Traditional Chinese medicine body constitution (TCMBC) reflects a person’s vulnerability to diseases. Thus, identifying body constitutions prone to depression can help prevent and treat depression. The review aimed to assess and summarize the existing evidence that explores the relationship between TCMBC and depression. Psychology and Behavioral Sciences Collection, MEDLINE, PubMed, CNKI, Wanfang, SinoMed, Embase, VIP, CINAHL, and CMJ were searched from inception to April 2021. Observational studies assessing the association between TCMBC and depression were selected. The quality of the included studies were assessed using the Newcastle–Ottawa Scale (NOS). Eighteen studies were included in the systematic review and thirteen in the meta-analysis. The pooled odd ratios of developing depression for Qi-stagnation, Qi-deficiency, Yang-deficiency, Yin-deficiency, and Balanced constitutions were 3.12 (95% CI, 1.80–5.40; I² = 94%), 2.15 (95% CI, 1.54–3.01; I² = 89%), 1.89 (95% CI, 0.71–5.03; I² = 81%), 1.41 (95% CI, 0.91–2.20; I² = 57%), and 0.60 (95% CI, 0.40–0.90; I² = 94%), respectively. The findings suggest that the evaluation of a person’s TCMBC could be useful the in prevention and treatment of depression. However, more case-control and cohort studies are required to further confirm the association between TCMBC and depression.

Depression is commonly observed in university students, who are a high risk group for developing psychiatric disorders during adulthood. This study aimed to determine the prevalence of depression and its traditional Chinese medicine body constitutions and psychological determinants among university students in Malaysia. A cross-sectional pilot study was conducted between 9 and 28 September 2020 among 80 university students in Malaysia. Participants completed online survey questionnaires, including the validated Patient Health Questionnaire (PHQ-9), Constitution in Chinese Medicine Questionnaire (CMCQ), Dysfunctional Attitude Scale (DAS), Depression Anxiety Stress Scale (DASS-21) stress subscale, Perceived Stress Scale (PSS-10), and Rosenberg Self-Esteem Scale (RSES), which assess depression, body constitution, dysfunctional attitude, stress, perceived stress, and self-esteem. Multiple linear regression analyses were performed to determine the associated risk factors for depression. The overall prevalence of depression among university students was 33.8%. The multiple regression analysis showed a significant relationship between depression and qi-stagnation constitution (B = 0.089, p = 0.011), balanced constitution (B = −0.077, p = 0.049), and self-esteem (B = −0.325, p = 0.001). Our findings suggest that some traditional Chinese medicine body constitutions and self-esteem are significant risk factors affecting depression among university students. Identifying risk factors of depression is vital to aid in the early detection of depression among university students.

Depression is a serious psychological disorder with high prevalence rates, especially among university students. Serum proteins related to the immune system and oxygen and lipid transfer could have contributing roles in the development of depression and could act as biomarkers for depression. Currently, there is a lack of accurate biological methods that can be used to diagnose depression. Biomarkers could be an inexpensive and convenient way to predict depression and understand its pathophysiology. This study aimed to screen the serum proteome profile of a depressed student for the identification of potential depression biomarkers. A Malaysian private university student who was recruited from the pre-test study (n = 10) was further analyzed for serum proteome due to the fact that he was depressed, with scores of 15 out of 27 on the Patient Health Questionnaire (PHQ-9). After depleting the high-abundance proteins from the serum sample, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to identify the expressed proteins. A total of 224 proteins were identified. Globins, globulins, apolipoproteins and glycoproteins were most commonly detected. Here, we show the potential biomarkers that can be used to identify depression vulnerable individuals. These findings may be relevant to the development of new diagnostic and treatment strategies. However, further studies with larger sample sizes and healthy controls are needed to confirm the role of these candidate biomarkers for the prediction and diagnosis of depression.