Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: The enduring value of body composition, nutritional, and inflammatory indices

Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: The enduring value of body composition, nutritional, and inflammatory indices | 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 Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: The enduring value of body composition, nutritional, and inflammatory indices Norihiko Tsuchiya, Sei Naito, Hiroki Fukuhara, Hayato Nishida, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6914364/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in International Journal of Clinical Oncology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Immune checkpoint inhibitors (ICIs) are now the standard first-line treatment for metastatic renal cell carcinoma (mRCC), yet many risk factors identified during the tyrosine kinase inhibitor era remain unvalidated in current practice. This study aimed to evaluate the prognostic value of body composition, nutritional, and inflammatory indices in the era of ICI-based first-line therapy. Methods We retrospectively analyzed 136 mRCC patients who received systemic therapy. Body composition indices (skeletal muscle index [SMI], visceral adipose tissue index [VATI], subcutaneous adipose tissue index [SATI]), nutritional markers (prognostic nutritional index [PNI], geriatric nutritional risk index [GNRI]), and inflammatory markers (Glasgow Prognostic Score [GPS], systemic inflammatory index [SII], and other indices) were assessed for their association with overall survival (OS). We also compared their prognostic impact on patients treated with non-ICI-based and ICI-based regimens as first-line therapy. Results Lower body mass index (HR 1.49, P = 0.033), VATI (HR 1.66, P = 0.017), and SATI (HR 1.89, P = 0.002) were associated with shorter survival. PNI (HR 1.72, P < 0.001) and GNRI (HR 1.59, P < 0.001) showed strong prognostic value, as did GPS (HR 2.53, P < 0.001) and SII (HR 2.01, P < 0.001) in the overall cohort. In the ICI-based regimen group, GNRI, PNI, and SATI demonstrated higher prognostic performance (C-indices 0.756, 0.739, and 0.687, respectively), with PNI and SATI providing clear OS stratification. Conclusion Several indices reflecting body composition, nutritional status, and systemic inflammation remain valuable prognostic markers in patients with mRCC receiving ICI-based first-line therapy. Metastatic renal cell carcinoma Immune checkpoint inhibitor Body composition Nutritional status Systemic inflammation Figures Figure 1 Figure 2 Introduction First-line systemic therapy for advanced renal cell carcinoma (RCC) or metastatic RCC (mRCC) has increasingly adopted combination strategies using tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), which have led to substantial improvements in patient prognosis [ 1 ]. The ICI-based therapy has markedly prolonged survival, highlighting the need for a new risk stratification system beyond the International Metastatic RCC Database Consortium (IMDC) risk stratification, which was originally developed based on outcomes from anti-vascular endothelial growth factor (VEGF)-targeted therapies [ 2 ]. Given the wide range of available treatments for advanced RCC, more accurate risk classification is essential to select the optimal therapy. While conventional prognostic models are based on clinical and hematological/biochemical parameters and offer practical utility, recent advances in systemic therapy underscore the potential value of incorporating prognostic factors from alternative domains. Such factors may contribute to improved prognostic accuracy in the current therapeutic landscape. Previous epidemiological studies have reported that obesity is a risk factor for RCC [ 3 ], while obesity or increased adipose tissue mass has been reported as a favorable prognostic factor for patients with RCC [ 4 , 5 ]. Obesity being a risk factor for diseases while also being a favorable prognostic factor is known as the "obesity paradox" [ 6 , 7 ]. Although this paradox remains unexplained, many hypotheses have been put forward. The mechanisms by which obesity affects disease prognosis have been suggested as reverse causation and possibly confounded by other prognostic factors and treatment effect determinants rather than a direct effect of obesity [ 6 ]. Various indicators that represent the physical state of the patient, such as nutritional status and inflammation, have been reported to be possible predictors of cancer prognosis [ 8 , 9 ]. The prognostic nutritional index (PNI) was initially developed as an indicator of nutritional status to assess the risk of surgical complications of gastrointestinal malignancies [ 10 ], and the geriatric nutritional risk index (GNRI) was developed to assess nutritional risk in the elderly [ 11 ]. Complete blood count–derived parameters, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), are indicators that reflect silent inflammation [ 12 ]. The Glasgow Prognostic Score (GPS) is a unique prognostic indicator that reflects both nutritional status and systemic inflammation, based on serum albumin levels and C-reactive protein concentrations [ 13 ]. These nutritional and inflammatory indices indicate the prognosis of RCC [ 12 – 14 ]. Most of these markers, however, were identified in the TKI era, and their prognostic significance in the era of ICI-based therapy is yet to be established. Therefore, this study aimed to evaluate the prognostic significance of body composition, nutritional, and inflammatory indices in patients with mRCC treated with ICI-based first-line regimens and to compare their predictive performance to identify those with superior prognostic utility. Materials and Methods Patient selection and ethical considerations A total of 202 patients with mRCC received molecular targeted therapy from 2010 to 2023 at Yamagata University Hospital in Japan. A total of 136 patients who received TKIs, mammalian target of rapamycin inhibitors (mTORIs), ICI + ICI, or ICI + TKI as first-line therapy and for whom evaluable CT imaging and relevant clinical data were available for assessing body composition, nutritional status, and inflammatory markers were included in the analysis. Fourteen patients were excluded because of the unavailability of CT imaging, 20 were omitted because of missing biochemical or hematological data, and 32 were not included in the analysis as they received first-line therapies other than TKI- or ICI-based regimens. This study was approved by the institutional review board of the Faculty of Medicine at Yamagata University (2018-43). An opt-out method was used to obtain informed consent. Measurement of muscle and adipose tissue components Patients' clinical and CT imaging data were obtained immediately before the first-line treatment. The measurement of muscle and adipose tissue components was conducted using the Automated Muscle and Adipose Tissue Composition Analysis (AutoMATiCA) system [ 15 ]. This open-source software was obtained from https://gitlab.com/Michael_Paris/AutoMATiCA . Subcutaneous adipose tissue area, visceral adipose tissue area, and skeletal muscle area were automatically measured at the L3 level. The skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), and visceral adipose tissue index (VATI) were defined as the corresponding area divided by body height squared. The visceral-to-subcutaneous adipose tissue ratio (VSR) was calculated as the visceral adipose tissue area divided by the subcutaneous adipose tissue area. These indices were dichotomized separately for male and female individuals using their respective medians because significant sex differences were found in body composition (Supplementary Table S1). A body mass index (BMI) of < 18.5 kg/m 2 was defined as underweight, 18.5–25.0 kg/m 2 as normal weight, ≥ 25.0 kg/m 2 as overweight, and ≥ 30.0 kg/m 2 as obese. Calculation of nutritional and inflammatory indices The PNI was calculated using the following formula: serum albumin (g/L) + 0.005 × total lymphocyte count (/µl) [ 10 ]. On the basis of the PNI score, nutritional status was classified into three categories: malnutrition: PNI < 40; mild malnutrition: 40 ≤ PNI < 45; and no malnutrition: PNI ≥ 45 [ 16 ]. The formula for GNRI is [1.489 x serum albumin (g/L)] + [41.7 × (weight/WLo)], where WLo is an ideal weight calculated from the Lorentz equations [ 11 ]. GNRI was categorized as follows: ≥ 98: no risk; ≥ 92 and < 98: low risk; ≥ 82 and < 92: moderate risk; and < 82: major risk [ 11 ]. The GPS was used as a marker of both nutritional status and systemic inflammation [ 17 ] and is a composite index ranging from 0 to 2, with one point assigned for a C-reactive protein level of > 1 mg/dL and one point for a serum albumin level of < 3.5 g/L. The systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) were used as systemic immune-inflammatory biomarkers. SII was calculated from the equation: P x N/L, where P, N, and L are the preoperative peripheral blood platelet, neutrophil, and lymphocyte counts, respectively [ 18 ]. SII was dichotomized using a cut-off value of 788x10 9 /L, based on a previous study [ 19 ]. Similarly, the cut-offs for NLR, PLR, and LMR were set as 3.0 [ 20 ], 150.0 [ 21 ], and 3.0 [ 22 ], respectively, based on previous reports and meta-analyses. Statistical analysis R software version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analyses. The Mann–Whitney test was used to compare continuous variables between two groups. The Chi-square test was used to compare categorical variables. Overall survival (OS) was defined as the time from initiation of the first-line systemic therapy to death from any cause and was analyzed using Kaplan–Meier curves with a log-rank test, while Cox regression analysis was used to assess the effects of each variable on survival. A Pearson correlation coefficient was calculated for each pair of indices. The concordance index (C-index) was calculated using Uno's method to assess the model's time-dependent discriminatory ability. A P -value of < 0.05 was considered statistically significant. Results Patients’ clinical demographics The patients’ demographics are summarized in Table 1 . A total of 136 patients (108 male and 28 female) were included in the analysis. The median age was 66 years (interquartile range [IQR], 61–71), and the median follow-up duration was 22.7 months (IQR, 8.9–47.2). Pathological confirmation revealed clear cell RCC in 106 patients (78%), followed by papillary RCC (6.6%), chromophobe RCC (3.7%), collecting duct carcinoma (2.9%), acquired cystic disease-associated RCC (2.2%), other subtypes (3.7%), and unknown (2.9%). According to the IMDC risk classification, 8.8% of patients were categorized as favorable risk, 52% as intermediate risk, and 39% as poor risk. The most common first-line treatment was TKI in 57% of patients, followed by ICI-based treatments (ICI + ICI: 29%; ICI + TKI: 9.6%) and mTORI (4.4%). Table 1 Baseline demographics and clinical characteristics of patients according to first-line regimen Variable Overall (n = 136) Non-ICI-based regimen (n = 84) ICI-based regimen (n = 52) Age, year (IQR) 66 (61, 71) 68 (63, 71) 68 (63, 72) Sex (%) Male 108 (79) 66 (79) 42 (81) Female 28 (21) 18 (21) 10 (19) Follow-up, month (IQR) 22.7 (8.9, 47.2) 21.6 (8.8, 43.6) 25.2 (10.5, 49.2) Pathological type (%) Clear cell RCC 106 (78) 68 (81) 38 (73) Papillary RCC 9 (6.6) 6 (7.1) 3 (5.8) Chromophobe cell renal carcinoma 5 (3.7) 3 (3.6) 2 (3.8) Collecting duct carcinoma 4 (2.9) 1 (1.2) 3 (5.8) Acquired cystic disease-associated RCC 3 (2.2) 1 (1.2) 2 (3.8) Other types 5 (3.7) 2 (2.4) 3 (5.8) Unknown 4 (2.9) 3 (3.6) 1 (1.9) IMDC risk criteria (%) Favorable 12 (8.8) 7 (8.3) 5 (9.6) Intermediate 71 (52) 49 (58) 22 (42) Poor 53 (39) 28 (33) 25 (48) First-line treatment (%) TKI 78 (57) 78 (93) ‒ mTORI 6 (4.4) 6 (7.1) ‒ ICI + ICI 39 (29) ‒ 39 (75) ICI + TKI 13 (9.6) ‒ 13 (25) RCC, Renal cell carcinoma; TKI, Tyrosine kinase inhibitor; mTORI, mTOR inhibitor; ICI, Immune checkpoint inhibitor Sex differences in prognostic markers Significant sex differences were observed in several body composition indices: male individuals had higher values than female individuals for SMI (48.7 vs. 37.1 cm²/m², P < 0.001), VATI (33.4 vs. 12.4 cm²/m², P < 0.001), and VSR (0.9 vs. 0.4, P < 0.001). By contrast, SATI did not differ significantly between sexes (32.2 vs. 35.5 cm²/m², P = 0.761). Among inflammatory markers, PLR was significantly higher in female individuals (261.5 vs. 206.4, P = 0.031), while other indices showed no significant sex-based differences (Supplementary Table S1). Influence of prognostic markers on OS in the overall cohort As shown in Table 2 , in the overall cohort, lower BMI (hazard ratio [HR] 1.49, 95% CI 1.03–2.16, P = 0.033), VATI (HR 1.66, 95% CI 1.09–2.51, P = 0.017), and SATI (HR 1.89, 95% CI 1.25–2.86, P = 0.002) were significantly associated with shorter OS, whereas SMI and VSR had no significant effect on survival. Nutritional markers demonstrated strong prognostic value: patients with malnutrition, as defined by PNI, had significantly shorter OS (HR 1.72, 95% CI 1.35–2.20, P < 0.001), and those at higher risk according to GNRI also exhibited poorer survival (HR 1.59, 95% CI 1.33–1.91, P < 0.001). Table 2 Univariable analysis of overall survival by first-line regimen Variable Overall Non-ICI regimen ICI regimen HR 95% CI P HR 95% CI P HR 95% CI P Age (1 year increment) 1.01 0.99, 1.03 0.547 0.99 0.97, 1.02 0.56 1.03 0.98, 1.07 0.204 IMDC (Poor vs. Intermediate vs. Favorable) 2.32 1.60, 3.35 < 0.001 2.62 1.69, 4.05 < 0.001 2.98 1.33, 6.71 0.004 BMI (Underweight vs. Normal vs. Overweight/Obese) 1.49 1.03, 2.16 0.033 1.51 0.99, 2.29 0.052 1.41 0.66, 3.04 0.373 SMI (Low vs. High) 1.27 0.84, 1.90 0.255 1.21 0.76, 1.92 0.429 1.45 0.62, 3.36 0.386 VATI (Low vs. High) 1.66 1.09, 2.51 0.017 1.95 1.21, 3.15 0.006 1.64 0.69, 3.90 0.258 SATI (Low vs. High) 1.89 1.25, 2.86 0.002 1.68 1.06, 2.69 0.029 3.25 1.27, 8.33 0.009 VSR (Low vs. High) 1.30 0.86, 1.96 0.213 1.69 1.05, 2.72 0.032 1.06 0.46, 2.46 0.889 PNI (Malnutrition vs. Mild malnutrition vs. Normal) 1.72 1.35, 2.20 < 0.001 1.75 1.32, 2.33 < 0.001 2.80 1.52, 5.17 < 0.001 GNRI (Major vs. Moderate vs. Low vs. No risk) 1.59 1.33, 1.91 < 0.001 1.74 1.39, 2.19 < 0.001 1.96 1.32, 2.90 < 0.001 GPS (2 vs. 1 vs. 0) 2.53 1.66, 3.85 < 0.001 2.29 1.42, 3.67 < 0.001 4.49 1.64, 12.3 0.001 SII (High vs. Low) 2.01 1.32, 3.06 < 0.001 2.01 1.25, 3.22 0.004 2.53 0.99, 6.46 0.041 NLR (High vs. Low) 1.99 1.30, 3.05 0.001 1.80 1.12, 2.89 0.014 3.71 1.25, 11.0 0.008 PLR (High vs. Low) 1.57 1.04, 2.37 0.030 1.72 1.08, 2.75 0.022 1.91 0.75, 4.88 0.160 LMR (Low vs. High) 1.75 1.16, 2.63 0.009 1.43 0.89, 2.30 0.146 3.05 1.30, 7.17 0.010 ICI, immune checkpoint inhibitor; IMDC, international mRCC database consortium; BMI, body mass index; SMI, skeletal muscle index; VATI, visceral adipose tissue index; SATI, subcutaneous adipose tissue index; VSR, visceral to subcutaneous fat ratio; PNI, prognostic nutritional index; GNRI, geriatric nutritional risk index; GPS, Glasgow prognostic score; SII, systemic immune inflammation index; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; LMR, lymphocyte to monocyte ratio GPS emerged as a robust predictor, with higher scores correlating with worse outcomes (HR 2.53, 95% CI 1.66–3.85, P < 0.001). Among inflammatory markers, elevated SII (HR 2.01, 95% CI 1.32–3.06, P < 0.001), NLR (HR 1.99, 95% CI 1.30–3.05, P = 0.001), and PLR (HR 1.57, 95% CI 1.04–2.37, P = 0.030), as well as lower LMR (HR 1.75, 95% CI 1.16–2.63, P = 0.009), were all significantly associated with shorter OS. Comparison of prognostic markers between non-ICI and ICI-based regimens Detailed subgroup analyses, as summarized in Table 2 , reveal distinct patterns. Kaplan–Meier curves for five prognostic indices, including the IMDC risk classification and four representative indices of body composition, nutritional status, and systemic inflammation, are displayed separately for the two treatment subgroups in Fig. 1 . In the non-ICI group, low VATI (HR 1.95, 95% CI 1.21–3.15, P = 0.006) and low SATI (HR 1.68, 95% CI 1.06–2.69, P = 0.029) were significantly associated with poorer OS. Nutritional markers, including PNI (HR 1.75, 95% CI 1.32–2.33, P < 0.001) and GNRI (HR 1.74, 95% CI 1.39–2.19, P < 0.001), also showed strong associations with OS. Inflammatory markers SII (HR 2.01, 95% CI 1.25–3.22, P = 0.004), NLR (HR 1.80, 95% CI 1.12–2.89, P = 0.014), and PLR (HR 1.72, 95% CI 1.08–2.75, P = 0.022), as well as GPS (HR 2.29 95% CI 1.42–3.67, P < 0.001), demonstrated significant prognostic value in this group. Conversely, among patients treated with ICIs, while the IMDC classification remained significant (HR 2.98, 95% CI 1.33–6.71, P = 0.004), Kaplan–Meier curves showed no clear separation between the favorable- and intermediate-risk groups (Fig. 1 B). Among body composition indices, SATI emerged as a strong predictor (HR 3.25, 95% CI 1.27–8.33, P = 0.009), showing the highest C-index (0.679 as a continuous variable) among body composition indices (Supplementary Table S3). Both PNI (HR 2.80, 95% CI 1.52–5.17, P < 0.001) and GNRI (HR 1.96, 95% CI 1.32–2.90, P < 0.001) were significant prognostic factors, demonstrating high predictive performance (C-indices 0.759 and 0.773 as continuous variables, respectively) (Supplementary Table S3). Notably, PNI showed clear separation of survival curves among the three groups in Kaplan–Meier analysis (Fig. 1 F). Among inflammatory markers, GPS (HR 4.49, 95% CI 1.64–12.3, P = 0.002) and NLR (HR 3.71, 5% CI 1.25–11.0, P = 0.008) (Table 2 ) exhibited higher hazard ratios compared with the non-ICI group, with survival curves demonstrating significant stratification ( P = 0.014 and 0.011, respectively) (Fig. 1 H and 1 J). Correlation analysis among prognostic markers A correlation heatmap of the evaluated indices is shown in Fig. 2 . Moderate correlations were observed between the IMDC classification and several nutritional and inflammatory markers: PNI (r = − 0.48), GNRI (r = − 0.51), GPS (r = 0.49), and SII (r = 0.46). By contrast, BMI showed moderate to strong correlations with body composition and nutritional indices, including SMI (r = 0.63), VATI (r = 0.74), SATI (r = 0.75), PNI (r = 0.38), and GNRI (r = 0.53). The adipose tissue-related indices VATI and SATI demonstrated moderate correlations with the nutritional marker GNRI (r = 0.44 and 0.42, respectively). Notably, both PNI and GNRI demonstrated strong correlations with each other (r = 0.92) and moderate associations with inflammatory markers, body composition indices, and IMDC classification, highlighting their roles as well-balanced and comprehensive prognostic markers. Discussion The relationship between obesity and survival in patients with mRCC has been widely reported, but varying results have been obtained depending on the obesity index used. While BMI has been identified as an independent prognostic factor in several studies [ 5 , 20 , 21 ], others have shown that visceral or subcutaneous adipose tissue, rather than BMI, more accurately predict survival outcomes [ 22 , 23 ]. CT imaging enables separate measurements of visceral and subcutaneous adipose tissue, offering more precise body composition data than BMI. In our study, both VATI and SATI demonstrated stronger associations with prolonged survival compared with BMI, which may be partly explained by the lower prevalence of obesity (BMI ≥ 25 kg/m²) in the Japanese population, and the fact that BMI does not directly reflect adipose tissue distribution [ 24 ]. Although obesity is a recognized risk factor for RCC incidence [ 3 ], paradoxically, several studies have reported a favorable impact of obesity on OS in advanced RCC [ 4 , 5 ]. Proposed explanations include the less aggressive nature of RCC in obese patients and lead-time bias because of cancer cachexia. Higher BMI has been associated with more favorable clinicopathologic features at diagnosis, such as lower stage, lower Fuhrman grade, and smaller tumor size [ 25 ]. Our findings demonstrate that adipose tissue indices are moderately correlated with nutritional markers but show weaker correlations with the IMDC classification (Fig. 2 ), suggesting that while adipose tissue-related indices partially reflect nutritional status, they may have distinct prognostic significance independent of conventional risk models. Furthermore, IL-6, which is preferentially released from visceral rather than subcutaneous adipose tissue in obese individuals [ 26 ], has been reported to attenuate the efficacy of ICIs [ 27 ]. By contrast, adiponectin, which is more abundantly secreted from subcutaneous than visceral adipose tissue [ 28 ], helps preserve anti-tumor immunity during ICI therapy by resolving inflammation [ 29 ]. Consistent with these observations, our findings suggest that the association between VATI and OS was attenuated in the ICI-based regimen group; however, the role of visceral adipose tissue in modulating ICI efficacy remains unclear and warrants further investigation. This study used PNI and GNRI as nutritional indices, and GPS was applied as a composite index of nutrition and systemic inflammation. Consistent with previous reports [ 8 , 13 , 30 ], these indices were significantly associated with OS in patients with mRCC, and they were strongly correlated with each other, possibly because of the influence of albumin as a common factor. A favorable nutritional status could enhance immune competence, attenuate systemic inflammation, preserve metabolic reserves, and improve treatment tolerance, all of which may collectively contribute to improved survival outcomes in cancer patients. In the ICI treatment group, these indices remained strong predictors of OS, indicating that poor nutritional status continues to be an adverse prognostic factor even in the era of ICI therapy. Although such associations have been reported in other cancer types [ 31 ], to our knowledge, no previous study has specifically examined the association between PNI, GNRI, GPS, and outcomes in mRCC patients treated with ICIs, highlighting the novelty and clinical relevance of our findings. Systemic inflammatory markers have been reported to be associated with survival in patients with mRCC in several meta-analyses [ 32 – 34 ]. In particular, the prognostic and predictive significance of SII [ 19 , 35 ], NLR [ 35 ], and LMR [ 35 , 36 ] have been suggested in small cohort studies of patients with mRCC treated with first-line ICI-based regimens, especially the combination of ipilimumab and nivolumab. However, the predictive accuracy of these markers has varied across studies, and few investigations have directly compared their prognostic value. In the subgroup analyses of the present study, some markers also lost their significant association with OS. This inconsistency may be attributed to small sample sizes, heterogeneity in patient populations, variations in treatment regimens, and the lack of standardized cut-off values among studies. These findings further underscore the need for standardized definitions and large-scale validation in diverse patient populations. To establish more accurate and clinically applicable risk stratification models, it will be essential to incorporate these inflammatory markers into predictive algorithms validated through prospective studies. Importantly, such efforts will require the adoption of fixed, standardized cut-off values to ensure consistency and comparability across future trials. This study has several limitations. First, among the 137 patients with mRCC who received first-line systemic therapy, only 52 patients were treated with regimens including ICIs. Therefore, the sample size might have been insufficient to adequately evaluate the impact of each index on OS in this subgroup. Second, the study could not examine the association between the indices and the treatment response or progression-free survival as an endpoint. An accurate evaluation of these endpoints was difficult and was not included because the study used retrospective real-world data. Third, sex-adjusted median values were applied as cut-off points for each index. The lack of universally established cut-off values represents a significant limitation, not only for body composition indices but also for other indices with continuous values. For systemic inflammation indices, cut-off values that are generally considered acceptable were employed; therefore, the C-index for each index may not correspond directly to its HR value. While continuous variables were dichotomized for analytical purposes, it is conceivable that categorization into three or four groups would yield greater clinical utility, in which case the C-index values would also be expected to differ. In conclusion, several indices reflecting body composition, nutritional status, and systemic inflammation remain valuable prognostic markers in patients with mRCC receiving ICI-based first-line therapy. In particular, SATI and PNI demonstrated strong predictive performance, allowing clear stratification of OS. These findings suggest that incorporating such markers into current prognostic models may improve risk stratification and guide treatment decisions in the era of ICI-based first-line regimens. Declarations Acknowledgments We thank Matthew Grimshaw, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. Authors contribution N.T.: Conceptualization, Supervision, Project administration, Formal analysis, Writing − original draft. S.N.: Data curation, Validation, Supervision, Formal analysis, Writing − review & editing. H.F.: Data curation, Methodology, Supervision, Formal analysis, Writing − review & editing. H.N.: Validation, Supervision, Formal analysis, Writing − review & editing. M.Y., A.Y., Y.T., T.N., and S.S.: Data curation, Formal analysis, Validation. Data availability The datasets generated and analyzed in the current study are available from the corresponding author upon reasonable request. Conflict of Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: N.T. received honoraria from Pfizer Inc., Eisai Co. Ltd., Novartis Pharmaceuticals Co., Ono Pharmaceutical Co. Ltd., Merck & Co. Inc., MSD Japan, and Takeda Pharmaceutical Co. Ltd. S.N. received honoraria from Bristol Myers Squibb Co., Ono Pharmaceutical Co. Ltd., Takeda Pharmaceutical Co. Ltd., Eisai Co. Ltd., Merck & Co. Inc., Pfizer Inc., and MSD Japan. T.N. received honoraria from MSD Japan, and Bristol-Myers Squibb Co. Ethical Approval This study was approved by the institutional review board of the Faculty of Medicine at Yamagata University (2018-43). References Naito S, Kato T, Numakura K et al (2021) Prognosis of Japanese metastatic renal cell carcinoma patients in the targeted therapy era. Int J Clin Oncol 26:1947–1954. https://doi.org/10.1007/s10147-021-01979-9 Heng DY, Xie W, Regan MM et al (2013) External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study. 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Med (Baltim) 100:e25127. https://doi.org/10.1097/MD.0000000000025127 Paris MT, Tandon P, Heyland DK et al (2020) Automated body composition analysis of clinically acquired computed tomography scans using neural networks. Clin Nutr 39:3049–3055. https://doi.org/10.1016/j.clnu.2020.01.008 Zhang X, Zhang J, Liu F et al (2023) Prognostic Nutritional Index (PNI) as a Predictor in Patients with Metabolic Syndrome and Heart Failure. Diabetes Metab Syndr Obes Volume 16:2503–2514. https://doi.org/10.2147/DMSO.S420924 Zahorec R (2001) Ratio of neutrophil to lymphocyte counts–rapid and simple parameter of systemic inflammation and stress in critically ill. Bratisl Lek Listy 102:5–14 Hu B, Yang X-R, Xu Y et al (2014) Systemic Immune-Inflammation Index Predicts Prognosis of Patients after Curative Resection for Hepatocellular Carcinoma. Clin Cancer Res 20:6212–6222. https://doi.org/10.1158/1078-0432.CCR-14-0442 Stühler V, Herrmann L, Rausch S et al (2022) Role of the Systemic Immune-Inflammation Index in Patients with Metastatic Renal Cell Carcinoma Treated with First-Line Ipilimumab plus Nivolumab. Cancers 14:2972. https://doi.org/10.3390/cancers14122972 Sanchez A, Furberg H, Kuo F et al (2020) Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study. Lancet Oncol 21:283–293. https://doi.org/10.1016/S1470-2045(19)30797-1 Albiges L, Hakimi AA, Xie W et al (2016) Body Mass Index and Metastatic Renal Cell Carcinoma: Clinical and Biological Correlations. J Clin Oncol 34:3655–3663. https://doi.org/10.1200/JCO.2016.66.7311 Mizuno R, Miyajima A, Hibi T et al (2017) Impact of baseline visceral fat accumulation on prognosis in patients with metastatic renal cell carcinoma treated with systemic therapy. Med Oncol 34:47. https://doi.org/10.1007/s12032-017-0908-3 Gu W, Zhu Y, Wang H et al (2015) Prognostic Value of Components of Body Composition in Patients Treated with Targeted Therapy for Advanced Renal Cell Carcinoma: A Retrospective Case Series. PLoS ONE 10:e0118022. https://doi.org/10.1371/journal.pone.0118022 Chatterjee S, Kleinman N, Gharajeh A et al (2009) Computerized Tomography Measurement of Visceral Adiposity Predicts Plasma Adiponectin Levels and Metastatic Disease in Patients with Clear Cell Renal Cell Carcinoma. Curr Urol 2:188–193. https://doi.org/10.1159/000209831 Choi Y, Park B, Jeong BC et al (2013) Body mass index and survival in patients with renal cell carcinoma: A clinical-based cohort and meta‐analysis. Int J Cancer 132:625–634. https://doi.org/10.1002/ijc.27639 Wueest S, Konrad D (2020) The controversial role of IL-6 in adipose tissue on obesity-induced dysregulation of glucose metabolism. Am J Physiol-Endocrinol Metab 319:E607–E613. https://doi.org/10.1152/ajpendo.00306.2020 Laino AS, Woods D, Vassallo M et al (2020) Serum interleukin-6 and C-reactive protein are associated with survival in melanoma patients receiving immune checkpoint inhibition. J Immunother Cancer 8:e000842. https://doi.org/10.1136/jitc-2020-000842 Frederiksen L, Nielsen TL, Wraae K et al (2009) Subcutaneous Rather than Visceral Adipose Tissue Is Associated with Adiponectin Levels and Insulin Resistance in Young Men. J Clin Endocrinol Metab 94:4010–4015. https://doi.org/10.1210/jc.2009-0980 Braun LM, Giesler S, Andrieux G et al (2025) Adiponectin reduces immune checkpoint inhibitor-induced inflammation without blocking anti-tumor immunity. Cancer Cell 43:269–291e19. https://doi.org/10.1016/j.ccell.2025.01.004 Saal J, Bald T, Eckstein M et al (2023) Integrating On-Treatment Modified Glasgow Prognostic Score and Imaging to Predict Response and Outcomes in Metastatic Renal Cell Carcinoma. JAMA Oncol 9:1048. https://doi.org/10.1001/jamaoncol.2023.1822 Ni L, Huang J, Ding J et al (2022) Prognostic Nutritional Index Predicts Response and Prognosis in Cancer Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. Front Nutr 9:823087. https://doi.org/10.3389/fnut.2022.823087 Shao Y, Wu B, Jia W et al (2020) Prognostic value of pretreatment neutrophil-to-lymphocyte ratio in renal cell carcinoma: a systematic review and meta-analysis. BMC Urol 20:90. https://doi.org/10.1186/s12894-020-00665-8 Jin M, Yuan S, Yuan Y, Yi L (2021) Prognostic and Clinicopathological Significance of the Systemic Immune-Inflammation Index in Patients With Renal Cell Carcinoma: A Meta-Analysis. Front Oncol 11:735803. https://doi.org/10.3389/fonc.2021.735803 Chen X, Meng F, Jiang R (2021) Neutrophil-to-Lymphocyte Ratio as a Prognostic Biomarker for Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. Front Oncol 11:746976. https://doi.org/10.3389/fonc.2021.746976 Iinuma K, Enomoto T, Kawada K et al (2021) Utility of Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Systemic Immune Inflammation Index as Prognostic, Predictive Biomarkers in Patients with Metastatic Renal Cell Carcinoma Treated with Nivolumab and Ipilimumab. J Clin Med 10:5325. https://doi.org/10.3390/jcm10225325 Numakura K, Sekine Y, Osawa T et al (2024) The lymphocyte-to-monocyte ratio as a significant inflammatory marker associated with survival of patients with metastatic renal cell carcinoma treated using nivolumab plus ipilimumab therapy. Int J Clin Oncol 29:1019–1026. https://doi.org/10.1007/s10147-024-02538-8 Supplementary Files SuppleTable01.pdf SuppleTable02.pdf SuppleTable03.pdf Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in International Journal of Clinical Oncology → Version 1 posted Editorial decision: Major revisions 07 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 18 Jun, 2025 First submitted to journal 17 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6914364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475500729,"identity":"4e6151c5-18ab-45a4-8745-db7350e8728c","order_by":0,"name":"Norihiko Tsuchiya","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-4242-2618","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":true,"prefix":"","firstName":"Norihiko","middleName":"","lastName":"Tsuchiya","suffix":""},{"id":475500730,"identity":"7f883482-0627-4ddc-a974-4c0b04b0b465","order_by":1,"name":"Sei Naito","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Sei","middleName":"","lastName":"Naito","suffix":""},{"id":475500731,"identity":"cb83de9d-82af-495c-93e6-05dab5037770","order_by":2,"name":"Hiroki Fukuhara","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Fukuhara","suffix":""},{"id":475500732,"identity":"0e6e478a-0772-4533-8842-5868172b35b6","order_by":3,"name":"Hayato Nishida","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Hayato","middleName":"","lastName":"Nishida","suffix":""},{"id":475500733,"identity":"460ebbab-fb36-4e4f-bd6f-8e192dd58a05","order_by":4,"name":"Mayu Yagi","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Mayu","middleName":"","lastName":"Yagi","suffix":""},{"id":475500734,"identity":"b25e4c9e-d71d-4a02-9b41-3a8bb119f0e6","order_by":5,"name":"Yuki Takai","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Takai","suffix":""},{"id":475500735,"identity":"080572ce-9dd4-4d1e-8fe2-c5916c48b31d","order_by":6,"name":"Atsushi Yamagishi","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Yamagishi","suffix":""},{"id":475500736,"identity":"22d2b8f4-a97f-4c49-9968-56fc4623db3d","order_by":7,"name":"Takafumi Narisawa","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Takafumi","middleName":"","lastName":"Narisawa","suffix":""},{"id":475500737,"identity":"b25952e2-30dd-4f1f-87c5-3670a63ca0b8","order_by":8,"name":"Shinata Suenaga","email":"","orcid":"","institution":"Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Shinata","middleName":"","lastName":"Suenaga","suffix":""}],"badges":[],"createdAt":"2025-06-17 12:26:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6914364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6914364/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10147-025-02855-6","type":"published","date":"2026-01-23T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85725059,"identity":"d772a3b6-98b8-4559-a3c1-a87c9a64cd27","added_by":"auto","created_at":"2025-07-01 06:25:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79763,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of overall survival stratified by IMDC risk classification, SATI, PNI, and NLR in non-ICI and ICI-based first-line therapy. (A and B) IMDC risk classification. (C and D) Subcutaneous adipose tissue index (SATI). (E and F) Prognostic nutritional index (PNI). (G and H) Glasgow Prognostic Score (GPS). (I and J) Neutrophil-to-lymphocyte ratio (NLR). Left panels (A, C, E, G, and I) show non-ICI-based regimens; right panels (B, D, F, H, and J) show ICI-based regimens. The survival probability is plotted over time (months), with log-rank \u003cem\u003eP\u003c/em\u003e-values and numbers at risk indicated.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/309f89ebf5134a55d425a866.png"},{"id":85725066,"identity":"eb716ba1-9f42-4909-8876-ff68552881d3","added_by":"auto","created_at":"2025-07-01 06:25:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49652,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix among body composition, nutritional, and inflammatory indices. The heatmap shows pairwise correlation coefficients between variables including body mass index (BMI), skeletal muscle index (SMI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI), visceral-to-subcutaneous fat ratio (VSR), prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI), Glasgow Prognostic Score (GPS), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). Positive correlations are shown in blue and negative correlations in pink, with stronger correlations indicated by more intense colors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/b6d3869edaeba96ed70bcc66.png"},{"id":101151728,"identity":"7fb49912-3e25-4748-996b-2100552c2b88","added_by":"auto","created_at":"2026-01-26 16:03:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/75b0a40c-a04c-4250-be96-71880c48eb40.pdf"},{"id":85725455,"identity":"b912c5b3-03a0-4663-9615-b3a01f13f64a","added_by":"auto","created_at":"2025-07-01 06:33:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170540,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleTable01.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/ca9c539d5eedb61c84048789.pdf"},{"id":85725456,"identity":"2f36d035-b013-4ba3-97f2-95bc3db208a8","added_by":"auto","created_at":"2025-07-01 06:33:29","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":182456,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleTable02.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/37cb6df5b77585224082b6de.pdf"},{"id":85726362,"identity":"4358df31-aaa6-4119-81dc-a2476be2c4f8","added_by":"auto","created_at":"2025-07-01 06:41:29","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":138506,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleTable03.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914364/v1/c3df2826b97f4b890e2e97b5.pdf"}],"financialInterests":"","formattedTitle":"Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: The enduring value of body composition, nutritional, and inflammatory indices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFirst-line systemic therapy for advanced renal cell carcinoma (RCC) or metastatic RCC (mRCC) has increasingly adopted combination strategies using tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), which have led to substantial improvements in patient prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The ICI-based therapy has markedly prolonged survival, highlighting the need for a new risk stratification system beyond the International Metastatic RCC Database Consortium (IMDC) risk stratification, which was originally developed based on outcomes from anti-vascular endothelial growth factor (VEGF)-targeted therapies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given the wide range of available treatments for advanced RCC, more accurate risk classification is essential to select the optimal therapy. While conventional prognostic models are based on clinical and hematological/biochemical parameters and offer practical utility, recent advances in systemic therapy underscore the potential value of incorporating prognostic factors from alternative domains. Such factors may contribute to improved prognostic accuracy in the current therapeutic landscape.\u003c/p\u003e \u003cp\u003ePrevious epidemiological studies have reported that obesity is a risk factor for RCC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while obesity or increased adipose tissue mass has been reported as a favorable prognostic factor for patients with RCC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Obesity being a risk factor for diseases while also being a favorable prognostic factor is known as the \"obesity paradox\" [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although this paradox remains unexplained, many hypotheses have been put forward. The mechanisms by which obesity affects disease prognosis have been suggested as reverse causation and possibly confounded by other prognostic factors and treatment effect determinants rather than a direct effect of obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious indicators that represent the physical state of the patient, such as nutritional status and inflammation, have been reported to be possible predictors of cancer prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prognostic nutritional index (PNI) was initially developed as an indicator of nutritional status to assess the risk of surgical complications of gastrointestinal malignancies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and the geriatric nutritional risk index (GNRI) was developed to assess nutritional risk in the elderly [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Complete blood count\u0026ndash;derived parameters, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), are indicators that reflect silent inflammation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Glasgow Prognostic Score (GPS) is a unique prognostic indicator that reflects both nutritional status and systemic inflammation, based on serum albumin levels and C-reactive protein concentrations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These nutritional and inflammatory indices indicate the prognosis of RCC [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of these markers, however, were identified in the TKI era, and their prognostic significance in the era of ICI-based therapy is yet to be established. Therefore, this study aimed to evaluate the prognostic significance of body composition, nutritional, and inflammatory indices in patients with mRCC treated with ICI-based first-line regimens and to compare their predictive performance to identify those with superior prognostic utility.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection and ethical considerations\u003c/h2\u003e \u003cp\u003eA total of 202 patients with mRCC received molecular targeted therapy from 2010 to 2023 at Yamagata University Hospital in Japan. A total of 136 patients who received TKIs, mammalian target of rapamycin inhibitors (mTORIs), ICI\u0026thinsp;+\u0026thinsp;ICI, or ICI\u0026thinsp;+\u0026thinsp;TKI as first-line therapy and for whom evaluable CT imaging and relevant clinical data were available for assessing body composition, nutritional status, and inflammatory markers were included in the analysis. Fourteen patients were excluded because of the unavailability of CT imaging, 20 were omitted because of missing biochemical or hematological data, and 32 were not included in the analysis as they received first-line therapies other than TKI- or ICI-based regimens. This study was approved by the institutional review board of the Faculty of Medicine at Yamagata University (2018-43). An opt-out method was used to obtain informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of muscle and adipose tissue components\u003c/h3\u003e\n\u003cp\u003ePatients' clinical and CT imaging data were obtained immediately before the first-line treatment. The measurement of muscle and adipose tissue components was conducted using the Automated Muscle and Adipose Tissue Composition Analysis (AutoMATiCA) system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This open-source software was obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gitlab.com/Michael_Paris/AutoMATiCA\u003c/span\u003e\u003cspan address=\"https://gitlab.com/Michael_Paris/AutoMATiCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Subcutaneous adipose tissue area, visceral adipose tissue area, and skeletal muscle area were automatically measured at the L3 level. The skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), and visceral adipose tissue index (VATI) were defined as the corresponding area divided by body height squared. The visceral-to-subcutaneous adipose tissue ratio (VSR) was calculated as the visceral adipose tissue area divided by the subcutaneous adipose tissue area. These indices were dichotomized separately for male and female individuals using their respective medians because significant sex differences were found in body composition (Supplementary Table S1). A body mass index (BMI) of \u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e was defined as underweight, 18.5\u0026ndash;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e as normal weight, \u0026ge;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e as overweight, and \u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e as obese.\u003c/p\u003e\n\u003ch3\u003eCalculation of nutritional and inflammatory indices\u003c/h3\u003e\n\u003cp\u003eThe PNI was calculated using the following formula: serum albumin (g/L)\u0026thinsp;+\u0026thinsp;0.005 \u0026times; total lymphocyte count (/\u0026micro;l) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the basis of the PNI score, nutritional status was classified into three categories: malnutrition: PNI\u0026thinsp;\u0026lt;\u0026thinsp;40; mild malnutrition: 40\u0026thinsp;\u0026le;\u0026thinsp;PNI\u0026thinsp;\u0026lt;\u0026thinsp;45; and no malnutrition: PNI\u0026thinsp;\u0026ge;\u0026thinsp;45 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The formula for GNRI is [1.489 x serum albumin (g/L)] + [41.7 \u0026times; (weight/WLo)], where WLo is an ideal weight calculated from the Lorentz equations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. GNRI was categorized as follows: \u0026ge; 98: no risk; \u0026ge; 92 and \u0026lt;\u0026thinsp;98: low risk; \u0026ge; 82 and \u0026lt;\u0026thinsp;92: moderate risk; and \u0026lt;\u0026thinsp;82: major risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The GPS was used as a marker of both nutritional status and systemic inflammation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and is a composite index ranging from 0 to 2, with one point assigned for a C-reactive protein level of \u0026gt;\u0026thinsp;1 mg/dL and one point for a serum albumin level of \u0026lt;\u0026thinsp;3.5 g/L. The systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) were used as systemic immune-inflammatory biomarkers. SII was calculated from the equation: P x N/L, where P, N, and L are the preoperative peripheral blood platelet, neutrophil, and lymphocyte counts, respectively [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. SII was dichotomized using a cut-off value of 788x10\u003csup\u003e9\u003c/sup\u003e/L, based on a previous study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, the cut-offs for NLR, PLR, and LMR were set as 3.0 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], 150.0 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and 3.0 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], respectively, based on previous reports and meta-analyses.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eR software version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analyses. The Mann\u0026ndash;Whitney test was used to compare continuous variables between two groups. The Chi-square test was used to compare categorical variables. Overall survival (OS) was defined as the time from initiation of the first-line systemic therapy to death from any cause and was analyzed using Kaplan\u0026ndash;Meier curves with a log-rank test, while Cox regression analysis was used to assess the effects of each variable on survival. A Pearson correlation coefficient was calculated for each pair of indices. The concordance index (C-index) was calculated using Uno's method to assess the model's time-dependent discriminatory ability. A \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; clinical demographics\u003c/h2\u003e \u003cp\u003eThe patients\u0026rsquo; demographics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 136 patients (108 male and 28 female) were included in the analysis. The median age was 66 years (interquartile range [IQR], 61\u0026ndash;71), and the median follow-up duration was 22.7 months (IQR, 8.9\u0026ndash;47.2). Pathological confirmation revealed clear cell RCC in 106 patients (78%), followed by papillary RCC (6.6%), chromophobe RCC (3.7%), collecting duct carcinoma (2.9%), acquired cystic disease-associated RCC (2.2%), other subtypes (3.7%), and unknown (2.9%). According to the IMDC risk classification, 8.8% of patients were categorized as favorable risk, 52% as intermediate risk, and 39% as poor risk. The most common first-line treatment was TKI in 57% of patients, followed by ICI-based treatments (ICI\u0026thinsp;+\u0026thinsp;ICI: 29%; ICI\u0026thinsp;+\u0026thinsp;TKI: 9.6%) and mTORI (4.4%).\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\u003eBaseline demographics and clinical characteristics of patients according to first-line regimen\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-ICI-based regimen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICI-based regimen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (61, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (63, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (63, 72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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 \u003cp\u003e108 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (81)\u003c/p\u003e \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\u003e28 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up, month (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7 (8.9, 47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6 (8.8, 43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.2 (10.5, 49.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear cell RCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary RCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromophobe cell renal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollecting duct carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquired cystic disease-associated RCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMDC risk criteria (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-line treatment (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emTORI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u0026thinsp;+\u0026thinsp;ICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u0026thinsp;+\u0026thinsp;TKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eRCC, Renal cell carcinoma; TKI, Tyrosine kinase inhibitor; mTORI, mTOR inhibitor; ICI, Immune checkpoint inhibitor\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSex differences in prognostic markers\u003c/h3\u003e\n\u003cp\u003eSignificant sex differences were observed in several body composition indices: male individuals had higher values than female individuals for SMI (48.7 vs. 37.1 cm\u0026sup2;/m\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VATI (33.4 vs. 12.4 cm\u0026sup2;/m\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and VSR (0.9 vs. 0.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By contrast, SATI did not differ significantly between sexes (32.2 vs. 35.5 cm\u0026sup2;/m\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.761). Among inflammatory markers, PLR was significantly higher in female individuals (261.5 vs. 206.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), while other indices showed no significant sex-based differences (Supplementary Table S1).\u003c/p\u003e\n\u003ch3\u003eInfluence of prognostic markers on OS in the overall cohort\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in the overall cohort, lower BMI (hazard ratio [HR] 1.49, 95% CI 1.03\u0026ndash;2.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), VATI (HR 1.66, 95% CI 1.09\u0026ndash;2.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), and SATI (HR 1.89, 95% CI 1.25\u0026ndash;2.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were significantly associated with shorter OS, whereas SMI and VSR had no significant effect on survival. Nutritional markers demonstrated strong prognostic value: patients with malnutrition, as defined by PNI, had significantly shorter OS (HR 1.72, 95% CI 1.35\u0026ndash;2.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those at higher risk according to GNRI also exhibited poorer survival (HR 1.59, 95% CI 1.33\u0026ndash;1.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eUnivariable analysis of overall survival by first-line regimen\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eNon-ICI regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eICI regimen\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 \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% 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 \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\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 (1 year increment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99, 1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97, 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.98, 1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMDC (Poor vs. Intermediate vs. Favorable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60, 3.35\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.69, 4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.33, 6.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Underweight vs. Normal vs. Overweight/Obese)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03, 2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99, 2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.66, 3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (Low vs. High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84, 1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76, 1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.62, 3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATI (Low vs. High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09, 2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.21, 3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.69, 3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSATI (Low vs. High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25, 2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.06, 2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.27, 8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSR (Low vs. High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86, 1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05, 2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.46, 2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI (Malnutrition vs. Mild malnutrition vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35, 2.20\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.32, 2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.52, 5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\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\u003eGNRI (Major vs. Moderate vs. Low vs. No risk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33, 1.91\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.39, 2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.32, 2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\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\u003eGPS (2 vs. 1 vs. 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66, 3.85\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.42, 3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.64, 12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (High vs. Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32, 3.06\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25, 3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.99, 6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (High vs. Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30, 3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.12, 2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.25, 11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR (High vs. Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04, 2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.08, 2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.75, 4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR (Low vs. High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16, 2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89, 2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.30, 7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eICI, immune checkpoint inhibitor; IMDC, international mRCC database consortium; BMI, body mass index; SMI, skeletal muscle index; VATI, visceral adipose tissue index; SATI, subcutaneous adipose tissue index; VSR, visceral to subcutaneous fat ratio; PNI, prognostic nutritional index; GNRI, geriatric nutritional risk index; GPS, Glasgow prognostic score; SII, systemic immune inflammation index; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; LMR, lymphocyte to monocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGPS emerged as a robust predictor, with higher scores correlating with worse outcomes (HR 2.53, 95% CI 1.66\u0026ndash;3.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among inflammatory markers, elevated SII (HR 2.01, 95% CI 1.32\u0026ndash;3.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NLR (HR 1.99, 95% CI 1.30\u0026ndash;3.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and PLR (HR 1.57, 95% CI 1.04\u0026ndash;2.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), as well as lower LMR (HR 1.75, 95% CI 1.16\u0026ndash;2.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), were all significantly associated with shorter OS.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of prognostic markers between non-ICI and ICI-based regimens\u003c/h2\u003e \u003cp\u003eDetailed subgroup analyses, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal distinct patterns. Kaplan\u0026ndash;Meier curves for five prognostic indices, including the IMDC risk classification and four representative indices of body composition, nutritional status, and systemic inflammation, are displayed separately for the two treatment subgroups in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the non-ICI group, low VATI (HR 1.95, 95% CI 1.21\u0026ndash;3.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and low SATI (HR 1.68, 95% CI 1.06\u0026ndash;2.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) were significantly associated with poorer OS. Nutritional markers, including PNI (HR 1.75, 95% CI 1.32\u0026ndash;2.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GNRI (HR 1.74, 95% CI 1.39\u0026ndash;2.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), also showed strong associations with OS. Inflammatory markers SII (HR 2.01, 95% CI 1.25\u0026ndash;3.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), NLR (HR 1.80, 95% CI 1.12\u0026ndash;2.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), and PLR (HR 1.72, 95% CI 1.08\u0026ndash;2.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), as well as GPS (HR 2.29 95% CI 1.42\u0026ndash;3.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrated significant prognostic value in this group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConversely, among patients treated with ICIs, while the IMDC classification remained significant (HR 2.98, 95% CI 1.33\u0026ndash;6.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), Kaplan\u0026ndash;Meier curves showed no clear separation between the favorable- and intermediate-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among body composition indices, SATI emerged as a strong predictor (HR 3.25, 95% CI 1.27\u0026ndash;8.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), showing the highest C-index (0.679 as a continuous variable) among body composition indices (Supplementary Table S3). Both PNI (HR 2.80, 95% CI 1.52\u0026ndash;5.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GNRI (HR 1.96, 95% CI 1.32\u0026ndash;2.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant prognostic factors, demonstrating high predictive performance (C-indices 0.759 and 0.773 as continuous variables, respectively) (Supplementary Table S3). Notably, PNI showed clear separation of survival curves among the three groups in Kaplan\u0026ndash;Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Among inflammatory markers, GPS (HR 4.49, 95% CI 1.64\u0026ndash;12.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and NLR (HR 3.71, 5% CI 1.25\u0026ndash;11.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) exhibited higher hazard ratios compared with the non-ICI group, with survival curves demonstrating significant stratification (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014 and 0.011, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis among prognostic markers\u003c/h2\u003e \u003cp\u003eA correlation heatmap of the evaluated indices is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Moderate correlations were observed between the IMDC classification and several nutritional and inflammatory markers: PNI (r = \u0026minus;\u0026thinsp;0.48), GNRI (r = \u0026minus;\u0026thinsp;0.51), GPS (r\u0026thinsp;=\u0026thinsp;0.49), and SII (r\u0026thinsp;=\u0026thinsp;0.46). By contrast, BMI showed moderate to strong correlations with body composition and nutritional indices, including SMI (r\u0026thinsp;=\u0026thinsp;0.63), VATI (r\u0026thinsp;=\u0026thinsp;0.74), SATI (r\u0026thinsp;=\u0026thinsp;0.75), PNI (r\u0026thinsp;=\u0026thinsp;0.38), and GNRI (r\u0026thinsp;=\u0026thinsp;0.53). The adipose tissue-related indices VATI and SATI demonstrated moderate correlations with the nutritional marker GNRI (r\u0026thinsp;=\u0026thinsp;0.44 and 0.42, respectively). Notably, both PNI and GNRI demonstrated strong correlations with each other (r\u0026thinsp;=\u0026thinsp;0.92) and moderate associations with inflammatory markers, body composition indices, and IMDC classification, highlighting their roles as well-balanced and comprehensive prognostic markers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between obesity and survival in patients with mRCC has been widely reported, but varying results have been obtained depending on the obesity index used. While BMI has been identified as an independent prognostic factor in several studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], others have shown that visceral or subcutaneous adipose tissue, rather than BMI, more accurately predict survival outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. CT imaging enables separate measurements of visceral and subcutaneous adipose tissue, offering more precise body composition data than BMI. In our study, both VATI and SATI demonstrated stronger associations with prolonged survival compared with BMI, which may be partly explained by the lower prevalence of obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;) in the Japanese population, and the fact that BMI does not directly reflect adipose tissue distribution [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough obesity is a recognized risk factor for RCC incidence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], paradoxically, several studies have reported a favorable impact of obesity on OS in advanced RCC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Proposed explanations include the less aggressive nature of RCC in obese patients and lead-time bias because of cancer cachexia. Higher BMI has been associated with more favorable clinicopathologic features at diagnosis, such as lower stage, lower Fuhrman grade, and smaller tumor size [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our findings demonstrate that adipose tissue indices are moderately correlated with nutritional markers but show weaker correlations with the IMDC classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that while adipose tissue-related indices partially reflect nutritional status, they may have distinct prognostic significance independent of conventional risk models. Furthermore, IL-6, which is preferentially released from visceral rather than subcutaneous adipose tissue in obese individuals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], has been reported to attenuate the efficacy of ICIs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By contrast, adiponectin, which is more abundantly secreted from subcutaneous than visceral adipose tissue [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], helps preserve anti-tumor immunity during ICI therapy by resolving inflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent with these observations, our findings suggest that the association between VATI and OS was attenuated in the ICI-based regimen group; however, the role of visceral adipose tissue in modulating ICI efficacy remains unclear and warrants further investigation.\u003c/p\u003e \u003cp\u003eThis study used PNI and GNRI as nutritional indices, and GPS was applied as a composite index of nutrition and systemic inflammation. Consistent with previous reports [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], these indices were significantly associated with OS in patients with mRCC, and they were strongly correlated with each other, possibly because of the influence of albumin as a common factor. A favorable nutritional status could enhance immune competence, attenuate systemic inflammation, preserve metabolic reserves, and improve treatment tolerance, all of which may collectively contribute to improved survival outcomes in cancer patients. In the ICI treatment group, these indices remained strong predictors of OS, indicating that poor nutritional status continues to be an adverse prognostic factor even in the era of ICI therapy. Although such associations have been reported in other cancer types [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], to our knowledge, no previous study has specifically examined the association between PNI, GNRI, GPS, and outcomes in mRCC patients treated with ICIs, highlighting the novelty and clinical relevance of our findings.\u003c/p\u003e \u003cp\u003eSystemic inflammatory markers have been reported to be associated with survival in patients with mRCC in several meta-analyses [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In particular, the prognostic and predictive significance of SII [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], NLR [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and LMR [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] have been suggested in small cohort studies of patients with mRCC treated with first-line ICI-based regimens, especially the combination of ipilimumab and nivolumab. However, the predictive accuracy of these markers has varied across studies, and few investigations have directly compared their prognostic value. In the subgroup analyses of the present study, some markers also lost their significant association with OS. This inconsistency may be attributed to small sample sizes, heterogeneity in patient populations, variations in treatment regimens, and the lack of standardized cut-off values among studies. These findings further underscore the need for standardized definitions and large-scale validation in diverse patient populations. To establish more accurate and clinically applicable risk stratification models, it will be essential to incorporate these inflammatory markers into predictive algorithms validated through prospective studies. Importantly, such efforts will require the adoption of fixed, standardized cut-off values to ensure consistency and comparability across future trials.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, among the 137 patients with mRCC who received first-line systemic therapy, only 52 patients were treated with regimens including ICIs. Therefore, the sample size might have been insufficient to adequately evaluate the impact of each index on OS in this subgroup. Second, the study could not examine the association between the indices and the treatment response or progression-free survival as an endpoint. An accurate evaluation of these endpoints was difficult and was not included because the study used retrospective real-world data. Third, sex-adjusted median values were applied as cut-off points for each index. The lack of universally established cut-off values represents a significant limitation, not only for body composition indices but also for other indices with continuous values. For systemic inflammation indices, cut-off values that are generally considered acceptable were employed; therefore, the C-index for each index may not correspond directly to its HR value. While continuous variables were dichotomized for analytical purposes, it is conceivable that categorization into three or four groups would yield greater clinical utility, in which case the C-index values would also be expected to differ.\u003c/p\u003e \u003cp\u003eIn conclusion, several indices reflecting body composition, nutritional status, and systemic inflammation remain valuable prognostic markers in patients with mRCC receiving ICI-based first-line therapy. In particular, SATI and PNI demonstrated strong predictive performance, allowing clear stratification of OS. These findings suggest that incorporating such markers into current prognostic models may improve risk stratification and guide treatment decisions in the era of ICI-based first-line regimens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Matthew Grimshaw, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.T.: Conceptualization, Supervision, Project administration, Formal analysis, Writing \u0026minus; original draft. S.N.: Data curation, Validation, Supervision, Formal analysis,\u0026nbsp;Writing \u0026minus; review \u0026amp; editing. H.F.: Data curation, Methodology, Supervision, Formal analysis, Writing \u0026minus; review \u0026amp; editing. H.N.: Validation, Supervision, Formal analysis,\u0026nbsp;Writing \u0026minus; review \u0026amp; editing. M.Y., A.Y., Y.T., T.N., and S.S.: Data curation, Formal analysis, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: N.T. received honoraria from\u0026nbsp;Pfizer Inc., Eisai Co. Ltd., Novartis Pharmaceuticals Co., Ono Pharmaceutical Co. Ltd., Merck \u0026amp; Co. Inc., MSD Japan, and Takeda Pharmaceutical Co. Ltd. S.N. received honoraria from\u0026nbsp;Bristol Myers Squibb Co., Ono Pharmaceutical Co. Ltd., Takeda Pharmaceutical Co. Ltd., Eisai Co. Ltd., Merck \u0026amp; Co. Inc., Pfizer Inc., and MSD Japan. T.N. received honoraria from\u0026nbsp;MSD Japan, and Bristol-Myers Squibb Co.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional review board of the Faculty of Medicine at Yamagata University (2018-43).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaito S, Kato T, Numakura K et al (2021) Prognosis of Japanese metastatic renal cell carcinoma patients in the targeted therapy era. 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Int J Clin Oncol 29:1019\u0026ndash;1026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10147-024-02538-8\u003c/span\u003e\u003cspan address=\"10.1007/s10147-024-02538-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metastatic renal cell carcinoma, Immune checkpoint inhibitor, Body composition, Nutritional status, Systemic inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6914364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6914364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eImmune checkpoint inhibitors (ICIs) are now the standard first-line treatment for metastatic renal cell carcinoma (mRCC), yet many risk factors identified during the tyrosine kinase inhibitor era remain unvalidated in current practice. This study aimed to evaluate the prognostic value of body composition, nutritional, and inflammatory indices in the era of ICI-based first-line therapy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe retrospectively analyzed 136 mRCC patients who received systemic therapy. Body composition indices (skeletal muscle index [SMI], visceral adipose tissue index [VATI], subcutaneous adipose tissue index [SATI]), nutritional markers (prognostic nutritional index [PNI], geriatric nutritional risk index [GNRI]), and inflammatory markers (Glasgow Prognostic Score [GPS], systemic inflammatory index [SII], and other indices) were assessed for their association with overall survival (OS). We also compared their prognostic impact on patients treated with non-ICI-based and ICI-based regimens as first-line therapy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLower body mass index (HR 1.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), VATI (HR 1.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), and SATI (HR 1.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were associated with shorter survival. PNI (HR 1.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GNRI (HR 1.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed strong prognostic value, as did GPS (HR 2.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SII (HR 2.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the overall cohort. In the ICI-based regimen group, GNRI, PNI, and SATI demonstrated higher prognostic performance (C-indices 0.756, 0.739, and 0.687, respectively), with PNI and SATI providing clear OS stratification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSeveral indices reflecting body composition, nutritional status, and systemic inflammation remain valuable prognostic markers in patients with mRCC receiving ICI-based first-line therapy.\u003c/p\u003e","manuscriptTitle":"Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: The enduring value of body composition, nutritional, and inflammatory indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 06:25:24","doi":"10.21203/rs.3.rs-6914364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-07-07T04:48:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-24T04:39:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T04:31:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T04:00:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2025-06-17T08:25:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd01f1c8-0471-4b61-a917-699d2d10caef","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:00:38+00:00","versionOfRecord":{"articleIdentity":"rs-6914364","link":"https://doi.org/10.1007/s10147-025-02855-6","journal":{"identity":"international-journal-of-clinical-oncology","isVorOnly":false,"title":"International Journal of Clinical Oncology"},"publishedOn":"2026-01-23 15:57:36","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2025-07-01 06:25:24","video":"","vorDoi":"10.1007/s10147-025-02855-6","vorDoiUrl":"https://doi.org/10.1007/s10147-025-02855-6","workflowStages":[]},"version":"v1","identity":"rs-6914364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6914364","identity":"rs-6914364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}