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Metabolomics in the Interventions of Metabolic Diseases and Physical Activity

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A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 14201

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Special Issue Editors

Prof. Dr. Tiemin Liu

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Guest Editor

Department of Physiology and Biophysics, the School of Life Science, Fudan University, Shanghai 200438, China
Interests: physiology and neuroscience; neuromodulation; pathogenesis of metabolic disorders and neuroendocrine homeostasis in mammals; muscle and exercise
Special Issues, Collections and Topics in MDPI journals

Dr. Hongmei Yan

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Guest Editor

Department of Endocrinology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Interests: obesity; non-alcoholic fatty liver disease; metabolic syndrome; sarcopenia
Special Issues, Collections and Topics in MDPI journals

Dr. Qiongyue Zhang

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Guest Editor

Department of Endocrinology, Huashan Hospital, Fudan University, Shanghai 200032, China
Interests: obesity; type 2 diabetes; standardized and individualized diagnosis and treatment of metabolic diseases; bariatric surgery, especially the preoperative evaluation and postoperative follow-up of differential diagnosis of obesity and perioperative blood glucose management
Special Issues, Collections and Topics in MDPI journals

Dr. Ting Yao

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Guest Editor

Department of School of Basic Medical College, Xi'an Jiaotong University, Xi'an 710049, China
Interests: obesity; diabetes; energy balance; metabolic network regulation; central nervous regulation of metabolism

Special Issue Information

Dear Colleagues,

Metabolic diseases such as obesity, diabetes, and fatty liver disease have attained the status of a global pandemic, threatening human health and causing a tremendous socio-economic burden. Currently, there are intervention methods for preventing and treating metabolic diseases, including diet, drugs, exercise, and bariatric surgery. However, there is significant heterogeneity in treatment approaches, and the mechanisms of these approaches are not yet completely clear. Among them, the importance of exercise interventions has been consistently underestimated, and the specific mechanism of exercise to improve metabolism has not been fully elucidated.

The rapid development and increasing accessibility of multiple omics techniques, including metabolomics, lipidomics, and proteomics, can not only provide a holistic overview of the metabolite profiling and the chemical phenotype in human subjects and animal models, but can also play an essential role in the study of biological systems and metabolic disorders, especially in the diagnosis and evaluation, treatment, and prediction of diseases.

Therefore, this Special Issue aims to study metabolic spectrum differences in metabolic diseases, various interventions for metabolic illnesses, exercise-related biological effects, and metabolic changes as well as to discover potential mechanisms and to identify novel biomarkers of metabolic disorders. It is hoped that more researchers can share the new progress and discoveries that have been made in metabolic disease prevention and exercise intervention through this academic platform. As such, we can expand the applications of multiple omics, including metabolomics, in the prevention and treatment of metabolic diseases and exercise promotion for health and can facilitate the development of novel strategies to combat metabolic diseases.

We welcome different types of articles, including original research articles, clinical trials, reviews, and perspectives, that address these questions across diverse fields, and topics may include but are not limited to:

Explorations of the pathogenesis and efficacy prediction of different metabolic diseases (such as diabetes, obesity, fatty liver disease, hyperuricemia, etc.) through multiple omics methods, including metabolomics;
Identification of exercise response factors of different types of physical activity, including aerobics and resistance exercise, to study the role and mechanism of exercise intervention in the prevention and treatment of metabolic diseases;
Extension of the application prospects of metabolomics, lipidomics, proteomics, and other omics in metabolic diseases and in exercise intervention;
Revelations of the efficacy and mechanism of different intervention methods (such as diet, exercise, drugs, surgery, etc.) for metabolic diseases;
Development of accurate diet programs and exercise prescriptions for metabolic diseases.

Prof. Dr. Tiemin Liu
Dr. Hongmei Yan
Dr. Qiongyue Zhang
Dr. Ting Yao
Guest Editors

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

multi-omics: metabolomics, lipidomics, proteomics etc
physical activity: exercise, exercise prescription, muscle metabolism, sport, aerobics, sports physiology, muscle-strengthening physical activity, sarcopenia, etc
metabolic diseases: obesity, diabetes, NAFLD, MAFLD, NASH etc
intervention methods: bariatric surgery, pharmacological treatment, aerobics, diet, nutrition, treatment, etc

Published Papers (6 papers)

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Research

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23 pages, 2024 KiB

Open AccessArticle

Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis

by Amarnath Singh, Garrett Kinnebrew, Ping-Ching Hsu, Daniel Y. Weng, Min-Ae Song, Sarah A. Reisinger, Joseph P. McElroy, Brittney Keller-Hamilton, Amy K. Ferketich, Jo L. Freudenheim and Peter G. Shields

Metabolites 2023, 13(8), 899; https://doi.org/10.3390/metabo13080899 - 30 Jul 2023

Viewed by 1524

Abstract

Obesity in children and adolescents has increased globally. Increased body mass index (BMI) during adolescence carries significant long-term adverse health outcomes, including chronic diseases such as cardiovascular disease, stroke, diabetes, and cancer. Little is known about the metabolic consequences of changes in BMI in adolescents outside of typical clinical parameters. Here, we used untargeted metabolomics to assess changing BMI in male adolescents. Untargeted metabolomic profiling was performed on urine samples from 360 adolescents using UPLC–QTOF-MS. The study includes a baseline of 235 subjects in a discovery set and 125 subjects in a validation set. Of them, a follow-up of 81 subjects (1 year later) as a replication set was studied. Linear regression analysis models were used to estimate the associations of metabolic features with BMI z-score in the discovery and validation sets, after adjusting for age, race, and total energy intake (kcal) at false-discovery-rate correction (FDR) ≤ 0.1. We identified 221 and 16 significant metabolic features in the discovery and in the validation set, respectively. The metabolites associated with BMI z-score in validation sets are glycylproline, citrulline, 4-vinylsyringol, 3′-sialyllactose, estrone sulfate, carnosine, formiminoglutamic acid, 4-hydroxyproline, hydroxyprolyl-asparagine, 2-hexenoylcarnitine, L-glutamine, inosine, N-(2-Hydroxyphenyl) acetamide glucuronide, and galactosylhydroxylysine. Of those 16 features, 9 significant metabolic features were associated with a positive change in BMI in the replication set 1 year later. Histidine and arginine metabolism were the most affected metabolic pathways. Our findings suggest that obesity and its metabolic outcomes in the urine metabolome of children are linked to altered amino acids, lipid, and carbohydrate metabolism. These identified metabolites may serve as biomarkers and aid in the investigation of obesity’s underlying pathological mechanisms. Whether these features are associated with the development of obesity, or a consequence of changing BMI, requires further study. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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16 pages, 5423 KiB

Open AccessArticle

High-Intensity Interval Training Induces Protein Lactylation in Different Tissues of Mice with Specificity and Time Dependence

by Wenhua Huang, Jie Su, Xuefei Chen, Yanjun Li, Zheng Xing, Lanlan Guo, Shitian Li and Jing Zhang

Metabolites 2023, 13(5), 647; https://doi.org/10.3390/metabo13050647 - 09 May 2023

Cited by 4 | Viewed by 2057

Abstract

Protein lysine lactylation (Kla) is a novel protein acylation reported in recent years, which plays an important role in the development of several diseases with pathologically elevated lactate levels, such as tumors. The concentration of lactate as a donor is directly related to the Kla level. High-intensity interval training (HIIT) is a workout pattern that has positive effects in many metabolic diseases, but the mechanisms by which HIIT promotes health are not yet clear. Lactate is the main metabolite of HIIT, and it is unknown as to whether high lactate during HIIT can induce changes in Kla levels, as well as whether Kla levels differ in different tissues and how time-dependent Kla levels are. In this study, we observed the specificity and time-dependent effects of a single HIIT on the regulation of Kla in mouse tissues. In addition, we aimed to select tissues with high Kla specificity and obvious time dependence for lactylation quantitative omics and analyze the possible biological targets of HIIT-induced Kla regulation. A single HIIT induces Kla in tissues with high lactate uptake and metabolism, such as iWAT, BAT, soleus muscle and liver proteins, and Kla levels peak at 24 h after HIIT and return to steady state at 72 h. Kla proteins in iWAT may affect pathways related to glycolipid metabolism and are highly associated with de novo synthesis. It is speculated that the changes in energy expenditure, lipolytic effects and metabolic characteristics during the recovery period after HIIT may be related to the regulation of Kla in iWAT. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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14 pages, 2029 KiB

Open AccessArticle

Lipidomics and Transcriptomics Differ Liposarcoma Differentiation Characteristics That Can Be Altered by Pentose Phosphate Pathway Intervention

by Zhengqing Song, Shuaikang Wang, Lili Lu, Jingshen Xu, Qiwen Zhou, Weiqi Lu, Hanxing Tong, Yong Zhang, Wenshuai Liu, Zhiming Wang, Wei Li, Yang You, Chenlu Zhang, Xi Guo, Rongkui Luo, Yingyong Hou, Chunmeng Wang, Yuexiang Wang, Lei Sun, He Huang and Yuhong Zhou Show full author list Hide full author list

Metabolites 2022, 12(12), 1227; https://doi.org/10.3390/metabo12121227 - 07 Dec 2022

Cited by 2 | Viewed by 1665

Abstract

Liposarcoma (LPS) is a rare and heterogeneous malignancy of adipocytic origin. Well-differentiated liposarcoma (WDLPS) and dedifferentiated liposarcoma (DDLPS) are two of the most common subtypes, showing similar genetic characterizations but distinct biological behaviors and clinical prognosis. Compared to WDLPS, DDLPS is more aggressive and has the potential of metastasis, as the malignant adipocytic tumor’s metabolic changes may have taken place during the tumorigenesis of LPSs. Therefore, to investigate the lipid alterations between the two subtypes, high-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS) based untargeted lipidomic analysis was performed onto LPS tissues from 6 WDLPS and 7 DDLPS patients. The lipidomic analysis showed the upregulated phosphatidylcholines and phosphoethanolamines in DDLPS, and the upregulated triglycerides and diglycerides in WDLPS, which might be due to the uncompleted adipocytic dedifferentiation leading to such tumorigenesis. Such a finding was also confirmed by the similarity comparison of two LPS subtypes to the transcriptome of stromal vascular fraction at different differentiation stages. Transcriptomic analysis also demonstrated that metabolic pathways including the pentose phosphate pathway (PPP) were upregulated in WDLPS compared to DDLPS. Therefore, the cell line LPS853 was treated with the PPP inhibitor 6-aminonicotinamide ex vivo and the proliferation and invasion of LPS853 was significantly promoted by PPP inhibition, suggesting the potential role of PPP in the development and differentiation of LPS. In conclusion, this study described the altered lipid profiles of WDLPS and DDLPS for the first time, revealing the different differentiation stages of the two subtypes and providing a potential metabolic target for LPS treatment. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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17 pages, 1497 KiB

Open AccessArticle

Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods

by Ning Wang, Haonan Guo, Yingyu Jing, Lin Song, Huan Chen, Mengjun Wang, Lei Gao, Lili Huang, Yanan Song, Bo Sun, Wei Cui and Jing Xu

Metabolites 2022, 12(11), 1040; https://doi.org/10.3390/metabo12111040 - 29 Oct 2022

Cited by 3 | Viewed by 1753

Abstract

Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women’s and Children’s Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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17 pages, 2165 KiB

Open AccessArticle

Plasma Metabolomics Reveals β-Glucan Improves Muscle Strength and Exercise Capacity in Athletes

by Ruwen Wang, Xianmin Wu, Kaiqing Lin, Shanshan Guo, Yuning Hou, Renyan Ma, Qirong Wang and Ru Wang

Metabolites 2022, 12(10), 988; https://doi.org/10.3390/metabo12100988 - 18 Oct 2022

Cited by 4 | Viewed by 2124

Abstract

The present study aimed to assess the changes in muscle strength and plasma metabolites in athletes with β-glucan supplementation. A total of 29 athletes who met the inclusion criteria were recruited for this study (ChiCTR2200058091) and were randomly divided into a placebo group (n = 14) and β-glucan group (n = 15). During the trial, the experimental group received β-glucan supplementation (2 g/d β-glucan) for 4 weeks and the control group received an equal dose of placebo supplementation (0 g/d β-glucan), with both groups maintaining their regular diet and exercise habits during the trial. The athletes’ exercise performance, muscle strength, and plasma metabolome changes were analyzed after 4 weeks of β-glucan supplementation. The results showed a significant increase in mean grip strength (kg), right hand grip strength (kg), left triceps strength (kg), and upper limb muscle mass (kg) in the experimental group after the 4-week intervention compared to the preintervention period (p < 0.05). A comparison of the difference between the two groups after the intervention showed that there were significant differences between the control group and the experimental group in mean grip strength (kg) and right-hand grip strength (kg) (p < 0.05). Athletes in the experimental group showed significant improvements in 1 min double rocking jump (pcs), VO_2max (ml/kg-min) (p < 0.05). The β-glucan intake increased the creatine-related pathway metabolites in plasma. Overall, these results suggest that 4 weeks of β-glucan supplementation can improve muscle strength in athletes, with the potential to increase aerobic endurance and enhance immune function, possibly by affecting creatine-related pathways. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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Review

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15 pages, 1025 KiB

Open AccessReview

Exercise Metabolome: Insights for Health and Performance

by Aayami Jaguri, Asmaa A. Al Thani and Mohamed A. Elrayess

Metabolites 2023, 13(6), 694; https://doi.org/10.3390/metabo13060694 - 26 May 2023

Cited by 5 | Viewed by 3172

Abstract

Exercise has many benefits for physical and mental well-being. Metabolomics research has allowed scientists to study the impact of exercise on the body by analyzing metabolites released by tissues such as skeletal muscle, bone, and the liver. Endurance training increases mitochondrial content and oxidative enzymes, while resistance training increases muscle fiber and glycolytic enzymes. Acute endurance exercise affects amino acid metabolism, fat metabolism, cellular energy metabolism, and cofactor and vitamin metabolism. Subacute endurance exercise alters amino acid metabolism, lipid metabolism, and nucleotide metabolism. Chronic endurance exercise improves lipid metabolism and changes amino acid metabolism. Acute resistance exercise changes several metabolic pathways, including anaerobic processes and muscular strength. Chronic resistance exercise affects metabolic pathways, resulting in skeletal muscle adaptations. Combined endurance–resistance exercise alters lipid metabolism, carbohydrate metabolism, and amino acid metabolism, increasing anaerobic metabolic capacity and fatigue resistance. Studying exercise-induced metabolites is a growing field, and further research can uncover the underlying metabolic mechanisms and help tailor exercise programs for optimal health and performance. Full article

(This article belongs to the Special Issue Metabolomics in the Interventions of Metabolic Diseases and Physical Activity)

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