Does the Additional Component of Calf Circumference Refine Metabolic Syndrome in Correlating With Cardiovascular Risk?

Does the Additional Component of Calf Circumference Refine Metabolic Syndrome in Correlating With... Abstract Context Calf circumference (CC) was a useful anthropometric tool, but there was limited study on the effect of CC on metabolic syndrome (MetS) for cardiovascular risk. Objective The objective of our study was to determine whether adding CC as a component of MetS refined correlating MetS with cardiovascular, all-cause, and cancer mortality risks. Design, Setting, Patients, and Interventions From the National Health and Nutrition Examination Survey data set for 1999 through 2002, we analyzed four types of MetS: (1) increased waist circumference and two or more of four MetS components (WaistMetS); (2) decreased CC and two or more of four MetS components (CalfMetS); (3) increased waist-to-calf ratio and two or more of four MetS components (WCRMetS); and (4) decreased CC and three or more of five MetS components (CC+MetS). Primary Outcome Measure The cause-specific hazard ratios were measured as categorized by the four types of MetS. Results For cardiovascular mortality, the adjusted hazard ratios for WaistMetS, CalfMetS, WCRMetS, and CC+MetS were 1.867, 1.871, 1.949, and 2.306, respectively (all P < 0.001). Notably, CalfMetS showed the strongest positive correlation with serum C-reactive protein levels, and WCRMetS had the strongest positive relationship with homeostasis model assessment of insulin resistance. Conclusions Adding CC to the components of MetS correlated with higher cardiovascular and all-cause mortality risk than the traditional definition of MetS. Metabolic syndrome (MetS), a constellation of metabolic abnormalities characterized by central obesity, elevated blood pressure, impaired glucose tolerance, and atherogenic dyslipidemia, has been linked to the growing risk of type 2 diabetes mellitus and excessive cardiovascular disease (1, 2). An earlier meta-analysis of longitudinal studies demonstrated that the overall cardiovascular risk was increased by up to 78% in the subjects with MetS compared with those without MetS (3). Moreover, one epidemiological study indicated that ∼40% of US adults were recognized as having MetS (4). Therefore, it has become a compelling public issue over the past few decades. Most importantly, MetS was also related to other systemic diseases, such as nonalcoholic steatohepatitis (5), polycystic ovary syndrome (6), systemic lupus erythematosus (7), and chronic renal disease (8). Moreover, there was accumulating evidence indicating that all-cause and cardiovascular mortality increased with MetS (9–13), but the relative risk varied depending on different definitions, regions, and populations. Among the components of MetS, besides blood pressure, waist circumference (WC) was the other feasible assessment measured by noninvasive tools without blood sampling. WC is an anthropometric parameter and is commonly regarded as a surrogate marker of adiposity among the general population (14). Emerging studies have reported that WC is directly related to insulin resistance (15, 16), which has been proposed as one of the predominant underlying risk factors for MetS (17, 18). However, calf circumference (CC) is one useful anthropometric parameter now being investigated in many fields; it has been reported that subjects with lower CCs had increased frequency of carotid plaques (19). Notably, lower CC was positively associated with carotid atherosclerosis and insulin resistance in patients with diabetes (20). Korean patients with diabetes who had low CC and high WC had an increased risk of carotid atherosclerosis (21). Despite the growing awareness of the interplay between CC and cardiovascular events, the combination of CC with WC, as another component of MetS, has not yet been explored, to our knowledge. The aim of this article is to outline several approaches to clarify different phenotypes of MetS that might be more suitable for the prediction of mortality risk than the traditional definition of MetS in the US population. Materials and Methods Ethics statement The information in our study was derived from the National Health and Nutrition Examination Survey (NHANES) database, and was approved by the National Center for Health Statistics (NCHS) institutional review board in accordance with the revised Declaration of Helsinki. All informed consents had been obtained before data collection and the comprehensive examinations. Data source and participants The data sets were derived from the NHANES for the years 1999 through 2002. Participants with missing values of CC, MetS components, demographic information, body mass index (BMI), past medical history, and recreational activity were excluded. Our study procedure for the selection of participants is shown in Fig. 1. The final sample included 7,448 eligible subjects (n = 3,910 women; n = 3,538 men). The NCHS of the Centers for Disease Control and Prevention conducted the NHANES, which included national and cross-sectional samples of noninstitutionalized US citizens with a stratified, multistage probability design with planned oversampling of minority groups and certain ages. All in-home personal interviews and physical examinations are contained in the NHANES database. Detailed survey operation manuals and consent documents are accessible on the NHANES website. Figure 1. View largeDownload slide Study flow diagram. CalfMet, decreased calf circumference and two or more of four metabolic syndrome components; CC+MetS, decreased calf circumference and three or more of five metabolic syndrome components; ROC, receiver operating characteristic; WCR, waist-to-calf ratio; WCRMetS, increased waist-to-calf ratio and two or more of four metabolic syndrome components. Figure 1. View largeDownload slide Study flow diagram. CalfMet, decreased calf circumference and two or more of four metabolic syndrome components; CC+MetS, decreased calf circumference and three or more of five metabolic syndrome components; ROC, receiver operating characteristic; WCR, waist-to-calf ratio; WCRMetS, increased waist-to-calf ratio and two or more of four metabolic syndrome components. Criteria of components in traditional MetS According to the revised National Cholesterol Education Program’s Adult Treatment Panel III (ATP III) (22), eligible subjects were diagnosed as having MetS if they had three or more of the following features (1): WC ≥102 cm in men and ≥88 cm in women (MetWaist); (2) elevated blood pressure defined as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or current hypertension medication use (MetBP); (3) high serum triglyceride levels defined as ≥150 mg/dL among subjects who did not use lipid-lowering medications (MetTG); (4) low high-density lipoprotein cholesterol, defined as <40 mg/dL in men and <50 mg/dL in women or patients under medical control (MetHDL); and (5) high fasting glucose level defined as ≥100 mg/dL or current oral hypoglycemic agent or insulin use (MetGlu). Different definitions of MetS For the prediction of cardiovascular mortality, receiver operator characteristic curves were used to identify optimal cutoff values for CC and the waist-to-calf ratio (WCR) in our study, and the area under the receiver operator characteristic (AUROC) and the corresponding 95% confidence intervals (CIs) were calculated. The best cutoff values of CC were, in women, 33.65 cm (AUROC, 0.748; 95% CI, 0.697 to 0.799) with a sensitivity and specificity of 85% and 53%, respectively; and, in men, 36.65 cm (AUROC, 0.673; 95% CI, 0.623 to 0.723) with a sensitivity and specificity of 70%, and 59%, respectively. Based on this finding, the subjects who met the criteria of CC <33.65 cm in women and <36.65 cm in men were categorized as MetCalf. In addition, the optimal cutoff value of the WCR was 2.69 (AUROC, 0.738; 95% CI, 0.706 to 0.769) with a sensitivity and specificity of 65%, and 73%, respectively, in our study population. To compare the effects of the different definitions of MetS with the traditional definition of MetS on cause-specific mortality, we analyzed four novel types of MetS, as follows: WaistMetS was defined as MetWaist along with the presence of at least two of four components of MetS. CalfMetS was defined as MetCalf combined with at least two of four components of MetS (i.e., MetBP, MetTG, MetHDL, and MetGlu). WCRMetS was defined as subjects with a WCR >2.69 coupled with at least two of four components of MetS (i.e., MetBP, MetTG, MetHDL, and MetGlu). Last, CC+MetS was defined as MetCalf along with at least three of five components of MetS (i.e., MetWaist, MetBP, MetTG, MetHDL, and MetGlu). Outcome assessment The primary outcomes of interest for different definitions of MetS in our study were time to cause-specific death, including cardiovascular-, all-cause–, and malignancy-related mortality. The follow-up data on mortality were obtained from the time of the study participants' examination dates to death or 31 December 2006 in the NHANES database. The NCHS provided the data based on probabilistic matching according to the National Death Index death certificate records (23). Codes for the underlying cause of death have been validated to have a discrepancy rate of ∼5% (24). The key secondary end point was the associations among the MetS phenotypes, C-reactive protein (CRP), and homeostasis model assessment of insulin resistance (HOMA-IR), which may elucidate possible pathophysiological mechanisms underlying phenotypic differences. Measurement: covariates The self-reported data included age, sex, race/ethnicity, smoking history, recreational activity, and medical history, including heart attack, stroke, coronary heart disease, congestive heart failure, angina pectoris, emphysema, and cancer. Recreational activity was defined as increased breath frequency or elevated heart rate when the subjects underwent the exercise for a time without break. The biochemistry profiles related to MetS components and anthropometric parameters were described in our previous article (25). All the procedures adhered to the standardized protocols based on the Centers for Disease Control and Prevention guidelines. Statistical analysis All data analyses were executed by the SPSS software, version 18.0, for Windows (IBM, Armonk, NY). Continuous variables are presented as the mean and standard deviation (SD), whereas discrete variables are presented as frequency counts and percentages. The χ2 test was applied to discrete data, and the Student t test was applied to continuous data. A two-sided P < 0.05 was considered to be statistically significant. Survival analyses using the Kaplan-Meier method, with significance according to the log-rank test, were performed. Survival was plotted using Kaplan-Meier curves stratified by different definitions of MetS. We performed multivariate regression analysis using Cox proportional hazard regression analysis to examine the risk of different phenotypes of MetS among cardiovascular, all-cause, and cancer mortality. In addition, we investigated the association of different definitions of MetS with CRP level and HOMA-IR, using the multivariable linear regression models. Results Study sample characteristics Demographic characteristics of all subjects with MetWaist and phenotypes of MetS are listed in Table 1. Compared with subjects with MetWaist, those with MetCalf were more likely to be men and have lower BMI and CRP level. Additionally, a significantly higher prevalence of heart attack, emphysema, and malignancy was found in the MetCalf group. MetWaist phenotype included mostly women, whereas the MetBP phenotype included a mostly elderly population. The MetGlu phenotype included mostly patients who were prediabetic. Table 1. Characteristics of Study Participants With Different MetS Components   MetWaist  MetBP  MetGlu  MetTG  MetHDL  MetCalf  Continuousa   Age, y  50.87 (17.88)  59.76 (16.43)  59.13 (15.89)  50.62 (17.74)  47.92 (18.18)  52.28 (20.92)   BMI, kg/m2  32.12 (5.70)  29.34 (6.41)  30.57 (6.63)  30.02 (5.77)  30.27 (6.47)  23.23 (3.28)   Albumin, g/dL  4.21 (0.36)  4.29 (0.31)  4.27 (0.33)  4.26 (0.39)  4.29 (0.33)  4.38 (0.36)   Uric acid, mg/dL  5.44 (1.53)  5.65 (1.49)  5.75 (1.53)  5.68 (1.56)  5.52 (1.54)  5.24 (1.46)   TB, μmol/L  10.44 (4.14)  11.17 (4.40)  11.13 (4.38)  10.62 (4.11)  10.76 (4.62)  11.88 (5.43)   CRP, mg/dL  0.61 (0.88)  0.49 (0.74)  0.65 (1.36)  0.55 (1.05)  0.56 (0.95)  0.41 (1.16)  Categoricalb   Male sex  1368 (35.9)  1171 (51.6)  918 (56.5)  1271 (50.6)  1150 (43.8)  1080 (63.9)   Elderly  938 (24.6)  932 (41.0)  620 (38.1)  595 (23.7)  552 (21.0)  565 (33.4)   Prediabetes  687 (18.0)  473 (20.9)  1110 (68.3)  483 (19.2)  482 (18.4)  246 (14.6)   Race/ethnicity     Non-Hispanic white  1908 (50.1)  1145 (50.4)  739 (45.4)  1296 (51.6)  1274 (48.5)  726 (42.9)   Mexican American  935 (24.5)  499 (22.0)  448 (27.6)  727 (28.9)  720 (27.4)  561 (33.2)   Other Hispanic  191 (5.0)  108 (4.8)  92 (5.7)  141 (5.6)  181 (6.9)  102 (6.0)   Non-Hispanic black  675 (17.7)  453 (19.9)  295 (18.1)  255 (10.1)  364 (13.9)  235 (13.9)   Other race  102 (2.7)  66 (2.9)  52 (3.2)  95 (3.8)  86 (3.3)  67 (4.0)   CHF  121 (3.2)  100 (4.4)  80 (4.9)  84 (3.3)  97 (3.7)  56 (3.3)   CHD  167 (4.4)  128 (5.6)  116 (7.1)  127 (5.1)  140 (5.3)  80 (4.7)   Angina pectoris  160 (4.2)  110 (4.8)  101 (6.2)  113 (4.5)  117 (4.5)  62 (3.7)   Heart attack  156 (4.1)  120 (5.3)  115 (7.1)  127 (5.1)  140 (5.3)  90 (5.3)   Stroke  126 (3.3)  120 (5.3)  79 (4.9)  89 (3.5)  96 (3.7)  65 (3.8)   Emphysema  50 (1.3)  46 (2.0)  35 (2.2)  27 (1.1)  23 (0.9)  43 (2.5)   Malignancy  308 (8.1)  229 (10.1)  177 (10.9)  186 (7.4)  167 (6.4)  138 (8.2)   Smoking  1788 (46.9)  1106 (48.7)  832 (51.2)  1299 (51.7)  1299 (49.5)  925 (54.7)   Recreational activity  1685 (44.2)  1040 (45.8)  733 (45.1)  1150 (45.7)  1202 (45.8)  794 (47.0)    MetWaist  MetBP  MetGlu  MetTG  MetHDL  MetCalf  Continuousa   Age, y  50.87 (17.88)  59.76 (16.43)  59.13 (15.89)  50.62 (17.74)  47.92 (18.18)  52.28 (20.92)   BMI, kg/m2  32.12 (5.70)  29.34 (6.41)  30.57 (6.63)  30.02 (5.77)  30.27 (6.47)  23.23 (3.28)   Albumin, g/dL  4.21 (0.36)  4.29 (0.31)  4.27 (0.33)  4.26 (0.39)  4.29 (0.33)  4.38 (0.36)   Uric acid, mg/dL  5.44 (1.53)  5.65 (1.49)  5.75 (1.53)  5.68 (1.56)  5.52 (1.54)  5.24 (1.46)   TB, μmol/L  10.44 (4.14)  11.17 (4.40)  11.13 (4.38)  10.62 (4.11)  10.76 (4.62)  11.88 (5.43)   CRP, mg/dL  0.61 (0.88)  0.49 (0.74)  0.65 (1.36)  0.55 (1.05)  0.56 (0.95)  0.41 (1.16)  Categoricalb   Male sex  1368 (35.9)  1171 (51.6)  918 (56.5)  1271 (50.6)  1150 (43.8)  1080 (63.9)   Elderly  938 (24.6)  932 (41.0)  620 (38.1)  595 (23.7)  552 (21.0)  565 (33.4)   Prediabetes  687 (18.0)  473 (20.9)  1110 (68.3)  483 (19.2)  482 (18.4)  246 (14.6)   Race/ethnicity     Non-Hispanic white  1908 (50.1)  1145 (50.4)  739 (45.4)  1296 (51.6)  1274 (48.5)  726 (42.9)   Mexican American  935 (24.5)  499 (22.0)  448 (27.6)  727 (28.9)  720 (27.4)  561 (33.2)   Other Hispanic  191 (5.0)  108 (4.8)  92 (5.7)  141 (5.6)  181 (6.9)  102 (6.0)   Non-Hispanic black  675 (17.7)  453 (19.9)  295 (18.1)  255 (10.1)  364 (13.9)  235 (13.9)   Other race  102 (2.7)  66 (2.9)  52 (3.2)  95 (3.8)  86 (3.3)  67 (4.0)   CHF  121 (3.2)  100 (4.4)  80 (4.9)  84 (3.3)  97 (3.7)  56 (3.3)   CHD  167 (4.4)  128 (5.6)  116 (7.1)  127 (5.1)  140 (5.3)  80 (4.7)   Angina pectoris  160 (4.2)  110 (4.8)  101 (6.2)  113 (4.5)  117 (4.5)  62 (3.7)   Heart attack  156 (4.1)  120 (5.3)  115 (7.1)  127 (5.1)  140 (5.3)  90 (5.3)   Stroke  126 (3.3)  120 (5.3)  79 (4.9)  89 (3.5)  96 (3.7)  65 (3.8)   Emphysema  50 (1.3)  46 (2.0)  35 (2.2)  27 (1.1)  23 (0.9)  43 (2.5)   Malignancy  308 (8.1)  229 (10.1)  177 (10.9)  186 (7.4)  167 (6.4)  138 (8.2)   Smoking  1788 (46.9)  1106 (48.7)  832 (51.2)  1299 (51.7)  1299 (49.5)  925 (54.7)   Recreational activity  1685 (44.2)  1040 (45.8)  733 (45.1)  1150 (45.7)  1202 (45.8)  794 (47.0)  Abbreviations: CHD, coronary heart disease; CHF, congestive heart failure; TB, total bilirubin. a Continuous values expressed as mean (standard deviation). b Categorical variables data are expressed as no. (%). View Large Survival analyses of cardiovascular diseases classified by different definitions of MetS Survival analysis for cardiovascular mortality was plotted using Kaplan-Meier curves of the different definitions of MetS (Supplemental Fig. 1). Table 2 shows the hazard ratios (HRs) of cardiovascular mortality in participants with different definitions of MetS. The HRs and 95% CI of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS in the adjusted model were 1.867 (95% CI, 1.328 to 2.625), 1.871 (95% CI, 1.334 to 2.625), 1.949 (95% CI, 1.421 to 2.672), and 2.306 (95% CI, 1.586 to 3.352), respectively (all P < 0.001). Taken together, these findings indicate the CC-incorporated definition of MetS tended to have a better predictive ability for cardiovascular mortality. Table 2. HRs of Cardiovascular Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  2.068 (1.573–2.720)  <0.001  1.867 (1.328–2.625)  <0.001  CalfMetS  5.887 (4.453–7.784)  <0.001  1.871 (1.334–2.625)  <0.001  WCRMetS  3.884 (2.954–5.108)  <0.001  1.949 (1.421–2.672)  <0.001  CC+MetS  6.687 (4.686–9.542)  <0.001  2.306 (1.586–3.352)  <0.001    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  2.068 (1.573–2.720)  <0.001  1.867 (1.328–2.625)  <0.001  CalfMetS  5.887 (4.453–7.784)  <0.001  1.871 (1.334–2.625)  <0.001  WCRMetS  3.884 (2.954–5.108)  <0.001  1.949 (1.421–2.672)  <0.001  CC+MetS  6.687 (4.686–9.542)  <0.001  2.306 (1.586–3.352)  <0.001  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Survival analyses of all-cause mortality stratified by different definitions of MetS Survival analysis for all-cause mortality was plotted using Kaplan-Meier curves of the different definitions of MetS (Supplemental Fig. 2). The HRs of all-cause mortality in participants with different MetS are summarized in Table 3. The HRs and 95% CIs of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS domains in the adjusted model were 1.614 (95% CI, 1.305 to 1.995), 1.573 (95% CI, 1.261 to 1.963), 1.728 (95% CI, 1.415 to 2.111), and 1.854 (95% CI, 1.443 to 2.382), respectively (all P < 0.001). Collectively, the findings indicate this CC-incorporated definition of MetS might be another useful prediction tool for all-cause mortality, except for CalfMetS. Table 3. HRs of All-Cause Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.811 (1.511–2.170)  <0.001  1.614 (1.305–1.995)  <0.001  CalfMetS  4.011 (3.277–4.908)  <0.001  1.573 (1.261–1.963)  <0.001  WCRMetS  3.248 (2.711–3.891)  <0.001  1.728 (1.415–2.111)  <0.001  CC+MetS  4.780 (3.758–6.079)  <0.001  1.854 (1.443–2.382)  <0.001    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.811 (1.511–2.170)  <0.001  1.614 (1.305–1.995)  <0.001  CalfMetS  4.011 (3.277–4.908)  <0.001  1.573 (1.261–1.963)  <0.001  WCRMetS  3.248 (2.711–3.891)  <0.001  1.728 (1.415–2.111)  <0.001  CC+MetS  4.780 (3.758–6.079)  <0.001  1.854 (1.443–2.382)  <0.001  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Survival analyses of cancer mortality categorized by different definitions of MetS Survival analysis for cancer mortality was plotted using Kaplan-Meier curves concerning the different definitions of MetS (Supplemental Fig. 3). Table 4 lists the HRs of cancer mortality in participants with different newly defined MetS. The HRs and 95% CI of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS domains in the adjusted model were 1.461 (95% CI, 0.949 to 2.251; P = 0.085), 1.258 (95% CI, 0.764 to 2.072; P = 0.368), 1.599 (95% CI, 1.069 to 2.392; P = 0.022), and 1.670 (95% CI, 0.940 to 2.966; P = 0.080), respectively. These findings illustrated a statistically significant association only between WCRMetS and cancer mortality. WC was substituted with the WCR as one component of MetS, and the WCR coupled with at least two of four metabolic abnormalities had a substantial increased risk of cancer mortality compared with those without WCRMetS. Table 4. HRs of Cancer Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.612 (1.115–2.329)  0.011  1.461 (0.949–2.251)  0.085  CalfMetS  2.862 (1.808–4.530)  <0.001  1.258 (0.764–2.072)  0.368  WCRMetS  3.028 (2.103–4.360)  <0.001  1.599 (1.069–2.392)  0.022  CC+MetS  3.566 (2.046–6.216)  <0.001  1.670 (0.940–2.966)  0.080    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.612 (1.115–2.329)  0.011  1.461 (0.949–2.251)  0.085  CalfMetS  2.862 (1.808–4.530)  <0.001  1.258 (0.764–2.072)  0.368  WCRMetS  3.028 (2.103–4.360)  <0.001  1.599 (1.069–2.392)  0.022  CC+MetS  3.566 (2.046–6.216)  <0.001  1.670 (0.940–2.966)  0.080  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Association of different definitions of MetS with CRP and insulin resistance To explore whether the inflammatory process and insulin resistance could explain the connection between several definitions of MetS and different causes of mortality, we further determined the interplay among the different definitions of MetS, serum CRP level, and HOMA-IR. Table 5 lists the positive associations of the CC-incorporated definition of MetS with CRP level in the adjusted models (P < 0.05); CalfMetS showed the strongest positive correlation. In addition, all MetS in our study revealed positive relationships with HOMA-IR in the adjusted models (P < 0.001), and WCRMetS showed the strongest positive correlation. Collectively, these findings indicate the CC-incorporated definition of MetS demonstrated positive associations with CRP level and insulin resistance. Table 5. Associations of CRP Levels and HOMA-IR With Different Definitions of MetS   Serum CRP Level  HOMA-IR  Total Adjusted βa (95% CI)  P Value  Total Adjusted βa (95% CI)  P Value  WaistMetS  0.019 (−0.032 to 0.070)  0.472  2.941 (2.499–3.384)  <0.001  CalfMetS  0.184 (0.107–0.261)  <0.001  1.416 (0.742–2.091)  <0.001  WCRMetS  0.107 (0.050–0.165)  <0.001  3.010 (2.509–3.511)  <0.001  CC+MetS  0.159 (0.059–0.260)  0.002  2.574 (1.703–3.444)  <0.001    Serum CRP Level  HOMA-IR  Total Adjusted βa (95% CI)  P Value  Total Adjusted βa (95% CI)  P Value  WaistMetS  0.019 (−0.032 to 0.070)  0.472  2.941 (2.499–3.384)  <0.001  CalfMetS  0.184 (0.107–0.261)  <0.001  1.416 (0.742–2.091)  <0.001  WCRMetS  0.107 (0.050–0.165)  <0.001  3.010 (2.509–3.511)  <0.001  CC+MetS  0.159 (0.059–0.260)  0.002  2.574 (1.703–3.444)  <0.001  a β coefficients were interpreted as the degree of association of CRP and HOMA-IR with each different definition of MetS. View Large Discussion In this cohort study of the US general population, the most prominent finding was that the CC-incorporated definition of MetS was useful in correlating MetS with cardiovascular risk. The CC-incorporated domains, especially CC in addition to the traditional definition of MetS, were associated with higher cardiovascular mortality risk than was MetS alone. In addition, the CC-incorporated definition of MetS, except when CC was simply substituted for WC, also had a higher correlation with all-cause and cancer mortality risk than the traditional definition of MetS. As far as we are aware, this is the first study to propose the idea of incorporating CC into the components of MetS in different ways for the prediction of mortality risk. A large cohort study that enrolled 23,998 participants with 4.8 years of follow-up tried to add elevated CRP level to the ATP III criteria as a sixth component and showed a similar mortality risk as that determined with the traditional definition of MetS (26). Similarly, our study aimed to add low CC to the MetS of ATP III criteria as another component, on the basis of the accumulating evidence (19–21). Findings of another large cohort study, the Canada Fitness Survey of 10,638 subjects with over 12 years of follow-up, indicated that WC and CC seemed to demonstrate contrary effects on mortality risk (27). Emerging studies suggested that low CC had the highest accuracy for predicting mortality in patients with chronic obstructive pulmonary disease compared with other anthropometric parameters (28). Moreover, CC had a better ability to predict emerging care needs in the elderly in a national cohort study (29). Findings of a cross-sectional study indicated subjects with high WCR had a significantly greater risk of MetS and arterial stiffness and researchers suggested that large CC might indicate a reduced risk for cardiovascular disease (30). Collectively, these studies’ results implied the potential role of CC in predicting cardiovascular mortality risk, and its importance and implications could be supported from the results of our study. A possible mechanism for the CC-incorporated definition of MetS, which demonstrated a higher cardiovascular mortality risk in our study, might be related to the connection between CC and skeletal muscle. An earlier study suggested that higher amounts of lean mass and strength might be protective factors for premature death in the obese group (31). Mostly, the extremities had a relatively higher fat-free mass than other body sites, and the CC was reported to have the closest relationship with muscle mass compared with other anthropometric parameters (32, 33). Zuliani et al. (34) indicated that insulin resistance and low-grade systemic inflammation were the main predisposing factors for overall and cardiovascular mortality in a 9-year follow-up cohort study. In regard to low-grade systemic inflammation, higher serum levels of interleukin-6 and CRP were associated with cardiovascular mortality in previous studies (35, 36). CRP levels were higher among patients with greater WC than among those with lower WC (37). Moreover, recent findings clearly demonstrated a strong link between chronic low-grade inflammation and skeletal muscle. In a study of differentially expressed genes in insulin-resistant skeletal muscle tissue, the inflammatory gene expression in skeletal muscle of women with MetS was negatively correlated with insulin sensitivity (38). Specifically, there were a substantial number of studies examining the advantageous metabolic effects of skeletal muscle in the peripheral target tissue for adiponectin. A review of recent research observed that adiponectin was an adipose tissue–derived hormone with antiatherogenic and anti-inflammatory functions, and its levels were shown to be negatively associated with insulin resistance (39). Preliminary data from recent observational studies had indicated that inflammation, insulin resistance, and metabolic dysfunction in skeletal muscle play a major role in the progress of MetS and type 2 diabetes via adiponectin expression and action (40). Another study suggested that effects of muscle-derived adiponectin on skeletal muscle had major insulin-sensitizing and metabolic effects that contributed to the antidiabetic outcome (41). Furthermore, according to several studies, the insulin-sensitizing effects of exercise might be associated with increased adiponectin receptor 1 (AdipoR1) expression in skeletal muscle instead of circulating adiponectin levels (42). A previous publication indicated AdipoR1 was abundantly expressed in skeletal muscle (43), and other studies using experimental animal models reported the associated results connected AdipoR1 expression with exercise (44–46). Evidence also suggests exercise might increase AdipoR1 expression in the muscle of rats (44). Another animal study suggested that long-term exercise training affected the expression level of AdipoR1, thereby improving insulin resistance in obese mice (45). In addition, chronic exercise training might improve insulin sensitivity and reduce AdipoR1 gene expression in the soleus muscle of obese rats (46). Although there were a limited number of studies regarding the correlation between AdipoR1 expression and exercise in humans, we speculated that these relationships might exist in human subjects. The phenomenon of long-term exercise increasing the soleus muscle mass was considerable (47); therefore, we hypothesized that a higher CC might be associated with increased AdipoR1 gene expression and vice versa. Moreover, there were several reports in the review literature supporting chronic inflammation–induced loss of skeletal muscle (48). Nevertheless, skeletal muscle had recently been identified as an endocrine organ that secretes myokines and exerts either paracrine or autocrine effects (49). According to two studies (50, 51), some myokines are involved in inducing anti-inflammatory responses with each bout of exercise and mediating beneficial effects on health via long-term exercise in the protection against cardiovascular risk factors and low-grade inflammation. Therefore, a vicious cycle of chronic inflammation was formed in patients with systemic inflammatory diseases, such as type 2 diabetes mellitus and atherosclerosis, leading to decreased physical activity and disability that was accompanied with deconditioned skeletal muscles and exacerbated inflammation (50). In addition, results of a pilot study conducted by Takagi et al. (52) suggested that a reverse association was detected between resting pulse rate and CC. A longitudinal cohort study demonstrated that capillary supply was inversely related to resting heart rate at ages of 50 and 70 years (53). Furthermore, the percentage of slow-twitch muscle fiber was predominant in the soleus muscle (54), and a lower percentage of slow-twitch muscle fiber in MetS muscle was associated with the severity of insulin resistance (55). A previous publication had indicated the capillary density of slow-twitch muscle fibers was higher than that of fast-twitch muscle fibers (56). Consequently, the subjects with low CC might have a relatively high resting heart rate and lower capillary density. The greater elevation in heart rate was secondary to reduced stroke volume and associated with exercise intolerance (57), and the lower capillary density was correlated with muscular blood flow (58). Therefore, these negative factors related with low CC may contribute to an increase in the risk of cardiovascular events. Taken together, the plausible underlying pathophysiological effects, including adiponectin, myokines, and resting heart rate, might explain the findings in our study that the CC-incorporated definition of MetS demonstrated better relationships with inflammation and insulin resistance than the traditional definition of MetS, and was correlated with higher cardiovascular mortality risk, especially WCRMetS and CC+MetS. Another interesting finding of our study was that WCRMetS was associated with a statistically significant increased risk of cancer mortality by nearly 60% compared with subjects without WCRMetS. The plausible mechanisms might be attributed to skeletal muscle wasting and cachexia, which were characterized as a state of constantly depleting skeletal muscle mass that cannot be completely recovered by any conservative nutritional treatment and develops further functional impairment (59). Evidence from a review article further highlighted that patients with cancer who had low lean body mass, especially those with sarcopenic obesity, were at increased risk of treatment-related toxicities from chemotherapy and were associated with increased overall mortality (60). Additionally, Fukawa et al. (61) indicated that cachectic cancer cells produced several inflammatory factors that rapidly caused fatty acid oxidation and led to a manifestation of cachectic muscle atrophy. Taken together, these findings revealed that the individuals with cancer who had low muscle mass had increased mortality risk, which was associated with inflammation. Although we cannot clarify the reason why only WCRMetS had a significant correlation with cancer mortality, the finding suggests that the incorporation of low CC into the components of MetS might also help detect earlier low muscle mass status of patients with cancer and help identify the need for intervention to reduce mortality risk. Several limitations of this study should be mentioned. First, the optimal cutoff values of CC and WCR were calculated from the NHANES data, which mainly represent noninstitutionalized US citizens. Because the definitions of CC cutoffs differ depending on ethnicity, the generalization of the results to other populations of different race or ethnicity may be limited (62). Second, we did not assess the risk of incident cardiovascular events and type 2 diabetes mellitus in subjects with different definitions of MetS, because there was no available information. Third, self-report past history rather than objective measures may be prone to recall bias. Last, we did not compare waist-hip ratio (WHR) with CC in the additional components of MetS in correlating with cardiovascular risk because there was a lack of these data in the National Health and Nutrition Examination Survey data set for 1999 through 2002. Therefore, we collected the data of WHR from the NHANES III, and the cut point of WHR was defined on the basis of the report of a World Health Organization expert consultation. The adjusted HRs in the WHR-incorporated definition of MetS and traditional MetS were 1.196 (95% CI, 1.106 to 1.294), and 1.214 (95% CI, 1.123 to 1.313), respectively. The results implied that the WHR-incorporated definition of MetS was not superior to traditional MetS. From the NHANES of 1999 through 2002, The adjusted HRs in the CC-incorporated definition of MetS and traditional MetS were 1.916 (95% CI, 1.513 to 2.425), and 1.421 (95% CI, 1.196 to 1.687), respectively. It was tempting to speculate that the CC-incorporated definition of MetS might be more suitable for the prediction of mortality risk than the WHR-incorporated definition of MetS (Supplemental Table 1). In conclusion, the CC-incorporated definition of MetS, particularly in addition to the traditional definition of MetS, demonstrated higher correlation with cardiovascular and all-cause mortality risk than did the traditional definition of MetS alone. In addition, WCRMetS was significantly and positively associated with cancer mortality. More studies are warranted to determine the ethnicity-specific cutoff values of CC and WCR for their application in clinical practice. Abbreviations: Abbreviations: AdipoR1 adiponectin receptor 1 ATP Adult Treatment Panel III AUROC area under the receiver operator characteristic curve BMI body mass index CalfMetS decreased calf circumference and two or more of four metabolic syndrome components CC calf circumference CC+MetS decreased calf circumference and three or more of five metabolic syndrome components metabolic syndrome metabolic syndrome CI confidence interval CRP C-reactive protein HOMA-IR homeostasis model assessment of insulin resistance HR hazard ratio MetBP elevated blood pressure defined as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or current hypertension medication use MetCalf calf circumference <33.65 cm in women and <36.65 cm in men MetGlu high fasting-glucose level defined as ≥100 mg/dL or current oral hypoglycemic agent or insulin use MetHDL low high-density lipoprotein cholesterol, defined as <40 mg/dL in men and <50 mg/dL in women or patients under medical control MetTG high serum triglyceride levels defined as ≥150 mg/dL among subjects who did not use lipid-lowering medications MetWaist category in which waist circumference ≥102 cm in men and ≥88 cm in women NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination Survey WaistMetS increased waist circumference and two or more of four metabolic syndrome components WC waist circumference WCR waist-to-calf ratio WCRMetS increased waist-to-calf ratio and two or more of four metabolic syndrome components WHR waist-hip ratio Acknowledgments Financial Support: No financial support was received for this study. Author Contributions: W.-L.C. conceived the study; W.-L.C. and C.-J.W. designed the study; C.-J.W. drafted the manuscript; all authors contributed to data analysis; W.-L.C. supervised all aspects of the study, critically reviewed and revised the manuscript; and all authors read and approved the final manuscript. Disclosure Summary: The authors have nothing to disclose. References 1. Grassi G, Quarti-Trevano F, Seravalle G, Dell’Oro R. Cardiovascular risk and adrenergic overdrive in the metabolic syndrome. Nutr Metab Cardiovasc Dis . 2007; 17( 6): 473– 481. Google Scholar CrossRef Search ADS PubMed  2. Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation . 2005; 112( 20): 3066– 3072. Google Scholar CrossRef Search ADS PubMed  3. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, Montori VM. 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Does the Additional Component of Calf Circumference Refine Metabolic Syndrome in Correlating With Cardiovascular Risk?

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Endocrine Society
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Copyright © 2018 Endocrine Society
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0021-972X
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1945-7197
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10.1210/jc.2017-02320
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Abstract

Abstract Context Calf circumference (CC) was a useful anthropometric tool, but there was limited study on the effect of CC on metabolic syndrome (MetS) for cardiovascular risk. Objective The objective of our study was to determine whether adding CC as a component of MetS refined correlating MetS with cardiovascular, all-cause, and cancer mortality risks. Design, Setting, Patients, and Interventions From the National Health and Nutrition Examination Survey data set for 1999 through 2002, we analyzed four types of MetS: (1) increased waist circumference and two or more of four MetS components (WaistMetS); (2) decreased CC and two or more of four MetS components (CalfMetS); (3) increased waist-to-calf ratio and two or more of four MetS components (WCRMetS); and (4) decreased CC and three or more of five MetS components (CC+MetS). Primary Outcome Measure The cause-specific hazard ratios were measured as categorized by the four types of MetS. Results For cardiovascular mortality, the adjusted hazard ratios for WaistMetS, CalfMetS, WCRMetS, and CC+MetS were 1.867, 1.871, 1.949, and 2.306, respectively (all P < 0.001). Notably, CalfMetS showed the strongest positive correlation with serum C-reactive protein levels, and WCRMetS had the strongest positive relationship with homeostasis model assessment of insulin resistance. Conclusions Adding CC to the components of MetS correlated with higher cardiovascular and all-cause mortality risk than the traditional definition of MetS. Metabolic syndrome (MetS), a constellation of metabolic abnormalities characterized by central obesity, elevated blood pressure, impaired glucose tolerance, and atherogenic dyslipidemia, has been linked to the growing risk of type 2 diabetes mellitus and excessive cardiovascular disease (1, 2). An earlier meta-analysis of longitudinal studies demonstrated that the overall cardiovascular risk was increased by up to 78% in the subjects with MetS compared with those without MetS (3). Moreover, one epidemiological study indicated that ∼40% of US adults were recognized as having MetS (4). Therefore, it has become a compelling public issue over the past few decades. Most importantly, MetS was also related to other systemic diseases, such as nonalcoholic steatohepatitis (5), polycystic ovary syndrome (6), systemic lupus erythematosus (7), and chronic renal disease (8). Moreover, there was accumulating evidence indicating that all-cause and cardiovascular mortality increased with MetS (9–13), but the relative risk varied depending on different definitions, regions, and populations. Among the components of MetS, besides blood pressure, waist circumference (WC) was the other feasible assessment measured by noninvasive tools without blood sampling. WC is an anthropometric parameter and is commonly regarded as a surrogate marker of adiposity among the general population (14). Emerging studies have reported that WC is directly related to insulin resistance (15, 16), which has been proposed as one of the predominant underlying risk factors for MetS (17, 18). However, calf circumference (CC) is one useful anthropometric parameter now being investigated in many fields; it has been reported that subjects with lower CCs had increased frequency of carotid plaques (19). Notably, lower CC was positively associated with carotid atherosclerosis and insulin resistance in patients with diabetes (20). Korean patients with diabetes who had low CC and high WC had an increased risk of carotid atherosclerosis (21). Despite the growing awareness of the interplay between CC and cardiovascular events, the combination of CC with WC, as another component of MetS, has not yet been explored, to our knowledge. The aim of this article is to outline several approaches to clarify different phenotypes of MetS that might be more suitable for the prediction of mortality risk than the traditional definition of MetS in the US population. Materials and Methods Ethics statement The information in our study was derived from the National Health and Nutrition Examination Survey (NHANES) database, and was approved by the National Center for Health Statistics (NCHS) institutional review board in accordance with the revised Declaration of Helsinki. All informed consents had been obtained before data collection and the comprehensive examinations. Data source and participants The data sets were derived from the NHANES for the years 1999 through 2002. Participants with missing values of CC, MetS components, demographic information, body mass index (BMI), past medical history, and recreational activity were excluded. Our study procedure for the selection of participants is shown in Fig. 1. The final sample included 7,448 eligible subjects (n = 3,910 women; n = 3,538 men). The NCHS of the Centers for Disease Control and Prevention conducted the NHANES, which included national and cross-sectional samples of noninstitutionalized US citizens with a stratified, multistage probability design with planned oversampling of minority groups and certain ages. All in-home personal interviews and physical examinations are contained in the NHANES database. Detailed survey operation manuals and consent documents are accessible on the NHANES website. Figure 1. View largeDownload slide Study flow diagram. CalfMet, decreased calf circumference and two or more of four metabolic syndrome components; CC+MetS, decreased calf circumference and three or more of five metabolic syndrome components; ROC, receiver operating characteristic; WCR, waist-to-calf ratio; WCRMetS, increased waist-to-calf ratio and two or more of four metabolic syndrome components. Figure 1. View largeDownload slide Study flow diagram. CalfMet, decreased calf circumference and two or more of four metabolic syndrome components; CC+MetS, decreased calf circumference and three or more of five metabolic syndrome components; ROC, receiver operating characteristic; WCR, waist-to-calf ratio; WCRMetS, increased waist-to-calf ratio and two or more of four metabolic syndrome components. Criteria of components in traditional MetS According to the revised National Cholesterol Education Program’s Adult Treatment Panel III (ATP III) (22), eligible subjects were diagnosed as having MetS if they had three or more of the following features (1): WC ≥102 cm in men and ≥88 cm in women (MetWaist); (2) elevated blood pressure defined as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or current hypertension medication use (MetBP); (3) high serum triglyceride levels defined as ≥150 mg/dL among subjects who did not use lipid-lowering medications (MetTG); (4) low high-density lipoprotein cholesterol, defined as <40 mg/dL in men and <50 mg/dL in women or patients under medical control (MetHDL); and (5) high fasting glucose level defined as ≥100 mg/dL or current oral hypoglycemic agent or insulin use (MetGlu). Different definitions of MetS For the prediction of cardiovascular mortality, receiver operator characteristic curves were used to identify optimal cutoff values for CC and the waist-to-calf ratio (WCR) in our study, and the area under the receiver operator characteristic (AUROC) and the corresponding 95% confidence intervals (CIs) were calculated. The best cutoff values of CC were, in women, 33.65 cm (AUROC, 0.748; 95% CI, 0.697 to 0.799) with a sensitivity and specificity of 85% and 53%, respectively; and, in men, 36.65 cm (AUROC, 0.673; 95% CI, 0.623 to 0.723) with a sensitivity and specificity of 70%, and 59%, respectively. Based on this finding, the subjects who met the criteria of CC <33.65 cm in women and <36.65 cm in men were categorized as MetCalf. In addition, the optimal cutoff value of the WCR was 2.69 (AUROC, 0.738; 95% CI, 0.706 to 0.769) with a sensitivity and specificity of 65%, and 73%, respectively, in our study population. To compare the effects of the different definitions of MetS with the traditional definition of MetS on cause-specific mortality, we analyzed four novel types of MetS, as follows: WaistMetS was defined as MetWaist along with the presence of at least two of four components of MetS. CalfMetS was defined as MetCalf combined with at least two of four components of MetS (i.e., MetBP, MetTG, MetHDL, and MetGlu). WCRMetS was defined as subjects with a WCR >2.69 coupled with at least two of four components of MetS (i.e., MetBP, MetTG, MetHDL, and MetGlu). Last, CC+MetS was defined as MetCalf along with at least three of five components of MetS (i.e., MetWaist, MetBP, MetTG, MetHDL, and MetGlu). Outcome assessment The primary outcomes of interest for different definitions of MetS in our study were time to cause-specific death, including cardiovascular-, all-cause–, and malignancy-related mortality. The follow-up data on mortality were obtained from the time of the study participants' examination dates to death or 31 December 2006 in the NHANES database. The NCHS provided the data based on probabilistic matching according to the National Death Index death certificate records (23). Codes for the underlying cause of death have been validated to have a discrepancy rate of ∼5% (24). The key secondary end point was the associations among the MetS phenotypes, C-reactive protein (CRP), and homeostasis model assessment of insulin resistance (HOMA-IR), which may elucidate possible pathophysiological mechanisms underlying phenotypic differences. Measurement: covariates The self-reported data included age, sex, race/ethnicity, smoking history, recreational activity, and medical history, including heart attack, stroke, coronary heart disease, congestive heart failure, angina pectoris, emphysema, and cancer. Recreational activity was defined as increased breath frequency or elevated heart rate when the subjects underwent the exercise for a time without break. The biochemistry profiles related to MetS components and anthropometric parameters were described in our previous article (25). All the procedures adhered to the standardized protocols based on the Centers for Disease Control and Prevention guidelines. Statistical analysis All data analyses were executed by the SPSS software, version 18.0, for Windows (IBM, Armonk, NY). Continuous variables are presented as the mean and standard deviation (SD), whereas discrete variables are presented as frequency counts and percentages. The χ2 test was applied to discrete data, and the Student t test was applied to continuous data. A two-sided P < 0.05 was considered to be statistically significant. Survival analyses using the Kaplan-Meier method, with significance according to the log-rank test, were performed. Survival was plotted using Kaplan-Meier curves stratified by different definitions of MetS. We performed multivariate regression analysis using Cox proportional hazard regression analysis to examine the risk of different phenotypes of MetS among cardiovascular, all-cause, and cancer mortality. In addition, we investigated the association of different definitions of MetS with CRP level and HOMA-IR, using the multivariable linear regression models. Results Study sample characteristics Demographic characteristics of all subjects with MetWaist and phenotypes of MetS are listed in Table 1. Compared with subjects with MetWaist, those with MetCalf were more likely to be men and have lower BMI and CRP level. Additionally, a significantly higher prevalence of heart attack, emphysema, and malignancy was found in the MetCalf group. MetWaist phenotype included mostly women, whereas the MetBP phenotype included a mostly elderly population. The MetGlu phenotype included mostly patients who were prediabetic. Table 1. Characteristics of Study Participants With Different MetS Components   MetWaist  MetBP  MetGlu  MetTG  MetHDL  MetCalf  Continuousa   Age, y  50.87 (17.88)  59.76 (16.43)  59.13 (15.89)  50.62 (17.74)  47.92 (18.18)  52.28 (20.92)   BMI, kg/m2  32.12 (5.70)  29.34 (6.41)  30.57 (6.63)  30.02 (5.77)  30.27 (6.47)  23.23 (3.28)   Albumin, g/dL  4.21 (0.36)  4.29 (0.31)  4.27 (0.33)  4.26 (0.39)  4.29 (0.33)  4.38 (0.36)   Uric acid, mg/dL  5.44 (1.53)  5.65 (1.49)  5.75 (1.53)  5.68 (1.56)  5.52 (1.54)  5.24 (1.46)   TB, μmol/L  10.44 (4.14)  11.17 (4.40)  11.13 (4.38)  10.62 (4.11)  10.76 (4.62)  11.88 (5.43)   CRP, mg/dL  0.61 (0.88)  0.49 (0.74)  0.65 (1.36)  0.55 (1.05)  0.56 (0.95)  0.41 (1.16)  Categoricalb   Male sex  1368 (35.9)  1171 (51.6)  918 (56.5)  1271 (50.6)  1150 (43.8)  1080 (63.9)   Elderly  938 (24.6)  932 (41.0)  620 (38.1)  595 (23.7)  552 (21.0)  565 (33.4)   Prediabetes  687 (18.0)  473 (20.9)  1110 (68.3)  483 (19.2)  482 (18.4)  246 (14.6)   Race/ethnicity     Non-Hispanic white  1908 (50.1)  1145 (50.4)  739 (45.4)  1296 (51.6)  1274 (48.5)  726 (42.9)   Mexican American  935 (24.5)  499 (22.0)  448 (27.6)  727 (28.9)  720 (27.4)  561 (33.2)   Other Hispanic  191 (5.0)  108 (4.8)  92 (5.7)  141 (5.6)  181 (6.9)  102 (6.0)   Non-Hispanic black  675 (17.7)  453 (19.9)  295 (18.1)  255 (10.1)  364 (13.9)  235 (13.9)   Other race  102 (2.7)  66 (2.9)  52 (3.2)  95 (3.8)  86 (3.3)  67 (4.0)   CHF  121 (3.2)  100 (4.4)  80 (4.9)  84 (3.3)  97 (3.7)  56 (3.3)   CHD  167 (4.4)  128 (5.6)  116 (7.1)  127 (5.1)  140 (5.3)  80 (4.7)   Angina pectoris  160 (4.2)  110 (4.8)  101 (6.2)  113 (4.5)  117 (4.5)  62 (3.7)   Heart attack  156 (4.1)  120 (5.3)  115 (7.1)  127 (5.1)  140 (5.3)  90 (5.3)   Stroke  126 (3.3)  120 (5.3)  79 (4.9)  89 (3.5)  96 (3.7)  65 (3.8)   Emphysema  50 (1.3)  46 (2.0)  35 (2.2)  27 (1.1)  23 (0.9)  43 (2.5)   Malignancy  308 (8.1)  229 (10.1)  177 (10.9)  186 (7.4)  167 (6.4)  138 (8.2)   Smoking  1788 (46.9)  1106 (48.7)  832 (51.2)  1299 (51.7)  1299 (49.5)  925 (54.7)   Recreational activity  1685 (44.2)  1040 (45.8)  733 (45.1)  1150 (45.7)  1202 (45.8)  794 (47.0)    MetWaist  MetBP  MetGlu  MetTG  MetHDL  MetCalf  Continuousa   Age, y  50.87 (17.88)  59.76 (16.43)  59.13 (15.89)  50.62 (17.74)  47.92 (18.18)  52.28 (20.92)   BMI, kg/m2  32.12 (5.70)  29.34 (6.41)  30.57 (6.63)  30.02 (5.77)  30.27 (6.47)  23.23 (3.28)   Albumin, g/dL  4.21 (0.36)  4.29 (0.31)  4.27 (0.33)  4.26 (0.39)  4.29 (0.33)  4.38 (0.36)   Uric acid, mg/dL  5.44 (1.53)  5.65 (1.49)  5.75 (1.53)  5.68 (1.56)  5.52 (1.54)  5.24 (1.46)   TB, μmol/L  10.44 (4.14)  11.17 (4.40)  11.13 (4.38)  10.62 (4.11)  10.76 (4.62)  11.88 (5.43)   CRP, mg/dL  0.61 (0.88)  0.49 (0.74)  0.65 (1.36)  0.55 (1.05)  0.56 (0.95)  0.41 (1.16)  Categoricalb   Male sex  1368 (35.9)  1171 (51.6)  918 (56.5)  1271 (50.6)  1150 (43.8)  1080 (63.9)   Elderly  938 (24.6)  932 (41.0)  620 (38.1)  595 (23.7)  552 (21.0)  565 (33.4)   Prediabetes  687 (18.0)  473 (20.9)  1110 (68.3)  483 (19.2)  482 (18.4)  246 (14.6)   Race/ethnicity     Non-Hispanic white  1908 (50.1)  1145 (50.4)  739 (45.4)  1296 (51.6)  1274 (48.5)  726 (42.9)   Mexican American  935 (24.5)  499 (22.0)  448 (27.6)  727 (28.9)  720 (27.4)  561 (33.2)   Other Hispanic  191 (5.0)  108 (4.8)  92 (5.7)  141 (5.6)  181 (6.9)  102 (6.0)   Non-Hispanic black  675 (17.7)  453 (19.9)  295 (18.1)  255 (10.1)  364 (13.9)  235 (13.9)   Other race  102 (2.7)  66 (2.9)  52 (3.2)  95 (3.8)  86 (3.3)  67 (4.0)   CHF  121 (3.2)  100 (4.4)  80 (4.9)  84 (3.3)  97 (3.7)  56 (3.3)   CHD  167 (4.4)  128 (5.6)  116 (7.1)  127 (5.1)  140 (5.3)  80 (4.7)   Angina pectoris  160 (4.2)  110 (4.8)  101 (6.2)  113 (4.5)  117 (4.5)  62 (3.7)   Heart attack  156 (4.1)  120 (5.3)  115 (7.1)  127 (5.1)  140 (5.3)  90 (5.3)   Stroke  126 (3.3)  120 (5.3)  79 (4.9)  89 (3.5)  96 (3.7)  65 (3.8)   Emphysema  50 (1.3)  46 (2.0)  35 (2.2)  27 (1.1)  23 (0.9)  43 (2.5)   Malignancy  308 (8.1)  229 (10.1)  177 (10.9)  186 (7.4)  167 (6.4)  138 (8.2)   Smoking  1788 (46.9)  1106 (48.7)  832 (51.2)  1299 (51.7)  1299 (49.5)  925 (54.7)   Recreational activity  1685 (44.2)  1040 (45.8)  733 (45.1)  1150 (45.7)  1202 (45.8)  794 (47.0)  Abbreviations: CHD, coronary heart disease; CHF, congestive heart failure; TB, total bilirubin. a Continuous values expressed as mean (standard deviation). b Categorical variables data are expressed as no. (%). View Large Survival analyses of cardiovascular diseases classified by different definitions of MetS Survival analysis for cardiovascular mortality was plotted using Kaplan-Meier curves of the different definitions of MetS (Supplemental Fig. 1). Table 2 shows the hazard ratios (HRs) of cardiovascular mortality in participants with different definitions of MetS. The HRs and 95% CI of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS in the adjusted model were 1.867 (95% CI, 1.328 to 2.625), 1.871 (95% CI, 1.334 to 2.625), 1.949 (95% CI, 1.421 to 2.672), and 2.306 (95% CI, 1.586 to 3.352), respectively (all P < 0.001). Taken together, these findings indicate the CC-incorporated definition of MetS tended to have a better predictive ability for cardiovascular mortality. Table 2. HRs of Cardiovascular Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  2.068 (1.573–2.720)  <0.001  1.867 (1.328–2.625)  <0.001  CalfMetS  5.887 (4.453–7.784)  <0.001  1.871 (1.334–2.625)  <0.001  WCRMetS  3.884 (2.954–5.108)  <0.001  1.949 (1.421–2.672)  <0.001  CC+MetS  6.687 (4.686–9.542)  <0.001  2.306 (1.586–3.352)  <0.001    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  2.068 (1.573–2.720)  <0.001  1.867 (1.328–2.625)  <0.001  CalfMetS  5.887 (4.453–7.784)  <0.001  1.871 (1.334–2.625)  <0.001  WCRMetS  3.884 (2.954–5.108)  <0.001  1.949 (1.421–2.672)  <0.001  CC+MetS  6.687 (4.686–9.542)  <0.001  2.306 (1.586–3.352)  <0.001  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Survival analyses of all-cause mortality stratified by different definitions of MetS Survival analysis for all-cause mortality was plotted using Kaplan-Meier curves of the different definitions of MetS (Supplemental Fig. 2). The HRs of all-cause mortality in participants with different MetS are summarized in Table 3. The HRs and 95% CIs of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS domains in the adjusted model were 1.614 (95% CI, 1.305 to 1.995), 1.573 (95% CI, 1.261 to 1.963), 1.728 (95% CI, 1.415 to 2.111), and 1.854 (95% CI, 1.443 to 2.382), respectively (all P < 0.001). Collectively, the findings indicate this CC-incorporated definition of MetS might be another useful prediction tool for all-cause mortality, except for CalfMetS. Table 3. HRs of All-Cause Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.811 (1.511–2.170)  <0.001  1.614 (1.305–1.995)  <0.001  CalfMetS  4.011 (3.277–4.908)  <0.001  1.573 (1.261–1.963)  <0.001  WCRMetS  3.248 (2.711–3.891)  <0.001  1.728 (1.415–2.111)  <0.001  CC+MetS  4.780 (3.758–6.079)  <0.001  1.854 (1.443–2.382)  <0.001    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.811 (1.511–2.170)  <0.001  1.614 (1.305–1.995)  <0.001  CalfMetS  4.011 (3.277–4.908)  <0.001  1.573 (1.261–1.963)  <0.001  WCRMetS  3.248 (2.711–3.891)  <0.001  1.728 (1.415–2.111)  <0.001  CC+MetS  4.780 (3.758–6.079)  <0.001  1.854 (1.443–2.382)  <0.001  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Survival analyses of cancer mortality categorized by different definitions of MetS Survival analysis for cancer mortality was plotted using Kaplan-Meier curves concerning the different definitions of MetS (Supplemental Fig. 3). Table 4 lists the HRs of cancer mortality in participants with different newly defined MetS. The HRs and 95% CI of the individuals with WaistMetS, CalfMetS, WCRMetS, and CC+MetS domains in the adjusted model were 1.461 (95% CI, 0.949 to 2.251; P = 0.085), 1.258 (95% CI, 0.764 to 2.072; P = 0.368), 1.599 (95% CI, 1.069 to 2.392; P = 0.022), and 1.670 (95% CI, 0.940 to 2.966; P = 0.080), respectively. These findings illustrated a statistically significant association only between WCRMetS and cancer mortality. WC was substituted with the WCR as one component of MetS, and the WCR coupled with at least two of four metabolic abnormalities had a substantial increased risk of cancer mortality compared with those without WCRMetS. Table 4. HRs of Cancer Mortality Categorized by Different Definitions of MetS   Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.612 (1.115–2.329)  0.011  1.461 (0.949–2.251)  0.085  CalfMetS  2.862 (1.808–4.530)  <0.001  1.258 (0.764–2.072)  0.368  WCRMetS  3.028 (2.103–4.360)  <0.001  1.599 (1.069–2.392)  0.022  CC+MetS  3.566 (2.046–6.216)  <0.001  1.670 (0.940–2.966)  0.080    Unadjusted Model HR (95% CI)  P Value  Adjusted Model HR (95% CI)  P Value  WaistMetS  1.612 (1.115–2.329)  0.011  1.461 (0.949–2.251)  0.085  CalfMetS  2.862 (1.808–4.530)  <0.001  1.258 (0.764–2.072)  0.368  WCRMetS  3.028 (2.103–4.360)  <0.001  1.599 (1.069–2.392)  0.022  CC+MetS  3.566 (2.046–6.216)  <0.001  1.670 (0.940–2.966)  0.080  Adjusted for age; sex; race/ethnicity; BMI; levels of serum albumin, serum uric acid, serum total bilirubin, and CRP; history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, stroke, cancer/malignancy, emphysema, smoking, and moderate to vigorous levels of recreational activity. View Large Association of different definitions of MetS with CRP and insulin resistance To explore whether the inflammatory process and insulin resistance could explain the connection between several definitions of MetS and different causes of mortality, we further determined the interplay among the different definitions of MetS, serum CRP level, and HOMA-IR. Table 5 lists the positive associations of the CC-incorporated definition of MetS with CRP level in the adjusted models (P < 0.05); CalfMetS showed the strongest positive correlation. In addition, all MetS in our study revealed positive relationships with HOMA-IR in the adjusted models (P < 0.001), and WCRMetS showed the strongest positive correlation. Collectively, these findings indicate the CC-incorporated definition of MetS demonstrated positive associations with CRP level and insulin resistance. Table 5. Associations of CRP Levels and HOMA-IR With Different Definitions of MetS   Serum CRP Level  HOMA-IR  Total Adjusted βa (95% CI)  P Value  Total Adjusted βa (95% CI)  P Value  WaistMetS  0.019 (−0.032 to 0.070)  0.472  2.941 (2.499–3.384)  <0.001  CalfMetS  0.184 (0.107–0.261)  <0.001  1.416 (0.742–2.091)  <0.001  WCRMetS  0.107 (0.050–0.165)  <0.001  3.010 (2.509–3.511)  <0.001  CC+MetS  0.159 (0.059–0.260)  0.002  2.574 (1.703–3.444)  <0.001    Serum CRP Level  HOMA-IR  Total Adjusted βa (95% CI)  P Value  Total Adjusted βa (95% CI)  P Value  WaistMetS  0.019 (−0.032 to 0.070)  0.472  2.941 (2.499–3.384)  <0.001  CalfMetS  0.184 (0.107–0.261)  <0.001  1.416 (0.742–2.091)  <0.001  WCRMetS  0.107 (0.050–0.165)  <0.001  3.010 (2.509–3.511)  <0.001  CC+MetS  0.159 (0.059–0.260)  0.002  2.574 (1.703–3.444)  <0.001  a β coefficients were interpreted as the degree of association of CRP and HOMA-IR with each different definition of MetS. View Large Discussion In this cohort study of the US general population, the most prominent finding was that the CC-incorporated definition of MetS was useful in correlating MetS with cardiovascular risk. The CC-incorporated domains, especially CC in addition to the traditional definition of MetS, were associated with higher cardiovascular mortality risk than was MetS alone. In addition, the CC-incorporated definition of MetS, except when CC was simply substituted for WC, also had a higher correlation with all-cause and cancer mortality risk than the traditional definition of MetS. As far as we are aware, this is the first study to propose the idea of incorporating CC into the components of MetS in different ways for the prediction of mortality risk. A large cohort study that enrolled 23,998 participants with 4.8 years of follow-up tried to add elevated CRP level to the ATP III criteria as a sixth component and showed a similar mortality risk as that determined with the traditional definition of MetS (26). Similarly, our study aimed to add low CC to the MetS of ATP III criteria as another component, on the basis of the accumulating evidence (19–21). Findings of another large cohort study, the Canada Fitness Survey of 10,638 subjects with over 12 years of follow-up, indicated that WC and CC seemed to demonstrate contrary effects on mortality risk (27). Emerging studies suggested that low CC had the highest accuracy for predicting mortality in patients with chronic obstructive pulmonary disease compared with other anthropometric parameters (28). Moreover, CC had a better ability to predict emerging care needs in the elderly in a national cohort study (29). Findings of a cross-sectional study indicated subjects with high WCR had a significantly greater risk of MetS and arterial stiffness and researchers suggested that large CC might indicate a reduced risk for cardiovascular disease (30). Collectively, these studies’ results implied the potential role of CC in predicting cardiovascular mortality risk, and its importance and implications could be supported from the results of our study. A possible mechanism for the CC-incorporated definition of MetS, which demonstrated a higher cardiovascular mortality risk in our study, might be related to the connection between CC and skeletal muscle. An earlier study suggested that higher amounts of lean mass and strength might be protective factors for premature death in the obese group (31). Mostly, the extremities had a relatively higher fat-free mass than other body sites, and the CC was reported to have the closest relationship with muscle mass compared with other anthropometric parameters (32, 33). Zuliani et al. (34) indicated that insulin resistance and low-grade systemic inflammation were the main predisposing factors for overall and cardiovascular mortality in a 9-year follow-up cohort study. In regard to low-grade systemic inflammation, higher serum levels of interleukin-6 and CRP were associated with cardiovascular mortality in previous studies (35, 36). CRP levels were higher among patients with greater WC than among those with lower WC (37). Moreover, recent findings clearly demonstrated a strong link between chronic low-grade inflammation and skeletal muscle. In a study of differentially expressed genes in insulin-resistant skeletal muscle tissue, the inflammatory gene expression in skeletal muscle of women with MetS was negatively correlated with insulin sensitivity (38). Specifically, there were a substantial number of studies examining the advantageous metabolic effects of skeletal muscle in the peripheral target tissue for adiponectin. A review of recent research observed that adiponectin was an adipose tissue–derived hormone with antiatherogenic and anti-inflammatory functions, and its levels were shown to be negatively associated with insulin resistance (39). Preliminary data from recent observational studies had indicated that inflammation, insulin resistance, and metabolic dysfunction in skeletal muscle play a major role in the progress of MetS and type 2 diabetes via adiponectin expression and action (40). Another study suggested that effects of muscle-derived adiponectin on skeletal muscle had major insulin-sensitizing and metabolic effects that contributed to the antidiabetic outcome (41). Furthermore, according to several studies, the insulin-sensitizing effects of exercise might be associated with increased adiponectin receptor 1 (AdipoR1) expression in skeletal muscle instead of circulating adiponectin levels (42). A previous publication indicated AdipoR1 was abundantly expressed in skeletal muscle (43), and other studies using experimental animal models reported the associated results connected AdipoR1 expression with exercise (44–46). Evidence also suggests exercise might increase AdipoR1 expression in the muscle of rats (44). Another animal study suggested that long-term exercise training affected the expression level of AdipoR1, thereby improving insulin resistance in obese mice (45). In addition, chronic exercise training might improve insulin sensitivity and reduce AdipoR1 gene expression in the soleus muscle of obese rats (46). Although there were a limited number of studies regarding the correlation between AdipoR1 expression and exercise in humans, we speculated that these relationships might exist in human subjects. The phenomenon of long-term exercise increasing the soleus muscle mass was considerable (47); therefore, we hypothesized that a higher CC might be associated with increased AdipoR1 gene expression and vice versa. Moreover, there were several reports in the review literature supporting chronic inflammation–induced loss of skeletal muscle (48). Nevertheless, skeletal muscle had recently been identified as an endocrine organ that secretes myokines and exerts either paracrine or autocrine effects (49). According to two studies (50, 51), some myokines are involved in inducing anti-inflammatory responses with each bout of exercise and mediating beneficial effects on health via long-term exercise in the protection against cardiovascular risk factors and low-grade inflammation. Therefore, a vicious cycle of chronic inflammation was formed in patients with systemic inflammatory diseases, such as type 2 diabetes mellitus and atherosclerosis, leading to decreased physical activity and disability that was accompanied with deconditioned skeletal muscles and exacerbated inflammation (50). In addition, results of a pilot study conducted by Takagi et al. (52) suggested that a reverse association was detected between resting pulse rate and CC. A longitudinal cohort study demonstrated that capillary supply was inversely related to resting heart rate at ages of 50 and 70 years (53). Furthermore, the percentage of slow-twitch muscle fiber was predominant in the soleus muscle (54), and a lower percentage of slow-twitch muscle fiber in MetS muscle was associated with the severity of insulin resistance (55). A previous publication had indicated the capillary density of slow-twitch muscle fibers was higher than that of fast-twitch muscle fibers (56). Consequently, the subjects with low CC might have a relatively high resting heart rate and lower capillary density. The greater elevation in heart rate was secondary to reduced stroke volume and associated with exercise intolerance (57), and the lower capillary density was correlated with muscular blood flow (58). Therefore, these negative factors related with low CC may contribute to an increase in the risk of cardiovascular events. Taken together, the plausible underlying pathophysiological effects, including adiponectin, myokines, and resting heart rate, might explain the findings in our study that the CC-incorporated definition of MetS demonstrated better relationships with inflammation and insulin resistance than the traditional definition of MetS, and was correlated with higher cardiovascular mortality risk, especially WCRMetS and CC+MetS. Another interesting finding of our study was that WCRMetS was associated with a statistically significant increased risk of cancer mortality by nearly 60% compared with subjects without WCRMetS. The plausible mechanisms might be attributed to skeletal muscle wasting and cachexia, which were characterized as a state of constantly depleting skeletal muscle mass that cannot be completely recovered by any conservative nutritional treatment and develops further functional impairment (59). Evidence from a review article further highlighted that patients with cancer who had low lean body mass, especially those with sarcopenic obesity, were at increased risk of treatment-related toxicities from chemotherapy and were associated with increased overall mortality (60). Additionally, Fukawa et al. (61) indicated that cachectic cancer cells produced several inflammatory factors that rapidly caused fatty acid oxidation and led to a manifestation of cachectic muscle atrophy. Taken together, these findings revealed that the individuals with cancer who had low muscle mass had increased mortality risk, which was associated with inflammation. Although we cannot clarify the reason why only WCRMetS had a significant correlation with cancer mortality, the finding suggests that the incorporation of low CC into the components of MetS might also help detect earlier low muscle mass status of patients with cancer and help identify the need for intervention to reduce mortality risk. Several limitations of this study should be mentioned. First, the optimal cutoff values of CC and WCR were calculated from the NHANES data, which mainly represent noninstitutionalized US citizens. Because the definitions of CC cutoffs differ depending on ethnicity, the generalization of the results to other populations of different race or ethnicity may be limited (62). Second, we did not assess the risk of incident cardiovascular events and type 2 diabetes mellitus in subjects with different definitions of MetS, because there was no available information. Third, self-report past history rather than objective measures may be prone to recall bias. Last, we did not compare waist-hip ratio (WHR) with CC in the additional components of MetS in correlating with cardiovascular risk because there was a lack of these data in the National Health and Nutrition Examination Survey data set for 1999 through 2002. Therefore, we collected the data of WHR from the NHANES III, and the cut point of WHR was defined on the basis of the report of a World Health Organization expert consultation. The adjusted HRs in the WHR-incorporated definition of MetS and traditional MetS were 1.196 (95% CI, 1.106 to 1.294), and 1.214 (95% CI, 1.123 to 1.313), respectively. The results implied that the WHR-incorporated definition of MetS was not superior to traditional MetS. From the NHANES of 1999 through 2002, The adjusted HRs in the CC-incorporated definition of MetS and traditional MetS were 1.916 (95% CI, 1.513 to 2.425), and 1.421 (95% CI, 1.196 to 1.687), respectively. It was tempting to speculate that the CC-incorporated definition of MetS might be more suitable for the prediction of mortality risk than the WHR-incorporated definition of MetS (Supplemental Table 1). In conclusion, the CC-incorporated definition of MetS, particularly in addition to the traditional definition of MetS, demonstrated higher correlation with cardiovascular and all-cause mortality risk than did the traditional definition of MetS alone. In addition, WCRMetS was significantly and positively associated with cancer mortality. More studies are warranted to determine the ethnicity-specific cutoff values of CC and WCR for their application in clinical practice. Abbreviations: Abbreviations: AdipoR1 adiponectin receptor 1 ATP Adult Treatment Panel III AUROC area under the receiver operator characteristic curve BMI body mass index CalfMetS decreased calf circumference and two or more of four metabolic syndrome components CC calf circumference CC+MetS decreased calf circumference and three or more of five metabolic syndrome components metabolic syndrome metabolic syndrome CI confidence interval CRP C-reactive protein HOMA-IR homeostasis model assessment of insulin resistance HR hazard ratio MetBP elevated blood pressure defined as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or current hypertension medication use MetCalf calf circumference <33.65 cm in women and <36.65 cm in men MetGlu high fasting-glucose level defined as ≥100 mg/dL or current oral hypoglycemic agent or insulin use MetHDL low high-density lipoprotein cholesterol, defined as <40 mg/dL in men and <50 mg/dL in women or patients under medical control MetTG high serum triglyceride levels defined as ≥150 mg/dL among subjects who did not use lipid-lowering medications MetWaist category in which waist circumference ≥102 cm in men and ≥88 cm in women NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination Survey WaistMetS increased waist circumference and two or more of four metabolic syndrome components WC waist circumference WCR waist-to-calf ratio WCRMetS increased waist-to-calf ratio and two or more of four metabolic syndrome components WHR waist-hip ratio Acknowledgments Financial Support: No financial support was received for this study. 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Journal of Clinical Endocrinology and MetabolismOxford University Press

Published: Mar 1, 2018

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