Temporary relationship between sleep duration and depression and its impact on future risk of cardiovascular disease

Development and validation of a nomogram for predicting depression risk in patients with chronic kidney disease based on NHANES 2005-2018

BACKGROUND

Depression is common among patients with chronic kidney disease (CKD) and is associated with poor outcomes. This study aims to develop and validate a nomogram for predicting depression risk in patients with CKD.

METHODS

This cross-sectional study utilized data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES) database. Participants were randomly divided into training and validation sets (7:3 ratio). A nomogram was developed based on predictors identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis.

RESULTS

A total of 4414 participants were included. Gender, age, race, poverty-to-income ratio, diabetes mellitus, cardiovascular diseases, trouble sleeping, sleep hours, and smoking were included as predictors in the nomogram. The area under the curve (AUC) of the nomogram for predicting depression risk in patients with CKD was 0.785 (95% CI: 0.761-0.809) in the training set and 0.773 (95% CI: 0.737-0.810) in the validation set. The corrected C-index, calculated using bootstrap resampling, was 0.776, indicating good predictive performance. Calibration curves and decision curve analysis showed good calibration and clinical utility. Subgroup and sensitivity analyses further confirmed the robustness of the nomogram. A web-based risk calculator based on the nomogram was developed to enhance clinical applicability. A flowchart demonstrating the application of the nomogram for risk assessment and clinical decision-making in routine practice is provided.

CONCLUSIONS

This nomogram effectively predicts depression risk in patients with CKD and may serve as a user-friendly tool for the early identification of patients with CKD at high risk for depression using key demographic, comorbid, and lifestyle factors.

The relationship between night sleep duration, sleep quality and depressive symptoms in Chinese elderly couples

Although there are many studies analysing the relationship between sleep and depression in individuals, there is currently no bi-directional causal relationship between sleep and depression between older couples analysed from a binary perspective.Thus, this study used the CHARLS database to analyse the relationship between sleep and depression among older couples. A binary cross-lagged model was used to analyse the data.The study found an interaction between nighttime sleep duration and depression among older couples. Wife's depressive symptoms were significant predictors of husband's nocturnal sleep duration. Husband's depression and sleep quality predicted wife's sleep quality and depression, respectively. Therefore, when screening community-dwelling older adults for depressive symptoms, we need to be aware of the impact of different sleep profiles of older adults of different genders on spousal depressive symptoms. In addition, when dealing with patients with depressive symptoms, we need to consider not only the patient but also the impact of their depressive symptoms on their spouse.

Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning

OBJECTIVE

A plethora of studies have unequivocally established the profound significance of harmonious familial relationships on the psychological well-being of the elderly. In this study, we elucidate the intergenerational relationships, probing the association between frequent interactions or encounters with their children and the incidence of depression in old age.

METHODOLOGY

We employed a retrospective cross-sectional study design, sourcing our data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS). To identify cases of depression, we utilized the 10-item Center for Epidemiologic Studies Depression Scale (CESD). Employing a five-fold cross-validation methodology, we endeavored to fashion five distinct machine learning models. Subsequently, we crafted learning curves to facilitate the refinement of hyperparameters, assessing model classification performance through metrics such as accuracy and the Area Under the Receiver Operating Characteristic (AUROC) curve. To further elucidate the relationship between variables and geriatric depression, logistic regression was subsequently applied.

RESULTS

Our findings accentuated that sleep patterns emerged as the paramount determinants influencing the onset of depression in the elderly. Relationships with offspring ranked as the second most significant determinant, only surpassed by sleep habits. A negative correlation was observed between sleep patterns (Odds Ratio [OR]: 0.78, 95 % Confidence Interval [CI]: 0.75-0.81, P < 0.01), communication with offspring (OR: 0.86, 95 % CI: 0.82-0.90, P < 0.01), and the prevalence of depressive symptoms. Among the evaluated models, the k-Near Neighbor algorithm demonstrated commendable discriminative power. However, it was the Random Forest algorithm that manifested unparalleled discriminative prowess and precision, establishing itself as the most efficacious classifier.

CONCLUSION

Prolonging the duration of nocturnal sleep, and elevating the frequency of communication with offspring have been identified as measures conducive to mitigating the onset of geriatric depression.