World No Tobacco Day 2019 puts the spotlight on lung health

The influence of smoking on retinal ganglion cell-inner plexiform layer complex in male diabetes

PURPOSE

This study aims to evaluate the influence of smoking on ganglion cell-inner plexiform layer complex (GC-IPL) thickness and central macular thickness (CMT) measured by spectral domain optical coherence tomography (OCT) in male diabetes.

METHODS

90 smoking and 90 never-smoking male subjects were included in this study. They were divided into six groups based on the diagnostic criteria for diabetes and the Early Treatment Diabetic Retinopathy Study (ETDRS) classification: smoking healthy subjects (SH, n = 20), non-smoking healthy subjects (NSH, n = 20), smoking diabetic patients without diabetic retinopathy (SNDR, n = 40), non-smoking diabetic patients without diabetic retinopathy (NSNDR, n = 40), smoking diabetic patients with diabetic retinopathy (SDR, n = 30), and non-smoking diabetic patients with diabetic retinopathy (NSDR, n = 30). After a full ophthalmologic examination, GC-IPL thickness and central macular thickness (CMT) were measured by OCT. Statistical analysis was performed to compare GC-IPL thickness and CMT between groups. Multiple linear regression equations were constructed to explore the potential risk factors of mean GC-IPL thickness.

RESULTS

There were no significant differences in GC-IPL thickness and CMT between SH and NSH (all p > 0.05). Mean, superonasal, superior, superotemporal, inferonasal, inferior GC-IPL (p＜0.001, p＜0.001, p＜0.001, p = 0.003, p = 0.001, and p = 0.005, respectively) were thinner in the SNDR than NSNDR except for inferotemporal GC-IPL thickness and CMT (p = 0.066, p = 0.605, respectively). Mean, superonasal, superior, and inferonasal GC-IPL were thinner in the SDR than NSDR (p = 0.019, p = 0.045, p = 0.037, and p = 0.049, respectively). Multiple regression analysis demonstrated that age (β [SE], -0.141 [0.060]; p = 0.020) and smoking (β [SE], -4.470 [1.015]; p＜0.001) were the most important determinants for mean GC-IPL thickness.

CONCLUSION

Smoking is associated with reduced retinal GC-IPL thickness in male diabetes. Smoking behavior and age are important determinants of mean GC-IPL thickness.

Association between Smoking and Periodontal Disease in South Korean Adults

Smoking poses a threat to global public health. This study analyzed data from the 2016-2018 National Health and Nutrition Examination Survey to investigate smoking's impact on periodontal health and identify potential risk factors associated with poor periodontal health in Korean adults. The final study population was 9178 patients, with 4161 men and 5017 women. The dependent variable was the Community Periodontal Index (CPI), to investigate periodontal disease risks. Smoking was the independent variable and was divided into three groups. The chi-squared test and multivariable logistic regression analyses were used in this study. Current smokers had a higher risk of periodontal disease than non-smokers (males OR: 1.78, 95% CIs = 1.43-2.23, females OR: 1.44, 95% CIs = 1.04-1.99). Age, educational level, and dental checkups affected periodontal disease. Men with a higher number of pack years had a higher risk of periodontal disease than non-smokers (OR: 1.84, 95% CIs = 1.38-2.47). Men who quit smoking for less than five years had a higher risk of periodontal disease than non-smokers but lower than current smokers (current OR: 1.78, 95% CIs = 1.43-2.23, ex OR: 1.42, 95% CIs = 1.04-1.96). Those who had quit smoking for less than five years had a higher risk of periodontal disease than non-smokers but lower than current smokers (males OR: 1.42, 95% CIs = 1.04-1.96, females OR: 1.11, 95% CIs = 1.71-1.74). It is necessary to motivate smokers by educating them on the importance of early smoking cessation.

Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs' latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.