Low-carbohydrate high-fat weight reduction diet induces changes in human gut microbiota

Gut microbiota in the association between obesity and kidney function decline: a metagenomics-based study in a rat model

OBJECTIVES

Obesity can induce dysbiosis in the gut microbiota and is considered a separate risk factor for kidney function decline. Nonetheless, the precise function of intestinal microorganisms in facilitating the connection between obesity and kidney function decline remains uncertain. Hence, the objective of this study was to investigate the alterations in the gut microbiota composition that take place during obesity and their correlations with renal function utilizing a rat model.

METHODS

For 20 weeks, 25 Sprague-Dawley rats were fed either a high-fat diet (HFD) or a normal-fat normal diet (ND). Physiological indices, peripheral plasma, kidney tissue, and colon contents were collected for comparison between groups. Metagenomic analysis of intestinal flora was performed.

RESULTS

The HFD group demonstrated significantly increased levels of creatinine and urea nitrogen in the peripheral blood. Additionally, the HFD rats exhibited a significantly larger glomerular diameter compared to the ND group, accompanied by the presence of glomerulosclerosis, tubular vacuolar transformation, and other pathological changes in certain glomeruli. Metagenomics analysis revealed a notable rise in the prevalence of the Firmicutes phylum within the HFD group, primarily comprising the Rumenococcus genus. Functional analysis indicated that the gut microbiota in the HFD group primarily correlated with infectious diseases, signal transduction, and signaling molecules and interactions.

CONCLUSIONS

This study provides evidence that the consumption of a HFD induces modifications in the composition and functionality of the gut microbiome in rats, which may serve as a potential mechanism underlying the relationship between obesity and the progression of kidney function decline.

The Development and Evaluation of a Literature-Based Dietary Index for Gut Microbiota

The aim of the study was to develop and evaluate a novel dietary index for gut microbiota (DI-GM) that captures dietary composition related to gut microbiota profiles. We conducted a literature review of longitudinal studies on the association of diet with gut microbiota in adult populations and extracted those dietary components with evidence of beneficial or unfavorable effects. Dietary recall data from the National Health and Nutrition Examination Survey (NHANES, 2005-2010, n = 3812) were used to compute the DI-GM, and associations with biomarkers of gut microbiota diversity (urinary enterodiol and enterolactone) were examined using linear regression. From a review of 106 articles, 14 foods or nutrients were identified as components of the DI-GM, including fermented dairy, chickpeas, soybean, whole grains, fiber, cranberries, avocados, broccoli, coffee, and green tea as beneficial components, and red meat, processed meat, refined grains, and high-fat diet (≥40% of energy from fat) as unfavorable components. Each component was scored 0 or 1 based on sex-specific median intakes, and scores were summed to develop the overall DI-GM score. In the NHANES, DI-GM scores ranged from 0-13 with a mean of 4.8 (SE = 0.04). Positive associations between DI-GM and urinary enterodiol and enterolactone were observed. The association of the novel DI-GM with markers of gut microbiota diversity demonstrates the potential utility of this index for gut health-related studies.

Metagenomic analysis reveals distinct changes in the gut microbiome of obese Chinese children

BACKGROUND

The prevalence of obese children in China is increasing, which poses a great challenge to public health. Gut microbes play an important role in human gut health, and changes in gut status are closely related to obesity. However, how gut microbes contribute to obesity in children remains unclear. In our study, we performed shotgun metagenomic sequencing of feces from 23 obese children, 8 overweight children and 22 control children in Chengdu, Sichuan, China.

RESULTS

We observed a distinct difference in the gut microbiome of obese children and that of controls. Compared with the controls, bacterial pathogen Campylobacter rectus was significantly more abundant in obese children. In addition, functional annotation of microbial genes revealed that there might be gut inflammation in obese children. The guts of overweight children might belong to the transition state between obese and control children due to a gradient in relative abundance of differentially abundant species. Finally, we compared the gut metagenomes of obese Chinese children and obese Mexican children and found that Trichuris trichiura was significantly more abundant in the guts of obese Mexican children.

CONCLUSIONS

Our results contribute to understanding the changes in the species and function of intestinal microbes in obese Chinese children.

Gut Microbiota Patterns in Patients with Non-Alcoholic Fatty Liver Disease: A Comprehensive Assessment Using Three Analysis Methods

Non-alcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease worldwide, affecting nearly 25% of the global adult population. Increasing evidence suggests that functional and compositional changes in the gut microbiota may contribute to the development and promote the progression of NAFLD. 16S rRNA gene next-generation sequencing is widely used to determine specific features of the NAFLD microbiome, but a complex system such as the gut microbiota requires a comprehensive approach. We used three different approaches: MALDI-TOF-MS of bacterial cultures, qPCR, and 16S NGS sequencing, as well as a wide variety of statistical methods to assess the differences in gut microbiota composition between NAFLD patients without significant fibrosis and the control group. The listed methods showed enrichment in Collinsella sp. and Oscillospiraceae for the control samples and enrichment in Lachnospiraceae (and in particular Dorea sp.) and Veillonellaceae in NAFLD. The families, Bifidobacteriaceae, Lactobacillaceae, and Enterococcaceae (particularly Enterococcus faecium and Enterococcus faecalis), were also found to be important taxa for NAFLD microbiome evaluation. Considering individual method observations, an increase in Candida krusei and a decrease in Bacteroides uniformis for NAFLD patients were detected using MALDI-TOF-MS. An increase in Gracilibacteraceae, Chitinophagaceae, Pirellulaceae, Erysipelatoclostridiaceae, Muribaculaceae, and Comamonadaceae, and a decrease in Acidaminococcaceae in NAFLD were observed with 16S NGS, and enrichment in Fusobacterium nucleatum was shown using qPCR analysis. These findings confirm that NAFLD is associated with changes in gut microbiota composition. Further investigations are required to determine the cause-and-effect relationships and the impact of microbiota-derived compounds on the development and progression of NAFLD.

Gut microbiota profile and the influence of nutritional status on bacterial distribution in diabetic and healthy Tunisian subjects

Gut microbiota plays a key role in the regulation of metabolism and immunity. We investigated the profile of gut microbiota and the impact of dietary intake on gut bacterial distribution in diabetic and healthy Tunisian subjects, aiming to identify a dysbiotic condition, hence opening the way to restore eubiosis and facilitate return to health. In the present research, we enrolled 10 type 1 diabetic (T1D), 10 type 2 diabetic (T2D) patients and 13 healthy (H) subjects. Illumina Miseq technology was used to sequence V3-V4 hypervariable regions of bacterial 16SrRNA gene. Data were analyzed referring to QIIME 2 pipeline. RStudio software was used to explore the role of nutrition in gut bacterial distribution. At the phylum level, we identified an imbalanced gut microbiota composition in diabetic patients marked by a decrease in the proportion of Firmicutes and an increase in the abundance of Bacteroidetes compared with H subjects. We observed higher amounts of Fusobacteria and a decline in the levels of TM7 phyla in T1D patients compared with H subjects. However, we revealed a decrease in the proportions of Verrucomicrobia in T2D patients compared with H subjects. At the genus level, T2D subjects were more affected by gut microbiota alteration, showing a reduction in the relative abundance of Faecalibacterium, Akkermansia, Clostridium, Blautia and Oscillibacter, whereas T1D group shows a decrease in the proportion of Blautia. The gut bacteria distribution was mainly affected by fats and carbohydrates consumption. Gut microbiota composition was altered in Tunisian diabetic patients and affected by dietary habits.

Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study

Background

Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia.

Methods

One hundred and four participants between 10 and 70 years were recruited from Odisha's Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.

Results

Significant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.

Conclusion

These findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.

Could Gut Microbiota Composition Be a Useful Indicator of a Long-Term Dietary Pattern?

Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject's dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual's microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.

Gluten-free diet affects fecal small non-coding RNA profiles and microbiome composition in celiac disease supporting a host-gut microbiota crosstalk

Current treatment for celiac disease (CD) is adhering to a gluten-free diet (GFD), although its long-term molecular effects are still undescribed. New molecular features detectable in stool may improve and facilitate noninvasive clinical management of CD. For this purpose, fecal small non-coding RNAs (sncRNAs) and gut microbiome profiles were concomitantly explored in CD subjects in relation to strict (or not) GFD adherence over time. In this observational study, we performed small RNA and shotgun metagenomic sequencing in stool from 63 treated CD (tCD) and 3 untreated subjects as well as 66 sex- and age-matched healthy controls. tCD included 51 individuals on strict GFD and with negative transglutaminase (TG) serology (tCD-TG-) and 12 symptomatic with not strict/short-time of GFD adherence and positive TG serology (tCD-TG+). Samples from additional 40 healthy adult individuals and a cohort of 19 untreated pediatric CD subjects and 19 sex/age matched controls were analyzed to further test the outcomes. Several miRNA and microbial profiles were altered in tCD subjects (adj. p < .05). Findings were validated in the external group of adult controls. In tCD-TG-, GFD duration correlated with five miRNA levels (p < .05): for miR-4533-3p and miR-2681-3p, the longer the diet adherence, the less the expression differed from controls. tCD-TG+ and untreated pediatric CD patients showed a similar miRNA dysregulation. Immune-response, trans-membrane transport and cell death pathways were enriched in targets of identified miRNAs. Bifidobacterium longum, Ruminococcus bicirculans, and Haemophilus parainfluenzae abundances shifted (adj. p < .05) with a progressive reduction of denitrification pathways with GFD length. Integrative analysis highlighted 121 miRNA-bacterial relationships (adj. p < .05). Specific molecular patterns in stool characterize CD subjects, reflecting either the long-term GFD effects or the gut inflammatory status, in case of a not strict/short-time adherence. Our findings suggest novel host-microbial interplays and could help the discovery of biomarkers for GFD monitoring over time.

Intake of slow-digesting carbohydrates is related to changes in the microbiome and its functional pathways in growing rats with obesity induced by diet

INTRODUCTION

The main cause of insulin resistance in childhood is obesity, which contributes to future comorbidities as in adults. Although high-calorie diets and lack of exercise contribute to metabolic disease development, food quality rather than the quantity of macronutrients is more important than food density. The purpose of the present study was to examine the effects of changing the quality of carbohydrates from rapidly to slowly digestible carbohydrates on the composition of the gut microbiota and the profiles of the functional pathways in growing rats with obesity due to a high-fat diet (HFD).

METHODS

During the course of 4 weeks, rats growing on an HFD-containing carbohydrates with different digestive rates were fed either HFD-containing carbohydrates with a rapid digestion rate (OBE group) or HFD-containing carbohydrates with a slow digestion rate (OBE-ISR group). A non-obese group (NOB) was included as a reference, and rats were fed on a rodent standard diet (AIN93G). An analysis of gut microbiota was conducted using 16S rRNA-based metagenomics; a linear mixed-effects model (LMM) was used to determine changes in abundance between baseline and 4 weeks of treatment, and functional pathways were identified. Gut microbiota composition at bacterial diversity and relative abundance, at phylum and genus levels, and functional profiles were analyzed by integrating the Integrated Microbial Genomes (IMG) database.

RESULTS

The groups showed comparable gut microbiota at baseline. At the end of the treatment, animals from the ISR group exhibited differences at the phylum levels by decreasing the diversity of Fisher's index and Firmicutes (newly named as Bacillota), and increasing the Pielou's evenness and Bacteroidetes (newly named as Bacteroidota); at the genus level by increasing Alistipes, Bifidobacterium, Bacteroides, Butyricimonas, Lachnoclostridium, Flavonifractor, Ruminiclostridium 5, and Faecalibaculum and decreasing Muribaculum, Blautia, and Ruminiclostridium 9. Remarkably, relative abundances of genera Tyzzerella and Angelakisella were higher in the OBE group compared to NOB and OBE-ISR groups. In addition, some microbiota carbohydrate metabolism pathways such as glycolysis, glucuronic acid degradation, pentose phosphate pathway, methanogenesis, and fatty acid biosynthesis exhibited increased activity in the OBE-ISR group after the treatment. Higher levels of acetate and propionate were found in the feces of the ISR group compared with the NOB and OBE groups.

CONCLUSION

The results of this study demonstrate that replacing rapidly digestible carbohydrates with slowly digestible carbohydrates within an HFD improve the composition of the gut microbiota. Consequently, metabolic disturbances associated with obesity may be prevented.

Role of the Gut Microbiota in the Increased Infant Body Mass Index Induced by Gestational Diabetes Mellitus

The connection between gestational diabetes mellitus (GDM) and the offspring's development, such as obesity, is well established. Emerging evidence indicates that the microbiota of the neonate's meconium is associated with maternal GDM status. To explore whether the association between GDM and infant body mass index (BMI) in early childhood is affected by the meconium microbiota, we recruited 120 mothers (60 healthy women and 60 with GDM) and their newborns from the Women's Hospital of Nanjing Medical University. Meconium of 120 neonates was collected within a few hours after birth and sequenced using 16S rRNA sequencing analysis. Children's BMI was measured at 12 months of age. The results revealed that infants born to mothers with GDM had increased BMI Z-scores at 12 months old and that the β-diversity of their meconium microbiota was reduced. Several genera were observed to be significantly different between the GDM and control groups. The genus Burkholderia-Caballeronia-Paraburkholderia and an untitled genus in the family Enterobacteriaceae enriched in neonates born to healthy mothers were found to be negatively associated with infant BMI by using regression analysis. A coabundance group depleted in the GDM group was correlated negatively with 12-month BMI and mediated 21.65% of the association between GDM and infant BMI by mediation analyses. This study provided evidence for the associations among maternal GDM, the meconium microbiota, and infant BMI. Maternal GDM was demonstrated to affect infant BMI, mediated by the gut microbiome. Gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity. IMPORTANCE Using 16S rRNA sequencing analysis, regression analysis and mediation analysis were used to explore whether maternal gestational diabetes mellitus (GDM) changed the function and composition of the meconium microbiota and whether this explained the GDM-induced alterations of infant body mass index (BMI). This study showed that gut microbiome dysbiosis induced by maternal GDM might play an important role in the increased infant BMI during the first 12 months of life. Therefore, gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity.

Impact of Food-Based Weight Loss Interventions on Gut Microbiome in Individuals with Obesity: A Systematic Review

The observation that the gut microbiota is different in healthy weight as compared with the obese state has sparked interest in the possible modulation of the microbiota in response to weight change. This systematic review investigates the effect of food-based weight loss diets on microbiota outcomes (α-diversity, β-diversity, relative bacterial abundance, and faecal short-chain fatty acids, SCFAs) in individuals without medical comorbidities who have successfully lost weight. Nineteen studies were included using the keywords ‘obesity’, ‘weight loss’, ‘microbiota’, and related terms. Across all 28 diet intervention arms, there were minimal changes in α- and β-diversity and faecal SCFA concentrations following weight loss. Changes in relative bacterial abundance at the phylum and genus level were inconsistent across studies. Further research with larger sample sizes, detailed dietary reporting, and consistent microbiota analysis techniques are needed to further our understanding of the effect of diet-induced weight loss on the gut microbiota.

Effects of caloric restriction on the gut microbiome are linked with immune senescence

BACKGROUND

Caloric restriction can delay the development of metabolic diseases ranging from insulin resistance to type 2 diabetes and is linked to both changes in the composition and metabolic function of the gut microbiota and immunological consequences. However, the interaction between dietary intake, the microbiome, and the immune system remains poorly described.

RESULTS

We transplanted the gut microbiota from an obese female before (AdLib) and after (CalRes) an 8-week very-low-calorie diet (800 kcal/day) into germ-free mice. We used 16S rRNA sequencing to evaluate taxa with differential abundance between the AdLib- and CalRes-microbiota recipients and single-cell multidimensional mass cytometry to define immune signatures in murine colon, liver, and spleen. Recipients of the CalRes sample exhibited overall higher alpha diversity and restructuring of the gut microbiota with decreased abundance of several microbial taxa (e.g., Clostridium ramosum, Hungatella hathewayi, Alistipi obesi). Transplantation of CalRes-microbiota into mice decreased their body fat accumulation and improved glucose tolerance compared to AdLib-microbiota recipients. Finally, the CalRes-associated microbiota reduced the levels of intestinal effector memory CD8+ T cells, intestinal memory B cells, and hepatic effector memory CD4+ and CD8+ T cells.

CONCLUSION

Caloric restriction shapes the gut microbiome which can improve metabolic health and may induce a shift towards the naïve T and B cell compartment and, thus, delay immune senescence. Understanding the role of the gut microbiome as mediator of beneficial effects of low calorie diets on inflammation and metabolism may enhance the development of new therapeutic treatment options for metabolic diseases.

TRIAL REGISTRATION

NCT01105143 , "Effects of negative energy balance on muscle mass regulation," registered 16 April 2010. Video Abstract.

Disparity of Gut Microbiota Composition Among Elite Athletes and Young Adults With Different Physical Activity Independent of Dietary Status: A Matching Study

Objective

This study aimed to investigate the disparity of gut microbiota among elite athletes and young adults with different physical activity independent of dietary status.

Methods

In Hangzhou, China, an age and sex matching study was conducted between April and May 2021. A total of 66 Chinese young adults were recruited in this study and divided into an elite athlete group, physically active group, and physically inactive group. Fecal samples were collected to assess gut microbiota composition. Dietary status was measured using a food-frequency questionnaire. Comparisons in gut microbiota and blood biomarkers among three groups were analyzed by using the analysis of covariance.

Results

The findings depicted a tendency to form clusters for beta diversity among three groups, while no significant difference was observed in both alpha and beta diversity. In the multiple analysis model, by adjusting dietary status, a significantly higher abundance of Clostridiaceae (p = 0.029) and Megamonas_rupellensis (p = 0.087) was observed in elite athletes compared to that in general young adults. Furthermore, inflammation-related bacteria such as Bilophila (p = 0.011) and Faecalicoccus (p = 0.050) were enriched in physically inactive young adults compared to two other groups. Pearson's correlation analysis showed a positive association between Bilophila and circulating white body cell count (r = 0.332, p = 0.006) and its subtypes including neutrophils (r = 0.273, p = 0.027), and lymphocytes (r = 0.327, p = 0.007). Megamonas_rupellensis has been shown associated positively with serum lymphocytes levels (r = 0.268, p = 0.03). Although no significant differences were observed, the elite athletes tended to have lower levels of blood biomarkers of immunity within a normal range, which may reflect a better immune function.

Conclusion

This matching study indicated that physically inactive young adults are more likely to have a lower immune function and a higher abundance of pro-inflammatory gut bacteria than elite athletes and physically active young adults. Dietary status should be considered as an important factor that may affect the association of physical activity with immune function and gut microbiota.

Evaluating the Effects of Diet-Gut Microbiota Interactions on Sleep Traits Using the UK Biobank Cohort

Previous studies showed that diet and gut microbiota had a correlation with sleep. However, the potential interaction effects of diet and gut microbiota on sleep are still unclear. The phenotypic data of insomnia (including 374,505 subjects) and sleep duration (including 372,805 subjects) were obtained from the UK Biobank cohort. The Single Nucleotide Polymorphisms (SNPs) associated with 114 gut microbiota, 84 dietary habits, and 4 dietary compositions were derived from the published Genome-wide Association Study (GWAS). We used Linkage Disequilibrium Score Regression (LDSC) to estimate the genetic correlation and colocalization analysis to assess whether dietary habits and insomnia/sleep duration shared a causal variant in a region of the genome. Using UK Biobank genotype data, the polygenetic risk score of gut microbiota, dietary habits, and dietary compositions were calculated for each subject. Logistic regression and linear regression models were used to assess the potential effects of diet-gut microbiota interactions on sleep phenotypes, including insomnia and sleep duration. Insomnia and sleep duration were used as dependent variables, and sex, age, the Townsend Deprivation Index scores, and smoking and drinking habits were selected as covariates in the regression analysis. All statistical analyses were conducted using R-3.5.1 software. Significant genetic correlations were discovered between insomnia/sleep duration and dietary habits. Further, we found several significant dietary compositions-gut microbiota interactions associated with sleep, such as fat × G_Collinsella_RNT (p = 1.843 × 10-2) and protein × G_Collinsella_HB (p = 7.11 × 10-3). Besides, multiple dietary habits-gut microbiota interactions were identified for sleep, such as overall beef intake × G_Desulfovibrio_RNT (p = 3.26 × 10-4), cups of coffee per day × G_Escherichia_Shigella_RNT (p = 1.14 × 10-3), and pieces of dried fruit per day × G_Bifidobacterium_RNT (p = 5.80 × 10-3). This study reported multiple diet-gut microbiota interactions associated with sleep, which may provide insights into the biological mechanisms of diet and gut microbiota affecting sleep.