A Combinatory Approach of Plasma Lipidomic and Proteomic Markers to Predict Glycemic Deterioration in Type 2 Diabetes
Main Article Content
Keywords
Plasma Lipidomic, Proteomic Markers, Glycemic Deterioration, Type 2 Diabetes
Abstract
Background: Precision medicine aims to take step further by not applying generic treatment methods to treat complex metabolic conditions such as type 2 diabetes mellitus (T2D) but instead customizing interventions to individual variation. The multi-omics research indicates that lipidomic and proteomic data can be used to explain the heterogeneity of T2D. The metabolic syndrome raises circulating pro-inflammatory cytokines including interleukin-18 (IL-18) before the onset of T2D but signaled by integrated lipids including triglycerides and fasting glucose are becoming emerging surrogate indicators of insulin resistance and cardiovascular risk.
Objective: To develop a quantitative multi-omics study, which investigates the role of plasma lipids (triacylglycerols and sphingomyelin) and proteomic (IL-18 receptor 1 and testican-1) markers in predicting glycaemic deterioration in T2D patients in combination with clinical variables (age, body-mass index (BMI), and fasting glucose).
Methods: A hospital-based analytical cohort of 200 adults with T2D attending Teaching Hospital, Narowal, Pakistan, was structured to examine age, BMI, fasting glucose, triacylglycerols, sphingomyelin, IL-18 receptor 1, testican-1, baseline HbA1c, follow-up HbA1c, and triglyceride-glucose (TyG) index. Glycaemic deterioration (ΔHbA1c) was defined as the change in HbA1c over one year. Independent associations between biochemical markers and glycaemic deterioration were assessed using multiple linear regression adjusted for age and BMI. Descriptive statistics, Pearson correlation analysis, and graphical summaries were used to present the findings.Results: IL -18 receptor 1 was most significantly positively correlated with glycaemic deterioration (= 0.42 per cent change in HbA1c per increase in ng mL -1 = -0.001). Triacylglycerides were positively but not significantly related following a multivariate adjustment of other variables (β = 0.0004% per mg dL -1; p = 0.27). Age had a small negative relationship with glycaemic deterioration (0.003 by per cent/year; p 0.03), but there were no significant relationships between testican-1 and BMI. The heatmaps of correlation showed that there was a high correlation between the TyG index and triacylglycerols/fasting glucose, as well as IL-18 receptor 1 and glycaemic deterioration. A scatter plot was used to show the positive linear relationship between IL-18 receptor 1 and ΔHbA1c, the regression coefficient plot gave precedence to the predictors.
Conclusion: This hospital-based analytical study indicates that, among commonly measured plasma lipids and proteins, IL-18 receptor 1 is a relevant predictor of glycaemic deterioration in T2D. These results align with recent multi-omics studies indicating that IL-18 signalling and triacylglycerols represent significant molecular markers that distinguish T2D sub-populations and that circulating IL-18 is up-regulated during metabolic syndrome and before the development of T2D. The combination of lipidomic and proteomic biomarkers may improve risk stratification and support personalized interventions in T2D.
References
2. Guan, H., Zhao, S., Li, J., Wang, Y., Niu, P., Zhang, Y., Zhang, Y., Fang, X., Miao, R., & Tian, J. (2024). Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus. Frontiersin.Org, 15. https://doi.org/10.3389/FENDO.2024.1363877/FULL
3. Hameed, A., Mojsak, P., … A. B.-J. of clinical, & 2020, undefined. (n.d.). Altered metabolome of lipids and amino acids species: a source of early signature biomarkers of T2DM. Mdpi.Com. Retrieved October 26, 2025, from https://www.mdpi.com/2077-0383/9/7/2257
4. Kahn, S., Cooper, M., Lancet, S. D. P.-T., & 2014, undefined. (n.d.). Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Thelancet.Com. Retrieved October 26, 2025, from https://www.thelancet.com/journals/a/article/PIIS0140-6736(13)62154-6/abstract
5. Kupai, K., Várkonyi, T., Török, S., Gáti, V., Life, Z. C.-, & 2022, undefined. (n.d.). Recent progress in the diagnosis and management of type 2 diabetes mellitus in the era of COVID-19 and single cell multi-omics technologies. Mdpi.Com. Retrieved October 26, 2025, from https://www.mdpi.com/2075-1729/12/8/1205
6. Lappas, M., Mundra, P. A., Wong, G., Huynh, K., Jinks, D., Georgiou, H. M., Permezel, M., & Meikle, P. J. (2015). The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics. Springer, 58(7), 1436–1442. https://doi.org/10.1007/S00125-015-3587-7
7. Lee, S., Rauch, J., & Kolch, W. (2020). Targeting MAPK signaling in cancer: Mechanisms of drug resistance and sensitivity. International Journal of Molecular Sciences, 21(3), 1–29. https://doi.org/10.3390/ijms21031102
8. Lin, M., Weng, S., Chai, K., advances, Z. M.-R., & 2019, undefined. (2019). Lipidomics as a tool of predicting progression from non-alcoholic fatty pancreas disease to type 2 diabetes mellitus. Pubs.Rsc.Org. https://doi.org/10.1039/c9ra07071k
9. Liu, J., Semiz, S., van der Lee, S. J., van der Spek, A., Verhoeven, A., van Klinken, J. B., Sijbrands, E., Harms, A. C., Hankemeier, T., van Dijk, K. W., van Duijn, C. M., & Demirkan, A. (2017). Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study. pringer, 13(9), 104. https://doi.org/10.1007/S11306-017-1239-2
10. Montgomery, M. K., Lin, S., Yang, C. H., Prasad, K., Cheng, Z. L., Bayliss, J., Leeming, M. G., Williamson, N. A., Loh, K., Dong, L., & Watt, M. J. (2025). -FC protein therapy increases skeletal muscle glucose uptake and improves glycaemic control in mice with insulin resistance and in a mouse model of type 2 diabetes. Springer, 68(7), 1530–1543. https://doi.org/10.1007/S00125-025-06413-7
11. Nagamuruganandam, A., Chandran, C. P., Rajathi, S., & Agilandeswari, V. (2025). Machine Learning Approaches for Lung Cancer Prediction. Springer, 2425 CCIS, 184–198. https://doi.org/10.1007/978-3-031-86293-9_13
12. O’Sullivan, S., Qi, L., & Zalloua, P. (2025). From omics to AI—mapping the pathogenic pathways in type 2 diabetes. Wiley Online Library. https://doi.org/10.1002/1873-3468.70115
13. Pollé, O. G., Pyr Dit Ruys, S., Lemmer, J., Hubinon, C., Martin, M., Herinckx, G., Gatto, L., Vertommen, D., & Lysy, P. A. (123 C.E.). Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission. Nature.Com, 14, 20798. https://doi.org/10.1038/s41598-024-71717-4
14. Qiu, Y., Rajagopalan, D., Connor, S. C., Damian, D., Zhu, L., Handzel, A., Hu, G., Amanullah, A., Bao, S., Woody, N., MacLean, D., Lee, K., Vanderwall, D., & Ryan, T. (2008). Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus. Springer, 4(4), 337–346. https://doi.org/10.1007/S11306-008-0123-5
15. Slieker, R. C., Donnelly, L. A., Lopez-Noriega, L., Muniangi-Muhitu, H., Akalestou, E., Sheikh, M., Georgiadou, E., Giordano, G. N., Åkerlund, M., Ahlqvist, E., Barovic, M., Bouland, G. A., Burdet, F., Dragan, I., Elders, P. J., Fernandez, C., Festa, A., Fitipaldi, H., Froguel, P., … Rutter, G. A. (n.d.). Novel biomarkers for glycaemic deterioration in type 2 diabetes: an IMI RHAPSODY study. Medrxiv.Org. https://doi.org/10.1101/2021.04.22.21255625.ABSTRACT
16. Slieker, R. C., Münch, M., Donnelly, L. A., Bouland, G. A., Dragan, I., Kuznetsov, D., Elders, P. J. M., Rutter, G. A., Ibberson, M., Pearson, E. R., ’t Hart, L. M., van de Wiel, M. A., & Beulens, J. W. J. (2024). An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study. Springer, 67(5), 885–894. https://doi.org/10.1007/S00125-024-06105-8
17. Slieker, R. C., Münch, M., Donnelly, L. A., Bouland, G. A., Dragan, I., Kuznetsov, D., Petra, ·, Elders, J. M., Guy, ·, Rutter, A., Ibberson, · Mark, Pearson, E. R., Leen, ·, ’t Hart, M., Van De Wiel, M. A., & Beulens, J. W. J. (2021). Heterogeneity of lipid and protein cartilage profiles associated with human osteoarthritis with or without type 2 diabetes mellitus. ACS Publications, 20(5), 2973–2982. https://doi.org/10.1021/ACS.JPROTEOME.1C00186
18. Soares, N. P., Magalhaes, G. C., Mayrink, P. H., & Verano-Braga, T. (2024). Omics to unveil diabetes mellitus pathogenesis and biomarkers: focus on proteomics, lipidomics, and metabolomics. Springer, 1443, 211–220. https://doi.org/10.1007/978-3-031-50624-6_11
19. Tofte, N., Persson, F., & Rossing, P. (2020). Omics research in diabetic kidney disease: new biomarker dimensions and new understandings? Springer, 33(5), 931–948. https://doi.org/10.1007/S40620-020-00759-4
20. Yu, J., Ren, J., Ren, Y., Wu, Y., Zeng, Y., Zhang, Q., EBioMedicine, X. X.-, & 2024, undefined. (n.d.). Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes. Thelancet.Com. Retrieved October 26, 2025, from https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(24)00043-4/fulltext
21. Zubair, A., Fatima, S., Nizami, S. A., Khalil, A., Sattar, R., & Ahmed, B. (2021). Biochemical Changesakistan. 15(9), 2890–2892.

