Effects of garlic supplementation on components of metabolic syndrome: a systematic review, meta-analysis, and meta-regression of randomized controlled trials

Abstract

Background: Garlic (Allium sativum), the underground bulb of the Allium genus, has been consumed on Earth for thousands of years. Many clinical trials of garlic supplementation on components of metabolic syndrome (MetS) have emerged in recent years, but there is no consensus on the effect. This meta-analysis aimed at systematically evaluating the effect of garlic supplementation on components of MetS. Methods: In this meta-analysis, we searched Pubmed, Embase, Cochrane, Medline, Web of Science databases, and clinical trials online sites from inception to November 1, 2022, with language restrictions to English. We engaged participants > 18 years and eligible for the clinical diagnosis of MetS or those with metabolic disorders and garlic was the only intervention. Outcomes included waist circumference, and body mass index, triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, blood pressure, and fasting blood glucose. Meta-regression and subgroup analyses were conducted based on six covariates (total sample size, the mean age, the mean dose, the duration of intervention, the oral form of garlic, and the dietary intervention). Results: Results from 19 RCTs were included engaging 999 participants. Compared to placebo, garlic significantly reduced TG [SMD (95%CI) = -0.66 (-1.23, -0.09)], TC [SMD (95%CI) = -0.43 (-0.86, -0.01)], LDL [SMD (95%CI) = -0.44(-0.88, -0.01)], DBP [SMD (95%CI) = -1.33 (-2.14, -0.53)], BMI [SMD (95%CI) = -1.10(-1.90, -0.20)], and WC [SMD (95%CI) = -0.78(-1.09, -0.47)]. Meta-regression showed age and sample size are potential effect modifiers. Conclusion: According to the results of meta-analysis, the modulatory effect of garlic on some MetS components is evident. More high-quality, large-scale RCTs are needed to confirm iat based on the high heterogeneity and potential publication bias of the current data. Trial registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373228 , ID: CRD42022373228.