Associations between untraditional risk factors, pneumonia/lung cancer, and hospital fatality among hypertensive men in Guangzhou downtown

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis

BACKGROUND

Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT.

OBJECTIVES

To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status.

MATERIALS AND METHODS

This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status.

RESULTS

This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia.

CONCLUSION

Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD.

CLINICAL RELEVANCE STATEMENT

Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients.

KEY POINTS

•Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.

Microbiome-Induced Autoimmunity and Novel Therapeutic Intervention

Microorganisms' flora, which colonize in many parts of our body, stand out as one of the most important components for a healthy life. This microbial organization called microbiome lives in integration with the body as a single and whole organ/system. Perhaps, the human first encounters the microbial activity it carries through the immune system. This encounter and interaction are vital for the development of immune system cells that protect the body against pathogenic organisms and infections throughout life. In recent years, it has been determined that some disruptions in the host-microbiome interaction play an important role in the physiopathology of autoimmune diseases. Although the details of this interaction have not been clarified yet, the focus is on leaky gut syndrome, dysbiosis, toll-like receptor ligands, and B cell dysfunction. Nutritional regulations, prebiotics, probiotics, fecal microbiota transplantation, bacterial engineering, and vaccination are being investigated as new therapeutic approaches in the treatment of problems in these areas. This article reviews recent research in this area.

Exploration of the Potential Link, Hub Genes, and Potential Drugs for Coronavirus Disease 2019 and Lung Cancer Based on Bioinformatics Analysis

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has a huge influence on global public health and the economy. Lung cancer is one of the high-risk factors of COVID-19, but the molecular mechanism of lung cancer and COVID-19 is still unclear, and further research is needed. Therefore, we used the transcriptome information of the public database and adopted bioinformatics methods to identify the common pathways and molecular biomarkers of lung cancer and COVID-19 to further understand the connection between them. The two RNA-seq data sets in this study—GSE147507 (COVID-19) and GSE33532 (lung cancer)—were both derived from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) for lung cancer and COVID-19 patients. We conducted Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and found some common features between lung cancer and COVID-19. We also performed TFs-gene, miRNAs-gene, and gene-drug analyses. In total, 32 DEGs were found. A protein-protein interaction (PPI) network was constructed by DEGs, and 10 hub genes were screened. Finally, the identified drugs may be helpful for COVID-19 treatment.

Elevated Blood Pressure Increases Pneumonia Risk: Epidemiological Association and Mendelian Randomization in the UK Biobank

Background

Small studies have correlated hypertension with pneumonia risk; whether this is recapitulated in larger prospective studies, and represents a causal association, is unclear.

Methods

We estimated the risk for prevalent hypertension with incident respiratory diseases over mean follow-up of 8 years among 377,143 British participants in the UK Biobank. Mendelian randomization of blood pressure on pneumonia was implemented using 75 independent, genome-wide significant variants associated with systolic and diastolic blood pressures among 299,024 individuals not in the UK Biobank. Secondary analyses with pulmonary function tests were performed.

Findings

In total, 107,310 participants (30%) had hypertension at UK Biobank enrollment, and 9,969 (3%) developed pneumonia during follow-up. Prevalent hypertension was independently associated with increased risk for incident pneumonia (HR: 1.36; 95% CI: 1.29–1.43; p < 0.001), as well as other incident respiratory diseases. Genetic predisposition to a 5 mm Hg increase in blood pressure was associated with increased risk for incident pneumonia for systolic blood pressure (HR: 1.08; 95% CI: 1.04–1.13; p < 0.001) and diastolic blood pressure (HR: 1.11; 95% CI: 1.03–1.20; p = 0.005). Additionally, consistent with epidemiologic associations, increased blood pressure genetic risk was significantly associated with reduced performance on pulmonary function tests (p < 0.001).

Conclusions

These results suggest that elevated blood pressure increases risk for pneumonia. Maintaining adequate blood pressure control, in addition to other measures, may reduce risk for pneumonia.

Funding

S.M.Z. (1F30HL149180-01), M.H. (T32HL094301-07), and P.N. (R01HL1427, R01HL148565, and R01HL148050) are supported by the National Institutes of Health. J.P. is supported by the John S. LaDue Memorial Fellowship.

Epidemiologic analyses have correlated hypertension with pneumonia risk. Whether this represents a direct consequence of hypertension or the influence of co-morbid risk factors such as age, diabetes mellitus, or smoking is unclear. Here, across 377,143 individuals from the UK Biobank, we show that hypertension is independently associated with significantly increased risk for incident pneumonia. Additionally, using Mendelian randomization, we show that a genetic predisposition to elevated blood pressure across 75 independent genetic variants is associated with increased risk for incident pneumonia and reduced pulmonary function test performance. These results suggest that elevated blood pressure may be a causal risk factor for pneumonia. Maintaining adequate blood pressure control, in addition to other measures, may reduce risk for pneumonia.