Gender difference in the association between lower muscle mass and metabolic syndrome independent of insulin resistance in a middle-aged and elderly Taiwanese population

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Contents lists available at ScienceDirect

Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger

Gender difference in the association between lower muscle mass and metabolic syndrome independent of insulin resistance in a middle-aged and elderly Taiwanese population You-Ci Oua,b, Hai-Hua Chuangc, Wen-Cheng Lid, I-Shiang Tzenge, Jau-Yuan Chena,b,

MARK

⁎

a

Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan, ROC Linkou Branch Chang-Gung University, School of Medicine, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 333, Taiwan, ROC c Department of Family Medicine, Chang-Gung Memorial Hospital, Taipei Branch, No. 199, Tung Hwa North Road, Taipei City 105, Taiwan, ROC d Department of Occupation Medicine, Chang-Gung Memorial Hospital, Taipei Branch, No. 199, Tung Hwa North Road, Taipei City 105, Taiwan, ROC e Department of Statistic, National Taipei University, No. 67, Sec. 3, Ming-shen E. Road, Taipei 104, Taiwan, ROC b

A R T I C L E I N F O

A B S T R A C T

Keywords: Lower muscle mass Sarcopenia Metabolic syndrome Gender difference Insulin resistance

Background: Loss of muscle mass was reported to be associated with metabolic syndrome (MetS), but little is known about the gender difference. Thus, the aim of this study was to evaluate the relationship between lower muscle mass and MetS and determine whether there was any gender difference or not. Methods: A total of 394 middle-aged and elderly Taiwanese adults (138 males and 256 females) were enrolled and completed our health survey. They were stratified into three groups according to appendicular skeletal muscle mass divided by weight. Participants distributed into the lower tertile were defined as people having lower muscle mass. MetS was defined using the Adult Treatment Panel III Asian diagnostic criteria. Multivariate logistic regression analysis was performed to assess the association between muscle and MetS. Results: We found an inverse association between MetS and muscle mass in both males and females. Participants with lower muscle mass had a higher risk of MetS in univariate analysis. The same results were observed when adjusted for age and when also adjusted for living condition factors. However, after additional adjustment for potential confounders and HOMA-IR, we only found it to be statistically significant in the female group (OR in male = 3.60; 95% CI = 0.62–20.83, p = 0.153; OR in female = 3.03; 95% CI = 1.16–7.94, p = 0.024). Conclusions: We examined the relationship between lower muscle mass and metabolic syndrome in a middleaged and elderly Taiwanese population. We found that lower muscle mass was associated with the risk of metabolic syndrome in the aged, particularly in females.

1. Introduction

2014). In addition to MetS, lower muscle mass and sarcopenia are other highly prevalent problems in middle-aged and elderly populations (Iannuzzi-Sucich, Prestwood, & Kenny, 2002; Osuna-Pozo et al., 2014). The leading characteristic of sarcopenia is the loss of muscle mass with increasing age (Limpawattana, Kotruchin, & Pongchaiyakul, 2015), which starts at the age of 40 at the rate of 8% per decade and increases to 15% per decade after the age of 70 (Kim & Choi, 2013). Sarcopenia has a crucial negative impact on seniors’ physical activity, independence and quality of life (Yu, 2015). Sarcopenic obesity, which refers to elevated body fat mass combined with reduced muscle mass, has also been proposed as characterizing age-related changes in body composition in elderly people (Roubenoff, 2004). It is known that muscles use large amounts of glucose

Metabolic syndrome (MetS), which is known to be an increasing global health burden, is a common metabolic disorder resulting from a cluster of interacting metabolic risk factors including glucose intolerance, insulin resistance, abdominal obesity, hypertension, and dyslipidemia (Eckel, Grundy, & Zimmet, 2005). It was reported that MetS increased the risk of diabetes, cardiovascular disease, and all-cause mortality (Ford, 2005b). Some previous studies showed that the prevalence of MetS differed in age, sex, and ethnicity (Ford, 2005a; Hildrum, Mykletun, Dahl, & Midthjell, 2009; Lin, Caffrey, Chang, & Lin, 2010). Other studies conducted in Taiwan found that MetS is a common disease among middle-aged and elderly individuals (Hwang, Bai, & Chen, 2006; Tsou & Chang, 2013; Tsou, Chang, Huang, & Hsu,

⁎

Corresponding author at: No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan, ROC. E-mail addresses: [email protected], [email protected] (J.-Y. Chen).

http://dx.doi.org/10.1016/j.archger.2017.04.006 Received 31 August 2016; Received in revised form 21 April 2017; Accepted 26 April 2017 Available online 08 May 2017 0167-4943/ © 2017 The Authors. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

mass (ASM) and total body fat mass (kg). The participants stood upright with their arms abducted apart from their trunk and legs slightly spread. ASM was calculated as the sum of muscle mass estimated individually for two arms and two legs. For biochemistry laboratory examinations, values were analyzed in the central laboratory of Linkou Chang Gung Memorial Hospital, including fasting blood glucose (FBG), serum total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and homeostatic model assessment of insulin resistance (HOMA-IR) levels.

(Karlsson & Zierath, 2007), and lower muscle mass is believed relating to an adverse glucose metabolism (Srikanthan, Hevener, & Karlamangla, 2010) and a higher prevalence of cardiovascular disease (Oterdoom et al., 2009). It is also known that MetS is closely associated with cardiovascular risk and type 2 diabetes mellitus in the middle- and old-aged populations (Miranda, DeFronzo, Califf, & Guyton, 2005; Mottillo et al., 2010). Thus, previous studies, which focused mainly on elderly people having lower muscle mass or sarcopenia, investigated the relationship between muscle mass and metabolic risk factors (Chung, Kang, Lee, Lee, & Lee, 2013; Lim et al., 2010). It had been reported that sarcopenia defined in terms of muscle mass and sarcopenic obesity had a significant relationship with metabolic syndrome (Lee, Hong, Shin, & Lee, 2016). In a study conducted in an US population, lower muscle mass was found to increase insulin resistance in both obese and non-obese subjects (Srikanthan et al., 2010). Another study conducted in the Korean population found that sarcopenia increased the risk of metabolic abnormalities beyond what was predicted by the abdominal obesity category (Park et al., 2014). Another study conducted in the Korean population pointed to statistically significant associations between sarcopenia, defined in terms of muscle mass and sarcopenic obesity, with metabolic syndrome (Lee et al., 2016). However, little is known about the gender difference in the relationship between lower muscle mass and metabolic risk factors in the middle- and old-aged Taiwanese populations. We therefore aim in the following pages to determine whether there was a higher risk of MetS and any gender difference when lower muscle mass is compared to middle and higher muscle mass. Additionally, if there was gender difference, we might evaluate which sex should be more aware of their body composition and muscle mass loss problem, and discuss the possibilities of the reasons.

2.3. Definition of lower muscle mass Appendicular skeletal muscle mass (ASM) was measured by an 8contact electrode bioelectrical impedance analysis (BIA) device. According to a previous study, ASM divided by weight (ASM/Wt) was more closely associated with MetS (Lim et al., 2010). Thus, we used ASM divided by weight (ASM/Wt) for evaluating lower muscle mass status. Participants were divided into three groups according to ASM/ Wt ratio and people distributed into the lower ASM/Wt tertile were defined as people having lower muscle mass. The same applied to the middle and higher tertiles. For males, the lower, middle, and higher ASM/Wt tertiles each had 46 participants. For females, 86 participants were in the lower ASM/Wt tertile, and 85 participants were in the middle and higher ASM/Wt tertiles.

2.4. Definition of MetS Based on the Third Adult Treatment Panel (ATP III) Asian diagnostic criteria of the National Cholesterol Education Program (NCEP) (2002); Grundy et al., 2005; Tan, Ma, Wai, Chew, & Tai, 2004), the diagnosis of metabolic syndrome in our study was defined as a subject presenting at least 3 of the 5 following factors: (1) abdominal obesity (abdominal waist circumference > 90 cm in men or > 80 cm in women); (2) high serum triglycerides (serum TG ≥150 mg/dL or under treatment); (3) decreased serum high-density lipoproteins cholesterol (serum HDLC < 40 mg/dL in men and < 50 mg/dL in women or under treatment); (4) high blood pressure (a systolic blood pressure ≥130 mmHg and/or diastolic pressure ≥85 mmHg, under treatment, or already diagnosed with hypertension); (5) hyperglycemia (FBG ≥100 mg/dL, under treatment, or previously diagnosed with diabetes mellitus).

2. Materials and methods 2.1. Study design and population The present study was a cross-sectional and observational study based on a health survey conducted by Linkou Chang Gung Memorial Hospital in 2014. The 400 participants were 50–90 year-olds randomly selected from the residents of Guishan district, Taoyuan City, Taiwan. The subjects completed our health questionnaires, body composition analysis, and laboratory tests. Subjects who did not undergo bioelectrical impedance analysis (BIA) and with incomplete data were excluded (n = 6). Finally, a total of 394 subjects (138 males and 256 females) were included in our analysis. This study was approved by the institutional review board (IRB) of the study hospital and written informed consent was given by all the participants before enrollment.

2.5. Statistical analysis We used SPSS version 19.0 (SPSS Inc., Chicago, IL) to perform the statistical analysis. The data were presented as n (%) for categorical variables and mean ± standard deviation (SD) for continuous variables. The categorical variables, including marital status (currently unmarried or not), living status (living alone or not), eating habits (vegetarian or not), exercise times (exercising ≥3 times/week or not), food intake (eating fruits or vegetables ≥3 times/week or not), alcohol intake (drinking ≥2 days/week or not), smoking status (current smoker or not) and MetS (meeting the criteria or not), were compared using Chi-square or Fisher's exact test. The continuous variables, including age, waist circumference, BMI, ASM, ASM/Wt, total body fat mass, total body fat percentage, SBP, DBP, FBG, serum total cholesterol, LDL-C, HDL-C, TG, and HOMA-IR levels, were compared using the independent t-test or one-way ANOVA. Linear trend test was applied to identify the association of ASM/Wt with the prevalence of metabolic syndrome. In addition, a binary logistic regression model was performed to evaluate the association of muscle mass defined by ASM/Wt with metabolic syndrome after adjusting for age, regular exercise ≥3 times/week, current smoker, alcohol intake ≥2 days/week, eating fruits or vegetables ≥3 times/week, FBG, HDL-C, TG, SBP, DBP, and HOMA-IR. In our study, a p-value < 0.05 was considered statistically significant.

2.2. Parameter measurements The contents of the health survey included questionnaires of age, marital status (currently married or not), living status (living alone or not), exercise times (exercising ≥3 times/week or not), food intake (eating fruits or vegetables≥3 times/week or not), alcohol intake (drinking ≥2 days/week or not), and smoking status (current smoker or not). The health survey also contained the measurements of waist circumference (WC), and blood pressure. Body weight and height were measured with the subjects dressed in light clothing and barefoot. Body mass index (BMI) was calculated as an individual's body weight (kg) divided by their height squared (m2). Waist circumference was taken midway between the inferior margin of the last rib and the crest of the ilium in the horizontal plane whilst in an upright position. Resting systolic and diastolic blood pressures were measured at least two times. Body composition was analyzed using an 8-contact electrode bioelectrical impedance analysis (BIA) device (Tanita BC-418, Tokyo, Japan), which was used to measure participants’ appendicular skeletal muscle 13

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

Table 1 The general characteristics of study participants by sex. Variables

Total (n = 394)

Male (n = 138)

Female (n = 256)

p value

Age (years) Marital status (currently unmarried), n (%) Living status (living alone), n (%) Regular exercising ≥3 times/week, n (%) Eating fruits or vegetables ≥3 times/week, n (%) Alcohol drinking ≥2 days/week, n (%) Currently smoking, n (%) Metabolic syndrome, n (%) Waist circumference (cm) BMI (kg/m2) ASM/Wt (%) SBP (mmHg) DBP (mmHg) FBG (mg/dL) Total cholesterol (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) TG (mg/dL) HOMA-IR Total body fat mass (kg)

64.41 ± 8.46 74 (18.8) 21 (5.3) 323 (82.0) 354 (89.8) 75 (19.0) 42 (10.1) 141 (35.8) 85.04 ± 9.60 24.55 ± 3.51 30.91 ± 4.80 129.68 ± 16.70 77.11 ± 11.27 95.61 ± 22.40 197.34 ± 35.79 118.64 ± 32.23 54.37 ± 13.79 121.81 ± 62.94 1.87 ± 1.40 18.76 ± 6.91

66.01 ± 8.61 14 (10.1) 6 (4.3) 112 (81.2) 116 (84.1) 47 (34.1) 34 (24.6) 48 (34.8) 89.4 ± 10.03 24.86 ± 3.84 35.99 ± 3.00 130.47 ± 17.26 79.33 ± 12.18 97.86 ± 25.14 184.70 ± 35.69 111.49 ± 33.04 48.57 ± 14.03 123.43 ± 65.49 1.77 ± 1.20 15.68 ± 6.83

63.55 ± 8.26 60 (23.4) 15 (5.9) 211 (82.4) 238 (93.0) 28 (10.9) 8 (3.1) 93 (36.3) 82.64 ± 8.45 24.38 ± 3.31 28.17 ± 3.02 129.25 ± 16.42 75.92 ± 10.59 94.40 ± 20.73 204.16 ± 34.01 122.50 ± 31.18 57.49 ± 12.62 120.94 ± 61.64 1.93 ± 1.49 20.41 ± 6.37

0.006 0.001 0.524 0.756 0.005 < 0.001 < 0.001 0.760 < 0.001 0.192 < 0.001 0.490 0.004 0.145 < 0.001 0.001 < 0.001 0.708 0.275 < 0.001

Clinical characteristics are expressed as mean ± SD for continuous variables and n (%) for categorical variables. Abbreviations: BMI, body mass index; ASM/Wt, appendicular skeletal muscle mass divided by weight; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; HOMA-IR, homeostatic model assessment of insulin resistance. p values were derived from independent two-sample t-test for continuous variables and Chi-square or Fisher's exact test for categorical variables.

3. Result

3.3. Association between ASM/Wt ratio and MetS

3.1. General characteristics of the study population

The prevalence of MetS was 35.8% among the 394 participants. MetS prevalence was present in 52.2%, 32.6%, and 19.6% of males and 50.0%, 29.4%, and 29.4% of females in the tertile groups (Fig. 1). Significant linear trend associations across increasing tertiles in both sexes were observed (p values for trend < 0.05). To examine the independent associations of the muscle mass and MetS among two sex groups, we applied multivariate logistic regression models (Table 4): model 1, non-adjusted; model 2, adjusted for age; model 3, additionally adjusted for FBG, HDL-C, TG, SBP, DBP, and HOMA-IR; model 4, further adjusted for regular exercise ≥3 times/week, currently smoking, alcohol intake ≥2 days/week, and eating fruits or vegetables ≥3 times/week. Males in the lower ASM/Wt tertile had a 4.49-fold increase in risk of MetS compared to those in the higher ASM/ Wt tertile in model 1 (OR = 4.49, 95% CI = 1.77–11.37, p < 0.05). Males in the lower ASM/Wt tertile had a 4.58-fold increase in risk of MetS compared to those in the higher ASM/Wt tertile in model 2 (OR = 4.58, 95% CI = 1.80–11.68, p < 0.05). However, there was no significant association between MetS and ASM/Wt in the lower ASM/ Wt tertile in model 3 (OR = 2.44, 95% CI = 0.50–11.89, p = 0.269) and model 4 (OR = 3.60, 95% CI = 0.62–20.83, p = 0.153). Females in the lower ASM/Wt tertile had a 2.40-fold increase in risk of MetS comparing to those in the higher ASM/Wt tertile in model 1 (OR = 2.40, 95% CI = 1.28–4.50, p < 0.05). Females in the lower ASM/Wt tertile had a 2.77-fold increase in risk of MetS comparing to those in the higher ASM/Wt tertile in model 2 (OR = 2.77, 95% CI = 1.44–5.31, p < 0.05). Consistent result was observed in model 3 (OR = 2.94, 95% CI = 1.13–7.60, p < 0.05) and model 4 (OR = 3.03, 95% CI = 1.16–7.94, p < 0.05). In both males and females, there was no significant association between MetS and ASM/Wt in the middle ASM/Wt tertile comparing to the higher ASM/Wt tertile.

Gender differences were analyzed using the independent t-test and Chi-square test in Table 1. Subjects consisted of 394 participants with 138 males (35%) and 256 (65%) females. The average age of participants in the study sample was 64.41 years. There was no significant difference in metabolic syndrome prevalence between males and females (34.8% vs. 36.3%, p = 0.760). For living habits, there were significantly more males drinking alcohol more than 2 days/week, and currently smoking, but there were significantly less males currently unmarried, and eating fruit or vegetable more than 3 times/week. Values in regular exercising showed no significant difference between males and females. For laboratory data and measurements, males had significantly larger WC, higher levels of ASM/Wt, DBP, and lower levels of total cholesterol, LDL-C, HDL-C, and total body fat mass. There was no significant difference in the levels of BMI, SBP, FBG, TG, and HOMAIR between males and females.

3.2. Characteristics according to ASM/Wt ratio tertiles The general characteristics according to tertiles of ASM/Wt are shown in Table 2 (males) and Table 3 (females). Participants were categorized into 3 groups based on their ASM/Wt ratio from low to high. In Table 2, each group contained 46 participants. In Table 3, there were 86 participants in the lower tertile, and 85 participants in the middle and higher tertiles. Across the increasing tertiles in Table 2, we observed a significant decrease in percentage of metabolic syndrome prevalence, and significant decrease in levels of WC, BMI, TG, and HOMA-IR. We also observed a significant increase in percentage of regular exercise and eating fruit or vegetable, as well as significant increase in levels of HDL-C. Other characteristics showed no significant difference. In Table 3, a significant decrease in percentage of metabolic syndrome prevalence and levels of WC, BMI, FBG, and HOMA-IR were observed across the increasing tertiles. We also observed a significant increase in percentage of regular exercise and the levels of HDL-C. Other characteristics showed no significant difference.

4. Discussion In this community-based and cross-sectional study, we investigated the gender difference associated with lower muscle mass and MetS prevalence in the middle-aged and elderly Taiwanese adults. We found evidence that participants with lower muscle mass in both male and 14

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

Table 2 The general characteristics of male according to tertile of appendicular skeletal muscle mass divided by weight. Males (n = 138)

ASM/Wt lower tertile (n = 46)

ASM/Wt middle tertile (n = 46)

ASM/Wt upper tertile (n = 46)

p value

p value (for trend)

Age (years) Marital status (currently unmarried), n (%) Living status (living alone), n (%) Regular exercising ≥3 times/week, n (%) Eating fruits or vegetables ≥3 times/week, n (%) Alcohol drinking ≥2 days/week, n (%) Currently smoking, n (%) Metabolic syndrome, n (%) Waist circumference (cm) BMI (kg/m2) ASM/Wt (%) SBP (mmHg) DBP (mmHg) FBG (mg/dL) Total cholesterol (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) TG (mg/dL) HOMA-IR

65.07 ± 9.00 6 (13.0) 3 (6.5) 30 (65.2) 35 (76.1)

66.00 ± 8.90 4 (8.7) 1 (2.2) 40 (87.0) 37 (80.4)

66.98 ± 7.98 4 (8.7) 2 (4.3) 42 (91.3) 44 (95.7)

0.570 0.728 0.593 0.003 0.027

0.290 0.491 0.610 0.001 0.011

21 (45.7) 14 (30.4) 24(52.2) 95.39 ± 11.46 27.03 ± 4.43 32.81 ± 2.31 134.04 ± 15.56 81.13 ± 10.73 101.37 ± 32.38 187.87 ± 33.98 115.83 ± 33.01 43.30 ± 11.45 144.04 ± 63.52 2.38 ± 1.48

11 (23.9) 11 (23.9) 15(32.6) 88.21 ± 7.51 24.38 ± 3.01 36.21 ± 0.68 129.22 ± 16.92 79.20 ± 11.83 94.70 ± 12.88 184.89 ± 35.14 110.43 ± 32.92 48.78 ± 11.56 128.35 ± 67.42 1.66 ± 0.90

15 (32.6) 9(19.6) 9 (19.6) 84.88 ± 7.71 23.17 ± 2.86 38.95 ± 1.52 128.15 ± 18.93 77.65 ± 13.79 97.50 ± 26.22 181.35 ± 38.28 108.22 ± 33.44 53.61 ± 16.73 97.89 ± 57.90 1.26 ± 0.88

0.086 0.476 0.004 < 0.001 < 0.001 < 0.001 0.219 0.393 0.445 0.684 0.528 0.002 0.002 < 0.001

0.188 0.228 0.001 < 0.001 < 0.001 < 0.001 0.103 0.173 0.462 0.384 0.273 < 0.001 0.001 < 0.001

Clinical characteristics are expressed as mean ± SD for continuous variables and n (%) for categorical variables. Abbreviations: BMI, body mass index; ASM/Wt, appendicular skeletal muscle mass divided by weight; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; HOMA-IR, homeostatic model assessment of insulin resistance.

trend < 0.05). This result corresponded to previous studies indicating sarcopenia defined by ASM/Wt was associated with metabolic syndrome, and cardiovascular disease prevalence (Chung et al., 2013; Moon, Choo, & Kim, 2015). Sarcopenia defined by ASM/Wt ratio with metabolic syndrome was reported as being statistically significant (Lee et al., 2016). Another research even showed that sarcopenia defined by ASM/Wt ratio might increase the risk of metabolic abnormalities beyond what was predicted by the abdominal obesity category (Park et al., 2014). In our study, we also found that male participants (Table 2) in the lower muscle mass group had a larger WC, higher TG level, higher HOMA-IR level, and lower HDL-C level. Similarly, female participants (Table 3) in the lower muscle mass group had a larger WC, higher FBG level, higher HOMA-IR level, and lower HDL-C level. Both males and females in the lower

female groups had higher MetS prevalence. When comparing the lower and middle muscle mass groups to the higher muscle mass group, we found that participants with lower muscle mass had a higher risk of MetS compared to those with a higher muscle mass, particularly in females. We used ASM/Wt to define muscle mass because a previous study proposed that sarcopenia defined as ASM/Wt was more closely associated with metabolic parameters than sarcopenia defined by ASM/square height (Lim et al., 2010). Participants were divided into three groups according to ASM/Wt ratio and people distributed into lower ASM/Wt tertile were defined as people having lower muscle mass. The same was also applied to the middle and higher tertiles. In our study, we found an inverse association between MetS prevalence and muscle mass in both males and females (Fig. 1, p value for

Table 3 The general characteristics of female according to tertile of appendicular skeletal muscle mass divided by weight. Females (n = 256)

ASM/Wt lower tertile (n = 86)

ASM/Wt middle tertile (n = 85)

ASM/Wt upper tertile (n = 85)

p value

p value (for trend)

Age (years) Marital status (currently unmarried), n (%) Living status (living alone), n (%) Regular exercising ≥3 times/week, n (%) Eating fruits or vegetables ≥3 times/week, n (%) Alcohol drinking ≥2 days/week, n (%) Currently smoking, n (%) Metabolic syndrome, n (%) Waist circumference (cm) BMI (kg/m2) ASM/Wt (%) SBP (mmHg) DBP (mmHg) FBG (mg/dL) Total cholesterol (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) TG (mg/dL) HOMA-IR

62.72 ± 8.04 17 (19.8) 1 (1.2) 63 (73.3) 80 (93.0)

63.04 ± 8.25 20 (23.5) 6 (7.1) 74 (87.1) 79 (92.9)

64.89 ± 8.43 23 (27.1) 8 (9.4) 74 (87.1) 79 (92.9)

0.179 0.531 0.061 0.023 0.996

0.086 0.261 0.022 0.018 0.983

7 (8.1) 3 (3.5) 43 (50.0) 89.09 ± 8.22 27.25 ± 2.97 25.12 ± 2.01 130.86 ± 15.51 75.20 ± 11.60 98.99 ± 24.10 206.64 ± 30.87 126.00 ± 28.22 55.26 ± 11.10 126.72 ± 64.25 2.50 ± 1.74

14 (16.5) 2 (2.4) 25 (29.4) 81.75 ± 5.99 24.05 ± 2.08 28.27 ± 0.55 128.12 ± 16.45 75.88 ± 9.47 93.11 ± 21.28 202.11 ± 36.93 121.48 ± 34.59 57.28 ± 12.53 116.99 ± 56.51 1.87 ± 1.48

7 (8.2) 3 (3.5) 25 (29.4) 77.01 ± 6.10 21.80 ± 2.18 31.17 ± 2.18 128.75 ± 17.32 76.68 ± 10.65 91.06 ± 15.15 203.69 ± 34.23 119.98 ± 30.48 59.96 ± 13.82 119.04 ± 64.11 1.41 ± 0.94

0.135 0.882 0.005 < 0.001 < 0.001 < 0.001 0.521 0.658 0.034 0.678 0.422 0.05 0.554 < 0.001

0.979 0.989 0.005 < 0.001 < 0.001 < 0.001 0.403 0.361 0.012 0.573 0.208 0.015 0.416 < 0.001

Clinical characteristics are expressed as mean ± SD for continuous variables and n (%) for categorical variables. Abbreviations: BMI, body mass index; ASM/Wt, appendicular skeletal muscle mass divided by weight; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; HOMA-IR, homeostatic model assessment of insulin resistance.

15

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

Fig. 1. Prevalence of MetS according to ASM/Wt tertiles by sex: male group (A) and female group (B). A linear decreasing trend was observed across ASM/Wt tertiles in both sexes.

compared to those with higher muscle mass, particularly in females. The mechanisms underlying the sex-related differences in the association between MetS and muscle mass are unclear, but we can find some possible reasons by exploring recent research. One possible explanation is the effect of sex hormones, testosterone and estradiol, on skeletal muscles. It was reported that testosterone level was positively related to both muscle mass and muscle strength (Auyeung et al., 2011), and the level decreased in older adults and women. It was also reported that MetS was associated with lower testosterone level (Kupelian, Hayes, Link, Rosen, & McKinlay, 2008). Thus, one of the possible pathways through which MetS exerts its adverse effects on muscle mass is via testosterone. Since testosterone level decreases along with age and is lower in females than males, female adults will have a relatively lower level of testosterone, leading to their lower muscle mass and higher risk of MetS compared to male adults. The level of another sex hormone, estradiol, also plays an important role by regulating whole-body insulin sensitivity, although the mechanisms are unclear (Moran et al., 2008; Van Pelt, Gozansky, Schwartz, & Kohrt, 2003). When menopause causes the cessation of female sex hormone production, females start under-regulating insulin sensitivity and have increased insulin resistance. In addition, human skeletal muscles have estrogen receptors and estradiol plays a role in the glucose metabolism of skeletal muscles (Lemoine et al., 2003). Females with lower muscle mass have fewer estrogen receptors. Once their menopause sets in and causes decreasing estradiol level, their glucose metabolism will be seriously affected. Thus, there is a gradual increase in susceptibility to metabolic complications in older female adults (Polotsky & Polotsky, 2010), and lower muscle mass seems to be a risk factor. Another possible explanation is the effect of adiposity and adipo-

muscle mass group had at least three significant metabolic risk factors compared to the middle- and higher-muscle mass groups. These participants also had a significantly higher HOMA-IR index, a valuable and useful tool to assess insulin resistance. Insulin resistance plays an important pathophysiological role in the development of many metabolic abnormalities (Eckel et al., 2005; Singh & Saxena, 2010). The above results were consistent with earlier reports which indicated that lower muscle mass was associated with metabolic risk factors (Lee et al., 2016; Park et al., 2014). In Table 4 we performed a binary logistic regression model of the association between MetS and ASM/Wt tertiles. We set the higher tertile as the reference and compared the odd ratios of the lower and middle tertiles to the higher tertile. The result showed that the association between MetS and the lower ASM/Wt tertile compared to the higher tertile with no adjustments for any factor was statistically significant in both males and females (model 1; OR in male = 4.49; 95% CI = 1.77–11.37, p = 0.002; OR in female = 2.40; 95% CI = 1.28–4.50, p = 0.006). When adjusted for age (model 2; OR in male = 4.58; 95% CI = 1.80–11.68, p = 0.001; OR in female = 2.77; 95% CI = 1.44–5.31, p = 0.002). However, when we additionally adjusted for FBG, HDL-C, TG, SBP, DBP, and HOMA-IR, we only found it to be statistically significant in the female group (model 3; OR in male = 2.44; 95% CI = 0.50–11.89, p = 0.269; OR in female = 2.94; 95% CI = 1.13–7.60, p = 0.027). Moreover, some living status factors also adjusted (model 4; OR in male = 3.06; 95% CI = 0.62–20.83, p = 0.153; OR in female = 3.03; 95% CI = 1.16–7.94, p = 0.024), it still suggested that participants with lower muscle mass had a higher risk of MetS than those with higher muscle mass. Above results suggest that participants with lower muscle mass had a higher risk of MetS

Table 4 Multivariate logistic regression analysis of the association between muscle mass and metabolic syndrome according to tertiles of ASM/Wt, stratified by sex. Model 1

Males (n = 138) Lower tertile Middle tertile Higher tertile Females (n = 256) Lower tertile Middle tertile Higher tertile

Model 2

Model 3

Model 4

OR (95% CI)

p value

OR (95% CI)

p value

OR (95% CI)

p value

OR (95% CI)

p value

4.49 (1.77–11.37) 1.99 (0.77–5.17) 1 (Reference)

0.002 0.158

4.58 (1.80–11.68) 2.01 (0.77–5.23) 1 (Reference)

0.001 0.152

2.44 (0.50–11.89) 2.38 (0.48–11.84) 1 (Reference)

0.269 0.288

3.60 (0.62–20.83) 3.23 (0.56–18.53) 1 (Reference)

0.153 0.189

2.40 (1.28–4.50) 1.00 (0.52–1.93) 1 (Reference)

0.006 1.000

2.77 (1.44–5.31) 1.10 (0.56–2.16) 1 (Reference)

0.002 0.787

2.94 (1.13–7.60) 0.99 (0.38–2.56) 1 (Reference)

0.027 0.989

3.03 (1.16–7.94) 1.02 (0.39–2.69) 1 (Reference)

0.024 0.962

Model 1: non-adjusted; model 2: adjusted for age; model 3: adjusted for factors in model 2 plus FBG, HDL-C, TG, SBP, DBP, and HOMA-IR; model 4: adjusted for factors in model 3 plus regular exercise ≥3 times/week, currently smoking, alcohol intake ≥2 days/week, and eating fruits or vegetables ≥3 times/week.

16

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

aware of their loss of muscle mass problem.

kines. A US cohort of 16,000 12 to 80 years old males and females suggested that females had higher mean total body fat estimates than males at each age group (Chumlea et al., 2002). The greater total body fat in women is likely to have a greater effect on adipokine productions and secretions, such as serum leptin, adiponectin, resistin, retinolbinding protein 4 (RBP4), tumor necrosis factor α (TNFα), and interleukin 6 (IL-6) concentrations (Deng & Scherer, 2010). Some adipokines like leptin and adiponectin increase insulin sensitivity while others like resistin and TNFα lead to impaired insulin sensitivity (Dyck, Heigenhauser, & Bruce, 2006). Generally speaking, adipokines have been linked to pathogenesis of MetS and its comorbidities through their effects on vascular function and inflammation (Conde et al., 2011; Lago et al., 2011). These factors are upregulated in adipocytes undergoing pro-inflammatory stimulation and can cause insulin resistance, which is associated with MetS (Eckel et al., 2005). On the other hand, increasing levels of adiposity and adipokines will also be associated with lower muscle mass or sarcopenia (Lutz & Quinn, 2012), and body fat is also distributed differently in males and females. Females have more peripheral subcutaneous fat than males, which leads to fatty infiltration of appendicular muscle mass (Lemieux, Prud’homme, Bouchard, Tremblay, & Despres, 1993). This situation may therefore induce a lack of muscle quality and cause the inverse association between muscle mass and MetS. In our study, female participants had higher total body fat mass than males (total body fat mass in males, 15.68 ± 6.83 kg; in females, 20.41 ± 6.37 kg; p value < 0.001). People having more body fat mass tend to have higher level of adipokines. This may be the reason why females with lower muscle mass had a higher risk of MetS than males. There are a few strengths in our study. First, this is a communitybased study. Our participants were recruited from a community of a single ethnic background. Therefore, the effects of important confounders including ethnicity, residential area, and environmental factors were minimized. Second, while evaluating the association between metabolic syndrome and muscle mass, we studied the effects of male and female participants separately to provide more relevant information in the fields. Our study also has limitations to be discussed. First, this is a crosssectional study. Thus, the causal relationship between lower muscle mass and MetS cannot be evaluated and determined. Second, the number of participants in this study was relatively small and they were recruited from a single community. The participants could therefore only be distributed into three groups and the results cannot be generalized for other ethnicities. Besides, we found that the lower muscle mass group defined by ASM/Wt had a higher risk of having metabolic syndrome than the higher tertile groups. Although ASM/Wt is wide spread use in sports research and industry, but is not widely accepted that ASM or ASM/Wt analysis is an accurate measure of muscle mass in the human muscle physiology research community. In fact, DXA and MRI are not suitable measures for the older individuals living in the community because of these measured instrument and equipment usually located in large-scale medical institutions. With intuition, for one having a high fat mass with low muscle mass seems likely to lead to more functional limitations and metabolic disorders. The use of ASM or ASM/Wt is potential limitation of the study. Third, even though we used a standardized questionnaire, the exercise times, smoking frequency, alcohol consumption, and food intake habits were self-reported and reporting bias (or recalling bias) was also possible. Lastly, we consider sex hormones and adipokines as the factors causing gender differences, but the absence of the values for these substances is also a limitation. In conclusion, we examined the relationship between lower muscle mass and metabolic syndrome prevalence in a middle-aged and elderly Taiwanese population. We found that lower muscle mass was positively associated with the risk of metabolic syndrome in Taiwanese females, but not in males. Thus, preventing loss of muscle mass at older ages might have the potential to reduce MetS and females should be more

Authors’ contributions Y-CO was involved in analyzing the data and writing of the manuscript. H-HC and W-CL conceived and supervised the study. I-ST provided statistical advice. J-YC contributed in conceiving and designing the experiments, performed the experiments, data collection, analyzed the data, revising it critically for important intellectual content and final approval of the version to be submitted. Conflict of interests The authors declare that they have no conflict of interests. Acknowledgements This study was supported by Chang Gung Memorial Hospital, Linkou (grants CORPG3C0171∼3C0172, CZRPG3C0053). References Auyeung, T. W., Lee, J. S., Kwok, T., Leung, J., Ohlsson, C., Vandenput, L., et al. (2011). Testosterone but not estradiol level is positively related to muscle strength and physical performance independent of muscle mass: A cross-sectional study in 1489 older men. European Journal of Endocrinology, 164, 811–817. Chumlea, W. C., Guo, S. S., Kuczmarski, R. J., Flegal, K. M., Johnson, C. L., Heymsfield, S. B., et al. (2002). Body composition estimates from NHANES III bioelectrical impedance data. International Journal of Obesity and Related Metabolic Disorders, 26, 1596–1609. Chung, J. Y., Kang, H. T., Lee, D. C., Lee, H. R., & Lee, Y. J. (2013). Body composition and its association with cardiometabolic risk factors in the elderly: A focus on sarcopenic obesity. Archives of Gerontology and Geriatrics, 56, 270–278. Conde, J., Scotece, M., Gomez, R., Lopez, V., Gomez-Reino, J. J., Lago, F., et al. (2011). Adipokines: Biofactors from white adipose tissue. A complex hub among inflammation, metabolism, and immunity. Biofactors, 37, 413–420. Deng, Y., & Scherer, P. E. (2010). Adipokines as novel biomarkers and regulators of the metabolic syndrome. Annals of the New York Academy of Sciences, 1212, E1–E19. Dyck, D. J., Heigenhauser, G. J., & Bruce, C. R. (2006). The role of adipokines as regulators of skeletal muscle fatty acid metabolism and insulin sensitivity. Acta Physiologica (Oxford), 186, 5–16. Eckel, R. H., Grundy, S. M., & Zimmet, P. Z. (2005). The metabolic syndrome. The Lancet, 365, 1415–1428. Ford, E. S. (2005a). Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S. Diabetes Care, 28, 2745–2749. Ford, E. S. (2005b). Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: A summary of the evidence. Diabetes Care, 28, 1769–1778. Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Franklin, B. A., et al. (2005). Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart Lung, and Blood Institute Scientific Statement. Circulation, 112, 2735–2752. Hildrum, B., Mykletun, A., Dahl, A. A., & Midthjell, K. (2009). Metabolic syndrome and risk of mortality in middle-aged versus elderly individuals: The Nord-Trondelag Health Study (HUNT). Diabetologia, 52, 583–590. Hwang, L.-C., Bai, C.-H., & Chen, C.-J. (2006). Prevalence of obesity and metabolic syndrome in Taiwan. Journal of the Formosan Medical Association, 105, 626–635. Iannuzzi-Sucich, M., Prestwood, K. M., & Kenny, A. M. (2002). Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 57, M772–M777. Karlsson, H. K., & Zierath, J. R. (2007). Insulin signaling and glucose transport in insulin resistant human skeletal muscle. Cell Biochemistry and Biophysics, 48, 103–113. Kim, T. N., & Choi, K. M. (2013). Sarcopenia: Definition, epidemiology, and pathophysiology. Journal of Bone Metabolism, 20, 1–10. Kupelian, V., Hayes, F. J., Link, C. L., Rosen, R., & McKinlay, J. B. (2008). Inverse association of testosterone and the metabolic syndrome in men is consistent across race and ethnic groups. Journal of Clinical Endocrinology and Metabolism, 93, 3403–3410. Lago, F., Gomez, R., Conde, J., Scotece, M., Gomez-Reino, J. J., & Gualillo, O. (2011). Cardiometabolic comorbidities and rheumatic diseases: Focus on the role of fat mass and adipokines. Arthritis Care & Research (Hoboken), 63, 1083–1090. Lee, J., Hong, Y. P., Shin, H. J., & Lee, W. (2016). Associations of sarcopenia and sarcopenic obesity with metabolic syndrome considering both muscle mass and muscle strength. Journal of Preventive Medicine & Public Health, 49, 35–44. Lemieux, S., Prud’homme, D., Bouchard, C., Tremblay, A., & Despres, J. P. (1993). Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. American Journal of Clinical Nutrition, 58, 463–467. Lemoine, S., Granier, P., Tiffoche, C., Rannou-Bekono, F., Thieulant, M. L., & Delamarche, P. (2003). Estrogen receptor alpha mRNA in human skeletal muscles. Medicine and

17

Archives of Gerontology and Geriatrics 72 (2017) 12–18

Y.-C. Ou et al.

Estudio ELLI. Revista Española de Geriatría y Gerontología, 49, 72–76. Oterdoom, L. H., Gansevoort, R. T., Schouten, J. P., de Jong, P. E., Gans, R. O., & Bakker, S. J. (2009). Urinary creatinine excretion, an indirect measure of muscle mass, is an independent predictor of cardiovascular disease and mortality in the general population. Atherosclerosis, 207, 534–540. Park, S. H., Park, J. H., Park, H. Y., Jang, H. J., Kim, H. K., Park, J., et al. (2014). Additional role of sarcopenia to waist circumference in predicting the odds of metabolic syndrome. Clinical Nutrition, 33, 668–672. Polotsky, H. N., & Polotsky, A. J. (2010). Metabolic implications of menopause. Seminars in Reproductive Medicine, 28, 426–434. Roubenoff, R. (2004). Sarcopenic obesity: The confluence of two epidemics. Obesity Research, 12, 887–888. Singh, B., & Saxena, A. (2010). Surrogate markers of insulin resistance: A review. World J Diabetes, 1, 36–47. Srikanthan, P., Hevener, A. L., & Karlamangla, A. S. (2010). Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: Findings from the National Health and Nutrition Examination Survey III. PLoS One, 5, e10805. Tan, C. E., Ma, S., Wai, D., Chew, S. K., & Tai, E. S. (2004). Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care, 27, 1182–1186. Tsou, M.-T., & Chang, B. C.-C. (2013). Sex differences in metabolic syndrome of metropolitan elderly people in Northern Taiwan. Journal of Clinical Gerontology and Geriatrics, 4, 42–50. Tsou, M.-T., Chang, B. C.-C., Huang, W.-H., & Hsu, C.-P. (2014). Prevalence of metabolic syndrome and risk factor analysis among urban elderly in one medical center in Northern Taiwan. International Journal of Gerontology, 8, 127–132. Van Pelt, R. E., Gozansky, W. S., Schwartz, R. S., & Kohrt, W. M. (2003). Intravenous estrogens increase insulin clearance and action in postmenopausal women. American Journal of Physiology. Endocrinology and Metabolism, 285, E311–E317. Yu, J. (2015). The etiology and exercise implications of sarcopenia in the elderly. International Journal of Nursing Sciences, 2, 199–203.

Science in Sports and Exercise, 35, 439–443. Lim, S., Kim, J. H., Yoon, J. W., Kang, S. M., Choi, S. H., Park, Y. J., et al. (2010). Sarcopenic obesity: Prevalence and association with metabolic syndrome in the Korean Longitudinal Study on Health and Aging (KLoSHA). Diabetes Care, 33, 1652–1654. Limpawattana, P., Kotruchin, P., & Pongchaiyakul, C. (2015). Sarcopenia in Asia. Osteoporosis and Sarcopenia, 1, 92–97. Lin, J. W., Caffrey, J. L., Chang, M. H., & Lin, Y. S. (2010). Sex, menopause, metabolic syndrome, and all-cause and cause-specific mortality – Cohort analysis from the Third National Health and Nutrition Examination Survey. Journal of Clinical Endocrinology and Metabolism, 95, 4258–4267. Lutz, C. T., & Quinn, L. S. (2012). Sarcopenia, obesity, and natural killer cell immune senescence in aging: Altered cytokine levels as a common mechanism. Aging (Albany, NY), 4, 535–546. Miranda, P. J., DeFronzo, R. A., Califf, R. M., & Guyton, J. R. (2005). Metabolic syndrome: Definition, pathophysiology, and mechanisms. American Heart Journal, 149, 33–45. Moon, J. H., Choo, S. R., & Kim, J. S. (2015). Relationship between low muscle mass and metabolic syndrome in elderly people with normal body mass index. Journal of Bone Metabolism, 22, 99–106. Moran, A., Jacobs, D. R., Jr., Steinberger, J., Steffen, L. M., Pankow, J. S., Hong, C. P., et al. (2008). Changes in insulin resistance and cardiovascular risk during adolescence: Establishment of differential risk in males and females. Circulation, 117, 2361–2368. Mottillo, S., Filion, K. B., Genest, J., Joseph, L., Pilote, L., Poirier, P., et al. (2010). The metabolic syndrome and cardiovascular risk: A systematic review and meta-analysis. Journal of the American College of Cardiology, 56, 1113–1132. Third Report of the National Cholesterol Education Program (NCEP). Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation, 106, 3143–3421. Osuna-Pozo, C. M., Serra-Rexach, J. A., Viña, J., Gómez-Cabrera, M.d. C., Salvá, A., Ruiz, D., et al. (2014). Prevalencia de sarcopenia en consultas de geriatría y residencias.

18