Endocrinology and Metabolism

Namgok Lecture 2023

Diabetes, obesity and metabolism
Hypothalamic AMP-Activated Protein Kinase as a Whole-Body Energy Sensor and Regulator: Se Hee Min, Do Kyeong Song, Chan Hee Lee, Eun Roh, Min-Seon Kim; Endocrinol Metab. 2024;39(1):1-11. Published online February 14, 2024; DOI: https://doi.org/10.3803/EnM.2024.1922

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Abstract PDF PubReader ePub: 5´-Adenosine monophosphate (AMP)-activated protein kinase (AMPK), a cellular energy sensor, is an essential enzyme that helps cells maintain stable energy levels during metabolic stress. The hypothalamus is pivotal in regulating energy balance within the body. Certain neurons in the hypothalamus are sensitive to fluctuations in food availability and energy stores, triggering adaptive responses to preserve systemic energy equilibrium. AMPK, expressed in these hypothalamic neurons, is instrumental in these regulatory processes. Hypothalamic AMPK activity is modulated by key metabolic hormones. Anorexigenic hormones, including leptin, insulin, and glucagon-like peptide 1, suppress hypothalamic AMPK activity, whereas the hunger hormone ghrelin activates it. These hormonal influences on hypothalamic AMPK activity are central to their roles in controlling food consumption and energy expenditure. Additionally, hypothalamic AMPK activity responds to variations in glucose concentrations. It becomes active during hypoglycemia but is deactivated when glucose is introduced directly into the hypothalamus. These shifts in AMPK activity within hypothalamic neurons are critical for maintaining glucose balance. Considering the vital function of hypothalamic AMPK in the regulation of overall energy and glucose balance, developing chemical agents that target the hypothalamus to modulate AMPK activity presents a promising therapeutic approach for metabolic conditions such as obesity and type 2 diabetes mellitus.

Review Article

Miscellaneous
Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer: Aliya Lakhani, Da Hyun Kang, Yea Eun Kang, Junyoung O. Park; Endocrinol Metab. 2023;38(6):619-630. Published online November 21, 2023; DOI: https://doi.org/10.3803/EnM.2023.1814

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Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing’s syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing’s syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

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Editorial: Tumor metabolism and programmed cell death
Dan-Lan Pu, Qi-Nan Wu
Frontiers in Endocrinology.2024;[Epub] CrossRef

Original Article

Diabetes, obesity and metabolism
Association between Serum Amyloid A Levels and Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis: Ting Liu, Meng Li, Chunying Cui, Jielin Zhou; Endocrinol Metab. 2023;38(3):315-327. Published online June 7, 2023; DOI: https://doi.org/10.3803/EnM.2023.1621

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Background
To date, consistent data have not been reported on the association between serum amyloid A (SAA) levels and type 2 diabetes mellitus (T2DM). The purpose of this study was to systematically summarize their relationship.
Methods
Databases including PubMed, Cochrane Library, Embase, Web of Science, and MEDLINE were searched until August 2021. Cross-sectional and case-control studies were included.
Results
Twenty-one studies with 1,780 cases and 2,070 controls were identified. SAA levels were significantly higher in T2DM patients than in healthy groups (standardized mean difference [SMD], 0.68; 95% confidence interval [CI], 0.39 to 0.98). A subgroup analysis showed that the mean age of participants and the continent that participants were from were related to differences in SAA levels between cases and controls. Furthermore, in T2DM patients, SAA levels were positively associated with body mass index (r=0.34; 95% CI, 0.03 to 0.66), triglycerides (r=0.12; 95% CI, 0.01 to 0.24), fasting plasma glucose (r=0.26; 95% CI, 0.07 to 0.45), hemoglobin A1c (r=0.24; 95% CI, 0.16 to 0.33), homeostasis model assessment for insulin resistance (r=0.22; 95% CI, 0.10 to 0.34), C-reactive protein (r=0.77; 95% CI, 0.62 to 0.91), and interleukin-6 (r=0.42; 95% CI, 0.31 to 0.54), but negatively linked with highdensity lipoprotein cholesterol (r=–0.23; 95% CI, –0.44 to –0.03).
Conclusion
The meta-analysis suggests that high SAA levels may be associated with the presence of T2DM, as well as lipid metabolism homeostasis and the inflammatory response.

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Correlation between insulin resistance and the rate of neutrophils-lymphocytes, monocytes-lymphocytes, platelets-lymphocytes in type 2 diabetic patients
Yuanyuan Zhang, Huaizhen Liu
BMC Endocrine Disorders.2024;[Epub] CrossRef
Antioxidant and Anti-Inflammatory Functions of High-Density Lipoprotein in Type 1 and Type 2 Diabetes
Damien Denimal
Antioxidants.2023; 13(1): 57. CrossRef

Review Article

Miscellaneous
Brown Adipose Tissue: Activation and Metabolism in Humans: Imane Hachemi, Mueez U-Din; Endocrinol Metab. 2023;38(2):214-222. Published online March 27, 2023; DOI: https://doi.org/10.3803/EnM.2023.1659

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Brown adipose tissue (BAT) is a thermogenic organ contributing to non-shivering thermogenesis. BAT becomes active under cold stress via sympathetic nervous system activation. However, recent evidence has suggested that BAT may also be active at thermoneutrality and in a postprandial state. BAT has superior energy dissipation capacity compared to white adipose tissue (WAT) and muscles. Thus, it has been proposed that the recruitment and activation of additional BAT may increase the overall energy-expending capacity in humans, potentially improving current whole-body weight management strategies. Nutrition plays a central role in obesity and weight management. Thus, this review discusses human studies describing BAT hyper-metabolism after dietary interventions. Nutritional agents that can potentially recruit brown adipocytes via the process of BAT-WAT transdifferentiation are also discussed.

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Spermidine activates adipose tissue thermogenesis through autophagy and fibroblast growth factor 21
Yinhua Ni, Liujie Zheng, Liqian Zhang, Jiamin Li, Yuxiang Pan, Haimei Du, Zhaorong Wang, Zhengwei Fu
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MRI Methods to Visualize and Quantify Adipose Tissue in Health and Disease
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Original Articles

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

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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

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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

Thyroid Big Data Articles (National Health Insurance Service Database)
Repeated Low High-Density Lipoprotein Cholesterol and the Risk of Thyroid Cancer: A Nationwide Population- Based Study in Korea: Jinyoung Kim, Mee Kyoung Kim, Ki-Hyun Baek, Ki-Ho Song, Kyungdo Han, Hyuk-Sang Kwon; Endocrinol Metab. 2022;37(2):303-311. Published online April 6, 2022; DOI: https://doi.org/10.3803/EnM.2021.1332

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Background
High-density lipoprotein cholesterol (HDL-C) plays an important role in the reverse cholesterol transport pathway and prevents atherosclerosis-mediated disease. It has also been suggested that HDL-C may be a protective factor against cancer. However, an inverse correlation between HDL-C and cancer has not been established, and few studies have explored thyroid cancer.
Methods
The study participants received health checkups provided by the Korean National Health Insurance Service from 2009 to 2013 and were followed until 2019. Considering the variability of serum HDL-C level, low HDL-C level was analyzed by grouping based on four consecutive health checkups. The data analysis was performed using univariate and multivariate Cox proportional hazard regression models.
Results
A total of 3,134,278 total study participants, thyroid cancer occurred in 16,129. In the crude model, the hazard ratios for the association between repeatedly measured low HDL-C levels and thyroid cancer were 1.243, 1.404, 1.486, and 1.680 (P for trend <0.01), respectively, which were significant even after adjusting for age, sex, lifestyle factors, and metabolic diseases. The subgroup analysis revealed that low HDL-C levels likely had a greater impact on the group of patients with central obesity (P for interaction= 0.062), high blood pressure (P for interaction=0.057), impaired fasting glucose (P for interaction=0.051), and hyperlipidemia (P for interaction=0.126).
Conclusion
Repeatedly measured low HDL-C levels can be considered a risk factor for cancer as well as vascular disease. Low HDL-C levels were associated with the risk of thyroid cancer, and this correlation was stronger in a metabolically unhealthy population.

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Xiaowen Zhang, Yuyang Ze, Jianfeng Sang, Xianbiao Shi, Yan Bi, Shanmei Shen, Xinlin Zhang, Dalong Zhu
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Exposure to multiple trace elements and thyroid cancer risk in Chinese adults: A case-control study
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Namgok Lecture 2021

Diabetes, Obesity and Metabolism
The Influence of Obesity and Metabolic Health on Vascular Health: Eun-Jung Rhee; Endocrinol Metab. 2022;37(1):1-8. Published online February 28, 2022; DOI: https://doi.org/10.3803/EnM.2022.101

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The prevalence of obesity is rapidly increasing worldwide. Obesity should not be understood only as the accumulation of fat in the body, but instead as a phenomenon that exerts different effects on our health according to the place of fat deposition and its stability. Obesity is the starting point of most metabolic diseases, such as diabetes, hypertension, metabolic syndrome, sleep apnea, and eventually cardiovascular disease. There are different kinds of obesity, ranging from simple obesity to sarcopenic obesity. The main purpose of intervening to address obesity is to decrease the ultimate consequence of obesity—namely, cardiovascular disease. The main mechanism through which obesity, especially abdominal obesity, increases cardiovascular risk is the obesity-induced derangement of metabolic health, leading to the development of metabolic diseases such as diabetes, non-alcoholic fatty liver disease, and metabolic syndrome, which are the main initiators of vascular damage. In this review, I discuss the influence of various types of obesity on the risk of metabolic diseases, and how these diseases increase cardiovascular disease risk.

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Review Article

Diabetes, Obesity and Metabolism
Homeostatic Regulation of Glucose Metabolism by the Central Nervous System: Jong Han Choi, Min-Seon Kim; Endocrinol Metab. 2022;37(1):9-25. Published online February 28, 2022; DOI: https://doi.org/10.3803/EnM.2021.1364

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Evidence for involvement of the central nervous system (CNS) in the regulation of glucose metabolism dates back to the 19th century, although the majority of the research on glucose metabolism has focused on the peripheral metabolic organs. Due to recent advances in neuroscience, it has now become clear that the CNS is indeed vital for maintaining glucose homeostasis. To achieve normoglycemia, specific populations of neurons and glia in the hypothalamus sense changes in the blood concentrations of glucose and of glucoregulatory hormones such as insulin, leptin, glucagon-like peptide 1, and glucagon. This information is integrated and transmitted to other areas of the brain where it eventually modulates various processes in glucose metabolism (i.e., hepatic glucose production, glucose uptake in the brown adipose tissue and skeletal muscle, pancreatic insulin and glucagon secretion, renal glucose reabsorption, etc.). Errors in these processes lead to hyper- or hypoglycemia. We here review the current understanding of the brain regulation of glucose metabolism.

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Original Articles

Adrenal Gland
Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea: Eu Jeong Ku, Chaelin Lee, Jaeyoon Shim, Sihoon Lee, Kyoung-Ah Kim, Sang Wan Kim, Yumie Rhee, Hyo-Jeong Kim, Jung Soo Lim, Choon Hee Chung, Sung Wan Chun, Soon-Jib Yoo, Ohk-Hyun Ryu, Ho Chan Cho, A Ram Hong, Chang Ho Ahn, Jung Hee Kim, Man Ho Choi; Endocrinol Metab. 2021;36(5):1131-1141. Published online October 21, 2021; DOI: https://doi.org/10.3803/EnM.2021.1149

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Background
Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids.
Methods
The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors.
Results
The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT.
Conclusion
The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.

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Diabetes, Obesity and Metabolism
Role of TRPV4 Channel in Human White Adipocytes Metabolic Activity: Julio C. Sánchez, Aníbal Valencia-Vásquez, Andrés M. García; Endocrinol Metab. 2021;36(5):997-1006. Published online October 14, 2021; DOI: https://doi.org/10.3803/EnM.2021.1167

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Background
Intracellular calcium (Ca²⁺) homeostasis plays an essential role in adipocyte metabolism and its alteration is associated with obesity and related disorders. Transient receptor potential vanilloid 4 (TRPV4) channels are an important Ca²⁺ pathway in adipocytes and their activity is regulated by metabolic mediators such as insulin. In this study, we evaluated the role of TRPV4 channels in metabolic activity and adipokine secretion in human white adipocytes.
Methods
Human white adipocytes were freshly cultured and the effects of the activation and inhibition of TRPV4 channels on lipolysis, glucose uptake, lactate production, and leptin and adiponectin secretion were evaluated.
Results
Under basal and isoproterenol-stimulated conditions, TRPV4 activation by GSK1016709A decreased lipolysis whereas HC067047, an antagonist, increased lipolysis. The activation of TRPV4 resulted in increased glucose uptake and lactate production under both basal conditions and insulin-stimulated conditions; in contrast HC067047 decreased both parameters. Leptin production was increased, and adiponectin production was diminished by TRPV4 activation and its inhibition had the opposite effect.
Conclusion
Our results suggested that TRPV4 channels are metabolic mediators involved in proadipogenic processes and glucose metabolism in adipocyte biology. TRPV4 channels could be a potential pharmacological target to treat metabolic disorders.

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Namgok Lecture 2020

Obesity and Metabolism
Cellular and Intercellular Homeostasis in Adipose Tissue with Mitochondria-Specific Stress: Min Jeong Choi, Saet-Byel Jung, Joon Young Chang, Minho Shong; Endocrinol Metab. 2021;36(1):1-11. Published online February 24, 2021; DOI: https://doi.org/10.3803/EnM.2021.956

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Paracrine interactions are imperative for the maintenance of adipose tissue intercellular homeostasis, and intracellular organelle dysfunction results in local and systemic alterations in metabolic homeostasis. It is currently accepted that mitochondrial proteotoxic stress activates the mitochondrial unfolded protein response (UPR^mt) in vitro and in vivo. The induction of mitochondrial chaperones and proteases during the UPR^mt is a key cell-autonomous mechanism of mitochondrial quality control. The UPR^mt also affects systemic metabolism through the secretion of cell non-autonomous peptides and cytokines (hereafter, metabokines). Mitochondrial function in adipose tissue plays a pivotal role in whole-body metabolism and human diseases. Despite continuing interest in the role of the UPR^mt and quality control pathways of mitochondria in energy metabolism, studies on the roles of the UPR^mt and metabokines in white adipose tissue are relatively sparse. Here, we describe the role of the UPR^mt in adipose tissue, including adipocytes and resident macrophages, and the interactive roles of cell non-autonomous metabokines, particularly growth differentiation factor 15, in local adipose cellular homeostasis and systemic energy metabolism.

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Mitochondrial stress-induced GFRAL signaling controls diurnal food intake and anxiety-like behavior
Carla Igual Gil, Bethany M Coull, Wenke Jonas, Rachel N Lippert, Susanne Klaus, Mario Ost
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Stress-induced FGF21 and GDF15 in obesity and obesity resistance
Susanne Keipert, Mario Ost
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Review Article

Miscellaneous
Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome: Jang Won Son, Saeed Shoaie, Sunjae Lee; Endocrinol Metab. 2020;35(3):507-514. Published online September 22, 2020; DOI: https://doi.org/10.3803/EnM.2020.303

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The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

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Huan Li, Chang Wang, Qing Wang, Xuehua Liu, Juanjuan Zhang, He Zhang, Wenjie Fei, Hang Zhao, Luping Ren
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Original Article

Endocrine Research
Liver X Receptor β Related to Tumor Progression and Ribosome Gene Expression in Papillary Thyroid Cancer: Seonhyang Jeong, In-Kyu Kim, Hyunji Kim, Moon Jung Choi, Jandee Lee, Young Suk Jo; Endocrinol Metab. 2020;35(3):656-668. Published online August 20, 2020; DOI: https://doi.org/10.3803/EnM.2020.667

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Background
Intracellular lipid deposition has been reported in thyroid glands in obese animal and human. To understand the regulatory mechanism of lipid metabolism in thyroid cancer, we investigated the expression status of liver X receptor (LXR) and analyzed its clinicopathological characteristics and molecular biological features.
Methods
Expression status of LXR and its transcriptional targets in human cancers were analyzed using The Cancer Genome Atlas (TCGA). The gene-sets related to high LXRβ expression was investigated by gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene ontology biologic process. Quantitative reverse transcription polymerase chain reaction was performed in thyroid cancer samples using our validation cohort.
Results
In contrast to low expression of LXRα, LXRβ was highly expressed in thyroid cancer compared to the other types of human cancers. High LXRβ expression was correlated with the expression of LXRβ transcriptional targets genes, such as apolipoprotein C1 (APOC1), APOC2, apolipoprotein E (APOE), ATP binding cassette subfamily G member 8 (ABCG8), sterol regulatory elementbinding protein 1c (SREBP1c), and SPOT14. Furthermore, High LXRβ expression group indicated poor clinicopathological characteristics and aggressive molecular biological features independently from the drive mutation status. Mechanistically, high LXRβ expression was coordinately related to ribosome-related gene sets.
Conclusion
The mechanistic link between LXRβ and ribosomal activity will be addressed to develop new diagnostic and therapeutic targets in thyroid cancers.

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Review Articles

Miscellaneous
Machine Learning Applications in Endocrinology and Metabolism Research: An Overview: Namki Hong, Heajeong Park, Yumie Rhee; Endocrinol Metab. 2020;35(1):71-84. Published online March 19, 2020; DOI: https://doi.org/10.3803/EnM.2020.35.1.71

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Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

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Obesity and Metabolism
Recent Progress on Branched-Chain Amino Acids in Obesity, Diabetes, and Beyond: Md Abu Bakkar Siddik, Andrew C. Shin; Endocrinol Metab. 2019;34(3):234-246. Published online September 26, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.3.234

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Branched-chain amino acids (BCAAs) are essential amino acids that are not synthesized in our body; thus, they need to be obtained from food. They have shown to provide many physiological and metabolic benefits such as stimulation of pancreatic insulin secretion, milk production, adipogenesis, and enhanced immune function, among others, mainly mediated by mammalian target of rapamycin (mTOR) signaling pathway. After identified as a reliable marker of obesity and type 2 diabetes in recent years, an increasing number of studies have surfaced implicating BCAAs in the pathophysiology of other diseases such as cancers, cardiovascular diseases, and even neurodegenerative disorders like Alzheimer's disease. Here we discuss the most recent progress and review studies highlighting both correlational and potentially causative role of BCAAs in the development of these disorders. Although we are just beginning to understand the intricate relationships between BCAAs and some of the most prevalent chronic diseases, current findings raise a possibility that they are linked by a similar putative mechanism.

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