Circadian rhythms regulate key physiological processes through clock genes in central and peripheral tissues. While circadian gene expression during development has been well studied, the temporal dynamics of metabolism across tissues remain less understood. Here, we present the Circadian Ontogenetic Metabolomics Atlas (COMA), which maps circadian metabolic rhythms across 16 rat anatomical structures. The brain (suprachiasmatic nuclei, medial prefrontal cortex) and periphery (liver, plasma) span developmental stages from embryonic E19 to postnatal P2, P10, P20, and P28. Fecal samples include all four postnatal stages, while additional peripheral tissues were analyzed at P20 and P28. Using a multiplatform liquid chromatography-mass spectrometry approach, we annotated 851 metabolites from 1610 samples. We identified distinct circadian shifts, particularly during the transition from nursing to solid food intake (P10-P20), with an average of 24% of metabolites exhibiting circadian oscillations across sample types, as determined by JTK_CYCLE. Our study also underscores the importance of standardized sampling, as metabolite intensities fluctuate with both circadian rhythms and development. COMA serves as an open-access resource ( https://coma.metabolomics.fgu.cas.cz ) for exploring circadian metabolic regulation and its role in developmental biology.

• Keywords
• Atlas, Circadian rhythm, Lipidomics, Metabolomics, Resource,
• MeSH
• Chromatography, Liquid MeSH
• Circadian Rhythm * physiology   MeSH
• Feces * chemistry   MeSH
• Liver metabolism   MeSH
• Rats MeSH
• Metabolome * MeSH
• Metabolomics * methods   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

• MeSH
• Biomarkers metabolism   MeSH
• Diabetes Mellitus, Type 1 metabolism   complications   MeSH
• Diabetes Mellitus, Type 2 * metabolism   MeSH
• Cardiovascular Diseases * metabolism   diagnosis   MeSH
• Humans MeSH
• Lipidomics * methods   MeSH
• Metabolic Syndrome * metabolism   MeSH
• Metabolomics * methods   MeSH
• Fatty Liver metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Biomarkers MeSH