Effects on human metabolism

2010 ◽  
pp. 527-531
Keyword(s):  
Human Metabolism

Trace Elements and Iron in Human Metabolism

1979 ◽  
Vol 7 (6) ◽  
pp. 1330-1331
Author(s):  
E. D. WILLS
Keyword(s):  
Trace Elements ◽  
Human Metabolism

Human metabolism and obesity: the influence of exercise

2015 ◽  
pp. 21-24
Author(s):  
Naomi Brooks ◽  
Stuart Galloway
Keyword(s):  
Human Metabolism

Inter-Tissue and Intra-Tissue Co-Expression Networks in Human Metabolism: Morphological Evaluation of the Link Between Transcription Factors ERβ and NFAT in Morbid Obesity

2020 ◽  
Vol 17 (1) ◽  
pp. 63-80
Author(s):  
Athina Chasapi ◽  
Kostas Balampanis ◽  
Eleni Kourea ◽  
Fotios Kalfaretzos ◽  
Vaia Lambadiari ◽  
...  
Keyword(s):  
Morbid Obesity ◽  
Adipose Tissue ◽  
Clinicopathological Parameters ◽  
Morbidly Obese ◽  
Human Metabolism ◽  
Activated T Cells ◽  
Alcoholic Fatty Liver ◽  
Morphological Evaluation ◽  
Underlying Mechanisms

Background: Estrogen receptor β (ERβ) plays an important role in human metabolism and some of its metabolic actions are mediated by a positive “cross-talk” with Nuclear Factor of Activated T cells (NFAT) and the key metabolic transcriptional coregulator Transcriptional Intermediary Factor 2 (TIF2). Introduction: Our study is an “in situ” morphological evaluation of the communication between ERβ, NFAT and TIF2 in morbid obesity. Potential correlations with clinicopathological parameters and with the presence of diabetes and non-alcoholic fatty liver disease (NAFLD) were also explored. The aim of the present study was to determine the role of ERβ and NFAT in the underlying pathophysiology of obesity and related comorbidities. We have investigated the expression of specific proteins using immunochemistry methodologies. Methods: Our population consists of 50 morbidly obese patients undergoing planned bariatric surgery, during which biopsies were taken from visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), skeletal muscle (SM), extramyocellular adipose tissue (EMAT) and liver and the differential protein expression was evaluated by immunohistochemistry. Results: We demonstrated an extensive intra- and inter-tissue co-expression network, which confirms the tissue-specific and integral role of each one of the investigated proteins in morbid obesity. Moreover, a beneficial role of ERβ and NFATc1 against NAFLD is implicated, whereas the distinct roles of TIF2 still remain an enigma. Conclusions: We believe that our findings will shed light on the complex underlying mechanisms and that the investigated biomarkers could represent future targets for the prevention and therapy of obesity and its comorbidities.

scholarly journals Chemical Attribution of Fentanyl: The Effect of Human Metabolism

2021 ◽  
pp. 100330
Author(s):  
Mirjam de Bruin-Hoegée ◽  
Djarah Kleiweg ◽  
Daan Noort ◽  
Arian C. Van Asten
Keyword(s):  
Human Metabolism

Human metabolism and urinary excretion kinetics of di-n-butyl adipate (DnBA) after oral and dermal administration in three volunteers

2021 ◽  
Vol 343 ◽  
pp. 11-20
Author(s):  
Benedikt Ringbeck ◽  
Daniel Bury ◽  
Alexandra Gotthardt ◽  
Heiko Hayen ◽  
Rainer Otter ◽  
...  
Keyword(s):  
Urinary Excretion ◽  
Human Metabolism ◽  
Excretion Kinetics ◽  
Dermal Administration ◽  
Kinetics Of

scholarly journals Gut microbiota influence in type 2 diabetes mellitus (T2DM)

Gut Pathogens ◽  
2021 ◽  
Vol 13 (1) ◽  
Author(s):  
A. L. Cunningham ◽  
J. W. Stephens ◽  
D. A. Harris
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiota ◽  
Potential Role ◽  
Evidence Base ◽  
Human Metabolism ◽  
Altered Glucose

AbstractA strong and expanding evidence base supports the influence of gut microbiota in human metabolism. Altered glucose homeostasis is associated with altered gut microbiota, and is clearly associated with the development of type 2 diabetes mellitus (T2DM) and associated complications. Understanding the causal association between gut microbiota and metabolic risk has the potential role of identifying susceptible individuals to allow early targeted intervention.

scholarly journals A cross-platform approach identifies genetic regulators of human metabolism and health

2021 ◽  
Vol 53 (1) ◽  
pp. 54-64
Author(s):  
Luca A. Lotta ◽  
◽  
Maik Pietzner ◽  
Isobel D. Stewart ◽  
Laura B. L. Wittemans ◽  
...  
Keyword(s):  
Human Metabolism ◽  
Cross Platform

scholarly journals Compartment and hub definitions tune metabolic networks for metabolomic interpretations

GigaScience ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
T Cameron Waller ◽  
Jordan A Berg ◽  
Alexander Lex ◽  
Brian E Chapman ◽  
Jared Rutter
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Shortest Paths ◽  
Large Data ◽  
Differential Regulation ◽  
Large Data Sets ◽  
Data Sets ◽  
Human Metabolism ◽  
Experimental Conditions ◽  
Systemic Model

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism’s cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

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