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4 "Metabolomics"
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Original Article
Carnitine Metabolite as a Potential Circulating Biomarker for Sarcopenia in Men
Je Hyun Seo, Jung-Min Koh, Han Jin Cho, Hanjun Kim, Young‑Sun Lee, Su Jung Kim, Pil Whan Yoon, Won Kim, Sung Jin Bae, Hong-Kyu Kim, Hyun Ju Yoo, Seung Hun Lee
Received July 28, 2024  Accepted October 7, 2024  Published online November 28, 2024  
DOI: https://doi.org/10.3803/EnM.2024.2117    [Epub ahead of print]
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  • 24 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Sarcopenia, a multifactorial disorder involving metabolic disturbance, suggests potential for metabolite biomarkers. Carnitine (CN), essential for skeletal muscle energy metabolism, may be a candidate biomarker. We investigated whether CN metabolites are biomarkers for sarcopenia.
Methods
Associations between the CN metabolites identified from an animal model of sarcopenia and muscle cells and sarcopenia status were evaluated in men from an age-matched discovery (72 cases, 72 controls) and a validation (21 cases, 47 controls) cohort.
Results
An association between CN metabolites and sarcopenia showed in mouse and cell studies. In the discovery cohort, plasma C5-CN levels were lower in sarcopenic men (P=0.005). C5-CN levels in men tended to be associated with handgrip strength (HGS) (P=0.098) and were significantly associated with skeletal muscle mass (P=0.003). Each standard deviation increase in C5-CN levels reduced the odds of low muscle mass (odd ratio, 0.61; 95% confidence interval [CI], 0.42 to 0.89). The area under the receiver operating characteristic curve (AUROC) of CN score using a regression equation of C5-CN levels, for sarcopenia was 0.635 (95% CI, 0.544 to 0.726). In the discovery cohort, addition of CN score to HGS significantly improved AUROC from 0.646 (95% CI, 0.575 to 0.717; HGS only) to 0.727 (95% CI, 0.643 to 0.810; P=0.006; HGS+CN score). The improvement was confirmed in the validation cohort (AUROC=0.563; 95% CI, 0.470 to 0.656 for HGS; and AUROC=0.712; 95% CI, 0.569 to 0.855 for HGS+CN score; P=0.027).
Conclusion
C5-CN, indicative of low muscle mass, is a potential circulating biomarker for sarcopenia in men. Further studies are required to confirm these results and explore sarcopenia-related metabolomic changes.
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Review Article
Adrenal Gland
Recent Updates on the Management of Adrenal Incidentalomas
Seung Shin Park, Jung Hee Kim
Endocrinol Metab. 2023;38(4):373-380.   Published online August 16, 2023
DOI: https://doi.org/10.3803/EnM.2023.1779
  • 15,904 View
  • 2,280 Download
  • 3 Web of Science
  • 7 Crossref
AbstractAbstract PDFPubReader   ePub   
Adrenal incidentalomas represent an increasingly common clinical conundrum with significant implications for patients. The revised 2023 European Society of Endocrinology (ESE) guideline incorporates cutting-edge evidence for managing adrenal incidentalomas. This paper provides a concise review of the updated contents of the revised guideline. In the 2023 guideline, in patients without signs and symptoms of overt Cushing’s syndrome, a post-dexamethasone cortisol level above 50 nmol/L (>1.8 μg/dL) should be considered as mild autonomous cortisol secretion. Regarding the criteria of benign adrenal adenomas, a homogeneous adrenal mass with ≤10 Hounsfield units on non-contrast computed tomography requires no further follow-up, irrespective of its size. The updated guideline also discusses steroid metabolomics using tandem mass spectrometry to discriminate malignancy. It underscores the importance of high-volume surgeons performing adrenalectomy and emphasizes the pivotal role of a multidisciplinary team approach in deciding the treatment plan for indeterminate adrenal masses. The guideline advocates for more proactive surgical treatment for indeterminate adrenal masses in young patients (<40 years) and pregnant women. This review of the 2023 ESE guideline underscores the ongoing evolution of the adrenal incidentaloma management landscape, emphasizing the need for further research and adaptation of diagnostic and therapeutic strategies.

Citations

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  • Personalized Management of Malignant and Non-Malignant Ectopic Mediastinal Thyroid: A Proposed 10-Item Algorithm Approach
    Mara Carsote, Mihai-Lucian Ciobica, Oana-Claudia Sima, Adrian Ciuche, Ovidiu Popa-Velea, Mihaela Stanciu, Florina Ligia Popa, Claudiu Nistor
    Cancers.2024; 16(10): 1868.     CrossRef
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    Hypertension Research.2024; 47(8): 2019.     CrossRef
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    Acta Medica Bulgarica.2024; 51(2): 1.     CrossRef
  • Adrenal incidentalomas
    Ivana Ságová
    Vnitřní lékařství.2024; 70(5): E9.     CrossRef
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    Cureus.2024;[Epub]     CrossRef
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    So Yoon Kwon, Kyeong-Jin Kim, Soo-Youn Lee, Jae Hyeon Kim
    Endocrinology and Metabolism.2024; 39(6): 965.     CrossRef
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Original Article
Thyroid
Metabolite Changes during the Transition from Hyperthyroidism to Euthyroidism in Patients with Graves’ Disease
Ho Yeop Lee, Byeong Chang Sim, Ha Thi Nga, Ji Sun Moon, Jingwen Tian, Nguyen Thi Linh, Sang Hyeon Ju, Dong Wook Choi, Daiki Setoyama, Hyon-Seung Yi
Endocrinol Metab. 2022;37(6):891-900.   Published online December 26, 2022
DOI: https://doi.org/10.3803/EnM.2022.1590
  • 3,951 View
  • 286 Download
  • 3 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
An excess of thyroid hormones in Graves’ disease (GD) has profound effects on systemic energy metabolism that are currently partially understood. In this study, we aimed to provide a comprehensive understanding of the metabolite changes that occur when patients with GD transition from hyperthyroidism to euthyroidism with methimazole treatment.
Methods
Eighteen patients (mean age, 38.6±14.7 years; 66.7% female) with newly diagnosed or relapsed GD attending the endocrinology outpatient clinics in a single institution were recruited between January 2019 and July 2020. All subjects were treated with methimazole to achieve euthyroidism. We explored metabolomics by performing liquid chromatography-mass spectrometry analysis of plasma samples of these patients and then performed multivariate statistical analysis of the metabolomics data.
Results
Two hundred metabolites were measured before and after 12 weeks of methimazole treatment in patients with GD. The levels of 61 metabolites, including palmitic acid (C16:0) and oleic acid (C18:1), were elevated in methimazole-naïve patients with GD, and these levels were decreased by methimazole treatment. The levels of another 15 metabolites, including glycine and creatinine, were increased after recovery of euthyroidism upon methimazole treatment in patients with GD. Pathway analysis of metabolomics data showed that hyperthyroidism was closely related to aminoacyl-transfer ribonucleic acid biosynthesis and branched-chain amino acid biosynthesis pathways.
Conclusion
In this study, significant variations of plasma metabolomic patterns that occur during the transition from hyperthyroidism to euthyroidism were detected in patients with GD via untargeted metabolomics analysis.

Citations

Citations to this article as recorded by  
  • Serum metabolome analysis in hyperthyroid cats before and after radioactive iodine therapy
    Molly A. Bechtold, Yimei Lin, Meredith L. Miller, Jennifer M. Prieto, Carol E. Frederick, Lucinda L. Bennett, Mark E. Peterson, Kenneth W. Simpson, John P. Loftus, Anu Sayal
    PLOS ONE.2024; 19(6): e0305271.     CrossRef
  • Associations of serum keratin 1 with thyroid function and immunity in Graves’ disease
    Chao-Wen Cheng, Wen-Fang Fang, Jiunn-Diann Lin, Appuwawadu Mestri Nipun Lakshitha de Silva
    PLOS ONE.2023; 18(11): e0289345.     CrossRef
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Review Article
Obesity and Metabolism
Understanding Metabolomics in Biomedical Research
Su Jung Kim, Su Hee Kim, Ji Hyun Kim, Shin Hwang, Hyun Ju Yoo
Endocrinol Metab. 2016;31(1):7-16.   Published online March 16, 2016
DOI: https://doi.org/10.3803/EnM.2016.31.1.7
  • 8,842 View
  • 157 Download
  • 56 Web of Science
  • 55 Crossref
AbstractAbstract PDFPubReader   

The term "omics" refers to any type of specific study that provides collective information on a biological system. Representative omics includes genomics, proteomics, and metabolomics, and new omics is constantly being added, such as lipidomics or glycomics. Each omics technique is crucial to the understanding of various biological systems and complements the information provided by the other approaches. The main strengths of metabolomics are that metabolites are closely related to the phenotypes of living organisms and provide information on biochemical activities by reflecting the substrates and products of cellular metabolism. The transcriptome does not always correlate with the proteome, and the translated proteome might not be functionally active. Therefore, their changes do not always result in phenotypic alterations. Unlike the genome or proteome, the metabolome is often called the molecular phenotype of living organisms and is easily translated into biological conditions and disease states. Here, we review the general strategies of mass spectrometry-based metabolomics. Targeted metabolome or lipidome analysis is discussed, as well as nontargeted approaches, with a brief explanation of the advantages and disadvantages of each platform. Biomedical applications that use mass spectrometry-based metabolomics are briefly introduced.

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    Jaewoo Choi, Muhammad Shoaib, Tai Yin, Gautam Nayyar, Koichiro Shinozaki, Jan F. Stevens, Lance B. Becker, Junhwan Kim
    Journal of the American Heart Association.2019;[Epub]     CrossRef
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    Nurul Amir Hashim, Sharaniza Ab‑Rahim, Leny Suddin, Mohd Ahmad Saman, Musalmah Mazlan
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    Kai Zhao, Jianzhong Zhang, Zhen Xu, Yue Xu, Aiming Xu, Wei Chen, Chenkui Miao, Shouyong Liu, Zengjun Wang, Ruipeng Jia
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    Won-Young Lee
    Endocrinology and Metabolism.2017; 32(1): 62.     CrossRef
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    Rachel Culp‐Hill, Julie A. Reisz, Kirk C. Hansen, Angelo D'Alessandro
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    Darrell D. Marshall, Robert Powers
    Progress in Nuclear Magnetic Resonance Spectroscopy.2017; 100: 1.     CrossRef
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    Jianxin Tan, Yajun Wang, Siliang Wang, Nannan Zhang, Simeng Wu, Zhe Yuan, Xike Zhu
    Cell Biology International.2017; 41(4): 447.     CrossRef
  • Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health
    Pamela Vernocchi, Federica Del Chierico, Lorenza Putignani
    Frontiers in Microbiology.2016;[Epub]     CrossRef
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