Does smoking influence tryptophan metabolism in periodontal inflammation? A cross-sectional study

OBJECTIVES

The aim of this study was to identify the effects of smoking and periodontal inflammation on tryptophan-kynurenine metabolism as well as the correlation between these findings and clinical periodontal parameters.

BACKGROUND

It has been shown that the tryptophan amino acid's primary catabolic pathway, the kynurenine pathway (KP), may serve as a key biomarker for periodontal disease. Although there are studies investigating the effect of smoking on KYN-TRP metabolism, the effect of smoking on periodontal disease through KP has not been revealed so far.

METHODS

The salivary and serum samples were gathered from 24 nonsmoker (NS-P) stage III, grade B generalized periodontitis and 22 smoker (S-P) stage III, grade C generalized periodontitis patients, in addition to 24 nonsmoker (NS-C) and 24 smoker (S-C) periodontally healthy control individuals. Saliva and serum IL-6, kynurenine (KYN), and tryptophan (TRP) values, and KYN/TRP ratio were analyzed by liquid chromatography-mass spectrometry. Clinical periodontal measurements were recorded.

RESULTS

Salivary TRP values were significantly higher in both periodontitis groups than control groups (p < .05). Salivary KYN values were highest in NS-P group (p < .05). Salivary KYN values did not differ significantly between periodontitis groups (p = .84). Salivary KYN/TRP ratio was significantly lower in NS-P group compared to other groups (p < .001). Serum TRP value is higher in S-P group than other groups; however, significant difference was found in S-C group (p < .05). Serum KYN values were significantly lower in smokers than nonsmokers. Serum KYN/TRP ratio is higher in NS-P group. NS-P group has the highest salivary IL-6 levels, NS-C group has the lowest values (p < .05).

CONCLUSIONS

Our results point out that smoking exacerbates inflammation in the periodontium and increases TRP destruction and decreases IDO activity by suppressing KP in serum. As a result, kynurenine and its metabolites may be significant biomarkers in the link between smoking and periodontal disease.

Association of serum ADMA, SDMA and L-NMMA concentrations with disease progression in COVID-19 patients

Introduction

This study determines and compares the concentrations of arginine and methylated arginine products ((asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), n-monomethyl-1-arginine (L-NMMA) and homoarginine (HA)) for assessment of their association with disease severity in serum samples of COVID-19 patients.

Materials and methods

Serum arginine and methylated arginine products of 57 mild-moderate and 29 severe (N = 86) COVID-19 patients and 21 controls were determined by tandem mass spectrometry. Moreover, the concentrations of some of the routine clinical laboratory parameters -neutrophil lymphocyte ratio (NLR), C-reactive protein, ferritin, D-dimer, and fibrinogen measured during COVID-19 follow-up were also taken into consideration and compared with the concentrations of arginine and methylated arginine products.

Results

Serum ADMA, SDMA and L-NMMA were found to be significantly higher in severe COVID-19 patients, than in both mild-moderate patients and the control group (P < 0.001 for each). In addition, multiple logistic regression analysis indicated L-NMMA (cut-off =120 nmol/L OR = 34, 95% confidence interval (CI) = 3.5-302.0, P= 0.002), CRP (cut-off = 32 mg/L, OR = 37, 95% CI = 4.8-287.0, P < 0.001), and NLR (cut-off = 7, OR = 22, 95% CI = 1.4-335.0, P = 0.020) as independent risk factors for identification of severe patients.

Conclusions

The concentration of methylated arginine metabolites are significantly altered in COVID-19 disease. The results of this study indicate a significant correlation between the severity of COVID-19 disease and concentrations of CRP, NLR and L-NMMA.

Kynurenine pathway in Coronavirus disease (COVID‐19): Potential role in prognosis

Background

It is known that inflammatory responses play an important role in the pathophysiology of COVID‐19.

Aims

In this study, we aimed to examine the role of kynurenine (KYN) metabolism on the severity of COVID‐19 disease AQ5.

Materials & Methods

Seventy COVID‐19 patients of varying severity and 30 controls were included in the study. In addition to the classical laboratory parameters, KYN, tryptophan (TRP), kynurenic acid (KYNA), 3 hydroxykynurenine (3OHKYN), quinolinic acid (QA), and picolinic acid (PA) were measured with mass spectrometry.

Results

TRP, KYN, KYN:TRP ratio, KYNA, 3OHKYN, PA, and QA results were found to be significantly different in COVID‐19 patients (p < 0.001 for all). The KYN:TRP ratio and PA of severe COVID‐19 patients was statistically higher than that of mild‐moderate COVID‐19 patients (p < 0.001 for all). When results were examined, statistically significant correlations with KYN:TRP ratio, IL‐6, ferritin, and procalcitonin were only found in COVID‐19 patients. ROC analysis indicated that highest AUC values were obtained by KYN:TRP ratio and PA (0.751 vs 0.742). In determining the severity of COVID‐19 disease, the odd ratios (and confidence intervals) of KYN:TRP ratio and PA levels that were adjusted according to age, gender, and comorbidity were determined to be 1.44 (1.1–1.87, p = 0.008) and 1.06 (1.02–1.11, p = 0.006), respectively.

Discussion & Conclusion

According to the results of this study, KYN metabolites play a role in the pathophysiology of COVID‐19, especially KYN:TRP ratio and PA could be markers for identification of severe COVID‐19 cases.

Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.