Introduction

Coronavirus disease-2019 (Covid-19) has created a global pandemic with a significant fatality rate worldwide with severe or critical illness. Furthermore, it has been associated with SARS-CoV-2 infection-induced hyperinflammation of the innate and adaptive immune systems and the subsequent cytokine storm [1]. As a result, d-dimer, fibrinogen, and ferritin have influenced clinical decisions [2,3[. Tobacco smoke contains a complex mixture of chemicals, including reactive oxygen and nitrogen species, which can damage lipids, proteins, and nucleic acids, among other cellular and subcellular targets. Accumulating evidence shows that smoking-induced reactive oxygen species (ROS) and the consequent oxidative stress play a crucial role in inflammation and carcinogenesis [4]. Genetic support for a causal relationship between smoking behaviors and the Covid-19 phenotypes studied was found through Mendelian randomization analyses, which showed that genetic variants predicting differences in smoking initiation and heaviness were linked to an increased risk of SARS-CoV-2 infection and severe Covid-19-related outcomes [5]. Monitoring biochemical markers help understand a patient's clinical course in the context of other exams and clinical findings; however, other factors, such as smoking status, must be considered when concluding a patient's outcomes [6].

When performing diagnosis and treatment in patients or healthy individuals, it should be known that results within the population-based reference range may be pathological. In contrast, results outside it may be physiological [7]. One of the two main reasons for these differences is analytical variation, and the other is biological variation. Laboratory professionals regularly strive to minimize analytical variation. However, natural changes cannot be intervened due to their nature and are often overlooked. Therefore, it is essential to know the variations in diseased and healthy individuals to provide the most clinical benefit, which may affect the clinical picture. In order to clarify this point, we examined the interday changes of d-dimer, ferritin, and fibrinogen, which are important laboratory parameters for Covid-19 and various other inflammatory diseases in healthy individuals. In addition, increases in these parameters have been reported in healthy smokers [8,9]. These increases can be confusing for smokers in the case of Covid-19 or the like. Therefore, incorrect treatment may be applied by interpreting the clinical course of the patients differently. To prevent this situation, it is essential to have information about the changes in inflammatory parameters related to smoking. This study aims to investigate the effect of smoking on inflammatory laboratory parameters, which are essential in the follow-up of Covid-19 patients. Another aim is to provide a different perspective on the parameters specified in Covid-19 patients who smoke by detecting the changes in D-dimer, ferritin, and fibrinogen levels resulting from smoking.

Materials Methods

Subjects and study design

Thirty healthy participants participated in the study of which 15 were smokers, and 15 were non-smokers and did not have tobacco exposure. Volunteers were chosen from the laboratory staff. The smokers smoked one pack (20 cigarettes) daily for at least ten years. As a result of smoking's prothrombotic, inflammatory, and endothelial effects, the groups were divided. On the first, third, and fifth days, subjects provided samples on three occasions. Pregnant women and current Covid-19 patients were excluded from the study.

Ethical approval

The subjects were given a thorough description of the study. Individuals provided informed consent. The study with the 2021-012 number was approved by the Amasya University non-invasive studies ethics committee.

Samples

On the first, third, and fifth day, the participants' fasting blood was taken in a yellow-capped tube with no gel addition and a blue-capped citrate tube. The same skilled phlebotomist collected blood samples between 8 and 10 a.m. after an 8-hour overnight fast. After drawing blood for 20 minutes, plasma and serum samples were collected by centrifuging the blood at 1500 G for 15 minutes. Samples were stored at -80 degrees Celsius until the day of the investigation. On the day of analysis, after waiting for the samples to dissolve at room temperature, they were centrifuged at 1500 G for 15 minutes. All tests were conducted in duplicate to rule out analytical variation. On a Siemens Advia Centaur XPT immunoassay autoanalyzer (Siemens Healthineers AG, Erlangen, Germany) with a reference range of 10-322 ng/mL, 2.1-3.0 within run % CV, and 2.7-5.4 between run % CV, serum ferritin measurements were done. Fibrinogen levels were detected using the Claus clotting technique and a Stago Compact Max 3 automated coagulation analyzer (Diagnostica Stago, Inc., Parsippany, NJ, USA), with a reference interval of 200-400 mg/dL, 1.60-1.72 within-run %CV, and 3.77-4.77 between-run %CV. Plasma d-dimer levels were analyzed with an Erba XL 1000 clinical chemistry analyzer using a turbidimetric method and Archem brand reagent (Archem Health Ind. Inc. Ba?ak?ehir, Istanbul, Turkey). The 99th percentile limit is 0.5 g FEU/mL, within the run % CV range of 1.3-3.7 and between the run % CV range of 3.3-4.5. All analyses were conducted on the same day using calibrators, controls, and reagents from the same lot. On the dates of the tests, one normal and one pathological quality control sample were assessed for every analyte to ensure they were both appropriate for analysis. All volunteers' assays were conducted using the same calibrator curve to reduce analytical variation.

Statistics

Between replicates, within people, and between individual values were evaluated, and the Shapiro-Wilk test was used to determine distribution. The Dixon-Reed criterion was applied to find outliers in the mean between-subject values for analytes. Furthermore, Cochran's C test was used to exclude outliers from within-subject results, such as repeated measurements. After processing the extreme values' findings, the variances' homogeneity was studied by applying the Bartlett test to all data, including repeated measurements. Due to the parametric nature of the data, Student's t-test was employed to evaluate day and daytime smoking. The one-way ANOVA test determined the parameters' changes over time and between individuals. A p-value less than 0.05 was deemed significant. Excel 2019 (Microsoft Corporation, Redmond, Washington, United States) and Minitab 19 were utilized to conduct statistical analyses (Minitab Ltd., Coventry CV3 2TE, UK).

Results

Thirteen women with a mean age of 42.58.4 and 17 men with a mean age of 44.19.5 were included in our study. Non-smokers were younger and comprised more women than smokers. There is no discernible difference in sex between smokers and non-smokers. The levels of d-dimer on the first day and fibrinogen on the third and fifth days were substantially higher in smokers than in non-smokers (Table 1). When all participants were evaluated, d-dimer levels differed considerably on days one and five. In the same examination, the mean values for d-dimer on the third and fifth days and ferritin on the first and third days differed significantly (Table 2). ANOVA revealed significant differences between days and between individuals. Table 3 displays the variations in parameters over time and across individuals.

Table 1: Descriptive statistics according to smoking.

Table 2: Day-to-day changes of parameters.

Table 3: Variations in parameters over time and between individuals were shown in three groups.

Discussion

Life is a continually evolving biological process. Laboratory parameters vary according to an individual's genetic traits, environmental variables, and smoking habits. Consequently, population-based reference ranges, commonly employed in medical laboratories, may not be adequate for the clinical evaluation of individuals. Additionally, distinct individual responses to infection may occur, particularly in pandemics such as COVID-19, for which the complete picture is not yet known.

We found significant increases in first-day d-dimer and third-fifth days fibrinogen values of smokers. A study on nearly three thousand elderly individuals determined that smoking increases c-reactive protein, d-dimer, and fibrinogen [10]. In another study on Sudanese smokers, d-dimer levels were higher than in healthy individuals [11]. A study on healthy individuals showed that smokers had increased fibrinogen levels [12]. In a study conducted with young women, it was stated that high fibrinogen levels were determined in smokers and that these individuals should be followed up with fibrinogen levels [13]. In a Korean study of 50405 participants, it was found that serum ferritin values were higher in former or current smokers and with the smoking amount in all subgroups of participants [14]. With the studies we mentioned, our data is compatible with the contribution to the literature except for ferritin. However, we could not detect any difference between smokers and non-smokers in terms of d-dimer levels on the third and fifth days and fibrinogen levels on the first day. In addition, when ferritin values were considered, there was a difference between groups. At this point, intra-individual and inter-individual biological variation came to the fore. In addition, smoking, a determining factor in inflammation, did not have a similar effect among smokers. One way Anova analysis revealed significant differences in the repeated measurements of both groups and individuals on different days.

The European Federation of Laboratory Medicine (EFLM) has created a database that reveals the effect of biological variables on laboratory results [15]. At this point, the aim is to consider the impacts of natural changes when interpreting laboratory results. For example, a study dealing with thromboembolic events in pregnant women argued that a biological variation-based approach would be helpful when interpreting d-dimer levels [16]. A different study revealed that biological variation was detected in fibrinogen, albeit at a low level [17]. In a survey conducted with a small number of individuals, researchers found the rate of biological variation in ferritin to be over 21 percent [18].

The inflammatory effects of cigarette smoking will differ according to individuals with different physical structures. Although we could not find any research on this subject in the literature, it is natural for smokers to have biological variations in laboratory parameters like non-smokers. This approach is essential for individuals who smoke and have inflammatory diseases such as Covid-19. According to the World Health Organization, smokers are more likely than never smokers to have more severe COVID-19 outcomes, such as admission to intensive care units and death. Furthermore, severe COVID-19 or fatalities from COVID-19 are more common in patients with tobacco-related comorbidities, such as COPD, lung cancer, and cardiovascular disease [19]. Therefore, when making clinical decisions in treating these patients, changes due to Covid-19 inflammation and those due to smoking should be considered in addition to biological variation. While the patient is recovering from Covid-19, the expected changes in parameters may not occur due to long-term smoking, and malpractice may be applicable. Alternatively, the source of the increase in inflammatory parameters may not be found. In the approach to different inflammatory diseases, knowing the basal inflammatory events such as smoking and interpreting the laboratory results with this knowledge will provide more patient benefits.

Conclusions

Variations in inflammatory laboratory parameters can be detected in healthy people based on their biological characteristics, and the population-based reference period may be inadequate for clinical evaluation. Therefore, every person should be examined internally. Clinicians will employ the same methodology when treating smoking and other comparable inflammatory diseases. From this perspective, it is beneficial to examine the factors contributing to the inflammatory response, particularly in diagnosing and treating Covid-19 and other systemic inflammatory illnesses.

Limitations

First, this study was done with healthy individuals and did not represent the clinical and biological features of patients with Covid-19 or other related diseases. In addition, the research is not an average biological variation analysis prepared according to the protocols outlined in the EFLM standards; rather, it is meant to track daily change. Moreover, since the amount and rate tobacco smokers consume are not precisely established, no interpretation was made. Lastly, the fact that we could not examine the effect of smoking on inflammatory parameters in individuals who had Covid-19 since the Covid-19 history of the individuals we selected is unknown was another factor that limited our study.

Acknowledgments

None.

Declaration of interest

All authors agree that there is no conflict of interest.

Funding

There is no funding for this research.

Authors’ contributions

All authors contributed equally to preparing the manuscript.

References

1. Hsu RJ, Yu WC, Peng GR, Ye CH, Hu S, Chong PCT, Yap KY, Lee JYC, Lin WC, Yu SH. The Role of Cytokines and Chemokines in Severe Acute Respiratory Syndrome Coronavirus 2 Infections. Front Immunol. 13 (2022): 832394
2. Kaur S, Bansal R, Kollimuttathuillam S, et al. The looming storm: blood and cytokines in COVID-19. Blood Rev 46 (2021): 100743.
3. Henry BM, de Oliveira MHS, Benoit S, et al. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med 58 (2020): 1021-1028.
4. Caliri AW, Tommasi S, Besaratinia A. Relationships among smoking, oxidative stress, inflammation, macromolecular damage, and cancer. Mutat Res Rev Mutat Res 787 (2021): 108365.
5. Clift AK, Von Ende A, Tan PS, et al. Smoking and COVID-19 outcomes: an observational and Mendelian randomization study using the UK Biobank cohort. Thorax 77 (2022): 65-73.
6. Hay?ro?lu M?, Ç?nar T, Tekke?in A?. Fibrinogen and D-dimer variances and anticoagulation recommendations in Covid-19: current literature review. Rev Assoc Med Bras 66 (2020): 842-848.
7. Fraser CG. Biological variation: from principles to practice. Amer. Assoc. for Clinical Chemistry (2001).
8. Petersen PH, Sandberg S, Fraser CG, et al. Influence of index of individuality on false positives in repeated sampling from healthy individuals. Clin Chem Lab Med. 39 (2001): 160-165.
9. Koczkodaj P, Cedzy?ska M, Przepiórka I, et al. The COVID-19 Pandemic and Smoking Cessation-A Real-Time Data Analysis from the Polish National Quitline. Int J Environ Res Public Health 19 (2022): 2016.
10. Rumley A, Lennon L, Whincup PH. Associations between cigarette smoking, pipe/cigar smoking, and smoking cessation, and haemostatic and inflammatory markers for cardiovascular disease. Eur Heart J. 26 (2005): 1765-1773.
11. AbdElkarim SA, Khalil HB. Assessment of Quantitative D.Dimer Level Among Sudanese Smokers. International Journal of Information Research and Review 2 (2015): 1464-1466.
12. Gitte RN. Effect of Cigarette Smoking on Plasma Fibrinogen and Platelet Count. Asian J Med Sci 2 (2012): 181-184.
13. Ramanathan G, Araujo JA, Gornbein J, et al. Cigarette smoking is associated with dose-dependent adverse effects on paraoxonase activity and fibrinogen in young women. Inhal Toxicol 26 (2014): 861-865.
14. Lee CH, Goag EK, Lee SH, et al. Association of serum ferritin levels with smoking and lung function in the Korean adult population: analysis of the fourth and fifth Korean National Health and Nutrition Examination Survey. Int J Chron Obstruct Pulmon Dis. 11 (2016): 3001-3006.
15. Biological variation database EFLM. Retrieved from https://biologicalvariation.eu/ (2021).
16. Kristoffersen AH, Petersen PH, Sandberg S. A model for calculating the within-subject biological variation and likelihood ratios for analytes with a time-dependent change in concentrations; exemplified with the use of D-dimer in suspected venous thromboembolism in healthy pregnant women. Ann Clin Biochem 49 (2012): 561-569.
17. Giuseppe B, Del Fabbro M. Biological variation in tests of hemostasis. Seminars in thrombosis and hemostasis 35 (2009): 119-26.
18. Gnanou JV, Muthayya S, Kurpad AV. Biological variation of plasma ferritin in healthy adult males in south Indian population-A sample study. Indian journal of clinical biochemistry. Indian J Clin Biochem. 21 (2006): 193-195.
19. World Health Organisation. Coronavirus disease (COVID-19): Tobacco (2002)