Plantain flour: A potential anti-obesity ingredient for intestinal flora regulation and improved hormone secretion
Bibliographic record
Abstract
Introduction Development of functional food ingredients with anti-obesity is a growing interest in the global food industry. Plantain ( Musa spp. AAB), a special type of cooking/starchy banana, is widely growing in African and Latin American countries. The flour made from unripe plantain pulp, which is considered as a natural source of indigestible carbohydrates such as resistant starch (RS), could be used in the formulation of diverse functional foods due to its anti-obesity properties. However, the mechanisms underlying the anti-obesity properties of plantain flour are not explored. Methods In this study, we investigated the changes in serum hormone levels, liver transcriptome profiles, and the modulation of gut microbiota in high-fat-fed Sprague-Dawley (SD) rats. The male SD rats were divided into six groups, viz. two control groups [non-obese (NC) or obese (OC)] which were not given the supplementation, one positive control (PC) group which received orlistat supplementation (60 mg/kg body weight/day), and three groups of obese rats which were supplemented with unripe plantain flour (UPF) at a dosage (body weight/day) of 1.25 g/kg (low-dose, LD), 2.50 g/kg (intermediate-dose, MD) or 5.0 g/kg (high-dose, HD). Results and discussion It was found that UPF supplementation could lower the insulin levels of the obese rats. Moreover, UPF supplementation had a positive impact on gut microbiota, decreasing the relative abundances of Blautia, Parasutterella and Fusicatenibacter which were closely related to obesity, and increasing the relative abundances of probiotics ( Allobaculum, Romboutsia, Staphylococcus , and Bacteroides ). The spearman correlation analysis revealed that UPF supplementation reduced the relative abundance of Parasutterella and possibly decreased the blood sugar levels, leading to a decrease in the relative abundances of Blautia and Fusicatenibacter and a subsequent decrease in insulin levels. Furthermore, transcriptomic analysis of the liver tissues displayed that the peroxisome proliferator activated receptor-1α (PPAR) and AMP-activated protein kinase (AMPK) signaling pathway genes ( Pparaa, Cpt1a, Prkaa1, Prkab1, Prkaa2 , and Ppargc1a ) were upregulated in those groups supplemented with UPF. These results indicated that UPF could mediate the glucolipid metabolism in the obese rats. Taken together, our findings suggested that the anti-obesity properties of UPF could be achieved by decreasing the insulin levels, positive-regulating of the gut microbiota composition as well as altering gene expression related to glucolipid metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".