Yousf S, Batra HS, Jha RM, Sardesai DM, Ananthamohan K, Chugh J, Sharma S. Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics.
Clin Chim Acta 2024;
557:117857. [PMID:
38484908 DOI:
10.1016/j.cca.2024.117857]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND
The prevalence of type 2 diabetes mellitus (T2DM), a progressive metabolic disorder characterized by chronic hyperglycemia and the development of insulin resistance, has increased globally, with worrying statistics coming from children, adolescents, and young adults from developing countries like India. Here, we investigated unique circulating metabolic signatures associated with prediabetes and T2DM in an Indian cohort using NMR-based metabolomics.
MATERIALS AND METHODS
The study subjects included healthy volunteers (N = 101), prediabetic subjects (N = 75), and T2DM patients (N = 108). Serum metabolic profiling was performed using 1H NMR spectroscopy and major perturbed metabolites were identified by multivariate analysis and receiver operating characteristic (ROC) modules.
RESULTS
Of the 36 aqueous abundant metabolites, 24 showed a statistically significant difference between healthy volunteers, prediabetics, and established T2DM subjects. On performing multivariate ROC curve analysis with 5 commonly dysregulated metabolites (namely, glucose, pyroglutamate, o-phosphocholine, serine, and methionine) in prediabetes and T2DM, AUC values obtained were 0.96 (95 % confidence interval (CI) = 0.93, 0.98) for T2DM; and 0.88 (95 % CI = 0.81, 0.93) for prediabetic subjects, respectively.
CONCLUSION
We propose that the identified metabolite panel can be used in the future as a biomarker for clinical diagnosis, patient surveillance, and for predicting individuals at risk for developing diabetes.
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