Muscle mass is an important prognostic factor for patients with lung cancer: a cross-sectional multicenter prospective cohort study

Muscle mass is an important prognostic factor for patients with lung cancer: a cross-sectional multicenter prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Muscle mass is an important prognostic factor for patients with lung cancer: a cross-sectional multicenter prospective cohort study Hanping Shi, Xin Wang, Hong Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5255311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Muscle dysfunction and loss of mass are significant clinical concerns and key features of cancer cachexia. However, the potential predictive role of muscle mass, especially hand grip strength (HGS), as a prognostic factor in lung cancer remains underexplored. The aim of this study is to determine whether HGS, as a measure of muscle mass, is an effective predictor of clinical outcomes in patients with lung cancer. Methods This research is a cross-sectional multicenter prospective cohort study, encompassing patients aged over 18 from 29 medical centers. These patients were diagnosed with lung cancer between June 2012 and December 2019. We assessed muscle mass using several indicators: hand grip strength (HGS), mid-arm circumference (MAC), left mid-arm muscle circumference (MAMC), mid-arm muscle area (MAMA), left maximum calf circumference (MCC), and an overall muscle wasting score (OMWS). These indicators serve to measure muscle strength and mass. The primary outcomes of this study are overall survival (OS) time and all-cause mortality. Hazard ratios (HRs) were applied to analyze the impact of muscle mass on the all-cause mortality. Results Totally 3496 patients from 29 medical centers were included. Patients were stratified into four groups based on the quartiles of their HGS scores, with group sizes of 874, 860, 887 and 875, respectively. Subgroup analysis revealed that patients with lower HGS scores had significantly poorer outcomes than those with higher scores. The 5-year survival probabilities were 30.73%, 35.43%, 31.04 and 39.06% for each group respectively (p < 0.0001), even after adjusting for tumor stage and gender. Univariate Cox regression analysis revealed that higher HGS was an independent protective factor for patients with lung cancer (hazard ratio = 0.69, 95% confidence interval [CI]: 0.59–0.81). Multivariable Cox proportional hazards regression analysis corroborated this finding. Other muscle mass metrics, such as MAC, MAMC, MAMA, MLC and OMWS, also underscore the protective role of maintaining muscle mass in lung cancer prognosis. Conclusions The progressive and widespread reduction of skeletal muscle mass and strength is a critical negative prognostic indicator in lung cancer patients. Among all the evaluated parameters, HGS demonstrates the most significant correlation with overall survival. It stands out as a key factor in predicting the prognosis of patients with lung cancer. Muscle mass hand grip strength Prognosis Lung cancer Sarcopenia Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Despite extensive research and advancements in treatment, lung cancer continues to be one of the most prevalent cancers, with the highest mortality rates worldwide( 1 , 2 ). Recent data indicated that lung cancer is responsible for approximately 2.09 million new cases and about 1.8 million deaths each year( 1 , 3 , 4 ). The impact of lung cancer goes beyond health concerns, imposing significant economic strain on societies( 5 ). While there has been significant progress in understanding lung cancer’s pathogenesis, molecular characteristics, and treatment methodologies, the overall survival rates remain disappointingly low( 6 ). Precise prognosis prediction is crucial for determining the appropriate treatment strategies and offering personalized care to lung cancer patients( 7 ). This highlights the critical need for developing effective prognostic tools. Presently, the prognostic predictors for lung cancer are currently limited, emphasizing the urgent need for the identification and validation of reliable prognostic indicators. Traditionally, the survival outcomes of lung cancer patients have been assessed using their pathological stage and histological subtype. However, patients with the same stage or subtype often show varying outcomes, highlighting the necessity for more refined prognostic tools( 8 ). In addition to cost, the inconvenience and significant time investment required are additional drawbacks of these evaluation methods. Cancer-associated cachexia is a multifactorial syndrome characterized by significant weight loss, primarily in skeletal muscle( 9 ). It is known as a leading cause of death in cancer patients, accounting for up to 20% of cancer-related deaths. Besides, approximately 50–80% of cancer patients undergo carious degree of this debilitating syndrome( 10 ). A key feature of cancer-associated cachexia is muscle wasting, which is responsible for low physical performance and functional impairments( 11 ). Several studies have linked reduced muscle mass to inflammation( 12 ), immune response,( 13 ) mortality( 14 ), and complications( 15 ), demonstrating the deleterious effects of muscle depletion across various cancers( 16 , 17 ). Despite this, it remains unclear whether muscle mass, which includes muscle mass and function, can serve as prognostic predictors of lung cancer patients. This study aims to analyze a comprehensive dataset from multiple centers to evaluate the prognostic value of muscle mass in lung cancer patients, and to identify the most effective parameter. Muscle mass was evaluated using hand grip strength (HGS)( 18 , 19 ), mid-arm circumference (MAC), mid-arm muscle circumference (MAMC), left mid-arm muscle area (MAMA), left maximum-calf circumference (MCC) and overall muscle wasting score (OMWS), reflecting both muscle strength and muscle mass( 20 ). The goal of this study is to refine and improve the prognostic assessment tools for lung cancer, enabling more personalized treatment strategies, and ultimately improving the overall management and outcomes of lung cancer patients. 2 Method and materials 2.1 Population We abstracted data from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC; chictr.org.cn), which was a national multicenter cross-sectional prospective cohort study conducted in 34 medical centers in China. This study was conducted according to the Declaration of Helsinki and was approved by the Medical Ethics Committee. All participants or their legal guardian assigned written informed consent before the study. Patients were included if they satisfied the following inclusion criteria: ( 1 ) Patients who were pathologically diagnosed with lung cancer at the participating medical centers between January 1, 2016, and December 31, 2021; ( 2 ) Age of 18 years or older; ( 3 ) Absence of other conditions that could potentially cause cachexia or significant weight change, such as HIV infection, pregnancy, organ transplantation or end-stage renal disease, besides lung cancer; ( 4 ) Have the ability to communicate clearly and complete the study questionnaire; ( 5 ) Were willing to participate in the study. Patients who meet the following criteria were excluded: ( 1 ) Patients with missing or incomplete clinicopathological data; ( 2 ) Patients who were lost to follow-up. This work has been reported in line with the STROCSS criteria.( 21 ) 2.2 Data collection Patients who met the included criteria were from 29 medical centers. Patient demographic, clinical characteristics, and oncological data were collected by experienced physicians within 48 hours of admission from 29 medical centers. The baseline data encompassed age at diagnosis, gender, marital status, occupation, weight and height at admission, body mass index (BMI), past medical history, smoking history, alcohol consumption habits, and family history. BMI was calculated as weight (Kg) divided by height squared (m 2 ). Clinical data included weight loss, Karnofsky performance status (KPS), Patient-Generated Subjective Global Assessment (PG-SGA) score, nutritional risk screening 2002 (NRS2002), triceps skinfold thickness (TSF), fat deficiency, HGS, MAC, MAMC, MAMA, MCC and OMWS. Low muscle strength was defined as the HGS < 28 kg for men and < 18 kg for women( 20 ). Tumor-related information included tumor location, differentiation status, histological and pathological stage, TNM stage and type of treatment received, such as surgery, radiotherapy and chemotherapy. These data were retrieved from patients' medical records in the Health Information System (HIS; Dayinjunhui company, Beijing, China). Patients were consistently followed through regular outpatient visits or telephone interviews until either their passing or until December 31, 2021, which occurred first. Those who remained symptom-free for 72 months were considered cured and were no longer followed up. The study received ethical approval from the research ethics committees of all participating medical centers, ensuring adherence to the Declaration of Helsinki guidelines. Informed consent was obtained from all participants prior to their enrollment in the study. 2.3 Measurement of muscle mass Patient muscle mass assessment focused on two aspects: muscle mass and muscle function. Left MAC and MCC were measured using a millimeter-marked flexible, non-stretchable measuring tape. The midpoint of the upper arm was determined by measuring the distance between the tip of the shoulder (acromion process) and the tip of the elbow (olecranon process), with the midpoint being equidistant from these points. MAMC and MAMA were derived from MAC and TSF. The triceps skinfold thickness was measured at the midpoint of the upper arm using a skinfold caliper. MAMC ( cm ) = MAC ( cm )−( π × TSF ( cm )) MAMA (cm 2 )= \(\:\frac{1}{4\pi\:}{\left[MAC\right(cm)-\pi\:\times\:TSF(cm\left)\right]}^{2}\) Muscle function was evaluated using HGS and OMWS. HGS was tested with a hydraulic dynamometer. Patients, seated with their arm at a right angle and elbow by their side, squeezed the dynamometer with maximum effort for approximately 5 seconds, using only their left hand without additional body assistance. The highest value recorded in kilograms was noted. The OMWS was determined by a comprehensive assessment of the muscle mass in the limbs and trunk, as well as grip strength. The scoring was set as follows: 3 for extreme wasting, 2 for severe wasting, 1 for significant wasting, 0 for no noticeable change, -1 for slight muscle increase, and − 2 for significant increase. To ensure accuracy, all the measurement were taken at least twice. For HGS, allow a rest period of at least 15–30 seconds between each attempt to avoid muscle fatigue. 2.4 Statistical analysis Statistical analyses were performed using SPSS (SPSS Inc., Chicago, IL, USA) and R Project (StataCorp LLC, Texas, USA). Overall survival (OS) rates and median survival times at 3- and 5-year intervals were calculated using the Kaplan-Meier method, with the optimal cut-off value for lung cancer determined by the optimal stratification method. Survival differences between groups were analyzed using the log-rank test. Both univariate and multivariate survival analyses were conducted using Cox regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI). Continuous variables were compared using either Student’s t-test or the Kruskal-Wallis test, while categorical variables were analyzed using the Chi-squared test or Fisher’s exact test. Variables with a P-value below 0.10 in univariate analysis were included in a multivariate Cox proportional hazards regression model. Data was presented as mean ± standard deviation (SD) for normally distributed data, and median (interquartile range) or frequency (percentage) for non-normally distributed data. A P-value of less than 0.05 was considered statistically significant. 3 Results 3.1 Demographic and oncological data of patients with lung cancer Our study commenced with an initial screening of 5025 patients in 34 medical centers across China from the INSCOC study. 1529 of these patients were excluded due to the lack of complete parameters. The specific screening process and the reasons for exclusion are shown in Fig. 1 . A total of 3496 patients from 29 medical centers were finally enrolled, including 2331 (66.68%) men and 1165 (33.32%) women with the mean age of 59.95 ± 9.82. The distribution of patients according to the tumor stage of their lung cancer was as follows: 323 patients (9.24%) had stage I disease, 571 (16.33%) had stage II, 758 (21.68%) had stage III, and 1844 patients (52.75%) presented with stage IV disease. In the study cohort, 2149 (61.47%) of patients underwent chemotherapy, 394 (11.27%) had lung surgery and 281 (8.04%) received radiotherapy. Following a comprehensive 6-year follow-up period, 1793 (51.29%) patients succumbed to lung cancer. The detailed demographic and oncological data were presented in Table 1 . Table 1 Characteristic of patients with lung cancer stratified by HGS. Variable Total (n = 3496) HGS group Statistic P 1 (n = 874) 2 (n = 860) 3 (n = 887) 4 (n = 875) Age, Mean ± SD 59.95 ± 9.82 62.14 ± 10.37 60.48 ± 9.71 60.07 ± 9.15 57.12 ± 9.34 F = 40.970 < 0.001 BMI, Mean ± SD 22.89 ± 3.27 22.17 ± 3.40 22.64 ± 3.18 22.75 ± 3.05 24.01 ± 3.18 F = 52.632 < 0.001 KPS, Mean ± SD 85.85 ± 12.23 80.96 ± 16.34 85.93 ± 10.73 87.15 ± 10.29 89.34 ± 8.56 F = 78.454 < 0.001 Total protein, Mean ± SD 68.51 ± 7.42 68.05 ± 7.83 68.57 ± 7.03 68.62 ± 7.35 68.80 ± 7.43 F = 1.626 0.181 NRS2002, Mean ± SD 1.54 ± 1.40 1.88 ± 1.57 1.59 ± 1.40 1.47 ± 1.32 1.21 ± 1.20 F = 35.551 < 0.001 PGSGA, Mean ± SD 5.39 ± 4.66 7.30 ± 5.23 5.34 ± 4.55 4.89 ± 4.34 4.05 ± 3.78 F = 81.765 < 0.001 Fat deficiency, Mean ± SD 0.31 ± 0.75 0.48 ± 0.83 0.30 ± 0.76 0.32 ± 0.71 0.15 ± 0.63 F = 30.228 < 0.001 MAC, Mean ± SD 26.69 ± 3.42 25.58 ± 3.32 26.27 ± 3.24 26.86 ± 3.24 28.06 ± 3.38 F = 88.671 < 0.001 TSF, Mean ± SD 15.38 ± 7.49 15.92 ± 7.28 15.91 ± 7.49 14.55 ± 7.62 15.16 ± 7.46 F = 6.866 < 0.001 MAMC, Mean ± SD 21.87 ± 3.24 20.59 ± 2.88 21.27 ± 2.94 22.29 ± 3.18 23.30 ± 3.26 F = 130.831 < 0.001 MAMA, Mean ± SD 38.90 ± 11.42 34.40 ± 9.72 36.71 ± 9.98 40.35 ± 11.25 44.08 ± 12.11 F = 134.416 < 0.001 MLC, Mean ± SD 33.30 ± 3.86 31.80 ± 3.70 32.76 ± 3.55 33.37 ± 3.79 35.26 ± 3.53 F = 140.200 < 0.001 HGS, Mean ± SD 25.59 ± 9.70 13.76 ± 3.78 21.70 ± 1.78 28.50 ± 2.12 38.29 ± 5.43 F = 7354.873 < 0.001 Muscle score, Mean ± SD 0.85 ± 0.56 0.95 ± 0.61 0.88 ± 0.54 0.85 ± 0.53 0.72 ± 0.51 F = 26.15 < 0.001 Gender, n (%) χ²=1009.408 < 0.001 Male 2331 (66.68) 282 (32.27) 459 (53.37) 742 (83.65) 848 (96.91) Female 1165 (33.32) 592 (67.73) 401 (46.63) 145 (16.35) 27 (3.09) Diabetes, n (%) χ²=0.854 0.837 No 3185 (91.1) 794 (90.85) 779 (90.58) 809 (91.21) 803 (91.77) Yes 311 (8.9) 80 (9.15) 81 (9.42) 78 (8.79) 72 (8.23) Hypertension, n (%) χ²=28.934 < 0.001 No 2822 (80.72) 660 (75.51) 691 (80.35) 759 (85.57) 712 (81.37) Yes 674 (19.28) 214 (24.49) 169 (19.65) 128 (14.43) 163 (18.63) Family history, n (%) χ²=3.347 0.341 No 2958 (84.61) 747 (85.47) 739 (85.93) 744 (83.88) 728 (83.20) Yes 538 (15.39) 127 (14.53) 121 (14.07) 143 (16.12) 147 (16.80) Smoke, n (%) χ²=342.168 < 0.001 No 1381 (39.5) 534 (61.10) 402 (46.74) 242 (27.28) 203 (23.20) Yes 2115 (60.5) 340 (38.90) 458 (53.26) 645 (72.72) 672 (76.80) Alcohol, n (%) χ²=200.942 < 0.001 No 2642 (75.57) 781 (89.36) 701 (81.51) 602 (67.87) 558 (63.77) Yes 854 (24.43) 93 (10.64) 159 (18.49) 285 (32.13) 317 (36.23) Tumor stage, n (%) χ²=38.872 < 0.001 Ⅰ 323 (9.24) 93 (10.64) 82 (9.53) 75 (8.46) 73 (8.34) Ⅱ 571 (16.33) 106 (12.13) 135 (15.70) 166 (18.71) 164 (18.74) Ⅲ 758 (21.68) 167 (19.11) 172 (20.00) 198 (22.32) 221 (25.26) Ⅳ 1844 (52.75) 508 (58.12) 471 (54.77) 448 (50.51) 417 (47.66) Surgery, n (%) χ²=0.837 0.841 No 3102 (88.73) 777 (88.90) 764 (88.84) 780 (87.94) 781 (89.26) Yes 394 (11.27) 97 (11.10) 96 (11.16) 107 (12.06) 94 (10.74) Chemotherapy, n (%) χ²=61.734 < 0.001 No 1347 (38.53) 422 (48.28) 342 (39.77) 314 (35.40) 269 (30.74) Yes 2149 (61.47) 452 (51.72) 518 (60.23) 573 (64.60) 606 (69.26) Radiotherapy, n (%) χ²=7.767 0.051 No 3215 (91.96) 787 (90.05) 804 (93.49) 822 (92.67) 802 (91.66) Yes 281 (8.04) 87 (9.95) 56 (6.51) 65 (7.33) 73 (8.34) HGS group, n (%) χ²=10488.000 < 0.001 1 874 ( 25 ) 874 (100.00) 0 (0.00) 0 (0.00) 0 (0.00) 2 860 (24.6) 0 (0.00) 860 (100.00) 0 (0.00) 0 (0.00) 3 887 (25.37) 0 (0.00) 0 (0.00) 887 (100.00) 0 (0.00) 4 875 (25.03) 0 (0.00) 0 (0.00) 0 (0.00) 875 (100.00) 3.2 Relationship between the muscle mass and clinicopathologic characteristics HGS was a reliable and robust metric for assessing muscular quality in our study, providing significant insights into overall muscle health and functionality. Patients were stratified into four groups based on the quartiles of their HGS scores. Group 1(~ 18.6 kg), Group 2 (18.6–24.9), Group 3 (24.9–32.0) and Group 4 (32.0~) had 874, 860, 887 and 875 patients, respectively. We conducted a comparison of the clinicopathological characteristics among patients within different groups. Low HGS was significantly related to increased age (Group 1 vs 2 vs 3 vs 4: 62.14 ± 10.37 vs 60.48 ± 9.71 vs 60.07 ± 9.15 vs 57.12 ± 9.34, P < 0.001), decreased BMI (22.17 ± 3.40 vs 22.64 ± 3.18 vs 22.75 ± 3.05 vs 24.01 ± 3.18, P < 0.001), poor KPS (80.96 ± 16.34 vs 85.93 ± 10.73 vs 87.15 ± 10.29 vs 89.34 ± 8.56, P < 0.001), and severe Fat deficiency (0.48 ± 0.83 vs 0.30 ± 0.76 vs 0.32 ± 0.71 vs 0.15 ± 0.63, P < 0.001). NRS2002 and PG-SGA were used to assess the risk of malnutrition and the additional require for nutritional support interventions in patients respectively. Higher HGS was associated with decreased NRS2002 (1.88 ± 1.57 vs 1.59 ± 1.40 vs 1.47 ± 1.32 vs 1.21 ± 1.20, P < 0.001) and reduced PG-SGA (7.30 ± 5.23 vs 5.34 ± 4.55 vs 4.89 ± 4.34 vs 4.05 ± 3.78, P < 0.001) (Table 1 ). For muscle mass indicators, low HGS was associated with decreased MAC (25.58 ± 3.32 vs 26.27 ± 3.24 vs 26.86 ± 3.24 vs 28.06 ± 3.38, P < 0.001), smaller MAMA (34.40 ± 9.72 vs 36.71 ± 9.98 vs 40.35 ± 11.25 vs 44.08 ± 12.11, P < 0.001), reduced MAMC (20.59 ± 2.88 vs 21.27 ± 2.94 vs 22.29 ± 3.18 vs 23.30 ± 3.26, P < 0.001), declined MLC (31.80 ± 3.70 vs 32.76 ± 3.55 vs 33.37 ± 3.79 vs 35.26 ± 3.53, P < 0.001) and increased muscle wasting score (0.95 ± 0.61 vs 0.88 ± 0.54 vs 0.85 ± 0.53 vs 0.72 ± 0.51, P < 0.001) (Table 1 ). 3.3 Kaplan-Meier curves of overall survival in lung cancer patients with different HGS The mean follow-up time in this study was 17.27 months. After a six-year follow-up period, 1793 out of 3496 (51.29%) patients succumbed to lung cancer. A strong correlation between HGS and patient survival was observed upon dividing the cohort into quartiles based on their HGS scores. Patients with a higher HGS showed significantly improved 5-year probability of survival than those with a lower HGS (Group 1 vs 2 vs 3 vs 4: 30.73% vs 35.43% vs 31.04 vs 39.06%, log-rank P < 0.001). Median survival of these four groups were 23.07, 27.83, 24.43 and 31.23 months, respectively ( P < 0.001). Stratified analysis of lung cancer patients with high and low HGS revealed that higher HGS was associated with better prognosis at all stages and in different genders. 3.4 Univariate and multivariable survival analyses Univariate analysis revealed that the risk of death increased with age (HR:1.01 (1.00-1.01), P = 0.022), NRS2002 (1.17 (1.12–1.21), P < 0.001), PGSGA (1.06 (1.05–1.07), P < 0.001), fat deficiency (1.22 (1.14–1.31), P < 0.001), OMWS (1.27 (1.17–1.38), P < 0.001), Smoking (1.49 (1.32–1.67), P < 0.001), alcohol abuse (1.13 (1.00-1.29), P < 0.001). Compared with stage Ⅰ, stage Ⅱ 2.29 (1.58–3.31), Ⅲ (4.55 (3.21–6.44)), Ⅳ (7.52 (5.38–10.52), P < 0.001) showed significant increased risk of death. Surprisingly, patients who received radiotherapy were associated with increased risk compared with those who did not underwent radiotherapy (1.30 (1.08–1.57), P < 0.001). In contrast, BMI (0.95 (0.93–0.96), P < 0.001), KPS (0.98 (0.98–0.99), P < 0.001) and total protein (0.99 (0.98–0.99) P < 0.001) were associated with a lower risk of death. Female patients had lower risk of death compared with male (0.73 (0.64–0.82), P < 0.001) and those who received surgery had lower risk (0.37 (0.29–0.46), P < 0.001). (Table 2 ). The relationship between various factors and hazard risk of overall survival in patients with lung cancer was listed in Fig. 3 . In addition, we performed a nomogram, through which we constructed a predictive model of the correlation between patients' muscle mass and clinical outcomes, which revealed a positive correlation between patients' overall survival and higher HGS scores (Fig. 4 ). Under the same conditions, patients with a higher HGS score may have a higher survival probability. Table 2 Univariate and multivariate cox regression analysis of factors associated with overall survival in patients with lung cancer. Variables Univariate analysis Multivariate analysis HR (95%CI) P HR (95%CI) P Age 1.01 (1.00–1.01) 0.022 BMI 0.95 (0.93–0.96) < 0.001 0.98 (0.97–0.99) 0.043 KPS 0.98 (0.98–0.99) < 0.001 0.99 (0.98–0.99) < 0.001 Total protein 0.99 (0.98–0.99) < 0.001 0.99 (0.98–0.99) 0.011 NRS2002 1.17 (1.12–1.21) < 0.001 PGSGA 1.06 (1.05–1.07) < 0.001 1.02 (1.01–1.03) < 0.001 Fat deficiency 1.22 (1.14–1.31) < 0.001 MAC 0.96 (0.95–0.98) < 0.001 TSF 0.98 (0.97–0.98) < 0.001 0.99 (0.98–0.99) 0.015 MAMC 0.99 (0.98–1.01) 0.491 MAMA 1.00 (0.99–1.00) 0.343 MLC 0.96 (0.95–0.98) < 0.001 HGS 0.99 (0.98–0.99) < 0.001 Muscle score 1.27 (1.17–1.38) < 0.001 Gender Male Ref Ref Female 0.73 (0.64–0.82) < 0.001 0.77 (0.67–0.89) < 0.001 Diabetes No Ref Yes 1.05 (0.87–1.27) 0.598 Hypertension No Ref Yes 1.00 (0.87–1.16) 0.965 Family history No Ref Ref Yes 0.87 (0.74–1.03) 0.107 0.87 (0.76–0.99) 0.046 Smoke No Ref Ref Yes 1.49 (1.32–1.67) < 0.001 1.26 (1.11–1.42) < 0.001 Alcohol No Ref Yes 1.13 (1.00–1.29) 0.058 Tumor stage Ⅰ Ref Ref Ⅱ 2.29 (1.58–3.31) < 0.001 2.09 (1.53–2.85) < 0.001 Ⅲ 4.55 (3.21–6.44) < 0.001 3.89 (2.90–5.21) < 0.001 Ⅳ 7.52 (5.38–10.52) < 0.001 6.35 (4.77–8.44) < 0.001 Surgery No Ref Ref Yes 0.37 (0.29–0.46) < 0.001 0.64 (0.52–0.78) < 0.001 Chemotherapy No Ref Yes 1.02 (0.91–1.14) 0.769 Radiotherapy No Ref Ref Yes 1.30 (1.08–1.57) 0.006 0.87 (0.74–1.02) 0.077 HGS group 1 Ref Ref 2 0.85 (0.72–0.99) 0.028 0.85 (0.75–0.98) 0.023 3 0.94 (0.81–1.09) 0.115 0.89 (0.77–1.03) 0.110 4 0.74 (0.64–0.87) < 0.001 0.68 (0.58–0.80) < 0.001 In the multivariate Cox proportional analysis, after adjusting these confounding factors, patients with lower HGS were significantly more likely to have worse clinical outcomes than those with higher HGS. When HGS was divided into quintiles, the lowest quintile Group 1 was used as a reference. Group 2, Group 3 and Group 4 were all positively associated with better prognosis with (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.72–1.01; 0.88 [0.74, 1.04] and 0.69 [0.59, 0.81], respectively (Table 2 ). 4 Discussion Cancer cachexia is a severe lethal wasting syndrome, notably affecting up to 50%-80% of cancer cases and account for up to 20% of cancer deaths( 22 , 23 ). This syndrome is not just a symptom, but a complex metabolic disorder characterized by loss of skeletal muscle mass, with or without fat loss. Such loss leads to a decline in overall physical function, severely undermining the quality of life and treatment effectiveness for cancer patients( 24 ). The relationship between cancer cachexia and lung cancer prognosis has been extensively studied, revealing a substantial impact on patient outcomes( 25 , 26 ). However, despite its significance, there is a lack of a standardized cachexia parameter for the prediction of lung cancer prognosis. This gap in clinical assessment tools presents presenting a major challenge in the effective management and treatment of lung cancer patients( 27 ). BMI has been widely used to assess nutritional status of patients and has shown a significant correlation with tumor prognosis( 28 ). However, the rise in the number of cancer patients with sarcopenic obesity—a condition where obesity coexists with reduced muscle mass—casts doubt on the effectiveness of BMI and other traditional cachexia parameters as reliable indicators( 29 ). Furthermore, the accuracy of BMI as an assessment tool is compromised by various confounders. For example, dystrophic edema can artificially increase weight without reflecting true nutritional status. Malignancy-associated pleural effusion can alter body weight and composition, complicating the assessment of patients' nutritional and health status. Increases in visceral adiposity, the accumulation of fat around the internal organs in the abdominal cavity, can skewed BMI interpretations and mask muscle loss and sarcopenia. All these factors challenge the conventional approaches to evaluating the nutritional and health status of cancer patients, underlining the need for more sophisticated and nuanced assessment tools. To enhance the assessment of nutritional status and prognosis prediction in lung cancer patients, we performed an in-depth analysis of data from a comprehensive multicenter cross-sectional prospective cohort study. The results of our study highlighted a strong correlation between muscle mass and the prognosis of lung cancer patients. As per our current knowledge, this study represents the most extensive investigation of its kind, being a multicenter, cross-sectional prospective cohort investigation concentrated on evaluating muscle mass in lung cancer patients. HGS offers a quick and non-invasive method to evaluate muscular strength, which is a key component of physical health and an important factor in the quality of life, especially for patients with cancer. Our findings distinctly established that higher HGS was associated with better survival outcomes in these patients. This correlation underscores the pivotal importance of muscle mass in determining the prognosis and influencing the survival rates of lung cancer patients. The significance of muscle mass in providing protective effects is evident, yet the effective management options for cancer-associated cachexia and muscle wasting are currently limited( 30 ). The American Society of Clinical Oncology (ASCO) guidelines highlight the uncertain outcomes of pharmacological interventions aimed at treating cancer cachexia. Nonetheless, dietary strategies such as nutritional counseling, increasing protein consumption, and supplementing diets with omega-3 (n-3) fatty acids are advisable and beneficial for patients( 31 , 32 ). In addition, combined muscle-strengthening and aerobic activities were associated with reduced risk and mortality rate( 33 ). Recent foundational studies are actively investigating new potential therapeutic targets to combat muscle wasting in lung cancer patients. For example, the LCN2 secreted by tissue-infiltrating neutrophils was a potential target in the treatment of cancer cachexia( 34 ). Furthermore, antagonists that target RAGE (Receptor for Advanced Glycation End-products) are emerging as a promising therapeutic approach to prevent or alleviate the symptoms associated with cachexia syndrome( 35 ). At present, these studies are predominantly in the preclinical phase, emphasizing the necessity for more extensive research. Looking ahead, the execution of clinical randomized controlled trials (RCTs) is essential to thoroughly assess and confirm the therapeutic efficacy of these potential treatments. Our study, while comprehensive, faces several limitations that need to be acknowledged. Firstly, the exclusive inclusion of Chinese individuals in our cohort casts uncertainty on the applicability of these findings to other ethnic groups. This limitation is significant as it may affect the applicability of our results in a broader, more diverse context. Secondly, although this national multicenter prospective cohort study mitigates the selection bias typically associated with single-center studies, there remain potential biases due to variations in the quality of treatment and diagnostic equipment across different participating centers. This aspect of our study warrants further external validation to solidify our findings. Additionally, our research primarily focused on exploring the predictive roles of muscle mass in lung cancer patients. Despite this focus, our data also revealed that nutrition-related metrics, such as total protein, NRS2002, and PG-SGA, significantly influence the prognosis of lung cancer patients. It is important to note that, based on our current findings, we cannot conclusively state that HGS surpasses these nutrition-related indicators in evaluating prognosis. Future efforts will necessitate a more detailed refinement and analysis of these results to clarify their implications. 5 Conclusion In conclusion, our study provides a comprehensive overview of the muscle mass in lung cancer patients, underscored by its multicenter prospective design and substantial sample size. This research robustly substantiates the significant impact of muscle mass, especially HSG, on the prognosis of patients with lung cancer. Key muscle mass indicators, such as the HGS, MAMC, MAMA, MAC, MCC and OMWS are closely associated with survival outcomes in these patients. Furthermore, our findings bring to light the notably poor muscle mass observed in patients with stage 4 lung cancer, highlighting a critical need for strength and muscle restoration in their treatment and care. The data gathered through our study is invaluable in informing therapeutic strategies and rehabilitation programs for lung cancer patients. It highlights the importance of incorporating muscle mass assessment into routine clinical practice, not only as a prognostic tool but also as a guide for personalized treatment and care plans. This approach could potentially improve quality of life and survival outcomes for individuals battling lung cancer. Declarations Acknowledgement Data availability statement The datasets analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate. Not applicable. Role of the Funder/Sponsor This research was funded by Beijing Hospitals Authority Youth Programme. grant number QMS20220726. Consent for publication Not applicable. Competing interests The authors declare that they have no potential conflicts of interest. Disclaimer The statements in this article are solely the responsibility of the authors. Provenance and peer review Not commissioned, externally peer-reviewed. Author Contributions Conceptualization, X.W., Y.H., and Y.F.; Data curation, X.W., Y.G. and H.S.; Formal analysis, X.W., L.S. and H.S.; Funding acquisition, X.W.; Methodology, X.W., Y.H., and Y.F.; Software, H.Z., Y.H., and Y.F.; Writing – original draft, X.W., Y.H., and Y.F; Writing – review & editing, L.S., Y.G. and H.S.. 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Chen Y, Zitello E, Guo R, Deng Y. The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Med. 2021;11(4):e367. Xie H, Ruan G, Wei L, Deng L, Zhang Q, Ge Y, et al. The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non-small cell lung cancer. J Cachexia Sarcopenia Muscle. 2023;14(2):869–78. Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. Cancer-associated cachexia. Nat Rev Dis Primers. 2018;4:17105. Argilés JM, López-Soriano FJ, Stemmler B, Busquets S. Cancer-associated cachexia - understanding the tumour macroenvironment and microenvironment to improve management. Nat Rev Clin Oncol. 2023;20(4):250–64. Jain R, Coss C, Whooley P, Phelps M, Owen DH. The Role of Malnutrition and Muscle Wasting in Advanced Lung Cancer. Curr Oncol Rep. 2020;22(6):54. Xie HL, Ruan GT, Wei L, Zhang Q, Ge YZ, Song MM, et al. The prognostic value of the combination of body composition and systemic inflammation in patients with cancer cachexia. J Cachexia Sarcopenia Muscle. 2023;14(2):879–90. Cury SS, de Moraes D, Oliveira JS, Freire PP, Dos Reis PP, Batista ML, et al. Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment. J Transl Med. 2023;21(1):116. Wang K, Long W, Sima X, Zhao Y, Xiao B, Gulizeba H, et al. Sarcopenia defined by skeletal muscle mass index at the third lumbar vertebra is a prognostic factor for extensive-stage small cell lung cancer patients: a retrospective study. J Thorac Dis. 2022;14(7):2645–51. Cameron ME, Ayzengart AL, Oduntan O, Judge SM, Judge AR, Awad ZT. Low Muscle Mass and Radiodensity Associate with Impaired Pulmonary Function and Respiratory Complications in Patients with Esophageal Cancer. J Am Coll Surg. 2023;236(4):677–84. Sun C, Anraku M, Kawahara T, Karasaki T, Kitano K, Nagayama K et al. Respiratory strength and pectoralis muscle mass as measures of sarcopenia: Relation to outcomes in resected non-small cell lung cancer. J Thorac Cardiovasc Surg. 2022;163(3). Vigneron C, Laousy O, Chassagnon G, Vakalopoulou M, Charpentier J, Alexandre J et al. Assessment of Functional and Nutritional Status and Skeletal Muscle Mass for the Prognosis of Critically Ill Solid Cancer Patients. Cancers (Basel). 2022;14(23). Song M, Zhang Q, Song C, Liu T, Zhang X, Ruan G, et al. Handgrip weakness, systemic inflammation indicators, and overall survival in lung cancer patients with well performance status: A large multicenter observational study. Cancer Med. 2023;12(3):2818–30. Xie H, Ruan G, Deng L, Zhang H, Ge Y, Zhang Q, et al. Comparison of absolute and relative handgrip strength to predict cancer prognosis: A prospective multicenter cohort study. Clin Nutr. 2022;41(8):1636–43. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21(3):300–e72. Mathew G, Agha R, Albrecht J, Goel P, Mukherjee I, Pai P, et al. STROCSS 2021: Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int J Surg. 2021;96:106165. Cala MP, Agulló-Ortuño MT, Prieto-García E, González-Riano C, Parrilla-Rubio L, Barbas C, et al. Multiplatform plasma fingerprinting in cancer cachexia: a pilot observational and translational study. J Cachexia Sarcopenia Muscle. 2018;9(2):348–57. Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer. 2014;14(11):754–62. Setiawan T, Sari IN, Wijaya YT, Julianto NM, Muhammad JA, Lee H, et al. Cancer cachexia: molecular mechanisms and treatment strategies. J Hematol Oncol. 2023;16(1):54. Bye A, Sjøblom B, Wentzel-Larsen T, Grønberg BH, Baracos VE, Hjermstad MJ, et al. Muscle mass and association to quality of life in non-small cell lung cancer patients. J Cachexia Sarcopenia Muscle. 2017;8(5):759–67. Biswas AK, Acharyya S. Understanding cachexia in the context of metastatic progression. Nat Rev Cancer. 2020;20(5):274–84. Halvorsen TO, Valan CD, Slaaen M, Grønberg BH. Associations between muscle measures, survival, and toxicity in patients with limited stage small cell lung cancer. J Cachexia Sarcopenia Muscle. 2020;11(5):1283–90. Renfro LA, Loupakis F, Adams RA, Seymour MT, Heinemann V, Schmoll HJ, et al. Body mass index Is prognostic in metastatic colorectal cancer: pooled analysis of patients from first-line clinical trials in the ARCAD database. J Clin Oncol. 2016;34(2):144–50. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513–37. Ferrer M, Anthony TG, Ayres JS, Biffi G, Brown JC, Caan BJ, et al. Cachexia: A systemic consequence of progressive, unresolved disease. Cell. 2023;186(9):1824–45. Roeland EJ, Bohlke K, Baracos VE, Bruera E, Del Fabbro E, Dixon S, et al. Management of cancer cachexia: ASCO guideline. J Clin Oncol. 2020;38(21):2438–53. Kiss N, Curtis A. Current insights in nutrition assessment and intervention for malnutrition or muscle loss in people with lung cancer: A narrative review. Adv Nutr. 2022;13(6):2420–32. Momma H, Kawakami R, Honda T, Sawada SS. Muscle-strengthening activities are associated with lower risk and mortality in major non-communicable diseases: a systematic review and meta-analysis of cohort studies. Br J Sports Med. 2022;56(13):755–63. Wang D, Li X, Jiao D, Cai Y, Qian L, Shen Y, et al. LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia. J Hematol Oncol. 2023;16(1):30. Chiappalupi S, Sorci G, Vukasinovic A, Salvadori L, Sagheddu R, Coletti D, et al. Targeting RAGE prevents muscle wasting and prolongs survival in cancer cachexia. J Cachexia Sarcopenia Muscle. 2020;11(4):929–46. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5255311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":365714789,"identity":"38f3f0ef-c813-45ed-834c-52e291a2a2c4","order_by":0,"name":"Hanping Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACCcY2BoYKKIeHeC1nSNPCwMYA0kW8FvnZzW2PC+fZyevOSGB88LaNQd6ckBaDOwfbjWduSzbcdiOB2XBuG4PhzgZCWiQS26R5tx1IMLuRwCbN28aQYHCAkMNmgLTMAWth/02UFoYbIC0NEFuYidJiANIy4xjQL2ceNkvOOSdhuIGww9KfSRfU2MmbHU8++OFNmY08YYcBATOEYmxgAEUTUYCZOGWjYBSMglEwYgEA2NM9fqQigNMAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hanping","middleName":"","lastName":"Shi","suffix":""},{"id":365714790,"identity":"6ae747a1-2207-4044-80e0-6bdc06ab90e8","order_by":1,"name":"Xin Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":365714791,"identity":"46e9cac3-d6cd-4052-b100-8d6980d3a4ce","order_by":2,"name":"Hong Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-10-13 12:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5255311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5255311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68655898,"identity":"6cb4fa97-8cc5-4c68-a17c-3ac88dc0f74f","added_by":"auto","created_at":"2024-11-10 14:11:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54392,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the cohort study and the inclusion and exclusion criteria.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5255311/v1/a80471463f7e985dbc6c6e89.png"},{"id":68654612,"identity":"80a43f40-1c81-40dd-b4ac-ead5c40b3c0d","added_by":"auto","created_at":"2024-11-10 13:55:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188981,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival and stratified analysis of patients with lung cancer.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5255311/v1/1c9112111c327ebd14ffa2d2.png"},{"id":68655212,"identity":"cc052e92-95c7-4145-9480-b815fa10edd9","added_by":"auto","created_at":"2024-11-10 14:03:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68899,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between various factors and hazard risk of overall survival in patients with lung cancer.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5255311/v1/735abf8e205328fe1c44ddd3.png"},{"id":68654609,"identity":"cbb01d18-edd3-49f6-a9c0-396878f6b3f3","added_by":"auto","created_at":"2024-11-10 13:55:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41320,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram between survival and muscle status in patients with lung cancer.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5255311/v1/fc1586195efbb3f583bd83f6.png"},{"id":68656588,"identity":"e41438a1-3b88-4f6e-bc96-5172e538e453","added_by":"auto","created_at":"2024-11-10 14:27:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383380,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5255311/v1/5356d9cc-86f0-4778-8d96-a60f15463738.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Muscle mass is an important prognostic factor for patients with lung cancer: a cross-sectional multicenter prospective cohort study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite extensive research and advancements in treatment, lung cancer continues to be one of the most prevalent cancers, with the highest mortality rates worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Recent data indicated that lung cancer is responsible for approximately 2.09\u0026nbsp;million new cases and about 1.8\u0026nbsp;million deaths each year(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The impact of lung cancer goes beyond health concerns, imposing significant economic strain on societies(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While there has been significant progress in understanding lung cancer\u0026rsquo;s pathogenesis, molecular characteristics, and treatment methodologies, the overall survival rates remain disappointingly low(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Precise prognosis prediction is crucial for determining the appropriate treatment strategies and offering personalized care to lung cancer patients(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This highlights the critical need for developing effective prognostic tools. Presently, the prognostic predictors for lung cancer are currently limited, emphasizing the urgent need for the identification and validation of reliable prognostic indicators.\u003c/p\u003e \u003cp\u003eTraditionally, the survival outcomes of lung cancer patients have been assessed using their pathological stage and histological subtype. However, patients with the same stage or subtype often show varying outcomes, highlighting the necessity for more refined prognostic tools(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition to cost, the inconvenience and significant time investment required are additional drawbacks of these evaluation methods. Cancer-associated cachexia is a multifactorial syndrome characterized by significant weight loss, primarily in skeletal muscle(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). It is known as a leading cause of death in cancer patients, accounting for up to 20% of cancer-related deaths. Besides, approximately 50\u0026ndash;80% of cancer patients undergo carious degree of this debilitating syndrome(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A key feature of cancer-associated cachexia is muscle wasting, which is responsible for low physical performance and functional impairments(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Several studies have linked reduced muscle mass to inflammation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), immune response,(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) mortality(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and complications(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), demonstrating the deleterious effects of muscle depletion across various cancers(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Despite this, it remains unclear whether muscle mass, which includes muscle mass and function, can serve as prognostic predictors of lung cancer patients.\u003c/p\u003e \u003cp\u003eThis study aims to analyze a comprehensive dataset from multiple centers to evaluate the prognostic value of muscle mass in lung cancer patients, and to identify the most effective parameter. Muscle mass was evaluated using hand grip strength (HGS)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), mid-arm circumference (MAC), mid-arm muscle circumference (MAMC), left mid-arm muscle area (MAMA), left maximum-calf circumference (MCC) and overall muscle wasting score (OMWS), reflecting both muscle strength and muscle mass(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The goal of this study is to refine and improve the prognostic assessment tools for lung cancer, enabling more personalized treatment strategies, and ultimately improving the overall management and outcomes of lung cancer patients.\u003c/p\u003e"},{"header":"2 Method and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Population\u003c/h2\u003e \u003cp\u003eWe abstracted data from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC; chictr.org.cn), which was a national multicenter cross-sectional prospective cohort study conducted in 34 medical centers in China. This study was conducted according to the Declaration of Helsinki and was approved by the Medical Ethics Committee. All participants or their legal guardian assigned written informed consent before the study.\u003c/p\u003e \u003cp\u003ePatients were included if they satisfied the following inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Patients who were pathologically diagnosed with lung cancer at the participating medical centers between January 1, 2016, and December 31, 2021; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Age of 18 years or older; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Absence of other conditions that could potentially cause cachexia or significant weight change, such as HIV infection, pregnancy, organ transplantation or end-stage renal disease, besides lung cancer; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Have the ability to communicate clearly and complete the study questionnaire; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Were willing to participate in the study. Patients who meet the following criteria were excluded: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Patients with missing or incomplete clinicopathological data; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Patients who were lost to follow-up.\u003c/p\u003e \u003cp\u003eThis work has been reported in line with the STROCSS criteria.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003ePatients who met the included criteria were from 29 medical centers. Patient demographic, clinical characteristics, and oncological data were collected by experienced physicians within 48 hours of admission from 29 medical centers. The baseline data encompassed age at diagnosis, gender, marital status, occupation, weight and height at admission, body mass index (BMI), past medical history, smoking history, alcohol consumption habits, and family history. BMI was calculated as weight (Kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e). Clinical data included weight loss, Karnofsky performance status (KPS), Patient-Generated Subjective Global Assessment (PG-SGA) score, nutritional risk screening 2002 (NRS2002), triceps skinfold thickness (TSF), fat deficiency, HGS, MAC, MAMC, MAMA, MCC and OMWS. Low muscle strength was defined as the HGS\u0026thinsp;\u0026lt;\u0026thinsp;28 kg for men and \u0026lt;\u0026thinsp;18 kg for women(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Tumor-related information included tumor location, differentiation status, histological and pathological stage, TNM stage and type of treatment received, such as surgery, radiotherapy and chemotherapy. These data were retrieved from patients' medical records in the Health Information System (HIS; Dayinjunhui company, Beijing, China).\u003c/p\u003e \u003cp\u003ePatients were consistently followed through regular outpatient visits or telephone interviews until either their passing or until December 31, 2021, which occurred first. Those who remained symptom-free for 72 months were considered cured and were no longer followed up. The study received ethical approval from the research ethics committees of all participating medical centers, ensuring adherence to the Declaration of Helsinki guidelines. Informed consent was obtained from all participants prior to their enrollment in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of muscle mass\u003c/h2\u003e \u003cp\u003ePatient muscle mass assessment focused on two aspects: muscle mass and muscle function. Left MAC and MCC were measured using a millimeter-marked flexible, non-stretchable measuring tape. The midpoint of the upper arm was determined by measuring the distance between the tip of the shoulder (acromion process) and the tip of the elbow (olecranon process), with the midpoint being equidistant from these points. MAMC and MAMA were derived from MAC and TSF. The triceps skinfold thickness was measured at the midpoint of the upper arm using a skinfold caliper.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMAMC\u003c/em\u003e (\u003cem\u003ecm\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eMAC\u003c/em\u003e (\u003cem\u003ecm\u003c/em\u003e)\u0026minus;(\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eTSF\u003c/em\u003e (\u003cem\u003ecm\u003c/em\u003e))\u003c/p\u003e \u003cp\u003eMAMA (cm\u003csup\u003e2\u003c/sup\u003e)=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{4\\pi\\:}{\\left[MAC\\right(cm)-\\pi\\:\\times\\:TSF(cm\\left)\\right]}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMuscle function was evaluated using HGS and OMWS. HGS was tested with a hydraulic dynamometer. Patients, seated with their arm at a right angle and elbow by their side, squeezed the dynamometer with maximum effort for approximately 5 seconds, using only their left hand without additional body assistance. The highest value recorded in kilograms was noted. The OMWS was determined by a comprehensive assessment of the muscle mass in the limbs and trunk, as well as grip strength. The scoring was set as follows: 3 for extreme wasting, 2 for severe wasting, 1 for significant wasting, 0 for no noticeable change, -1 for slight muscle increase, and \u0026minus;\u0026thinsp;2 for significant increase. To ensure accuracy, all the measurement were taken at least twice. For HGS, allow a rest period of at least 15\u0026ndash;30 seconds between each attempt to avoid muscle fatigue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS (SPSS Inc., Chicago, IL, USA) and R Project (StataCorp LLC, Texas, USA). Overall survival (OS) rates and median survival times at 3- and 5-year intervals were calculated using the Kaplan-Meier method, with the optimal cut-off value for lung cancer determined by the optimal stratification method. Survival differences between groups were analyzed using the log-rank test. Both univariate and multivariate survival analyses were conducted using Cox regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI). Continuous variables were compared using either Student\u0026rsquo;s t-test or the Kruskal-Wallis test, while categorical variables were analyzed using the Chi-squared test or Fisher\u0026rsquo;s exact test. Variables with a P-value below 0.10 in univariate analysis were included in a multivariate Cox proportional hazards regression model. Data was presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed data, and median (interquartile range) or frequency (percentage) for non-normally distributed data. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and oncological data of patients with lung cancer\u003c/h2\u003e \u003cp\u003eOur study commenced with an initial screening of 5025 patients in 34 medical centers across China from the INSCOC study. 1529 of these patients were excluded due to the lack of complete parameters. The specific screening process and the reasons for exclusion are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 3496 patients from 29 medical centers were finally enrolled, including 2331 (66.68%) men and 1165 (33.32%) women with the mean age of 59.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82. The distribution of patients according to the tumor stage of their lung cancer was as follows: 323 patients (9.24%) had stage I disease, 571 (16.33%) had stage II, 758 (21.68%) had stage III, and 1844 patients (52.75%) presented with stage IV disease. In the study cohort, 2149 (61.47%) of patients underwent chemotherapy, 394 (11.27%) had lung surgery and 281 (8.04%) received radiotherapy. Following a comprehensive 6-year follow-up period, 1793 (51.29%) patients succumbed to lung cancer. The detailed demographic and oncological data were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic of patients with lung cancer stratified by HGS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;3496)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eHGS group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;874)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;860)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;887)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;875)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;40.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eBMI, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;52.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eKPS, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.96\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.93\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;78.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eTotal protein, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.80\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRS2002, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;35.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003ePGSGA, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;81.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eFat deficiency, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;30.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMAC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;88.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eTSF, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.55\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMAMC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;130.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMAMA, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.90\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.71\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;134.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMLC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;140.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eHGS, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7354.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMuscle score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;26.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=1009.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2331 (66.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (32.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e459 (53.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e742 (83.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e848 (96.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1165 (33.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e592 (67.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401 (46.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145 (16.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3185 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e794 (90.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e779 (90.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e809 (91.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e803 (91.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (9.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 (8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=28.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2822 (80.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e660 (75.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e691 (80.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e759 (85.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e712 (81.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e674 (19.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (24.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (19.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128 (14.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163 (18.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=3.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2958 (84.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e747 (85.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e739 (85.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e744 (83.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e728 (83.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e538 (15.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (14.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143 (16.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (16.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=342.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1381 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e534 (61.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 (46.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e242 (27.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e203 (23.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2115 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (38.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458 (53.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e645 (72.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e672 (76.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=200.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2642 (75.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e781 (89.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e701 (81.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e602 (67.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e558 (63.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e854 (24.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159 (18.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285 (32.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e317 (36.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=38.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (9.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (8.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571 (16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166 (18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164 (18.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (21.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (19.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198 (22.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221 (25.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1844 (52.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e508 (58.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471 (54.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e448 (50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e417 (47.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3102 (88.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e777 (88.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e764 (88.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e780 (87.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e781 (89.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (11.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 (12.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94 (10.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=61.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1347 (38.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422 (48.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342 (39.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (35.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e269 (30.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2149 (61.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452 (51.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e518 (60.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573 (64.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e606 (69.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=7.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3215 (91.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e787 (90.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e804 (93.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e822 (92.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e802 (91.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (6.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS group, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003cp\u003eχ\u0026sup2;=10488.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e874 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e874 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e860 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e860 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e887 (25.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e887 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e875 (25.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e875 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Relationship between the muscle mass and clinicopathologic characteristics\u003c/h2\u003e \u003cp\u003eHGS was a reliable and robust metric for assessing muscular quality in our study, providing significant insights into overall muscle health and functionality. Patients were stratified into four groups based on the quartiles of their HGS scores. Group 1(~\u0026thinsp;18.6 kg), Group 2 (18.6\u0026ndash;24.9), Group 3 (24.9\u0026ndash;32.0) and Group 4 (32.0~) had 874, 860, 887 and 875 patients, respectively. We conducted a comparison of the clinicopathological characteristics among patients within different groups. Low HGS was significantly related to increased age (Group 1 vs 2 vs 3 vs 4: 62.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37 vs 60.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71 vs 60.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15 vs 57.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), decreased BMI (22.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40 vs 22.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18 vs 22.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05 vs 24.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), poor KPS (80.96\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34 vs 85.93\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73 vs 87.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29 vs 89.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and severe Fat deficiency (0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 vs 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 vs 0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71 vs 0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). NRS2002 and PG-SGA were used to assess the risk of malnutrition and the additional require for nutritional support interventions in patients respectively. Higher HGS was associated with decreased NRS2002 (1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57 vs 1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 vs 1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32 vs 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced PG-SGA (7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23 vs 5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55 vs 4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34 vs 4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For muscle mass indicators, low HGS was associated with decreased MAC (25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32 vs 26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24 vs 26.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24 vs 28.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smaller MAMA (34.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72 vs 36.71\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98 vs 40.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.25 vs 44.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reduced MAMC (20.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88 vs 21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 vs 22.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18 vs 23.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), declined MLC (31.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70 vs 32.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55 vs 33.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79 vs 35.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased muscle wasting score (0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 vs 0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 vs 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 vs 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Kaplan-Meier curves of overall survival in lung cancer patients with different HGS\u003c/h2\u003e \u003cp\u003eThe mean follow-up time in this study was 17.27 months. After a six-year follow-up period, 1793 out of 3496 (51.29%) patients succumbed to lung cancer. A strong correlation between HGS and patient survival was observed upon dividing the cohort into quartiles based on their HGS scores. Patients with a higher HGS showed significantly improved 5-year probability of survival than those with a lower HGS (Group 1 vs 2 vs 3 vs 4: 30.73% vs 35.43% vs 31.04 vs 39.06%, log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Median survival of these four groups were 23.07, 27.83, 24.43 and 31.23 months, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stratified analysis of lung cancer patients with high and low HGS revealed that higher HGS was associated with better prognosis at all stages and in different genders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Univariate and multivariable survival analyses\u003c/h2\u003e \u003cp\u003eUnivariate analysis revealed that the risk of death increased with age (HR:1.01 (1.00-1.01), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), NRS2002 (1.17 (1.12\u0026ndash;1.21), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PGSGA (1.06 (1.05\u0026ndash;1.07), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fat deficiency (1.22 (1.14\u0026ndash;1.31), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), OMWS (1.27 (1.17\u0026ndash;1.38), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Smoking (1.49 (1.32\u0026ndash;1.67), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol abuse (1.13 (1.00-1.29), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with stage Ⅰ, stage Ⅱ 2.29 (1.58\u0026ndash;3.31), Ⅲ (4.55 (3.21\u0026ndash;6.44)), Ⅳ (7.52 (5.38\u0026ndash;10.52), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed significant increased risk of death. Surprisingly, patients who received radiotherapy were associated with increased risk compared with those who did not underwent radiotherapy (1.30 (1.08\u0026ndash;1.57), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, BMI (0.95 (0.93\u0026ndash;0.96), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KPS (0.98 (0.98\u0026ndash;0.99), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total protein (0.99 (0.98\u0026ndash;0.99) \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with a lower risk of death. Female patients had lower risk of death compared with male (0.73 (0.64\u0026ndash;0.82), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those who received surgery had lower risk (0.37 (0.29\u0026ndash;0.46), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The relationship between various factors and hazard risk of overall survival in patients with lung cancer was listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In addition, we performed a nomogram, through which we constructed a predictive model of the correlation between patients' muscle mass and clinical outcomes, which revealed a positive correlation between patients' overall survival and higher HGS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Under the same conditions, patients with a higher HGS score may have a higher survival probability.\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\u003eUnivariate and multivariate cox regression analysis of factors associated with overall survival in patients with lung cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\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 \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.93\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eTotal protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRS2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.12\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eFat deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.14\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (1.17\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.64\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.77 (0.67\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.87\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.87\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.74\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \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 \u003cp\u003e0.87 (0.76\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.32\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e1.26 (1.11\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.00\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.29 (1.58\u0026ndash;3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e2.09 (1.53\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.55 (3.21\u0026ndash;6.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e3.89 (2.90\u0026ndash;5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.52 (5.38\u0026ndash;10.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e6.35 (4.77\u0026ndash;8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.29\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.64 (0.52\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.91\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.08\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \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 \u003cp\u003e0.87 (0.74\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.72\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \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 \u003cp\u003e0.85 (0.75\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.81\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \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 \u003cp\u003e0.89 (0.77\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.64\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \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 \u003cp\u003e0.68 (0.58\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate Cox proportional analysis, after adjusting these confounding factors, patients with lower HGS were significantly more likely to have worse clinical outcomes than those with higher HGS. When HGS was divided into quintiles, the lowest quintile Group 1 was used as a reference. Group 2, Group 3 and Group 4 were all positively associated with better prognosis with (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;0.85, 95% confidence interval [CI]: 0.72\u0026ndash;1.01; 0.88 [0.74, 1.04] and 0.69 [0.59, 0.81], respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eCancer cachexia is a severe lethal wasting syndrome, notably affecting up to 50%-80% of cancer cases and account for up to 20% of cancer deaths(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This syndrome is not just a symptom, but a complex metabolic disorder characterized by loss of skeletal muscle mass, with or without fat loss. Such loss leads to a decline in overall physical function, severely undermining the quality of life and treatment effectiveness for cancer patients(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The relationship between cancer cachexia and lung cancer prognosis has been extensively studied, revealing a substantial impact on patient outcomes(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, despite its significance, there is a lack of a standardized cachexia parameter for the prediction of lung cancer prognosis. This gap in clinical assessment tools presents presenting a major challenge in the effective management and treatment of lung cancer patients(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). BMI has been widely used to assess nutritional status of patients and has shown a significant correlation with tumor prognosis(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, the rise in the number of cancer patients with sarcopenic obesity\u0026mdash;a condition where obesity coexists with reduced muscle mass\u0026mdash;casts doubt on the effectiveness of BMI and other traditional cachexia parameters as reliable indicators(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, the accuracy of BMI as an assessment tool is compromised by various confounders. For example, dystrophic edema can artificially increase weight without reflecting true nutritional status. Malignancy-associated pleural effusion can alter body weight and composition, complicating the assessment of patients' nutritional and health status. Increases in visceral adiposity, the accumulation of fat around the internal organs in the abdominal cavity, can skewed BMI interpretations and mask muscle loss and sarcopenia. All these factors challenge the conventional approaches to evaluating the nutritional and health status of cancer patients, underlining the need for more sophisticated and nuanced assessment tools.\u003c/p\u003e \u003cp\u003eTo enhance the assessment of nutritional status and prognosis prediction in lung cancer patients, we performed an in-depth analysis of data from a comprehensive multicenter cross-sectional prospective cohort study. The results of our study highlighted a strong correlation between muscle mass and the prognosis of lung cancer patients. As per our current knowledge, this study represents the most extensive investigation of its kind, being a multicenter, cross-sectional prospective cohort investigation concentrated on evaluating muscle mass in lung cancer patients. HGS offers a quick and non-invasive method to evaluate muscular strength, which is a key component of physical health and an important factor in the quality of life, especially for patients with cancer. Our findings distinctly established that higher HGS was associated with better survival outcomes in these patients. This correlation underscores the pivotal importance of muscle mass in determining the prognosis and influencing the survival rates of lung cancer patients.\u003c/p\u003e \u003cp\u003eThe significance of muscle mass in providing protective effects is evident, yet the effective management options for cancer-associated cachexia and muscle wasting are currently limited(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The American Society of Clinical Oncology (ASCO) guidelines highlight the uncertain outcomes of pharmacological interventions aimed at treating cancer cachexia. Nonetheless, dietary strategies such as nutritional counseling, increasing protein consumption, and supplementing diets with omega-3 (n-3) fatty acids are advisable and beneficial for patients(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, combined muscle-strengthening and aerobic activities were associated with reduced risk and mortality rate(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Recent foundational studies are actively investigating new potential therapeutic targets to combat muscle wasting in lung cancer patients. For example, the LCN2 secreted by tissue-infiltrating neutrophils was a potential target in the treatment of cancer cachexia(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Furthermore, antagonists that target RAGE (Receptor for Advanced Glycation End-products) are emerging as a promising therapeutic approach to prevent or alleviate the symptoms associated with cachexia syndrome(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). At present, these studies are predominantly in the preclinical phase, emphasizing the necessity for more extensive research. Looking ahead, the execution of clinical randomized controlled trials (RCTs) is essential to thoroughly assess and confirm the therapeutic efficacy of these potential treatments.\u003c/p\u003e \u003cp\u003eOur study, while comprehensive, faces several limitations that need to be acknowledged. Firstly, the exclusive inclusion of Chinese individuals in our cohort casts uncertainty on the applicability of these findings to other ethnic groups. This limitation is significant as it may affect the applicability of our results in a broader, more diverse context. Secondly, although this national multicenter prospective cohort study mitigates the selection bias typically associated with single-center studies, there remain potential biases due to variations in the quality of treatment and diagnostic equipment across different participating centers. This aspect of our study warrants further external validation to solidify our findings. Additionally, our research primarily focused on exploring the predictive roles of muscle mass in lung cancer patients. Despite this focus, our data also revealed that nutrition-related metrics, such as total protein, NRS2002, and PG-SGA, significantly influence the prognosis of lung cancer patients. It is important to note that, based on our current findings, we cannot conclusively state that HGS surpasses these nutrition-related indicators in evaluating prognosis. Future efforts will necessitate a more detailed refinement and analysis of these results to clarify their implications.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, our study provides a comprehensive overview of the muscle mass in lung cancer patients, underscored by its multicenter prospective design and substantial sample size. This research robustly substantiates the significant impact of muscle mass, especially HSG, on the prognosis of patients with lung cancer. Key muscle mass indicators, such as the HGS, MAMC, MAMA, MAC, MCC and OMWS are closely associated with survival outcomes in these patients. Furthermore, our findings bring to light the notably poor muscle mass observed in patients with stage 4 lung cancer, highlighting a critical need for strength and muscle restoration in their treatment and care. The data gathered through our study is invaluable in informing therapeutic strategies and rehabilitation programs for lung cancer patients. It highlights the importance of incorporating muscle mass assessment into routine clinical practice, not only as a prognostic tool but also as a guide for personalized treatment and care plans. This approach could potentially improve quality of life and survival outcomes for individuals battling lung cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder/Sponsor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Beijing Hospitals Authority Youth Programme. grant number QMS20220726.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statements in this article are solely the responsibility of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance and peer review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot commissioned, externally peer-reviewed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, X.W., Y.H., and Y.F.; Data curation, X.W., Y.G. and H.S.; Formal analysis, X.W., L.S. and H.S.; Funding acquisition, X.W.; Methodology, X.W., Y.H., and Y.F.; Software, H.Z., Y.H., and Y.F.; Writing \u0026ndash; original draft, X.W., Y.H., and Y.F; Writing \u0026ndash; review \u0026amp; editing, L.S., Y.G. and H.S.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuma N, Evans N, Mitchell E. Disparities in lung cancer. J Natl Med Assoc. 2023;115(2S):S46\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clin Chest Med. 2020;41(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Current insights in nutrition assessment and intervention for malnutrition or muscle loss in people with lung cancer: A narrative review. Adv Nutr. 2022;13(6):2420\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMomma H, Kawakami R, Honda T, Sawada SS. Muscle-strengthening activities are associated with lower risk and mortality in major non-communicable diseases: a systematic review and meta-analysis of cohort studies. Br J Sports Med. 2022;56(13):755\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Li X, Jiao D, Cai Y, Qian L, Shen Y, et al. LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia. J Hematol Oncol. 2023;16(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiappalupi S, Sorci G, Vukasinovic A, Salvadori L, Sagheddu R, Coletti D, et al. 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J Cachexia Sarcopenia Muscle. 2020;11(4):929\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Muscle mass, hand grip strength, Prognosis, Lung cancer, Sarcopenia","lastPublishedDoi":"10.21203/rs.3.rs-5255311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5255311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eMuscle dysfunction and loss of mass are significant clinical concerns and key features of cancer cachexia. However, the potential predictive role of muscle mass, especially hand grip strength (HGS), as a prognostic factor in lung cancer remains underexplored. The aim of this study is to determine whether HGS, as a measure of muscle mass, is an effective predictor of clinical outcomes in patients with lung cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis research is a cross-sectional multicenter prospective cohort study, encompassing patients aged over 18 from 29 medical centers. These patients were diagnosed with lung cancer between June 2012 and December 2019. We assessed muscle mass using several indicators: hand grip strength (HGS), mid-arm circumference (MAC), left mid-arm muscle circumference (MAMC), mid-arm muscle area (MAMA), left maximum calf circumference (MCC), and an overall muscle wasting score (OMWS). These indicators serve to measure muscle strength and mass. The primary outcomes of this study are overall survival (OS) time and all-cause mortality. Hazard ratios (HRs) were applied to analyze the impact of muscle mass on the all-cause mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTotally 3496 patients from 29 medical centers were included. Patients were stratified into four groups based on the quartiles of their HGS scores, with group sizes of 874, 860, 887 and 875, respectively. Subgroup analysis revealed that patients with lower HGS scores had significantly poorer outcomes than those with higher scores. The 5-year survival probabilities were 30.73%, 35.43%, 31.04 and 39.06% for each group respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), even after adjusting for tumor stage and gender. Univariate Cox regression analysis revealed that higher HGS was an independent protective factor for patients with lung cancer (hazard ratio\u0026thinsp;=\u0026thinsp;0.69, 95% confidence interval [CI]: 0.59\u0026ndash;0.81). Multivariable Cox proportional hazards regression analysis corroborated this finding. Other muscle mass metrics, such as MAC, MAMC, MAMA, MLC and OMWS, also underscore the protective role of maintaining muscle mass in lung cancer prognosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe progressive and widespread reduction of skeletal muscle mass and strength is a critical negative prognostic indicator in lung cancer patients. Among all the evaluated parameters, HGS demonstrates the most significant correlation with overall survival. It stands out as a key factor in predicting the prognosis of patients with lung cancer.\u003c/p\u003e","manuscriptTitle":"Muscle mass is an important prognostic factor for patients with lung cancer: a cross-sectional multicenter prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-10 13:54:59","doi":"10.21203/rs.3.rs-5255311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"November 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-10T13:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-10 13:54:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5255311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5255311","identity":"rs-5255311","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}