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International Urology and Nephrology

, Volume 50, Issue 9, pp 1679–1685 | Cite as

Sagittal abdominal diameter and Framingham risk score in non-dialysis chronic kidney disease patients

  • Hua Xiao
  • Yu Bao
  • Ming-Yue Liu
  • Jun-Hua Yang
  • Yan-Ting Li
  • Yi-An Wang
  • Ying Wang
  • Yue Yan
  • Zhu Zhu
  • Mei Ni
  • Xiao-Yan Huang
  • Xin-Kui Tian
  • Tao Wang
  • Xing-Wei Zhe
Nephrology - Original Paper

Abstract

Background

Chronic kidney disease (CKD) is very common now and is associated with high overall and cardiovascular mortality. Numerous studies have reported that abdominal obesity is a risk factor for cardiovascular mortality. We investigated the link between sagittal abdominal diameter (SAD) and Framingham risk score in non-dialysis CKD patients.

Methods

In a cross-sectional study, we enrolled 307 prevalent non-dialysis CKD patients (175 males, aged 50.7 ± 17.04 years). SAD and Framingham risk score were measured.

Results

Framingham cardiovascular disease risk score was independently predicted by SAD (P < 0.01), GFR (P < 0.01) and diabetic history (P < 0.05). Adjusted R2 of the model was 0.178. SAD could be independently predicted by BMI (P < 0.01), diabetic history (P < 0.01), GFR (P < 0.01) and age (P < 0.01). Adjusted R2 of the model was 0.409. Using receiver operating characteristic (ROC) curve, a cutoff SAD value of 16.55 cm was determined with sensitivity of 63.7%, specificity of 58.3%.

Conclusion

Elevated SAD is significantly associated with increased Framingham risk score in non-dialysis CKD patients. SAD can be predicted by patients’ BMI, diabetic history, renal function and age. Further investigation is needed to explore the potential benefits of central obesity lowering therapy in this patient group.

Keywords

Central obesity CVD Anthropometry Body weight 

Notes

Acknowledgements

The authors thank all the patients and staff of the Division of Nephrology.

This work was funded by grant from Chinese Society of Blood Purification Administration (CHABP2016-07), was supported by Chinese Society of Nephrology Grant (13030310416) and was funded by Special Supporting Program for Young Teachers in Kunming Medical University.

BY was supported by a Grant from Graduate Innovation Fund in Kunming Medical University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Zhang L et al (2012) Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet 379(9818):815–822CrossRefPubMedGoogle Scholar
  2. 2.
    Go AS et al (2004) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351(13):1296–1305CrossRefPubMedGoogle Scholar
  3. 3.
    Keith DS et al (2004) Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch Intern Med 164(6):659–663CrossRefPubMedGoogle Scholar
  4. 4.
    Kopelman P (2007) Health risks associated with overweight and obesity. Obes Rev 8(Suppl 1):13–17CrossRefPubMedGoogle Scholar
  5. 5.
    Kannel WB et al (1991) Regional obesity and risk of cardiovascular disease; the Framingham Study. J Clin Epidemiol 44(2):183–190CrossRefPubMedGoogle Scholar
  6. 6.
    Kamimura MA et al (2013) Visceral obesity assessed by computed tomography predicts cardiovascular events in chronic kidney disease patients. Nutr Metab Cardiovasc Dis 23(9):891–897CrossRefPubMedGoogle Scholar
  7. 7.
    Zhe XW et al (2008) Pulse wave velocity is associated with metabolic syndrome components in CAPD patients. Am J Nephrol 28(4):641–646CrossRefPubMedGoogle Scholar
  8. 8.
    Kvist H et al (1988) Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 48(6):1351–1361CrossRefPubMedGoogle Scholar
  9. 9.
    van der Kooy K et al (1993) Abdominal diameters as indicators of visceral fat: comparison between magnetic resonance imaging and anthropometry. Br J Nutr 70(1):47–58CrossRefPubMedGoogle Scholar
  10. 10.
    Clasey JL et al (1999) The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 7(3):256–264CrossRefPubMedGoogle Scholar
  11. 11.
    Empana JP et al (2004) Sagittal abdominal diameter and risk of sudden death in asymptomatic middle-aged men: the Paris Prospective Study I. Circulation 110(18):2781–2785CrossRefPubMedGoogle Scholar
  12. 12.
    Iribarren C et al (2006) Value of the sagittal abdominal diameter in coronary heart disease risk assessment: cohort study in a large, multiethnic population. Am J Epidemiol 164(12):1150–1159CrossRefPubMedGoogle Scholar
  13. 13.
    Lee MJ et al (2013) Sagittal abdominal diameter is an independent predictor of all-cause and cardiovascular mortality in incident peritoneal dialysis patients. PLoS ONE 8(10):e77082CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Levey AS et al (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 130(6):461–470CrossRefPubMedGoogle Scholar
  15. 15.
    Nordhamn K et al (2000) Reliability of anthropometric measurements in overweight and lean subjects: consequences for correlations between anthropometric and other variables. Int J Obes Relat Metab Disord 24(5):652–657CrossRefPubMedGoogle Scholar
  16. 16.
    Anderson KM et al (1991) Cardiovascular disease risk profiles. Am Heart J 121(1 Pt 2):293–298CrossRefPubMedGoogle Scholar
  17. 17.
    Haslam DW, James WP (2005) Obesity. Lancet 366(9492):1197–1209CrossRefPubMedGoogle Scholar
  18. 18.
    Scuteri A et al (2005) The metabolic syndrome in older individuals: prevalence and prediction of cardiovascular events: the Cardiovascular Health Study. Diabetes Care 28(4):882–887CrossRefPubMedGoogle Scholar
  19. 19.
    Zhe X et al (2012) Hypertriglyceridemic waist is associated with increased carotid atherosclerosis in chronic kidney disease patients. Nephron Clin Pract 122(3–4):146–152CrossRefPubMedGoogle Scholar
  20. 20.
    Smith SR et al (2001) Contributions of total body fat, abdominal subcutaneous adipose tissue compartments, and visceral adipose tissue to the metabolic complications of obesity. Metabolism 50(4):425–435CrossRefPubMedGoogle Scholar
  21. 21.
    Clasey JL et al (1999) The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 7(3):256–264CrossRefPubMedGoogle Scholar
  22. 22.
    van der Velde M et al (2011) Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int 79(12):1341–1352CrossRefPubMedGoogle Scholar
  23. 23.
    Mafham M et al (2011) Estimated glomerular filtration rate and the risk of major vascular events and all-cause mortality: a meta-analysis. PLoS ONE 6(10):e25920CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Segall L, Nistor I, Covic A (2014) Heart failure in patients with chronic kidney disease: a systematic integrative review. Biomed Res Int 2014:937398CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Anjana M et al (2004) Visceral and central abdominal fat and anthropometry in relation to diabetes in Asian Indians. Diabetes Care 27(12):2948–2953CrossRefPubMedGoogle Scholar
  26. 26.
    Kim SR et al (2011) Relationship of visceral and subcutaneous adiposity with renal function in people with type 2 diabetes mellitus. Nephrol Dial Transplant 26(11):3550–3555CrossRefPubMedGoogle Scholar
  27. 27.
    Fouque D et al (2008) A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int 73(4):391–398CrossRefPubMedGoogle Scholar
  28. 28.
    Despres JP (2012) Abdominal obesity and cardiovascular disease: is inflammation the missing link? Can J Cardiol 28(6):642–652CrossRefPubMedGoogle Scholar
  29. 29.
    Vasques AC et al (2015) Sagittal abdominal diameter as a surrogate marker of insulin resistance in an admixtured population-Brazilian Metabolic Syndrome Study (BRAMS). PLoS ONE 10(5):e0125365CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pajunen P et al (2013) Sagittal abdominal diameter as a new predictor for incident diabetes. Diabetes Care 36(2):283–288CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Riserus U et al (2010) Sagittal abdominal diameter as a screening tool in clinical research: cutoffs for cardiometabolic risk. J Obes 2010:1–7CrossRefGoogle Scholar
  32. 32.
    Lee MJ et al (2013) Sagittal abdominal diameter is an independent predictor of all-cause and cardiovascular mortality in incident peritoneal dialysis patients. PLoS ONE 8(10):e77082CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Hua Xiao
    • 1
  • Yu Bao
    • 1
  • Ming-Yue Liu
    • 2
  • Jun-Hua Yang
    • 3
  • Yan-Ting Li
    • 4
  • Yi-An Wang
    • 1
  • Ying Wang
    • 1
  • Yue Yan
    • 1
  • Zhu Zhu
    • 1
  • Mei Ni
    • 1
  • Xiao-Yan Huang
    • 5
  • Xin-Kui Tian
    • 6
  • Tao Wang
    • 6
  • Xing-Wei Zhe
    • 1
  1. 1.Division of NephrologyThe First Affiliated Hospital of Kunming Medical UniversityKunmingChina
  2. 2.Division of NephrologyThe Second Affiliated Hospital of Kunming Medical UniversityKunmingChina
  3. 3.Division of NephrologyPuer People’s HospitalPuerChina
  4. 4.Intensive Care UnitGeneral Hospital of Ningxia Medical UniversityYinchuanChina
  5. 5.Department of NephrologyThe First People’s Hospital of XuzhouXuzhouChina
  6. 6.Division of NephrologyPeking University Third HospitalBeijingChina

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