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Atul Khajuria 1,
Mahesh Gaba 2 *,
- Dean, Department of Allied & Health Care Sciences, Rayat Bahra Professional University, Hoshiarpur, Chandigarh Rd, VPO, Bohan, Hoshiarpur, Punjab, India
- Faculty, department of Medical Laboratory Science, PCTE Group of Institutes, Jhande [Ludhiana], punjab, India
Abstract
Metabolomics provides a comprehensive snapshot of smallmolecule intermediates and end products of cellular processes and thereby links genotype, environment, and phenotype in human disease . Over the last decade, advances in liquid chromatography–mass spectrometry (LC–MS) and nuclear magnetic resonance (NMR) spectroscopy have enabled high-throughput profiling of thousands of metabolites from microliter volumes of clinical samples . Clinical metabolomics is now emerging as a tool for disease diagnosis, risk prediction, therapeutic monitoring, and mechanistic discovery across cardiometabolic, neurologic, renal, and inflammatory disorders. In diabetic kidney disease, metabolomics has identified early alterations in aminoacid, lipid, and energy pathways and candidate biomarkers that precede albuminuria or decline in estimated glomerular filtration rate (eGFR) . In Alzheimer’s disease, targeted and untargeted metabolomics in plasma, cerebrospinal fluid (CSF), and brain tissue have
highlighted disturbances in branchedchain amino acids, nicotinamide adenine dinucleotide (NAD⁺) homeostasis, polyamine and Larginine metabolism, and tryptophan–kynurenine pathways, some of which correlate with amyloid and tau biomarkers and cognitive decline . Despite impressive discovery output, translation into clinical tests remains limited, impeded by issues of analytical standardization, inter- laboratory reproducibility, causal inference, and regulatory and implementation barriers. This
review summarizes principles of clinical metabolomics, highlights key disease-related metabolites in diabetic kidney disease and Alzheimer’s disease, and discusses the main technical, statistical, and translational challenges for bringing metabolomic biomarkers and signatures into routine clinical practice .
Keywords: KEYWORDS -Clinical metabolomics; Disease biomarkers; Diabetic kidney disease; Diabetic nephropathy; Alzheimer’s disease; Mass spectrometry; Metabolic profiling; Translational medicine.
Atul Khajuria 1, Mahesh Gaba 2 *. Clinical metabolomics: Useful insights, perspectives, and challenges.. Emerging Trends in Metabolites. 2026; 03(01):-.
Atul Khajuria 1, Mahesh Gaba 2 *. Clinical metabolomics: Useful insights, perspectives, and challenges.. Emerging Trends in Metabolites. 2026; 03(01):-. Available from: https://journals.stmjournals.com/etm/article=2026/view=239640
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Emerging Trends in Metabolites
| Volume | 03 |
| 01 | |
| Received | 21/02/2026 |
| Accepted | 14/03/2026 |
| Published | 25/03/2026 |
| Publication Time | 32 Days |
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