Analysis of the association between glomerular filtration rate, proteinuria and metabolic syndrome in chronic kidney patients based on longitudinal data

Abstract

Chronic kidney disease (CKD) is a group of chronic diseases caused by kidney damage from multiple causes. Metabolic syndrome (MS) manifests as dysfunction of endothelial cells and chronic functional inflammatory states, and may be involved in pathological changes related to renal impairment. Based on longitudinal data analysis of the association between estimated glomerular filtration rate (eGFR), proteinuria and MS in patients with CKD, this study aims to provide new ideas for the pathophysiological mechanism of CKD and a theoretical basis for the early prevention and effective intervention of MS‐related kidney damage. A total of 126 patients with CKD were divided into non‐MS group and MS group. According to the eGFR level, 126 patients with CKD were divided into G1 group, G2 group, G3a group, G3b group, G4 group and G5 group. Serum markers such as eGFR, urine protein, and triglycerides (TG) were collected. The correlation between eGFR, urine protein and MS‐related indexes was analysed, and the risk factors affecting CKD complicated by MS were analysed. In patients with CKD, the levels of urine protein, abdominal circumference, TG, systolic blood pressure (SBP), diastolic blood pressure (DBP), and fasting blood glucose (FPG) were increased with the course of the disease, but the levels of eGFR and high density lipoprotein (HDL‐C) were decreased (p < 0.05). Abdominal circumference, TG, SBP, DBP, FP were significantly negatively correlated with eGFR, but HDL‐C was positively correlated with eGFR (p < 0.05). Diabetes, hyperlipidemia, UA, and SBP were independent risk factors affecting CKD complicated MS, and eGFR were independent protective factors (p < 0.05). The combination of diabetes, hyperlipidemia, UA, SBP, and eGFR exhibited higher prediction value for the CKD patients complicated by MS. There was a certain correlation between between MS components with eGFR and urinary protein in patients with CKD. The early intervention treatment of MS was helpful in delaying the development of CKD and reducing proteinuria.

The incidence rate of MS in patients with CKD was high. Components of MS was correlated with EGFR and urinary protein in patients with CKD. Early intervention treatment of MS is helpful to delay the development of CKD and reduce proteinuria.

1. INTRODUCTION

Chronic kidney disease (CKD) is a chronic disease group with irreversible kidney damage caused by various reasons, accompanied by high clinical morbidity, mortality and disability rates. Due to the atypical clinical manifestations of CKD in the early stage, most patients only manifest as fatigue, lack of diet, anemia, proteinuria, foamy urine and so on, which are easy to be ignored. Thus, most patients were already in the middle and late stages at the time of diagnosis and treatment, which not only greatly endangers the life and health of patients but also brings huge economic burden to families and society [1, 2].

CKD induces diabetes and cardiovascular disease but also may develop into end‐stage renal disease (ESRD). At present, the main principle of clinical treatment of CKD is to delay the course of CKD and prevent the occurrence of ESRD [3]. The main way to evaluate the course of CKD is to detect the level of estimated glomerular filtration rate (eGFR). Recent studies have shown that the change of the proteinuria level is closely related to the course of CKD. eGFR and proteinuria are important biological detection indicators for evaluating renal function [4, 5]. eGFR is the amount of ultrafiltration fluid produced by both kidneys per minute, the decrease of whose level indicates the existence of renal functional diseases. eGFR in the elderly may also be reduced to a certain level. Proteinuria is a typical symptom of chronic kidney disease and the presence of proteinuria suggests that patients have impaired renal function.

Metabolic syndrome (MS) is a metabolic disorder in which many metabolic factors such as insulin resistance, abnormal glucose and lipid metabolism, hypertension, endothelial dysfunction etc., act on the body. Recent studies have found that obesity, elevated blood glucose, hypertension, lipid metabolism disorder and other components of MS are related to the incidence and development of CKD [6]. Epidemiological studies have shown that the risk of CKD in MS patients is 3.9 times higher than that in other control populations, suggesting that MS is an important cause of chronic kidney damage [7]. In addition, in experimental studies on animal models, it is found that glomerular filtration rate and renal plasma flow in the experimental group with MS are significantly increased compared with the control group, suggesting that MS can cause hyperperfusion and hyperfiltration of the glomerulus, and patients will eventually face renal fibrosis and progressive loss of renal function if appropriate intervention is not carried out in the course of its disease [8]. However, the correlation between eGFR and proteinuria in MS and CKD patients remains to be confirmed.

In the present study, a total of 126 patients with CKD treated in our hospital from July 2019 to July 2020 were selected to explore the association between eGFR, proteinuria and MS based on longitudinal data, thereby clarifying the risk factors for the occurrence of CKD, determining the best treatment principle and choosing the appropriate medication for the prevention of CKD.

2. MATERIALS AND METHODS

2.1. General information

This study was a longitudinal study based on cross‐sectional population data, which was derived from the hospital quality monitoring system database. The sample size calculation adopted a simple random sampling formula taking α = 0.05 and Z _α/2 = 1.96. According to the study of the international incidence of CKD [9], it was estimated that the incidence of CKD was 12%, the error was taken by 5% and the required sample size was calculated to be 203 patients. Additionally, considering that there might be an invalid questionnaire, the required sample size was increased by 15%; thus, a total of 233 patients were selected. According to the inclusion and exclusion criteria, the final included study subjects were 126 CKD patients. They were admitted to our hospital from July 2019 to July 2020, all of whom came from the same hospital in the same area, and their diet and lifestyle were in line with local characteristics. Inclusion criteria: (1) all patients met the diagnostic criteria for CKD according to the KDIGO guidelines [10]; (2) patients with no previous nervous system and mental‐related diseases; (3) the patient and his/her family members were informed and had good compliance, could cooperate with the examination and treatment and signed the informed consent form. Exclusion criteria: (1) patients less than 18 years old; (2) patients with serious dysfunction of important organs; (3) patients complicated with systemic or severe infection; (4) patients combined with malignant tumour; (5) patients with pleural effusion and ascites. The patients were divided into non‐MS group and the MS group according to whether they were combined with MS or not (according to MS diagnostic standards in the international standard of 2005). They were divided into G1 group (eGFR≥90), G2 group (60≤eGFR≤89), G3a group (45≤eGFR≤59), G3b group (30≤eGFR≤89), G4 group (15≤eGFR≤29) and G5 group (eGFR≤15) according to the level of EGFR. All the included experiments were approved by the Ethics Committee of the hospital. The process of general data selection was shown in Figure 1.

FIGURE 1.

The process of general data selection.

2.2. Outcome measures

2.2.1. Data collection

The clinical data including age, gender, body mass index (BMI), waist circumference, hip circumference, waist hip ratio, hypertension history, diabetes history, use of renin‐angiotensin‐aldosterone system antagonists (RASI), payment methods, family support, systolic blood pressure (SBP), diastolic blood pressure (DBP) and so on were collected and compared.

2.2.2. Laboratory indicators examination

• (1)Serum collection: The morning fasting venous blood of patients in each group was collected, and the urine samples were taken. The blood in an ordinary test tube naturally coagulated at room temperature for about 15 min. A portion of venous blood was placed into an anticoagulant test tube and was centrifuged at 3000 r/min for 20 min to separate the serum. The serum was stored in an −80°C for standby. The serum was sent for examination in the laboratory of the Affiliated Hospital of Hebei University.
• (2)Measurement of blood glucose levels: Fasting blood glucose (FPG) was measured by the glucose oxidase method. 0.50 ml β‐D‐glucose solution (purchased in Beijing Ita Biotechnology Co., Ltd., Taihubi Beili, Tongzhou District, Beijing, China), 0.10 ml peroxidase solution (purchased in Shanghai Baisai Biotechnology Co., Ltd., Minhang District, Shanghai, China) and 2.40 ml o‐diammonium solution (purchased in Beijing Bio Labs Technology Co., Ltd., Haidian District, Beijing, China) were mixed and were adjusted to 25°C. The mixture was added to a 1 cm cuvette. A total of 0.10 ml enzyme was added to the glucose oxidase solution to be tested, and the absorbance was measured at 436 nm every 1 min for 15 min. Calculate the FPG content in the sample according to the absorbance.
• (3)Measurement of blood lipid levels: The levels of total cholesterol (TC), triglyceride (TG), uric acid (UA), high density lipoprotein cholesterol (HDL‐C), creatinine (SCr), low density lipoprotein cholesterol (LDL‐C), glycosylated hemoglobin (HbA1c) and vitamin D were measured by the enzymatic method. All the kits were purchased from Shanghai Qincheng Biotechnology Co., Ltd., (Baoshan District, Shanghai, China). Restore all reagents and components in the kit to room temperature, and prepare working solutions for all components in the kit. Take the required strip and set the standard hole, sample hole and blank hole. A total of 50 μL standard at different concentrations was added to the standard sample hole, t50 μL samples to be tested was added to the sample hole and nothing was added to the blank hole. A total of 100 μL horseradish peroxidase labelled detection antibody (Purchased in Yeasen Biotechnology (Shanghai) Co., Ltd., Pingnan High‐tech Zone, Pudong, Shanghai, China) was added to the standard sample hole and the sample hole. The strip was incubated in 37°C water bath or thermostat for 60 min. Discard the liquid in the hole, and pat the strip dry on the absorbent paper. Then, fill each hole with washing liquid, stand for 20 s, throw away the washing liquid and pat the strip dry on the absorbent paper. The washing process was repeated 4 times. Then, the substrates A and B are fully mixed in 1:1 volume, and a 100 μL substrate mixture was added to each well. The well was incubated in 37°C water bath or thermostat for 15 min. A total of 50 μL termination liquid was added to all holes, and the absorbance of each hole was read on the microplate reader. Take the standard concentration as the ordinate and the corresponding absorbance as the abscissa, and a standard curve equation was created using a standardised curve‐fitting four‐parameter logistic method. Calculate the concentration value of the sample according to the standard curve equation.
• (4)Measurement of insulin levels: Fast insulin (fins) was measured by radioimmunoassay. The kit was purchased from Jiangxi Zhonghong Boyuan Biotechnology Co., Ltd., (Nanchang County, Nanchang City, Jiangxi Province, China). Unlabelled antigen, labelled antigen and specific antiserum were added to the reaction tube for competitive inhibition reaction. After the separation of binding and free markers, the labelled antigens and reagent antibodies form immune complexes. Add appropriate precipitant to precipitate the immune complex thoroughly, and centrifuge the labelled antigen. The radioactivity of the labelled antigen antibody complex was measured. Take different concentrations of standard antigen as the abscissa and the corresponding radiation count as the ordinate to draw the standard curve for the quantification of the antigen to be tested.

The level of urinary protein was detected by the biuret method: Twelve test tubes were divided into two groups. Add 0, 0.2, 0.4, 0.6, 0.8 and 1.0 ml of standard protein solution respectively; make up to 1 mL with water, and then add 4 mL of biuret reagent to the tubes. After fully shaking, the tubes were placed at room temperature for 30 min, and colourimetric determination was conducted at 540 nm. The first tube without protein solution was used as the blank control solution. Take the average value of the two groups, and draw the standard curve with the protein content as the abscissa and the light absorption value as the ordinate. Take two or three tubes, and the protein concentration of unknown samples was determined with the same method as above. The insulin resistance index (HOMA‐IR) was calculated by fasting blood glucose (mmol/L) × Fasting blood insulin (IU/L)/22.5.

• (5)Glomerular filtration rate (EGFR) was calculated according to the CKD‐EPI formula [11]. CKD‐EPI formula: male: serum creatinine≤0.9 mL/dl, eGFR = 144 × (serum creatinine/0.9)−0.411 × 0.993 × age; serum creatinine>0.9 mL/dl, eGFR = 144 × (serum creatinine/0.9)−1.209 × 0.993 × age. Female: serum creatinine≤0.7 mL/dl, eGFR = 144 × (serum creatinine/0.7)−0.329 × 0.993 × age, serum creatinine>0.7 mL/dl, eGFR = 144 × (serum creatinine/0.7)−1.209 × 0.993 × age.

2.3. Statistical analysis

Spss20.0 software was used to analyse the experimental data. Kolmogorov–Smirnov was used to test whether continuous variables conformed to a normal distribution. Age, Fins, urinary protein, HOMA‐IR index and other measurement data were expressed by $(\overline{x}\pm s)$. The variables that conformed to the normal distribution were compared between groups by independent sample t‐test, and repeated measure ANOVA was used for intra‐group comparison, and the skewed distribution variables were analysed by Kruskal–Walllist test. Gender, diabetes, hyperlipidemia, hypertension and other enumeration data were expressed in (%) and were compared using the χ ² text. The K–S method was used to test the normal distribution of the grade information, the K–W rank sum test was used to compare the differences between groups with the test level of α = 0.05 two‐sided test and then the Mann–Whitney U test was used to compare pairs between groups. Pearson correlation analysis was used to analyse the correlation between eGFR, urinary protein and MS; the risk factors of CKD complicated with MS were analysed using multivariate logistic regression, and p < 0.05 was considered as statistically significant.

3. RESULTS

3.1. Comparison of clinical indexes between the two groups

The clinical indicators of the two groups of patients were analysed, and the results displayed that the age, waist circumference, hip circumference, TC, TG, FBG, SCr, UA, SBP, DBP, Fins, urinary protein, HOMA‐IR index and the proportion of diabetes, hyperlipidemia and hypertension in the MS group were much higher than these in the non‐MS group, and the levels of HDL‐C and eGFR in the MS group were sharply lower than these in the non‐MS group (p < 0.05, Table 1).

TABLE 1.

Comparison of clinical indexes between the two groups $(\overline{x}\pm s)$.

Indicators		The non‐MS group (n = 83)	The MS group (n = 43)	t	p
Age (years)		68.52 ± 4.52	72.65 ± 2.58	5.535	<0.001
Gender (case)	Male	43 (51.81%)	22 (51.16%)	0.004	0.945
	Female	40 (48.19%)	21 (48.84%)
Use of RASI	Yes	66 (79.52%)	32 (74.42%)	0.426	0.514
	No	17 (20.48%)	11 (25.58%)
Payment methods	Medical insurance	47 (56.63%)	26 (60.47%)	0.294	0.864
	Agricultural insurance	23 (27.71%)	10 (23.26%)
	Self‐paying	13 (15.66%)	7 (16.28%)
Family support	High	62 (74.70%)	35 (81.40%)	0.717	0.397
	Low	21 (25.30%)	8 (18.60%)
BMI (kg/m2)		22.16 ± 2.46	22.38 ± 1.85	0.515	0.607
Waistline (cm)		84.15 ± 3.15	93.56 ± 2.15	17.567	<0.001
Hipline (cm)		96.73 ± 3.85	105.12 ± 4.75	10.691	<0.001
Waist‐to‐hip ratio		0.87 ± 0.12	0.89 ± 0.16	0.789	0.431
TC (mmol/L)		4.15 ± 0.56	4.79 ± 1.05	4.469	<0.001
TG (mmol/L)		1.15 ± 0.52	1.85 ± 0.45	7.490	<0.001
LDL‐C (mmol/L)		2.58 ± 0.37	2.65 ± 0.58	0.823	0.411
HDL‐C (mmol/L)		1.20 ± 0.26	0.79 ± 0.15	9.539	<0.001
Diabetes (case)		8 (9.64%)	18 (41.86%)	17.957	<0.001
Hyperlipidemia (case)		12 (14.46%)	30 (69.77%)	38.993	<0.001
Hypertension (case)		14 (16.87%)	35 (81.40%)	49.627	<0.001
FBG (mmol/L)		5.02 ± 0.56	6.89 ± 1.25	11.596	<0.001
HbA1c (%)		5.95 ± 0.25	6.23 ± 1.15	2.130	0.035
SCr (mg/dl)		0.81 ± 0.15	0.95 ± 0.09	5.612	<0.001
UA (μmol/L)		359.63 ± 23.51	398.52 ± 46.15	6.278	<0.001
Vitamin D (mg/dl)		13.25 ± 5.46	12.05 ± 3.62	1.299	0.196
SBP (mmHg)		124.63 ± 3.56	136.95 ± 4.15	17.391	<0.001
DBP (mmHg)		79.63 ± 2.45	88.25 ± 2.41	18.828	<0.001
Fins (mU/L)		9.82 ± 1.59	12.56 ± 1.32	9.696	<0.001
Urine protein (g/L)		1.26 ± 0.15	2.52 ± 1.05	10.846	<0.001
HOMA‐IR index		1.12 ± 0.26	1.79 ± 0.14	15.737	<0.001
eGFR		102.63 ± 21.45	78.05 ± 10.46	7.080	<0.001

Abbreviations: BMI, bodymassindex; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; Fins, fast insulin; FPG, fasting blood glucose; HbA1c, glycosylated hemoglobin; HDL‐C, high density lipoprotein cholesterol; HOMA‐IR, the insulin resistance index; LDL‐C, low density lipoprotein cholesterol; RASI, renin‐angiotensin‐aldosterone system antagonists; SBP, systolic blood pressure; SCr, creatinine; TC, total cholesterol; TG, triglyceride; UA, uric acid.

3.2. Comparison of eGFR and urinary protein levels in patients with CKD at different stages

The above result analysis showed that there was a difference in the clinical data of patients with MS. The results of this analysis of renal function in patients with CKD showed that the urine protein level of CKD patients increased, and eGFR decreased with the progress of the disease was statistically significant (p < 0.05, Table 2).

TABLE 2.

Comparison of eGFR and urinary protein levels in patients with CKD at different stages $(\overline{x}\pm s)$.

Group	eGFR	Urine protein
G1 group (n = 56)	93.26 ± 2.52	1.32 ± 0.28
G2 group (n = 28)	75.96 ± 6.38 a	1.67 ± 0.15 a
Ga group (n = 16)	50.12 ± 2.15 a , b	2.56 ± 0.26 a , b
Gb group (n = 13)	35.25 ± 1.32 a , b , c	3.04 ± 0.12 a , b , c
G4 group (n = 10)	22.56 ± 1.48 a , b , c , d	3.59 ± 0.08 a , b , c , d
G5 group (n = 3)	9.02 ± 0.08 a , b , c , d , e	4.08 ± 0.09 a , b , c , d , e
F	212.310	334.850
p	<0.001	<0.001

Abbreviation: eGFR, estimated glomerular filtration rate.

^a p < 0.05 compared to G1 group.

^b p < 0.05 compared with G2 group.

^c p < 0.05 compared to G3a group.

^d p < 0.05 compared to G3b group.

^e p < 0.05 compared to G4 group.

3.3. Comparison of MS related indexes in patients with CKD at different stages

The analysis of the above results showed that there were differences in patients with CKD with different processes. The results of this study analysed the MS‐related indexes of patients with CKD with different processes, and the results showed that the levels of abdominal circumference, TG, SBP, DBP and FPG in CKD patients increased, and the level of HDL‐C decreased with the progress of the disease. The difference between the two groups was statistically significant (p < 0.05) (Table 3).

TABLE 3.

Comparison of MS‐related indexes in patients with CKD at different stages $(\overline{x}\pm s)$.

Group	Abdomen circumference	TG	HDL‐C	SBP	DBP	FPG
G1 group (n = 56)	85.60 ± 6.85	4.21 ± 0.25	1.21 ± 0.26	125.63 ± 2.15	79.25 ± 6.45	5.04 ± 0.12
G2 group (n = 28)	88.95 ± 4.45 a	4.56 ± 0.36 a	0.98 ± 0.12 a	130.14 ± 1.45 a	83.16 ± 1.85 a	5.69 ± 0.45 a
Ga group (n = 16)	92.63 ± 1.26 a , b	4.89 ± 0.25 a , b	0.82 ± 0.11 a , b	135.63 ± 3.01 a , b	87.66 ± 1.02 a , b	6.35 ± 0.25 a , b
Gb group (n = 13)	98.35 ± 1.52 a , b , c	5.32 ± 0.30 a , b , c	0.67 ± 0.03 a , b , c	146.35 ± 2.15 a , b , c	94.67 ± 1.25 a , b , c	6.79 ± 0.36 a , b , c
G4 group (n = 10)	106.35 ± 1.45 b , c , d	5.84 ± 0.15 b , c , d	0.50 ± 0.01 b , c , d	156.34 ± 4.58 b , c , d	106.35 ± 2.14 b , c , d	8.45 ± 0.35 b , c , d
G5 group (n = 3)	120.36 ± 1.56 a , b , c , d , e	6.21 ± 0.25 a , b , c , d , e	0.22 ± 0.01 a , b , c , d , e	167.53 ± 3.25 a , b , c , d , e	114.38 ± 1.45 a , b , c , d , e	9.05 ± 0.25 a , b , c , d , e
F	55.940	106.600	48.290	495.120	100.950	365.090
p	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

Abbreviations: DBP, diastolic blood pressure; FPG, fasting blood glucose; LDL‐C, low density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglyceride.

^a p < 0.05 compared to G1 group.

^b p < 0.05 compared with G2 group.

^c p < 0.05 compared to G3a group.

^d p < 0.05 compared to G3b group.

^e p < 0.05 compared to G4 group.

3.4. Correlation analysis of eGFR, urinary protein and MS in patients with CKD

The results of the above studies showed that there were differences in renal function and MS indexes in patients with different processes of CKD, and further Pearson correlation analysis showed that abdominal circumference, TG, SBP, DBP and FPG were significantly negatively correlated with eGFR (r = −0.526, −0.412, −0.582, −0.396, −0.435, all p < 0.05), and HDL‐C was significantly positively correlated with eGFR (r = 0.356, p < 0.05); Abdominal circumference, TG, SBP, DBP and FPG were positively correlated with urinary protein (r = 0.412, 0.362, 0.359, 0.647, 0.558, all p < 0.05), and HDL‐C was negatively correlated with urinary protein (r = −0.485, p < 0.05, Table 4).

TABLE 4.

Correlation analysis of eGFR, urinary protein and MS in patients with CKD.

Group	eGFR		Urinary protein
	r	p	r	p
Abdominal circumference	−0.526	0.005	0.412	0.029
TG	−0.412	0.012	0.362	0.018
HDL‐C	0.356	0.006	−0.485	0.035
SBP	−0.582	0.015	0.359	0.027
DBP	−0.396	0.026	0.647	0.008
FPG	−0.435	0.038	0.558	0.001

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting blood glucose; LDL‐C, low density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglyceride.

3.5. Univariate logistic regression analysis of influencing factors of CKD complicated with MS

The results of the above studies showed that eGFR, urine protein and MS were correlated in patients with CKD. Further Univariate Logistic regression analysis showed that age, waist circumference, TG, FBG, SCr, UA, SBP, DBP, urinary protein, HOMA‐IR index, diabetes and hyperlipidemia were the influencing factors of CKD complicated with MS (p < 0.05), and HDL‐C and eGFR were the protective factors of CKD complicated with MS (p < 0.05, Table 5).

TABLE 5.

Univariate logistic regression analysis of influencing factors of CKD complicated with MS.

Indicators	β	SE	Wald	p	OR	95% CI
Age	0.958	0.185	1.265	0.025	2.635	1.251–3.849
Waist circumference	2.156	0.715	9.154	0.001	8.154	2.659–14.851
Hip circumference	0.528	0.158	1.246	0.215	1.625	0.982–2.451
TC	0.481	0.214	1.254	0.051	1.969	1.145–3.485
TG	2.465	0.254	1.564	0.032	1.298	0.048–3.154
HDL‐C	−2.154	0.784	9.256	0.015	0.758	0.012–0.895
Diabetes	0.416	0.254	0.521	0.025	1.694	1.021–2.854
Hyperlipidemia	0.685	0.346	0.851	0.014	2.051	1.024–3.879
Hypertension	0.845	0.245	0.638	0.068	1.578	0.982–4.158
FBG	0.748	0.295	0.354	0.025	2.636	1.024–4.857
SCr	1.561	0.854	0.516	0.015	3.054	1.257–5.895
UA	1.786	0.945	0.617	0.008	2.649	1.587–5.162
SBP	1.485	0.567	0.524	0.042	1.325	1.001–3.451
DBP	1.685	0.695	0.451	0.036	1.678	1.025–4.593
FIns	0.485	0.325	0.652	0.148	1.569	1.045–4.572
Urinary protein	2.154	0.785	9.265	0.005	8.451	3.745–16.524
HOMA‐IR index	1.754	0.685	8.495	0.008	6.321	2.594–10.451
eGFR	−2.465	0.985	7.468	0.002	0.582	0.151–0.954

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; Fins, fast insulin; FPG, fasting blood glucose; HDL‐C, high density lipoprotein cholesterol; HOMA‐IR, the insulin resistance index; SBP, systolic blood pressure; SCr, creatinine; TC, total cholesterol; TG, triglyceride; UA, uric acid.

3.6. Multivariate logistic regression analysis of risk factors for CKD complicated with MS

On the basis of univariate analysis, further multivariate logistic regression analysis showed that diabetes, hyperlipidemia, UA and SBP were independent risk factors for CKD complicated with MS (p < 0.05), and eGFR an was independent protective factor for CKD complicated with MS (p < 0.05, Table 6).

TABLE 6.

Multivariate logistic regression analysis of risk factors for CKD complicated with MS.

Indicators	β	SE	Wald	p	OR	95% CI
Diabetes	2.465	0.254	1.564	0.032	1.298	0.048–3.154
Hyperlipidemia	0.667	0.249	7.156	0.007	1.949	1.195–3.182
SBP	0.015	0.006	6.372	0.011	1.015	1.001–1.258
UA	0.457	0.026	6.192	0.018	1.670	1.521–1.857
eGFR	−0.030	0.010	9.215	0.001	0.831	0.747–0.925

Abbreviations: eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; UA, uric acid.

3.7. Clinical value of ROC curve analysis in predicting CKD complicated with MS

ROC curve analysis was performed according to the results of multivariate logistic regression analysis. The results showed that the AUC of diabetes, hyperlipidemia, UA, SBP and eGFR alone and jointly predicted CKD complicated with MS was 0.815, 0.767, 0.804, 0.895, 0.811 and 0.919, respectively. The value of combined prediction was higher (Table 7 and Figure 2).

TABLE 7.

Clinical value of ROC curve analysis in predicting CKD complicated with MS.

Indicators	AUC	Sensibility	Specificity	p	95% CI
Diabetes	0.815	87.50	77.50	0.000	0.723–0.907
Hyperlipidemia	0.767	77.50	60.00	0.000	0.663–0.870
SBP	0.804	90.00	72.50	0.000	0.710–0.89
UA	0.895	92.50	77.50	0.000	0.827–0.963
eGFR	0.811	87.55	75.00	0.000	0.710–0.912
Combined prediction	0.919	95.00	87.50	0.000	0.862–0.976

Abbreviations: eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; UA, uric acid.

FIGURE 2.

ROC curve analysis.

4. DISCUSSION

4.1. CKD epidemiology

CKD is a chronic irreversible disease with high incidence rate in the world. With the change of diet structure and the aggravation of population aging in recent years, the number of patients with CKD is gradually increasing [12]. The early symptoms of CKD are hidden, and most patients were already at ESRD stage when they were diagnosed, which poses a serious threat to the normal life and health of patients [13]. According to an incomplete statistics, there are about 850 million CKD patients in the world, and 2.4 million people die of CKD every year [14]. CKD has become the focus of scholars worldwide because of its low awareness, high prevalence, high mortality, high prevalence and low mortality.

4.2. CKD and MS

MS is a common clinical syndrome of systemic chronic inflammation and metabolic disorder caused by insulin resistance, mainly manifested as hypertension, hyperlipidemia, hyperglycemia and obesity. With the deepening of MS‐related research in recent years, it has been found that MS can increase the risk of cardiovascular disease and cardiovascular events [15]. Kyung c y et al. [16] found that compared with the normal population, MS patients had much higher probability of atherosclerotic heart disease. Aghasizadeh m et al. [17] showed that the number of patients with diabetes induced by MS accounted for 82.00% of the total number, the number of patients with hypertension induced by MS accounted for 64.70% of the total number and the number of patients with microalbuminuria induced by MS accounted for 54.50% of the total number. In recent years, it has been found that the components of MS are related to the incidence and development of CKD. Research shows that obese patients with hypertension have higher incidence of kidney disease than the healthy people. Diabetes is an important risk factor for CKD. Hyperglycemia can induce glomerular fibrosis by damaging glomerulus and eventually destroy renal structure and function [18]. Hyperlipidemia destroys renal function by damaging mesangial cells, promoting the migration and proliferation of monocytes and inducing systemic low inflammatory response. In this study, the age, waist circumference, TG, FPG, SBP, DBP, urinary protein, HOMA‐IR, HDL‐C and eGFR were increased, and HDL‐C and EGFR were decreased in MS group compared with non‐MS group. It is suggested that MS damaged renal function by mediating eGFR and urinary protein and accelerated the course of CKD, which is similar to the results of Wang Fen et al. [19].

4.3. Influencing factors of CKD concurrent MS

The incidence rate of CKD in China is as high as 10.8%. Compared with the normal population, CKD patients are accompanied with premature death, reduced cognitive function and quality of life, which not only brings great pain to patients but also strongly increases the medical burden [20]. Early diagnosis of CKD and delaying the development of CKD are of great help to improve the quality of life and reduce the mortality of CKD patients. In order to further analyse the relationship between eGFR, urinary protein and MS components in CKD patients, Pearson correlation analysis in this study showed that abdominal circumference, TG, SBP, DBP, FPG and HDL‐C were significantly correlated with eGFR and urinary protein. It is suggested that MS itself and its metabolic components are both involved in the occurrence and development of CKD in varying degrees. Early targeted intervention in MS is of great significance to prevent the occurrence of CKD and delay the process of CKD. Multivariate logistic regression analysis showed that diabetes, hyperlipidemia, UA and SBP were independent risk factors for CKD complicated with MS, and eGFR was an independent protective factor for CKD complicated with MS. In addition, the ROC curve analysis results of this study showed that the AUC of diabetes, hyperlipidemia, UA, SBP and eGFR in the joint prediction of CKD complicated MS was 0.919, and the joint prediction value was higher than that of a single index. Some scholars have found that [21] type 2 diabetes with MS and its metabolic factors are closely related to early chronic kidney disease. Waist circumference, body mass index, fasting blood glucose, glycosylated hemoglobin A, systolic blood pressure, triglycerides and the course of diabetes are independent risk factors for CKD in type 2 diabetes patients, which is similar to the conclusion of the present study. The pathological mechanism of renal injury caused by MS includes micro inflammation, endothelial dysfunction, internal environment imbalance and many other aspects. MS aggravates the occurrence and development of CKD, mainly in the impact on proteinuria and EGFR. In addition, dyslipidemia promotes the occurrence and development of glomerulosclerosis and aggravates renal function damage by damaging renal microvessels. As a complex metabolic disorder syndrome, each component of MS plays an important role in renal function damage [22], and renal damage in turn will affect MS. The effects of CKD patiens' treatment on body weight, blood glucose, blood lipid, blood pressure and other MS components need to be further studied.

5. CONCLUSION

To sum up, there exists a significant correlation between MS components with eGFR and urinary protein in CKD patients. Early intervention and adjustment of MS components are very important to delay the development of CKD and reduce proteinuria. The results of this study can provide a basis for the clinicopathological diagnosis of MS‐related renal damage, new ideas for the study of its pathophysiological mechanism and a theoretical basis for the early prevention and effective intervention of MS‐related renal injury.

5.1. Innovation

Based on the longitudinal data system, this experiment analysed the relationship between MS and CKD (proteinuria and renal insufficiency), and described the relationship between HbAlc and the prevalence of MS and CKD. In the introduction and discussion section, the relationship between MS and the risk of CKD in different countries and ethnic groups was further evaluated through systematic evaluation and analysis of prospective cohort studies at home and abroad. By comparing the renal pathological manifestations of MS and non‐MS population, the possible pathological characteristics of MS‐related renal damage were reported, and a combined detection model of the ROC curve was established for the first time. In addition, the exposure factor MS in this study was evaluated from multiple aspects so that it could be scored from multiple dimensions, and the total score continuity variable could be transformed into an ordered categorical variable, which could strengthen the interpretation of the relationship between the variables and better reflect the relationship between metabolic syndrome and CKD in this study. Moreover, this study used a trend test to convert continuous variables into categorical variables, which was more likely to produce positive results.

5.2. Limitations

However, this study was retrospective and no prospective follow‐up studies were conducted. In addition, the mechanism of action of MS in renal injury in patients with CKD should be analysed in depth. Due to the limited research time and small sample size, the present experimental results might be accidental. Also, the patients in this study were all from the same hospital and might not be generalised to people in other regions. Thus, in our following research, the sample size and the research time and range will be expanded for further exploration.

AUTHOR CONTRIBUTIONS

Guarantor of integrity of the entire study: Li Guo, Shan‐Shan Guo, You‐Lan Gong, Jing Li, Jian‐Dong Li. Li Guo: study concepts; study design; clinical studies; manuscript preparation; manuscript editing. Shan‐Shan Guo: literature research; manuscript editing. You‐Lan Gong: experimental studies; manuscript editing. Jing Li: data acquisition. Jian‐Dong Li: definition of intellectual content; data analysis; statistical analysis; manuscript review.

CONFLICT OF INTEREST STATEMENT

None.

CONSENT FOR PUBLICATION

Informed consent was obtained from all individual participants included in the study.

Guo, L. , et al.: Analysis of the association between glomerular filtration rate, proteinuria and metabolic syndrome in chronic kidney patients based on longitudinal data. IET Nanobiotechnol. 17(5), 476–484 (2023). 10.1049/nbt2.12146

DATA AVAILABILITY STATEMENT

All data generated or analysed during this study are included in this published article.