Association analysis between vitamin D level and depression in women perimenopause: A protocol of systematic review and meta-analysis

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker

Background

Depression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview.

Methods

A total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student's T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model.

Results

A total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800.

Conclusion

The possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants' words during an interview show significant potential as an objective and diagnostic marker through machine learning.

The Problem of Malnutrition Associated with Major Depressive Disorder from a Sex-Gender Perspective

Major depressive disorder (MDD) is an incapacitating condition characterized by loss of interest, anhedonia and low mood, which affects almost 4% of people worldwide. With rising prevalence, it is considered a public health issue that affects economic productivity and heavily increases health costs alone or as a comorbidity for other pandemic non-communicable diseases (such as obesity, cardiovascular disease, diabetes, inflammatory bowel diseases, etc.). What is even more noteworthy is the double number of women suffering from MDD compared to men. In fact, this sex-related ratio has been contemplated since men and women have different sexual hormone oscillations, where women meet significant changes depending on the age range and moment of life (menstruation, premenstruation, pregnancy, postpartum, menopause…), which seem to be associated with susceptibility to depressive symptoms. For instance, a decreased estrogen level promotes decreased activation of serotonin transporters. Nevertheless, sexual hormones are not the only triggers that alter neurotransmission of monoamines and other neuropeptides. Actually, different dietary habits and/or nutritional requirements for specific moments of life severely affect MDD pathophysiology in women. In this context, the present review aims to descriptively collect information regarding the role of malnutrition in MDD onset and course, focusing on female patient and especially macro- and micronutrient deficiencies (amino acids, ω3 polyunsaturated fatty acids (ω3 PUFAs), folate, vitamin B12, vitamin D, minerals…), besides providing evidence for future nutritional intervention programs with a sex-gender perspective that hopefully improves mental health and quality of life in women.