Effects of probiotics, prebiotics and synbiotics on anthropometric, cardiometabolic and inflammatory markers: An umbrella review of meta-analyses.

BACKGROUND & AIMS: Though probiotics, prebiotics and synbiotics have been shown to confer health benefits, their effects on cardiometabolic risk factors remain unclear. Therefore, we conducted an umbrella review to examine their effectiveness on anthropometric, cardiometabolic and inflammatory markers. METHODS: We conducted an umbrella review on eligible systematic reviews with meta-analysis (SRMA) published from journals' inception till 13 January 2023 retrieved from seven electronic databases (CINAHL, EMBASE, ProQuest, PubMed, Scopus, The Cochrane Library, and Web of Science). Methodological quality was appraised using the Assessment of Multiple Systematic Reviews 2 (AMSTAR2) tool and certainty of evidence was graded into five classes. Random-effects meta-analyses were performed on outcome effect sizes at the SRMA and primary study levels. Extent of overlapping articles were evaluated using corrected cover area. RESULTS: 24 systematic reviews representing 265 unique studies, 1076 unique effect sizes and 25,973 subjects were included. Synbiotics were evidently more effective in improving weight (-1.91 kg, 95%CI -3.45 kg to -0.37 kg, p = 0.02), total cholesterol (-12.17 mg/dl, 95%CI -17.89 mg/dl to -6.46 mg/dl, p < 0.001), low-density lipoprotein (-12.26 mg/dl, 95%CI -18.27 mg/dl to -6.25 mg/dl, p < 0.01), waist circumference (-1.85 cm, 95%CI -2.77 cm to -0.94 cm, p < 0.01), and fasting plasma glucose (-9.68 mg/dl, 95%CI -16.18 mg/dl to -3.18 mg/dl, p < 0.01). Prebiotics were more effective in improving body mass index (-0.34 kg/m2, 95%CI -0.48 kg/m2 to -0.20 kg/m2, p < 0.01), and HOMA-IR (-0.92, 95%CI -1.91 to 0.07, p = 0.06). Probiotics were shown to be more effective in reducing diastolic blood pressure (-1.34 mmHg, 95%CI -2.14 mmHg to -0.55 mmHg, P < 0.01) improving insulin level change (-0.84 mIU/mL, 95%CI -1.27 mIU/mL to -0.41 mIU/mL, p < 0.01), and the percentage of body fat (-0.66%, 95%CI -0.70% to -0.61%, p < 0.01). For all outcomes, the credibility of evidence was classified as class IV. CONCLUSION: Pre-, pro-, and synbiotics can significantly enhance anthropometric indices, glucose and lipid profiles, blood pressure, and inflammatory markers in individuals confronting obesity. While suggesting their supplementation holds promise for this population, the true clinical impact hinges on tailoring these interventions to specific indications and customizing treatment strategies to align with individual patient needs.

Anthropometric and cardiometabolic effects of polyphenols in people with overweight and obesity: an umbrella review.

CONTEXT: Polyphenols are plant-based compounds with potential anti-inflammatory, antioxidant, and anti-obesogenic properties. However, their effects on health outcomes remain unclear. OBJECTIVE: To evaluate the effects of polyphenols on anthropometric and cardiometabolic markers. DATA SOURCES: Six electronic databases-namely, EMBASE, CINAHL, PubMed, Scopus, The Cochrane Library (reviews only), and Web of Science-were searched for relevant systematic reviews with meta-analyses (SRMAs). DATA EXTRACTION: Three reviewers performed the data extraction via a data-extraction Microsoft Excel spreadsheet. DATA ANALYSIS: An umbrella review and meta-analysis of existing SRMAs was conducted. Eighteen SRMAs published from 2015 to 2023, representing 445 primary studies and 838 unique effect sizes, were identified. Meta-analyses were conducted using random-effects models with general inverse variance. Polyphenol-containing foods were found to significantly improve weight (-0.36 kg; 95% confidence interval [CI]: -0.62, 0.77 kg; P < 0.01, I2 = 64.9%), body mass index (-0.25 kg/m2; 95% CI: -0.34, -0.17 kg/m2; P < 0.001, I2 = 82.4%), waist circumference (-0.74 cm; 95% CI: -1.34, -0.15 cm; P < 0.01, I2 = 99.3%), low-density-lipoprotein cholesterol (-1.75 mg/dL; 95% CI: -2.56, -0.94; P < 0.001, I2 = 98.6%), total cholesterol (-1.23 mg/dL; 95% CI: -2.00, -0.46; P = 0.002, I2 = 94.6%), systolic blood pressure (-1.77 mmHg; 95% CI: -1.77, -0.93 mmHg; P < 0.001, I2 = 72.4%), diastolic blood pressure (-1.45 mmHg; 95% CI: -2.09, -0.80 mmHg; P < 0.001, I2 = 61.0%), fat percentage (-0.70%; 95% CI: -1.03, -0.36%; P < 0.001, I2 = 52.6%), fasting blood glucose (-0.18 mg/dL; 95% CI: -0.35, -0.01 mg/dL; P = 0.04, I2 = 62.0%), and C-reactive protein (CRP; including high-sensitivity-CRP [hs-CRP]) (-0.2972 mg/dL; 95% CI: -0.52, -0.08 mg/dL; P = 0.01, I2 = 87.9%). No significant changes were found for high-density-lipoprotein cholesterol (-0.12 mg/dL; 95% CI: -1.44, 0.69; P = 0.67, I2 = 89.4%) and triglycerides (-1.29 mg/dL; 95% CI: -2.74, 0.16; P = 0.08, I2 = 85.4%). Between-study heterogeneity could be explained by polyphenol subclass differences. CONCLUSION: The findings of this umbrella review support the beneficial effects of polyphenols on anthropometric and metabolic markers, but discretion is warranted to determine the clinical significance of the magnitude of the biomarker improvements. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews no. CRD42023420206.

A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia.

Background and Aims: Pharmaceutical therapy for NASH is associated with lipid modulation, but the consensus on drug treatment is limited and lacks comparative analysis of effectiveness. A network meta-analysis was conducted to compare NASH drug classes in lipid modulation. Methods: Online databases were searched for randomized controlled trails (RCTs) evaluating NASH treatments in biopsy-proven NASH patients. Treatments were classified into four groups: (1) inflammation, (2) energy, (3) bile acids, and (4) fibrosis based on the mechanism of action. A Bayesian network analysis was conducted with outcome measured by mean difference (MD) with credible intervals (Crl) and surface under the cumulative ranking curve (SUCRA). Results: Forty-four RCTs were included in the analysis. Bile acid modulating treatments (MD: 0.05, Crl: 0.03-0.07) were the best treatment for improvement in high-density lipid (HDL) cholesterol, followed by treatments modulating energy (MD: 0.03, Crl: 0.02-0.04) and fibrosis (MD: 0.01, Crl: -0.12 to 0.14) compared with placebo. The top three treatments for reduction in triglycerides were treatments modulating energy (MD: -0.46, Crl: -0.49 to -0.43), bile acids (MD: -0.22, Crl: -0.35 to -0.09), and inflammation (MD: -0.08, Crl: -0.13 to -0.03) compared with placebo. SUCRA found treatment modulating fibrosis (MD: -1.27, Crl: -1.76 to -0.79) was the best treatment for reduction in low-density lipid (LDL) cholesterol followed by treatment modulating inflammation (MD: -1.03, Crl: -1.09 to -0.97) and energy (MD: -0.37, Crl: -0.39 to -0.34) compared with placebo, but LDL cholesterol was worsened by treatments modulating bile acids. Conclusions: Network analysis comparing the class effects of dyslipidemia modulation in NASH found that treatment targets can include optimization of atherogenic dyslipidemia. Future studies are required to evaluate the cardiovascular outcomes.