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Probiotic Lactobacillus sp. as a strategy for modulation of non-comorbid obesity: A systematic meta-analysis and GRADE assessment of randomized controlled trials Lele, Juan AJMN.; Sihaloho, Karlos B.; Vighneswara, Dewa; Rampengan , Derren DCH.; Rizqiansyah, Chrisandi Y.; Permatasari, Happy K.; Mayulu, Nelly; Tallei, Trina E.; Taslim, Nurpudji A.; Kim, Bonglee; Kezia, Immanuelle; Nurkolis, Fahrul; Syahputra, Rony A.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1562

Abstract

Given the high prevalence of obesity worldwide, effective therapeutic strategies are crucial to prevent and manage obesity-related health conditions. Existing studies indicate that Lactobacillus sp. showed beneficial effects on body weight and adiposity by modifying the gut microbiota; however, no meta-analysis has been conducted assessing the efficacy of Lactobacillus sp-based probiotics on anthropometric parameters, leptin and adiponectin levels, and gut microbiota composition. The aim of this study was to evaluate the efficacy and safety of probiotic supplementation with Lactobacillus sp. in obese individuals without comorbidities. A systematic search was conducted on November 28, 2024, using five databases: PubMed, Wiley, ScienceDirect, Epistemonikos, and Cochrane. Primary outcomes included changes in body mass index (BMI), body weight, waist and hip circumferences, visceral and subcutaneous fat areas, and total body fat content. Secondary outcomes included alterations in leptin and adiponectin levels, gut microbiota composition, and the incidence of adverse events. A total of 1,058 individuals were included across 12 clinical trials. Significant reductions were observed in BMI (mean difference (MD): -0.40 kg/m²; 95%CI: -0.48–(-0.32), p<0.00001), body weight (MD: -1.16 kg; 95%CI: -1.79–(-0.53), p=0.0003), waist circumference (MD: -1.41 cm; 95%CI: -1.75–(-1.08), p<0.00001), and hip circumference (MD: -0.85 cm; 95%CI: -1.09–(-0.61), p<0.00001) compared to controls. Additionally, compared to control group, significant reductions were observed in visceral and subcutaneous fat mass (MD: -7.35; 95%CI: -9.95–(-4.75); p<0.00001) and overall body fat (MD: -1.11; 95%CI: -1.31–(-0.91); p<0.00001). Leptin levels significantly decreased (MD: -2.11 μg/mL; 95%CI: -3.59–(-0.64), p=0.005) compared to before Lactobacillus sp. supplementation, while adiponectin levels increased (MD: 0.71 μg/mL; 95%CI: 0.22–1.20, p=0.004) following Lactobacillus sp. supplementation compared to placebo group. No significant adverse events were reported in either the intervention or control groups. In conclusion, Lactobacillus sp. probiotic supplementation may serve as an adjuvant therapy to enhance obesity management in non-comorbid obese individuals.
Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning Kezia, Immanuelle; Erlina, Linda; tedjo, aryo; Fadilah, Fadilah
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 1, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pancreatic cancer is one of the deadliest cancers in the world. This cancer is caused by multiple factors and mostly detected at late stadium. Biomarker is a marker that can identify some diseases very specific. For pancreatic cancer, biomarker has been recognized using blood sample known as liquid biopsy, breath, pancreatic secret, and tumor marker CA19-9. Those biomarkers are invasive, so we want to identify the disease using a very convenient method. Metabolite is product from cell metabolism. Metabolites can become a biomarker especially from difficult diseases. In this paper, we want to find biomarker from metabolite using machine learning and enrichment. Metabolites data was obtained from Metabolomic workbench, while the detection and identification is done using in silico. From 106 samples, control and cancer, we found 61 metabolites and analyze them. We got 8 metabolites that play important role in pancreatic cancer and found out 2 of them are the most impactful. From that we found that ethanol is one of the best candidate of biomarker that we provide for pancreatic detection cancer. However, the simulation need to be improved to find another biomarker that provide a better marker for prognosis. Keyword : metabolite, pancreatic, cancer, machine learning