BMC psychiatry · 2026
Uncovering the metabolomic signatures in adolescent depression using a targeted metabolomics approach.
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Jadhav Kaustubh Kishor, Avesani Simone, Piironen Aino-Kaisa, Giugno Rosalba, Lakka Timo, Tolmunen Tommi, Kanninen Katja M
Аннотация
BACKGROUND: Depression is a leading cause of mortality worldwide and remains a growing public health concern. Most research has focused on adults, despite evidence that adolescence is a common period of onset of mental health problems. Metabolomics, which reflects the ongoing bodily biochemical processes through metabolite profiling, has emerged as a promising avenue to identify possible indicators of disease state. To contribute to this growing field, this study aims to identify early occurring metabolomic signatures associated with adolescent depression using a targeted metabolomics approach.
METHODS: A total of 204 adolescents diagnosed with clinical depression were compared with 78 healthy peers using targeted nuclear magnetic resonance spectroscopy using serum samples (Nightingale Health Ltd.). In total, 250 metabolite measures were quantified, including both absolute concentrations and percentage measures. A combination of univariate and multivariate analyses was conducted to identify metabolomic signatures in adolescent depression. Additionally, subgroup analyses for drug-naïve and medicated adolescents with depression were performed using the same analytical approach.
RESULTS: Of the 250 metabolite measures analyzed, 61 were identified as metabolomic signatures associated with adolescent depression based on statistical significance (adj.p-value < 0.05) and multivariate contribution (VIP > 1.0). These belong to ketone bodies, glycolysis-related metabolites, amino acids, inflammatory markers, fatty acids, lipids, lipoprotein subclasses, and particle sizes. In subgroup analyses, 24 and 74 metabolomic signatures were identified among drug-naïve adolescents with depression and those consuming medications.
CONCLUSION: By applying a targeted metabolomics approach, this study identifies metabolomic signatures associated with adolescent depression. These early biological signatures offer valuable insight into the metabolic alterations associated with adolescent depression, particularly within the drug-naïve group, where confounding effects of medication were minimized. Continued research on peripheral metabolic markers may help advance early detection strategies and contribute to the development of more individualized and precision-based approaches.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: A total of 204 adolescents diagnosed with clinical depression were compared with 78 healthy peers using targeted nuclear magnetic resonance spectroscopy using serum samples (Nightingale Health Ltd.). In total, 250 metabolite measures were quantified, including both absolute concentrations and percentage measures. A combination of univariate and multivariate analyses was conducted to identify metabolomic signatures in adolescent depression. Additionally, subgroup analyses for drug-naïve and medicated adolescents with depression were performed using the same analytical approach.
RESULTS: Of the 250 metabolite measures analyzed, 61 were identified as metabolomic signatures associated with adolescent depression based on statistical significance (adj.p-value < 0.05) and multivariate contribution (VIP > 1.0). These belong to ketone bodies, glycolysis-related metabolites, amino acids, inflammatory markers, fatty acids, lipids, lipoprotein subclasses, and particle sizes. In subgroup analyses, 24 and 74 metabolomic signatures were identified among drug-naïve adolescents with depression and those consuming medications.
CONCLUSION: By applying a targeted metabolomics approach, this study identifies metabolomic signatures associated with adolescent depression. These early biological signatures offer valuable insight into the metabolic alterations associated with adolescent depression, particularly within the drug-naïve group, where confounding effects of medication were minimized. Continued research on peripheral metabolic markers may help advance early detection strategies and contribute to the development of more individualized and precision-based approaches.
CLINICAL TRIAL NUMBER: Not applicable.