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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.
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.
It is unclear how brain reserve interacts with gender and apolipoprotein E4 (APOE4) genotype, and how this influences the progression of Alzheimer's disease (AD). The association between intracranial volume (ICV) and progression to AD in subjects with mild cognitive impairment (MCI), and differences according to gender and APOE4 genotype, was investigated.
Data from subjects initially diagnosed with MCI and at least two visits were downloaded from the ADNI database. Those who progressed to AD were defined as converters. The longitudinal influence of ICV was determined by survival analysis. The time of conversion from MCI to AD was set as a fiducial point, as all converters would be at a similar disease stage then, and longitudinal trajectories of brain atrophy and cognitive decline around that point were compared using linear mixed models.
Large ICV increased the risk of conversion to AD in males (HR: 4.24, 95% confidence interval (CI): 1.17–15.40) and APOE4 non-carriers (HR: 10.00, 95% CI: 1.34–74.53), but not in females or APOE4 carriers. Cognitive decline and brain atrophy progressed at a faster rate in males with large ICV than in those with small ICV during the two years before and after the time of conversion.
Large ICV increased the risk of conversion to AD in males and APOE4 non-carriers with MCI. This may be due to its influence on disease trajectory, which shortens the duration of the MCI stage. A longitudinal model of progression trajectory is proposed.
The study's aim was to examine the association of alcohol consumption with verbal and visuospatial memory impairment in older people.
Participants were 1,572, aged ≥60 years, in the hospital-based registry of the Clinical Research Center for Dementia of South Korea (CREDOS). Moderate drinking was defined as no more than seven drinks per week and three drinks per day. Memory impairment was defined as performance with more than 1 standard deviation below the mean value on the Seoul Verbal Learning Test and Rey Complex Figure Test.
Those who consumed alcohol moderately, compared with abstainers, had a lower odds of verbal memory impairment (Odds Ratio [OR] = 0.64; 95% Confidence Interval [CI]: 0.46–0.87), adjusting for covariates. Visuospatial memory, however, was not significantly associated with alcohol consumption.
Moderate alcohol drinking is associated with a reduced likelihood of verbal memory impairment among older people attending memory clinics.
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