The association of depression with use of prescription drugs in adults with noncommunicable diseases: Based on NHANES in 2005-2016

Effects of Reactive Oxygen and Nitrogen Species on Male Fertility

Significance: In recent decades, male fertility has been severely reduced worldwide. The causes underlying this decline are multifactorial, and include, among others, genetic alterations, changes in the microbiome, and the impact of environmental pollutants. Such factors can dysregulate the physiological levels of reactive species of oxygen (ROS) and nitrogen (RNS) in the patient, generating oxidative and nitrosative stress that impairs fertility. Recent Advances: Recent studies have delved into other factors involved in the dysregulation of ROS and RNS levels, such as diet, obesity, persistent infections, environmental pollutants, and gut microbiota, thus leading to new strategies to solve male fertility problems, such as consuming prebiotics to regulate gut flora or treating psychological conditions. Critical Issues: The pathways where ROS or RNS may be involved as modulators are still under investigation. Moreover, the extent to which treatments can rescue male infertility as well as whether they may have side effects remains, in most cases, to be elucidated. For example, it is known that prescription of antioxidants to treat nitrosative stress can alter sperm chromatin condensation, which makes DNA more exposed to ROS and RNS, and may thus affect fertilization and early embryo development. Future Directions: The involvement of extracellular vesicles, which might play a crucial role in cell communication during spermatogenesis and epididymal maturation, and the relevance of other factors such as sperm epigenetic signatures should be envisaged in the future.

Associations of Lifestyle Patterns With Overweight and Depressive Symptoms Among United States Emerging Adults With Different Employment Statuses

Objective: To identify lifestyle patterns in emerging adults and examine the association of lifestyle patterns with overweight and depression. Methods: Data was from the National Health and Nutrition Examination Survey between 2011 and 2018. A latent class analysis (LCA) was conducted with 2,268 US emerging adults based on sedentary behavior, moderate-to-vigorous physical activity, diet, sleep, alcohol drinking, and cigarette smoking. The associations of lifestyle groups with overweight and depression were examined by logistic regression and were further stratified by employment status. Results: The LCA results favored a four-class solution: "unhealthy but non-substance use" (59%), "healthy but sleepless and drinking" (12%), "unhealthy lifestyle" (15%), and "healthy but sedentary" group (14%). Compared to the "unhealthy lifestyle" group, participants in the "unhealthy but non-substance use" (coef. = -1.44, SE = 0.27; OR = 0.40, 95% CI [0.26, 0.61]), "healthy but sleepless and drinking" (coef. = -1.49, SE = 0.35; OR = 0.38, 95% CI [0.20, 0.72]), and "healthy but sedentary" (coef. = -1.97, SE = 0.34; OR = 0.29, 95% CI [0.14, 0.57]) groups had lower depression severity and reported fewer depressive symptoms. Moreover, lifestyle groups and health outcomes showed different relationships among employed and unemployed participants. Conclusion: This study found that the combinations of lifestyle behaviors had synergistic effects on mental health, and such effects differed by employment status.

Variations in sociodemographic and health-related factors are linked to distinct clusters of individuals with depression based on the PHQ-9 instrument: NHANES 2007-2018

BACKGROUND

Depression is a heterogeneous disease. Identification of latent depression subgroups and differential associations across these putative groups and sociodemographic and health-related factors might pave the way toward targeted treatment of individuals.

METHODS

We used model-based clustering to identify relevant subgroups of 2900 individuals with moderate to severe depression (defined as scores ≥10 on the PHQ-9 instrument) from the NHANES cross-sectional survey. We used ANOVA and chi-squared tests to assess associations between cluster membership and sociodemographics, health-related variables, and prescription medication use.

RESULTS

We identified six latent clusters of individuals, three based on depression severity and three differentially loaded by somatic and mental components of the PHQ-9. The Severe mental depression cluster had the most individuals with low education and income (P < 0.05). We observed differences in the prevalence of numerous health conditions, with the Severe mental depression cluster showing the worst overall physical health. We observed marked differences between the clusters regarding prescription medication use: the Severe mental depression cluster had the highest use of cardiovascular and metabolic agents, while the Uniform severe depression cluster showed the highest use of central nervous system and psychotherapeutic agents.

LIMITATIONS

Due to the cross-sectional design we cannot make conclusions about causal relationships. We used self-reported data. We did not have access to a replication cohort.

CONCLUSIONS

We show that socioeconomic factors, somatic diseases, and prescription medication use are differentially associated with distinct and clinically relevant clusters of individuals with moderate to severe depression.

Association of suicidal ideation and depression with the use of proton pump inhibitors in adults: a cross-sectional study

Proton pump inhibitors (PPIs) were found to be associated with depression. This study aimed to find the cross-sectional association between recent PPI use and suicidal ideation. Item 9 of Patient Health Questionnaire-9 (PHQ-9) of the US National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 was used to categorize whether or not the participants had suicidal ideation. The secondary outcome of this study was depression and the scores of the PHQ-9 were used as the depression diagnostic instrument. The study population included 16,881 participants who were over 20 years old. The bivariate Rao-Scott χ² test showed a significant association between PPI use and suicidal ideation (P < 0.001) and a stronger association was observed between PPIs and depression (P < 0.001). Multiple logistic regression analysis of the education, gender, race and age-adjusted model revealed that the PPI users had a 2.34 (95% CI 1.66-3.31) greater risk of having suicidal ideation than the non-PPI users. Middle-aged participants (40-49 years) showed the greatest number of differences in suicidal ideation between PPI and non-PPI users (P < 0.001). Future research should continue to consider the psychiatric effects of taking PPIs.

Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018

Depression is one of the most common mental health problems in middle-aged and elderly people. The establishment of risk factor-based depression risk assessment model is conducive to early detection and early treatment of high-risk groups of depression. Five machine learning models (logistic regression (LR); back propagation (BP); random forest (RF); support vector machines (SVM); category boosting (CatBoost) were used to evaluate the depression among 8374 middle-aged people and 4636 elderly people in the NHANES database from 2011 to 2018. In the 2011-2018 cycle, the estimated prevalence of depression was 8.97% in the middle-aged participants and 8.02% in the elderly participants. Among the middle-aged and elderly participants, CatBoost was the best model to identify depression, and its area under the working characteristic curve (AUC) reaches the highest. The second is LR model and SVM model, while the performance of BP and RF model was slightly worse. The primary influencing factor of depression in middle-aged male is alanine aminotransferase. All five machine learning models can identify the occurrence of depression in the NHANES data set through social demographics, lifestyle, laboratory data and other data of middle-aged and elderly people, and among five models, the CatBoost model performed best.