Etienne Vachon-Presseau

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.

Self-reported cognitive difficulties are a common, yet underexplored, complaint of patients with chronic pain (CP). These deficits hinder daily functioning and reduce quality of life, contributing to disability. However, the extent and profile of these impairments remain unclear. Our study synthesized findings from a meta-analysis (Study 1; n = 67 studies, 6,899 patients and controls) with data from the UK Biobank (Study 2; n = 37,139 participants with CP and controls) to ascertain the magnitude and profile of cognitive deficits on performance-based measures in CP sufferers relative to healthy controls. Study 1 considered various factors, including cognitive domains, pain diagnosis and intensity, and psychological variables; Study 2 used a novel predictive model of cognitive age to evaluate cognitive deficits between different pain, neurological, and psychiatric conditions. Results from Study 1 demonstrated moderate cognitive difficulties in CP ( g = −0.48 [95% CI −0.52 to −0.43], I ² = 82.03) across pain diagnoses and cognitive domains, and proportional to pain severity and psychological symptoms. Study 2 replicated these findings, though to a lesser magnitude ( d = 0.04-0.30). Furthermore, participants with higher pain severity (4+ pain sites) showed an advanced cognitive age pattern. The cognitive difficulties observed among CP participants may be indicative of stress-related advanced cognitive age, resulting from persistent and uncontrollable pain. However, it is important to note that these deficits are small to moderate in severity, and, therefore, the observation of more pronounced impairment in CP warrants further clinical evaluation, particularly if their extent appears disproportionate to pain severity.

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.

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.

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. Here, in this multidataset study of over 523,000 participants, we applied machine learning to multidimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (for example, rheumatoid arthritis and gout) or self-reported chronic pain (for example, back pain and knee pain). 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). Notably, all biomarkers worked in synergy with psychosocial factors, accurately predicting both medical conditions (AUC 0.69–0.91) and self-reported pain (AUC 0.71–0.92). These findings underscore the necessity of adopting a holistic approach in the development of biomarkers to enhance their clinical utility.

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.

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants ( n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue ‘Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation’.

From adolescence, women become more likely to experience fear dysregulation. Oral contraceptives (OCs) can modulate the brain regions involved in fear processes. OCs are generally used for years and often initiated during adolescence, a sensitive period where certain brain regions involved in the fear circuitry are still undergoing important reorganization. It remains unknown whether OC use during adolescence may induce long‐lasting changes in the fear circuitry. This study aimed to examine whether age of onset moderated the relationship between duration of use and fear‐related brain structures. We collected structural MRI data in 98 healthy adult women (61 current users, 37 past users) and extracted grey matter volumes (GMV) and cortical thickness (CT) of key regions of the fear circuitry. Non‐linear multiple regressions revealed interaction effects between age of onset and quadratic duration of use on GMV of the right hippocampus and right ventromedial prefrontal cortex (vmPFC). Among women who initiated OCs earlier in adolescence, a short duration of use was associated with smaller hippocampal GMV and thicker vmPFC compared to a longer duration of use. For both GMV and CT of the right vmPFC, women with an early OC onset had more grey matter at a short duration of use than those with a later onset. Our results suggest that OC use earlier in adolescence may induce lasting effects on structural correlates of fear learning and its regulation. These findings support further investigation into the timing of OC use to better comprehend how OCs could disrupt normal brain development processes.

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.