (Table 3 ). Table 3 Univariate and multivariate logistic regression analyses of the study variables Univariate Multivariate Variable OR 95% CI Lower-Upper p OR 95% CI Lower-Upper p Age 0.917 0.868–0.967 0.002 0.932 0.878–0.990 0.022 Sex 2.455 1.379–4.370 0.002 3.092 1.565–6.111 0.001 Albumin 1.129 1.064–1.197 < 0.001 1.118 1.046–1.195 0.001 CAR 0.536 0.167–1.772 0.295 1.129 0.073–1.393 0.319 SII 1.000 0.999-1.000 0.386 1.002 1.000-1.003 0.028 WBC 0.791 0.654–0.957 0.016 1.000 0.754–1.325 0.999 Neutrophil 0.701 0.555–0.884 0.033 0.494 0.317–0.771 0.002 Platelet 0.996 0.992-1.00 0.057 0.994 0.988-1.000 0.048 WBC: white blood cell; SII: systemic inflammatory index; CAR: C-reactive protein albumin ratio; ROC analysis revealed a cutoff value of the SII for sarcopenia of 370.88, with a sensitivity of 63.8% and specificity of 58% (AUC 0.588; 95% CI 0.519–0.655; p = 0.003) (Fig. 1 ). Discussion The primary findings of the present study indicated a significant association between the risk of sarcopenia and the SII in the geriatric population over 80 years of age. Comparative analysis revealed significant differences in additional inflammatory parameters between patients with and without suspected sarcopenia. The study findings revealed that 61.2% of the samples were at risk of sarcopenia. A study from Spain involving geriatric individuals over 65 years of age residing in nursing homes reported a sarcopenia prevalence of 81.7%. In contrast, another study reported figures ranging from 22–85.4% [ 21 , 22 ]. The prevalence of sarcopenia in geriatric nursing home residents has been reported to be between 73% and 91%, whereas Bingyang Liu et al. reported a prevalence of 48.8% in individuals aged over 60 years [ 21 – 25 ]. Our findings are consistent with the previous literature. This difference in prevalence rates may be due to the ages of the participants or to socioeconomic, cultural, or healthcare-based factors derived from regional differences. In a study from Spain, women represented 84.6% of patients with sarcopenia, compared with 49.5% in a study from China [ 21 , 25 ]. In the present study, women constituted 51.8% of the subjects at risk of sarcopenia. We attribute these inconsistencies between studies to the broad prevalence of sarcopenia and to differences in the sociodemographic characteristics of the geriatric populations in which they were conducted. Zhang et al. reported that sarcopenic patients were older and had lower BMI values [ 26 ]. A significant positive correlation was detected between age and the risk of sarcopenia in the present study. The mean BMI values of sarcopenic patients in the cited studies from China and Spain were 22.14 kg/m² and 27 kg/m², respectively [ 21 , 25 ]. The mean BMI in the present study was consistent with the established data, indicating that BMI was not significantly associated with the risk of sarcopenia. Go et al. investigated the relationships among sarcopenia, cancer-related inflammation, and the neutrophil‒lymphocyte ratio (NLR). Those authors reported a higher PLT-to-lymphocyte ratio in patients with sarcopenia than in those without sarcopenia [ 27 ]. Sarcopenic patients reportedly present higher levels of white and red blood cells and proinflammatory factors, with lower levels of Hgb, albumin, and anti-inflammatory factors [ 28 ]. A high NLR has been associated with inflammation, indicating that an inflammatory state may accelerate the development of sarcopenia and trigger this condition by increasing inflammation levels in the body [ 27 ]. The neutrophil count in the present study was significantly low, whereas the PLT values were significantly high in patients at risk of sarcopenia. In addition, the SII, a novel inflammatory marker, was significantly greater in patients with a risk of sarcopenia than in those with no such risk. Yiqian Jiang et al. examined the relationship between albumin and CRP levels among individuals aged 60–80 years in the USA. These findings indicate a negative correlation between the two parameters, suggesting that hypoalbuminemia may serve as a promising predictive biomarker for inflammation [ 29 ]. Consistent with previous studies supporting the relationship between inflammation and sarcopenia, in the present research, the levels of albumin, an acute-phase reactant, were low, whereas the CAR, a novel inflammatory marker, was high in patients at risk of sarcopenia. The use of novel biomarkers such as the SII is important for more effective management and monitoring of patients at risk of sarcopenia, one of the most common health problems encountered in the aging process in the very old geriatric population. In the present study, and consistent with the previous literature, heart disease was the most common chronic condition among patients over 80 years of age at risk of sarcopenia. HT was the most prevalent disease in the study population [ 25 , 13 ]. The present study revealed a significant relationship between the risk of sarcopenia and frailty, dependency, falls, malnutrition, and polypharmacy in terms of a comprehensive geriatric assessment. A Spanish study yielded similar results, with malnutrition, urinary incontinence, and immobility emerging as the most prevalent health concerns experienced by sarcopenic patients during comprehensive geriatric evaluations [ 22 ]. Conclusion Individuals over the age of 80 years experience various health problems as part of the aging process, with sarcopenia being one of the most prevalent conditions in this population. While the SARC-F test is a viable option for individuals over 80 years of age, the incorporation of a combined SII assessment may yield a more systematic and effective monitoring process in the clinical setting. The present study underscores the importance of biomarkers such as the SII and lends support to the potential use of these biomarkers in the monitoring of patients at risk of sarcopenia. Declarations Conflicts of Interest: The authors declare that they have no conflict of interest. No conflicts interest this study Funding This study has been supported by the Recep Tayyip Erdoğan University Development Foundation. Ethics approval and consent to participate This research was evaluated by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee and deemed to be ethically appropriate (decision number 2024/175). Consent for publication Not Applicable. Competing interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution H.D. conceived and designed the study, collected the data, performed the data analysis, and drafted the manuscript. D.T. contributed to the interpretation of the results and the drafting of the manuscript, and critically revised it for important intellectual content. Both authors read and approved the final manuscript. Acknowledgements Not applicable. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Jones K. The problem of an aging global population, shown by country. Geogr Bull. 2020;52:21–3. Skirbekk VF, Staudinger UM, Cohen JE. How to measure population aging? The answer is less than obvious: a review. Gerontology. 2019;65:136–44. Rahimi FA, Estebsari F, Mostafaei D, et al. 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11:32:08","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89197,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7349098/v1/0e203f0655e5d83a52f5f840.html"},{"id":92801995,"identity":"ded6499e-af27-405b-828f-5e7dff1af064","added_by":"auto","created_at":"2025-10-05 11:40:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50020,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the systemic immune-inflammation index\u003c/p\u003e","description":"","filename":"figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7349098/v1/50ed7d421011767a776abca9.jpeg"},{"id":93400311,"identity":"4eeaf9d6-2f99-4c82-8898-9d98f6753892","added_by":"auto","created_at":"2025-10-13 12:23:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7349098/v1/75d6a308-861c-4974-a4fe-30bf1e59d4a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship between Sarcopenia and the Systemic Immune-Inflammation Index in a Patient Age 80 and Above Short Title: Sarcopenia and Systemic Inflammatory Index","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe world is experiencing a significant demographic shift, characterized by an aging population in most countries due to rising life expectancy and declining birth rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2006, individuals aged 60 and over accounted for 11% of the global population, a figure projected to reach 22% by 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In T\u0026uuml;rkiye, which is traditionally classified as a developing nation with a lower proportion of older individuals than younger individuals, the elderly population recently exceeded 10% for the first time. Projections indicate that this figure will rise to 23.1% by 2050 and to 31.7% by 2075 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This demographic shift represents one of the most pressing challenges of modern times, necessitating the development of innovative approaches to aging. As the geriatric population grows, new classifications have emerged to define this group. Terms such as \u0026ldquo;young-elderly,\u0026rdquo; \u0026ldquo;middle-elderly,\u0026rdquo; and \u0026ldquo;very old\u0026rdquo; are commonly used, with the geriatric population being categorized into \u0026ldquo;young-elderly\u0026rdquo; (60\u0026ndash;69 years), \u0026ldquo;middle-elderly\u0026rdquo; (70\u0026ndash;79 years), and \u0026ldquo;very old\u0026rdquo; (80\u0026thinsp;+\u0026thinsp;years) [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe increase in the elderly population has brought the issue of sarcopenia, a condition characterized by the loss of muscle mass and strength often associated with chronic diseases, to the forefront of medical research. Sarcopenia is defined as age-related muscle loss, and a decline in muscle strength, mass, and motor functions reduces individuals\u0026rsquo; quality of life by making their activities of daily living difficult [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While it is not adequately addressed in routine health assessments, sarcopenia, with a prevalence ranging from 6\u0026ndash;22% in the geriatric population, places a significant burden on healthcare spending [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to the European Sarcopenia in Advanced Age Study Group, the diagnosis of sarcopenia is based on a decrease in muscle function and bone mass, and research into early diagnostic biomarkers is ongoing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Strength, Assistance with Walking, Rising from a Chair, Climbing Stairs, and Falls (SARC-F) score, a straightforward and pragmatic instrument, is employed for the screening of sarcopenia and is defined as a decline in muscle mass and function attributable to aging or disease. In the context of primary care, the Sarcopenia Assessment Risk Calculator SARC-F questionnaire has been documented as representing a pragmatic approach for sarcopenia screening and early diagnosis in elderly individuals under the supervision of family physicians [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFactors such as a decrease in anabolic hormones (insulin-like growth factor-1, estrogen, and testosterone) with increasing age, the induction of catabolism by inflammatory cytokines (tumor necrosis factor-α and interleukin-6), a sedentary lifestyle, and nutritional disorders have been implicated in the etiopathogenesis of sarcopenia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Researchers have postulated that inflammation and oxidative stress, which increase with age, play pivotal roles in the pathogenesis of this condition [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This process is characterized by the development of chronic low-grade inflammation, known as \u0026lsquo;inflammaging\u0026rsquo; [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous studies have demonstrated that inflammatory biomarkers exhibit a high degree of efficacy in predicting morbidity and mortality in geriatric populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The systemic immune-inflammation index (SII) is an important marker of inflammation and the immune response and is calculated by means of platelet (PLT), neutrophil, and lymphocyte counts. Increased SII values have been correlated with severity and unfavorable prognoses in chronic diseases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study evaluated the relationship between the SII and the sarcopenia screening tool SARC-F score in the elderly population and examined the potential role of that score in the early diagnosis and management of sarcopenia.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003ePatient Population and Data Collection: This study included individuals aged 80 years and older who presented to the LIFE Unit and the endocrine outpatient clinic of the Recep Tayyip Erdoğan University Training and Research Hospital in Rize, T\u0026uuml;rkiye. This research was evaluated by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee and deemed to be ethically appropriate (decision number 2024/175). Written informed consent was obtained from all participants or their legally authorized representatives. All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\u003cp\u003eThe variables of age, sex, chronic diseases, medications, and sarcopenia status were evaluated. The exclusion criteria were age younger than 80 years; the presence of active malignancy, acute infection, immunodeficiency, or advanced chronic renal failure; and a history of cerebrovascular events, end-stage heart failure, trauma, and psychiatric disorders. The risk of sarcopenia was assessed via the SARC-F questionnaire, which is known to exhibit low to medium sensitivity and high specificity in determining muscle strength [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The SARC-F score evaluates five domains: strength, walking, climbing stairs, and falling. A score of four or above indicates a high probability of sarcopenia.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBlood samples\u003c/h2\u003e\u003cp\u003eHemoglobin (Hgb), C-reactive protein (CRP), and albumin levels (Abbott Architect c1600 Clinical Chemistry; Abbott Core Laboratory, Germany, normal range: 3.5\u0026ndash;5.5 g/dL) were measured from blood samples via standard methods. Complete blood counts, including PLT and white blood cell (WBC) counts, were assessed via a CELL-DYN Ruby hematology analyzer (Abbott Laboratories). The CRP/albumin ratio (CAR) was calculated as the ratio of CRP to albumin multiplied by 100. The SII (neutrophil count \u0026times; platelet count/lymphocyte count) was obtained from complete blood count values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe normality of the distribution of variables was evaluated via both visual methods (such as histograms and probability plots) and the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests. The Levene test was used to assess the homogeneity of variance. Continuous variables are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables are expressed as percentages. Data for nonnormally distributed data are presented as medians and interquartile ranges. Categorical group comparisons were conducted via the chi-square test or Fisher's exact test, with the latter being employed when the chi-square assumption was violated due to low expected frequencies. Normally distributed continuous variables were analyzed via the independent samples t test, whereas the Mann‒Whitney U test was used for nonnormally distributed data. Univariate and backward stepwise multivariate logistic regression analyses were conducted. Multivariate logistic regression analysis was employed to identify independent factors associated with sarcopenia. The findings are reported as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided p value of less than 0.05 was considered statistically significant. All the statistical analyses were performed via SPSS version 21.0 software (IBM SPSS, Inc.). Multivariate logistic regression analysis was conducted to determine independent predictors of the risk of sarcopenia. Receiver operating characteristic (ROC) curve analysis was applied to establish CRP, albumin, and CAR cutoff values. Predictive performance was evaluated by calculating the area under the ROC curve (AUC) or c statistic, and comparisons of c statistics were carried out via MedCalc statistical software.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTwo hundred fourteen 214 patients, with a mean age of 86.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84 years, were included in the study. A risk of sarcopenia was detected in 61.2% of the patients. A statistically significant difference in terms of age was observed between the group with sarcopenia (87.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79) and the nonsarcopenia group (85.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women represented 64.5% of the entire study group, 51.8% of the sarcopenia group and 48.2% of the nonsarcopenic group. The sex distributions differed significantly between the two groups (p\u0026thinsp;=\u0026thinsp;0.002). No significant differences were detected in terms of diabetes mellitus (DM), hypertension (HT), or body mass index (BMI) between the participants with and without a risk of sarcopenia (36.6% vs 30.1%, p\u0026thinsp;=\u0026thinsp;0.203; 68.7% vs 71.1%, p\u0026thinsp;=\u0026thinsp;0.416; and 29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00 vs 30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07, p\u0026thinsp;=\u0026thinsp;0.454, respectively).\u003c/p\u003e\u003cp\u003eAnalysis of laboratory values revealed no significant differences between the groups in terms of fasting blood glucose, lymphocyte, or CRP values. Albumin levels were 38 (35\u0026ndash;40) in the sarcopenia group and 40 (37\u0026ndash;45) in the nonsarcopenic group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CAR values were 0.12 (0.08\u0026ndash;0.16) in the sarcopenia group and 0.09 (0.08\u0026ndash;0.12) in the nonsarcopenic group (p\u0026thinsp;=\u0026thinsp;0.021). The SII was 445.91 (280.66\u0026ndash;558.84) in the sarcopenia group and 340.71 (218.03\u0026ndash;505.88) in the nonsarcopenic group. These differences between the sarcopenia and nonsarcopenia groups were statistically significant (p\u0026thinsp;=\u0026thinsp;0.029). The neutrophil count was 4.16 (3.41\u0026ndash;5.06) in the sarcopenia group and 4.24 (3.52\u0026ndash;5.88) in the nonsarcopenic group, and the difference was also significant (p\u0026thinsp;=\u0026thinsp;0.002). The platelet counts also differed significantly between the sarcopenia group at 237.00 (209.00\u0026ndash;281.00) and the nonsarcopenic group at 206.00 (181.00\u0026ndash;268.00) (p\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The prevalence of polypharmacy was 27.6%. Polypharmacy was observed in 94.9% of patients with sarcopenia and 5.1% of nonsarcopenic patients. This difference was also statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and laboratory characteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients (n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia (+) (n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarcopenia (-) (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, male %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes, %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension, %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolypharmacy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemoglobin, g/dL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose\u003c/b\u003e mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99(94\u0026ndash;103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(94\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103(92\u0026ndash;120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.29 (3.2\u0026ndash;5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3 (3.4\u0026ndash;6.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (3.1\u0026ndash;5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (35\u0026ndash;42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (35\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (37\u0026ndash;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCAR \u0026times;100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.08\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,12(0.08\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09(0.08\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e370.88(259.35-556.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e445.91(280.66-558.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e340.71(218.03-505.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC count \u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.49(5.43\u0026ndash;7.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.57(5.37\u0026ndash;7.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.11(5.46\u0026ndash;7.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophil \u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00(3.28\u0026ndash;4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.16(3.41\u0026ndash;5.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.24(3.52\u0026ndash;5.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLymphocyte %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30(1.75-3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.30(1.75\u0026ndash;3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.40(1.75\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelet \u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229,00(192.00-277.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237.00(209.00-281.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206.00(181.00-268.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNormally distributed parameters are shown as the median\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The median (IQR) values are given ​​for parameters not normally distributed. BMI \u0026ndash; body mass index; WBC: white blood cell; SII: systemic inflammatory index; CAR: C-reactive protein albumin ratio\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn comprehensive geriatric evaluations, basic activities of daily living, instrumental activities of daily living, the Clinical Frailty Scale, the Mini Nutritional Assessment, and the Get Up and Walk test scores were significantly higher in participants at risk of sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComprehensive Clinical and Geriatric Characteristics of the Participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia (+) (n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarcopenia (-) (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBADL \u0026ndash; Dependent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIADL- Dependent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCFS- Frailty\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMNA-Normal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGet Up and Go Test (-)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations: Basic Activities of Daily Living (BADL), Instrumental Activities of Daily Living (IADL), Clinical Frailty Scale (CFS), Mini Nutritional Assessment (MNA)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThose parameters that differed significantly between the two groups were subjected to univariate and multivariate logistic regression analysis (backward method). Age, sex, and the serum ALB concentration, SII, neutrophil count, and PLT were identified as independent predictors of the risk of sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate logistic regression analyses of the study variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI Lower-Upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI Lower-Upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.868\u0026ndash;0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.878\u0026ndash;0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.379\u0026ndash;4.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.565\u0026ndash;6.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.064\u0026ndash;1.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.046\u0026ndash;1.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCAR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.167\u0026ndash;1.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u0026ndash;1.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999-1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000-1.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.654\u0026ndash;0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.754\u0026ndash;1.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophil\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.555\u0026ndash;0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.317\u0026ndash;0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelet\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.992-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.988-1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eWBC: white blood cell; SII: systemic inflammatory index; CAR: C-reactive protein albumin ratio;\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eROC analysis revealed a cutoff value of the SII for sarcopenia of 370.88, with a sensitivity of 63.8% and specificity of 58% (AUC 0.588; 95% CI 0.519\u0026ndash;0.655; p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary findings of the present study indicated a significant association between the risk of sarcopenia and the SII in the geriatric population over 80 years of age. Comparative analysis revealed significant differences in additional inflammatory parameters between patients with and without suspected sarcopenia.\u003c/p\u003e\u003cp\u003eThe study findings revealed that 61.2% of the samples were at risk of sarcopenia. A study from Spain involving geriatric individuals over 65 years of age residing in nursing homes reported a sarcopenia prevalence of 81.7%. In contrast, another study reported figures ranging from 22\u0026ndash;85.4% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The prevalence of sarcopenia in geriatric nursing home residents has been reported to be between 73% and 91%, whereas Bingyang Liu et al. reported a prevalence of 48.8% in individuals aged over 60 years [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our findings are consistent with the previous literature. This difference in prevalence rates may be due to the ages of the participants or to socioeconomic, cultural, or healthcare-based factors derived from regional differences. In a study from Spain, women represented 84.6% of patients with sarcopenia, compared with 49.5% in a study from China [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the present study, women constituted 51.8% of the subjects at risk of sarcopenia. We attribute these inconsistencies between studies to the broad prevalence of sarcopenia and to differences in the sociodemographic characteristics of the geriatric populations in which they were conducted. Zhang et al. reported that sarcopenic patients were older and had lower BMI values [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A significant positive correlation was detected between age and the risk of sarcopenia in the present study. The mean BMI values of sarcopenic patients in the cited studies from China and Spain were 22.14 kg/m\u0026sup2; and 27 kg/m\u0026sup2;, respectively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The mean BMI in the present study was consistent with the established data, indicating that BMI was not significantly associated with the risk of sarcopenia. Go et al. investigated the relationships among sarcopenia, cancer-related inflammation, and the neutrophil‒lymphocyte ratio (NLR). Those authors reported a higher PLT-to-lymphocyte ratio in patients with sarcopenia than in those without sarcopenia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Sarcopenic patients reportedly present higher levels of white and red blood cells and proinflammatory factors, with lower levels of Hgb, albumin, and anti-inflammatory factors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A high NLR has been associated with inflammation, indicating that an inflammatory state may accelerate the development of sarcopenia and trigger this condition by increasing inflammation levels in the body [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The neutrophil count in the present study was significantly low, whereas the PLT values were significantly high in patients at risk of sarcopenia. In addition, the SII, a novel inflammatory marker, was significantly greater in patients with a risk of sarcopenia than in those with no such risk.\u003c/p\u003e\u003cp\u003eYiqian Jiang et al. examined the relationship between albumin and CRP levels among individuals aged 60\u0026ndash;80 years in the USA. These findings indicate a negative correlation between the two parameters, suggesting that hypoalbuminemia may serve as a promising predictive biomarker for inflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent with previous studies supporting the relationship between inflammation and sarcopenia, in the present research, the levels of albumin, an acute-phase reactant, were low, whereas the CAR, a novel inflammatory marker, was high in patients at risk of sarcopenia. The use of novel biomarkers such as the SII is important for more effective management and monitoring of patients at risk of sarcopenia, one of the most common health problems encountered in the aging process in the very old geriatric population.\u003c/p\u003e\u003cp\u003eIn the present study, and consistent with the previous literature, heart disease was the most common chronic condition among patients over 80 years of age at risk of sarcopenia. HT was the most prevalent disease in the study population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe present study revealed a significant relationship between the risk of sarcopenia and frailty, dependency, falls, malnutrition, and polypharmacy in terms of a comprehensive geriatric assessment. A Spanish study yielded similar results, with malnutrition, urinary incontinence, and immobility emerging as the most prevalent health concerns experienced by sarcopenic patients during comprehensive geriatric evaluations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIndividuals over the age of 80 years experience various health problems as part of the aging process, with sarcopenia being one of the most prevalent conditions in this population. While the SARC-F test is a viable option for individuals over 80 years of age, the incorporation of a combined SII assessment may yield a more systematic and effective monitoring process in the clinical setting. The present study underscores the importance of biomarkers such as the SII and lends support to the potential use of these biomarkers in the monitoring of patients at risk of sarcopenia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eNo conflicts interest this study\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study has been supported by the Recep Tayyip Erdoğan University Development Foundation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis research was evaluated by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee and deemed to be ethically appropriate (decision number 2024/175).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.D. conceived and designed the study, collected the data, performed the data analysis, and drafted the manuscript. D.T. contributed to the interpretation of the results and the drafting of the manuscript, and critically revised it for important intellectual content. Both authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJones K. The problem of an aging global population, shown by country. Geogr Bull. 2020;52:21\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkirbekk VF, Staudinger UM, Cohen JE. How to measure population aging? The answer is less than obvious: a review. 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Med (Baltim). 2023;102:e34726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000034726\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000034726\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Systemic immune-inflammation index, Elderly population","lastPublishedDoi":"10.21203/rs.3.rs-7349098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7349098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eSarcopenia, defined as the loss of muscle mass and function associated with aging, significantly impacts the quality of life of elderly individuals. This study investigated the relationship between the risk of sarcopenia and the systemic immune-inflammation index (SII) in elderly individuals aged 80 years and above.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study involved 214 patients aged 80 years and above who presented at the Rize Recep Tayyip Erdoğan University Training and Research Hospital. The risk of sarcopenia was assessed via the Strength, Assistance with Walking, Rising from a Chair, Climbing Stairs, and Falls test, and laboratory tests were used to analyze inflammation levels.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe findings of this study revealed significant differences in inflammation parameters among patients at risk of sarcopenia. SII and C-reactive protein/albumin ratios are particularly associated with the risk of sarcopenia. Age, sex, the serum ALB level, the neutrophil count, the platelet count, and the SII emerged as independent predictors of the risk of sarcopenia. Receiver operating characteristic analysis demonstrated that the SII may serve as a predictor of the risk of sarcopenia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe integration of biomarkers such as the SII in the detection of sarcopenia in elderly individuals may play an important role in early diagnosis and treatment management. These findings further our understanding of the relationship between sarcopenia and inflammation while supporting the potential use of the SII in clinical practice.\u003c/p\u003e\u003ch2\u003eTrial Registration:\u003c/h2\u003e\u003cp\u003eThis is not a clinical trial.\u003c/p\u003e","manuscriptTitle":"The Relationship between Sarcopenia and the Systemic Immune-Inflammation Index in a Patient Age 80 and Above Short Title: Sarcopenia and Systemic Inflammatory Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:32:03","doi":"10.21203/rs.3.rs-7349098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3986365f-22a6-4991-bd2d-1087551d466c","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T12:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-05 11:32:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7349098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7349098","identity":"rs-7349098","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}