Secondhand Smoke in the Workplace Is Associated With Depression in Korean Workers

Systemic inflammation mediates the association between environmental tobacco smoke and depressive symptoms: A cross-sectional study of NHANES 2009-2018

BACKGROUND

Depression is associated with both environmental tobacco smoke (ETS) and inflammation. However, whether systemic inflammation mediates the ETS-depression relationship is unclear.

METHODS

We analyzed 19,612 participants from the 2009-2018 National Health and Nutrition Examination Survey (representing approximately 206,284,711 USA individuals), utilizing data of depressive symptoms (assessed by Patient Health Questionnaire-9), blood cotinine level (an ETS biomarker), dietary inflammatory index (DII, assessed by 24-h dietary recall) and inflammation, represented by immune-inflammation index (SII) and systemic inflammation response index (SIRI).

RESULTS

Weighted multivariable logistic regression showed that a higher blood cotinine level is significantly associated with a higher depressive symptoms risk (OR = 1.79, 1.35-2.38). After adjusting for covariates, the effect in smokers (OR = 1.220, 95 % CI: 1.140-1.309) is larger than that in non-smokers (OR = 1.150, 95 % CI: 1.009-1.318). Compared to the lowest level, depressive symptoms risks in participants with the highest level of SII, SIRI and DII are 19 % (OR = 1.19, 1.05-1.35), 15 % (OR = 1.15, 1.01-1.31) and 88 % (OR = 1.88, 1.48-2.39) higher, respectively. Weighted linear regression demonstrated positive correlations of SII (β = 0.004, 0.001-0.006), SIRI (β = 0.009, 0.005-0.012) and DII (β = 0.213, 0.187-0.240) with blood cotinine level. Restricted cubic splines model showed a linear dose-response relationship between blood cotinine and depressive symptoms (Pnon-linear = 0.410), with decreasing risk for lower DII. And SII and SIRI respectively mediate 0.21 % and 0.1 % of the association between blood cotinine and depressive symptoms.

LIMITATION

Cross-sectional design, and lack of medication data for depression.

CONCLUSIONS

Positive association of ETS (blood cotinine) with depressive symptoms risk is partly mediated by systemic inflammation, and anti-inflammatory diet could be beneficial.

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

BACKGROUND

The prevalence of depression is increasing in the elderly population, and growing evidence suggests that malnutrition impacts mental health. Despites, research on the factors that predict depression is limited.

METHODS

We included 2946 elderly individuals from National Health and Nutrition Examination Survey (NHANES) spanning the years 2011 through 2014. Depressive symptoms were assessed using the PHQ-9 scale. Multinomial logistic regression was performed to evaluate the independent association between Geriatric Nutritional Risk Index (GNRI) and depression prevalence and scores. Subgroup analysis was conducted to explore potential factors influencing the negative correlation between GNRI and depression. Restricted cubic spline graph was employed to examine the presence of a non-linear relationship between GNRI and depression.

RESULTS

The depression group had a significantly lower GNRI than the non-depression group, and multivariate logistic regression showed that GNRI was a significant predictor of depression (P < 0.001). Subgroup analysis revealed that certain demographic characteristics were associated with a lower incidence of depression in individuals affected by GNRIs. These characteristics included being female (P < 0.0001), non-Hispanic black (P = 0.0003), having a moderate BMI (P = 0.0005), having a college or associates (AA) degree (P = 0.0003), being married (P = 0.0001), having a PIR between 1.50 and 3.49 (P = 0.0002), being a former smoker (P = 0.0002), and having no history of cardiovascular disease (P < 0.0001), hypertension (P < 0.0001), and diabetes (P = 0.0027). Additionally, a non-linear negative correlation (non-linear P < 0.01) was found between GNRI and depression prevalence, with a threshold identified at GNRI = 104.17814.

CONCLUSION

The GNRI demonstrates efficacy as a reliable indicator for forecasting depression in the elderly population. It exhibits a negative nonlinear correlation with the prevalence of depression among geriatric individuals.

Association between secondhand smoke exposure across the life course and depressive symptoms among Chinese older adults

BACKGROUND

There are limited data on the association between secondhand smoke (SHS) exposure across the life course and depressive symptoms among older adults. We aimed to investigate the association of childhood household SHS exposure, adulthood household SHS exposure, lifetime social SHS exposure, and their coexistence with depressive symptoms in older adults.

METHODS

Data were from the 2011-2012 and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey. About 4000 participants (aged 60 years or older) were recruited in a randomly selected half of the counties and cities in China. Data on SHS exposure, past-year depressive symptoms, and covariates were collected using a questionnaire. The chi-square test (for categorical variables) and t-test (for continuous variables) were used to assess differences in the participant characteristics across groups of SHS exposures. We estimated the odds ratios (ORs) and 95 % confidence intervals (CIs) of depressive symptom according to different types of SHS exposure.

RESULTS

Childhood household SHS exposure (OR = 1.42, 95%CI = 1.22-1.66), adulthood household SHS exposure (OR = 1.41, 95%CI = 1.21-1.63) and lifetime social SHS exposure (OR = 1.35, 95%CI = 1.14-1.58) were associated with higher odds of depressive symptoms. Additionally, those with a higher SHS exposure score had higher odds of depressive symptoms (1 point: OR = 1.56, 95%CI = 1.22-2.00; 2 points: OR = 1.77, 95%CI = 1.39-2.25; 3 points: OR = 1.83, 95%CI = 1.45-2.31). The results were similar when stratified by lifetime nonsmoking, former smoking, and current smoking.

LIMITATIONS

Retrospective design may introduce recall bias.

CONCLUSIONS

SHS exposure was associated with higher odds of depressive symptoms in older adults, with the effect seeming to be addictive.

Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey

Objective

Depression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers.

Methods

ML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 (N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features.

Results

The light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP.

Conclusions

Compared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.