Publications by authors named "Mathilde M V Pascal"
Article Synopsis
- The study explored genetic links to neuropathic pain by comparing individuals with the condition to those who had injuries but did not experience neuropathic pain.
- Key findings included significant associations with the KCNT2 gene and pain intensity, as well as other genes like LHX8 and TCF7L2 connected to neuropathic pain.
- The research also highlighted the influence of polygenic risk scores related to depression and inflammation on neuropathic pain, while discovering novel genetic variants tied to specific sensory profiles.
Article Synopsis
- The study investigates the reasons behind why some patients experience painful polyneuropathy while others do not, utilizing data from 1181 patients in the DOLORISK database.
- Researchers used multivariate logistic regression and machine learning to identify key factors related to painful neuropathy, including severity of neuropathy, family history of chronic pain, fatigue, depression scores, and pain-related worrying.
- The findings suggest that emotional and clinical factors play a significant role in the development of painful neuropathy, with predictive models achieving over 76% accuracy, which could help in identifying patients at risk in the future.
Chronic pain (CP) is a common and often debilitating disorder that has major social and economic impacts. A subset of patients develop CP that significantly interferes with their activities of daily living and requires a high level of healthcare support. The challenge for treating physicians is in preventing the onset of refractory CP or effectively managing existing pain.
View Article and Find Full Text PDFBackground: To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors.
Methods: The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN.
Purpose: Neuropathic pain is a common disorder of the somatosensory system that affects 7%-10% of the general population. The disorder places a large social and economic burden on patients as well as healthcare services. However, not everyone with a relevant underlying aetiology develops corresponding pain.
View Article and Find Full Text PDFNeuropathic pain is an increasingly prevalent condition and has a major impact on health and quality of life. However, the risk factors for the development and maintenance of neuropathic pain are poorly understood. Clinical, genetic and psychosocial factors all contribute to chronic pain, but their interactions have not been studied in large cohorts.
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