Second-hand smoke and fine particulate matter exposures among multi-unit housing residents in Singapore: A pilot study

Association between secondhand smoke and liver injury among US non-smoking adults: Mediation analysis of body mass index in the NHANES

INTRODUCTION

Liver injury is a primary factor in the pathogenesis of most liver diseases, which can lead to liver failure. Secondhand smoke (SHS) is a serious public problem. This research explored the correlation between SHS and the indicators of liver injury.

METHODS

This cross-sectional study was based on the National Health and Nutrition Examination Survey (NHANES) 2011-2016. The relationship between SHS and indicators of liver injury was explored by the weighted linear regression model and smooth curve fitting. The weighted threshold saturation effect model tested the relationship and inflection point between them. Mediation analyses were used to explore whether body mass index (BMI) mediates the correlation between SHS and liver injury indicators.

RESULTS

Our cross-sectional study included 3811 non-smoking participants (aged 20-80 years). The full covariate adjustment model (β= -0.05; 95% CI: -0.08 - -0.02) showed a significant and negative correlation between log cotinine and albumin (ALB). Compared to the unexposed group, the ALB, and total protein (TP) were decreased by 0.16 g/dL, 0.26 g/dL in the heavy exposure group [ALB: -0.16 (-0.26 - -0.05), TP: -0.26 (-0.38 - -0.13)], respectively. Smoothed curve fitting revealed a nonlinear relationship between log cotinine and fibrosis-4 index (FIB-4 score), with the inflection point of log cotinine at -1.72. When log cotinine was < -1.72, the log cotinine significantly and positively correlated with the FIB-4 score (β=0.27; 95% CI: 0.06-0.49). BMI partially mediated the effect of SHS exposure on ALB or TP.

CONCLUSIONS

SHS has harmful effects on the liver in never-smoking adults. BMI partially mediated the effect of SHS exposure on ALB or TP. More prospective and basic research in the future is necessary to focus on validating our results.

Development of a novel predictive model for interstitial lung disease in ANCA-associated vasculitis prognostications within the Chinese population

Antineutrophil cytoplasmic antibody vasculitis-associated interstitial lung disease (AAV-ILD) is a potentially life-threatening disease. However, very little research has been done on the condition's mortality risk. Hence, our objective is to find out the factors influencing the prognosis of AAV-ILD and employ these findings to create a nomogram model. Patients with AAV-ILD who received treatment at the First Affiliated Hospital of Zhengzhou University during the period from March 1, 2011, to April 1, 2022 were selected for this research. The development of nomogram entailed a synergistic integration of univariate, Lasso, and multivariate Cox regression analyses. Internal validation ensued through bootstrap techniques involving 1000 re-sampling iterations. Discrimination and calibration were assessed utilizing Harrell's C-index, receiver operating characteristic (ROC) curve, and calibration curve. Model performance was evaluated through integrated discrimination improvement (IDI), net reclassification improvement (NRI), and likelihood ratio test. The net benefit of the model was evaluated using decision curve analysis (DCA). A cohort comprising 192 patients was enrolled for analysis. Throughout observation period, 32.29% of the population died. Key factors such as cardiac involvement, albumin, smoking history, and age displayed substantial prognostic relevance in AAV-ILD. These factors were incorporated to craft a predictive nomogram. Impressively, the model exhibited robust performance, boasting a Harrell's C index of 0.826 and an AUC of 0.940 (95% CI 0.904-0.976). The calibration curves depicted a high degree of harmony between predicted outcomes and actual observations. Significantly enhancing discriminative ability compared to the ILD-GAP model, the nomogram was validated through the IDI, NRI, and likelihood ratio test. DCA underscored the superior predictive value of the predictive model over the ILD-GAP model. The internal validation further affirmed this efficacy, with a mean Harrell's C-index of 0.815 for the predictive model. The nomogram model can be employed to predict the prognosis of patients with AAV-ILD. Moreover, the model performance is satisfactory. In the future, external datasets could be utilized for external validation.