• Users Online: 683
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Browse Articles Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 118

The effects of smoking on metabolic syndrome and its components using causal methods in the Iranian Population


1 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2 Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
4 Prevention of Metabolic Disorders Research Center; Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Date of Submission07-Feb-2021
Date of Acceptance27-Feb-2021
Date of Web Publication29-Sep-2021

Correspondence Address:
Davood Khalili
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, PO Box: 19395-4763, Tehran
Iran
Mohammad Ali Mansournia
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijpvm.ijpvm_45_21

Rights and Permissions
  Abstract 


Background: The aim of this study was to estimate the effect of smoking on metabolic syndrome (MS) and its components applying inverse probability-of-treatment weighting (IPTW) and propensity score (PS) matching. Methods: Using data from Tehran Lipid and Glucose Study, 4857 participants aged over 20 years with information on smoking and confounders in the third phase (2005–2008) were included, and the MS was assessed in the fifth phase (2011–2014). IPTW and PS matching were used to adjust for confounders. Results: Based on average treatment effect (ATE) estimates, smoking decreased the risk of hypertension (RR: 0.62; 95% CI: 0.43, 0.88), but increased the risk of low HDL cholesterol (1.20; 0.98, 1.48). Similarly, the average treatment effect in the treated (ATT) estimates using IPTW and PS matching suggested that smoking decreased the risk of hypertension (0.63; 0.52, 0.76, and 0.68; 0.54, 0.85), and increased the risk of low HDL cholesterol (1.24; 1.07, 1.43, and 1.28; 1.06, 1.54), respectively. Conclusions: Smoking seems to increase the risk of low HDL cholesterol but decreases the risk of hypertension.

Keywords: Metabolic syndrome, propensity score, smoking.


How to cite this article:
Khodamoradi F, Nazemipour M, Mansournia N, Yazdani K, Khalili D, Mansournia MA. The effects of smoking on metabolic syndrome and its components using causal methods in the Iranian Population. Int J Prev Med 2021;12:118

How to cite this URL:
Khodamoradi F, Nazemipour M, Mansournia N, Yazdani K, Khalili D, Mansournia MA. The effects of smoking on metabolic syndrome and its components using causal methods in the Iranian Population. Int J Prev Med [serial online] 2021 [cited 2021 Dec 2];12:118. Available from: https://www.ijpvmjournal.net/text.asp?2021/12/1/118/327230




  Introduction Top


Metabolic syndrome (MS) is a set of conditions including central obesity, high blood pressure (BP), increased fasting blood sugar (FBS), increased triglycerides, and decreased high-density lipoprotein cholesterol (HDL-C).[1] The prevalence of MS is globally increasing and, depending on some background factors, ranges from less than 10% to 84%.[2] MS patients are at risk of a two-fold increase in type 2 diabetes and a five-fold increase in cardiovascular disease over the next 5 and 10 years, respectively.[2]

Modifications of life styles such as smoking are useful for chronic diseases prevention.[3] Smoking is associated with many noncommunicable diseases and contributes to mortality and disability-adjusted life year (DALYs).[4],[5] According to the world health organization, there will be 1.5 billion smokers worldwide by 2050.[5] The relationship between smoking and MS,[6],[7] BP,[8],[9] abdominal obesity,[10] FBS,[11] and triglycerides[9],[12] has been already studied using conventional regression for confounding adjustment. An alternative is propensity score (PS) methods. PS, defined as the probability of exposure given the set of confounders,[13] can be used in different procedures for confounding adjustment: matching, inverse probability-of-treatment weighting (IPTW), stratification, and regression adjustment.[14],[15],[16],[17]

PS methods are preferred to outcome regression for inferring causality because i) it is easier to determine whether the exposure models are correctly specified in terms of yielding covariate-balancing PSs using standardized differences, ii) these methods effectively emulate a randomized experiment without any reference to the outcome, and iii) the overlap in the distribution of confounders can be explicitly assessed between two exposure groups: the small number of matched subjects or huge inverse probability-of-treatment weights indicate low overlap.[18] The aim of this study is to estimate the effect of smoking on MS and its components using IPTW and PS matching.


  Methods Top


Using the third phase data (2005-2008) of Tehran Lipid and Glucose cohort Study (TLGS) as the baseline, 4857 participants aged over 20 years without MS and with information on smoking and confounders were selected. MS was assessed in the fifth phase (2011–2014). This study was approved by the Research Council of Research Institute for Endocrine Sciences of Shahid Beheshti University of Medical Sciences, and a consent form was obtained from all participants.

The exposure was cigarette smoking status assessed by the question: “is person smoking daily?”. The outcome was MS, defined as having at least three out of the following variables: abdominal obesity (waist circumference ≥95 cm), low HDL-C (<40 in men or <50 mg/dL in women), hypertriglyceridemia (TG ≥150 mg/dL), hypertension (systolic BP ≥130 or diastolic BP ≥85 mmHg) and impaired blood glucose (FBS ≥100 mg/dL).[19],[20],[21]

The confounders were identified using a causal diagram [Figure 1] for the study population.[22],[23],[24],[25],[26],[27] The minimally sufficient set for confounding adjustment, derived based on Pearl's back-door criterion,[28] included gender, age, physical activity, marital status, education, and job, measured by a questionnaire, as well as the unmeasured variables income and alcohol. Fractional polynomials were used to identify any nonlinear association between age and smoking in the PS model.[29]
Figure 1: Causal diagram for the effect of smoking on MS

Click here to view


Statistical methods

IPTW was used to adjust for confounders. We first estimated PS through logistic regression, with smoking as the response variable and confounders as predictors. Then the average treatment effect (ATE) was estimated using weighted risk ratio (RR) between smoking and MS with weights equal to 1/PS for the smokers and 1/(1 ̶ PS) for the non-smokers. Moreover, the average treatment effect in the treated (ATT) was estimated with weights equal to 1 for the smokers and PS/(1 ̶ PS) for the non-smokers.[30],[31] IPTW produces a pseudo-population in which confounders do not predict the exposure, and the effect in the pseudo-population is the same as that in the population.

Confounders were also adjusted for using PS matching. A PS-matched dataset was created by matching, without replacement, one unexposed person to one exposed based on the nearest value of PS (±0.05).[32] Then the ATT effect was estimated using the RR between smoking and MS in the matched sample.[18]

The 95% confidence intervals (CIs) for the IPTW estimates were derived using robust standard errors.[33] The 95% CIs for the PS matching was obtained based on nonparametric bootstrapping by 1000 repetitions with 2.5th and 97.5th percentiles as 95% confidence limits.[34]

The correct specification of the PS model was assessed based on the balance of measured confounders between exposure groups in the matched sample for PS matching, and in the weighted sample for the IPTW. The standardized difference was used to compare the mean and proportion of continuous and binary confounders between the exposed and unexposed, respectively. The standardized difference for continuous confounders is



Where and are the mean estimates, and and are variance estimates in exposed and unexposed, respectively.

The standardized difference for binary confounders is



Where and are the proportion estimates of the binary confounders in the exposed and unexposed, respectively.

A standardized difference of more than 0.1 was considered as an important difference in mean or proportion of confounders between exposure groups.[18] All statistical analyses were performed using stata.


  Results Top


Of 4857 participants, 2959 (60.9%) were female, and the mean (SD) of age was 39.10 (13.48) years. There were 512 (10.5%) cigarette smokers at baseline, and 922 (19.0%) developed MS during the study follow-up. We excluded 25 (0.5%) participants due to missing MS data in phase 5. In PS matching, 511 unexposed were matched to 511 exposed. The mean (SD) of weights for ATE and ATT estimates were 2.02 (5.22) and 0.21 (0.30), respectively. The baseline characteristics of participants have been shown in [Table 1]. [Table 2] represents the standardized differences for confounders in original, weighted and matched data. In the original data, eight variables had standardized differences above 0.1, but in both weighted and matched data all variables had standardized differences less than 0.1, a sufficient balance on the confounders. The effects of smoking on MS and its components have been presented in [Table 3]. Based on ATE estimates, smoking decreased the risk of hypertension, RR: 0.62 (95% CI: 0.43, 0.88), but increased the risk of low HDL-C: 1.20 (95% CI: 0.98, 1.48). Similarly the ATT estimates using IPTW and PS matching suggested that smoking decreased the risk of hypertension, 0.63 (95% CI: 0.52, 0.76) and 0.68 (95% CI: 0.54, 0.85), and increased the risk of low HDL-C, 1.24 (95% CI: 1.07, 1.43) and 1.28 (95% CI: 1.06, 1.54), respectively. There was no strong evidence against no effect of smoking on MS, abdominal obesity, hypertriglyceridemia and impaired blood glucose.
Table 1: Baseline characteristics of the study participants

Click here to view
Table 2: Standardized differences for confounders

Click here to view
Table 3: The ATE and ATT estimates of the effect of smoking on MS and its components using IPTW and PS matching

Click here to view



  Discussion Top


We did not find strong evidence against no effect of smoking on MS using causal methods which are consistent with previous studies.[35],[36] However, some studies indicated a positive relationship between smoking and MS.[37],[38] The difference in study population, MS definition, and statistical analysis may justify the controversial results.

Our estimates suggest that smoking increases the risk of low HDL-C which is consistent with the results of previous studies.[39],[40] This finding can be explained by the fact that smoking decreases lipid metabolism through diminished lipoprotein lipase activity.[40] Also, nicotine can cause lipolysis which in turn may reduce HDL-C.[6]

Our analyses revealed that smoking lowers BP. According to some studies, smokers have a lower risk of hypertension and a cluster analysis on a national survey in Iran showed that people in a cluster of smokers were less likely to experience elevated BP, though they had a very high level of work-related physical activity.[41],[42],[43] These results may be interpreted as an indirect protective effect of smoking on BP through the body weight.[44] However, some studies indicated a higher risk of hypertension for the smokers.[45],[46] Moreover, previous studies have shown the positive effect of smoking on cardiovascular disease (2-6 times for >20 vs. <10 cigarette per day)[47],[48] which should always be considered as an important factor.

Our study had some limitations. The confounders alcohol consumption and income, were not available. Also there was measurement bias as smoking was dichotomized and self-reported. Finally some confounders like physical activity had measurement error leading to residual confounding.


  Conclusions Top


In summary, there was no strong evidence against no effect of smoking on MS, abdominal obesity, hypertriglyceridemia and impaired blood glucose. Smoking seems to increase the risk of low HDL-Cbut decrease the risk of hypertension. More studies are needed to understand better if and how smoking affects MS and its components.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Huang LN, Wang HJ, Wang ZH, Zhang JG, Jia XF, Zhang B, et al. Association of red meat usual intake with serum ferritin and the risk of metabolic syndrome in Chinese adults: A longitudinal study from the China health and nutrition survey. Biomed Environ Sci 2020;33:19-29.  Back to cited text no. 1
    
2.
Annani-Akollor ME, Laing EF, Osei H, Mensah E, Owiredu E-W, Afranie BO, et al. Prevalence of metabolic syndrome and the comparison of fasting plasma glucose and HbA1c as the glycemic criterion for MetS definition in non-diabetic population in Ghana. Diabetol Metab Syndr 2019;11:26.  Back to cited text no. 2
    
3.
Hwang G-Y, Cho Y-J, Chung R-H, Kim S-H. The relationship between smoking level and metabolic syndrome in male health check-up examinees over 40 years of age. Korean J Fam Med 2014;35:219.  Back to cited text no. 3
    
4.
Huang C, Chen G, Zhang M, Lu Y, Hua Y, Hu Y, et al. Association between environmental tobacco smoke exposure and risk of type 2 diabetes mellitus in Chinese female never smokers: A population-based cohort study. J Diabetes 2020;12:339-46.  Back to cited text no. 4
    
5.
Campagna D, Alamo A, Di Pino A, Russo C, Calogero A, Purrello F, et al. Smoking and diabetes: dangerous liaisons and confusing relationships. Diabetol Metab Syndr 2019;11:1-12.  Back to cited text no. 5
    
6.
Kim BJ, Han JM, Kang JG, Rhee EJ, Kim BS, Kang JH. Relationship of cotinine-verified and self-reported smoking status with metabolic syndrome in 116,094 Korean adults. J Clin Lipidol 2017;11:638-45.e2.  Back to cited text no. 6
    
7.
Slagter SN, van Vliet-Ostaptchouk JV, Vonk JM, Boezen HM, Dullaart RP, Kobold ACM, et al. Associations between smoking, components of metabolic syndrome and lipoprotein particle size. BMC Med 2013;11:195.  Back to cited text no. 7
    
8.
Li G, Wang H, Wang K, Wang W, Dong F, Qian Y, et al. The association between smoking and blood pressure in men: A cross-sectional study. BMC Public Health 2017;17:797.  Back to cited text no. 8
    
9.
Kim J-Y, Yang Y, Sim Y-J. Effects of smoking and aerobic exercise on male college students' metabolic syndrome risk factors. J Phys Ther Sci 2018;30:595-600.  Back to cited text no. 9
    
10.
Kim Y, Jeong SM, Yoo B, Oh B, Kang H-C. Associations of smoking with overall obesity, and central obesity: A cross-sectional study from the Korea National Health and Nutrition Examination Survey (2010-2013). Epidemiol Health 2016;38:e2016020.  Back to cited text no. 10
    
11.
Yankey BN, Strasser S, Okosun IS. A cross-sectional analysis of the association between marijuana and cigarette smoking with metabolic syndrome among adults in the United States. Diabetes Metab Syndr 2016;10:S89-95.  Back to cited text no. 11
    
12.
Horie M, Noguchi S, Tanaka W, Goto Y, Yoshihara H, Kawakami M, et al. Relationships among smoking habits, airflow limitations, and metabolic abnormalities in school workers. PloS One 2013;8):e81145.  Back to cited text no. 12
    
13.
Elze MC, Gregson J, Baber U, Williamson E, Sartori S, Mehran R, et al. Comparison of propensity score methods and covariate adjustment: Evaluation in 4 cardiovascular studies. J Am Coll Cardiol 2017;69:345-57.  Back to cited text no. 13
    
14.
Aryaie M, Sharifi H, Saber A, Nazemipour M, Mansournia MA. Longitudinal Causal Effects of Normalized Protein Catabolic Rate on All-Cause Mortality in Patients With End-Stage Renal Disease: Adjusting for Time-Varying Confounders Using the G-Estimation Method. Am J Epidemiol. 2021;190:1133-41.  Back to cited text no. 14
    
15.
Mansournia MA, Danaei G, Forouzanfar MH, Mahmoodi M, Jamali M, Mansournia N, et al. Effect of physical activity on functional performance and knee pain in patients with osteoarthritis: Analysis with marginal structural models. Epidemiology 2012:631-40.  Back to cited text no. 15
    
16.
Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ 2017;359:j4587.  Back to cited text no. 16
    
17.
Almasi-Hashiani A, Nedjat S, Ghiasvand R, Safiri S, Nazemipour M, Mansournia N, et al. The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran. BMC Public Health. 2021;21:1219.  Back to cited text no. 17
    
18.
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399-24.  Back to cited text no. 18
    
19.
Alberti K, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation 2009;120:1640-5.  Back to cited text no. 19
    
20.
Delavari A, Forouzanfar MH, Alikhani S, Sharifian A, Kelishadi R. First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: The national survey of risk factors for noncommunicable diseases of Iran. Diabetes Care 2009;32:1092-7.  Back to cited text no. 20
    
21.
AZIZI F, Hadaegh F, KHALILI D, Esteghamati A, HOSSEIN PF, Delavari A, et al. Appropriate definition of metabolic syndrome among Iranian adults: Report of the Iranian National Committee of Obesity. Arch Iran Med 2010;13:426-8.  Back to cited text no. 21
    
22.
Etminan M, Brophy JM, Collins G, Nazemipour M, Mansournia MA. To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies. Am Heart J. 2021;237:62-7.  Back to cited text no. 22
    
23.
Mansournia MA, Higgins JP, Sterne JA, Hernán MA. Biases in randomized trials: A conversation between trialists and epidemiologists. Epidemiology (Cambridge, Mass). 2017;28:54.  Back to cited text no. 23
    
24.
Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol 2013;42:860-9.  Back to cited text no. 24
    
25.
Etminan M, Nazemipour M, Sodhi M, Mansournia MA. Potential biases in studies of acid suppressing drugs and COVID-19 infection. Gastroenterology 2020. doi: 10.1053/j.gastro. 2020.11.053.  Back to cited text no. 25
    
26.
Mansournia MA, Nazemipour M, Etminan M. Causal diagrams for immortal time bias. Int J Epidemiol. 2021.  Back to cited text no. 26
    
27.
Mansournia MA, Collins GS, Nielsen RO, Nazemipour M, Jewell NP, Altman DG, et al. A CHecklist for statistical assessment of medical papers (the CHAMP statement): Explanation and elaboration. Br J Sports Med 2021. doi: 10.1136/bjsports-2020-103652.  Back to cited text no. 27
    
28.
Pearl J. Causality: Models, reasoning and inference cambridge university press. Cambridge, MA, USA. 2000.  Back to cited text no. 28
    
29.
Abdollahpour I, Nedjat S, Mansournia MA, Sahraian MA, Kaufman JS. Estimating the marginal causal effect of fish consumption during adolescence on multiple sclerosis: A population-based incident case-control study. Neuroepidemiology 2018;50:111-8.  Back to cited text no. 29
    
30.
Mansournia MA, Altman DG. Inverse probability weighting. BMJ 2016;352:i189.  Back to cited text no. 30
    
31.
Abdollahpour I, Nedjat S, Mansournia MA, Schuster T. Estimation of the marginal effect of regular drug use on multiple sclerosis in the Iranian population. PloS One 2018;13:e0196244.  Back to cited text no. 31
    
32.
Hernán MA, Robins JM. Causal inference. 2016.  Back to cited text no. 32
    
33.
Mansournia MA, Nazemipour M, Naimi AI, Collins GS, Campbell MJ. Reflections on modern methods: Demystifying robust standard errors for epidemiologists. Int J Epidemiol 2020. doi: 10.1093/ije/dyaa260.  Back to cited text no. 33
    
34.
Austin PC, Small DS. The use of bootstrapping when using propensity-score matching without replacement: A simulation study. Stat Med 2014;33:4306-19.  Back to cited text no. 34
    
35.
Santos A-C, Ebrahim S, Barros H. Alcohol intake, smoking, sleeping hours, physical activity and the metabolic syndrome. Prev Med 2007;44:328-34.  Back to cited text no. 35
    
36.
Ishizaka N, Ishizaka Y, Toda E-I, Nagai R, Yamakado M. Association between cigarette smoking, white blood cell count, and metabolic syndrome as defined by the Japanese criteria. Intern Med 2007;46:1167-70.  Back to cited text no. 36
    
37.
Wada T, Urashima M, Fukumoto T. Risk of metabolic syndrome persists twenty years after the cessation of smoking. Intern Med 2007;46:1079-82.  Back to cited text no. 37
    
38.
Sun K, Liu J, Ning G. Active smoking and risk of metabolic syndrome: A meta-analysis of prospective studies. PloS One 2012;7:e47791.  Back to cited text no. 38
    
39.
Cheng E, Burrows R, Correa P, Güichapani CG, Blanco E, Gahagan S. Light smoking is associated with metabolic syndrome risk factors in Chilean young adults. Acta Diabetol 2019;56:473-9.  Back to cited text no. 39
    
40.
Wakabayashi I. Relationship between smoking and metabolic syndrome in men with diabetes mellitus. Metab Syndr Relat Disord 2014;12:70-8.  Back to cited text no. 40
    
41.
Gharipour M, Kelishadi R, Sarrafzadegan N, Baghaei A, Yazdani M, Anaraki J, et al. The association of smoking with components of the metabolic syndrome in non-diabetic patients. Ann Acad Med Singap 2008;37:919.  Back to cited text no. 41
    
42.
Akbarpour S, Khalili D, Zeraati H, Mansournia MA, Ramezankhani A, Pishkuhi MA, et al. Relationship between lifestyle pattern and blood pressure-Iranian national survey. Sci Rep 2019;9:1-8.  Back to cited text no. 42
    
43.
Akbarpour S, Khalili D, Zeraati H, Mansournia MA, Ramezankhani A, Fotouhi A. Healthy lifestyle behaviors and control of hypertension among adult hypertensive patients. Sci Rep 2018;8:1-9.  Back to cited text no. 43
    
44.
Leone A. Smoking and hypertension. J Cardiol Curr Res 2015;2:00057.  Back to cited text no. 44
    
45.
Al-khalifa II, Mohammed SM, Ali ZM. Cigarette smoking as a relative risk factor for metabolic syndrome. J Endocrinol Metab 2017;6:178-82.  Back to cited text no. 45
    
46.
Bowman TS, Gaziano JM, Buring JE, Sesso HD. A prospective study of cigarette smoking and risk of incident hypertension in women. J Am Coll Cardiol 2007;50:2085-92.  Back to cited text no. 46
    
47.
Khalili D, Sheikholeslami FH, Bakhtiyari M, Azizi F, Momenan AA, Hadaegh F. The incidence of coronary heart disease and the population attributable fraction of its risk factors in Tehran: A 10-year population-based cohort study. PloS One 2014;9:e105804.  Back to cited text no. 47
    
48.
Ehteshami-Afshar S, Momenan A, Hajshekholeslami F, Azizi F, Hadaegh F. The impact of smoking status on 9.3 years incidence of cardiovascular and all-cause mortality among Iranian men. Ann Hum Biol 2014;41:249-54.  Back to cited text no. 48
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusions
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed86    
    Printed0    
    Emailed0    
    PDF Downloaded15    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]