Obesity represents a heterogeneous metabolic disorder characterized by substantial interindividual variation in inflammatory status, insulin sensitivity, and cardiometabolic risk. Traditional anthropometric measures fail to capture this metabolic diversity, limiting risk stratification and personalized intervention strategies. This review critically examines nutritional and metabolic biomarkers that reflect the physiological dysregulation underlying obesity, including adipokines (leptin, adiponectin, resistin), inflammatory markers (C-reactive protein, interleukin-6, TNF-α), insulin resistance indices (HOMA-IR, fasting insulin, HbA1c), and lipid metabolism indicators (LDL cholesterol, triglycerides, HDL cholesterol, and liver enzymes such as ALT and GGT). Among these, elevated CRP, reduced adiponectin, and increased HOMA-IR have demonstrated the strongest clinical utility for early metabolic risk identification. We further evaluate emerging biomarkers—including circulating microRNAs, gut microbiota-derived metabolites (short-chain fatty acids, TMAO, lipopolysaccharides), and bile acid profiles—which offer additional mechanistic insight into diet–microbiome–host interactions. We systematically assess the mechanistic basis, clinical relevance, and nutritional modulation of each biomarker class, emphasizing how dietary composition—particularly fatty acid quality, fiber intake, and overall dietary patterns such as the Mediterranean diet—influences biomarker profiles and metabolic outcomes. Furthermore, we explore how biomarker-based phenotyping enables precision nutrition approaches by identifying individuals most likely to benefit from specific dietary interventions. Integration of multi-biomarker panels with clinical and genetic data holds promise for advancing from population-based dietary guidelines toward individualized nutrition strategies that optimize metabolic health and prevent obesity-related complications. Future research should prioritize validating biomarker-guided intervention frameworks, establishing standardized thresholds across diverse populations, and developing clinically implementable tools for personalized nutritional medicine.
Maio et al. (Mon,) studied this question.