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. 2023 Jan 6;12(2):275.
doi: 10.3390/foods12020275.

Lactobacillus acidophilus (LA) Fermenting Astragalus Polysaccharides (APS) Improves Calcium Absorption and Osteoporosis by Altering Gut Microbiota

Junhua Zhou  1 , Jing Cheng  1 , Liu Liu  1 , Jianming Luo  1 , Xichun Peng  1
Affiliations

Lactobacillus acidophilus (LA) Fermenting Astragalus Polysaccharides (APS) Improves Calcium Absorption and Osteoporosis by Altering Gut Microbiota

Junhua Zhou et al. Foods. .

Abstract

Lactobacillus acidophilus (LA) and Astragalus polysaccharides (APS) have each been shown to have anti-osteoporotic activity, and the aim of this study was to further investigate whether the LA fermenting APS was more effective in improving calcium absorption and osteoporosis than the unfermented mixed solution (MS). We found that the fermentation solution (FS) intervention improved the calcium absorption, BMD, and bone microarchitecture in osteoporotic rats and resulted in better inhibition of osteoclast differentiation markers ACP-5 and pro-inflammatory cytokines TNF-α and IL-6 and promotion of osteoblast differentiation marker OCN VSports手机版. This better performance may be due to the improved restoration of the relative abundance of specific bacteria associated with improved calcium absorption and osteoporosis such as Lactobacillus, Allobaculum, and UCG-005. Several key metabolites, including indicaxanthin, chlorogenic acid, and 3-hydroxymelatonin, may also be the key to the better improvement. In conclusion, the LA fermenting APS can better improve calcium absorption and osteoporosis by increasing active metabolites and altering gut microbiota. This finding should become a solid foundation for the development of LA fermenting APS in functional foods. .

Keywords: Astragalus polysaccharides; Lactobacillus acidophilus; calcium absorption; fermentation; gut microbiota; osteoporosis. V体育安卓版.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures (V体育2025版)

Figure 1
Figure 1
FS more significantly restored apparent calcium absorption in osteoporotic rats. Rats in each group were fed in metabolic cages 3 days before the end of the experiment, and the intake, gavage volumes, and feces were recorded for 3 days. Calcium content was detected by ICP-MS. Apparent calcium absorption (%) = (calcium intake − fecal calcium)/calcium intake × 100%; calcium intake (g) = calcium content in feed (g/kg) × feed consumption (kg) + gavage dose (mL) × gavage calcium content (g/mL); fecal calcium (g/kg) = calcium content in feces (g/kg) × total mass of feces (kg). Values are expressed as mean ± SD (n = 6). With p < 0.05, values with various superscript letters differ significantly.
Figure 2
Figure 2
FS more significantly restored BMD in osteoporotic rats. Three groups of female rats were injected intramuscularly with dexamethasone to induce osteoporosis, and the rats in the Con group were injected with an equal volume of saline. Meanwhile, the Fer group was intragastrically administered 1 mL/100 g of FS, the Mix group was intragastrically administered 1 mL/100 g of MS, and the Con and Mod groups were intragastrically administered an equal volume of sterile water. After execution, double-energy X-ray absorptiometry (Lunar iDXA, GE) was used to analyze the BMD of the left femur. The values are given as mean ± SD (n = 6). With p < 0.05, values with various superscript letters differ significantly.
Figure 3
Figure 3
FS repaired the impairment of bone microarchitecture in osteoporotic rats. Representative pathological sections of the Con group (A), Mod group (B), Fer group (C) and Mix group (D). The Con, Mod, Fer, and Mix groups’ right tibias were sampled and prepared for paraffin sectioning, H&E staining, and bone microarchitecture.
Figure 4
Figure 4
Serum levels of ACP-5 (A) and OCN (B). Serum of rats was collected at the time of execution, and the levels of ACP-5 and OCN were measured using ELISA kits. Values are expressed as mean ± SD (n = 6). With p < 0.05, values with various superscript letters differ significantly.
Figure 5
Figure 5
Serum levels of TNF-α (A) and IL-6 (B). Serum of rats was collected at the time of execution, and the levels of TNF-α and IL-6 were measured using ELISA kits. Values are expressed as mean ± SD (n = 6). With p < 0.05, values with various superscript letters differ significantly.
Figure 6
Figure 6
Gut microbiota diversity in different groups. The contents of rats’ ceca were collected, sequenced, and evaluated for community structure. Differences in gut microbiota diversity across groups were determined by using PLS−DA analysis (A). The Venn diagram depicts the number of common and individual OTUs (B) and genera (C).
Figure 7
Figure 7
Changes in gut microbiota at different levels and interactions between them. LEfSe analysis was performed to identify the taxa that changed significantly. A cladogram (A) was used to analyze the changes in the gut microbiota occurring in each group across taxa, and LDA score histograms (B) were used to analyze the bacterial markers in each group.
Figure 8
Figure 8
Composition at the level of phylum (A), family (B), and genus (C) of gut microbiota. Relative abundance of different group of gut microbiota was analyzed at the genus level to identify potential key bacteria.
Figure 9
Figure 9
Analysis of differential metabolites. PLS−DA analysis (A) was performed to show the variation in metabolites diversity between the Fer and Mix groups. A volcano plot (B) was used to analyze the differential metabolites in the Fer and Mix groups. Gray dots denote non−significant differential metabolites, blue dots denote significantly downregulated metabolites (p < 0.05), and red dots denote significantly upregulated metabolites (p < 0.05). HMDB compound classification analysis (C) was used to classify and annotate differential metabolites.
Figure 10
Figure 10
Correlation analysis between other parameters and the relative abundance of inferred key bacteria. The Majorbio cloud platform’s web tool (https://cloud.majorbio.com/page/tools/, accessed on 20 November 2022) was utilized to perform correlation analysis, and R values are indicated by colors with different depths (* |R| ≥ 0.5).
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