Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
4.1
Journals
Metabolites
Special Issues
Metabolomics in Obesity Related Diseases
share announcement

Special Issue "Metabolomics in Obesity Related Diseases"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Lipid Metabolism".

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 2409

Special Issue Editor

Dr. Ming Ding

E-Mail Website
Guest Editor
Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA
Interests: lipidomics; genetics; metabolomics; nutrition

Special Issue Information

Dear Colleagues,

Obesity is a worldwide problem and can cause a number of diseases, such as type 2 diabetes, cardiovascular dieases, some types of cancer, and reproductive system disorders. Metabolomics is a state-of-the-art technology that allows for high-throughput profiling of small molecules, and has emerged as a powerful tool to understand complex diseases. In particular, lipid metabolites are the main components of known metablites. Large epidemiological studies with collected biological samples and ascertained disease outcomes provide ideal resources to conduct metabolomic studies. These studies identify novel molecular markers that can be used for disease prediction and reveal pathways that elucidate biological mechanisms of obesity-related diseases.

This Special Issue aims to highlight epidemiological studies utilizing metabolomics or metabolomics integrated with other -omics technologies (such as proteomics, transcriptomics, epigenetics, and gut microbiota) to enhance our understanding of obesity-related diseases. Therefore, we invite original and review articles based on population data in humans related to this topic. Methodological studies and COVID-19 related studies are also welcome.

Dr. Ming Ding
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • obesity
  • metabolomics
  • lipidomics
  • multi-omics
  • cardiovascular disease
  • type 2 diabetes
  • cancer
  • reproductive system disorders

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

get_app
Article
Cord Blood Metabolome and BMI Trajectory from Birth to Adolescence: A Prospective Birth Cohort Study on Early Life Biomarkers of Persistent Obesity
by
Tingyi Cao
,
Jiaxuan Zhao
,
Xiumei Hong
,
Guoying Wang
,
Frank B. Hu
,
Xiaobin Wang
and
Liming Liang
Metabolites 2021, 11(11), 739; https://doi.org/10.3390/metabo11110739 - 28 Oct 2021
Cited by 12 | Viewed by 1871
Abstract
There is increasing recognition on the role of early life metabolic programming in childhood obesity. This study sought to investigate whether newborn cord blood metabolome can predict future BMI. It included 946 children in the Boston Birth Cohort, a sample of high-risk yet [...] Read more.
There is increasing recognition on the role of early life metabolic programming in childhood obesity. This study sought to investigate whether newborn cord blood metabolome can predict future BMI. It included 946 children in the Boston Birth Cohort, a sample of high-risk yet understudied US urban, low-income, predominantly Black and Hispanic children, who were enrolled at birth and followed prospectively up to age 18 years. A total of 376 metabolites were measured in cord blood plasma. Longitudinal BMI trajectories were defined and categorized into three distinct patterns: early onset overweight and obesity (early-OWO), late onset OWO (late-OWO), and normal weight trajectory (NW). Multinomial logistic regression models were used to identify metabolites individually or as network modules associated with BMI trajectories. Of the 946 children, 388, 254, and 304 were classified as early-OWO, late-OWO, and NW, respectively. Of the seven co-metabolomic network modules defined, two were inversely correlated with early-OWO. Among the 68 metabolites within the two modules, 22 triacylglycerols and diacylglycerols were negatively associated with early-OWO; 5 cholesterol esters were positively associated with early-OWO. In this prospective birth cohort, we demonstrated distinctive longitudinal BMI trajectories and identified multiple cord plasma metabolites in relevant biological pathways that were associated with early-OWO. Full article
(This article belongs to the Special Issue Metabolomics in Obesity Related Diseases)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop