Scholarlink

Introduction <p>The intricate interplay between organs can give rise to a multitude of physiological conditions. Disruptions such as inflammation or tissue damage can precipitate the development of chronic diseases such as tumors or diabetes mellitus (DM). While both lung cancer and DM are the consequences of disruptions in homeostasis, the relationship between them is intricate. This study sought to investigate the potential influence of DM on lung cancer by employing total-body dynamic PET imaging.</p> Methods <p>The present study proposes a framework for metabolic network analysis using total-body dynamic PET imaging of 20 lung cancer patients with DM (DM group) and 20 lung cancer patients without DM (Non-DM group), with the residuals of a third-order polynomial fit serving as an indicator of Pearson correlation.</p> Results <p>The framework successfully captured the deviation of the DM group from the Non-DM group at both the edge and organ levels. At the edge level, there was a significant difference in the lesion- left ventricle (LV) between the DM and Non-DM groups (<i>P</i> &lt; 0.05). Furthermore, we discovered a positive correlation between the absolute value of Z-score (ZCC) of lesion - LV and the duration of DM (<i>R</i> = 0.680, <i>P</i> &lt; 0.001). At the organ level, there was a significant difference in the kidney, brain, and abdominal fat between the DM and Non-DM groups (<i>P</i> &lt; 0.05).</p> Conclusion <p>This study demonstrated the feasibility of constructing metabolic networks to uncover complex alterations in lung cancer patients with DM. The findings contribute to understanding the systemic effects of DM on lung cancer metabolism and highlight the importance of personalized metabolic network analysis to comprehend the implications of concurrent diseases.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Influence of diabetes mellitus on metabolic networks in lung cancer patients: an analysis using dynamic total-body PET/CT imaging

  • 期刊论文

  • 发表日期：

    2025年1月20日

查看全文
  • Lubing Sun,
  • Yaping Wu,
  • Tao Sun,
  • Panlong Li,
  • Junting Liang,
  • Xuan Yu,
  • Junpeng Yang,
  • Nan Meng,
  • Meiyun Wang,
  • Chuanliang Chen

摘要

Introduction

The intricate interplay between organs can give rise to a multitude of physiological conditions. Disruptions such as inflammation or tissue damage can precipitate the development of chronic diseases such as tumors or diabetes mellitus (DM). While both lung cancer and DM are the consequences of disruptions in homeostasis, the relationship between them is intricate. This study sought to investigate the potential influence of DM on lung cancer by employing total-body dynamic PET imaging.

Methods

The present study proposes a framework for metabolic network analysis using total-body dynamic PET imaging of 20 lung cancer patients with DM (DM group) and 20 lung cancer patients without DM (Non-DM group), with the residuals of a third-order polynomial fit serving as an indicator of Pearson correlation.

Results

The framework successfully captured the deviation of the DM group from the Non-DM group at both the edge and organ levels. At the edge level, there was a significant difference in the lesion- left ventricle (LV) between the DM and Non-DM groups (P < 0.05). Furthermore, we discovered a positive correlation between the absolute value of Z-score (ZCC) of lesion - LV and the duration of DM (R = 0.680, P < 0.001). At the organ level, there was a significant difference in the kidney, brain, and abdominal fat between the DM and Non-DM groups (P < 0.05).

Conclusion

This study demonstrated the feasibility of constructing metabolic networks to uncover complex alterations in lung cancer patients with DM. The findings contribute to understanding the systemic effects of DM on lung cancer metabolism and highlight the importance of personalized metabolic network analysis to comprehend the implications of concurrent diseases.