Izabela Szołtysek-Bołdys

Estimation of urinary cotinine cut-off points distinguishing non-smokers, passive and active smokers

Abstract An objective assessment of exposure to tobacco smoke may be accomplished by means of examining particular biomarkers in body fluids. The most common biomarker of tobacco smoke exposure is urinary, or serum, cotinine. In order to distinguish non-smokers from passive smokers and passive smokers from active smokers, it is necessary to estimate cotinine cut-off points. The objective of this article was to apply statistical distribution of urinary cotinine concentration to estimate cut-off points distinguishing the three above-mentioned groups. The examined group consisted of 327 volunteers (187 women and 140 men) who were ethnically homogenous inhabitants of the same urban agglomeration (Sosnowiec, Poland). The values which enabled differentiation of the examined population into groups and subgroups were as follows: 50 µg l−1 (differentiation of non-smokers from passive smokers), 170 µg l−1 (to divide the group of passive smokers into two subgroups: minimally and highly exposed to environmental tobacco smoke), 550 µg l−1 (differentiation of passive smokers from active smokers), and 2100 µg l−1 (to divide group of active smokers into two subgroups: minimally and highly exposed to tobacco smoke). The results suggest that statistical distribution of urinary cotinine concentration is useful for estimating urinary cotinine cut-off points and for assessing the smoking status of persons exposed to tobacco smoke.

ADMA and SDMA levels in healthy men exposed to tobacco smoke

Dear Editor, Smoking cigarettes is one of the most important modifiable risk factors for the development of cardiovascular disease (CVD), which includes coronary and cerebrovascular diseases, peripheral arterial diseases and congestive heart failure [1]. Only few reports about the relationship between newly emerging risk factors for CVD and smoking cigarettes exist [2]. These compounds include two biological mediators of interconnected metabolic pathways, namely asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) [3]. ADMA is an endogenous inhibitor of endothelial nitric oxide synthase. When elevated, ADMA plasma levels are associated with an increased risk of atherosclerosis in patients with chronic renal injury [4]. Recent studies also suggest that ADMA may be an independent risk factor of coronary artery disease [5]. In addition, there is evidence that SDMA may be a biomarker of renal disease and useful in the assessment of coronary artery disease risk [6]. We investigated 168 healthy male volunteers aged 18–60 years (mean age 37.9 ± 9.1). They all lived in the city of Katowice (Poland), did not give any history of ischemic heart disease, diabetes mellitus, liver disease, or hypertension. All participants declared alcohol abstinence, practiced a sedentary lifestyle (no daily exercise) and having at least one cup of coffee a day. They also declared that they did not take vitamin supplements during 3 months before examination. Initial plasma lipid levels in most cases were within the reference range used in the laboratory practice. Plasma creatinine levels were measured and found to be 30 ng/mL). ADMA and SDMA levels were assessed simultaneously using high-performance liquid chromatography (HPLC) [7]. Our results show that ADMA plasma levels were 15.4% higher in passive smokers (0.45 ± 0.16 μmol/L) than in the control group (0.39 ± 0.16 μmol/L), and 7.7% higher in the active smokers group (0.42 ± 0.14 μmol/L) vs. passive smokers. These differences were not statistically significant (P = 0.110 and P = 0.322, respectively). Among passive smokers, mean plasma SDMA level (0.42 ± 0.14 μmol/L) was insignificantly higher by 13.5% (P = 0.183) than in the control group (0.37 ± 0.10 μmol/L), and only by 8.1% (P = 0.595) higher than in active smokers (0.40 ± 0.15 μmol/L). There were no statistically significant relationships found between ADMA or SDMA and the tobacco exposure assessed by plasma cotinine for the non-smokers group. In passive and active smokers, the relationships were weak (ADMA: r = 0.173 (passive) vs. r = 0.218 (active); SMDA: r = 0.097 (passive) vs. r = 0.239 (active)) and statistically non-significant. Plasma ADMA and SDMA levels in non-smokers and in passive smokers are highly correlated (r = 0.671 and r = 0.643, respectively, P < 0.001). Strong correlation was also found between plasma ADMA and SDMA levels in active smokers (r = 0.803, P < 0.001). Multiple regression analysis was used in the case of serum ADMA. Plasma homocysteine (tHcy) and creatinine concentrations were found to be significantly correlated with plasma ADMA concentration in the passive smokers group (Table 1). Although in the active smokers group the β coefficient was the highest for tHcy, it was not significant (β = 0.2810, P = 0.090). Also in the whole examined population tHcy and creatinine concentration were significantly correlated with ADMA (β = 0.2598, P = 0.008 and β = 0.2452, P = 0.034, respectively). Table 1 Beta coefficients of the multiple regression analysis for plasma ADMA concentration. In summary, ADMA levels are not significantly different for active and passive smokers when compared to non-smokers. Our results show that the increased ADMA levels by 7.7% and 15.4% for active and passive smokers, respectively, in comparison with non-smokers, are not significant. The correlation between plasma ADMA level and plasma cotinine in both groups are weak and statistically not significant. Results from this study were compared with published findings of plasma ADMA levels. Table 2 presents the PubMed database papers reporting plasma ADMA levels up to the end of 2007. In most cases, presented data regard patients with some diseases which makes it difficult to compare with our results. Only one study included healthy men [10]. The authors of this study observed highly significant differences (more than 80%) in plasma ADMA levels between smokers and non-smokers. However this result differs significantly from the other studies presented in Table 2. Kielstein et al. [14] pointed to lack of correlation with clinical investigations and a low number of examined persons in the above-citied study. The largest studies to date on plasma ADMA levels were performed by Meinitzer et al. [13]. Multiple regression analysis showed that tHcy is the strongest predictor of plasma ADMA level. In our study, predictive factors for plasma ADMA level were as follows in order of strength of relationship: tHcy > creatinine > age > cotinine > BMI, but they were statistically non-significant. The same order was observed among passive smokers, but in that case the predictive values of tHcy and creatinine were significant. Among the entire examined population, both tHcy and creatinine showed significant predictive value. The other possible risk factors for CVD, such as coexisting diseases, dislipidemias, alcohol and coffee intake, occupational exposures, drugs and renal impairment were not accounted for because those factors were part of our exclusion criteria. Controlling confounding variables improves the internal validity of our results. Table 2 Literature data on plasma ADMA levels changes in non-smokers and smokers. Apart from the publications listed in Table 2, there are additional studies on the relationship between ADMA and cigarette smoking; however the studies do not report any exact ADMA levels nor their ranges. A study published by Lu et al. [15] examined the relationship between ADMA as a predictor for the outcomes in patients with angina pectoris who underwent angioplasty (n = 153). They found that increases in plasma ADMA levels were independent from other factors, such as age, hypercholesterolemia, application of stents and cigarette smoking. In 2003, Schiel et al. [16] examined 554 patients with type I diabetes mellitus and renal impairment. The obtained results showed higher plasma ADMA and SDMA levels in patients in comparison with the control group, but no significant effect of smoking tobacco on the examined parameters was observed in any of the groups. In CARDIAC (Coronary Artery Risk Determination Investigating the Influence of ADMA Concentration) study, 816 persons were evaluated for the relationship between plasma ADMA level and the risk of coronary artery disease [17]. Active smokers showed significantly lower plasma ADMA levels than non-smokers. However, in ex-smokers plasma ADMA levels were higher than in non-smokers. Tonstad et al. [18] selected a group of 207 women and men aged 18–39 years with a high risk of coronary artery disease (dyslipidemia, family history of coronary artery disease). They found that ADMA level was related only to BMI but is not related to age, sex, and the number of cigarettes smoked. Wang et al. [11] observed a non-significant increase in plasma SDMA by 2.7% (P = 0.58) in smokers in comparison with non-smokers (0.38 μmol/L vs. 0.37 μmol/L, respectively). On the other hand, in human endothelial cell culture, there was an insignificant SDMA level increase (by 43.7%) after supplementing the culture medium with 10% tobacco smoke extract (0.102 nmol/mg protein vs. 0.071 nmol/mg protein, respectively) [10]. Our results showed elevated plasma SDMA levels by 13.5% in passive smokers and only by 8.1% in active smokers relative to control group of non-smokers. In conclusion, there was no significant effect of tobacco smoke on ADMA and SDMA levels. Therefore, the endothelial dysfunction caused by tobacco smoke is probably not related to the inhibition of nitric oxide synthase by ADMA.

Effect of occupational exposure to lead on new risk factors for cardiovascular diseases.

Objective The cardiovascular effects of lead are caused primarily through an effect on blood pressure but are not just limited to an increased risk of hypertension. The aim of our study was to determine to what extent chronic exposure to lead affects new risk factors for cardiovascular disease (CVD) development, such as biomarkers of inflammation (C reactive protein (CRP) and fibrinogen) and biomarkers of endothelial dysfunction (homocysteine, asymmetric dimethylarginine (ADMA) and L-homoarginine). Methods A cross-sectional study was performed on a sample of 231 male volunteers, aged 20–60 years, working for at least 2 years in jobs with exposure to lead during the mining and processing of lead–zinc ores. The association between lead in blood and CVD biomarkers was evaluated using multiple linear regression, and the effects of exposure level were observed in workers divided into subgroups according to their blood lead concentration: <250, 250–400 and >400 µg/L. Results Lead in the blood correlated with new risk factors for CVD except for ADMA. Multiple regression analysis revealed that predictive properties for lead in the blood increased for particular biomarkers in the following order: L-homoarginine, fibrinogen, CRP and homocysteine. Among the specified groups, significant differences were observed only between the groups with the most and least exposure to lead, which differed in concentrations by 54.3% for CRP, 19.3% for fibrinogen, 10.6% for homocysteine and −25.5% for L-homoarginine. Conclusions These findings support the hypothesis that occupational exposure to lead can promote atherosclerosis, particularly in highly exposed individuals.

Evaluation of the Blood Antioxidant Capacity in Two Selected Phases of the Training Cycle in Professional Soccer Players

Evaluation of the Blood Antioxidant Capacity in Two Selected Phases of the Training Cycle in Professional Soccer Players The aim of this study was to evaluate the effects of a regular pre-season training on the aerobic performance and the blood antioxidant defense capacity in soccer players from the Polish Premier League club (n=19) and IVth League team (n=15). The players participated in an incremental treadmill running exercise to volitional fatigue twice (i.e., at the beginning (Trial A) and the end (Trial B) of the pre-season spring training). In venous blood samples, taken at rest and 3 min post-test, the activities of antioxidant enzymes (SOD, GSH-Px, CAT, GR) and concentrations of non-enzymatic antioxidants (GSH, tocopherols, retinol, uric acid) and malondialdehyde as a lipid peroxidation biomarker were measured. With the aim of between-group comparisons and possible conclusions on training-induced changes in the capacity of the blood antioxidant defense, the POTAX index was calculated as a sum of standardized activities of antioxidant enzymes and concentrations of non-enzymatic antioxidants. The results of the present study indicate that the players from the Premier League club were characterized by only slightly higher maximal oxygen uptake rates, the differences compared to IVth League team, as assessed in both trials, were statistically insignificant. Participation in the pre-season training resulted in a moderate improvement of aerobic performance, although only a few players were characterized by VO2max comparable to the international-class elite performers. No distinct differences were observed in the level of aerobic performance between higher- and lower-classified players. Pre-season training led to an improvement in the global blood antioxidant capacity expressed in terms of POTAOX indices, although the changes in the activities and concentrations of individual components of the antioxidant system were less pronounced. Training-induced level of antioxidant conditioning was higher among the Premier League players, which may be related to differences in the training schedule and nutritional preparation of the athletes.

Cessation of alcohol consumption decreases rate of nicotine metabolism in male alcohol-dependent smokers

BACKGROUND Rate of nicotine metabolism is an important factor influencing cigarette smoking behavior, dependence, and efficacy of nicotine replacement therapy. The current study examined the hypothesis that chronic alcohol abuse can accelerate the rate of nicotine metabolism. Nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism) and patterns of nicotine metabolites were assessed at three time points after alcohol cessation. METHODS Participants were 22 Caucasian men randomly selected from a sample of 165 smokers entering a 7-week alcohol dependence treatment program in Poland. Data were collected at three time points: baseline (week 1, after acute alcohol detoxification), week 4, and week 7. Urine was analyzed for nicotine and metabolites and used to determine the nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism), and total nicotine equivalents (TNE, a biomarker for total daily nicotine exposure). RESULTS AND CONCLUSIONS There was a significant decrease in urine NMR over the 7 weeks after alcohol abstinence (F(2,42)=18.83, p<0.001), indicating a decrease in rate of nicotine metabolism. On average NMR decreased 50.0% from baseline to week 7 (9.6±1.3 vs 4.1±0.6). There was no change in urine TNE across the three sessions, indicating no change daily nicotine intake. The results support the idea that chronic alcohol abuse may increase the rate of nicotine metabolism, which then decreases over time after alcohol cessation. This information may help to inform future smoking cessation interventions in this population.

Effect of occupational lead exposure on α- and γ-tocopherol concentration in plasma

Objectives Changes in enzymatic antioxidant activity are frequently observed in workers occupationally exposed to lead. Few studies have investigated the influence of lead on the non-enzymatic antioxidant system. The aim of our study was to assess the influence of occupational exposure to lead on the plasma concentration of two hydrophobic forms of vitamin E: α-tocopherol and γ-tocopherol. Methods A sample of 401 healthy men, aged 19–62, participated in the study. In total, 340 of these subjects were employed at the Mine and Metallurgical Plant in southern Poland. The workers who were occupationally exposed to lead were divided into quartiles (groups of 85 subjects). The lead concentrations in the blood of the subjects in the control group and in the lead exposure quartiles correspond to the following ranges: 10–72 μg/l (control group); 82–206 μg/l (Q1); 209–308 μg/l (Q2); 308–394 μg/l (Q3) and 395–644 μg/l (Q4), respectively. Results Significant differences were observed only for the plasma concentration of γ-tocopherol, which differed between the control group and Q1 (by 24.1%, p=0.0368), between Q1 and Q3 (by −18.8%, p=0.0115) and between Q1 and Q4 (by −25.7%, p=0.0002). Multiple linear regression analysis showed that the statistically significant, predictive properties of the γ-tocopherol plasma concentration were as follows: triglycerides (β=0.440)> age (β=0.131)> whole cholesterol (β=0.117)> blood lead concentration (β=−0.108). For α-tocopherol, significant prognostic properties were triglycerides and total cholesterol (β=0.485 and β=0.399, respectively). Conclusions Occupational exposure to lead is strongly correlated with the concentration of γ-tocopherol but not α-tocopherol.