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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Biomarkers and Mental Disorders: A Relevance Analysis Using a Random Forest Algorithm.

Joice M A Rodolpho1, Krissia F Godoy1, Bruna D L Fragelli2

  • 1Laboratory of Inflammation and Infectious Diseases, Federal University of São Carlos (UFSCar), São Carlos 13565-905, SP, Brazil.

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Summary

Biomarkers like vitamin D and inflammatory cytokines show sex-specific relevance in predicting depression and anxiety. Machine learning identified key indicators, highlighting differences between men and women for these common mental health disorders.

Keywords:
IL-6TNFanxietybiomarkerscortisolcytokinesdepressionmachine learningrandom forestvitamin D

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Area of Science:

  • Mental Health Research
  • Biomarker Discovery
  • Machine Learning in Medicine

Background:

  • Depression and anxiety affect millions globally, impairing daily life and leading to severe outcomes.
  • Inflammatory cytokines (IL-1β, IL-6, TNF), cortisol, and vitamin D are implicated in the pathophysiology of these disorders.
  • Existing research suggests potential links between specific biomarkers and mental health conditions, but sex-specific differences require further investigation.

Purpose of the Study:

  • To identify and assess the importance of various biomarkers in predicting depression and anxiety.
  • To investigate potential sex-specific differences in the predictive relevance of biomarkers for mental health disorders.
  • To apply machine learning, specifically Random Forest, for robust biomarker analysis.

Main Methods:

  • Utilized the Random Forest machine learning algorithm with cross-validation.
  • Analyzed a panel of biomarkers including inflammatory cytokines, cortisol, vitamin D, and cardiac markers in 96 participants (50 women, 46 men) diagnosed with mental disorders.
  • Assessed the predictive importance of biomarkers for depression and anxiety, examining sex-specific patterns.

Main Results:

  • Sex-specific differences in biomarker relevance were observed for both depression and anxiety.
  • For depression, vitamin D, C-reactive protein (CRP), and D-dimer were most predictive in men; IL-6, CRP, and vitamin D were significant in women.
  • For anxiety, vitamin D and myoglobin were important in men; IL-8 and vitamin D were key in women.

Conclusions:

  • The study successfully identified key biomarkers for depression and anxiety with notable sex-specific variations.
  • Vitamin D emerged as a significant biomarker across sexes and conditions, underscoring its potential role in mental health.
  • The Random Forest approach proved effective in robustly identifying predictive biomarkers, offering insights into the complex pathophysiology of mental disorders.