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Objective <p>Metabolic disorders are common in cancer patients. This study aimed to classify the metabolic disorder status of cancer patients using hematological indicators and to examine the association between disorder types and prognosis.</p> Methods <p>A cohort of 6307 patients from INSCOC was classified into three clusters via K-means clustering based on hematological indicators. Logistic regression and Cox models assessed each cluster's impact on adverse outcomes.</p> Results <p>A total of 6,307 participants were included in the study, K-means clustering divided the population into three groups, Cluster 1 (Normal Group, NG), Cluster 2 (Mild Disorder Group, MDG) and Cluster 3 (Severe Disorder Group, SDG). Compared to NG, MDG (OR = 2.268; 95% CI: 1.967–2.616) and SDG (OR = 4.317; 95% CI: 2.441–7.634) had significantly higher risks of sarcopenia. MDG (OR = 1.943; 95% CI: 1.717–2.198) was associated with a higher risk of moderate malnutrition, and both MDG (OR = 3.786; 95% CI: 3.282–4.368) and SDG (OR = 14.501; 95% CI: 6.847–30.709) were identified as risk factors for severe malnutrition (p &lt; 0.05). Cox regression analysis indicated that MDG and SDG were independent risk factors for all-cause mortality (MDG: HR = 1.460, 95% CI: 1.341–1.590; SDG: HR = 2.257, 95% CI: 1.622–3.140) and cancer-specific mortality (MDG: HR = 1.192, 95% CI: 1.039–1.367; SDG: HR = 2.068, 95% CI: 1.825–2.343) (<i>p</i> &lt; 0.05).</p> Conclusion <p>K-means clustering effectively categorized metabolic disorder subgroups, supporting targeted interventions and demonstrating a significant link between disorder severity and adverse outcomes.</p>

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The association of metabolic disorders and prognosis in cancer patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月17日

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  • Yi Li,
  • Chenan Liu,
  • Jinyu Shi,
  • Xin Zheng,
  • Yue Chen,
  • Xiaoyue Liu,
  • Zhaoting Bu,
  • Hong Zhao,
  • Changhong Xu,
  • Bing Yin,
  • Shuyao Wang,
  • Hanping Shi

摘要

Objective

Metabolic disorders are common in cancer patients. This study aimed to classify the metabolic disorder status of cancer patients using hematological indicators and to examine the association between disorder types and prognosis.

Methods

A cohort of 6307 patients from INSCOC was classified into three clusters via K-means clustering based on hematological indicators. Logistic regression and Cox models assessed each cluster's impact on adverse outcomes.

Results

A total of 6,307 participants were included in the study, K-means clustering divided the population into three groups, Cluster 1 (Normal Group, NG), Cluster 2 (Mild Disorder Group, MDG) and Cluster 3 (Severe Disorder Group, SDG). Compared to NG, MDG (OR = 2.268; 95% CI: 1.967–2.616) and SDG (OR = 4.317; 95% CI: 2.441–7.634) had significantly higher risks of sarcopenia. MDG (OR = 1.943; 95% CI: 1.717–2.198) was associated with a higher risk of moderate malnutrition, and both MDG (OR = 3.786; 95% CI: 3.282–4.368) and SDG (OR = 14.501; 95% CI: 6.847–30.709) were identified as risk factors for severe malnutrition (p < 0.05). Cox regression analysis indicated that MDG and SDG were independent risk factors for all-cause mortality (MDG: HR = 1.460, 95% CI: 1.341–1.590; SDG: HR = 2.257, 95% CI: 1.622–3.140) and cancer-specific mortality (MDG: HR = 1.192, 95% CI: 1.039–1.367; SDG: HR = 2.068, 95% CI: 1.825–2.343) (p < 0.05).

Conclusion

K-means clustering effectively categorized metabolic disorder subgroups, supporting targeted interventions and demonstrating a significant link between disorder severity and adverse outcomes.