A recent study published in the journal Nutrients has revealed the potent effects of garlic in reducing blood sugar and cholesterol levels in humans. The study, conducted by a group of researchers in China, aimed to investigate the impact of garlic on blood lipid and glucose levels through a systematic review and meta-analysis.
Chronic non-communicable diseases, such as cardiovascular diseases, chronic respiratory diseases, cancers, and diabetes, are responsible for millions of deaths annually. Dysregulation of glucose and lipids can lead to various health issues, including atherosclerosis, diabetes, and fatty liver disease. Dyslipidemia, characterized by high total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and low high-density lipoprotein cholesterol (HDL-C), poses a significant cardiovascular risk.
Current treatments for metabolic diseases primarily focus on symptom management and often come with side effects. Garlic, known for its high content of compounds like allicin, has shown promise in regulating glucose and lipids. However, further research is needed to explore its mechanisms of action, optimal dosage, and long-term effects.
The study involved a comprehensive search of four databases – Embase, PubMed, Cochrane Library, and Web of Science – up to February 2024. The researchers used specific terms related to garlic, glucose, and lipid metabolism to identify relevant randomized clinical trials. In addition to the database search, manual searches were conducted to include all eligible trials.
The inclusion criteria for the trials required randomized clinical trials lasting over two weeks, with outcomes such as Hemoglobin A1c (HbA1c), fasting blood glucose (FBG), TC, HDL-C, LDL-C, and TG. The participants were adults aged 18 or older, and there was a placebo control group for comparison. Exclusions were made for non-garlic interventions, combined supplements, pregnant participants, non-clinical studies, and incomplete data.
Data extraction and analysis were conducted by two independent researchers, focusing on study details, sample sizes, demographics, and glucose and lipid indicator values. The quality of the studies was assessed using Cochrane Collaboration tools to evaluate bias risk factors.
The data analysis process involved converting glycated hemoglobin units and standardizing blood glucose and lipid levels. Mean outcome changes were calculated based on baseline and endpoint data, with heterogeneity assessed using chi-square tests and the I2 index. In cases of significant heterogeneity, a random-effects model was applied with a significance threshold of 0.05. Subgroup analyses and sensitivity analyses were also performed to ensure the robustness of the findings.
