The Effect of Microbiome-Modulating Agents (MMAs) on Type 1 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.
Ying ZhangAiying HuangJun LiWilliam MunthaliSaiying CaoUlfah Mahardika Pramono PutriLina YangPublished in: Nutrients (2024)
Gut microbiome-modulating agents (MMAs), including probiotics, prebiotics, postbiotics, and synbiotics, are shown to ameliorate type 1 diabetes (T1D) by restoring the microbiome from dysbiosis. The objective of this systematic review and meta-analysis was to assess the impact of MMAs on hemoglobin A1c (HbA1c) and biomarkers associated with (T1D). A comprehensive search was conducted in PubMed, Web of Science, Embase, Cochrane Library, National Knowledge Infrastructure, WeiPu, and WanFang Data up to 30 November 2023. Ten randomized controlled trials ( n = 630) were included, with study quality evaluated using the Cochrane risk-of-bias tool. Random-effect models with standardized mean differences (SMDs) were utilized. MMA supplementation was associated with improvements in HbA1c (SMD = -0.52, 95% CI [-0.83, -0.20]), daily insulin usage (SMD = -0.41, 95% confidence interval (CI) [-0.76, -0.07]), and fasting C-peptide (SMD = 0.99, 95% CI [0.17, 1.81]) but had no effects on FBG, CRP, TNF-α, IL-10, LDL, HDL, and the Shannon index. Subgroup analysis of HbA1c indicated that a long-term intervention (>3 months) might exert a more substantial effect. These findings suggest an association between MMAs and glycemic control in T1D. Further large-scale clinical trials are necessary to confirm these findings with investigations on inflammation and gut microbiota composition while adjusting confounding factors such as diet, physical activity, and the dose and form of MMA intervention.
Keyphrases
- glycemic control
- type diabetes
- physical activity
- blood glucose
- randomized controlled trial
- insulin resistance
- clinical trial
- weight loss
- cardiovascular disease
- systematic review
- healthcare
- quality improvement
- oxidative stress
- signaling pathway
- adipose tissue
- body mass index
- metabolic syndrome
- deep learning
- depressive symptoms
- big data
- sleep quality
- data analysis