Method Article

Meta-analysis of Effects of Nutritional Therapies on Nutritional Status of Gastric Cancer Patients

DOI:

10.3791/69720

⸱

January 16th, 2026

 ,  ,  , 
1Department of Nutrition, the 8th Medical Center of Chinese PLA General Hospital, 2Department of Ultrasound, the 8th Medical Center of Chinese PLA General Hospital

* These authors contributed equally

In This Article

Summary

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This study found that oral supplements significantly increased weight, and early tube feeding improved prealbumin more than parenteral nutrition. However, specialized immunonutrition showed no added benefit over standard formulas for key blood proteins. Further research is needed to confirm these results.

Abstract

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This protocol provides a methodological framework for conducting a meta-analysis to evaluate the effects of nutritional therapies on the nutritional status of gastric cancer patients. By following this protocol, researchers will be able to systematically identify, appraise, and synthesize evidence from randomized controlled trials and comparative studies. The steps include: formulating a comprehensive search strategy across multiple databases; dual-independent screening and selection of studies using predefined PICOS criteria (population, interference, comparison, outcome, and study design); standardized data extraction and harmonization of outcome measures; assessment of study quality using the Cochrane Risk of Bias 2.0 tool; and statistical synthesis using appropriate models, with exploration of heterogeneity and sensitivity analyses. Application of this protocol to the available evidence suggests potential benefits of specific nutritional interventions, e.g., oral supplementation for body weight, though high heterogeneity underscores the need for rigorous and standardized methodology in this field. A continuous model with fixed or random effects was used to get the mean difference (MD) with 95% confidence intervals (CIs). A total of 18 studies, involving 3,586 subjects, were selected for the meta-analysis. Oral nutritional supplementation had a significantly increased body weight (MD, 0.74; 95% CI, 0.20-1.27, p = 0.007) compared to the control in patients with gastric cancer. Early enteral nutrition had significantly improved prealbumin levels (MD, 22.53; 95% CI, 13.37-31.69, p < 0.001) compared to parenteral nutrition in patients. However, no significant differences were found between enteral immunonutrition and standard enteral nutrition for albumin (MD, 0.57, 95%CI, -0.31-1.44, p=0.20), prealbumin (MD, 0.23, 95%CI, -0.29-0.76, p=0.38), or transferrin levels (MD, 0.11, 95%CI, -0.09-0.32, p=0.28). The studied data showed that using oral nutritional supplementation had significantly increased body weight compared to control, and early enteral nutrition had significantly improved prealbumin levels compared to parenteral nutrition. However, more studies are required to validate this finding.

Introduction

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Annually, almost 1 million instances of gastric cancer are diagnosed globally1. Despite a consistent decrease in incidence, gastric cancer continues to be one of the most prevalent and lethal neoplasms, accounting for 783,000 cases (8.2% of all cancer fatalities) in 20182. Surgery is the principal treatment for gastric cancer, with a 5-year survival rate of about 25.7% in cases of locally advanced disease in Europe and the United States3. Despite advancements in surgical techniques and perioperative care in recent years, a majority of gastric cancer patients experience significant malnutrition due to the characteristics of gastric cancer, surgical trauma, perioperative dietary management, and inadequate caloric intake, which adversely affect their nutritional status and body composition4. Furthermore, malnutrition is exacerbated by intensified catabolism resulting from immune response dysregulation and metabolic disturbances, leading to anorexia, elevated energy consumption, and weight loss5. These factors constitute the foundation of what is termed disease-related malnutrition6. In this context, laboratory and clinical instruments have been established to evaluate the nutritional status of cancer patients6. The evaluation of nutritional status includes data on body weight, body mass index, body composition metrics, e.g., fat-free mass or fat mass, and biochemical indicators, e.g., albumin and prealbumin levels6. Various nutritional status markers, including the prognosis nutritional index, body mass index, serum albumin, and preoperative body weight reduction, have been evaluated as prognostic indicators in gastric cancer7,8. Indeed, deteriorations in nutritional status can result in an amplified occurrence of complications, diminished progression-free survival, and reduced overall survival7. These findings underscore the imperative to identify malnutrition and provide appropriate nutritional support to enhance nutritional status, hence optimizing the quality of life and survival of gastric cancer patients. The effects of various nutritional treatments on nutritional status across different contexts remain contentious.

To study such contentious effects, we need to use a standardized methodology. Traditional narrative reviews lack the systematic rigor to synthesize this heterogeneous evidence, while individual trial data alone cannot provide definitive conclusions about comparative effectiveness. This protocol employs a meta-analysis approach, which offers distinct advantages over these alternatives by providing a structured and reproducible methodology for evidence synthesis. Meta-analysis minimizes bias through comprehensive search strategies and explicit inclusion criteria. It also enables quantitative pooling of results to increase statistical power and precision9. The chosen approach is particularly valuable in nutritional oncology research, where interventions vary considerably in composition, timing, and administration routes.

Specifically, this protocol addresses gaps in existing methodologies by providing explicit guidance for categorizing different nutritional interventions, standardizing outcome measurement timepoints, offering transparent criteria for model selection based on heterogeneity, and incorporating comprehensive sensitivity analyses. The protocol is applicable to studies examining any nutritional intervention in gastric cancer patients, with outcomes including anthropometric measures (body weight), biochemical markers (albumin, prealbumin, transferrin), and functional assessments. However, conclusions may not generalize to other cancer types or nutritional interventions not specifically addressed, and applicability may be limited for studies with substantial methodological heterogeneity or incomplete outcome reporting. The aim of the study was to systematically evaluate and compare the effects of different nutritional therapies, including oral nutritional supplementation, early enteral nutrition, parenteral nutrition, and enteral immunonutrition, on the nutritional status of gastric cancer patients, as measured by changes in body weight, albumin, prealbumin, and transferrin levels.

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Protocol

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This study involves analysis of previously published data and does not require ethics approval or patient consent.

1. Developing the search strategy 9

  1. Search the following databases: PubMed, Cochrane Library, Embase, Google Scholar, and OVID. Use both Medical Subject Headings (MeSH) terms and free-text keywords in all searches. Apply no language or date restrictions.
  2. Document the search date and the number of records retrieved from each database.
  3. Develop search strings using the PICOS framework (population, intervention, comparison, outcome, and study design)10.
    1. For population, use terms such as: Stomach Neoplasms, gastric cancer, stomach cancer, gastric carcinoma.
    2. For intervention, use terms such as: Enteral Nutrition, Parenteral Nutrition, Dietary Supplements, Nutrition Therapy, enteral nutrition, parenteral nutrition, oral nutritional supplement, immunonutrition.
    3. For outcome, use terms such as: Nutritional Status, nutritional status, body weight, albumin, prealbumin, transferrin.
    4. For study design, use terms such as randomized controlled trial, controlled clinical trial, randomized, randomized, trial.
  4. Combine all terms with appropriate Boolean operators (AND, OR, NOT). Adapt the search syntax for the specific requirements of each database.

2. Screening and selection of studies 11

  1. Import all search results into reference management software (e.g., EndNote X20). Perform automated deduplication followed by manual verification to remove duplicate records.
  2. Use dedicated systematic review software (e.g., Covidence) for the screening process12. Assign two independent authors to screen titles and abstracts against the predefined inclusion and exclusion criteria. Resolve any discrepancies between authors through discussion or, if necessary, by consulting a third author.
  3. Use the following inclusion criteria: Randomized controlled trials or comparative studies; adult gastric cancer patients (≥18 years); comparison of a nutritional intervention vs. control or alternative intervention; report of at least one relevant nutritional status outcome (body weight, albumin, prealbumin, transferrin).
  4. Use the following exclusion criteria: Non-comparative designs; non-gastric cancer populations; no nutritional outcomes; insufficient data for meta-analysis.
  5. Retrieve the full-text articles for all records that pass the title/abstract screening13. Assign two independent authors to assess each full-text article against the inclusion criteria. Document the reason for exclusion for every article excluded at this stage. Contact the corresponding authors of studies to request missing data or clarifications when necessary.

3. Extracting the data 13

  1. Develop a standardized data extraction form. Create the form using software such as Microsoft Excel. Include fields for: study characteristics (author, year, country, design); participant characteristics (sample size, age, gender, cancer stage); intervention details (type, composition, duration, timing); comparator details; outcome data (means, standard deviations, sample sizes for each group and timepoint); and methodological quality indicators.
  2. Assign two independent authors to extract data from the included studies using the standardized form. Resolve discrepancies through consensus or by referring to the original publication.
  3. Extract outcome data for the following metrics at the specified timepoints: body weight change from baseline to 1-month post-intervention; and albumin, prealbumin, and transferrin levels at postoperative day 7 (± 2 days).
  4. Convert all extracted data into standardized units: body weight in kilograms (kg), albumin in grams per liter (g/L), prealbumin in milligrams per liter (mg/L), and transferrin in grams per liter (g/L). If means and standard deviations are not reported, calculate them from available statistics using established conversion methods.

4. Assessing the quality of included studies 10

  1. Assess the risk of bias for each study. Use the Cochrane Risk of Bias 2.0 (RoB 2) tool for randomized trials.
  2. Assign two independent authors to assess each study across the five domains: bias arising from the randomization process; bias due to deviations from intended interventions; bias due to missing outcome data; bias in measurement of the outcome; and bias in selection of the reported result. Classify the risk of bias for each domain as low, some concerns, or high. Resolve any assessment discrepancies through discussion.
  3. Make an overall quality judgment for each study. Classify studies as: Low risk (low risk in all domains), Some concerns (some concerns in at least one domain), or High risk (high risk in at least one domain or some concerns in multiple domains).

5. Statistical analysis

  1. Prepare the data for analysis. Enter the extracted data into meta-analysis software (e.g., Review Manager 5.4). For continuous outcomes, calculate the mean difference (MD) and 95% confidence interval (CI) for each study. Use the standardized mean difference (SMD) if different measurement scales are used for the same outcome.
  2. Assess statistical heterogeneity. Calculate the I² statistic to quantify the proportion of total variation due to heterogeneity. Interpret the I² value as follows: 0-25% (might not be important), 25-50% (low), 50-75% (moderate), 75-100% (high). Use Cochran's Q test (p < 0.10) to assess the presence of significant heterogeneity.
  3. Select the appropriate statistical model. Use a random-effects model when I² > 50% or when clinical heterogeneity is evident. Use a fixed-effect model when I² ≤ 50% and the studies are clinically homogeneous. Report results from both models as part of the sensitivity analyses.
  4. Conduct subgroup and sensitivity analyses. Perform subgroup analyses based on intervention type, timing of intervention, geographic region, and cancer stage. Perform sensitivity analyses by excluding studies with a high risk of bias, using alternative effect measures, applying different statistical models, and conducting leave-one-out analyses.
  5. Assess publication bias14. Create a funnel plot to visually inspect for asymmetry for any outcome that includes 10 or more studies. Perform Egger's regression test for outcomes with 10 or more studies to statistically test for funnel plot asymmetry. Interpret the results with caution if fewer than 10 studies are available for an outcome.

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Results

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Search results and study selection
A comprehensive literature search conducted on July 1, 2025, identified 3675 records from five databases. After removing 1226 duplicates, 2449 records underwent title and abstract screening. Of these, 148 full-text articles were assessed for eligibility, with 130 excluded for not meeting the inclusion criteria. Eighteen studies15,16,17,18<...

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Discussion

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Meta-analysis outcomes
For the current meta-analysis, 18 studies with 3586 subjects were studied15,16,17,18,19,20,21,22,23,24,25<...

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Disclosures

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The authors declare that they have no competing interests.

Acknowledgements

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Not applicable.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Covidence for study screeningVeritas Health Innovation Ltd.https://www.covidence.org/
EndNote X9 Clarivatehttps://support.clarivate.com/Endnote/s/article/Download-EndNote?language=en_US
R statistical software (version 4.3.1) with metafor package for additional analysesthe r foundation for statistical computinghttps://www.r-project.org/
Review Manager 5.4 (The Cochrane Collaboration) for meta-analysis.The Nordic Cochrane Centre, the Cochrane Collaborationhttps://www.cochrane.org/learn

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Oral Nutritional SupplementationEnteral NutritionParenteral NutritionRandomized Controlled TrialsBody WeightPrealbumin Levels

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