Advertisement

European Journal of Nutrition

, Volume 57, Issue 1, pp 191–198 | Cite as

Anthropometrically predicted visceral adipose tissue and blood-based biomarkers: a cross-sectional analysis

  • Justin C. BrownEmail author
  • Michael O. Harhay
  • Meera N. Harhay
Original Contribution

Abstract

Purpose

We hypothesized that anthropometrically predicted visceral adipose tissue (apVAT) accounts for more variance in blood-based biomarkers of glucose homeostasis, inflammation, and lipid metabolism than body mass index (BMI), waist circumference (WC), and the combination of BMI and WC (BMI + WC).

Methods

This was a cross-sectional analysis of 10,624 males and females who participated in the Third National Health and Nutrition Examination Survey (NHANES III; 1988–1994). apVAT was predicted from a validated regression equation that included age, height, weight, waist, and thigh circumferences. Bootstrapped linear regression models were used to compare the proportion of variance (R 2) in biomarkers explained by apVAT, BMI, WC, and BMI + WC.

Results

apVAT accounted for more variance in biomarkers of glucose homeostasis than BMI (∆R 2 = 8.4–11.8 %; P < 0.001), WC (∆R 2 = 5.5–8.4 %; P < 0.001), and BMI + WC (∆R 2 = 5.1–7.7 %; P < 0.001). apVAT accounted for more variance in biomarkers of inflammation than BMI (ΔR 2 = 3.8 %; P < 0.001), WC (ΔR 2 = 3.1 %; P < 0.001), and BMI + WC (ΔR 2 = 2.9 %; P < 0.001). apVAT accounted for more variance in biomarkers of lipid metabolism than BMI (ΔR 2 = 2.9–9.2 %; P < 0.001), WC (ΔR 2 = 2.9–5.2 %; P < 0.001), and BMI + WC (ΔR 2 = 2.4–4.1 %; P ≤ 0.01).

Conclusions

apVAT, estimated with simple and widely used anthropometric measures, accounts for more variance in blood-based biomarkers than BMI, WC, and BMI + WC. Clinicians and researchers may consider utilizing apVAT to characterize cardio-metabolic health, particularly in settings with limited availability of imaging and laboratory data.

Keywords

Adiposity Body composition Waist–hip ratio Population-based 

Notes

Acknowledgments

Research reported in this publication was supported by the National Cancer Institute (F31-CA192560, R21-CA182726), National Heart, Lung, and Blood Institute (F31-HL127947), and the National Institute of Diabetes and Digestive and Kidney Diseases (K23-DK105207) of the National Institutes of Health.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

394_2016_1308_MOESM1_ESM.docx (51 kb)
Supplementary material 1 (DOCX 50 kb)

References

  1. 1.
    Despres JP, Lemieux I, Prud’homme D (2001) Treatment of obesity: need to focus on high risk abdominally obese patients. BMJ 322:716–720CrossRefGoogle Scholar
  2. 2.
    Despres JP (2012) Body fat distribution and risk of cardiovascular disease: an update. Circulation 126:1301–1313CrossRefGoogle Scholar
  3. 3.
    Tchernof A, Despres JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93:359–404CrossRefGoogle Scholar
  4. 4.
    Shen W, Wang Z, Punyanita M, Lei J, Sinav A, Kral JG, Imielinska C, Ross R, Heymsfield SB (2003) Adipose tissue quantification by imaging methods: a proposed classification. Obes Res 11:5–16CrossRefGoogle Scholar
  5. 5.
    Pouliot M, Després J, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ (1994) 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 73:460–468CrossRefGoogle Scholar
  6. 6.
    Bouchard C (2007) BMI, fat mass, abdominal adiposity and visceral fat: where is the ‘beef’? Int J Obes 31:1552–1553CrossRefGoogle Scholar
  7. 7.
    Samouda H, Dutour A, Chaumoitre K, Panuel M, Dutour O, Dadoun F (2013) VAT = TAAT-SAAT: innovative anthropometric model to predict visceral adipose tissue without resort to CT-Scan or DXA. Obesity 21:E41–E50CrossRefGoogle Scholar
  8. 8.
    Brown JC, Harhay MO, Harhay MN (2016) Anthropometrically-predicted visceral adipose tissue and mortality in men and women. Am J Hum Biol. doi: 10.1002/ajhb.22898 Google Scholar
  9. 9.
    Emerging Risk Factors Collaboration (2010) Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375:2215–2222CrossRefGoogle Scholar
  10. 10.
    Zhang Y, Hu G, Yuan Z, Chen L (2012) Glycosylated hemoglobin in relationship to cardiovascular outcomes and death in patients with type 2 diabetes: a systematic review and meta-analysis. PLoS ONE 7:e42551CrossRefGoogle Scholar
  11. 11.
    Benderly M, Graff E, Reicher-Reiss H, Behar S, Brunner D, Goldbourt U (1996) Fibrinogen is a predictor of mortality in coronary heart disease patients. The Bezafibrate Infarction Prevention (BIP) Study Group. Arterioscler Thromb Vasc Biol 16:351–356CrossRefGoogle Scholar
  12. 12.
    Emerging Risk Factors Collaboration (2010) C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. The Lancet 375:132–140CrossRefGoogle Scholar
  13. 13.
    Prospective Studies Collaboration (2007) Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths. The Lancet 370:1829–1839CrossRefGoogle Scholar
  14. 14.
    Pouliot MC, Despres JP, Nadeau A, Moorjani S, Prud’Homme D, Lupien PJ, Tremblay A, Bouchard C (1992) Visceral obesity in men. Associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes 41:826–834CrossRefGoogle Scholar
  15. 15.
    Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C (1990) Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 10:497–511CrossRefGoogle Scholar
  16. 16.
    Pou KM, Massaro JM, Hoffmann U, Vasan RS, Maurovich-Horvat P, Larson MG, Keaney JF Jr, Meigs JB, Lipinska I, Kathiresan S, Murabito JM, O’Donnell CJ, Benjamin EJ, Fox CS (2007) Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham heart study. Circulation 116:1234–1241CrossRefGoogle Scholar
  17. 17.
    Centers for Disease Control and Prevention (2007) NHANES III anthropometric procedures video. Government Printing Office, Washington, DC 1996a (stock no.017-022-01335-5) Google Scholar
  18. 18.
    National Center for Health Statistics (1996) NHANES III body measurements (anthropometry). https://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/nchs/manuals/anthro.pdf
  19. 19.
    Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC (1990) Validity of self-reported waist and hip circumferences in men and women. Epidemiology 1:466–473CrossRefGoogle Scholar
  20. 20.
    Gunter EW, McQuillan G (1990) Quality control in planning and operating the laboratory component for the Third National Health and Nutrition Examination Survey. J Nutr 120(Suppl 11):1451–1454CrossRefGoogle Scholar
  21. 21.
    National Center for Health Statistics. Laboratory Procedures Used for the Third National Health and Nutrition Exam Survey (NHANES III), 1988–1994. http://wonder.cdc.gov/wonder/sci_data/surveys/hanes/hanes3/type_txt/lab.asp
  22. 22.
    Lacher DA, Hughes JP, Carroll MD (2005) Estimate of biological variation of laboratory analytes based on the third national health and nutrition examination survey. Clin Chem 51:450–452CrossRefGoogle Scholar
  23. 23.
    Kennedy ET, Ohls J, Carlson S, Fleming K (1995) The healthy eating index: design and applications. J Am Diet Assoc 95:1103–1108CrossRefGoogle Scholar
  24. 24.
    Genuth S, Alberti K, Bennett P, Buse J, DeFronzo R, Kahn R, Kitzmiller J, Knowler WC, Lebovitz H, Lernmark A (2003) Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26:3160–3168CrossRefGoogle Scholar
  25. 25.
    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr, International Diabetes Federation Task Force on Epidemiology and Prevention, Hational Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, International Association for the Study of Obesity (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120:1640–1645CrossRefGoogle Scholar
  26. 26.
    Rosner B (2015) Fundamentals of biostatistics. Thomson-Brooks/Cole, BelmontGoogle Scholar
  27. 27.
    Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New YorkCrossRefGoogle Scholar
  28. 28.
    Kershaw EE, Flier JS (2004) Adipose tissue as an endocrine organ. J Clin Endocrinol Metab 89:2548–2556CrossRefGoogle Scholar
  29. 29.
    Kaul S, Rothney MP, Peters DM, Wacker WK, Davis CE, Shapiro MD, Ergun DL (2012) Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity 20:1313–1318CrossRefGoogle Scholar
  30. 30.
    Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL (2012) Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat. Obesity 20:1109–1114CrossRefGoogle Scholar
  31. 31.
    Rankinen T, Kim S, Perusse L, Despres J, Bouchard C (1999) The prediction of abdominal visceral fat level from body composition and anthropometry: ROC analysis. Int J Obes 23:801–809CrossRefGoogle Scholar
  32. 32.
    Lemieux I, Pascot A, Couillard C, Lamarche B, Tchernof A, Almeras N, Bergeron J, Gaudet D, Tremblay G, Prud’homme D, Nadeau A, Despres JP (2000) Hypertriglyceridemic waist: a marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men? Circulation 102:179–184CrossRefGoogle Scholar
  33. 33.
    Després J (2001) Health consequences of visceral obesity. Ann Med 33:534–541CrossRefGoogle Scholar
  34. 34.
    Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM, Kushner RF (2014) 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol 63:2985–3023CrossRefGoogle Scholar
  35. 35.
    Apovian CM, Aronne LJ, Bessesen DH, McDonnell ME, Murad MH, Pagotto U, Ryan DH, Still CD (2015) Pharmacological management of obesity: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 100:342–362CrossRefGoogle Scholar
  36. 36.
    Rice B, Janssen I, Hudson R, Ross R (1999) Effects of aerobic or resistance exercise and/or diet on glucose tolerance and plasma insulin levels in obese men. Diabetes Care 22:684–691CrossRefGoogle Scholar
  37. 37.
    Scafoglieri A, Clarys JP, Cattrysse E, Bautmans I (2013) Use of anthropometry for the prediction of regional body tissue distribution in adults: benefits and limitations in clinical practice. Aging Dis 5:373–393CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Division of Population Sciences, Department of Medical OncologyDana-Farber Cancer InstituteBostonUSA
  2. 2.Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Division of Nephrology, Department of MedicineDrexel University College of MedicinePhiladelphiaUSA

Personalised recommendations