Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study
IntroductionThis study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies.MethodsA Bayesian network approach was applied to 2,400 adult participants (18+ years; 50% male, 50% female) from the Q...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Pain Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpain.2025.1573465/full |
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| author | Aisha Ahmad M. A. Al-Khinji Aisha Ahmad M. A. Al-Khinji Dhafer Malouche Dhafer Malouche |
| author_facet | Aisha Ahmad M. A. Al-Khinji Aisha Ahmad M. A. Al-Khinji Dhafer Malouche Dhafer Malouche |
| author_sort | Aisha Ahmad M. A. Al-Khinji |
| collection | DOAJ |
| description | IntroductionThis study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies.MethodsA 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%).ResultsThe 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).DiscussionThese 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. |
| format | Article |
| id | doaj-art-c2d3050d475d43b0bc3069857fc15656 |
| institution | OA Journals |
| issn | 2673-561X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pain Research |
| spelling | doaj-art-c2d3050d475d43b0bc3069857fc156562025-08-20T02:29:20ZengFrontiers Media S.A.Frontiers in Pain Research2673-561X2025-05-01610.3389/fpain.2025.15734651573465Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank studyAisha Ahmad M. A. Al-Khinji0Aisha Ahmad M. A. Al-Khinji1Dhafer Malouche2Dhafer Malouche3College of Medicine, Qatar University, Doha, QatarClinical Translational Science Research Group, QU Health, Qatar University, Doha, QatarClinical Translational Science Research Group, QU Health, Qatar University, Doha, QatarDepartment of Mathematicsand Statistics, College of Arts and Sciences, Qatar University, Doha, QatarIntroductionThis study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies.MethodsA 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%).ResultsThe 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).DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fpain.2025.1573465/fullBayesian networkQatar Biobank (QBB)pain interdependenciesconditional probabilitiessystemic painprobabilistic modeling |
| spellingShingle | Aisha Ahmad M. A. Al-Khinji Aisha Ahmad M. A. Al-Khinji Dhafer Malouche Dhafer Malouche Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study Frontiers in Pain Research Bayesian network Qatar Biobank (QBB) pain interdependencies conditional probabilities systemic pain probabilistic modeling |
| title | Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study |
| title_full | Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study |
| title_fullStr | Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study |
| title_full_unstemmed | Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study |
| title_short | Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study |
| title_sort | modeling chronic pain interconnections using bayesian networks insights from the qatar biobank study |
| topic | Bayesian network Qatar Biobank (QBB) pain interdependencies conditional probabilities systemic pain probabilistic modeling |
| url | https://www.frontiersin.org/articles/10.3389/fpain.2025.1573465/full |
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