Osteosarcopenia as a Prognostic Indicator of Gastric Cancer Compared with Established Nutritional Markers

Osteosarcopenia as a Prognostic Indicator of Gastric Cancer Compared with Established Nutritional Markers | 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 Osteosarcopenia as a Prognostic Indicator of Gastric Cancer Compared with Established Nutritional Markers Keiichiro Ryujin, Tetsuro Kawazoe, Yasuo Tsuda, Kensuke Kudou, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9331253/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Preoperative nutritional and immunological status is closely associated with postoperative outcomes in gastric cancer. Although osteopenia and sarcopenia have been reported as prognostic factors, the significance of their coexistence as osteosarcopenia and its value relative to established nutritional indices remain unclear. This study aimed to clarify the prognostic impact of osteosarcopenia in patients undergoing curative gastrectomy. Methods This study included 273 patients who underwent curative gastrectomy for gastric cancer. Osteopenia and sarcopenia were assessed using preoperative computed tomography–derived bone mineral density and skeletal muscle index, respectively, and osteosarcopenia was defined as their coexistence. Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards models, and prognostic predictive ability was evaluated by time-dependent ROC analysis. Results Osteopenia, sarcopenia, and osteosarcopenia were observed in 40.7%, 49.5%, and 22.3% of patients, respectively. Osteopenia and osteosarcopenia were independent predictors of poor OS and RFS, whereas sarcopenia alone was not. Osteosarcopenia was associated with older age, systemic inflammation, and poor nutritional status. A composite Cox model–based risk score integrating body composition and nutritional parameters demonstrated superior prognostic accuracy. Conclusion Preoperative osteosarcopenia is a strong prognostic indicator in gastric cancer, and multidimensional assessment improves survival prediction. metabolic bone diseases prognosis stomach neoplasms sarcopenia osteopenia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastric cancer (GC) is the fifth most common malignancy worldwide and the fourth leading cause of cancer-related deaths, despite a global decline in its incidence and mortality rates 1 . Throughout Asia, GC continues to pose a major health concern, ranking third in incidence and fourth in mortality among all cancers in Japan 2 . Although advances in multimodal treatments such as surgical techniques and chemotherapy have improved patient outcomes, the identification of reliable prognostic factors to guide therapeutic decision-making remains essential. Preoperative nutritional and immunological statuses play a crucial role in determining postoperative outcomes in patients with cancer, with several indices, including the platelet-to-lymphocyte ratio (PLR) and prognostic nutritional index (PNI), reported to be significant prognostic markers of surgical outcomes 3 , 4 . Osteopenia (reduced bone mineral density) and sarcopenia (indicating loss of skeletal muscle mass and strength) have recently garnered increasing attention as potential prognostic markers, with several studies showing them to be prognostic factors for various malignancies. Additionally, the concept of osteosarcopenia, defined as the co-occurrence of osteopenia and sarcopenia, has recently emerged 5 , further highlighting the relevance of nutritional indicators in the perioperative setting. Although previous studies have evaluated individual nutritional or inflammatory indices 6 – 8 , these assessments often capture limited aspects of cancer-related nutritional impairment and systemic inflammation. Moreover, the relationships among osteopenia, sarcopenia, and established nutritional markers remain unclear. Therefore, this retrospective study aimed to elucidate the interrelationships and prognostic significance of preoperative osteopenia, sarcopenia, osteosarcopenia, and representative nutritional indices in patients with GC. Material and methods Patients We screened a total of 292 consecutive patients who underwent gastrectomy for primary GC at the Department of Gastroenterological Surgery at Kyushu University Hospital between January 2014 and December 2018, of whom 273 were retrospectively included in this study. A total of 19 patients were excluded for unresectable disease or distant metastases (Fig. 1 ). The following clinical and pathological data were extracted from medical records: age, sex, body mass index (BMI), smoking history, preoperative laboratory findings (hemoglobin, white blood cell, C-reactive protein [CRP], tumor markers, etc.), surgical procedure, reconstruction method, pathological stage (Japanese Classification of Gastric Carcinoma, 15th edition 9 ), postoperative complications, hospital stay, and use of neoadjuvant or adjuvant chemotherapy. Definition of osteopenia, sarcopenia, and osteosarcopenia Bone mineral density (BMD) was assessed using computed tomography (CT) performed within 2 months prior to surgery. Osteopenia was defined as a value < 140 Hounsfield unit (HU), as determined by the mean HU value within a circular region of interest at the center of the 11th thoracic vertebral body, based on a previously published study 10 (Fig. 2 a). Skeletal muscle mass was quantified using the cross-sectional skeletal muscle area at the level of the 3rd lumbar vertebra on CT performed within 2 months prior to surgery and was normalized by height squared to calculate the skeletal muscle index (SMI). As cut-off values for SMI vary widely across different definitions and among different populations, we divided participants into three groups: men were divided into two groups based on BMI (≥ 25 and < 25), and women formed a single group. We then used the median SMI values for each group (43.5, 50.7, and 34.6 cm²/m², respectively) as cutoff points, defining sarcopenia as values below these thresholds 11 (Fig. 2 b). Osteosarcopenia was defined as the co-occurrence of osteopenia and sarcopenia. Nutritional and Inflammatory Markers Preoperative blood data (obtained within 2 months prior to surgery) were used to calculate nutritional and inflammatory indices as follows: PLR: total platelet count (TPC) [/µL] / total lymphocyte count (TLC) [/µL] Neutrophil to lymphocyte ratio (NLR): total neutrophil count (TNC) [/µL] / TLC [/µL] CRP to albumin [Alb] ratio (CAR): CRP [mg/dL] / Alb [g/dL] Nutritional Risk Index (NRI): (1.519 × Alb [g/L]) / (41.7 * body weight [kg] / ideal body weight [kg]) Systemic Immune-Inflammation Index (SII): TPC [/µL] * TNC [/µL] / TLC [/µL] PNI: (10 × Alb [g/dL]) + (0.005 × TLC [/µL]) Controlling Nutritional Status (CONUT) Score: determined by the combination of Alb, TLC, and total cholesterol level 12 . Modified Glasgow Prognostic Score (mGPS): determined by the combination of CRP and Alb level 13 . Analyses of Risk Factors for Survival and Recurrence The relationships among clinicopathological factors, nutritional and inflammatory markers, body composition indicators (osteopenia, sarcopenia, and osteosarcopenia), and postoperative oncological outcomes were evaluated. Univariate analyses were performed to examine the relationships between each variable and overall survival (OS) and recurrence-free survival (RFS). Variables with P < 0.05 in the univariate analysis were subsequently included in a multivariate Cox proportional hazards regression model to identify independent prognostic factors. In cases of suspected multicollinearity among covariates, the corresponding variables were removed before the model was constructed. Osteopenia and sarcopenia were analyzed concomitantly, given their lack of correlation, whereas osteosarcopenia was analyzed independently owing to its correlation with both osteopenia and sarcopenia. Continuous variables, except age, were dichotomized using the following thresholds: carcinoembryonic antigen (CEA), 5 ng/mL; carbohydrate antigen (CA)19 − 9, 37 U/mL; NLR, 2.5; PLR, 150; CAR, 0.05; NRI, 100; SII, 50×10; and PNI, 45. Pathological stages were grouped as pStage I vs. pStage II/III, CONUT score as 0–1 vs. 2–12, and mGPS as 0 vs. 1–2. Time-dependent receiver operating characteristic (ROC) curve analysis To evaluate prognostic performance with respect to OS, time-dependent ROC analyses were performed for preoperative nutritional and inflammatory indices. Based on the significant variables identified in the univariate Cox analysis (PNI ≥ 45, mGPS 1–2, osteopenia, and sarcopenia), four quantitative indicators (PNI, mGPS, BMD, and SMI) were included in the ROC analysis. The area under the curve (AUC) at 3 years was calculated for each parameter. A composite prognostic model (Cox model risk score) was developed by integrating these indices (low PNI [< 45], high mGPS [1–2], osteopenia, and sarcopenia were coded as 1; all else, 0) using a Cox proportional hazards model. Linear predictor values were generated for each case, and the optimal cutoff value was determined using the Youden index. Predictive performance was assessed by calculating the sensitivity and specificity for 3-year OS using the timeROC package in R ( https://www.r-project.org/ version 4.3.3), and ROC curves for individual and composite models were plotted on the same graph. Statistical Analysis All statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were expressed as medians with interquartile ranges (IQRs) and compared using the Mann–Whitney U test. Categorical variables are expressed as counts and percentages and compared using the chi-squared test. Survival curves for OS and RFS were generated using the Kaplan–Meier method, and differences between groups were evaluated using the log-rank test. Cox proportional hazards regression was used for both univariate and multivariate analyses to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical significance was set at P < 0.05. Results Patient Characteristics Details of the patients’ background characteristics are summarized in Table 1 and Supplementary Tables 1 and 2. Among the 273 included patients, 189 (69.2%) were men, the median age was 68 years, and the median BMI was 22.7 kg/m². Osteopenia, sarcopenia, and osteosarcopenia were observed in 111 (40.7%), 135 (49.5%), and 61 (22.3%) patients, respectively. No significant correlations were observed between osteopenia and sarcopenia. Table 1 List of clinical and pathological factors for osteosarcopenia group. Total Osteosarcopenia Yes No (n = 273) (n = 61) (n = 212) P Sex Male, n (%) 189 (69.2) 49 (80.3) 140 (66.0) 0.05 Age (year) median [IQR] 68.0 [61.0, 76.0] 72.0 [65.0, 79.0] 68.0 [60.0, 74.0] 0.002 BMI (kg×m − 2 ) median [IQR] 22.7 [20.3, 24.4] 21.1 [19.3, 23.2] 23.0 [20.8, 24.5] 0.002 Smoking Yes, n (%) 167 (61.2) 43 (70.5) 124 (58.5) 0.12 Neoadjuvant chemotherapy Yes, n (%) 18 (6.6) 6 (9.8) 12 (5.7) 0.39 CEA (mg/dl) ≥ 5, n (%) 33 (12.1) 13 (21.3) 20 (9.4) 0.02 CA19-9 (U/dl) ≥ 37, n (%) 13 (4.8) 1 (1.6) 12 (5.7) 0.34 PLR ≥ 150, n (%) 117 (42.9) 23 (37.7) 94 (44.3) 0.44 NLR ≥ 2.5, n (%) 96 (35.2) 29 (47.5) 67 (31.6) 0.03 CAR ≥ 0.05, n (%) 64 (23.4) 21 (34.4) 43 (20.3) 0.03 NRI < 100, n (%) 81 (29.7) 26 (42.6) 55 (25.9) 0.02 SII ≥ 50x10^4, n (%) 131 (48.0) 33 (54.1) 98 (46.2) 0.35 PNI < 45, n (%) 67 (24.5) 20 (32.8) 47 (22.2) 0.13 CONUT score ≥ 2, n (%) 105 (38.5) 25 (41.0) 80 (37.7) 0.76 mGPS 1–2, n (%) 28 (10.3) 8 (13.1) 20 (9.4) 0.55 Operative time (min) median [IQR] 274 [234, 317] 274 [236, 300] 273 [233, 321] 0.60 Bleeding (ml) median [IQR] 43.0 [20.0, 105] 40.0 [20.0, 100] 44.0 [20.0, 105] 0.74 pStage I, n (%) 174 (63.7) 40 (65.6) 134 (63.2) 0.39 II, n (%) 53 (19.4) 14 (23.0) 39 (18.4) III, n (%) 46 (16.8) 7 (11.5) 39 (18.4) Postoperative complication (CD) ≥ Grade III, n (%) 29 (10.6) 5 (8.2) 24 (11.3) 0.64 Postoperative hospital stay (day) median [IQR] 10.0 [9.0, 14.0] 11.0 [9.0, 14.0] 10.0 [8.0, 14.0] 0.42 Adjuvant chemotherapy Yes, n (%) 63 (23.1) 10 (16.4) 53 (25.0) 0.22 IQR, interquartile range; BMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein–to–albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score; CD, Clavien–Dindo classification. The patients with osteosarcopenia had a higher proportion of men than those without (80.3% vs. 66.0%, respectively; P = 0.048) and were significantly older (72 vs. 68 years, respectively; P < 0.002). No significant differences were observed in the pathological stage, surgical procedure, or reconstruction method among the groups. Regarding tumor markers, the osteosarcopenia group had a significantly higher rate of elevated serum CEA than those without (21.3% vs. 9.4%, respectively; P = 0.02). The osteosarcopenia group also showed higher proportions of elevated NLR (≥ 2.5: 47.5% vs. 31.6%, respectively; P = 0.02), elevated CAR (≥ 0.05: 34.4% vs. 20.3%, respectively; P = 0.03), and low NRI (42.6% vs. 25.9%; P = 0.02) (Table 1 ) than those without. Furthermore, upon evaluating osteopenia and sarcopenia as distinct entities, patients with osteopenia were significantly older (74 vs. 64 years, respectively; P < 0.001) and had longer postoperative hospital stays (11 vs. 10 days, respectively; P = 0.004). They also tended to have a higher incidence of postoperative complications classified as Clavien–Dindo grade III or higher (15.3% vs. 7.4%, respectively; P = 0.06) (Supplementary Table 1). Among the sarcopenia group, BMI was significantly lower (20.8 vs. 23.5; P < 0.001), and the proportion of patients receiving preoperative treatment was higher (10.4% vs. 2.9%; P = 0.03) than those in the osteopenia group, respectively. Among the nutritional and inflammatory markers, low PNI was more frequent in the osteopenia group (PNI < 45: 32.4% vs. 19.1%; P = 0.02), and low NRI was more common in the sarcopenia group (NRI < 100: 42.2% vs. 17.4%; P < 0.001) (Supplementary Table 2). Impact of osteopenia, sarcopenia, and osteosarcopenia on OS Figure 3 shows the Kaplan–Meier curves for OS among patients with osteopenia, sarcopenia, and osteosarcopenia. Patients with osteopenia had a significantly poorer OS than those without (5-year OS: 70.7% vs. 84.4%, respectively; P = 0.002) (Fig. 3 a). Similarly, patients with sarcopenia had worse OS than those without (5-year OS: 72.7% vs. 85.4%, respectively; P = 0.03) (Fig. 3 b). Among all groups, osteosarcopenia was associated with the poorest prognosis (5-year OS: 64.9% vs. 83.0%; P = 0.003) (Fig. 3 c). Osteopenia remained a significant independent prognostic factor for OS (HR = 1.71; 95% CI: 1.00–3.11; P = 0.049), whereas sarcopenia did not (HR = 1.46; 95% CI: 0.81–2.63; P = 0.20). In contrast, osteosarcopenia was identified as an independent predictor of poor OS (HR = 1.95; 95% CI: 1.06–3.60; P = 0.03) (Table 2 ). Table 2 Multivariate analysis of overall survival in osteopenia, sarcopenia and osteosarcopenia. Overall Survival Univariate analysis Multivariate analysis Osteopenia and Sarcopenia Osteosarcopenia HR 95%CI P HR 95%CI P HR 95%CI P Sex (male) 1.66 0.87–3.15 0.13 Age 1.07 1.04–1.10 < 0.001 1.04 1.01–1.07 0.004 1.05 1.02–1.08 0.001 Smoke (yes) 1.70 0.93–3.14 0.09 BMI (≥ 25 kg×m-2) 1.40 0.77–2.54 0.27 CEA (≥ 5 mg/dl) 2.96 1.51–5.78 0.001 2.39 1.19–4.82 0.014 2.30 1.13–4.65 0.02 CA19-9 (≥ 37 U/dl) 2.22 0.69–7.17 0.18 PLR (≥ 150) 1.00 0.57–1.74 0.99 NLR (≥ 2.5) 1.48 0.85–2.57 0.16 CAR (≥ 0.05) 1.92 1.08–3.42 0.03 NRI (< 100) 2.27 1.31–3.92 0.003 SII (≥ 50×10 4 ) 1.39 0.81–2.41 0.23 PNI (< 45) 3.25 1.87–5.66 < 0.001 1.67 0.85–3.30 0.14 1.67 0.85–3.30 0.14 CONUT score (≥ 2) 1.97 1.13–3.44 0.02 mGPS (≥ 1) 4.13 2.12–8.06 < 0.001 1.67 0.74–3.79 0.22 1.76 0.77–3.99 0.18 Osteopenia (yes) 2.29 1.33–3.94 0.003 1.66 1.00-2.89 0.05 Sarcopenia (yes) 1.84 1.06–3.21 0.03 1.57 0.89–2.76 0.12 Osteosarcopenia (yes) 2.28 1.30-4.00 0.004 1.97 1.09–3.55 0.02 Operative time (≥ 270 min) 1.33 0.77–2.33 0.31 Bleeding (≥ 100 ml) 1.68 0.97–2.90 0.06 pStage (≥ II) 3.99 2.27–7.01 < 0.001 3.42 1.90–6.13 < 0.001 3.56 1.98–6.40 < 0.001 Cravien-Dindo (≥ Grade III) 1.38 0.59–3.24 0.45 BMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; HR, hazard ratio; CI, confidence interval; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein–to–albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score. Impact of osteopenia, sarcopenia, and osteosarcopenia on RFS Figure 4 presents the Kaplan–Meier curves for RFS among patients with osteopenia, sarcopenia, and osteosarcopenia. RFS was significantly worse in the osteopenia group than in the non-osteopenia group (5-year RFS: 67.8% vs. 82.1%, respectively; P = 0.002) (Fig. 4 a). Similarly, patients with sarcopenia had poorer RFS than those without (5-year RFS: 69.2% vs. 83.6%, respectively; P = 0.02) (Fig. 4 b). Osteosarcopenia was associated with the most unfavorable RFS among all groups (5-year RFS: 62.1% vs. 80.5%; P = 0.005) (Fig. 4 c). Multivariate analysis of RFS revealed that osteopenia was an independent predictor of recurrence (HR = 1.82; 95% CI: 1.02–3.26; P = 0.04), whereas sarcopenia was not (HR = 1.48; 95% CI: 0.83–2.64; P = 0.19). Osteosarcopenia remained a significant independent factor for recurrence (HR = 2.03; 95% CI: 1.10–3.72; P = 0.02) (Table 3 ). Table 3 Multivariate analysis of recurrence-free survival in osteopenia, sarcopenia and osteosarcopenia. Recurrence-Free Survival Univariate analysis Multivariate analysis Osteopenia and Sarcopenia Osteosarcopenia HR 95%CI P HR 95%CI P HR 95%CI P Sex (male) 1.98 1.05–3.71 0.04 0.9 0.42–1.92 0.78 0.86 0.41–1.83 0.70 Age 1.06 1.03–1.08 < 0.001 1.04 1.01–1.07 0.008 1.04 1.02–1.07 0.002 Smoke (yes) 1.91 1.06–3.41 0.03 2.34 1.09-5.00 0.03 2.23 1.05–4.72 0.04 BMI (≥ 25 kg×m-2) 1.30 0.73–2.30 0.37 CEA (≥ 5 mg/dl) 2.99 1.61–5.55 < 0.001 1.87 0.95–3.66 0.07 1.88 0.96–3.67 0.07 CA19-9 (≥ 37 U/dl) 3.56 1.41–8.96 0.007 3.66 1.24–10.8 0.02 3.73 1.30–10.7 0.02 PLR (≥ 150) 1.13 0.68–1.89 0.63 NLR (≥ 2.5) 1.38 0.82–2.32 0.23 CAR (≥ 0.05) 2.15 1.27–3.66 0.004 NRI (< 100) 1.97 1.18–3.30 0.01 SII (≥ 50×10 4 ) 1.42 0.85–2.37 0.18 PNI (< 45) 3.14 1.87–5.28 < 0.001 1.79 0.94–3.39 0.08 1.79 0.95–3.38 0.07 CONUT score (≥ 2) 2.05 1.21–3.47 0.002 mGPS (≥ 1) 4.04 2.18–7.49 < 0.001 1.82 0.85–3.90 0.13 1.89 0.90–3.99 0.09 Osteopenia (yes) 2.17 1.31–3.59 0.003 1.71 1.02–2.97 0.05 Sarcopenia (yes) 1.84 1.10–3.09 0.02 1.51 0.88–2.59 0.13 Osteosarcopenia (yes) 2.10 1.24–3.57 0.006 1.84 1.05–3.24 0.04 Operative time (≥ 270 min) 1.20 0.72-2.00 0.48 Bleeding (≥ 100 ml) 1.56 0.94–2.62 0.09 pStage (≥ II) 4.03 2.39–6.81 < 0.001 3.08 1.76–5.39 < 0.001 3.15 1.80–5.51 < 0.001 Cravien-Dindo (≥ Grade III) 1.56 0.74–3.29 0.24 BMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; HR, hazard ratio; CI, confidence interval; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein–to–albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score. Prognostic value of nutritional and inflammatory indices based on ROC analysis Figure 5 a compares the predictive performance of nutritional, inflammatory, and body composition indicators for 3-year OS using ROC analyses. PNI, mGPS, osteopenia, sarcopenia, and osteosarcopenia, the five variables that were significant in univariate analyses, were evaluated. The AUC values for the predicted 3-year OS were 0.70, 0.61, 0.61, and 0.58, respectively, and osteosarcopenia (yes/no) = 0.64. Although each indicator demonstrated moderate discriminatory ability, their individual performance was limited. Therefore, a composite prognostic model (Cox model risk score) was developed by integrating these (low PNI [< 45], high mGPS [1–2], osteopenia, and sarcopenia were coded as 1; all else, 0) using a Cox proportional hazards model (Cox model risk score = 0.907 × high PNI + 0.569 × low mGPS + 0.641 × osteopenia + 0.516 × sarcopenia). The resulting Cox model risk score yielded the highest predictive accuracy, with an AUC of 0.78, outperforming all individual indicators. Patients were divided into low- and high-risk groups according to the optimal cutoff value determined using the Cox model risk score (0.907). A significant difference was observed in OS between the groups (5-year OS: 88.8% vs. 63.2%, respectively; P < 0.001) (Fig. 5 b). These results indicate that combining bone and muscle composition markers with nutritional and inflammatory parameters provided a more robust prognostic assessment than any single factor alone. Discussion Among patients with advanced cancer, reduced oral intake, malabsorption, and chronic inflammation often lead to cancer cachexia, which is characterized by the loss of skeletal muscle mass and decreased BMD 14 . Among patients with GC, osteopenia has been reported to influence surgical stress, postoperative complications, and long-term outcomes 15 , 16 , whereas sarcopenia has been associated with systemic inflammatory responses, treatment-related toxicity, immunosuppression, and poor prognosis 17 , 18 . In the present study, patients with osteopenia were significantly older than those without, suggesting an age-related deterioration in BMD. Furthermore, osteopenia was associated with a higher incidence of severe postoperative complications (Clavien–Dindo grade III or higher), which may have contributed to patients’ prolonged postoperative hospital stays. The proportion of patients who received preoperative chemotherapy was higher in the sarcopenia group than in the non-sarcopenia group, suggesting that preoperative treatment may have induced and/or exacerbated muscle loss. Although BMI was significantly lower in the sarcopenia group, it was inherently incorporated into the definition of sarcopenia in this study. Therefore, background factors such as BMI and indices that include standard body weight, such as the NRI, are expected to correlate with sarcopenia by definition and are thus not considered independent explanatory variables for evaluating the impact of sarcopenia itself. We investigated the effects of preoperative osteopenia, sarcopenia, and their co-occurrence as osteosarcopenia on the prognosis of patients who underwent curative gastrectomy for GC. Osteopenia was identified as an independent prognostic factor associated with significantly shorter OS and RFS, while sarcopenia was not an independent prognostic factor in the multivariate analysis, contrary to previous reports 19 . Among our cohort, a higher proportion of patients in the sarcopenia group received preoperative chemotherapy, some of whom may have transitioned from a non-sarcopenic to a sarcopenic state within a relatively short period owing to the treatment. Consequently, the preoperative “sarcopenia” group may have included patients who originally had relatively preserved muscle mass, potentially allowing early recovery of muscle mass once adequate postoperative oral intake was achieved, thereby mitigating the negative prognostic impact of this change. It is also possible that improved oncological outcomes owing to preoperative therapy attenuated the survival difference between patients with and without sarcopenia. Previous studies have reported no association between sarcopenia and prognosis 20 , and the lack of a standardized cutoff for SMI across populations and studies 11 further complicates this interpretation. Therefore, the prognostic significance of sarcopenia in GC remains unclear. In the present study, patients with osteosarcopenia, characterized by a simultaneous reduction in bone and muscle mass, had the worst prognoses. Although osteopenia alone was found to be a prognostic factor, the Cox proportional hazards model revealed an additive effect of sarcopenia in osteosarcopenia. This finding is consistent with the results reported by Kai et al. 21 , and Hirase et al. 5 . The present study represents the largest case series to date. Hirase et al. 5 . demonstrated that patients with osteosarcopenia exhibit significantly reduced infiltration of CD8-positive T cells, programmed cell death 1 (PD-1)-positive cells, and programmed cell Death Ligand 1 (PD-L1)-positive cells within the tumor microenvironment. Their results suggested that an immunosuppressive shift in the tumor immune microenvironment may contribute to poor outcomes. In the present study, patients with osteosarcopenia were characterized by older age, male predominance, low BMI, high NLR, and low NRI, indicating a systemic proinflammatory state and poor nutritional status, which may facilitate tumor progression and subsequently worsen prognosis. Nutritional and inflammatory indices such as PNI and mGPS have been widely reported as useful prognostic markers among patients with cancer 22 , 23 . PNI, a simple index based on serum albumin level and lymphocyte count is commonly used as a predictor of poor postoperative prognosis in GC 24 , whereas mGPS and CAR reflect CRP-mediated systemic inflammation and allow for a comprehensive assessment of immunonutritional status 25 . In the present study, we integrated PNI and mGPS with bone and muscle status markers to construct a composite risk score using a Cox proportional hazards model. This Cox model-based risk score achieved an AUC of 0.78 for predicting 3-year OS, outperforming each individual indicator, suggesting that a multidimensional risk assessment combining nutritional, inflammatory, and body composition parameters is more informative than any single metric in predicting survival in GC. The results of the present study demonstrated that the preoperative evaluation of osteopenia and sarcopenia is useful for risk stratification of patients with GC. Wada et al. 26 reported that preoperative nutritional and exercise interventions in patients undergoing gastrectomy improved postoperative outcomes, including a reduction in postoperative complications and a shortened length of hospital stay. Additionally, interventions aimed at maintaining bone mass, such as vitamin D and calcium supplementation and bisphosphonate therapy, have been reported in patients with GC after surgical intervention 27 , 28 . The de novo development of sarcopenia after surgery has also been associated with poor prognosis 29 , suggesting that comprehensive perioperative management combining nutritional support, exercise therapy, and pharmacological interventions may be an important future strategy. The present study had some limitations that should be discussed. First, this was a single-center retrospective analysis with a potential selection bias and an imbalance in demographic characteristics. Nonetheless, compared with previous studies on osteopenia, sarcopenia, and osteosarcopenia in GC, this study included a relatively large sample size and utilized multivariate analyses with adjustment for confounding factors, thereby providing statistically robust results despite its retrospective design. Second, immunological analyses among our cohort were limited; however, when interpreted in conjunction with prior immunological studies, our findings help bridge molecular-level mechanisms and clinically-observed outcomes. Conclusion In conclusion, the present study, which included the largest number of cases among relevant literature, elucidated the association between osteosarcopenia, nutritional indicators, and prognosis for the first time in GC. Preoperative osteopenia and sarcopenia, especially when co-occurring as osteosarcopenia, are associated with a poor prognosis in patients undergoing curative gastrectomy for GC. These conditions are closely linked to nutritional status, systemic inflammation, and tumor immune microenvironment; therefore, a multifactorial model incorporating a composite risk score based on these factors may enable a more accurate prognostic prediction and facilitate individualized treatment strategies. Given that perioperative nutritional interventions and body composition-targeted management may improve these risk profiles, proactive interventions should be considered in clinical practice. Declarations Ethics Statement All procedures were performed in accordance with the ethical standards of the responsible committees on human experimentation (institutional and national) and the Helsinki Declaration of 1964 and its later versions. The Ethics Review Committee of Kyushu University Graduate School of Medicine Sciences approved the study protocol, and informed consent was obtained from all patients (approval no. 23329). Conflicts of Interest: No conflicts of interest. Funding Information: No funding was received for this study. Author Contribution KR, TK, and MO conceived and designed the study. KR performed data collection, data analysis, and statistical analysis. KR drafted the manuscript. TK, KK, MO, and KA critically revised the manuscript for important intellectual content. YT, TN, EO, and TY provided clinical interpretation and editorial support. All of the authors have read and approved the final manuscript. Acknowledgement The authors would like to thank Editage (www.editage.jp) for English language editing. Data Availability The data are not publicly available due to privacy or ethical restrictions but are available from the corresponding author upon reasonable request and with permission of the institutional review board. References Yang WJ, Zhao HP, Yu Y et al (2023) Updates on global epidemiology, risk and prognostic factors of gastric cancer. World J Gastroenterol 29:2452–2468 2 Cancer Statistics in Japan (1958–2022). Available from: https://ganjoho.jp/reg_stat/statistics/data/dl/en.html Fang T, Wang Y, Yin X et al (2020) Diagnostic Sensitivity of NLR and PLR in Early Diagnosis of Gastric Cancer. Journal of Immunology Research ; 2020: 9146042 Migita K, Takayama T, Saeki K et al (2013) The prognostic nutritional index predicts long-term outcomes of gastric cancer patients independent of tumor stage. Ann Surg Oncol 20:2647–2654 Hirase Y, Arigami T, Matsushita D et al (2024) Prognostic significance of osteosarcopenia in patients with stage IV gastric cancer undergoing conversion surgery. Langenbecks Arch Surg 410:7 Kudou K, Nakashima Y, Haruta Y et al (2021) Comparison of Inflammation-Based Prognostic Scores Associated with the Prognostic Impact of Adenocarcinoma of Esophagogastric Junction and Upper Gastric Cancer. Ann Surg Oncol 28:2059–2067 Jiang X, Hiki N, Nunobe S et al (2012) Prognostic importance of the inflammation-based Glasgow prognostic score in patients with gastric cancer. Br J Cancer 107:275–279 Takagi K, Domagala P, Polak WG, Buettner S, Wijnhoven BPL, Ijzermans JNM (2019) Prognostic significance of the controlling nutritional status (CONUT) score in patients undergoing gastrectomy for gastric cancer: a systematic review and meta-analysis. BMC Surg 19:129 Japanese Gastric Cancer Association (2017) Japanese Classification of Gastric Carcinoma, 15 edn. Kanehara, Tokyo du Fossé NA, Grootjans W, Navas A et al (2024) Exploring bone density analysis on routine CT scans as a tool for opportunistic osteoporosis screening. Sci Rep 14:18359 Jovanovic N, Chinnery T, Mattonen SA, Palma DA, Doyle PC, Theurer JA (2022) Sarcopenia in head and neck cancer: A scoping review. PLoS ONE 17:e0278135 Kuroda D, Sawayama H, Kurashige J et al (2018) Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer 21:204–212 Takeno S, Hashimoto T, Shibata R et al (2014) The High-Sensitivity Modified Glasgow Prognostic Score Is Superior to the Modified Glasgow Prognostic Score as a Prognostic Predictor in Patients with Resectable Gastric Cancer. Oncology 87:205–214 Fearon K, Strasser F, Anker SD et al (2011) Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12:489–495 Shimizu S, Matsunaga T, Sawata S et al (2023) Preoperative Osteopenia Is a Risk Factor for Death in Patients Undergoing Gastrectomy for Gastric Cancer. Anticancer Res 43:3665–3672 Fukushima N, Tsuboi K, Nyumura Y et al (2023) Prognostic significance of preoperative osteopenia on outcomes after gastrectomy for gastric cancer. Annals Gastroenterological Surg 7:255–264 Kawamura T, Makuuchi R, Tokunaga M et al (2018) Long-Term Outcomes of Gastric Cancer Patients with Preoperative Sarcopenia. Ann Surg Oncol 25:1625–1632 Kuwada K, Kuroda S, Kikuchi S et al (2019) Clinical Impact of Sarcopenia on Gastric Cancer. Anticancer Res 39:2241 Zheng H-L, Wei L-H, Xu B-B et al (2024) Prognostic value of preoperative sarcopenia in gastric cancer: A 10-year follow-up study. Eur J Surg Oncol 50:108004 Tegels JJ, van Vugt JL, Reisinger KW et al (2015) Sarcopenia is highly prevalent in patients undergoing surgery for gastric cancer but not associated with worse outcomes. J Surg Oncol 112:403–407 Kai W, Takano Y, Kobayashi Y, Kanno H, Hanyu N, Eto K (2025) Impact of osteosarcopenia on short- and long-term outcomes in patients with gastric cancer. Jpn J Clin Oncol 55:477–483 Hayasaka K, Notsuda H, Onodera K et al (2024) Prognostic value of perioperative changes in the prognostic nutritional index in patients with surgically resected non-small cell lung cancer. Surg Today 54:1031–1040 Takahashi M, Aoyama A, Hamaji M et al (2025) Clinical significance of the preoperative prognostic nutritional index in patients with resectable non-small cell lung cancer: a multicenter study. Surg Today 55:918–929 Jiang N, Deng JY, Ding XW et al (2014) Prognostic nutritional index predicts postoperative complications and long-term outcomes of gastric cancer. World J Gastroenterol 20:10537–10544 Zhang H, Shi J, Xie H et al (2023) Superiority of CRP-albumin-lymphocyte index as a prognostic biomarker for patients with gastric cancer. Nutrition 116:112191 Wada Y, Nishi M, Yoshikawa K et al (2022) Preoperative nutrition and exercise intervention in frailty patients with gastric cancer undergoing gastrectomy. Int J Clin Oncol 27:1421–1427 Suzuki Y, Ishibashi Y, Omura N et al (2005) Alendronate improves vitamin D-resistant osteopenia triggered by gastrectomy in patients with gastric cancer followed long term. J Gastrointest Surg 9:955–960 Lim JS, Jin SH, Kim SB, Lee JI (2012) Effect of bisphosphonates on bone mineral density and fracture prevention in gastric cancer patients after gastrectomy. J Clin Gastroenterol 46:669–674 Kudou K, Saeki H, Nakashima Y et al (2019) Postoperative development of sarcopenia is a strong predictor of a poor prognosis in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer. Am J Surg 217:757–763 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 12 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 06 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9331253","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625438029,"identity":"4347d72a-a746-4a6c-85d1-0d132f7f4d4b","order_by":0,"name":"Keiichiro Ryujin","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Keiichiro","middleName":"","lastName":"Ryujin","suffix":""},{"id":625438030,"identity":"9b258ef3-fe31-45de-b6a7-6f24fab6b7b8","order_by":1,"name":"Tetsuro Kawazoe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACCRDxocJGDkQfeECsFsYZZ9KMwVoSiNXCzNt2OLEBxCNKi2T/4YOPediY0+eHHX4ItMVOTreBgBZphmPJhnN42HI33k4zAGpJNjY7QECLHGOPmcQbCZ7cjbMTQFoOJG4jqIWZx/wHj4FEuuHs9A/EaZFm4zFj5EkwSJCXziHSFsketmTJGQcSDDdI5xQcSDAgwi8S5w8f/PDx3395+dnpmz98qLCTI6gFDgzAKg2IVQ4C8g2kqB4Fo2AUjIIRBQAcA0Qtg2Xd/AAAAABJRU5ErkJggg==","orcid":"","institution":"Kyushu University","correspondingAuthor":true,"prefix":"","firstName":"Tetsuro","middleName":"","lastName":"Kawazoe","suffix":""},{"id":625438031,"identity":"852fd1c8-25ee-44a6-8f70-570ac373ce0e","order_by":2,"name":"Yasuo Tsuda","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Yasuo","middleName":"","lastName":"Tsuda","suffix":""},{"id":625438032,"identity":"d2bbdd6b-441b-4c4d-891b-ea80b33e067a","order_by":3,"name":"Kensuke Kudou","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Kensuke","middleName":"","lastName":"Kudou","suffix":""},{"id":625438033,"identity":"0b53271f-39e0-4773-8a40-d8de620e9ade","order_by":4,"name":"Tomonori Nakanoko","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Tomonori","middleName":"","lastName":"Nakanoko","suffix":""},{"id":625438034,"identity":"e7fb7488-0e53-4515-8f09-9f7078c9b9e9","order_by":5,"name":"Mitsuhiko Ota","email":"","orcid":"","institution":"National Hospital Organization Kyushu Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mitsuhiko","middleName":"","lastName":"Ota","suffix":""},{"id":625438035,"identity":"85469414-c544-4d6e-8b0f-addb07fe4609","order_by":6,"name":"Koji Ando","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Koji","middleName":"","lastName":"Ando","suffix":""},{"id":625438036,"identity":"5e2c9c10-4dda-4917-abcc-a52c16866c37","order_by":7,"name":"Eiji Oki","email":"","orcid":"","institution":"Kyushu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eiji","middleName":"","lastName":"Oki","suffix":""},{"id":625438037,"identity":"ce697595-7e80-45b3-bc44-cf8136fd1c48","order_by":8,"name":"Tomoharu Yoshizumi","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Tomoharu","middleName":"","lastName":"Yoshizumi","suffix":""}],"badges":[],"createdAt":"2026-04-06 07:54:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9331253/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9331253/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107355959,"identity":"c01b9e51-c4fd-4071-8215-14e1384b0e9f","added_by":"auto","created_at":"2026-04-20 16:55:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132719,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient selection. Among primary gastric cancer surgery cases (292 consecutive cases) in our department from 2014 to 2018, 273 cases meeting the criteria of pStage I–III and R0 resection were included, after the exclusion of 14 cases of stage IV cancer and 5 cases of R1 resection.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/fe61e4577373f0982c57ba1c.png"},{"id":107355949,"identity":"e0dce577-af6d-4dc3-9039-1cace1e80958","added_by":"auto","created_at":"2026-04-20 16:55:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3888586,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative CT images for assessment of BMD and skeletal muscle mass. (a) Measurement of BMD at the center of the 11\u003csup\u003eth\u003c/sup\u003e thoracic vertebral body on a preoperative CT image. Images illustrate representative examples of osteopenia (BMD = 37.5) and non-osteopenia (BMD = 175). (b) Measurement of the skeletal muscle area at the 3\u003csup\u003erd\u003c/sup\u003e lumbar vertebral level to calculate SMI on a preoperative CT image. The images illustrate representative examples of sarcopenia (SMI = 26.0) and non-sarcopenia (SMI = 67.7).\u003c/p\u003e\n\u003cp\u003eCT, computed tomography; Th11, 11\u003csup\u003eth\u003c/sup\u003e thoracic vertebral body; L3, 3\u003csup\u003erd\u003c/sup\u003e lumbar vertebra; BMD, bone mineral density; SMI, Skeletal Muscle Index\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/a6a07a1fb7f11f851cef14d8.png"},{"id":107355962,"identity":"4f163bb6-27c8-4d14-96d4-88cf188fd82f","added_by":"auto","created_at":"2026-04-20 16:55:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2138629,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for OS according to body composition status. (a) Osteopenia and non-osteopenia groups (5-year OS: 70.7% vs. 84.4%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.002). (b) Sarcopenia and non-sarcopenia groups (5-year OS: 72.7% vs. 85.4%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.03). (c) Osteosarcopenia and non-osteosarcopenia groups (5-year OS: 64.9% vs. 83.0%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.003).\u003c/p\u003e\n\u003cp\u003eDifferences between groups were assessed using the log-rank test.\u003c/p\u003e\n\u003cp\u003eOS, overall survival\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/dfe0e3ca34077576b70b1797.png"},{"id":107355929,"identity":"f0844099-0423-4dfa-8e4d-f5e2b4fc53f6","added_by":"auto","created_at":"2026-04-20 16:55:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2214155,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for RFS according to body composition status. (a) Osteopenia and non-osteopenia groups (5-year RFS: 67.8% vs. 82.1%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.002). (b) Sarcopenia and non-sarcopenia groups (5-year RFS: 69.2% vs. 83.6%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.02). (c) Osteosarcopenia and non-osteosarcopenia groups (5-year RFS: 62.1% vs. 80.5%, respectively; \u003cem\u003eP\u003c/em\u003e = 0.005).\u003c/p\u003e\n\u003cp\u003eDifferences between groups were assessed using the log-rank test.\u003c/p\u003e\n\u003cp\u003eRFS, recurrence-free survival\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/9e6f213c4b754619c6f0ed01.png"},{"id":107355947,"identity":"dd15bfd7-11d6-4104-a33a-46af6019dfe3","added_by":"auto","created_at":"2026-04-20 16:55:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":878450,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic performance of nutritional, inflammatory, and body composition indices; PNI, mGPS, BMD, SMI, osteosarcopenia, and Cox model risk score. (a) Time-dependent receiver operating characteristic curves for 3-year OS. The point on the Cox model risk score line that gave the highest Youden index was 0.47. The AUC for each factor and the equation for the Cox model risk score are as follows: AUC – PNI, 0.70; mGPS, 0.61; BMD, 0.61; SMI, 0.58; osteosarcopenia, 0.64; and Cox model risk score, 0.78. Cox model risk score = 0.907 × high PNI + 0.569 × low mGPS + 0.641 × osteopenia + 0.516 × sarcopenia optimal cut-off value = 0.907. (b) Kaplan–Meier curves for overall survival according to the Cox model risk score.\u003c/p\u003e\n\u003cp\u003ePNI, Prognostic Nutritional Index; mGPS, modified Glasgow Prognostic Score; BMD, Bone Mineral Density; SMI, Skeletal Muscle Index; OS, overall survival; AUC, area under the curve\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/454e02854813e7a3531b9e7a.png"},{"id":107356018,"identity":"b49688bf-881a-4766-84d7-e96d0b1849db","added_by":"auto","created_at":"2026-04-20 16:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9284987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/20d16bb7-1c21-4bb5-b899-1db2f80f0f2c.pdf"},{"id":107355937,"identity":"5fadb7c2-bf6c-42c4-b995-85debc2b61e2","added_by":"auto","created_at":"2026-04-20 16:55:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45760,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9331253/v1/e4d2c0b67d2fba17b3190538.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Osteosarcopenia as a Prognostic Indicator of Gastric Cancer Compared with Established Nutritional Markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fifth most common malignancy worldwide and the fourth leading cause of cancer-related deaths, despite a global decline in its incidence and mortality rates\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Throughout Asia, GC continues to pose a major health concern, ranking third in incidence and fourth in mortality among all cancers in Japan\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although advances in multimodal treatments such as surgical techniques and chemotherapy have improved patient outcomes, the identification of reliable prognostic factors to guide therapeutic decision-making remains essential.\u003c/p\u003e \u003cp\u003ePreoperative nutritional and immunological statuses play a crucial role in determining postoperative outcomes in patients with cancer, with several indices, including the platelet-to-lymphocyte ratio (PLR) and prognostic nutritional index (PNI), reported to be significant prognostic markers of surgical outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Osteopenia (reduced bone mineral density) and sarcopenia (indicating loss of skeletal muscle mass and strength) have recently garnered increasing attention as potential prognostic markers, with several studies showing them to be prognostic factors for various malignancies. Additionally, the concept of osteosarcopenia, defined as the co-occurrence of osteopenia and sarcopenia, has recently emerged\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, further highlighting the relevance of nutritional indicators in the perioperative setting.\u003c/p\u003e \u003cp\u003eAlthough previous studies have evaluated individual nutritional or inflammatory indices\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, these assessments often capture limited aspects of cancer-related nutritional impairment and systemic inflammation. Moreover, the relationships among osteopenia, sarcopenia, and established nutritional markers remain unclear. Therefore, this retrospective study aimed to elucidate the interrelationships and prognostic significance of preoperative osteopenia, sarcopenia, osteosarcopenia, and representative nutritional indices in patients with GC.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe screened a total of 292 consecutive patients who underwent gastrectomy for primary GC at the Department of Gastroenterological Surgery at Kyushu University Hospital between January 2014 and December 2018, of whom 273 were retrospectively included in this study. A total of 19 patients were excluded for unresectable disease or distant metastases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The following clinical and pathological data were extracted from medical records: age, sex, body mass index (BMI), smoking history, preoperative laboratory findings (hemoglobin, white blood cell, C-reactive protein [CRP], tumor markers, etc.), surgical procedure, reconstruction method, pathological stage (Japanese Classification of Gastric Carcinoma, 15th edition\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e), postoperative complications, hospital stay, and use of neoadjuvant or adjuvant chemotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of osteopenia, sarcopenia, and osteosarcopenia\u003c/h3\u003e\n\u003cp\u003eBone mineral density (BMD) was assessed using computed tomography (CT) performed within 2 months prior to surgery. Osteopenia was defined as a value\u0026thinsp;\u0026lt;\u0026thinsp;140 Hounsfield unit (HU), as determined by the mean HU value within a circular region of interest at the center of the 11th thoracic vertebral body, based on a previously published study\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Skeletal muscle mass was quantified using the cross-sectional skeletal muscle area at the level of the 3rd lumbar vertebra on CT performed within 2 months prior to surgery and was normalized by height squared to calculate the skeletal muscle index (SMI). As cut-off values for SMI vary widely across different definitions and among different populations, we divided participants into three groups: men were divided into two groups based on BMI (\u0026ge;\u0026thinsp;25 and \u0026lt;\u0026thinsp;25), and women formed a single group. We then used the median SMI values for each group (43.5, 50.7, and 34.6 cm\u0026sup2;/m\u0026sup2;, respectively) as cutoff points, defining sarcopenia as values below these thresholds\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Osteosarcopenia was defined as the co-occurrence of osteopenia and sarcopenia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eNutritional and Inflammatory Markers\u003c/h3\u003e\n\u003cp\u003ePreoperative blood data (obtained within 2 months prior to surgery) were used to calculate nutritional and inflammatory indices as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePLR: total platelet count (TPC) [/\u0026micro;L] / total lymphocyte count (TLC) [/\u0026micro;L]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeutrophil to lymphocyte ratio (NLR): total neutrophil count (TNC) [/\u0026micro;L] / TLC [/\u0026micro;L]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCRP to albumin [Alb] ratio (CAR): CRP [mg/dL] / Alb [g/dL]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNutritional Risk Index (NRI): (1.519 \u0026times; Alb [g/L]) / (41.7 * body weight [kg] / ideal body weight [kg])\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSystemic Immune-Inflammation Index (SII): TPC [/\u0026micro;L] * TNC [/\u0026micro;L] / TLC [/\u0026micro;L]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePNI: (10 \u0026times; Alb [g/dL]) + (0.005 \u0026times; TLC [/\u0026micro;L])\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eControlling Nutritional Status (CONUT) Score: determined by the combination of Alb, TLC, and total cholesterol level\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModified Glasgow Prognostic Score (mGPS): determined by the combination of CRP and Alb level\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eAnalyses of Risk Factors for Survival and Recurrence\u003c/h3\u003e\n\u003cp\u003eThe relationships among clinicopathological factors, nutritional and inflammatory markers, body composition indicators (osteopenia, sarcopenia, and osteosarcopenia), and postoperative oncological outcomes were evaluated. Univariate analyses were performed to examine the relationships between each variable and overall survival (OS) and recurrence-free survival (RFS). Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were subsequently included in a multivariate Cox proportional hazards regression model to identify independent prognostic factors. In cases of suspected multicollinearity among covariates, the corresponding variables were removed before the model was constructed. Osteopenia and sarcopenia were analyzed concomitantly, given their lack of correlation, whereas osteosarcopenia was analyzed independently owing to its correlation with both osteopenia and sarcopenia.\u003c/p\u003e \u003cp\u003eContinuous variables, except age, were dichotomized using the following thresholds: carcinoembryonic antigen (CEA), 5 ng/mL; carbohydrate antigen (CA)19\u0026thinsp;\u0026minus;\u0026thinsp;9, 37 U/mL; NLR, 2.5; PLR, 150; CAR, 0.05; NRI, 100; SII, 50\u0026times;10; and PNI, 45. Pathological stages were grouped as pStage I vs. pStage II/III, CONUT score as 0\u0026ndash;1 vs. 2\u0026ndash;12, and mGPS as 0 vs. 1\u0026ndash;2.\u003c/p\u003e\n\u003ch3\u003eTime-dependent receiver operating characteristic (ROC) curve analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate prognostic performance with respect to OS, time-dependent ROC analyses were performed for preoperative nutritional and inflammatory indices. Based on the significant variables identified in the univariate Cox analysis (PNI\u0026thinsp;\u0026ge;\u0026thinsp;45, mGPS 1\u0026ndash;2, osteopenia, and sarcopenia), four quantitative indicators (PNI, mGPS, BMD, and SMI) were included in the ROC analysis. The area under the curve (AUC) at 3 years was calculated for each parameter.\u003c/p\u003e \u003cp\u003eA composite prognostic model (Cox model risk score) was developed by integrating these indices (low PNI [\u0026lt;\u0026thinsp;45], high mGPS [1\u0026ndash;2], osteopenia, and sarcopenia were coded as 1; all else, 0) using a Cox proportional hazards model. Linear predictor values were generated for each case, and the optimal cutoff value was determined using the Youden index. Predictive performance was assessed by calculating the sensitivity and specificity for 3-year OS using the timeROC package in R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e version 4.3.3), and ROC curves for individual and composite models were plotted on the same graph.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were expressed as medians with interquartile ranges (IQRs) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables are expressed as counts and percentages and compared using the chi-squared test. Survival curves for OS and RFS were generated using the Kaplan\u0026ndash;Meier method, and differences between groups were evaluated using the log-rank test. Cox proportional hazards regression was used for both univariate and multivariate analyses to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eDetails of the patients\u0026rsquo; background characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Tables\u0026nbsp;1 and 2. Among the 273 included patients, 189 (69.2%) were men, the median age was 68 years, and the median BMI was 22.7 kg/m\u0026sup2;. Osteopenia, sarcopenia, and osteosarcopenia were observed in 111 (40.7%), 135 (49.5%), and 61 (22.3%) patients, respectively. No significant correlations were observed between osteopenia and sarcopenia.\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\u003eList of clinical and pathological factors for osteosarcopenia group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.0 [61.0, 76.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.0 [65.0, 79.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.0 [60.0, 74.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg\u0026times;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7 [20.3, 24.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1 [19.3, 23.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0 [20.8, 24.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (U/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;37, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.5, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.05, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50x10^4, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 [234, 317]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274 [236, 300]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e273 [233, 321]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0 [20.0, 105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.0 [20.0, 100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.0 [20.0, 105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003epStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (63.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complication (CD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge; Grade III, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative hospital stay (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 [9.0, 14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 [9.0, 14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 [8.0, 14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eIQR, interquartile range; BMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein\u0026ndash;to\u0026ndash;albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score; CD, Clavien\u0026ndash;Dindo classification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe patients with osteosarcopenia had a higher proportion of men than those without (80.3% vs. 66.0%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) and were significantly older (72 vs. 68 years, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002). No significant differences were observed in the pathological stage, surgical procedure, or reconstruction method among the groups. Regarding tumor markers, the osteosarcopenia group had a significantly higher rate of elevated serum CEA than those without (21.3% vs. 9.4%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). The osteosarcopenia group also showed higher proportions of elevated NLR (\u0026ge;\u0026thinsp;2.5: 47.5% vs. 31.6%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), elevated CAR (\u0026ge;\u0026thinsp;0.05: 34.4% vs. 20.3%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and low NRI (42.6% vs. 25.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) than those without.\u003c/p\u003e \u003cp\u003eFurthermore, upon evaluating osteopenia and sarcopenia as distinct entities, patients with osteopenia were significantly older (74 vs. 64 years, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had longer postoperative hospital stays (11 vs. 10 days, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). They also tended to have a higher incidence of postoperative complications classified as Clavien\u0026ndash;Dindo grade III or higher (15.3% vs. 7.4%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06) (Supplementary Table\u0026nbsp;1). Among the sarcopenia group, BMI was significantly lower (20.8 vs. 23.5; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the proportion of patients receiving preoperative treatment was higher (10.4% vs. 2.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) than those in the osteopenia group, respectively. Among the nutritional and inflammatory markers, low PNI was more frequent in the osteopenia group (PNI\u0026thinsp;\u0026lt;\u0026thinsp;45: 32.4% vs. 19.1%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), and low NRI was more common in the sarcopenia group (NRI\u0026thinsp;\u0026lt;\u0026thinsp;100: 42.2% vs. 17.4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImpact of osteopenia, sarcopenia, and osteosarcopenia on OS\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Kaplan\u0026ndash;Meier curves for OS among patients with osteopenia, sarcopenia, and osteosarcopenia. Patients with osteopenia had a significantly poorer OS than those without (5-year OS: 70.7% vs. 84.4%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Similarly, patients with sarcopenia had worse OS than those without (5-year OS: 72.7% vs. 85.4%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Among all groups, osteosarcopenia was associated with the poorest prognosis (5-year OS: 64.9% vs. 83.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOsteopenia remained a significant independent prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;1.71; 95% CI: 1.00\u0026ndash;3.11; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), whereas sarcopenia did not (HR\u0026thinsp;=\u0026thinsp;1.46; 95% CI: 0.81\u0026ndash;2.63; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20). In contrast, osteosarcopenia was identified as an independent predictor of poor OS (HR\u0026thinsp;=\u0026thinsp;1.95; 95% CI: 1.06\u0026ndash;3.60; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (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\u003eMultivariate analysis of overall survival in osteopenia, sarcopenia and osteosarcopenia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c12\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eOsteopenia and Sarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.10\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (\u0026ge;\u0026thinsp;25 kg\u0026times;m-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026ge;\u0026thinsp;5 mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u0026ndash;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u0026ndash;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.13\u0026ndash;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (\u0026ge;\u0026thinsp;37 U/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR (\u0026ge;\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (\u0026ge;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR (\u0026ge;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026ndash;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRI (\u0026lt;\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u0026ndash;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026ge;\u0026thinsp;50\u0026times;10\u003csup\u003e4\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI (\u0026lt;\u0026thinsp;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026ndash;5.66\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.85\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026ndash;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emGPS (\u0026ge;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026ndash;8.06\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u0026ndash;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.77\u0026ndash;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u0026ndash;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00-2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u0026ndash;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30-4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09\u0026ndash;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time (\u0026ge;\u0026thinsp;270 min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding (\u0026ge;\u0026thinsp;100 ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage (\u0026ge;\u0026thinsp;II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u0026ndash;7.01\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90\u0026ndash;6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.98\u0026ndash;6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eCravien-Dindo (\u0026ge;\u0026thinsp;Grade III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026ndash;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eBMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; HR, hazard ratio; CI, confidence interval; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein\u0026ndash;to\u0026ndash;albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImpact of osteopenia, sarcopenia, and osteosarcopenia on RFS\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Kaplan\u0026ndash;Meier curves for RFS among patients with osteopenia, sarcopenia, and osteosarcopenia. RFS was significantly worse in the osteopenia group than in the non-osteopenia group (5-year RFS: 67.8% vs. 82.1%, respectively; P\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Similarly, patients with sarcopenia had poorer RFS than those without (5-year RFS: 69.2% vs. 83.6%, respectively; P\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Osteosarcopenia was associated with the most unfavorable RFS among all groups (5-year RFS: 62.1% vs. 80.5%; P\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariate analysis of RFS revealed that osteopenia was an independent predictor of recurrence (HR\u0026thinsp;=\u0026thinsp;1.82; 95% CI: 1.02\u0026ndash;3.26; P\u0026thinsp;=\u0026thinsp;0.04), whereas sarcopenia was not (HR\u0026thinsp;=\u0026thinsp;1.48; 95% CI: 0.83\u0026ndash;2.64; P\u0026thinsp;=\u0026thinsp;0.19). Osteosarcopenia remained a significant independent factor for recurrence (HR\u0026thinsp;=\u0026thinsp;2.03; 95% CI: 1.10\u0026ndash;3.72; P\u0026thinsp;=\u0026thinsp;0.02) (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\u003eMultivariate analysis of recurrence-free survival in osteopenia, sarcopenia and osteosarcopenia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecurrence-Free Survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c12\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eOsteopenia and Sarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026ndash;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.41\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026ndash;1.08\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05\u0026ndash;4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (\u0026ge;\u0026thinsp;25 kg\u0026times;m-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026ge;\u0026thinsp;5 mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026ndash;5.55\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u0026ndash;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96\u0026ndash;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (\u0026ge;\u0026thinsp;37 U/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u0026ndash;8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u0026ndash;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.30\u0026ndash;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR (\u0026ge;\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (\u0026ge;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR (\u0026ge;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026ndash;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRI (\u0026lt;\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026ge;\u0026thinsp;50\u0026times;10\u003csup\u003e4\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI (\u0026lt;\u0026thinsp;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026ndash;5.28\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u0026ndash;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.95\u0026ndash;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026ndash;3.47\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emGPS (\u0026ge;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026ndash;7.49\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026ndash;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90\u0026ndash;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u0026ndash;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u0026ndash;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026ndash;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026ndash;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05\u0026ndash;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time (\u0026ge;\u0026thinsp;270 min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding (\u0026ge;\u0026thinsp;100 ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage (\u0026ge;\u0026thinsp;II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u0026ndash;6.81\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76\u0026ndash;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.80\u0026ndash;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eCravien-Dindo (\u0026ge;\u0026thinsp;Grade III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eBMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen; HR, hazard ratio; CI, confidence interval; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; CAR, C-reactive protein\u0026ndash;to\u0026ndash;albumin ratio; NRI, nutritional risk index; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; CONUT score, controlling nutritional status score; mGPS, modified Glasgow prognostic score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of nutritional and inflammatory indices based on ROC analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea compares the predictive performance of nutritional, inflammatory, and body composition indicators for 3-year OS using ROC analyses. PNI, mGPS, osteopenia, sarcopenia, and osteosarcopenia, the five variables that were significant in univariate analyses, were evaluated. The AUC values for the predicted 3-year OS were 0.70, 0.61, 0.61, and 0.58, respectively, and osteosarcopenia (yes/no)\u0026thinsp;=\u0026thinsp;0.64.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough each indicator demonstrated moderate discriminatory ability, their individual performance was limited. Therefore, a composite prognostic model (Cox model risk score) was developed by integrating these (low PNI [\u0026lt;\u0026thinsp;45], high mGPS [1\u0026ndash;2], osteopenia, and sarcopenia were coded as 1; all else, 0) using a Cox proportional hazards model (Cox model risk score\u0026thinsp;=\u0026thinsp;0.907 \u0026times; high PNI\u0026thinsp;+\u0026thinsp;0.569 \u0026times; low mGPS\u0026thinsp;+\u0026thinsp;0.641 \u0026times; osteopenia\u0026thinsp;+\u0026thinsp;0.516 \u0026times; sarcopenia). The resulting Cox model risk score yielded the highest predictive accuracy, with an AUC of 0.78, outperforming all individual indicators.\u003c/p\u003e \u003cp\u003ePatients were divided into low- and high-risk groups according to the optimal cutoff value determined using the Cox model risk score (0.907). A significant difference was observed in OS between the groups (5-year OS: 88.8% vs. 63.2%, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). These results indicate that combining bone and muscle composition markers with nutritional and inflammatory parameters provided a more robust prognostic assessment than any single factor alone.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong patients with advanced cancer, reduced oral intake, malabsorption, and chronic inflammation often lead to cancer cachexia, which is characterized by the loss of skeletal muscle mass and decreased BMD\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Among patients with GC, osteopenia has been reported to influence surgical stress, postoperative complications, and long-term outcomes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, whereas sarcopenia has been associated with systemic inflammatory responses, treatment-related toxicity, immunosuppression, and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, patients with osteopenia were significantly older than those without, suggesting an age-related deterioration in BMD. Furthermore, osteopenia was associated with a higher incidence of severe postoperative complications (Clavien\u0026ndash;Dindo grade III or higher), which may have contributed to patients\u0026rsquo; prolonged postoperative hospital stays. The proportion of patients who received preoperative chemotherapy was higher in the sarcopenia group than in the non-sarcopenia group, suggesting that preoperative treatment may have induced and/or exacerbated muscle loss.\u003c/p\u003e \u003cp\u003eAlthough BMI was significantly lower in the sarcopenia group, it was inherently incorporated into the definition of sarcopenia in this study. Therefore, background factors such as BMI and indices that include standard body weight, such as the NRI, are expected to correlate with sarcopenia by definition and are thus not considered independent explanatory variables for evaluating the impact of sarcopenia itself.\u003c/p\u003e \u003cp\u003eWe investigated the effects of preoperative osteopenia, sarcopenia, and their co-occurrence as osteosarcopenia on the prognosis of patients who underwent curative gastrectomy for GC. Osteopenia was identified as an independent prognostic factor associated with significantly shorter OS and RFS, while sarcopenia was not an independent prognostic factor in the multivariate analysis, contrary to previous reports\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Among our cohort, a higher proportion of patients in the sarcopenia group received preoperative chemotherapy, some of whom may have transitioned from a non-sarcopenic to a sarcopenic state within a relatively short period owing to the treatment. Consequently, the preoperative \u0026ldquo;sarcopenia\u0026rdquo; group may have included patients who originally had relatively preserved muscle mass, potentially allowing early recovery of muscle mass once adequate postoperative oral intake was achieved, thereby mitigating the negative prognostic impact of this change. It is also possible that improved oncological outcomes owing to preoperative therapy attenuated the survival difference between patients with and without sarcopenia. Previous studies have reported no association between sarcopenia and prognosis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and the lack of a standardized cutoff for SMI across populations and studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e further complicates this interpretation. Therefore, the prognostic significance of sarcopenia in GC remains unclear.\u003c/p\u003e \u003cp\u003eIn the present study, patients with osteosarcopenia, characterized by a simultaneous reduction in bone and muscle mass, had the worst prognoses. Although osteopenia alone was found to be a prognostic factor, the Cox proportional hazards model revealed an additive effect of sarcopenia in osteosarcopenia. This finding is consistent with the results reported by Kai et al.\u003csup\u003e21\u003c/sup\u003e, and Hirase et al.\u003csup\u003e5\u003c/sup\u003e. The present study represents the largest case series to date. Hirase et al.\u003csup\u003e5\u003c/sup\u003e. demonstrated that patients with osteosarcopenia exhibit significantly reduced infiltration of CD8-positive T cells, programmed cell death 1 (PD-1)-positive cells, and programmed cell Death Ligand 1 (PD-L1)-positive cells within the tumor microenvironment. Their results suggested that an immunosuppressive shift in the tumor immune microenvironment may contribute to poor outcomes. In the present study, patients with osteosarcopenia were characterized by older age, male predominance, low BMI, high NLR, and low NRI, indicating a systemic proinflammatory state and poor nutritional status, which may facilitate tumor progression and subsequently worsen prognosis.\u003c/p\u003e \u003cp\u003eNutritional and inflammatory indices such as PNI and mGPS have been widely reported as useful prognostic markers among patients with cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. PNI, a simple index based on serum albumin level and lymphocyte count is commonly used as a predictor of poor postoperative prognosis in GC\u003csup\u003e24\u003c/sup\u003e, whereas mGPS and CAR reflect CRP-mediated systemic inflammation and allow for a comprehensive assessment of immunonutritional status\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In the present study, we integrated PNI and mGPS with bone and muscle status markers to construct a composite risk score using a Cox proportional hazards model. This Cox model-based risk score achieved an AUC of 0.78 for predicting 3-year OS, outperforming each individual indicator, suggesting that a multidimensional risk assessment combining nutritional, inflammatory, and body composition parameters is more informative than any single metric in predicting survival in GC.\u003c/p\u003e \u003cp\u003eThe results of the present study demonstrated that the preoperative evaluation of osteopenia and sarcopenia is useful for risk stratification of patients with GC. Wada et al.\u003csup\u003e26\u003c/sup\u003ereported that preoperative nutritional and exercise interventions in patients undergoing gastrectomy improved postoperative outcomes, including a reduction in postoperative complications and a shortened length of hospital stay. Additionally, interventions aimed at maintaining bone mass, such as vitamin D and calcium supplementation and bisphosphonate therapy, have been reported in patients with GC after surgical intervention\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003ede novo\u003c/em\u003e development of sarcopenia after surgery has also been associated with poor prognosis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, suggesting that comprehensive perioperative management combining nutritional support, exercise therapy, and pharmacological interventions may be an important future strategy.\u003c/p\u003e \u003cp\u003eThe present study had some limitations that should be discussed. First, this was a single-center retrospective analysis with a potential selection bias and an imbalance in demographic characteristics. Nonetheless, compared with previous studies on osteopenia, sarcopenia, and osteosarcopenia in GC, this study included a relatively large sample size and utilized multivariate analyses with adjustment for confounding factors, thereby providing statistically robust results despite its retrospective design. Second, immunological analyses among our cohort were limited; however, when interpreted in conjunction with prior immunological studies, our findings help bridge molecular-level mechanisms and clinically-observed outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the present study, which included the largest number of cases among relevant literature, elucidated the association between osteosarcopenia, nutritional indicators, and prognosis for the first time in GC. Preoperative osteopenia and sarcopenia, especially when co-occurring as osteosarcopenia, are associated with a poor prognosis in patients undergoing curative gastrectomy for GC. These conditions are closely linked to nutritional status, systemic inflammation, and tumor immune microenvironment; therefore, a multifactorial model incorporating a composite risk score based on these factors may enable a more accurate prognostic prediction and facilitate individualized treatment strategies. Given that perioperative nutritional interventions and body composition-targeted management may improve these risk profiles, proactive interventions should be considered in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eAll procedures were performed in accordance with the ethical standards of the responsible committees on human experimentation (institutional and national) and the Helsinki Declaration of 1964 and its later versions. The Ethics Review Committee of Kyushu University Graduate School of Medicine Sciences approved the study protocol, and informed consent was obtained from all patients (approval no. 23329).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e \u003cp\u003eNo conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Information:\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKR, TK, and MO conceived and designed the study. KR performed data collection, data analysis, and statistical analysis. KR drafted the manuscript. TK, KK, MO, and KA critically revised the manuscript for important intellectual content. YT, TN, EO, and TY provided clinical interpretation and editorial support. All of the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are not publicly available due to privacy or ethical restrictions but are available from the corresponding author upon reasonable request and with permission of the institutional review board.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang WJ, Zhao HP, Yu Y et al (2023) Updates on global epidemiology, risk and prognostic factors of gastric cancer. World J Gastroenterol 29:2452\u0026ndash;2468\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2 Cancer Statistics in Japan (1958\u0026ndash;2022). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ganjoho.jp/reg_stat/statistics/data/dl/en.html\u003c/span\u003e\u003cspan address=\"https://ganjoho.jp/reg_stat/statistics/data/dl/en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang T, Wang Y, Yin X et al (2020) Diagnostic Sensitivity of NLR and PLR in Early Diagnosis of Gastric Cancer. \u003cem\u003eJournal of Immunology Research\u003c/em\u003e ; 2020: 9146042\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMigita K, Takayama T, Saeki K et al (2013) The prognostic nutritional index predicts long-term outcomes of gastric cancer patients independent of tumor stage. Ann Surg Oncol 20:2647\u0026ndash;2654\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirase Y, Arigami T, Matsushita D et al (2024) Prognostic significance of osteosarcopenia in patients with stage IV gastric cancer undergoing conversion surgery. Langenbecks Arch Surg 410:7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKudou K, Nakashima Y, Haruta Y et al (2021) Comparison of Inflammation-Based Prognostic Scores Associated with the Prognostic Impact of Adenocarcinoma of Esophagogastric Junction and Upper Gastric Cancer. Ann Surg Oncol 28:2059\u0026ndash;2067\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang X, Hiki N, Nunobe S et al (2012) Prognostic importance of the inflammation-based Glasgow prognostic score in patients with gastric cancer. Br J Cancer 107:275\u0026ndash;279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakagi K, Domagala P, Polak WG, Buettner S, Wijnhoven BPL, Ijzermans JNM (2019) Prognostic significance of the controlling nutritional status (CONUT) score in patients undergoing gastrectomy for gastric cancer: a systematic review and meta-analysis. BMC Surg 19:129\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapanese Gastric Cancer Association (2017) Japanese Classification of Gastric Carcinoma, 15 edn. Kanehara, Tokyo\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edu Foss\u0026eacute; NA, Grootjans W, Navas A et al (2024) Exploring bone density analysis on routine CT scans as a tool for opportunistic osteoporosis screening. Sci Rep 14:18359\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJovanovic N, Chinnery T, Mattonen SA, Palma DA, Doyle PC, Theurer JA (2022) Sarcopenia in head and neck cancer: A scoping review. PLoS ONE 17:e0278135\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuroda D, Sawayama H, Kurashige J et al (2018) Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer 21:204\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeno S, Hashimoto T, Shibata R et al (2014) The High-Sensitivity Modified Glasgow Prognostic Score Is Superior to the Modified Glasgow Prognostic Score as a Prognostic Predictor in Patients with Resectable Gastric Cancer. Oncology 87:205\u0026ndash;214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFearon K, Strasser F, Anker SD et al (2011) Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12:489\u0026ndash;495\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu S, Matsunaga T, Sawata S et al (2023) Preoperative Osteopenia Is a Risk Factor for Death in Patients Undergoing Gastrectomy for Gastric Cancer. Anticancer Res 43:3665\u0026ndash;3672\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukushima N, Tsuboi K, Nyumura Y et al (2023) Prognostic significance of preoperative osteopenia on outcomes after gastrectomy for gastric cancer. Annals Gastroenterological Surg 7:255\u0026ndash;264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawamura T, Makuuchi R, Tokunaga M et al (2018) Long-Term Outcomes of Gastric Cancer Patients with Preoperative Sarcopenia. Ann Surg Oncol 25:1625\u0026ndash;1632\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuwada K, Kuroda S, Kikuchi S et al (2019) Clinical Impact of Sarcopenia on Gastric Cancer. Anticancer Res 39:2241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng H-L, Wei L-H, Xu B-B et al (2024) Prognostic value of preoperative sarcopenia in gastric cancer: A 10-year follow-up study. Eur J Surg Oncol 50:108004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTegels JJ, van Vugt JL, Reisinger KW et al (2015) Sarcopenia is highly prevalent in patients undergoing surgery for gastric cancer but not associated with worse outcomes. J Surg Oncol 112:403\u0026ndash;407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKai W, Takano Y, Kobayashi Y, Kanno H, Hanyu N, Eto K (2025) Impact of osteosarcopenia on short- and long-term outcomes in patients with gastric cancer. Jpn J Clin Oncol 55:477\u0026ndash;483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayasaka K, Notsuda H, Onodera K et al (2024) Prognostic value of perioperative changes in the prognostic nutritional index in patients with surgically resected non-small cell lung cancer. Surg Today 54:1031\u0026ndash;1040\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi M, Aoyama A, Hamaji M et al (2025) Clinical significance of the preoperative prognostic nutritional index in patients with resectable non-small cell lung cancer: a multicenter study. Surg Today 55:918\u0026ndash;929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang N, Deng JY, Ding XW et al (2014) Prognostic nutritional index predicts postoperative complications and long-term outcomes of gastric cancer. World J Gastroenterol 20:10537\u0026ndash;10544\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Shi J, Xie H et al (2023) Superiority of CRP-albumin-lymphocyte index as a prognostic biomarker for patients with gastric cancer. Nutrition 116:112191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWada Y, Nishi M, Yoshikawa K et al (2022) Preoperative nutrition and exercise intervention in frailty patients with gastric cancer undergoing gastrectomy. Int J Clin Oncol 27:1421\u0026ndash;1427\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki Y, Ishibashi Y, Omura N et al (2005) Alendronate improves vitamin D-resistant osteopenia triggered by gastrectomy in patients with gastric cancer followed long term. J Gastrointest Surg 9:955\u0026ndash;960\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim JS, Jin SH, Kim SB, Lee JI (2012) Effect of bisphosphonates on bone mineral density and fracture prevention in gastric cancer patients after gastrectomy. J Clin Gastroenterol 46:669\u0026ndash;674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKudou K, Saeki H, Nakashima Y et al (2019) Postoperative development of sarcopenia is a strong predictor of a poor prognosis in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer. Am J Surg 217:757\u0026ndash;763\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"metabolic bone diseases, prognosis, stomach neoplasms, sarcopenia, osteopenia","lastPublishedDoi":"10.21203/rs.3.rs-9331253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9331253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003ePreoperative nutritional and immunological status is closely associated with postoperative outcomes in gastric cancer. Although osteopenia and sarcopenia have been reported as prognostic factors, the significance of their coexistence as osteosarcopenia and its value relative to established nutritional indices remain unclear. This study aimed to clarify the prognostic impact of osteosarcopenia in patients undergoing curative gastrectomy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 273 patients who underwent curative gastrectomy for gastric cancer. Osteopenia and sarcopenia were assessed using preoperative computed tomography\u0026ndash;derived bone mineral density and skeletal muscle index, respectively, and osteosarcopenia was defined as their coexistence. Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards models, and prognostic predictive ability was evaluated by time-dependent ROC analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOsteopenia, sarcopenia, and osteosarcopenia were observed in 40.7%, 49.5%, and 22.3% of patients, respectively. Osteopenia and osteosarcopenia were independent predictors of poor OS and RFS, whereas sarcopenia alone was not. Osteosarcopenia was associated with older age, systemic inflammation, and poor nutritional status. A composite Cox model\u0026ndash;based risk score integrating body composition and nutritional parameters demonstrated superior prognostic accuracy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePreoperative osteosarcopenia is a strong prognostic indicator in gastric cancer, and multidimensional assessment improves survival prediction.\u003c/p\u003e","manuscriptTitle":"Osteosarcopenia as a Prognostic Indicator of Gastric Cancer Compared with Established Nutritional Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 16:54:57","doi":"10.21203/rs.3.rs-9331253/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T03:30:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T12:39:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T15:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159424010153751459428091992867820740585","date":"2026-04-18T13:11:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40431892458270166172162931442264824979","date":"2026-04-13T12:41:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T05:20:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-12T15:09:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T07:24:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Langenbeck's Archives of Surgery","date":"2026-04-06T07:40:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf81387d-8fae-4edc-83fe-62fa7681238c","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T03:30:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T12:39:17+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T20:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 16:54:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9331253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9331253","identity":"rs-9331253","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}