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Sagittal abdominal diameter shows better correlation with cardiovascular risk factors than waist circumference and BMI

Journal of Diabetes & Metabolic Disorders volume 12, Article number: 41 (2013) Cite this article

Abstract

Background

Obesity (abdominal adiposity) is a risk factor for cardiovascular diseases and the most used methods to measure the adiposity are body mass index (BMI), waist circumference (WC), and sagittal abdominal diameter (SAD).

Objective

To correlate BMI, WC, and SAD with biochemical parameters and blood pressure in adults.

Methods

A non-experimental exploratory/descriptive and cross sectional study was developed and it was assessed 133 subjects (59 men and 74 women) aging between 18 and 87 years. It was registered the patients’ weight (kg), height (m), BMI (kg/m2), WC (cm) and SAD (cm), and these parameters were correlated with glycemia, triglycerides, total cholesterol, HDL-c, LDL-c and blood pressure.

Results

After adjustment for gender and age, it was observed a positive correlation between SAD and systolic arterial blood pressure (r = 0.20), glycemia (r = 0.20), triglycerides (r = 0.32), LDL (r = 0.26), total cholesterol (TC) (r = 0.33), and a negative correlation with HDL-c (r = −0.21) (p < 0.05). It was observed a positive correlation between WC and systolic arterial blood pressure (r = 0.14), triglycerides (r = 0.31), total cholesterol (r = 0.21), and a negative correlation with HDL-c (r = −0.24) (p < 0.05). BMI showed a positive correlation with systolic arterial blood pressure (r = 0.22), total cholesterol (r = 0.20), and triglycerides (r = 0.23) (p < 0.05).

Conclusion

SAD correlated with almost all the cardiovascular risk factors analyzed and it might be considered the best predictor of abdominal fat and cardiovascular risk.

Background

According to the World Health Organization, obesity is characterized by the accumulation of body fat and might be responsible for the appearance of non-transmissible chronic diseases (NTCD) [1]. According to data gathered in 2001, about 60% of deaths were caused by NTCD [2].

In general, body mass index (BMI) is used to verify if subjects are overweight or obese [3], without considering muscle mass. It was observed that high BMI and advanced age increased the predisposition to hypertension and diabetes mellitus (DM), which are two metabolic syndrome (MS) components that are also associated to cardiovascular disease (CVD) [4].

Abdominal adiposity is also a risk factor for the development of CVDs [5, 6], glucose intolerance [7, 8], hypertension and dyslipidemia [9]. Hence, it is necessary to measure and quantify abdominal adiposity [10].

Computed tomography, magnetic resonance and dual-energy x-ray absorptiometry are considered “gold standard” to assess abdominal adiposity. However, these methods are expensive and also expose subjects to radiation [11, 12].

Therefore, some anthropometric measuring methods like waist circumference (WC) and sagittal abdominal diameter (SAD) [3] are used as abdominal obesity markers due to the fact that they offer lower costs, and are not only easy to measure but also harmless. They are also more applicable in clinical practice as well as in epidemiologic studies [13, 14].

Waist circumference is the measurement that is more commonly used to verify abdominal adiposity [15] because it is easy to be assessed by any person that received little training and may be used to predict MS [16, 17]. However, WC presents some limitations, because there are subjects whose abdominal adiposity seems to move the umbilical cord characterizing an “apron type belly” [18].

SAD seems to be the best predictor of abdominal adiposity regarding cardiovascular morbity and mortality [19]. When SAD is measured with the subject at supine position, fat slides to the sides of the waist, thus reflecting visceral adipose tissue [20], and making it a more efficient method than the WC [16, 21, 22]. WC as well as SAD, has specific measurement sites: the narrowest waist measurement between thorax and waist [23], the highest abdominal diameter site [24], umbilical level [25] and the midpoint of iliac crests [26]. The last one is the most commonly used [26, 27] and it coincides with the location of L4 and L5 [28, 29].

Hence, this study aims to correlate SAD, WC, and BMI with biochemical parameters as well as arterial blood pressure of adults.

Methods

A non-experimental exploratory/descriptive and cross sectional study was developed and it assessed 133 subjects (59 men and 74 women) aging between 18 and 87 years in the cardiology and general clinic ambulatory at “Dr. Jacob Casseb” health care Center in Agudos, São Paulo, Brazil. All the subjects signed a free-consent form, and the research project was approved by the Research Ethics Committee (document no. CEP 191.038) of the Paulista University (UNIP), Brazil.

Anthropometry

Body weight was measured with barefooted individuals wearing as few clothes as possible. As for height, it was measured with barefooted subjects with their feet together and heads held up high [30]. Both were measured using a digital scale with a Welmy built-in stadiometer.

BMI was calculated dividing their weight by their height and it was classified according to World Health Organization, 2000 [31].

WC was measured using a Sanny inelastic and flexible tape measure with the individual at a standing position. It was assessed at the midpoint between the last rib and iliac crest [31].

SAD was measured using an abdominal caliper (Holtain Kahn Abdominal Caliper) with the individual lying down in supine position at the iliac crests’ midpoint. One caliper arm was placed at the back of the subject and the other one was placed on his/her abdomen [26]. The adopted cutoffs to indicate cardiovascular risk were > 23.1 cm for men and > 20.1 cm for women [32].

Biochemical data and arterial blood pressure (ABP)

Biochemical data and ABP values were obtained from the patients’ records, and it was taken into consideration only the examinations that were performed up to one month prior to the experiment’s data collection. Enzyme colorimetric assay kits were used to quantify total cholesterol, glycemia, triglycerides (TG) and high density lipoprotein (HDL-c). Low density lipoprotein (LDL-c) was calculated by Friedewald equation [33].

ABP was assessed according to the VI Brazilian Arterial Hypertension guidelines (2010) [34].

Statistical analysis

Data were expressed in mean ± standard deviation. Sample normality was tested by the Shapiro-Wilk test. It was used the Student’s t test to compare the characteristics of individuals of both genders. Pearson correlation (with adjustment for gender and age) was used to correlate BMI, WC, and SAD with biochemical and arterial blood pressure parameters. Significance level was set at p < 0.05 and the software STATISTICA 6.0 was used for statistical analysis.

Results

The present study assessed adult men and women. Men showed higher values for weight and height when compared to women and their BMI was classified as overweight. WC and SAD were altered; and Systolic arterial blood pressure (SAP) as well as Diastolic arterial blood pressure (DAP) were appropriate. Glycemia, LDL-c, total cholesterol and triglycerides were altered and HDL was adequate. Women showed a higher HDL-c concentrations than men (Table 1).

Table 1 Characteristics of the individuals
Full size table

It was observed a positive correlation between SAD and systolic arterial blood pressure (r = 0.20), glycemia (r = 0.20), triglycerides (r = 0.32), LDL (r = 0.26), total cholesterol (TC) (r = 0.33), and a negative correlation with HDL-c (r = −0.21) (p < 0.05). It was observed a positive correlation between WC and systolic arterial blood pressure (r = 0.14), triglycerides (r = 0.31), total cholesterol (r = 0.21), and a negative correlation with HDL-c (r = −0.24) (p < 0.05). BMI showed a positive correlation with systolic arterial blood pressure (r = 0.22), total cholesterol (r = 0.20) and triglycerides (r = 0.23) (p < 0.05) (Figure 1).

Figure 1
figure 1

Biochemical variables and arterial blood pressure correlations with SAD, WC, and BMI (adjusted for gender and age). *p < 0.05 / SAP: Systolic arterial blood pressure / DAP: Diastolic Arterial blood pressure / HDL-c: High density lipoprotein / TG: Triglycerides / LDL-c: Low density lipoprotein / TC: Total cholesterol / SAD: Sagittal abdominal diameter / WC: Waist circumference / BMI: Body mass index.

Full size image

Discussion

The results of the present study showed that SAD correlated with more cardiovascular risk parameters than WC and BMI. WC and BMI correlated with four and three cardiovascular parameters, respectively, whereas SAD correlated with six out of seven parameters that were assessed in this study. Just like the present study, other researchers showed that SAD might be considered a good marker for metabolic blood disorders [16, 35].

In our study we observed that SAD is a better predictor of cardiovascular risk and metabolic syndrome components, if compared to BMI and WC. Other studies presented the very same correlation regarding SAD. It was observed that SAD was a better predictor for MS [36], but this is not unanimous, since that others studies showed that SAD, WC, and BMI had similar correlation with the parameters analyzed [20, 25].

WC has been the most commonly used abdominal adiposity marker due to the fact that it is easy to perform, has a very low cost and does not expose individuals to risks. The present study showed that WC correlated with SAP, HDL-c, TC, TG, and it was more efficient than BMI but not as much as SAD. Lopes de La Terra et al., observed that increased WC was strongly correlated with diabetes, but in the present study it was only observed for SAD [37].

it was showed in a cross-sectional study of over one hundred adults that only SAD was correlated with glycemia, TG, and HDL-c whereas TC and LDL-c did not correlate with any of the measurements [35]. In our study SAD showed correlation with PAS, glycemia, TC, TG, HDL-c and LDL-c. It was also more efficient than WC and BMI, thus proving that it is an adiposity marker that is more strongly correlated with cardiovascular risk factors. Other studies showed that SAD was the only anthropometric measurement that could predict insulin resistance, hyperinsulinemia, and glycemia [16, 38], thus making this measurement a strong insulin resistance marker, which was confirmed in our study.

We have assessed both WC and SAD to check which one would be more accurate to estimate visceral fat tissue (VFT), since it is correlated with various factors that were analyzed in the present study. VFT is the most active and it is also an insulin-resistant tissue. It also releases higher concentrations of adipokines that are related to pro-inflammatory processes, and contributes to the development of hypertension, insulin resistance, metabolic syndrome, and cardiovascular diseases [39–42]. The subcutaneous fat tissue (SFT) presents properties of intermediate order if compared to visceral fat, thus showing lower adipokine secretion [43].

WC generally evaluates the abdominal extension and it does not single out the two types of adipose tissue. SAD can perform a more accurate evaluation of VFT because its measurement is taken with the subject at supine position and the SFT tends to slide to the sides of the body due to its higher malleability. VFT is a more rigid tissue so it does not slide and makes the SAD a better analysis of VFT.

BMI is the most commonly used marker to assess nutritional status. In our study it only correlated with SAP, TC, and TG. Hence, BMI was not a strong marker to demonstrate the influence of adiposity upon the risk factor of cardiovascular diseases. It was already demonstrated that BMI presented weaker correlation with VFT if compared to SAD and WC [26, 44]. Two other studies showed that BMI was not as efficient as SAD and WC to identify insulin resistance in men [16, 45], which is in line with our study.

Conclusion

We can conclude that SAD correlated with almost all the cardiovascular risk factors analyzed at present study. Thus, it is better to predict the quantity of abdominal fat tissue and cardiovascular risk; and should be employed in clinical practice.

References

  1. WHO Expert Committee on Physical Status – World Health Organization: Physical status: the use and interpretation of anthropometry, Report of a WHO Expert Committee. Geneva: Technical Report Series No. 854; 1995. Acesso em: 17/08/2012. Disponível em: http://www.who.int/childgrowth/publications/physical_status/en/index.html

    Google Scholar 

  2. WHO – World Health Organization: Diet, nutrition and the prevention of chronic diseases, Volume No. 916 (TRS 916). Geneva: Report of the joint WHO/FAO expert consultation, WHO Technical Report Series; 2003. Acesso em: 20/08/2012. Disponível em: http://www.who.int/dietphysicalactivity/publications/trs916/en/

    Google Scholar 

  3. Ho SY, Lam TH, Janus ED: Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol 2003,13(10):683–691. 10.1016/S1047-2797(03)00067-X

    PubMed  Google Scholar 

  4. Coltro RS, Mizutani BN, Mutti A, Barbieri MPD, Baptista LMM, Cogni AL, et al.: Frequência de fatores de risco cardiovascular em voluntários participantes de evento de educação em saúde. Rev Assoc Med Bras 2009,55(5):606–610. 10.1590/S0104-42302009000500028

    PubMed  Google Scholar 

  5. Nicklas BJ, Cesari M, Penninx BW, Kritchevsky SB, Ding J, Newman A, et al.: Abdominal obesity is an independent risk factor for chronic heart failure in older people. J Am Geriatr Soc 2006,54(3):413–420. 10.1111/j.1532-5415.2005.00624.x

    PubMed  Google Scholar 

  6. Kuk JL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R: Visceral fat is an independent predictor of all-cause mortality in men. Obesity 2006,14(2):336–341. 10.1038/oby.2006.43

    PubMed  Google Scholar 

  7. Goodpaster BH, Krishnaswami S, Resnick H, Kelley DE, Haggerty C, Harris TB, et al.: Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care 2003,26(2):372–379. 10.2337/diacare.26.2.372

    PubMed  Google Scholar 

  8. Hermans MP, Pepersack TM, Godeaux LH, Beyer I, Turc AP: Prevalence and determinants of impaired glucose metabolism in frail elderly patients: the Belgian elderly diabetes survey (BEDS). J Gerontol Med Sci 2005,60(2):241–247. 10.1093/gerona/60.2.241

    Google Scholar 

  9. Carneiro G, Faria AN, Ribeiro Filho FF, Lerario D, Ferreira SR, Zanella MT: Influence of body fat distribution on the prevalence of arterial hypertension and other cardiovascualr risk factors in obese patients. Rev Assoc Med Bras 2003,49(3):306–311. 10.1590/S0104-42302003000300036

    PubMed  Google Scholar 

  10. Vasques ACJ, Priore SE, Lima LEFR, Franceschini SCF: Utilização de medidas antropométricas para a avaliação do acúmulo de gordura visceral. Revista de Nutrição, Campinas. 2010,23(1):107–118.

    Google Scholar 

  11. Fuller NJ, Jebb SA, Laskey MA, Coward WA, Elia M: Four-component model for the assessment of body composition in humans: comparison with alternative methods, and evaluation of the density and hydration of fat-free mass. Clin Sci 1992,82(6):687–693.

    CAS  Google Scholar 

  12. Biaggi RR, Vollman MW, Nies MA, Brener CE, Flakoll PJ, Levenhagen DK, et al.: Comparison of air-displacement plethysmography with hydrostatic weighing and bioelectrical impedance analysis for the assessment of body composition in healthy adults. Am J Clin Nutr 1999,69(5):898–903.

    CAS  PubMed  Google Scholar 

  13. Guedes DP: Recursos antropométricos para análise da composição corporal. Rev Bras Educ Fís Esp 2006,20(5):115–119.

    Google Scholar 

  14. Ribeiro-Filho FF, Mariosa LS, Ferreira SRG, Zanella MT: Gordura visceral e síndrome metabólica: mais que uma simples associação. Arq Bras Endocrinol Metabol 2006,50(2):230–238. 10.1590/S0004-27302006000200009

    PubMed  Google Scholar 

  15. Vasques ACJ, Paez LEFRL, Rosado GP, Ribeiro RCL, Franceschini SCC, Geloneze B: Diferentes aferições do diâmetro abdominal sagittal e do perímetro da cintura na predição de HOMA-IR. Arq Bras Cardiol 2009,93(5):473–479. 10.1590/S0066-782X2009001100006

    Google Scholar 

  16. Riserus U, Arnlöv J, Brismar K, Zethelius B, Berglund L, Vessby B: Sagittal abdominal diameter is a strong anthropometric marker of insulin resistance and hyperproinsulinemia in obese men. Diabetes Care 2004,27(8):2041–2046. 10.2337/diacare.27.8.2041

    PubMed  Google Scholar 

  17. Pimentel GD, Portero-Mclellan KC, Oliveira EP, Spada AP, Oshiiwa M, Zemdegs JC, et al.: Long-term nutrition education reduces several risk factors for type 2 diabetes mellitus in Brazilians with impaired glucose tolerance. Nutr Res 2010,30(3):186–190. 10.1016/j.nutres.2010.03.003

    CAS  PubMed  Google Scholar 

  18. Nordhamn K, Södergren E, Olsson E, Karlström B, Vessby B, Berglund L, et al.: Reliability of anthropometric measurements in overweight and lean subjects: consequences for correlations between anthropometric and other variables. Int J Obes 2000,24(5):652–657. 10.1038/sj.ijo.0801216

    CAS  Google Scholar 

  19. Kahn HS, Simoes EJ, Koponen M, Hanzlick R: The abdominal diameter index and sudden coronary death in men. Am J Cardiol 1996, 78: 961–964. 10.1016/S0002-9149(96)00479-1

    CAS  PubMed  Google Scholar 

  20. Sjostrom L: A computer-tomography based multicompartment body composition technique and anthropometric predictions of lean body mass, total and subcutaneous adipose tissue. Int J Obes Relat Metab Disord 1991,15(Suppl 2):19–30.

    Google Scholar 

  21. Petersson H, Daryani A, Risérus U: Sagittal abdominal diameter as a marker of inflammation and insulin resistance among immigrant women from the Middle East and native Swedish women: a cross-sectional study. Card Diabetol 2001, 6: 1–7.

    Google Scholar 

  22. Frenhani PB, Pimentel GD, Portero-Mclellan KC, Burini RC: Sagittal abdominal diameter as a predictor of visceral abdominal fat, insulin resistance, dyslipidemia and inflammation in overweight Brazilian adults. Clin Nutr Suppl 2008,3(Suppl. 1):126.

    Google Scholar 

  23. Richelsen B, Pedersen SB: Associations between different anthropometric measurements of fatness and metabolic risk parameters in non-obese, healthy, middle-aged men. Int J Obes Relat Metab Disord 1995, 19: 169–174.

    CAS  PubMed  Google Scholar 

  24. Turcato E, Bosello O, Di Francesco V, Harris TB, Zoico E, Bissoli L, et al.: Waist circumference and abdominal sagittal diameter as surrogates of body fat distribution in the elderly: their relation with cardiovascular risk factors. Int J Obes Relat Metab Disord 2000, 24: 1005–1010. 10.1038/sj.ijo.0801352

    CAS  PubMed  Google Scholar 

  25. Ohrvall M, Berglund L, Vessby B: Sagittal abdominal diameter compared with other anthropometric measurements in relation to cardiovascular risk. Int J Obes Relat Metab Disord 2000, 24: 497–501. 10.1038/sj.ijo.0801186

    CAS  PubMed  Google Scholar 

  26. Sampaio LR, Simoes EJ, Assis AM, Ramos LR: Validity and reliability of the sagittal abdominal diameter as a predictor of visceral abdominal fat. Arq Bras Endocrinol Metab. 2007, 51: 980–986. 10.1590/S0004-27302007000600013

    Google Scholar 

  27. Kahn HS: Alternative anthropometric measures of risk: possible improvements on the waist-hip ratio. In Progress in obesity research. 9th edition. Edited by: Medeiros-Neto G, Halpern A, Bouchard C. London: John Libbey Eurotext Ltd; 2003:639–643.

    Google Scholar 

  28. Kvist H, Chowdhury B, Grangard U, Tylen U, Sjostrom L: Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 1988, 48: 1351–1361.

    CAS  PubMed  Google Scholar 

  29. Sjostrom L, Kvist H: Regional body fat measurements with CT-scan and evaluation of anthropometric predictions. Acta Med Scand Suppl 1988, 723: 169–177.

    CAS  PubMed  Google Scholar 

  30. Waitzberg D, Ferrini MT: Exame físico e antropometria. In Nutrição Oral, enteral e parenteral na prática clínica. Edited by: Waitzberg DL. São Paulo: Atheneu; 2000:255–278.

    Google Scholar 

  31. WHO – World Health Organization: Obesity and other diet-related chronic diseases list of publications. Geneva: Obesity: preventing and managing the global epidemic Report of a WHO Consultation Acesso em; 2000. Disponível em: http://www.who.int/nutrition/publications/obesity/en/index.html

    Google Scholar 

  32. Pimentel GD, Portero-McLellan KC, Maesta N, Corrente JE, Burini RC: Accuracy of sagittal abdominal diameter as predictor of abdominal fat among Brazilian adults: a comparation with waist circumference. Nutr Hosp 2010,25(4):656–661.

    Google Scholar 

  33. Friedewald T, Levy RI, Frederickson DS: Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972,18(6):499–502.

    CAS  PubMed  Google Scholar 

  34. Sociedade Brasileira de Cardiologia: VI Diretrizes brasileiras de hipertensão. Arq Bras Cardiol 2010,95(1 supl. 1):1–51.

    Google Scholar 

  35. Pimentel GD, Moreto F, Takahashi MM, Portero-Mclellan KC, Burini RC: Sagittal abdominal diameter, but not waist circumference is strongly associated with glycemia, triacilglycerols and HDL-c levels in overweitht adults. Nutr Hosp 2011,26(5):1125–1129.

    CAS  PubMed  Google Scholar 

  36. Valsamakis G, Chetty R, Anwar A, Banerjee AK, Barnett A, Kumar S: Association of simple anthropometric measures of obesity with visceral fat and the metabolic syndrome in male Caucasian and Indo-Asian subjects. Diabet Med 2004,21(12):1339–1345. 10.1111/j.1464-5491.2004.01361.x

    CAS  PubMed  Google Scholar 

  37. Lopez de la Torre M, Bellido D, Soto A, Carreira J, Hernandez-Mijares A: Standardisation of the waist circumference (WC) for each range of body mass index (BMI) in adult outpatients attended to in Endocrinology and Nutrition departments. Nutr Hosp 2010,25(2):262–269.

    CAS  PubMed  Google Scholar 

  38. Gustat J, Elkasabany A, Srinivasan S, Berenson GS: Relation of abdominal height to cardiovascular risk factors in young adults: the Bogalusa heart study. Am J Epidemiol 2000,151(9):885–891. 10.1093/oxfordjournals.aje.a010292

    CAS  PubMed  Google Scholar 

  39. Arner P: Differences in lipolysis between human subcutaneous and omental adipose tissues. Ann Med 1995,27(7):435–438.

    CAS  PubMed  Google Scholar 

  40. Giacchetti G, Faloia E, Mariniello B, Sardu C, Gatti C, Camilloni MA, et al.: Over expression of the rennin-angiotensin system in human visceral adipose tissue in normal and overweight subjects. Am J Hypertens 2002, 15: 381–388. 10.1016/S0895-7061(02)02257-4

    CAS  PubMed  Google Scholar 

  41. Hsueh WA, Law R: The central role of fat and effect of peroxisome proliferator-activated receptor-g on progression of insulin resistance and cardiovascular disease. Am J Cardiol 2003, 92: 3j-9j.

    CAS  PubMed  Google Scholar 

  42. Wajchenberg BL: Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 2000,21(6):697–738. 10.1210/er.21.6.697

    CAS  PubMed  Google Scholar 

  43. Hermsdorff HHM, Josefina MJBR: Gordura visceral, subcutanea ou intramuscular: onde esta o problema? Arq Bras Endocrinol Metab 2004,48(6):803–811. 10.1590/S0004-27302004000600005

    Google Scholar 

  44. Després JP, Prud’homme D, Pouliot MC, Tremblay A, Bouchard C: Estimation of deep abdominal adipose tissue accumulation from simple anthropometric measurements in men. Am J Clin Nutr 1991, 54: 471–477.

    PubMed  Google Scholar 

  45. Ybarra J, Sanchez-Hernandez J, Pou JM, Fernández S, Gich I, Ordóñez-Llanos J, et al.: Anthropometrical measures are easily obtainable sensitive and specific predictors of insulin resistance in healthy individuals. Prev Control 2005,1(2):175–181. 10.1016/j.precon.2005.05.001

    Google Scholar 

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Authors and Affiliations

  1. Paulista University (UNIP), Bauru, São Paulo, Brazil

    Natalia Cavalheri de Souza

  2. School of Medicine, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil

    Erick Prado de Oliveira

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NCS collected the data and wrote the manuscript, EPO was the mentor of the research, wrote and corrected the manuscript. Both authors read and approved.

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de Souza, N.C., de Oliveira, E.P. Sagittal abdominal diameter shows better correlation with cardiovascular risk factors than waist circumference and BMI. J Diabetes Metab Disord 12, 41 (2013). https://doi.org/10.1186/2251-6581-12-41

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Keywords

  • Waist circumference
  • Sagittal abdominal diameter
  • Blood pressure
  • Glycemia
  • Dyslipidemia