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The menopause transition is a vulnerable period that can be associated with changes in mood and cognition. The present study aimed to investigate whether a symptomatic menopausal transition increases the risks of depression, anxiety, and sleep disorders.
This population-based, retrospective cohort study analysed data from five electronic health record databases in South Korea. Women aged 45–64 years with and without symptomatic menopausal transition were matched 1:1 using propensity-score matching. Subgroup analyses were conducted according to age and use of hormone replacement therapy (HRT). A primary analysis of 5-year follow-up data was conducted, and an intention-to-treat analysis was performed to identify different risk windows over 5 or 10 years. The primary outcome was first-time diagnosis of depression, anxiety, and sleep disorder. We used Cox proportional hazard models and a meta-analysis to calculate the summary hazard ratio (HR) estimates across the databases.
Propensity-score matching resulted in a sample of 17,098 women. Summary HRs for depression (2.10; 95% confidence interval [CI] 1.63–2.71), anxiety (1.64; 95% CI 1.01–2.66), and sleep disorders (1.47; 95% CI 1.16–1.88) were higher in the symptomatic menopausal transition group. In the subgroup analysis, the use of HRT was associated with an increased risk of depression (2.21; 95% CI 1.07–4.55) and sleep disorders (2.51; 95% CI 1.25–5.04) when compared with non-use of HRT.
Our findings suggest that women with symptomatic menopausal transition exhibit an increased risk of developing depression, anxiety, and sleep disorders. Therefore, women experiencing a symptomatic menopausal transition should be monitored closely so that interventions can be applied early.
Attention deficit-hyperactivity disorder (ADHD) is related to depressive disorder, and adolescents with both present poor outcomes. However, evidence for the safety of concomitantly using a methylphenidate (MPH) and a selective serotonin reuptake inhibitor (SSRI) among adolescent ADHD patients is limited, a literature gap aimed to address through this investigation.
We conducted a new-user cohort study using a nationwide claims database in South Korea. We identified a study population as adolescents who were diagnosed both ADHD and depressive disorder. MPH-only users were compared with patients who prescribed both a SSRI and a MPH. Fluoxetine and escitalopram users were also compared to find a preferable treatment option. Thirteen outcomes including neuropsychiatric, gastrointestinal, and other events were assessed, taking respiratory tract infection as a negative control outcome. We matched the study groups using a propensity score and used the Cox proportional hazard model to calculate the hazard ratio. Subgroup and sensitivity analyses were conducted in various epidemiologic settings.
The risks of all the outcomes between the MPH-only and SSRI groups were not significantly different. Regarding SSRI ingredients, the risk of tic disorder was significantly lower in the fluoxetine group than the escitalopram group [HR 0.43 (0.25–0.71)]. However, there was no significant difference in other outcomes between the fluoxetine and escitalopram groups.
The concomitant use of MPHs and SSRIs showed generally safe profiles in adolescent ADHD patients with depression. Most of the differences between fluoxetine and escitalopram, except those concerning tic disorder, were not significant.
Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.
Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.
A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.
We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.
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