Metabolomic Diagnostic Technology as a Basis for the Establishing the Principles of Metabolic Health

Aim: to examine current evidence on the key metabolic alterations that reflect the major pathogenetic axes of metabolic syndrome (MS) and to assess their potential for the diagnosis and stratification of MS.

Key points. The review highlights five major metabolomic pathogenetic axes of MS: anabolic resistance, mitochondrial dysfunction, systemic inflammation, insulin resistance, and lysosomal insufficiency. Each represents an essential pathogenic link influencing the development of MS complications. Characteristic alterations of low-molecular-weight metabolites (such as amino acids and organic acids) and lipids identified through modern metabolomic analytical methods are described for each axis, and their clinical significance is discussed. Special attention is given to the role of combined metabolite panels, which improve early diagnosis of MS and prediabetes, allow risk stratification for complications (type 2 diabetes, atherosclerosis, nonalcoholic fatty liver disease, etc.), and enable monitoring of treatment effectiveness. It is noted that metabolomic biomarkers possess high diagnostic and prognostic value, complementing standard clinical indicators. Evidence is presented showing that integrating metabolomic data with clinical parameters increases diagnostic accuracy (for example, combining metabolomic markers with the glucose tolerance test improves its predictive value). The development of integrated metabolomic indices (such as MetSCORE) provides high accuracy in identifying MS (AUROC ~0.9). Metabolomic studies confirm the heterogeneity of MS and allow subclassification according to predominant pathophysiological disturbances, opening prospects for precision medicine.

Conclusion. Thus, the metabolomic approach substantially expands the possibilities for diagnosis and personalized therapy in patients with MS. It enables the detection of latent metabolic disturbances at the preclinical stage of disease, complementing routine diagnostic methods. Implementation into clinical practice requires standardization of metabolomic analytical protocols, validation of metabolomic biomarkers, and integration of multi-omics approaches, which will ensure the reproducibility of results and their broad application in medicine.

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