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. 2026 Jun 30;40(4):2289–2299. doi: 10.21873/invivo.14381

Nutritional Support for Treatment Continuity and Survival in Advanced Gastric Cancer: A Propensity Score-matched Study

KEIJI SUGIYAMA 1,2, TAKUMI ITO 1, MASAHIRO YAGUCHI 1, SEIRA OWAKI 1, RIKU INO 1, MARIKO SATO 1, KAZUHIRO SHIRAISHI 1, KYOKO KATO 1, YOSHINORI IMAIZUMI 3, NAO HINO 3, MUTSUMI MURAYAMA 4, MASAAKI SHIMADA 4, YASUO UNO 5, MASASHI HATTORI 5, MASAYA SUENAGA 5, YASUHIRO KODERA 5, MITSURO KANDA 2, CHIYOE KITAGAWA 1
PMCID: PMC13321963  PMID: 42379804

Abstract

Background/Aim

This study aimed to clarify the effect of nutritional support in patients with advanced gastric cancer (AGC) receiving first-line chemotherapy.

Patients and Methods

We retrospectively reviewed the medical records of consecutive patients with unresectable or recurrent AGC treated with first-line chemotherapy at Nagoya Medical Center between 2007 and 2024. Patients who received nutritional support were compared with those without nutritional support. Nutritional support included individualized dietary counselling, assessment of energy and protein requirements, and multidisciplinary evaluation. Propensity score matching (PSM, 1:1) was applied to adjust for baseline nutritional and inflammatory status. The primary endpoint was time to treatment failure (TTF). Subgroup analyses were conducted by baseline albumin level.

Results

Among the 324 eligible patients included in the analysis, 76 received nutritional support. Before PSM, patients who were provided nutritional support more frequently presented with poor nutritional profiles. After PSM (n=46 per group), the median TTF was 5.0 months in patients without nutritional support and 4.6 months in those with it. In patients with hypoalbuminemia (albumin <3.5 g/dl), nutritional support significantly prolonged TTF. Finally, multivariate analysis in the pre-PSM cohort identified nutritional support as an independent predictor of TTF.

Conclusion

Although nutritional support was not associated with significant TTF improvement in the overall AGC population after propensity score matching, it emerged as an independent predictor of treatment continuity in multivariate analysis of the pre-PSM cohort. This benefit was particularly evident in patients with hypoalbuminemia, indicating that targeted nutritional intervention is essential for this vulnerable subgroup to prevent premature treatment failure.

Keywords: Advanced gastric cancer, nutritional support, propensity score matching, chemotherapy, hypoalbuminemia

Introduction

Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide (1). For patients with advanced gastric cancer (AGC), systemic chemotherapy prolongs survival, but outcomes remain unsatisfactory, with median overall survival rarely exceeding 14-20 months (2-5).

Cancer-associated malnutrition and cachexia are highly prevalent in AGC, affecting up to 80% of patients (6-9), and are linked to reduced treatment tolerance, impaired quality of life, and poor survival (10, 11). These conditions are driven by a complex interplay of systemic inflammation, metabolic dysregulation, and insufficient nutritional intake (12). Reflecting this clinical relevance, international guidelines (13, 14), including those from the European Society for Clinical Nutrition and Metabolism (ESPEN), recommend early nutritional screening and timely nutritional support as an essential component of comprehensive cancer care.

In the perioperative setting, several trials and meta-analyses have demonstrated that structured nutritional interventions improve perioperative outcomes and adherence to chemotherapy (15-19). However, robust evidence for the impact of nutritional support in advanced disease remains limited. Previous retrospective studies, including our earlier single-center analysis, suggested that nutritional support may help maintain treatment delivery and potentially improve outcomes; however, these studies were restricted by small sample sizes and inadequate adjustment for confounders (20).

To evaluate the survival impact of nutritional intervention, we conducted a retrospective analysis of data from patients with AGC treated with first-line chemotherapy at our institution. Using propensity score matching (PSM), we investigated the impact of nutritional support on survival outcomes and treatment continuity. As hypoalbuminemia is a well-recognized marker of malnutrition and systemic inflammation and is associated with poor survival and treatment intolerance in gastrointestinal cancers, we also performed a subgroup analysis for patients with hypoalbuminemia.

Patients and Methods

Selection of patients. We retrospectively reviewed the medical records of consecutive patients with unresectable or recurrent AGC who received first-line systemic chemotherapy at the National Hospital Organization Nagoya Medical Center between January 2007 and December 2024. The eligibility criteria included histologically confirmed adenocarcinoma of the stomach or gastroesophageal junction, Eastern Cooperative Oncology Group performance status (ECOG PS) of 0-2, and no synchronous malignancy. Patients treated with best supportive care alone, those with insufficient clinical data, or those who experienced recurrence during adjuvant therapy or within six months after its completion were excluded.

Nutritional support intervention. Nutritional support was delivered by registered dietitians within a multidisciplinary team, in accordance with the Japanese Society for Clinical Nutrition and Metabolism and the ESPEN guidelines (13). At baseline, energy requirements were estimated (25-30 kcal/kg/day), and intake was assessed using 24-hour food recall. Individualized dietary counselling included recommendations on food amount and form, protein/fat supplementation, and recipes. Follow-up sessions were conducted during outpatient visits or infusion appointments. Nutritional markers (albumin, C-reactive protein, and electrolytes), body weight, and nutrition impact symptoms (e.g., anorexia, nausea, vomiting, diarrhea, dysphagia, mucositis, and malaise) were monitored, and counselling was adjusted accordingly. For hospitalized patients, a wider team of specialists (nutritionists, gastroenterologists, dentists, hygienists, physiotherapists, and speech therapists) was involved. Patients categorized as “without nutritional support” received routine oncological care. Brief, non-structured dietary advice from ward nurses or physicians, and occasional unscheduled contacts with a dietitian during hospitalization or at discharge, were permitted and regarded as routine advice only. These interactions did not involve formal assessment of nutritional requirements, personalized meal planning, or scheduled follow-up. Oral nutritional supplements could be prescribed ad hoc at the clinician’s discretion, but in the absence of the structured program described above, they were considered part of routine care.

Endpoints. Time to treatment failure (TTF) was selected as the primary endpoint as it reflects both disease progression and treatment discontinuation from adverse events or nutritional decline, thereby capturing treatment continuity in real-world practice. TTF was defined as the time from initiation of first-line chemotherapy to treatment discontinuation for any reason, including progression, toxicity, or death. The secondary endpoint was overall survival (OS), defined as the time from initiation of first-line chemotherapy to death from any cause. Subgroup analyses were pre-defined according to baseline albumin level, with hypoalbuminemia defined as albumin <3.5 g/dl.

Statistical analysis. Survival curves were estimated using the Kaplan-Meier method and compared with the log-rank test. Hazard ratios (HRs) with 95% confidence intervals (CIs) were obtained from Cox proportional hazards models. To minimize confounding, we performed 1:1 nearest-neighbor PSM (without replacement) using a caliper of 0.20 standard deviations of the logit of the propensity score. The propensity model included: age, sex, ECOG PS, body mass index (BMI), human epidermal growth factor-2 (HER2) status, tumor differentiation, tumor location (stomach vs. gastroesophageal junction), peritoneal involvement, number of metastatic sites (<2 vs. ≥2), prior gastrectomy, period of diagnosis (2007-2014, 2015-2019, 2020-2024), lactate dehydrogenase level (normal vs. high), and prognostic nutritional index (PNI; <40 vs. ≥40) (21, 22). Covariate balance was assessed using standardized mean differences (SMDs) and is summarized in Table I.

Table I. Patient characteristics before and after propensity score matching.

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BMI: Body mass index; diff: differentiated (well/moderately); EGJ: esophagogastric junction; F: female; G: stomach (gastric); HER2: human epidermal growth factor receptor 2; LDH high: elevated lactate dehydrogenase; M: male; met: metastatic; mix: mixed type; NA: not assessed/unknown; No.: number; PER: peritoneal metastasis; PNI: prognostic nutritional index; por: poorly differentiated; ECOG PS: Eastern Cooperative Oncology Group performance status; PSM: propensity score matching; SMD: standardized mean difference.

Missing data were handled by case-wise exclusion. For the propensity score model, patients with missing values in any of the covariates were excluded before matching. Likewise, for survival and multivariate analyses, only patients with complete data for the variables used in each model were included. Accordingly, the analyses were conducted on datasets without missing values for the relevant covariates; however, a complete-case analysis across all variables was not applied.

In addition, univariate and multivariate Cox regression analyses were performed in the pre-PSM cohort to identify independent prognostic factors for TTF and OS. Variables included were known clinical and pathological factors (ECOG PS, BMI, prior gastrectomy, tumor differentiation, HER2 status, peritoneal metastasis, number of metastatic sites, and Glasgow Prognostic Score), as well as nutritional support. Variables with p≤0.10 in univariate analyses were entered into the multivariate model. Statistical significance was defined as a two-sided p <0.05. All analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria).

Ethical approval. The study was approved by the Committee for Clinical Review in the NHO Nagoya Medical Center (date of approval: 4 September 2025; approval number: 2025-022). Given the retrospective design of this study, the requirement for written informed consent was waived. Instead, an opt-out approach was employed in accordance with institutional policy, and information regarding the study was disclosed to patients on the hospital website.

Results

Patient flow. During the study period, 676 patients with AGC were identified from our institutional records. After excluding patients treated with best supportive care alone (n=254), those transferred to other hospitals or treated with emergency care only (n=56), and those with missing data (n=10), synchronous cancer (n=6), poor performance status (n=2), and prior adjuvant failure (n=24), a total of 324 patients were eligible for the analysis (Figure 1). Of these, 76 (23.5%) received nutritional support and 248 were managed without nutritional support. The median follow-up time was 54.2 months [95% confidence interval (CI)=50.9-57.4].

Figure 1.

Figure 1

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Patient flow diagram. Of the 676 registered patients with advanced gastric cancer (AGC), 324 met the eligibility criteria and were included in the analysis. After propensity score matching, 46 patients remained in each group (nutritional support vs. no nutritional support). BSC: best supportive care; GEJ: gastro-esophageal junction; ECOG PS: Eastern Cooperative Oncology Group performance status.

Patient characteristics. Before PSM, patients who received nutritional support more frequently presented with poor performance status, multiple metastatic sites, hypoalbuminemia, and low PNI, and were treated in later periods, compared with those without nutritional support. After 1:1 PSM, 46 patients were included in each group, and the baseline characteristics were well-balanced across most covariates, with SMDs <0.10 (Table I).

Survival outcomes. In the matched cohort, the median TTF was 5.0 months in patients without nutritional support and 4.6 months in those with it [hazard ratio (HR)=0.91; 95%CI=0.59-1.41; p=0.65]. The median OS was 16.6 months in patients without nutritional support and 12.0 months in those with it (HR=1.31; 95%CI=0.68-2.52; p=0.41). The Kaplan-Meier survival curves for TTF and OS are shown in Figure 2. In the pre-propensity score-matched cohort, the median TTF was 4.6 months in patients without nutritional support and 4.8 months in those with it (p=0.23). The median OS was 11.3 months and 11.4 months, respectively (p=0.37). These results indicate comparable outcomes between the two groups (Figure 3).

Figure 2.

Figure 2

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Kaplan-Meier curves for time to treatment failure (TTF) and overall survival (OS) in patients receiving nutritional support versus those without nutritional support after propensity score matching. NS: Nutritional support; Non-NS: non-nutritional support; HR: hazard ratio; CI: confidence interval.

Figure 3.

Figure 3

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Kaplan-Meier curves for time to treatment failure (TTF) and overall survival (OS) by nutritional support in the pre-PSM cohort. NS: Nutritional support; Non-NS: non-nutritional support; HR: hazard ratio; CI: confidence interval.

Subgroup analysis by serum albumin level. In patients with hypoalbuminemia (albumin <3.5 g/dl), nutritional support was associated with significantly longer TTF, compared with those without it (median 5.0 vs. 3.7 months; HR= 0.33; 95%CI=0.12-0.92; p=0.03). No significant difference in the OS was observed in this subgroup (median 10.2 vs. 6.9 months; HR=0.80; 95%CI=0.32-2.03; p=0.643) (Figure 4).

Figure 4.

Figure 4

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Kaplan-Meier curves for time to treatment failure (TTF) and overall survival (OS) stratified by baseline albumin status. Patients with hypoalbuminemia (albumin <3.5 g/dl) who received nutritional support showed significantly longer TTF, compared with those without nutritional support. NS: Nutritional support; non-NS: non-nutritional support; HR: hazard ratio; CI: confidence interval.

Multivariable analyses. In the pre-PSM dataset, univariate and multivariate Cox regression analyses were performed to identify the prognostic factors for TTF and explore the crude associations observed in the unadjusted cohort. Nutritional support was independently associated with improved TTF (HR=0.58; 95%CI=0.43-0.78; p<0.001), with other independent factors, including ECOG PS (HR=1.77; 95%CI=1.46-2.13; p<0.001), HER2 positivity (HR=0.70; 95%CI=0.51-0.97; p=0.032), and higher Glasgow prognostic score (HR=1.32; 95%CI=1.02-1.72; p=0.037) (Table II).

Table II. Univariate and multivariate analyses for time to treatment failure.

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ECOG performance status was analyzed as a binary variable (PS 1-2 vs. PS 0). For variables with more than two ordered categories (BMI and period of diagnosis), hazard ratios represent the effect per category increase, modelled as ordinal variables in the Cox regression analysis. BMI: Body mass index; CI: confidence interval; GPS: Glasgow prognostic score; HER2: human epidermal growth factor receptor 2; HR: hazard ratio; No.: number; ECOG PS: Eastern Cooperative Oncology Group performance status; PER: peritoneal metastasis; Period: year of diagnosis/chemotherapy initiation; Ref: reference category.

Discussion

The present retrospective propensity score-matched study provides clinically relevant insights into the role of nutritional support for patients with AGC receiving first-line chemotherapy, with a primary focus on treatment continuity as reflected by TTF. Although nutritional support was not associated with improved OS in the overall cohort, it significantly prolonged TTF in patients with pre-existing hypoalbuminemia. These findings suggest that, although nutritional support is not sufficient to confer a survival advantage on its own, it may contribute to maintaining treatment continuity for vulnerable subgroups. In the unadjusted pre-PSM analysis, TTF and OS were comparable between patients with and without nutritional support, despite there being a higher prevalence of established poor prognostic factors in the nutritional support group. Therefore, nutritional support may have partially offset the negative effect of poor baseline condition on treatment continuity, although a causal effect cannot be inferred from this observational analysis.

Interpretation of outcomes in the context of cachexia. The absence of an OS benefit in the overall population is consistent with the multifactorial nature of cancer-related cachexia, which involves systemic inflammation and complex metabolic pathways (23, 24). Nutritional intervention alone is unlikely to fully counteract these processes. Consistently, crude analyses in the pre-PSM cohort demonstrated comparable outcomes for both TTF and OS, suggesting that nutritional support does not exert a strong population-level effect when assessed without adjustment. However, the observed benefit in patients with hypoalbuminemia, in whom poor nutritional status and systemic inflammation often coexist, suggests that nutritional support may mitigate some of the catabolic effects of advanced cancer and help patients tolerate chemotherapy for longer. This is an essential prerequisite for achieving meaningful clinical benefit.

Ethical considerations and the role of observational studies. Randomized trials comparing nutritional care with no nutritional care are ethically infeasible, as international guidelines such as those from ESPEN recommend nutritional intervention as part of standard practice (13). Consequently, well-designed retrospective analyses remain valuable for addressing this question. Present data highlight the biological relevance of supportive care and suggest that nutritional support, even as a single modality, may contribute to treatment continuity – a key prerequisite for deriving long-term benefit.

Strengths and limitations. This study has several strengths, including a relatively large sample size, long-term follow-up, and the use of PSM to minimize baseline imbalances. Nonetheless, some limitations must be acknowledged. This was a single-institution retrospective analysis, and residual confounding cannot be excluded. In particular, patients who received nutritional support may have differed systematically from those who did not in terms of baseline characteristics, such as performance status, comorbidities, or disease burden. Although PSM was employed, unmeasured or incompletely measured factors related to treatment tolerance and clinical decision-making could still have influenced treatment outcomes, independently of nutritional support. Importantly, although nutritional support was identified as a significant predictor of TTF in the pre-PSM multivariate analysis, this significance was not maintained in the matched cohort. This discrepancy may be attributed to a reduction in statistical power due to the smaller sample size after matching (n=92), potentially leading to a type II error. Alternatively, the pre-PSM findings might partially reflect residual confounding and confounding by indication, whereby patients receiving nutritional support represent a more vulnerable population not fully captured by measured covariates. Accordingly, the numerically shorter OS observed in the nutritional support group after PSM should be interpreted with caution and likely reflects underlying frailty rather than a detrimental effect of nutritional intervention itself. The reduced sample size after matching (n=92) may have limited statistical power to detect modest differences. Given the small sample size after matching and the secondary nature of OS in this study, the OS findings should be considered exploratory. Although nutritional support was generally initiated early during treatment, the timing and intensity of nutritional intervention were heterogeneous, and residual time-related bias cannot be completely excluded.

Nutritional support was more frequently implemented in recent years, coinciding with advances in systemic therapy, including the introduction of immune checkpoint inhibitors. To mitigate potential bias arising from such temporal trends, the period of diagnosis (2007-2014, 2015-2019, 2020-2024) was included as a covariate in the propensity score model; however, residual effects of evolving treatment strategies cannot be entirely ruled out.

Critically, we assessed only hard outcomes (TTF and OS) and did not evaluate detailed longitudinal changes in body weight, serum albumin levels, or other nutritional indicators. These parameters are essential for a comprehensive evaluation of nutritional intervention, and their omission represents a major limitation. There was also a lack of data on adherence to nutritional support and its influence on chemotherapy-related toxicities.

Future directions. From a therapeutic perspective, the efficacy and tolerability of systemic therapies, including immune checkpoint inhibitors, are increasingly recognized as being influenced by host-related factors, such as systemic inflammation (25, 26) and sarcopenia (27). Emerging evidence also suggests that exercise interventions may enhance the efficacy of systemic and immune therapies (28-30). Future research should therefore integrate nutritional support and exercise (31) into treatment regimens, assessing skeletal muscle mass, quality of life, and treatment efficacy in a regimen-specific manner. Such approaches will enable a more precise understanding of how multimodal supportive care can be optimally combined with pharmacological therapies for patients with cancer cachexia and malnutrition. Novel pharmacological strategies, such as growth differentiation factor 15-targeted therapies (32-34), may further enhance outcomes when combined with nutritional and exercise interventions, potentially enhancing survival and quality of life in patients with gastrointestinal cancer, particularly those affected by cancer cachexia or malnutrition.

Conclusion

In the present study, we found that nutritional support did not improve OS in patients with AGC. However, nutritional support was associated with prolonged TTF in patients with hypoalbuminemia, highlighting its potential role in maintaining treatment continuity during first-line chemotherapy. These findings highlight the importance of evaluating treatment-related endpoints, such as TTF and progression-free survival, in future research on nutritional and other supportive interventions in AGC.

Conflicts of Interest

The Authors declare no conflicts of interest in relation to this study.

Authors’ Contributions

Keiji Sugiyama: Conceptualization, Methodology, Formal analysis, Data curation, Investigation, Visualization, Project administration, Writing - original draft, Writing - review & editing. Takumi Ito, Masahiro Yaguchi, Seira Owaki, Riku Ino, Mariko Sato, Kazuhiro Shiraishi, Kyoko Kato: Data curation, Investigation, Writing - review & editing. Yoshinori Imaizumi, Nao Hino, Mutsumi Murayama, Masaaki Shimada, Yasuo Uno, Masashi Hattori, Masaya Suenaga: Validation, Writing - review & editing. Mitsuro Kanda, Yasuhiro Kodera: Supervision, Writing - review & editing. Chiyoe Kitagawa: Resources, Supervision, Writing - review & editing.

Acknowledgements

The Authors would like to thank the Department of Nutrition and the multidisciplinary nutritional support team at the National Hospital Organization Nagoya Medical Center for their dedicated involvement in patient care. We are also grateful to Ms. Mika Yokoi from the Department of Medical Information for her assistance with data extraction and registry management, and to Ms. Mika Mano for her valuable support as a research assistant.

Funding

This work was supported by the Department of Medical Oncology, National Hospital Organization, Nagoya Medical Center (no grant number).

Artificial Intelligence (AI) Disclosure

No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.

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