Endocrinology and Metabolism

Original Articles

Diabetes, obesity and metabolism
Antilipolytic Insulin Sensitivity Indices Measured during an Oral Glucose Challenge: Associations with Insulin-Glucose Clamp and Central Adiposity in Women without Diabetes: Foued Naimi, Christophe Richer dit Laflèche, Marie-Claude Battista, André C. Carpentier, Jean-Patrice Baillargeon; Endocrinol Metab. 2025;40(4):561-573. Published online March 18, 2025; DOI: https://doi.org/10.3803/EnM.2024.2129

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Background
Tissue overexposure to non-esterified fatty acids (NEFA) contributes to the development of metabolic conditions, with insulin-mediated suppression of lipolysis being an important mechanism in limiting this overexposure. We investigated which dynamic NEFA insulin-suppression indices derived from the oral glucose tolerance test (OGTT) were best associated with those derived from the insulin-glucose clamp, as well as with central adiposity and glucoregulatory parameters.
Methods
This cross-sectional study recruited 29 women without diabetes, 15 healthy women, and 14 women with polycystic ovary syndrome. The OGTT indices of NEFA insulin-suppression were the decremental NEFA area under the curve, negative log-linear NEFA slope, percentage of NEFA suppression (%NEFA_supp) and time to suppress NEFA levels by 50% (T50_NEFA). The indices derived from the two-step euglycemic-hyperinsulinemic clamp (low-dose insulin step) were delta NEFA and %NEFA_supp.
Results
Among the OGTT and clamp indices, T50_NEFA[OGTT] and %NEFA_supp[clamp] showed the closest associations in both subgroups (r=–0.58). Additionally, T50_NEFA correlated significantly in all women with waist circumference (r=0.64), body fat percentage (r=0.60), fasting insulinemia (r=0.53), and M-value insulin sensitivity index (r=–0.45). Similarly, %NEFA_supp[clamp] correlated significantly in all women with waist circumference (r=–0.57), body fat percentage (r=–0.54), fasting insulinemia (r=–0.55), and M-value insulin sensitivity index (r=0.51). T50_NEFA and %NEFA_supp[clamp] also correlated with other anthropometric and metabolic parameters associated with lipotoxicity.
Conclusion
For dynamic testing of NEFA insulin-suppression in women, T50_NEFA was the OGTT-derived index best correlated with a clamp index (%NEFA_supp). These indices were also the most closely associated with anthropometric and glucoregulatory parameters. Thus, the OGTT-derived T50_NEFA appears valid for assessing dynamic antilipolytic insulin action.

Citations

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Effects of Dietary Spirulina Supplementation on Cecal Microbiota, Serum Biochemistry, and Antioxidant Capacity in Lambs
Yuxuan Wang, Yushan Jia, Gentu Ge, Jian Bao, Xia Ding, Xiangdong Liu, Zhijun Wang
Microorganisms.2026; 14(2): 288. CrossRef
From Adipose Dysfunction to Multi-Organ Steatosis: Defining the Metabolic Steatotic Axis
Almir Fajkić, Yun Wah Lam, Rijad Jahić, Ivan Ćavar, Antonio Markotić, Andrej Belančić
Current Issues in Molecular Biology.2026; 48(2): 178. CrossRef
Impact of a high dietary fiber cereal meal intervention on the progression of liver fibrosis in T2DM with MASLD
Xi-Shuang Chen, Hui-Zhen Liu, Fang Huang, Jian Meng, Jing-Xian Fang, Yu Han, Hui-Ming Zou, Qing Gu, Xue Hu, Qian-Wen Ma, Yue-Xia Han, Sui-Jun Wang
Frontiers in Endocrinology.2025;[Epub] CrossRef

Thyroid
TSHR Gene (rs179247) Polymorphism and Susceptibility to Autoimmune Thyroid Disease: A Systematic Review and Meta-Analysis: Hendra Zufry, Timotius Ivan Hariyanto; Endocrinol Metab. 2024;39(4):603-614. Published online August 1, 2024; DOI: https://doi.org/10.3803/EnM.2024.1987

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Background
Both Graves’ disease (GD) and Hashimoto’s thyroiditis (HT) are classified as autoimmune thyroid diseases (AITDs). It has been hypothesized that changes in the thyroid-stimulating hormone receptor (TSHR) gene may contribute to the development of these conditions. This study aimed to analyze the correlation between the TSHR rs179247 gene polymorphism and susceptibility to AITD.
Methods
We conducted a thorough search of the Google Scholar, Scopus, Medline, and Cochrane Library databases up until March 2, 2024, utilizing a combination of relevant keywords. This review examines data on the association between TSHR rs179247 and susceptibility to AITD. Random-effect models were employed to assess the odds ratio (OR), and the findings are presented along with their respective 95% confidence intervals (CIs).
Results
The meta-analysis included 12 studies. All genetic models of the TSHR rs179247 gene polymorphism were associated with an increased risk of developing GD. Specifically, the associations were observed in the dominant model (OR, 1.65; P<0.00001), recessive model (OR, 1.65; P<0.00001), as well as for the AA genotype (OR, 2.09; P<0.00001), AG genotype (OR, 1.39; P<0.00001), and A allele (OR, 1.44; P<0.00001). Further regression analysis revealed that these associations were consistent regardless of the country of origin, sample size, age, and sex distribution. However, no association was found between TSHR rs179247 and the risk of HT across all genetic models.
Conclusion
This study suggests that the TSHR rs179247 gene polymorphism is associated with an increased risk of GD, but not with HT, and may therefore serve as a potential biomarker.

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Genetic Risk Factors in Autoimmune Thyroid Diseases: An Umbrella Review of Meta-Analyses and Evidence Credibility
Anu Shibi Anilkumar, Nithya Ajay, Ramakrishnan Veerabathiran
Immunological Investigations.2026; 55(5): 1054. CrossRef

Review Articles

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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Development and validation of an interpretable machine learning model for predicting intraoperative HDI in PPGL based on intratumoral and peritumoral CT radiomics
Shurong Li, Zhiqiang Zhang, Yubing Zhang, Yulong Chen, Jian Ling, Qingfen Hong, Xuanling Wu, Fufu Zheng, Cheng Luo
European Journal of Radiology.2026; 195: 112559. CrossRef
Features of bioenergetic metabolism in physiological and pathological conditions: focus on oncogenesis
A. S. Zhdanova, Z. E. Belaya, G. A. Melnichenko
Problems of Endocrinology.2026; 71(6): 56. CrossRef
Advances in Intestinal Glucose Absorption Regulation for Ruminant Energy Efficiency Improvement
Yan Ye, Xiongfei Zhang, Junhu Yao, Xinjian Lei
Animals.2026; 16(4): 659. CrossRef
Factors Associated with the Development of Skin Lesions in Hospitalized Patients Admitted to a Nursing Preventive Care Program in Colombia
Gaby E. Escobar, Ángela F Espinosa, Olga L. Cortés, Nicolás Molano González
Investigación y Educación en Enfermería.2025;[Epub] CrossRef
Editorial: Tumor metabolism and programmed cell death
Dan-Lan Pu, Qi-Nan Wu
Frontiers in Endocrinology.2024;[Epub] CrossRef
Molecular subtypes of clear cell renal carcinoma based on PCD-related long non-coding RNAs expression: insights into the underlying mechanisms and therapeutic strategies
Han Wang, Yang Liu, Aifa Tang, Xiansheng Zhang
European Journal of Medical Research.2024;[Epub] CrossRef

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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The Journal of Clinical Endocrinology & Metabolism.2026; 111(3): e892. CrossRef
Advances in Machine Learning and Deep Learning for Hormonal Disorder Diagnosis: an Exhaustive Review on PCOS, Thyroid, and Optimization Techniques
Sanjay Dhanka, Ankur Kumar, Abhinav Sharma, Haswanth Vundavilli, Surita Maini, Elakkiya Rajasekar
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Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning
Min Chen, Yuxin Li, Sumei Zhou, Linbo Zou, Lei Yu, Tianfang Deng, Xian Rong, Shirong Shao, Jijun Wu
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Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan C. Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark A. Swerdlow, David C. Klonoff
Journal of Diabetes Science and Technology.2023; 17(1): 224. CrossRef
Expressions of Cushing’s syndrome in multiple endocrine neoplasia type 1
William F. Simonds
Frontiers in Endocrinology.2023;[Epub] CrossRef
Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review
Francisco J. Barrera, Ethan D.L. Brown, Amanda Rojo, Javier Obeso, Hiram Plata, Eddy P. Lincango, Nancy Terry, René Rodríguez-Gutiérrez, Janet E. Hall, Skand Shekhar
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Predictors of rituximab effect on modified Rodnan skin score in systemic sclerosis: a machine-learning analysis of the DesiReS trial
Satoshi Ebata, Koji Oba, Kosuke Kashiwabara, Keiko Ueda, Yukari Uemura, Takeyuki Watadani, Takemichi Fukasawa, Shunsuke Miura, Asako Yoshizaki-Ogawa, Asano Yoshihide, Ayumi Yoshizaki, Shinichi Sato
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Automating and improving cardiovascular disease prediction using Machine learning and EMR data features from a regional healthcare system
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International Journal of Medical Informatics.2022; 163: 104786. CrossRef
An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data
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International Journal of Environmental Research and Public Health.2022; 19(10): 5800. CrossRef
Ensemble blood glucose prediction in diabetes mellitus: A review
M.Z. Wadghiri, A. Idri, Touria El Idrissi, Hajar Hakkoum
Computers in Biology and Medicine.2022; 147: 105674. CrossRef
The maze runner: navigating through basic kinetics to AI models of human metabolism pathology
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Procedia Computer Science.2022; 213: 271. CrossRef
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Endocrinology and Metabolism.2021; 36(5): 928. CrossRef
Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition
Danning Wu, Shi Chen, Yuelun Zhang, Huabing Zhang, Qing Wang, Jianqiang Li, Yibo Fu, Shirui Wang, Hongbo Yang, Hanze Du, Huijuan Zhu, Hui Pan, Zhen Shen
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Real World Data and Artificial Intelligence in Diabetology
Kwang Joon Kim
The Journal of Korean Diabetes.2020; 21(3): 140. CrossRef
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Mathematical Problems in Engineering.2020; 2020: 1. CrossRef

Adrenal gland
Is Follow-up of Adrenal Incidentalomas Always Mandatory?: Giuseppe Reimondo, Alessandra Muller, Elisa Ingargiola, Soraya Puglisi, Massimo Terzolo; Endocrinol Metab. 2020;35(1):26-35. Published online March 19, 2020; DOI: https://doi.org/10.3803/EnM.2020.35.1.26

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Adrenal masses are mainly detected unexpectedly by an imaging study performed for reasons unrelated to any suspect of adrenal diseases. Such masses are commonly defined as “adrenal incidentalomas” and represent a public health challenge because they are increasingly recognized in current medical practice. Management of adrenal incidentalomas is currently matter of debate. Although there is consensus on the need of a multidisciplinary expert team evaluation and surgical approach in patients with significant hormonal excess and/or radiological findings suspicious of malignancy demonstrated at the diagnosis or during follow-up, the inconsistency between official guidelines and the consequent diffuse uncertainty on management of small adrenal incidentalomas still represents a considerable problem in terms of clinical choices in real practice. The aim of the present work is to review the proposed strategies on how to manage patients with adrenal incidentalomas that are not candidates to immediate surgery. The recent European Society of Endocrinology/European Network for the Study of Adrenal Tumors guidelines have supported the view to avoid surveillance in patients with clear benign adrenal lesions <4 cm and/or without any hormonal secretion; however, newer prospective studies are needed to confirm safety of this strategy, in particular in younger patients.

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Is muscle strength an overlooked parameter in patients affected by mild autonomous cortisol secretion?
Martina Romanisio, Chiara Mele, Sara Sturnia, Carola Ciamparini, Rosa Pitino, Alice Ferrero, Lorenza Scotti, Madalina Elena Iftimie, Gianluca Aimaretti, Paolo Marzullo, Flavia Prodam, Marina Caputo
Frontiers in Endocrinology.2026;[Epub] CrossRef
Imaging of Adrenal Incidentalomas: What Actually Happens in Everyday Clinical Practice?
Brian Ngo, Tracy Liu, Eddie Lau
Journal of Medical Imaging and Radiation Oncology.2025; 69(3): 328. CrossRef
The Landmark Series: Evaluation and Management of Adrenal Incidentalomas
Lily Owei, Heather Wachtel
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Prolonged symptom duration and the potential for gradual progression in pediatric adrenocortical tumors: observations from the MET studies
Michaela Kuhlen, Stefan A. Wudy, Clara Baumann, Christian Vokuhl, Michaela F. Hartmann, Marina Kunstreich, Rainer Claus, Antje Redlich
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Adrenal Incidentaloma—Innocent Bystander or Intruder?
Laurence Amar, Inga Harbuz-Miller, Adina F Turcu
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Diagnosing and managing adrenal incidentalomas
Justine Herndon, Irina Bancos
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Tumor enlargement in adrenal incidentaloma is related to glaucoma: a new prognostic feature?
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Miscellaneous
Search for Novel Mutational Targets in Human Endocrine Diseases: So Young Park, Myeong Han Seo, Sihoon Lee; Endocrinol Metab. 2019;34(1):23-28. Published online March 21, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.1.23

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The identification of disease-causing genetic variations is an important goal in the field of genetics. Advancements in genetic technology have changed scientific knowledge and made it possible to determine the basic mechanism and pathogenesis of human disorders rapidly. Many endocrine disorders are caused by genetic variations of a single gene or by mixed genetic factors. Various genetic testing methods are currently available, enabling a more precise diagnosis of many endocrine disorders and facilitating the development of a concrete therapeutic plan. In this review article, we discuss genetic testing technologies for genetic endocrine disorders, with relevant examples. We additionally describe our research on implementing genetic analysis strategies to identify novel causal mutations in hypocalcemia-related disorders.

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A novel gain-of-function mutation (W818R) of calcium-sensing receptor in a family with autosomal dominant hypocalcemia type 1
Min Fu, Yanxiang Luo, Kexin Xu, Lixin Guo, Qi Pan
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Frontiers in Endocrinology.2025;[Epub] CrossRef

Original Articles

Miscellaneous
The Status and Distinct Characteristics of Endocrine Diseases in North Korean Articles Published between 2006 and 2015: Kyeong Jin Kim, Shin Ha, Yo Han Lee, Jung Hyun Noh, Sin Gon Kim; Endocrinol Metab. 2018;33(2):268-272. Published online June 21, 2018; DOI: https://doi.org/10.3803/EnM.2018.33.2.268

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Background

Past decades of division have led to substantial differences in medical environments between South and North Korea. However, little is known about North Korea's medical status and research field, especially regarding endocrinology. In this study, we report the characteristics of North Korea's articles regarding endocrine-related diseases.

Methods

Among the nine medical journals, articles published in Internal Medicine between 2006 and 2015 were reviewed. A total of 2,092 articles were included; among them, 96 articles were associated with endocrinology. We analyzed these articles according to the disease categories they focused on and evaluated their features.

Results

Articles related to diabetes mellitus accounted for 55.2% (n=53) and those to thyroid disease accounted for 28.1% (n=27). Other disease categories, including adrenal gland (n=1), pituitary gland (n=1), and osteoporosis (n=3), comprised minor portions. Regarding diabetes mellitus, more than half the articles (n=33) focused on treatment and complications. Experimental studies were conducted with old hypoglycemic drugs or natural substances for the treatment of hyperglycemia. Regarding thyroid disease, articles related to hyperthyroidism were the most common (51.9%, n=14), followed by thyroid nodule/cancer (18.5%, n=5). Unique article features were short length, no figures, and less than five references.

Conclusion

North Korea's endocrinology articles mainly focused on diabetes mellitus and thyroid disease. Persistent studies have been carried out in North Korea with dedication despite the poor medical environment. We hope that this study will be the beginning of mutual medical exchange and collaboration between North and South Korea.

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Surgical Diseases in North Korea: An Overview of North Korean Medical Journals
Sejin Choi, Taehoon Kim, Soyoung Choi, Hee Young Shin
International Journal of Environmental Research and Public Health.2020; 17(24): 9346. CrossRef
Endocrinology and Metabolism Has Been Indexed in MEDLINE: A Major Achievement
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Endocrinology and Metabolism.2019; 34(2): 138. CrossRef

Clinical Study
Factors Associated with Postoperative Diabetes Insipidus after Pituitary Surgery: Antonio L. Faltado, Anna Angelica Macalalad-Josue, Ralph Jason S. Li, John Paul M. Quisumbing, Marc Gregory Y. Yu, Cecilia A. Jimeno; Endocrinol Metab. 2017;32(4):426-433. Published online November 21, 2017; DOI: https://doi.org/10.3803/EnM.2017.32.4.426

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Background

Determining risk factors for diabetes insipidus (DI) after pituitary surgery is important in improving patient care. Our objective is to determine the factors associated with DI after pituitary surgery.

Methods

We reviewed records of patients who underwent pituitary surgery from 2011 to 2015 at Philippine General Hospital. Patients with preoperative DI were excluded. Multiple logistic regression analysis was performed and a predictive model was generated. The discrimination abilities of the predictive model and individual variables were assessed using the receiving operator characteristic curve.

Results

A total of 230 patients were included. The rate of postoperative DI was 27.8%. Percent change in serum Na (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.15 to 1.69); preoperative serum Na (OR, 1.19; 95% CI, 1.02 to 1.40); and performance of craniotomy (OR, 5.48; 95% CI, 1.60 to 18.80) remained significantly associated with an increased incidence of postoperative DI, while percent change in urine specific gravity (USG) (OR, 0.53; 95% CI, 0.33 to 0.87) and meningioma on histopathology (OR, 0.05; 95% CI, 0.04 to 0.70) were significantly associated with a decreased incidence. The predictive model generated has good diagnostic accuracy in predicting postoperative DI with an area under curve of 0.83.

Conclusion

Greater percent change in serum Na, preoperative serum Na, and performance of craniotomy significantly increased the likelihood of postoperative DI while percent change in USG and meningioma on histopathology were significantly associated with a decreased incidence. The predictive model can be used to generate a scoring system in estimating the risk of postoperative DI.

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Perioperative Complications Associated with Routine Preoperative Glucocorticoid Use among Patients undergoing Pituitary Surgery with Normal Preoperative HPA Axis
Franz Michael Magnaye, Elizabeth Paz-Pacheco
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