- Open Access
Prevalence rate of Metabolic Syndrome in a group of light and heavy smokers
Diabetology & Metabolic Syndromevolume 5, Article number: 28 (2013)
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.
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.
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.
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.
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 .
It is widely accepted as a major risk factor for metabolic and cardiovascular disease . 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 [5–8]. Furthermore, several studies show that smoking is associated withmetabolic abnormalities and increases the risk of Metabolic Syndrome (MBS) [9–11]. This syndrome is associated with multiple metabolic alterations andhemodynamic disorders. Weitzman et al.  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.  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 , 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 . 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.
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:
Body weight, measured on subjects wearing only underwear and without shoes, by means of a steel yard scale (precision ± 100 g);
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);
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 .
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.
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.
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).
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.
Three smokers category have been considered: light smokers consuming till 19 Packper Years (PY), moderate smokers ≥ 20–39 PY and heavysmokers ≥ 40 PY .
PY means cigarettes smoked per day times years of smoking, divided by 20 .
Diagnostic criteria for metabolic syndrome
Several definitions exist for MBS .
In our study we used the International Diabetes Federation (IDF) clinicalcriteria , 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.
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.
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.
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 .
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.
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.
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).
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 .
Currently smoking men and women are at significantly higher risk of developing MBS,increasing directly the risk of atherosclerotic cardiovascular disease development .
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.  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 . Cigarette smoking as well as physical inactivity and obesity areassociated with higher risk of the metabolic syndrome in elderly men too ; stopping smoking is one of the lifestyle changes, even at older ages,associated with a significant lowering risk of developing MBS .
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 . 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 .
Claire C et al.  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 .
Waist circumference as well as WHR is an indicator of the amount of visceral adiposetissue . 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. .
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 . Distribution of body fat is more important than the amount of fat as aprognostic factor for life expectancy .
Nicotine, carbon monoxide, and other metabolites from smoking also play importantroles in insulin resistance . 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 [35–37].
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 .
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 .
The visceral fat accumulation and insulin resistance may represent an important linkbetween cigarette smoking and the risk of cardiovascular disease . 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  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 .
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 .
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 , 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.
Body mass index
Coronary heart disease
Fasting plasma glucose
High-density lipoprotein cholesterol
International Diabetes Federation
Pack per Years
Systolic blood pressure
Diastolic blood pressure
World Health Organization: Tobacco free initiative: Why is tobacco a public health priority. Available at: http://www.who.int/tobacco/health_priority/en/Accessed on January 31, 2011,
Frati AC, Iniestra F, Ariza CR: Acute effect of cigarette smoking on glucose tolerance and othercardiovascular risk factors. Diabetes Care. 1996, 19: 112-118. 10.2337/diacare.19.2.112.
Targher G, Alberiche M, Zenere MB, et al: Cigarette smoking and insulin resistance in patients withnoninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1997, 82: 3619-3624. 10.1210/jc.82.11.3619.
Wada T, Urashima M, Fukumoto T: Risk of metabolic syndrome persists twenty years after the cessation ofsmoking. Intern Med. 2007, 46: 1079-1082. 10.2169/internalmedicine.46.0026.
Lee WY, Jung CH, Park JS, et al: Effects of smoking, alcohol, exercise, education, and family history on themetabolic syndrome as defined by the ATP III. Diabetes Res Clin Pract. 2005, 67: 70-77. 10.1016/j.diabres.2004.05.006.
Tsiara S, Elisaf M, Mikhailidis DP: Influence of smoking on predictors of vascular disease. Angiology. 2003, 54: 507-530. 10.1177/000331970305400501.
Steiner G, Schwartz L, Shumak S, et al: The association of increased levels of intermediate-density lipoproteins withsmoking and with coronary artery disease. Circulation. 1987, 75: 124-130. 10.1161/01.CIR.75.1.124.
Criqui MH, Wallace RB, Heiss G, et al: Cigarette smoking and plasma high-density lipoprotein cholesterol. The LipidResearch Clinics Program Prevalence Study. Circulation. 1980, 62: IV70-IV76.
Cena H, Fonte ML, Turconi G: Relationship between smoking and Metabolic Syndrome. Nutr Rev. 2011, 69 (12): 745-753. 10.1111/j.1753-4887.2011.00446.x.
Miyatake N, Wada J, Kawasaki Y, et al: Relationship between metabolic syndrome and cigarette smoking in the Japanesepopulation. Intern Med. 2006, 45: 1039-1043. 10.2169/internalmedicine.45.1850.
Geslain-Biquez C, Vol S, Tichet J, et al: The metabolic syndrome in smokers. The D.E.S.I.R. study. Diabetes Metab. 2003, 29: 226-234. 10.1016/S1262-3636(07)70031-9.
Weitzman M, Cook S, Auinger P, et al: Tobacco smoke exposure is associated with the metabolic syndrome inadolescents. Circulation. 2005, 112: 862-869. 10.1161/CIRCULATIONAHA.104.520650.
Saarni SE, Pietilainen K, Kantonen S, et al: Association of smoking in adolescence with abdominal obesity in adulthood: afollow-up study of 5 birth cohorts of Finnish twins. Am J Public Health. 2009, 99: 348-354. 10.2105/AJPH.2007.123851.
Kawada T, Otsuka T, Inagaki H, et al: Association of smoking status, insulin resistance, body mass index, andmetabolic syndrome in workers: a 1-year follow-up study. Obes Res Clin Pract. 2010, 4: e163-e169. 10.1016/j.orcp.2009.12.004.
Reaven G, Tsao PS: Insulin resistance and compensatory hyperinsulinemia: the key player betweencigarette smoking and cardiovascular disease?. J Am Coll Cardiol. 2003, 41: 1044-1047. 10.1016/S0735-1097(02)02982-0.
Durnin JV, Womersley J: Body fat assessed from total body density and its estimation from skinfoldthickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr. 1974, 32 (1): 77-97. 10.1079/BJN19740060.
Li Y, Swensen SJ, Karabekmez LG, et al: Effect of Emphysema on Lung Cancer Risk in Smokers: A ComputedTomography-based Assessment. Cancer Prev Res. 2011, 4 (1): 43-50. 10.1158/1940-6207.CAPR-10-0151.
Forey BA, Thornton AJ, Lee PN: Systematic review with meta-analysis of the epidemiological evidence relatingsmoking to COPD, chronic bronchitis and emphysema. BMC Pulm Med. 2011, 11: 36-10.1186/1471-2466-11-36.
Assmann G, Guerra R, Fox G, et al: Harmonizing the definition of the metabolic syndrome: comparison of thecriteria of the Adult Treatment Panel III and the International DiabetesFederation in United States American and European populations. Am J Cardiol. 2007, 99: 541-548. 10.1016/j.amjcard.2006.08.045.
Alberti KG, Eckel RH, Grundy SM, National Heart, Lung, and Blood Institute, et al: Harmonizing the metabolic syndrome: a joint interim statement of theInternational Diabetes Federation Task Force on Epidemiology andPrevention. Circulation. 2009, 120: 1640-1645. 10.1161/CIRCULATIONAHA.109.192644.
Alberti KG, Zimmet P: Shaw Metabolic syndrome–a new world-wide definition. A ConsensusStatement from the International Diabetes Federation. J Diabetes Med. 2006, 23 (5): 469-480. 10.1111/j.1464-5491.2006.01858.x.
Ford ES, Li C, Zhao G: Prevalence and correlates of metabolic syndrome based on a harmoniousdefinition among adults in the US. J Diabetes. 2010, 2 (3): 180-193. 10.1111/j.1753-0407.2010.00078.x.
Grundy SM, Cleeman JI, Daniels SR, et al: American Heart Association; National Heart, Lung, and Blood Institute.Diagnosis and management of the metabolic syndrome Diagnosis and managementof the metabolic syndrome: an American Heart Association/National Heart,Lung, and Blood Institute Scientific Statement. Circulation. 2005, 112 (17): 2735-2752. 10.1161/CIRCULATIONAHA.105.169404.
Hayes L, Pearce MS, Unwin NC: Lifecourse predictors of normal metabolic parameters in overweight and obeseadults. Int J Obes. 2006, 30 (6): 970-976. 10.1038/sj.ijo.0803213.
Wannamethee SG, Shaper AG, Whincup PH: Modifiable lifestyle factors and the metabolic syndrome in older men: Effectsof lifestyle changes. J Am Geriatr Soc. 2006, 54 (12): 1909-1914. 10.1111/j.1532-5415.2006.00974.x.
Zhu S, St-Onge MP, Heshka S, et al: Lifestyle Behaviors associeted with lower risk of having the MetabolicSyndrome. Metabolism. 2004, 53 (11): 1503-1511. 10.1016/j.metabol.2004.04.017.
Pinto Pereira SM, Ki M, Power C: Sedentary behaviour and biomarkers for cardiovascular disease and diabetes inmid-life: the role of television-viewing and sitting at work. PLoS One. 2012, 7 (2): e31132-10.1371/journal.pone.0031132.
Tankó LB, Christiansen C: An update on the antiestrogenic effect of smoking: a literature review withimplications for researchers and practitioners. Menopause. 2004, 11 (1): 104-109. 10.1097/01.GME.0000079740.18541.DB.
Kwok S, Canoy D, Soran H, et al: Body fat distribution in relation to smoking and exogenous hormones inBritish women. Clin Endocrinol. 2011, 10.1111/j.1365-2265.2011.04331.x.
Clair C, Chiolero A, Faeh D, et al: Dose-dependent positive association between cigarette smoking, abdominalobesity and body fat: cross-sectional data from a population-basedsurvey. BMC Public Health. 2011, 11: 23-10.1186/1471-2458-11-23.
Chiolero A, Faeh D, Paccaud F, et al: Consequences of smoking for body weight, body fat distribution, and insulinresistance. Am J Clin Nutr. 2008, 87 (4): 801-809.
Shimokata H, Muller DC, Andres R: Studies in the distribution of body fat. Effects of cigarettes smoking. JAMA. 1989, 26: 1169-1173.
Kishida K, Funahashi T, Matsuzawa Y, et al: Visceral adiposity as a target for the management of the metabolicsyndrome. Ann Med. 2011, 44 (3): 233-241.
Petursson H, Sigurdsson JA, Bengtsson C, et al: Body configuration as a predictor of mortality: comparison of fiveanthropometric measures in a 12 year follow-up of the Norwegian HUNT 2study. PLoS One. 2011, 6 (10): e26621-10.1371/journal.pone.0026621.
Will JC, Galuska DA, Ford ES, et al: Cigarette smoking and diabetes mellitus: evidence of a positive associationfrom a large prospective cohort study. Int J Epidemiol. 2001, 30 (3): 540-546. 10.1093/ije/30.3.540.
Tanko LB: Novel association between bioaviable estradiol and adipokines in ederlywomenwith different phenotypes of obesity: implication for atherogenesis. Circulation. 2004, 110 (15): 2246-2252. 10.1161/01.CIR.0000144470.55149.E5.
Cryer PE, Haymond MW, Santiago JV, et al: Norepinephrine and epinephrine release and adrenergic mediation ofsmoking-associated hemodynamic and metabolic events. N Engl J Med. 1976, 295 (11): 573-577. 10.1056/NEJM197609092951101.
Kannel WB: Cigarettes, coronary occlusion, and myocardial infarction. JAMA. 1981, 246 (8): 871-872. 10.1001/jama.1981.03320080057033.
Beaser RS, Levy P: Metabolic syndrome: a work in progress, but a useful construct. Circulation. 2007, 115: 1812-1818. 10.1161/CIRCULATIONAHA.106.673616.
Eliasson B, Attvall S, Taskinen MR, et al: The insulin resistance syndrome in smokers is related to smoking habits. Arterioscler Thromb. 1994, 14 (12): 1946-1950. 10.1161/01.ATV.14.12.1946.
Barter P: Raising HDL-C can be achieved by both lifestyle changes and pharmacologicalmeans. Atheroscler Suppl. 2011, 12 (3): 265-266. 10.1016/S1567-5688(11)00035-3.
Kontush A, Chapman MJ: Antiatherogenic small, dense HDL–guardian angel of the arterialwall?. Nat Clin Pract Cardiovasc Med. 2006, 3 (3): 144-153. 10.1038/ncpcardio0500.
Graham I, Atar D, Borch-Johnsen K, et al: European guidelines on cardiovascular disease prevention in clinicalpractice: Fourth Joint Task Force of the European Society of Cardiology andother societies. Eur J Cardiovasc Prev Rehabil. 2007, 14 (Suppl. 2): S1-S113.
Alberico L, et al, The Task Force for the management of dyslipidaemias of the EuropeanSociety of Cardiology (ESC) and the European Atherosclerosis Society(EAS), Authors/Task Force Members: Review et ESC/EAS Guidelines for the management of dyslipidaemias. Atherosclerosis. 2011, 217S: S1-S44.
Travier N, Agudo A, May A, et al: Longitudinal changes in weight in relation to smoking cessation inparticipants of the EPIC-PANACEA study. Prev Med. 2012, 54 (3–4): 183-192.
Katano S, Nakamura Y, Nakamura A, et al: Relationship among phisycal activity, smoking, drinking and clustering of theMetabolic Syndrome Diagnostic Components. J Atheroscler Thromb. 2010, 17 (6): 644-650. 10.5551/jat.3699.
Kleppinger A, Litt MD, Kenny AM, et al: Effects of smoking cessation on body composition in postmenopausal women. J Womens Health. 2010, 19 (9): 1651-1657. 10.1089/jwh.2009.1853.
Gorber SC, Tremblay M, Moher D, et al: A comparison of direct vs. self-report measures for assessing height, weightand body mass index: a systematic review. Obes Rev. 2007, 8 (4): 307-326. 10.1111/j.1467-789X.2007.00347.x.
This study did not receive any funding.
The authors declare that they have no competing interests.
HC conceived the study hypothesis, supervised data analyses and wrote the manuscript,AT and RN took a lead role in the data collection. IC contributed to the studydesign; CR contributed to the data interpretation. GT made substantial contributionto data analysis and writing of the manuscript. All authors read and approved thefinal manuscript.