Skip to main content

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

Abstract

Background

The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.

Methods

In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005–2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014–2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.

Results

Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.

Conclusions

Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Peer Review reports

Background

The American Heart Association and the National Heart, Lung, and Blood Institute have considered metabolic syndrome as presence of three or more metabolic syndrome components, including Increased waist circumference (abdominal obesity), hypertriglyceridemia, low HDL cholesterol, impaired fasting blood sugar, and hypertension [1, 2]. The metabolic syndrome prevalence is increasing worldwide and its prevalence varies in different parts of the world depending on environmental factors, sex, age, race and ethnicity [3, 4]. In Asian countries, the prevalence of this syndrome is between 10 and 20% [5,6,7]. The metabolic syndrome prevalence in Iranian youth is dramatically high, ranging from 4.8 to 24.5%. Interestingly, its prevalence in the elderly was significantly higher than in the young and 49.5% reported [8]. The risk of death, stroke and heart attack in people with metabolic syndrome is 2 to 3 times higher than healthy people. Also, metabolic syndrome increases the risk of diseases such as diabetes, cardiovascular disease, fatty liver, asthma, ovarian cysts and a number of cancers [9, 10].

Although metabolic syndrome is traditionally recognized as an ‘all or one’ condition, it is unknown whether this definition is accurate and to date has not been validated [11]. A proposed method for classifying metabolic syndrome is latent class analysis (LCA). LCA is a model that shows that there is a latent classification variable that divides population into latent classes [12]. Latent classes are created to show unobserved heterogeneity among individuals according to observed variables [9, 13]. Metabolic syndrome does not have a standard diagnostic test and the use of LCA can help to identify more [11].

Smoking is one of the major causes of mortality and disease in the world [14] and is responsible for about 7.2 million deaths per year. Moreover, smoking is one of the modifiable risk factors for non-communicable diseases such as cardiovascular disease and type 2 diabetes [15].

In previous studies, regression methods have often been used to investigate the relationship between smoking and metabolic syndrome and adjust for confounding variables [15,16,17]. An alternative approach for confounding adjustment is exposure modeling with propensity score (PS) methods. PS which is the conditional probability of exposure, given the set of measured confounders [18, 19] can be used in different procedures for balance of confounding variables among exposure groups, including matching, stratification, inverse probability-of-treatment weighting (IPTW), and use of the PS as a covariate [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34, 83].

Although regression models are widely used in practice, PS methods are preferred for inferring causality for the following reasons: First, it is easier to determine whether the exposure models are adequately specified in terms of yielding covariate-balancing propensity scores using standardized differences. Second, these methods effectively emulate a randomized experiment without any reference to the outcome. Third, the overlap in the distribution of confounders can be explicitly assessed between two exposure groups [20].

Any analysis of observational data, including the effect of an exposure on latent class members, is subject to confounding and here we apply an approach to causal inference in LCA using PS methods. Therefore, the aim of this study was to investigate the causal effect of smoking on the latent hazard classes of metabolic syndrome using latent class causal analysis.

Methods

Participants

In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS), designed to investigate risk factors of non-communicable diseases. TLGS is an ongoing study that started in 1998 and performed in several phases. The current study was based on a sample of 4857 participants. We used the third phase data (2005–2008) as the baseline, and participants aged over 20 with complete information on the variables including age, gender, physical activity, marital status, education, job and smoking status were selected. In the third phase, people with metabolic syndrome criteria were excluded, that is, people who had 3 or more metabolic syndrome components. We measured the metabolic syndrome components to obtain latent classes of metabolic syndrome in the fifth phase data. (2011–2014). The TLGS main project has been approved by the IRB of the Iranian National Scientific Research Council and the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, under the Helsinki Declaration and an informed consent form was obtained from all participants [35, 36]. The ethics committee of the School of Health, Tehran University of Medical Sciences (code of IR.TUMS.SPH.REC.1398.032) has approved this project.

.

Measures

Exposure and outcome

The exposure variable was smoking measured by asking the question: “is person smoking daily?”. The outcome variable was metabolic syndrome. Components measuring metabolic syndrome, included abdominal obesity (waist circumference for men and women ≥ 95 cm), low HDL cholesterol (< 40 mg/dl in men or < 50 mg dl in women), hypertriglyceridemia (TG ≥ 150 mg/dl), hypertension (systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg) and impaired blood glucose (fasting blood glucose ≥ 100 mg/dl) [37,38,39].

Confounders

A causal-directed acyclic graph (cDAG) [40,41,42,43,44,45,46,47,48, 82] for the study population was created using the DAGitty package [49] (Fig. 1). The diagram shows the causal relationships between exposure, outcome and covariates which was drawn based on the literature review. The minimally sufficient set for confounding adjustment, derived based on Pearl’s back-door criterion [50], included gender, age, physical activity, marital status, education, and job as well as the unmeasured variables income and alcohol. Fractional polynomials were used to identify any nonlinear association between age and exposure (smoking) in the PS model [51,52,53,54,55].

Fig. 1
figure 1

Causal diagram for the effect of smoking on MS

Statistical methods

Steps of causal inference in LCA using PS methods

Step 1: PS estimation and evaluation of overlap

PS, the probability of exposure conditional on confounders [19], was estimated through logistic regression, with smoking as the response variable and confounders as predictors. We evaluated \(\widehat{PS}\) overlap in the exposed and unexposed groups using a histogram. The correlations between the predictors were assessed with the correlation matrix. The highest correlation was less than 0.3, so collinearity in the exposure model was not present.

Step 2: calculate IPTW and PS matching

Inverse probability-of-treatment weighting (IPTW)

IPTW was used to adjust for the minimally sufficient set of confounders. The rationale for weighting in IPTW is that over-represented persons (people with a high probability of exposure to cigarettes) take a low weight and under-represented persons (people with a low probability of exposure to cigarettes) take a high weight. Average treatment effect (ATE) in the whole population was estimated with weights equal to \(\frac{1}{\widehat{PS}}\) for the smokers and \(\frac{1}{(1-\widehat{PS})}\)for the non-smokers. The rationale behind the IPTW is that it produces a pseudo-population in which confounders do not predict the exposure anymore, and the causal effect of interest in the pseudo-population is the same as that in the population [56,57,58].

PS matching

A PS-matched dataset was created by matching, without replacement, one unexposed person to one exposed based on the nearest value of \(\widehat{PS}\) (± 0.05) [57]. Of note, a caliper width of 0.2 of the standard deviation of the logit of the PS [59] was deemed to be 0.25 which was considered too large for matching and thus was not included. The PS matching was performed using the R package Match It [60].

Step 3: evaluating balance of confounding variables between exposure groups

PS is a balancing score, so in persons with the same PS, the distribution of confounders should be similar between the exposed and unexposed. The correct specification of the PS model can be assessed based on the balance of measured confounders between exposure groups. The balance was evaluated 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 defined as

$$d=\frac{({\stackrel{-}{x}}_{exposed}-{\stackrel{-}{x}}_{unexposed})}{\sqrt{\frac{{s}_{exposed}^{2}+{s}_{unexposed}^{2}}{2}}}$$

where \({\stackrel{-}{x}}_{exposed}\) and \({\stackrel{-}{x}}_{unexposed}\) are the mean estimates and \({s}_{exposed}^{2}\) and \({s}_{unexposed}^{2}\) are variance estimates in the exposed and unexposed, respectively.

The standardized difference for binary confounders is defined as

$$d=\frac{{\widehat{p}}_{exposed}-{\widehat{p}}_{unexposed}}{\sqrt{\frac{{\widehat{p}}_{exposed}\left(1-{\widehat{p}}_{exposed}\right)+{\widehat{p}}_{unexposed}(1-{\widehat{p}}_{unexposed})}{2}}}$$

where \({\widehat{p}}_{exposed}\) and \({\widehat{p}}_{unexposed}\) are the proportion estimates of the binary confounders in the exposed and unexposed, respectively.

Although there is no consensus on the cutpoint of the standardized difference for defining an important imbalance, a standardized difference of less than 0.1 was considered as an unimportant difference in mean or proportion of confounders between exposure groups [20].

Step 4: Conduct LCA using the weighted or matched data set

LCA is a latent variable model that classifies homogeneous individuals. LCA is used to find groups in classified data, which are called latent classes. LCA has two parameters, class prevalence and item-response probability. The probability of membership in each latent class is called class prevalence. Item-response probability is the conditional probability of “yes” response to metabolic syndrome components. The probability of “no” response can be calculated by subtracting item-response probabilities from 1. These probabilities constitute the basis for interpreting and naming latent classes: class in which all the metabolic syndrome components have a probability less than 0.5 as without metabolic syndrome, one component has a probability higher than 0.5 as low risk, two of components have a probability higher than 0.5 as moderate risk, and three or more components have a probability higher than 0.5 are considered high risk. To conduct LCA, five observed dichotomous variables (metabolic syndrome components) were used to classify metabolic syndrome as a latent variable.

We conducted the LCA model in three types of data, original data, weighted data using IPTW, and matched data using PS matching. To select the best model, we compared LCA models 1 to 6 classes. Akaike’s Information Criterion (AIC) [61], Bayesian Information Criterion (BIC) [62], Consistent Akaike’s Information Criterion (CAIC) [61], and Adjusted Bayesian Information Criterion (ABIC) [63] were used to select the best model. Lower values of these indices indicate better model fit. Next, in each type of data, we examined the relationship between smoking and the latent classes of metabolic syndrome through multinomial logistic regression model and estimated the odds ratio (OR) with 95% confidence interval (CI) [64, 65]. The 95% CIs for the IPTW estimates were derived using robust standard errors [66]. 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 [67].

Software

R software was used to perform IPTW and PS matching analyses and calculate the standardized differences for confounders. The PS matching was performed using R package Match It [60]. The R package tableone was used for calculating standardized differences for IPTW and PS matching [68]. SAS package PROC LCA was used to obtain LCA [69].

Results

Of the 4857 participants included in this study, 2959 (60.9%) were female, and the mean (standard deviation) of age of participants was 39.10 (13.48) years, ranging from 20 to 90. Moreover, there were 512 (10.5%) cigarette smokers at baseline. In the PS matching, 508 unexposed subjects were matched to 508 exposed subjects. The mean (SD) of inverse probability-of-treatment weights for ATE estimate was 2.02 (5.57). The baseline characteristics of participants based on smoking have been shown in Table 1.

Table 1 Baseline characteristics of the study participants

Figure 2 shows the \(\widehat{PS}\) overlap in the exposed and unexposed groups using a histogram. Based on the figure, the distribution of \(\widehat{PS}\) in the smoker and non-smokers groups shows that there is sufficient overlap between two groups.

Fig. 2
figure 2

Histogram diagram of estimated propensity scores for the exposed and unexposed groups

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, indicating that a sufficient balance on the confounders was established between exposure groups.

Table 2 Standardized differences before (unadjusted) and after (adjusted) applying IPTW and use of PS matching

Table 3 compared LCA models 1 to 6 classes in the original data, weighted data using IPTW, and matched data to select the best model. Based on this table, in the weighted data using IPTW, the four-class model had the lowest values of BIC, CAIC, and ABIC, and the five-class model had the lowest value of AIC. Based on the lower values of BIC, CAIC and ABIC indicators, we preferred the four-class model for the weighted data. In the matched data, the three-class model had the lowest values of AIC, BIC, CAIC and ABIC. Therefore, based on the lower values of these indicators, the three-class model was preferred. Based on the original data, the three-class model had the lowest values of BIC, CAIC and ABIC, and the four-class model had the lowest value of AIC. Based on lower values of BIC, CAIC and ABIC indicators, the three-class model was preferred.

Table 3 Summary of information for selecting number of latent classes for metabolic syndrome

Table 4 shows the item-response probabilities for the four-class, three-class and three-class metabolic syndrome models in the weighted data using IPTW, matched data, and original data. Probability higher than 0.5 was considered as high probability. Based on this table, the class without metabolic syndrome shows people who had a low probability (less than 0.5) of metabolic syndrome components: in the weighted, matched, and original data, they comprised of 22%, 30%, and 57% of the population, respectively. Based on Table 4 in the weighted data using IPTW, the low-risk class comprised of 31% of the population and included people at high-risk of abdominal obesity. The moderate-risk class comprised of 39% of the population and also included people who were at a higher risk for hypertriglyceridemia and low HDL cholesterol levels. The high-risk class comprised of 8% of the population and included people who are at high-risk for all metabolic syndrome components except hypertension. In the matched data, the low-risk class comprised of 36% of the population and included people at high-risk for low HDL cholesterol. The moderate-risk class was comprised of 34% of the population and included people at high-risk of abdominal obesity and hypertriglyceridemia. In the original data, the low-risk class comprised of 27% of the population and included people who are at high-risk of abdominal obesity. The moderate-risk class comprised of 16% of the population and included people at high-risk for hypertriglyceridemia and low HDL cholesterol.

Table 4 Item-response probabilities for the four-class, three-class and three-class metabolic syndrome models in IPTW-weighted data, PS-matched data and original data

Table 5 shows the causal effect of smoking on metabolic syndrome latent classes in the weighted data using IPTW and matched data. Also, this table shows the effect of smoking on the metabolic syndrome latent classes in the original data. In the matched data, smoking increased the chances of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63) compared to the class without metabolic syndrome. Based on the matched data, 95% CI was compatible with both increase and decrease chance of being in moderate-risk class of metabolic syndrome. The same pattern of inconclusive CIs was seen for being in the low, moderate, and high risk class in the weighted analysis.

Table 5 Odds ratios and confidence intervals for the relationship between smoking and latent classes of metabolic syndrome before (unadjusted) and after (adjusted) applying IPTW and use of PS matching

Discussion

In this cohort study, we investigated the causal effect of smoking on the latent hazard classes of metabolic syndrome by integrating causal inference methods in the LCA. Integration of PS methods in LCA provides a better understanding of the causal mechanism of behavior or characteristics that are not directly measurable and allows researcher to easily control for many confounders simultaneously [56]. The most important advantage of using the causal inference approach to estimate each effect is that it allows researcher needs to ask a specific causal question. Average treatment effect (ATE) and average treatment effect in the treated (ATT) expresses different questions that differ in terms of the population to which we generalize the results. We first estimated ATE by IPTW, which ask” if everyone in the community smoked, what difference is expected in the pattern of metabolic syndrome compared to those who had never smoked”. We subsequently estimated ATT by PS matching, which asks “among people who smoked, assuming they all did,, what difference is expected in the pattern of metabolic syndrome compared to those who had never smoked” none of them having smoked? We believe that ATT provides us with a more realistic feeling. In fact, to answer this question, we are comparing smokers in terms of actual behavior of smoking with their expected behavior if they do not smoke. Thinking about the expected effects of smoking in the whole population does not seem logical, because in practice, many people in a population do not smoke (since different people in society have different behavioral characteristics). The results by PS matching, which considers exposed individuals, appear to be more plausible than the results of the IPTW, which measures the whole population.

In causal LCA, we first selected the model and obtained the relationship between smoking and metabolic syndrome latent classes by comparing the low, medium and high risk classes of metabolic syndrome with class without metabolic syndrome through polynomial regression. Based on the results of unadjusted model, we considered the three-class model, which included people without metabolic syndrome (57%), people with low risk of metabolic syndrome (26%) and people with moderate risk of metabolic syndrome (15%). Based on this model, smoking increased the chances of being in the low-risk and moderate-risk classes of metabolic syndrome compared to the class without metabolic syndrome. The purpose of adjustment and inclusion of IPTW and PS matching in the model is to analyze by re-weighing everyone based on IPTW or matching and removing confounders similar to a randomized controlled trial. Based on IPTW, no association was found between smoking and the metabolic syndrome latent classes.

In a study from the Czech Republic on 805 people in the age group of 18 to 65 years, the prevalence of metabolic syndrome did not differ between smokers and non-smokers [70]. In another study by Ishizaka et al. in Japan on 3687 people, smoking was not a predictor of metabolic syndrome and no relationship was found between these two variables [71]. In a study by Santos et al. in Porto, Portugal on 2165 people in the age group of 18 to 92 years, the prevalence of metabolic syndrome was not different in smokers and non-smokers [72]. Based on PS matching, smoking increased the chances of being in the low-risk class of metabolic syndrome compared to the class without metabolic syndrome. Previous studies have shown an association between smoking and metabolic syndrome, for example, in a multinational study with different ethnicities, non-Hispanic white, African-American, Hispanic, and American-Chinese, conducted in six U.S. regions, smoking increased the chances of developing metabolic syndrome compared to those who did not smoke [73]. In addition, Slagter et al. conducted a study in the Netherlands with 59,467 people, observed a higher prevalence of metabolic syndrome in smokers [17]. Sun et al. Conducted a meta-analysis of several cohort studies in different parts of Asia, Europe and North America, they found that smoking increases the risk of metabolic syndrome [74]. Also, an increased risk of metabolic syndrome associated with smoking, was observed in results of other studies in other parts of the world [1, 75,76,77]. The effects of smoking on the cardiovascular system can be caused by an increase in nicotine receptors. Nicotine receptor activation can secrete neurotransmitters and hormones such as vasopressin, growth hormone, dopamine, serotonin and glutamate in the central nervous system, acetylcholine in the peripheral nervous system, and catecholamine and cortisol from the adrenal glands. All these molecules affect metabolism [78]. Also, studies show that smoking causes inflammation, which predisposes to metabolic syndrome. Smoking increases the production of procytokines, decreases the level of anti-inflammatory cytokines, and increases the pathological level of inflammatory-sensitive proteins such as Alpha 1-antitrypsin and fibrinogen [79].

This study has some limitations. First, the validity of the causal analyses using this study relies on no unmeasured confounding. However, some confounders such as alcohol consumption and income were not available. Although we did not have income data, job and education variables were included in the model as suitable proxies for income. Alcohol is expected to have a positive relationship with smoking and also outcome: had adjusted for it, the effect estimate would have been even weaker than the current estimate. Second, there might have been some measurement bias as smoking was dichotomized and self-reported so subject to recall and under-reporting biases [80, 81]. Third, some adjusted confounders like physical activity might have had measurement error leading to residual confounding. Fourth, the exclusion of the subjects with missing data on baseline confounders might be subject to selection bias. However, only 10% of the participants were excluded for this reason with the mean age of 41 years and 57% female, which is somewhat similar to the people included in the study.

Conclusion

In summary, the results of this study showed that in unadjusted analyses, there were association between smoking and the chances of being in the low-risk and moderate-risk classes of metabolic syndrome compared to the class without metabolic syndrome, but after adjustment with IPTW, no strong evidence of an association between smoking and metabolic syndrome latent classes was observed Based on PS matching, smoking increased the chances of being in the low-risk class of metabolic syndrome compared to the class without metabolic syndrome. The differences in results can be explained by no confounding adjustment in the unadjusted analysis as well as different effect targets for the IPTW and PS matched adjusted analyses.

Data Availability

Data are available from the authors upon reasonable request to the corresponding authors.

Abbreviations

LCA:

Latent class analysis

IPTW:

Inverse probability of treatment weighting

PS:

Propensity score

OR:

Odds ratio

CI:

Confidence interval

AIC:

Akaike information criterion

BIC:

Bayesian information criterion

CAIC:

Consistent Akaike’s Information Criterion

ABIC:

Adjusted Bayesian Information Criterion

ATE:

Average treatment effect

References

  1. Al-khalifa II, Mohammed SM, Ali ZM. Cigarette smoking as a relative risk factor for metabolic syndrome. J Endocrinol Metabolism. 2017;6(6):178–82.

    Google Scholar 

  2. Riahi SM, et al. Patterns of clustering of the metabolic syndrome components and its association with coronary heart disease in the multi-ethnic study of atherosclerosis (MESA): a latent class analysis. Int J Cardiol. 2018;271:13–8.

    PubMed  Google Scholar 

  3. Subramani SK, et al. Prevalence of metabolic syndrome in Gwalior region of Central India: a comparative study using NCEP ATP III, IDF and Harmonized criteria. Volume 13. Diabetes & Metabolic Syndrome: Clinical Research & Reviews; 2019. pp. 816–21. 1.

    Google Scholar 

  4. Annani-Akollor ME, 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(1):26.

    PubMed  PubMed Central  Google Scholar 

  5. Ervin RB. Prevalence of Metabolic Syndrome Among Adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index; United States, 2003–2006 2009.

  6. Nestel P et al. Metabolic syndrome: recent prevalence in East and Southeast Asian populations. Asia Pac J Clin Nutr, 2007. 16(2).

  7. Keller KB, Lemberg L. Obesity and the metabolic syndrome. Am J Crit Care. 2003;12(2):167–70.

    PubMed  Google Scholar 

  8. Sarrafzadegan N, et al. Metabolic syndrome and the risk of ischemic stroke. J Stroke Cerebrovasc Dis. 2017;26(2):286–94.

    PubMed  Google Scholar 

  9. Abbasi-Ghahramanloo A, et al. Clustering and combining pattern of metabolic syndrome components among iranian population with latent class analysis. Med J Islamic Repub Iran. 2016;30:445.

    Google Scholar 

  10. Dalvand S, et al. Prevalence of metabolic syndrome among iranian population: a systematic review and meta-analysis. Iran J Public Health. 2017;46(4):456.

    PubMed  PubMed Central  Google Scholar 

  11. Boyko EJ, et al. Latent class analysis of the metabolic syndrome. Diabetes Res Clin Pract. 2010;89(1):88–93.

    PubMed  PubMed Central  Google Scholar 

  12. Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157–68.

    PubMed  PubMed Central  Google Scholar 

  13. Arguelles W. A latent class analysis of metabolic syndrome among Hispanics/Latinos living in the United States in relation to cardiovascular disease prevalence: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). 2012, University of Miami.

  14. Soflaei SS, et al. Hookah smoking is strongly associated with diabetes mellitus, metabolic syndrome and obesity: a population-based study. Diabetol Metab Syndr. 2018;10(1):1–6.

    Google Scholar 

  15. Bermudez V et al. Cigarette smoking and metabolic syndrome components: a cross-sectional study from Maracaibo City, Venezuela [version 1; referees: 1 approved with reservations] 2018.

  16. Takayama S et al. Smoking cessation without educational instruction could promote the development of metabolic syndrome. J Atheroscler Thromb, 2017: p. 40063.

  17. Slagter SN, et al. Associations between smoking, components of metabolic syndrome and lipoprotein particle size. BMC Med. 2013;11(1):195.

    PubMed  PubMed Central  Google Scholar 

  18. Inacio MC, et al. Statistics in brief: an introduction to the use of propensity scores. Springer; 2015.

  19. Elze MC, et al. Comparison of propensity score methods and covariate adjustment: evaluation in 4 cardiovascular studies. J Am Coll Cardiol. 2017;69(3):345–57.

    PubMed  Google Scholar 

  20. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46(3):399–424.

    Google Scholar 

  21. Almasi-Hashiani A, Nedjat S, Mansournia MA. Causal methods for Observational Research: a primer. Archives of Iranian Medicine (AIM), 2018. 21(4).

  22. Mansournia MA et al. Effect of physical activity on functional performance and knee pain in patients with osteoarthritis: analysis with marginal structural models. Epidemiology, 2012: p. 631–40.

  23. Mansournia MA, et al. Handling time varying confounding in observational research. BMJ. 2017;359:j4587.

    PubMed  Google Scholar 

  24. Khodamoradi F et al. The effects of smoking on metabolic syndrome and its components using causal methods in the iranian population. Int J Prev Med, 2021. 12.

  25. Abdollahpour I, et al. Estimating the marginal causal effect and potential impact of waterpipe smoking on risk of multiple sclerosis using the targeted maximum likelihood estimation method: a large, population-based incident case-control study. Am J Epidemiol. 2021;190(7):1332–40.

    PubMed  Google Scholar 

  26. Almasi-Hashiani A, 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(1):1–8.

    Google Scholar 

  27. Aryaie M, et al. 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. 2020;190(6):1133–41.

    Google Scholar 

  28. Gharibzadeh S, et al. Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores. Commun Statistics-Simulation. 2018;47(4):964–76.

    Google Scholar 

  29. Gharibzadeh S et al. Standardization as a Tool for Causal Inference in Medical Research. 2016. 19(9): p. 0–0.

  30. Smith MJ, et al. Introduction to computational causal inference using reproducible Stata, R, and Python code: a tutorial. Stat Med. 2022;41(2):407–32.

    PubMed  Google Scholar 

  31. Koohi F, et al. Longitudinal effects of lipid indices on incident cardiovascular diseases adjusting for time-varying confounding using marginal structural models: 25 years follow-up of two US cohort studies. Global Epidemiol. 2022;4:100075.

    Google Scholar 

  32. Shakiba M, et al. Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial. Sci Rep. 2023;13(1):5833.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mansournia MA, Nazemipour M, Etminan M. A practical guide to handling competing events in etiologic time-to-event studies. Global Epidemiol. 2022;4:100080.

    Google Scholar 

  34. Aryaie M, et al. Longitudinal causal effect of modified creatinine index on all-cause mortality in patients with end-stage renal disease: accounting for time-varying confounders using G-estimation. PLoS ONE. 2022;17(8):e0272212.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Azizi F, Zadeh-Vakili A, Takyar M. Review of rationale, design, and initial findings: Tehran lipid and glucose study. Int J Endocrinol Metabolism, 2018. 16(4 Suppl).

  36. Daneshpour MS et al. Cohort profile update: Tehran cardiometabolic genetic study. Eur J Epidemiol, 2023: p. 1–13.

  37. Alberti K, 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(16):1640–5.

    CAS  PubMed  Google Scholar 

  38. Delavari A, et al. 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(6):1092–7.

    PubMed  PubMed Central  Google Scholar 

  39. AZIZI F et al. Appropriate definition of metabolic syndrome among Iranian adults: report of the Iranian National Committee of Obesity 2010.

  40. Etminan M, Collins GS, Mansournia MA. Using causal diagrams to improve the design and interpretation of medical research. Chest. 2020;158(1):S21–8.

    PubMed  Google Scholar 

  41. Mansournia MA, et al. Biases in randomized trials: a conversation between trialists and epidemiologists. Epidemiol (Cambridge Mass). 2017;28(1):54.

    Google Scholar 

  42. Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol. 2013;42(3):860–9.

    PubMed  PubMed Central  Google Scholar 

  43. Kyriacou DN, Greenland P, Mansournia MA. Using Causal Diagrams for Biomedical Research. Annals of Emergency Medicine; 2022.

  44. Etminan M, et al. To adjust or not to adjust: the role of different Covariates in Cardiovascular Observational Studies. Am Heart J. 2021;237:62–7.

    PubMed  Google Scholar 

  45. Mansournia MA, Nazemipour M, Etminan M. Causal diagrams for immortal time bias. Int J Epidemiol. 2021;50(5):1405–9.

    PubMed  Google Scholar 

  46. Mansournia MA, Nazemipour M, Etminan M. Time-fixed vs time-varying causal diagrams for immortal time bias. Int J Epidemiol. 2022;51(3):1030–1.

    PubMed  Google Scholar 

  47. Mansournia MA, Nazemipour M, Etminan M. Interaction contrasts and Collider Bias. Am J Epidemiol. 2022;191(10):1813–9.

    PubMed  Google Scholar 

  48. Taheri Soodejani M et al. Adjustment for collider bias in the hospitalized Covid-19 setting Global Epidemiology, 2023: p. 100120.

  49. Textor J, et al. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol. 2016;45(6):1887–94.

    PubMed  Google Scholar 

  50. Judea P. Causality: models, reasoning, and inference Cambridge University Press. ISBN 0, 2000. 521(77362): p. 8.

  51. Abdollahpour I, et al. Estimating the marginal causal effect of fish consumption during adolescence on multiple sclerosis: a population-based incident case-control study. Neuroepidemiology. 2018;50(2–3):111–8.

    PubMed  Google Scholar 

  52. Mansournia MA, et al. A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration. Br J Sports Med. 2021;55(18):1009–17.

    PubMed  Google Scholar 

  53. Mansournia MA, et al. CHecklist for statistical Assessment of Medical Papers: the CHAMP statement. Br J Sports Med. 2021;55(18):1002–3.

    PubMed  Google Scholar 

  54. Abdollahpour I, et al. Estimation of the marginal effect of regular drug use on multiple sclerosis in the iranian population. PLoS ONE. 2018;13(4):e0196244.

    PubMed  PubMed Central  Google Scholar 

  55. Elduma AH, et al. The targeted maximum likelihood estimation to estimate the causal effects of the previous tuberculosis treatment in Multidrug-resistant tuberculosis in Sudan. PLoS ONE. 2023;18(1):e0279976.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Lanza ST, Coffman DL, Xu S. Causal inference in latent class analysis. Struct Equation Modeling: Multidisciplinary J. 2013;20(3):361–83.

    Google Scholar 

  57. Hernán MA, Robins JM. Causal Inference September 11, 2016.

  58. Mansournia MA, Altman DG. Inverse probability weighting. Br J Sports Med. 2016;352:i189.

    Google Scholar 

  59. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150–61.

    PubMed  Google Scholar 

  60. Stuart EA, et al. MatchIt: nonparametric preprocessing for parametric causal inference. Journal of statistical software; 2011.

  61. Bozdogan H. Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika. 1987;52(3):345–70.

    Google Scholar 

  62. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–4.

    Google Scholar 

  63. Sclove SL. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika. 1987;52(3):333–43.

    Google Scholar 

  64. Greenland S, Mansournia MA, Joffe M. To curb research misreporting, replace significance and confidence by compatibility: a Preventive Medicine golden jubilee article. Prev Med, 2022: p. 107127.

  65. Mansournia MA, Nazemipour M, Etminan M. P-value, compatibility, and S-value. Global Epidemiol. 2022;4:100085.

    Google Scholar 

  66. Mansournia MA, et al. Reflection on modern methods: demystifying robust standard errors for epidemiologists. Int J Epidemiol. 2020;50(1):346–51.

    Google Scholar 

  67. Austin PC, Small DS. The use of bootstrapping when using propensity-score matching without replacement: a simulation study. Stat Med. 2014;33(24):4306–19.

    PubMed  PubMed Central  Google Scholar 

  68. Yoshida K, Bohn J, Yoshida MK. Package ‘tableone’ R Foundation for Statistical Computing, Vienna, Austria (30 November 2016), 2020.

  69. Lanza ST, et al. Proc LCA & Proc LTA users’ guide (Version 1.3. 2). University Park: The Methodology Center, Penn State,; 2015.

    Google Scholar 

  70. Vosátková M, et al. Prevalence of obesity and metabolic syndrome in adult population of selected regions of the Czech Republic. Relation to eating habits and smoking. Prague Med Rep. 2012;113(3):206–16.

    PubMed  Google Scholar 

  71. Ishizaka N, et al. Association between cigarette smoking, white blood cell count, and metabolic syndrome as defined by the japanese criteria. Intern Med. 2007;46(15):1167–70.

    PubMed  Google Scholar 

  72. Santos A-C, Ebrahim S, Barros H. Alcohol intake, smoking, sleeping hours, physical activity and the metabolic syndrome. Prev Med. 2007;44(4):328–34.

    PubMed  Google Scholar 

  73. Berlin I, et al. Smoking status and metabolic syndrome in the multi-ethnic study of atherosclerosis. A cross-sectional study. Tob Induc Dis. 2012;10(1):9.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Sun K, Liu J, Ning G. Active smoking and risk of metabolic syndrome: a meta-analysis of prospective studies. PLoS ONE. 2012;7(10):e47791.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Yim E, et al. The prevalence of metabolic syndrome and health-related behavior changes: the Korea National Health Examination Survey. In Healthcare. Multidisciplinary Digital Publishing Institute; 2020.

  76. Chen C-C, et al. Association among cigarette smoking, metabolic syndrome, and its individual components: the metabolic syndrome study in Taiwan. Metabolism. 2008;57(4):544–8.

    CAS  PubMed  Google Scholar 

  77. Calo WA, et al. Association of cigarette smoking and metabolic syndrome in a puerto rican adult population. Int J Prev Med. 2014;5(7):849.

    PubMed  PubMed Central  Google Scholar 

  78. Yildiz D. Nicotine, its metabolism and an overview of its biological effects. Toxicon. 2004;43(6):619–32.

    CAS  PubMed  Google Scholar 

  79. 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. Volume 10. Diabetes & Metabolic Syndrome: Clinical Research & Reviews; 2016. pp. S89–S95. 2.

    Google Scholar 

  80. Pakzad R, et al. Effect of smoking on breast Cancer by adjusting for Smoking Misclassification Bias and Confounders using a probabilistic Bias Analysis Method. Clin Epidemiol. 2020;12:557–68.

    PubMed  PubMed Central  Google Scholar 

  81. Pakzad R, et al. Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders. BMC Med Res Methodol. 2023;23(1):157.

    PubMed  PubMed Central  Google Scholar 

  82. Etminan M, Nazemipour M, Mansournia MA, et al. Potential Biases in Studies of Acid-Suppressing Drugs and COVID-19 Infection. Gastroenterology. 2021;160(5):1443–1446.

  83. Mansournia MA, Naimi AI, Greenland S, et al. The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models Abstract American Journal of Epidemiology 2019;188(4):753–759.

Download references

Funding

No funding was received for this work.

Author information

Authors and Affiliations

Authors

Contributions

FKH: Conceptualization, Formal analysis, Methodology, Writing - original draft. MN: Writing - review & editing. NM: Writing - review & editing. KY: Writing - review & editing. DKH: Data curation, review & editing. MA: Writing - review & editing. ME: Writing - review & editing. MAM: Conceptualization, Methodology, Supervision, Writing - review & editing.

Corresponding author

Correspondence to Mohammad Ali Mansournia.

Ethics declarations

Ethics approval and consent to participate

The TLGS main project has been approved by the IRB of the Iranian National Scientific Research Council and the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, under the Helsinki Declaration and an informed consent form was obtained from all participants. The ethics committee of the School of Health, Tehran University of Medical Sciences (code of IR.TUMS.SPH.REC.1398.032) has approved this project.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khodamoradi, F., Nazemipour, M., Mansournia, N. et al. The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population. BMC Public Health 23, 2058 (2023). https://doi.org/10.1186/s12889-023-16863-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-16863-6

Keywords