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. 2022 Nov 1;23(1):349.
doi: 10.1186/s12882-022-02981-7.

Waist-hip ratio measured by bioelectrical impedance analysis as a valuable predictor of chronic kidney disease development

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Waist-hip ratio measured by bioelectrical impedance analysis as a valuable predictor of chronic kidney disease development

Younghoon Song et al. BMC Nephrol. .

Abstract

Obesity is a major health problem worldwide and is associated with chronic kidney disease (CKD). Body mass index (BMI) is a common method of diagnosing obesity, but there are concerns about its accuracy and ability to measure body composition. This study evaluated the risk of CKD development in a middle-aged population in association with various body composition metrics. From a prospective cohort of 10,030 middle-aged adults, we enrolled 6727 for whom baseline and follow-up data were available. We collected data pertaining to participants' BMI, manually measured waist-hip ratio (WHR), and various measurements of bioelectrical impedance analysis (BIA), including total body fat content, muscle content, and calculated WHR, and classified the participants into quintiles accordingly. CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 in follow-up laboratory tests. While an increase in BMI, WHR, and total body fat were associated with an elevated risk of CKD, an increase in total body muscle decreased the risk. Among the body composition metrics, WHR measured by BIA had the highest predictive value for CKD (C-statistics: 0.615). In addition, participants who were "healthy overweight, (defined as low WHR but high BMI), exhibited a 62% lower risk of developing CKD compared to those with "normal-weight obesity," (defined as high WHR despite a normal BMI). In conclusion, we suggest that central obesity measured by BIA is a more accurate indicator than BMI for predicting the development of CKD.

Keywords: Bioelectrical impedance analysis; Body mass index; Chronic kidney disease; Obesity.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Flow chart of the enrollment of participants
Fig. 2
Fig. 2
Cumulative incidence of chronic kidney disease (CKD) according to quintiles of BMI (left) and WHR measured by BIA (right). BMI, body mass index; WHR, waist hip ratio; BIA, bioelectrical impedance analysis
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) analysis for chronic kidney disease (CKD) development according to various body composition profiles. The numbers in the legend represent areas under the ROC curve (C-statistics). WHR, waist–hip ratio; BIA, bioelectrical impedance analysis; BMI, body mass index; AUROC, area under receiver operating characteristic curve
Fig. 4
Fig. 4
Comparison of the cumulative incidence of chronic kidney disease (CKD) in the “healthy overweight” and “normal-weight obesity” groups. The blue line represents “normal-weight obesity,” defined as a high WHR with a normal BMI. The green line represents “healthy overweight” defined as a low WHR with a high BMI. The red line represents other participants not included in the “healthy overweight” or “normal-weight obesity” categories. WHR, waist–hip ratio; BIA, bioelectrical impedance analysis; BMI, body mass index

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References

    1. Collaborators GBDRF Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659–1724. doi: 10.1016/S0140-6736(16)31679-8. - DOI - PMC - PubMed
    1. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age, 2007–2008 to 2015–2016. JAMA. 2018;319(16):1723–1725. doi: 10.1001/jama.2018.3060. - DOI - PMC - PubMed
    1. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. doi: 10.1016/j.metabol.2018.09.005. - DOI - PubMed
    1. Seidell JC, Halberstadt J. The global burden of obesity and the challenges of prevention. Ann Nutr Metab. 2015;66(Suppl 2):7–12. doi: 10.1159/000375143. - DOI - PubMed
    1. Tai ES, Ho SC, Fok AC, Tan CE. Measurement of obesity by anthropometry and bioelectric impedance analysis: correlation with fasting lipids and insulin resistance in an Asian population. Ann Acad Med Singap. 1999;28(3):445–450. - PubMed

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