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在复杂的多重疾病途径中发现桥梁性疾病:一种网络科学方法。

Uncovering bridging diseases in complex multimorbidity pathways: A network science approach.

作者信息

Alvarez-Galvez Javier, Arroyo Javier

机构信息

Computational Social Science DataLab (CS2 DataLab), University Institute for Sustainable Social Development, University of Cádiz, Cádiz, Spain.

Department of General Economy (Sociology), Faculty of Nursing and Physiotherapy, University of Cádiz, Cádiz, Spain.

出版信息

PLoS One. 2025 May 7;20(5):e0323208. doi: 10.1371/journal.pone.0323208. eCollection 2025.

Abstract

Multimorbidity, the co-occurrence of multiple chronic diseases, represents a significant challenge in healthcare, necessitating advanced analytical methods for a better understanding. Although numerous studies focus on characterizing chronicity profiles across different population groups, there is still a need to identify specific diseases that play a crucial role in shaping multimorbidity patterns. This study applies network science to analyze multimorbidity structures and identify bridging diseases that facilitate the development of complex multimorbidity patterns, using data from a representative sample of 2,200 individuals aged 50 and older residing in southern Spain. Our findings reveal significant gender-based differences in multimorbidity patterns, with women experiencing a higher burden of chronic diseases, resulting in more complex and tightly linked disease networks. The analysis highlights the relevance of specific conditions, such as liver dysfunction in men and depression in women, as key contributors to the formation of complex multimorbidity structures. These findings emphasize the importance of sex/gender-specific healthcare strategies aimed at controlling and preventing diseases that may act as catalysts for multisystem multimorbidity, which have a profound impact on both mortality rates and healthcare utilization.

摘要

多重疾病,即多种慢性病同时出现,是医疗保健领域的一项重大挑战,需要先进的分析方法来更好地理解。尽管众多研究聚焦于刻画不同人群的慢性病概况,但仍有必要识别在塑造多重疾病模式中起关键作用的特定疾病。本研究运用网络科学分析多重疾病结构,并识别促进复杂多重疾病模式发展的桥梁性疾病,使用的数据来自西班牙南部2200名50岁及以上的代表性个体样本。我们的研究结果揭示了多重疾病模式中显著的性别差异,女性承受着更高的慢性病负担,导致疾病网络更复杂且联系更紧密。分析突出了特定病症的相关性,如男性的肝功能障碍和女性的抑郁症,是形成复杂多重疾病结构的关键因素。这些发现强调了针对性别制定医疗保健策略的重要性,旨在控制和预防可能成为多系统多重疾病催化剂的疾病,这些疾病对死亡率和医疗保健利用率都有深远影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d0f/12058136/393b236d097a/pone.0323208.g001.jpg

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