Introduction

Smoking has been widely believed to be the strongest single adverse health factor.1-4 Epidemiological studies have shown a fairly constant association between socioeconomic status and smoking, with a higher prevalence of smoking observed among those who are poorer and less educated.5,6 Although a negative effect of a low socioeconomic status and smoking on mortality risk and health status has been clearly established, associations between smoking and other biological indicators of health status have not been elucidated yet. Some studies indicated associations among smoking, single clinical parameters, and socioeconomic status.7-9 Lower body mass was also associated with smoking.10 Furthermore, smoking, loneliness, and mental health problems were demonstrated to be related.11 In the search for a more precise model of adverse effects of nicotine, attempts have been made to characterize in detail how smoking and other health behaviors are related to biochemical and even metabolomic correlates of smoking effects.12-14 Elucidating the associations between smoking and selected biological determinants of smoking-related disease may help to understand their pathomechanism and identify possible ways to decrease the risk. Additionally, the role of new cardiovascular risk factors, such as serum bilirubin and creatinine levels, in the prediction of cardiovascular events need to be better understood also in the context of smoking.15,16 So far, no study has attempted to compare multiple, both biochemical and socioeconomic, cardiovascular risk profiles between smokers and nonsmokers in Central-Eastern Europe.

The aim of this study was to assess the relationship among smoking, the pattern of biochemical risk factors, and sociodemographic characteristics in a Central-Eastern European population, based on data collected in a representative sample of the Polish general population in the NATPOL 2011 (Arterial Hypertension and Other Cardiovascular Disease Risk Factors in Poland [Polish: Nadciśnienie tętnicze oraz inne czynniki ryzyka chorób serca i naczyń w Polsce]) study.

Patients and methods

The NATPOL 2011 survey was designed as a cross-sectional, representative observational study. It was carried out in a representative sample of Polish residents aged 18 to 79 years. The participants were randomly selected in clusters using a stratified, proportional draw performed in 3 stages. Overall, 2413 subjects (1245 women and 1168 men) participated in the survey. The response rate among respondents who were invited and eligible for the study was 66.4%. The survey fieldwork was carried out by 234 well-trained nurses who lived in or close to the randomly selected geographical clusters. The participants were examined during 2 home visits. The evaluation of an individual included the following components: completion of a questionnaire, blood pressure readings and anthropometric measurements, and blood and urine sample collection. The questionnaire was completed during the first visit. Blood pressure readings were taken 3 times during the first and the second visit using a fully automatic oscillometric device (A&D, UA 767, Tokyo, Japan). Arterial hypertension was diagnosed according to the 2007 European Society of Hypertension / European Society of Cardiology guidelines for the management of arterial hypertension if during both visits systolic blood pressure was higher than or equal to 140 mm Hg or diastolic blood pressure was higher than or equal to 90 mm Hg, or the patient was taking antihypertensive drugs over the previous 2 weeks because of diagnosed hypertension. Overweight was defined as a body mass index (BMI) between 25 and 29.9 kg/m2, and obesity, as BMI ≥30 kg/m2. Smoking was defined as active regular smoking of at least 1 cigarette per day. Education levels were divided into the following categories: primary (includes vocational), secondary (includes incomplete higher education, ie, without a master’s degree), and higher education. Blood and urine samples were taken during the second visit, after 10 to 12 hours of fasting. However, participants were allowed to drink water while fasting. Frozen samples were transported to a central laboratory for blood and urine analysis.

The study protocol was approved by the institutional ethics committee at Medical University of Gdańsk and all participants provided written informed consent.

Detailed data on the questionnaire, sample selection, and laboratory parameters were provided elsewhere.17

Statistical analysis

The sample size was calculated based on the assumption that the acceptable (or allowable) margin of error in the estimation for prevalence of smoking or hypertension within different sex groups was not greater than 3%. The calculated sample size included 2400 participants. Data were presented as the number and percentage of patients, mean (SD), or median (interquartile range) for nonnormally distributed data. For normally distributed continuous variables, the t test for independent samples was applied. For variables that did not follow normal distribution, the Mann–Whitney test was used to compare independent measurements. The Kruskal–Wallis test or 1-way analysis of variance were used to compare multiple groups depending on whether the data fitted the assumptions of normality. Differences between categorical variables were tested using the χ2 test. Data on income were grouped into quartiles. Logistic regression analysis was performed to identify characteristics associated with smoking status. The following variables, for which the P value was less than or equal to 0.2 in univariable analysis, were included in multivariable logistic regression analysis: BMI, education, income, place of residence, marital status, hypercholesterolemia, and hypertension. Former smokers were classified as nonsmokers in all analyses. A P value less than 0.05 was considered significant. Smoking was a dependent variable for models with sociodemographic and some clinical characteristics, and it was regarded as an independent variable in other health outcome analyses. All statistical analyses were performed using the STATA software, version 12.1 (STATA Corp., College Station, Texas, Unites States).

Patient and public involvement

This study was performed without patient involvement. Patients were not invited to comment on the study design and were not consulted to develop patient-relevant outcomes or interpret the results. Patients did not contribute to the writing or editing of this document for readability or accuracy.

Results

Overall, the study included 2413 respondents (1245 women and 1168 men). The mean (SD) age was 47 (17) years in women and 45 (16) years in men. The study group included 331 female smokers (26.6%) and 402 male smokers (34.4%). Former smokers accounted for 21.2% of women and 31.6% of men. More than half of the surveyed women (52.2%) and 34% of men declared that they had never smoked tobacco. The percentage of smokers differed significantly between men and women (P <0.001).

The mean or median values of the parameters analyzed in the study in men and women are presented in table 1. Men were found to have significantly higher BMI, apolipoprotein B (apoB), bilirubin, creatinine, potassium, and fasting blood glucose levels as well as systolic and diastolic blood pressure.

Table 1. Characteristics of the study population by sex

Characteristics

Women

Men

P value

Patients, n (%)

1245 (51.6)

1168 (48.4)

–

Smoking, n (%)

331 (26.6)

402 (34.4)

<0.001

BMI, kg/m2, median (IQR)

25.1 (22–29.2)

26.8 (24.1–30)

<0.001

Fasting blood glucose, mg/dl, median (IQR)

88 (83–96)

92 (86–101)

<0.001

Total cholesterol, mg/dl, median (IQR)

196 (171–226)

196 (168–227)

0.53

apoB, g/l, median (IQR)

0.86 (0.7–1.04)

0.92 (0.74–1.1)

<0.001

CRP, mg/dl, median (IQR)

1.4 (0.6–3.1)

1.3 (0.6–2.7)

0.22

Creatinine, mg/dl, median (IQR)

0.74 (0.69–0.79)

0.88 (0.8–0.97)

<0.001

Bilirubin, mg/dl, median (IQR)

0.57 (0.43–0.77)

0.71 (0.51–0.96)

<0.001

Potassium, mmol/l, median (IQR)

4.3 (4.1–4.6)

4.4 (4.2–4.7)

<0.001

SBP, mm Hg, mean (SD)

127.2 (21)

134.3 (18.6)

<0.001

DBP, mm Hg, mean (SD)

80.2 (10.6)

81.6 (11.5)

0.002

SI conversion factors: to convert glucose to mmol/l, multiply by 0.0555; total cholesterol to mmol/l, multiply by 0.0259; apoB to mmol/l, multiply by 0.0019; CRP to nmol/l, multiply by 95.2381; creatinine to mmol/l, multiply by 0,0884; and bilirubin to mmol/l, multiply by 0,0171.

Abbreviations: apoB, apolipoprotein B; BMI, body mass index; CRP, C-reactive protein, DBP, diastolic blood pressure; SBP, systolic blood pressure

The prevalence of hypertension was higher in smoking men than in smoking women (table 2). We found no significant increase in the risk of hypertension among smoking men and women compared with nonsmokers. Both smoking women and men had a 40% lower risk of excess body weight in comparison to nonsmokers. The proportion of women who believed that they were overweight or obese was about 40% and was similar in smokers and nonsmokers. Smoking men had a significantly lower tendency to perceive their body weight as excessive (odds ratio [OR], 0.64; 95% CI, 0.49–0.83).

Table 2. Prevalence and univariable odds ratios for hypertension, overweight, and obesity in relation to smoking

Women

Men

Smokers

Nonsmokers

OR (95% CI)

Smokers

Nonsmokers

OR (95% CI)

Arterial hypertension

86 (26.06)

304 (33.63)

0.78 (0.58–1.06)

139 (34.84)

286 (37.48)

0.9 (0.69–1.17)

Overweight or obesity

48 (14.5)

215 (23.52)

0.61 (0.42–0.88)

75 (18.66)

214 (27.94)

0.59 (0.43–0.81)

Self-perceived overweight or obesity

126 (38.07)

369 (40.37)

0.91 (0.69–1.19)

127 (31.59)

325 (42.43)

0.64 (0.49–0.83)

Data are presented as the number (percentage) of patients unless otherwise indicated.

Abbreviations: OR, odds ratio

Smokers were younger and had lower body weight than nonsmokers. We found significant differences in the mean age and BMI between smoking and nonsmoking women and men (table 3). Significantly higher fasting blood glucose levels were observed in the oldest group of female nonsmokers (P = 0.02) and in male smokers aged 18 to 39 years (P = 0.02) in comparison to their peers with the opposite smoking status. Significantly higher cholesterol levels were observed in smoking women, and this difference between smokers and nonsmokers increased with age. In smoking men, significantly higher cholesterol levels were noted in all respondents compared with nonsmokers (P = 0.01). In smoking women, significantly higher apoB levels were observed in all respondents (P = 0.002), in those aged 40 to 59 years (P = 0.001), and in the oldest age group (P = 0.046). In smoking men, significantly higher apoB levels were observed in all respondents (P = 0.007) and in the oldest age group (P = 0.046). Mean C-reactive protein (CRP) levels did not significantly differ between smoking and nonsmoking women. However, significant differences in CRP levels were noted between smoking and nonsmoking men, and these differences increased with respondents’ age (table 3). No significant differences in creatinine levels were found between smoking and nonsmoking women (table 3). In men, creatinine levels were significantly higher in nonsmokers except for the oldest age group. Bilirubin levels were significantly lower in smoking women and men, except for the group of the oldest men (P = 0.88). Potassium levels were significantly higher (P <0.05) in smoking women and men, except for the group of the oldest women (P = 0.09).

Table 3. Body mass index and laboratory parameters in women and men in relation to age and smoking status

Variable

Women

Men

Smokers

Nonsmokers

P value

Smokers

Nonsmokers

P value

Age, y

43.55 (14.22)

47.82 (18.04)

<0.001

43.22 (14.36)

45.75 (16.78)

<0.001

BMI, kg/m2

Overall

25.22 (5.07)

26.48 (5.64)

0.03

26.67 (4.58)

27.67 (4.53)

<0.001

18–39 y

23.39 (4.31)

23.4 (4.46)

0.14

25.42 (4.9)

26 (4.18)

0.3

40–59 y

26.59 (5.32)

27.01 (5.29)

0.41

26.64 (4.05)

29.03 (4.47)

<0.001

60–79 y

26.74 (4.71)

29.68 (5.27)

<0.001

27.3 (4.88)

28.57 (4.28)

0.08

Fasting blood glucose, mg/dl

Overall

90.54 (18.29)

92.02 (18.13)

0.29

97.32 (29.85)

97.09 (23.22)

0.92

18–39 y

84.36 (8.58)

84.06 (8.32)

0.53

90.14 (10.78)

88.7 (9.7)

0.02

40–59 y

95.79 (24.47)

93.15 (17.20)

0.3

101.84 (40.57)

100.79 (27.06)

0.32

60–79 y

93.6 (12.34)

100.61 (22.83)

0.02

104.28(22.84

105.64 (28.21)

0.78

Total cholesterol, mg/dl

Overall

203.82 (42.83)

198.54 (40.07)

0.01

202.98 (45.06)

196.12 (43.10)

0.01

18–39 y

181.26 (30.86)

184.59 (34.17)

0.34

188.14 (40.83)

185.85 (38.35)

0.65

40–59 y

221.91 (42.72)

209.48 (37.74)

0.01

218.54 (42.35)

210.61 (43.51)

0.28

60–79 y

217.46 (43.36)

205.63 (43.72)

0.008

195.25 (49.62)

192.82 (44.97)

0.29

apoB, g/l

Overall

0.92 (0.27)

0.87 (0.25)

0.002

0.96 (0.28)

0.92 (0.26)

0.007

18–39 y

0.78 (0.22)

0.78 (0.21)

0.76

0.88 (0.24)

0.84 (0.24)

0.35

40–59 y

1.02 (0.25)

0.93 (0.24)

0.001

1.04 (0.28)

1.01 (0.26)

0.27

60–79 y

1.02 (0.29)

0.93 (0.26)

0.005

0.97 (0.31)

0.91 (0.26)

0.046

CRP, mg/l

Overall

1.99 (2)

2.01 (1.9)

0.73

2.06 (1.95)

1.76 (1.82)

<0.001

18–39 y

1.68 (1.79)

1.64 (1.81)

0.59

1.56 (1.71)

1.45 (1.62)

0.28

40–59 y

2.19 (2.11)

2.10 (1.92)

0.91

2.29 (2.07)

1.94 (1.96)

0.01

60–79 y

2.35 (2.16)

2.40 (1.91)

0.48

2.85 (1.83)

2.05 (1.86)

<0.001

Creatinine, mg/dl

Overall

0.74 (0.1)

0.76(0.20)

0.12

0.88 (0.17)

0.91 (0.17)

<0.001

18–39 y

0.72 (0.07)

0.72 (0.07)

0.52

0.88 (0.14)

0.9 (0.11)

0.008

40–59 y

0.74 (0.1)

0.74 (0.1)

0.97

0.86 (0.12)

0.9 (0.14)

<0.001

60–79 y

0.78 (0.13)

0.8 (0.25)

0.41

0.97 (0.31)

0.97 (0.26)

0.23

Bilirubin, mg/dl

Overall

0.56 (0.25)

0.68 (0.36)

<0.001

0.72 (0.35)

0.84 (0.45)

<0.001

18–39 y

0.59 (0.29)

0.68 (0.36)

0.01

0.77 (0.38)

0.86 (0.46)

0.02

40–59 y

0.53 (0.21)

0.67 (0.4)

<0.001

0.68 (0.31)

0.82 (0.47)

0.001

60–79 y

0.58 (0.24)

0.69 (0.32)

0.001

0.97 (0.31)

0.84 (0.41)

0.88

Potassium, mmol/l

Overall

4.45 (0.45)

4.34 (0.39)

<0.001

4.51 (0.42)

4.39 (0.38)

<0.001

18–39 y

4.37 (0.39)

4.24 (0.31)

0.001

4.46 (0.39)

4.38 (0.34)

0.04

40–59 y

4.5 (0.43)

4.38 (0.43)

0.01

4.5 (0.4)

4.4 (0.36)

0.006

60–79 y

4.57 (0.59)

4.42 (0.41)

0.09

4.7 (0.54)

4.42 (0.44)

0.001

Data are presented as mean (SD).

Abbreviations: see table 1

Although smoking rates declined with age, the relationship between smoking and age was of borderline significance (table 4). Women who completed secondary education were at approximately 40% lower risk of smoking, as demonstrated in univariable and multivariable analyses, in comparison to women who completed primary education. The effect of education was stronger among men: the likelihood of smoking was about 70% lower in men with secondary and incomplete higher education, and about 35% lower in men with higher education compared with primary education (OR, 0.33; 95% CI, 0.21–0.52 and OR, 0.66; 95% CI, 0.49–0.9, respectively). We found a strong relationship between the smoking status and financial situation of the respondents. Univariable and multivariable analyses showed that the lower the income, the higher the likelihood of smoking. Men and women in the upper quartile of income had a 50% lower likelihood of smoking compared with those in the lower quartile (OR, 0.51; 95% CI, 0.35–0.76 and OR, 0.55; 95% CI, 0.36–0.85, respectively). In both sexes, the highest smoking rates were observed in the group with the lowest income. In univariable and multivariable analyses, the likelihood of smoking was also related to the marital status and was significantly higher in single subjects compared with the married ones (adjusted OR, 2.39; 95% CI, 1.3–4.4 and OR, 1.9; 95% CI, 1.2–3 in men and women, respectively).

Table 4. Odds ratios with 95% CIs for smoking in relation to socioeconomic and demographic factors

Variable

Women

Men

OR (95% CI)

OR (95% CI)a

OR (95% CI)

OR (95% CI)a

Age (per 1 year)

0.99 (0.98–1)

0.98 (0.97–1)

0.99 (0.99–1)

0.99 (0.98–1)

Education level

Primary

1

1

1

1

Secondary

0.61 (0.42–0.91)

0.62 (0.4–0.97)

0.30 (0.2–0.47)

0.33 (0.21–0.52)

High

1.15 (0.41–1.56)

1.17 (0.83–1.64)

0.63 (0.48–0.83)

0.66 (0.49–0.9)

Place of residence

Rural area

1

1

1

1

Town (<50 000 inhabitants)

1.32 (0.93–1.89)

1.45 (1–2.11)

1.37 (0.99–1.9)

1.64 (1.15–2.33)

Town (50 000–200 000 inhabitants)

1.01 (0.66–1.54)

1.14 (0.73–1.78)

1.12 (0.78–1.6)

1.38 (0.94–2.03)

City (>200 000 inhabitants)

1.18 (0.82–1.7)

1.42 (0.96–2.09)

0.82 (0.57–1.19)

1.17 (0.78–1.75)

Income quartiles

1 (lowest)

1

1

1

1

2

0.58 (0.4–0.83)

0.63 (0.43–0.94)

0.59 (0.42–0.83)

0.67 (0.47–0.97)

3

0.5 (0.33–0.74)

0.57 (0.37–0.89)

0.39 (0.27–0.57)

0.48 (0.32–0.73)

4 (highest)

0.47 (0.31–0.69)

0.55 (0.36–0.85)

0.41 (0.29–0.59)

0.51 (0.35–0.76)

Marital status

Married

1

1

1

1

Widowed

0.93 (0.66–1.3)

0.75 (0.5–1.12)

1.04 (0.77–1.39)

0.82 (0.55–1.21)

Single

1.83 (1.18–2.85)

1.90 (1.2–3)

2.14 (1.21–3.74)

2.39 (1.3–4.4)

Divorced

0.77 (0.51–1.16)

0.98 (0.61–1.58)

1.25 (0.59–2.64)

1.17 (0.52–2.62)

Data are presented as mean (SD).

a Multivariable analysis

Abbreviations: see tables 1 and 2

The results of univariable logistic regression analyses in women and men showed a significant relationship between BMI and smoking (table 5). However, 95% CI reached only a borderline value in the multivariable analysis for women. There was no association in logistic regression analysis between smoking status and hypertension. We found a 60% increased risk of hypercholesterolemia in smoking women yet no association between smoking and hypercholesterolemia in men (OR, 1.59; 95% CI, 1.15–2.21 and OR, 1.21; 95% CI, 0.89–1.64, respectively).

Table 5. Odds ratios with 95% CIs for selected clinical outcomes in relation to smoking

Outcome

Women

Men

OR (95% CI)

OR (95% CI)a

OR (95% CI)

OR (95% CI)a

BMI (per 1 kg/m2)

0.97 (0.94–0.99)

0.97 (0.94–1)

0.93 (0.91–0.96)

0.93 (0.9–0.97)

Hypertension

No

1

1

1

1

Yes

0.78 (0.58–1.06)

0.95 (0.65–1.4)

0.9 (0.69–1.17)

1.07 (0.78–1.46)

Hypercholesterolemia

No

1

1

1

1

Yes

1.21 (0.91–1.60)

1.59 (1.15–2.21)

1.04 (0.8–1.35)

1.21 (0.89–1.64)

a Multivariable analysis

Abbreviations: see table 1

Discussion

Smoking is an established risk factor for malignancies, metabolic disease, and cardiovascular disease. Numerous observations have indicated that smoking is related to metabolic syndrome and changes in lipid levels.18 There is a well-known, documented association between smoking and lower body mass, although this may lead to insulin resistance and an elevated risk of type 2 diabetes in heavy smokers.19 Our findings confirm the observation of a lower rate of excess body weight in smokers compared with nonsmokers.10,20 The risk of obesity or overweight was significantly lower in smokers—by around 40% in both women and men (table 2). In our study, smoking was associated with a significantly lower likelihood of self-perceived overweight or obesity in men yet not in women (table 2). The reason for a lower self-awareness of excess body weight among smoking men compared with women may be related to their ignorance regarding positive health behaviors, probably due to their lower socioeconomic status and a lower level of education.21 Smoking is more common among those socially and economically disadvantaged, which was also found in our study.5,22 Studies have indicated that smokers consume less fruit and vegetables, drink more alcohol, and are less physically active compared with nonsmokers.23,24 Hypercholesterolemia is a major risk factor for cardiovascular disease. In our study, we found a significantly higher cholesterol level in smokers compared with nonsmokers (table 3). Similar observations were reported in other studies, which also showed significantly higher cholesterol levels in smokers compared with nonsmokers.25,26 Our findings regarding apoB are also consistent with other reports of a significantly higher apoB level in smokers compared with nonsmokers (table 3).25 Insulin plays a key role in the regulation of apoB levels, and the insulin-resistant state is associated with increased secretion and decreased clearance of apoB. This is the possible mechanism of observed increased blood glucose levels in some age groups of smokers. Overall, fasting blood glucose levels did not significantly differ between smokers and nonsmokers in our study. However, a detailed subgroup analysis showed that, among women at the oldest age (60–79 years), the mean fasting blood glucose level was significantly higher in nonsmokers compared with smokers, while among men in the youngest age group (18–39 years), the mean fasting blood glucose level was significantly higher in smokers compared with nonsmokers (table 3). The effect of smoking on blood glucose control may vary depending on the history of smoking and the presence of type 2 diabetes in a smoking person. It is known that the risk of type 2 diabetes and glucose intolerance depends on the nicotine dose in smoked cigarettes.27 Longitudinal studies have shown that, in smokers with type 2 diabetes, quitting was paradoxically associated with worse glycemic control for up to 3 years.28 As we did not include ex-smokers as a separate group in the analysis, we were not informed about the possible reversibility of these changes; nonetheless, our survey could not verify a causal relationship. In the short-term perspective, smoking is usually associated with increased glucose levels and hormonal effects accompanied by a reduction of visceral fat, which also results in higher long-term blood glucose levels despite reduced BMI.29,30 Based on our cross-sectional study, it is difficult to draw conclusions about causal relationships, but the observed differences in blood glucose levels between age groups and sexes may have been related to differences in behaviors and smoking history in these groups. Similarly, it is challenging to clearly interpret the specific patterns found in our study in terms of the relationship between smoking and CRP and creatinine levels (table 3). C-reactive protein levels were higher, and creatinine levels were lower in smokers compared with non-smokers, which is in line with previous data.31 Studies have indicated a potential role of CRP in smokers as a mediator of glomerular hyperfiltration, increased proteinuria, and kidney dysfunction in a healthy population.32 Smoking is known to have varying effects on the physiology of vascular endothelium and inflammatory biomarkers depending on sex, which may, to some extent, explain the various patterns of the levels of these markers observed in men and women.33,34

Epidemiological studies have shown an inverse relationship between bilirubin levels and the risk of cardiovascular disease, stroke, and metabolic syndrome.35-37 Our study showed significantly lower bilirubin levels in smokers compared with nonsmokers, both overall and in all age- and sex-specific groups except for men aged 60–79 years (table 3). A role of bilirubin in biological pathways that lead to smoking-related disease has been suggested by reports of a higher risk of lung cancer in individuals with low bilirubin levels.14 In our study, potassium levels were significantly lower in nonsmokers, which is also associated with a lower risk of cardiovascular mortality.38 The increase in potassium levels may be attributed to cigarette smoking–induced skeletal muscle damage, which may cause leakage of cellular contents along with potassium into circulating plasma.39

Rates of smoking are known to be inversely proportional to socioeconomic status.5,6,40 In the present study, we found clear associations between socioeconomic factors and smoking. In multivariable analysis, the risk of smoking in women with secondary or incomplete higher education were nearly 40% lower compared with women with primary or incomplete secondary education (table 4). The proportion of smokers among women who completed higher education was similar to that among women with primary education, which probably reflects the increase in the rate of smokers among women with higher education, which has been observed in recent years. A simpler relationship was observed in men: the higher the level of education, the lower the risk of being a smoker (table 4). Another trend clearly seen in our data showed that the higher the income level, the lower the rate of smoking. However, there was no significant correlation between income and education. These findings are consistent with other reports showing that individuals with the lowest income are at the highest risk of initiating smoking and the least likely to quit it.41 The reasons for the highest rates of smoking among those with the lowest income level are believed to include stress, family problems, daily life struggles (including financial problems), boredom, and social influences in a community with a large proportion of smokers.22 These factors are most likely to characterize small-town communities, in which we found the highest risk of smoking in our study (table 4). In addition, we noted that being alone was associated with a higher risk of smoking. These findings are consistent with data from other studies, which have shown that the rate of smoking was more than 2-fold higher among single people compared with those nonsingle.42,43 Data from a Polish study showed that being a single woman increased the risk of smoking.44

These patterns of relationships among smoking, socioeconomic status, and loneliness correspond with the reported association of smoking with depressed mood, stress, and depression among smokers.11 Longitudinal studies have even shown an increased risk of suicide in the smoking population.45 Although we did not evaluate mood changes in the present study, the role of inflammatory markers as mediators of mood disorders suggests that smoking and an increased CRP level may represent 2 components of a larger spectrum encompassing stress and depression. Loneliness is also associated with an increased CRP level, which was reported in the context of atherogenesis.46,47 These associations are consistent with the postulated pathomechanism underlying the effect of smoking on the cardiovascular risk and they are supported by the results of our study. The inability to disentangle the effect of smoking, via insulin resistance, from different fasting blood glucose levels in diverse age and sex groups due to lack of data on smoking history and type 2 diabetes was a significant limitation in the interpretation of the study findings.

The cross-sectional design was another major limitation of the present study, precluding conclusions regarding temporality and potential causation. The practical application of potential risk prediction algorithms that incorporate different biochemical profiles of nonclassic risk factors requires further research.

We observed differences regarding major biochemical and clinical parameters between smokers and nonsmokers in Poland, which indicate an adverse cardiovascular risk profile in smokers. We also found clear socioeconomic differences between smokers and nonsmokers, which are probably the major determinants of the increased cardiovascular risk associated with smoking in the Polish population. We observed that smoking behavior substantially differs between individuals with lower and higher socioeconomic status, those single and married, and with regard to place of residence.

In order to revisit the role of socioeconomic factors in the development of the unfavorable biochemical profile among smokers, future studies would need to employ designs based on follow-up data collected in controlled settings.