Toward objective diagnostics in depression: blood metabolomics for symptom profiling, suicide risk, and personalized psychiatry

Depression is a major global health burden, yet its diagnosis still relies largely on subjective clinical interviews and questionnaires. This review explores how blood-based metabolomics could provide objective biomarkers for depressive states, suicidal ideation, and treatment responsiveness. Advances in mass spectrometry now allow the identification of metabolite profiles that reflect biochemical changes in patients with depression. Notably, metabolites such as 3-hydroxybutyrate, betaine, citrate, creatinine, and γ-aminobutyric acid (GABA) have shown reproducible associations with depressive severity across multiple clinical cohorts .

Beyond overall depression severity, metabolomic signatures also correlate with specific symptom dimensions. For instance, elevated 3-hydroxybutyrate levels were linked with suicidal ideation and treatment responsiveness, while altered citrate and kynurenine levels predicted suicide risk when incorporated into machine learning models. These findings support the potential for minimally invasive blood tests to stratify patients based on risk and symptom subtype. Parallel animal studies reinforce these associations, revealing consistent disruptions in tryptophan and alanine metabolism, implicating neurotransmitter biosynthesis, stress response, and gut–brain axis interactions in depressive pathology .

The review further highlights emerging tools for refining diagnosis, including personality-based biotyping and artificial intelligence–driven analyses. Combining blood metabolomics with personality traits and machine learning improved diagnostic performance, pointing toward a future of precision psychiatry. The authors also emphasize the translational value of metabolomic monitoring in treatment response, such as in repetitive transcranial magnetic stimulation (rTMS) and ketamine therapy, where metabolic shifts may predict therapeutic outcomes .

While promising, the field faces challenges including reproducibility, standardization of sample handling, and ethical concerns around biomarker use. Large-scale, multicenter validation studies are needed before metabolomics can be integrated into routine psychiatry. Nevertheless, the accumulating evidence supports blood metabolomics as a powerful complementary approach to improve early detection, suicide prevention, and personalized treatment strategies in depression .