The Role of the Gut Microbiota in Lipid and Lipoprotein Metabolism

Yu, Yifan; Raka, Fitore; Adeli, Khosrow

doi:10.3390/jcm8122227

Cited by 107 publications

(79 citation statements)

References 166 publications

(203 reference statements)

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“…Recent studies have revealed that alteration of gut microbiota not only could affect the bile acid pool but also could influence the bile acid receptor signaling (i.e., FXR and TGR5). The FXR has been reported to be involved in glucose homeostasis, energy expenditure, and lipid metabolism ( 26 ). These observations provide insight into the potential function and mechanism of our identified microbial features, represented by the MRS, in host metabolism.…”

Section: Resultsmentioning

confidence: 99%

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

et al. 2020

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OBJECTIVE To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features. RESEARCH DESIGN AND METHODS We used an interpretable machine learning framework to identify the type 2 diabetes–related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS–type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832). RESULTS The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23–1.33), 1.23 (1.13–1.34), and 1.12 (1.06–1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota–derived blood metabolites. Animal study further confirmed the MRS–type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome–type 2 diabetes relationship. CONCLUSIONS Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

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Section: Resultsmentioning

confidence: 99%

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

et al. 2020

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“…Recent studies have revealed that alteration of gut microbiota could 218 not only affect the bile acid pool, but also influence the bile acid receptor signaling 219 (i.e., FXR and TGR5). The FXR has been reported to be involved in glucose 220 homeostasis, energy expenditure, and lipid metabolism (20).These observations 221 provide insight into the potential function and mechanism of our identified microbial Mice transplanted with the gut microbiota from high MRS individuals, either at non-230 T2D or T2D status, showed significant increase in fasting glucose levels compared 231 with those from the low MRS individuals or germ-free control mice ( Fig.3E to F).…”

mentioning

confidence: 73%

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gou

Ling

et al. 2020

Preprint

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New interpretable machine-learning analytic framework identifies a combination 55 of microbes consistently associated with type 2 diabetes risk across three 56 independent cohorts involving 9111 participants 57 • Faecal microbiota transplantation from humans to germ-free mice demonstrates a 58 causal role of the identified combination of microbes in the type 2 diabetes 59 development 60 • Body shape could modify the gut microbiome-diabetes relationship 61 4 Abstract 62Gut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts 63 have been controversial. Using an interpretable machine learning-based analytic 64 framework, we identified robust human gut microbiome features, with their optimal 65 threshold, in predicting T2D. Based on the results, we constructed a microbiome risk 66 score (MRS), which was consistently associated with T2D across 3 independent 67 Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also 68 predict future glucose increment, and was correlated with a variety of gut microbiota-69 derived blood metabolites. Faecal microbiota transplantation from humans to germ-70 free mice demonstrated a causal role of the identified combination of microbes in the 71 T2D development. We further identified adiposity and dietary factors which could 72 prospectively modulate the MRS, and found that body fat distribution may be the key 73 factor modulating the gut microbiome-T2D relationship. Taken together, we proposed 74 a new analytical framework for the investigation of microbiome-disease relationship. 75 The identified microbiota may serve as potential drug targets for T2D in future. 77Type 2 diabetes (T2D) is a complex disorder influenced by both host genetic and 78 environmental factors (1), and its prevalence is rising rapidly in both developed and 79 developing countries (2). Gut microbiome is considered as a modifiable 80 environmental factor, which plays an important role in the development of T2D (3-7). 81The research interest to identify gut microbiome-related treatment/prevention target is 82 emerging recently (8). Although there are a few human studies investigating the 83 association of gut microbiome with T2D in the past few years, the results are 84 inconsistent, and the causality is lacking (9). So far, there are sparse human evidence 85 robustly linking specific gut microbiome features to T2D. 87Machine learning has been widely used in biomedical fields in recent years (10). 88However, its application in the clinical setting is still limited as their predictions are 89 usually difficult to interpret. Of note, with the methodology development in the past 90 few years, interpretable algorithms could unlock the traditional "black box" of 91 machine learning results (11). The integration of the new algorithms with large-scale 92 gut microbiome data have the potential to radically unveil the relationship between 93 gut microbiome and T2D. Yet, no such investigation has been done. 95Therefore, in the present study, we aimed to identify robust h...

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“…This is in accordance with a recent proteomic investigation by Simon et al ( 31 ) where 24-h-long fasting was shown to cause an upregulation of Apo A-I in the jejunum of broiler chicken. Additionally, there are data indicating that both intestinal microbiota as well as gut microbiome-derived short-chain fatty acids (SCFAs) are also a potent stimulus of intestinal and liver lipoprotein formation ( 32 , 33 ). Taken together, the dynamic changes of APOA1 gene expression demonstrated in our study could be attributed to a concomitant combination of the aforementioned mechanism.…”

Section: Discussionmentioning

confidence: 99%

Molecular Response in Intestinal and Immune Tissues to in Ovo Administration of Inulin and the Combination of Inulin and Lactobacillus lactis Subsp. cremoris

et al. 2021

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Intestinal microbiota are a key factor in maintaining good health and production results in chickens. They play an important role in the stimulation of immune responses, as well as in metabolic processes and nutrient digestion. Bioactive substances such as prebiotics, probiotics, or a combination of the two (synbiotic) can effectively stimulate intestinal microbiota and therefore replace antibiotic growth promoters. Intestinal microbiota might be stimulated at the early stage of embryo development in ovo. The aim of the study was to analyze the expression of genes related to energy metabolism and immune response after the administration of inulin and a synbiotic, in which lactic acid bacteria were combined with inulin in the intestines and immune tissues of chicken broilers. The experiment was performed on male broiler chickens. Eggs were incubated for 21 days in a commercial hatchery. On day 12 of egg incubation, inulin as a prebiotic and inulin with Lactobacillus lactis subsp. cremoris as a synbiotic were delivered to the egg chamber. The control group was injected with physiological saline. On day 35 post-hatching, birds from each group were randomly selected and sacrificed. Tissues (spleen, cecal tonsils, and large intestine) were collected and intended for RNA isolation. The gene panel (ABCG8, HNF4A, ACOX2, APBB1IP, BRSK2, APOA1, and IRS2) was selected based on the microarray dataset and biological functions of genes related to the energy metabolism and immune responses. Isolated RNA was analyzed using the RT-qPCR method, and the relative gene expression was calculated. In our experiment, distinct effects of prebiotics and synbiotics following in ovo delivery were manifested in all analyzed tissues, with the lowest number of genes with altered expression shown in the large intestines of broilers. The results demonstrated that prebiotics or synbiotics provide a potent stimulation of gene expression in the spleen and cecal tonsils of broiler chickens. The overall number of gene expression levels and the magnitude of their changes in the spleen and cecal tonsils were higher in the group of synbiotic chickens compared to the prebiotic group.

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The Role of the Gut Microbiota in Lipid and Lipoprotein Metabolism

Cited by 107 publications

References 166 publications

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Molecular Response in Intestinal and Immune Tissues to in Ovo Administration of Inulin and the Combination of Inulin and Lactobacillus lactis Subsp. cremoris

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