Long COVID is a recently described entity that is responsible for significant morbidity and that has consequences ranging from mild to life-threatening. The underlying mechanisms are not completely understood, and treatment options are currently limited, as existing data focus more on risk factors and predictors. Smoking has been reported as a risk factor for poor outcomes of acute SARS-CoV-2 infection and seems to also play a role in mediating post-COVID-19 symptoms. We aimed to review relevant work addressing the interaction between smoking and long COVID in order to characterize smoking’s role as a risk factor and possibly identify new research directions. Methods: The PubMed/MEDLINE database was searched using the keywords ‘smoking’, ‘long COVID’, and ‘post-acute COVID’ to identify relevant English-language articles published up to October 2023. Results and conclusions: From the 374 initial hits, a total of 36 papers were deemed relevant to the aim of the review. There was significant variability concerning the ways in which tobacco usage was quantified and reported; still, there is compelling evidence linking smoking to an increased risk of developing manifestations of post-acute-COVID disease. Some clinical conditions, such as dyspnea, cardiovascular symptoms, and cognitive or mental-health impairment, seem to be relatively strongly associated with smoking, while the connection between smoking and upper-airway involvement seems less certain. The available data support recommending smoking cessation as a clinical tool for the prevention of long COVID.

Tobacco consumption is the most incriminated and studied risk factor for Chronic obstructive pulmonary disease (COPD), but other factors such as air pollution, are also linked to this disease. One of the known aspects of this chronic lung disease is that its occurrence is mainly due to the chronic inflammation of the airways. Lipid metabolism seems to be affected by smoking, with studies showing a correlation between this habit and high levels of triglycerides and low levels of high-density lipoprotein cholesterol (HDL-CHOL). Uric acid concentration is thought to reflect the antioxidative capacity of the body because it is the most abundant aqueous antioxidant. The aim of this study was to investigate the lipid profile and biochemical parameters of COPD patients in relation to smoking status. The present study was conducted between 2020 and 2021 in the Clinical Hospital of Pneumology in Iasi, Romania. Patients diagnosed with COPD (n = 52) were included and divided in three groups depending on their smoking status: non-smokers, smokers and ex-smokers. The obtained results show low correlations between COPD stages and serum uric acid concentrations (r = 0.4; p ˂ 0.05), smoking status (smoker/non-smoker/ex-smoker) and total serum cholesterol values (r = 0.45; p ˂ 0.05), but also between serum urea concentrations and the number of packs-years for the smoker/ex-smoker groups (r = 0.45, p ˂ 0.05). Smoking was associated with changes in the lipid profile of smokers and ex-smokers, along with increased low-density lipoprotein cholesterol (LDL-CHOL) and low serum uric acid values.

Oxidative stress is induced by tobacco smoking and is also associated with exposure to air pollution, which are two of the most important risk factors for chronic obstructive pulmonary disease (COPD). The aim of this study was to correlate tobacco use and exposure to air pollution with oxidative stress markers useful in clinical practice in patients with COPD. A total of 102 patients were included and the levels of polycyclic aromatic hydrocarbons (PAHs), malondialdehyde, uric acid and number of packs-years (PY) were determined. Also, six different ratios were used to assess the source of exposure. The results obtained in this study show an admission of pollutants according to smoking status (former smokers/smoker/non-smokers) quantified in average total concentrations for the group of patients with COPD of 4.12 ng/mL, 6.76 ng/mL, 6.04 ng/mL. The six ratios used show that in COPD, the content of PAHs in the blood could be a result of diesel emissions and fuel combustion. Uric acid levels were lower in the smoker group of COPD patients (mean = 5.21 mg/dL), which indicates that oxidative stress is intensified with each cigarette smoked. Additionally, high concentrations of malondialdehyde were quantified for smoking patients diagnosed with COPD (mean = 2.72 µmol/L) compared to former smokers (mean = 2.43 µmol/L) and non-smoking (mean = 2.32 µmol/L) patients, which is another indicator of the implication of smoking in oxidative stress in COPD patients.

Long COVID-19 or post-COVID infection (PCI) refers to the prolongation of symptoms in people who have been infected with the SARS-CoV-2 virus. Some meta-analysis studies have shown that patients with comorbidities, such as diabetes, obesity or hypertension, have severe complications after infection with the SARS-CoV-2 virus. The presence of chronic respiratory diseases such as bronchial asthma, COPD, pulmonary hypertension or cystic fibrosis increases the risk of developing severe forms of the COVID-19 disease. The risk of developing the severe form of COVID-19 was observed in patients with bronchial asthma being treated with corticosteroids, but also in those hospitalized with severe asthma. The biological variables determined in patients with PCI infection showed changes, especially in the hematological parameters, but also in some inflammatory markers. The aim of this study was to investigate some biological predictors in post-COVID-19 infection in patients with asthma and various comorbidities. In the case of patients diagnosed with moderate and severe forms of COVID-19, the variation in biological tests has shown high concentrations for serum glucose, lactate dehydrogenase and C-reactive protein. Additionally, the calculation of the relative risk (RR) based on the associated comorbidities in patients with PCI points to higher values for patients with asthma, hypertension, diabetes and obesity (RR moderate/severe form = 0.98/1.52), compared to patients with PCI and asthma (RR moderate/severe form = 0.36/0.63). Based on the statistical results, it can be concluded that the alanine aminotransferase (ALT) activity (p = 0.006) and the age of patients (p = 0.001) are the variables that contribute the most to the separation of the four classes of comorbidities considered.

In recent decades, the burden of TB has been gradually declining; however, with the emergence of COVID-19 and ongoing political conflicts, including the war in Ukraine, the proper functioning of healthcare services and TB control programs has been jeopardized. Recently, research has emphasized the importance of hematological parameters associated with inflammation, which can be easily analyzed through routine blood tests. Combining these parameters may have predictive value for various diseases, including pulmonary tuberculosis and even help monitor the effectiveness of treatment. Since there is no single hematological or inflammatory biomarker that provides precise and dynamic information about the success or failure of treatment, identifying individual markers or sets of biomarkers with higher sensitivity and specificity is essential. This is particularly important since sputum culture conversion at two months remains insufficiently sensitive and microscopy conversion has limited sensitivity and specificity in detecting treatment failure. Also, the analysis of the impact of the standard directly observed treatment, short-course regimen on pathogenic mechanisms also focuses on how it influences the interaction between inflammation and oxidative tissue degradation, by measuring plasma levels of glutathione. Utilizing a combination of hematological, inflammatory, and antioxidant biomarkers offers significant insights into systemic inflammatory responses in pulmonary tuberculosis patients, both before commencing treatment and during the entire duration of antituberculosis therapy. Combining different inflammatory parameters into a multiple biomarker can significantly enhance the accuracy of predicting prognosis and response to antibiotic chemotherapy. Identifying an optimal combination of biomarkers with predictive value is crucial for assessing treatment response and evaluating the effectiveness of anti-TB medication. Rather than developing or testing a composite prediction model, this review summarizes reported performance metrics from individual studies and highlights priorities for future prospective validation of integrated biomarker panels.