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Classifying depression using blood biomarkers: A large population study.

Ziqiang Lin1, Wayne R Lawrence2, Yanhong Huang3

  • 1Department of Preventive Medicine, School of Basic Medicine and Public Health, Jinan University, Guangzhou, 510632, China; Department of Psychiatry, New York University School of Medicine, One Park Ave, New York, NY, 10016, USA; Department of Mathematics and Statistics, College of Arts and Sciences, University at Albany, State University of New York, 1400 Washington Ave, Albany, NY, 12222, USA.

Journal of Psychiatric Research
|June 18, 2021
PubMed
Summary
This summary is machine-generated.

Routine blood tests can help identify depression, even in individuals with chronic conditions like obesity and diabetes. This study developed a low-cost diagnostic tool using biomarkers to aid primary care providers in diagnosing depression more objectively.

Keywords:
DepressionDiabetesMachine learningMetabolic syndromeObesityRandom forest

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Area of Science:

  • Biomarkers and diagnostics
  • Mental health research
  • Public health

Background:

  • Depression is a common mood disorder linked to chronic conditions like obesity and diabetes.
  • Diagnosing depression is challenging for primary care providers due to time and training limitations.
  • Objective, low-cost diagnostic tools for depression are needed.

Purpose of the Study:

  • To develop an objective, low-cost diagnostic tool for depression using patient characteristics and blood biomarkers.
  • To assess the predictive value of routine blood tests for depression in the general population and in individuals with metabolic diseases.

Main Methods:

  • A random forest (RF) prediction model was developed and validated using data from the National Health and Nutrition Examination Survey (NHANES).
  • The model was trained on data from 2007-2014 and externally validated on data from 2015-2016.
  • Feature selection was performed using backward elimination from RF, with subgroup analyses for general, overweight/obese, diabetic, and metabolic syndrome populations.

Main Results:

  • Key predictors included family income, Gamma-glutamyl transferase (GGT), glucose, Triglyceride, red cell distribution width (RDW), creatinine, Basophils, Eosinophils, and Bilirubin.
  • Area Under the Curve (AUC) in the training set ranged from 0.80 to 0.83 across datasets.
  • AUC in the validation set ranged from 0.63 to 0.69 across datasets.

Conclusions:

  • Routine blood laboratory tests demonstrate significant predictive value for identifying depression.
  • This approach is effective in both the general population and individuals with metabolism-related chronic diseases.
  • Blood biomarkers offer a promising avenue for developing more objective depression diagnostic tools.