Clinical measures in chronic neuropathic pain are related to the Kennedy and endocannabinoid pathways.

Background: Chronic neuropathic pain (CNP) is a debilitating condition, often refractory to currently available drugs. Understanding biochemical alterations in peripheral tissues such as blood will be useful for understanding underlying pathophysiological processes relating to CNP.

Methods: We collected blood from two independent cohorts of CNP and pain-free controls (CNP n = 129/Controls n = 127) in the UK and Ireland to investigate the relationship between CNP-associated molecular/biochemical alterations and a range of clinical and pain metric parameters. Multiple statistical comparisons were conducted on the data, with selected variables included in one or more of the intended inferential analyses (six models).

Results: Gene expression analysis showed that choline phosphotransferase (CHPT1) was increased (p < .001) in the CNP group compared to controls. The levels of phosphatidylcholine, a metabolite of CHPT1 in the Kennedy Pathway, were significantly (p = .008) decreased in the plasma of patients with CNP. Given the relationship between the Kennedy pathway and endocannabinoids, plasma endocannabinoids and related N-acylethanolamines were quantified in clinical samples by HPLC-Tandem Mass Spectrometry. Plasma levels of the endocannabinoid 2-arachidonoylglycerol were higher in CNP samples compared to controls, and in the statistical models applied, 2-arachidonoylglycerol significantly increased the odds of CNP (p < .001). The expression of genes related to the synthesis and catabolism of endocannabinoids also corroborated the increased plasma 2-arachidonoylglycerol levels in patients with CNP.

Conclusions: Endocannabinoid levels, expression of genes related to endocannabinoid metabolism, age, sex, depression and anxiety state together were strong predictors of CNP. The observed molecular changes indicate that lipid metabolism is altered in CNP and thus may represent a viable target for novel analgesics or biomarker development.