Fat-to-muscle ratio is a useful index for cardiometabolic risks: A population-based observational study
Fat-to-muscle ratio is a useful index for cardiometabolic risks: A population-based observational study
Yuan-Yuei Chen 0 2
Wen-Hui Fang 2
Chung-Ching Wang 2
Tung-Wei Kao 2
Hui- Fang Yang 2
Chen-Jung Wu 1 2
Yu-Shan Sun 2
Ying-Chuan Wang 2
Liang ChenID 2
0 Department of Internal Medicine, Tri-Service General Hospital Songshan Branch , Taipei, Taiwan , Republic of China, 2 Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center , Taipei, Taiwan , Republic of China, 3 Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center , Taipei, Taiwan , Republic of China, 4 Graduate Institute of Clinical Medical, College of Medicine, National Taiwan University , Taipei, Taiwan , Republic of China
1 Division of Family Medicine, Department of Community Medicine, Taoyuan Armed Forces General Hospital , Taoyuan, Taiwan , Republic of China
2 Editor: Ying-Mei Feng, Beijing Key Laboratory of Diabetes Prevention and Research , CHINA
Metabolic disorders are prevalent worldwide and have recently become public health problems recently. Previous studies have proposed different body composition indices for predicting future cardiovascular risks. We hypothesized an association among fat-to-muscle ratio (FMR), metabolic syndrome (MetS), hypertension (HTN), prediabetes, type 2 diabetes mellitus (DM), and cardiovascular risk in an adult population. A total of 66829 eligible subjects composed of 34182 males and 32647 females aged 20 years or older were obtained from health examinations in the Tri-Service General Hospital from 2011 to 2017. The body composition indices included fat and muscle mass measured by bioelectrical impedance analysis. A multivariable regression model was performed in a large population-based cross-sectional study. FMR was significantly associated with MetS, prediabetes, DM and HTN in all models of both genders. Based on quartile analysis, higher FMR had higher predictive ability for adverse health outcomes. The association between different definitions of MetS and the Framingham risk score was analyzed, and FMR-incorporated MetS was more useful for predicting higher Framingham risk scores than traditional definitions. FMR was a useful indicator for the presence of adverse cardiometabolic risks. Compared to traditional definition of MetS, FMR-incorporated MetS had a greater ability to predict incident cardiovascular risks. FMR seemed to be a simple and effective index for the early prevention and management of cardiometabolic events.
Data Availability Statement: The data set is
owned by the Institutional Review Board (IRB) of
Tri-Service General Hospital (TSGH). TSGH IRB
only approved the data analysis in our study and
did not approve data sharing. Therefore, we do not
have permission to share the data set. Interested
researchers can submit data access requests to the
Tri-Service General Hospital IRB using the
following email address: .
tw. Others would be able to access these data in
the same manner as the authors and the authors
also did not have any special access privileges.
The current worldwide prevalence of obesity has increased progressively. As a major public
health problem in the world, an increasing number of individuals have been diagnosed with
Funding: The authors received no specific funding
for this work.
obesity and metabolic syndrome (MetS) in Taiwan with high risks for the development of
diabetes mellitus (DM) and hypertension (HTN)[
]. An emerging concept called ?sarcopenic
obesity?, which reflect a combination of age-associated skeletal muscle loss and fat mass
], was also recognized as a critical public health risk in the aging society. Previous
studies have proposed an association between sarcopenic obesity and MetS in both sexes[
and between sarcopenic obesity and insulin resistance in the adult population[
Increased total fat mass and its distribution were significantly associated with insulin
resistance, glucose intolerance and high risks of DM and cardiovascular diseases[
], wthile loss of
skeletal muscle was reported to contribute to MetS and DM in the adult population[
However, the associations among simultaneous skeletal muscle mass loss, fat mass
accumulation and metabolic disorders have not been well established. The ratio of visceral fat to thigh
muscle area was considered as a single anthropometric index for insulin resistance and glucose
metabolism. Park et al. suggested muscle-to-fat ratio as a useful indicator for predicting
Although different types of body composition indices have valid predictions for metabolic
dysfunction, there is no comprehensive index that can be used simultaneously for the risk of
cardiometabolic disorders. The objective of this cross-cohort analysis was to critically examine
whether fat-to-muscle ratio (FMR) was associated with the presence of MetS, prediabetes, DM
and HTN and to develop sound definitions of MetS.
Study design and participants
All data were derived from health examinations in the Tri-Service General Hospital from 2010
to 2016. The study design met the requirements of the Helsinki Declaration and the design
was approved by the institutional review board of Tri-Service General Hospital. Because the
data were analyzed anonymously, the institutional review board of Tri-Service General
Hospital waived the need to acquire individual informed consent. Based on the flow chart of the
study shown in Fig 1, subjects who attended the health check-up and finished comprehensive
examinations, including laboratory biochemistry tests, body composition exams and
questionnaires of the personal history were included in this study. 66829 eligible subjects were analyzed
in a step-by-step manner in the following orders. First, the ORs of FMR in males and females
for the presence of MetS, prediabetes, DM and HTN were conducted by multivariate logistic
regression. Next, FMR was divided into quartiles to analyze its association with the presence of
adverse health outcomes. Third, multivariable linear regression was used to assess the
association between FMR and individual MetS components. Last, we calculated the optimal cut-off
values of FMR for MetS in both genders and then created different definitions of MetS to
compare the effect of inflammatory process with the traditional MetS criteria. In addition, we
analyzed the association between different definitions of MetS and the Framingham risk score by
using multivariable linear regression.
Measurement of body composition
Percentage of skeletal muscle mass and percentage of body fat were measured by bioelectric
impedance analysis (BIA) (InBody720, Biospace, Inc., Cerritos, CA, USA) in the present study.
BIA has been proven to be one of the most practical procedures to estimate body composition
among different groups because of its ready accessibility, quick assessment, low cost, and its
high validity against DEXA as the reference method[
]. FMR was defined as the ratio of fat
mass to lean muscle mass.
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Fig 1. Flow chart which represented the steps of analysis performed in the study.
General definition of MetS
According to the Taiwan Health Promotion Administration of the Ministry of Health and
Welfare in 2007, the diagnosis of MetS was defined if an individual manifested 3 or more of
the following components: (1) waist circumference>90 cm for male participants and >80 cm
for female participants.; (2) systolic blood pressure 130 mmHg, diastolic blood pressure 80
mmHg, or self-reported hypertension (3) triglyceride 150 mg/dL (1.7 mmol/L); (4) fasting
plasma glucose 100 mg/dL, a past history of diabetes status, or the use of antidiabetic agents;
and (5) HDL-C<40 mg/dL (1.03 mmol/L) for male participants and <50 mg/dL (1.3 mmol/L)
for female participants.
Different definitions of MetS
In our study, we created two different definitions of MetS to compare the effects of the
inflammatory process with the traditional MetS. To assess the cut-off values of FMR for MetS, a
receiver operating characteristic (ROC) curve analysis was performed. In males, the AUROC
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value was 0.673 (95%CI: 0.660?0.686), and the optimal cut-off value was 0.76 using the
maximal Youden index, with a sensitivity of 0.763 and a specificity of 0.494. In females, the
AUROC value was 0.701 (95%CI: 0.685?0.717), and the optimal cut-off value was 1.51 with a
sensitivity of 0.792 and a specificity of 0.509. Subjects who had FMR above the cut-off values
(males: 0.76; females: 1.51) were categorized as ?MetFMR?.
First, ?FMRMetS? was defined as participants with ?MetFMR? along with at least two of
four components of MetS except waist circumference. Second, ?FMR incorporated MetS? was
defined as MetFMR along with at least three out of five components of MetS.
Definition of Type 2 DM
Type 2 DM was defined base on the American Diabetes Association criteria as follows: fasting
plasma glucose 126 mg/dL; hemoglobin A1c test 6.5%; random plasma glucose 200 mg/
dL; and past history of diabetes status, or use of antidiabetic agents[
Definition of HTN
Based on the guidelines of the Taiwan Society of Cardiology and the Taiwan Hypertension
Society for the management of hypertension, HTN was defined as blood pressure being higher
than 140/90 mmHg or subjects taking antihypertensive agents[
Measurement o covariates
The regular health examinations included standard evaluations of comprehensive
biochemistry tests and anthropometric measurements. The body mass index (BMI) was obtained based
on the formula in which the weight of the subject in kilograms is divided by the square of their
height in meters(kg/m2). The waist circumference was measured at the mid-level between the
iliac crest and the lower border of the 12th rib while the subject stood with feet 25?30 cm apart.
Hemodynamic status included systolic blood pressure (SBP) and diastolic blood pressure
(DBP) estimated when the participants were seated. Biochemical analysis was conducted by
drawing blood samples from subjects after fasting for at least 8 hours. The fasting plasma
glucose (FPG) was detected using a glucose oxidase method. Serum levels of lipid profiles such as
total cholesterol (TC), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C),
were measured using an enzymatic colorimetric method.
All statistical estimations were performed using the Statistical Package for the Social Sciences,
version18.0 (SPSS Inc., Chicago, IL, USA) for Windows. Student?s t-tests and Pearson?s
chisquare tests were performed to examine the differences between the gender groups in terms of
demographic information and laboratory data. A two-sided p-value of 0.05 was regarded as
the threshold for statistical significance. The extend-model approach was performed in the
study with multivariable adjustment for pertinent clinical variables. Linear regression with
beta coefficients was conducted for the association of FMR with MetS components,
inflammation and the Framingham risk score. Logistic regression for ORs was used to examine the
association between FMR and the presence of MetS, prediabetes, DM and HTN in a
crosssectional analysis. A receiver operating characteristic (ROC) curve analysis was calculated for
the area under the ROC (AUROC), 95% confidence intervals (CI), sensitivity and specificity to
assess the cut-off values of FMR.
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Characteristics of the study population
All data were obtained from the annual health examinations conducted in the Tri-Service
General Hospital (TSGH) from 2010 to 2016. There were 34182 eligible males and 32647 eligible
females enrolled in the study after excluding those with missing data. The mean age of male
subjects was 42.35?16.14 years old, and the mean age of female was 42.63?15.95 years old. The
prevalence of MetS, FMRMetS, and FMR-incorporated MetS were significantly higher in
males than females (P<0.05). All demographic characteristics listed in Table 1, such as body
composition index, components of MetS and laboratory biochemistry data, had significant
Association between FMR and the presence of MetS, Prediabetes, DM and
PBF, percentage body fat; FMR, fat-muscle ratio; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG,
triglyceride; HDL-C, high density lipoprotein cholesterol; FPG, fasting plasma glucose; TC, total cholesterol; UA, uric acid; Cr, creatinine; AST, aspartate
aminotransferase; hsCRP, high sensitivity C-reactive protein
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health outcomes in all adjusted models of male (ORs = 5.69, 4.63, 4.20; 95%CI = 4.05?7.99,
3.26?6.56, 2.91?6.05, respectively). In females, the ORs of MetS and DM were similar in all
models. The FMR tended to have more predictive ability for the presence of DM in fully
adjusted model (ORs = 1.92; 95%CI = 1.14?3.23).
Association between quartiles of FMR and the presence of MetS,
Prediabetes, DM and HTN
In Table 3, the FMR in each gender was divided into quartiles and the higher quartiles (Q2, Q3
and Q4) were compared to baseline (Q1) in subgroups to analyze the association between the
FMR and the presence of adverse health outcomes. The intervals of FMR in quartiles were
<0.66, 0.66?0.81, 0.81?0.96, and >0.96 in males and <1.30, 1.30?1.55, 1.55?1.80, and >1.80
in females from Q1 to Q4, respectively. Obviously, the higher quartile of FMR had more
predictive ability for the presence of MetS, prediabetes, DM and HTN in male and female
Association between different definitions of MetS and Framingham risk
We analyzed the association of MetS, FMRMetS and FMR-incorporated MetS with the
Framingham risk score listed in Table 4. All definitions of MetS had significant association with
increased Framingham risk score. FMR-incorporated MetS (? = 3.64, 95%CI = 3.25?4.03) was
more closely associated with the Framingham risk score than MetS (? = 3.59, 95%CI = 3.26?
3.92) in the fully adjusted model in males. However, in females, not only FMR-incorporated
MetS (fully adjusted model: ? = 2.10, 95%CI = 1.84?2.35) but also FMRMetS (fully adjusted
model: ? = 1.90, 95%CI = 1.66?2.15) were more closely associated with the Framingham risk
score than MetS (fully adjusted model: ? = 1.74, 95%CI = 1.53?1.96) in all models.
Association between FMR and individual components of MetS
Multivariable linear regressions of FMR and MetS components performed with the adjusted
extend-model approach are shown in S1 Table. As expected, the FMR was significantly
associated with higher blood pressure, central obesity, hypertriglyceridemia, hyperglycemia and
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PLOS ONE | https://doi.org/10.1371/journal.pone.0214994
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Association between different definitions of MetS with inflammation
Multivariable beta coefficients regression was performed for the association between different
definitions of MetS and levels of CRP, as shown in S2 Table. It was surprising that different
definitions of MetS including MetS, MetFMR, FMRMetS and FMR-incorporated MetS had
significant associations with increased levels of CRP in both sexes, except MetS in the fully
adjusted model in males.
In the cross-sectional study of data from the annual health examinations of a medical center in
Taiwan for the general population, a novel indicator, FMR, was suggested as an excellent body
composition index for predicting the presence of MetS, prediabetes, DM and HTN. FMR was
significantly associated with adverse health outcomes and a substantial dose dependent effect
was noted in both genders. Furthermore, FMR-incorporated MetS had better predictive ability
for the Framingham risk score than other definitions, particularly in females, indicating the
possibility that FMR might have the potential capacity for predicting the incident risks of
cardiovascular disease mortality.
In a Korean study composed of 264 adults, an increased visceral fat-to-thigh muscle ratio
was significantly associated with MetS with an OR of 6.72 (95%CI = 1.60?28.14)[
finding obtained from a Korean cohort study indicated that the ratio of skeletal muscle mass
to visceral fat was associated with MetS with an OR of 5.43 (95%CI = 2.56?13.34)[
et al. demonstrated that adverse body composition characterized by the ratio of whole body fat
to lean mass was independently associated with metabolic dysfunction in women with
polycystic ovary syndrome[
]. Compared to the above different body composition indices, our
findings suggested that FMR was a useful indicator for predicting the presence of MetS,
prediabetes, DM and HTN in the general population. To the best of our knowledge, the
present study was the first to propose that FMR was strongly associated with adverse health
outcomes in both males and females in a large-scale cross-sectional observational study.
Accumulated evidence has supported the relationship between fat mass and
cardiometabolic outcomes. The distribution of body fat is associated with MetS in elderly adults, especially
those with normal body weight[
]. In a longitudinal cohort study, those with more visceral
fat had higher risks for developing incident MetS during a five-year follow-up[
et al. demonstrated that a higher amount of visceral fat was more useful in predicting the
incident prediabetes and DM than other indices in a longitudinal study[
]. Visceral fat was
considered an important predictor of insulin resistance in the non-diabetic population[
]. In a
cohort study of 903 normotensive participants examining the development of HTN, visceral
adipose tissue was associated with incident hypertension (relative risk: 1.22; 95%CI: 1.06?1.39)
after multivariable adjustment[
]. Collectively, the above results were consistent with our
findings that increased fat mass was associated with the presence of MetS, prediabetes, DM
and HTN. Several studies have proposed the important role of fat tissue in cardiometabolic
risks through different pathways. Dysfunction in adipose tissue, such as excessive free fatty
acid metabolism changes, was caused by fat tissue accumulation[
]. Adipose alternation
might lead to the impairment of hepatic metabolism[
]. It could also contribute to
degradation of insulin, reduced degradation of apolipoprotein B, and increased hepatic glucose
production, leading to hyperinsulinemia, hypertriglyceridemia and eventually DM[
Another mechanism was the inflammation of adipose tissue caused by adipocyte hypertrophy,
adipose tissue stresses and apoptosis. Impaired insulin sensitivity and deteriorated glucose
and lipid metabolism were related to adipocyte hypertrophy, which was described as a
predominant and large volume of adipose tissue[
]. Increasing the secretion of chemoattractants
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and proinflammatory cytokines, such as MCP-1, TNF-?, IL-1, and IL-6, caused by adipocyte
hypertrophy contributed to immune cell infiltration[
]. Increased numbers of macrophages
caused by phenotypic switching were related to adaptive immune systems[
]. Changes in
Tcell phenotype and the recruitment of B cells and T cells preceded macrophage infiltration
]. A series of inflammatory changes in adipose tissue induced a chronic inflammation
strongly implicated in the mechanisms underpinning whole-body metabolic dysregulation.
A progressive loss of muscle mass and an increment of fat mass were prevalent in the aging
process. Excessive loss of appendicular lean mass was associated with Type 2 DM in
community-dwelling older adults, particularly undiagnosed cases[
]. An inverse association was
found between skeletal muscle mass with insulin resistance and the risk of prediabetes. In a
recent Taiwanese study composed of 394 middle-aged and elderly adults, lower muscle mass
was associated with the risk of metabolic syndrome, especially in the aging female population
]. Emerging studies have proposed an association between sarcopenia and metabolic
dysfunction. Chung et al. reported that the sarcopenic obese group showed close associations with
insulin resistance, MetS, and cardiovascular disease risk factors in the elderly population[
Subjects with sarcopenia obesity were considered to have a greater risk of hypertension than
]. The significant associations between sarcopenia, defined in terms of
muscle mass, sarcopenic obesity and MetS were observed in both men (RR = 1.31, 95%CI = 1.10?
1.56) and women (RR = 1.17, 95%CI = 1.10?1.25)[
]. The mechanisms of the relationship
between muscle mass and cardiometabolic risks were unclear. There were several plausible
explanations, as follows. As an organ of an insulin-responsive target, the loss of muscle mass
contributed to insulin resistance, MetS and HTN[
]. Levels of HOMA-IR were higher in
sarcopenia participants than in control subjects[
]. The pathophysiology of DM caused an
atrophy of muscles and included declines in the activity of anabolic hormones (e.g. IGF-I,
], and increased protein degradation caused by elevated expression of
]. The reported loss of lean mass was caused by decreased responsiveness to insulin
for the stimulation of muscle protein synthesis and for inhibiting protein breakdown[
Macrophage infiltration, one of the potential pathways of adipose dysfunction, was also related
to inflammation in muscle mass[
]. Increased levels of IL-6 and CRP induced by elevated
numbers of macrophages were significantly associated with the loss of total appendicular lean
]. Elderly adults with higher inflammatory levels such as TNF-? revealed the
strongest associations and might be important markers of loss of muscle mass and strength
]. In a recent study, the negative effects of CRP on muscle mass were identified by a
reduction in the size of human myotubes along with a reduction in muscle protein synthesis.
Increased CRP levels reduced the phosphorylation of Akt, the major upstream regulator of the
mTOR cascade involved in the regulation of muscle growth, and contributed to the
impairment of muscle protein synthesis[
]. Another pathway was CRP-mediated cellular
energy stress that increased the upregulation of AMPK, leading to the suppression of
Interestingly, the gender difference is noted in the association between different definitions
of MetS and Framingham risk score in the present study. FMRMetS is more closely associated
with the risk score than MestS in females, but not in males. Several studies have reported that
females have substantially greater body fat percentage, while males have greater visceral fat[
]. This difference might be associated with the sexual dimorphism of body fat distribution
and sex hormones.
The strengths of our study were a large population-based survey, and we proposed novel
findings for the effect of a body composition index on cardiometabolic events. However, there
were several potential limitations among our study. First, causal inference was not suitable
because the present study was a cross-sectional design; thus, we could not explain whether
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FMR affected metabolic dysfunction. Second, the data for insulin resistance and HOMA-IR
were not accessible in the health examination. If we could examine the association between
insulin resistance and fat and muscle, interesting findings could be uncovered. Third, BIA is
quite variable and it is not regarded by many as providing an accurate measure of body
composition. Dehydration is an important factor affecting accuracy of BIA measurement that it
causes an increase in the body?s electrical resistance and an overestimation of body fat[
Exercise before BIA measurement contributes to an underestimation of body fat percentage
and overestimation of fat-free mass because of reduced impedance[
]. Next, the information
regarding drug use for DM, HTN, and dyslipidemia is not available in the study because these
data is not assessing in the health examinations that may confound findings. Finally, the
dataset was derived from only an Asian population. Thus, the limited ethnicity diversity in the
participants might not reflect the association between FMR and metabolic risk factors in terms of
The present study highlighted a significant association between FMR and MetS, prediabetes,
DM and HTN. FMR might be incorporated in newly constructed MetS definitions, which
were better able to predict the incident cardiovascular risks than traditional criteria. We
provided a simple and useful body composition indicator for the early prevention and
management of cardiometabolic risks and improvement of public health. Further studies should focus
more effort on the underlying mechanisms of the interaction between body composition and
S1 Table. Association between fat-muscle ratio with individual MetS components.
S2 Table. Association between the CRP and different definitions of MetS.
Conceptualization: Wei-Liang Chen.
Data curation: Yuan-Yuei Chen, Wen-Hui Fang, Chung-Ching Wang, Tung-Wei Kao,
Fang Yang, Chen-Jung Wu, Yu-Shan Sun, Ying-Chuan Wang, Wei-Liang Chen.
Formal analysis: Yuan-Yuei Chen, Wen-Hui Fang, Chung-Ching Wang, Tung-Wei Kao,
Hui-Fang Yang, Chen-Jung Wu, Yu-Shan Sun, Ying-Chuan Wang, Wei-Liang Chen.
Investigation: Yuan-Yuei Chen, Wen-Hui Fang, Chung-Ching Wang, Tung-Wei Kao,
Fang Yang, Chen-Jung Wu, Yu-Shan Sun, Ying-Chuan Wang.
Methodology: Yuan-Yuei Chen, Wen-Hui Fang, Chung-Ching Wang, Tung-Wei Kao,
Fang Yang, Chen-Jung Wu, Yu-Shan Sun, Ying-Chuan Wang, Wei-Liang Chen.
Project administration: Yuan-Yuei Chen, Wei-Liang Chen.
Supervision: Wei-Liang Chen.
Validation: Yuan-Yuei Chen, Wei-Liang Chen.
Visualization: Yuan-Yuei Chen, Wei-Liang Chen.
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Writing ? original draft: Yuan-Yuei Chen.
Writing ? review & editing: Yuan-Yuei Chen, Wei-Liang Chen.
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