Metabolic Predictors of Response to Immunotherapy in Unresectable Locally Advanced Esophageal Cancer: Unveiling the Obesity Paradox

Metabolic Predictors of Response to Immunotherapy in Unresectable Locally Advanced Esophageal Cancer: Unveiling the Obesity Paradox | 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 Article Metabolic Predictors of Response to Immunotherapy in Unresectable Locally Advanced Esophageal Cancer: Unveiling the Obesity Paradox Jiaqi Tan, Zhaoyu Pan, Haoyu Zhang, Yuhan Chen, Xiaoqiao Cui, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7875131/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: PD-1 inhibitor-based immunotherapy is increasingly used for unresectable locally advanced esophageal cancer, yet the impact of metabolic factors—and the emerging "obesity paradox"—on treatment efficacy remains largely unknown. Methods Patients were stratified by body mass index (BMI) and metabolic syndrome (MetS) to assess survival (OS/PFS) and treatment response (CR/PR/SD/PD); a prognostic nomogram was subsequently developed via LASSO and multivariable Cox regression and validated internally and externally using the respective institutional cohorts. Results A total of 305 eligible patients with unresectable locally advanced esophageal cancer from two medical centers were included. Patients with high BMI or MetS demonstrated significantly superior overall survival, progression-free survival, and objective response rates to anti-PD-1 therapy. These groups exhibited favorable immune profiles, characterized by a lower pan-immune inflammation value (PIV) and a higher combined positive score (CPS). A prognostic nomogram was successfully constructed and validated, incorporating four independent predictors: gender, BMI, TNM stage, and PIV. The model demonstrated high predictive accuracy for 24- and 36-month overall survival in both internal and external validation cohorts, with well-fitted calibration curves. Conclusion Elevated BMI and MetS correlate with improved outcomes following immunotherapy in unresectable locally advanced esophageal cancer patients. Furthermore, our validated nomogram enables refined prognostic assessment through metabolic risk stratification. Biological sciences/Cancer Health sciences/Gastroenterology Health sciences/Oncology prognosis immunotherapy unresectable locally advanced esophageal cancer obesity nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Esophageal cancer (EC) ranks as the eleventh most prevalent malignancy globally and the seventh leading cause of cancer mortality, representing 2.6% of new cancer diagnoses and 4.6% of cancer deaths worldwide (GLOBOCAN 2022) 1 . The two major EC subtypes show distinct geographical distributions: squamous cell carcinoma (ESCC) predominates in East Asia, South-Central Asia, and Southern Africa, whereas adenocarcinoma (EAC) is most prevalent in Northern Europe and North America 2 . Despite advancements in minimally invasive surgical and endoscopic techniques, as well as precision radiotherapy technologies, which have contributed to improved patient prognosis, the overall survival rate remains poor, with a 5-year survival of approximately 20% across all stages, largely due to late-stage diagnosis in most patients when curative treatment options are limited 3 , 4 . The past decade has witnessed a paradigm shift with the advent of immune checkpoint inhibitors (ICIs), which have established a new standard of care and brought hope for potential cures to patients with unresectable locally advanced EC in both first-line metastatic and adjuvant settings 5 , 6 . However, ICIs still face considerable limitations: only a subset of patients achieves a meaningful response, while a significant proportion experience primary or secondary resistance. Consequently, a central challenge in the field remains the development of strategies to accurately predict responses to anti-PD-1 therapy and then screen patients suitable for immunotherapy, with the goal of enabling personalized treatment and improving overall survival 7 . Against the backdrop of increasing global obesity rates 8 , 9 , the role of obesity in cancer development has come under intense scrutiny. Extensive research has established a strong association, underscoring the carcinogenic role of excess body weight in the etiology of multiple malignancies 10 – 13 . Contrary to the initially postulated negative impact of obesity on therapeutic outcomes, empirical findings reveal that overweight and obese patients with several solid tumors do not experience reduced immunotherapy responses or poorer survival. In fact, these patients demonstrate a lower risk of all-cause cancer mortality and superior efficacy from immunotherapy compared to those with a low-to-normal BMI, which is called “obesity paradox” 14–16 . However, despite this emerging trend across multiple immunotherapy-responsive malignancies, the specific role and clinical relevance of the “obesity paradox” in the context of EC remain incompletely understood. While body mass index (BMI) serves as the operational definition for overweight and obesity in clinical and epidemiological contexts, it is an inadequate proxy that does not fully represent the pathophysiological state of obesity or its mechanistic effects on tumors 17 . The potential for obesity to enhance anti-tumor immunity is multifaceted. Obesity may potentiate anti-tumor immunity through multiple mechanisms. A key link is the chronic, low-grade inflammatory state characteristic of obesity, which drives a pro-inflammatory milieu and alters immune cell populations, consequently reshaping systemic and local anti-tumor immune responses 18 , 19 . Therefore, merely comparing efficacy differences between obese and non-obese groups based on BMI—a descriptive approach—is insufficient. Clinical practice necessitates an integrated tool that incorporates obesity status alongside other key prognostic factors. At the core of high-BMI obesity often lies metabolic syndrome (MetS), a condition of systemic metabolic dysfunction which, in turn, fosters a chronic pro-inflammatory state and associated alterations in immune function. Thus, this study was designed with a dual objective: firstly, to explore the implications of the "obesity paradox" for anti-PD-1 immunotherapy in esophageal cancer, and secondly, to build upon that foundation to develop an integrated prognostic prediction model. This model aims to equip clinicians with an intuitive and personalized tool for enhancing the accuracy of survival prognostication post-immunotherapy, thus supporting more informed treatment strategies. Material and methods Study design The study design and patient selection process are illustrated in Fig. 1 . Study participants were diagnosed with unresectable locally advanced esophageal cancer and underwent anti-PD-1 treatment at Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital from January 2020 to May 2023. Patients were excluded for: (1) other concomitant primary tumors; (2) immunotherapy exposure before; (3) HER2 overexpression positive and received trastuzumab treatment; (4) being in active clinical trials; (5) underweight status (BMI < 18.5); (6) lost to follow-up; and (7) therapy changes before disease progression or death. The study enrolled 305 eligible patients. The training cohort comprised 212 patients from Xiangya Hospital, Central South University and the validation cohort consisted of 93 patients from Harbin Medical University Cancer Hospital. The ethics committees of Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital granted study approval. And all of the study was performed in accordance with relevant regulations and the Declaration of Helsinki. Written informed consent was obtained from all patients for data use in research, and prospective follow-up employing standardized outcome recording ensured data accuracy for analyses. We adopted the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines to uphold methodological rigor and transparency throughout this study. Prognosis analysis Prognostic associations were analyzed by stratifying patients: (1) by BMI (normal 18.5–24.0 kg/m², high > 24.0 kg/m²) using the Chinese National Health Commission’s standard Criteria of Weight for Adults (WS/T 428–2013), and (2) by MetS status (normal, MetS) based on the diagnostic criteria established by the International Diabetes Federation (IDF) consensus guidelines. Overall survival (OS) and progression-free survival (PFS) were compared across BMI/MetS subgroups via Kaplan-Meier curves. The log-rank test determined inter-group survival differences (significance threshold: two-sided *p* < 0.05). Using RECIST v1.1 criteria, tumor responses were classified as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Fisher’s exact test or the χ² test was applied for intergroup response rate comparisons based on suitability. Nomogram construction Prognostically significant variables were selected using Least absolute shrinkage and selection operator (LASSO) regression. Of the eleven candidate variables, continuous variables included age, BMI, PIV value, and CPS; categorical variables comprised gender, MetS, pathology type, tumor site, TNM stage, chemotherapy, and anti-PD-1 agent. Baseline characteristics showed no statistically significant differences across training and validation cohorts per chi-square analysis. LASSO-selected variables (non-zero coefficients) were analyzed using multivariate Cox regression to identify independent prognostic factors and estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Independent prognostic factors defined by multivariate statistical significance (p < 0.05) were retained for inclusion in the prognostic nomogram. Nomogram discrimination was assessed via receiver operating characteristic (ROC) curve analysis, quantified by the area under the curve (AUC). The training cohort underwent internal validation through 500 bootstrap-resampled calibration curves, while the validation cohort received independent external validation using the same calibration approach. Predictive calibration plots were generated using parametric calibration to extend model assessment. Statistical analysis We employed chi-square (χ²) tests for categorical variable associations, switching to Fisher’s exact test for sparse contingency tables (expected frequency < 5). Training and validation cohort equivalence was confirmed by non-significant baseline comparisons. Subgroup differences in continuous variables were tested via independent sample t-tests. Statistical significance (p < 0.05, two-tailed) was assessed using R 4.4.2 and GraphPad Prism 8.0.2. Results Patient Characteristics A total of 332 patients with unresectable locally advanced esophageal cancer who underwent anti-PD-1 therapy at Xiangya Hospital or Harbin Medical University Cancer Hospital from January 2020 to May 2023 were initially enrolled. Following application of predefined exclusion criteria, 27 patients were excluded, resulting in a final cohort of 305 eligible patients. Of these, 212 patients from Xiangya Hospital comprised the training cohort, while 93 patients from Harbin Medical University Cancer Hospital constituted the validation cohort for subsequent analyses (Fig. 1 ). The patient characteristics in the training and validation cohort are presented in Table 1 . Table 1 Patient characteristics. BMI, body mass index; ESCC, esophageal Squamous Cell Carcinoma; EAC, esophageal adenocarcinoma; EUC, Esophageal Undifferentiated Carcinoma; CPS, combined positive score; PIV, pan-immune inflammation value. Variable Total (n = 305) Training cohort (n = 212) Validation cohort (n = 93) Median age, years (range) 64 (43–83) 65 (43–83) 62 (47–79) Gender Male 278 189 89 Female 27 23 4 Median BMI (range) 22.4 (18.5–32.1) 23.0 (18.5–30.8) 21.3 (18.5–32.1) Metabolic syndrome Yes 47 31 16 No 258 181 77 Pathology type SCC 286 198 88 AC 16 11 5 NEC 3 3 0 Tumor site Cervical 31 22 9 Upper thoracic 26 15 11 Middle thoracic 175 126 49 Lower thoracic 71 47 24 Abdominal 2 2 0 TNM stage II 15 10 5 III 126 89 37 IV A 119 86 33 IV B 45 27 18 Chemotherapy TP 162 101 61 Paclitaxel + Oxaliplatin 90 90 0 Others 53 21 32 Immunotherapy Camrelizumab 21 15 6 Nivolumab 18 13 5 Pembrolizumab 56 36 20 Serplulimab 34 27 7 Sintilimab 45 33 12 Tislelizumab 33 25 8 Toripalimab 98 63 35 Efficacy evaluation CR 35 20 15 PR 182 127 55 SD 75 55 20 PD 13 10 3 Median CPS (range) 21.9 (0–90) 23.3 (0–90) 20.5 (0–80) Median PIV (range) 661 (0-4385) 707 (0-4385) 557 (76-1367) Prognostic Value of BMI and MetS To evaluate the prognostic value of BMI and MetS in patients with unresectable locally advanced esophageal cancer, we assessed survival outcomes and anti-PD-1 treatment response across different BMI/MetS subgroups. Both the high BMI group and the MetS group demonstrated superior OS (Fig. 2 A, D) and PFS (Fig. 2 B, E) compared to the normal patient groups. Comparative analysis of treatment efficacy following anti-PD-1 therapy revealed that patients in the high BMI (Fig. 2 C) and MetS (Fig. 2 F) groups exhibited increased rates of CR and PR, alongside decreased rates of PD and SD. These findings demonstrate that both elevated BMI and the presence of MetS are significantly associated with prognosis and may serve as predictive factors in patients with unresectable locally advanced esophageal cancer undergoing immunotherapy. Current evidence indicates that obesity influences tumor progression primarily by driving pathological inflammation and altering tumor immunity. Therefore, we further analyzed the relationships between BMI and MetS with the PIV (pan-immune inflammation value) and CPS (combined positive score), representing systemic inflammation and immunotherapy responsiveness, respectively in patients with unresectable locally advanced esophageal cancer receiving anti-PD-1 therapy. The results demonstrated that patients in both the high BMI and MetS groups exhibited significantly lower PIV values compared to the normal group (Fig. 3 E, K). Furthermore, the proportion of patients with a CPS score ≥ 10 was significantly higher within the high BMI and MetS groups than in the normal group (Fig. 3 F, L). Notably, analysis of the individual components comprising the PIV formula revealed that platelet (Fig. 3 C, I) and lymphocyte counts (Fig. 3 D, J) were significantly elevated in patients with high BMI and MetS. Monocyte counts were significantly reduced in the high BMI group specifically (Fig. 3 B, H), while neutrophil counts showed no significant difference across the groups (Fig. 3 A, G). Taken together, these findings suggest that unique systemic immune-inflammatory and tumor immune microenvironment profiles potentially contribute to the superior anti-PD-1 therapy responses observed in overweight patients, including those with metabolic syndrome. Nomogram Construction From an initial set of eleven variables (gender, age, BMI, MetS, tumor site, stage, PIV, tumor pathological subtype, CPS, anti-PD-1 agents and chemotherapy regimens) subjected to LASSO regression, five were retained based on non-zero coefficients, signifying potential prognostic value (Fig. 4 A, B). In the subsequent multivariable Cox regression, four independent factors demonstrated statistically significant (p < 0.05) prognostic value: gender, BMI, TNM stage, and PIV value (Table 2 ). Employing these four variables, we developed a prognostic nomogram to predict survival probabilities for patients receiving anti-PD-1 therapy against unresectable locally advanced esophageal cancer (Fig. 4 C). The model facilitates personalized clinical decision-making through quantitative risk stratification and outcome prediction. Table 2 Multivariate Cox regression analyses of the selected characteristics for overall survival in the training cohort. Characteristics HR 95% CI P Gender (male vs. female) 1.69 1.01–2.84 0.046 * Age 1.02 1.00-1.03 0.054 Group BMI (high BMI vs. normal) 0.70 0.55–0.98 0.002 ** TNM stage 1.70 1.41–2.05 < 0.001 *** Pan-immune-inflammation value (PIV) 1.03 1.01–1.05 0.005 ** Clinical Implementation and Validation of the nomogram The nomogram's discriminative ability underwent internal validation via area under the curve (AUC) analysis within the training cohort. This assessment revealed high predictive precision for 24- and 36-month overall survival, evidenced by AUCs of 0.848 and 0.793, respectively (Fig. 5 A). Subsequent external validation using an independent cohort produced consistent AUC metrics at the corresponding time intervals (Fig. 5 B), thereby reinforcing the model's reliability and broad applicability. Excellent agreement between predicted and observed survival probabilities was evident in calibration plots for the training (Fig. 5 C, D) and validation cohorts (Fig. 5 E, F), indicative of strong model calibration. This performance underscores the nomogram's reliability and clinical value for survival prognostication in unresectable locally advanced esophageal cancer managed with anti-PD-1 therapy. Discussion We conducted a multi-center study of anti-PD-1 therapy in patients with unresectable locally advanced esophageal cancer from two large Chinese hospitals and found that obesity, whether classified by high BMI or MetS, predicts significantly improved survival and treatment response to anti-PD-1 therapy. Consequently, we developed and validated a prognostic prediction model that integrates obesity status with other key clinical determinants. This finding aligns with the growing body of evidence describing a similar "obesity paradox" in other malignancies treated with immunotherapy 15 , 16 , 18 , 20 . However, the implications of obesity for anti-PD-1 efficacy in esophageal cancer remain unexplored, paradoxically despite its well-established role as an independent risk factor for the disease 13 , 21 , 22 . This gap in knowledge is particularly critical for patients with unresectable locally advanced disease, in whom anti-PD-1 therapy has emerged as a promising new standard-of-care. A key contribution of our work is the systematic elucidation of this phenomenon in esophageal cancer immunotherapy, which both broadens the landscape of the "obesity paradox" in oncology and provides a transformative framework for patient stratification, paving the way for more refined treatment approaches. The most immediate clinical implication of our study lies in the construction of a practical prognostic model. By incorporating obesity status alongside conventional clinical factors, this tool enables a more nuanced stratification of patients likely to derive significant survival benefit from anti-PD-1 therapy. A review of the existing literature indicated that the association between obesity and treatment outcomes could be influenced by kinds of clinical factors. A sex-specific obesity paradox has been reported in metastatic melanoma patients receiving immunotherapy, wherein the association between obesity and a lower risk of mortality or disease progression is driven predominantly by the male population 23 , 24 . Furthermore, a striking association exists between obesity and gynecologic cancers, implicating the role of female sex steroids in their pathogenesis. Notably, obesity exhibits differential risk profiles by gender for several cancers, including those of the colon, rectum, gallbladder, kidney, and pancreas 13 , 21 , 25 , 26 . Age must also be considered within the obesity paradox framework, particularly for cancers with wide age distributions like leukemia, as evidenced by a study of acute myeloid leukemia where advanced age and low BMI jointly predicted significantly increased mortality—a relationship potentially mediated by age-related physiological changes, such as increased fat stores and decreased lean body mass, which alter the association between BMI and mortality 26 , 27 . Given the close relationship between obesity and a chronic, low-grade inflammatory state 28 , we investigated PIV as a novel biomarker reflective of this dysregulated immune response. By integrating multiple immune and inflammatory signals, PIV offers a more comprehensive profile of a patient's immune status than traditional biomarkers, establishing it as a promising tool for predicting immunotherapy outcomes and personalizing treatment 29 – 31 . Therefore, these variables were included in the analysis to account for their potential influence. Consequently, our model transcends observational phenomenon to become a direct clinical tool. By incorporating readily available variables like obesity, it achieves a synergistic enhancement of prognostic precision, offering direct potential for clinical implementation. Despite the intriguing findings presented above, it is important to acknowledge several limitations of this study. First, our definition of obesity relied solely on BMI and MetS status. The practicality of BMI is offset by its inability to accurately assess body composition, as it does not differentiate lean from fat mass. Given this limitation, it is imperative to include supplementary measures that more comprehensively reflect adiposity. Parameters such as waist and hip circumference, and when feasible, body composition analysis for fat and muscle percentage, are essential for a correct definition of overweight and obesity as a comprehensive trait 32 – 35 . Second, although this study utilized data from two large Chinese hospitals, the sample size in certain subgroups became limited after stratification, which may have constrained the robustness of our sub-analyses. Furthermore, the predictive model requires external validation in large-scale, prospective, multi-center cohorts to establish its generalizability. Our ongoing research efforts are directed toward clinical application, with the aim of iteratively refining and optimizing the model through real-world deployment. Third, a deep mechanistic understanding of the obesity paradox in esophageal cancer is still lacking. Current explanations are predominantly speculative and await experimental confirmation. This uncertainty charts a clear path for our future work, aimed at probing the molecular and immunology basis of this phenomenon, with the ultimate goal of leveraging these insights to confer the metabolic benefits of obesity to non-obese individuals. We recognize that the principal value of this study lies in its hypothesis-generating nature and the development of a predictive tool. Our findings should be considered exploratory, not definitive, and they illuminate a clear path for future mechanistic investigations and large-scale clinical validation. Conclusion Our analysis reveals that elevated BMI and MetS predict enhanced survival and ICI response in unresectable locally advanced esophageal cancer, associations linked to a favorable immune milieu (elevated CPS, reduced PIV). This prompted us to develop and validate an accurate prognostic nomogram integrating gender, BMI, TNM stage, and PIV for clinical risk stratification. By elucidating this metabolism-immunity axis, our work provides novel prognostic insights and a practical tool for future validation. Declarations Contributorship Statement: We confirm that all authors meet the ICMJE criteria for authorship. The individual contributions of each author are as follows: Jia qi Tan for conceptualization, methodology, formal analysis, and data curation for the work and manuscript drafting. Zhaoyu Pan for investigation and data curation for the work and manuscript drafting. Haoyu Zhang for methodology and formal analysis for the work. Yuhan Chen, Xiaoqiao Cui, and Xueying Wang for investigation and data curation of the work. Gangcai Zhu for conceptualization, supervision and funding acquisition of the work. Acknowledgments : We gratefully acknowledge the patients who participated in this study and provided written informed consent for the use of their clinical data. Declaration of conflicting interests : The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding statement: This work was supported by National Natural Science Foundation of China (82173341), Natural Science Foundation of Hunan (2023JJ20087), Changsha Distinguished Young Scholars grant (ka2209007), and The science and technology innovation Program of Hunan Province (2023RC3082) from Gangcai Zhu. E thical approval and informed consent statements : This study was approved by the Ethics Review Committees of both Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital. All enrolled patients signed the consent, agreeing to include their clinical information in this study. Data availability statement : The authors confirm that the data supporting the findings of this study are available within the article. Generative AI Tool Declaration: We declare that no Generative AI tools were utilized during the preparation or creation of this manuscript. References Freddie Bray ML, Hyuna Sung, Jacques Ferlay, Rebecca L Siegel, Isabelle Soerjomataram, Ahmedin Jemal. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2024; 74 (3): 205-313. Eileen Morgan IS, Harriet Rumgay, Helen G Coleman, Aaron P Thrift, Jérôme Vignat, Mathieu Laversanne, Jacques Ferlay, Melina Arnold. The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020. Gastroenterology 2022; 163 (3): 649-658. Nathaniel Deboever CMJ, Kohei Yamashita, Jaffer A Ajani, Wayne L Hofstetter Advances in diagnosis and management of cancer of the esophagus. BMJ 2024; 385. Jiang W, Zhang B, Xu J et al. Current status and perspectives of esophageal cancer: a comprehensive review. Cancer Communications 2024; 45 (3): 281-331. Yang H, Wang F, Hallemeier CL et al. Oesophageal cancer. The Lancet 2024; 404 (10466): 1991-2005. Network® NCC. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®): Esophageal and Esophagogastric Junction Cancers, Version 3.2025. NCCN Guidelines®. 2025. Ma F, Li Y, Xiang C et al. Proteomic characterization of esophageal squamous cell carcinoma response to immunotherapy reveals potential therapeutic strategy and predictive biomarkers. Journal of Hematology & Oncology 2024; 17 (1). Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism 2022; 133. Ng M, Gakidou E, Lo J et al. Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. The Lancet 2025; 405 (10481): 813-838. Conner SJ, Borges HB, Guarin JR et al. Obesity Induces Temporally Regulated Alterations in the Extracellular Matrix That Drive Breast Tumor Invasion and Metastasis. Cancer Research 2024; 84 (17): 2761-2775. Argyrakopoulou G, Dalamaga M, Spyrou N et al. Gender Differences in Obesity-Related Cancers. Current Obesity Reports 2021; 10 (2): 100-115. Mandic M, Safizadeh F, Niedermaier T et al. Association of Overweight, Obesity, and Recent Weight Loss With Colorectal Cancer Risk. JAMA Network Open 2023; 6 (4). Avgerinos KI, Spyrou N, Mantzoros CS et al. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism 2019; 92: 121-135. Piening A, Ebert E, Gottlieb C et al. Obesity-related T cell dysfunction impairs immunosurveillance and increases cancer risk. Nature Communications 2024; 15 (1). Delaye M, Rousseau A, Mailly-Giacchetti L et al. Obesity, cancer, and response to immune checkpoint inhibitors: Could the gut microbiota be the mechanistic link? Pharmacology & Therapeutics 2023; 247. Petrelli F, Cortellini A, Indini A et al. Association of Obesity With Survival Outcomes in Patients With Cancer. JAMA Network Open 2021; 4 (3). Wen H, Deng G, Shi X et al. Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies. ESMO Open 2024; 9 (3). Rathmell JC. Obesity, Immunity, and Cancer. The New England journal of medicine 2022; 384 (12): 1160-1162. Assumpção JAF, Pasquarelli-do-Nascimento G, Duarte MSV et al. The ambiguous role of obesity in oncology by promoting cancer but boosting antitumor immunotherapy. Journal of Biomedical Science 2022; 29 (1). Lee JH, Kang D, Ahn JS et al. Obesity paradox in patients with non‐small cell lung cancer undergoing immune checkpoint inhibitor therapy. Journal of Cachexia, Sarcopenia and Muscle 2023; 14 (6): 2898-2907. Kyrgiou M, Kalliala I, Markozannes G et al. Adiposity and cancer at major anatomical sites: umbrella review of the literature. Bmj 2017. Rask-Andersen M, Ivansson E, Höglund J et al. Adiposity and sex-specific cancer risk. Cancer Cell 2023; 41 (6): 1186-1197.e1184. McQuade JL, Daniel CR, Hess KR et al. Association of body-mass index and outcomes in patients with metastatic melanoma treated with targeted therapy, immunotherapy, or chemotherapy: a retrospective, multicohort analysis. The Lancet Oncology 2018; 19 (3): 310-322. Naik GS, Waikar SS, Johnson AEW et al. Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition. Journal for ImmunoTherapy of Cancer 2019; 7 (1). Gong J, Liu F, Peng Y et al. Sex disparity in the association between metabolic-anthropometric phenotypes and risk of obesity-related cancer: a prospective cohort study. BMC Medicine 2024; 22 (1). Martinez-Tapia C, Diot T, Oubaya N et al. The obesity paradox for mid- and long-term mortality in older cancer patients: a prospective multicenter cohort study. The American Journal of Clinical Nutrition 2021; 113 (1): 129-141. Brunner AM, Sadrzadeh H, Feng Y et al. Association between baseline body mass index and overall survival among patients over age 60 with acute myeloid leukemia. American Journal of Hematology 2013; 88 (8): 642-646. Schleh MW, Caslin HL, Garcia JN et al. Metaflammation in obesity and its therapeutic targeting. Science Translational Medicine 2023; 15 (723). Fucà G, Guarini V, Antoniotti C et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. British Journal of Cancer 2020; 123 (3): 403-409. Kuang T, Qiu Z, Wang K et al. Pan-immune inflammation value as a prognostic biomarker for cancer patients treated with immune checkpoint inhibitors. Frontiers in Immunology 2024; 15. Huang J-s, Zhong Q-h, Zhang Y-q et al. Combined immunescore and pan-immune inflammation value associate with pathological response and survival outcomes in esophageal squamous cell carcinoma receiving neoadjuvant immunotherapy. International Journal of Surgery 2025. Simati S, Kokkinos A, Dalamaga M et al. Obesity Paradox: Fact or Fiction? Current Obesity Reports 2023; 12 (2): 75-85. Caan BJ, Cespedes Feliciano EM, Kroenke CH. The Importance of Body Composition in Explaining the Overweight Paradox in Cancer—Counterpoint. Cancer Research 2018; 78 (8): 1906-1912. Di Filippo Y, Dalle S, Mortier L et al. Relevance of body mass index as a predictor of systemic therapy outcomes in metastatic melanoma: analysis of the MelBase French cohort data☆. Annals of Oncology 2021; 32 (4): 542-551. Park Y, Peterson LL, Colditz GA. The Plausibility of Obesity Paradox in Cancer—Point. Cancer Research 2018; 78 (8): 1898-1903. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 30 Oct, 2025 Editor invited by journal 29 Oct, 2025 Submission checks completed at journal 25 Oct, 2025 First submitted to journal 25 Oct, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7875131","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542222427,"identity":"4df39307-cbe3-437a-a2d9-4a6bcba07f09","order_by":0,"name":"Jiaqi Tan","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Tan","suffix":""},{"id":542222430,"identity":"4698d501-b864-4d4d-9f93-9ad3d20cccff","order_by":1,"name":"Zhaoyu Pan","email":"","orcid":"","institution":"Hunan Children ’ s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyu","middleName":"","lastName":"Pan","suffix":""},{"id":542222432,"identity":"2dd5b969-153b-48fd-b5f9-bd9c5babe6c8","order_by":2,"name":"Haoyu Zhang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Zhang","suffix":""},{"id":542222434,"identity":"f6c457ca-b4dd-4c40-a2b7-7bc331eeca70","order_by":3,"name":"Yuhan Chen","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Chen","suffix":""},{"id":542222438,"identity":"d3933b79-5876-4545-a03a-54144f065aa7","order_by":4,"name":"Xiaoqiao Cui","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqiao","middleName":"","lastName":"Cui","suffix":""},{"id":542222440,"identity":"526e904a-b526-4e13-bff3-6713943c9fde","order_by":5,"name":"Xueying Wang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Wang","suffix":""},{"id":542222441,"identity":"817d3a72-37d4-4161-a7ab-68c19d0f081b","order_by":6,"name":"Gangcai Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZhgCgQ8QyoBILWwMDIwziNLCgKSFmYcYLXzHmZ89LmyzluOf33zss01NXWIDe/M2CYaaOzi1SB5mMzee2ZZuLHGMLXl2zrHDiQ08x8okGI49w6nF4DCDmTRv2+HEDWw8xsy5DQcSGyRyzCQYGw7j0cL+DaHFsgHoMPk3hLTwINnC2MAMtIUHvxbJwzxl0jznQH5JS2bsOXbYuI0nrdgi4RhuLXznj2+T5ikDhljz4cMMP2rqZPvZD2+88aEGtxaGA+gCbCAiAbcGLFpGwSgYBaNgFKADAKyVSq8QMZY1AAAAAElFTkSuQmCC","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":true,"prefix":"","firstName":"Gangcai","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-10-16 08:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7875131/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7875131/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95663381,"identity":"17fe96ac-aade-4915-9094-afe9d6f6c3ba","added_by":"auto","created_at":"2025-11-11 16:38:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1212674,"visible":true,"origin":"","legend":"","description":"","filename":"02Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/3447e5117bb15e9912a60753.docx"},{"id":95663331,"identity":"a2ea7245-69b6-454e-a4da-6197287c2b58","added_by":"auto","created_at":"2025-11-11 16:38:43","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8587,"visible":true,"origin":"","legend":"","description":"","filename":"470ebd3fda94405d8f0d0855ca42e9f9.json","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/fec1023a3af3a57184122dc5.json"},{"id":95663286,"identity":"3e5f419d-2b6d-4715-875a-2b4b86ba66ad","added_by":"auto","created_at":"2025-11-11 16:38:40","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94387,"visible":true,"origin":"","legend":"","description":"","filename":"470ebd3fda94405d8f0d0855ca42e9f91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/4c995351d5f61a3c99bd82cb.xml"},{"id":95663374,"identity":"449d3453-efba-440d-b7a4-49908a798ba8","added_by":"auto","created_at":"2025-11-11 16:38:47","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9244862,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/f0ee9aaad2b80658f4e89832.jpeg"},{"id":95663254,"identity":"891d5c33-f88e-4a29-9bd9-d540d55d6218","added_by":"auto","created_at":"2025-11-11 16:38:37","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2809770,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/d0b7aa87fcd110c1d6dc26dd.jpeg"},{"id":95663231,"identity":"1c68ab2b-ac9d-455c-a3b3-224b881f7bc7","added_by":"auto","created_at":"2025-11-11 16:38:34","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2455720,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/8b4744dac426042d80b7db38.jpeg"},{"id":95663217,"identity":"bc58a40e-6765-410e-a1d1-3dd9753b6887","added_by":"auto","created_at":"2025-11-11 16:38:32","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3725184,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/14f5b870ce4a535b0c2f78b8.jpeg"},{"id":95663229,"identity":"05e551be-9676-4098-a5e9-3744bb34defa","added_by":"auto","created_at":"2025-11-11 16:38:34","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7216408,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/ecdc88e99eb6e99126d01a9d.jpeg"},{"id":95663321,"identity":"7622bb9a-fd35-4d88-b17d-5a6b5a2f71aa","added_by":"auto","created_at":"2025-11-11 16:38:41","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62222,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/39dcdc12cf4fbf3baed874f9.png"},{"id":95663232,"identity":"1f261e2e-0cbb-4da6-95a4-5f4b29433189","added_by":"auto","created_at":"2025-11-11 16:38:34","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92933,"visible":true,"origin":"","legend":"","description":"","filename":"470ebd3fda94405d8f0d0855ca42e9f91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/965cf0e1bc1e33cba94e3686.xml"},{"id":95663328,"identity":"83890ba9-3322-4e6a-8788-33fae06ee132","added_by":"auto","created_at":"2025-11-11 16:38:43","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102309,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/1e86b6dc7adc5807f30951dc.html"},{"id":95663258,"identity":"dd91ff43-0920-4ea9-9304-f9a7029c9f63","added_by":"auto","created_at":"2025-11-11 16:38:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214236,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design flowchart. Patients from Xiangya Hospital were designated as training cohort to screen out independent risk factors and construct a nomogram. Patients from Harbin Medical University Cancer Hospital were designated as test cohort to validate the constructed nomogram.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/d967567905f539042d5df042.jpg"},{"id":95663378,"identity":"e26e8323-d224-4fe9-91a7-ff32b790c9d3","added_by":"auto","created_at":"2025-11-11 16:38:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60923,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of treatment outcomes based on BMI category and metabolic syndrome (MetS) status among unresectable locally advanced esophageal cancer patients receiving anti-PD-1 therapy. (A-B) Kaplan-Meier curves to compare overall survival (OS) and progression-free survival (PFS) between different BMI groups. (C) Efficacy evaluation between different BMI groups. (D-E) Kaplan-Meier curves to compare OS and PFS between different metabolic syndrome (MetS) status. (F) Efficacy evaluation between different MetS status.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/eb451f1acc3c2e91a61c4ab3.png"},{"id":95663248,"identity":"4a58ddfc-3598-475d-a0df-3a1287ef89a5","added_by":"auto","created_at":"2025-11-11 16:38:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45978,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Systemic Inflammatory and Tumor Immune Profiles by BMI and Metabolic Syndrome Status. Associations of (A, G) neutrophil, (B, H) monocyte, (C, I) platelet, (D, J) lymphocyte, (E, K) pan-immune inflammation value (PIV) and (F, L) combined positive score (CPS) between different BMI groups (A-F) and different MetS status (G-L).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/b03339bc168025896f4e1f74.png"},{"id":95663235,"identity":"2ff6bee2-c56e-44f7-8e35-4fb940fc113e","added_by":"auto","created_at":"2025-11-11 16:38:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50003,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram construction. (A) The plot shows coefficient magnitudes (y-axis) for individual variables (colored lines) across values of the tuning parameter log(λ) (x-axis). Coefficients shrink progressively to zero as log(λ) increases, enabling optimal parameter selection. (B) LASSO coefficient paths for 11 candidate features versus logλ (x-axis), with partial likelihood deviance on the y-axis. The optimal logλ, determined by minimum deviance (lowest point), selects the features comprising the final model. (C) Nomogram predicting 2- and 3-year overall survival in unresectable locally advanced esophageal cancer patients patients.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/a751498f6c22eaf2c3e9a861.png"},{"id":95663376,"identity":"a2fc5e28-89ac-4398-8ced-3a1048e1a793","added_by":"auto","created_at":"2025-11-11 16:38:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90976,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance Validation of the Prognostic Nomogram. Receiver operating characteristic (ROC) curves for the prediction of 2- and 3-year OS by the nomogram in the (A) training and (B) validation cohorts. (C-F) Calibration curves for prognostic prediction in the training cohort (C, E) and validation cohort (D, F).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/e8c7a740df3696e1097721ba.png"},{"id":95664041,"identity":"4f40fabe-0a01-4a56-bab3-09630c3144de","added_by":"auto","created_at":"2025-11-11 16:39:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1336824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7875131/v1/7eb7ff5a-adda-4ddc-82b3-285eea934785.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Predictors of Response to Immunotherapy in Unresectable Locally Advanced Esophageal Cancer: Unveiling the Obesity Paradox","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer (EC) ranks as the eleventh most prevalent malignancy globally and the seventh leading cause of cancer mortality, representing 2.6% of new cancer diagnoses and 4.6% of cancer deaths worldwide (GLOBOCAN 2022)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The two major EC subtypes show distinct geographical distributions: squamous cell carcinoma (ESCC) predominates in East Asia, South-Central Asia, and Southern Africa, whereas adenocarcinoma (EAC) is most prevalent in Northern Europe and North America\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite advancements in minimally invasive surgical and endoscopic techniques, as well as precision radiotherapy technologies, which have contributed to improved patient prognosis, the overall survival rate remains poor, with a 5-year survival of approximately 20% across all stages, largely due to late-stage diagnosis in most patients when curative treatment options are limited\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The past decade has witnessed a paradigm shift with the advent of immune checkpoint inhibitors (ICIs), which have established a new standard of care and brought hope for potential cures to patients with unresectable locally advanced EC in both first-line metastatic and adjuvant settings\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, ICIs still face considerable limitations: only a subset of patients achieves a meaningful response, while a significant proportion experience primary or secondary resistance. Consequently, a central challenge in the field remains the development of strategies to accurately predict responses to anti-PD-1 therapy and then screen patients suitable for immunotherapy, with the goal of enabling personalized treatment and improving overall survival\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAgainst the backdrop of increasing global obesity rates\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the role of obesity in cancer development has come under intense scrutiny. Extensive research has established a strong association, underscoring the carcinogenic role of excess body weight in the etiology of multiple malignancies\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Contrary to the initially postulated negative impact of obesity on therapeutic outcomes, empirical findings reveal that overweight and obese patients with several solid tumors do not experience reduced immunotherapy responses or poorer survival. In fact, these patients demonstrate a lower risk of all-cause cancer mortality and superior efficacy from immunotherapy compared to those with a low-to-normal BMI, which is called \u0026ldquo;obesity paradox\u0026rdquo;\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. However, despite this emerging trend across multiple immunotherapy-responsive malignancies, the specific role and clinical relevance of the \u0026ldquo;obesity paradox\u0026rdquo; in the context of EC remain incompletely understood.\u003c/p\u003e\u003cp\u003eWhile body mass index (BMI) serves as the operational definition for overweight and obesity in clinical and epidemiological contexts, it is an inadequate proxy that does not fully represent the pathophysiological state of obesity or its mechanistic effects on tumors\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The potential for obesity to enhance anti-tumor immunity is multifaceted. Obesity may potentiate anti-tumor immunity through multiple mechanisms. A key link is the chronic, low-grade inflammatory state characteristic of obesity, which drives a pro-inflammatory milieu and alters immune cell populations, consequently reshaping systemic and local anti-tumor immune responses\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, merely comparing efficacy differences between obese and non-obese groups based on BMI\u0026mdash;a descriptive approach\u0026mdash;is insufficient. Clinical practice necessitates an integrated tool that incorporates obesity status alongside other key prognostic factors. At the core of high-BMI obesity often lies metabolic syndrome (MetS), a condition of systemic metabolic dysfunction which, in turn, fosters a chronic pro-inflammatory state and associated alterations in immune function. Thus, this study was designed with a dual objective: firstly, to explore the implications of the \"obesity paradox\" for anti-PD-1 immunotherapy in esophageal cancer, and secondly, to build upon that foundation to develop an integrated prognostic prediction model. This model aims to equip clinicians with an intuitive and personalized tool for enhancing the accuracy of survival prognostication post-immunotherapy, thus supporting more informed treatment strategies.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThe study design and patient selection process are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Study participants were diagnosed with unresectable locally advanced esophageal cancer and underwent anti-PD-1 treatment at Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital from January 2020 to May 2023. Patients were excluded for: (1) other concomitant primary tumors; (2) immunotherapy exposure before; (3) HER2 overexpression positive and received trastuzumab treatment; (4) being in active clinical trials; (5) underweight status (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5); (6) lost to follow-up; and (7) therapy changes before disease progression or death. The study enrolled 305 eligible patients. The training cohort comprised 212 patients from Xiangya Hospital, Central South University and the validation cohort consisted of 93 patients from Harbin Medical University Cancer Hospital.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe ethics committees of Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital granted study approval. And all of the study was performed in accordance with relevant regulations and the Declaration of Helsinki. Written informed consent was obtained from all patients for data use in research, and prospective follow-up employing standardized outcome recording ensured data accuracy for analyses. We adopted the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines to uphold methodological rigor and transparency throughout this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrognosis analysis\u003c/h3\u003e\n\u003cp\u003ePrognostic associations were analyzed by stratifying patients: (1) by BMI (normal 18.5\u0026ndash;24.0 kg/m\u0026sup2;, high\u0026thinsp;\u0026gt;\u0026thinsp;24.0 kg/m\u0026sup2;) using the Chinese National Health Commission\u0026rsquo;s standard Criteria of Weight for Adults (WS/T 428\u0026ndash;2013), and (2) by MetS status (normal, MetS) based on the diagnostic criteria established by the International Diabetes Federation (IDF) consensus guidelines. Overall survival (OS) and progression-free survival (PFS) were compared across BMI/MetS subgroups via Kaplan-Meier curves. The log-rank test determined inter-group survival differences (significance threshold: two-sided *p* \u0026lt; 0.05). Using RECIST v1.1 criteria, tumor responses were classified as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Fisher\u0026rsquo;s exact test or the χ\u0026sup2; test was applied for intergroup response rate comparisons based on suitability.\u003c/p\u003e\n\u003ch3\u003eNomogram construction\u003c/h3\u003e\n\u003cp\u003ePrognostically significant variables were selected using Least absolute shrinkage and selection operator (LASSO) regression. Of the eleven candidate variables, continuous variables included age, BMI, PIV value, and CPS; categorical variables comprised gender, MetS, pathology type, tumor site, TNM stage, chemotherapy, and anti-PD-1 agent. Baseline characteristics showed no statistically significant differences across training and validation cohorts per chi-square analysis. LASSO-selected variables (non-zero coefficients) were analyzed using multivariate Cox regression to identify independent prognostic factors and estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Independent prognostic factors defined by multivariate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained for inclusion in the prognostic nomogram. Nomogram discrimination was assessed via receiver operating characteristic (ROC) curve analysis, quantified by the area under the curve (AUC). The training cohort underwent internal validation through 500 bootstrap-resampled calibration curves, while the validation cohort received independent external validation using the same calibration approach. Predictive calibration plots were generated using parametric calibration to extend model assessment.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe employed chi-square (χ\u0026sup2;) tests for categorical variable associations, switching to Fisher\u0026rsquo;s exact test for sparse contingency tables (expected frequency\u0026thinsp;\u0026lt;\u0026thinsp;5). Training and validation cohort equivalence was confirmed by non-significant baseline comparisons. Subgroup differences in continuous variables were tested via independent sample t-tests. Statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, two-tailed) was assessed using R 4.4.2 and GraphPad Prism 8.0.2.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 332 patients with unresectable locally advanced esophageal cancer who underwent anti-PD-1 therapy at Xiangya Hospital or Harbin Medical University Cancer Hospital from January 2020 to May 2023 were initially enrolled. Following application of predefined exclusion criteria, 27 patients were excluded, resulting in a final cohort of 305 eligible patients. Of these, 212 patients from Xiangya Hospital comprised the training cohort, while 93 patients from Harbin Medical University Cancer Hospital constituted the validation cohort for subsequent analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The patient characteristics in the training and validation cohort are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics. BMI, body mass index; ESCC, esophageal Squamous Cell Carcinoma; EAC, esophageal adenocarcinoma; EUC, Esophageal Undifferentiated Carcinoma; CPS, combined positive score; PIV, pan-immune inflammation value.\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\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;305)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;212)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age, years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (43\u0026ndash;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (43\u0026ndash;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (47\u0026ndash;79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89\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\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian BMI (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.4 (18.5\u0026ndash;32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.0 (18.5\u0026ndash;30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.3 (18.5\u0026ndash;32.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic syndrome\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathology 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\u003eSCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor site\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\u003eCervical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper thoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle thoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower thoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaclitaxel\u0026thinsp;+\u0026thinsp;Oxaliplatin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunotherapy\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\u003eCamrelizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNivolumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePembrolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerplulimab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSintilimab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTislelizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToripalimab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEfficacy evaluation\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\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian CPS (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.9 (0\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3 (0\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5 (0\u0026ndash;80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian PIV (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e661 (0-4385)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e707 (0-4385)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e557 (76-1367)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrognostic Value of BMI and MetS\u003c/h3\u003e\n\u003cp\u003eTo evaluate the prognostic value of BMI and MetS in patients with unresectable locally advanced esophageal cancer, we assessed survival outcomes and anti-PD-1 treatment response across different BMI/MetS subgroups. Both the high BMI group and the MetS group demonstrated superior OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D) and PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, E) compared to the normal patient groups. Comparative analysis of treatment efficacy following anti-PD-1 therapy revealed that patients in the high BMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and MetS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) groups exhibited increased rates of CR and PR, alongside decreased rates of PD and SD. These findings demonstrate that both elevated BMI and the presence of MetS are significantly associated with prognosis and may serve as predictive factors in patients with unresectable locally advanced esophageal cancer undergoing immunotherapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCurrent evidence indicates that obesity influences tumor progression primarily by driving pathological inflammation and altering tumor immunity. Therefore, we further analyzed the relationships between BMI and MetS with the PIV (pan-immune inflammation value) and CPS (combined positive score), representing systemic inflammation and immunotherapy responsiveness, respectively in patients with unresectable locally advanced esophageal cancer receiving anti-PD-1 therapy. The results demonstrated that patients in both the high BMI and MetS groups exhibited significantly lower PIV values compared to the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, K). Furthermore, the proportion of patients with a CPS score\u0026thinsp;\u0026ge;\u0026thinsp;10 was significantly higher within the high BMI and MetS groups than in the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, L). Notably, analysis of the individual components comprising the PIV formula revealed that platelet (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, I) and lymphocyte counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, J) were significantly elevated in patients with high BMI and MetS. Monocyte counts were significantly reduced in the high BMI group specifically (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, H), while neutrophil counts showed no significant difference across the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, G). Taken together, these findings suggest that unique systemic immune-inflammatory and tumor immune microenvironment profiles potentially contribute to the superior anti-PD-1 therapy responses observed in overweight patients, including those with metabolic syndrome.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eNomogram Construction\u003c/h3\u003e\n\u003cp\u003eFrom an initial set of eleven variables (gender, age, BMI, MetS, tumor site, stage, PIV, tumor pathological subtype, CPS, anti-PD-1 agents and chemotherapy regimens) subjected to LASSO regression, five were retained based on non-zero coefficients, signifying potential prognostic value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). In the subsequent multivariable Cox regression, four independent factors demonstrated statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) prognostic value: gender, BMI, TNM stage, and PIV value (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Employing these four variables, we developed a prognostic nomogram to predict survival probabilities for patients receiving anti-PD-1 therapy against unresectable locally advanced esophageal cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The model facilitates personalized clinical decision-making through quantitative risk stratification and outcome prediction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox regression analyses of the selected characteristics for overall survival in the training cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003cp\u003e(male vs. female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026ndash;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00-1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup BMI\u003c/p\u003e\u003cp\u003e(high BMI vs. normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.41\u0026ndash;2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePan-immune-inflammation value (PIV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implementation and Validation of the nomogram\u003c/h2\u003e\u003cp\u003eThe nomogram's discriminative ability underwent internal validation via area under the curve (AUC) analysis within the training cohort. This assessment revealed high predictive precision for 24- and 36-month overall survival, evidenced by AUCs of 0.848 and 0.793, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Subsequent external validation using an independent cohort produced consistent AUC metrics at the corresponding time intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), thereby reinforcing the model's reliability and broad applicability. Excellent agreement between predicted and observed survival probabilities was evident in calibration plots for the training (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D) and validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F), indicative of strong model calibration. This performance underscores the nomogram's reliability and clinical value for survival prognostication in unresectable locally advanced esophageal cancer managed with anti-PD-1 therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a multi-center study of anti-PD-1 therapy in patients with unresectable locally advanced esophageal cancer from two large Chinese hospitals and found that obesity, whether classified by high BMI or MetS, predicts significantly improved survival and treatment response to anti-PD-1 therapy. Consequently, we developed and validated a prognostic prediction model that integrates obesity status with other key clinical determinants. This finding aligns with the growing body of evidence describing a similar \"obesity paradox\" in other malignancies treated with immunotherapy\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the implications of obesity for anti-PD-1 efficacy in esophageal cancer remain unexplored, paradoxically despite its well-established role as an independent risk factor for the disease\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This gap in knowledge is particularly critical for patients with unresectable locally advanced disease, in whom anti-PD-1 therapy has emerged as a promising new standard-of-care. A key contribution of our work is the systematic elucidation of this phenomenon in esophageal cancer immunotherapy, which both broadens the landscape of the \"obesity paradox\" in oncology and provides a transformative framework for patient stratification, paving the way for more refined treatment approaches.\u003c/p\u003e\u003cp\u003eThe most immediate clinical implication of our study lies in the construction of a practical prognostic model. By incorporating obesity status alongside conventional clinical factors, this tool enables a more nuanced stratification of patients likely to derive significant survival benefit from anti-PD-1 therapy. A review of the existing literature indicated that the association between obesity and treatment outcomes could be influenced by kinds of clinical factors. A sex-specific obesity paradox has been reported in metastatic melanoma patients receiving immunotherapy, wherein the association between obesity and a lower risk of mortality or disease progression is driven predominantly by the male population\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, a striking association exists between obesity and gynecologic cancers, implicating the role of female sex steroids in their pathogenesis. Notably, obesity exhibits differential risk profiles by gender for several cancers, including those of the colon, rectum, gallbladder, kidney, and pancreas\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Age must also be considered within the obesity paradox framework, particularly for cancers with wide age distributions like leukemia, as evidenced by a study of acute myeloid leukemia where advanced age and low BMI jointly predicted significantly increased mortality\u0026mdash;a relationship potentially mediated by age-related physiological changes, such as increased fat stores and decreased lean body mass, which alter the association between BMI and mortality\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Given the close relationship between obesity and a chronic, low-grade inflammatory state\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we investigated PIV as a novel biomarker reflective of this dysregulated immune response. By integrating multiple immune and inflammatory signals, PIV offers a more comprehensive profile of a patient's immune status than traditional biomarkers, establishing it as a promising tool for predicting immunotherapy outcomes and personalizing treatment\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, these variables were included in the analysis to account for their potential influence. Consequently, our model transcends observational phenomenon to become a direct clinical tool. By incorporating readily available variables like obesity, it achieves a synergistic enhancement of prognostic precision, offering direct potential for clinical implementation.\u003c/p\u003e\u003cp\u003eDespite the intriguing findings presented above, it is important to acknowledge several limitations of this study. First, our definition of obesity relied solely on BMI and MetS status. The practicality of BMI is offset by its inability to accurately assess body composition, as it does not differentiate lean from fat mass. Given this limitation, it is imperative to include supplementary measures that more comprehensively reflect adiposity. Parameters such as waist and hip circumference, and when feasible, body composition analysis for fat and muscle percentage, are essential for a correct definition of overweight and obesity as a comprehensive trait\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Second, although this study utilized data from two large Chinese hospitals, the sample size in certain subgroups became limited after stratification, which may have constrained the robustness of our sub-analyses. Furthermore, the predictive model requires external validation in large-scale, prospective, multi-center cohorts to establish its generalizability. Our ongoing research efforts are directed toward clinical application, with the aim of iteratively refining and optimizing the model through real-world deployment. Third, a deep mechanistic understanding of the obesity paradox in esophageal cancer is still lacking. Current explanations are predominantly speculative and await experimental confirmation. This uncertainty charts a clear path for our future work, aimed at probing the molecular and immunology basis of this phenomenon, with the ultimate goal of leveraging these insights to confer the metabolic benefits of obesity to non-obese individuals. We recognize that the principal value of this study lies in its hypothesis-generating nature and the development of a predictive tool. Our findings should be considered exploratory, not definitive, and they illuminate a clear path for future mechanistic investigations and large-scale clinical validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur analysis reveals that elevated BMI and MetS predict enhanced survival and ICI response in unresectable locally advanced esophageal cancer, associations linked to a favorable immune milieu (elevated CPS, reduced PIV). This prompted us to develop and validate an accurate prognostic nomogram integrating gender, BMI, TNM stage, and PIV for clinical risk stratification. By elucidating this metabolism-immunity axis, our work provides novel prognostic insights and a practical tool for future validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributorship Statement:\u0026nbsp;\u003c/strong\u003eWe confirm that all authors meet the ICMJE criteria for authorship. The individual contributions of each author are as follows: Jia qi Tan for conceptualization, methodology, formal analysis, and data curation for the work and manuscript drafting. Zhaoyu Pan for investigation and data curation for the work and manuscript drafting. Haoyu Zhang for methodology and formal analysis for the work. Yuhan Chen, Xiaoqiao Cui, and Xueying Wang for investigation and data curation of the work. Gangcai Zhu for conceptualization, supervision and funding acquisition of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e We gratefully acknowledge the patients who participated in this study and provided written informed consent for the use of their clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u0026nbsp;\u003c/strong\u003eThis work was supported by National Natural Science Foundation of China (82173341), Natural Science Foundation of Hunan (2023JJ20087), Changsha Distinguished Young Scholars grant (ka2209007), and The science and technology innovation Program of Hunan Province\u0026nbsp;(2023RC3082) from Gangcai Zhu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003ethical approval and informed consent statements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e This study was approved by the Ethics Review Committees of both Xiangya Hospital, Central South University, and Harbin Medical University Cancer Hospital. All enrolled patients signed the consent, agreeing to include their clinical information in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI Tool Declaration:\u003c/strong\u003e We declare that no Generative AI tools were utilized during the preparation or creation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFreddie Bray ML, Hyuna Sung, Jacques Ferlay, Rebecca L Siegel, Isabelle Soerjomataram, Ahmedin Jemal. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2024; 74 (3): 205-313.\u003c/li\u003e\n\u003cli\u003eEileen Morgan IS, Harriet Rumgay, Helen G Coleman, Aaron P Thrift, J\u0026eacute;r\u0026ocirc;me Vignat, Mathieu Laversanne, Jacques Ferlay, Melina Arnold. The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020. Gastroenterology 2022; 163 (3): 649-658.\u003c/li\u003e\n\u003cli\u003eNathaniel Deboever CMJ, Kohei Yamashita, Jaffer A Ajani, Wayne L Hofstetter Advances in diagnosis and management of cancer of the esophagus. BMJ 2024; 385.\u003c/li\u003e\n\u003cli\u003eJiang W, Zhang B, Xu J et al. Current status and perspectives of esophageal cancer: a comprehensive review. Cancer Communications 2024; 45 (3): 281-331.\u003c/li\u003e\n\u003cli\u003eYang H, Wang F, Hallemeier CL et al. Oesophageal cancer. The Lancet 2024; 404 (10466): 1991-2005.\u003c/li\u003e\n\u003cli\u003eNetwork\u0026reg; NCC. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;): Esophageal and Esophagogastric Junction Cancers, Version 3.2025. \u003cem\u003eNCCN Guidelines\u0026reg;.\u003c/em\u003e 2025.\u003c/li\u003e\n\u003cli\u003eMa F, Li Y, Xiang C et al. Proteomic characterization of esophageal squamous cell carcinoma response to immunotherapy reveals potential therapeutic strategy and predictive biomarkers. Journal of Hematology \u0026amp; Oncology 2024; 17 (1).\u003c/li\u003e\n\u003cli\u003eBoutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism 2022; 133.\u003c/li\u003e\n\u003cli\u003eNg M, Gakidou E, Lo J et al. Global, regional, and national prevalence of adult overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. The Lancet 2025; 405 (10481): 813-838.\u003c/li\u003e\n\u003cli\u003eConner SJ, Borges HB, Guarin JR et al. Obesity Induces Temporally Regulated Alterations in the Extracellular Matrix That Drive Breast Tumor Invasion and Metastasis. Cancer Research 2024; 84 (17): 2761-2775.\u003c/li\u003e\n\u003cli\u003eArgyrakopoulou G, Dalamaga M, Spyrou N et al. Gender Differences in Obesity-Related Cancers. Current Obesity Reports 2021; 10 (2): 100-115.\u003c/li\u003e\n\u003cli\u003eMandic M, Safizadeh F, Niedermaier T et al. Association of Overweight, Obesity, and Recent Weight Loss With Colorectal Cancer Risk. JAMA Network Open 2023; 6 (4).\u003c/li\u003e\n\u003cli\u003eAvgerinos KI, Spyrou N, Mantzoros CS et al. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism 2019; 92: 121-135.\u003c/li\u003e\n\u003cli\u003ePiening A, Ebert E, Gottlieb C et al. Obesity-related T cell dysfunction impairs immunosurveillance and increases cancer risk. Nature Communications 2024; 15 (1).\u003c/li\u003e\n\u003cli\u003eDelaye M, Rousseau A, Mailly-Giacchetti L et al. Obesity, cancer, and response to immune checkpoint inhibitors: Could the gut microbiota be the mechanistic link? Pharmacology \u0026amp; Therapeutics 2023; 247.\u003c/li\u003e\n\u003cli\u003ePetrelli F, Cortellini A, Indini A et al. Association of Obesity With Survival Outcomes in Patients With Cancer. JAMA Network Open 2021; 4 (3).\u003c/li\u003e\n\u003cli\u003eWen H, Deng G, Shi X et al. Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies. ESMO Open 2024; 9 (3).\u003c/li\u003e\n\u003cli\u003eRathmell JC. Obesity, Immunity, and Cancer. The New England journal of medicine 2022; 384 (12): 1160-1162.\u003c/li\u003e\n\u003cli\u003eAssump\u0026ccedil;\u0026atilde;o JAF, Pasquarelli-do-Nascimento G, Duarte MSV et al. The ambiguous role of obesity in oncology by promoting cancer but boosting antitumor immunotherapy. Journal of Biomedical Science 2022; 29 (1).\u003c/li\u003e\n\u003cli\u003eLee JH, Kang D, Ahn JS et al. Obesity paradox in patients with non‐small cell lung cancer undergoing immune checkpoint inhibitor therapy. Journal of Cachexia, Sarcopenia and Muscle 2023; 14 (6): 2898-2907.\u003c/li\u003e\n\u003cli\u003eKyrgiou M, Kalliala I, Markozannes G et al. Adiposity and cancer at major anatomical sites: umbrella review of the literature. Bmj 2017.\u003c/li\u003e\n\u003cli\u003eRask-Andersen M, Ivansson E, H\u0026ouml;glund J et al. Adiposity and sex-specific cancer risk. Cancer Cell 2023; 41 (6): 1186-1197.e1184.\u003c/li\u003e\n\u003cli\u003eMcQuade JL, Daniel CR, Hess KR et al. Association of body-mass index and outcomes in patients with metastatic melanoma treated with targeted therapy, immunotherapy, or chemotherapy: a retrospective, multicohort analysis. The Lancet Oncology 2018; 19 (3): 310-322.\u003c/li\u003e\n\u003cli\u003eNaik GS, Waikar SS, Johnson AEW et al. Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition. Journal for ImmunoTherapy of Cancer 2019; 7 (1).\u003c/li\u003e\n\u003cli\u003eGong J, Liu F, Peng Y et al. Sex disparity in the association between metabolic-anthropometric phenotypes and risk of obesity-related cancer: a prospective cohort study. BMC Medicine 2024; 22 (1).\u003c/li\u003e\n\u003cli\u003eMartinez-Tapia C, Diot T, Oubaya N et al. The obesity paradox for mid- and long-term mortality in older cancer patients: a prospective multicenter cohort study. The American Journal of Clinical Nutrition 2021; 113 (1): 129-141.\u003c/li\u003e\n\u003cli\u003eBrunner AM, Sadrzadeh H, Feng Y et al. Association between baseline body mass index and overall survival among patients over age 60 with acute myeloid leukemia. American Journal of Hematology 2013; 88 (8): 642-646.\u003c/li\u003e\n\u003cli\u003eSchleh MW, Caslin HL, Garcia JN et al. Metaflammation in obesity and its therapeutic targeting. Science Translational Medicine 2023; 15 (723).\u003c/li\u003e\n\u003cli\u003eFuc\u0026agrave; G, Guarini V, Antoniotti C et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. British Journal of Cancer 2020; 123 (3): 403-409.\u003c/li\u003e\n\u003cli\u003eKuang T, Qiu Z, Wang K et al. Pan-immune inflammation value as a prognostic biomarker for cancer patients treated with immune checkpoint inhibitors. Frontiers in Immunology 2024; 15.\u003c/li\u003e\n\u003cli\u003eHuang J-s, Zhong Q-h, Zhang Y-q et al. Combined immunescore and pan-immune inflammation value associate with pathological response and survival outcomes in esophageal squamous cell carcinoma receiving neoadjuvant immunotherapy. International Journal of Surgery 2025.\u003c/li\u003e\n\u003cli\u003eSimati S, Kokkinos A, Dalamaga M et al. Obesity Paradox: Fact or Fiction? Current Obesity Reports 2023; 12 (2): 75-85.\u003c/li\u003e\n\u003cli\u003eCaan BJ, Cespedes Feliciano EM, Kroenke CH. The Importance of Body Composition in Explaining the Overweight Paradox in Cancer\u0026mdash;Counterpoint. Cancer Research 2018; 78 (8): 1906-1912.\u003c/li\u003e\n\u003cli\u003eDi Filippo Y, Dalle S, Mortier L et al. Relevance of body mass index as a predictor of systemic therapy outcomes in metastatic melanoma: analysis of the MelBase French cohort data☆. Annals of Oncology 2021; 32 (4): 542-551.\u003c/li\u003e\n\u003cli\u003ePark Y, Peterson LL, Colditz GA. The Plausibility of Obesity Paradox in Cancer\u0026mdash;Point. Cancer Research 2018; 78 (8): 1898-1903.\u003c/li\u003e\n\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prognosis, immunotherapy, unresectable locally advanced esophageal cancer, obesity, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7875131/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7875131/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e\u003cp\u003ePD-1 inhibitor-based immunotherapy is increasingly used for unresectable locally advanced esophageal cancer, yet the impact of metabolic factors\u0026mdash;and the emerging \"obesity paradox\"\u0026mdash;on treatment efficacy remains largely unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003ePatients were stratified by body mass index (BMI) and metabolic syndrome (MetS) to assess survival (OS/PFS) and treatment response (CR/PR/SD/PD); a prognostic nomogram was subsequently developed via LASSO and multivariable Cox regression and validated internally and externally using the respective institutional cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 305 eligible patients with unresectable locally advanced esophageal cancer from two medical centers were included. Patients with high BMI or MetS demonstrated significantly superior overall survival, progression-free survival, and objective response rates to anti-PD-1 therapy. These groups exhibited favorable immune profiles, characterized by a lower pan-immune inflammation value (PIV) and a higher combined positive score (CPS). A prognostic nomogram was successfully constructed and validated, incorporating four independent predictors: gender, BMI, TNM stage, and PIV. The model demonstrated high predictive accuracy for 24- and 36-month overall survival in both internal and external validation cohorts, with well-fitted calibration curves.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated BMI and MetS correlate with improved outcomes following immunotherapy in unresectable locally advanced esophageal cancer patients. Furthermore, our validated nomogram enables refined prognostic assessment through metabolic risk stratification.\u003c/p\u003e","manuscriptTitle":"Metabolic Predictors of Response to Immunotherapy in Unresectable Locally Advanced Esophageal Cancer: Unveiling the Obesity Paradox","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:30:45","doi":"10.21203/rs.3.rs-7875131/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T10:46:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T14:59:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T18:35:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96545195268935277391835167979225801823","date":"2025-11-30T23:53:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105120445616630405476786465747052395386","date":"2025-11-26T18:42:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T16:02:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T07:30:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T04:59:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-25T12:49:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-25T12:46:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a8fc0f7-3100-4082-8019-6fa5e227f389","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57681098,"name":"Biological sciences/Cancer"},{"id":57681099,"name":"Health sciences/Gastroenterology"},{"id":57681100,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-13T06:26:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:30:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7875131","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7875131","identity":"rs-7875131","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}