Introduction

Sarcopenia, an age-associated progressive loss of skeletal muscle mass, is accompanied by decreased muscle strength and function [1]. It has been estimated that sarcopenia will affect more than 500 million older adults by 2050 [2]. Numerous studies reported that sarcopenia significantly increased the risk of falls, disability, frailty, hospitalization, and mortality [3, 4]. Given the serious consequences of sarcopenia, identifying modifiable risk factors has become an urgent public health priority.

Smoking is the single most preventable cause of disease, disability, and death [5]. Studies have demonstrated that active or passive smoking is associated with a variety of diseases, including cancer, cardiovascular, respiratory, and neurological diseases [6, 7]. Recently, the detrimental effects of smoking on muscle metabolism have attracted much attention. Several basic studies have revealed that smoking inhibits the synthesis and promotes the degradation of muscle proteins [8, 9]. Furthermore, some researchers reported that cigarette smoking was associated with reduced muscle strength [10]. However, few studies reported the potential effect of cigarette smoke exposure on muscle mass. Cigarette smoke contains large amounts of harmful compounds, including nicotine, nitrosamines, and heavy metals [11]. Cotinine, a major metabolite of nicotine, is currently used as a reliable biomarker of exposure to cigarette smoke [12]. Thus, we conducted this study to investigate the association between cigarette smoke exposure, defined based on serum cotinine, and muscle mass in the US population.

Materials and methods

Source of data

The NHANES is a national survey conducted by the Centers for Disease Control and Prevention, which monitors the health and nutritional status of children and adults across the US. The National Center for Health Statistics (NCHS) Ethics Review Board approved NHANES protocols, and all participants gave informed consent for using their data for research [13].

Study population

We utilized data from four 2-year cycles of NHANES (2011–2018) for analysis.

Participants with complete data on serum cotinine, appendicular skeletal muscle mass (ASM), standing height, and cigarette use were enrolled in this study. We excluded participants who were pregnant (n = 840), had cancer (n = 2036), or lacked data for cotinine (n = 8542), ASM (n = 12681), standing height (n = 37), gender (n = 44), and cigarette use (n = 5068), age ≤ 18 years (n = 1904). (The flowchart of the selection processes is shown in Fig. 1.)

Fig. 1
figure 1

The flowchart of selection processes

Full size image

For each included participant, data on serum cotinine, ASM, standing height, cigarette use, and covariates were extracted and analyzed.

Study variables

Serum cotinine

Serum cotinine (ng/mL) was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry (ID HPLC-APCI MS/MS).

Muscle mass

Body composition was evaluated by dual-energy X-ray absorptiometry (DXA) scan (Hologic, Inc., Bedford, Massachusetts), which measured bone and soft tissue for the whole body, including both arms and legs, the truck, and the head. The ASM (Kg) was defined as the sum of the lean soft tissue mass of both arms and legs [14]. We used appendicular skeletal muscle mass index (ASMI, Kg/m2) to further quantify muscle mass. It was calculated by ASM divided by height square (ASM/ht2) [15].

Covariates

Based on the literature, the following variables were selected as covariates in this study [13, 16,17,18]. Data on demographic variables, including age, gender (male or female), race (Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Races), and family income to poverty ratio (FIPR), were collected from demographic data. The FIPR was categorized as < 1.3, 1.3–3.5, > 3.5 [15]. White blood cell counts (WBC, 1000 cells/uL) and hemoglobin (g/dL) were measured using the Coulter® DxH 800 analyzer. Glycohemoglobin was measured using G7/G8 Glycohemoglobin analyzer. Serum albumin (g/dL), globulin (g/dL), AST (IU/L), iron (ug/dL), uric acid (mg/dL), and creatinine (mg/dL) were measured using Beckman UniCel® DxC800 Synchron. HDL-cholesterol, LDL-cholesterol, total cholesterol, and triglyceride were measured using Roche/Hitachi Modular P Chemistry Analyzer. Vitamin D was measured using High-Performance Liquid Chromatography-Tandem Mass Spectrometry.

The presence or absence of hypertension and diabetes was defined by participants’ self-reports of their doctor’s diagnosis. Smoking status was collected from Questionnaire Data. Participants who smoked less than 100 cigarettes in their life were defined as never smokers; participants who had smoked at least 100 cigarettes, but did not smoke at the time of survey, were defined as former smokers; and participants who had smoked more than 100 cigarettes and smoked cigarettes at the time of survey were considered current smokers [19].

Statistical analysis

The statistical analyses were conducted using R 3.4.3 (https://www.r-project.org/) and EmpowerStats 2.0 (http://www.empowerstats.com). P < 0.05 was considered statistically significant. The NHANES sample weights were used as recommended by the NCHS. The weighted linear regression model and weighted chi-square test were used to compare the continuous and categorical variables, respectively. Weighted multivariable linear regression analyses were performed to investigate the association between serum cotinine and ASMI. Subgroup analyses stratified by gender, race and smoking status were also performed. The generalized additive models and smooth curve fittings were conducted to explore the nonlinear relationship. When nonlinearity was detected, a two-piecewise linear regression model was performed to analyze the threshold effect.

Results

In total, 8004 participants were enrolled based on the inclusion and exclusion criteria. There were 4750 men and 3254 women, and their mean ages were 38.406 ± 11.909 years and 37.477 ± 11.747 years, respectively. Men were more likely than women to have higher levels of hemoglobin, albumin, AST, iron, uric acid, creatinine, LDL-cholesterol, triglyceride, vitamin D, ASMI, and cotinine, higher rate of current and former smoking, and lower levels of WBC, globulin, and HDL-cholesterol (Table 1).

Table 1 Baseline characteristics of study participants
Full size table

Association between serum cotinine and muscle mass

Table 2 presents the association between serum cotinine and muscle mass in three multivariate linear regression models. In the unadjusted model, there was no significant association between serum cotinine and muscle mass (model 1: β = 0.000, 95%CI: -0.000 to 0.001). When adjusted for covariates, we observed negative associations between serum cotinine and muscle mass in model 2: β=-0.001, 95%CI: -0.001 to -0.000 and model 3: β=-0.001, 95%CI: -0.001 to -0.001. Furthermore, by comparing participants in the highest vs. the lowest tertile of serum cotinine in model 3, we found that ASMI decreased by 0.135 Kg/m2, P trend = 0.01.

Table 2 Association between serum cotinine and ASMI.
Full size table

In subgroup analysis stratified by gender, the association between serum cotinine and ASMI remained significant in both man and woman. In subgroup analysis stratified by race, the association between serum cotinine and ASMI remained significant in all races (Table 3). The association remained significant in current smokers and former smokers, but not in never smokers (Table 4).

Table 3 Association between serum cotinine and ASMI stratified by gender and race
Full size table
Table 4 Association between serum cotinine and ASMI stratified by smoking status
Full size table

Threshold effect analysis

There was a nonlinear relationship between serum cotinine and ASMI (Fig. 2), with the inflection point identified at 356 ng/mL. When serum cotinine levels were < 356 ng/mL, a one-unit increase in serum cotinine level was associated with 0.001 units reduction in ASMI. When serum cotinine levels were > 356 ng/mL, no significant association was observed with ASMI. (Table 5)

Fig. 2
figure 2

The relationship between serum cotinine and ASMI. (a) Each black point represents a sample. (b) Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit

Full size image
Table 5 Threshold effect analysis of serum cotinine on ASMI using two-piecewise linear regression model
Full size table

A nonlinear relationship was detected between serum cotinine and ASMI in man and woman. When serum cotinine levels were < 386 ng/mL, a one-unit increase in serum cotinine level was associated with 0.002 units reduction of ASMI in man. When serum cotinine levels were > 386 ng/mL, no significant association was observed with ASMI. When serum cotinine levels were > 92 ng/mL, a one-unit increase in serum cotinine level was associated with 0.003 units reduction of ASMI in woman. When serum cotinine levels were < 92 ng/mL, no significant association was observed with ASMI. (Fig. 3a; Table 5)

Fig. 3
figure 3

The subgroup analysis of the relationship between serum cotinine and ASMI. (a) Stratified by gender. (b) Stratified by race. (c) Stratified by smoking status

Full size image

Similarly, a nonlinear relationship was detected between serum cotinine and ASMI among participants of the Non-Hispanic White race, with the inflection point identified at 393 ng/mL. When serum cotinine levels were < 393 ng/mL, a one-unit increase in serum cotinine level was associated with 0.001 units reduction in ASMI. When serum cotinine levels were > 393 ng/mL, no significant association was observed with ASMI. (Fig. 3b; Table 5)

There was a nonlinear relationship among current smokers, with the inflection point identified at 459 ng/mL. When serum cotinine levels were < 459 ng/mL, a one-unit increase in serum cotinine level was associated with 0.001 units reduction in ASMI. When serum cotinine levels were > 459 ng/mL, no significant association was observed with ASMI. (Fig. 3c; Table 5)

Discussion

With the aging population, the prevalence of sarcopenia continues to rise. A previous study reported that the incidence of sarcopenia varied from 6.1 to 44.3% among the elderly population [20]. To date, there is no effective treatment for sarcopenia; therefore, preventive measures are of great importance [21]. Although several risk factors have been identified, the effect of smoking on muscle mass remains poorly defined. Therefore, we performed this study to explore the role of cigarette smoke exposure on muscle loss. The results demonstrated a negative association between serum cotinine and muscle mass.

Although various tobacco control measures have been implemented, the prevalence of smoking remains considerably high, particularly in low-income and middle-income countries [22, 23]. To make matters worse, the prevalence of smoking among adolescents increased from 8.3% in 2003 to 12.5% in 2013 [22]. Cigarette smoking is closely related to chronic non-communicable diseases, such as chronic obstructive pulmonary diseases, ischemic stroke, ischemic heart disease, diabetes, osteoporosis, and cancers [24, 25]. Besides, accumulating evidence showed a strong link between cigarette smoke exposure and skeletal muscle dysfunction. Hansen et al. reported a significant loss of skeletal muscles (soleus and gastrocnemius) in mice exposed to cigarette smoke compared with sham mice [26]. Similarly, Caron et al. found a 10.8% reduction in gastrocnemius weight in mice exposed to cigarette smoke compared to mice inhaling room air [8]. Other studies also observed a significant muscle type redistribution (shift from type I to type II), and a reduction in muscle capillary to fiber ratio in mice exposed to cigarette smoke [27]. In addition to structural alteration of skeletal muscle, cigarette smoke exposure has deleterious effects on muscle function. Wang et al. found that the average and maximum grip strength were significantly lower in mice exposed to cigarette smoke compared with sham mice [28]. In a cross-sectional study, Carrasco-Rios et al. revealed that participants with the highest quartile of serum cotinine had 1.41 kg reduction in combined grip strength compared with participants with the lowest quartile of serum cotinine [10]. Similar results were also reported by other studies [29, 30]. This study is the largest population-based study that investigated the association between serum cotinine and muscle mass. The results demonstrated a negative association between serum cotinine and muscle mass. Participants with the highest tertile of serum cotinine had 0.135 Kg/m2 reduction in ASMI compared with participants with the lowest tertile of serum cotinine.

Although the mechanisms that lead to muscle dysfunction have not been fully elucidated, multiple factors, such as systemic and local inflammation, oxidative stress, and anti-estrogen effect, may be involved [26, 31, 32]. Animal studies indicated a significantly higher abundance of inflammatory cells, proinflammatory cytokines, and chemokines in mice exposed to cigarette smoke [26, 28]. Human studies further confirmed systemic low-grade inflammation in smokers, evidenced by elevated serum levels of interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) [33, 34]. Several studies demonstrated that exposure to cigarette smoke disrupted the oxidative/antioxidant balance by increasing superoxide radicals and decreasing superoxide dismutase and catalase levels [35]. Additionally, smoking has potential anti-estrogenic effects by increasing estradiol 2-hydroxylation and producing metabolites with minimal estrogenic activity [36]. Moreover, smoking may reduce IGF-1 and vitamin-D levels [26, 37]. The combination of these factors enhances proteolysis, inhibits protein synthesis, and finally reduces muscle mass [38].

Although we cannot effectively prevent the aging process, we can reduce cigarette exposure and maintain serum cotinine at low levels for better muscle health. In vivo study showed that quitting smoking for 60 days in BALB/c mice attenuated the pro-catabolic and anti-anabolic signaling induced by cigarette exposure, suggesting that smoking cessation has beneficial effects on skeletal muscle metabolism [8]. In a twelve-month longitudinal study, the authors reported that adult smokers who abstained from cigarette smoking gained 1.26 kg lean mass and 3.6 kg handgrip strength compared with those who continued smoking [39]. Another study focusing on postmenopausal women found that smoking cessation was associated with increased muscle mass and functional capacity [40]. Consistently, a meta-analysis performed by Aubin et al. revealed that smoking cessation was associated with an average weight gain of 4–5 kg after 12 months of abstinence [41]. Thus, avoiding cigarette smoke exposure may help prevent or delay the progression of age-related muscle loss.

Limitations

There were some limitations to this study. First, since this was a cross-sectional study using NHANES data, the causal relationship could not be verified. Second, although the present study adjusted for several relevant variables, potential residual confounding, such as physical activity, dietary habits, volatile organic compounds exposure, and use of nicotine replacement therapy or corticosteroid agents may have altered our results. Third, the DXA scan was performed only on 8- to 59-years-old participants; therefore, the relationship between serum cotinine and muscle mass among elderly participants could not be explored. Fourth, there are many techniques can be used to evaluate the muscle mass, including DXA, CT, MRI, ultrasound, etc. Compared with CT and MRI, DXA can only estimate the value of muscle mass, but can’t measure the area of the muscle and the rate of intermuscular adipose infiltration. Thus, more studies are needed to confirm these findings and explore the underlying mechanisms.

Conclusion

The present study provides compelling evidence that the serum level of cotinine is negatively associated with muscle mass. This finding improves our understanding of the deleterious effects of cigarette smoke on muscle mass and highlights the importance of smoking cessation for muscle health. Hence, more practical and effective policies are needed to reduce prevalence of smoking.