Clinical relevance of different handgrip strength indexes and metabolic syndrome in Chinese community-dwelling elderly individuals

Archives of Gerontology and Geriatrics 87 (2020) 104010

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Clinical relevance of different handgrip strength indexes and metabolic syndrome in Chinese community-dwelling elderly individuals

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Peiyu Songa,b, Yuanyuan Zhangb,1, Yue Wangb,1, Peipei Hanb, Liyuan Fub, Xiaoyu Chenb, Hairui Yub, Lin Houb, Xing Yub, Lu Wangb, Fengying Yangb, Qi Guoc,* a

Department of Rehabilitation Medicine, TEDA International Cardiovascular Hospital, Cardiovascular Clinical College of Tianjin Medical University, Tianjin, China Department of Rehabilitation Medicine, Tianjin Medical University, Tianjin, China c College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China b

ARTICLE INFO

ABSTRACT

Keywords: Metabolic syndrome Muscle strength Body composition ROC analysis Elderly

Purpose: Currently there is no consensus on the correlation between metabolic syndrome (MetS) and muscle strength. The objective of this study was to examine the associations between MetS and its components and different handgrip strength (HS) indexes among Chinese community-dwelling elderly individuals. In addition, we hoped to find an optimal cutoff point for the index most relevant to MetS. Methods: Data were obtained from 909 participants aged ≥ 60 years (385 men, average age, 68.0 ± 5.9 y). We used the International Diabetes Federation metabolic syndrome guidelines to define MetS. General data of all participants were collected through questionnaires and anthropometric data were measured. At the same time, blood samples were collected. Results: The prevalence of MetS was 26.8 % in men and 46.9 % in women. In all HS indexes, HS/body fat mass was most strongly correlated with MetS, and the areas under the receiver-operating characteristic curve were 0.723 (95 % confidence interval [CI] = 0.669-0.776) in men and 0.619 (95 % CI = 0.571-0.667) in women, and the optimal cutoffs were 1.92 in men and 1.25 in women. The adjusted odds ratios (ORs) of MetS for low HS/ body fat mass were 5.38 (95 % CI = 3.03–9.56, p < 0.001) in men and 2.39 (95 % CI = 1.56–3.64, p < 0.001) in women. Conclusions: HS/body fat mass appears to be the index best associated with MetS and its components, and in men it is more relevant than in women.

1. Introduction

indexes are needed to detect and prevent MetS early. In response MetS, recently, muscle strength has also been proved to be an important biomarker of cardiovascular disease, cardiovascular death and all-cause death (Sayer & Kirkwood, 2015). In addition, a large number of studies have found a correlation between MetS and muscle strength. MetS is significantly related to changes in muscle fiber cross-section, decreased cytochrome c oxidase activity in muscle fibers, and lipid accumulation in muscle fibers (Gueugneau et al., 2015). Handgrip strength (HS) is a manifestation of whole-body muscle strength, and we have reason to believe that there is a link between HS and MetS. Importantly, a growing body of evidence has suggested that impaired HS is associated with increased odds of having MetS (Sayer et al., 2007), but this association has not been found in some studies (Chang et al., 2015). The difference in results may be due to

Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors, including central obesity, hypertension, hyperglycemia, and dyslipidemia (He et al., 2006). Many studies conducted in China have found that MetS is a frequent disease in the elderly (He et al., 2006; Liu et al., 2013; Wang et al., 2010) and is significantly associated with cardiovascular disease and all-cause death (Huang, Su et al., 2014, 2014b; Kopin, 2017; Smith, 2015). However, due to the complexity of the examination procedures and the possible absence of symptoms, the population does not pay enough attention to MetS (Li et al., 2013). However, effective lifestyle modifications (such as exercise, weight control and nutrition, etc.) could improve all parameters of MetS (Vassallo & Driver, 2016). Therefore, efficient and simple clinical

Corresponding author at: College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China. E-mail address: [email protected] (Q. Guo). 1 These authors contributed equally to this work and should be considered co-first author. ⁎

https://doi.org/10.1016/j.archger.2020.104010 Received 30 October 2019; Received in revised form 22 December 2019; Accepted 2 January 2020 Available online 03 January 2020 0167-4943/ © 2020 Elsevier B.V. All rights reserved.

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confounding factors, such as body size, and there are indeed articles confirming differences in body composition between different races (Shah et al., 2005), so relative HS (absolute HS divided by weight, BMI, etc.) has been recommended to address this confounding factor (Lee, Peng, Chiou, & Chen, 2016; Li et al., 2018), but it remains unclear which relative HS indexes are most relavant to MetS and its components. To our knowledge, there is currently no research analyzing the HS index that is most relevant to MetS in the elderly Chinese population. Although our previous study found that HS/body fat mass appears to be the index best associated with cardiovascular disease risk factors (Yu et al., 2018), the index most relevant to the MetS is unclear. Therefore, in order to find out the handgrip strength index most relevant to the onset of MetS among community-dwelling elderly Chinese individuals, we performed a cross-sectional study to observe the association between different HS indexes and MetS and its components. In addition, we attempted to find optimal cutoffs for the index most relevant to MetS for screening MetS and its components.

2.2.2. HS measurement and relative HS indexes Isometric HS was measured using a handheld dynamometer (DRIPD; Takei Ltd., Niigata, Japan) in a standing position with arm extended straight down to the side. Participants were asked to exert their maximum effort twice using their dominant hand and the average grip strength was recorded (Ma et al., 2018). Relative HS indexes are ratios of HS to body composition measurements (body weight, BMI, body fat mass, BF%, fat-free mass, soft lean mass, skeletal muscle mass and upper limb muscle mass). All possible combinations were assessed.

2. Methods

According to the International Diabetes Federation (IDF), people with MetS are defined by having central obesity (WC ≥ 90 cm in men and≥ 80 cm in women) along with two or more of the following abnormalities: (1) elevated triglycerides (≥150 mg/dL); (2) reduced HDL cholesterol (< 40 mg/dL in men and < 50 mg/dL in women); (3) elevated blood pressure (≥130/85 mm Hg or known treatment for hypertension); (4) elevated FPG (≥100 mg/dL, or known treatment for diabetes) (Yu et al., 2018).

2.2.2.1. Analysis of blood samples and blood pressure. A blood sample was obtained from the antecubital vein from patients who fasted overnight for at least 10 h. Blood sample analysis and blood pressure collection methods have been explained in our previous studies (Han et al., 2017). 2.3. Definition of MetS

2.1. Study population The population included in our study came from Hangu District, Tianjin, China. From 2016–2018, 933 elderly people (age 60 and above) who participated in the national free physical examination program were included. All subjects were invited to participate in a comprehensive geriatric assessment. Exclusion criteria have been explained in our previous study (Yu et al., 2019). We excluded 5 subjects who did not complete informed consent, 11 subjects who lacked HS test due to hand trauma, and 8 subjects who could not complete body composition test due to pacemaker. After excluding 24 participants, 909 participants were included in the final analysis. The study was approved by the Ethics Committee of Tianjin Medical University. Informed consent was obtained from all subjects. The methods were carried out in accordance with the approved guidelines.

2.4. Statistical analysis The continuous variables with a normal distribution were recorded by mean and standard deviation, and classification variables were recorded by percentages. The area under receiver operating characteristic (ROC) curves was used to evaluate the abilities of the HS indexes to screen MetS and its components. The cut-off points were determined by Youden’s Index. The ability of each HS index to screen MetS and its components was shown as areas under the ROC curves (AUC) and 95 % confidence interval (CI). Using logistic regression analysis to assess the odds ratio (OR) of MetS and its components associated with HS indexes. Several confounding factors were adjusted: age, educational level, family income, smoking status, drinking status, occupation, physical activity level and nutritional status. All analysis were conducted using SPSS22.0 software.

2.2. Baseline variable All the participants were invited to a face-to-face interview to answer a standardized questionnaire after they completed their medical examination. The questionnaire included questions about age, sex, occupation, educational level, marital status (with spouse, widowed, unmarried, divorced), family income (< 1000 yuan, 1000–3000 yuan, 3000–5000 yuan, > 5000 yuan), smoking habits (current smoker or not), drinking habits (drinking alcohol once a week, drinking in the past, and never drinking were all considered as no drinking). Physical activity was assessed using the short form of the International Physical Activity Questionnaire (IPAQ) (Craig et al., 2003). Depressive symptoms were assessed using the Geriatric Depression Scale (GDS) (Mui, 1996). Nutritional status assessment using the Mini Nutritional Assessment-short form (MNA-SF) (Montejano Lozoya, Martinez-Alzamora, Clemente Marin, Guirao-Goris, & Ferrer-Diego, 2017). We also reviewed whether participants had chronic medical conditions, such as type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, cardiovascular disease (CVD), stroke, kidney disease, hepatic disease, biliary tract disease, peptic ulcer, osteoarthritis and gout.

3. Results General characteristics of 909 participants (385 men; mean age: 68.0 ± 5.9) are given in Table 1. The overall prevalence of MetS was 38.4 % (26.8 % in males and 46.9 % in females). Male and female subjects with MetS had a higher percentage of high waist circumference, high blood pressure, elevated blood glucose, and dyslipidemia than subjects without MetS (all p < 0.01). Moreover, subjects with MetS had significantly higher weight, BMI, body fat mass, BF% and skeletal muscle mass than those without MetS (all p < 0.01). As consistent with the above results, their nutritional status is also superior to participants without MetS (all p < 0.01). However, male and female subjects with MetS had significantly lower HS/weight, HS/BMI and HS/ body fat mass than subjects without MetS (all p < 0.01).

2.2.1. Body composition measurements Body composition, like fat-free mass, soft lean mass, skeletal muscle mass, upper body muscle mass, body fat mass and the percentage of body fat (BF%), was measured by a bioelectrical impedance analyzer (Inbody 720; Biospace Co., Ltd, Seoul, Korea). Subjects were tested for body composition 24 h after breakfast in the morning, and they were required to be dressed in light clothes and be barefoot for body composition measurements. Besides, we operated according to strict testing standards to minimize error (Yu et al., 2019).

4. Cut-off points of HS indexes associated with MetS and its components ROC curves were created to quantify sensitivity, specificity, areas under the ROC curves (AUC) and optimal cutoff points of HS indexes (Table 2 for males and Table 3 for females). Our results indicated that the AUC of HS/body fat mass was largest for those with MetS and its components among all indexes in both gender, HS/body fat mass 2

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Table 1 General Characteristics of participants with and without MetS. Characteristics

N Age(y) Family income (%) ≤1000 RMB/mo 1000–3000 RMB/mo 3000–5000 RMB/mo ≥5000 RMB/mo Illiteracy (%) Farming (%) Smoking (%) Drinking (%) IPAQ(METs/week) MNA GDS Weight(kg) BMI (kg/m2) Body fat mass(kg) BF% Skeletal muscle mass(kg) Upper limb muscle mass(kg) Soft lean mass(kg) Fat free mass(kg) HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/upper limb muscle mass HS/skeletal muscle mass HS/soft lean mass HS/fat free mass MetS components (%) High WC High blood pressure Elevated fasting glucose Dyslipidemiaa Chronic conditions (%) CVD Stroke Cancer Kidney disease Hepatic disease Biliary tract disease Peptic ulcer Osteoarthritis Gout

Men

Women

MetS

Not MetS

103 69.1 ± 6.1

282 69.0 ± 6.5

61.2 34 2.9 1.9 20.4 52.4 33 20.4 3360.0 (973.0, 6930.0) 10.8 ± 0.9 6.0 ± 3.5 77.8 ± 8.0 26.3 ± 2.1 21.1 ± 4.9 27.0 ± 5.0 31.2 ± 3.7 6.0 ± 1.5 47.6 ± 7.8 50.3 ± 8.2 32.3 ± 7.4 0.42 ± 0.09 1.23 ± 0.30 1.62 ± 0.59 1.25 ± 0.43 5.71 ± 1.96 1.03 ± 0.22 0.70 ± 0.20 0.66 ± 0.18

51.2 35.6 9.3 3.9 16.3 61.3 36.2 29.4 3164.0 (1399.5,6825.0) 10.4 ± 1.1 6.4 ± 5.0 68.6 ± 10.1 23.4 ± 3.0 15.3 ± 6.1 21.8 ± 6.8 29.2 ± 3.9 5.8 ± 1.3 47.7 ± 8.9 50.3 ± 9.1 31.6 ± 7.4 0.46 ± 0.11 1.36 ± 0.34 2.54 ± 0.67 1.66 ± 0.91 5.78 ± 1.99 1.08 ± 0.22 0.68 ± 0.20 0.64 ± 0.18

100 96.1 64.1 72.8 13.7 6.8 0.0 1.0 1.0 3.9 2.0 9.8 2.0

P value

MetS

Not MetS

246 67.4 ± 5.1

278 67.0 ± 5.5

0.215 0.115 0.387 0.345 0.108 0.002 0.371 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.156 0.928 0.999 0.422 < 0.001 < 0.001 < 0.001 < 0.001 0.781 0.054 0.49 0.5

61.4 30.5 5.3 2.8 70.3 70.3 18.4 0.8 4105.5 (1680.0,8106.0) 10.7 ± 0.8 8.1 ± 5.8 66.0 ± 9.6 25.9 ± 3.3 23.2 ± 6.5 34.6 ± 6.2 23.1 ± 3.8 5.0 ± 1.2 43.1 ± 7.7 45.7 ± 8.1 20.1 ± 5.4 0.31 ± 0.09 0.79 ± 0.23 0.96 ± 0.54 0.62 ± 0.38 4.23 ± 1.42 0.88 ± 0.22 0.48 ± 0.14 0.45 ± 0.13

54.7 34.2 7.6 3.6 65.8 65.8 25.3 0.7 4053.0 (1680.0,9492.0) 10.4 ± 1.0 8.0 ± 5.4 59.2 ± 10.1 23.6 ± 3.6 18.9 ± 6.5 31.2 ± 6.9 21.5 ± 3.1 4.6 ± 1.1 40.8 ± 7.2 43.2 ± 7.5 19.7 ± 5.5 0.34 ± 0.10 0.85 ± 0.26 1.19 ± 0.63 0.67 ± 0.29 4.50 ± 1.53 0.92 ± 0.23 0.49 ± 0.15 0.46 ± 0.14

0.611 0.271 0.125　 0.628 0.053 0.002 0.789 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.001 0.001 0.392 < 0.001 0.005 < 0.001 0.08 0.037 0.043 0.272 0.277

34.3 63.9 23.8 18.1

< 0.001 < 0.001 < 0.001 < 0.001

100 90.2 69.9 72.8

58.6 50 16.5 27.3

< 0.001 < 0.001 < 0.001 < 0.001

17.0 8.4 0.5 3.6 4.5 6.3 4.9 14.3 1.4

0.449 0.608 0.496 0.186 0.103 0.389 0.205 0.263 0.683

36.7 4.1 1.6 3.7 4.1 7.8 4.5 13.3 0.4

32.7 4.8 0.5 1.0 1.9 5.2 6.7 18.4 0.5

0.364 0.730 0.241 0.061 0.178 0.265 0.314 0.112 0.909

0.871 0.097

P value 0.402 0.42

Notes: BF%, percentage of body fat; BMI, body mass index; GDS, Geriatric Depression Scale; HS, handgrip strength; MNA, Mini-Nutritional Assessment ; IPAQ, International Physical Activity Questionnaire; WC, waist circumference; MetS, metabolic syndrome. Data are presented as mean ± SD or n (%). a Dyslipidemia, defined as triglycerides≥150 mg/dL and/or HDL cholesterol < 40 mg/dL in men and < 50 mg/dL in women.

showed a moderate correlation with high WC and MetS in men (AUC > 0.7), and a certain correlation with other MetS indicators of men and MetS and its components in women (0.5 < AUC < 0.7). In men, the AUC of HS/ body fat mass for those with high WC, high blood pressure, elevated blood glucose, dyslipidemia were 0.766, 0.633, 0.589, 0.653, respectively. And its corresponding optimal cutoff points were 1.99, 1.72, 1.98, 2.13. For those with MetS, the AUC was 0.723, and its optimal cutoff point was 1.92. In women, the AUC of HS/ body fat mass for those with high WC, high blood pressure, elevated blood glucose, dyslipidemia, were 0.615, 0.619, 0.599, 0.550, respectively. And its corresponding optimal cutoff points were 1.26, 1.30, 0.90, 1.02. The AUC of HS/ body fat mass for those with MetS was 0.619, and its optimal cutoff point was 1.25.

associated with HS indexes in men and women. Men in the weaker muscle strength categories identified by HS/body fat mass had OR values for high WC, high blood pressure, elevated blood glucose, dyslipidemia and MetS were 6.39 (95 %CI: 3.88, 10.51), 2.70 (1.58, 4.63), 2.03 (1.28, 3.24), 3.51 (2.04, 6.04), 5.38 (3.03, 9.56), independent of age, smoking status, drinking status, physical activity level, occupation, family income, education level and nutritional status; Similarly, women in the weaker muscle strength categories identified by HS/body fat mass had adjusted ORs for high WC, high blood pressure, elevated blood glucose, dyslipidemia and MetS were 3.88 (2.11, 5.76), 2.18 (1.41, 3.36), 1.78 (1.24, 2.57), 1.43 (1.00, 2.05), 2.39 (1.56, 3.64).

5. ORs for MetS and its components according to cutoff points of HS indexes

This cross-sectional study was performed to examine the relationship between HS indexes and metabolic syndrome and its components and determined its thresholds. The principal finding of this study was that relative HS indexes are better than HS for screening patients with

6. Discussion

Tables 4 and 5 presents the ORs for MetS and its components 3

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Table 2 AUC and cutoff point of HS and relative HS indexes in males.

High WC HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass High blood pressure HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass Elevated blood glucose HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass Dyslipidemia HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass MetS HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass

AUC(95 %CI)

P value

Cutoff

Sensitivity

Specificity

0.415 0.643 0.612 0.766 0.668 0.549 0.477 0.431 0.431

(0.358-0.472) (0.587-0.698) (0.555-0.669) (0.719-0.813) (0.614-0.722) (0.491-0.607) (0.419-0.535) (0.373-0.488) (0.374-0.488)

0.004 < 0.001 < 0.001 < 0.001 < 0.001 0.099 0.44 0.019 0.02

32.03 0.50 1.47 1.99 1.54 1.15 4.99 0.36 0.34

0.384 0.465 0.432 0.658 0.527 0.462 0.665 0.989 0.989

0.420 0.785 0.790 0.740 0.750 0.655 0.402 0.025 0.025

0.458 0.539 0.537 0.633 0.605 0.491 0.522 0.501 0.499

(0.390-0.526) (0.472-0.606) (0.470-0.604) (0.569-0.697) (0.538-0.671) (0.422-0.560) (0.453-0.591) (0.432-0.569) (0.431-0.568)

0.208 0.239 0.262 < 0.001 0.002 0.78 0.505 0.983 0.981

37.53 0.52 1.59 1.72 1.47 1.27 5.99 0.83 0.78

0.263 0.324 0.257 0.790 0.590 0.238 0.467 0.238 0.238

0.784 0.770 0.849 0.437 0.621 0.859 0.653 0.850 0.850

0.508 0.557 0.554 0.589 0.576 0.531 0.534 0.513 0.513

(0.448-0.569) (0.498-0.616) (0.495-0.613) (0.530-0.648) (0.517-0.636) (0.471-0.590) (0.474-0.595) (0.452-0.574) (0.452-0.574)

0.789 0.068 0.082 0.004 0.014 0.325 0.27 0.669 0.672

26.40 0.50 1.49 1.98 1.37 1.15 4.77 0.57 0.54

0.782 0.389 0.353 0.510 0.570 0.430 0.711 0.735 0.731

0.293 0.752 0.827 0.654 0.609 0.677 0.371 0.318 0.326

0.519 0.600 0.591 0.653 0.628 0.552 0.470 0.470 0.471

(0.450-0.581) (0.540-0.660) (0.532-0.651) (0.596-0.710) (0.570-0.686) (0.490-0.614) (0.406-0.534) (0.406-0.534) (0.407-0.535)

0.538 0.001 0.004 < 0.001 < 0.001 0.098 0.347 0.348 0.358

27.48 0.44 1.23 2.13 1.42 1.03 4.34 0.53 0.50

0.737 0.589 0.664 0.479 0.552 0.649 0.790 0.813 0.805

0.349 0.595 0.500 0.776 0.696 0.480 0.250 0.242 0.250

0.470 0.634 0.616 0.723 0.650 0.567 0.506 0.462 0.462

(0.374-0.535) (0.574-0.694) (0.555-0.676) (0.669-0.776) (0.592-0.708) (0.503-0.631) (0.438-0.573) (0.395-0.529) (0.395-0.529)

0.374 < 0.001 0.001 < 0.001 < 0.001 0.043 0.86 0.256 0.257

38.38 0.49 1.49 1.92 1.42 1.15 4.98 0.44 0.96

0.199 0.411 0.358 0.584 0.548 0.441 0.651 0.932 0.061

0.825 0.835 0.893 0.786 0.748 0.709 0.417 0.097 0.971

Notes: AUC, area under the receiver-operating characteristics curve; CI, confidence interval; BF%, percentage of body fat; BMI, body mass index; MetS, metabolic syndrome; WC, waist circumference; HS, handgrip strength.

Kang, 2018) and Taiwanese (Lee et al., 2016) studies. Many of the above studies have demonstrated the superiority of relative HS in identifying MetS. This result may be caused by the following two reasons. First, absolute HS may inadequately address the issue of body size and body composition when targeting metabolic profiles and diseases (Li et al., 2018). Relative HS indexes focus on body size and HS simultaneously, which may explain why relative HS was better than HS alone in cardiometabolic indicators. Second, racial factors may explain why our population has a significantly lower mean absolute handgrip strength when the average age difference is small; for example, our cohort and a Hertfordshire cohort yielded HSs of 31.8 and 44.3 kg in males, respectively, and 19.9 and 26.7 kg in females, respectively (Sayer et al., 2007). As far as we know, although previous studies have recommended HS divided by body weight or BMI as a measurement of relative handgrip strength when studying the correlation between muscle strength and MetS, our study is the first to investigate the relationship between

MetS and its components in both gender, and HS/body fat mass was the most efficient index for men and women in our population. HS is a good reflection of overall muscle strength and is associated with mortality and adverse health outcome (Wu, Wang, Liu, & Zhang, 2017). Several studies have found a link between decreased muscle strength and MetS. However, there are differences in these results. A cross-sectional study in the UK found a negative correlation between absolute grip strength and the incidence of MetS (Sayer et al., 2007), but the association between HS and MetS was not found among elderly populations in Taiwanese communities (Chang et al., 2015). There was no correlation between absolute grip strength and metabolic syndrome in our population. The inconsistencies regarding absolute handgrip strength limit its application while relative grip strength is more consistent. A study of elderly people in a rural area of Japan showed an association between lower cardiometabolic risk and higher HS/weight (Kawamoto et al., 2016). A significant negative correlation between HS/BMI and MetS was found in both Korean (Yi, Khang, Lee, Son, & 4

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Table 3 AUC and cutoff point of HS and relative HS indexes in females.

High WC HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass High blood pressure HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass Elevated blood glucose HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass Hyperlipidemia HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass MetS HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass HS/soft lean mass HS/fat free mass

AUC(95 %CI)

P value

Cutoff

Sensitivity

Specificity

0.385 0.545 0.514 0.615 0.537 0.490 0.506 0.489 0.489

(0.327-0.442) (0.481-0.608) (0.452-0.576) (0.551-0.679) (0.473-0.600) (0.429-0.552) (0.447-0.565) (0.431-0.547) (0.430-0.547)

< 0.001 0.149 0.654 < 0.001 0.234 0.757 0.839 0.721 0.715

31.83 0.40 1.25 1.26 1.12 0.73 3.76 0.41 0.34

0.017 0.313 0.122 0.460 0.150 0.788 0.699 0.708 0.814

0.985 0.815 0.958 0.771 0.98 0.254 0.388 0.343 0.236

0.533 0.587 0.591 0.619 0.593 0.558 0.559 0.540 0.540

(0.479-0.586) (0.534-0.639) (0.539-0.643) (0.567-0.670) (0.541-0.645) (0.505-0.611) (0.505-0.613) (0.485-0.594) (0.486-0.594)

0.234 0.002 0.001 < 0.001 0.001 0.034 0.032 0.15 0.148

15.93 0.39 0.64 1.30 0.76 0.80 4.73 0.54 0.64

0.822 0.325 0.853 0.374 0.356 0.736 0.466 0.405 0.178

0.268 0.804 0.288 0.809 0.784 0.362 0.660 0.689 0.916

0.531 0.599 0.581 0.599 0.559 0.583 0.560 0.547 0.547

(0.480-0.581) (0.549-0.649) (0.532-0.630) (0.549-0.649) (0.509-0.609) (0.534-0.633) (0.509-0.610) (0.496-0.598) (0.496-0.597)

0.233 < 0.001 0.002 < 0.001 0.023 0.001 0.022 0.072 0.074

16.73 0.34 0.95 0.90 0.45 0.87 4.05 0.46 0.43

0.754 0.498 0.321 0.610 0.836 0.636 0.597 0.601 0.601

0.333 0.685 0.810 0.542 0.266 0.523 0.522 0.522 0.517

0.451 0.513 0.503 0.550 0.516 0.485 0.507 0.483 0.483

(0.402-0.501) (0.463-0.562) (0.453-0.552) (0.500-0.599) (0.466-0.566) (0.435-0.534) (0.456-0.557) (0.433-0.533) (0.433-0.533)

0.054 0.619 0.919 0.05 0.528 0.546 0.797 0.508 0.508

20.63 0.41 1.05 1.02 0.76 1.24 5.41 0.72 0.63

0.383 0.212 0.193 0.487 0.296 0.082 0.240 0.072 0.103

0.498 0.849 0.848 0.611 0.766 0.952 0.807 0.951 0.922

0.475 0.581 0.559 0.619 0.558 0.545 0.549 0.526 0.526

(0.425-0.525) (0.533-0.630) (0.509-0.608) (0.571-0.667) (0.509-0.608) (0.495-0.594) (0.499-0.599) (0.475-0.576) (0.475-0.576)

0.328 0.001 0.021 < 0.001 0.022 0.079 0.058 0.319 0.322

13.58 0.35 1.05 1.25 0.76 1.11 5.39 0.67 0.63

0.874 0.435 0.227 0.366 0.319 0.217 0.273 0.164 0.160

0.169 0.708 0.885 0.815 0.794 0.860 0.841 0.905 0.905

Notes: AUC, area under the receiver-operating characteristics curve; CI, confidence interval; BF%, percentage of body fat; BMI, body mass index; MetS, metabolic syndrome; WC, waist circumference; HS, handgrip strength.

accumulation of body fat and the degradation of skeletal muscle (Chang et al., 2015). Therefore, the strong correlation between HS/body fat mass and MetS is also reflected by these results. Our previous studies also found that HS/body fat mass appears to be the index best associated with cardiovascular disease risk factors (i.e., hypertension, diabetes, and dyslipidemia) (Yu et al., 2018).Therefore, the strong correlation between HS/body fat mass and the metabolic syndrome is very convincing. In our population, ROC analysis showed that HS/body fat mass is more strongly associated with MetS than female, while low muscle strength based on cut-off point also showed higher ORs in men. This indicates that the correlation between HS/body fat mass and MetS is stronger in men than in women. Some studies have shown that MetS is associated with muscle weakness, especially in men (Yang et al., 2012). The differences between gender may be caused by the following reasons. First, although men's muscle strength is stronger than women's, men's grip strength declines more during the aging process (Janssen,

different HS indexes and MetS and its components. In our study HS/ body fat mass showed the strongest correlation with MetS and its components in both gender when compared to HS combined with other obesity or lean body mass indicators. It is well known that obesity is closely related to MetS, and it is widely recognized as a prerequisite for MetS (O’Neill & O’Driscoll, 2015). Although BMI is a common measure of obesity, it is not measured precisely by body composition (Rothman, 2008). Studies have shown that South Asians have higher levels of body fat while having lower body mass indexes than Whites (Shah et al., 2005). It was also found in this study that the association between handgrip strength combined with fat mass and MetS was superior to that between grip strength combined with lean body weight or muscle mass and MetS. Previous studies have also shown that fat mass in the trunk in men and fat mass in the arms in women are independently associated with a cluster of cardiovascular risk factors, whereas lean mass is not associated in a stepwise logistic regression analysis (Joseph et al., 2011). Moreover, the aging process is accompanied by the 5

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Table 4 Odds ratio of the risk factors in subjects determined by the cutoffs of each indexes in males. Variable

High WC HS HS/weight HS/BMI HS/body fat mass HS/BF% HS/soft lean mass HS/fat free mass High blood pressure HS/body fat mass HS/BF% Elevated fasting glucose HS/body fat mass HS/BF% Hyperlipidemia HS/weight HS/BMI HS/body fat mass HS/BF% MetS HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass

Odds ratio(95 %CI) Crude

P value

Adjusted model

P value

2.22 2.97 2.70 5.38 3.29 1.30 1.28

(1.47–3.34) (1.91–4.64) (1.73–4.21) (3.47–8.34) (2,14-5.06) (0.77–2.18) (0.75–2.17)

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.328 0.363

3.09 3.67 2.96 6.39 3.77 1.94 1.86

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.036 0.052

2.58 (1.55–4.28) 2.34 (1.48–3.70)

< 0.001 < 0.001

2.70 (1.58–4.63) 2.29 (1.40–3.76)

< 0.001 0.001

1.86 (1.21–2.86) 1.72 (1.11–2.65)

0.005 0.014

2.03 (1.28–3.24) 1.92 (1.19–3.08)

0.003 0.007

2.09 1.86 3.04 2.82

(1.35–3.21) (1.20–2.86) (1.88–4.92) (1.79–4.44)

0.001 0.005 < 0.001 < 0.001

2.42 2.08 3.51 2.98

(1.46–4.00) (1.25–3.44) (2.04–6.04) (1.78–5.00)

0.001 0.005 < 0.001 < 0.001

2.85 3.92 5.16 3.41 1.89

(1.67–4.87) (2.09–7.37) (3.05–8.75) (2.07–5.62) (1.17–3.08)

< 0.001 < 0.001 < 0.001 < 0.001 0.01

3.17 4.03 5.38 3.42 2.00

(1.78–5.65) (2.05–7.92) (3.03–9.56) (1.97–5.93) (1.18–3.40)

< 0.001 < 0.001 < 0.001 < 0.001 0.01

(1.88–5.07) (2.22–6.06) (1.81–4.85) (3.88–10.51) (2.33–6.09) (1.05–3.61) (0.99–3.47)

Notes: CI, confidence interval; BF%, percentage of body fat; BMI, body mass index; MetS, metabolic syndrome; HS, handgrip strength; WC, waist circumference. Adjusted model is adjusted with age, smoking status, drinking status, occupation, educational level, family income, nutritional status and physical activity level.

Heymsfield, Wang, & Ross, 2000; Metter, Conwit, Tobin, & Fozard, 1997). The incidence of MetS may be more strongly related to the change of muscle strength. Second, gender differences in skeletal muscle energy metabolism may explain this result. Male patients with MetS have lower levels of androgens, and testosterone levels decrease as the number of MetS indicators increases (Kupelian, Hayes, Link, Rosen, & McKinlay, 2008). Testosterone retains nitrogen, an action

essential for the development and maintenance of muscle mass (SinhaHikim, Cornford, Gaytan, Lee, & Bhasin, 2006), and improves physical strength and performance (Page et al., 2005). However, studies have shown that total testosterone is not associated with incident MetS in women (Soriguer et al., 2012). Third, the reason that the association between HS/body fat mass and MetS was stronger in men may be that the average age of men was higher in this study. Moreover, according to

Table 5 Odds ratio of the risk factors in subjects determined by the cutoffs of each indexes in females. Variable

High WC HS HS/body fat mass High blood pressure HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass Elevated fasting glucose HS/weight HS/BMI HS/body fat mass HS/BF% HS/skeletal muscle mass HS/upper limb muscle mass Hyperlipidemia HS/body fat mass MetS HS/weight HS/BMI HS/body fat mass HS/BF%

Odds ratio(95 %CI) Crude

P value

Adjusted model

P value

1.43 (0.308–6.61) 2.77 (1.79–4.29)

0.649 < 0.001

0.90 (0.18–4.43) 3.48 (2.11–5.76)

0.897 < 0.001

1.75 2.26 2.40 1.97 1.57 1.63

(1.12–2.68) (1.39–3.67) (1.59–3.64) (1.31–2.96) (1.04–2.36) (1.12–2.38)

0.01 0.001 < 0.001 0.001 0.032 0.012

1.57 2.04 2.18 1.90 1.42 1.53

(1.00–2.48) (1.22–3.41) (1.41–3.36) (1.22–2.94) (0.92–2.19) (1.03–2.28)

0.05 0.007 < 0.001 0.004 0.117 0.036

2.16 2.02 1.79 1.49 1.89 1.59

(1.49–3.15) (1.33–3.06) (1.25–2.54) (0.99–2.25) (1.33–2.70) (1.12–2.28)

< 0.001 0.001 0.001 0.056 < 0.001 0.01

2.12 1.97 1.78 1.46 1.85 1.59

(1.44–3.13) (1.28–3.02) (1.24–2.57) (0.94–2.28) (1.28–2.67) (1.09–2.30)

< 0.001 0.002 0.002 0.094 0.001 0.015

1.45 (1.02–2.05)

0.038

1.43 (1.02–2.05)

0.047

1.83 2.25 2.47 1.75

0.001 0.001 < 0.001 0.007

1.78 2.12 2.39 1.77

0.003 0.004 < 0.001 0.009

(1.27–2.63) (1.39–3.65) (1.65–3.70) (1.17–2.61)

(1.22–2.60) (1.28–3.51) (1.56–3.64) (1.16–2.72)

Notes: CI, confidence interval; BF%, percentage of body fat; BMI, body mass index; MetS, metabolic syndrome; HS, handgrip strength; WC, waist circumference. Adjusted model is adjusted with age, smoking status, drinking status, occupation, educational level, family income, nutritional status and physical activity level. 6

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our statistical analysis, the correlation among participants over 70 years old is stronger than that of those under 70 years old. The AUC of participants over 70 years old is 0.709, and that of those under 70 years old is 0.685. These finding support our result for gender differences in HS/ body fat mass screening for MetS and its components. In our study, the AUCs of MetS and its components show significant moderate accuracy in statistics for both men and women because the 95 % CI of the AUC is bigger than 0.5. Muscle strength thresholds have been identified as indicators of insulinemic profiles, low cardiorespiratory fitness, and independence regarding activities of daily living in the elderly. Very few studies have identified a threshold of muscle strength associated with MetS in suburb-dwelling elderly Chinese. According to the ROC analysis of the subjects, the cut-off values for HS/ body fat mass to predict MetS were 1.92 for males and 1.25 for females. In males, the sensitivity and specificity of HS/body fat mass for screening MetS were 58.4 % and 78.6 %, respectively, implying that this cutoff could identify three fifth of people with MetS, but false positives will occur in 21.4 % of cases. Similarly, the sensitivity and specificity of HS/body fat mass in women were 36.6 % and 81.5 %, respectively, implying that this cutoff would fail to identify two third of people with mobility limitations but the false positives rate is less than one in five. High specificity reduces unessential testing and associated costs to the patient and community and makes it valuable for early community screening. Furthermore, logistic regression analyses demonstrated that the cutoff points for HS/body fat mass had the highest ORs for MetS and its components. For example, compared with individuals with normal muscle strength, men with weak muscles were 5.38 times (95 % CI: 3.03, 9.56) more likely to develop MetS, and women with weak muscles were 2.39 times (95 % CI: 1.56, 3.64) more likely to develop MetS. Overall, these cutoffs are valuable in screening older adults at high risk for MetS and thus facilitate early interventions. The population selected for our study was older than 60 years of age, while younger populations were not included. First, studies have shown that the incidence of MetS rises with age and reaches its highest level above the age of 60, with a relatively low incidence in young individuals (Aguilar, Bhuket, Torres, Liu, & Wong, 2015). Today's aging is getting worse, the survey in the elderly group highlights the significance of this research. Secondly, the decrease in muscle strength is significantly associated with adverse outcomes in the elderly. It has been well documented that the age-associated loss of strength is more pronounced with advancing age. Compared with the young population, muscle strength in the old population is reduced by about half, and the decline speed of muscle strength in the old population is also more rapid (Goodpaster et al., 2006; Marty, Liu, Samuel, Or, & Lane, 2017). Therefore, the prediction ability of muscle strength in the old population for various adverse outcomes is far higher than that in the younger population. Our study was the first to investigate the association between different HS indexes and MetS and its components in elderly Chinese people. The Foundation for the National Institutes of Health sarcopenia project has suggested that HS/BMI is a good marker for incident adverse health outcomes. However, the validation of corrected HS in Asian older adults is still not well established. Our main achievement of the present study is identification of a simple and efficient index for screening elderly adults at high risk of MetS. This index is very useful in large-scale clinical practice. Several limitations should be considered in this study. First, all participants in the present study were relatively healthy as we did not include participants who were unable to participate in the free annual national physical examination (e.g. those bedridden or with serious disease). Due to this, our results might in fact underestimate of the prevalence of metabolic syndrome and its associated health impact. Secondly, because of the nature of cross-sectional studies, we cannot draw conclusions about causality. Third, BIA is not a gold standard for measuring body composition, and its measurement results may not be as accurate as DXA. Fourth, due to differences in demographic characteristics of different regions and ethnicities, the

study findings may not be applicable to other regions. Therefore, it is necessary to further carry out such projects in other regions to verify whether this standard can predict MetS and its components. Last, although HS/body fat mass is the most relevant index for MetS in men and women, it is more relevant in men. But the predictive value is still not very high. In the future, we will increase the sample size and followup to verify the clinical significance of this indicator. 7. Conclusion In conclusion, we found that HS/body fat mass is the best indicator of MetS and its components screening among Chinese communitydwelling elderly individuals. We also determined the optimal cut-off point for people at high risk of MetS. The underlying mechanism behind this relationship is still unclear, and future research needs to determine its mechanisms and its ability to predict MetS. Declaration of interests Nothing declared. Funding This work was supported by National Natural Science Foundation of China [Grant No. 81601952]; and Tianjin Municipal Science and Technology Commission [Grant No 16ZXMJSY00070]. Acknowledgments The authors thank Guiyan Shao from the Chadian Public Health Center and Xiaofang Ren from the Hangu Welfare House for providing infrastructural and planning support. References Aguilar, M., Bhuket, T., Torres, S., Liu, B., & Wong, R. J. (2015). Prevalence of the metabolic syndrome in the United States, 2003-2012. JAMA, 313, 1973–1974. Chang, K. V., Hung, C. Y., Li, C. M., Lin, Y. H., Wang, T. G., Tsai, K. S., et al. (2015). Reduced flexibility associated with metabolic syndrome in community-dwelling elders. PLoS One, 10, e0117167. Craig, C. L., Marshall, A. L., Sjostrom, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., et al. (2003). International physical activity questionnaire: 12-Country reliability and validity. Medicine and Science In Sports And Exercise, 35, 1381–1395. Goodpaster, B. H., Park, S. W., Harris, T. B., Kritchevsky, S. B., Nevitt, M., Schwartz, A. V., et al. (2006). The loss of skeletal muscle strength, mass, and quality in older adults: The health, aging and body composition study. The Journals Of Gerontology Series A, Biological Sciences And Medical Sciences, 61, 1059–1064. Gueugneau, M., Coudy-Gandilhon, C., Theron, L., Meunier, B., Barboiron, C., Combaret, L., et al. (2015). Skeletal muscle lipid content and oxidative activity in relation to muscle fiber type in aging and metabolic syndrome. The Journals Of Gerontology Series A, Biological Sciences And Medical Sciences, 70, 566–576. Han, P., Yu, H., Ma, Y., Kang, L., Fu, L., Jia, L., et al. (2017). The increased risk of sarcopenia in patients with cardiovascular risk factors in Suburb-Dwelling older Chinese using the AWGS definition. Scientific Reports, 7, 9592. He, Y., Jiang, B., Wang, J., Feng, K., Chang, Q., Fan, L., et al. (2006). Prevalence of the metabolic syndrome and its relation to cardiovascular disease in an elderly Chinese population. Journal of the American College of Cardiology, 47, 1588–1594. Huang, Y., Cai, X., Chen, P., Mai, W., Tang, H., Huang, Y., et al. (2014). Associations of prediabetes with all-cause and cardiovascular mortality: A meta-analysis. Annals of Medicine, 46, 684–692. Huang, Y., Su, L., Cai, X., Mai, W., Wang, S., Hu, Y., et al. (2014). Association of all-cause and cardiovascular mortality with prehypertension: A meta-analysis. American Heart Journal, 167 160-8.e1. Janssen, I., Heymsfield, S. B., Wang, Z. M., & Ross, R. (2000). Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr. Journal of Applied Physiology (Bethesda, Md: 1985), 89, 81–88. Joseph, L., Wasir, J. S., Misra, A., Vikram, N. K., Goel, K., Pandey, R. M., et al. (2011). Appropriate values of adiposity and lean body mass indices to detect cardiovascular risk factors in Asian Indians. Diabetes Technology & Therapeutics, 13, 899–906. Kawamoto, R., Ninomiya, D., Kasai, Y., Kusunoki, T., Ohtsuka, N., Kumagi, T., et al. (2016). Handgrip strength is associated with metabolic syndrome among middleaged and elderly community-dwelling persons. Clinical and Experimental Hypertension (New York, NY: 1993), 38, 245–251. Kopin, L. (2017). Lowenstein C. Dyslipidemia. Annals of Internal Medicine, 167, Itc81–itc96.

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