Quantile regression to explore association of sleep duration with depression among adults in NHANES and KNHANES

A dynamic online nomogram for predicting depression risk in cancer patients based on NHANES 2007-2018

BACKGROUND

Cancer, recognized as a significant global public health issue, exhibits a notably elevated prevalence of depression among its patient population. This study aimed to construct a nomogram to predict depression risk in cancer patients.

METHODS

In this study, the training set comprises 70 % of the dataset, while the test set comprises 30 %. On the training set, we employed the least absolute shrinkage and selection operator (LASSO) regression in conjunction with multivariable logistic regression to identify key variables, subsequently constructing a prediction model. ROC curves, calibration tests, and decision curve analysis (DCA) were used to evaluate model performance.

RESULTS

A total of 2604 participants were included in this study. The nomogram predictors encompassed age, poverty-income ratio (PIR), sleep disorder, and food security. We have developed a web-based dynamic nomogram incorporating these factors (available at https://xiaoshuweiya.shinyapps.io/DynNomapp/). The area under the model's ROC curve (AUC) was 0.803 and 0.766 when evaluated on the training and test sets, respectively. These AUC values highlight the model's robustness and reliability in making accurate predictions across different datasets. The calibration curves demonstrated consistency between the model's predicted and actual results. Additionally, the decision curve analysis further substantiated the potential clinical utility of the nomograms.

CONCLUSIONS

This study developed a nomogram to help clinicians identify high-risk populations for depression among cancer patients, providing a scientific method for early detection and assessment of depression risk.

Association between pain, sleep and intrinsic capacity in Chinese older adults: Evidence from CHARLS

OBJECTIVES

To examine the relationship between pain, sleep, and intrinsic capacity (IC).

DESIGN

A cohort study.

SETTING AND PARTICIPANTS

Data were obtained from participants in China Health and Retirement Longitudinal Study (CHARLS) 2011-2015. The study population consisted of older adults who completed assessments on pain, sleep duration, sleep quality and IC at baseline.

MEASUREMENTS

Pain, sleep duration, and sleep quality were assessed through self-reports from participants. The total IC score was derived from five domains: psychological, sensory, cognitive, locomotor, and vitality. The relationships between pain, sleep duration, sleep quality and IC were analyzed using linear mixed models. The relationship between sleep duration and IC was analyzed using quadratic analysis. Stratified analyses by gender and age were also performed.

RESULTS

A total of 3517 participants were included in the analysis. After adjusting for all covariates, single-site pain (β = -0.29, 95% confidence interval [CI] = -0.38 to -0.20) and multisite pain (β = -0.41, 95% CI = -0.48 to -0.34) were significantly associated with a decrease in IC compared with older adults without pain; long sleep duration (β = -0.15, 95% CI = -0.24 to -0.06) was significantly associated with a decrease in IC compared with older adults with moderate sleep duration; and poor sleep quality (β = -0.63, 95% CI = -0.71 to -0.55) and fair sleep quality (β = -0.33, 95% CI = -0.40 to -0.27) were significantly associated with a decrease in IC compared with older adults with good sleep quality.

CONCLUSION

To maintain IC, it is important to ensure approximately 7.5 h of sleep duration, improve sleep quality, and manage pain. Interventions should begin as early as possible.

Joint association of sleep onset time and sleep duration with depression in patients with chronic kidney disease: Insights from the NHANES 2015-2020

Background

The prevalence of depression among patients with chronic kidney disease (CKD) is high and closely related to poor prognosis. However, the association between sleep onset time, sleep duration, and depression in CKD patients has not been thoroughly studied.

Methods

This study utilized cross-sectional data from CKD patients who participated in the National Health and Nutrition Examination Survey from 2015 to 2020, analyzing their sleep onset time, sleep duration, and Patient Health Questionnaire-Nine. Logistic regression models and restricted cubic spline models were used to explore the association between sleep onset time, sleep duration, and depression in CKD patients.

Results

A total of 2141 CKD patients aged 20 and above were included in this study, among whom 246 (11.5 %) had depression. Compared to those reporting optimal sleep onset (22:00-23:59) and sufficient sleep duration (7-8 h), CKD patients with late sleep onset (≥24:00) and either insufficient (<7 h) or excessive (≥9 h) sleep had a significantly higher risk of depression, with adjusted OR of 2.03 (95 % CI:1.29-3.19) and 2.07 (95 % CI:1.07-4.00), respectively. Additionally, the association between sleep onset time, sleep duration, and depression showed a U-shaped pattern, with the inflection point for sleep onset time at 23:00 and for sleep duration at 7.5 h.

Conclusion

Inappropriate sleep onset time and sleep duration are significantly associated with depression in CKD patients. This association may be important to consider in clinical practice for the prevention and management of depressive symptoms in CKD patients.

Single and joint exposure of Pb, Cd, Hg, Se, Cu, and Zn were associated with cognitive function of older adults

BACKGROUND

Impaired cognitive function following exposure to heavy metals has emerged as a significant global health concern. Nevertheless, the impact of combined exposure to multiple heavy metals on cognitive impairment remains unclear.

OBJECTIVE

This study aimed to explore the association between multiple heavy metals exposure and cognitive function to provide theoretical evidence to guide prevention strategies.

METHODS

The blood levels of lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), copper (Cu) and zinc (Zn) and the results of the cognitive function tests were extracted from 811 elderly Americans who completed the NHANES between 2011 and 2014. Quantile regression (QR), restricted cubic splines (RCS), and Bayesian kernel machine regression (BKMR) were used to explore the individual and joint association between heavy metals exposure and performance in 4 standardized cognitive tests; Item Response Theory (IRT), Delayed Recall Test (DRT), Animal Fluency Test (AFT) and Digit Symbol Substitution Test (DSST).

RESULTS

A negative association was noted between Cd levels and IRT (p = 0.048, 95%CI: -2.7, -0.1). Se concentrations ranging between 2.197 µg/L (95%CI: 0.004, 0.15) to 2.29 µg/L (95%CI: 2.56, 7.64) (log10Se) was postively associated with DSST (p = 0.001 ). Cu was negatively associated with DSST (p = 0.049, 95%CI: -37.75, -0.09), while Zn was positively associated with IRT (p = 0.022, 95%CI: 0.55, 11.73). Exposure to the 6 heavy metals combined showed a positive linear association with IRT, DRT, and a negative linear association with DSST. An interaction between Cd and the other heavy metals (excepted for Pb).

CONCLUSION

Exposure to Pb, Cd, Hg, Se, Cu, and Zn was associated with cognitive function. Joint exposure to the 6 heavy metals showed a positive linear association with IRT, DRT, contrarily, a negative linear association with DSST.

The association between nap time, nighttime sleep and depression in Chinese older adults: A cross-sectional study

OBJECTIVE

To explore the relationship among nap time, night sleep time, and depression among the elderly and to determine the recommended sleep time to provide a scientific and reasonable basis for the prevention and control of depression in residents.

METHODS

Based on the 2020 China Health and Elderly Care Longitudinal Survey (CHARLS) database, the demographic data and the health and lifestyle information of the study subjects were obtained. A total of 2,959 valid samples were included, and the relationship between sleep and depression was explored by logistic regression, restricted cubic spline, and isotemporal substitution model.

RESULTS

In the cross-sectional analysis, no statistical relationship was observed between napping time and depression in the elderly. The optimal sleep interval for the elderly at night is 6-7.5 hours, and the health benefits are the largest. A sleep duration of < 6 hours at night (OR = 2.25, 95% CI: 1.90 to 2.65) was associated with a high likelihood of depression. The probability of depression in the elderly continues to decrease with the increase of time after the nighttime sleep duration reaches 6 hours and is at the lowest level of about 7.5 hours. Moreover, the probability of depression will increase after the sleep duration exceeds 9.5 hours. In the range of 6-7.5 hours of recommended sleep duration, the likelihood of depression in the elderly will be reduced by 0.311 for every 30-minute increase in nighttime sleep time instead of noon sleep time.

CONCLUSION

The duration of nighttime sleep and the probability of depression have a U-shaped relationship. The likelihood of depression was lowest in the elderly who slept for 6-8 hours at night, and the likelihood of depression could be reduced by increasing the nighttime sleep time instead of napping time within the optimal nighttime sleep range.

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.