Probable Sarcopenia and Inflammatory Indices in Very Old Adults (≥80) in Primary Care: The Role of SII and CAR

Probable Sarcopenia and Inflammatory Indices in Very Old Adults (≥80) in Primary Care: The Role of SII and CAR | 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 Probable Sarcopenia and Inflammatory Indices in Very Old Adults (≥80) in Primary Care: The Role of SII and CAR Handan Duman, Damla Tüfekçi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8067935/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 Cross-sectional study of 214 adults aged 80 years and older. SARC-F for case-finding; handgrip dynamometry for muscle strength. Probable sarcopenia per EWGSOP2 (< 27 kg men, < 16 kg women). Systemic immune-inflammation index (SII) and C-reactive protein/albumin ratio (CAR) calculated from routine laboratory tests; muscle mass not measured. Results Median handgrip strength was 10 (5–26) kg in probable sarcopenia versus 25 (17–40) kg in non-sarcopenia (p < 0.001). Significant differences in inflammatory parameters were also observed; the systemic immune-inflammation index (SII) and the C-reactive protein/albumin ratio (CAR) were particularly associated with probable sarcopenia. In multivariable analysis, age, sex, serum albumin, neutrophil count, platelet count, and SII emerged as independent predictors. Receiver operating characteristic analysis indicated that SII may serve as a predictor of probable sarcopenia. Conclusion Among very old adults (≥ 80 years), SII was associated with probable sarcopenia and provided modest discrimination. Incorporating SII into primary care screening pathways may support triage of SARC-F/handgrip–positive patients for confirmatory testing and management; longitudinal validation is needed. Trial Registration: This is not a clinical trial. Sarcopenia systemic immune-inflammation index very old adults Figures Figure 1 Introduction Population ageing is accelerating worldwide because of longer life expectancy and sustained declines in fertility [ 1 , 2 ]. In 2006, adults aged 60 years and older accounted for 11% of the global population and are projected to reach about 22% by 2050 [ 3 , 4 ]. In Türkiye, the share of older adults has recently exceeded 10% and is expected to rise to 23.1% by 2050 and 31.7% by 2075 [ 5 ]. Within gerontology, older adults are often stratified as young-old (60–69 years), middle-old (70–79 years), and very old (≥ 80 years), reflecting heterogeneity in health needs and outcomes [ 6 – 9 ]. Sarcopenia, characterized by loss of muscle strength and mass, is associated with disability, falls, hospitalization, and reduced quality of life, imposing a substantial healthcare burden [ 10 , 11 ]. The 2018 European Working Group on Sarcopenia in Older People (EWGSOP2) reframed sarcopenia as a strength-based condition: probable sarcopenia is identified by low muscle strength; confirmed sarcopenia requires additional evidence of low muscle quantity/quality assessed by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA); and severe sarcopenia also involves impaired physical performance [ 12 ]. The SARC-F questionnaire is recommended for case-finding rather than diagnosis and should be complemented by objective handgrip dynamometry to identify probable sarcopenia in line with EWGSOP2 [ 12 , 13 ]. This pragmatic, strength-based approach is particularly relevant in primary care and long-term care settings, where access to DXA/BIA is limited. Age-related hormonal changes, physical inactivity, nutritional disorders, and pro-inflammatory cytokine activity contribute to sarcopenia pathophysiology, while chronic low-grade inflammation (“inflammaging”) provides a unifying framework [ 11 , 14 ]. Metabolic disturbances such as hyperuricemia have also been linked to muscle mass and strength [ 13 ]. Inflammation and oxidative stress, which accumulate with age, are thought to play pivotal roles within the framework of chronic low-grade inflammation known as inflammaging [ 11 , 14 ]. Consistent with this, inflammatory biomarkers show prognostic value for morbidity and mortality in older populations [ 15 , 16 ]. The systemic immune-inflammation index (SII), derived from platelet, neutrophil, and lymphocyte counts, integrates immune and inflammatory activity; higher SII values correlate with disease severity and adverse outcomes across chronic conditions [ 17 , 18 ]. Accordingly, this study examined the association between SII and handgrip-defined probable sarcopenia in adults aged ≥ 80 years. Accordingly, we aimed to evaluate the association between inflammatory indices—primarily the systemic immune-inflammation index (SII) and secondarily the C-reactive protein/albumin ratio (CAR)—and handgrip-defined probable sarcopenia in adults aged 80 years and older. We further examined independent predictors in multivariable models and assessed the discriminatory ability of SII by receiver operating characteristic analysis. This pragmatic, EWGSOP2-aligned approach is intended to inform feasible screening and triage in settings where dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are not routinely available. We aimed to determine whether inflammatory indices (SII, CAR) are associated with handgrip-defined probable sarcopenia in adults aged 80 years and older and to assess SII’s independent discriminatory performance within an EWGSOP2-aligned framework where DXA/BIA are not routinely available. Material and Methods Study design, setting, and ethics: This cross-sectional study included individuals aged 80 years and older who presented to the LIFE Unit and the Endocrinology Outpatient Clinic of the Recep Tayyip Erdoğan University Training and Research Hospital (Rize, Türkiye). The study was approved by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee (decision no. 2024/175). Written informed consent was obtained from all participants or their legally authorized representatives. All procedures complied with the Declaration of Helsinki and its amendments. Participants: Baseline variables included age, sex, chronic diseases, medication use, and sarcopenia case-finding/handgrip measurements. Exclusion criteria were age < 80 years; active malignancy, acute infection, immunodeficiency, or advanced chronic kidney failure; and a history of cerebrovascular events, end-stage heart failure, trauma, or psychiatric disorders. Sarcopenia case-finding and handgrip strength (EWGSOP2-aligned): Sarcopenia case-finding was performed with the SARC-F questionnaire (score 0–10; ≥4 indicates high risk), which has been shown to demonstrate good performance in relation to sarcopenia definitions, muscle mass, and functional measures [ 19 ]. In line with the 2018 European Working Group on Sarcopenia in Older People (EWGSOP2), muscle strength was measured using a Baseline® Hydraulic Hand Dynamometer (Fabrication Enterprises Inc. [FEI], White Plains, NY, USA). Participants were seated with the shoulder adducted, elbow flexed at 90°, forearm in neutral, and wrist in neutral to slight extension; the handle position was standardized and adjusted for hand size when necessary. Each hand was tested three times with brief rest; the highest value (kg) was recorded. Low muscle strength was defined as < 27 kg in men and < 16 kg in women, classifying participants as probable sarcopenia per EWGSOP2. SARC-F was used for screening only and not as a diagnostic criterion. Objective muscle mass assessment by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) was not available; therefore, all analyses pertain to probable rather than confirmed sarcopenia [ 12 ]. Geriatric assessments: Activities of daily living were evaluated using the Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL) scales; higher dependence indicates worse function. Frailty was assessed with the Clinical Frailty Scale (CFS; 1–9), with higher scores indicating greater frailty. Nutritional status was assessed using the Mini Nutritional Assessment (MNA) (short form if applicable), with standard cut-offs. Physical performance was captured by the Timed Up and Go (TUG) test and recorded in seconds; higher times indicate worse performance which aligns with geriatric care principles emphasizing functional assessment in preventive care approaches [ 20 ]. Blood sampling and inflammatory indices: Venous blood samples were analyzed using standard hospital laboratory procedures. Hemoglobin (Hgb), C-reactive protein (CRP) and albumin (Abbott Architect c1600 Clinical Chemistry; Abbott Core Laboratory, Germany; albumin reference 3.5–5.5 g/dL) were measured by routine methods. Complete blood counts, including platelet (PLT) and white blood cell (WBC) counts and differentials, were obtained on a CELL-DYN Ruby hematology analyzer (Abbott Laboratories). The systemic immune-inflammation index (SII) was calculated as SII = (platelets × neutrophils) / lymphocytes, using absolute counts in ×10³/µL. The C-reactive protein/albumin ratio (CAR) was calculated as CRP (mg/L) / albumin (g/dL) × 100. 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). Males represented 64.5% of the study population, with females comprising the remaining 35.5%. 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 overall prevalence of polypharmacy was 27.6%. It was higher in the sarcopenia group than in the non-sarcopenia group (p < 0.001). 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 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/L 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 ×10³/µL 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 BMI: body mass index; WBC: white blood cell; SII: systemic immune-inflammation index; CAR: C-reactive protein/albumin ratio. 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 Handgrip strength, kg 13 (5–40) 10 (5–26) 25 (17–40) < 0.001 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; Adjusted odds ratios (OR) with 95% confidence intervals are reported. Outcome coding: 1 = sarcopenia, 0 = non-sarcopenia. Continuous predictors are scaled as follows: SII per 100-unit increase; other continuous variables per 1-unit increase unless otherwise specified. Categorical variables are compared with the reference category indicated in the table. Two-sided α = 0.05. ROC analysis showed that the AUC for SII was 0.588 (95% CI 0.519–0.655; p = 0.031); at a threshold of 370.9, sensitivity was 63.8% and specificity was 58.0%. (Fig. 1 ). Discussion In this cross-sectional cohort of adults aged 80 years and older, the systemic immune-inflammation index (SII) was independently associated with handgrip-defined probable sarcopenia after adjustment for demographic and clinical covariates. In unadjusted comparisons, several inflammatory parameters differed between participants with and without probable sarcopenia, but SII retained the most consistent association in multivariable models. Handgrip strength was markedly lower in the probable-sarcopenia group than in the non-sarcopenia group, in line with the EWGSOP2 strength-based framework. Receiver operating characteristic analysis indicated modest discriminatory ability of SII for identifying probable sarcopenia, supporting its potential role in screening/triage where confirmatory muscle-mass assessment (DXA/BIA) is not routinely available. 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% to 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 ]. Limitations This study did not include objective assessment of muscle mass by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA); therefore, findings apply to probable sarcopenia based on low handgrip strength, supported by SARC-F case-finding, rather than confirmed sarcopenia. We did not perform standardized gait speed or SPPB testing, which precluded EWGSOP2 severity staging. The cross-sectional, single-center design limits causal inference and generalizability, and residual confounding (e.g., nutritional status, comorbidity burden, habitual physical activity) cannot be fully excluded despite multivariable adjustment. Conclusion In adults aged 80 years and older, the systemic immune-inflammation index (SII) was independently associated with handgrip-defined probable sarcopenia and showed modest discriminatory ability. In settings where DXA/BIA are not routinely available, incorporating SII alongside SARC-F and handgrip strength may help screening and triage, prioritizing patients for confirmatory assessment and appropriate interventions. Prospective studies are warranted to validate thresholds and clinical utility. Declarations Conflicts of Interest: The authors declare that they have no conflict of interest. Acknowledgements Not applicable. Authors’ contributions 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. Funding This study has been supported by the Recep Tayyip Erdoğan University Development Foundation. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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. References Jones K. The problem of an aging global population, shown by country. 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Duman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBACAyCWAGLGBhDvA4zBwMBMnBbGGSha2IjQwsxDjBZz9rMHb/zcwSC74drhZ49tKu7J9vMvfvaAocI6sUG+9wE2LZY9ecmWvWcYjDfcTjM3zjlTbDxzxjNzA4Yz6YkNbOwGWB12IMdMgreNIXHD7QQz6dy2hMQNNw6YSTC2HQZqwe4yg/NvzCT/grWkf5O2/AfScvybBOM/PFpu5JhJQ2wBMhgbgFrO9wBtacCn5Y2xtWybhPHM2zllkj3HEoB+4SmTSDiWbtzGlobDYTmGN9+22cj23U7fJvGjJgEYYse3SXyosZbtZz6GVQsUSCCzExgYgAhXtGAD/AeIVzsKRsEoGAUjAgAAsx9jm5yIs0AAAAAASUVORK5CYII=","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":true,"prefix":"","firstName":"Handan","middleName":"","lastName":"Duman","suffix":""},{"id":556450756,"identity":"a6f76306-9c19-41be-963c-82b130bdac9c","order_by":1,"name":"Damla Tüfekçi","email":"","orcid":"","institution":"Recep Tayyip Erdoğan 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15:35:55","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93224,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8067935/v1/5b1f338ce77956cd4c4d3e2f.html"},{"id":97720607,"identity":"e1e1a1c1-1497-4d9c-a653-8b95e271dbd3","added_by":"auto","created_at":"2025-12-08 15:35:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50020,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the systemic immune-inflammation index (SII) for discriminating probable sarcopenia: AUC 0.588 (95% CI 0.519–0.655; p = 0.031). Optimal cutoff 370.9 (sensitivity 63.8%, specificity 58.0%).\u003c/p\u003e","description":"","filename":"figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8067935/v1/90070c4574a2e03da74b75a6.jpeg"},{"id":108081032,"identity":"c682f094-e642-4d44-8e76-f2265e743d5b","added_by":"auto","created_at":"2026-04-29 07:41:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":343739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8067935/v1/af81477d-f687-4974-847c-7bb99b0781d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probable Sarcopenia and Inflammatory Indices in Very Old Adults (≥80) in Primary Care: The Role of SII and CAR","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation ageing is accelerating worldwide because of longer life expectancy and sustained declines in fertility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2006, adults aged 60 years and older accounted for 11% of the global population and are projected to reach about 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, the share of older adults has recently exceeded 10% and is expected to rise to 23.1% by 2050 and 31.7% by 2075 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Within gerontology, older adults are often stratified as young-old (60\u0026ndash;69 years), middle-old (70\u0026ndash;79 years), and very old (\u0026ge;\u0026thinsp;80 years), reflecting heterogeneity in health needs and outcomes [\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\u003eSarcopenia, characterized by loss of muscle strength and mass, is associated with disability, falls, hospitalization, and reduced quality of life, imposing a substantial healthcare burden [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The 2018 European Working Group on Sarcopenia in Older People (EWGSOP2) reframed sarcopenia as a strength-based condition: probable sarcopenia is identified by low muscle strength; confirmed sarcopenia requires additional evidence of low muscle quantity/quality assessed by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA); and severe sarcopenia also involves impaired physical performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The SARC-F questionnaire is recommended for case-finding rather than diagnosis and should be complemented by objective handgrip dynamometry to identify probable sarcopenia in line with EWGSOP2 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This pragmatic, strength-based approach is particularly relevant in primary care and long-term care settings, where access to DXA/BIA is limited.\u003c/p\u003e\u003cp\u003eAge-related hormonal changes, physical inactivity, nutritional disorders, and pro-inflammatory cytokine activity contribute to sarcopenia pathophysiology, while chronic low-grade inflammation (\u0026ldquo;inflammaging\u0026rdquo;) provides a unifying framework [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Metabolic disturbances such as hyperuricemia have also been linked to muscle mass and strength [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Inflammation and oxidative stress, which accumulate with age, are thought to play pivotal roles within the framework of chronic low-grade inflammation known as inflammaging [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consistent with this, inflammatory biomarkers show prognostic value for morbidity and mortality in older 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), derived from platelet, neutrophil, and lymphocyte counts, integrates immune and inflammatory activity; higher SII values correlate with disease severity and adverse outcomes across chronic conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Accordingly, this study examined the association between SII and handgrip-defined probable sarcopenia in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years.\u003c/p\u003e\u003cp\u003eAccordingly, we aimed to evaluate the association between inflammatory indices\u0026mdash;primarily the systemic immune-inflammation index (SII) and secondarily the C-reactive protein/albumin ratio (CAR)\u0026mdash;and handgrip-defined probable sarcopenia in adults aged 80 years and older. We further examined independent predictors in multivariable models and assessed the discriminatory ability of SII by receiver operating characteristic analysis. This pragmatic, EWGSOP2-aligned approach is intended to inform feasible screening and triage in settings where dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are not routinely available.\u003c/p\u003e\u003cp\u003eWe aimed to determine whether inflammatory indices (SII, CAR) are associated with handgrip-defined probable sarcopenia in adults aged 80 years and older and to assess SII\u0026rsquo;s independent discriminatory performance within an EWGSOP2-aligned framework where DXA/BIA are not routinely available.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e Study design, setting, and ethics: This cross-sectional study included individuals aged 80 years and older who presented to the LIFE Unit and the Endocrinology Outpatient Clinic of the Recep Tayyip Erdoğan University Training and Research Hospital (Rize, T\u0026uuml;rkiye). The study was approved by the Recep Tayyip Erdoğan University Clinical Research Ethics Committee (decision no. 2024/175). Written informed consent was obtained from all participants or their legally authorized representatives. All procedures complied with the Declaration of Helsinki and its amendments.\u003c/p\u003e\u003cp\u003eParticipants: Baseline variables included age, sex, chronic diseases, medication use, and sarcopenia case-finding/handgrip measurements. Exclusion criteria were age\u0026thinsp;\u0026lt;\u0026thinsp;80 years; active malignancy, acute infection, immunodeficiency, or advanced chronic kidney failure; and a history of cerebrovascular events, end-stage heart failure, trauma, or psychiatric disorders.\u003c/p\u003e\u003cp\u003eSarcopenia case-finding and handgrip strength (EWGSOP2-aligned): Sarcopenia case-finding was performed with the SARC-F questionnaire (score 0\u0026ndash;10; \u0026ge;4 indicates high risk), which has been shown to demonstrate good performance in relation to sarcopenia definitions, muscle mass, and functional measures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In line with the 2018 European Working Group on Sarcopenia in Older People (EWGSOP2), muscle strength was measured using a Baseline\u0026reg; Hydraulic Hand Dynamometer (Fabrication Enterprises Inc. [FEI], White Plains, NY, USA). Participants were seated with the shoulder adducted, elbow flexed at 90\u0026deg;, forearm in neutral, and wrist in neutral to slight extension; the handle position was standardized and adjusted for hand size when necessary. Each hand was tested three times with brief rest; the highest value (kg) was recorded. Low muscle strength was defined as \u0026lt;\u0026thinsp;27 kg in men and \u0026lt;\u0026thinsp;16 kg in women, classifying participants as probable sarcopenia per EWGSOP2. SARC-F was used for screening only and not as a diagnostic criterion. Objective muscle mass assessment by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) was not available; therefore, all analyses pertain to probable rather than confirmed sarcopenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGeriatric assessments: Activities of daily living were evaluated using the Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL) scales; higher dependence indicates worse function. Frailty was assessed with the Clinical Frailty Scale (CFS; 1\u0026ndash;9), with higher scores indicating greater frailty. Nutritional status was assessed using the Mini Nutritional Assessment (MNA) (short form if applicable), with standard cut-offs. Physical performance was captured by the Timed Up and Go (TUG) test and recorded in seconds; higher times indicate worse performance which aligns with geriatric care principles emphasizing functional assessment in preventive care approaches [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBlood sampling and inflammatory indices: Venous blood samples were analyzed using standard hospital laboratory procedures. Hemoglobin (Hgb), C-reactive protein (CRP) and albumin (Abbott Architect c1600 Clinical Chemistry; Abbott Core Laboratory, Germany; albumin reference 3.5\u0026ndash;5.5 g/dL) were measured by routine methods. Complete blood counts, including platelet (PLT) and white blood cell (WBC) counts and differentials, were obtained on a CELL-DYN Ruby hematology analyzer (Abbott Laboratories). The systemic immune-inflammation index (SII) was calculated as SII = (platelets \u0026times; neutrophils) / lymphocytes, using absolute counts in \u0026times;10\u0026sup3;/\u0026micro;L. The C-reactive protein/albumin ratio (CAR) was calculated as CRP (mg/L) / albumin (g/dL) \u0026times; 100.\u003c/p\u003e\u003cdiv id=\"Sec3\" 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). Males represented 64.5% of the study population, with females comprising the remaining 35.5%. 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 (181.00-268.00) (p\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall prevalence of polypharmacy was 27.6%. It was higher in the sarcopenia group than in the non-sarcopenia group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). 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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"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\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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, mg/dL\u003c/b\u003e\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=\"char\" char=\".\" 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, mg/L\u003c/b\u003e\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=\"char\" char=\".\" 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, g/dL\u003c/b\u003e\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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 \u0026times;10\u0026sup3;/\u0026micro;L\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003eBMI: body mass index; WBC: white blood cell; SII: systemic immune-inflammation 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\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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" 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\u003eHandgrip strength, kg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (5\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (5\u0026ndash;26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (17\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\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=\"left\" 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; Adjusted odds ratios (OR) with 95% confidence intervals are reported. Outcome coding: 1\u0026thinsp;=\u0026thinsp;sarcopenia, 0\u0026thinsp;=\u0026thinsp;non-sarcopenia. Continuous predictors are scaled as follows: SII per 100-unit increase; other continuous variables per 1-unit increase unless otherwise specified. Categorical variables are compared with the reference category indicated in the table. Two-sided α\u0026thinsp;=\u0026thinsp;0.05.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eROC analysis showed that the AUC for SII was 0.588 (95% CI 0.519\u0026ndash;0.655; p\u0026thinsp;=\u0026thinsp;0.031); at a threshold of 370.9, sensitivity was 63.8% and specificity was 58.0%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional cohort of adults aged 80 years and older, the systemic immune-inflammation index (SII) was independently associated with handgrip-defined probable sarcopenia after adjustment for demographic and clinical covariates. In unadjusted comparisons, several inflammatory parameters differed between participants with and without probable sarcopenia, but SII retained the most consistent association in multivariable models. Handgrip strength was markedly lower in the probable-sarcopenia group than in the non-sarcopenia group, in line with the EWGSOP2 strength-based framework. Receiver operating characteristic analysis indicated modest discriminatory ability of SII for identifying probable sarcopenia, supporting its potential role in screening/triage where confirmatory muscle-mass assessment (DXA/BIA) is not routinely available.\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% to 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\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study did not include objective assessment of muscle mass by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA); therefore, findings apply to probable sarcopenia based on low handgrip strength, supported by SARC-F case-finding, rather than confirmed sarcopenia. We did not perform standardized gait speed or SPPB testing, which precluded EWGSOP2 severity staging. The cross-sectional, single-center design limits causal inference and generalizability, and residual confounding (e.g., nutritional status, comorbidity burden, habitual physical activity) cannot be fully excluded despite multivariable adjustment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn adults aged 80 years and older, the systemic immune-inflammation index (SII) was independently associated with handgrip-defined probable sarcopenia and showed modest discriminatory ability. In settings where DXA/BIA are not routinely available, incorporating SII alongside SARC-F and handgrip strength may help screening and triage, prioritizing patients for confirmatory assessment and appropriate interventions. Prospective studies are warranted to validate thresholds and clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been supported by the Recep Tayyip Erdoğan University Development Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\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\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJones K. 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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, very old adults","lastPublishedDoi":"10.21203/rs.3.rs-8067935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8067935/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\u003eCross-sectional study of 214 adults aged 80 years and older. SARC-F for case-finding; handgrip dynamometry for muscle strength. Probable sarcopenia per EWGSOP2 (\u0026lt;\u0026thinsp;27 kg men, \u0026lt;\u0026thinsp;16 kg women). Systemic immune-inflammation index (SII) and C-reactive protein/albumin ratio (CAR) calculated from routine laboratory tests; muscle mass not measured.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMedian handgrip strength was 10 (5\u0026ndash;26) kg in probable sarcopenia versus 25 (17\u0026ndash;40) kg in non-sarcopenia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences in inflammatory parameters were also observed; the systemic immune-inflammation index (SII) and the C-reactive protein/albumin ratio (CAR) were particularly associated with probable sarcopenia. In multivariable analysis, age, sex, serum albumin, neutrophil count, platelet count, and SII emerged as independent predictors. Receiver operating characteristic analysis indicated that SII may serve as a predictor of probable sarcopenia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAmong very old adults (\u0026ge;\u0026thinsp;80 years), SII was associated with probable sarcopenia and provided modest discrimination. Incorporating SII into primary care screening pathways may support triage of SARC-F/handgrip\u0026ndash;positive patients for confirmatory testing and management; longitudinal validation is needed.\u003c/p\u003e\u003ch2\u003eTrial Registration:\u003c/h2\u003e\u003cp\u003eThis is not a clinical trial.\u003c/p\u003e","manuscriptTitle":"Probable Sarcopenia and Inflammatory Indices in Very Old Adults (≥80) in Primary Care: The Role of SII and CAR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 15:35:50","doi":"10.21203/rs.3.rs-8067935/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":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T07:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 15:35:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8067935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8067935","identity":"rs-8067935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}