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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
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Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study.

Bernard X W Liew1, Jan Hartvigsen2, Marco Scutari3

  • 1School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, UK.

Journal of Clinical Epidemiology
|November 17, 2022
PubMed
Summary

Psychological factors significantly impact low back pain (LBP) disability in distinct patient subgroups. Tailoring treatments to these differing pathways, especially psychological ones, may improve outcomes for chronic LBP.

Keywords:
Bayesian networksChronic painLow back painMachine learningNetwork analysisStructural equation modeling

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Area of Science:

  • Pain research
  • Clinical psychology
  • Rehabilitation science

Background:

  • Low back pain (LBP) is a complex condition with multifactorial causes of disability.
  • Understanding the interplay of physical, psychological, and activity-related factors is crucial for effective LBP management.
  • Previous research indicates heterogeneity in LBP patient experiences and contributing factors.

Purpose of the Study:

  • To investigate the distinct pathways linking physical, activity, pain, and psychological factors to disability in individuals with long-lasting nonspecific LBP.
  • To determine if these pathways differ between identified patient subgroups.

Main Methods:

  • Utilized baseline data from 3849 participants in the GLA:D Back program for long-lasting nonspecific LBP.
  • Employed clustering for subgroup identification and Bayesian networks (BN) for structural learning.
  • Applied structural equation modeling (SEM) for statistical inference on 15 measured variables.

Main Results:

  • Identified two distinct clinical subgroups with differing symptom severity.
  • Psychological factors were directly associated with disability in both subgroups, most strongly in subgroup 1 (β=0.363).
  • In subgroup 2, pain was the strongest predictor of disability (β=0.408), while psychological factors commonly predicted physical factors, pain, activity, and disability.

Conclusions:

  • Psychological factors are critically important for LBP disability in both identified subgroups, highlighting their significance in treatment.
  • Recognizing the differential contributions of physical, pain, and psychological factors across subgroups can inform more personalized and optimal LBP treatment strategies.