The Effect of Garlic Tablets on the Endometriosis-Related Pains: A Randomized Placebo-Controlled Clinical Trial.
Sudabeh AmirsalariZahra Behboodi MoghadamZiba TaghizadehMina Naghi Jafar AbadiParichehr Sabaghzadeh IraniSaied GoodarziHadi RanjbarPublished in: Evidence-based complementary and alternative medicine : eCAM (2021)
Endometriosis is a common chronic inflammatory disease. Garlic contains components that have antiproliferative, anti-inflammatory, and antioxidative effects. The current study aimed to evaluate the effectiveness of garlic on endometriosis symptoms. This was a randomized placebo-controlled triple-blind clinical trial. A convenience sample of 60 women was randomly allocated into two groups. The intervention group received usual care supplemented with 400 mg garlic tablets, and the placebo group received identical placebo tablets. A four-part Visual Analogue Scale (VAS) was used to measure the severity of pains. The pains were measured on four occasions (before the intervention and on one-, two-, and three-month follow-ups). Data were analyzed using the t-test, chi-square, repeated measures ANOVA, and ANCOVA by SPSS 16. The overall severity of pain reduced from 6.51 ± 0.86 to 1.83 ± 1.25 in the intervention group (p < 0.05). It increased from 6.41 ± 1.12 to 6.65 ± 1.37 in the control group (p = 0.02). The repeated measures ANOVA showed that there is a significant difference in the change of pain scores between intervention and control groups (p < 0.001, np2 = 0.572). Garlic extract can reduce pelvic and back pain, dysmenorrhea, and dyspareunia which are important symptoms of endometriosis.
Keyphrases
- double blind
- randomized controlled trial
- placebo controlled
- clinical trial
- anti inflammatory
- study protocol
- phase iii
- phase ii
- chronic pain
- pain management
- oxidative stress
- systematic review
- open label
- neuropathic pain
- polycystic ovary syndrome
- pregnant women
- rectal cancer
- squamous cell carcinoma
- metabolic syndrome
- phase ii study
- big data
- spinal cord
- adipose tissue
- depressive symptoms
- machine learning
- electronic health record
- physical activity
- pregnancy outcomes
- artificial intelligence