Genetic and Environmental Influences on Chronic Pain Conditions

Chronic pain conditions result from a complex interplay between inherited genetic factors and a range of environmental influences, including psychological stress, sleep quality, occupational exposures and diet. Genetic predisposition accounts for an estimated 30–50% of variability in susceptibility to chronic pain, with numerous loci implicated in nociception, inflammatory pathways and central nervous system processing. Environmental and lifestyle factors such as mood disorders, physical inactivity, obesity and socioeconomic disadvantage interact with this inherited risk, modulating neuronal sensitisation and gene expression through epigenetic mechanisms. Biopsychosocial models emphasise how psychological comorbidities amplify genetic vulnerability, driving transitions from acute injury to persistent pain. Advances in large-scale genomic analyses, causal inference methods and integrative phenotyping have refined our understanding of how multiple low-penetrance variants combine with modifiable exposures to shape individual trajectories of pain. This integrated perspective underpins emerging strategies for personalised risk stratification and prevention, targeting both biological pathways and environmental triggers.

Research from Nature Portfolio

Recent studies have harnessed population cohorts to develop predictive frameworks and dissect the genetic architecture of chronic pain. A novel risk score derived from nearly half a million participants distilled six biopsychosocial variables into a tool that forecasts the onset and anatomical spread of multisite chronic pain over almost a decade. Validated in independent birth-cohort and clinical samples, this model exemplifies how genetic predisposition and environmental stress factors can be operationalised into a clinically actionable prognostic index. In parallel, an analysis of genetically independent phenotypes across four musculoskeletal sites applied principal component methods to uncover shared genetic signals. The leading phenotype captured a core biopsychological dimension of pain, enriched for genes involved in neurogenesis, synaptic function and central pain processing, underscoring common molecular pathways through which genetic and environmental factors converge.

Genetic and Environmental Influences on Chronic Pain Conditions publication trend

The graph below shows the total number of articles in genetic and environmental influences on chronic pain conditions across all publications each year (not limited to Nature Index journals).

Technical terms

Genome-wide association study (GWAS): A large-scale scan of common genetic variants across the genome to identify loci associated with a trait.

Mendelian randomisation (MR): An analytical approach using genetic variants as proxies for exposures to infer causality between risk factors and outcomes.

Biopsychosocial model: A framework integrating biological, psychological and social factors in disease development and progression.

Polygenic risk score (PRS): An individual’s aggregate genetic liability computed by summing risk alleles weighted by effect sizes from GWAS.

Genetic loci: Specific chromosomal regions where variation influences a phenotype or disease risk.

References

1. A prognostic risk score for development and spread of chronic pain. Nature Medicine (2023).
2. Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Communications Biology (2020).
3. Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models. BMC Medicine (2024).
4. Exploring the bidirectional relationship between pain and mental disorders: a comprehensive Mendelian randomization study. The Journal of Headache and Pain (2023).
5. Dried fruit intake causally protects against low back pain: A Mendelian randomization study. Frontiers in Nutrition (2023).
6. Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis. PLOS Medicine (2016).