Antropometry-based BFP Predicts Hs-CRP in CKD (Thaha M, et al.)
Indones Biomed J. 2018; 10(2): 184-91
DOI: 10.18585/inabj.v10i2.397
RESEARCH ARTICLE
Anthropometry-based Body Fat Percentage Predicts High hs-CRP in
Chronic Kidney Disease Patients
Mochammad Thaha1,2, Maulana Antiyan Empitu3,, Ika Nindya Kadariswantiningsih4,
Cahyo Wibisono Nugroho1,2,Nurina Hasanatuludhhiyah3, Haerani Rasyid5, Zaky El Hakim6,
Maulana Muhtadin Suryansyah6, Rieza Rizqi Alda6, Mohammad Yusuf Alsagaff7,8,
Mochammad Amin9, Djoko Santoso1, Yusuke Suzuki10
1
Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo 47, Surabaya, Indonesia
2
Department of Internal Medicine, Universitas Airlangga Hospital, Jl. Mulyorejo, Surabaya, Indonesia
3
Department of Pharmacology and Therapeutics, Faculty of Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo 47, Surabaya, Indonesia
4
Department of Medical Microbiology, Faculty of Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo 47, Surabaya, Indonesia
5
Department of Internal Medicine, Faculty of Medicine, Universitas Hasanudin, Jl. Perintis Kemerdekaan Km. 10, Makassar, Indonesia
6
Medical Science Program, Faculty of Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo 47, Surabaya, Indonesia
7
Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo 47, Surabaya, Indonesia
8
Department of Cardiology and Vascular Medicine, Universitas Airlangga Hospital, Jl. Mulyorejo, Surabaya, Indonesia
9
Institute of Tropical Disease, Universitas Airlangga, Jl. Mulyorejo, Surabaya, Indonesia
10
Department of Nephrology, Juntendo University Faculty of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo, Japan
Corresponding author. E-mail: mochthaha@yahoo.com
Received date: Oct 20, 2017; Revised date: May 29, 2018; Accepted date: June 5, 2018
Abstract
B
ACKGROUND: 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.
Introduction
In last decades, chronic kidney disease (CKD) has been
one of the global health problems with high morbidity and
mortality rates. The prevalence of CKD in United States was
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
Indones Biomed J. 2018; 10(2): 184-91
around 6-10% of adult population with mortality rate 2050%.(1) In Indonesia, the prevalence of CKD was 10%.(2)
Chronic kidney disease is a progressive disease with wide
spectrum of manifestations, from mild decreased kidney
function denoted by micro albuminuria to chronic renal
failure with necessity for renal replacement therapy.(3)
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The Indonesian Biomedical Journal, Vol.10, No.2, August 2018, p.184-91
Obesity is an important CKD risk factor with high
prevalence in general population.(4,5). Study by Kelly, et
al., estimated 937 million or 23.2% of the world’s adult
population was overweight and 396 million (9.8%) was
obese.(6,7) They also projected the absolute numbers of
overweight and obese individuals in 2030 will increase to
be 2.16 billion and 1.12 billion, respectivley.(7) In 2010,
overweight and obesity were estimated to cause 3.4 million
deaths, 3.9 % of years of life lost, and 3.8 % of disabilityadjusted life-years (DALYs) worldwide.(8) Available
data show that the most dramatic increases in obesity are
in developing countries such as Mexico, China, and
Thailand.(9,10)
Inflammation is considered as one of the key elements
in almost all of kidney diseases. In CKD, particularly in
patients with end-stage renal disease (ESRD), prevalence
of chronic low-grade inflammation is quite high. One of
the potential causes of low-grade inflammation in CKD
patients is excess of adipose tissue. Recently, adipose
tissue is considered as an endocrine organ and major
site that plays important role for inflammatory and
metabolic pathways.(11) The adipose tissue is a complex
organ with functions other than energy storage, and secretes
several adipokines, such as tumor necrosis factor (TNF)-α,
interleukin (IL)-6, type 1 plasminogen activator inhibitor
(PAI-1), C-reactive Protein (CRP), resistin, and acylationstimulating protein.(12) In the human body, the major fat
deposit sites are subcutaneous fat and visceral abdominal
fat. Both of the fat deposit sites stated previously are
associated with insulin resistance and pro inflammation in
obese subjects.(11)
Excess of adipose tissue and chronic inflammation
are associated with early pathogenesis of kidney injury,
such as glomerular hyperfiltration. Moreover, a harmful
renal hemodynamic profile is also linked to obesity
both in patients with ESRD and general population. Patients
with advanced CKD had increased expression of proinflammatory genes in their subcutaneous abdominal fat.
This provides evidence that obesity in CKD also associated
with inflammation at the cellular level.(13) Positive
association also found between total body fat, abdominal
body fat, and markers of inflammation not only in general
population but also in CKD patients.(11,12)
High-sensitivity CRP (hs-CRP) is a most frequently
used marker of inflammation. CRP does not show a
significant circadian variability in stable condition, and
its stability in laboratory measurements is comparable to
serum cholesterol levels. This biomarker also associated
with higher risk of cardiovascular events and mortality.
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(14,15,16) The objective of this study was to assess the
association between body fat percentage (BFP) with serum
hs-CRP level in CKD patients. This study compared the
ability of BFP with widely used body mass index (BMI)
to predict the value of serum hs-CRP in CKD patients.
Considering these findings that link body fat with the
presence of inflammation, we hypothesized that higher
body fat are associated with elevated serum hs-CRP in CKD
patients.
Methods
Study Design and Population Selection
This was an analytic observational study using crosssectional design. Ninety patients from outpatient clinics
and hemodialysis (HD) units at two government and a
private hospitals in Surabaya and Makasar were recruited
from May to July 2017. This study was approved by Ethical
Committee & Law of Universitas Airlangga Hospital with
No. 093/KEH/2017.
Seventy three patients were diagnosed with CKD.
Seventy one patients were qualified to participate in
this study since 2 patients were excluded due to acute
inflammatory conditions. The inclusion criteria were adult
CKD patients, aged >21 years old and agreed to participate
in this study. The diagnosis of CKD was confirmed by
National Kidney Foundation Kidney Disease Outcomes
Quality Initiative (NKF K-DOQI) criteria based on Chronic
Kidney Disease Epidemiology Collaboration (CKD-EPI)
equation (estimated glomerular filtration rate (eGFR) ≤60
mL/min/1.73 m2, urine albumin >300 mg/day, albumin to
creatinine ratio (ACR) >250 mg/g for men and >355 mg/g
for women). Exclusion criteria were overt infection, fever
in the last 3 days, signs of infection, acute inflammatory
disease and malignancy.
Anthropometric Measurement
Anthropometric measurements, included body weight,
height, BMI, BFP, skinfold thickness (SKF) of triceps
and biceps were performed by trained physician. Body
weight was measured to the nearest 0.1 kg in balance-beam
scale. Height was measured to the nearest 0.5 cm while
patients standing erect with their head in plane. BMI was
calculated using formula of body weight (kg) divided by
square of height (m2). SKF was measured using Lange
skinfold caliper on the left arm or on the arm contrary to
arteriovenous fistule for patients underwent hemodialysis.
BFP was calculated using Kwok’s Formula:
DOI: 10.18585/inabj.v10i2.397
BFP = -27.149 + 6.137(SexB) + 1.120(BMI) + 17.308 log10(Sum 2).
Antropometry-based BFP Predicts Hs-CRP in CKD (Thaha M, et al.)
Indones Biomed J. 2018; 10(2): 184-91
Table 1. Characteristics of study participants.
Notes: SexB, female=2, male=1; Sum 2, triceps + biceps.(17)
Laboratory Parameters
Blood and urine samples were collected early in the
morning. Measurement of kidney function parameters
(estimated glomerular filtration rate (eGFR), urine ACR,
serum creatinine, serum cystatin-C) were performed
using routine laboratory methods. High-sensitivity CRP
was measured by Particle enhanced Turbidimetry (Roche
Diagnostic, California, USA)
Characteristic
CKD HD and Non-HD
(n=71)
Age (years)
59 (53-63)
Sex ratio (M/F)
41/30
Kidney Function
2
eGFR (mL/minute/1.73 m )
Urine ACR
408 (19-2174)
Serum Creatinine (mg/L)
2.9 (1.7-12.7)
Serum Cystatin-C (mg/L)
2.8 (1.7-6.7)
HD, n (%)
Statistical Analysis
Normality test was performed using Shapiro Wilk. For
normally distributed variables, data were shown as
mean±SD. For skewed variables, data were shown as
median and interquartile range (1st quartile-3rd quartile).
Correlation between variables was performed using
Pearson correlation test. Receiver operating characteristic
(ROC) curve were plotted using absolute value of BFP
and hs-CRP value (high hs-CRP >3 mg/L). Optimal cut-off
was defined as BFP value with highest cumulative value of
sensitivity and specificity.
All testing were two-tailed at the 0.05 significance
level. The statistical analyses were performed using
XLSTAT software version 2016.02.28451.
Results
Characteristic of Study Participants
Seventy one patients were evaluated in this study (median
age 59, 41 male). The baseline characteristics of study
participants were depicted in Table 1. The median of
eGFR was 20 mL/minute/1.73 m2. From anthropometric
data, it was shown that the averaged BMI of our subjects
was 25.8±4.4. Overweight and obesity prevalence were
42% and 14% respectively. Only 2 (3%) subjects were
undernourished. The majority of subjects had some
cardiometabolic diseases, such as 73% have type 2 diabetes
mellitus, 87% percents have hypertension and 41% have
congestive heart failure (CHF).
Comparison of Characteristic between Non-HD and HD
Patients
The comparison between non-HD and HD patients
characteristic was depicted in Table 2. There were no
significant differences in age and sex ratio between nonHD and HD patients. There were significant differences
20 (3-39)
26 (37)
Anthropometry
Body Fat Percentage (%)
19.1 (16.0-24.0)
Body weight (kg)
67.0 (54.4-76.9)
2
BMI, (kg/m )
25.8±4.4
Biceps skinfold (cm)
1.2 (1.0-1.7)
Triceps skinfold (cm)
2.3 (1.6-3.0)
BMI Categories
18.5-24.9, n (%)
29 (40.845)
25-29.9, n (%)
30 (42.254)
<18.5, n (%)
2 (2.817)
>=30, n (%)
10 (14.085)
Lipid parameters
Total Cholesterols (mg/dL)
204 (167-236)
LDL Cholesterols (mg/dL)
122 (49)
HDL Cholesterols (mg/dL)
39 (13)
Cardiovascular Disease and Risk Factors
Type 2 Diabetes, n (%)
54 (76)
Hypertension, n (%)
62 (87)
High SBP, n (%)
26 (37)
High DBP, n (%)
10 (14)
CHF, n (%)
hs-CRP (mg/L)
High hs-CRP, n (%)
29 (41)
1.8 (1.1-4.3)
24 (34)
in kidney function parameters for eGFR, urine ACR,
serum creatinine, serum cystatin-C between two groups.
Those were, eGFR (<0.0001), urine ACR (<0.0001),
serum creatinine (<0.0001), serum cystatin-C (<0.0001).
Anthropometry measurement results mostly demostrated no
significant differences. Those were for body fat percentage,
body weight, BMI, and biceps skinfold. However, triceps
skinfold of HD patients was significantly lower than non-HD
patients (p<0.05). Our study showed that the HD patients
had significantly higher hs-CRP level than non-HD patients
(p<0.01). However there was no significant difference of the
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The Indonesian Biomedical Journal, Vol.10, No.2, August 2018, p.184-91
Table 2. Comparison of characteristic between non-HD and HD patients.
Characteristic
Age (years)
Sex ratio (M/F)
Non HD
(n=45)
HD
(n=26)
p -value
58.13±5.92
57.62±10.98
0.9
27/18
14/12
0.613
29 (22-60)
3 (2-4)
<0.0001*
Kidney Function
2
eGFR (ml/minute/1.73 m )
184 (18-803)
1789 (834-3473)
<0.0001*
Serum Creatinine (mg/dL)
Urine ACR
1.88 (1.29-2.59)
14.14 (11.51-15.95)
<0.0001*
Serum Cystatin-C (mg/L)
1.88 (1.28-2.61)
7.42(6.58-7.94)
<0.0001*
20.7±7.2
18.7±7.9
0.205
69 (62.0-78.0)
61.8 (52.6-74.9)
0.11
Anthropometry
Body Fat Percentage (%)
Body weight (kg)
2
BMI (kg/m )
26.31±4.25
25.02±4.59
0.254
Biceps Skinfold (cm)
1.3 (1.0-1.8)
1.1 (1.0-1.6)
0.586
Triceps Skinfold (cm)
2.4 (2.1-3.1)
1.8 (1.3-2.8)
0.026*
Total Cholesterols (mg/dL)
215 (182-245)
189 (151-206)
0.013*
LDL Cholesterols (mg/dL)
131±54
105±34
0.037*
HDL Cholesterols (mg/dL)
44±15
39±14
0.237
0.001*
Lipid parameters
Cardiovascular Disease and Risk Factors
Type 2 Diabetes, n (%)
41 (91)
15 (58)
Hypertension, n (%)
39 (87)
26 (100)
0.337
High SBP, n (%)
6 (13)
20 (77)
<0.0001*
High DBP, n (%)
4 (9)
6 (23)
0.098
CHF, n (%)
hs-CRP (mg/L)
High hs-CRP, n (%)
15 (33)
14 (54)
0.09
1.40 (0.90-3.20)
2.10 (1.65-8.05)
0.010*
12 (26.67)
12 (46.12)
0.094
* : significant correlation
proportion of patients with high hs-CRP between non-HD
and HD patients.
Comparison for BMI and BFP to Predict High hs-CRP
BFP and BMI may predict inflammation in our CKD
patients with cut off point 18.4% (Figure 1, Figure 2). The
BFP value above 18.4% were positively associated with
high hs-CRP, indicated by significant crude and adjusted
odd ratio (OR) (4.705 and 5.056 respectively). The crude
OR of high hs-CRP was also significant for BMI (3.447).
hs-CRP Concentration in CKD Patients
Positive correlation was found between BFP and hs-CRP
(r= 0.266; p<0.05) as shown in Table 3. However there was
no significant correlation between BMI and hs-CRP.
Table 3. Pearson’s matrix of association between BFP and hs-CRP.
hs-CRP
Parameters
Correlation
Coefficient
p -value
p -value
BFP
0.025*
0.266
0.006*
0.324
BMI
0.448
0.092
0.117
0.188
Total Cholesterol
0.129
0.182
0.728
0.042
LDL Cholesterol
0.099
-0.197
0.117
-0.188
HDL Cholesterol
0.004*
-0.341
0.001*
-0.378
<0.0001*
0.637
0.004*
0.339
Ratio of Total/HDL Cholesterol
* : significant correlation
187
log hs-CRP
Correlation
Coefficient
Antropometry-based BFP Predicts Hs-CRP in CKD (Thaha M, et al.)
Indones Biomed J. 2018; 10(2): 184-91
DOI: 10.18585/inabj.v10i2.397
ROC CURVES
1
0.9
0.8
True positive rate (Sensitivity)
0.7
0.6
Body Fat Percentage
0.5
Body Mass Index
0.4
Series2
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False negative rate (1-Specificity)
Figure 1. ROC curve of BFP and BMI to predict high hs-CRP (p-value= 0.001).
It remained significant after adjusted by age (3.487). The
area under curve (AUC) of receiver operating characteristic
(ROC) for BMI was lower than BFP (Table 4).
Discussion
Since there were no significant differences in age, sex,
BMI, BFP, and the prevalence of high hs-CRP between
non-HD and HD groups, we combined HD and non-HD
groups in the sensitivity and specificity analysis of BFP and
BMI to predict high hs-CPR. In this observational study, we
demonstrated positive correlation between BFP and hs-CRP
in CKD populations.
CRP is an established marker of systemic
inflammation in the general population as well as patients
with CKD. Inflammation plays fundamental role in
pathophysiology of CKD and has beed linked with
cardiovascular disease, protein energy wasting and
mortality in CKD patients.(18) Our study showed that the HD
patients group with significantly lower kidney function had
significantly higher hs-CRP level than the non HD patients.
Several studies has shown predictive values of hs-CRP
in CKD. Persistently elevated hs-CRP level is associated
with all cause mortality in ESRD. It is also considered as
a predictor for malnutrition and cardiovascular disease in
hemodialysis patients.(14)
The most prevalent nutritional status in our study was
overweight (BMI 25-29.9). This was in line with the finding
of a large cohort study conducted by Herrington, et al.,
which concluded that BMI >25 kg/m2 was associated with
a progressively increased risk of CKD stages 4-5.(19) Prior
studies also found high prevalence of overweight and
obesity in predialysis and CKD patients.(20,21) However,
our study could not show correlation between BMI and hsCRP level. Several literatures have stated that BMI is less
than ideal metric for obesity in CKD population.(22) Others
also highlight the limitations of BMI as a reflection of body
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The Indonesian Biomedical Journal, Vol.10, No.2, August 2018, p.184-91
Figure 2. Sensitivity and
specificity of body fat
percentage for predicting
high hs-CRP.
composition (21), since it is unable to discriminate fat and
muscle mass as well as hydration status which often
alter in CKD patients.(23,24) Therefore we also included
BFP as anthropometric parameter other than BMI.
The correlation between BFP and hs-CRP in this
study was concordance with previous study which
demonstrate association between CRP and BFP measured
by bioimpedance method in non dialysis stage 3-5 CKD
patients. Inflammatory cytokines in CKD are produced not
only by lymphocytes, but also by adipose tissue, which
becomes dysfunctional in CKD (25) characterized by
increased hs-CRP production (26). Additionally, this study
observed that the hs-CRP also correlate negatively with
HDL cholesterol and positively with total/HDL cholesterol
ratio. Some previous extensive studies have shown that the
impaired cholesterol metabolism was strongly correlated
with increased inflammatory state in CKD patients.(27)
The cholesterol profile was also correlated well with BMI
and BFP, although not at the same degree of association
compared to triglycerides.(27) Therefore theoretically,
greater BFP and BMI would reflect as a higher cholesterol
levels. In this study, the total/HDL cholesterol ratio was the
strongest factor associated with hs-CRP levels compared
to others lipid parameters as well as anthropometric
parameters. However, this study failed to observe the
association between laboratory lipid parameters with either
BMI and BFP (data not shown).
We used anthropometry based BFP calculation using
Kwok’s formula. We assumed that the characteristics of
subjects in Kwok’s study were similar with our study.(28)
Anthropometry based BFP is considered to be a valid
discriminator for cardiometabolic risk in comparison
with dual energy X-ray absorptiometry (DEXA) scan and
Bioelectrical impedance analysis (BIA) methods, as shown
by Krachler, et al., in general population.(29) This method
is cheaper and easier compared to DEXA Scan and BIA.
Table 4. AUC, specificity, sensitivity, and OR (CI 95%) of high hs-CRP value using BFP’s
optimal cut-off 18.4% and BMI cut-off 27.3 kg/m2.
BFP
189
BMI
Test Value
CI 95%
Test Value
CI 95%
AUC
0.71
(0.592-0.828)
0.637
0.501-0.774
Sensitivity
0.833
(0.634-0.938)
0.583
0.388-0.755
Specificity
0.553
(0.412-0.686)
0.723
0.581-0.831
Odds Ratio (Unadjusted)
4.705
(1.560-14.192)
3.447
1.247-9.530
Odds Ratio (Adjusted by Age)
5.056
(4.343-5.885)
3.487
3.046-3.992
Antropometry-based BFP Predicts Hs-CRP in CKD (Thaha M, et al.)
Indones Biomed J. 2018; 10(2): 184-91
DOI: 10.18585/inabj.v10i2.397
The finding of this study should be viewed with some
limitation. The small sample size of this study might not
reflect accurately the predictive ability of BFP and BMI to
estimate the value of hs-CRP. Specificity of hs-CRP was
considered to be low since many factors may influence its
measurement i.e., diabetes, infection, smoking, chronic
diseases, sleep disorders, malnutrition, anemia, old age,
alcohol consumption, CVD, hypertension, vascular access,
pregnancy, pharmacological therapy, dialysis and CRP gene
polymorphism, which this study did not estimate in the
statistical analysis. Additionally, the relatively narrow range
of BFP and BMI among the study participants may limit the
ability of statistical analysis to generate prediction model
for hs-CRP. Therefore, this study could demonstrate some
degree of association between anthropometric-based BFP
and hs-CRP but with poor correlation and predictive value.
Another limitation regarding this study is its applicability
aspect. The relatively poor correlation between BFP and hsCRP has limited the usability of the specificity, sensitivity,
and predictive value generated from this study in the daily
clinical practice. However, the association of BFP and
hs-CRP showed in this study might indicate a potential
relationship between the two variables, particularly when a
significantly bigger sample size is introduced.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Conclusion
In overall, this study emphasized the need to regularly
assess nutritional status in CKD using BFP rather than BMI
alone since it demonstrated better association with hs-CRP
in our study. It needs further study to evaluate larger and
more heterogenous samples to explore malnutrition as a risk
factor for chronic inflammation as well as cardiovascular
complications in CKD patients.
16.
17.
18.
19.
References
20.
1. 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.
2. 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.
3. Andrassy KM. Comments on'KDIGO 2012 clinical practice guideline
for the evaluation and management of chronic kidney disease'.
Kidney Int. 2013; 84: 622-3.
4. Kovesdy CP, Furth S, Zoccali C. Obesity and kidney disease: hidden
consequences of the epidemic. JEMDSA. 2017; 22: 5-11.
21.
22.
23.
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.
Arroyo-Johnson C, Mincey KD. Obesity Epidemiology Worldwide.
Gastroenterol Clin North Am. 2016; 45: 571-9.
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.
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.
Caballero B. The global epidemic of obesity: an overview. Epidemiol
Rev. 2007; 29: 1-5.
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.
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.
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.
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.
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.
Lacson E, Levin NW. Poor nutritional status and inflammation:
C-reactive protein and end-stage renal disease. Semin Dial. 2004;
17: 438-448.
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.
Akchurin OM, Kaskel F. Update on inflammation in chronic kidney
disease. Blood Purif. 2015; 39: 84-92.
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: e0173515. doi:
10.1371/journal.pone.0173515.
Silva MIB, da Silva Lemos CC, Torres MRSG, Bregman R. Waistto-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.
Johansen KL, Lee C. Body composition in chronic kidney disease.
Curr Opin Nephrol Hypertens. 2015; 24: 268-75.
Mallamaci F, Tripepi G. Obesity and CKD progression: hard facts
on fat CKD patients. Nephrology Dialysis Transplantation. 2013;
28(Suppl 4): iv105-8.
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.
190
The Indonesian Biomedical Journal, Vol.10, No.2, August 2018, p.184-91
24. Cuppari L. Diagnosis of obesity in chronic kidney disease: BMI or
body fat? Nephrology Dialysis Transplantation. 2013; 28(Suppl 4):
iv119-21.
25. Akchurin M, Kaskel F. Update on inflammation in chronic kidney
disease. Blood Purif. 2015; 39: 84-92.
26. 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.
191
Print ISSN: 2085-3297, Online ISSN: 2355-9179
27. Reiss AB, Voloshyna I, De Leon J, Miyawaki N, Mattana J.
Cholesterol metabolism in CKD. Am J Kidney Dis. 2015; 66: 107182.
28. Kwok T, Woo J, Lau E. Prediction of body fat by anthropometry in
older Chinese people. Obes Res. 2001; 9: 97-101.
29. 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. doi:
10.1155/2013/862514.