Classifying depression using blood biomarkers: A large population study.

BACKGROUND

Depression is a common mood disorder characterized by persistent low mood or lack of interest in activities. People with other chronic medical conditions such as obesity and diabetes are at greater risk of depression. Diagnosing depression can be a challenge for primary care providers and others who lack specialized training for these disorders and have insufficient time for in-depth clinical evaluation. We aimed to create a more objective low-cost diagnostic tool based on patients' characteristics and blood biomarkers.

METHODS

Blood biomarker results were obtained from the National Health and Nutrition Examination Survey (NHANES, 2007-2016). A prediction model utilizing random forest (RF) in NHANES (2007-2014) to identify depression was derived and validated internally using out-of-bag technique. Afterwards, the model was validated externally using a validation dataset (NHANES, 2015-2016). We performed four subgroup comparisons (full dataset, overweight and obesity dataset (BMI≥25), diabetes dataset, and metabolic syndrome dataset) then selected features using backward feature selection from RF.

RESULTS

Family income, Gamma-glutamyl transferase (GGT), glucose, Triglyceride, red cell distribution width (RDW), creatinine, Basophils count or percent, Eosinophils count or percent, and Bilirubin were the most important features from four models. In the training set, AUC from full, overweight and obesity, diabetes, and metabolic syndrome datasets were 0.83, 0.80, 0.82, and 0.82, respectively. In the validation set, AUC were 0.69, 0.63, 0.66, and 0.64, respectively.

CONCLUSION

Results of routine blood laboratory tests had good predictive value for distinguishing depression cases from control groups not only in the general population, but also individuals with metabolism-related chronic diseases.