Association between coffee and caffeine intake and risk of COPD: Findings based on NHANES 2007-2012.

BACKGROUND: The association between coffee and caffeine intake and the risk of COPD and lung function has not been thoroughly discussed in Americans, with subgroup and threshold effects remaining unclear. OBJECTIVES: This study investigated the association between coffee and caffeine consumption and the risk of chronic obstructive pulmonary disease (COPD) as well as lung function utilizing data from the NHANES 2007-2012. METHODS: We assessed the associations of coffee and caffeine consumption with the risk of COPD and lung function parameters, including FEV1 and FVC, adjusting for common demographic and disease characteristics in a cross-sectional analysis of NHANES data. RESULTS: A total of 9763 participants were included in the study, and 592 were diagnosed with COPD. Multivariate regression models revealed positive associations between coffee and caffeine consumption and the risk of COPD and lung function. Subgroup analyses stratified by sex, DM, hypertension status, and smoking habits identified potential effect modifiers as well as inflection points from threshold effect examinations. CONCLUSIONS: The results of this cross-sectional study indicated significant positive correlations between coffee and caffeine consumption and the risk of COPD. Additionally, positive correlations between exposure variables and FEV1 and FVC were detected. Among the stratification factors, smoking status exhibited the most potential for modifying effects. Future practices and research are needed to validate the results and explore the underlying mechanisms.

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.

Background: Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods: Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results: In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions: This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.

Composite Dietary Antioxidant Index Is Negatively Associated with Hyperuricemia in US Adults: An Analysis of NHANES 2007-2018.

Hyperuricemia and its complications are severe risks to human health. Dietary intervention is considered an essential part of the management of hyperuricemia. Studies have reported that the intake of antioxidants has a positive effect on hyperuricemia. Here, we collected data from 8761 participants of the National Health and Nutrition Examination Survey for this analysis. Daily intakes of vitamins A, C, and E; manganese; selenium; and zinc were calculated as the composite dietary antioxidant index (CDAI). The participants were divided into four groups (Q1, Q2, Q3, and Q4) according to the CDAI. Univariate analysis was used to assess the association of covariates with hyperuricemia. The association between the CDAI and hyperuricemia was evaluated using multinomial logistic regression, and its stability was determined by stratified analysis. Our results revealed that the CDAI has a significant negative association with hyperuricemia (Q2: 0.81 (0.69, 0.95); Q3: 0.75 (0.62, 0.90); Q4: 0.65 (0.51, 0.82); P < 0.01). The results of stratified analysis emphasize that this association between CDAI and hyperuricemia is stable. In conclusion, this study suggested a negative association between the CDAI and hyperuricemia.