Temporal trends of blood-based markers in various psychiatric disorders and their cross-sectional brain structure associations - Yu-Jia Wang; Zairen Zhou; Yu-Zhu Li; Ju-Jiao Kang; Jin-Tai Yu; Jian-Feng Feng; Wei Cheng; Guoqing Pan; Jia You; Linbo Wang (Communications Medicine)
Abstract Background: Understanding the temporal trends of blood-based biomarkers and their associations with brain structure is crucial for early detection and intervention in psychiatric disorders. This study aimed to explore these trends in the decade before and after diagnosis, along with the cross-sectional relationships with brain structures. Methods: Utilizing UK Biobank data, we conducted a nested case-control analysis of individuals aged 40-69 years diagnosed with anxiety (n = 27,216), bipolar disorder (n = 1325), depression (n = 36,570), or schizophrenia (n = 1478) within 10 years of baseline. We used multivariable linear regression to analyze temporal trends and brain structure associations for 31 blood cell counts, 28 biochemistry markers, and 168 serum metabolites. Results: Here we show that compared to controls, significant temporal divergence is observed in 39, 6, 55, and 12 blood-based markers for anxiety, bipolar disorder, depression, and schizophrenia, respectively. Common biomarkers like cystatin C, red blood cells, hemoglobin, hematocrit, and total bilirubin are identified. Biomarkers cluster into groups with either linear or non-linear trends. Among the linearly changing biomarkers, some have a widening difference from controls while others have a narrowing one. For example, in the case of depression, HDL-TG demonstrates an increasing disparity over time, while cholesterol exhibits a decreasing trend. Non-linear clusters often show reversals around diagnosis, indicating potential treatment effects. Differential associations are found between biomarkers and brain regions, including the orbitofrontal cortex, hippocampus, and accumbens. Conclusions: This study reveals the temporal trends of blood-based biomarkers in psychiatric disorders and their correlations with brain structure, aiding early detection and potentially enhancing clinical outcomes.