Few studies have quantitatively characterised the shared and distinct features of the epigenetic age signature of schizophrenia, bipolar disorder and major depressive disorder.

To construct a multi-platform epigenetic clock tailored to human blood and brain tissues, and to characterise variations in epigenetic age acceleration across these three common psychiatric disorders.

We integrated 31 publicly available DNA methylation data-sets generated on the platforms Illumina 27K, 450K and EPIC (850K) from patients with schizophrenia, bipolar disorder or major depressive disorder, and from matched controls. Using elastic net regression combined with sure independence screening, we developed the blood–brain clock and applied it to assess disorder-specific epigenetic age acceleration in blood and brain.

The blood–brain clock achieved high accuracy across tissues and outperformed established predictors, particularly in brain samples. Epigenetic age acceleration was reduced in schizophrenia, increased in bipolar disorder and major depressive disorder and strongly elevated in Alzheimer’s disease (positive control). Alterations appeared earlier in blood than in brain. Meta-analysis confirmed that both reduced acceleration (schizophrenia) and increased acceleration (bipolar disorder, major depressive disorder, Alzheimer’s disease) were significantly associated with disease prevalence. Differential methylation analyses further revealed that the blood–brain clock probes captured disease-associated signals, with schizophrenia showing the greatest overlap with causal risk loci, and opposite methylation patterns distinguishing schizophrenia from bipolar disorder or major depressive disorder. A subset of blood DNA methylation probes enabled high-precision classification between schizophrenia and bipolar disorder or major depressive disorder.

This blood–brain clock reveals distinct patterns of epigenetic age acceleration across psychiatric disorders, reflecting disorder-specific and shared biological ageing signatures. The manifestation of these alterations in peripheral blood highlights its potential as a non-invasive biomarker for early detection, risk stratification and differential classification of schizophrenia, bipolar disorder and major depressive disorder.