Abdominal Obesity Is Associated with Albuminuria in Women: The 2011 Korea National Health and Nutrition Examination Survey

Abstract

Background:

The effects of obesity on the kidney, apart from diabetes or hypertension, have not drawn much attention. Moreover, only a few studies have reported the relationship between obesity status and albuminuria in Asian countries, including South Korea. Therefore, this study aimed to investigate the association between obesity status and albuminuria in Korean adults.

Methods:

We analyzed data from the 2011 Korea National Health and Nutrition Examination Survey. Of the 4,979 subjects included in the general-population group, 3,274 were sorted into a nondiabetic and nonhypertensive population group. Obesity status was measured by body mass index and waist circumference. Albuminuria was defined as a urine albumin-to-creatinine ratio ≥30 mg/g.

Results:

Abdominally obese women were at higher risk for albuminuria than were women without abdominal obesity both in the general population (odds ratio [OR], 95% confidence interval [CI]: 2.08 [1.04–4.16]) and in the nondiabetic and nonhypertensive population (OR [95% CI]: 6.96 [2.34–20.64]) after further adjustment for confounders. Among generally nonobese women, abdominally obese women were at higher risk for albuminuria than were women without abdominal obesity both in the general population (OR [95% CI]: 2.82 [1.51–5.29]) and in the nondiabetic and nonhypertensive population (OR [95% CI]: 5.32 [1.47–19.22]).

Conclusion:

Abdominal obesity is associated with an increased risk for albuminuria in Korean women, independently of diabetes or hypertension. Screening for abdominal obesity, especially in women, may therefore provide earlier identification of individuals at risk for developing renal disease and cardiovascular disease, even those who are nondiabetic and nonhypertensive.

Introduction

Albuminuria initially was well known to be predictive of diabetic nephropathy and originally ranged from the risk-prediction curve for nephropathy in diabetic patients. Albuminuria is conventionally defined as a urine albumin-to-creatinine ratio (UACR) ≥30 mg/g measured from a random urine sample.¹ Albuminuria is prevalent not only in patients with diabetes or hypertension but also in the general population.² Furthermore, albuminuria is independently associated with an increased risk of future cardiovascular morbidity and mortality in diabetic and hypertensive patients and even in the general population.^3
–5 Hence, early detection and management of albuminuria and its modifiable risk factors are essential to prevent adverse health outcomes.

Although the underlying pathogenesis between albuminuria and cardiovascular disease (CVD) remains unclear, it is evident that urinary albumin leakage from the glomerulus reflects general vascular damage signifying subclinical atherosclerosis.^6,7 More recently, several studies have shown that insulin resistance involves the mechanism that links albuminuria and CVD, demonstrating that metabolic syndrome, which is closely related to insulin resistance, is associated with renal manifestations.⁸

Metabolic syndrome is a concomitant condition that consists of established contributors to albuminuria, such as poor glycemic control, elevated blood pressure, and abnormal lipid profiles.^9,10 Obesity, as a component of metabolic syndrome, is a widespread health problem, and the prevalence of obesity has increased rapidly worldwide, including in South Korea. But as shown recently in developed nations, the growth rate has become stable over the past decade.¹¹ However, obesity-related diseases, including type 2 diabetes, hypertension, dyslipidemia, chronic kidney disease (CKD), CVD, and cancer are still prevalent and lead to substantial burdens on society.^12

–15

Obesity has been documented to affect renal disease because of its close association with diabetes and hypertension, which are the most common causes of end-stage renal disease. However, other effects of obesity on the kidney have not received much study. In South Korea and other Asian countries, few studies have focused on fat distribution—based on both body mass index (BMI) and waist circumference (WC)—to relate obesity status and albuminuria as a predominant predictor of renal failure and CVD. The present study investigated the association between obesity status (as measured by BMI and WC) and albuminuria (as measured by UACR) in Korean adults, using nationwide representative data.

Materials and Methods

Survey overview and study subjects

This study analyzed the data from the 2011 Korea National Health and Nutrition Examination Survey (KNHANES), a nationwide survey that has been performed since 1998 by the Division of Chronic Disease Surveillance at the Korean Center for Disease Control and Prevention (KCDC). KNHANES, designed to assess national health and nutritional levels, consists of a health interview, a nutritional assessment, and a health examination. The survey subjects were randomly selected using stratified, multistage, and cluster-sampling designs, with proportional allocation based on geographic area, sex, and age from the 2005 National Census Registry to represent the entire noninstitutionalized civilian population in Korea.

Of the 6,566 subjects aged ≥19 years who participated in both a health interview and a health examination, we excluded 838 subjects who had been treated for CKD or whose estimated glomerular filtration rate (eGFR) was <60 mL/min/1.73 m² and 108 subjects who had a history of cirrhosis or cancer. We then excluded 113 subjects who did not fast for at least 8 hours before the blood test, 18 who were pregnant, 281 who were menstruating, and 229 with missing data. Thus, a total of 4,979 individuals were included in the general-population group. After excluding 1,705 subjects with diabetes mellitus or hypertension from the general-population group, 3,274 participants were sorted into the nondiabetic and nonhypertensive population group. Diabetes mellitus was defined by fasting plasma glucose (FPG) levels (after at least 8 hours of fasting) ≥126 mg/dL, current use of insulin or oral hypoglycemic agents, or diagnosis by a physician.¹⁶ Hypertension was defined by blood pressure ≥140/90 mm Hg or treatment with antihypertensive medication.¹⁷ All participants provided written informed consent, and the institutional review board of the KCDC approved the study protocol.

Lifestyle variables

As expected confounding factors, alcohol consumption, smoking status, and physical activity were investigated based on the response to the self-report questionnaire. Based on the amount of alcohol consumed per day during the 1-month period before the interview, the subjects were classified into three groups: nondrinkers, light drinkers (<15 g/day), and moderate to heavy drinkers (≥15 g/day).¹⁸ The subjects' smoking status was categorized as either nonsmoker or current smoker. The amount of physical activity performed was assessed using the International Physical Activity Questionnaire short form modified for the Korean population.¹⁹ Regular physical exercise was defined by moderate exercise (more than five times per week for more than 30 minutes per session) or vigorous exercise (more than three times per week for more than 20 minutes per session).

Anthropometric and laboratory measurements

Trained staffs conducted a physical examination following standard procedures. Body weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, while the subjects were wearing light clothing but no shoes. WC was measured during exhalation at the narrowest point, between the lower costal margin and the iliac crest. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using a standard mercury sphygmomanometer three times at 5-minute intervals, and the mean values of the second and third measurements were used in the analyses.

Blood samples were obtained after at least 8 hours of fasting, and random midstream urine samples were obtained from participants. After proper processing and immediate refrigeration, samples were transported in cold-storage units to the Central Testing Institute in Seoul, Korea, and analyzed within 24 hours of transportation. FPG, total cholesterol (TC), and triglyceride (TG) levels were measured by enzymatic methods using a Hitachi Automatic Analyzer 7600 (Tokyo, Japan). According to the criteria of the National Cholesterol Education Program Adult Treatment Panel III,²⁰ hypercholesterolemia was defined as TC levels ≥240 mg/dL in a blood test after fasting or the use of lipid-lowering drugs, and hypertriglyceridemia was defined as TG levels ≥200 mg/dL. Glycated hemoglobin (HbA1c) level was measured by high-performance liquid chromatography using a Tosoh HLC-723G7 (Tokyo, Japan). Serum 25-hydroxyvitamin D [25(OH)D] levels were measured using a PerkinElmer 1470 Wizard gamma counter (Turku, Finland) by radioimmunoassay (RIA) using a DiaSorin 25-hydroxyvitamin D ¹²⁵I RIA kit (Stillwater, MN). Serum and urine creatinine levels were measured by kinetic colorimetry using a Hitachi Automatic Analyzer 7600, and urine albumin level was measured by performing a turbidimetric assay using the same equipment. Based on the UACR, albuminuria was defined as UACR ≥30 mg/g; eGFR was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation: eGFR (mL/min/1.73 m²)=141×min(serum creatinine/κ, 1)^α×max(serum creatinine/κ, 1)^−1.209×0.993^Age×1.018[if female]×1.159[if African American], where κ is 0.7 for women and 0.9 for men, α is −0.329 for women and −0.411 for men, min indicates the minimum of serum creatinine/κ or 1, and max indicates the maximum of serum creatinine/κ or 1.²¹

Definitions of obesity status

BMI was calculated as weight in kilograms divided by the square of the height in meters. Based on BMI, we defined general obesity as a BMI ≥25 kg/m² and general overweight as 23≤BMI<25 kg/m².^22,23 The cutoff points for abdominal obesity were defined as WC ≥90 cm for men and ≥85 cm for women.²⁴

Statistical analyses

Statistical analyses were conducted using SAS version 9.2 survey procedures (Cary, NC) in a manner that reflected sampling weights and provided nationally representative estimates. All data were presented as mean±standard error (SE) or as proportions (SE). One-way analysis of variance, chi-square test, or independent t-test were performed to assess the differences in mean values and prevalence of clinical and biochemical characteristics by sex and according to urinary albumin excretion (UAE) or obesity status. Multivariate logistic regression analyses were conducted to investigate the relationship between obesity status and albuminuria, and odds ratios (ORs) and confidence intervals (CIs) were estimated after adjustment for age, alcohol consumption, smoking status, physical activity, diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, eGFR, 25(OH)D, and BMI (or WC). A p value of <0.05 was considered statistically significant.

Results

The baseline characteristics of the study subjects according to UAE are shown in Table 1. Mean WC was higher in men with albuminuria than in men without albuminuria in the general-population group. Mean values of WC, SBP, DBP, FPG, TC, and TG were higher among albuminuric women than among normoalbuminuric women in both population groups. Mean values of WC, DBP, FPG, TG, and serum creatinine were higher in albuminuric men than in albuminuric women in the general-population group.

Table 1.

Baseline Characteristics According to Urinary Albumin Excretion Among Subjects in the 2011 Korea National Health and Nutrition Examination Survey

	Men			Women
	Normo-albuminuria	Albuminuria	p ^a	Normo-albuminuria	Albuminuria	p ^a	p ^b
General population group
n	2,107	136		2,540	196
Age (years)	43.1±0.5	53.7±1.4	<0.001	45.8±0.5	57.7±1.6	<0.001	0.03
Postmenopause (%)				25.5 (1.3)	33.4 (7)	0.234
BMI (kg/m²)	24.1±0.1	24.6±0.4	0.229	23.2±0.1	24.7±0.3	<0.001	0.893
WC (cm)	84.3±0.3	88±1.2	0.004	77.9±0.3	83.7±0.9	<0.001	0.006
SBP (mm Hg)	119.4±0.4	130.6±1.7	<0.001	113.7±0.4	132±1.8	<0.001	0.553
DBP (mm Hg)	79.1±0.3	83±1.1	0.001	72.9±0.2	79.1±1	<0.001	0.008
FPG (mg/dL)	97.6±0.6	118.8±3.7	<0.001	93.2±0.4	104.3±1.7	<0.001	<0.001
HbA1c (%)	5.7±0.02	6.3±0.1	<0.001	5.6±0.01	6.1±0.1	<0.001	0.065
TC (mg/dL)	187.9±1.2	198.8±3.7	0.005	189.9±0.9	205.8±3.3	<0.001	0.183
TG (mg/dL)^c	153.4±3.5	204.4±17.2	<0.001	108.2±1.8	153.7±9.2	<0.001	0.009
Serum creatinine (mg/dL)	0.96±0.004	0.96±0.02	0.775	0.7±0.003	0.7±0.01	0.854	<0.001
UACR (mg/g)	3.7±0.1	235.8±57.3	<0.001	4.2±0.1	170.7±25	<0.001	0.283
eGFR (mL/min/1.73 m²)	93.6±0.5	90.9±2.1	0.203	98.1±0.6	93.3±1.7	0.008	0.368
25(OH)D (ng/mL)	18.1±0.3	17.9±0.7	0.78	16.4±0.2	17.3±0.5	0.048	0.463
Current smoker (%)	44.5 (1.4)	38 (5)	0.235	5.2 (0.6)	6.6 (2.5)	0.551	<0.001
Moderate to heavy drinker (%)	87.1 (0.9)	88.2 (3.4)	0.767	68.5 (1.2)	52 (4.4)	<0.001	<0.001
Regular exerciser (%)	23.2 (1.1)	24.2 (4.6)	0.82	17.4 (1)	11.3 (2.5)	0.046	0.013
DM (%)	8.2 (0.7)	37.8 (5.7)	<0.001	6.1 (0.5)	22 (3.3)	<0.001	0.009
HTN (%)	26.3 (1.2)	62.4 (5.6)	<0.001	19.9 (1.0)	62.2 (4.8)	<0.001	0.001
Hypercholesterolemia (%)	10.5 (0.9)	18.9 (4.4)	0.025	13 (0.7)	30.5 (4.7)	<0.001	0.065
Hypertriglyceridemia (%)	20.3 (1.2)	39 (5.9)	<0.001	8.3 (0.7)	17.5 (3.5)	<0.001	0.001
History of CVD or stroke (%)	2.1 (0.3)	5.1 (1.7)	0.009	2 (0.3)	3.9 (1.5)	0.103	0.621
Nondiabetic and nonhypertensive population group
n	1,353	28		1,825	68
Age (years)	40±0.6	49±3.3	0.008	41.6±0.5	48±2.4	0.008	0.793
Postmenopause (%)				25.8 (1.3)	33.7 (6.9)	0.235
BMI (kg/m²)	23.7±0.1	24±0.5	0.558	22.7±0.1	23.7±0.6	0.091	0.726
WC (cm)	82.9±0.4	84.4±1.3	0.275	76±0.3	80.3±1.6	0.009	0.065
SBP (mm Hg)	114.1±0.4	117.9±2.5	0.117	108.4±0.3	116.2±1.9	<0.001	0.61
DBP (mm Hg)	75.8±0.3	76.9±1.7	0.53	71.1±0.2	73.5±1.1	0.03	0.129
FPG (mg/dL)	92.1±0.4	96.9±2.4	0.05	89.6±0.3	92.6±1.4	0.036	0.165
HbA1c (%)	5.5±0.01	5.7±0.1	0.161	5.5±0.01	5.5±0.05	0.132	0.318
TC (mg/dL)	187.7±1.5	197.8±6.7	0.142	186.9±1	201±4.5	0.003	0.702
TG (mg/dL)^b	141.9±4.1	184.5±30.1	0.046	99.1±1.9	143.7±10	<0.001	0.156
Serum creatinine (mg/dL)	1±0.004	1±0.02	0.399	0.7±0.003	0.7±0.02	0.771	<0.001
UACR (mg/mmol)	2.9±0.1	151.8±44.9	0.001	3.5±0.1	111.6±19.1	<0.001	0.408
eGFR (mL/min/1.73 m²)	94.7±0.6	92.1±2.3	0.266	100±0.6	99.1±3.2	0.684	0.077
25(OH)D (ng/mL)	18±0.3	17.2±0.7	0.419	16±0.2	15.7±0.7	0.689	0.159
Current smoker (%)	44.9 (1.7)	25.1 (9.8)	0.084	5.4 (0.7)	12.3 (6.1)	0.109	0.282
Moderate to heavy drinker (%)	86.8 (1.2)	87.9 (6.2)	0.868	72.2 (1.2)	58.5 (8.1)	0.061	0.004
Regular exerciser (%)	23.9 (1.5)	29.3 (11.7)	0.619	17.5 (1.1)	16.4 (4.9)	0.838	0.266
Hypercholesterolemia (%)	8 (1)	2.3 (2.3)	0.167	8.7 (0.7)	15.8 (5.3)	0.093	0.029
Hypertriglyceridemia (%)	15.6 (1.4)	29.8 (15)	0.241	6.1 (0.7)	5.8 (2.8)	0.936	0.001
History of CVD or stroke (%)	0.8 (0.2)	0.2 (0.2)	0.153	0.9 (0.3)	0

Values represent mean±standard error or proportions (standard error).

Obtained by t-test or chi-square test.

Obtained by t-test for comparing baseline characteristics between albuminuric men and albuminuric women.

Log transformation of variables was performed to calculate p values.

BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; HTN, hypertension; 25(OH)D, 25-hydroxyvitamin D; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; UACR, urinary albumin to creatinine ratio; WC, waist circumference.

Table 2 shows the risk of having albuminuria according to obesity status in both sexes after adjustment for confounders. Compared with men without general obesity or abdominal obesity, the adjusted ORs for having albuminuria in generally obese men and abdominally obese men were not significant in either the general-population group or the nondiabetic and nonhypertensive population group. Meanwhile, in both population groups, compared with women with a normal BMI, the risks for having albuminuria were not significantly associated with general overweight and general obesity after all the confounders in model 2. However, abdominally obese women had a higher risk of albuminuria than women without abdominal obesity in the general-population group, and this relationship persisted even after further adjustment (OR [95% CI]: 1.79 [1.15–2.78] in model 1; 2.08 [1.04–4.16] in model 2). In the nondiabetic and nonhypertensive population, women with abdominal obesity were also at higher risk for albuminuria (OR [95% CI]: 4.11 [1.71–9.89] in model 1; 6.96 [2.34–20.64] in model 2).

Table 2.

Adjusted Odds Ratios (Confidence Intervals) for Having Albuminuria According to Obesity Status

	Men		Women
General population group	Model 1 ^a	Model 2 ^a	Model 1 ^a	Model 2 ^a
Without overweight or obesity (reference)	1	1	1	1
With overweight	0.75 (0.38–1.46)	0.57 (0.26–1.25)	1.73 (1.04–2.89)	1.61 (0.96–2.7)
With general obesity	0.86 (0.44–1.71)	0.49 (0.19–1.28)	1.71 (1.1–2.66)	1.45 (0.81–2.6)
Without abdominal obesity (reference)	1	1	1	1
With abdominal obesity	0.98 (0.5–1.93)	0.87 (0.42–1.81)	1.79 (1.15–2.78)	2.08 (1.04–4.16)
Nondiabetic and nonhypertensive population group	Model 1 ^b	Model 2 ^b	Model 1 ^b	Model 2 ^b
Without overweight or obesity (reference)	1	1	1	1
With overweight	1.34 (0.48–3.73)	1.23 (0.32–4.77)	0.71 (0.3–1.68)	0.5 (0.22–1.14)
With general obesity	1.02 (0.18–5.85)	0.88 (0.13–5.77)	1.81 (0.74–4.41)	0.79 (0.29–2.15)
Without abdominal obesity (reference)	1	1	1	1
With abdominal obesity	1.08 (0.19–6.09)	1.08 (0.2–5.72)	4.11 (1.71–9.89)	6.96 (2.34–20.64)

Model 1 is adjusted for age, alcohol consumption, smoking status, physical activity, diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, eGFR, 25(OH)D; Model 2 is adjusted for confounders in Model 1 plus waist circumference or body mass index.

Model 1 is adjusted for age, alcohol consumption, smoking status, physical activity, hypercholesterolemia, hypertriglyceridemia, eGFR, 25(OH)D; Model 2 is adjusted for confounders in Model 1 plus waist circumference or body mass index.

Even in generally nonobese women, abdominally obese women were at higher risk for albuminuria than women without abdominal obesity in both the general population and the nondiabetic and nonhypertensive population group (OR [95% CI]: 2.82 [1.51–5.29] and 5.32 [1.47–19.22], respectively) (Table 3).

Table 3.

Adjusted Odds Ratios(Confidece Intervals) for Having Albuminuria According to Waist Circumference Among Body Mass Index Categories

	General-population group ^a		Nondiabetic and nonhypertensive population group ^b
	Men	Women	Men	Women
BMI <25 kg/m²
Without abdominal obesity (reference)	1	1	1	1
With abdominal obesity	0.42 (0.16–1.13)	2.82 (1.51–5.29)	0.22 (0.03–1.76)	5.32 (1.47–19.22)
p	0.085	0.001	0.154	0.011
BMI ≥25 kg/m²
Without abdominal obesity (reference)	1	1	1	1
With abdominal obesity	1.75 (0.63–4.87)	1.36 (0.57–3.27)	5.9 (0.47–74.97)	11.6 (1.38–97.69)
p	0.283	0.49	0.171	0.024

Adjusted for age, alcohol consumption, smoking status, physical activity, diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, eGFR, and 25(OH)D.

Adjusted for age, alcohol consumption, smoking status, physical activity, hypercholesterolemia, hypertriglyceridemia, eGFR, and 25(OH)D.

Table 4 presents prevalence and multivariate adjusted ORs for albuminuria according to menopausal status. The prevalence of albuminuria was significantly higher among abdominally obese women regardless of menopausal status in both population groups. In the nondiabetic and nonhypertensive population group, abdominally obese women had higher risk for albuminuria compared with abdominally nonobese women in both premenopausal women and postmenopausal women (OR [95% CI]: 6.4 [1.94–21.17] in premenopausal women and 5.75 [1.06–31.24] in postmenopausal women).

Table 4.

Prevalence and Adjusted Odds Ratios (Confidence Intervals) for Albuminuria According to Menopausal Status

	General-population group ^a		Nondiabetic and nonhypertensive population group ^b
	Prevalence, % (SE)	OR (95% CIs)	Prevalence, % (SE)	OR (95% CIs)
Premenopausal women
Without overweight or obesity (reference)	6.7 (1.3)	1	2.6 (0.9)	1
With overweight	12.3 (2.3)	2.11 (1.13–3.95)	3.8 (1.7)	0.8 (0.29–2.23)
With general obesity	13.8 (2.2)	1.92 (0.87–4.26)	4.8 (2.2)	0.21 (0.03–1.56)
p for trend	0.004	0.123	0.309	0.09
Without abdominal obesity (reference)	7.8 (1.1)	1	1.5 (0.6)	1
With abdominal obesity	15.4 (2.2)	2 (0.99–4.05)	7.9 (2.3)	6.4 (1.94–21.17)
p for trend	0.001	0.054	<0.001	0.002
Postmenopausal women
Without overweight or obesity (reference)	2.3 (0.6)	1	2.3 (0.6)	1
With overweight	3.9 (1.7)	1.11 (0.41–3.02)	1.2 (0.8)	0.34 (0.07–1.59)
With general obesity	5.4 (1.7)	0.96 (0.23–3.91)	4.1 (1.5)	1.18 (0.25–5.7)
p for trend	0.034	0.959	0.321	0.959
Without abdominal obesity (reference)	2.6 (0.6)	1	1.9 (0.5)	1
With abdominal obesity	7.2 (2.1)	2.63 (0.56–12.38)	6 (2.3)	5.75 (1.06–31.24)
p for trend	0.005	0.22	0.015	0.043

Adjusted for age, alcohol consumption, smoking status, physical activity, diabetes, hypertension, hypercholesterolemia, hypertriglyceridemia, eGFR, and 25(OH)D.

Adjusted for age, alcohol consumption, smoking status, physical activity, hypercholesterolemia, hypertriglyceridemia, eGFR, and 25(OH)D.

CI, confidence interval; OR, odds ratio; SE, standard error.

Discussion

In the present study, we found that abdominal obesity as measured by WC was associated with an increased risk of albuminuria among women in the Korean general-population group. In women with normal BMI in that group, abdominal obesity was associated with an increased risk of albuminuria. This suggests that fat distribution is more relevant than body weight with respect to the risk of albuminuria in Korean women. Moreover, because these associations persisted among nondiabetic and nonhypertensive women, albuminuria might be involved in a mechanism other than hyperglycemia or high blood pressure.

Generally, progression to a higher BMI is associated with a higher risk of development of CKD and end-stage renal disease, and obesity is known to be a powerful predictor of such diseases.²⁵ However, the underlying mechanisms are not fully elucidated, owing to the association of obesity with hypertension and diabetes. Obese individuals have abnormalities in renal structures and vascular alterations, and these structural abnormalities accompany functional abnormalities, such as hyperperfusion and hyperfiltration, and lead to albuminuria.²⁶ Additionally, obesity could cause characteristic renal alteration in the form of focal segmental glomerulosclerosis, which is suggested to be linked to abdominal obesity and metabolic syndrome.²⁷

Focused more on abdominal obesity, the mechanism involved in the association with albuminuria is presumably proposed as following a complex pathogenesis. Abdominal obesity is related to metabolic syndrome and end-organ damage by elevation of insulin levels, peripheral tissue resistance to the insulin-sensitizing action of leptin, and increased macrophage infiltration in fat tissues and concomitant release of proinflammatory cytokines.^28,29 These metabolic changes cause intracellular fat deposition that might decrease the functional integrity of the endothelial wall and lead to albuminuria.^30,31 Second, as additional possible mediators, inflammatory proteins and circulating hormones, such as adiponectin and angiotensinogen, that are released from abdominal adipose tissue can change the glomerular function and cause albuminuria through sympathetic activation and activation of the renin-angiotensin system.^32,33 Third, compared with general obesity, abdominal obesity is more relevant to inflammation and oxidative stress, which have been associated with the increased risk of CVD that was confirmed to be closely related to albuminuria.^34
–36 Additionally, a visceral pattern of fat increased the risk of diminished epidermal growth factor receptor even in individuals with a BMI more than 25 kg/m².³⁷

In several trials conducted to identify the association between abdominal obesity and albuminuria targeting various subject groups, the results were inconsistent. In the Look Action for Health in Diabetes study, an increase in BMI and abdominal obesity was associated with albuminuria in overweight and obese adults with type 2 diabetes.³⁸ A study from the Diabetes Control and Complications Trial showed that WC predicts the subsequent development of microalbuminuria in patients with type 1 diabetes.³⁹ A cross-sectional study of hypertensive subjects in 26 countries also reported similar findings.⁴⁰ A 6-year follow-up cohort study among nondiabetic subjects in France demonstrated that abdominal obesity is related to the development of elevated albuminuria.⁴¹ A study of South Asian subjects showed that central obesity assessed by waist-to-hip ratio is an independent risk factor for increased albuminuria in nondiabetic subjects.⁴² A study of nondiabetic subjects in the Netherlands found an association between central fat distribution and microalbuminuria and renal insufficiency.³⁷ A study of healthy glucose-tolerant Hispanic subjects showed no relationship between central obesity and microalbuminuria.⁴³ In a recent longitudinal cohort study in the Chinese general population, an independent association of central obesity with albuminuria was found in female subjects.⁴⁴

Meanwhile, a few studies of the relationship between abdominal obesity and albuminuria have been performed in the South Korean population, and the findings have been inconsistent. A study of the Korean general population showed that central obesity measured by waist-to-hip ratio was associated with microalbuminuria.⁴⁵ A study of normotensive and euglycemic Korean men found that abdominal obesity was significantly associated with microalbuminuria but that BMI was not.⁴⁶ Additionally, the relationship between abdominal obesity as a component of metabolic syndrome and microalbuminuria was examined by only a small number of studies conducted at single centers in South Korea. For example, in a study of healthy Korean subjects, increased WC did not show a graded association with microalbuminuria.⁴⁷ In a study of nonhypertensive type 2 diabetic patients, abdominal obesity did not contribute to the risk of microalbuminuria.⁴⁸ Although different from the results of prior studies performed in South Korea, those of our study might be more meaningful in that we used a large sample and nationally representative data. Furthermore, we examined both a general-population group and a nondiabetic and nonhypertensive population group with subgroup analyses.

Another point worthy of notice in this study is the difference between sexes. Our study results showed that abdominal obesity is remarkably associated with albuminuria only in women. A cross-sectional study from the Framingham cohort showed that visceral adipose tissue is associated with microalbuminuria in men but not in women.⁴⁹ However, in an aforementioned study of Chinese adults, an independent association of central obesity with albuminuria was found in female subjects.⁴⁴ Inflammation has been recently understood to be a main mechanism in the link between obesity and CVD by showing the association of obesity with C-reactive protein in a large number of studies.^50,51 Moreover, these associations were found to be strong in women of various ethnicities.^52,53 As previously mentioned, UAE as an early marker of renal disease and CVD reflects endothelial dysfunction and subclinical inflammation. Therefore, the mechanism for sex differences in the relationship of abdominal obesity with albuminuria is uncertain, but it may involve inflammation. Additionally, the sex difference in that association suggests that sex hormones might play a part in the underlying inflammatory mechanism. It was reported that men might have lower incidences of inflammatory diseases than women because men have a relatively lower level of 17ß-estradiol, which would enable dehydroepiandrosterone to be converted to 5-androsten-3ß, 17ß-diol and provide a potent antiinflammatory effect.⁵⁴ Also, estrogen might explain this finding as a possible medicator that is known to alter serum levels of inflammatory marker, such as soluble CD40 ligand.^55,56 Further study is needed to identify the mechanism and whether sex hormones are responsible for the association of obesity with albuminuria.

The current study has several limitations. First, the cross-sectional design could not determine the causal relationship between abdominal obesity and albuminuria. Second, our study did not take into account the effect of medications, including the specific types of antihypertensive agents or nonsteroidal anti-inflammatory drugs, on albuminuria or renal function. Third, more accurate methods, such as computed tomography or dual-energy x-ray absorptiometry, were not used to measure abdominal adiposity. Fourth, UAE was assessed from a single-void random urine sample measurement, which could be incorrect.

Despite these limitations, however, our study has several strengths. We studied a general-population group, as well as a nondiabetic and nonhypertensive population group, using nationally representative data of the Korean population. We assessed both sexes and defined obesity in relation to a specific cutoff of BMI and WC for Korean subjects.

Conclusion

Abdominal obesity is associated with an increased risk of albuminuria as a surrogate marker of adverse renal and cardiovascular outcomes in Korean women, independent of diabetes or hypertension. Screening for abdominal obesity, especially in women, may be helpful for early identification of those at risk for developing renal disease and CVD, even those who are nondiabetic and nonhypertensive.

Footnotes

Disclosure Statement

No competing financial interests exist.

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