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Phenotyping in clinical nutrition

Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer

European Journal of Clinical Nutrition volume 75, pages 1291–1301 (2021)Cite this article

Subjects

Abstract

Background

Malnutrition is prevalent that can impair multiple clinical outcomes in oncology populations. This study aimed to develop and utilize a tool to optimize the early identification of malnutrition in patients with cancer.

Methods

We performed an observational cohort study including 3998 patients with cancer at two teaching hospitals in China. Hierarchical clustering was performed to classify the patients into well-nourished or malnourished clusters based on 17 features reflecting the phenotypic and etiologic dimensions of malnutrition. Associations between the identified clusters and patient characteristics were analyzed. A nomogram for predicting the malnutrition probability was constructed and independent validation was performed to explore its clinical significance.

Results

The cluster analysis identified a well-nourished cluster (n = 2736, 68.4%) and a malnourished cluster (n = 1262, 31.6%) in the study population, which showed significant agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria (both P < 0.001). The malnourished cluster was negatively associated with the nutritional status, physical status, quality of life, short-term outcomes and was an independent risk factor for survival (HR = 1.38, 95%CI = 1.22–1.55, P < 0.001). Sex, gastrointestinal symptoms, weight loss percentages (within and beyond 6 months), calf circumference, and body mass index were incorporated to develop the nomogram, which showed high performance to predict malnutrition (AUC = 0.972, 95%CI = 0.960–0.983). The decision curve analysis and independent external validation further demonstrated the effectiveness and clinical usefulness of the tool.

Conclusions

Nutritional features-based clustering analysis is a feasible approach to define malnutrition. The derived nomogram shows effectiveness for the early identification of malnutrition in patients with cancer.

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Fig. 1: Cluster analysis on the nutritional features of the study population.
Fig. 2: Evaluation of the clustering results.
Fig. 3: Development, validation, and clinical usefulness of a nomogram for predicting the probability of being in the malnourished cluster.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available to protect patient confidentiality but are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (81673167), the National Key Research and Development Program (2017YFC1309200), and the Chongqing Technology Innovation and Application Demonstration Project for Social Livelihood (cstc2018jscx-msybX0094).

Author information

Authors and Affiliations

  1. Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China

    Liangyu Yin, Jie Liu, Xin Lin, Na Li, Jing Guo, Yang Fan, Ling Zhang, Muli Shi, Hongmei Zhang & Hongxia Xu

  2. Cancer Center of the First Hospital of Jilin University, Changchun, 130021, Jilin, China

    Xiao Chen, Chang Wang, Li Deng, Wei Li & Jiuwei Cui

  3. Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China

    Zhenming Fu

  4. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China

    Chunhua Song

  5. Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China

    Zengqing Guo

  6. Department of Gastrointestinal Surgery | Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 100038, Beijing, China

    Hanping Shi

Authors
  1. Liangyu Yin
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Contributions

Conceptualization and study design: HXX, HPS, JWC, and LYY. investigation: LYY and HXX. Statistical analysis: LYY. Data interpretation: JL, XL, NL, JG, YF, LZ, MLS, HMZ, XC, CW, LD, WL, ZMF, CHS, and ZQG. Paper preparation: LYY. All authors have read and approved the final paper.

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Correspondence to Jiuwei Cui, Hanping Shi or Hongxia Xu.

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Yin, L., Liu, J., Lin, X. et al. Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer. Eur J Clin Nutr 75, 1291–1301 (2021). https://doi.org/10.1038/s41430-020-00844-8

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