scholarly journals Gut microbiome activity contributes to individual variation in glycemic response in adults

2019 ◽  
Author(s):  
Hal Tily ◽  
Ally Perlina ◽  
Eric Patridge ◽  
Stephanie Gline ◽  
Matvey Genkin ◽  
...  
Keyword(s):  
Individual Variation ◽  
Gut Microbiome ◽  
Metabolic Diseases ◽  
Nutritional Composition ◽  
Gradient Boosting ◽  
Glycemic Response ◽  
Activity Data ◽  
Gradient Boosting Machine ◽  
Functional Features ◽  
First Time

AbstractLimiting post-meal glycemic response is an important factor in reducing the risk of chronic metabolic diseases, and contributes to significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity (i.e., metatranscriptomic) data and measured the glycemic responses of 550 adults who consumed more than 30,000 meals from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity makes a statistically significant contribution to individual variation in glycemic response, in addition to anthropometric factors and the nutritional composition of foods. We describe predictive models (multilevel mixed-effects regression and gradient boosting machine) of variation in glycemic response among individuals ingesting the same foods. We introduce functional features aggregated from microbial activity data as candidates for association with mechanisms of glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with glycemic response among adults.

scholarly journals Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helena Marcos-Pasero ◽  
Gonzalo Colmenarejo ◽  
Elena Aguilar-Aguilar ◽  
Ana Ramírez de Molina ◽  
Guillermo Reglero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Childhood Obesity ◽  
Metabolic Diseases ◽  
Variable Importance ◽  
The Body ◽  
Gradient Boosting ◽  
Gradient Boosting Machine ◽  
Potential Risk Factors ◽  
Variable Importance Measures ◽  
Near Future

AbstractThe increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.

scholarly journals Gut Microbiome Activity Contributes to Prediction of Individual Variation in Glycemic Response in Adults

2021 ◽  
Author(s):  
Hal Tily ◽  
Eric Patridge ◽  
Ying Cai ◽  
Vishakh Gopu ◽  
Stephanie Gline ◽  
...  
Keyword(s):  
Individual Variation ◽  
Gut Microbiome ◽  
Glycemic Response

Folate and vitamin B-12 deficiencies additively impaired memory function and disturbed the gut microbiota in amyloid-β infused rats

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sunmin Park ◽  
Sunna Kang ◽  
Da Sol Kim
Keyword(s):  
Insulin Resistance ◽  
Gut Microbiota ◽  
Insulin Signaling ◽  
Gut Microbiome ◽  
Metabolic Diseases ◽  
Memory Function ◽  
Amyloid Β ◽  
Impaired Memory ◽  
Ca1 Region ◽  
Folate And Vitamin B12

Abstract. Folate and vitamin B12(V-B12) deficiencies are associated with metabolic diseases that may impair memory function. We hypothesized that folate and V-B12 may differently alter mild cognitive impairment, glucose metabolism, and inflammation by modulating the gut microbiome in rats with Alzheimer’s disease (AD)-like dementia. The hypothesis was examined in hippocampal amyloid-β infused rats, and its mechanism was explored. Rats that received an amyloid-β(25–35) infusion into the CA1 region of the hippocampus were fed either control(2.5 mg folate plus 25 μg V-B12/kg diet; AD-CON, n = 10), no folate(0 folate plus 25 μg V-B12/kg diet; AD-FA, n = 10), no V-B12(2.5 mg folate plus 0 μg V-B12/kg diet; AD-V-B12, n = 10), or no folate plus no V-B12(0 mg folate plus 0 μg V-B12/kg diet; AD-FAB12, n = 10) in high-fat diets for 8 weeks. AD-FA and AD-VB12 exacerbated bone mineral loss in the lumbar spine and femur whereas AD-FA lowered lean body mass in the hip compared to AD-CON(P < 0.05). Only AD-FAB12 exacerbated memory impairment by 1.3 and 1.4 folds, respectively, as measured by passive avoidance and water maze tests, compared to AD-CON(P < 0.01). Hippocampal insulin signaling and neuroinflammation were attenuated in AD-CON compared to Non-AD-CON. AD-FAB12 impaired the signaling (pAkt→pGSK-3β) and serum TNF-α and IL-1β levels the most among all groups. AD-CON decreased glucose tolerance by increasing insulin resistance compared to Non-AD-CON. AD-VB12 and AD-FAB12 increased insulin resistance by 1.2 and 1.3 folds, respectively, compared to the AD-CON. AD-CON and Non-AD-CON had a separate communities of gut microbiota. The relative counts of Bacteroidia were lower and those of Clostridia were higher in AD-CON than Non-AD-CON. AD-FA, but not V-B12, separated the gut microbiome community compared to AD-CON and AD-VB12(P = 0.009). In conclusion, folate and B-12 deficiencies impaired memory function by impairing hippocampal insulin signaling and gut microbiota in AD rats.

scholarly journals MiRNA Expression in Neuroendocrine Neoplasms of Frequent Localizations

2021 ◽  
Vol 7 (3) ◽  
pp. 38
Author(s):  
Alexandra Korotaeva ◽  
Danzan Mansorunov ◽  
Natalya Apanovich ◽  
Anna Kuzevanova ◽  
Alexander Karpukhin
Keyword(s):  
Mirna Expression ◽  
Malignant Tumors ◽  
Neuroendocrine Neoplasms ◽  
Specific Expression ◽  
Clinical Biomarkers ◽  
Different Types ◽  
Functional Features ◽  
First Time ◽  
Potential Biomarkers

Neuroendocrine neoplasms (NEN) are infrequent malignant tumors of a neuroendocrine nature that arise in various organs. They occur most frequently in the lungs, intestines, stomach and pancreas. Molecular diagnostics and prognosis of NEN development are highly relevant. The role of clinical biomarkers can be played by microRNAs (miRNAs). This work is devoted to the analysis of data on miRNA expression in NENs. For the first time, a search for specificity or a community of their functional characteristics in different types of NEN was carried out. Their properties as biomarkers were also analyzed. To date, more than 100 miRNAs have been characterized as differentially expressed and significant for the development of NEN tumors. Only about 10% of the studied miRNAs are expressed in several types of NEN; differential expression of the remaining 90% was found only in tumors of specific localizations. A significant number of miRNAs have been identified as potential biomarkers. However, only a few miRNAs have values that characterized their quality as markers. The analysis demonstrates the predominant specific expression of miRNA in each studied type of NEN. This indicates that miRNA’s functional features are predominantly influenced by the tissue in which they are formed.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

scholarly journals Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balamurugan Sadaiappan ◽  
Chinnamani PrasannaKumar ◽  
V. Uthara Nambiar ◽  
Mahendran Subramanian ◽  
Manguesh U. Gauns
Keyword(s):  
Machine Learning ◽  
Iron Transport ◽  
Meta Analysis ◽  
Zooplankton Community ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
16S Rdna Gene ◽  
Marine Copepod ◽  
Form Of Life ◽  
First Time

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.

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