Multi-study analysis reveals dynamic postprandial biomarkers for metabolic syndrome by low-level fusion of multi-platform data from nutritional challenge test studies.

backgroundMetabolic syndrome (MetS) is a global health issue linked to increased disease risk and economic burden. Traditional biomarkers for MetS reflect disrupted homeostasis but may fail to detect earlier stages of imbalance, i.e., an increase in allostatic load. Health can be defined as the capacity to maintain or recover homeostatic control, and this capacity can be assessed via a standardized nutritional challenge test (e.g., PhenFlex), which provokes adaptive metabolic responses. We aimed to identify a biomarker signature indicative of MetS-associated allostatic load, using data from multiple nutritional challenge test (NCT) studies.

resultsA multi-study analysis was performed using data from four human studies with nutritional challenge test (n = 195). We identified 64 analytes with significantly altered postprandial responses in individuals with MetS (n = 78) compared to healthy controls (n = 117). Cluster analysis revealed four clusters with a distinct temporal dynamic response to the NCT. The most prominently affected analytes included total triacylglycerides, C-peptide, leptin, and a range of amino acids (e.g., L-glutamic acid, L-serine, and L-leucine). The response of a subset of these analytes to the nutritional challenge test also changed after a 12-week, 20% energy-restriction intervention in an independent trial (n = 72), supporting their potential as markers for monitoring intervention efficacy. Further network and ontology-based analyses revealed that the altered analytes represented key biological processes, particularly lipid and protein metabolism, as well as higher-level physiological function domains, such as liver, muscle, adipose tissue, and pancreas function. These domains were also responsive to the energy-restriction intervention.

conclusionsThis study identified a robust, dynamic biomarker signature for MetS that emerges in response to a NCT. This signature reflects increased allostatic load and highlights disrupted adaptability across multiple metabolic functions. A subset of the identified markers were modifiable through dietary intervention, supporting their potential as early, actionable biomarkers of metabolic health.