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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.

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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.

Christophe Tanguay-Sabourin 1,2,3 , Matt Fillingim1,3, Gianluca V. Guglietti1,3,4, Azin Zare 1,4, Marc Parisien 1,3,4, Jax Norman1,4, Hilary Sweatman 5, Ronrick Da-ano1,4, Eveliina Heikkala6,7, PREVENT-AD Research Group*, Jordi Perez3,8, Jaro Karppinen6,9,10, Sylvia Villeneuve11,12, Scott J. Thompson13, Marc O. Martel1,3,4, Mathieu Roy1,3,14, Luda Diatchenko 1,3,4 & Etienne Vachon-Presseau 1,3,4

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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.

Vachon-Presseau E., Berger SE, Abdullah TB, Griffith JW, Schnitzer TJ, & Apkarian AV. Under revisions PDF available here

Because of my academic background in psychology, I was particularly interested in the psychological factors and personality traits influencing the clinical profile of chronic pain patients. In an ongoing study, we are demonstrating that specific dimensions of the psychology of pain can actually be predicted from resting state functional connectivity in chronic pain patients. Moreover, we are showing that biotypes of patients can be derived from the expression of each of these predictive brain markers: some being vulnerable to pain, or to comorbid mood disorders, while others being resilient and protected. For the first time, we are showing that psychology of pain actually shapes specific circuitry of the brain that can be used to profile the clinical portrait of chronic pain patients.

Vachon-Presseau E, Berger SE, Abdullah TB, Huang L, Griffith JW, Schnitzer TJ, & Apkarian AV.  PDF available here

I am highly interested in the placebo response in the settings of randomized controlled trials (RCTs). Several seminal papers have already identified neural correlates of the placebo response in healthy individuals, but these studies involved conditioning and were performed in the setting of a laboratory. Here, we first analyzed data from osteoarthritis patients (OA) in the settings of a RCT and showed that individuals likely to respond to a placebo treatment showed stronger connectivity between the dorsolateral prefrontal cortex (DLPFC) and the rest of the brain (Tétreault, Mansour, Vachon-Presseau et al., PLOS Biology, 2017). Building on these findings, we conducted a prospective neuroimaging-based RCT specifically designed to investigate the placebo response in low back pain patients. In this study, we tracked daily pain levels in chronic low back pain patients using a smartphone application for 8 weeks; we followed them before, during, and after receiving placebo pill treatment for their pain, with a subset of control patients receiving no treatment for the duration of the study. Our results uncovered a multiplicity of parameters underlying placebo pill analgesia: stable brain anatomical properties and a functional network specifically related to placebo pill response (also involving the DLPFC circuitry), with components showing stable or transient properties. Moreover, we used machine learning and demonstrate that patients can be classified as placebo responders and non-responders from psychological factors and that the magnitude of the response can a be predicted from a combination of psychological factors and functional connections collected before the administration of placebo treatment. These studies are important because they suggest that the placebo effect, observed in clinical trials, can be partially predicted and is a consequence of uncontrollable artifacts. There is a Phase 2 to this trial where we validated our model in a second clinical trial specially designed to test our predictive model. The preprint of this Phase 2 study should be posted shortly.

Vachon-Presseau E, Tétreault P, Petre B, Huang L, Berger S, Torbey S, Baria AT, Mansour AR, Hashmi JA, Griffith JW, Comasco E, Schnitzer TJ, Baliki MN, & Apkarian AV.  PDF available here

This is a prospective longitudinal study where we tracked brain properties in subacute back pain patients (experiencing pain for less than 12 weeks) over several years as they either recovered from, or transitioned to, chronic pain. The patients were scanned 4 times over one year and a subsample of patients were scanned a fifth time 3 years after pain onset. We combined white matter tractography, functional connectivity and high-resolution morphometric analyses to demonstrate that higher connections in the mPFC-amygdala-accumbens circuit, as well as smaller amygdala and hippocampal volumes represented independent risk factors for chronic pain. Examination of candidate genes also revealed opioid receptor mediated neural susceptibility to chronic pain . We posit that a circuitry encompassing the medial prefrontal cortex, the nucleus accumbens, the amygdala, and the hippocampus act as a predictor and an amplifier of chronic pain. Our current theory about these brain properties contributing to chronic pain and comorbid pathologies have been summarized in Vachon-Presseau et al., Journal of Dental Research, 2016.

Vachon-Presseau E, Roy M, Martel MO, Caron E, Marin MF, Chen J, Albouy G, Plante I, Sullivan MJ, Lupien SJ, & Rainville P.  PDF available here

The main topic of my PhD dissertation was to determine how stress hormones impact chronic pain. We conducted a comprehensive study where we collected daily saliva sample (5x per day) during 7 consecutive days starting immediately after the MRI session. We showed that higher basal levels of cortisol were associated with smaller volume of the hippocampus, and higher functional brain activity in response to thermal noxious stimuli in chronic pain patients compared to healthy controls.Based on these findings, a conceptual framework about how stress impact the limbic circuitry and its implications for the development of chronic pain has been provided in Vachon-Presseau Prog Neuropsychopharmacol Biol Psychiatry, 2017. These publications were important because they addressed the timely question of the role of stress on the limbic brain plasticity in chronic pain patients.

Vachon-Presseau E, Martel MO, Roy M, Caron E, Albouy G, Marin MF, Plante I, Sullivan MJ, Lupien SJ, & Rainville P.  PDF available here

Following up on the previous publication (using the same dataset), we further showed that individuals showing higher acute cortisol in response response to thermal pain administered in the MRI showed stress induced analgesic response mediated by lower pain-related brain activity in the mid cingulate cortex.