Objective To develop and validate a prediction model to assess the risk of depression in patients with chronic kidney disease (CKD) based on National Health and Nutrition Examination Survey (NHANES) database. Methods Data on patients with CKD were selected from the NHANES between 2005 and 2018. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio for model development and validation, respectively. Multivariable logistic regression was used in the training set to identify independent risk factors associated with depression in CKD patients, with stepwise selection applied to determine the final predictors. Model performance was assessed using receiver operating characteristic curve (ROC), calibration plots, and decision curve analysis (DCA). Internal validation was performed through bootstrap resampling, and a predictive model was ultimately established. Results A total of 4413 CKD patients were included, including 2112 males (47.86%) and 2301 females (52.14%). Among them, 3089 patients were assigned to the training set and 1324 to the validation set. In the training set, 332 patients (10.75%) presented with depressive symptoms, while 143 patients (10.80%) in the validation set had depressive symptoms. Multivariate logistic regression analysis showed that other hispanic, current smoking, and sleep disorders were risk factors (P＜0.05). Male, middle or high-income, high school grad/ged or above, married or widowed were protective factors (P＜0.05). Finally, 7 variables were included to construct a prediction model, including gender, poverty income ratio, education level, marital status, smoking status, body mass index, and sleep disorders. The ROC curve showed that the AUC=0.773 [95% confidence interval (0.747, 0.799)] in the training set, the internal validation was evaluated by 1000 Bootstrap resampling methods, and the corrected C-index=0.763. The validation set AUC=0.778 [95% confidence interval (0.740, 0.815)], showed good discrimination ability. The calibration curve showed that the model’s predicted probability was highly consistent with the actual occurrence. Decision curve analysis showed that the model provided a significant net benefit for clinical decision-making at a threshold probability of 20%～50%. Conclusions The prediction model constructed in this study can effectively predict the risk of depression in patients with CKD and can provide guidance for early screening and personalized intervention for high-risk groups. However, the external validation and localization of the model still needed further research.
Objective To investigate the relationship between the mean corpuscular volume/red blood cell distribution width ratio (MRR) and stroke, providing a new perspective for the risk assessment and early prevention of stroke. Methods The study was based on the complex sampling design of data from the National Health and Nutrition Examination Survey from 1999 to 2018 and included 17434 eligible participants aged 18 years and older. MRR was calculated based on the mean corpuscular volume and red blood cell distribution width, and stroke patients were determined based on the health status questionnaire. According to the quartiles of MRR, the included participants were divided into Q1, Q2, Q3, and Q4 groups. Multivariate logistic regression analysis was used to explore the association between MRR and the incidence of stroke. Official sampling weights, stratification variables, and primary sampling units were incorporated throughout this study for weighting adjustment, and weighted statistical methods suitable for complex samples were adopted for data analysis and intergroup comparisons. Results Among all the participants, there were 625 stroke patients, with a weighted stroke prevalence of 2.7%. The MRR of the overall population were (6.58±0.78) fL/%. The results of the multivariate logistic regression analysis showed that, after adjusting for age, gender, race, education level, diabetes, hyperlipidemia, hypertension, smoking history and drinking history, a high level of MRR was independently associated with a reduced risk of stroke [Q2 vs. Q1: odds ratio (OR)=0.986, 95% confidence interval (CI) (0.976, 0.996), P=0.005; Q3 vs. Q1: OR=0.986, 95%CI (0.975, 0.996), P=0.008; Q4 vs. Q1: OR=0.984, 95%CI (0.974, 0.994), P=0.002]. Conclusions The higher the MRR, the lower the risk of stroke. A higher MRR level provides a protective effect against stroke, and the MRR index has potential value in the prevention and management of stroke.
Objective To investigate the relationship between sleep factors (sleep duration and sleep complaints) and human papillomavirus (HPV) infection in adult women based on the data of National Health and Nutrition Examination Survey (NHANES). Methods A total of 7844 women aged 20-59 years with valid HPV testing and complete sleep survey data from the 2005-2016 cycles of the NHANES database were included in the study. Weighted logistic regression was used to analyze the relationship between sleep duration, sleep complaints and HPV infection. Subgroup analyses and sensitivity analyses were performed to ensure robustness. Results Among the 7844 participants, 3439 participants (43.8%) tested positive for HPV; participants with abnormal sleep duration and sleep complaints were 3179 (weighted prevalence: 36.35%) and 2285 (weighted prevalence: 31.02%), respectively. After adjusting for all other covariates considered, abnormal sleep duration [short sleep duration: odds ratio (OR)=1.18, 95% confidence interval (CI) (1.04, 1.35), P<0.05; long sleep duration: OR=1.52, 95%CI (1.14, 2.03), P<0.05] and sleep complaints [OR=1.17, 95%CI (1.00, 1.38), P<0.05] were both positively associated with HPV infection. Conclusions Sleep duration and sleep complaints are correlated with HPV infection among women aged 20-59 years in the United State. Poor sleep quality elevates the risk of HPV infection. Improving sleep quality and maintaining adequate sleep duration may help reduce the risk of HPV infection in women.
