Abstract
Objective: To identify the potential biomarkers for predicting depression in diabetes mellitus using support vector machine to analyze routine biochemical tests and vital signs between two groups: subjects with both diabetes mellitus and depression, and subjects with diabetes mellitus alone.Methods: Electronic medical records upon admission and biochemical tests and vital signs of 135 patients with both diabetes mellitus and depression and 187 patients with diabetes mellitus alone were identified for this retrospective study. After matching on factors of age and sex, the two groups (n = 72 for each group) were classified by the recursive feature elimination-based support vector machine, of which, the training data, validation data, and testing data were split for ranking the parameters, determine the optimal parameters, and assess classification performance. The biomarkers were identified by 10-fold cross validation.Results: The experimental results identified 8 predictive biomarkers with classification accuracy of 78%. The 8 biomarkers are magnesium, cholesterol, AST/ALT, percentage of monocytes, bilirubin indirect, triglyceride, lactic dehydrogenase, and diastolic blood pressure. Receiver operating characteristic curve analysis was also adopted with area under the curve being 0.72.Conclusions: Some biochemical parameters may be potential biomarkers to predict depression among the subjects with diabetes mellitus.
Highlights
Diabetes mellitus is a chronic illness affecting about 347 million people worldwide in 2017, and this number is expected to increase more than half by 2035 [1, 2]
We proposed using Support Vector Machine (SVM) to identify potential prediction biomarkers for depression in patients with diabetes mellitus
Eight features were computed in 10-fold cross-validation experiments, repeated 1,000 times with SVM, including magnesium, cholesterol, AST/ALT, percentage of monocytes, bilirubin indirect, triglyceride, lactic dehydrogenase (LDH), and diastolic blood pressure (Table 2)
Summary
Diabetes mellitus is a chronic illness affecting about 347 million people worldwide in 2017, and this number is expected to increase more than half by 2035 [1, 2]. The disease will lead to emotional distress other than physical symptoms and impose psychosocial impacts on life quality, which complicates its management. Depression and diabetes mellitus are common comorbid conditions [3]. A meta-analysis reported that patients with diabetes mellitus more than doubled the odds of developing depression [3]. Another study described that depression was highly prevalent, affecting ∼26% of the patients with diabetes mellitus [4]. Depression was found to be associated with a greater number of complications of diabetes mellitus [5]. Depression itself is a disabling disease and imposes a significant impact on life quality by undermining physical health [6] and impairing
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