Outcomes of metabolic syndrome and anxiety levels in light and heavy smokers

Introduction

Smoking constitutes a chronic addiction to nicotine, acknowledged as a significant risk factor for a spectrum of diseases and disabilities impacting both physical and mental well-being (Pawlina et al., 2015). Tobacco smoking is one of the biggest public health risks in the general population. According to the World Health Organization (WHO), over 8 million people die annually due to tobacco-related causes, with over 7 million directly attributable to tobacco use, and 1.2 million fatalities resulting from health complications associated with passive smoking (WHO, 2022). If current smoking trends persist, projections suggest that more over 8 million individuals will succumb annually to tobacco-related illnesses by the year 2030 (WHO, 2011). Given the conclusive evidence of nicotine’s harm in health contexts, the latest International Classification of Diseases (ICD-11) introduces substantial improvments, significant updates and perspectives on nicotine-related disorders (World Health Organisation). Notably, individuals with psychiatric disorders exhibit a heightened prevalence of nicotine addiction, as nicotine’s psychoactive properties often temporarily alleviate psychiatric symptoms to some extent (El-Sherbiny & Elsary, 2022). Despite mounting evidence of its detrimental effects, smoking remains ingrained in the belief system of many, who perceive it as a stress reliever and anxiety-reducer (CDC, 2013). Particularly noteworthy is the disproportionately elevated smoking prevalence among individuals with psychiatric conditions such as schizophrenia, anxiety disorders, eating disorders, and attention deficit hyperactivity disorder (Sciberras et al., 2017).

A literature review highlights a significant failure rate in smoking cessation efforts. Studies examining smoking cessation programs underscore a concerning trend of high failure rates, largely attributed to elevated levels of depression, anxiety, and stress, coupled with a general reluctance to adopt behavioral changes (Ghali et al., 2019; Shepherd et al., 2022). Anxiety, given its role as a potential motivator for smoking initiation, emerges as a crucial focus area warranting investigation (McDermott et al., 2013). Notably, smokers grappling with heightened anxiety levels often experience poorer outcomes in cessation efforts, marked by intensified withdrawal symptoms and a heightened urge to smoke (Johnson et al., 2019; Lewis et al., 2020). Moreover, apprehensions surrounding nicotine withdrawal symptoms and heightened state anxiety further compound the challenges of quitting smoking for individuals with elevated anxiety levels (McLaughlin, Dani & De Biasi, 2015). On a positive note, emerging evidence suggests smoking cessation can complement treatment strategies for mental disorders, particularly anxiety disorders, yielding favorable effects on mental health outcomes (Cavazos-Rehg et al., 2014; Taylor et al., 2014).

Metabolic syndrome, characterized by a cluster of cardiovascular risk factors, including abdominal obesity, hypertension, hyperglycemia, and dyslipidemia, represents a burgeoning public health concern with implications for diabetes and cardiovascular morbidity; approximately one-quarter of the global adult population is documented to have metabolic syndrome (American Diabetes Association, 2013; Wang et al., 2022). Lifestyle shifts toward sedentary behaviors and high-calorie diets have fueled the escalating prevalence of metabolic syndrome, posing a formidable threat to recent strides in health advancement (Saklayen, 2018; Cena et al., 2013).

The association between anxiety and smoking behavior often leads individuals to smoke more, thereby increasing their exposure to the adverse effects of smoking and the risk of developing metabolic syndrome. This underscores the importance of conducting comprehensive evaluations and providing integrated services for individuals seeking smoking cessation assistance at outpatient clinics. There is a need in the literature in terms of studies which concurrently investigate smoke consumption, anxiety, and metabolic syndrome. Additionally, studies on the effect of nicotine addiction on metabolic syndrome have yielded conflicting results, highlighting the need for further research. Therefore, this study aimed to evaluate the effect of smoking status, nicotine dependence on metabolic syndrome parameters and simultaneously examining anxiety levels in smokers. By examining these factors concurrently, we aim to contribute to a better understanding of the complex interplay between smoking behavior, psychological factors, and metabolic health, thereby informing more effective strategies for smoking cessation interventions and metabolic syndrome management.

Materials and Methods

Results

In Table 1, heavy smoking was represented by the median, with all subjects having smoked for a median duration of 25.0 years. Table 1 also outlines the characteristics of the sample and compares values between genders. The data comprises 208 patients, with 97 males and 111 females. The average age of the entire sample was 43.8 ± 11.8 years, while the mean age upon starting smoking was 12.5 ± 9.8 years, with an average smoking duration of 23.3 ± 10.7 years. Basal glucose levels averaged 96.0 ± 18.6 mg/dL. STAI-A and STAI-T Anxiety scores were recorded as 49.4 ± 6.6 and 50.0 ± 7.0, respectively. When categorizing smoking status into heavy and light based on median values, 53.7% of participants were identified as heavy smokers. Prevalence rates of meeting metabolic syndrome criteria were 41.8% for TG, 33.7% for HDL, 23.6% for BP, and 16.4% for FG.

Females had significantly lower HDL levels compared to males (p < 0.001). Females exhibited higher systolic blood pressure (p < 0.001) but lower diastolic blood pressure (p < 0.001). The proportion of heavy smokers was significantly higher among males (p = 0.008). Males also had a higher prevalence of elevated triglycerides (p = 0.001) and elevated blood pressure (p = 0.006) compared to females.

Table 2 categorized values based on light, moderate, and heavy smoking levels by pack years, further divided into quartiles of 10 years each. Age increases significantly with smoking intensity. Light smokers are younger on average (35.4 years), while heavy smokers are older (53.3 years). A similar trend is seen in the quartiles, with the 1st quartile being the youngest (31.3 years) and the 4th quartile the oldest (50.2 years). Significant age differences were noted across the smoking intensity categories and within the 3rd and 4th quartiles (p < 0.05).

In the quartile analysis, only the 1st quartile (the lightest smokers) shows a statistically significant longer delay in smoking their first cigarette, with an average of 19.0 min. This delay is significantly longer than that observed in the other quartiles (p < 0.05).

The duration of smoking years impacted the smoking level across all quartiles. LDL levels increase with smoking intensity, showing a significant difference between light smokers (113.6 mg/dL) and moderate/heavy smokers (133.3, 130.8, p < 0.05). A similar trend appears across quartiles, with the 1st quartile having the lowest LDL (101.1, p < 0.05). HbA1c increases from light to heavy smokers, with a statistically significant difference between light smokers (5.5) and moderate/heavier groups (5.8, 5.9, p < 0.05). SBP is significantly higher in heavy smokers (125.0 mm Hg) compared to light and moderate smokers (113.0, 118.1, p < 0.05). DBP also increases with smoking intensity, with heavy smokers having significantly higher DBP (78.0 mm Hg) than light and moderate smokers (71.2, 75.3, p < 0.05).

Heavy smokers have a significantly higher Trait Anxiety score (51.6) compared to light smokers (48.3) and moderate smokers (50.5), with a significant difference observed (p < 0.05). Significant differences in State Anxiety scores are observed in the 3rd quartile of smoking intensity, with p < 0.05.

In Table 3, Significantly lower mean age (p = 0.012) and duration of the first cigarette (p < 0.001) were noted when comparing normal to moderate addiction levels with high levels. Significantly lower mean age (p = 0.012) and shorter duration until the first cigarette (p < 0.001) were noted when comparing normal to moderate addiction levels with high levels. However, in multivariate analysis, age was not significant (OR = 1.0, p = 0.299), while the duration until the first cigarette remained significant (OR = 0.9, p < 0.001). A downward trend was observed in all values except TG, LDL, insulin, HOMA-Ir, and SBP from normal to moderate to high addiction levels. However, in the multivariate analysis, none of the variables remained statistically significant.

In Table 4, each anxiety type is divided into two groups, “Normal to Moderate” and “Severe”. State and trait anxiety scores were elevated in 52.4% and 52.9% of the entire sample, respectively.

Across the spectrum from normal to severe state anxiety levels, there was a tendency for all values to increase, while the time to the first cigarette smoked tended to decrease. State anxiety was associated with differences in baseline glycemia, with the ‘Severe’ group exhibiting higher levels (p = 0.050). Additionally, state anxiety showed significant differences in HbA1c levels, with higher levels in the ‘Severe’ group (p = 0.034). Systolic blood pressure was significantly higher in the ‘Severe’ group for state anxiety, while diastolic blood pressure did not differ (p = 0.029; p = 0.233). State anxiety was significantly associated with trait anxiety, with higher values observed in the ‘Severe’ group (p = 0.000). Regarding anxiety scores across state anxiety subgroups, the mean state anxiety scores were 44.8 ± 4.5 and 53.6 ± 5.2, while the mean trait anxiety scores were 47.7 ± 5.7 and 52.1 ± 7.4 (p = 0.000).

State anxiety increased with the severity of anxiety, while the time to the first cigarette smoked tended to decrease. Baseline glycemia also showed a significant relationship with State anxiety. Specifically, the severe anxiety group exhibited higher baseline glycemia levels (p = 0.05) compared to other groups. In addition to baseline glycemia, state anxiety was associated with HbA1c levels. The severe anxiety group exhibited significantly higher HbA1c levels compared to the other groups (p = 0.034). SBP was significantly higher in the severe anxiety group compared to other groups (p = 0.029), whereas DBP did not show significant differences across anxiety levels (p = 0.233). State anxiety was significantly associated with trait anxiety, with higher trait anxiety scores observed in the severe anxiety group (p = 0.000). Regarding anxiety scores across state anxiety subgroups, the mean state anxiety scores were 44.8 ± 4.5 and 53.6 ± 5.2, while the mean trait anxiety scores were 47.7 ± 5.7 and 52.1 ± 7.4. These differences were statistically significant (p = 0.000).

Discussion

The etiology of the metabolic syndrome remains subject to debate, yet smoking emerges as a notable modifiable risk factor. Extensive research indicates that smoking is linked to lipid irregularities, endothelial dysfunction, and a prothrombotic state, all of which constitute components of metabolic syndrome (Golbidi, Edvinsson & Laher, 2020; Slagter et al., 2013). This association is further underscored by evidence suggesting that smoking may exacerbate insulin resistance, contributing to metabolic and hemodynamic aberrations (Slagter et al., 2013). While certain investigations have demonstrated a positive correlation between smoking and the prevalence of metabolic syndrome (Calo et al., 2013; Chen et al., 2008; Shin, Oh & Cho, 2018; Wada, Urashima & Fukumoto, 2007), conflicting findings have been reported in other studies (Katano et al., 2010; Yu et al., 2014). Notably, a study among Turkish women suggested a reduced risk of metabolic syndrome among smokers (Onat et al., 2007). While previous studies have typically focused on daily tobacco consumption or cumulative smoking duration, our study offers a nuanced analysis incorporating gender-specific median values, pack years, a three-tier assessment (light, moderate, heavy), and quartiles, aligning with prior research (Cena et al., 2013; Wada, Urashima & Fukumoto, 2007; Zhu et al., 2011). Additionally, our study unveils, for the first time, parallels between key metabolic components and anxiety scores within a sample of 208 smokers.

In exploring the relationship between anxiety and metabolic syndrome, evidence suggests a tenuous link (Butnoriene et al., 2014; Skilton et al., 2007). A recent meta-analysis of 18 cross-sectional studies examining anxiety’s impact on metabolic syndrome risk reported a modest yet discernible increase in risk (OR = 1.07) (Tang, Wang & Lian, 2017). Furthermore, a registry review revealed elevated cardiometabolic risks among individuals with anxiety, encompassing conditions such as diabetes, hypertension, hyperlipidemia, and obesity (Pérez-Piñar et al., 2017). Given the profound influence of lifestyle on metabolic syndrome and the generally unhealthy lifestyles prevalent among psychiatric patients, it is posited that poor lifestyle choices may contribute to the observed association between anxiety disorders and metabolic syndrome (Penninx & Lange, 2022).

Nicotine, carbon monoxide, and other smoking metabolites have been implicated in inducing insulin resistance. Cena et al. (2013) have noted that the effects previously attributed to insulin resistance persist even when considering smoking effects independently of insulin resistance. The absence of dose-dependent worsening in insulin and HOMA-IR levels in our study compared to the existing literature is a notable and distinctive finding. Despite this, we observed that criteria for metabolic syndrome and important metabolic components were affected at various levels, including SBP, DBP, HbA1c, and LDL. Consequently, mechanisms other than insulin resistance can adversely influence metabolic syndrome parameters, revealing an ongoing deterioration independent of insulin resistance, just as suggested by Cena et al. (2013).

A comparative analysis of men and women revealed no significant differences in BMI and age. However, as anticipated, disparities were observed in HDL values, with men exhibiting higher SBP and DBP levels and a higher prevalence of poor triglyceride profiles. Another notable difference was the higher proportion of heavy smokers among men; this may be evidence of a clear relationship between the smoking profile and SBP, DBP and triglyceride profiles. This observation is supported by our study sample, which adjusts for confounding variables such as BMI, age, metabolic syndrome-related diseases, or psychiatric illness status. Previous research by Chen et al. (2008) found no significant relationship between fasting blood glucose levels and tobacco consumption. At the same time, conflicting evidence was reported by Will et al. (2001), who suggested an association between increasing tobacco consumption and the incidence of type 2 diabetes (Chen et al., 2008). These contradictory findings regarding the effect of smoking on fasting blood glucose levels underscore the complexity of this relationship. Thus, while our study did not reveal a distinct difference in fasting blood glucose, definitive conclusions cannot be drawn.

Our study also evidently impacted LDL profile, an important risk factor for coronary artery disease, although LDL is not among the criteria for metabolic syndrome. Specifically, LDL profile was lower in light smokers or those with less than 10 years of smoking history. However, the duration of this effect could not be determined statistically in our study. While triglyceride levels increased significantly in accordance with the median smoking duration (25 years), the duration of this effect remained unclear statistically like LDL profile. This ambiguity may be attributed to the cross-sectional nature of our study design. As anticipated, SBP and DBP profiles were significantly impaired in heavy smokers, consistent with findings in the literature (Cena et al., 2013).

Research suggests that trait anxiety is more likely to be associated with metabolic syndrome markers, as persistent anxiety could lead to sustained stress responses that impact physiological processes, including metabolic pathways. Research highlights that trait anxiety could exacerbate the negative effects of smoking on metabolic health, as individuals with high trait anxiety may use smoking as a coping mechanism, which intensifies nicotine dependence and metabolic syndrome risk factors (Chida & Steptoe, 2009; Cohen, Edmondson & Kronish, 2015). A study by McEwen (1998) demonstrated that individuals with high trait anxiety tend to have higher levels of smoking-related biomarkers, such as cortisol and catecholamines, potentially worsening lipid profiles and blood pressure. Consistent with previous research, our findings suggest that SBP and DBP values are influenced by the anxiety levels of patients, as discussed elsewhere in the literature (Lemche, Chaban & Lemche, 2016; Marvar et al., 2014). Furthermore, it has been established in the literature that a poor metabolic profile can lead to anxiety (Butnoriene et al., 2014; Diamanti-Kandarakis, 2008; Diamanti-Kandarakis et al., 1999). Given these findings, it is evident that individuals seeking smoking cessation, particularly those with a compromised metabolic profile, may need integrated services addressing both physical and mental health concerns, including anxiety.

Anxiety levels and blood pressure have been found to be related in our study. Potential mechanisms explaining this relationship could involve physiological stress responses. Anxiety often activates the sympathetic nervous system, leading to the release of stress hormones such as cortisol and adrenaline, which can increase heart rate and cause vasoconstriction, ultimately raising blood pressure (Chrousos, 2009). Chronic anxiety may result in sustained sympathetic activation, contributing to long-term increases in blood pressure (Wenner, 2018). Moreover, behavioral factors could also play a role. Individuals with heightened anxiety levels may be more likely to engage in unhealthy behaviors, such as poor dietary choices, smoking, or lack of exercise, all of which are risk factors for hypertension (Hering, Lachowska & Schlaich, 2015; King et al., 1996). Furthermore, anxiety may influence the autonomic nervous system and the regulation of blood pressure through altered baroreceptor sensitivity (Ziegler, 2012).

Individuals with a history of smoking are reported to be more susceptible to metabolic syndrome than non-smokers, with a significant risk across age groups and, as in our study, across genders (Ford, Li & Zhao, 2010; Grundy et al., 2005). Dietary habits, physical activity levels, and socioeconomic status are also important factors influencing metabolic differences (Hildrum et al., 2007; Lakka & Laaksonen, 2007; Matthews et al., 2008; Santos-Marcos, Perez-Jimenez & Camargo, 2019). The majority of our sample is in stages just before the age groups most relevant for metabolic syndrome. According to reports, age may partially influence the prevalence of metabolic syndrome, which increases significantly with age. However, given that the entire sample was cross-sectional and collected over a 1-year period from a specific center, we assume that these factors did not significantly influence the study outcomes. The uniformity in the sample, along with uninterrupted data collection during this period, helps minimize variability due to these confounders. Furthermore, the specific center’s population characteristics were consistent, which we believe reduces the potential impact of socioeconomic status, diet, and physical activity on the findings.

Research on post-cessation recovery reveals that the timeline and degree of metabolic improvements are often proportional to pre-cessation tobacco use, with studies observing a gradual normalization of lipid, glucose, and blood pressure values up to 5–20 years post-cessation, depending on baseline smoking intensity (Chiolero et al., 2008; Park et al., 2021; Wada, Urashima & Fukumoto, 2007; Will et al., 2001; Zhu et al., 2011). For instance, a study demonstrated that those with heavier smoking histories took longer to exhibit improvements in glucose metabolism and insulin resistance (Wada, Urashima & Fukumoto, 2007). Significant improvements were observed one month post-cessation: respiratory function markers such as FEV1 and FEF25/75 improved, eCO levels dropped substantially, and respiratory symptoms alleviated. Metabolic parameters also showed positive changes, including a modest increase in vitamin D levels (without supplementation) and reductions in total cholesterol (Pezzuto et al., 2023).

In terms of mental health, incorporating follow-up data could further elucidate whether anxiety decreases alongside metabolic improvements post-cessation. While our study suggests elevated anxiety in heavy smokers, studies indicate that cessation may reduce anxiety and improve quality of life, likely due to metabolic and neurochemical stabilization (Hajek, Taylor & McRobbie, 2010; Zvolensky et al., 2018). As anxiety and smoking cessation are intertwined, addressing metabolic health post-cessation may thus provide dual benefits for smokers. Besides our current study was limited to specific psychological metrics, we agree that incorporating additional nuanced psychological measurements, such as stress resilience or coping mechanisms, could provide a more comprehensive understanding of the observed trends.

Conclusions

In conclusion, our study underscores the interconnectedness of nicotine dependence and anxiety levels with metabolic syndrome parameters among smokers. Metabolic syndrome components—including systolic and diastolic blood pressure, HbA1c, LDL, and triglycerides—show an increase in individuals who smoke, with distinct differences between light and heavy smokers. Notably, systolic blood pressure exhibits a dose-response relationship with smoking intensity, suggesting that heavier smoking may further elevate this risk factor. Additionally, there is an observed link between anxiety levels and blood pressure, emphasizing a potential interplay between psychological stress and cardiovascular risk in smokers. These findings highlight the critical link between physical and mental health in individuals who smoke. Moving forward, it is imperative to conduct further research in this area and to integrate mental health considerations into smoking cessation programs. We suggest that future research employs more detailed psychiatric assessments to better understand the psychological impact of smoking. By addressing both physical and mental health aspects, we can better support individuals in their efforts to quit smoking and improve overall well-being.

Supplemental Information