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网络分析显示,与头痛相关的、心理和心身方面的结果代表了偏头痛女性的不同方面。

Network Analysis Reveals That Headache-Related, Psychological and Psycho-Physical Outcomes Represent Different Aspects in Women with Migraine.

作者信息

Fernández-de-Las-Peñas César, Florencio Lidiane L, Varol Umut, Pareja Juan A, Ordás-Bandera Carlos, Valera-Calero Juan A

机构信息

Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, 28922 Alcorcón, Spain.

VALTRADOFI Research Group, Department of Physiotherapy, Faculty of Health, Camilo Jose Cela University, 28692 Villafranca del Castillo, Spain.

出版信息

Diagnostics (Basel). 2022 Sep 26;12(10):2318. doi: 10.3390/diagnostics12102318.

Abstract

Evidence supports that migraine is a complex pain condition with different underlying mechanisms. We aimed to quantify potential associations between demographic, migraine-related, and psychophysical and psychophysical variables in women with migraine. Demographic (age, height, and weight), migraine-related (intensity, frequency, and duration), related-disability (Migraine Disability Assessment Scale, Headache Disability Inventory), psychological (Hospital Anxiety and Depression Scale), and psycho-physical (pressure pain thresholds -PPTs-) variables were collected from a sample of 74 women suffering from migraine. We calculated adjusted correlations between the variables by using a network analysis. Additionally, we also calculated centrality indices to identify the connectivity among the variables within the network and the relevance of each variable in the network. Multiple positive correlations (ρ) between PPTs were observed ranging from 0.1654 (C5-C6 and tibialis anterior) to 0.40 (hand and temporalis muscle). The strongest associations within the network were those between migraine attack frequency and diagnosis of chronic migraine (ρ = 0.634) and between the HDI-E and HDI-P (ρ = 0.545). The node with the highest strength and betweenness centrality was PPT at the second metacarpal, whereas the node with the highest harmonic centrality was PPT at the tibialis anterior muscle. This is the first study applying a network analysis to understand the underlying mechanisms in migraine. The identified network revealed that a model where each subgroup of migraine-related, psychological, and psycho-physical variables showed no interaction between each variable. Current findings could have clinical implications for developing multimodal treatments targeting the identified mechanisms.

摘要

有证据支持偏头痛是一种具有不同潜在机制的复杂疼痛病症。我们旨在量化偏头痛女性人群中人口统计学、偏头痛相关因素以及心理物理学和心理因素之间的潜在关联。从74名偏头痛女性样本中收集了人口统计学变量(年龄、身高和体重)、偏头痛相关变量(强度、频率和持续时间)、相关残疾变量(偏头痛残疾评估量表、头痛残疾问卷)、心理变量(医院焦虑抑郁量表)以及心理物理学变量(压痛阈值-PPTs-)。我们通过网络分析计算了变量之间的校正相关性。此外,我们还计算了中心性指标,以确定网络中变量之间的连通性以及每个变量在网络中的相关性。观察到PPTs之间存在多个正相关(ρ),范围从0.1654(C5-C6和胫骨前肌)到0.40(手部和颞肌)。网络中最强的关联是偏头痛发作频率与慢性偏头痛诊断之间的关联(ρ = 0.634)以及头痛残疾问卷-情绪维度(HDI-E)和头痛残疾问卷-身体维度(HDI-P)之间的关联(ρ = 0.545)。强度中心性和中介中心性最高的节点是第二掌骨处的PPT,而调和中心性最高的节点是胫骨前肌处的PPT。这是第一项应用网络分析来理解偏头痛潜在机制的研究。所确定的网络表明,偏头痛相关、心理和心理物理学变量的每个亚组之间不存在相互作用的模型。目前的研究结果可能对针对所确定机制开发多模式治疗方法具有临床意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb1b/9600561/444a56724e63/diagnostics-12-02318-g001.jpg

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