Metabolomics India 2026

Small-for-gestational age (SGA3) is a high-risk phenotype driven largely by placental insufficiency and associated with heightened perinatal morbidity. Growing evidence points to impaired placental amino-acid transport, dysregulated lipid metabolism, and sphingolipid-mediated trophoblast dysfunction as convergent pathways that disrupt mTOR signalling and fetoplacental vascular function. These domains have rarely been quantified together on a single platform. Using a case-control study design nested within the parent cohort absolute quantification of amino acids, biogenic amines, acylcarnitines, glycerophospholipids and sphingolipids in maternal plasma collected at early trimester of pregnancy, will be profiled by targeted LCMS/MS and FIA-MS/MS using the Biocrates MxP Quant 500 platform. The study aims to (i) validate the candidate amino acids and delineate metabolic alterations linked to placental dysfunction, (ii) derive a multi-analyte amino acid-lipid-sphingolipid panel predictive of SGA3 risk and severity, and (iii) integrate this panel with clinical and ultrasound parameters to improve risk stratification beyond conventional markers. The work would support earlier screening, more precise monitoring, and identification of therapeutic targets.

Environmental chemicals such as bisphenols, PFAS and trace elements rarely act alone, and their biological footprint is often easier to see in the metabolome than in the exposure measurement itself. This talk shows how pairing exposure biomarkers with targeted and untargeted metabolomics in cohort and case-control studies can reveal the pathways (bile acids, lipids, steroids, amino acids) through which everyday exposures may shape metabolic and reproductive health. Drawing on examples from Indian cohorts, including bisphenol A exposure and polycystic ovary syndrome, I will discuss study design, mixture analysis, the limits of cross-sectional data, and how quantitative, standardised platforms can make exposome-health findings comparable across populations.

Human metabolism follows daily rhythms that are critical for health. These rhythms arise from endogenous body clocks and cyclic environmental and behavioural factors, including light-dark, sleep-wake and feeding-fasting cycles. Shift work, sleep deprivation and irregular eating can disrupt these rhythms and increase metabolic disease risk.
Chrono-metabolomics combines chronobiology and metabolomics, profiling metabolites and lipids in biological fluids sampled at high frequency (30 minutes to 2 hours) over multiple days, alongside the circadian phase markers melatonin and cortisol. Using targeted metabolomics (biocrates) in controlled laboratory studies, we showed that ~25% of circulating metabolites exhibited endogenously driven circadian rhythms, while the presence of cyclic behaviours generated a higher proportion (~75%) of diurnal rhythms, in a metabolite class-dependent manner. Investigation of the same set of metabolites showed altered temporal profiles under simulated shift-work and acute sleep deprivation, while the central pacemaker remained aligned with the light-dark cycle.
Building on these studies, our current work is focusing on how meal timing plays a role in circadian synchronisation. In parallel, we are also exploring metabolomic profiling of interstitial fluid collected every 20 minutes using the non-invasive U-RHYTHM device during simulated shift work.
These foundational studies provide temporal baselines and highlight the need for open-access, time-resolved metabolite and pathway resources, alongside field-based studies, to understand how circadian and behavioural influences on metabolism translate into real-world health.

Untargeted metabolomics is rapidly becoming a mainstream technique to discover biomarkers, to study the effects of interventions, and to discover the function of circulating bioactive molecules. Metabolomics is an ‘omics’ research technology that deals with the systematic identification and quantification of metabolome and complete set of low-molecular-weight molecules produced in biological system. The metabolome is highly dynamic, context-dependent, and the concentration varies according to the physiological, developmental or pathological state of an organism. The study of the entire metabolome is technically challenging, and several strategies have been used to achieve broad metabolic coverage which is made possible by the analytical technique Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) and establish in silico pipeline to identify the potent molecule that modulate the cancer-associated molecular pathway.

Harmonized measurements are essential to understand how exposures influence health. We have automated mass spectrometry workflows for metabolite and chemical exposure analysis, including the standardized biocrates Quant 500 kit. Through EIRENE (European Infrastructure for Human Exposome Research), these workflows are being adopted across multiple laboratories, providing reproducible analytical capacity for population-scale exposome research.

This work focuses on non-invasive urinary profiling to characterize gliomas as well as correlating tissue and serum metabolomics to see the relationship between tumour localized and systemic changes. We also did metabolomics in Lennox Gastaut Syndrome which is a severe developmental epileptic encephalopathy and compared it with not only healthy subjects but Controlled Focal Epilepsy group to see the profile associated with disease severity and pharmacoresistance. In the methodology part we developed an F1-PSYCHE TOCSY experiment nested with 1H–13C HSQC experiment in a NOAH supersequence for confident metabolite assignment in lesser time.

Metabolomics provides a powerful approach for characterising molecular changes associated with environmental, lifestyle and physiological factors in human populations. In cohort research, metabolic profiles can help bridge the gap between external exposures, biological responses and health outcomes. This presentation will discuss how metabolomics can be applied in human cohort studies to identify metabolic signatures associated with health and disease, and how these data can be integrated with complementary clinical and multi-omics information to improve biological interpretation. Examples from clinical and population-based metabolomics research will be used to illustrate the value of robust study design, analytical platforms, and computational approaches for translating complex metabolic data into meaningful biological insights. The presentation will also highlight opportunities and challenges in connecting metabolomics with the broader exposome framework and in moving towards more comprehensive approaches to understanding exposure–health relationships.