New form of addiction: An emerging hazardous addiction problem of milk tea among youths

Association between bubble tea consumption and mental health among adults in mainland China: A national cross-sectional analysis of cohort study

BACKGROUND

Bubble tea has emerged as an essential beverage in the daily lives of many individuals, which is particularly pronounced among nurses. However, few studies have investigated the potential effects of bubble tea consumption on mental health among nurses. In this study, we aimed to investigate the association between bubble tea consumption and mental health among nurses.

METHODS

A cross-sectional analysis using baseline data from the Nurses' Mental Health Study (NMHS), a national cohort study. Odds ratios (ORs) were employed to evaluate the relationship between bubble tea consumption and mental health.

RESULTS

A total of 132,910 participants were enumerated nationwide in our survey. 8666 (6.52 %) were male and 124,244 (93.48 %) were female. The median age of participants was 34 years. After adjusting for potential confounders, the odds ratios (ORs) for depressive symptoms, anxiety symptoms, psychiatric diagnosis, fatigue, burnout, loneliness, and well-being trend changed across categories of bubble tea consumption. Low frequency of bubble tea consumption was associated with a lower risk of suicidal ideation, while high frequency of consumption was related to an increased suicidal risk. The relation between bubble tea and mental health varies with gender and age.

CONCLUSIONS

This large-sample cross-sectional study found that consumption of bubble tea was significantly associated with increased anxiety, depression, psychiatric diagnosis, fatigue, job burnout and loneliness, and decreased well-being among nurses. Moreover, research indicates that limited consumption of bubble tea may be correlated with a reduction in suicidal ideation, whereas excessive consumption may be linked to an increase in such ideation.

L-Cysteine: A promising nutritional supplement for alleviating anxiety disorders

Anxiety disorders are prevalent chronic psychological disease with complex pathogenic mechanisms. Current anxiolytics have limited efficacy and numerous side effects in many anxiety patients, highlighting the urgent need for new therapies. Recent research has been focusing on nutritional supplements, particularly amino acids, as potential therapies for anxiety disorders. Among these, L-Cysteine plays a crucial role in various biological processes. L-Cysteine exhibits antioxidant properties that can enhance the antioxidant functions of the central nervous system (CNS). Furthermore, metabolites of L-cysteine, such as glutathione and hydrogen sulfide have been shown to alleviate anxiety through distinct molecular mechanisms. Long-term administration of L-Cysteine has anxiolytic, antidepressant, and memory-improving effects. L-Cysteine depletion can lead to increased oxidative stress in the brain. This review delves into the potential mechanisms of L-Cysteine and its main products, glutathione (GSH) and hydrogen sulfide (H2S) in the management of anxiety and related diseases.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis

OBJECTIVE

This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China.

METHODS

Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix.

RESULTS

Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group.

LIMITATION

This study didn't carry out external validation.

CONCLUSIONS

This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.