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Distinct Biomarker Patterns Reveal Metabolic-Inflammatory Profiles Across Mental Disorders.

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Summary

Mental disorders are linked to metabolic and inflammatory changes. This study found higher body mass index (BMI), elevated inflammatory markers like C-Reactive Protein (CRP), and lower vitamin D levels in individuals with anxiety, depression, and bipolar disorder.

Keywords:
anxietybiomarkersbipolar disorderdepressioninflammationmetabolism

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Area of Science:

  • Psychiatry
  • Metabolic Medicine
  • Immunology

Background:

  • Mental disorders often co-occur with metabolic and inflammatory dysregulation.
  • These alterations can influence disease presentation and increase comorbidity risk.

Purpose of the Study:

  • To compare inflammatory, metabolic, and cardiac biomarkers in individuals with anxiety, depression, and bipolar disorder against healthy controls.
  • To assess anthropometric and lifestyle factors in relation to these biomarkers.

Main Methods:

  • Cross-sectional study of 50 volunteers across four groups: control, anxiety, depression, and bipolar disorder.
  • Assessment included the DASS-21 questionnaire and blood tests for CRP, hs-CRP, IL-6, IL-1β, TNF-α, vitamin D, cortisol, D-dimer, NT-proBNP, CK-MB, cTnI, and Myo.
  • Body Mass Index (BMI) and lifestyle indicators like physical activity were also evaluated.

Main Results:

  • Clinical groups exhibited higher BMI, particularly anxiety and depression groups.
  • Anxiety group showed elevated CRP, hs-CRP, and IL-6.
  • Depression group had lower vitamin D levels.
  • Bipolar disorder group displayed higher CK-MB, CRP, IL-6, and IL-1β.
  • Sedentary lifestyle correlated with poorer emotional scores, increased inflammation, and vitamin D deficiency.

Conclusions:

  • Findings support an integrated axis between metabolism, inflammation, and behavior in mental disorders.
  • Excess weight, sedentary behavior, and nutritional deficits exacerbate psychiatric symptoms and metabolic vulnerability.
  • Integrating biomarkers, BMI, and behavioral data can help define clinical subphenotypes for personalized treatment.