The effect of Gymnema sylvestre supplementation on glycemic control in type 2 diabetes patients: A systematic review and meta-analysis.
Suneel DevanganBincy VargheseEbin JohnySurender GurramRamu AdelaPublished in: Phytotherapy research : PTR (2021)
This systematic review and meta-analysis aims to find the effect of Gymnema sylvestre (GS) supplementation on glycemic control in type-2 diabetes mellitus (T2DM). PubMed, Cochrane library, Google Scholar, and Science Direct were searched from inception to June 2020 to identify the studies that reported GS supplementation on glycemic parameters. Standardized mean difference (SMD) was calculated by comparing the post-intervention data with baseline data. SMDs with 95% confidence intervals (CIs) were pooled using a random-effects model. Our meta-analysis consisting of 10 studies with a total of 419 participants showed that GS supplementation significantly reduces fasting blood glucose (FBG) (SMD 1.57 mg/dl, 95% CI 2.22 to -0.93, p < .0001, I2 90%), postprandial blood glucose (PPBG) (SMD 1.04 mg/dl, 95% CI 1.53 to -0.54, p < .0001, I2 80%), and glycated haemoglobin (HbA1c) (SMD 3.91, 95% CI 7.35 to -0.16%, p < .0001, I2 99%) compared to baseline. Further, our study also found that GS significantly reduces triglycerides (SMD 1.81 mg/dl, 95% CI 2.95 to -0.66, p < .0001, I2 : 96%), and total cholesterol (SMD 4.10 mg/dl, 95% CI 7.21 to -0.99, p < .0001, I2 : 98%) compared to baseline. Our study shows that GS supplementation is effective in improving glycemic control and reducing lipid levels in T2DM patients and suggests that such supplementation might be used as an effective therapy for the management of T2DM and its associated complications to an extent.
Keyphrases
- glycemic control
- blood glucose
- type diabetes
- weight loss
- insulin resistance
- end stage renal disease
- systematic review
- ejection fraction
- newly diagnosed
- chronic kidney disease
- randomized controlled trial
- cardiovascular disease
- case control
- peritoneal dialysis
- clinical trial
- risk factors
- big data
- public health
- adipose tissue
- machine learning
- high density
- patient reported