Prevalence rate of Metabolic Syndrome in a group of light and heavy smokers

  • Hellas Cena1Email author,

    Affiliated with

    • Antonella Tesone1,

      Affiliated with

      • Rosanna Niniano2,

        Affiliated with

        • Isa Cerveri2,

          Affiliated with

          • Carla Roggi1 and

            Affiliated with

            • Giovanna Turconi1

              Affiliated with

              Diabetology & Metabolic Syndrome20135:28

              DOI: 10.1186/1758-5996-5-28

              Received: 30 January 2013

              Accepted: 11 May 2013

              Published: 30 May 2013

              Abstract

              Background

              Smoking is an important cause of morbidity and mortality worldwide. It iswidely accepted as a major risk factor for metabolic and cardiovasculardisease. Smoking reduces insulin sensitivity or induces insulin resistanceand enhances cardiovascular risk factors such as elevated plasmatriglycerides, decreases high-density lipoprotein cholesterol and causeshyperglycemia. Several studies show that smoking is associated withmetabolic abnormalities and increases the risk of Metabolic Syndrome. Theaim of this study was to estimate the prevalence of the metabolic syndromein a group of light and heavy smokers, wishing to give up smoking.

              Methods

              In this cross-sectional study all the enrolled subjects voluntary joined thesmoking cessation program held by the Respiratory Pathophysiology Unit ofSan Matteo Hospital, Pavia, Northern Italy.

              All the subjects enrolled were former smokers from at least 10 years and hadno cancer or psychiatric disorders, nor history of diabetes or CVD orcoronary artery disease and were not on any medication.

              Results

              The subjects smoke 32.3 ± 16.5 mean Pack Years. Theprevalence of the metabolic syndrome is 52.1%: 57.3% and 44.9% for males andfemales respectively. Analysing the smoking habit influence on the IDFcriteria for the metabolic syndrome diagnosis we found that all thevariables show an increasing trend from light to heavy smokers, except forHDL cholesterol. A statistical significant correlation among Pack Years andwaist circumference (R = 0.48, p < 0.0001),Systolic Blood Pressure (R = 0.18, p < 0.05),fasting plasma glucose (R = 0.19, p < 0.005) andHDL cholesterol (R = −0.26, p = 0.0005) hasbeen observed.

              Conclusions

              Currently smoking subjects are at high risk of developing the metabolicsyndrome.

              Therapeutic lifestyle changes, including smoking cessation are a desirablePublic health goal and should successfully be implemented in clinicalpractice at any age.

              Keywords

              Metabolic syndrome Smoking habit Insulin resistance Overweight Waist circumference

              Background

              Smoking is an important cause of morbidity and mortality worldwide. Currently,tobacco is the second leading cause of death in the world, accounting for about 5million deaths annually, equivalent to 1 out of 10 adults worldwide [1].

              It is widely accepted as a major risk factor for metabolic and cardiovascular disease [2]. Previous studies have shown that smoking reduces insulin sensitivity orinduces insulin resistance [3, 4] and enhances cardiovascular risk factors such as elevated plasmatriglycerides, decreases high-density lipoprotein cholesterol (HDL-C) and causeshyperglycemia [58]. Furthermore, several studies show that smoking is associated withmetabolic abnormalities and increases the risk of Metabolic Syndrome (MBS) [911]. This syndrome is associated with multiple metabolic alterations andhemodynamic disorders. Weitzman et al. [12] have demonstrated for the first time a dose-responsive,nicotine-confirmed relationship between tobacco smoke and the severity of MBS, alsoin adolescents, reporting that the exposure to tobacco smoke, whether by active orpassive smoking, is associated with a 4-fold increase in the risk of MBS amongadolescents who are overweight or at risk for overweight.

              Saarni et al. [13] investigated the association of adolescent smoking with overweight andabdominal obesity in adulthood, reporting that smoking is a risk factor forabdominal obesity among both genders and for overweight in women.

              In Kawada’s 1-year follow-up study [14], current smokers have a higher risk of MBS than non-smokers,independently of age, body mass index, insulin resistance, uric acid level and otherlifestyle factors. In contrast, ex-smokers do not have a significantly greater riskof MBS than non-smokers.

              The most effective way for smokers to decrease the risk of metabolic syndrome andcardiovascular disease is to quit smoking [15]. However, other authors highlight that smoking cessation is alsoassociated with an increased risk of MBS due to the subsequent body weight gain [4, 9].

              The aim of this cross sectional study is to estimate the prevalence of the metabolicsyndrome in a group of light and heavy smokers, wishing to give up smoking.

              Methods

              Sampling

              In this cross sectional study, the subject recruited were all smokers attendingthe respiratory physio-pathological surgical outpatient clinic of the San MatteoHospital in Pavia, Northern Italy, wishing to give up smoking. The inclusioncriteria were: former smokers from at least 10 years, age range from 28 to 70years, a medical history with no cancer or psychiatric disorders, nor history ofdiabetes or CVD or coronary artery disease and were not on any medication.

              They were enrolled consecutively.

              Anthropometric and functional measurements

              Each subject underwent a preliminary examination with a lung specialist and asubsequent examination to estimate his/her nutritional status withnutritionists: a medical doctor specialized in clinical nutrition and aregistered dietitian. The following parameters were measured:
              1. 1.

                Body weight, measured on subjects wearing only underwear and without shoes, by means of a steel yard scale (precision ± 100 g);

                 
              2. 2.

                Body height, measured on subjects without shoes by means of a stadiometer (precision ± 1 mm). BMI was calculated as the ratio between weight (in kilograms) and the square of height (in metres);

                 
              3. 3.

                Four skinfold thicknesses (mid-triceps, mid-biceps, subscapular and suprailiac), measured on subjects according to standard conditions on the non-dominant body side using a Harpenden skinfold thickness calliper (resolution 2 mm); three consecutive measurements were performed and the mean of the three values was considered. The sum of the four skinfold thicknesses was computed and the body fat percentage was calculated according to the predictive equations of Durnin and Womersley [16].

                 
              4. 4.

                Waist circumference, measured to the nearest mm in duplicate according to standard conditions, by placing a flexible tape midway between the lowest rib and the iliac crest. The tape was snug, but did not squeeze or compress the skin, and was parallel to the floor. The measure was collected on unclothed, relaxed subjects, after exhaling.

                 
              5. 5.

                Systolic (SBP) and diastolic (DBP) blood pressure, measured according to standard conditions using a sphygmomanometer; three measurements were performed at intervals of 2–5 minutes and then mean of the three values was considered.

                 
              6. 6.

                Heart rate measured according to standard conditions expressed as beats per minute (bpm), finding the pulse at the ventral aspect of the wrist on the side of the thumb (radial artery).

                 
              7. 7.

                Routine haematochemical levels and any drug therapy prescribed for cigarette withdrawal by the lung specialist were recorded at every medical examination. The patient’s life style was investigated by an interview conducted by a trained dietitian, in order to evaluate the kind, frequency and intensity of physical activity.

                 

              Smoking habits

              Three smokers category have been considered: light smokers consuming till 19 Packper Years (PY), moderate smokers ≥ 20–39 PY and heavysmokers ≥ 40 PY [17].

              PY means cigarettes smoked per day times years of smoking, divided by 20 [18].

              Diagnostic criteria for metabolic syndrome

              Several definitions exist for MBS [19].

              In our study we used the International Diabetes Federation (IDF) clinicalcriteria [20], providing an universally accepted diagnostic tool that is very easyto use in clinical practice.

              The IDF consensus definition includes:
              • ▪ Ethnic specific values for waist circumference: Central obesity is defined as waist circumference ≥94 cm forEuripides men and ≥80 cm for Euripides women, with ethnicity specificvalues for other groups; nevertheless if BMI is >30 kg/m2, centralobesity can be assumed and waist circumference does not need to be measured.

              • ▪ plus any two of the following four factors:
                • * raised Triglycerides (TG) level: ≥ 150 mg/dL (1.7 mmol/L), or undergoing specific treatment for this lipid abnormality;

                • * reduced HDL cholesterol: < 40 mg/dL (1.03 mmol/L) in malesand < 50 mg/dL (1.29 mmol/L) in females, or undergoing specifictreatment for this lipid abnormality;

                • * raised blood pressure: SBP ≥ 130 mm Hg orDBP ≥ 85 mm Hg, or undergoing a specific anti-hypertensivetreatment;

                • * raised fasting plasma glucose (FPG) ≥ 100 mg/dL(5.6 mmol/L), or previously diagnosed type 2 diabetes (if glucose concentrationis above 5.6 mmol/L or 100 mg/dL, OGTT is strongly recommended but is notnecessary to define presence of the syndrome).

              Informed consent and ethical approval

              Written informed consent was obtained from all participants prior to their inclusionin the study, which was performed in accordance with the ethical standards laid downin the appropriate version of the 1994 Declaration of Helsinki and approved by theUniversity of Pavia’s Faculty of Medicine Ethical Committee.

              Statistical analysis

              Comparison between males and females variables was analysed with pairedStudent’s t-test.

              Pearson’s correlation coefficient was used in order to determine theassociation among all the variables investigated, in particular with the smokinghabits (Packs per Year - PY).

              Analysis of variance was used to compare light, moderate and heavy smokers.

              Data were analyzed using the SPSS for PC statistical software package version 18(SPSS Inc., Chicago, IL, USA). All the results are reported asmean ± standard deviation. The statistical significance level wasset to p < 0.05 for all tests.

              Results

              Of the 160 subjects assessed for eligibility, 117 (73.1% of the whole sample) wereincluded in the study (68 males and 49 females). Forty-three individuals wereexcluded either because under the minimum age-range (n = 8) or affectedby metabolic, cardiovascular, psychiatric and/or oncological pathologies(n = 35). The mean age of the sample was 50.1 ± 11.3years. The mean BMI of the whole sample was 25.6 ± 4.8kg/m2. Table 1 shows the age and theanthropometric characteristics of the sample.
              Table 1

              Baseline characteristics of the sample

              Variables

              Men

              Women

              Total

              p-value

               

              n = 68

              n = 49

              n = 117

               

              Age (years)

              49.7 ± 11.2

              50.7 ± 11.7

              50.1 ± 11.3

              p = 0.639

              BMI (kg/m2)

              26.6 ± 4.9

              24.3 ± 4.3

              25.6 ± 4.8

              p < 0.01

              Fat mass (%)

              25.6 ± 6.1

              35.5 ± 6.7

              29.7 ± 7.9

              p < 0.0001

              Waist circumference (cm)

              97.7 ± 14.3

              86.5 ± 13.7

              93.0 ± 15.0

              p < 0.0001

              Triglycerides (mg/dL)

              188 ± 125

              140 ± 56

              168 ± 105

              p < 0.01

              HDL Cholesterol (mg/dL)

              43 ± 14

              51 ± 12

              46 ± 14

              p < 0.002

              Basal glycaemia (mg/dL)

              100 ± 27

              93 ± 18

              97 ± 24

              p = 0.157

              SBP1 (mmHg)

              129 ± 14

              126 ± 10

              128 ± 13

              p = 0.250

              DBP2 (mmHg)

              83 ± 8

              81 ± 6

              83 ± 7

              p = 0.148

              Pack years (n)

              35.2 ± 8.8

              27.4 ± 9.6

              32.3 ± 16.5

              p < 0.001

              1SBP Systolic Blood Pressure.

              2DBP Diastolic Blood Pressure.

              Waist circumference, body fat mass, BMI and PY are statistically different betweengender.

              Body fat percentage has been estimated by skinfolds thickness. We found that womenhave a higher fat mass percentage than men who show greater circumference waist andhigher BMI.

              A central fat distribution was found in the 73.7% of the sample; in the 71% of thesame subjects the waist circumference is above the 94 and 80 cm cut off levelsrespectively for men and women for the European population according to IDF [21].

              Biochemical and functional measurements, suggested by IDF, as MBS diagnosticcriteria, are reported in Table 1.

              Statistical significant differences between men and women for triglycerides and HDLcholesterol emerged. Waist circumference is positively correlated both to basalglycaemia (R = 0.41, p <0.0001), TG concentration(R = 0.26, p <0.00 5), SBP (R = 0.30,p < 0.001) and DBP (R = 0.27,p < 0.005).

              The subjects smoke 32.3 ± 16.5 mean PY. In Table 2 are reported the characteristics of the sample subdivided in light,moderate and heavy smokers.
              Table 2

              Characteristics of the whole sample subdivided in light, moderate andheavy smokers.

              Variable

              Light smokers

              Moderate smokers

              Heavy smokers

               

              (n = 17)

              (n = 41)

              (n = 59)

              Age (years)

              39.2 ± 12.7*

              46.2 ± 9.6

              56.6 ± 7.8

              BMI (kg/m2)

              22.3 ± 3.5*

              25.6 ± 4. 3

              26.6 ± 5.1

              Fat mass (%)

              35.5 ± 10.0

              40.5 ± 11.9

              41.1 ± 12.5

              Waist circumference (cm)

              82.7 ± 12.7*

              92.5 ± 13.3

              96.5 ± 13.6

              TG (mg/dL)

              124 ± 49*

              164 ± 72*

              190 ± 44

              HDL Col (mg/dL)

              52 ± 13

              46 ± 13

              45 ± 15

              Basal glycaemia (mg/dL)

              84 ± 14

              94 ± 16

              104 ± 29

              SBP (mmHg)

              124 ± 11

              126 ± 13

              132 ± 13

              DBP (mmHg)

              82 ± 6

              83 ± 8

              84 ± 7

              Years smoked (years)

              16.6 ± 7.8*

              29.1 ± 9.7*

              40.9 ± 8.6

              PY3 (n)

              12.5 ± 6.2*

              32.1 ± 9.9*

              63.4 ± 15.0

              * p < 0.05.

              1SBP Systolic Blood Pressure.

              2DBP Diastolic Blood Pressure.

              3PY Pack Years.

              Women resulted mainly light smokers (n = 13) and moderate smokers(n = 30) compared to men.

              All the variables show an increasing trend from light to heavy smokers, except forHDL cholesterol, which decreases as expected. The same trend has been observedadjusting for age, gender and BMI.

              In the whole sample there is a statistical significant correlation among PY and bodyweight (R = 0.40, p < 0.0001), BMI (R = 0.43,p < 0.0001), waist circumference (R = 0.48,p < 0.0001), fat mass (R = 0.45,p < 0.01), SBP (R = 0.18, p < 0.05),fasting plasma glucose (R = 0.19, p < 0.005).

              On the other hand, an inverse correlation between PY and HDL cholesterol(R = −0.26, p = 0.0005) has been observed. HDLcholesterol is also inversely correlated to body weight (R = −0.23,p < 0.01), BMI (R = − 0.22,p < 0.05), waist circumference (R = − 0.31,p < 0.0005), SBP (R = −0.19,p < 0.05), fasting plasma glucose (R = − 0.12,p < 0.001) and TG (R = − 0.46, p <0.0001).

              In the whole sample the MBS prevalence is 52.1%: 57.3% and 44.9% for males andfemales respectively.

              Analysing the prevalence of the single IDF criteria for the MBS diagnosis we foundthat a high waist circumference value is the most frequently relieved parameter (in88.2% of females and 91.5% of males), followed by raised TG in both genders (81.1%in females and 92.3% in males) and SBP in males (86.6%), and finally by lowered HDLlevels in females (75.6%) and high DBP in males (82.4%).

              Table 3 shows all the variables considered in the subjectswith or without MBS. All the parameters are significantly higher (p ranges from<0.05 to < 0.0001) in patients affected by MBS, except for HDLcholesterol value, which is lower.
              Table 3

              Criteria values for Metabolic Syndrome (MBS) diagnosis, age, BMI, bodyfat mass, PY in the two sub samples with and without MBS (MBS vsNMBS)

              Variable

              MBS (n 61)

              NMBS (n 56)

              p-value

              Age (years)

              53.5 ± 10.2

              46.5 ± 11.5

              p = 0.0006

              Males (n)

                35

                33

               

              BMI (kg/m2)

              27.9 ± 4.4

              23.1 ± 3.8

              p < 0.0001

              Fat mass (%)

              42.3 ± 14.6

              37.8 ± 8.5

              p = 0.04

              Waist circumference (cm)

              101.0 ± 12.8

              84.3 ± 12.3

              p < 0.0001

              Triglycerides (mg/dL)

              201 ± 12.1

              131 ± 66

              p = 0.0002

              HDL Cholesterol (mg/dL)

              53 ± 15

              41 ± 10

              p < 0.0001

              Basal glycaemia (mg/dL)

              107 ± 28

              86 ± 10

              p < 0.0001

              SBP1 (mmHg)

              135 ± 13

              121 ± 8

              p < 0.0001

              DBP2 (mmHg)

              86 ± 7

              79 ± 5

              p < 0.0001

              PY3 (n)

              51.0 ± 28.0

              36.8 ± 23.5

              p < 0.05

              1SBP Systolic Blood Pressure.

              2DBP Diastolic Blood Pressure.

              3PY Pack Years.

              The sample’s life style can be described overall as sedentary, since only 42.2%of the subjects regularly walk for not more than half an hour per day and 37% of thewhole sample regularly walk for not more than half an hour per week, withoutsignificant differences between gender; 80% of the sample does not practice anysports nor any programmed physical activity (78.8% of males and 81.6% offemales).

              Discussion

              Tobacco smoking is a major risk factor for several diseases, including MBS andconsequently cardiovascular disease (CDV).

              Using racial- or ethnic-specific International Diabetes Federation criteria for waistcircumference, the MBS age-adjusted prevalence in the USA is 38.5% and it is higherin former smokers [22].

              Currently smoking men and women are at significantly higher risk of developing MBS,increasing directly the risk of atherosclerotic cardiovascular disease development [23].

              Our data further highlight the correlation of central obesity, MBS risk as well asassociation with obesity and smoking.

              The association between smoking and MBS remains even after adjusting for othercovariates, possibly a reflection of the effect of cigarette smoking on insulinresistance.

              In according with Wada et al. [4] we found a positive dose–response relationship between the dailynumber of cigarettes and MBS prevalence rate.

              This relationship is dependent on the number of cigarettes smoked daily: BMI, waistcircumference, total cholesterol, TG and glucose concentration are positivelyassociated with smoking intensity.

              Cigarette smoking is an independent predictor of developing metabolic abnormalitiesin middle age overweight and obese adults, lowering cigarette smoking reduces riskof metabolic abnormalities, particularly in men [24]. Cigarette smoking as well as physical inactivity and obesity areassociated with higher risk of the metabolic syndrome in elderly men too [25]; stopping smoking is one of the lifestyle changes, even at older ages,associated with a significant lowering risk of developing MBS [25].

              In our study the prevalence of MBS in the females, although mainly light and moderatesmokers compared to men, and despite significantly lower mean BMI (overallnormo-weight females vs overweight males), may be partly explained bygender-difference, higher total body fat mass and fat distribution (higher waistcircumferences compared to the gender specific IDF cut-points for Euripids). Thewomen in our sample were mainly perimenopausal and it is possible that hormonalfactors, in part, exert their influence on body fat distribution, as well as the agerelated increase of physical inactivity or higher rates of sedentary [24, 26, 27]. The sex difference is explained by others as a stronger anti-estrogeniceffect of nicotine in women than in men [28]. Cigarette smoking, particularly smoking ≥20 cigarettes/day, hasbeen associated with larger waist circumference and higher waist:hip ratio (WHR) inpre- and post-menopausal women after adjusting for potential confounding factors [29].

              Claire C et al. [30] found that among middle-aged smokers of both sexes, waist circumferenceincreased with number of cigarettes smoked, the authors conclude that among smokers,cigarettes smoked per day were positively associated with central fat accumulation,particularly in women [30].

              Waist circumference as well as WHR is an indicator of the amount of visceral adiposetissue [31]. In our study smokers tend to have a large waist circumference thatincreased proportionally with the number of the pack years (R = 0.48,p < 0.0001) in agreement with Shimokata et al. [32].

              Smoking seems to accelerate visceral fat accumulation and promote obesity-relateddisorders. Medical research has focused on visceral adiposity as a target for themanagement of the MBS [33]. Distribution of body fat is more important than the amount of fat as aprognostic factor for life expectancy [34].

              Nicotine, carbon monoxide, and other metabolites from smoking also play importantroles in insulin resistance [31]. Indeed, several studies in the past have shown that nicotine leads toinsulin resistance, has an anti-estrogenic effect and increases the level of stresshormones like cortisol [3537].

              Cigarettes smoking is a strong independent risk factor for cardiovascular disease aswell as for non insulin dependent diabetes mellitus [8, 38].

              MBS and glucose intolerance are regarded as disturbances with a common background andstrong interrelations such as hyperglycemia, decreases high-density lipoproteincholesterol (HDL-C) and elevated plasma triglycerides [39].

              In according with other authors [11, 40] we found that smokers had features of insulin resistance syndromeincluding low HDL Cholesterol, high serum triacylglicerol, high fasting glucose. Inour study all these parameters are positively associated with smoking intensity:there is a statistical significant correlation among Packs Year (PY) and BMI, waistcircumference, fasting plasma glucose and there is an inverse correlation betweenpacks smoked per year and HDL cholesterol.

              Low serum concentrations of high-density lipoprotein-cholesterol (HDL-C), defined as<1 mmol/L (40 mg/dL) in both sexes, or <1 mmol/L in men and <1.3 mmol/L (50mg/dL) in women, are independent risk factors for coronary heart disease (CHD). Thecauses of low HDL-C include rare genetic disorders such as Tangier and secondaryfactors such as smoking, type 2 diabetes, metabolic syndrome and abdominal obesity [41, 42].

              The current International guidelines for the management of dyslipidemia recommend achange in lifestyle for people with low HDL-C, focussing on weight reduction,increased physical activity and smoking cessation [43, 44], with evident benefits on the overall CVD risk and specifically on HDL-C [43].

              The visceral fat accumulation and insulin resistance may represent an important linkbetween cigarette smoking and the risk of cardiovascular disease [31]. Further research is needed in this area, but these findings indicatethat more emphasis should be placed on the risk of central obesity among smokers andthose who are quitting smoking. Almost any smoker is aware of the associationbetween quitting smoking and the risk of subsequent body weight gain due toincreased energy intake, decreased metabolic rate, increased physical inactivity [45] but, on the other hand not all of them know that their unhealthylifestyle habits, such as scarce fruit and vegetable intake, excessive alcoholconsumption, sedentary lead to weight gain and might partly explain why smokers tendto accumulate fat specifically in the abdominal area [31, 46]. Besides a recent research reported that smoking cessation may beassociated not only with increased body weight, fat mass, but also with increasedlean and functional mass suggesting a novel and important finding on the benefits ofquitting smoking [47].

              Limitations of current study

              Our results must be interpreted in light of the study limitations. First, the studyis a cross-sectional one, our results do not investigate the MBS prevalence databefore and after quitting smoking. Second, inflammatory and procoagulant variablesuch as C-reactive protein, fibrinogen as well as citokynes concentrations were notmeasured.

              On the other hand one of the study's strength is the use of anthropometricmeasurements instead of self-reported weight and height, as well as waistcircumference assessment. People tend to over report their height and under reporttheir weight, resulting in an underestimation of BMI. Under reporting of weight ismore prevalent in those who are overweight or obese than in normal-weight persons [48].

              Conclusions

              Currently smoking subjects are at high risk of developing the metabolic syndrome.Intervention studies offering support to smokers willing to quit through physicalactivity promotion and healthy diet in order to reduce smoking prevalence whereasavoid weight gain following cessation is a desirable public health goal.

              Medical management and prevention programs should take into account that concernsabout post cessation weight gain may deter numerous persons from quitting smoking [48], such persons should be made aware that smoking is not an efficient wayto control body weight, does not help prevent obesity and could favourite visceralfat accumulation and increase the risk of metabolic syndrome.

              According to our data, we suggest to specifically target, heavy smokers, because oftheir increased MBS risk and find support to assist them in smoking cessation. Thisdeserves priority [49, 50] and should successfully be implemented in clinicalpractice at any age.

              Abbreviations

              BMI: 

              Body mass index

              CDV: 

              Cardiovascular disease

              CHD: 

              Coronary heart disease

              FPG: 

              Fasting plasma glucose

              HDL-C: 

              High-density lipoprotein cholesterol

              IDF: 

              International Diabetes Federation

              MBS: 

              Metabolic Syndrome

              PY: 

              Pack per Years

              SBP: 

              Systolic blood pressure

              DBP: 

              Diastolic blood pressure

              TG: 

              Triglycerides

              WHR: 

              Waist:hip ratio.

              Declarations

              Acknowledgements

              This study did not receive any funding.

              Authors’ Affiliations

              (1)
              Department of Public Health, Experimental and Forensic Medicine, Section of Human Nutrition, University of Pavia
              (2)
              Respiratory Pathophysiology Unit, Policlinico San Matteo IRCCS

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