Total Visitors: 141,405




Index Copernicus International


Turkiye Citation Index


Turk Medline

InfoBase Index

Google Scholar

The Association of Metabolic Syndrome Components with Anthropometric Measurements

Cagla Ozdemir, Cenk Aypak, Suleyman Gorpelioglu

Euras J Fam Med 2021;10(2):65-73. doi:10.33880/ejfm.2021100204


Original Research


Aim: This study aims to research the association of metabolic syndrome components with anthropometric measurements like arm circumference, neck circumference, hip circumference, waist-hip ratio, and waist-to-height ratio, which are applied rarely.

Methods: This cross-sectional study was conducted with 292 patients in November-December 2019. The arm circumference, neck circumference, waist circumference, hip circumference, waist-hip ratio, waist-to-height ratio, and body mass index measurements of the patients were made. Glucose, high-density lipoprotein, low-density lipoprotein, systolic, and diastolic blood pressure measurements were also recorded. The association between metabolic syndrome components and anthropometric measurements was analyzed. 

Results: Metabolic syndrome is diagnosed in 32.8% of the participants. According to body mass index, 18.6% of the patients were normal, 34.2% were overweight, and 47.2% were obese. There was a significant difference between the patients with and without metabolic syndrome in terms of laboratory parameters, blood pressure values, and anthropometric measurements. In the diagnosis of metabolic syndrome, optimal cut-off values for arm circumference, neck circumference, hip circumference, waist-hip ratio, and waist-to-height ratio were determined as 31.75 (AUC=0.703), 34.85 (AUC=0.763), 113.75 (AUC=0.757), 0.90 (AUC=0.701), 0.61 (AUC=0.769) for females while they were 35.75 (AUC=0.573), 39.75 (AUC=0.795), 111.5 (AUC=0.607), 0.96 (AUC=0.888), 0.61 (AUC=0.888) for males respectively. 

Conclusion: A significant correlation was detected between arm circumference, neck circumference, hip circumference, waist-hip ratio, and waist-to-height ratio, and metabolic syndrome components. However, low-density lipoprotein was not correlated with neck circumference and high-density lipoprotein with waist-to-height ratio.  

Keywords: metabolic syndrome, anthropometric measurement, body mass index, obesity, lipid profile


Metabolic syndrome (MS) is a disease characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia by World Health Organisation (WHO). It is known that MS and obesity are associated with cardiovascular diseases, insulin resistance, some cancer types, and psychiatric diseases (1-3). 

Determining some strategies to predict MS risk in adults and prevent its occurrence from the basics of preventive health care. For this reason, it is important to make an early diagnosis in high-risk individuals (4,5). There are several criteria for the evaluation of individuals in terms of obesity. The most common ones are the body mass index (BMI) and waist circumference (WC) measurement, however, it is not possible to receive more accurate information about body composition. WC measurement is not sufficient for bed-ridden patients, pregnant, and other patients having several diseases like liver diseases and so on. In such cases, other anthropometric measurements become important (6). 

Fatty acid release from visceral adipose tissue is different from subcutaneous adipose tissue. Fatty acid release from visceral adipose tissue causes a mild inflammation whereas fatty acid release from subcutaneous adipose tissue leads to a decrease in insulin sensitivity (7). This situation explains the association between anthropometric measurements and cardiometabolic parameters. Free fatty acid release from subcutaneous adipose tissue increases the risk independently of visceral adiposity (8,9). 

The more recent studies are deeply focused on body composition and cardiovascular disease risk. Neck circumference (NC), an indicator of upper body fat tissue, increases cardiovascular disease risk independently of visceral fat accumulation (9). 

Circumferential measurements may give information on the evaluation of fat localization (6,10,11). To the best of our knowledge, there is no study focusing on all the anthropometric measurements such as arm circumference (AC), NC, hip circumference (HC), waist-hip ratio (WHR), and waist-to-height ratio (WHtR) in our country. We aim to detect the association between the anthropometric measurements applied rarely like AC, NC, HC, WC, WHR, and WHtR and MS components and also to contribute to the literature.


This cross-sectional study was conducted with individuals between the ages of 18 and 69 who have applied to Ankara Dışkapı Yıldırım Beyazıt Training and Research Hospital Family Practice Policlinics in November-December 2019.  

Our study was approved by the Ethics Committee of Ankara Dışkapı Yıldırım Beyazıt Training and Research Hospital (Decree No: 72/08). This study was conducted with 292 volunteer participants who have maintained their weight for the last three months or not having been in the lactation period, and not having received an antihyperlipidemic treatment. 

Age, gender, educational levels, menopausal status, socio-demographic features examining the situation of present illness and drug utilization, high-density lipoprotein (HDL), low-density lipoprotein (LDL), glucose, and triglyceride levels of these patients were recorded. Biochemical parameters were studied using P800 Roche Hitachi and Olympus AU 5200. WC, HC, AC, and NC values of the patients were measured using an inelastic tape measure. 

The height and bodyweight of the patients were also measured and BMI was calculated by dividing patients' body weight by the square of their height. Horizontal size between the largest parts of the hip was measured for the HC. While WC was measured midway between the iliac crest and the last rib (12), AC was measured midway between the shoulder and the elbow (13).

According to the World Health Organization (WHO) classification, participants were categorized as normal, overweight, and obese. The participants were evaluated considering metabolic syndrome diagnostic criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and were split into groups as being with or without metabolic syndrome. Metabolic syndrome diagnosis was made in the presence of at least three of NCEP ATP III criteria mentioned below:

  •        Abdominal obesity (waist circumference >102 cm in males, >88 cm in females)
  •        Hypertriglyceridaemia (≥150 mg/dl)
  •        Low HDL (
  •        Hypertension (blood pressure ≥ 130/85)
  •        Hyperglycemia (fasting glucose ≥ 110 mg/dl)

Statistical analyses were performed using SPSS version 21.0 (IBM®, Chicago, The USA) packaged software. The convenience of variables to the normal distribution was analyzed through visual (histogram and probability graphics) and analytical (Shapiro-Wilk test) methods. Descriptive statistics were expressed as mean and standard deviation in normally distributed numeric data and as number and percentage in nominal data. Normally distributed numeric variables were analyzed through "Independent Samples T-Test" between two groups, "One-way Anova Test" among three groups, and "Paired T-Test" within the group. Non-normally distributed numeric variables were compared through the "Mann-Whitney U Test" between two groups and of the "Kruskal-Wallis Test" among three groups. Nominal data were evaluated between two groups by using the "Chi-square Test".  P values below 0.05 in statistical analyses were accepted as statistically significant.


Metabolic syndrome was present in 97 participants in our study. Mean ages of the females with MS were 47±14.9 years and without MS were 38.9±13.5 years (p<0.001); mean ages of the males with MS were 50.3±14.2 years and without MS 35.9±11.2 years (p<0.001). 78.8% of the participants were female (p<0.001). 47.7% of all the participants had an educational background shorter than 12 years about the presence of MS (p<0.001).

There was a significant difference between the females with and without MS in terms of laboratory parameters, tension values, and anthropometric measurements (p<0.001). All the metabolic parameters and measurements except HDL-cholesterol level were higher within the group consisting of females with MS whereas HDL-cholesterol levels were found out to be lower.  

There was also a significant difference between the males with and without MS in terms of laboratory parameters, blood pressure measurements, and anthropometric measurements such as NC, WHR, and WHtR apart from LDL-cholesterol levels (p<0.001). No significant difference was also detected among AC, HC, and HDL-cholesterol levels (p>0.05). 

The distribution of socio-demographic features, metabolic parameters, and anthropometric measurements according to gender differences and the presence of MS was summarized in Table 1. 

Table 1. The distribution of socio-demographic features, metabolic parameters, and of anthropometric measurements according to gender differences and  the presence of MS

Male (n=62)
  MS (+) (N=60) MS (-) (N=170) p MS (+) (N=37) MS (-) (N=25) p
Age (years) Mean ± SD 47±14.9 38.9±13.5 <0.001 50.3±14.2 35.9±11.2 <0.001
Glucose (mg/dL) Med  (min-max) 101 (66-232) 83 (60-266) <0.001 89 (67-256) 80 (48-115) 0.001
SBP (mmHg) Med  (min-max) 130 (100-180) 110 (75-160) <0.001 137 (90-155) 110 (90-140) <0.001
DBP(mmHg) Med  (min-max) 85 (65-120) 78 (50-110) <0.001 90 (60-110) 80 (60-90) <0.001
Triglyceride (mg/dL) Med  (min-max) 177 (48-422) 102 (32-404) <0.001 172 (48-501) 114 (50-342) <0.001
HDL(mg/dL) Med  (min-max) 42 (26-69) 51 (28-84) <0.001 38 (25-69) 44 (25-74) 0.036
LDL (mg/dL) Mean ± SD 135.5±33.3 124.3±29 0.016 137.2±32.8 134±29.9 0.698
BMI Mean ± SD 32.6±4 27.9±5.4 <0.001 32.4±4.5 28.5±3.8 0.001
NC (cm) Med  (min-max) 37.5 (32-48) 34 (29-44) <0.001 43 (38-51) 39.5 (32-47) <0.001
AC (cm) Med  (min-max) 33 (28-44) 31 (20.5-46) <0.001 33 (27.5-43) 32.5 (26-42) 0.246
HC (cm) Med  (min-max) 115 (98-149) 108 (31-135) <0.001 112 (99-135) 108.5 (101-127) 0.220
WHR Med  (min-max) 0.9 (0.6-1.1) 0.8 (0.6-2.9) <0.001 1 (0.8-1.1) 0.9 (0-0.9) <0.001
WHtT Med  (min-max) 0.6 (0.5-0.9) 0.5 (0.3-0.8) <0.001 0.6 (0.5-0.7) 0.5 (0-0.7) <0.001

HDL: High-Density Lipo-protein; LDL: Low-Density Lipo-protein; BMI: Body Mass Index; NC: Neck Circumference; AC: Arm Circumference; WC: Waist Circumference; HC: Hip Circumference; WHR: Waist- Hip Ratio; WHtR: Waist-To-Height Ratio; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MS: Metabolic Syndrome

A significant positive correlation was observed among glucose, triglyceride, LDL-cholesterol, systolic blood pressure (SBP), diastolic blood pressure (DBP) and arm circumference (AC), waist circumference (WC), hip circumference (HC), and BMI when all the patients within the groups were evaluated together (p<0.05). However, a significant negative correlation was observed with HDL level (p<0.05). The association between metabolic parameters and anthropometric measurements was summarized in Table 2.


Table 2. The association between metabolic parameters and anthropometric measurements

    Glucose** TRG** HDL** HDL** SBP** DBP**
NC  Rho 0.264 0.398 -0.331 0.056 0.502 0.452
  p <0.001 <0.001 <0.001 0.393 <0.001 <0.001
AC  Rho 0.272 0.347 -0.232 0.251 0.423 0.485
  p <0.001 <0.001 <0.001 0.015 <0.001 <0.001
WC  Rho 0.385 0.317 -0.180 0.177 0.533 0.490
  p <0.001 <0.001 0.002 0.003 <0.001 <0.001
HC  r 0.288 0.243 -0.155 0.191 0.425 0.402
  p <0.001 <0.001 0.009 0.001 <0.001 <0.001
BMI Rho 0.411 0.322 -0.189 0.197 0.487 0.436
  p <0.001 <0.001 0.001 0.001 <0.001 <0.001
WHR Rho 0.327 0.283 -0.152 0.134 0.432 0.391
  p <0.001 <0.001 0.011 0.025 <0.001 <0.001
WHtT Rho 0.429 0.304 -0.077 0.191 0.522 0.452
  p <0.001 <0.001 0.203 0.001 <0.001 <0.001

*Pearson Correlation Test; **Spearman Correlation Test;  HDL: High-Density Lipo-protein; LDL: Low-Density Lipo-protein; TRG: Triglyceride; BMI: Body Mass Index; NC: Neck Circumference; AC: Arm Circumference; WC: Waist Circumference; WHR: Waist-Hip Ratio; WHtR: Waist-To-Height Ratio; HC: Hip Circumference; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure

It was observed in the correlation analysis between BMI and anthropometric measurements that BMI was positively correlated with AC (Rho: 0.817), NC (Rho: 0.666), HC (Rho: 0.819), WC (Rho: 0.820), WHR (Rho: 0.535), and WHtR (Rho: 0.850) (p<0.05)

In Figure 1, the anthropometric measurements' accuracy for diagnosing MS was compared using plots of the ROC curves.

Figure 1. The anthropometric measurements' accuracy for diagnosing MetS was compared using plots of the ROC curves. WC: Waist Circumference; HC: Hip Circumference; AC: Arm circumference; NC: Neck circumference; WHR: Waist-Hip Ratio; WHtR: Waist-To-Height Ratio.


In Figure 2, the anthropometric measurements' accuracy for diagnosing obesity was compared using plots of the ROC curves. 

Figure 2. The anthropometric measurements' accuracy for diagnosing obesity was compared using plots of the ROC curves. WC: Waist Circumference; HC: Hip Circumference; AC: Arm circumference; NC: Neck circumference; WHR: Waist-Hip Ratio; WHtR: Waist-To-Height Ratio.


AUC, sensitivity, specificity, and cut-off values of anthropometric measurements for MS and obesity were demonstrated in Table 3. 

Table 3. Cut-off values of anthropometric measurements for metabolic syndrome and obesity

  AUC Cut-off value Sensitivity Specificity 95%CI
Females with metabolic syndrome
AC 0.703 31.75 68 58 0.621-0.785
NC 0.763 34.85 80 56 0.682-0.844
HC 0.757 113.75 63 80 0.677-0.836
WHR 0.701 0.90 78 57 0.614-0.787
WHtT 0.769 0.61 85 59 0.695-0.843
Males with metabolic syndrome 
AC 0.573 35.75 34 79 0.419-0.727
NC 0.795 39.75 84 58 0.675-0.915
HC 0.607 111.5 59 70 0.452-0.761
WHR 0.888 0.96 75 87 0.805-0.971
WHtT 0.888 0.61 84 87 0.797-0.979
Females with obesity
AC 0.892 32.25 74 87 0.847-0.938
NC 0.857 34.6 88 72 0.803-0.910
HC 0.919 110.75 81 80 0.882-0.956
WC 0.903 102.50 81 81 0.861-0.945
WHR 0.756 0.92 60 70 0.686-0.826
WHtT 0.922 0.61 89 77 0.884-0.959
Males with obesity
AC 0.775 33.5 60 78 0.653-0.897
NC 0.857 40.25 81 82 0.759-0.955
HC 0.791 110.50 72 78 0.669-0.913
WC 0.926 105.50 87 82 0.861-0.991
WHR 0.838 0.95 78 78 0.731-0.945
WHtT 0.968 0.61 93 91 0.929-1.000

WC: Waist Circumference; HC: Hip Circumference; AC: Arm circumference; NC: Neck circumference; WHR: Waist-Hip Ratio; WHtR: Waist-To-Height Ratio. AUC: Area Under The Curve


A significant correlation of MS components with anthropometric measurements has been detected in our study. There is also a significant correlation between anthropometric measurements with NC and AC. It turns out that NC is associated with a variety of cardiovascular diseases and impaired lipid metabolism. It is thought that it may be used as an indicator in many fatal or non-fatal cardiovascular cases (5,14-17). NC has been detected to be higher in individuals with obesity. Similarly, NC values are significantly found out to be higher in obese participants in our study. NC cut-off values for metabolic syndrome were determined as 39.75 for males (84% sensitivity, 58% specificity) and 34.85 for females (80% sensitivity, 56% specificity). However, they were determined for obesity as 40.25 for males (81% sensitivity, 82% specificity) and as 34.6 for females (88% sensitivity, 72% specificity). In two different studies conducted by Luo et al. (5) and Dai et al. (14) to determine the correlation with visceral obesity, NC cut-off values have been detected as 38.5 in males and 34.5 in females similar to our study. Also, that NC is associated with cardiometabolic risk factors, and increases prospective cardiovascular case incidence has been reported in this study. That there is a significant correlation between NC and glucose, triglyceride, HDL-cholesterol, SBP, and DBP in our study promotes this finding. Similarly, Ozkaya et al (16) revealed in their study including 264 participants that NC is significantly associated with SBP, DBP, HDL-cholesterol, and triglyceride. NC has also been found out to be associated with the whole cardiometabolic risk factor in a study conducted by Preis et al (17), however, there has not been a significant detected correlation with LDL-cholesterol in males and there has been a low correlation in females. In our study, similarly, no correlation has been detected between LDL-cholesterol and neck circumference. 

WHR, WHtR, WC, and HC have been associated with cardiovascular risk factors in several studies (18-20). However, there are a limited number of studies related to AC. Several studies have revealed that AC may be an alternative measurement in cases like pregnancy in which measuring waist circumference is not possible (21,22). Fakier et al. (23) have detected a high correlation between BMI and arm circumference in pregnancy up to 30th weeks in their study. The cut-off value has been determined as 30.57. That AC is associated with obesity has been detected in another study conducted by Okereke et al. (20) with 578 pregnant and the cut-off value has been determined as 33. Similarly, the cut-off value for obesity has been detected as 32.25 in our study. AC, WC, HC, WHtR, and WHR have been detected to be higher significantly in patients with metabolic syndrome in the study of Sagun et al. (24). The association among glucose, triglyceride, HDL-cholesterol, SBP, and DBP and these anthropometric measurements have been found out significantly similar to our study. 

In a prospective cohort study on abdominal obesity, cardiovascular diseases, and cancer-related deaths, 44.636 females have been followed up for 16 years and obesity has been found out to be associated with cardiovascular diseases. Our study has revealed that WHR and WHtR are associated with hypertension and hypercholesterolemia. Higher WC or WHR has been associated with an increased cardiovascular risk even in females with normal weight (18). It is known that WC is the main measurement for the risk estimation compared to the other anthropometric measurements because it reveals a high correlation with visceral fat accumulation. The status of WHR in risk estimation differs. Some studies are revealing that a large waist circumference and a narrow hip circumference reflect the cardiovascular risk more accurately. There are also studies showing that a narrow waist circumference and a wide hip circumference ratio are protective (21). According to some studies in literature, WHR is considered to be more effective than WC in risk estimation (22). American Heart Association recommends the ultimate value for WHR is 0.95 for males and 0.88 for females (25). Cut-off values for obesity have been determined as 0.95 for males and 0.92 for females in our study. WHtR can be an alternative to WHR because it indicates body frame size more accurately lately. This situation has been supported by some studies in the literature. Patel et al. (26) express that WC and WHtR are more associated with metabolic syndrome components compared to WHR. Our study has been supporting this finding. Also, the correlation between BMI and metabolic syndrome components is stronger than WHR.

There are some limitations of our study. Body compositions have been estimated through anthropometric measurements. Notwithstanding, some methods such as CT, MRI, and BEI have importance in the determination of body composition (27). Manuel anthropometric measurements have been preferred due to their practicality. MS frequency has been found out as 61% in females and 59% in males in our study. Compared to the other studies conducted in our country, MS frequency is higher because the participants have been chosen from the applicants to family medicine policlinics consecutively. For this reason, it is not possible to say that MS frequency represents the general population.


BMI may be a simple and adequate measurement for the early diagnosis of obesity. Some measurements like waist circumference, waist-to-height ratio, and waist-hip ratio may also be used to measure the abdominal fat distribution and to determine body frame size. However, considering neck or arm circumference may be a good alternative when waist circumference cannot be measured in patients with pregnancy, cirrhosis, acid, or bedridden patients. 


  1. Isomaa B, Almgren P, Tuomi T, Forsén B, Lahti K, Nissén M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 2001;24(4):683-9. doi: 10.2337/diacare.24.4.683. 
  2. Tasan E. Identification, evaluation and epidemiology of obesity. Turkiye Klinikleri J Int Med Sci 2005;1(37):1-4. 
  3. Tezcan S, Altintaş H, Sönmez R, Akinci A, Doğan B, Cakir B, et al. Cardiovascular risk factor levels in a lower middle-class community in Ankara, Turkey. Trop Med Int Health 2003;8(7):660-7. doi: 10.1046/j.1365-3156.2003.01057.x.
  4. Schneider HJ, Glaesmer H, Klotsche J, Böhler S, Lehnert H, Zeiher AM, et al. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 2007;92(2):589-94. doi: 10.1210/jc.2006-0254. 
  5. Luo Y, Ma X, Shen Y, Xu Y, Xiong Q, Zhang X, et al. Neck circumference as an effective measure for identifying cardio-metabolic syndrome: a comparison with waist circumference. Endocrine 2017;55(3):822-830. doi: 10.1007/s12020-016-1151-y. 
  6. Altuntas Y, Gunbey KS, Bayazit AD, Argon A, Kozok K, Karaca C, et al. Evaluation of body anthropometry and secretion of early phase insulin in obesity. Haseki Tıp Bülteni 1999;37(2):95-101. 
  7. Huang BX, Zhu MF, Wu T, Zhou JY, Liu Y, Chen XL, et al. Neck circumference, along with other anthropometric indices, has an independent and additional contribution in predicting fatty liver disease. PLoS One 2015;10(2):e0118071. doi: 10.1371/journal.pone.0118071.
  8. Shi J, Yang Z, Niu Y, Zhang W, Li X, Zhang H, et al. Large mid-upper arm circumference is associated with metabolic syndrome in middle-aged and elderly individuals: a community-based study. BMC Endocr Disord 2020;20(1):78. doi: 10.1186/s12902-020-00559-8.
  9. Ogbera AO. Prevalence and gender distribution of the metabolic syndrome. Diabetol Metab Syndr 2010;2:1. doi: 10.1186/1758-5996-2-1.
  10. Kozan O, Oguz A, Abaci A, Erol C, Ongen Z, Temizhan A, et al. Prevalence of the metabolic syndrome among Turkish adults. Eur J Clin Nutr 2007;61(4):548-53. doi: 10.1038/sj.ejcn.1602554.
  11. Day C. Metabolic syndrome, or what you will: definitions and epidemiology. Diab Vasc Dis Res 2007;4(1):32-8. doi: 10.3132/dvdr.2007.003.
  12. Oğuz A, Altuntaş Y, Karsidağ K, Güleç S, Temizhan A, Akalın AA, et al. The prevalence of metabolic syndrome in Turkey. Obesity Reviews 2010;11:486. 
  13. Lumeng CN, Saltiel AR. Inflammatory links between obesity and metabolic disease. J Clin Invest 2011;121(6):2111-7. doi: 10.1172/JCI57132.
  14. Dai Y, Wan X, Li X, Jin E, Li X. Neck circumference and future cardiovascular events in a high-risk population--A prospective cohort study. Lipids Health Dis 2016;15:46. doi: 10.1186/s12944-016-0218-3.
  15. Stabe C, Vasques AC, Lima MM, Tambascia MA, Pareja JC, Yamanaka A, et al. Neck circumference as a simple tool for identifying the metabolic syndrome and insulin resistance: results from the Brazilian Metabolic Syndrome Study. Clin Endocrinol (Oxf) 2013;78(6):874-81. doi: 10.1111/j.1365-2265.2012.04487.x. 
  16. Ozkaya I, Yardimci B, Tunckale A. Appropriate neck circumference cut-off points for metabolic syndrome in Turkish patients with type 2 diabetes. Endocrinol Diabetes Nutr 2017;64(10):517-23. doi: 10.1016/j.endinu.2017.07.006.
  17. Preis SR, Massaro JM, Hoffmann U, D'Agostino RB Sr, Levy D, Robins SJ, et al. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart study. J Clin Endocrinol Metab 2010;95(8):3701-10. doi: 10.1210/jc.2009-1779. 
  18. Zhang C, Rexrode KM, Van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation 2008;117(13):1658-67. doi: 10.1161/CIRCULATIONAHA.107.739714.
  19. Caprio S, Hyman LD, McCarthy S, Lange R, Bronson M, Tamborlane WV. Fat distribution and cardiovascular risk factors in obese adolescent girls: importance of the intraabdominal fat depot. Am J Clin Nutr 1996;64(1):12-7. doi: 10.1093/ajcn/64.1.12.
  20. Okereke CE, Anyaehie UB, Dim CC, Iyare EE, Nwagha UI. Evaluation of some anthropometric indices for the diagnosis of obesity in pregnancy in Nigeria: a cross-sectional study. Afr Health Sci 2013;13(4):1034-40. doi: 10.4314/ahs.v13i4.25.
  21. Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73(7):460-8. doi: 10.1016/0002-9149(94)90676-9.
  22. Madden AM, Smith S. Body composition and morphological assessment of nutritional status in adults: a review of anthropometric variables. J Hum Nutr Diet 2016;29(1):7-25. doi: 10.1111/jhn.12278.
  23. Fakier A, Petro G, Fawcus S. Mid-upper arm circumference: A surrogate for body mass index in pregnant women. S Afr Med J 2017;107(7):606-10. doi: 10.7196/SAMJ.2017.v107i7.12255.
  24. Sagun G, Oguz A, Karagoz E, Filizer AT, Tamer G, Mesci B. Application of alternative anthropometric measurements to predict metabolic syndrome. Clinics (Sao Paulo) 2014;69(5):347-53. doi: 10.6061/clinics/2014(05)09.
  25. Han L, You D, Ma W, Astell-Burt T, Feng X, Duan S, et al. National trends in American Heart Association Revised Life's Simple 7 Metrics associated with risk of mortality among US adults. JAMA Netw Open 2019;2(10):e1913131. doi: 10.1001/jamanetworkopen.2019.13131.
  26. Patel S, Unwin N, Bhopal R, White M, Harland J, Ayis SA, et al. A comparison of proxy measures of abdominal obesity in Chinese, European and South Asian adults. Diabet Med 1999;16(10):853-60. doi: 10.1046/j.1464-5491.1999.00163.x. 
  27. Lemos T, Gallagher D. Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes 2017;24(5):310-4. doi: 10.1097/MED.0000000000000360.

How to cite: Ozdemir C, Aypak C, Gorpelioglu S. The association of metabolic syndrome components with anthropometric measurements. Euras J Fam Med 2021;10(2):65-73. doi:10.33880/ejfm.2021100204.

Download Full Text Add to Favorites