Effects of PM2.5 on Skeletal Muscle Mass and Body Fat Mass of the Elderly in Taipei, Taiwan

Loss of skeletal muscle mass is common with aging and can cause morbidity and mortality in the elderly. The effects of particulate air pollution on skeletal muscle mass is not known. The study aims to assess the chronic effects of ambient fine particulates (PM2.5) on the body composition of the elderly. From October 2015 to November 2016, a cross-sectional survey on 530 elderly (age > = 65 years) was conducted in the Taipei Basin, Taiwan. The body composition was measured by bioelectrical impedance analysis (InBody 120). One year exposure to air pollution was estimated using the Kriging method at the participant’s residence. Multiple linear regression analysis, after adjustments for demographics and co-pollutants, was used to examine the effects of PM2.5 on body composition indices and force of handgrip. Changes in body composition for an interquartile (1.4 μm/m3) increase in PM2.5 concentration included a 0.4 kg (95% confidence interval (CI): −0.31, −0.58; p < 0.0001) decrease in skeletal muscle mass (2.0%) and a 0.7 kg (95% CI: 0.47, 0.91; p < 0.0001) increase in body fat mass (3.6%). While PM2.5 reduced fat free mass in the upper extremities and trunk, but not in the lower extremities, it increased body fat mass in the three parts. There was no significant effect of PM2.5 on handgrip force. Higher physical activity (versus lower than median) was associated with less detrimental effect of PM2.5 on skeletal muscle mass and body fat mass (p values for interaction term: 0.009 and 0.013, respectively). Long-term PM2.5 exposure is associated with decreased skeletal muscle mass and increased body fat mass in the elderly, which can be ameliorated by physical activity.

may induce muscle fiber atrophy, intra-muscular lipid accumulation, and mitochondrial dysfunction. These enhance muscle wasting, dysfunction and oxidative stress 2,3,9 . Sarcopenia further exacerbates obesity-associated insulin resistance and dysglycemia 10 . Moreover, local or systemic inflammatory states driven by pro-inflammatory cytokines or oxidative stress can enhance proteolysis and inhibit muscle synthesis [11][12][13] . The over-deposition of adipose tissue, especially in visceral site, augments pro-inflammatory cytokines (i.e. TNFα and IL-6) and have negative effect on muscles 14 . Loss of muscle mass and functionality may reduce physical activity, which in turn lowers energy expenditure and boosts the development of obesity 3 . Such mechanisms may set a vicious cycle between sarcopenia and obesity.
Particulate air pollution, especially particulate matter smaller than 2.5 μm (PM 2.5 ), are known to augment systemic inflammation, insulin resistance, and oxidative stress. In a mice study, PM 2.5 increases inflammation in adipose tissue and decreases glucose uptake in muscular tissue, resulting in increased systemic insulin resistance 15 . However, there is paucity of information regarding the effect of ambient PM 2.5 on human skeletal muscle and adipose tissue. Previous studies show that cigarette smoke leads to skeletal muscle cell damage, muscle protein breakdown 16 , skeletal muscle dysfunction 17 , and central obesity 18 . Because ambient PM 2.5 and cigarette smoke share some common patho-physiologic mechanisms like oxidative stress and inflammation, ambient PM 2.5 may have negative effects on muscle and adipose tissue in the elderly, a population susceptible to air pollution.

Materials and Methods
Study design and population. Between October 2015 and November 2016, a cross-sectional study on the elderly (age > = 65 years) was conducted in the Taipei Basin, Taiwan. Those who underwent their annual health exam in two hospitals were invited. Those with malignancy or ambulation and communication difficulties were excluded. The institutional review board of the National Health Research Institutes (EC1040508-E-R2) approved this study. All of the study participants provided a written informed consent. All experiments were performed in accordance with relevant guidelines and regulations.

Questionnaire.
A structured questionnaire was designed to collect information on personal habits (e.g. cigarette smoking and alcohol drinking), medical conditions (e.g. underling diseases and corresponding treatments), education, and physical activity. The Chinese edition of the Physical Activity Scale for the Elderly (PASE) questionnaire was used to assess physical activity 19,20 . Two well-trained interviewers provided the questionnaire. Because of visual impairment and reading difficulties among the elderly, the interviewers helped in reading and explaining the questions and in filling up the answers.

Measurements of body composition and handgrip force.
All of the participants underwent measurements of body composition in the morning after >8 hours of fasting to avoid the influence of food and fluid intake 21 . The participants were in a standing posture and lightly dressed for the examination. Bioelectric impedance analysis devices (BIA, Inbody 120, InBody Co., Ltd. Seoul, Korea) with tetrapolar-8-point-tactile electrodes were used. Each device had two different frequencies (20 and 100 kHz) for impedance measurement in five body segments (the four extremities and the trunk).
The body composition parameters used were skeletal muscle mass (SMM; in kg) of total body, fat free mass (FFM; in kg) of five body segments, and body fat mass (BFM; in kg) of the total body and of the five body segments. The FFM and BFM of the upper or lower extremities were calculated by adding up the component weights of the right and left sides of the arms or legs. Height and weight were measured at the time of body composition measurement.
Handgrip strength was measured using a digital handgrip-dynamometer (TTM-YD, Tokyo, Japan). Three trials for each hand were performed and the best reading was used for data analysis.
Assessment of air pollution exposure. The residential address of each participant was geocoded to estimate the ambient air pollution exposure. The annual mean concentrations of air pollutants, including PM 2.5 , sulphur dioxide (SO 2 ), ozone (O 3 ), carbon monoxide (CO), and nitrogen dioxide (NO 2 ), in 73 EPA monitoring stations in Taiwan in 2015 were calculated based on hourly measured data. A modified ordinary Kriging adopted from Liao et al. 22 was used to approximate the long-term residential exposure. ArcView GIS (version 93) and its Geostatistical Analysts Extension (ESRI Inc., Redland, CA) were used to construct the semi-variogram for spatial estimation of the concentrations of the air pollutants. The cross-validated R 2 values of PM 2.5 , NO 2 , CO, O 3 , and SO 2 were 0.61, 0.63, 0.28, 0.20, and 0.61, respectively. Statistical analysis. Linear regression for association between individual variables, muscle strength, and parameters of body composition, and Pearson's correlation for the relationship between each air pollutant were calculated using the JMP software version 5.0 (SAS Institute, Gary, NC, USA).
Multiple linear regression analysis was used to examine the association between air pollution and health outcomes. The models were adjusted for covariates such as age (years), sex (male/female), body height (cm), body weight (kg), cigarette smoking (never/former/current), alcohol drinking habit (none/less than once per week/ more than once per week), education (primary school or less/middle or high school or equivalent/university degree or more), physical activity (total score on the PASE questionnaire), diabetes mellitus (yes/no), asthma (yes/no), COPD (yes/no), stroke history (yes/no), heart diseases (yes/no), chronic renal disease (yes/no), arthritis (yes/no), osteoporosis (yes/no), and ambient temperature and humidity on the date of examination. Personal estimates of long-term exposure to air pollutant at residential the sites were fitted separately into the multiple linear regression model. The two-pollutant model was also applied to adjust for potential confounding effects of co-pollutants. Stratified analysis were done to examine potential modification effect of age (<or> = median), gender, physical activity (PASE score <or> = median), physician diagnosed diabetes mellitus (yes or no), physician diagnosed arthritis (yes or no), and physician diagnosed osteoporosis (yes or no). Interaction term in the www.nature.com/scientificreports www.nature.com/scientificreports/ full model was used to evaluate the statistical significance of modification effects. A p < 0.05 was considered statistically significant.

Results
The characteristics of participated elders. The study population had a mean age of 70.2 years, with male-female ratio of 0.71. Their mean BMI was 24.09 kg/m 2 and 36.6% had a BMI > 25 kg/m 2 . Among them, 5.1% were current smokers and 9.8% were frequent alcohol drinkers (more than once per week). The prevalence of chronic diseases was highest for arthritis (13.8%), followed by diabetes mellitus (12.8%) and osteoporosis (9.1%). The total score of PASE ranged from 0 to 286.2, with mean and median values of 107.4 and 103, respectively. The demographics, body composition indices, and handgrip force of the participants were summarized in Table 1, while their residential locations were shown in Fig. 1.
The distribution of air pollution at residential sites. The distributions of estimated concentrations of five air pollutants among the participants were shown in Table 2. The mean PM 2.5 at residential locations was 18.1 μg/m 3 , which exceeded the National Ambient Air Quality Standards of Taiwan (15 μg/m 3 ). The mean concentrations of NO 2 , CO, O 3 , and SO 2 , were below the national regulated levels. Moreover, NO 2 was highly correlated with CO (r = 0.89) but negatively correlated with O 3 (r = −0.73) ( Table 3). www.nature.com/scientificreports www.nature.com/scientificreports/ The effect of air pollution on body composition. The association between air pollution exposure and body composition was shown in Table 4. The 2015 average of PM 2.5 at the residential locations was associated with lower skeletal muscle mass and higher body fat mass. An interquartile increase of 1.41 μg/m 3 of PM 2.5 reduced skeletal muscle mass by 0.4 kg (95% confidence interval (CI): −0.31, −0.58; p < 0.0001), and increased fat mass by 0.7 kg (95% CI: 0.47, 0.91; p < 0.0001), representing 2.0% and 3.6% of change respectively. In terms of the effect of air pollutant on various body parts, PM 2.5 reduced the skeletal muscle mass of the upper extremities and trunk by 5.1% and 3.1%, respectively, but not the lower extremities. On the other hand, PM 2.5 increased body fat mass in each part (5.7% for the upper extremities, 2.2% for the trunk, and 5.6% for the lower extremities).   www.nature.com/scientificreports www.nature.com/scientificreports/ Two-pollutant model to clarify the most hazardous air pollutant. In the single pollutant model (Table 4), there were mild effects of CO on skeletal muscle mass and body fat mass, with a similar pattern to the effects of PM 2.5 . The two-pollutant model was therefore adjusted for the confounding effect of co-pollutants ( Table 5). The effect of PM 2.5 on body composition was consistent even after adjustments for the co-pollutants. However, the effect of CO became statistically non-significant after adjustments for PM 2.5 (Table 5).

Mean (SD) or %
Of the 530 relatively healthy elderly who participated in this study, there was no statistically significant effect of ambient air pollution on handgrip force (Table 4).  www.nature.com/scientificreports www.nature.com/scientificreports/ The modification effect of physical activity. In stratified analysis, we found physical activity significantly modified the PM 2.5 effect on body compositions, the lower the physical activity the more the loss of skeletal muscle mass and the increase in body fat mass related to PM 2.5 exposure (Table 6).

Discussion
This study demonstrates, for the first time, that exposure to ambient PM 2.5 is associated with a reduction in skeletal muscle mass and an increase in body fat mass in the Taiwanese elderly, a population vulnerable to the effects of air pollution and to sarcopenia. Such effects are consistent even after adjusting for co-pollutants. Long-term (average of one year) PM 2.5 exposure reduces muscle mass mainly on the upper extremities and trunk, but not for the lower extremities. On the other hand, ambient PM 2.5 is also associated with increased body fat mass in the same body parts. Physical activity ameliorates the detrimental effect of PM2.5 on body composition.
In this study, ambient PM 2.5 has a detrimental effect on muscle mass, particularly on the non-weight bearing muscles. Compared to previous research, where the average decrease in muscle mass is 6% per decade after mid-life 1 , the dose effect of PM 2.5 in this study is approximately a 2.0% reduction in skeletal muscle mass for every 1.4 μg/m 3 increase in PM 2.5 . This is a noteworthy issue. Although the mechanisms involved in muscle wasting among the elderly are probably multi-factorial and still poorly understood, chronic inflammation and insulin resistance are the probable explanations 2 . Many experimental and epidemiologic evidences have demonstrated the effects of ambient PM 2.5 on exacerbating systemic inflammation and insulin resistance 23 , providing the biological explanation for the observed effects in this study.
In addition, the non-observed effects of PM 2.5 on the lower extremities, the weight-bearing body part, and the protective role of physical activity, imply that exercise may ameliorate the deleterious effects of PM 2.5 on muscle mass. Because all of the study participants have full ambulation function, general weight bearing and walking may provide the basic resistance and aerobic training activities for their lower limbs. A previous study applying a short course exercise program for elderly with sarcopenia shows that both resistance and aerobic training can increase muscle mass and strength 24 , suggesting that exercise may be an effective way to overcome the pathologic process of sarcopenia. Further study with a longitudinal design and exercise intervention on non-weight bearing limbs may help to clarify how body activity modifies the detrimental effect of PM 2.5 .
There were also significant effects of PM 2.5 on increasing body fat mass. Previous animal studies have shown the direct effects of ambient PM 2.5 exposure on adipose tissue 15,25,26 . In mammals, adipocytes are classified into two types: white adipose tissue, or the primary site of energy storage, and brown adipose tissue, which are specialized for fatty acid metabolism, energy expenditure, and heat generation 27 . In mice studies, long-term exposure to PM 2.5 impairs the function of brown adipose tissue and changes the gene expression from brown to white adipocyte specific patterns 25,26 . Previous literature has linked the reduced functionality of brown adipose tissue to the propensity for obesity 28 . Such aforementioned evidences may explain how PM 2.5 influences the development of obesity. Because fat tissue expansion can further increase insulin resistance and pro-inflammatory states 3 , leading to more muscle wasting 2,11-14 , the differential or possibly even synergetic effects of PM 2.5 on muscle and fat tissue established in this study may enhance the pathologic process toward sarcopenia.
Although there was no significant effect of PM 2.5 on handgrip force, it may be too premature to conclude that ambient PM 2.5 has no effect on muscular function. This study enrolled the elderly mostly living in the Taipei Basin,  www.nature.com/scientificreports www.nature.com/scientificreports/ where the range of long-term PM 2.5 exposure is not wide. This may hamper the detectability of PM 2.5 -associated changes in muscular functionality. Future studies enrolling participants living in areas with higher and wider ranges of air pollution exposure may provide a better picture for examining the dose-response relationship.
There are several limitations to our study. First of all, our study has chosen the Kriging interpolation rather than the satellite-based approach [29][30][31] or land-use regression 32 for exposure assessments. This may lead to certain degree of non-differential misclassification bias. Yet due to the meteorological conditions and cloud contamination in Northern Taiwan, the satellite-based aerosol optical depth measurements have high missing rates in Taipei. This hampers the utilization of the satellite-based approach in our study. Furthermore, the published land-use regression model 33 for PM 2.5 in Taiwan was based on data from automatic beta attenuation measurements. However, this method can be interfered by the high humidity in Taiwan 34 . Accordingly, the Taiwan EPA has decided to report PM 2.5 concentration by manual sampling using the US Federal Reference Method as our standard methods since 2014. Moreover, the urbanization similarity and monitoring station density are high in Taipei, the misclassification bias by Kriging method should be small. (There are 16 monitoring stations in the Taipei Basin with approximately area of 243 km 2 . Most of our study subjects resided in a 15 km * 15 km area with 13 monitoring stations.) Owing to aforementioned reasons, our study finally decided to use the Kriging interpolation for PM 2.5 concentration estimation. This method may not totally avoid misclassification bias, but the influence may be trivial and acceptable. Also, a significant exposure response trend between quartile of PM 2.5 exposure and body composition parameters is found, which supports the existence of the correlation (see Supplementary  Fig. S1).
Secondly, since our study has used a cross-sectional design, precaution has to be taken in determining a causal relationship. Further longitudinal studies are needed to confirm the effect of PM 2.5 on declining muscle mass by age. To minimize the potential confounding effect on causal inference, this study statistically controlled several co-variables and chronic disease status related to body composition in previous literature. In particular, physical activity has been adjusted by applying the total score of the Physical Activity Scale for the Elderly, a value indicating daily activity-related caloric expenditure.
Lastly, the limitation of not controlling the nutrition intake should be noted. Because only generally healthy elderly were enrolled, or those who could walk to the hospital on their own to get the regular health exam, it is also likely that they can get their daily food without difficulty. Financial problems in buying daily food are also less likely in this study population because Taiwanese elderly have monthly paid pension, which fulfills their basic daily requirement. Also, there are different effects of PM 2.5 on muscle and on fat tissues, and on the upper and lower limb muscles. These are less likely to be explained by nutritional status since malnutrition reduces both muscle and fat mass throughout the entire body.
In conclusion, this study reveals that long-term exposure to urban PM 2.5 is associated with reduced skeletal muscle mass and increased body fat mass among healthy elderly living in Taipei Basin, where the annual average of PM 2.5 concentration is much higher than the regulated levels suggested by the World Health Organization and where traffic emission is the main source of ambient fine particles. Physical activity ameliorates the detrimental effect of PM 2.5 on skeletal muscle mass and body fat mass. Further studies with a longitudinal design and conducted in areas with higher and wider ranges of PM 2.5 exposure are warranted to verify the causal relationship and determine the effects on muscle function.