Yao ZY, Ma X, Cui YZ, Liu J, Han ZX, Song J. Impact of triglyceride-glucose index on the long-term prognosis of advanced gastric cancer patients receiving immunotherapy combined with chemotherapy. World J Gastroenterol 2025; 31(5): 102249 [DOI: 10.3748/wjg.v31.i5.102249]
Corresponding Author of This Article
Jun Song, PhD, Professor, Department of Gastrointestinal Surgery, The Affiliated Hospital of Xuzhou Medical College, No. 99 Huaihai West Road, Quanshan District, Xuzhou 221000, Jiangsu Province, China. 1447795006@qq.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Zhi-Yuan Yao, Xiao Ma, Zheng-Xiang Han, Department of Oncology, The Affiliated Hospital of Xuzhou Medical College, Xuzhou 221000, Jiangsu Province, China
Xiao Ma, Department of Oncology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210008, Jiangsu Province, China
Yong-Zheng Cui, Jie Liu, Department of Radiotherapy, The Affiliated Hospital of Xuzhou Medical College, Xuzhou 221000, Jiangsu Province, China
Jun Song, Department of Gastrointestinal Surgery, The Affiliated Hospital of Xuzhou Medical College, Xuzhou 221000, Jiangsu Province, China
Co-corresponding authors: Zheng-Xiang Han and Jun Song.
Author contributions: Yao ZY, Ma X, Han ZX, and Song J contributed to the conceptualization, writing-review and editing of this manuscript; Yao ZY and Song J were responsible for the methodology of this study; Yao ZY contributed to the formal analysis of this manuscript and the visualization of this article; Yao ZY, Ma X, and Cui YZ took part in the writing-original draft and investigation of this manuscript; Yao ZY, Han ZX, and Song J contributed to the project administration and the supervision of this manuscript; Ma X, Cui YZ, and Liu J took part in the data curation of this study; Yao ZY and Ma X were responsible for the validation of this manuscript; Liu J took part in the resources; Han ZX and Song J were involved in the supervision of this study; Yao ZY and Ma X contributed equally to the manuscript, they are co-first authors of this manuscript. Han ZX and Song J contributed to this manuscript equally, they are co-corresponding authors of this study.
Institutional review board statement: This research was carried out following the Declaration of Helsinki and received approval from the Ethics Committee at the Affiliated Hospital of Xuzhou Medical University (approval No. XYFY2023-KL277-01).
Informed consent statement: Given the retrospective design of this investigation, the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University granted us an exemption from obtaining written informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data included in this study can be obtained from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jun Song, PhD, Professor, Department of Gastrointestinal Surgery, The Affiliated Hospital of Xuzhou Medical College, No. 99 Huaihai West Road, Quanshan District, Xuzhou 221000, Jiangsu Province, China. 1447795006@qq.com
Received: October 12, 2024 Revised: November 9, 2024 Accepted: December 11, 2024 Published online: February 7, 2025 Processing time: 78 Days and 19.4 Hours
Abstract
BACKGROUND
Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of death worldwide. Despite advancements in immunotherapies, patient prognosis remains poor, necessitating the identification of key prognostic factors to optimize the treatment approaches. Insulin resistance, as indicated by the triglyceride glucose (TyG) index, is increasingly recognized for its impact on cancer progression and immune modulation, and its potential role in GC prognosis is of particular interest.
AIM
To investigate whether the TyG index, a surrogate marker of insulin resistance, can predict the prognosis of patients with advanced GC receiving immunotherapy combined with chemotherapy.
METHODS
This retrospective study included 300 patients with advanced GC who received sintilimab combined with chemotherapy. The patients were categorized into two groups according to high or low TyG index, and independent prognostic factors for overall survival (OS) were determined using Cox proportional hazards regression analysis, which led to the development of a nomogram model.
RESULTS
Of the included patients, 136 had a high TyG index and 164 had a low TyG index. The median progression-free survival of the high TyG index group was significantly longer than that of the low TyG index group. Similarly, the median OS of the high TyG index group was significantly longer than that of the low TyG index group. The objective response and disease control rates in the two groups were 18.38% vs 9.15% and 58.82% vs 46.95%, respectively. No significant difference was noted in the incidence of adverse reactions at any level between the two groups (P > 0.05). In multivariate analysis, the Eastern Cooperative Oncology Group score, programmed cell death ligand 1 expression, and TyG index acted as independent prognostic factors for OS. Of these factors, the hazard ratio of the TyG index was 0.36 (95% confidence interval: 0.36-0.55, P < 0.001), and the nomogram model re-emphasized its importance as the main predictor of patient prognosis, followed by programmed cell death ligand 1 expression and the Eastern Cooperative Oncology Group score.
CONCLUSION
The TyG index is a long-term predictor of the efficacy of immunotherapy combined with chemotherapy, and patients with a high index have a better prognosis.
Core Tip: Gastric cancer (GC) is the fifth most common malignancy. Despite advancements in immunotherapies, patient prognosis remains poor, necessitating the identification of key prognostic factors to optimize treatment approaches. Triglyceride glucose (TyG) index is increasingly recognized for its impact on cancer progression and immune modulation, and its potential role in GC prognosis is of particular interest. However, only a few studies have explored the effect of the TyG index on the prognosis of patients with advanced GC. Therefore, we initiated a retrospective clinical study to explore the impact of the TyG index on the prognosis of patients with advanced GC receiving immunosuppressive agents when combined with chemotherapy.
Citation: Yao ZY, Ma X, Cui YZ, Liu J, Han ZX, Song J. Impact of triglyceride-glucose index on the long-term prognosis of advanced gastric cancer patients receiving immunotherapy combined with chemotherapy. World J Gastroenterol 2025; 31(5): 102249
Although the incidence of gastric cancer (GC) has recently declined, it continues to be one of the most prevalent malignancies, and most patients are diagnosed with progressive or advanced disease[1]. GC is the fifth most common malignancy and the third leading cause of cancer-related deaths globally, with a particularly high prevalence in Southeast Asia, mainly China[2]. The common causes of GC include Helicobacter pylori infection, family genetic history, alcoholism, and obesity[3]. Among these, insulin resistance has gained attention as a significant factor influencing the progression and prognosis of GC. The triglyceride glucose (TyG) index, a simple and reliable surrogate marker of insulin resistance, has been shown to correlate with various cancer outcomes, including GC. Elevated TyG index levels have been associated with poor prognosis[4,5], tumor progression, and decreased survival rates in several malignancies, likely due to insulin resistance’s role in promoting inflammation, increasing oxidative stress, and altering metabolic pathways that favor tumor growth and metastasis[6]. There are several types of GC, and the World Health Organization classification comprises the papillary, tubular, mucinous, and signet ring cell subtypes[7]. Owing to the low early diagnosis rate, rapid progression, and poor prognosis, the survival time of patients with advanced GC is short, approximately 1 year[8]. Chemotherapy, targeted therapy, and immunotherapy are the conventional treatment methods in this direction[9]. With our deepening knowledge of the tumor microenvironment, related immunotherapy drugs such as sintilimab and camrelizumab have come to play a prominent role in treating patients with advanced GC[10,11]. In China, sintilimab combined with S-1 + oxaliplatin and capecitabine + oxaliplatin regimens and other chemotherapeutic regimens have become the key options for patients with advanced GC[12].
Sintilimab is a recombinant humanized immunoglobulin G4 monoclonal antibody that targets programmed cell death protein 1 (PD-1). By binding to PD-1 and preventing its interaction with programmed cell death ligand 1 (PD-L1) and PD-L2, sintilimab mitigates the immunosuppressive effects of PD-1, activates T-cell functions, boosts T-cell-mediated immune surveillance and tumor cell killing, and triggers immune responses against tumors[13,14]. Sintilimab is a PD-1 antibody produced in China, and several investigations have reported that it exhibits good antitumor effects on various cancer types, such as gastric, esophageal, liver, and lung cancers[15]. The CheckMate-649 trial demonstrated that PD-1 inhibitors combined with chemotherapy could improve the overall survival (OS) and progression-free survival (PFS) in patients with advanced GC, gastroesophageal junction, or esophageal adenocarcinoma, especially in those with PD-L1 combined positive score ≥ 5[16]. The TyG index acts as a noninvasive surrogate for insulin resistance and combines fasting plasma glucose and triglyceride levels[17]. In addition, the TyG index serves as a predictor for the development of various diseases, including type 2 diabetes, cardiovascular disease, and colorectal cancer[18,19]. Several studies have reported that insulin resistance is strongly associated with GC prognosis[20,21]. Insulin resistance fosters a pro-inflammatory microenvironment and alters immune responses, which can facilitate tumor growth and metastasis. It then impairs the function of immune cells, such as T cells, which are crucial for anti-tumor immunity, making the tumor more resistant to immunotherapy. Elevated insulin levels also promote the growth and survival of cancer cells by activating oncogenic pathways, including the phosphatidylinositol 3-kinase/protein kinase B pathway. A clinical study by Martini et al[22] on prostate cancer indicated that the survival benefit of the TyG index could be partially attributed to the downregulation of specific oncogenes and/or the upregulation of PD-1 expression. These effects are influenced by the immunosuppressive impact of obesity, which ultimately increases susceptibility to PD-1 inhibitors[22]. Okadome et al[23] suggested that TyG may influence the prognosis of esophageal cancer and that the systemic nutritional and immune status of patients may affect their prognosis via local tumor immunity. However, a few studies have explored the effect of the TyG index on the long-term prognosis of patients with advanced GC receiving immunosuppressive agents combined with chemotherapy. This gap in the knowledge is particularly important since insulin resistance has been shown to influence both cancer progression and immune response, which is crucial for the efficacy of immunotherapy. Therefore, this study retrospectively investigated whether the TyG index is an independent prognostic factor for these patients, specifically to address this gap in GC research.
MATERIALS AND METHODS
Study design and patients
The clinical data of patients with advanced GC admitted to the Affiliated Hospital of Xuzhou Medical University during January 2021 to 2024 were retrospectively analyzed. Patients who met the following criteria were included in the study: (1) Advanced GC confirmed via pathology and imaging; (2) Received sintilimab combined with chemotherapy, and biochemical blood indicators were measured before initiating sintilimab treatment; (3) Received at least two cycles of sintilimab treatment; (4) Exhibited at least one measurable tumor lesion; and (5) Age 18-75 years, Eastern Cooperative Oncology Group (ECOG) score ≤ 1, and expected survival time ≥ 2 months. Patients with the following characteristics were excluded from the study: (1) Prior antineoplastic therapy; (2) Severe infection or systemic inflammation; (3) History of autoimmune diseases; (4) Long-term use of steroids or immunosuppressants; (5) Serious organic diseases of the heart, liver, or kidney; (6) Complicated with other malignant tumors; and (7) Inaccurate or incomplete records of clinical data. Moreover, patients who were receiving lipid-altering medications (such as statins and fibrates) during the study period were excluded from the analysis to minimize any potential confounding effects on the TyG index calculation. Finally, 300 patients were included in the analysis. The study process is depicted in Figure 1. This retrospective study was approved by the Ethics Review Committee of the Affiliated Hospital of Xuzhou Medical University (approval No. XYFY2023-KL277-01) and adhered to the principles outlined in the Declaration of Helsinki. The requirement for written informed consent was waived because of the retrospective nature of the study.
Figure 1 Flow chart depicting the screening of gastric cancer patients who received sintilimab combined with chemotherapy.
PD-1: Programmed cell death protein 1; PD-L1: Programmed cell death ligand 1; TyG: Triglyceride glucose.
Routine clinical information retrieved from electronic medical records included baseline characteristics [e.g., age, sex, body mass index (BMI), and smoking and drinking history], blood parameters (e.g., fasting plasma glucose, triglyceride, alpha-fetoprotein, and carcinoembryonic antigen), and tumor characteristics (e.g., tumor location, pathological type, and the presence of peritoneal and liver metastases). All quality assessments and the risk of bias assessments were performed independently by two investigators blinded to other data.
Definition
The TyG index was calculated using the following formula: TyG index = ln [(fasting triglycerides) × fasting glucose/2] (mg/dL). Fasting glucose and triglyceride levels were measured after an overnight fast using enzymatic assays performed on an automated biochemical analyzer (Olympus, AU2700), following standard laboratory protocols to ensure accuracy and consistency across patients. The receiver operating characteristic curve was constructed using the TyG index of patients before sintilimab was combined with chemotherapy, and the diagnostic value was evaluated based on the area under the curve (AUC). The best cutoff value of the baseline TyG index was determined by maximizing the product of the specificity and sensitivity. Therefore, all patients were categorized into the high TyG index group and the low TyG index group according to the cutoff point.
Grouping and treatment protocol
The patients were classified into the high TyG (≥ 1.79) group and the low TyG (< 1.79) group according to the TyG index. Sintilimab was administered via intravenous infusion at a dose of 200 mg every 3 weeks. The chemotherapy regimen included the oxaliplatin + S-1 regimen, oxaliplatin + capecitabine regimen, docetaxel, and albumin paclitaxel. Chemotherapy was administered via intravenous infusion every 3 weeks, with doses adjusted based on patient-specific factors, including body surface area, renal and liver function, age, and overall ECOG performance status. Dose modifications were made in line with the clinical guidelines to maximize efficacy while minimizing toxicity, with standard dose reductions for patients showing compromised organ function or lower ECOG scores.
Evaluation
Tumor response was assessed using both Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and modified RECIST criteria to determine the baseline value and short-term efficacy. Baseline computed tomography (CT) images were taken before the first treatment, with the initial response evaluation conducted after the first two cycles of treatment. Subsequent abdominal CT evaluations were performed every 2 months to monitor disease progression. OS and PFS were determined. The modified RECIST criteria were used to assess the short-term efficacy. To ensure consistency, two independent radiologists with over 5 years of experience in oncology imaging evaluated all imaging data. In cases of disagreement, a third radiologist reviewed the images to reach a consensus, thereby minimizing inter-observer variability. All adverse events (AEs) were recorded and analyzed using the National Cancer Institute Common Terminology Criteria for Adverse Events (version 5.0).
Statistical analysis
Data was analyzed using R software (version 4.4.0). For the distribution of continuous data, the Kolmogorov-Smirnov test was applied to verify normality before hypothesis testing. Continuous variables were expressed as the mean ± SD of the mean. Categorical variables were presented as counts and their corresponding percentages. Fisher’s exact test, χ2 test, and t-test were used to compare the baseline characteristics between the groups. The Kaplan-Meier method was used to calculate the median OS and PFS. For both univariate and multivariate analyses, Cox proportional hazards regression was used. The multivariate analysis focused on targeting variables with P < 0.05 in univariate analysis to identify independent prognostic factors for OS and PFS. A nomogram prediction model was developed to predict the outcomes at 12, 15, and 18 months based on the independent prognostic factors identified in the multivariate OS analysis, specifically the TyG index, PD-L1 expression, and ECOG score. Variables were selected based on their statistical significance and clinical relevance, and each variable was weighted according to its hazard ratio (HR) derived from the multivariate analysis. Internal validation of the model was conducted using a bootstrapping method with 1000 resamples to assess the predictive accuracy and reduce overfitting. The predictions generated by the model were subsequently compared with the actual patient outcomes to evaluate calibration. All statistical tests were two-sided, and P < 0.05 were considered to indicate statistical significance.
RESULTS
Patient characteristics
This retrospective study analyzed 300 patients with advanced GC treated with sintilimab combined with chemotherapy from January 2021 to December 2023 (Figure 1). Of these patients, 164 were classified into the low TyG (< 1.79) index group and 136 into the high TyG (≥ 1.79) index group. A total of 407 patients were excluded because of incomplete documentation of clinical data (155), cooccurrence of other malignancies (36), loss of follow-up for > 6 months (82), use of alternative PD-1 or PD-L1 inhibitors (93), and lack of baseline TyG data (41). Table 1 provides a comprehensive overview of patient characteristics, including clinicopathological features such as age, sex, BMI, smoking and drinking status, ECOG score, pathological type, tumor location, presence or absence of peritoneal and liver metastases before PD-1 inhibitor treatment, and Epstein-Barr virus infection status. In addition, various laboratory parameters were recorded, including standardized fasting plasma glucose, fasting triglyceride, carcinoembryonic antigen, and alpha-fetoprotein. All blood parameters were measured in a fasting state (≥ 8 hours), following a standardized clinical protocol. Significant differences were observed between the two groups in BMI, TyG, fasting blood glucose, and fasting triglyceride (all P < 0.001).
Table 1 Baseline characteristics of triglyceride glucose index < 1.79 and triglyceride glucose index ≥ 1.79, n (%).
Table 2 depicts the tumor response. Among the enrolled patients, 1 achieved a complete response, 39 had a partial response, 117 experienced stable disease, and 143 developed progression disease. The objective response rates of the low and high TyG index groups were 9.15% and 18.38% (P = 0.020), respectively, and the disease control rates (DCR) were 46.95% and 58.82% (P = 0.040), respectively.
Table 2 Tumor responses of triglyceride glucose index < 1.79 and triglyceride glucose index ≥ 1.79, n (%).
Compared with the low TyG index group, the high TyG index group exhibited a significantly longer median PFS of 9.8 (9.2-10.9) months, whereas the low TyG index group had a median PFS of 8.0 (7.5-8.5) months [P < 0.001, HR = 0.583, 95% confidence interval (CI): 0.431-0.787), Figure 2A]. The median OS of the high TYG index group was significantly longer than that of the low TyG index group [23.1 (21.2-NA) months vs 16.5 (13.9-18.3) months, P < 0.001, HR = 0.298, 95%CI: 0.209-0.423, Figure 2B].
Figure 2 Effects of different triglyceride glucose indexes on the long-term prognosis of gastric cancer patients.
A: Kaplan-Meier plot of the triglyceride glucose (TyG) index < 1.79 and TyG index ≥ 1.79 groups; B: Kaplan-Meier plot of overall survival in the TyG index < 1.79 and TyG index ≥ 1.79 groups. TyG: Triglyceride glucose; HR: Hazard ratio; CI: Confidence interval.
Univariate and multifactorial analyses of PFS and OS
Table 3 presents the results of univariate and multivariate Cox proportional hazards regression analyses for PFS. The multivariate analysis revealed several independent prognostic factors associated with PFS, including ECOG score, higher ECOG performance scores significantly correlated with poorer outcomes for both PFS (HR = 1.58, 95%CI: 1.14-2.20, P = 0.006) and OS (HR = 1.68, 95%CI: 1.10-2.59, P = 0.017) (Table 4). This finding aligns with prior research indicating that reduced functional status limits treatment tolerance and effectiveness. A low TyG index, reflecting better metabolic function, showed strong associations with improved survival outcomes. Specifically, the TYG index was a significant prognostic factor for both PFS (HR = 0.66, 95%CI: 0.48-0.90, P = 0.010) and OS (HR = 0.36, 95%CI: 0.24-0.55, P < 0.001), indicating that patients with healthier metabolic profiles may respond more favorably to treatment. PD-L1 expression and higher PD-L1 expression (≥ 5 combined positive score) predicted favorable outcomes, which is associated with longer PFS (HR = 0.37, 95%CI: 0.25-0.56, P < 0.001) and OS (HR = 0.49, 95%CI: 0.29-0.82, P = 0.007). This finding supports evidence that PD-L1 positivity enhances response to immunotherapy, likely due to a more effective anti-tumor immune response and BMI. Although BMI was independently significant for PFS (HR = 0.56, 95%CI: 0.38-0.84, P = 0.005), it did not show any statistical significance in the OS multivariate analysis (P = 0.159), implying that a higher BMI may contribute to better treatment tolerance, extending PFS without necessarily impacting long-term survival. Factors such as age, sex, liver and peritoneal metastases, Epstein-Barr virus infection, and mismatch repair status did not achieve significance in multivariate analyses, potentially due to overlapping effects with more influential factors such as ECOG score and PD-L1 expression. Based on these identified independent prognostic factors, a nomogram prediction model was prepared for 12-, 15-, and 18-month survival predictions, which yielded strong predictive accuracy, with c-indexes of 0.700 in the training set and 0.779 in the validation set (Figure 3). The concordance indices (c-indexes) of the prediction model were 0.700 for the training dataset and 0.779 for the validation dataset, indicating good predictive accuracy.
The patients were randomly assigned to two groups, with 210 assigned to the training dataset and 90 to the internal validation dataset, in a 7:3 ratio. Table 5 lists the baseline characteristics of the two datasets. As shown in Figure 4A, the AUC for 12-month OS was 0.749 for the training set and 0.866 for the internal validation set. At 15 months, the AUCs for OS were 0.762 for the training set and 0.872 for the internal validation set (Figure 4B), while, at 18 months, the corresponding values were 0.778 and 0.855, respectively (Figure 4C). Calibration curves for OS at 12, 15, and 18 months for both the training and validation sets are shown in Figure 5. These curves demonstrated that the model’s predicted risk is closely aligned with the actual observed risk.
Figure 4 Graph depicting the operating characteristic evaluation plot for a prognostic model.
A: Graph showing the training set and validation set receiver operating characteristic (ROC) evaluation plots for 12-month prognostic prediction model; B: Graph showing the training set and validation set ROC evaluation plots for 15-month prognostic prediction model; C: Graph showing the training set and validation set ROC evaluation plots for 18-month prognostic prediction model. AUC: Area under the curve; CI: Confidence interval.
Figure 5 Graph illustrating the calibration plots for a prognostic model.
A: Calibration plots for the training set 12-month overall survival (OS); B: Calibration plots for the validation set 12-month OS; C: Calibration plots for the training set 15-month OS; D: Calibration plots for the validation set 15-month OS; E: Calibration plots for the training set 18-month OS; F: Calibration plots for the validation set 18-month OS.
Table 5 Comparison of features between the training and validation sets, n (%).
Characteristic
Test (n = 90)
Train (n = 210)
χ2
P value
Age, years
1.94
0.164
< 60
46 (51.11)
89 (42.38)
≥ 60
44 (48.89)
121 (57.62)
Sex
0.43
0.512
Female
43 (47.78)
109 (51.90)
Male
47 (52.22)
101 (48.10)
BMI, kg/m2
0.21
0.646
< 25
69 (76.67)
166 (79.05)
≥ 25
21 (23.33)
44 (20.95)
TyG
0.31
0.578
Low
47 (52.22)
117 (55.71)
High
43 (47.78)
93 (44.29)
Drinking history
0.24
0.627
No
63 (70.00)
141 (67.14)
Yes
27 (30.00)
69 (32.86)
Smoking history
1.58
0.209
No
67 (74.44)
141 (67.14)
Yes
23 (25.56)
69 (32.86)
ECOG
0.41
0.523
0
57 (63.33)
141 (67.14)
1
33 (36.67)
69 (32.86)
Site
0.08
0.772
Stomach
44 (48.89)
106 (50.72)
Gastric and esophageal binding
46 (51.11)
103 (49.28)
Histological
1.39
0.239
Adenocarcinoma
21 (23.33)
63 (30.00)
Other
69 (76.67)
147 (70.00)
CEA, ng/mL
3.70
0.054
< 3
34 (37.78)
56 (26.67)
≥ 3
56 (62.22)
154 (73.33)
AFP, ng/mL
1.83
0.176
< 15
72 (80.00)
181 (86.19)
≥ 15
18 (20.00)
29 (13.81)
CA-199, ng/mL
1.54
0.214
< 37
60 (66.67)
124 (59.05)
≥ 37
30 (33.33)
86 (40.95)
Liver metastasis
1.60
0.206
No
63 (70.00)
131 (62.38)
Yes
27 (30.00)
79 (37.62)
Peritoneal metastasis
0.73
0.392
No
75 (83.33)
166 (62.38)
Yes
15 (16.67)
79 (37.62)
EBV status
0.10
0.578
No-infect
75 (83.33)
178 (84.76)
Infect
15 (16.67)
32 (15.24)
PD-L1 expression
0.60
0.440
CPS < 5
13 (14.44)
38 (18.10)
CPS ≥ 5
77 (85.56)
172 (81.90)
MMR status
0.02
0.893
pMMR
4 (4.44)
7 (3.33)
dMMR
86 (95.56)
203 (96.67)
AEs
Patients in both groups reported AEs of different grades, and the most common ones were leukopenia, anemia, neutropenia, thrombocytopenia, nausea, and fever, but none of the patients in either group required treatment discontinuation due to AEs. No statistically significant difference was noted in the incidence or severity of AEs across groups (P > 0.05 for all AEs; Table 6). This consistent AE profile suggested that the metabolic status, as reflected by the TyG index, does not significantly impact the incidence of treatment-related toxicities in GC patients undergoing sintilimab and chemotherapy, possibly due to the nature of immune and hematologic AEs being more related to treatment mechanisms than metabolic variability.
Table 6 Adverse events associated with sintilimab plus chemotherapy in gastric cancer patients, n (%).
Variables
Low TyG index (n = 164)
High TyG index (n = 136)
χ2
P value
All grades: Leukopenia
71 (43.29)
62 (45.59)
0.26
0.607
All grades: Anemia
68 (41.46)
58 (42.65)
0.00
0.989
All grades: Neutropenia
63 (38.41)
50 (36.75)
0.09
0.769
All grades: Thrombocytopenia
58 (35.37)
47 (34.56)
0.00
0.959
All grades: Nausea
50 (30.49)
38 (27.94)
0.23
0.630
All grades: Pyrexia
40 (24.39)
36 (26.47)
0.17
0.680
All grades: Elevated ALT
34 (20.73)
24 (17.65)
0.45
0.501
All grades: Elevated AST
34 (20.73)
22 (16.18)
1.02
0.313
All grades: Hypothyroidism
32 (19.51)
31 (22.79)
0.48
0.487
All grades: Diarrhea
29 (17.68)
32 (23.52)
1.57
0.210
All grades: Hypertension
24 (14.63)
19 (13.97)
0.03
0.870
All grades: Pneumonitis
20 (12.20)
17 (12.50)
0.02
0.898
All grades: Proteinuria
18 (10.98)
12 (8.82)
0.38
0.536
All grades: Hyperbilirubinemia
17 (10.37)
12 (8.82)
0.20
0.653
All grades: Fatigue
16 (9.80)
13 (10.29)
0.00
0.954
≥ 3 grades: Thrombocytopenia
30 (18.29)
22 (16.18)
0.23
0.630
≥ 3 grades: Neutropenia
21 (12.80)
14 (10.29)
0.45
0.500
≥ 3 grades: Leukopenia
19 (11.59)
15 (11.03)
0.02
0.880
≥ 3 grades: Anemia
15 (9.15)
10 (7.35)
0.31
0.576
≥ 3 grades: Diarrhea
8 (4.88)
6 (4.41)
0.04
0.849
≥ 3 grades: Nausea
7 (4.27)
5 (3.68)
0.07
0.795
≥ 3 grades: Elevated ALT
5 (3.05)
4 (2.94)
0.08
0.775
≥ 3 grades: Elevated AST
4 (5.88)
2 (1.47)
0.03
0.855
≥ 3 grades: Hypothyroidism
3 (1.83)
4 (2.94)
0.04
0.833
≥ 3 grades: Hypertension
3 (1.83)
2 (1.47)
0.05
0.819
≥ 3 grades: Pneumonitis
3 (1.83)
2 (1.47)
0.05
0.819
≥ 3 grades: Hyperbilirubinemia
2 (1.22)
2 (1.47)
0.09
0.763
≥ 3 grades: Fatigue
0 (0.00)
0 (0.00)
-
-
≥ 3 grades: Pyrexia
0 (0.00)
0 (0.00)
-
-
≥ 3 grades: Proteinuria
0 (0.00)
0 (0.00)
-
-
DISCUSSION
This study is the first to comprehensively investigate the effect of the TyG index on the efficacy of immunotherapy combined with chemotherapy in patients with advanced GC as well as to develop a predictive model for comparing their long-term prognosis. The results indicated that patients in the high TyG index group had significantly better median OS, median PFS, objective response rate, and DCR compared to those in the low TyG index group. These findings, in contrast to some prior reports, could stem from differences in the study population characteristics, such as variations in cancer stages, types of PD-1 inhibitors used, or demographic factors, which may uniquely influence the TyG index outcomes. The independent prognostic factors for PFS were the ECOG score, TYG index of PD-L1 expression, and BMI. For OS, the ECOG score and TyG index of the PD-L1 expression were independent prognostic factors. There was no significant difference in AEs at any level between the two groups (P > 0.05), and death was not related to AEs.
We observed that a high TyG index acted as a protective factor for patients with advanced GC receiving immunotherapy combined with chemotherapy, which does not entirely agree with previously reported results[24]. The possible reasons for this observation were analyzed. Furthermore, it is important to note that prior studies often involved different patient populations and cancer stages, as well as distinct PD-1 inhibitors, which may partially explain the divergence in results. For instance, our study included patients with advanced GC who might demonstrate unique metabolic or immunological responses to the TyG index compared to that of other groups studied previously. First, the TyG index effectively reflected the status of the lipid metabolism. Generally, a higher TyG index is linked to better lipid metabolism capacity, which can enhance fatty acid oxidation, thereby providing adequate energy for T cells[25]. fatty acid oxidation not only provides energy for T cells but also regulates their metabolic state to a certain extent, thereby augmenting their function[26,27]. In addition, a high TYG index may be associated with the downregulation of some oncogenes and the upregulation of the PD-1 expression, which may be related to the immunosuppressive effect of obesity, thereby increasing patients’ sensitivity to PD-1 inhibitors[22]. Second, this index also reflects the glucose metabolism status of patients. A high TyG index may be linked to good glucose metabolism, which is crucial for maintaining the activity and proliferation of T-cells[28,29]. In a tumor microenvironment, tumor cells consume large amounts of glucose to support their rapid proliferation, resulting in T-cells facing the challenge of metabolic competition[30,31]. Good glucose metabolism can provide the necessary energy and metabolic precursors for T-cells[32] and reduce the accumulation of lactic acid in the tumor microenvironment, thereby maintaining their activity[33,34]. Previous reports have shown that TyG may affect the occurrence and development of GC via lipotoxic mechanisms[35,36]. Accordingly, the increase in and dysfunction of adipose tissues leads to lipid overflow and inflammation, which reduce the effect of immunosuppressive agents[23]. Nonetheless, although lipotoxicity may indeed affect the immune status, the complexity of the tumor microenvironment suggests that other factors, such as tumor type and systemic nutritional status of patients, can also affect the efficacy of immunotherapy[37,38]. In addition, the TyG index denotes the nutritional status of patients, and good nutritional and metabolic status may enhance the effect of immunotherapy by stimulating systemic immune function. Therefore, patients with advanced GC must receive immunotherapy combined with chemotherapy to improve the prognosis by taking appropriate nutrition before and after treatment.
Interestingly, the multivariate analysis performed in this study revealed that PD-L1 expression and TyG index were independent prognostic factors for OS. PD-L1 expression is an important predictor of the efficacy of PD-1 inhibitors, and the TyG index may enhance the response of patients with PD-L1 positivity to immunotherapy by improving their metabolic and immune status[39-41]. Therefore, the synergistic effect of the TyG index and PD-L1 expression may further optimize the efficacy of immunotherapy outcomes, warranting further investigation in the future and potentially in other cancer types. Future studies should explore this potential mechanism and determine the feasibility of applying the combined assessment of the TYG index and PD-L1 expression in other tumors.
In this study, the results of multivariate Cox proportional hazards regression analysis revealed that the ECOG score, PD-L1 expression, and TyG index were independent prognostic factors for OS. Subsequently, a nomogram prediction model that integrated these independent risk factors was created. The analysis revealed that the TyG index exerted the greatest effect on OS, followed by the PD-L1 expression, while the ECOG score had the least impact. To assess the validity of the nomogram prediction model, the c-index was computed and the calibration curve was generated. The c-index for the nomogram model in the validation set was 0.779. The calibration curve signified that the model agreed well with the actual data, which confirmed its reliability and accuracy. Nevertheless, external validation was hampered by the limited sample size.
Despite the valuable conclusions drawn from this study, some limitations should be noted. First, this study was a single-center retrospective study with the risk of selection bias; hence, the findings should be verified using multicenter prospective studies in the future. Second, the sintilimab plus chemotherapy regimen was specifically selected to minimize treatment bias. Although this centralized approach enhanced the robustness of our analysis, there is a need to further validate our conclusions regarding other PD-1/PD-L1 inhibitors in combination with chemotherapy. Third, although the association between the TYG index and patient prognosis was analyzed, the specific mechanism of insulin resistance in immunotherapy remains to be clarified. Insulin resistance could potentially impact the immune checkpoint inhibition process by modulating the tumor microenvironment. For example, insulin resistance may promote a pro-inflammatory environment, affecting the infiltration and function of immune cells within the tumor, which could, in turn, influence the effectiveness of immunotherapy. Further basic experiments and clinical trials are necessary to investigate these potential mechanisms and validate these hypotheses. Fourth, the cutoff value of the TyG index may vary based on the population and disease status. Future research should therefore determine whether this threshold is applicable across different tumor types, as this could impact the generalizability of our findings. Large-scale multicenter studies on patients with different tumor types can be considered in future research to confirm the prognostic value of the TyG index in patients with different types of cancer.
CONCLUSION
Patients with GC who have a high TyG index after immunotherapy combined with chemotherapy have a better prognosis. including longer PFS and OS, along with improved objective response and DCRs. In this study, the TyG index emerged as the strongest independent prognostic factor for OS, followed by PD-L1 expression and ECOG score, indicating its vital role as a predictor of long-term efficacy in patients with advanced GC undergoing combination therapy. In addition, the nomogram model, which integrated these factors, displayed high predictive accuracy, which further validates the TyG index’s importance. Importantly, no significant difference was observed in adverse effects between high and low TyG index groups, underscoring the TyG index as a useful prognostic tool without impacting safety profiles. These findings support the TyG index as a valuable metric for guiding treatment and improving prognostic assessments in advanced GC.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade C
Novelty: Grade A, Grade B, Grade C
Creativity or Innovation: Grade B, Grade B, Grade C
Scientific Significance: Grade B, Grade B, Grade C
P-Reviewer: Chen T; Rusman RD S-Editor: Wang JJ L-Editor: A P-Editor: Zheng XM
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