Prevalence and associated factors of psychotic symptoms in young first-episode, drug-naïve major depressive disorder patients with abnormal lipid metabolism.
Boxuan Li, Minghui Li, Mengqian Li, Xing Wang, Mingxiu Kang, Guoshuang Zhang, Guangming Xu, Xiangyang Zhang
Abstract
Open AccessBACKGROUND: Comorbidity of major depressive disorder (MDD) and abnormal lipid metabolism (ALM) is common. Psychotic major depression (PMD) is a severe subtype of MDD. However, the occurrence of psychotic symptoms in MDD patients with ALM has not been thoroughly investigated. This study aimed to explore the risk factors for psychotic symptoms in MDD patients with ALM. METHODS: A total of 1289 first-episode, drug-naïve (FEDN) MDD patients aged 18-45 years were recruited. Sociodemographic data and various clinical parameters, including lipid profiles, thyroid function tests, and thyroid antibodies, were measured. Patients were evaluated using the Hamilton Rating Scale for Depression (HAMD), Hamilton Anxiety Rating Scale (HAMA), and the positive subscale of the Positive and Negative Syndrome Scale (PANSS). RESULTS: In the FEDN MDD sample, the prevalence of ALM was 81.12% (1047/1289). Among MDD patients with ALM, levels of total cholesterol (TC), HAMA, HAMD, suicide attempts, TSH, TgAb, TPOAb, fasting glucose, and blood pressure were significantly higher in the PMD group than in the NPMD group. Binary logistic regression indicated that HAMD scores (OR = 1.187 [1.045, 1.349], P = 0.0085), HAMA scores (OR = 1.699 [1.510, 1.912], P < 0.0001), and TSH levels (OR = 1.116 [1.001, 1.234], P = 0.0472) were independent risk factors for psychotic symptoms in MDD patients with ALM. CONCLUSION: Our study identified a high prevalence of psychotic symptoms in young MDD patients with ALM. Key predictors, including HAMD, HAMA, and TSH, demonstrated strong predictive accuracy. These findings highlight the potential of these factors for early identification of psychotic symptoms in this patient population. Future research is needed to validate these results and explore the underlying mechanisms.