Association Between Obesity and Chronic Kidney Disease,Defined by Both Glomerular Filtration Rate and Albuminuria,in Korean Adults

Abstract

Background:

Chronic kidney disease (CKD) has often been defined based on glomerular filtration rate (GFR) alone. The Kidney Disease: Improving Global Outcomes guideline highlights albuminuria in the CKD definition. Thus, we investigated the association between obesity and CKD, as defined by both GFR and albuminuria, in Korean adults.

Methods:

We used Korea National Health and Nutrition Examination Survey 2011–2014 data (N = 19,331, ≥19 years old) representing the national Korean population. CKD was classified by (1) estimated GFR (eGFR) < 60 mL/min/1.73 m² (CKD_GFR); (2) albumin-to-creatinine ratio (ACR) ≥30 mg/gram (CKD_ACR); and (3) eGFR < 60 mL/min/1.73 m² or ACR ≥30 mg/gram (CKD_Risk). Associations between obesity and each CKD category were evaluated using multivariate logistic regression analysis.

Results:

The prevalence rates of CKD_GFR, CKD_ACR, and CKD_Risk were 2.2%, 6.7%, and 8.1%, respectively. Compared with the normal body mass index (BMI; 18.5–22.9 kg/m²) group, men with BMI ≥ 25 kg/m² had 1.88 times greater risk of CKD_GFR in the adjusted model [95% confidence interval (CI), 1.26–2.80; P = 0.002]; BMI was not significantly associated with CKD_GFR in women. In contrast, both men and women with BMI ≥ 25 kg/m² had 1.58 and 1.40 times higher risk of CKD_ACR (95% CI, 1.21–2.07 and 1.08–1.81, respectively, both P < 0.01). Obese men and women had 1.65 and 1.38 times higher risk of CKD_Risk (95% CI, 1.29–2.12 and 1.09–1.75, respectively, both P < 0.01).

Conclusions:

Obesity was significantly associated with an increased ACR-based CKD risk. Longitudinal studies are needed to investigate the role of overweight and obesity in the development and progression of CKD.

Introduction

The increasing socioeconomic burden of chronic kidney disease (CKD) has become globally widespread.¹ According to data from the Korea National Health and Nutrition Examination Survey (KNHANES) 2007–2010, the prevalence rates of CKD among Korean adults over the age of 20 years were 2.6%–7.7%.² Continuous decline in renal function leads to end-stage renal disease (ESRD) requiring dialysis or kidney transplant, while also increasing risk for cardiovascular complications and lowering 5-year survival rates to 60%–70%.³ Increasing life expectancy and diabetes prevalence have caused an increase in the number of dialysis patients. In 2014, there were 80,674 ESRD patients in Korea, which was 70% of the prevalence rate in the United States and 50% of the prevalence rate in Japan.⁴

The Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines published in 2002 by the US National Kidney Foundation are usually used to define and classify CKD. According to these guidelines, the presence of one or more markers of kidney damage or a glomerular filtration rate (GFR) <60 mL/min/1.73 m² lasting for 3 months or more, is defined as CKD.⁵

The Kidney Disease: Improving Global Outcomes (KDIGO) group analyzed the relative risks of total mortality rate, cardiovascular mortality rate, end-stage renal failure, acute renal damage, and progression of CKD using GFR and albuminuria categories.⁶ They suggested classifying CKD prognosis into low risk, moderate risk, high risk, and very high risk.⁶ In other words, the guidelines highlighted the need for CKD definition and classification, that can reflect the patient prognosis, by incorporating the etiology and albuminuria in the CKD evaluation, in addition to estimated GFR (eGFR). Albuminuria is typically expressed as a urine loss rate and is measured with albumin excretion rate or albumin-to-creatinine ratio (ACR). It is the earliest marker of glomerular diseases⁷ and is associated with CKD prognosis.^8
–10

Obesity has become a serious public health problem in Korea, with a 30%–35% prevalence rate defined by body mass index (BMI) ≥ 25 kg/m² among Korean adults¹¹ and may contribute to CKD progression as well as other conventional cardiovascular risk factors. Several studies have reported that obese individuals had greater risk of developing CKD.^12,13 However, in these studies, CKD was defined based one GFR or proteinuria. The relative risk for mortality and kidney outcomes is increased in subjects with urine ACR ≥ 30 mg/gram, even with their GFR > 60 mL/min/1.73 m².¹⁴ Furthermore, Asians tend to develop albuminuria more easily than Caucasians.¹⁵ For example, proteinuric diabetic kidney diseases are more prevalent in Asian ethnicities compared with non-Hispanic whites.^16,17 We therefore investigated the association between obesity and CKD, as defined by both GFR and albuminuria, in Korean adults.

Materials and Methods

Study participants and database

The data were derived from the KNHANES 2011–2014. This is a nationwide cross-sectional survey that has been conducted since 1998 by the Korea Centers for Disease Control and Prevention (KCDC) to assess the health and nutritional status of noninstitutionalized civilians in South Korea (http://knhanes.cdc.go.kr). Surveys were conducted once every 3 years until 2007; since then, they have been conducted annually. A multistage clustered probability sampling design was used for the selection of household units. Sample weights were constructed for sample participants to represent the Korean population by accounting for the complex survey design, survey nonresponse, and poststratification.¹⁸ Because urine albumin was measured during 2011–2014, we used data acquired in the second and third years (2011–2012) of KNHANES V and in first and second years (2013–2014) of KNHANES VI. Of the 30,447 participants, we excluded subjects <19 years old (n = 6759), those with any malignancies, hepatitis or liver cirrhosis, and pregnant, and without baseline serum creatinine, height or weight, urine albumin, or creatinine (n = 4357). The current analysis was based on a total of 19,331 subjects (8709 males, 10,622 females). The KNHANES was reviewed and approved by the Ethics Committee of the KCDC (2011-02CON-06-C, 2012-01EXP-01-2C, 2013-07CON-03-4C, and 2013-12EXP-03-5C) and the study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

Measures of anthropometric and biochemical traits

BMI was calculated by dividing weight (kg) by height squared (m²). Waist circumference (WC) was measured (in cm) during exhalation, at the narrowest region between the inferior costal margin and the iliac crest. According to standardized protocols, trained medical personnel performed all health examination procedures and all equipments were calibrated periodically. Laboratory performance was checked by the laboratory data quality control program to ensure the precision and accuracy of all analytical values. Blood samples were collected in the morning after fasting 8 hr. Fasting plasma glucose, total cholesterol, triglycerides, and high-density lipoprotein cholesterol were assessed on the Hitachi Automatic Analyzer 7600 (Tokyo, Japan) in 2011–2012 and the COBAS 8000 C702 (Roche, Mannheim, Germany) in 2013–2014. Low-density lipoprotein cholesterol levels were calculated by the Friedewald equation. Analysis of glycosylated hemoglobin (HbA1c) was performed with the HLC-723G7 (Tosoh, Tokyo, Japan) in 2011–2012 and the Tosoh G8 (Tosoh) in 2013–2014. Urine albumin was measured using an automated analyzer with turbidimetric assay (Hitachi Automatic Analyzer 7600).

Smoking status was classified into current smoker, former smoker, or never smoker. Drinker was defined as drinking more than 2 days per week during the last year. Regular exercise was defined as exercising more than 3 days per week.

Definition of obesity and CKD

Obesity was defined by BMI classification according to Asia–Pacific criteria.¹⁹ Low weight was defined as BMI <18.5 kg/m², normal weight as 18.5 to <23 kg/m², overweight as 23 to <25 kg/m², and obesity as ≥25 kg/m².

eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula.²⁰ The eGFR and albuminuria were classified according to the KDIGO guidelines.⁶ In these analyses, CKD defined as eGFR < 60 mL/min/1.73 m² was designated as CKD_GFR, while CKD defined as ACR ≥ 30 mg/gram was designated as CKD_ACR. According to the 2012 KDIGO guidelines, which provide the risk classification of CKD outcomes, we designated the groups with eGFR <60 mL/min/1.73 m² or ACR ≥ 30 mg/gram as CKD_Risk.

Statistical analyses

We used a survey procedure because KNHANES is based on a complex sampling design. For participants' baseline characteristics, the weighted mean and standard error or proportion (%) was calculated. Student's t test was used to compare continuous variables and the chi-squared test to compare categorical variables between men and women. A multivariate logistic regression analysis was performed to estimate the adjusted odds ratio and 95% confidence interval (CI) of BMI or WC categories for CKD. Because fasting glucose, HbA1c, triglyceride, aspartate and alanine aminotransferase, as well as urine ACR values showed a skewed distribution, they were log-transformed to improve normality. P < 0.05 was considered statistically significant. All analyses were performed using SAS 9.2 version (SAS Institute, Inc., Cary, NC).

Results

Baseline characteristics of study participants

We analyzed 19,331 participants over the age of 19 years (8709 males, 10,622 females) selected from KNHANES V and VI (2011–2014). The study design is shown in Supplementary Figure S1 (Supplementary Data are available online at www.liebertpub.com/met). The study participants' baseline characteristics are shown in Table 1.

Table 1.

Baseline Characteristics of Study Participants

Variables	Men (n = 8709)	Women (n = 10,622)	P
Age (years)	44.07 ± 0.23	46.97 ± 0.24	<0.001
Height (cm)	171.11 ± 0.10	157.39 ± 0.09	<0.001
Weight (kg)	71.09 ± 0.16	57.70 ± 0.13	<0.001
BMI (kg/m²)	24.23 ± 0.05	23.31 ± 0.05	<0.001
Waist circumference (cm)	84.21 ± 0.14	77.81 ± 0.15	<0.001
Systolic BP (mmHg)	119.31 ± 0.21	114.79 ± 0.24	<0.001
Diastolic BP (mmHg)	78.33 ± 0.16	73.10 ± 0.14	<0.001
Fasting glucose (mg/dL)	99.42 ± 0.28	96.29 ± 0.27	<0.001
HbA1c (%)	5.75 ± 0.10	5.72 ± 0.01	0.011
Total cholesterol (mg/dL)	187.43 ± 0.52	189.61 ± 0.44	0.001
Triglyceride (mg/dL)	158.32 ± 1.68	114.04 ± 1.19	<0.001
HDL-cholesterol (mg/dL)	49.18 ± 0.16	55.66 ± 0.16	<0.001
LDL-cholesterol (mg/dL)	108.38 ± 0.44	111.49 ± 0.37	<0.001
AST (IU/L)	24.03 ± 0.17	20.15 ± 0.11	<0.001
ALT (IU/L)	26.53 ± 0.30	17.69 ± 0.17	<0.001
Hemoglobin (grams/dL)	15.40 ± 0.02	13.13 ± 0.01	<0.001
WBC (thousands/μL)	6.52 ± 0.02	5.83 ± 0.02	<0.001
Vitamin D (ng/mL)	17.63 ± 0.14	16.12 ± 0.12	<0.001
BUN (mg/dL)	14.69 ± 0.05	13.45 ± 0.05	<0.001
Creatinine (mg/dL)	0.97 ± 0.00	0.72 ± 0.00	<0.001
eGFR (mL/min/1.73 m²)	95.06 ± 0.22	98.95 ± 0.24	<0.001
Urine ACR (mg/gram)	17.39 ± 1.45	17.81 ± 1.45	<0.001
Caloric intake (kcal/day)	2468.84 ± 16.79	1730.40 ± 9.85	<0.001
Diabetes mellitus (%)	1025 (9.82)	952 (7.82)	<0.001
Hypertension (%)	2774 (27.25)	2916 (22.99)	<0.001
Cardiovascular disease (%)	232 (3.46)	233 (3.38)	0.842
Hypercholesterolemia (%)	1052 (11.41)	1811 (15.13)	<0.001
Current/ex-/nonsmokers (%)	43.97/29.17/23.86	6.08/3.99/89.93	<0.001
Alcohol (≥2–3 times/week, %)	38.68	12.27	<0.001
Regular exercise (≥3 times/week, %)	41.30	33.71	<0.001
Family income (low, %)	37.80	44.07	<0.001
Education (<high, %)	19.89	33.57	<0.001

Data, mean ± standard error or n (%).

ACR, albumin-to-creatinine ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; WBC, white blood cell.

Prevalence of CKD by GFR and albuminuria categories in Korean adults

Prevalence rates for CKD_GFR and CKD_ACR were 2.2% and 6.7%, respectively. In the risk classification of CKD prognosis, the proportions of low, moderate, high, and very high risk were 91.9%, 6.5%, 1.2%, and 0.4%, respectively. The prevalence of CKD_Risk was 8.1% (Table 2).

Table 2.

Prevalence of Chronic Kidney Disease Based on Glomerular Filtration Rate and Albuminuria

		Albuminuria categories (ACR, mg/gram)
		<30	30–300	>300	Total
GFR categories (mL/min/1.73 m²)
G1	≥90	65.3^a	3.0^b	0.3^c	68.6
G2	60–89	26.6^a	2.3^b	0.3^c	29.2
G3a	45–59	1.2^b	0.4^c	0.1^d	1.7
G3b	30–44	0.2^c	0.1^d	0.1^d	0.4
G4	15–29	0.0^d	0.1^d	0.0^d	0.1
G5	<15	0.0^d	0.0^d	0.0^d	0.0
	Total	93.3	5.9	0.8	100.0

Gray shaded area represents CKD defined by eGFR and albuminuria.

Low risk (if no other markers of kidney disease, no CKD).

Moderately increased risk.

High risk.

Very high risk.

CKD, chronic kidney disease; GFR, glomerular filtration rate.

Prevalence of CKD_GFR, CKD_ACR, and CKD_Risk according to BMI categories

The prevalence rates of CKD_GFR, CKD_ACR, and CKD_Risk were higher in underweight and obese men compared with the other groups (Fig. 1A, C, E). In women, the prevalence rates of CKD_GFR, CKD_ACR, and CKD_Risk increased gradually with BMI categories (Fig. 1B, D, F). In men, the proportion of CKD stages 3b and 4, defined by eGFR, was the highest in the underweight group. The proportion of CKD stage 5 was the highest in women with BMI < 18.5 kg/m².

FIG. 1.

Prevalence of CKD_GFR (A, B), CKD_ACR (C, D), and CKD_Risk (E, F) according to BMI categories. ACR, albumin-to-creatinine ratio; CKD, chronic kidney disease.

Correlation between obesity indices and kidney functions

In the present study, both BMI and WC were negatively correlated with eGFR (r = −0.285 and r = −0.370, respectively; both P < 0.001) and positively correlated with urine ACR (r = 0.038 and r = 0.055, respectively; both P < 0.001).

Risk of BMI for CKD defined using different criteria

Multivariate logistic regression analysis was performed to estimate the odds of BMI for CKD defined using the different definitions (Table 3). Men with BMI ≥ 25 kg/m² had 1.88 times greater risk of CKD_GFR than the normal BMI group in the multivariate adjusted model (95% CI, 1.26–2.80; P = 0.002). In women, BMI was not associated with CKD_GFR in the adjusted model. Men with BMI ≥ 25 kg/m² had 1.58 times greater risk of CKD_ACR than the normal BMI group and those with BMI 23–24.9 kg/m² had 0.74 times lower risk than for those with normal weights (95% CI, 0.55–0.99; P = 0.045). Obese men had 1.65 times greater risk of CKD_Risk than those with normal weights (95% CI, 1.29–2.12; P < 0.001), while obese women had 1.38 times greater risk of CKD_Risk than those in the normal weight group (95% CI, 1.09–1.75; P = 0.008). The odds ratio for CKD_Risk among overweight men was 0.74 times lower than for those in the normal weight group (95% CI, 0.57–0.96; P = 0.022).

Table 3.

Multivariate-Adjusted Odds Ratios of Body Mass Index for CKD_Risk, CKD_GFR, And CKD_ACR

			CKD_Risk	CKD_GFR	CKD_ACR
			Odds ratio (95% CI)	Odds ratio (95% CI)	Odds ratio (95% CI)
Men	BMI (kg/m²)	<18.5	1.60 (0.76–3.54)	0.62 (0.22–1.77)	1.92 (0.86–4.29)
		18.5–22.9	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
		23.0–24.9	0.74^a (0.57–0.96)	0.87 (0.58–1.31)	0.74^a (0.55–0.99)
		≥25.0	1.65^a (1.29–2.12)	1.88^a (1.26–2.80)	1.58^a (1.21–2.07)
Women	BMI (kg/m²)	<18.5	1.32 (0.76–2.29)	2.39 (0.61–9.30)	1.133 (0.63–2.03)
		18.5–22.9	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
		23.0–24.9	1.03 (0.79–1.35)	0.69 (0.42–1.16)	1.09 (0.82–1.45)
		≥25.0	1.38^a (1.09–1.75)	1.37 (0.90–2.08)	1.40^a (1.08–1.81)

Data adjusted for: age, smoking, alcohol drinking, exercise habits, systolic BP, HbA1c, HDL cholesterol, and triglycerides.

P < 0.05.

CI, confidence interval; CKD_ACR, ACR ≥ 30 mg/gram; CKD_GFR, eGFR < 60 mL/min/1.73 m²; CKD_Risk, eGFR < 60 mL/min/1.73 m² or ACR ≥ 30 mg/gram.

After excluding the patients with diabetes, men with BMI < 18.5 kg/m² had 2.51 and 2.88 times higher risk of CKD_Risk and CKD_ACR than the respective reference groups (Supplementary Table S1). The associations between BMI ≥ 25.0 kg/m² and the risk of CKD were maintained in men and women without diabetes. After excluding individuals with diabetes or prediabetes, men with BMI < 18.5 kg/m² had 2.67 times higher risk of CKD_ACR (95% CI, 1.04–6.83, P < 0.05) (Supplementary Table S2). The associations between BMI ≥ 25.0 kg/m² and CKD were maintained in these men, but were attenuated in the women with normal glucose metabolism. In subgroup analysis of only subjects with diabetes, the associations were mostly attenuated (Supplementary Table S3).

Similar results were found for both men <65 years and men ≥65 years as was the case for all male subjects, except that the negative association between being overweight and CKD disappeared in men ≥65 years (Supplementary Tables S4 and S5). In women, the risks of high BMI for CKD were maintained regardless of age. Another subgroup analysis based on blood pressure showed that the associations between obesity and CKD were attenuated, but maintained in both normotensive and hypertensive subjects (Supplementary Tables S6 and S7, respectively).

Risk of WC for CKD defined using different criteria

A similar regression analysis was performed to estimate the association of WC with CKD defined as described above (Supplementary Table S8). The multivariate adjusted model showed that men and women with WC in the fourth quartile (>90.2 and >84.8 cm, respectively) had 1.68–2.17 times greater risk of CKD than the respective reference group (P < 0.05). Men with WC in the third quartile also had around 1.5 times greater risk of CKD than the reference group.

Discussion

Using KNHANES 2011–2014 data, obese individuals (with BMI ≥ 25 kg/m²) had significantly increased risk of CKD_Risk. However, CKD defined by eGFR alone was not significantly associated with BMI among female subjects.

In our study, obesity defined as BMI ≥ 25 kg/m² was significantly associated with CKD_GFR in men and women in the unadjusted model. However, after adjusting for confounding factors, a significant correlation was only found in men. Many Asian studies that have investigated the association between obesity and CKD have shown a significant correlation mainly in men.²¹ A Japanese cohort study conducted on health checkup data from Okinawa reported that the correlation between BMI ≥21 kg/m² and ESRD progression was only significant in men.²² A Singaporean study also found that the correlation between BMI-determined overweight or obesity and CKD was significant only in men.²³ In contrast, an Israeli cross-sectional study found that the association between obesity and CKD was significant only in women.²⁴ However, the higher smoking rate in female subjects and the smaller number of female participants in that study may have introduced a statistical bias.²⁴ Thus, the significant association between obesity and CKD_GFR among the male subjects in our study is consistent with findings from other Asian studies.

Several potential mechanisms may explain the gender difference in CKD progression. Animal studies have confirmed that estradiol increases the number of mesangial cells and decreases collagen synthesis to slow the progression of glomerulosclerosis, while testosterone does not exhibit such a kidney-protective effect.^25,26 Moreover, androgen is known to cause glomerular hypertension and kidney injury through activation of the renin–angiotensin system or increasing reabsorption in the proximal tubule.²⁷ Higher rates of smoking²⁸ and protein and phosphorous intake in males have also been implicated. However, more sophisticated studies are needed to explain any gender difference in the association between obesity and CKD.

Some evidence supports an association between obesity and renal function. Othman et al. found a CKD progression rate of 44.7% among nondiabetic patients with normal BMI, whereas the rate increased to 62% and 79.5% among those who were overweight and obese, respectively.²⁹ In contrast, a study using the Framingham offspring cohort data reported that, although obesity with BMI ≥ 30 kg/m² was correlated with CKD, no significant correlation was found after adjusting for cardiovascular risk factors such as diabetes, hypertension, and smoking.³⁰ A recent prospective cohort study reported that CKD defined as eGFR < 60 mL/min/1.73 m² was 3.5 times more prevalent in overweight subjects and 6.7 times more prevalent in obese subjects.³¹

Several mechanisms have been proposed to explain the association between obesity and CKD. Moorhead et al. showed that an excessive accumulation of lipids in the kidneys contributes to CKD progression,³² raising the concept of lipid toxicity. Other studies have reported that excessive free fatty acid in the cells, reactive oxygen species production, secretion of proinflammatory or profibrotic factors, and lipoapoptosis cause toxic metabolites in the kidneys.^7,33 Activation of the renin–angiotensin system due to hyperinsulinemia has also been reported to contribute to glomerular hyperfiltration and hypertension, or endothelial cell dysfunction.³⁴

Several studies have reported that obese patients seem initially to have a high GFR because of compensatory hyperfiltration to meet the heightened metabolic demands of increased body weight, leading to glomerulomegaly accompanied by deposition of adipose tissue in the glomerulus.³⁵ However, continued obesity may impact the kidneys locally and systemically via the production of unfavorable cytokines such as TNF-α, IL-6, plasminogen activator inhibitor-1, and resistin. This leads to initiation and aggravation of inflammation and oxidative stress, deterioration of lipid metabolism, activation of the renin–angiotensin system (RAS), and an increase in insulin resistance.^36,37 These phenomena result in pathologic changes in the kidneys, including increased glomerular permeability, and ultimately lead to the development of glomerulopathy.³⁸ Thus, obesity is fundamentally associated with a decline in kidney function.

In the present study, the prevalence rate of CKD showed a tendency to increase in underweight males compared with normal or overweight males. One recent study reported U-shaped associations of BMI with incident proteinuria,³⁹ and another study using KNHANES 2008–2011 showed a significant association between sarcopenia and CKD.⁴⁰ We speculate that low BMI reflects reduced muscle mass, which may exacerbate insulin resistance in low-body-weight individuals. Sarcopenia and poor physical function are common in patients with CKD.⁴¹ Therefore, loss of lean body mass, especially appendicular skeletal muscle, is likely to contribute to the decline in kidney function.

It has been suggested that Asians are more susceptible to developing albuminuria than Caucasians,¹⁵ and the prevalence of proteinuric diabetic kidney disease is also higher among Asians.^16,17 In our study, the prevalence of CKD defined as eGFR <60 mL/min/1.73 m² (CKD_GFR) was 2.2%. This figure is much lower than the 6.7% prevalence rate for CKD stage 3 and lower in the US NHANES 1999–2006, which included 18,026 respondents.⁵ However, the prevalence of CKD defined as ACR ≥ 30 mg/gram (CKD_ACR) in our study was 6.7%. These data suggest the importance of incorporating albuminuria measurement in evaluating CKD in Asian (or at least Korean) populations.

The association between central obesity and albuminuria has been also examined. A study of South Asians found that individuals with high waist-to-hip ratio had 4.1 times greater risk of developing albuminuria.⁴² Another study reported that WC was a more useful index than BMI for CKD patients, demonstrating that WC was correlated with reduced renal function, while BMI was not.⁴³ In the present study, men with WC in the fourth quartile (>90.2 cm) had 1.69 times greater risk of CKD_GFR than those in the second quartile in the adjusted model (Supplementary Table S8 and Supplementary Fig. S2). However, WC was not associated with CKD_GFR in female subjects. On the contrary, WC in the third or fourth quartiles in men and WC in the fourth quartile in women significantly increased risk for CKD_ACR and CKD_Risk.

Some study limitations should be noted. First, because the KNHANES is a cross-sectional survey, we were unable to use these data to identify causation. Second, to diagnose CKD precisely, repeated measurements of eGFR and ACR are required, while the present study was based on single measurement. Third, obesity was defined based on BMI alone; abdominal visceral fat may more robustly affect renal function. Fourth, duration of obesity was not assessed; because childhood obesity is highly correlated with CKD in adulthood,⁴⁴ past obesity and duration should also be considered in analyses.

Despite these relative limitations, the present study represents a large sample from the nationally representative KNHANES 2011–2014 data, thus representing national Korean population health characteristics. In addition, we used a survey procedure that reflected the complex sampling design in the statistical analyses, thereby increasing statistical accuracy and reliability.

In conclusion, the present study analyzed the associations between obesity and CKD, defined by both eGFR and ACR, in Korean adults. We found that obese individuals with BMI ≥25 kg/m² had increased risk of CKD_Risk compared with normal-weight individuals. Adding ACR to the CKD evaluation may avoid underestimating the impact of risk factors such as obesity on CKD. A longitudinal cohort study and experimental research are warranted to investigate the role of overweight or obesity on CKD progression.

Authors' Contributions

Study design: Y.J.K. and S.L. Data collection: Y.J.K. and S.D.H. Data analysis: Y.J.K., H.K., H.C.K., S.H.J., and S.L. Data interpretation: Y.J.K., S.D.H., T.J.O., K.M.K., H.C.J., H.K., H.C.K., S.H.J., and S.L. Drafting article: Y.J.K., S.H.J., and S.L. Approving final version of the article: Y.J.K., S.D.H., T.J.O., K.M.K., H.C.J., H.K., H.C.K., S.H.J., and S.L. S.L. and S.H.J. take responsibility for the integrity of the data analysis.

Footnotes

Author Disclosure Statement

No conflicting financial interests exist.

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Supplementary Material

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Association Between Obesity and Chronic Kidney Disease,Defined by Both Glomerular Filtration Rate and Albuminuria,in Korean Adults

Abstract

Background:

Methods:

Results:

Conclusions:

Introduction

Materials and Methods

Study participants and database

Measures of anthropometric and biochemical traits

Definition of obesity and CKD

Statistical analyses

Results

Baseline characteristics of study participants

Prevalence of CKD by GFR and albuminuria categories in Korean adults

Prevalence of CKDGFR, CKDACR, and CKDRisk according to BMI categories

Correlation between obesity indices and kidney functions

Risk of BMI for CKD defined using different criteria

Risk of WC for CKD defined using different criteria

Discussion

Authors' Contributions

Footnotes

Author Disclosure Statement

References

Supplementary Material

Prevalence of CKD_GFR, CKD_ACR, and CKD_Risk according to BMI categories