In this retrospective study, we found 12 important depression biomarkers using SVM. These biomarkers are hydroxybutyrate, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, total protein, high-density lipoprotein cholesterol, cholesterol, absolute value of the lymphocyte, blood urea nitrogen, chlorine, platelet count, and glutamyltranspeptidase, which differentiate depression in patients with diabetes mellitus at an overall classification accuracy of 74%. Twelve identified factors imply that modulation of the inflammatory, immune, energy metabolism, and lipid metabolism pathways were mainly involved in the pathophysiology process of depression in patients with diabetes mellitus.
We found three biomarkers involved in inflammatory and immune pathway including magnesium, absolute value of the lymphocyte, and glutamyltranspeptidase. Depression often coexists with diabetes, metabolic disorders and other diseases, and is linked to inflammatory and oxidative stress . The research found there is a link between depression and insulin resistance . Diabetes can cause a rise in blood sugar and insulin levels and has an effect on inflammation that may contribute to depression. Recent studies have shown that oxidative stress may enhance induction of HO-1 expression, which may result in insulin resistance and insufficiency [26, 27]. It is clear that increased oxidative stress may lead to insulin resistance and impose an impact on insulin secretion in patients having depressive disorder . One study demonstrated that reducing inflammation through non-drug treatments such as psychological interventions, physical exercises, and meditation can play a role in preventing depression . Magnesium has received great concern over its potential role in the pathophysiology of depression [29–31]. Lymphocytes are produced by lymphoid organs and constitute an important component of immune response. Previous studies indicated a decrease in lymphocyte counts among depressive patients , which was in agreement with our findings. One explanation is that inflammatory or chronic stress-induced cellular immunosuppression would cause elevated neutrophils and leukocytes and a relatively reduced lymphocyte counts [32–34]. Glutathione (GSH) is an important substance that protects cells from oxidative stress, and its synthesis requires the participation of Glutamyltranspeptidase [35, 36]. In addition, some researchers reported Glutamyltranspeptidase deficiency in human resulted specific symptoms such as abnormal behavior, mental retardation, and absence seizure [37, 38]. Emerging evidence showed that antidepressant treatments decrease inflammatory and improve mitochondrial dysfunction in patients with depression [39, 40].
We also found five biomarkers potentially related to energy metabolism. These biomarkers are hydroxybutyrate, hydroxybutyrate dehydrogenase, creatine kinase, total protein, and blood urea nitrogen. Hydroxybutyrate is a product of ketone body metabolism pathway. A previous study reported that synthesis and degradation of ketone bodies influenced immensely the pathophysiologic process of depression . Hydroxybutyrate might be helpful for screening depression and predicting its progress . Creatine kinase (CK) activity was reported to increase in the prefrontal cortex, hippocampus, and striatum of rats, and CK levels were increased in the serum of a patient with depression after antidepressant treatment [42, 43]. The normal role of CK is to catalyze the reversible transfer of the phosphoryl group from phosphocreatine to adenosine diphosphate (ADP), and through this process ATP used as energy by cells is generated . The final product of protein metabolism is urea . Hu et al. found that 10% of 260 hemodialysis patients had a diagnosis of depression using the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. They also found that patients with lower monthly income, shorter duration of hemodialysis, and lower levels of blood urea nitrogen were more likely to have a diagnosis of depression. They considered that depression symptoms were usually associated with poor appetite and poor nutrition in hemodialysis patients with depression . We observed lower concentrations of total protein in patients with both diabetes mellitus and depression compared to patients with diabetes mellitus alone, the result is consistent with the research by Peng et al. . The above results suggested that blood biochemical parameters, including urea nitrogen, lactate dehydrogenase, alanine transaminase, uric acid, and total protein, were significantly different between depression patients and healthy controls, and that multiple biochemical parameters in combination may improve the diagnostic effectiveness of depression and the comprehensive management for depressive patients.
Additionally, we found some other biomarkers that may be related to lipid metabolism, such as cholesterol and high-density lipoprotein cholesterol. One of the characteristics of depression is loss of appetite. Previous studies suggested that LDL-c increase is mostly determined by the severe loss of body fat [47, 48]. Higher level of cholesterol was observed in patients with depression than in controls . In the same way, increased levels of cholesterol were found to be associated with comorbidity of diabetes mellitus and depression in our study.
Changes of creatine kinase, cholesterol, total protein, and high-density lipoprotein cholesterol etc. in blood are not specific to depression and may be present in other psychiatric disorders such as eating disorders , schizophrenia [49, 50], and / or bipolar disorder [51, 52]. Researchers suggested that a single biomarker often lacks in sensitivity and specificity  and thus may not well distinguish depression from other diseases. Monitoring changes in multiple factor levels will provide a more comprehensive and accurate assessment, which can help us better understand the disease status and characteristics of specific diseases. Although the model of multiple biomarkers is more conducive for the diagnosis of diseases, it is usually used in the diagnosis of cancer instead of nervous system diseases [53, 54]. Our study is advantageous in that laboratory biochemical indexes are routine examinations in clinical settings, which could be obtained with minimal invasiveness, maximal convenience, and low cost, thus having a great potential for wider clinical access and more efficient population screening. Due to the inconsistency of biochemical test results between the two groups, different test items were deleted. The lack of biochemical tests as variables in SVM learning affected accuracy, which is one limitation of the present study. Second, the parameters chosen retrospectively instead of consecutively were inadequate and included only those that were clinically applicable. This may have caused an enrollment bias and an erroneous classification by the algorithm. This is one of the major methodological limitations of the present study, which should be remedied in future investigations using a prospective and consecutive design.