Pain
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Menopausal and postmenopausal women, characterized by a significant reduction in ovarian hormones, have a high prevalence of chronic pain with great pain intensity. However, the underlying mechanism of hyperalgesia induced by ovarian hormone withdrawal remains poorly understood. ⋯ Moreover, activation of the DRNGABA neurons projecting to the lateral parabrachial nucleus was critical for alleviating hyperalgesia in OVX mice. These findings show the essential role of DRNGABA neurons and their modulation by estrogen in regulating hyperalgesia induced by ovarian hormone withdrawal, providing therapeutic basis for the treatment of chronic pain in physiological or surgical menopausal women.
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Worse executive function (EF) is associated with chronic pain and could mechanistically contribute to pain chronification. It is unclear whether there is overall impairment in EFs or whether there are impairments in specific cognitive domains. Furthermore, the possible genetic risk underlying these associations has not been tested. ⋯ A twin model indicated that pain and Updating-specific variance share genetic risk (rA = -0.46, P = 0.005) but not environmental risk (rE = 0.05, P = 0.844). Updating working memory shares a phenotypic and genetic relationship with pain in young adults. Impairments in gating or monitoring pain signals may play a mechanistic role in pain development.
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This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. ⋯ However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP. A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.
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Pain anticipation during conditions of uncertainty can unveil intrinsic biases, and understanding these biases can guide pain treatment interventions. This study used machine learning and functional magnetic resonance imaging to predict anticipatory responses in a pain anticipation experiment. One hundred forty-seven participants that included healthy controls (n = 57) and individuals with current and/or past mental health diagnosis (n = 90) received cues indicating upcoming pain stimuli: 2 cues predicted high and low temperatures, while a third cue introduced uncertainty. ⋯ Three distinct response profiles emerged: subjects with a negative bias towards high pain anticipation, those with a positive bias towards low pain anticipation, and individuals whose predictions during uncertainty were unbiased. These profiles remained stable over one year, were consistent across diagnosed psychopathologies, and correlated with cognitive coping styles and underlying insula anatomy. The findings suggest that individualized and stable pain anticipation occurs in uncertain conditions.
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Our aim was to investigate relative contributions of central and peripheral mechanisms to knee osteoarthritis (OA) diagnosis and their independent causal association with knee OA. We performed longitudinal analysis using data from UK-Biobank participants. Knee OA was defined using International Classification of Diseases manual 10 codes from participants' hospital records. ⋯ Body mass index and MCP had independent causal effects on knee OA (OR 1.76 [95% CI, 1.64-1.88] and 1.83 [95% CI, 1.54-2.16] per unit change, respectively). In conclusion, peripheral risk factors (eg, BMI) contribute more to the development of knee OA than central risk factors (eg, MCP). Peripheral and central factors are independently causal on knee OA.