The effect of cumin supplementation on metabolic profiles in patients with metabolic syndrome: A randomized, triple blind, placebo-controlled clinical trial.
Ashti MorovatiBahram Pourghassem GargariParvin SarbakhshHushyar AzariLida Lotfi-DizajiPublished in: Phytotherapy research : PTR (2019)
Metabolic syndrome (MetS) is a cluster of interconnected serious disorders, which is a major health problem whose prevalence is increasing. Oxidative stress and inflammation contribute to the disease pathogenesis and its complications. The present study aimed to investigate the effect of Cuminum cyminum L. (which has antioxidant and anti-inflammatory properties) essential oil (CuEO) supplementation on inflammatory and antioxidant status in patients with MetS. In this clinical trial, 56 patients with MetS aged 18-60 years received either 75-mg CuEO or placebo soft gel, thrice daily, for 8 weeks. Data on anthropometric parameters, food consumption, tumor necrosis factor alpha, high-sensitivity C-reactive protein, superoxide dismutase (SOD), glutathione peroxidase, catalase, total antioxidant capacity (TAC), and malondialdehyde (MDA) were assessed at the beginning and at the end of the study. Compared with the placebo group, CuEO increased SOD (149.17; 95% CI, [67.93, 230.42]), TAC (0.24; 95% CI, [0.09, 0.38]) and decreased MDA (-0.36; 95% CI, [-0.66, 0.06]), (p < 0.01). In within-group analysis, CuEO led to 13.3% decrease in MDA and 6.7% increase in TAC levels (p < 0.04). The results indicated that CuEO supplementation can improve some antioxidative indices, as SOD and TAC, while decreasing MDA in patients with MetS.
Keyphrases
- oxidative stress
- clinical trial
- anti inflammatory
- metabolic syndrome
- double blind
- breast cancer cells
- placebo controlled
- phase iii
- healthcare
- public health
- essential oil
- risk factors
- phase ii
- rheumatoid arthritis
- study protocol
- cell cycle arrest
- amyotrophic lateral sclerosis
- hydrogen peroxide
- cardiovascular disease
- diabetic rats
- insulin resistance
- body composition
- machine learning
- type diabetes
- social media
- human health
- health promotion
- climate change