Christophe Tanguay-Sabourin

Pain is the leading cause of disability worldwide, yet no harmonized self-reported reference framework exists to characterize how its burden is distributed across the lifespan and world regions. In this study, we harmonized individual-level self-reported pain data from 6,125,459 participants across 902 population-based data sources in 118 countries to establish global reference trajectories of pain. We implemented these trajectories in an open-access benchmarking platform for positioning external datasets against global pain norms. Pain prevalence ranged from roughly 2% for facial pain to 40% for back pain, was consistently higher in women across all 11 anatomical sites (risk ratio range: 1.08−1.83) and increased most steeply before age 55 years. Contrary to existing estimates that generally project higher prevalence of pain conditions in higher Human Development Index (HDI) regions, we found that individuals in the lowest HDI countries experienced a substantially higher late-life prevalence of any bodily pain than those in the highest (risk difference: 30.6 percentage points (95% confidence interval: 26.9−34.3)), with low back pain approaching twice the prevalence at the oldest ages. Globally, 18.3% of pain burden across anatomical sites was attributable to three modifiable risk factors (smoking, obesity and low income), but this varied from 12.6% in sub-Saharan Africa to 27.1% in eastern Europe, indicating that the factors underlying pain in lower-HDI settings remain poorly characterized. Together, these reference trajectories provide a common framework for defining and comparing pain burden and informing more equitable global surveillance and prevention.

Pain is the leading cause of disability worldwide, yet no population-based reference exists against which individual cohorts, clinical populations, or countries can be benchmarked. Here, we harmonised individual-level self-reported pain data from 6,075,021 participants across 894 population-based data sources in 118 countries to establish global reference trajectories of pain across the lifespan, implemented in an open-access benchmarking platform. Pain prevalence ranged from 2.5% for facial pain to 45.0% for back pain, was consistently higher in women across all eleven anatomical sites (risk ratio range 1.09 to 1.83), and increased most steeply before age 55 years. Contrary to existing estimates that generally project higher prevalence of pain conditions in higher Human Development Index (HDI) regions, we found that individuals in the lowest HDI countries experienced nearly twice the late-life prevalence of any bodily pain compared with those in the highest (risk difference 31.8 percentage points [95% CI 30.1–33.6]). Globally, 18.3% of site-specific pain burden was attributable to three modifiable risk factors (smoking, obesity, and low income) but this varied from 12.6% in sub-Saharan Africa to 27.1% in eastern Europe, indicating that the drivers of pain in lower-HDI settings remain poorly characterised.

Pain is the leading cause of disability worldwide, yet no harmonised self-reported reference framework exists to characterise how its burden is distributed across the lifespan and world regions. Here, we harmonised individual-level self-reported pain data from 6,075,021 participants across 894 population-based data sources in 118 countries to establish global reference trajectories of pain. We implemented these trajectories in an open-access benchmarking platform for positioning external datasets against global pain norms. Pain prevalence ranged from 2.5% for facial pain to 45.0% for back pain, was consistently higher in women across all eleven anatomical sites (risk ratio range 1.09 to 1.83), and increased most steeply before age 55 years. Contrary to existing estimates that generally project higher prevalence of pain conditions in higher Human Development Index (HDI) regions, we found that individuals in the lowest HDI countries experienced nearly twice the late-life prevalence of any bodily pain compared with those in the highest (risk difference 31.8 percentage points [95% CI 30.1–33.6]). Globally, 18.3% of pain burden across anatomical sites was attributable to three modifiable risk factors (smoking, obesity, and low income) but this varied from 12.6% in sub-Saharan Africa to 27.1% in eastern Europe, indicating that the drivers of pain in lower-HDI settings remain poorly characterised.

The opioid epidemic remains a major global health crisis. To better understand who is most vulnerable to opioid prescribing, misuse, and opioid-related disorders, we analyzed data from the UK Biobank (n=195,808) and the All of Us Research Program (n=48,390). Opioids were more frequently prescribed to individuals with widespread pain and comorbid clinical conditions. We applied machine learning to evaluate both pain-related and non-pain risk factors separately, and the two models predicted opioid use with good accuracy in both cohorts (AUC 0.70–0.78). Longitudinal analyses showed that psychosocial and functional factors contributed as strongly as pain measurements, predicted opioid initiation nearly nine years later, and were elevated in individuals with opioid misuse and opioid-related disorders. These findings demonstrate that psychiatric, psychosocial, and functional vulnerabilities are sufficient to predict opioid use, and that integrating psychosocial risk profiling into routine pain care could support safer prescribing and mitigate opioid-related harms.

Subjective tinnitus is an auditory percept unrelated to external sounds, for which the limited understanding of its risk factors complicates the prevention and management. In this study, we train two distinct machine learning models to predict tinnitus presence (how often individuals perceive tinnitus) and severity separately using socio-demographic, psychological, and health-related predictors with the UK Biobank dataset (192,993 participants, 41,042 with tinnitus). We show that hearing health was the primary risk factor of both presence and severity, while mood, neuroticism, and sleep predicted severity. The severity model accurately predicts tinnitus progression over nine years, with a large effect size for individuals developing severe tinnitus (Cohen’s d = 1.3, ROC = 0.78). This result is validated on 463 individuals from the Tinnitus Research Initiative database. We simplify the severity model to a six-item clinical questionnaire that detects individuals at risk of severe tinnitus, for which early supportive care would be crucial.

Background: Subjective tinnitus is an auditory percept unrelated to an external sound source. The lack of curative treatments and limited understanding of its risk factors complicate the prevention and management of this distressing symptom. This study seeks to identify socio-demographic, psychological, and health-related risk factors predicting tinnitus presence (how often individuals perceive tinnitus) and severity separately, and their evolution over time. Methods: Using the UK Biobank dataset which encompasses data on the socio-demographic, physical, mental and hearing health from more than 170,000 participants, we trained two distinct machine learning models to identify risk scores predicting tinnitus presence and severity separately. These models were used to predict tinnitus over time and were replicated in 463 individuals from the Tinnitus Research Initiative database. Finding: Machine learning based approach identified hearing health as a primary risk factor for the presence and severity of tinnitus, while mood, neuroticism, hearing health, and sleep only predicted tinnitus severity. Only the severity model accurately predicted the evolution over nine years, with a large effect size for individuals developing severe tinnitus (Cohen’s d = 1.10, AUC-ROC = 0.70). To facilitate its clinical applications, we simplified the severity model and validated a five-item questionnaire to detect individuals at risk of developing severe tinnitus. Interpretation: This study is the first to clearly identify risk factors predicting tinnitus presence and severity separately. Hearing health emerges as a major predictor of tinnitus presence, while mental health plays a crucial role in its severity. The successful prediction of the evolution of tinnitus severity over nine years based on socio-emotional, hearing and sleep factors suggests that modifying these factors could mitigate the impact of tinnitus. The newly developed questionnaire represents a significant advancement in identifying individuals at risk of severe tinnitus, for which early supportive care would be crucial. Funding: Horizon Europe Marie Slodowska-Curie Actions, the Fondation des gueules cassées, the Fondation pour l’Audition, the Louise and Alan Edwards Foundation, the Canadian Institutes Health Research, the Institut TransMedTech and the Canada First Research Excellence Fund.

Chronic pain is a multifactorial condition presenting significant diagnostic and prognostic challenges. Biomarkers for the classification and the prediction of chronic pain are therefore critically needed. In this multi-dataset study of over 523,000 participants, we applied machine learning to multi-dimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (e.g., clinical diagnosis of rheumatoid arthritis, fibromyalgia, stroke, gout, etc.) or self-reported chronic pain (e.g., back pain, knee pain, etc). Biomarkers derived from blood immunoassays, brain and bone imaging, and genetics were effective in predicting medical conditions associated with chronic pain (area under the curve (AUC) 0.62-0.87) but not self-reported pain (AUC 0.50-0.62). Among the biomarkers identified was a composite blood-based signature that predicted the onset of various medical conditions approximately nine years in advance (AUC 0.59-0.72). Notably, all biomarkers worked in synergy with psychosocial factors, accurately predicting both medical conditions (AUC 0.69– 0.91) and self-report pain (AUC 0.71–0.92). Over a period of 15 years, individuals scoring high on both biomarkers and psychosocial risk factors had twice the cumulative incidence of diagnoses for pain-associated medical conditions (Hazard Ratio (HR): 2.26) compared to individuals scoring high on biomarkers but low on psychosocial risk factors (HR: 1.06). In summary, we identified various biomarkers for chronic pain conditions and showed that their predictive efficacy heavily depended on psychological and social influences. These findings underscore the necessity of adopting a holistic approach in the development of biomarkers to enhance their clinical utility.

Chronic pain is a complex condition influenced by a combination of biological, psychological and social factors. Using data from the UK Biobank (n = 493,211), we showed that pain spreads from proximal to distal sites and developed a biopsychosocial model that predicted the number of coexisting pain sites. This data-driven model was used to identify a risk score that classified various chronic pain conditions (area under the curve (AUC) 0.70–0.88) and pain-related medical conditions (AUC 0.67–0.86). In longitudinal analyses, the risk score predicted the development of widespread chronic pain, the spreading of chronic pain across body sites and high-impact pain about 9 years later (AUC 0.68–0.78). Key risk factors included sleeplessness, feeling ‘fed-up’, tiredness, stressful life events and a body mass index >30. A simplified version of this score, named the risk of pain spreading, obtained similar predictive performance based on six simple questions with binarized answers. The risk of pain spreading was then validated in the Northern Finland Birth Cohort (n = 5,525) and the PREVENT-AD cohort (n = 178), obtaining comparable predictive performance. Our findings show that chronic pain conditions can be predicted from a common set of biopsychosocial factors, which can aid in tailoring research protocols, optimizing patient randomization in clinical trials and improving pain management.

Chronic pain conditions are complex syndromes characterized by a mosaic of biological, psychological, and social factors. We derived predictive models for the number of co- existing pain sites in the UK Biobank and identified a common risk score that classified different chronic pain conditions in cross-sectional data, predicted the development of chronic pain in pain-free individuals, and determined the spreading of chronic pain to multiple sites or its recovery nine years later. The features with the strongest prognosis included sleeplessness, feeling ‘fed-up’, tiredness, stressful life events, and a BMI > 30. The risk score for pain was associated with an inflammatory blood marker, a polygenic risk score for pain, and a neuroimaging-based marker for sustained pain. The demonstration of a common biopsychosocial risk factor for different clinical pain conditions may help better characterize a general chronic pain syndrome, tailor research protocols, optimize patient randomization in clinical trials, and improve pain management.