Michele Costanzo
Shereen M. Aleidi
Anas Abdel Rahman
94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
- 1Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
- 2CEINGE–Biotecnologie Avanzate Franco Salvatore, Naples, Italy
- 3Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
- 4Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, Jordan
- 5Metabolomics Section, Department of Clinical Genomics, Genomics Medicine Center of Excellence, King Faisal Specialist Hospital and Research Centre (KFSHRC), Riyadh, Saudi Arabia
- 6Department of Biochemistry and Molecular Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
Editorial on the Research Topic
Omics in endocrinology: from biomarker discovery to targeting therapeutic strategies
Type 2 diabetes mellitus (T2DM) and thyroid disease (TD) are two endocrine disorders that are closely linked to metabolic dysfunction, thus representing established health concerns worldwide (Kalra et al., 2019). T2DM is a chronic condition marked by β-cell dysfunction, abnormal glucose homeostasis, and insulin resistance (DeFronzo et al., 2015). Thyroid diseases involve thyroid hormone production and regulation abnormalities, affecting glucose metabolism and the immune response (Chauhan and Patel, 2024; Yamauchi and Yabe, 2025). Both conditions significantly influence the quality of life of patients and increase their risk of developing other health complications, such as cardiovascular disease, kidney failure, and neurological impairments. Furthermore, obesity is associated with the incidence of these metabolic diseases (Pulgaron and Delamater, 2014). The key mechanisms underlying these metabolic disorders are complex and multifactorial. In diabetes, the dysfunction in pancreatic β-cells leads to inadequate insulin production, inducing loss of insulin sensitivity in peripheral tissues, such as muscle and adipose tissue (Donath and Shoelson, 2011). Moreover, systemic chronic inflammation, dysregulated adipokine secretion, and altered lipid metabolism relate to the progression of T2DM (Gasmi et al., 2021; Chen et al., 2022). Genetic and environmental factors in TD contribute to altered thyroid function, frequently driven by autoimmune mechanisms (Tomer and Davies, 2003; Bao et al., 2021).
With well-established guidelines to diagnose such disorders, the development of new classes of medications continues (McGill et al., 2024; Tywanek et al., 2024), whereas nanotechnology may revolutionize endocrine disorder treatments, bridging diagnostics and therapies (Yang et al., 2013; Yan et al., 2024). Nonetheless, omics technologies have significantly advanced the discovery of metabolic disorders (Costanzo et al., 2024), for monitoring biomarkers of disease progression, predicting treatment response, and improving follow-up care in diabetes and thyroid disorders. These approaches may enable diagnosis that is more precise and personalized treatment strategies (Olivier et al., 2019; Beger et al., 2020). Metabolomics, in particular, may lean on mass spectrometry (MS) or nuclear magnetic resonance (NMR) strategies to identify, characterize, and monitor molecules for clinical purposes (Roviello et al., 2014; Costanzo et al., 2022; Aleidi et al., 2023; Costanzo and Caterino, 2023). Further multi-omics data integration through advanced computational models has been proven instrumental in validating and prioritizing disease biomarkers for clinical use (Pietzner et al., 2018; Reel et al., 2021; Zhang et al., 2024).
This Research Topic collected original research articles that provide advances through multi-omics strategies to many aspects of endocrinological and metabolic diseases, including thyroid disease, diabetes, and obesity. Given the influence of thyroid hormones on lipids, insulin secretion, and carbohydrate metabolism, their involvement in developing T2DM is expected. To this purpose, using joint models of longitudinal and time-to-event data, Amirabadizadeh et al. investigated the association between serum changes of thyroid-stimulating hormone (TSH) and free thyroxine (FT4) levels and the incidence of T2DM [19]. They used the data from 1938 individuals in the Tehran Thyroid Study cohort and identified new cases of T2DM. Their findings revealed some dynamic variations in serum thyroid hormones connected with the development of T2DM. A significant reverse association between serum TSH levels and the risk of T2DM was demonstrated. These results suggested an intricate interplay between thyroid activity and diabetes risk, highlighting the significance of screening thyroid hormones as a preventive approach for T2DM.
Aleidi et al. investigated the metabolic profiles connected with Gestational Diabetes Mellitus (GDM) using an MS-based untargeted metabolomics approach [20]. Based on the oral glucose tolerance test (OGTT), 40 pregnant women at 24–28 weeks of gestation were screened, and 20 of them were identified as affected by GDM, while the other 20 were used as control. The analysis revealed distinctive metabolic differences between GDM and healthy women, with a significant number of dysregulated metabolites in serum from GDM ones. The most relevant metabolic pathways associated with the altered GDM metabolome included tryptophan metabolism, inositol phosphate metabolism, phenylalanine metabolism, and histidine metabolism. Moreover, the authors selected a set of 10 metabolites, including N-acetylproline and serylmethionine, whose combination showed high diagnostic importance with a high AUC value (0.978). Importantly, this set of metabolites could potentially be tested as novel biomarkers detectable with high specificity in place of standard OGTT to diagnose GDM.
Masood et al. explored the plasma proteome changes following the treatment with liraglutide, a long-acting agonist of GLP-1 receptor, showing strong therapeutic interest worldwide for its role in weight loss and obesity treatment. GLP-1 signaling is proposed to be activated by receptor binding and activation of the cAMP cascade, resulting in effective glucose regulation, improved insulin secretion, decreased glucagon secretion, lower food intake, and increased satiety. However, to elucidate the molecular mechanisms connected with liraglutide effects, the authors applied a label-free quantitative proteomics approach to identify altered pathways and potential biomarkers detectable in plasma from patients before initiating treatment and after 3 months of 3 mg liraglutide therapy. Proteome changes included a reduction of inflammation and oxidative stress pathways and a boost of glycolytic and lipolytic metabolic pathways.
Finally, Tang et al. investigated the metabolic changes and relative pathways associated with exposure to different doses of hexavalent chromium Cr(VI), being established its involvement in carbohydrate, lipid, and nucleic acid metabolism, as well as in the association with diseases such as cardiovascular diseases, diabetes, and depression. They studied the potential neuro-health risks associated with Cr(VI), analyzing the metabolome of rat astrocytes via untargeted metabolomics. Specifically, they highlighted the critical roles of sphingolipid metabolism and the methionine-cysteine cycle in neurotoxicity, with sphingolipid metabolism associated with apoptosis and the methionine-cysteine cycle playing a significant role in oxidative damage.
Author contributions
MC: Writing–original draft, Writing–review and editing. SA: Writing–original draft, Writing–review and editing. AA: Writing–original draft, Writing–review and editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
Author MC was affiliated with the CEINGE‒Biotecnologie Avanzate Franco Salvatore.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Aleidi, S. M., Al Fahmawi, H., Masood, A., and Abdel Rahman, A. (2023). Metabolomics in diabetes mellitus: clinical insight. Expert Rev. Proteomics 20, 451–467. doi:10.1080/14789450.2023.2295866
Bao, L., Xu, T., Lu, X., Huang, P., Pan, Z., and Ge, M. (2021). Metabolic reprogramming of metabolic reprogramming of thyroid cancer cells and crosstalk in their microenvironmenthyroid cancer cells and crosstalk in their microenvironment. Front. Oncol. 11, 773028. doi:10.3389/fonc.2021.773028
Beger, R. D., Schmidt, M. A., and Kaddurah-Daouk, R. (2020). Current concepts in pharmacometabolomics, current concepts in pharmacometabolomics, biomarker discovery, and precision medicineiomarker discovery, and precision medicine. Metabolites 10, 129. doi:10.3390/metabo10040129
Chauhan, A., and Patel, S. S. (2024). Thyroid thyroid hormone and diabetes mellitus interplay: making management of comorbid disorders complicatedormone and diabetes mellitus interplay: making management of comorbid disorders complicated. Horm. Metab. Res. 56, 845–858. doi:10.1055/a-2374-8756
Chen, J., Kan, M., Ratnasekera, P., Deol, L. K., Thakkar, V., and Davison, K. M. (2022). Blood blood chromium levels and their association with cardiovascular diseases, diabetes, and depression: national health and nutrition examination survey (NHANES) 2015-2016hromium levels and their association with cardiovascular diseases, diabetes, and depression: national health and nutrition examination survey (NHANES) 2015–2016. Nutrients 14, 2687. doi:10.3390/nu14132687
Costanzo, M., and Caterino, M. (2023). Targeted lipidomics data of COVID-19 patients. Data Brief. 48, 109089. doi:10.1016/j.dib.2023.109089
Costanzo, M., Caterino, M., and Ruoppolo, M. (2022). “Targeted metabolomics,” in Metabolomics perspectives (Elsevier), 219–236. doi:10.1016/B978-0-323-85062-9.00006-4
Costanzo, M., Cevenini, A., Kollipara, L., Caterino, M., Bianco, S., Pirozzi, F., et al. (2024). Methylmalonic acidemia triggers lysosomal-autophagy dysfunctions. Cell Biosci. 14, 63. doi:10.1186/s13578-024-01245-1
DeFronzo, R. A., Ferrannini, E., Groop, L., Henry, R. R., Herman, W. H., Holst, J. J., et al. (2015). Type 2 diabetes mellitus. Nat. Rev. Dis. Prim. 1, 15019. doi:10.1038/nrdp.2015.19
Donath, M. Y., and Shoelson, S. E. (2011). Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 11, 98–107. doi:10.1038/nri2925
Gasmi, A., Noor, S., Menzel, A., Doşa, A., Pivina, L., and Bjørklund, G. (2021). Obesity and obesity and insulin resistance: associations with chronic inflammation, genetic and epigenetic factorsnsulin resistance: associations with chronic inflammation, genetic and epigenetic factors. Curr. Med. Chem. 28, 800–826. doi:10.2174/0929867327666200824112056
Kalra, S., Aggarwal, S., and Khandelwal, D. (2019). Thyroid thyroid dysfunction and type 2 diabetes mellitus: screening strategies and implications for managementysfunction and type 2 diabetes mellitus: screening strategies and implications for management. Diabetes Ther. 10, 2035–2044. doi:10.1007/s13300-019-00700-4
McGill, J. B., Hirsch, I. B., Parkin, C. G., Aleppo, G., Levy, C. J., and Gavin, J. R. (2024). The current and future the current and future role of insulin therapy in the management of type 2 diabetes: a narrative reviewole of insulin therapy in the management of type 2 diabetes: a narrative review. Diabetes Ther. 15, 1085–1098. doi:10.1007/s13300-024-01569-8
Olivier, M., Asmis, R., Hawkins, G. A., Howard, T. D., and Cox, L. A. (2019). The the need for multi-omics biomarker signatures in precision medicineeed for multi-omics biomarker signatures in precision medicine. Int. J. Mol. Sci. 20, 4781. doi:10.3390/ijms20194781
Pietzner, M., Kacprowski, T., and Friedrich, N. (2018). Empowering thyroid hormone research in human subjects using OMICs technologies. J. Endocrinol. 238, R13-R29–R29. doi:10.1530/JOE-18-0117
Pulgaron, E. R., and Delamater, A. M. (2014). Obesity and Type 2 Obesity and type 2 diabetes in children: epidemiology and treatmentiabetes in Children: epidemiology and treatment. Curr. Diab Rep. 14, 508. doi:10.1007/s11892-014-0508-y
Reel, P. S., Reel, S., Pearson, E., Trucco, E., and Jefferson, E. (2021). Using machine learning approaches for multi-omics data analysis: using machine learning approaches for multi-omics data analysis: a review review. Biotechnol. Adv. 49, 107739. doi:10.1016/j.biotechadv.2021.107739
Roviello, G. N., Roviello, G., Musumeci, D., Capasso, D., Di Gaetano, S., Costanzo, M., et al. (2014). Synthesis and supramolecular assembly of 1,3-bis(1′-uracilyl)-2-propanone. RSC Adv. 4, 28691–28698. doi:10.1039/c4ra03713h
Tomer, Y., and Davies, T. F. (2003). Searching for the Searching for the autoimmune thyroid disease susceptibility genes: from gene mapping to gene functionutoimmune thyroid disease Susceptibility Genes: from Gene Mapping to Gene function. Endocr. Rev. 24, 694–717. doi:10.1210/er.2002-0030
Tywanek, E., Michalak, A., Świrska, J., and Zwolak, A. (2024). Autoimmunity, autoimmunity, new potential biomarkers and the thyroid gland-the perspective of hashimoto's thyroiditis and its treatmentew potential biomarkers and the thyroid gland—the perspective of hashimoto’s thyroiditis and its treatment. Int. J. Mol. Sci. 25, 4703. doi:10.3390/ijms25094703
Yamauchi, I., and Yabe, D. (2025). Best practices in the management of thyroid dysfunction induced by immune checkpoint inhibitors. Eur. Thyroid. J. 14, e240328. doi:10.1530/ETJ-24-0328
Yan, Y., Cai, H., and Yang, M. (2024). The application of the application of nanotechnology for the diagnosis and treatment of endocrine disorders: a review of current trends, toxicology and future perspectiveanotechnology for the diagnosis and treatment of endocrine disorders: a review of current trends, toxicology and future perspective. Int. J. Nanomedicine 19, 9921–9942. doi:10.2147/IJN.S477835
Yang, S., Liu, Y., Wang, Y., and Cao, A. (2013). Biosafety and bioapplication of biosafety and bioapplication of nanomaterials by designing protein-nanoparticle interactionsanomaterials by designing protein–nanoparticle interactions. Small 9, 1635–1653. doi:10.1002/smll.201201492
Zhang, H., Zhou, Y., Yu, B., Deng, Y., Wang, Y., Fang, S., et al. (2024). Multi-multi-omics approaches to discover biomarkers of thyroid eye disease: a systematic reviewmics approaches to discover biomarkers of thyroid eye disease: a systematic review. Int. J. Biol. Sci. 20, 6038–6055. doi:10.7150/ijbs.103977
Keywords: metabolic disease, metabolomics, precision medicine, endocrinolgy, diabetes mellitus, thyroid disease
Citation: Costanzo M, Aleidi SM and Abdel Rahman A (2025) Editorial: Omics in endocrinology: from biomarker discovery to targeting therapeutic strategies. Front. Mol. Biosci. 12:1567250. doi: 10.3389/fmolb.2025.1567250
Received: 26 January 2025; Accepted: 31 January 2025;
Published: 13 February 2025.
Edited and reviewed by:
Sara Rinalducci, University of Tuscia, ItalyCopyright © 2025 Costanzo, Aleidi and Abdel Rahman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Michele Costanzo, bWljaGVsZS5jb3N0YW56b0B1bmluYS5pdA==; Anas Abdel Rahman, YWFiZGVscmFobWFuNDZAa2ZzaHJjLmVkdS5zYQ==