Body composition change indices combined with Prognostic Nutritional Index predicts the clinical outcomes of patients with gastric cancer treated with immune checkpoint inhibitor

Abstract Objective This study aimed to investigate the prognostic significance of the Prognostic Nutritional Index (PNI) in conjunction with body composition change indices, namely subcutaneous fat area (SFA) and skeletal muscle index (SMI), with regard to clinical outcomes in patients with gastric cancer (GC) undergoing immune checkpoint inhibitors (ICIs) treatment. Methods This retrospective investigation encompassed patients with comprehensive clinical and pathological data, inclusive of portal phase enhanced CT images. Continuous variables underwent analysis utilizing the Student t‐test or Mann–Whitney U‐test, while categorical variables were assessed employing the Pearson chi‐squared test or Fisher test. Survival outcomes were evaluated using Kaplan–Meier survival curves and the Log‐rank test. Independent prognostic indicators were determined through Cox regression analysis, and a nomogram predicting survival probability for progression‐free survival (PFS) and overall survival (OS) was constructed. Results Within the PNI‐SFA groups, patients in Group 1 exhibited inferior PFS and OS compared to the other two groups. Similarly, among the PNI‐SMI groups, Group 1 patients demonstrated poorer PFS and OS. PNI‐SMI and Eosi were identified as independent prognostic factors through Cox regression analysis. Furthermore, positive associations with patient prognosis were observed for BMI, SAF, SMI, and PNI. Conclusion The comprehensive consideration of PNI‐SFA and PNI‐SMI proved to be a superior prognostic predictor for GC patients undergoing ICI treatment.


| INTRODUCTION
Gastric cancer (GC), ranking as the fifth most frequently diagnosed malignancy globally and the third leading cause of cancer-related mortality, 1 exhibits a notable gender disparity, being twice as prevalent in men compared to women. 2 The incidence of GC demonstrates an association with countries characterized by a high Human Development Index (HDI), with Helicobacter pylori infection prevalence further influencing its occurrence.East Asia, marked by elevated HDI and the prevalence of virulent H. pylori strains, bears a substantial burden of GC. 3 Immunosuppressant therapy has emerged as the primary treatment for GC, particularly benefiting patients with advanced stages. 46][7] Alongside disease status, nutritional considerations significantly impact the prognosis and treatment outcomes of GC patients.This study centers on evaluating the predictive capacity of body composition and blood indicators reflecting nutritional status in GC patients treated with ICIs.
Factors influencing patient nutritional status include decreased appetite and malaise. 8,9Cancer-associated cachexia, characterized by weight loss and specific skeletal muscle and adipose tissue depletion, is prevalent in GC patients. 10Malnutrition not only accelerates cancer progression but also hampers treatment efficacy. 11,12While body mass index (BMI) serves as an objective nutritional assessment, aiding in diagnosing malignant stasis alongside weight loss rates, it does not discern the distribution of visceral and subcutaneous fat. 13,14BMI is commonly employed to predict cancer risk; 15 however, its prognostic utility for cancer outcomes exhibits disparities, as illustrated in a systematic evaluation and meta-analysis demonstrating an association between high BMI and increased colorectal cancer-specific and overall mortality. 16his suggests obesity's significant correlation with heightened recurrence and progression risk, serving as a crucial indicator of cancer prognosis.The PNI, reflecting nutritional inflammation in the body, has been established as an indicator of the nutritional status of GC patients in multiple studies. 17,18everal studies have utilized CT images to calculate the fat and muscle areas of the third lumbar vertebra, examining the impact of abdominal fat and muscle loss on cancer prognosis.For instance, in GC, muscle loss is linked to a poorer prognosis, 19 while differences in abdominal fat distribution correlate with the response to intravesical Bacillus Calmette-Guerin immunotherapy. 20his study aims to analyze the prognostic value of PNI, BMI, and the third lumbar vertebral fat and muscle areas in patients with GC treated with ICIs, encompassing a hematological to body composition synthesis perspective.

| Patients
A total of 124 GC (GC) patients who underwent immunotherapy with immune checkpoint inhibitors (ICIs) at our institution between February 2016 and October 2022 were included in this study.Patient data, encompassing clinical and tumor characteristics, laboratory parameters, and portal phase CT images, were retrieved from medical records.Approval from the Institutional Review Board was obtained for this retrospective study, and due to its retrospective nature, the requirement for informed consent was waived.The ICIs immunotherapies comprised antibodies targeting programmed cell death protein 1 (PD-1), namely anti-PD-1 antibodies, anti-programmed death ligand 1 (PD-L1) antibodies, and combination regimens involving ICIs.Exclusion criteria comprised (1) absence of pre-treatment CT scans, (2) premature discontinuation of treatment, and (3) incomplete clinical data.

| Data collection
The study endpoints were PFS and OS, determined through telephone follow-up, with the last follow-up conducted in December 2022.OS and PFS were calculated from the initiation of treatment to the last follow-up, with disease progression identified primarily through enhanced CT scans.In the absence of evidence of disease progression, the last follow-up time was considered as PFS.

| Fat area, skeletal muscle area, and PNI
A radiologist with 15 years of imaging experience, blinded to clinical outcomes, analyzed CT images of all patients.The Prognostic Nutritional Index (PNI) was calculated as albumin level (g/L) + 5 × lymphocyte count (10^9/L).Cross-sectional images at the third lumbar vertebra level were extracted from pre-treatment CT images.Visceral fat area (VFA), subcutaneous fat area (SFA), and skeletal muscle area (SMA) were measured using a 3D slicer.Adipose tissue and skeletal muscle tissue were differentiated based on preset hounsfield unit (HU) thresholds.Total fat area (TFA) was computed as the sum of VFA and SFA, and the skeletal muscle index (SMI) was determined as SMA/height^2  13 and SFA below the optimal cutoff, Group 3 having PNI ≥48.13 and SFA greater than or equal to the optimal cutoff, and all other cases falling into Group 2. Gastrointestinal surgeons trained in 3D slicer verification ensured data accuracy by averaging the results.

| Statistical analysis
Normally distributed data were presented as mean ± standard deviation (SD), and non-normally distributed data as median (minimum and maximum values).Continuous variables underwent analysis using Student t-test or Mann-Whitney U-test, while categorical variables were assessed using Pearson chi-squared test or Fisher test.Kaplan-Meier survival curves and logrank test evaluated survival outcomes between different group, and multivariate Cox's regression analysis incorporated meaningful metrics from univariate analysis to identify independent prognostic factors associated with OS and PFS.These factors were used to establish predictive models for OS and PFS.R 4.1.3(Vienna, Austria) and SPSS 25.0 (Chicago, IL, USA) were employed for data analysis, with a two-sided p-value <0.05 considered statistically significant.

| Univariate and multivariate Cox's regression analysis
Univariate and multivariate Cox's proportional risk models were employed to analyze independent prognostic factors.All fat and muscle indicators were included in the univariate analysis, revealing that TP, PALB,  4).Although SFA and PNI did not attain statistical significance in predicting PFS, their median survival exhibited a notable difference.6).Notably, PNI-SMI seemed to exhibit an inconsistent performance in predicting PFS compared to PNI-SFA.The prognostic significance of PNI-SMI in patients treated with ICIs was confirmed as an independent factor.To assess its sensitivity, we conducted a subgroup analysis focusing on stage IV patients.Our findings demonstrated that PNI-SMI reliably predicted overall survival (OS) and progression-free survival (PFS) among patients diagnosed with stage IV GC, aligning with previous observations (Figure 7).

| Nomograms
Predictive models for PFS and OS were developed based on multivariate results.Independent prognosticators PNI-SMI and Eosi were incorporated into nomograms for both PFS and OS (Figure 8).

| DISCUSSION
In recent years, immunotherapy has emerged as a promising therapeutic avenue for various cancers, with monoclonal antibodies targeting immune checkpoints, such as PD-1 and CTLA-4, demonstrating clinical efficacy across diverse tumor types.This includes melanoma, non-small-cell lung cancer, renal cell carcinoma, bladder cancer, colorectal cancer, and others, fundamentally transforming the landscape of medical oncology. 21However, despite the favorable outcomes observed in some patients, a substantial proportion does not exhibit a sustained response to ICIs, leading to disease progression. 22This heterogeneity of response is often attributed to individualized immune resistance, which may be host-related. 23Host-related parameters, including BMI, play a significant role in influencing the outcomes of ICIs treatment. 24][27][28][29][30] The intricate relationship between obesity and cancer has been well-documented, with adipose tissue acting as a source of adipokines and inflammation-related factors, contributing to disrupted energy homeostasis and disease progression. 31,32Distinct abdominal fat distribution has been linked to the prognosis of various malignancies. 32,33Studies have demonstrated the association between visceral adipose tissue accumulation and the development and progression of early-stage colon cancer. 34Muscle mass, another indicator of nutritional status, has also been correlated with survival outcomes in cancer patients. 358][39][40] However, the limitations of individual indicators, such as the influence of multiple factors on albumin and lymphocytes in PNI, necessitate T A B L E 3 Univariate and multivariate analysis for PFS and OS.   a comprehensive assessment incorporating potential body composition prognostic indicators (SFA, SMI) to accurately determine the prognosis of GC patients undergoing ICIs treatment. 41,42MI has been extensively investigated within the GC research domain.For instance, studies such as that conducted by Feng-Min Zhang et al. have developed and validated predictive models, specifically column charts, aimed at assessing muscle mass and radiodensity in GC patients at various stages. 43Additionally, Qian-Tong Dong et al. conducted a comprehensive analysis exploring the relationship between body composition parameters, muscle strength, physical functioning, and their impact on postoperative complications and survival rates following radical gastrectomy for GC. 44Furthermore, Guang-Tan Lin et al. investigated the body parameter SMI as an independent risk factor for tumor regression in patients treated with neoadjuvant chemotherapy combined with immunotherapy. 45ur study utilized Kaplan-Meier analysis, revealing that high PNI-SFA and PNI-SMI groups correlated with prolonged PFS and OS compared to their low PNI-SFA and PNI-SMI counterparts.Notably, PNI-SMI exhibited a comparatively diminished predictive capacity for PFS compared to PNI-SFA.Positive correlations with PFS and OS were observed for BMI, SFA, and SMI, while VFA and TFA did not emerge as prognostic indicators.Multivariate analysis identified PNI-SMI and Eosi as independent prognostic factors for both PFS and OS.The establishment of an immune prediction model facilitated prognosis prediction based on PFS and OS columnline graphs.

PFS
Despite the insights gained, the study has limitations, primarily its retrospective, single-center nature, necessitating validation in a multicenter setting.Additionally, the inclusion of patients receiving different immunotherapy regimens introduces potential efficacy variations, requiring validation in cohorts with uniform treatment regimens.This study has encountered several limitations in its statistical methodology.First, the relatively small sample size led to a limited ROC curve in determining the cutoff value of SMI.Furthermore, the Kaplan-Meier method may yield unstable estimates with small sample sizes and a low number of events.Additionally, the Cox proportional hazards model permits only the comparison of relative risks and does not directly estimate absolute risks.This study contributes novel perspectives on hematological and body composition binding indices and the prognostic impact of fat area in GC patients receiving ICIs, offering innovative approaches for refining immune prediction models through comprehensive analyses of body composition changes, nutritional status, and inflammatory markers.

| CONCLUSION
In summary, the amalgamation of the PNI with body composition indicators, specifically SMI and SAF, effectively reflects the nutritional status of individuals.Lower scores in PNI-SMI and PNI-SAF are associated with poorer clinical outcomes for patients.This underscores the potential utility of integrating body composition alterations and nutritional indicators as a meaningful biomarker for evaluating the prognosis of GC patients undergoing treatment with Immune ICIs.

| 11 of 20 DENG
et al.F I G U R E 2 SMI related survival for (A) PFS and (B) OS.F I G U R E 3 SFA related survival curve for (A) PFS and (B) OS.| 13 of 20 DENG et al.F I G U R E 4 PNI related survival curve for (A) PFS and (B) OS.DENG et al.F I G U R E 5 PNI-SFA related survival curve for (A) PFS and (B) OS.F I G U R E 7 PNI-SMI related survival curve for (A) PFS and (B) OS in IV stage patients.| 17of 20 DENG et al.F I G U R E 8 Nomogram for predicting 1-, 3-and 5-year survival probability of PFS (A) and OS (B).