Al-Khinji Aisha Ahmad M A, Malouche Dhafer
College of Medicine, Qatar University, Doha, Qatar.
Clinical Translational Science Research Group, QU Health, Qatar University, Doha, Qatar.
Front Pain Res (Lausanne). 2025 May 27;6:1573465. doi: 10.3389/fpain.2025.1573465. eCollection 2025.
This study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies.
A Bayesian network approach was applied to 2,400 adult participants (18+ years; 50% male, 50% female) from the Qatar Biobank (QBB). Participants were categorized into young (18-35 years, 40.9%), middle-aged (36-60 years, 50.6%), and seniors (61+ years, 8.5%).
The model identified direct and indirect associations among pain locations and demographic factors, quantified by odds ratios (ORs). Younger females had the highest probability of headaches or migraines (48.6%) compared to younger males (31.2%), with probabilities decreasing across age (OR 1.917; 95% CI 1.609-2.284). Hand pain strongly correlated with hip pain (OR 8.691; 95% CI 6.074-12.434) and neck or shoulder pain (OR 4.451; 95% CI 3.302-6.000). Back pain was a key predictor of systemic pain, with a 37.9% probability of generalized pain when combined with hand pain (OR 7.682; 95% CI 5.293-11.149), dropping to 6.6% for back pain alone. Age, back pain, and foot pain collectively influenced knee pain, which reached 72.7% in older individuals with foot and back pain (OR 4.759; 95% CI 3.704-6.114).
These Bayesian network parameters explicitly reveal probabilistic interdependencies among pain locations, suggesting that targeted interventions for key anatomical regions could effectively mitigate broader chronic pain networks. The model also elucidates how demographic predispositions influence downstream pain patterns, providing a clear and actionable framework for personalized chronic pain management strategies.
本研究探讨了不同慢性疼痛部位之间的相互依存关系及其与年龄和性别的关系，这对有效的临床策略至关重要。
采用贝叶斯网络方法对来自卡塔尔生物样本库（QBB）的2400名成年参与者（18岁及以上；50%为男性，50%为女性）进行研究。参与者被分为青年组（18 - 35岁，40.9%）、中年组（36 - 60岁，50.6%）和老年组（61岁及以上，8.5%）。
该模型确定了疼痛部位与人口统计学因素之间的直接和间接关联，通过比值比（OR）进行量化。与青年男性（31.2%）相比，青年女性患头痛或偏头痛的概率最高（48.6%），且概率随年龄增长而降低（OR 1.917；95%置信区间1.609 - 2.284）。手部疼痛与髋部疼痛（OR 8.691；95%置信区间6.074 - 12.434）以及颈部或肩部疼痛（OR 4.451；95%置信区间3.302 - 6.000）密切相关。背痛是全身性疼痛的关键预测因素，与手部疼痛同时出现时，全身性疼痛的概率为37.9%（OR 7.682；95%置信区间5.293 - 11.149），单独背痛时降至6.6%。年龄、背痛和足部疼痛共同影响膝关节疼痛，在患有足部和背痛的老年人中，膝关节疼痛发生率达到72.7%（OR 4.759；95%置信区间3.704 - 6.114）。
这些贝叶斯网络参数明确揭示了疼痛部位之间的概率性相互依存关系，表明针对关键解剖区域的靶向干预可以有效减轻更广泛的慢性疼痛网络。该模型还阐明了人口统计学易感性如何影响下游疼痛模式，为个性化慢性疼痛管理策略提供了清晰且可操作的框架。
