Analysis of the relationship between components of metabolic syndrome and estimated glomerular filtration rate (eGFR)

Abstract

Early recognition and management of risk factors that reduce kidney function are essential. This study aims to analyze the relationship between the components of metabolic syndrome and estimated glomerular filtration rate (eGFR), and to provide basic data that can help develop educational materials for the prevention of kidney diseases. Data from the second round of the KNHNES(korea national health and nutrition examination survey, 8th) which was conducted by the Korea Disease Control and Prevention Agency (2020), were downloaded and analyzed for the current research. Binary logistic analysis was used to identify factors that were risk predictors for metabolic syndrome. Identified factors were categories of metabolic syndrome, including waist circumference, blood pressure, triglycerides, blood glucose, HDL-cholesterol, triglycerides, and eGFR. eGFR was 0.03 % lower in males with metabolic syndrome (OR:0.973) and 0.03 % lower in females (OR:0.974). The eGFR was significantly lower for those with metabolic syndrome. According to this study, there was a significant correlation between reduced GFR and metabolic syndrome. Therefore, the meaningful baseline data for decreasing the prevalence of kidney disease and preventing metabolic syndrome provided by this study's results are significant.

Highlights

• •Early management of risk factors that reduce kidney function is essential.
• •In general, GFR decreases with age, and metabolic syndrome increases.
• •The higher the incidence of metabolic syndrome, the worse the renal function is.
• •Blood pressure and HbA1C are the main factors that negatively affect GFR.

1. Introduction

According to the Health Insurance Review and Assessment Service, the number of patients with chronic kidney disease has significantly increased each year, from 203,978 in 2017 to 259,116 in 2020 and 277,252 in 2021 [1]. A total of 145,006 patients with patients with end-stage renal failure (ESRF) required renal replacement therapy in 2020; 117,398 of them were on hemodialysis (81.0 %), 5724 on peritoneal dialysis (3.9 %), and 21,884 in need of kidney transplants (15.1 %) [2]. Chronic kidney disease is a critical risk factor for heart disease and is emerging as a global public health issue; therefore, there is increased interest in its association with metabolic syndrome (MS), which is a risk factor for cardiovascular disease [3,4].

The diagnostic criteria for metabolic syndrome, as defined by the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), is having three or more of the following five conditions: abdominal obesity, hypertriglyceridemia, reduced HDL cholesterol, hypertension, and hyperglycemia [5]. Prior studies have shown that the risk of developing proteinuria increased 2.3-fold [6] in patients with metabolic syndrome, and the prevalence of chronic kidney disease was also higher in these patients [7]. Furthermore, metabolic syndrome was closely associated with comorbidities of end-stage renal disease patients, had adverse effects on treatments, and caused worse hemodialysis-related outcomes [8] Studies have also shown that metabolic syndrome is an independent risk factor for chronic kidney disease, and patients with metabolic syndrome had a higher risk of end-stage renal failure compared to those unaffected by metabolic syndrome [9,10].

In particular, the major factors of metabolic syndrome, blood glucose and blood sugar, are known to cause endothelial dysfunction and glomerular injury through mechanisms of insulin resistance and sodium retention, while obesity and dyslipidemia, including increased waist circumference, are known to cause renal injury through mechanisms such as ultrafiltration, activation of the renin-angiotensin-aldosterone system, and direct lipotoxicity [7,11,12].

In patients with advanced-to-end-stage renal failure, kidney diseases have lower survival rates than cancer and cause a greater burden on management measures and medical expenses at the national level. Domestically, at the end of 2018, the number of patients with ESRF was more than 40 times greater than that in 1986. During that time, the number of dialysis patients increased threefold, and between 46 % and 50 % of patients were receiving long-term dialysis (i.e., for more than five years), adding to the burden of medical expenses [13]. In addition, chronic kidney disease is the most common cause of vascular calcification, and the loss of arterial elasticity leads to an increase in pulse wave velocity and left ventricular hypertrophy, increasing the likelihood of cardiovascular complications and mortality rates [14]. However, early detection of chronic kidney disease is difficult, as no symptoms can be felt during the initial stages, even when renal injury persists for over 3 months or there is a continued decline in function [15]. In some studies, only 16.9 % of the group (235,059) included in the CKD criteria (eGFR and/or proteinuria) were diagnosed with CKD [16]; this suggests that early recognition and management of the risk factors that reduce kidney function is essential.

In addition, according to the data analyzed by UMAP (Uniform Manifold Approximation and Projection) based on the data from the Korea Disease Control and Prevention Agency, there is a strong correlation between heart disease, kidney disease, diabetes, and stroke [17]. However, according to the survey data from 2007 to 2017, blood glucose in subjects with metabolic syndrome increased from 28.5 % in 2008 to 39.7 % in 2017, and blood pressure and waist circumference also increased from 29.7 % to 26.4 % in 2008 to 36.1 % and 31.8 % in 2017, respectively. This means that the management of metabolic syndrome is very insufficient [18].

It is well known that metabolic syndrome negatively affects the kidney's function, but there are many studies on each risk factor, but studies examining all sub-items of metabolic syndrome are insufficient. In Korea, studies on the association between proteinuria and metabolic syndrome have been conducted, and in this study, proteinuria was significantly higher in the presence of metabolic syndrome [19]. However, it is necessary to study detailed items through glomerular filtration rate, which can check the function of the kidney rather than proteinuria specified by the test kit paper urine.

Therefore, this study aims to analyze the relationship between components of metabolic syndrome and GFR and find out whether it adversely affects renal function. In addition, through this, it aims to provide basic data that can be used to develop educational materials that can help prevent kidney disease.

2. Materials and methods

2.1. Study design and participants

Data from the second round of the 8th KNHANES, which was conducted in 2020, were downloaded (https://knhanes.kdca.go.kr/knhanes/sub03/sub03_02_05.do) and analyzed for this research The sampling framework of the National Health and Nutrition Examination Survey allowed representative samples to be extracted using the latest population and “Population and Housing Census” available at the time of sampling design as the baseline extraction framework. And the purpose of the survey is to calculate statistics with national representativeness and reliability on the health level, health behavior, food and nutritional intake status of the people, and use them as basic data for health policies such as setting and evaluating goals of the National Health Promotion Comprehensive Plan and developing health promotion programs. The survey was conducted using health questionnaires and mobile examination centers, and 3314 households and 7359 participants were included in the second year (2020) of the eighth survey. Due to the COVID-19 pandemic, 180 out of 192 examinations and 166 nutrition surveys were completed during the second round of the eighth survey (2020). In this study, 5235 adults aged >19 years were analyzed (Fig. 1). The 5235 people analyzed were anonymized and this study was approved by the Institutional Review Board of the J. University (approval no. JIRB-2022072601-01).

Fig. 1.

Simplified flow chart of study subject selection.

2.2. Measures

2.2.1. General characteristics

General characteristics of the sample included household income, sex, and educational level. The level of income was categorized into “highest”, “upper-middle”, “low-middle”, “lowest” groups, and the level of education was classified into “6 or less”, “7–9”, “10–12” and “13 or more” groups. Cigarette and electronic cigarette smokers were classified as current smokers. Waist circumference, body mass index (BMI), blood pressure, fasting blood glucose, lipid analysis, blood urea nitrogen (BUN), and albuminuria were measured and analyzed as factors affecting renal failure. Hypertension and dyslipidemia were classified based on the physician's diagnosis. The identification of metabolic syndrome was conducted per the Korean Society for the Study of Obesity and National Cholesterol Education Program Adult Treatment Panel III, and individuals that met at least three out of five diagnostic criteria (waist circumference of ≥90 cm for males and ≥85 cm for females, fasting blood glucose of ≥100 mg/dL, triglycerides of >150 mg, HDL cholesterol of <40 mg/dL for males and <50 mg/dL for females, and blood pressure of 130/85 mmHg) were considered to have metabolic syndrome [6].

2.2.2. Biochemical measurements

Primarily from the veins, after participants fasted for at least 8 h. The samples were refrigerated, transported to a laboratory medicine clinic, and analyzed within 24 h. Enzymatic methods using a Hitachi 7600 Automatic Analyzer (Tokyo, Japan) were used to measure triglyceride, high-density lipoprotein cholesterol, and fasting blood glucose levels. Kinetic colorimetric assay (Jaffe) was used to measure serum creatinine, the equipment was analyzed using Cobas 8000 (Roche, Germany), and the reagent was CREJ2 (Roche, Germany). In addition, albuminuria was analyzed using immunoturbidimetric assay, the equipment was Cobas 8000 (Roche, Germany), and the reagent was ALBT2 (Roche, Germany). This survey was conducted by the Korea Disease Control and Prevention Agency, and blood tests were performed by a state-designated analysis agency.

2.2.3. Renal function

The eGFR was used to assess renal function. The estimated glomerular filtration rate formula is combined with variables such as age, gender, race, and weight, which are variables that affect serum creatinine and cystatin-C concentration in addition to glomerular filtration. The formula is as follows: [20]. In particular, when developing and verifying the Korean CKD-EPI formula in 960 Koreans over 18 years of age who obtained the glomerular filtration rate value measured by 1Cr-EDTA, it was confirmed that the existing formula can be applied to Koreans because the average error is low to a level similar to the glomerular filtration rate estimated by the existing CKD-EPI formula: [21].

• 1.Females

Creatinine≤0.7 mg/dl: eGFR = 144 × (creatinine/0.7)^−0.329 × (0.993)^age × 1.159 (if black race).

Creatinine>0.7 mg/d: eGFR = 144 × (creatinine/0.7)^−1.209 × (0.993)^age × 1.159 (if black race).

• 2.Males

Creatinine≤0.9 mg/dl: eGFR = 141 × (creatinine/0.9)^−0.411 × (0.993)^age × 1.159 (if black race).

Creatinine>0.9 mg/dl: eGFR = 141 × (creatinine/0.9)^−1.209 × (0.993)^age × 1.159 (if black race).

Renal function was classified according to the Kidney Disease Improving Global Outcomes guidelines of the National Kidney Foundation in the United States, and the Korean Society of Nephrology, which are as follows: normal (eGFR ≥90), mildly decreased (60 ≤ eGFR <90), mildly to moderately decreased (45 ≤ eGFR <60), moderately to severely decreased (30 ≤ eGFR <45), or severely decreased (eGFR <30 mL/min/1.73 ㎡) [[20], [22], [23]]. In the present study, renal function was classified as normal (eGFR ≥90), mildly decreased (60 ≤ eGFR <90), or mildly to severely decreased (eGFR <60) to analyze the factors affecting renal function decline.

2.2.4. Statistical analysis

SPSS Statistics (Ver.22) was used for data analysis. Since the KNHANES data were collected using stratified multi-stage cluster sampling, weights were applied for the analysis using the SPSS complex sample analysis method; this improved the representation of the sample and the accuracy of the estimation. The weight value of the KNHANES is a multiplier, and the estimate represents the entire Korean population and reflects the extraction/response rate. When combining multiple variables to create a new variable or fitting statistical models that use multiple variables for analysis simultaneously, the survey sections, areas, and items of all analyzed variables were considered to determine the appropriate weight value. The weight values, including the survey sections, areas, and items, were identified as correlation analysis weights, and the weights were provided separately for each year. The collected general characteristics and difference values according to eGFR levels were analyzed using t-test, chi-square test, ANOVA. The components of MS and risk of renal failure were analyzed using generalized linear regression and binary logistic regression. Pearson's correlation coefficient was used to examine the correlation between each variable.

3. Results

3.1. Demographic and general characteristics

A total of 5235 patients were analyzed in this study, with 2327 males and 2908 females. The mean age was 50.7 years for males and 51.1 years for females. Among the household income groups, the “highest” group had the largest number of cases for both males and females. Among the education-level groups, the “13 years or more” group was the largest. A statistically significant difference was found between sexes with regard to smoking (p < .001), as 1694 males (72.8 %) were smokers and 2545 females (87.5 %) were non-smokers. The prevalence of hypertension was 23.8 % for males and 20.7 % for females, whereas the prevalence of dyslipidemia was 16.2 % for males and 19.2 % for females. The mean waist circumference was 88.6 ± 9.8 for males and 80.4 ± 10.1 for females. In addition, the mean blood glucose was 100.4 ± 16.3 for males and 96.1 ± 15.4 for females, and the mean HDL cholesterol was 47.8 ± 11.3 for males and 55.6 ± 12.5 for females. The prevalence of metabolic syndrome was 38.9 % in males and 27.5 % in females (p < .001) (Table 1).

Table 1.

Baseline characteristics of participants (N = 5235).

Category	Total (5,235)	Men (2,327)	Women (2,908)	p
	M±SD or N(%)	M±SD or N(%)	M±SD or N(%)
Age, year	50.68 ± 17.33	50.21 ± 17.54	51.06 ± 17.15	0.077
House Income
Lowest	893 (17.06)	377 (16.20)	516 (17.74)	0.015
Lower middle	1218 (23.27)	514 (22.09)	704 (24.21)
Upper middle	1471 (28.10)	654 (28.10)	817 (28.09)
Highest	1631 (31.16)	775 (33.30)	856 (29.44)
No response	22 (0.42)	7 (0.30)	15(0.52)
Education
0-6	701 (13.39)	227 (9.76)	474 (16.30)	<0.001
7-9	426 (8.14)	172 (7.39)	254 (8.73)
10-12	1747 (33.37)	831 (35.71)	916 (31.50)
13 or more	1945 (37.15)	918 (39.45)	1027 (35.32)
No response	416 (7.95)	179 (7.69)	237 (8.15)
Smoking
No	3178 (60.71)	633 (27.20)	2545 (87.52)	<0.001
Yes	2057 (39.29)	1694 (72.80)	363 (12.48)
Hypertension
No	4079 (77.92)	1773 (76.19)	2306 (79.30)	0.007
Yes	1156 (22.08)	554 (23.81)	602 (20.70)
Dyslipidemia
No	4300 (82.14)	1950 (83.80)	2350 (80.81)	0.005
Yes	934 (17.84)	376 (16.16)	558 (19.19)
No response	1 (0.02)	1 (0.04)	–
WC, cm	84.04 ± 10.81	88.61 ± 9.84	80.36 ± 10.13	<0.001
BMI, kg/m2	24.08 ± 3.79	24.83 ± 3.69	23.47 ± 3.76	<0.001
BP, Systolic, mmHg	118.88 ± 16.53	121.44 ± 14.93	116.82 ± 17.44	<0.001
BP, Diastolic, mmHg	75.92 ± 9.93	78.37 ± 10.01	73.95 ± 9.42	<0.001
FBS, ㎎/dL	98.01 ± 15.94	100.38 ± 16.33	96.12 ± 15.36	<0.001
HbA1C, %	5.66 ± 0.56	5.68 ± 0.57	5.63 ± 0.55	0.003
Triglyceride, ㎎/dL	130.88 ± 113.34	156.51 ± 143.30	110.34 ± 75.74	<0.001
HDL cholesterol, ㎎/dL	52.14 ± 12.62	47.78 ± 11.31	55.64 ± 12.52	<0.001
LDL cholesterol, ㎎/dL	115.41 ± 36.37	114.39 ± 35.72	117.44 ± 37.62	0.294
Total cholesterol, ㎎/dL	192.50 ± 37.44	190.75 ± 37.75	193.90 ± 37.13	0.003
BUN, ㎎/dL	14.94 ± 4.80	15.63 ± 4.88	14.38 ± 4.67	<0.001
Albuminuria, ㎍/㎖	17.33 ± 70.09	20.35 ± 85.88	14.87 ± 53.78	0.009
MetS
No	3451 (67.46)	1389 (61.11)	2062 (72.53)	<0.001
Yes	1665 (32.54)	884 (38.89)	781 (27.47)
eGFR, mL/min/1.73 m2	96.99 ± 17.72	94.37 ± 17.64	99.08 ± 17.51	<0.001

Abbreviations: WC, waist circumference; BMI, body mass index; BP, blood pressure; FBS, fasting blood sugar; MetS, metabolic syndrome; HDL, high-density lipoprotein; LDL, low-density lipoprotein; eGFR, estimated glomerular filtration rate; HbA₁C, hemoglobin A₁C; BUN, blood urea nitrogen. The difference in total is due to missing data.

3.2. General characteristics according to eGFR

When the GFR was categorized into stage 1 (eGFR ≥90), stage 2 (60 ≤ eGFR <90), and stage 3 (eGFR <60) and analyzed for differences in the general variables, a trend of increasing age with each stage was shown, with the mean age of 43.6 ± 14.6 for stage 1, 64.5 ± 12.9 for stage 2, and 73.8 ± 8.3 for stage 3. The prevalence of hypertension increased with each stage, with 12.6 % for stage 1, 43.1 % for stage 2, and 53.5 % for stage 3 (p < .001). The mean waist circumference, a component of metabolic syndrome, was 82.8 ± 11.3 in stage 1, 86.5 ± 9.2 in stage 2, and 88.7 ± 8.9 in stage 3. The mean systolic blood pressure was increased significantly with each stage (p < .001). In addition, mean blood glucose levels were significantly different between stages 1 (96.6 ± 15.1), 2 (100.9 ± 17.4), and 3 (102.8 ± 15.5). The mean HDL cholesterol levels were also significantly different at 53.2 ± 12.6, 50.3 ± 12.4, and 45.4 ± 11.3 at stages 1, 2, and 3, respectively (p < .001). The prevalence of metabolic syndrome showed statistically significant differences between stages, with 25.8 % for stage 1, 45.5 % for stage 2, and 60.4 % for stage 3 (p < .001) (Table 2).

Table 2.

General characteristics according to eGFR (N=5,235).

Category	Total (5,235)	90≥(3534)	60 ≤, <90(1557)	<60 (144)	p
	M±SD or N(%)	M±SD or N(%)	M±SD or N(%)	M±SD or N(%)
Age, year	50.68 ± 17.33	43.64 ± 14.60	64.52 ± 12.88	73.81 ± 8.27
Gender					<0.001
Men	2.327(44.5)	1434(61.6)	813(34.9)	80(3.4)	<0.001
Women	2908(55.5)	2100(72.2)	744(25.60	64(2.2)
House Income
Lowest	893 (17.13)	372 (10.56)	446 (28.83)	75 (52.45)	<0.001
Lower middle	1218 (23.36)	791 (22.45)	393 (25.40)	34 (23.78)
Upper middle	1471 (28.22)	1103 (31.31)	351 (22.69)	17 (11.89)
Highest	1631 (31.29)	1257 (35.68)	357 (23.08)	17 (11.89)
Education
0-6	701 (14.55)	248 (7.41)	392 (29.04)	61 (50.41)	<0.001
7-9	426 (8.84)	227 (6.78)	178 (13.19)	21 (17.36)
10-12	1747 (36.25)	1335 (39.87)	390 (28.89)	22 (18.18)
13 or more	1945 (40.36)	1538 (45.94)	390 (28.89)	17 (14.05)
Married
yes	4083 (77.99)	2460 (69.61)	1483 (95.25)	140 (97.22)	<0.001
no	1152 (22.01)	1074 (30.39)	74 (4.75)	4 (2.78)
Smoking
no	3178 (60.71)	2216 (62.71)	885 (56.84)	77 (53.47)	<0.001
yes	2057 (39.29)	1318 (37.29)	672 (43.16)	67 (46.53)
Hypertension
no	4079 (77.92)	3089 (87.41)	951 (61.08)	39 (27.08)	<0.001
yes	1156 (22.08)	445 (12.59)	606 (38.92)	105 (72.92)
Dyslipidemia
no	4300 (82.16)	3101 (87.77)	1107 (71.10)	92 (63.89)	<0.001
yes	934 (17.84)	432 (12.23)	450 (28.90)	52 (36.11)
WC, cm	84.04 ± 10.81	82.77 ± 11.29	86.48 ± 9.19	88.71 ± 8.88	<0.001
BMI, kg/m2	24.08 ± 3.79	23.98 ± 4.05	24.28 ± 3.17	24.20 ± 3.17	0.031
BP, Systolic, mmHg	118.88 ± 16.53	115.65 ± 15.21	124.98 ± 17.01	131.77 ± 17.40	<0.001
BP, Diastolic, mmHg	75.92 ± 9.93	76.07 ± 9.85	75.80 ± 9.89	73.39 ± 11.88	0.006
FBS, ㎎/dL	98.01 ± 15.94	96.57 ± 15.06	100.90 ± 17.41	102.77 ± 15.49	<0.001
HbA1C, %	5.66 ± 0.56	5.58 ± 0.54	5.80 ± 0.58	5.91 ± 0.54	<0.001
Triglycerides, ㎎/dL	130.88 ± 113.34	131.03 ± 127.40	129.16 ± 73.43	145.28 ± 93.29	0.262
HDL cholesterol, ㎎/dL	52.14 ± 12.62	53.23 ± 12.59	50.26 ± 12.39	45.40 ± 11.33	<0.001
LDL cholesterol, ㎎/dL	115.41 ± 36.37	114.88 ± 36.06	119.44 ± 37.07	92.87 ± 28.37	0.003
Total cholesterol, ㎎/dL	192.50 ± 37.44	193.32 ± 35.84	192.21 ± 40.33	175.68 ± 39.96	<0.001
BUN, ㎎/dL	14.94 ± 4.80	13.69 ± 3.77	16.91 ± 4.46	24.49 ± 10.10	<0.001
Albuminuria, ㎍/㎖	17.33 ± 70.09	13.66 ± 44.40	17.49 ± 54.18	107.28 ± 304.13	<0.001
MetS
No	3451 (67.46)	2575 (74.23)	819 (54.49)	57 (39.58)	<0.001
Yes	1665 (32.54)	894 (25.77)	684 (45.51)	87 (60.42)

Abbreviations: WC, waist circumference; BMI, body mass index; BP, blood pressure; FBS, fasting blood sugar; MetS, metabolic syndrome; HDL, high-density lipoprotein; LDL, low-density lipoprotein; eGFR, estimated glomerular filtration rate; HbA₁C, hemoglobin A₁C; BUN, blood urea nitrogen. The difference in total is due to missing data.

3.3. Generalized linear regression for metabolic syndrome among the sub-items (blood pressure, waist circumference, blood glucose, triglyceride, HDL-cholesterol) and eGFR

The analysis of the general variables and related factors of eGFR revealed age (β = −0.70, p < .001) and albuminuria (β = −0.03, p < .001) as significant factors for males, and age (β = −0.81, p < .001), diastolic blood pressure (β = −0.21, p < .001), Fasting Blood glucose(β = 0.09, p = .006), HbA1C (β = −2.11, p = .005), and BUN (β = 0.25, p = .009) as significant factors for females (Table 3).

Table 3.

Generalized linear regression for metabolic syndrome among the sub-items (blood pressure, waist circumference, blood glucose, triglyceride, HDL-cholesterol) and eGFR.

Category	Male						Female
	Beta	SE	t	p	R-Square	F (p)	Beta	SE	t	p	R-Square	F (p)
Intercept	209.280	3.419	61.206	<0.001	0.968	370.029 (<0.001)	201.743	6.306	31.993	<0.001	0.961	123.424 (<0.001)
Age, year	−0.698	0.021	−33.133	<0.001∗			−0.806	0.046	−17.616	<0.001∗
Smoking	−0.200	0.422	−0.474	0.636			0.122	1.175	0.104	0.917
WC, cm	−0.029	0.042	−0.686	0.493			−0.069	0.082	−0.841	0.401
BMI, kg/m2	0.110	0.103	1.069	0.286			−0.074	0.184	−0.401	0.689
BP, Systolic, mmHg	0.008	0.017	0.457	0.648			0.054	0.034	1.564	0.120
BP, Diastolic, mmHg	−0.038	0.023	−1.660	0.098			−0.212	0.056	−3.791	<0.001∗
FBS, ㎎/dL	−0.018	0.012	−1.416	0.158			0.088	0.032	2.782	0.006∗
HbA1C, %	0.024	0.337	0.070	0.944			−2.111	0.745	2.832	0.005∗
Triglycerides, ㎎/dL	0.000	0.001	−0.226	0.822			0.002	0.003	0.715	0.476
HDL cholesterol, ㎎/dL	−0.023	0.021	−1.108	0.268			0.015	0.043	0.339	0.735
LDL cholesterol, ㎎/dL	−0.003	0.012	−0.251	0.802			−0.006	0.025	0.227	0.820
Total cholesterol, ㎎/dL	0.000	0.011	−0.020	0.984			−0.007	0.022	0.301	0.764
BUN	0.053	0.039	1.372	0.171			0.249	0.094	2.648	0.009∗
Albuminuria, ㎍/㎖	0.030	0.002	17.394	<0.001∗			−0.002	0.005	0.520	0.604
MetS	−0.100	0.433	−0.231	0.817			0.869	0.989	0.879	0.381

∗ Abbreviations: WC, waist circumference; BMI, body mass index; BP, blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; LDL, low-density lipoprotein; eGFR, estimated glomerular filtration rate; HbA1C, hemoglobin A1C; BUN, blood urea nitrogen; MetS, metabolic syndrome.

3.4. Correlation for component of metabolic syndrome, and eGFR

Analysis of the correlations between eGFR and the components of metabolic syndrome revealed a relationship between them. In both men and women, eGFR elevation was negatively correlated with systolic and diastolic blood pressures, waist circumference, and fasting blood glucose (p < .005), and positively correlated with HDL cholesterol. The eGFR was negatively correlated with systolic and diastolic blood pressures, waist circumference, and fasting blood glucose (p < .005) and positively correlated with HDL cholesterol (Table 4).

Table 4.

Correlations for BP, WC, FBS, triglycerides, HDL cholesterol and eGFR.

Category		BP, Systolic (r/p)	BP, Diastolic (r/p)	WC (r/p)	HDL cholesterol(r/p)	FBS(r/p)	Triglycerides (r/p)
Male	BP, Diastolic, mmHg	0.557(<0.001)∗
	WC, cm	0.257(<0.001)∗	0.288(<0.001)∗
	HDL cholesterol, ㎎/dL	−0.045(<0.001)∗	−0.064(<0.001)∗	−0.311(<0.001)∗
	FBS, ㎎/dL	0.186(<0.001)∗	0.143(<0.001)∗	0.249(<0.001)∗	−0.092(<0.001)∗
	Triglycerides, ㎎/dL	0.136(<0.001)∗	0.230(<0.001)∗	0.230(<0.001)∗	−0.306(<0.001)∗	0.195(<0.001)∗
	eGFR	−0.230(<0.001)∗	−0.059(<0.05) ∗	−0.068(<0.001)∗	0.122(<0.001) ∗	−0.136(<0.001) ∗	−0.067(<0.05) ∗
Female	BP, Diastolic, mmHg	0.640(<0.001)∗
	WC, cm	0.396(<0.001)∗	0.296(<0.001)∗
	HDL cholesterol, ㎎/dL	−0.152(<0.001)∗	−0.051(<0.001)∗	−0.313(<0.001)∗
	FBS, ㎎/dL	0.206(<0.001)∗	0.127(<0.001)∗	0.293(<0.001)∗	−0.147(<0.001)∗
	Triglycerides, ㎎/dL	0.185(<0.001)∗	0.162(<0.001)∗	0.286(<0.001)∗	−0.380(<0.001)∗	0.247(<0.001)∗
	eGFR, mL/min/1.73 m2	−0.431(<0.001)∗	−0.089(<0.001) ∗	−0.291(<0.001)∗	0.142(<0.001) ∗	−0.177(<0.001) ∗	−0.126(<0.001) ∗

∗ Abbreviations: WC, waist circumference; BP, blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate.

3.5. Odds ratio for metabolic syndrome among the sub-items and eGFR

Binary logistic analysis using components of MS, including waist circumference, blood pressure, fasting blood glucose, HDL cholesterol, triglycerides, and eGFR, identified all these factors as predictors of risk for metabolic syndrome. The estimated eGFR was 0.03 % lower in males with metabolic syndrome (OR:0.97; 95 % Cl:0.97–0.98) and 0.03 % lower in females (OR:0.97; 95 % Cl:0.97–0.98). The estimated eGFR was significantly lower in those with metabolic syndrome (p < .001) (Table 5).

Table 5.

Odds ratio for metabolic syndrome among the sub-items and eGFR.

Category	Male		Female
	OR(CI)	p	OR(CI)	p
eGFR mL/min/1.73 m2	0.972(0.964–0.979)	<0.001∗	0.974(0.966–0.983)	<0.001∗
BP, Systolic, mmHg	1.039(1.005–1.038)	<0.001∗	1.043(1.032–1.054)	<0.001∗
BP, Diastolic, mmHg	1.021(1.005–1.038)	0.010∗	1.008(0.991–1.026)	0.355
WC, cm	1.161(1.139–1.183)	<0.001∗	1.152(1.133–1.173)	<0.001∗
FBS, ㎎/dL	1.078(1.064–1.091)	<0.001∗	1.082(1.068–1.096)	<0.001∗
Triglycerides, ㎎/dL	1.008(1.007–1.010)	<0.001∗	1.007(1.005–1.010)	<0.001∗
HDL cholesterol, ㎎/dL	0.972(0.964–0.979)	<0.001∗	0.926(0.914–0.939)	<0.001∗

∗ Abbreviations: WC, waist circumference; BP, blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate; OR, odds ratio; CI, confidence interval.

4. Discussion

This descriptive survey study aimed to investigate the correlation between eGFR and metabolic syndrome. Of the participants, 38.9 % of males and 27.5 % of females had metabolic syndrome, and significant differences were found in blood pressure, blood glucose, triglycerides, and waist circumference between males and females. This result is consistent with that of a similar study which showed a significantly higher prevalence of metabolic syndrome in males than in females [18]. The results were also similar to those reported in the “2020 National Health Screening Statistical Yearbook,” in which 10 million out of 14 million individuals who underwent general health screening (69.8 %) were found to have at least one risk factor for metabolic syndrome, 78.1 % of whom were male and 60.5 % female [24]. Furthermore, smoking rates (72.8 % for males and 12.5 % for females) in the present study imply that smoking may have increased the risk of metabolic syndrome in males. Smoking is a cause of insulin resistance and hyperinsulinemia and is known to be a risk factor for metabolic abnormalities, hemodynamic disorders, atherosclerosis, and cardiovascular disease, and is recognized as a major risk factor for metabolic syndrome [[25], [26], [27]].

In the present study, analysis of the differences in values according to the stages of GFR showed statistically significant differences in age, education level, and household income. Age acts as an important variable in terms of reducing GFR, and the regression analysis revealed that aging negatively affected GFR in both males and females GFR [[25], [26], [27], [28]]. However, various factors outside of age influence the glomerular filtration rate. Previous studies have confirmed the association between socioenvironmental factors, namely low education and income levels, and worse subjective health status. Additional assessments have been conducted for higher illness incidence rates, including those of chronic diseases, and a lower number of medical services used [29,30]. The study results are consistent with those of previous studies.

As a result of the study, it was found that the higher the incidence of metabolic syndrome, the more adverse it is on renal function. In particular, diastolic blood pressure and glycated hemoglobin were identified as the main factors that negatively affect GFR; this finding is similar to the results of previous studies which stated that diabetes and blood pressure negatively affect the kidneys [31].

A noteworthy finding of the present study is that the results of the regression analysis of eGFR and components of metabolic syndrome showed significant correlations with eGFR. Previous studies indicated that patients with metabolic syndrome have a high prevalence of microvascular diseases and decreased renal function over time [32]. In some cohort studies, metabolic syndrome was found to be closely associated with kidney function [32]. Some studies have shown a doubled rate of renal failure in patients with metabolic syndrome compared to that in patients without metabolic syndrome [33]. In addition, the intrarenal interlobar arteries of patients with metabolic syndrome show a high pulsatility and resistive index, suggesting that structural changes lead to atherosclerosis and glomerular ischemia. This explains the close association between renal function and metabolic syndrome [34]. Therefore, the results of the present and previous studies indicate a direct relationship between renal function and metabolic syndrome. It is essential to control metabolic syndrome to prevent chronic kidney disease.

However, according to Korea's National Health Examination statistics in 2018, 78.5 % of more than 4 million imprisoned people have at least one risk factor for metabolic syndrome [35]. In addition, chronic kidney disease has a worldwide prevalence of 9.1 % as of 2017, which is an increase of 29.3 % since 1990 [36]. In Korea, chronic kidney disease averaged 8.4 % and doubled medical expenses between 2011 and 2021 [37]. According to this study, the management of metabolic syndrome is closely related to a reduction in the glomerular filtration rate. Therefore, there is a need for diseases to be managed in an integrated way as quickly as possible. In particular, according to a previous study of 20,075 adults, 30.8 % of the total had metabolic syndrome, and albuminuria appeared in 3.4 % of them. This makes it possible to see that the metabolic syndrome has a very negative effect on the kidney [19]. It is also worth noting that albuminuria was most present in the third stage of this study.

Since this study was conducted on Koreans, it may be difficult to generalize the study results. However, a previous study based on data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES), a cohort survey conducted on various races in the United States, found that the elevation of HDL-cholesterol in subjects with metabolic syndrome, regardless of race, negatively affected the decrease of eGFR [38]. In addition, the NHANES survey from 2005 to 2016 also found that metabolic syndrome and obesity have an effect on the decrease of eGFR [39]. Therefore, although this study was conducted on Koreans, it can be said that it is very meaningful to reveal the relationship between metabolic syndrome and decreased renal function as in previous studies.

The study limitations are as follows.

• 1.It is difficult to accurately compare the trends of change because the data of the present study are not equal to those of previous studies. However, based on previous studies showing that the prevalence of chronic kidney disease (CKD) in adults aged ≥35 years in large cities has increased by 13.6 % annually over 5 years, identifying the various causes of reduced GFR is of paramount importance. Managing and controlling metabolic syndrome is also essential [15].
• 2.There were discrepancies between eGFR values returned by different serum creatinine-based formulas and GFR determined using plasma or renal clearance methods or the measured GFR. Therefore, we propose that the correlation between glomerular filtration rate and metabolic syndrome be analyzed using plasma or renal clearance methods.
• 3.Although statistically significant in this study, Pearson correlation coefficient was low. Therefore, future studies need to investigate additional samples and analyze various factors affecting glomerular filtration rate. In addition, I propose longitudinal studies including inflammatory indicators and genetic factors to overcome the limitations of associated studies.

5. Conclusions

In this study, a decrease in GFR was closely related to metabolic syndrome, including diabetes and blood pressure. In addition, it was found that elements of social environments, such as education level and household income, were also related to a decrease in GFR. Therefore, to prevent kidney failure, it is important to manage and prevent chronic diseases, including metabolic syndrome. In addition, the composition of a social environment that can prevent disease is very important. It is of great significance that this study provided meaningful baseline data to help decrease the prevalence of kidney disease and prevent metabolic syndrome. The limitations of this study include difficulties with controlling for many variables, such as participants’ living environments and health status, as well as raw materials not being analogous to the data of prior studies. Based on this study, we propose to systematically analyze the factors influencing the effect of metabolic syndrome on renal function over several years. This will aid in the development of living health guidelines and educational programs to prevent renal failure.

Informed consent statement

Not applicable because open data were used in this study.

Author contributions

Data collection: data analysis; manuscript writing: Park, YJ. I have read and agreed to the published version of the manuscript.

Institutional Review Board statement

This study was approved by the Institutional Review Board of the J. University (Approval no. JIRB-2022072601-01, approval date: August 17, 2022).

Data availability statement

The data can be found here: http://knhanes.cdc.go.kr; (Accessed date: July 4, 2022).

Funding statement

This research was funded by Joongbu university, South Korea; grant number 2024-01-10.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.