Next Article in Journal
Relative Association of Multi-Level Supportive Environments on Poor Health among Older Adults
Previous Article in Journal
Regretting Ever Starting to Smoke: Results from a 2014 National Survey
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Indices of Body Composition and Abnormal Metabolic Phenotype in Normal-Weight Chinese Adults

1
Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10, Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
2
China-Japan Friendship Hospital, Cherry Garden East Street, Chaoyang District, Beijing 100029, China
3
Department of Chronic Disease, Beijing Fangshan District Center for Disease Prevention and Control, Yuehua North Street, Fangshan District, Beijing 102446, China
4
Birth Defects Monitoring Center, West China Second University Hospital, People South Road, Wuhou District, Chengdu 610041, China
5
Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, No. 3, Dongdan, Dongcheng District, Beijing 100005, China
6
Departments of Medicine and Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(4), 391; https://doi.org/10.3390/ijerph14040391
Submission received: 30 January 2017 / Revised: 18 March 2017 / Accepted: 29 March 2017 / Published: 7 April 2017
(This article belongs to the Section Global Health)

Abstract

:
We aimed to determine the association of indices of body composition with abnormal metabolic phenotype, and to examine whether the strength of association was differentially distributed in different age groups in normal-weight Chinese adults. A total of 3015 normal-weight adults from a survey of Chinese people encompassing health and basic physiological parameters was included in this cross-sectional study. We investigated the association of body composition measured by bioelectrical impedance analysis and conventional body indices with metabolically unhealthy normal-weight (MUHNW) adults, divided by age groups and gender. Associations were assessed by multiple logistic regression analysis. We found abnormal metabolism in lean Chinese adults to be associated with higher adiposity indices (body mass index, BMI), waist circumference, and percentage body fat), lower skeletal muscle %, and body water %. Body composition was differentially distributed in age groups within the metabolically healthy normal weight (MHNW)/MUHNW groups. The impact of factors related to MUHNW shows a decreasing trend with advancing age in females and disparities of factors (BMI, body fat %, skeletal muscle %, and body water %) associated with the MUHNW phenotype in the elderly was noticed. Those factors remained unchanged in males throughout the age range, while the association of BMI, body fat %, skeletal muscle %, and body water % to MUHNW attenuated and grip strength emerged as a protective factor in elderly females. These results suggest that increased adiposity and decreased skeletal muscle mass are associated with unfavorable metabolic traits in normal-weight Chinese adults, and that MUHNW is independent of BMI, while increased waist circumference appears to be indicative of an abnormal metabolic phenotype in elderly females.

1. Introduction

The fact that obesity is a confirmed risk factor for chronic diseases including type 2 diabetes (T2DM) and cardiovascular diseases (CVD) has made it a major global health threat [1,2]. Body mass index (BMI) is the most commonly acknowledged diagnostic measurement for obesity. Nevertheless, individuals that are within a normal BMI range are not necessarily immune to metabolic disorders that are typically associated with obesity [3,4,5,6].
Lean individuals with abnormal metabolic profiles like hyperglycemia, hypertension, and dyslipidemia have been defined as “metabolically unhealthy normal-weight” (MUHNW), a concept first introduced by Ruderman et al. over 30 years ago [3]. MUHNW individuals may also be predisposed to similar adverse health outcomes as those observed with obese patients. However, since they are not overweight or obese, they may not get adequate medical attention, thus increasing their risk for untreated complications, which has been supported by a limited number of studies. A Korean study showed MUHNW adults exhibited increased arterial stiffness and carotid atherosclerosis compared with metabolically healthy obese adults [7]. MUHNW adults were three-fold more likely to develop diabetes than metabolically healthy obese individuals; and both groups had a similar risk in developing subclinical atherosclerosis as metabolically unhealthy obese adults [8,9]. Surprisingly, the highest risk of CVD and all-cause mortality of MUHNW adults was observed in a prospective cohort study with a median follow-up of ten years [6].
There have been several studies that attempted to estimate the prevalence of the MUHNW phenotype in the general population. Because of different criteria used to define MUHNW, the prevalence can be quite heterogeneous, ranging from 2.2% to 53.6% [10]. Even when defined by at least two metabolic abnormalities, the prevalence of MUHNW still differed in various population studies. For example, in a Korean study of the general population aged over 20 years old, MUHNW individuals accounted for 13% of the general population and 18.6% among normal-weight individuals, while in Americans of the same age range the proportions were 8.1% and 23.5%, respectively [11,12]. People with this phenotype have some common characteristics, including older age groups, alcohol drinker, smoker, and having a sedentary lifestyle [10,11,12].
No study has investigated the abnormal metabolic phenotypes assessed by the indices of body composition in a population-based study with a large sample size. Of particular note are those individuals predisposed to adiposity but with relatively low BMI, which is typical of the Asian population [13,14]; such a study is well suited to be performed in the Chinese population. The results from this study will determine the prevalence of the abnormal metabolic phenotype in the normal-weight population, and provide the rationale to propose earlier prevention of metabolic diseases in this medically neglected population. Since body composition varies with gender and age [15,16], this study has added value in assessing the specific association of abnormal metabolic phenotypes and body composition stratified into different gender and age groups.

2. Materials and Methods

2.1. Study Population

An ongoing population-based survey of Chinese people encompassing health and basic physiological parameters was conducted from 2013 onwards, which covered five provinces, including Hainan, Shanxi, Qinghai, Gansu, and Jiangxi. A stratified multistage, random cluster process was employed to select subjects. This study was approved by the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (approval number 029-2013). Written consent was obtained from all participants.
The data of the present study was from the site of the Shanxi province. The sampling method for this site was: first, four administrative regions were randomly sampled from the 104 districts in the Shanxi province, with two located in urban areas, one in a suburb area and one in a rural area, respectively. After that, six residential communities and six natural villages in those four regions were randomly chosen, based on a list provided by the Center for Disease Control of the Shanxi province. A total of 6410 residents aged 7–79 years old who lived in the selected communities were involved in this study. Among the 6410 participants, according to the results of a previous meta-analysis in the Chinese population [17], 681 (298 males, 383 females) were underweight (BMI < 18.5), 3220 (1248 males, 1972 females) were normal-weight (BMI 18.5–23.9), 1894 (902 males, 992 females) were overweight (BMI 24.0–27.9), and 1894 (902 males, 992 females) were obese (BMI ≥ 28). Exclusion criteria included those aged below 18 years (n = 588), BMI < 18.5 (n = 681), BMI ≥ 24 (n = 2509), and those with incomplete information to define the metabolic phenotype (n = 435). This left a total of 3015 normal-weight adults aged 18–79 years that were included in this study.

2.2. Data Collection

The information of demographic and socioeconomic status, smoking, and drinking status were obtained from participants using a questionnaire. Smoking status was divided into two categories: current smoker, or non-current smoker, according to the answers to “Are you currently a smoker”. Drinking status was categorized as current drinker and non-current drinker in the same way. Educational attainments were classified into three groups: ≤ middle school, high school, and ≥ college. Body compositions (fat mass, total body water, muscle mass), blood glucose and lipid profile, uric acid, alanine transaminase, and aspartate transaminase were collected. The latter three were chosen because they have been associated with insulin resistance in the liver [18] and overall metabolism [19,20].
Weight was measured to the nearest 0.1 kg using a SECA 813 digital scale (Seca, Vogel & Halke GmbH & Co., Hamburg, Germany), with individuals wearing only light underwear and after emptying the bladder. Body height was measured to the nearest 0.1 cm using a flexible anthropometer [21]. Waist circumference (WC) was measured at the midpoint between the lower border of the rib cage and the iliac crest. Blood pressure (BP) was measured in triplicate after a 10-min rest, using an Omron electronic sphygmomanometer (Omron Healthcare Co. Ltd., Kyoto, Japan). Handgrip was measured using a hand-held Takei dynamometer (Takei Scientific Instruments Co. Ltd., Niigata, Japan) in a standing position with the arm extended straight down to the side, and participants were asked to measure twice with their dominant hand. The larger reading of the two measurements was recorded as handgrip strength and expressed in kilograms.
Body fat mass, total body water, and impedance were measured by bioelectrical impedance (BI) analysis, which was performed with a TANITA body composition analyzer 420 (TANITA Co., Tokyo, Japan). BMI was calculated as weight (kg) divided by height squared (m2). Skeletal muscle mass (kg) was estimated via the following BI analysis equation of Janssen et al. [22]:
Skeletal muscle mass = (0.401 × (height2/BI) + (3.825 × sex) + (−0.071 × age)) + 5.102
With height measured in centimeters, BI measured in ohms, sex coded 1 for men and 0 for women, and age measured in years. The percentage of body fat, body water, and skeletal muscle were calculated as the according mass divided by the body weight.
Venous blood samples were obtained after an overnight fast of at least 8 h. All blood samples were analyzed in a national central laboratory in Beijing using the Olympus auto-analyzer 2700 (Olympus Instruments Inc., Tokyo, Japan), with strict quality control. Fasting glucose was measured by the glucose oxidase method (GOD-PAP) method (Randox Laboratories Ltd., Crumlin, UK). Serum triglyceride (TG) was measured by the glycerol lipase oxidase (GPO-PAP) method (Kyowa Medex Co. Ltd., Tokyo, Japan). Low-density lipoprotein cholesterol (LDLC) and high-density lipoprotein cholesterol (HDL-C) concentrations were measured enzymatically (Kyowa Medex Co. Ltd., Tokyo, Japan). Alanine transaminase (ALT) and aspartate transaminase (AST) were measured enzymatically (Randox Laboratories Ltd., Crumlin, UK). Serum uric acid (UA) was measured by the enzymatic colorimetric method (Randox Laboratories Ltd., Crumlin, UK).

2.3. Definitions of Metabolic Phenotypes

We used the criteria established by the National Cholesterol Education Program Adult Treatment Panel III (ATP III) to identify the MUHNW phenotype [23]. The presence of one or more of the following components: (1) high blood pressure (systolic blood pressure ≥ 130 or diastolic blood pressure ≥ 85 mmHg or known treatment for hypertension); (2) hypertriglyceridemia (fasting plasma triglycerides ≥ 1.69 mmol/L); (3) low HDL cholesterol (< 1.29 mmol/L); (4) hyperglycemia (fasting plasma glucose ≥ 5.6 mmol/L or known treatment for diabetes) was defined as metabolically unhealthy. The participants were classified into two groups according to the definition: metabolically healthy normal weight (MHNW) and MUHNW.

2.4. Statistical Analysis

All statistical analyses were conducted using the SPSS software version 18.0 for Windows (SPSS Inc., Chicago, IL, USA). The data was expressed as mean ± standard deviation (SD) for continuous variables and number (percentage) for categorical variables, respectively. Independent t-tests or ANOVA (analysis of variance) was used to compare the continuous variables between groups, and the chi-square test was used for the comparison of categorical variables. Factors associated with the unhealthy metabolic phenotype were evaluated by an unconditional logistic regression analysis, in which age, education, smoking, and drinking status were adjusted. Associations between body measurements and unhealthy metabolic phenotype within different age groups were also assessed in the logistic regression model, in which continuous variables were transformed to x’ = (x − mean)/SD to standardize OR (odds ratio). All tests were two-sided, and a value of p < 0.05 was considered as significant.

3. Results

3.1. General Characteristics of the Study Population

The 3015 normal weight adults had a mean age of 45.5 (ranged 18–79 years), with a sex distribution of 1158 (38.4%) males and 1857 (61.6%) females. Compared with females, males were older, more likely to be current smokers and drinkers, had less body fat and more muscle, and had less favorable metabolic profiles (Table 1). Fifty-one percent (n = 1539) of the subjects met the criteria of being metabolically unhealthy, and the detailed characteristics are shown in Table 2. Compared to metabolically healthy normal-weight individuals, besides less favorable metabolic traits, those with an abnormal metabolic phenotype were also older, more likely to be less educated, had a higher level of adiposity indexes (WC, BMI, and body fat %), and a lower level of body water % and muscle indexes (including skeletal muscle % and handgrip strength).

3.2. Factors Associated with Abnormal Metabolic Phenotype in Normal-Weight Adults

Characteristics of the normal and abnormal metabolic phenotype are shown in Table 3 and Table 4, stratified by gender. In both genders, compared with their metabolically healthy counterparts, metabolically unhealthy individuals were older, had higher levels of adiposity indices, uric acid (UA), and ALT, and lower levels of skeletal muscle % and body water %. All these factors were associated with metabolic abnormality after adjusting for age, education, smoking, and drinking status. Although MUHNW females had higher aspartate transaminase (AST) levels and weaker handgrip strength, the difference no longer existed after adjusting for confounders.

3.3. Characteristics of Body Composition Within Each Age Group and Within Each Metabolic Subgroup

We further investigated the characteristics of body measurements by metabolic phenotypes within the young adults (18–44 years old), the middle aged (45–59 years old) and the elderly (above 60 years old), and within each metabolic status (Table 5). In males of all age groups, all the adiposity indices were higher, but skeletal muscle % and body water % were lower in the MUHNW groups when compared to the MHNW groups. In females, however, the difference of all body compositions but waist circumference and skeletal muscle % between the two metabolic phenotypes was not significant in the elderly, and grip strength was higher in individuals with normal metabolism only in the elderly.
Among the MNHW and MUHNW subgroups, compared with the young male adults, the middle-aged males had larger WC, higher body water %, and lower grip strength, while the elderly had higher body water % and lower grip strength. Moreover, the elderly males had lower BMI, higher body water %, and weaker grip strength than the middle-aged group. However, in females, compared with the young adults, the middle aged and the elderly both had increased adiposity indices, decreased skeletal muscle %, and grip strength. The elderly females also had increased WC and body fat %, and decreased skeletal muscle % and grip strength when compared to the middle-aged females.

3.4. Association of Indices of Body Composition with Abnormal Metabolic Phenotype Was Differentially Distributed in Different Age Groups

The impact of BMI and body fat % on abnormal metabolism showed an increasing trend throughout the age groups, reaching a peak in the elderly with OR values of 2.34 and 1.98, while the waist circumference increased 68%, 50%, and 128% of the risk with each SD increment in young males, the middle aged, and the elderly males, respectively. Skeletal muscle % and body water % were protective of the abnormal metabolic phenotype in different age stages after adjusting for confounders in males (Table 6). In females, waist circumference was a risk factor of MUHNW in all age stages, posing the strongest impact (OR (95% confidence interval (CI): 1.83 (1.48–2.28)) in the middle aged, while the impact of BMI, body fat %, skeletal muscle %, and body water % seemed to attenuate in different age stages. Surprisingly, grip strength became a protective factor in the elderly females, decreasing 32% of the risk of being metabolically unhealthy (Table 6).

4. Discussion

To the best of our knowledge, the present study is the first to examine the factors associated with the abnormal metabolic phenotype in normal-weight Chinese adults of different age groups. Our findings suggest that higher adiposity indices (BMI, waist circumference, body fat %), and lower skeletal muscle % and body water % are associated with abnormal metabolism in lean Chinese adults, and that the impact of factors related to the unhealthy metabolic phenotype shows a decreasing trend with increasing age in females. Of note, there are disparities in the factors associated with the MUHNW phenotype in males and females aged over 60 years. Those factors remained unchanged in males throughout the age stages, while the association of BMI, body fat %, skeletal muscle %, and body water % to MUHNW attenuated and grip strength emerged as a protective factor in the elderly female.
Our finding of the association between adiposity indices and the abnormal metabolic phenotype in normal-weight adults is well in line with previous reports. The current study and other studies of the Chinese population [24,25] have shown that BMI and WC are higher in MUHNW individuals regardless of gender, and this result is further supported by studies conducted in Korea [26,27]. Moreover, the research conducted by Dvorak et al. [28] shows that the body fat % in young women with MUHNW is higher than in normal women, which also holds true in men and women below 60 years of age in our study. However, in the Dvorak et al. and Conus et al. [5] studies, the BMI and WC in women with abnormal metabolism were not significantly different from normal women. This inconsistency might have resulted from the ethnic differences, since Asians are verified to have more visceral fat than Europeans at a given WC or BMI [14,29]. Visceral fat (VAT) accumulation is a plausible mechanism for the metabolically unhealthy phenotype in our study. VAT not only acts as a fat-deposit site, but also as a highly secretory organ with a differential production of adipokines capable of regulating energy expenditure, lipid metabolism, insulin sensitivity, and inflammation [30,31]. A wealth of clinical studies have demonstrated that free fat acid (FFA), interleukin (IL)-6, C-reactive protein (CRP), and tumor necrosis factor (TNF)-α circulate at higher concentrations in individuals with greater VAT, indicating a pro-inflammatory feature [32,33,34,35]. In addition, increased macrophage infiltration has been found in both the subcutaneous and visceral adiposity tissue of individuals with abnormal metabolism [36,37], creating a low-grade chronic inflammation which is a common etiology of obesity-related complications.
Skeletal muscle is the most abundant tissue in non-obese adults, accounting for approximately 40% of the body weight and playing a critical role in energy expenditure and glucose homeostasis [38,39]. Although the impact of skeletal muscle mass on metabolism status has been less evaluated in normal-weight adults, previous reports can provide some clues. Recently, in the Korean sarcopenic obesity study, researchers used thigh muscle cross-sectional area corrected by weight as an index of muscle mass, and found that it decreased in MUHNW [16]. Furthermore, a clinical study demonstrated that in normal-weight young adults, malfunction of skeletal muscle diverted ingested glucose to the liver, leading to increased hepatic de novo lipogenesis and hyperlipidemia [40]. Since skeletal muscle accounts for the majority of glucose disposal, we presumed that the decreased skeletal muscle % accompanied by the increased fat accumulation observed in our study may play a critical role in the pathological process of the abnormal metabolic phenotype.
Our study supports previous observations that WC serves as a better indicator of metabolic risk in the elderly than BMI. Aging is associated with substantial changes in body composition, with a gradual loss of lean mass and a shift to central fat accumulation [15,41]. In this case, invisible obesity has already occurred with a perfectly normal BMI undermining metabolic health, which can partly explain a significantly higher proportion of the abnormal metabolic phenotype in the aged individuals in our study. On the contrary, WC reflects central obesity and has been validated in many epidemiological studies to be associated with increased FFA and adipokines, higher activity of inflammation, increased oxidative stress, blunt insulin sensitivity, and increased risk of developing insulin resistance and diabetes [42,43]. The shift of lean mass to fat accumulation in the elderly combined with the innate susceptibility to visceral fat deposition may account for the observed better performance of WC in both genders, even when the BMI can no longer predict unhealthy metabolism in the elderly females of our study. However, unlike elderly females, factors associated with MUHNW remained unchanged in elderly males; and although the difference in body composition is obvious, the underling mechanism of this gender disparity needs to be further investigated in a larger sample.
The present study has several limitations. First, the cross-sectional design limited our ability to infer causality from the associations observed. Second, no standard criteria for the definition of abnormal metabolism have been established. We adopted a strict definition in which satisfying any component of ATP III was considered as metabolically unhealthy. Our results might vary with different criteria. Third, the sample size of the elderly was relatively small, thus the disparity in the elderly needs to be further verified with a larger sample size and multi-ethnicities. Despite these limitations, there are several strengths of this study. We adopted a stratified multistage, randomized sampling, ensuring the representativeness of our population, thus enhancing the credibility of our results. Furthermore, the strength of association between body composition and the metabolically unhealthy normal-weight phenotype in different age groups has scarcely been investigated.

5. Conclusions

Our study shows that increased adiposity indices, and reduced skeletal muscle % and body water % are associated with the abnormal metabolic phenotype in normal-weight Chinese adults; however, this association between body composition and MUHNW may change with age. Moreover, waist circumference persists as a good indicator of abnormal metabolism in the elderly, especially in females.

Acknowledgments

This study was funded by the National Science and Technology Support Program of China, Grant #2013FY114100 to Guangliang Shan, and was supported by grants from the National Natural Science Foundation of China (No. 31672375) to Yan He and key projects in the National Science & Technology Pillar Program (No. SQ2015BA1300692). Data in this report was obtained by the Center for Disease Control of Shanxi province. SPSS software version 18.0 for Windows were purchased by Capital Medical University, China.

Author Contributions

Conceived and designed the study: Yan He and Guangliang Shan. Acquisition of data: Fen Dong, Haiying Gong, Guodong Xu, Ke Wang, and Li Pan. Guidance in data interpretation: Fen Liu, Ling Zhang, Yuxiang Yan, and Herbert Gaisano. Analyzed the data and wrote the paper: Lili Xia. Edited the paper: Yan He. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vazquez, G.; Duval, S.; Jacobs, D.R., Jr.; Silventoinen, K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol. Rev. 2007, 29, 115–128. [Google Scholar] [CrossRef] [PubMed]
  2. Aune, D.; Sen, A.; Norat, T.; Janszky, I.; Romundstad, P.; Tonstad, S.; Vatten, L.J. Body Mass Index, Abdominal Fatness, and Heart Failure Incidence and Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies. Circulation 2016, 133, 639–649. [Google Scholar] [CrossRef] [PubMed]
  3. Ruderman, N.B.; Schneider, S.H.; Berchtold, P. The "metabolically-obese," normal-weight individual. Am. J. Clin. Nutr. 1981, 34, 1617–1621. [Google Scholar] [CrossRef] [PubMed]
  4. Ruderman, N.; Chisholm, D.; Pi-Sunyer, X.; Schneider, S. The metabolically obese, normal-weight individual revisited. Diabetes 1998, 47, 699–713. [Google Scholar] [CrossRef] [PubMed]
  5. Conus, F.; Allison, D.B.; Rabasa-Lhoret, R.; St-Onge, M.; St-Pierre, D.H.; Tremblay-Lebeau, A.; Poehlman, E.T. Metabolic and behavioral characteristics of metabolically obese but normal-weight women. J. Clin. Endocrinol. Metab. 2004, 89, 5013–5020. [Google Scholar] [CrossRef] [PubMed]
  6. Choi, K.M.; Cho, H.J.; Choi, H.Y.; Yang, S.J.; Yoo, H.J.; Seo, J.A.; Kim, S.G.; Baik, S.H.; Choi, D.S.; Kim, N.H. Higher mortality in metabolically obese normal-weight people than in metabolically healthy obese subjects in elderly Koreans. Clin. Endocrinol. 2013, 79, 364–370. [Google Scholar] [CrossRef] [PubMed]
  7. Yoo, H.J.; Hwang, S.Y.; Hong, H.C.; Choi, H.Y.; Seo, J.A.; Kim, S.G.; Kim, N.H.; Choi, D.S.; Baik, S.H.; Choi, K.M. Association of metabolically abnormal but normal weight (MANW) and metabolically healthy but obese (MHO) individuals with arterial stiffness and carotid atherosclerosis. Atherosclerosis 2014, 234, 218–223. [Google Scholar] [CrossRef] [PubMed]
  8. Rhee, E.J.; Lee, M.K.; Kim, J.D.; Jeon, W.S.; Bae, J.C.; Park, S.E.; Park, C.Y.; Oh, K.W.; Park, S.W.; Lee, W.Y. Metabolic health is a more important determinant for diabetes development than simple obesity: A 4-year retrospective longitudinal study. PLoS ONE 2014, 9, e98369. [Google Scholar] [CrossRef] [PubMed]
  9. Laing, S.T.; Smulevitz, B.; Vatcheva, K.P.; Rahbar, M.H.; Reininger, B.; McPherson, D.D.; McCormick, J.B.; Fisher-Hoch, S.P. Subclinical atherosclerosis and obesity phenotypes among Mexican Americans. J. Am. Heart Assoc. 2015, 4, e001540. [Google Scholar] [CrossRef] [PubMed]
  10. Goday, A.; Calvo, E.; Vazquez, L.A.; Caveda, E.; Margallo, T.; Catalina-Romero, C.; Reviriego, J. Prevalence and clinical characteristics of metabolically healthy obese individuals and other obese/non-obese metabolic phenotypes in a working population: Results from the Icaria study. BMC Public Health 2016, 16, 248. [Google Scholar] [CrossRef] [PubMed]
  11. Wildman, R.P.; Muntner, P.; Reynolds, K.; McGinn, A.P.; Rajpathak, S.; Wylie-Rosett, J.; Sowers, M.R. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: Prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch. Intern. Med. 2008, 168, 1617–1624. [Google Scholar] [CrossRef] [PubMed]
  12. Jung, C.H.; Lee, M.J.; Kang, Y.M.; Jang, J.E.; Leem, J.; Hwang, J.Y.; Kim, E.H.; Park, J.Y.; Kim, H.K.; Lee, W.J. The risk of incident type 2 diabetes in a Korean metabolically healthy obese population: The role of systemic inflammation. J. Clin. Endocrinol. Metab. 2015, 100, 934–941. [Google Scholar] [CrossRef] [PubMed]
  13. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004, 363, 157–163. [Google Scholar]
  14. Nazare, J.A.; Smith, J.D.; Borel, A.L.; Haffner, S.M.; Balkau, B.; Ross, R.; Massien, C.; Almeras, N.; Despres, J.P. Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: The International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship With Cardiometabolic Risk/Intra-Abdominal Adiposity. Am. J. Clin. Nutr. 2012, 96, 714–726. [Google Scholar] [PubMed]
  15. Newman, A.B.; Lee, J.S.; Visser, M.; Goodpaster, B.H.; Kritchevsky, S.B.; Tylavsky, F.A.; Nevitt, M.; Harris, T.B. Weight change and the conservation of lean mass in old age: The Health, Aging and Body Composition Study. Am. J. Clin. Nutr. 2005, 82, 872–878. [Google Scholar] [PubMed]
  16. Kim, T.N.; Park, M.S.; Yang, S.J.; Yoo, H.J.; Kang, H.J.; Song, W.; Seo, J.A.; Kim, S.G.; Kim, N.H.; Baik, S.H.; et al. Body size phenotypes and low muscle mass: The Korean sarcopenic obesity study (KSOS). J. Clin. Endocrinol. Metab. 2013, 98, 811–817. [Google Scholar] [CrossRef] [PubMed]
  17. Zhou, B.F.; Cooperative Meta-Analysis Group of the Working Group on Obesity in China. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults—Study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed. Environ. Sci. BES 2002, 15, 83–96. [Google Scholar] [PubMed]
  18. Gray, B.; Muhlhausler, B.S.; Davies, P.S.; Vitetta, L. Liver enzymes but not free fatty acid levels predict markers of insulin sensitivity in overweight and obese, nondiabetic adults. Nutr. Res. 2013, 33, 781–788. [Google Scholar] [CrossRef] [PubMed]
  19. Lima, W.G.; Martins-Santos, M.E.; Chaves, V.E. Uric acid as a modulator of glucose and lipid metabolism. Biochimie 2015, 116, 17–23. [Google Scholar] [CrossRef] [PubMed]
  20. Liu, X.; Hamnvik, O.P.; Chamberland, J.P.; Petrou, M.; Gong, H.; Christophi, C.A.; Christiani, D.C.; Kales, S.N.; Mantzoros, C.S. Circulating alanine transaminase (ALT) and gamma-glutamyl transferase (GGT), but not fetuin-A, are associated with metabolic risk factors, at baseline and at two-year follow-up: The prospective Cyprus Metabolism Study. Metab. Clin. Exp. 2014, 63, 773–782. [Google Scholar] [CrossRef] [PubMed]
  21. Klipstein-Grobusch, K.; Georg, T.; Boeing, H. Interviewer variability in anthropometric measurements and estimates of body composition. Int. J. Epidemiol. 1997, 26, S174–S180. [Google Scholar] [CrossRef] [PubMed]
  22. Janssen, I.; Heymsfield, S.B.; Baumgartner, R.N.; Ross, R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J. Appl. Physiol. 2000, 89, 465–471. [Google Scholar] [PubMed]
  23. Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults. Executive Summary of The 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). JAMA 2001, 285, 2486–2497. [Google Scholar]
  24. Luo, D.; Liu, F.; Li, X.; Yin, D.; Lin, Z.; Liu, H.; Hou, X.; Wang, C.; Jia, W. Comparison of the effect of 'metabolically healthy but obese' and 'metabolically abnormal but not obese' phenotypes on development of diabetes and cardiovascular disease in Chinese. Endocrine 2015, 49, 130–138. [Google Scholar] [CrossRef] [PubMed]
  25. Du, T.; Yu, X.; Zhang, J.; Sun, X. Lipid accumulation product and visceral adiposity index are effective markers for identifying the metabolically obese normal-weight phenotype. Acta Diabetol. 2015, 52, 855–863. [Google Scholar] [CrossRef] [PubMed]
  26. Lee, S.H.; Ha, H.S.; Park, Y.J.; Lee, J.H.; Yim, H.W.; Yoon, K.H.; Kang, M.I.; Lee, W.C.; Son, H.Y.; Park, Y.M.; et al. Identifying metabolically obese but normal-weight (MONW) individuals in a nondiabetic Korean population: The Chungju Metabolic disease Cohort (CMC) study. Clin. Endocrinol. 2011, 75, 475–481. [Google Scholar] [CrossRef] [PubMed]
  27. Choi, J.Y.; Ha, H.S.; Kwon, H.S.; Lee, S.H.; Cho, H.H.; Yim, H.W.; Lee, W.C.; Park, Y.M. Characteristics of metabolically obese, normal-weight women differ by menopause status: The Fourth Korea National Health and Nutrition Examination Survey. Menopause 2013, 20, 85–93. [Google Scholar] [CrossRef] [PubMed]
  28. Dvorak, R.V.; DeNino, W.F.; Ades, P.A.; Poehlman, E.T. Phenotypic characteristics associated with insulin resistance in metabolically obese but normal-weight young women. Diabetes 1999, 48, 2210–2214. [Google Scholar] [CrossRef] [PubMed]
  29. Lear, S.A.; Humphries, K.H.; Kohli, S.; Birmingham, C.L. The use of BMI and waist circumference as surrogates of body fat differs by ethnicity. Obesity 2007, 15, 2817–2824. [Google Scholar] [CrossRef] [PubMed]
  30. Kershaw, E.E.; Flier, J.S. Adipose tissue as an endocrine organ. J. Clin. Endocrinol. Metab. 2004, 89, 2548–2556. [Google Scholar] [CrossRef] [PubMed]
  31. Nakamura, K.; Fuster, J.J.; Walsh, K. Adipokines: A link between obesity and cardiovascular disease. J. Cardiol. 2014, 63, 250–259. [Google Scholar] [CrossRef] [PubMed]
  32. Rexrode, K.M.; Pradhan, A.; Manson, J.E.; Buring, J.E.; Ridker, P.M. Relationship of total and abdominal adiposity with CRP and IL-6 in women. Ann. Epidemiol. 2003, 13, 674–682. [Google Scholar] [CrossRef]
  33. Park, H.S.; Park, J.Y.; Yu, R. Relationship of obesity and visceral adiposity with serum concentrations of CRP, TNF-αand IL-6. Diabetes Res. Clin. Pract. 2005, 69, 29–35. [Google Scholar] [CrossRef] [PubMed]
  34. Fontana, L.; Eagon, J.C.; Trujillo, M.E.; Scherer, P.E.; Klein, S. Visceral fat adipokine secretion is associated with systemic inflammation in obese humans. Diabetes 2007, 56, 1010–1013. [Google Scholar] [CrossRef] [PubMed]
  35. Fain, J.N.; Madan, A.K.; Hiler, M.L.; Cheema, P.; Bahouth, S.W. Comparison of the release of adipokines by adipose tissue, adipose tissue matrix, and adipocytes from visceral and subcutaneous abdominal adipose tissues of obese humans. Endocrinology 2004, 145, 2273–2282. [Google Scholar] [CrossRef] [PubMed]
  36. Bluher, M. The distinction of metabolically 'healthy' from ‘unhealthy’ obese individuals. Curr. Opin. Lipidol. 2010, 21, 38–43. [Google Scholar] [CrossRef] [PubMed]
  37. Moreno-Indias, I.; Oliva-Olivera, W.; Omiste, A.; Castellano-Castillo, D.; Lhamyani, S.; Camargo, A.; Tinahones, F.J. Adipose tissue infiltration in normal-weight subjects and its impact on metabolic function. Transl. Res. J. Lab. Clin. Med. 2016, 172, 6–17. [Google Scholar] [CrossRef] [PubMed]
  38. Turner, N.; Cooney, G.J.; Kraegen, E.W.; Bruce, C.R. Fatty acid metabolism, energy expenditure and insulin resistance in muscle. J. Endocrinol. 2014, 220, T61–T79. [Google Scholar] [CrossRef] [PubMed]
  39. Friedrichsen, M.; Mortensen, B.; Pehmoller, C.; Birk, J.B.; Wojtaszewski, J.F. Exercise-induced AMPK activity in skeletal muscle: Role in glucose uptake and insulin sensitivity. Mol. Cell. Endocrinol. 2013, 366, 204–214. [Google Scholar] [CrossRef] [PubMed]
  40. Petersen, K.F.; Dufour, S.; Savage, D.B.; Bilz, S.; Solomon, G.; Yonemitsu, S.; Cline, G.W.; Befroy, D.; Zemany, L.; Kahn, B.B.; et al. The role of skeletal muscle insulin resistance in the pathogenesis of the metabolic syndrome. Proc. Natl. Acad. Sci. USA 2007, 104, 12587–12594. [Google Scholar] [CrossRef] [PubMed]
  41. Vlassopoulos, A.; Combet, E.; Lean, M.E. Changing distributions of body size and adiposity with age. Int. J. Obes. 2014, 38, 857–864. [Google Scholar] [CrossRef] [PubMed]
  42. Rader, D.J. Effect of insulin resistance, dyslipidemia, and intra-abdominal adiposity on the development of cardiovascular disease and diabetes mellitus. Am. J. Med. 2007, 120, S12–S18. [Google Scholar] [CrossRef] [PubMed]
  43. Matsuda, M.; Shimomura, I. Increased oxidative stress in obesity: Implications for metabolic syndrome, diabetes, hypertension, dyslipidemia, atherosclerosis, and cancer. Obes. Res. Clin. Pract. 2013, 7, e330–e341. [Google Scholar] [CrossRef] [PubMed]
Table 1. General characteristics of the subjects.
Table 1. General characteristics of the subjects.
VariablesTotal (n = 3015)Male (n = 1158)Female (n = 1857)p Value
Age (years)45.5 ± 14.147.3 ± 15.044.3 ± 13.4<0.001
Education, n (%) 0.324
  ≤Middle school1448 (48.0)571 (49.3)877 (47.2)
  High school711 (23.6)276 (23.8)435 (23.4)
  ≥College856 (28.4)311 (26.9)545 (29.3)
Current smokers, n (%)667 (22.1)653 (56.4)14 (0.8)<0.001
Current drinkers, n (%)622 (20.6)531 (45.9)91 (4.9)<0.001
Waist (cm)78.8 ± 6.881.8 ± 6.776.9 ± 6.1<0.001
BMI (kg/m2)21.6 ± 1.521.7 ± 1.521.5 ± 1.5<0.001
Body fat (%)24.8 ± 6.617.2 ± 3.828.6 ± 3.4<0.001
Skeletal muscle (%)41.9 ± 5.947.2 ± 4.738.7 ± 3.9<0.001
Body water (%)53.1 ± 4.157.2 ± 2.950.6 ± 2.2<0.001
Handgrip strength (kg)28.0 ± 9.237.0 ± 7.122.3 ± 4.9<0.001
SBP (mmHg)116.6 ± 16.2121.1 ± 14.8113.8 ± 16.4<0.001
DBP (mmHg)72.7 ± 10.075.0 ± 9.971.2 ± 9.8<0.001
FPG (mmol/L)5.1 ± 1.05.2 ± 1.15.0 ± 1.00.002
TC (mmol/L)4.3 ± 0.94.2 ± 0.84.3 ± 1.00.001
TG (mmol/L)1.4 ± 0.91.5 ± 1.01.4 ± 0.9<0.001
HDL-C (mmol/L)1.3 ± 0.31.2 ± 0.31.4 ± 0.3<0.001
LDL-C (mmol/L)2.5 ± 0.82.5 ± 0.72.5 ± 0.80.660
UA (μmol/L)276.5 ± 72.0320.6 ± 67.5249.0 ± 60.1<0.001
ALT (U/L)18.8 ± 11.022.3 ± 13.216.5 ± 8.6<0.001
AST (U/L)21.9 ± 7.423.3 ± 8.421.1 ± 6.5<0.001
Data are expressed as mean ± standard distribution (SD) or number (percentage). BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, FPG: fasting plasma glucose, TC: total cholesterol, TG: triglyceride, HDL-C: high density lipoprotein cholesterol, LDL-C: low density lipoprotein cholesterol, UA: uric acid, ALT: alanine transaminase, AST: aspartate transaminase. p values were from independent two-sample t-tests or chi-square tests.
Table 2. Clinical and demographic characteristics of the subjects by metabolic phenotype.
Table 2. Clinical and demographic characteristics of the subjects by metabolic phenotype.
VariablesTotal (n = 3015)MHNW (n = 1476)MUHNW (n = 1539)p Value
Age (years)45.5 ± 14.142.7 ± 13.648.1 ± 14.1<0.001
Gender, n (%) 0.313
  Male1158 (38.4)581 (39.4)577 (37.5)
  Female1857 (61.6)895 (60.6)962 (62.5)
Education, n (%) <0.001
  ≤ Middle school1448 (48.0)647 (43.8)801 (52.0)
  High school711 (23.6)338 (22.9)373 (24.2)
  ≥ College856 (28.4)491 (33.3)365 (23.7)
Current smokers, n (%)667 (22.1)324 (22.0)343 (22.3)0.861
Current drinkers, n (%)622 (20.6)320 (21.7)302 (19.6)0.163
Waist (cm)78.8 ± 6.877.1 ± 6.580.5 ± 6.6<0.001
BMI (kg/m2)21.6 ± 1.521.3 ± 1.521.9 ± 1.4<0.001
Body fat (%)24.8 ± 6.623.2 ± 6.725.2 ± 6.3<0.001
Skeletal muscle (%)41.9 ± 5.943.0 ± 6.040.9 ± 5.6<0.001
Body water (%)53.1 ± 4.153.6 ± 4.252.7 ± 4.0<0.001
Handgrip strength (kg)28.0 ± 9.228.5 ± 9.127.4 ± 9.30.001
SBP (mmHg)116.6 ± 16.2112.9 ± 13.5120.2 ± 17.7<0.001
DBP (mmHg)72.7 ± 10.070.8 ± 9.074.5 ± 10.6<0.001
FPG (mmol/L)5.1 ± 1.04.8 ± 0.45.3 ± 1.3<0.001
TC (mmol/L)4.3 ± 0.94.2 ± 0.84.4 ± 1.0<0.001
TG (mmol/L)1.4 ± 0.91.0 ± 0.31.8 ± 1.2<0.001
HDL-C (mmol/L)1.3 ± 0.31.5 ± 0.31.2 ± 0.3<0.001
LDL-C (mmol/L)2.5 ± 0.82.4 ± 0.72.6 ± 0.8<0.001
UA (μmol/L)276.5 ± 72.0268.3 ± 67.3284.3 ± 75.4<0.001
ALT (U/L)18.8 ± 11.017.5 ± 9.620.0 ± 12.0<0.001
AST (U/L)21.9 ± 7.421.7 ± 7.222.2 ± 7.50.034
Note: Data are expressed as mean ± SD or number (percentage). MHNW: metabolically healthy normal weight, MUHNW: metabolically unhealthy normal-weight. p values were from independent two-sample t-tests or chi-square tests.
Table 3. Factors associated with an unhealthy metabolic phenotype in normal-weight males.
Table 3. Factors associated with an unhealthy metabolic phenotype in normal-weight males.
VariablesMHNW (n = 581)MUHNW (n = 577)p Value 1p Value 2OR (95% CI)
Age (years)44.6 ± 15.250.0 ± 14.3<0.001
Education, n (%) 0.620
  ≤ Middle school284 (48.9)287 (49.7)
  High school134 (23.1)142 (24.6)
  ≥ College163 (28.0)148 (25.7)
Current smokers, n (%)322 (55.4)331 (57.4)0.515
Current drinkers, n (%)263 (45.3)268 (46.4)0.723
Waist (cm)80.1 ± 6.583.6 ± 6.4<0.001<0.0011.09 (1.06–1.11)
BMI (kg/m2)21.3 ± 1.522.1 ± 1.3<0.001<0.0011.43 (1.31–1.55)
Body fat (%)16.2 ± 3.818.3 ± 3.4<0.001<0.0011.16 (1.12–1.21)
Skeletal muscle (%)48.2 ± 4.846.0 ± 4.2<0.001<0.0010.54 (0.46–0.64)
Body water (%)57.7 ± 3.056.7 ± 2.8<0.001<0.0010.84 (0.80–0.88)
Handgrip strength (kg)37.3 ± 7.036.6 ± 7.20.0990.2821.01 (0.99–1.03)
UA (μmol/L)312.5 ± 63.3328.7 ± 70.6<0.001<0.0011.01 (1.00–1.01)
ALT (U/L)21.0 ± 12.223.6 ± 14.00.001<0.0011.02 (1.01–1.03)
AST (U/L)23.4 ± 8.923.2 ± 7.90.7080.3280.99 (0.98–1.01)
Data are expressed as mean ± SD or number (percentage). p value 1 was calculated by an independent two-sample t-test or chi-square test. p Value 2 and odds ratio (OR) (95% confidence interval (CI)) were obtained from logistic regression analysis adjusted for age, education, smoking, and drinking status, and the variables were standardized before entering into the logistic regression model.
Table 4. Factors associated with an unhealthy metabolic phenotype in normal-weight females.
Table 4. Factors associated with an unhealthy metabolic phenotype in normal-weight females.
VariablesMHNW (n = 895)MUHNW (n = 962)p Value 1p Value 2OR (95% CI)
Age (years)41.5 ± 12.346.9 ± 13.9<0.001
Education, n (%) <0.001
  ≤ Middle school363 (40.6)514 (53.4)
  High school204 (22.8)231 (24.0)
  ≥ College328 (36.6)217 (22.6)
Current smokers, n (%)2 (0.2)12 (1.2)0.013
Current drinkers, n (%)57 (6.4)34 (3.5)0.005
Waist (cm)75.1 ± 5.878.6 ± 5.9<0.001<0.0011.09 (1.07–1.11)
BMI (kg/m2)21.2 ± 1.521.8 ± 1.4<0.001<0.0011.22 (1.14–1.31)
Body fat (%)27.7 ± 3.529.3 ± 3.1<0.001<0.0011.12 (1.09–1.16)
Skeletal muscle (%)39.6 ± 3.937.8 ± 3.7<0.001<0.0010.56 (0.48–0.66)
Body water (%)50.9 ± 2.250.2 ± 2.2<0.001<0.0010.86 (0.82–0.89)
Handgrip strength (kg)22.3 ± 4.721.9 ± 5.0<0.0010.1700.99 (0.97–1.01)
UA (μmol/L)239.7 ± 52.9257.6 ± 64.9<0.001<0.0011.01 (1.00–1.01)
ALT (U/L)15.2 ± 6.617.8 ± 10.0<0.001<0.0011.03 (1.02–1.05)
AST (U/L)20.5 ± 5.621.6 ± 7.2<0.0010.5421.01 (0.99–1.02)
Data are expressed as mean ± SD or number (percentage). p Value 1 was calculated by an independent two-sample t-test or chi-square test. p Value 2 and OR (95% CI) were obtained from logistic regression analysis adjusted for age, education, smoking, and drinking status, and variables were standardized before entering into the logistic regression model.
Table 5. Characteristics of body measurements in subjects by different metabolic phenotypes and age groups.
Table 5. Characteristics of body measurements in subjects by different metabolic phenotypes and age groups.
Body Measurements≤44 Years Oldp Value45–59 Years Oldp Value≥ 60 Years Oldp Valuep Value 1p Value 2
MHNW (282M/500F)MUHNW (193M/386F)MHNW (182M/321F)MUHNW (225M/406F)MHNW (117M/74F)MUHNW (159M/170F)
Waist
Males79.2 ± 5.782.0 ± 6.1<0.00181.7 ± 6.1 a84.2 ± 7.0 e0.00180.3 ± 8.284.7 ± 5.6 d<0.0010.0030.002
Females73.7 ± 5.576.3 ± 5.4<0.00176.4 ± 5.2 a79.7 ± 5.7 d<0.00179.3 ± 6.5 a,b81.3 ± 5.9 d,g0.030<0.001<0.001
BMI
Males21.4 ± 1.522.0 ± 1.5<0.00121.8 ± 1.522.3 ± 1.2<0.00121.3 ± 1.5 c22.2 ± 1.2<0.0010.0990.222
Females20.8 ± 1.421.4 ± 1.5<0.00121.4 ± 1.4 a21.9 ± 1.4 d<0.00121.9 ± 1.4 a22.0 ± 1.4 d0.176<0.001<0.001
Body fat %
Males16.2 ± 3.818.0 ± 3.6<0.00117.2 ± 3.818.8 ± 3.3<0.00116.7 ± 3.418.4 ± 3.4<0.0010.2200.070
Females26.8 ± 3.628.4 ± 3.1<0.00128.4 ± 3.0 a29.6 ± 2.9 d<0.00129.6 ± 3.0 a,c30.3 ± 3.0 d,g0.083<0.001<0.001
Skeletal muscle %
Males48.3 ± 5.246.3 ± 5.2<0.00147.9 ± 4.545.5 ± 3.9 e<0.00148.2 ± 4.446.4 ± 4.4 g0.0010.6440.046
Females40.7 ± 3.839.1 ± 3.5<0.00138.5 ± 3.6 a37.3 ± 3.5 d<0.00137.0 ± 3.0 a,b36.0 ± 3.6 d,f0.049<0.001<0.001
Body water %
Males56.7 ± 3.155.6 ± 2.5<0.00157.5 ± 2.8 a56.5 ± 2.5 d<0.00158.7 ± 2.3 a,b57.8 ± 2.8 d,f0.001<0.001<0.001
Females50.9 ± 2.449.9 ± 2.2<0.00150.9 ± 2.150.2 ± 2.1 e<0.00151.2 ± 1.950.8 ± 2.2 d,f0.1980.377<0.001
Grip strength
Males40.6 ± 5.840.8 ± 5.70.41036.9 ± 6.2 a37.3 ± 6.2 d0.70832.8 ± 7.1 a,b33.1 ± 6.9 d,f0.928<0.001<0.001
Females23.8 ± 4.523.7 ± 4.60.64422.1 ± 4.8 a22.0 ± 4.4 d0.77720.5 ± 4.4 a,b18.1 ± 3.8 d,f0.002<0.001<0.001
Data are expressed as mean ± SD. p values were calculated by an independent two-sample t-test. p value 1 and p value 2 were calculated by analysis of variance (ANOVA) within MHNW and MUHNW groups across the age groups, respectively. Comparisons between two different age groups within metabolic subgroups were analyzed by the least significant difference (LSD) post hoc test; a p < 0.001 vs. MHNW in young adults; b p < 0.001 vs. MHNW in the middle aged adults; c p < 0.05 vs. MHNW in the middle aged adults; d p < 0.001 vs. MUHNW in young adults; e p < 0.05 vs. MUHNW in young adults; f p < 0.001 vs. MUHNW in the middle aged adults; g p < 0.05 vs. MUHNW in the middle aged adults.
Table 6. Association between body measurements and abnormal metabolic phenotype in normal-weight adults by age groups.
Table 6. Association between body measurements and abnormal metabolic phenotype in normal-weight adults by age groups.
Body Measurements≤44 Years Old45–59 Years Old≥60 Years Old
OR (95% CI)p ValueOR (95% CI)p ValueOR (95% CI)p Value
Waist
Males1.68 (1.29–2.17)<0.0011.50 (1.13–2.00)0.0062.28 (1.56–3.31)<0.001
Females1.67 (1.40–2.00)<0.0011.83 (1.48–2.28)<0.0011.45 (1.03–2.04)0.032
BMI
Males1.55 (1.28–1.87)<0.0011.61 (1.28–2.01)<0.0012.34 (1.74–3.15)<0.001
Females1.45 (1.26–1.67)<0.0011.36 (1.15–1.60)<0.0011.28 (0.95–1.73)0.108
Body fat %
Males1.65 (1.34–2.03)<0.0011.87 (1.46–2.41)<0.0011.98 (1.48–2.65)<0.001
Females1.57 (1.35–1.82)<0.0011.47 (1.23–1.75)<0.0011.35 (0.99–1.85)0.062
Skeletal muscle %
Males0.63 (0.48–0.82)0.0010.45 (0.33–0.61)<0.0010.54 (0.38–0.76)0.001
Females0.51 (0.41–0.65)<0.0010.61 (0.47–0.79)<0.0010.65 (0.40–1.07)0.089
Body water %
Males0.64 (0.51–0.79)<0.0010.54 (0.42–0.70)<0.0010.61 (0.46–0.81)0.001
Females0.68 (0.59–0.78)<0.0010.72 (0.61–0.84)<0.0010.75 (0.56–1.01)0.060
Grip strength
Males1.07 (0.86–1.35)0.5391.08 (0.86–1.37)0.5461.08 (0.82–1.41)0.591
Females0.95 (0.83–1.10)0.4851.08 (0.92–1.28)0.3620.68 (0.47–0.97)0.035
p Value and OR (95% CI) were obtained from logistic regression analysis adjusted for age, education, smoking, and drinking status. Variables were standardized before entering into logistic regression mode.

Share and Cite

MDPI and ACS Style

Xia, L.; Dong, F.; Gong, H.; Xu, G.; Wang, K.; Liu, F.; Pan, L.; Zhang, L.; Yan, Y.; Gaisano, H.; et al. Association between Indices of Body Composition and Abnormal Metabolic Phenotype in Normal-Weight Chinese Adults. Int. J. Environ. Res. Public Health 2017, 14, 391. https://doi.org/10.3390/ijerph14040391

AMA Style

Xia L, Dong F, Gong H, Xu G, Wang K, Liu F, Pan L, Zhang L, Yan Y, Gaisano H, et al. Association between Indices of Body Composition and Abnormal Metabolic Phenotype in Normal-Weight Chinese Adults. International Journal of Environmental Research and Public Health. 2017; 14(4):391. https://doi.org/10.3390/ijerph14040391

Chicago/Turabian Style

Xia, Lili, Fen Dong, Haiying Gong, Guodong Xu, Ke Wang, Fen Liu, Li Pan, Ling Zhang, Yuxiang Yan, Herbert Gaisano, and et al. 2017. "Association between Indices of Body Composition and Abnormal Metabolic Phenotype in Normal-Weight Chinese Adults" International Journal of Environmental Research and Public Health 14, no. 4: 391. https://doi.org/10.3390/ijerph14040391

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop