This open-label, non-randomized observational study followed 600 chronic pain patients for 12 months to determine if there is any correlation between their genetic makeup and the effectiveness of cannabis medicine treatment. It was published in the journal Genes in October 2022.
This is an important study because it is well known that some people do not feel a benefit from cannabis for pain, while others feel a great benefit.
Participants had five different primary causes of chronic pain:
- central nervous system diseases
- rheumatoid, arthritic, inflammatory and autoimmune diseases
- headache and migraine
- neuropathic pain
- cancer pain
They used the following variables for measurement:
- Pain intensity: visual analogue scale (0–10)
- Presence/absence of benefits (sleep quality, muscle relaxation, etc.)
- Presence/absence of side effects
- Anxiety and depression: the hospital anxiety and depression scale (HADS)
The product used was a whole-plant extract in an olive oil base that contained varying amounts of THC and CBD, based on individual needs.
They identified 3 genes and specific expressions that were associated with significant reductions in pain. They also were able to predict which patients would respond to cannabis treatment positively and which patients would not respond positively, based on the expression of specific genes.
The overall conclusion was that human genetic makeup could be a strong predictive factor of the potential effectiveness of cannabis treatment for chronic pain.
Key quotes from the authors:
Results suggest that genetic makeup is, at the moment, a significant predictive factor of the variability in response to cannabis.
Three polymorphic genes (ABCB1, TRPV1 and UGT2B7) were identified as being significantly associated with decline in pain after treatment with cannabis.
Patients simultaneously carrying the most favourable allele combinations showed a greater reduction (polygenic effect) in pain compared to those with a less favourable combination.
Considering genotype combinations, we could group patients into good responders, intermediate responders and poor or non-responders.
The full text paper is here at MDPI.com.