Changes in the fecal gut microbiome of home healthcare patients with disabilities through consumption of malted rice amazake.

The aim of the present study was to investigate changes in the gut microbiome both during and after consumption of malted rice amazake (MR-Amazake), a fermented food from Japan, in-home healthcare patients with disabilities, including patients with severe motor and intellectual disabilities. We monitored 12 patients who consumed MR-Amazake for 6 wk and investigated them before and after the intervention as well as 6 wk after the end of intake to compare their physical condition, diet, type of their medication, constipation assessment scale, and analysis of their comprehensive fecal microbiome using 16S rRNA sequencing. Their constipation symptoms were significantly alleviated, and principal coordinate analysis revealed that 30% of patients showed significant changes in the gut microbiome after MR-Amazake ingestion. Furthermore, Bifidobacterium was strongly associated with these changes. These changes were observed only during MR-Amazake intake; the original gut microbiome was restored when MR-Amazake intake was discontinued. These results suggest that 6 wk is a reasonable period of time for MR-Amazake to change the human gut microbiome and that continuous consumption of MR-Amazake is required to sustain such changes.NEW & NOTEWORTHY The consumption of malted rice amazake (MR-Amazake) showed significant changes in the gut microbiome according to principal coordinate analysis in some home healthcare patients with disabilities, including those with severe motor and intellectual disabilities. After discontinuation of intake, the gut microbiome returned to its original state. This is the first pilot study to examine both the changes in the gut microbiome and their sustainability after MR-Amazake intake.

RepID represses megakaryocytic differentiation by recruiting CRL4A-JARID1A at DAB2 promoter.

BACKGROUND: Megakaryocytes (MKs) are platelet precursors, which arise from hematopoietic stem cells (HSCs). While MK lineage commitment and differentiation are accompanied by changes in gene expression, many factors that modulate megakaryopoiesis remain to be uncovered. Replication initiation determinant protein (RepID) which has multiple histone-code reader including bromodomain, cryptic Tudor domain and WD40 domains and Cullin 4-RING E3 ubiquitin ligase complex (CRL4) recruited to chromatin mediated by RepID have potential roles in gene expression changes via epigenetic regulations. We aimed to investigate whether RepID-CRL4 participates in transcriptional changes required for MK differentiation. METHODS: The PCR array was performed using cDNAs derived from RepID-proficient or RepID-deficient K562 erythroleukemia cell lines. Correlation between RepID and DAB2 expression was examined in the Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal. The acceleration of MK differentiation in RepID-deficient K562 cells was determined by estimating cell sizes as well as counting multinucleated cells known as MK phenotypes, and by qRT-PCR analysis to validate transcripts of MK markers using phorbol 12-myristate 13-acetate (PMA)-mediated MK differentiation condition. Interaction between CRL4 and histone methylation modifying enzymes were investigated using BioGRID database, immunoprecipitation and proximity ligation assay. Alterations of expression and chromatin binding affinities of RepID, CRL4 and histone methylation modifying enzymes were investigated using subcellular fractionation followed by immunoblotting. RepID-CRL4-JARID1A-based epigenetic changes on DAB2 promoter were analyzed by chromatin-immunoprecipitation and qPCR analysis. RESULTS: RepID-deficient K562 cells highly expressing MK markers showed accelerated MKs differentiation exhibiting increases in cell size, lobulated nuclei together with reaching maximum levels of MK marker expression earlier than RepID-proficient K562 cells. Recovery of WD40 domain-containing RepID constructs in RepID-deficient background repressed DAB2 expression. CRL4A formed complex with histone H3K4 demethylase JARID1A in soluble nucleus and loaded to the DAB2 promoter in a RepID-dependent manner during proliferation condition. RepID, CRL4A, and JARID1A were dissociated from the chromatin during MK differentiation, leading to euchromatinization of the DAB2 promoter. CONCLUSION: This study uncovered a role for the RepID-CRL4A-JARID1A pathway in the regulation of gene expression for MK differentiation, which can form the basis for the new therapeutic approaches to induce platelet production. Video Abstract.

DEMIX: Domain-Enforced Memory Isolation for Embedded System.

Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor's accessible memory into distinct units known as domains. Despite the security offered by this approach, the Memory Protection Unit (MPU), the most common memory isolation method provided in most lightweight systems, incurs overheads during domain switching due to the privilege level intervention. However, as IoT environments become increasingly interconnected and more resources become required for protection, the significant overhead associated with domain switching under this constraint is expected to be crucial, making it harder to operate with more granular domains. To mitigate these issues, we propose DEMIX, which supports efficient memory isolation for multiple domains. DEMIX comprises two mainelements-Domain-Enforced Memory Isolation and instruction-level domain isolation-with the primary idea of enabling granular access control for memory by validating the domain state of the processor and the executed instructions. By achieving fine-grained validation of memory regions, our technique safely extends the supported domain capabilities of existing technologies while eliminating the overhead associated with switching between domains. Our implementation of eight user domains shows that our approach yields a hardware overhead of a slight 8% in Ibex Core, a very lightweight RISC-V processor.

The differentially expressed gene signatures of the Cullin 3-RING ubiquitin ligases in neuroendocrine cancer.

Cullin-RING ubiquitin E3 ligase (CRLs) composed of four components including cullin scaffolds, adaptors, substrate receptors, and RING proteins mediates the ubiquitination of approximately 20% of cellular proteins that are involved in numerous biological processes. While CRLs deregulation contributes to the pathogenesis of many diseases, including cancer, how CRLs deregulation occurs is yet to be fully investigated. Here, we demonstrate that components of CRL3 and its transcriptional regulators are possible prognosis marker of neuroendocrine (NE) cancer. Analysis of Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal revealed that expression of CRL3 scaffold Cullin 3 (CUL3) highly correlates with NE signature, and CUL3 silencing inhibited NE cancer proliferation. Moreover, subset of 151 BTB (Bric-a-brac, Tramtrack, Broad complex) domain-containing proteins that have dual roles as substrate receptors and adaptor subunits in CRL3, as well as the expression of transcription factors (TFs) that control the transcription of BTB genes were upregulated in NE cancer. Analysis using published ChIP-sequencing data in small cell lung cancer (SCLC), including NE or non-NE SCLC verified that gene promoter of candidates which show high correlation with NE signature enriched H3K27Ac. These observations suggest that CRL3 is a master regulator of NE cancer and knowledge of specifically regulated CRL3 genes in NE cancer may accelerate new therapeutic approaches.

Altered gut microbiota and its association with inflammation in patients with chronic thromboembolic pulmonary hypertension: a single-center observational study in Japan.

BACKGROUND: The pathogenesis of chronic thromboembolic pulmonary hypertension (CTEPH) is considered to be associated with chronic inflammation; however, the underlying mechanism remains unclear. Recently, altered gut microbiota were found in patients with pulmonary arterial hypertension (PAH) and in experimental PAH models. The aim of this study was to characterize the gut microbiota in patients with CTEPH and assess the relationship between gut dysbiosis and inflammation in CTEPH. METHODS: In this observational study, fecal samples were collected from 11 patients with CTEPH and 22 healthy participants. The abundance of gut microbiota in these fecal samples was assessed using 16S ribosomal ribonucleic acid (rRNA) gene sequencing. Inflammatory cytokine and endotoxin levels were also assessed in patients with CTEPH and control participants. RESULTS: The levels of serum tumor necrosis factor-α (TNF-α), interleukin (IL)-6, IL-8, and macrophage inflammatory protein (MIP)-1α were elevated in patients with CTEPH. Plasma endotoxin levels were significantly increased in patients with CTEPH (P < 0.001), and were positively correlated with TNF-α, IL-6, IL-8, and MIP-1α levels. The 16S rRNA gene sequencing and the principal coordinate analysis revealed the distinction in the gut microbiota between patients with CTEPH (P < 0.01) and control participants as well as the decreased bacterial alpha-diversity in patients with CTEPH. A random forest analysis for predicting the distinction in gut microbiota revealed an accuracy of 80.3%. CONCLUSION: The composition of the gut microbiota in patients with CTEPH was distinct from that of healthy participants, which may be associated with the elevated inflammatory cytokines and endotoxins in CTEPH.

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extracted the corrected QT interval and five heart rate variability parameters from the ECG, along with glucose level from a continuous glucose monitoring system (CGMS). This feature set is used as input to the SVM, and hypoglycemic events are predicted every 5 min using the trained SVM model for up to 30 min in advance. The proposed algorithm was developed and evaluated for nine Type-1 diabetes patients in the D1NAMO dataset. The prediction sensitivity, specificity, and accuracy values for the test set were 91.1%, 87.0%, and 89.0% (10 min before); 88.0%, 84.3%, and 86.2% (20 min before); 80.1%, 83.3%, and 81.7% (30 min before), respectively. These results show higher performance of the proposed method compared to previous studies and suggest the possibility of predicting hypoglycemia in advance.

Comprehensive analysis of gut microbiota of a healthy population and covariates affecting microbial variation in two large Japanese cohorts.

BACKGROUND: Inter-individual variations in gut microbiota composition are observed even among healthy populations. The gut microbiota may exhibit a unique composition depending on the country of origin and race of individuals. To comprehensively understand the link between healthy gut microbiota and host state, it is beneficial to conduct large-scale cohort studies. The aim of the present study was to elucidate the integrated and non-redundant factors associated with gut microbiota composition within the Japanese population by 16S rRNA sequencing of fecal samples and questionnaire-based covariate analysis. RESULTS: A total of 1596 healthy Japanese individuals participated in this study via two independent cohorts, NIBIOHN cohort (n = 954) and MORINAGA cohort (n = 642). Gut microbiota composition was described and the interaction of these microorganisms with metadata parameters such as anthropometric measurements, bowel habits, medical history, and lifestyle were obtained. Thirteen genera, including Alistipes, Anaerostipes, Bacteroides, Bifidobacterium, Blautia, Eubacterium halli group, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Parabacteroides, Prevotella_9, Roseburia, and Subdoligranulum were predominant among the two cohorts. On the basis of univariate analysis for overall microbiome variation, 18 matching variables exhibited significant association in both cohorts. A stepwise redundancy analysis revealed that there were four common covariates, Bristol Stool Scale (BSS) scores, gender, age, and defecation frequency, displaying non-redundant association with gut microbial variance. CONCLUSIONS: We conducted a comprehensive analysis of gut microbiota in healthy Japanese individuals, based on two independent cohorts, and obtained reliable evidence that questionnaire-based covariates such as frequency of bowel movement and specific dietary habit affects the microbial composition of the gut. To our knowledge, this was the first study to investigate integrated and non-redundant factors associated with gut microbiota among Japanese populations.

Gut microbial composition in patients with atrial fibrillation: effects of diet and drugs.

Atrial fibrillation (AF) reduces the quality of life by triggering stroke and heart failure. The association between AF onset and gut metabolites suggests a causal relationship between AF and gut microbiota dysbiosis; however, the relationship remains poorly understood. We prospectively enrolled 34 hospitalized patients with AF and 66 age-, sex-, and comorbidity-matched control subjects without a history of AF. Gut microbial compositions were evaluated by amplicon sequencing targeting the 16S ribosomal RNA gene. We assessed differences in dietary habits by using a brief-type self-administered diet history questionnaire (BDHQ). Gut microbial richness was lower in AF patients, although the diversity of gut microbiota did not differ between the two groups. At the genus level, Enterobacter was depleted, while Parabacteroides, Lachnoclostridium, Streptococcus, and Alistipes were enriched in AF patients compared to control subjects. The BDHQ revealed that the intake of n-3 polyunsaturated fatty acids and eicosadienoic acid was higher in AF patients. Our results suggested that AF patients had altered gut microbial composition in connection with dietary habits.

Relationship between Nutrient Intake and Human Gut Microbiota in Monozygotic Twins.

Background and Objectives: The gut microbiota is associated with human health and dietary nutrition. Various studies have been reported in this regard, but it is difficult to clearly analyze human gut microbiota as individual differences are significant. The causes of these individual differences in intestinal microflora are genetic and/or environmental. In this study, we focused on differences between identical twins in Japan to clarify the effects of nutrients consumed on the entire gut microbiome, while excluding genetic differences. Materials and Methods: We selected healthy Japanese monozygotic twins for the study and confirmed their zygosity by matching 15 short tandem repeat loci. Their fecal samples were subjected to 16S rRNA sequencing and bioinformatics analyses to identify and compare the fluctuations in intestinal bacteria. Results: We identified 12 genera sensitive to environmental factors, and found that Lactobacillus was relatively unaffected by environmental factors. Moreover, we identified protein, fat, and some nutrient intake that can affect 12 genera, which have been identified to be more sensitive to environmental factors. Among the 12 genera, Bacteroides had a positive correlation with retinol equivalent intake (rs = 0.38), Lachnospira had a significantly negative correlation with protein, sodium, iron, vitamin D, vitamin B6, and vitamin B12 intake (rs = -0.38, -0.41, -0.39, -0.63, -0.42, -0.49, respectively), Lachnospiraceae ND3007 group had a positive correlation with fat intake (rs = 0.39), and Lachnospiraceae UCG-008 group had a negative correlation with the saturated fatty acid intake (rs = -0.45). Conclusions: Our study is the first to focus on the relationship between human gut microbiota and nutrient intake using samples from Japanese twins to exclude the effects of genetic factors. These findings will broaden our understanding of the more intuitive relationship between nutrient intake and the gut microbiota and can be a useful basis for finding useful biomarkers that contribute to human health.

Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks.

BACKGROUND: To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0-20 for QIIME1 and 0-30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. RESULTS: Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. CONCLUSIONS: Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.

BACKGROUND: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. METHODS: We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. RESULTS: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. CONCLUSION: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.

Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal.

In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation. The performance of the proposed method was evaluated on the training set from 63 patients and test set from 13 patients. The results of the LSTM model showed the following high performances: the positive predictive value of 94.16% for normal, 81.38% for apnea, and 97.92% for hypopnea; sensitivity of 86.03% for normal, 91.24% for apnea, and 99.38% for hypopnea events. The proposed method had especially higher performance of hypopnea classification which had been a drawback of previous studies. Furthermore, it can be applied to a system that can classify sleep apnea/hypopnea and normal events automatically without expert's intervention at home.

Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.

This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In particular, the BP signals are estimated according to a long short-term memory (LSTM) model using an Ear PPG. To investigate the proposed method, two statistical analyses were conducted for comparison between BP measured by the micromanometer-based gold standard method (BPMEAS) and the Ear PPG-based proposed method (BPEST) for swine cardiac model. First, Pearson's correlation analysis showed high positive correlations (r = 0.92, p < 0.01) between BPMEAS and BPEST. Second, the paired-samples t-test on the BP parameters (systolic and diastolic blood pressure) of the two methods indicated no significant differences (p > 0.05). Therefore, the proposed method has the potential for estimation of BP for CPR biofeedback based on LSTM using a single Ear PPG.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Effects of Asian dust events on atmospheric bacterial communities at different distances downwind of the source region.

Aeolian dust particles arising from arid and semiarid zones are known to carry microbes by air currents. The effect of wind-borne bacteria on atmospheric bacterial population at various downwind distances from the dust source regions must be clarified, but has not yet been reported. This study monitored the bacterial abundance and community composition in outdoor aerosol samples in Beijing, China, which is close to the Asian dust source regions, and compared them with the results obtained in a distant region (Osaka, Japan). The Asian dust collected in Beijing contained (4±3)×104bacterial cells/m3, approximately 4 times higher than in Osaka. On 15 April 2015, Beijing experienced severe Asian dust events with a 1000-fold increase in bacterial abundance, relative to non-Asian dust days. Dominant bacterial phyla and classes in Asian dust collected in Beijing were Actinobacteria, Bacilli and Acidobacteria, and the bacterial community composition varied more widely than in Osaka. The bacterial community compositions differed between the Beijing and Osaka dusts, even for the same Asian dust events. These results indicated that aerosol bacterial communities nearer the dust source are more affected by eolian dust than their distant counterparts.

Cardiopulmonary Coupling Analysis Using Home Sleep Monitoring System Based on Air Mattress.

This study investigates the feasibility of cardiopulmonary coupling (CPC) using home sleep monitoring system. We have designed a system to measure respiratory signals and normal-to-normal (NN) interval series in a non-contact based on air mattress. Then, CPC analysis was conducted using extracted respiratory signals and NN interval series, and six CPC parameters were extracted (VLFC, LFC, HFC, e-LFC, e-LFCNB and e-LFCBB). To evaluate the proposed method, two statistical analyses were conducted between the CPC parameters extracted by the electrocardiogram-based conventional method and the air mattress-based proposed method for five patients with obstructive sleep apnea and hypopnea (OSAH). Wilcoxon's signed rank test on the CPC parameters of the two methods indicated no significant differences (p > 0.05) and Spearman's rank correlation analysis showed high positive correlations (r ≥ 0.7, p < 0.05) between the two methods. Therefore, the proposed method has the potential for performing CPC analysis using air mattress-based system.

New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal.

We developed a rule-based algorithm for automatic real-time detection of sleep apnea and hypopnea events using a nasal pressure signal. Our basic premise was that the performance of our new algorithm using the nasal pressure signal would be comparable to that using other sensors as well as manual annotation labeled by a technician on polysomnography study. We investigated fifty patients with sleep apnea-hypopnea syndrome (age: 56.8 ± 10.5 years, apnea-hypopnea index (AHI): 36.2 ± 18.1/h) during full night PSG recordings at the sleep center. The algorithm was comprised of pre-processing with a median filter, amplitude computation and apnea-hypopnea detection parts. We evaluated the performance of the algorithm a confusion matric for each event and statistical analyses for AHI. Our evaluation achieved a good performance, with a sensitivity of 86.4 %, and a positive predictive value of 84.5 % for detection of apnea and hypopnea regardless of AHI severity. Our results indicated a high correlation with the manually labeled apnea-hypopnea events during PSG, with a correlation coefficient of r = 0.94 (p < 0.0001) and a mean difference of -2.9 ± 11.6 per hour. The proposed new algorithm could provide significant clinical and computational insights to design a PSG analysis system and a continuous positive airway pressure (CPAP) device for screening sleep quality related in patients with sleep apnea-hypopnea syndrome.

Effect of resistance training and chicken meat on muscle strength and mass and the gut microbiome of older women: A randomized controlled trial.

This study investigated the effects of white meat, such as chicken, intake combined with resistance training on muscle mass and strength in the elderly women, and whether the underlying mechanism involves changes in the gut microbiota. Ninety-three volunteers (age 59-79 years) were randomly allocated to sedentary control with placebo (Sed + PL) or chicken meat (Sed + HP) and resistance training with placebo (RT + PL) or chicken meat (RT + HP). Resistance training sessions were performed 3 d/week for 12 weeks using leg extensions and curls. Boiled chicken meat (110 g, containing 22.5 g protein) was ingested 3 d/week for 12 weeks. Maximal muscle strength and whole-body lean mass increased significantly in the RT + PL group compared to the Sed + HP group, and the RT + HP group showed a significantly greater increase than the Sed + HP and RT + PL groups. Additionally, the gut microbiota composition did not change before or after the interventions in any of the four groups. Moreover, the individual comparison of gut bacteria using false discovery rate-based statistical analysis showed no alterations before or after the interventions in the four groups. Resistance training combined with chicken meat intake may effective have increased muscle mass and strength without drastically modifying the gut microbiota composition in elderly women.

Group X phospholipase A2 links colonic lipid homeostasis to systemic metabolism via host-microbiota interaction.

The gut microbiota influences physiological functions of the host, ranging from the maintenance of local gut homeostasis to systemic immunity and metabolism. Secreted phospholipase A2 group X (sPLA2-X) is abundantly expressed in colonic epithelial cells but is barely detectable in metabolic and immune tissues. Despite this distribution, sPLA2-X-deficient (Pla2g10-/-) mice displayed variable obesity-related phenotypes that were abrogated after treatment with antibiotics or cohousing with Pla2g10+/+ mice, suggesting the involvement of the gut microbiota. Under housing conditions where Pla2g10-/- mice showed aggravation of diet-induced obesity and insulin resistance, they displayed increased colonic inflammation and epithelial damage, reduced production of polyunsaturated fatty acids (PUFAs) and lysophospholipids, decreased abundance of several Clostridium species, and reduced levels of short-chain fatty acids (SCFAs). These obesity-related phenotypes in Pla2g10-/- mice were reversed by dietary supplementation with ω3 PUFAs or SCFAs. Thus, colonic sPLA2-X orchestrates ω3 PUFA-SCFA interplay via modulation of the gut microbiota, thereby secondarily affecting systemic metabolism.

Effects of energy loads on energy and nutrient absorption rates and gut microbiome in humans: A randomized crossover trial.

OBJECTIVE: This study aimed to determine the effects of different energy loads on the gut microbiota composition and the rates of energy and nutrient excretion via feces and urine. METHODS: A randomized crossover dietary intervention study was conducted with three dietary conditions: overfeeding (OF), control (CON), and underfeeding (UF). Ten healthy men were subjected to each condition for 8 days (4 days and 3 nights in nonlaboratory and laboratory settings each). The effects of dietary conditions on energy excretion rates via feces and urine were assessed using a bomb calorimeter. RESULTS: Short-term energy loads dynamically altered the gut microbiota at the α-diversity (Shannon index), phylum, and genus levels (p < 0.05). Energy excretion rates via urine and urine plus feces decreased under OF more than under CON (urine -0.7%; p < 0.001, urine plus feces -1.9%; p = 0.049) and UF (urine -1.0%; p < 0.001, urine plus feces -2.1%; p = 0.031). However, energy excretion rates via feces did not differ between conditions. CONCLUSIONS: Although short-term overfeeding dynamically altered the gut microbiota composition, the energy excretion rate via feces was unaffected. Energy excretion rates via urine and urine plus feces were lower under OF than under CON and UF conditions.