Anthropometry-based Body Fat Percentage Predicts High hs-CRP in Chronic Kidney Disease Patients

Mochammad Thaha, Maulana Antiyan Empitu, Ika Nindya Kadariswantiningsih, Cahyo Wibisono Nugroho, Nurina Hasanatuludhhiyah, Haerani Rasyid, Zaky El Hakim, Maulana Muhtadin Suryansyah, Rieza Rizqi Alda, Mohammad Yusuf Alsagaff, Mochammad Amin, Djoko Santoso, Yusuke Suzuki

Abstract


BACKGROUND: Obesity is an important cardiovascular risk factor and associated with low grade inflammation in chronic kidney disease (CKD) patients. This study aims to assess the association between body fat with serum high sensitivity C-reactive protein (hs-CRP) level in CKD patients.

METHODS: A cross-sectional study was performed in 71 CKD patients. Anthropometric measurements included body weight, height, body mass index (BMI), body fat percentage (BFP), skinfold thickness (SKF) of triceps and biceps were performed by trained physician. BFP was calculated using Kwok’s Formula and hs-CRP was measured by Particle enhanced Turbidimetry.

RESULTS: The averaged BMI of our subjects was 25.8±4.4. There was no significant difference in BMI between pre-dialysis and hemodialysis CKD patients. Positive correlation was found between BFP and hs-CRP (r=0.266; p<0.05), while there was no significant correlation between BMI and hs-CRP.

CONCLUSION: Body fat percentage was associated with hs-CRP. Hence, it will be more beneficial to assess nutritional status in CKD using BFP rather than BMI alone since it was demonstrated to correlate with hs-CRP in our study

KEYWORDS: CKD, obesity, inflammation, body fat, hs-CRP


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References


Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013; 382: 260-72, CrossRef.

Levin A, Tonelli M, Bonventre J, Coresh J, Donner JA, Fogo AB, et al. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. Lancet. 2017; 390: 1888-917, CrossRef.

Andrassy KM. Comments on'KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease'. Kidney Int. 2013; 84: 622-3, CrossRef.

Kovesdy CP, Furth S, Zoccali C. Obesity and kidney disease: hidden consequences of the epidemic. JEMDSA. 2017; 22: 5-11, CrossRef.

Ejerblad E, Fored CM, Lindblad P, Fryzek J, McLaughlin JK, Nyrén O. Obesity and risk for chronic renal failure. J Am Soc Nephrol. 2006; 17: 1695-702, CrossRef.

Arroyo-Johnson C, Mincey KD. Obesity Epidemiology Worldwide. Gastroenterol Clin North Am. 2016; 45: 571-9, CrossRef.

Kelly T, Yang W, Chen C, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes. 2008; 32: 1431-7, CrossRef.

Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014; 384: 766-81, CrossRef.

Caballero B. The global epidemic of obesity: an overview. Epidemiol Rev. 2007; 29: 1-5, CrossRef.

Popkin BM, Gordon-Larsen P. The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obes Relat Metab Disord. 2004; 28 (Suppl 3): S2-9, CrossRef.

Carvalho LK, Barreto Silva MI, da Silva Vale B, Bregman R, Martucci RB, Carrero JJ, et al. Annual variation in body fat is associated with systemic inflammation in chronic kidney disease patients Stages 3 and 4: a longitudinal study. Nephrol Dial Transplant. 2012; 27: 1423-8, CrossRef.

Nihi MM, Manfro RC, Martins C, Suliman M, Murayama Y, Riella MC, et al. Association between body fat, inflammation and oxidative stress in hemodialysis. J Bras Nefrol. 2010; 32: 11-7.

Witasp A, Carrero JJ, Heimburger O, Lindholm B, Hammarqvist F, Stenvinkel P, et al. Increased expression of pro-inflammatory genes in abdominal subcutaneous fat in advanced chronic kidney disease patients. J Intern Med. 2011; 269: 410-9, CrossRef.

Abraham G, Sundaram V, Sundaram V, Mathew M, Leslie N, Sathiah V. C-Reactive protein, a valuable predictive marker in chronic kidney disease. Saudi J Kidney Dis Transpl. 2009; 20: 811-5, PMID.

Tsai YW, Lu MC, Lin YH, Lee YC, Li WC, Chen JY, et al. Combined body mass index with high-sensitivity C-reactive protein as independent predictors for chronic kidney disease in a relatively healthy population in Taiwan. Eur J Clin Nutr. 2016; 70: 766-72, CrossRef.

Lacson E, Levin NW. Poor Nutritional Status and Inflammation: C‐Reactive Protein and End‐Stage Renal Disease. Semin Dial. 2004; 17: 438-448, CrossRef.

Chambers AJ, Parise E, McCrory J, Cham R. A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States. J Nutr Health Aging. 2014; 18: 586-90, CrossRef.

Akchurin OM, Kaskel F. Update on inflammation in chronic kidney disease. Blood Purif. 2015; 39: 84-92, CrossRef.

Herrington WG, Smith M, Bankhead C, Matsushita K, Stevens S, Holt T, et al. Body-mass index and risk of advanced chronic kidney disease: Prospective analyses from a primary care cohort of 1.4 million adults in England. PloS one. 2017; 12(3): e0173515, CrossRef.

Silva MIB, da Silva Lemos CC, Torres MRSG, Bregman R. Waist-to-height ratio: an accurate anthropometric index of abdominal adiposity and a predictor of high HOMA-IR values in nondialyzed chronic kidney disease patients. Nutrition. 2014; 30: 279-85, CrossRef.

Johansen KL, Lee C. Body composition in chronic kidney disease. Curr Opin Nephrol Hypertens. 2015; 24: 268-75, CrossRef.

Mallamaci F, Tripepi G. Obesity and CKD progression: hard facts on fat CKD patients. Nephrology Dialysis Transplantation. 2013; 28(Suppl 4): iv105-8, CrossRef.

Mafra D, Guebre-Egziabher F, Fouque D. Body mass index, muscle and fat in chronic kidney disease: questions about survival. Nephrol Dial Transplant. 2008; 23: 2461-6, CrossRef.

Cuppari L. Diagnosis of obesity in chronic kidney disease: BMI or body fat? Nephrology Dialysis Transplantation. 2013; 28(Suppl 4): iv119-21, CrossRef.

Akchurin M, Kaskel F. Update on inflammation in chronic kidney disease. Blood Purif. 2015; 39: 84-92, CrossRef.

Lin CC, Kardia SL, Li CI, Liu CS, Lai MM, Lin WY, et al. The relationship of high sensitivity C-reactive protein to percent body fat mass, body mass index, waist-to-hip ratio, and waist circumference in a Taiwanese population. BMC Public Health. 2010; 10: 579. doi: 10.1186/1471-2458-10-579, CrossRef.

Reiss AB, Voloshyna I, De Leon J, Miyawaki N, Mattana J. Cholesterol metabolism in CKD. Am J Kidney Dis. 2015; 66: 1071-82, CrossRef.

Kwok T, Woo J, Lau E. Prediction of body fat by anthropometry in older Chinese people. Obes Res. 2001; 9: 97-101, CrossRef.

Krachler B, Völgyi E, Savonen K, Tylavsky FA, Alén M, Cheng S. BMI and an anthropometry-based estimate of fat mass percentage are both valid discriminators of cardiometabolic risk: a comparison with DXA and bioimpedance. J Obes. 2013; 2013: 862514, CrossRef.




DOI: https://doi.org/10.18585/inabj.v10i2.397

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