Association Between Endothelial Activation and Stress Index and Microalbuminuria

Abstract

Background

The aim of this study was to investigate the association between endothelial activation and stress index (EASIX), a composite marker of endothelial dysfunction, and microalbuminuria.

Methods

This cross-sectional study included adult patients from Departments of General Practice and Geriatrics at the First Affiliated Hospital of Fujian Medical University between January 2017 and December 2023. Microalbuminuria was defined as a urinary albumin-to-creatinine ratio (UACR) of 30–300 mg/g. EASIX was calculated as lactate dehydrogenase (U/L) × serum creatinine (mg/dL) / platelet count (10⁹/L). The association of EASIX with microalbuminuria was evaluated using logistic regression models, restricted cubic splines (RCS), and subgroup analyses.

Results

Among 3,126 patients, 403 (12.9%) had microalbuminuria and exhibited significantly higher EASIX levels compared with those with normal UACR [median (P25~P75): 0.63 (0.47–0.90) vs 0.62 (0.48–0.82) mg/g, P = 0.019]. Multivariate linear regression identified EASIX as an independent factor for lnUACR (β = 0.100, 95% CI: 0.115~0.233, P<0.001). Logistic regression further showed that EASIX was independently associated with microalbuminuria (OR = 1.420, 95% CI:1.167~1.728, P < 0.001). In the fully adjusted model, EASIX quartiles remained independently associated with microalbuminuria (OR = 1.395, 95% CI: 1.147~1.698, P < 0.001). RCS analysis revealed a U-shaped non-linear association (P for non-linear = 0.007), with an inflection point at EASIX = 0.625, above which the risk of microalbuminuria increased significantly. This association remained consistent across subgroups stratified by age, sex, body mass index (BMI), and hypertension or diabetes status in patients with EASIX > 0.625, with no significant interactions.

Conclusion

This study provides evidence that EASIX, a composite marker derived from routine blood tests, is independently associated with microalbuminuria. A threshold of 0.625 for the EASIX may warrant further investigation in longitudinal studies.

Introduction

Microalbuminuria serves as a sensitive marker of renal microvascular injury and an independent risk factor for cardiovascular events.¹ The population burden of microalbuminuria is substantial, with a detection rate of 8.98% in community-dwelling Chinese populations.² This rate increases significantly among high-risk individuals, reaching 28.8%–45.4% in those with diabetes or hypertension.^3,4 Approximately 20%–40% of patients with microalbuminuria experience a decline in estimated glomerular filtration rate (eGFR) within 5 to 10 years, and microalbuminuria is also associated with an approximately 2-fold increase in the risk of major cardiovascular events, establishing it as a key target for early intervention in patients with chronic kidney disease (CKD).⁵

Although the urinary albumin-to-creatinine ratio (UACR) is recommended by Kidney Disease: Improving Global Outcomes (KDIGO) guidelines as a core indicator for CKD risk stratification, its measurement is susceptible to transient factors such as exercise, infection, and urine concentration, exhibits considerable day-to-day variability.^6,7 This measurement has not been widely used in primary healthcare institutions. Moreover, microalbuminuria has been associated with subclinical cardiac dysfunction, including left ventricular hypertrophy and impaired diastolic function, in both landmark cohort studies and more recent prospective investigations.^8,9

Pathophysiologically, endothelial dysfunction serves as a common pathway connecting systemic microvascular injury to target organ damage.¹⁰ By increasing glomerular vascular permeability, endothelial dysfunction promotes albumin leakage and contributes to the development of glomerulosclerosis.^11,12 In hypertension, angiotensin II disrupts endothelial tight junctions by activating oxidative stress and chronic inflammation.¹³ Under hyperglycemic conditions, the accumulation of advanced glycation end-products exacerbates mitochondrial oxidative stress, directly leading to endothelial dysfunction.¹⁴ In addition, the accumulation of uremic toxins (e.g indoxyl sulfate, p-cresyl sulfate) during CKD progression directly impairs endothelial function by inducing oxidative stress and inflammation, which inhibits endothelial nitric oxide synthase activity and reduces nitric oxide bioavailability, thereby creating a vicious cycle of worsening renal function, toxin retention, endothelial dysfunction, and end-organ damage.¹⁵ Based on these theories, endothelial activation and stress index (EASIX) was constructed. EASIX, calculated as lactate dehydrogenase (LDH) × serum creatinine / platelet, is a composite index integrating endothelial injury, metabolic dysregulation, and microcirculatory disturbance.¹⁶ EASIX has been validated in clinical scenarios characterized by acute endothelial injury, including hematopoietic stem cell transplantation, Chimeric Antigen Receptor T-Cell Immunotherapy (CAR-T) therapy, and severe coronavirus disease 2019 (COVID-19) infection.^16,17 A close association of EASIX with renal microvascular endothelial dysfunction has been demonstrated within transplant-associated thrombotic microangiopathy.¹⁸ More recently, elevated EASIX has been independently associated with adverse cardiovascular outcomes in patients with chronic coronary syndrome and prediabetes, supporting its relevance to cardiovascular risk in chronic metabolic conditions.¹⁹ However, the relationship between EASIX and early renal microvascular injury remains unclear, and its dose–response relationship with microalbuminuria and the clinical warning threshold have yet to be elucidated.^20,21 This study therefore aimed to systematically investigate the association between EASIX and microalbuminuria and to explore the ability of EASIX to discriminate individuals with renal microvascular injury.

Methods

Statement of Ethics

This single-center cross-sectional study was conducted using data from the Fuzhou Study database: target organ damage and related risk factors in patients with hypertension (registered number: ChiCTR2000039448 [28/10/2020], URL: http://www.chictr.org.cn/index.aspx), a clinical study conducted in Fuzhou, China. The data from January 2017 to December 2023 were retrieved for analysis. This study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval no. [2020]306).

Subjects

Subjects aged ≥ 18 years were consecutively recruited from the Departments of General Practice and Geriatrics at the Chating Campus of the First Affiliated Hospital of Fujian Medical University. Inclusion criteria were as follows: (1) a primary diagnosis of essential hypertension or type 2 diabetes; (2) UACR testing performed during hospitalization or outpatient visits. A total of 3,532 patients were initially enrolled. According to predefined exclusion criteria, 406 patients were excluded for the following reasons: (1) acute infection, pregnancy, or active malignancy under treatment; (2) severe cardiac, hepatic, or renal dysfunction, including but not limited to left ventricular ejection fraction (LVEF) < 30%; alanine aminotransferase or aspartate aminotransferase > 3 × upper limit of normal (ULN); decompensated liver cirrhosis; eGFR < 15 mL/min/1.73 m²; or undergoing hemodialysis; (3) acute stroke within 6 months; (4) severe immune or hematological disorders, and patients with a history of severe immune or hematological disorders, including but not limited to severe combined immunodeficiency, hemophagocytic lymphohistiocytosis, acute leukemia, or lymphoma; (5) long-term use of medications affecting urinary protein excretion, including non-steroidal anti-inflammatory drugs, antineoplastic agents, and herbal medicines; (6) primary or secondary nephropathy other than diabetic or hypertensive nephropathy; (7) UACR ≥ 300 mg/g. Consequently, 3,126 patients were included in the final analysis (Figure 1).

Figure 1.

The flow chart of the study.

Abbreviations: UACR, urinary albumin to creatinine ratio; NSAIDs, nonsteroidal anti-inflammatory drugs; EASIX, endothelial activation and stress index; Q, quartile.

Demographic and Clinical Data

Demographic information, anthropometric measurements, and medical history including sex, age, smoking status, alcohol consumption, history of hypertension and diabetes, and medication use were collected. Height was measured in the morning with participants wearing light clothing without shoes, standing erect against a stadiometer, and recorded to the nearest 0.1 cm. Body weight was measured using a calibrated electronic scale after bladder emptying, and recorded to the nearest 0.1 kg. Body mass index (BMI) was calculated as body weight (kg) divided by height squared (m²). Office blood pressure was measured in accordance with the 2010 Chinese Guidelines for the Management of Hypertension.²² Participants refrained from smoking, alcohol, caffeine, and vigorous exercise for 30 minutes prior to measurement, emptied their bladder, and rested in a seated position for at least 5 minutes. Blood pressure was measured by trained physicians in a quiet, temperature-controlled room using an electronic sphygmomanometer (HBP-1300; Omron Healthcare, Kyoto, Japan). Two consecutive readings were obtained at 1-minute intervals and averaged; if the difference between the two readings exceeded 5 mmHg, a third measurement was taken, and the mean of all three readings was recorded.²²

Laboratory Measurement

Venous blood samples were collected in the early morning after an overnight fast of 6–8 hours. Complete blood count analysis was performed using a BC-6800Plus_4 automated hematology analyzer (Mindray, Shenzhen, China) with compatible reagents to determine hemoglobin and platelet. Fibrinogen was measured by optical coagulation using a CS5100 analyzer. Biochemical parameters including fasting plasma glucose, total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), serum creatinine, lactate dehydrogenase (LDH) were quantified on an automated analyzer (Roche Diagnostics, Mannheim, Germany). EASIX was calculated as: EASIX = [LDH (U/L) × serum creatinine (mg/dL)] / platelet (10⁹/L). Glycosylated hemoglobin A1c (HbA1c) was quantified by high-performance liquid chromatography using an automated analyzer (HLC-723G7, TOSOH, Japan). eGFR was calculated using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation: eGFR [mL/(min·1.73 m²)] = a × (serum creatinine/b)^c × 0.993^age, where a = 144 for females and 141 for males, b = 0.7 for females and 0.9 for males, and c was determined according to sex and serum creatinine level as follows: for females, c = −0.329 when serum creatinine ≤ 0.7 mg/dL and c = −1.209 when serum creatinine > 0.7 mg/dL; for males, c = −0.411 when serum creatinine ≤ 0.9 mg/dL and c = −1.209 when serum creatinine > 0.9 mg/dL.²³

Measurement of UACR

Patients were instructed to avoid vigorous exercise and a high-protein diet on the day prior to urine collection. A first morning midstream urine sample (3–5 mL) was collected. Urinary creatinine was measured by colorimetry assay (Boehringer Mannheim/Hitachi 717 analyzer, Indianapolis, US), and urinary albumin by immunoturbidimetry method (Roche P800 auto-matic analyzer, Switzerland). UACR was calculated as the ratio of urinary albumin (mg) to urinary creatinine (g). Microalbuminuria was defined as a UACR persistently within 30–300 mg/g.

Definitions

Smokers were defined as individuals who had smoked ≥ 100 cigarettes in their lifetime.²⁴ Alcohol consumers were defined as those who consumed alcohol (including liquor, beer, wine, or yellow rice wine) at least once per week for ≥ 1 year.²⁵ Hypertension was defined according to the 2017 Chinese National Primary Hypertension Prevention and Management Guidelines as office systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or a self-reported history of hypertension with current use of antihypertensive medications.²⁶ Diabetes was defined according to Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2013 Edition): the presence of typical diabetic symptoms (polydipsia, polyuria, polyphagia, and unexplained weight loss) accompanied by a random plasma glucose ≥ 11.1 mmol/L, fasting plasma glucose ≥ 7.0 mmol/L, or 2-hour plasma glucose ≥ 11.1 mmol/L during a 75 g oral glucose tolerance test (OGTT). In the absence of typical symptoms, two glycemic indicators meeting or exceeding the diagnostic thresholds at the same or different time points were required (excluding random glucose).²⁷

Statistical Analysis

Sample size was estimated a priori using G*Power 3.1.9.7 for a two-sided test of a single logistic regression coefficient, assuming an odds ratio of 1.5, α = 0.05, power = 0.90, and a baseline event rate of 12.0%. This yielded a minimum required sample of 749 patients (Figure S1). The final cohort of 3,126 patients (403 events) exceeded this requirement. The events-per-variable (EPV) ratio was 21.2 (403 events / 19 independent parameters), exceeding the conventional threshold of 10–15 and the more conservative threshold of 20.^28,29 Covariates were selected by combining established clinical relevance to microalbuminuria and endothelial dysfunction with P values from univariable analyses, rather than relying solely on univariable P values. Among lipid parameters, VLDL-C was selected as the representative variable; LDL-C was excluded due to collinearity with VLDL-C. To minimize overadjustment, the number of covariates was restricted relative to events (EPV = 21.2), and collinearity was assessed using the variance inflation factor (VIF > 5). Some covariates, e.g eGFR, HbA1c, VLDL-C, and fibrinogen, may lie on the causal pathway between EASIX and microalbuminuria; these were retained to provide a conservative estimate of the independent association.

Missing data were handled using multiple imputation by chained equations with 50 imputations in Stata, and pooled estimates were generated based on Rubin’s rules. Post-imputation results are reported. Statistical analyses were performed using SPSS 27.0 and R software (version 4.5.2). Continuous variables were presented as mean ± SD or median (P25~P75) depending on normality assessed by Kolmogorov–Smirnov test. Categorical variables were presented as counts (percentages). Comparisons between two groups were performed using the t-test, Mann–Whitney U-test, or chi-square test as appropriate. Multivariable stepwise linear regression with lnUACR as the dependent variable and multivariable logistic regression with microalbuminuria as the dependent variable were conducted. Restricted cubic spline (RCS) analysis with 4 knots at the 5th, 35th, 65th, and 95th percentiles of EASIX was used to examine the dose–response relationship with microalbuminuria and lnUACR, with the 25th percentile of EASIX as the reference. The inflection point was identified by the likelihood ratio test and piecewise regression, with its 95% CI estimated by bootstrap (1,000 resamples). Subgroup analyses stratified by age, sex, BMI, hypertension, and diabetes were performed, and interactions were tested. A two-sided P < 0.05 was considered statistically significant.

Results

Clinical Characteristics

A total of 3,126 participants were included in the final analysis, of whom 1,951 (62.4%) were male. The mean age of the study population was 59.58 ± 12.02 years. The prevalence of hypertension and diabetes was 76.9% (n = 2,403) and 30.0% (n = 938), respectively. Based on UACR, participants were categorized into two groups: normal UACR group (n = 2,723, 87.1%) and microalbuminuria group (n = 403, 12.9%).

Compared with participants in normal UACR group, those with microalbuminuria were significantly older, had higher BMI, systolic blood pressure, and diastolic blood pressure, and exhibited a higher prevalence and longer duration of both hypertension and diabetes (all P < 0.05). In addition, the use of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker/angiotensin receptor-neprilysin inhibitor (ACEI/ARB/ARNI), beta-blockers, and calcium channel blockers (CCB) was significantly more frequent in microalbuminuria group (all P < 0.05). No significant difference between two groups was observed in sex, smoking status, drinking status, or sodium-glucose cotransporter 2 inhibitor (SGLT2i) use (all P > 0.05).

With respect to laboratory parameters, the microalbuminuria group showed significantly lower levels of hemoglobin, eGFR, and HDL-C, along with significantly higher levels of platelet, LDH, serum creatinine, fasting plasma glucose, triglyceride, VLDL-C, HbA1c, fibrinogen, and EASIX (all P < 0.05). No significant difference was found in total cholesterol, LDL-C or statin use between the two groups (P > 0.05). Detailed comparisons are presented in Table 1.

Table 1.

Comparison of Clinical Characteristics and Blood Parameters in the Patients Between Normal UACR Group and Microalbuminuria Group (n=3,126)

Variables	Overall (n=3,126)	Normal UACR (n=2,723)	Microalbuminuria (n=403)	t/Z/χ2	P value
Age (years)	59.58 ±12.02	59.36 ±11.64	61.07 ±14.25	−2.297	0.022
Male (%)	1,951 (62.4)	1,717 (63.1)	234 (58.1)	3.727	0.054
Smoker (%)	723 (23.1)	618 (22.7)	98 (24.3)	0.523	0.470
Drinker (%)	489 (15.6)	439 (16.1)	50 (12.4)	3.671	0.055
Hypertension (%)	2,403 (76.9)	1,760 (64.6)	325 (80.6)	40.513	<0.001
Duration of hypertension (years)	3 (0,10)	2 (0, 10)	7 (1, 12)	−7.751	<0.001
Diabetes (%)	938 (30.0)	770 (28.3)	168 (41.7)	30.056	<0.001
Duration of diabetes (years)	0 (0, 1)	0 (0, 1)	0 (0, 6)	−6.058	<0.001
BMI (kg/m2)	24.67±3.18	24.62 ±3.13	24.99 ±3.51	−2.039	0.042
Systolic blood pressure (mmHg)	130.79 ±17.63	129.80 ±17.28	137.50 ±18.51	−7.860	<0.001
Diastolic blood pressure (mmHg)	79.19 ±12.08	78.98 ±11.87	80.64 ±13.35	−2.370	0.018
Hemoglobin (g/L)	140.58 ±15.11	140.96 ±14.69	138.03 ±17.51	3.196	0.001
Platelet (109/L)	227.87 ±57.81	227.03 ±56.63	233.55 ±64.99	−2.116	0.034
LDH (U/L)	181 (160, 205)	179 (160, 203)	193 (167, 215)	−5.323	<0.001
Serum creatinine (μmol/L)	69.67 ±18.66	69.06 ±15.77	73.73 ±31.67	−2.906	0.004
eGFR (mL/min/1.73m2)	96.95 ±18.22	97.70 ±17.51	91.86 ±21.80	5.138	<0.001
Fasting plasma glucose (mmol/L)	5.79 ±1.85	5.72 ±1.77	6.22 ±2.24	−4.283	<0.001
HbA1c (%)	6.23 ±1.45	6.16 ±1.37	6.70 ±1.83	−5.778	<0.001
Total cholesterol (mmol/L)	4.50 ±1.12	4.62 ±1.10	4.51 ±1.23	1.669	0.096
Triglyceride (mmol/L)	1.33 (0.95, 1.91)	1.31 (0.94, 1.88)	1.42 (1.01, 2.10)	−3.157	0.004
HDL-C (mmol/L)	1.23 ±0.37	1.24 ±0.37	1.18 ±0.37	2.749	0.006
LDL-C (mmol/L)	2.96 ±1.02	2.97 ±1.01	2.85 ±1.12	2.092	0.037
VLDL-C (mmol/L)	0.75 ±0.56	0.74 ±0.54	0.84 ±0.69	−2.825	0.005
Fibrinogen (g/L)	3.02 ±0.79	2.99 ±0.76	3.22 ±0.94	−4.876	<0.001
EASIX	0.62 (0.48, 0.83)	0.62 (0.48, 0.82)	0.63 (0.47, 0.90)	−2.337	0.019
ACEI/ARB/ARNI (%)	1094 (35.0)	913 (33.5)	181 (44.9)	19.998	<0.001
Beta-blocker (%)	550 (17.6)	453 (16.6)	97 (24.1)	13.379	<0.001
CCB (%)	987 (31.6)	811 (29.8)	176 (43.7)	31.345	<0.001
SGLT2i (%)	108 (3.5)	91 (3.3)	17 (4.2)	0.808	0.369
Statin	1,953 (62.5)	1,019 (37.4)	154 (38.2)	0.094	0.759

Abbreviations: BMI, body mass index; LDH, lactate dehydrogenase; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; EASIX, endothelial activation and stress index; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; CCB, calcium channel blockers; SGLT2i, sodium-dependent glucose transporter 2 inhibitor; UACR, urinary albumin to creatinine ratio.

Linear Regression Analysis of UACR

As shown in Table 2, stepwise linear regression was used to identify factors independently associated with lnUACR. After adjustment for all covariates, sex, age, alcohol consumption, hypertension duration, diabetes duration, CCB use, systolic blood pressure, hemoglobin, eGFR, fasting blood glucose, HbA1c, VLDL-C, fibrinogen, and EASIX (β = 0.100, 95% CI : 0.115~0.233, P < 0.001) were independently associated with lnUACR.

Table 2.

Univariate and Multivariate Linear Regression Analysis of lnUACR (n = 3,126)

	Univariate Analysis		Multivariate Analysis
Variables	β (95% CI)	P value	β (95% CI)	P value	VIF
Age (years)	0.135 (0.214~0.362)	<0.001	0.152 (0.231~0.414)	<0.001	1.789
Male (%)	0.064 (0.002~0.009)	<0.001	−0.058 (−0.009~-0.001)	0.009	1.875
Drinker (%)	−0.062 (−0.275~-0.076)	<0.001	−0.052 (−0.253~-0.041)	0.006	1.358
Smoker (%)	−0.045 (−0.196~-0.024)	<0.001	0.035 (−0.011~0.180)	0.083	1.488
Hypertension (%)	0.185 (0.329~0.480)	<0.001	0.045 (−0.002~0.199)	0.054	2.058
Duration of hypertension (years)	0.196 (0.022~0.032)	<0.001	0.081 (0.006~0.017)	<0.001	1.675
Diabetes (%)	0.144 (0.244~0.401)	<0.001	−0.009 (−0.134~0.095)	0.740	2.542
Duration of diabetes (years)	0.171 (0.026~0.039)	<0.001	0.115 (0.013~0.031)	<0.001	2.149
BMI (kg/m2)	0.063 (0.009~0.032)	<0.001	0.029 (−0.002~0.021)	0.113	1.218
Systolic blood pressure (mmHg)	0.240 (0.012~0.016)	<0.001	0.167 (0.007~0.012)	<0.001	1.998
Diastolic blood pressure (mmHg)	0.098 (0.005~0.011)	<0.001	0.012 (−0.003~0.005)	0.628	2.313
Hemoglobin (g/L)	−0.091 (−0.009~-0.004)	<0.001	−0.068 (−0.008~-0.002)	0.002	1.794
eGFR (mL/min/1.73m2)	−0.091 (−0.007~-0.003)	<0.001	−0.057 (−0.005~-0.001)	0.004	1.483
Fasting plasma glucose (mmol/L)	0.153 (0.066~0.104)	<0.001	0.056 (0.004~0.058)	0.022	1.490
HbA1c (%)	0.166 (0.094~0.143)	<0.001	0.074 (0.016~0.090)	0.005	2.292
Total cholesterol (mmol/L)	0.008 (−0.025~0.039)	0.672	/	/	/
Triglyceride (mmol/L)	0.092 (0.044~0.098)	<0.001	/	/	/
HDL-C (mmol/L)	−0.040 (−0.210~-0.013)	0.027	−0.007 (−0.128~0.089)	0.727	1.463
LDL-C (mmol/L)	−0.019 (−0.054~0.170)	0.300	/	/	/
VLDL-C (mmol/L)	0.097 (0.114~0.242)	<0.001	0.069 (0.060~0.192)	<0.001	1.268
Fibrinogen (g/L)	0.146 (0.145~0.236)	<0.001	0.110 (0.100~0.187)	<0.001	1.087
EASIX	0.086 (0.089~0.211)	<0.001	0.100 (0.115~0.233)	<0.001	1.127
ACEI/ARB/ARNI (%)	0.083 (0.103~0.254)	<0.001	−0.022 (−0.129~0.034)	0.257	1.399
Beta-blocker (%)	0.062 (0.072~0.262)	<0.001	0.005 (−0.078~0.106)	0.766	1.131
CCB (%)	0.147 (0.249~0.403)	<0.001	0.053 (0.036~0.200)	0.005	1.334
SGLT-2i (%)	0.027 (−0.044~0.351)	0.128	/	/	/
Statin (%)	−0.001 (−0.077~0.073)	0.957	/	/	/

Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; EASIX, endothelial activation and stress index; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; CCB, calcium channel blockers; SGLT2i, sodium-dependent glucose transporter 2 inhibitor.

Logistic Regression Analysis of Microalbuminuria

As shown in Table 3, forward stepwise multivariable logistic regression (entry P < 0.05, removal P > 0.10) identified sex, age, hypertension duration, diabetes duration, systolic blood pressure, eGFR, HbA1c, VLDL-C, fibrinogen, and EASIX (OR = 1.420, 95% CI:1.167~1.728, P < 0.001) as independent factors associated with microalbuminuria.

Table 3.

Univariate and Multiple Logistic Regression Analysis of Microalbuminuria (n = 3,126)

	Univariate Analysis		Multivariate Analysis
Variables	OR (95% CI)	P value	OR (95% CI)	P value	VIF
Age (years)	1.012 (1.003~1.021)	0.008	0.979 (0.967~0.991)	<0.001	1.810
Male	0.811 (0.656~1.003)	0.054	1.344 (1.025~1.762)	0.032	1.531
Drinker	0.737 (0.539~1.008)	0.155	/	/	/
Smoker	1.094 (0.857~1.398)	0.470	/	/	/
Hypertension	2.280 (1.759~2.955)	<0.001	1.093 (0.765~1.561)	0.626	2.051
Duration of hypertension (years)	1.445 (1.321~1.582)	<0.001	1.028 (1.012~1.045)	<0.001	1.656
Diabetes	1.813 (1.463~2.248)	<0.001	0.930 (0.648~1.334)	0.692	2.528
Duration of diabetes (years)	1.341 (1.231~1.460)	<0.001	1.032 (1.007~1.058)	0.013	2.145
BMI (kg/m2)	1.123 (1.013~1.245)	0.027	1.013 (0.977~1.051)	0.477	1.217
Systolic blood pressure (mmHg)	1.523 (1.375~1.687)	<0.001	1.019 (1.011~1.028)	<0.001	1.987
Diastolic blood pressure (mmHg)	1.147 (1.034~1.273)	0.010	0.999 (0.986~1.012)	0.904	2.300
Hemoglobin (g/L)	0.827 (0.746~0.916)	<0.001	0.992 (0.983~1.000)	0.058	1.584
eGFR (mL/min/1.73m2)	0.731 (0.660~0.810)	<0.001	0.987 (0.980~0.994)	<0.001	1.482
Fasting plasma glucose (mmol/L)	1.241 (1.139~1.353)	<0.001	1.022 (0.946~1.103)	0.583	2.259
HbA1c (%)	1.388 (1.230~1.455)	<0.001	1.142 (1.031~1.266)	0.011	2.531
Total cholesterol (mmol/L)	0.906 (0.815~1.008)	0.070	/	/	/
Triglyceride (mmol/L)	1.140 (1.045~1.244)	0.003	/	/	/
HDL-C (mmol/L)	0.855 (0.764~0.956)	0.006	1.038 (0.728~1.479)	0.837	1.392
LDL-C (mmol/L)	0.883 (0.794~0.983)	0.023	/	/	/
VLDL-C (mmol/L)	1.160 (1.063~1.266)	<0.001	1.245 (1.034~1.501)	0.021	1.243
Fibrinogen (g/L)	1.292 (1.118~1.415)	<0.001	1.268 (1.117~1.439)	<0.001	1.084
EASIX	1.280 (1.147~1.428)	<0.001	1.420 (1.167~1.728)	<0.001	1.118
ACEI/ARB/ARNI	1.616 (1.308~1.998)	<0.001	1.081 (0.838~1.394)	0.550	1.393
Beta-blocker	1.588 (1.238~2.039)	<0.001	1.141 (0.862~1.510)	0.355	1.129
CCB	1.828 (1.477~2.262)	<0.001	1.209 (0.937~1.559)	0.144	1.330
SGLT-2i	1.274 (0.751~2.161)	0.370	/	/	/
Statin	1.034 (0.834,1.283)	0.759	/	/	/

Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; EASIX, endothelial activation and stress index; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; CCB, calcium channel blockers; SGLT2i, sodium-dependent glucose transporter 2 inhibitor.

Clinical Characteristics Across EASIX Quartiles

Spearman rank correlation analysis showed a negative correlation between EASIX and lnUACR (r_s−0.058, P = 0.001). Participants were then divided into quartiles: quartile 1 (EASIX ≤ 0.479), quartile 2 (0.479 < EASIX ≤ 0.625), quartile 3 (0.625 < EASIX ≤ 0.835), and quartile 4 (EASIX > 0.835). As shown in Figure 2, median lnUACR values from quartile 1 to quartile 4 were 2.09, 1.92, 1.84, and 1.96, respectively, with corresponding microalbuminuria proportions of 13.7%, 12.0%, 10.0%, and 15.9% (χ² = 13.070, P = 0.004) (Figure 2).

Figure 2.

Composition ratio of microalbuminuria in the participants by EASIX quartiles.

Abbreviations: UACR, urinary albumin to creatinine ratio; EASIX, endothelial activation and stress index.

Based on the quartiles of EASIX, a significant increasing trend across quartile 1 to quartile 4 was observed in age, male proportion, LDH, serum creatinine, and serum uric acid (all P < 0.05), whereas platelet, eGFR, HDL-C, and fibrinogen showed significant decreasing trends (all P < 0.05). Significant difference among the patients in the quartiles of the EASIX was also observed in smoking status, alcohol use, hypertension prevalence and duration, BMI, systolic blood pressure, diastolic blood pressure, hemoglobin, total cholesterol, LDL-C, and use of ACEI/ARB/ARNI, beta-blockers, CCB and statin (all P < 0.05). However, no significant difference was found across quartiles in diabetes prevalence, diabetes duration, fasting blood glucose, HbA1c, triglyceride, VLDL-C, or SGLT2i use (all P > 0.05). Detailed comparisons are presented in Table 4.

Table 4.

Comparison of Clinical Characteristics According to Quartiles of the EASIX (n=3,126)

Variables	Quartile 1 (EASIX≤0.479) (n=782)	Quartile 2 (0.479≤ EASIX <0.625) (n=781)	Quartile 3 (0.625≤ EASIX <0.835) (n=782)	Quartile 4 (EASIX≥0.835) (n=781)	χ2/F/Z	P value
Age (years)	56.77 ±11.62	57.83 ±11.31	60.21 ±11.44	63.53 ±12.53	51.002	<0.001
Males (%)	307 (39.3)	451 (57.7)	557 (71.2)	636 (81.4)	147.533	<0.001
Smoker (%)	144 (18.4)	178 (22.8)	197 (25.2)	197 (25.2)	13.626	0.003
Drinker (%)	103 (13.2)	107 (13.7)	158 (20.2)	121 (15.5)	18.192	<0.001
Hypertension (%)	492 (62.9)	480 (61.5)	559 (71.5)	554 (70.9)	29.05	<0.001
Duration of hypertension (years)	2 (0, 10)	1 (0, 10)	4 (0, 10)	4 (0, 10)	38.767	<0.001
Diabetes (%)	242 (30.9)	210 (26.9)	241 (30.8)	245 (31.4)	4.879	0.181
Duration of diabetes (years)	0 (0, 2)	0 (0, 1)	0 (0, 1)	0 (0, 2)	5.081	0.023
BMI (kg/m2)	24.25 ±3.14	24.53 ±3.25	24.84 ±3.07	25.05 ±3.21	9.422	<0.001
Systolic blood pressure (mmHg)	129.66 ±17.37	129.98 ±17.32	131.19 ±17.60	132.33 ±18.13	3.754	0.023
Diastolic blood pressure (mmHg)	78.75 ±11.55	79.52 ±11.98	80.04 ±11.85	78.44 ±12.85	2.841	0.037
Hemoglobin (g/L)	136.27 ±13.84	140.54 ±14.37	143.22 ±14.87	142.3 ±16.334	33.455	<0.001
Platelet (109/L)	276.68 ±56.04	238.06 ±43.38	213.76 ±41.32	182.92 ±43.99	566.111	<0.001
LDH (U/L)	163 (144, 183)	176 (158, 197)	185 (166, 208)	201 (180, 228)	552.899	<0.001
Serum creatinine (μmol/L)	56.95 ±12.42	65.37 ±11.53	72.18 ±12.46	84.18 ±23,53	415.384	<0.001
eGFR (mL/min/1.73m2)	105.49 ±15.02	100.74 ±15.32	95.67 ±15.80	85.88 ±20.20	197.060	<0.001
Fasting plasma glucose (mmol/L)	5.92 ±2.09	5.81 ±1.95	5.69 ±1.49	5.72 ±1.80	2.530	0.055
HbA1c (%)	6.33 ±1.55	6.18 ±1.35	6.15 ±1.37	6.25 ±1.50	2.489	0.059
Cholesterol (mmol/L)	4.77 ±1.13	4.63 ±1.06	4.58 ±1.11	4.43 ±1.16	12.601	<0.001
Triglyceride (mmol/L)	1.39 (0.96, 2.03)	1.36 (0.97, 1.91)	1.32 (0.95, 1.87)	1.27 (0.92, 1.85)	7.491	0.058
HDL-C (mmol/L)	1.25 ±0.36	1.24 ±0.37	1.23 ±0.37	1.19 ±0.37	4.344	0.005
LDL-C (mmol/L)	3.14 ±1.05	2.96 ±0.97	2.93 ±1.00	2.81 ±1.04	13.837	<0.001
VLDL-C (mmol/L)	0.77 ±0.57	0.77 ±0.60	0.75 ±0.55	0.72 ±0.53	1.201	0.308
Fibrinogen (g/L)	3.10 ±0.85	3.03 ±0.78	2.97 ±0.75	2.96 ±0.77	5.636	<0.001
UACR (mg/g)	7.94 (4.89, 15.40)	6.85 (4.27, 13.73)	6.32 (3.97, 12.65)	7.12 (3.87, 16.43)	21.255	<0.001
lnUACR	2.09 (1.60, 2.83)	1.92 (1.45, 2.62)	1.84 (1.38, 2.54)	1.96 (1.35, 2.80)	21.256	<0.001
ACEI/ARB/ARNI (%)	241 (30.8)	240 (30.7)	322 (41.2)	291 (37.3)	27.129	<0.001
Beta-blocker (%)	109 (13.9)	118 (15.1)	166 (21.2)	157 (20.1)	21.038	<0.001
CCB (%)	232 (29.7)	220 (28.2)	271 (34.7)	264 (33.8)	10.734	0.013
SGLT-2i (%)	28 (3.6)	16 (2.0)	32 (4.1)	32 (4.1)	6.583	0.086
Statin (%)	258 (33.0)	275 (35.2)	318 (40.7)	322 (41.2)	16.491	<0.001

Abbreviations: BMI, body mass index; LDH, lactate dehydrogenase; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; EASIX, endothelial activation and stress index; UACR, urinary albumin to creatinine ratio; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; CCB, calcium channel blockers; SGLT2i, sodium-dependent glucose transporter 2 inhibitor.

Logistic Regression Analysis of Microalbuminuria Across EASIX Quartiles

Logistic regression models were constructed with microalbuminuria as the dependent variable. Three models were built. Model 1 was unadjusted. Model 2 adjusted for demographic characteristics and medical history including sex, age, drinking status, smoking status, history of hypertension, duration of hypertension, history of diabetes, duration of diabetes, BMI, systolic blood pressure, diastolic blood pressure, antihypertensive drug. Model 3 further adjusted for fasting plasma glucose, LDL-C, eGFR, HbA1c, and fibrinogen (Table 5). As a continuous variable, the quartile of EASIX was significantly associated with microalbuminuria in all models (Model 1: OR = 1.522, 95% CI: 1.264~1.833, P < 0.001; Model 2: OR = 1.563, 95% CI: 1.278~1.911, P < 0.001; Model 3: OR = 1.395, 95% CI: 1.147~1.698, P < 0.001).

Table 5.

Logistic Regression Analysis of the Quartile of EASIX with Microalbuminuria (n=3,126)

	β	SE	Wald	OR	95% CI	P value
	Model 1
EASIX (per quartile)	0.420	0.095	19.575	1.522	1.264~1.833	<0.001
Quartile 1(EASIX<0.479)	Reference
Quartile 2 (0.479≤EASIX <0.625)	−0.147	0.151	0.945	0.863	0.642~1.161	0.331
Quartile 3 (0.625≤EASIX <0.835)	−0.370	0.159	5.416	0.691	0.506~0.943	0.020
Quartile 4 (EASIX≥ 0.835)	0.173	0.143	1.466	1.189	0.898~1.573	0.226
	Model 2
EASIX (per quartile)	0.447	0.103	18.951	1.563	1.278~1.911	<0.001
Quartile 1 (EASIX<0.479)	Reference
Quartile 2 (0.479≤EASIX <0.625)	−0.970	0.158	0.376	0.908	0.666~1.237	0.540
Quartile 3 (0.625≤EASIX <0.835)	−0.363	0.170	4.576	0.695	0.499~0.970	0.032
Quartile 4 (EASIX≥ 0.835)	0.156	0.164	0.914	1.169	0.849~1.611	0.339
	Model 3
EASIX (per quartile)	0.333	0.100	11.084	1.395	1.147~1.698	<0.001
Quartile 1 (EASIX<0.479)	Reference
Quartile 2 (0.479≤EASIX <0.625)	−0.169	0.161	1.108	0.844	0.616~1.157	0.293
Quartile 3 (0.625≤EASIX <0.835)	−0.431	0.173	6.193	0.650	0.463~0.912	0.013
Quartile 4 (EASIX≥ 0.835)	−0.091	0.179	0.258	0.913	0.644~1.296	0.611

Notes: Model 1: unadjusted model; Model 2: adjusted for sex, age, drinking status, smoking status, history of hypertension, duration of hypertension, history of diabetes, duration of diabetes, BMI, systolic blood pressure, diastolic blood pressure, antihypertensive drug; Model 3: further adjusted for fasting plasma glucose, LDL-C, eGFR, HbA1c, and fibrinogen based on Model 2.

Abbreviation: EASIX, endothelial activation and stress index.

As the quartile of EASIX with quartile 1 as reference, quartile 3 was associated with lower microalbuminuria risk in all models (Model 1: OR = 0.691, 95% CI:0.506~0.943, P = 0.020; Model 2: OR = 0.695, 95% CI:0.499~0.970, P = 0.032; Model 3: OR = 0.650, 95% CI:0.463~0.912, P = 0.013). No significant associations were observed for quartile 2 or quartile 4 (P > 0.05). These data suggest a non-linear relationship between EASIX and microalbuminuria.

RCS Analysis for the Relationship Between EASIX and Microalbuminuria

RCS analysis revealed a non-linear dose-response relationship between EASIX and microalbuminuria after adjusting for all covariates (P for overall < 0.001, P for non-linear = 0.007), with OR > 1 when EASIX > 0.625, forming a U-shaped pattern (Figure 3A). Similarly, a non-linear association was observed between EASIX and lnUACR (P for overall < 0.001, P for non-linear < 0.001), with β > 0 when EASIX > 0.625, also showing a U-shaped pattern (Figure 3B).

Figure 3.

Restricted cubic spline plot of EASIX and microalbuminuria or UACR subgroups after adjustment for confounders. (A) EASIX and microalbuminuria; (B) EASIX and lnUACR.

Notes: The red solid line represents the OR value or β value, and the shaded area represents the 95% CI of OR value or β value. The horizontal black dotted line indicates an OR value or β value of 1. Adjusted confounders included sex, age, drinking status, smoking status, history of hypertension, duration of hypertension, history of diabetes, duration of diabetes, BMI, systolic blood pressure, diastolic blood pressure, antihypertensive drug, fasting plasma glucose, LDL-C, eGFR, HbA1c, and fibrinogen. n = 3,126.

Abbreviation: EASIX, endothelial activation and stress index.

Stratified Analysis for the Association of EASIX with Microalbuminuria

Participants were divided at the RCS inflection point (EASIX = 0.625) into two groups: ≤0.625 (n = 1,567) and >0.625 (n = 1,559). Subgroup analyses stratified by age, sex, BMI, hypertension, and diabetes were performed with full covariate adjustment. In the EASIX ≤ 0.625 group, no association of EASIX with microalbuminuria was observed (OR = 0.450, 95% CI: 0.100~1.970, P = 0.290) (Figure 4). In the EASIX > 0.625 group, EASIX was positively associated with microalbuminuria (OR = 1.460, 95% CI: 1.170~1.820, P < 0.001) (Figure 5). No significant interactions were found (all P > 0.05), indicating consistent associations across subgroups.

Figure 4.

Stratified analysis of EASIX (EASIX≤0.625) with microalbuminuria after adjustment for confounding factors.

Notes: Adjusted confounders included sex, age, drinking status, smoking status, history of hypertension, duration of hypertension, history of diabetes, duration of diabetes, BMI, systolic blood pressure, diastolic blood pressure, antihypertensive drug, fasting plasma glucose, LDL-C, eGFR, HbA1c, and fibrinogen. n =1,567.

Abbreviations: BMI, body mass index; OR, odds ratio; CI, confidence interval.

Figure 5.

Stratified analysis of EASIX (EASIX>0.625) with microalbuminuria after adjustment for confounding factors.

Notes: Adjusted confounders included sex, age, drinking status, smoking status, history of hypertension, duration of hypertension, history of diabetes, duration of diabetes, BMI, systolic blood pressure, diastolic blood pressure, antihypertensive drug, fasting glucose, LDL-C, eGFR, HbA1c, and fibrinogen. n = 1,559.

Abbreviations: BMI, body mass index; OR, odds ratio; CI, confidence interval.

Discussion

In this cross-sectional study of 3,126 patients with chronic metabolic diseases, predominantly hypertension and diabetes, we systematically investigated the relationship between EASIX and microalbuminuria in a Chinese population. Our data confirmed EASIX, as an independent composite marker of endothelial stress, was associated with early renal microvascular injury, revealed a U-shaped non-linear dose-response relationship with a defined inflection point (EASIX = 0.625), and demonstrated stability across key clinical subgroups. These findings suggest a potential role for EASIX to discriminate individuals with renal microvascular injury.

The data of this study were derived from the Fuzhou Study. In previous analyses based on this database, we identified several risk factors associated with target organ damage in patients with hypertension.^30,31 In the present study, we instead examined EASIX and its association with microalbuminuria, an early indicator of renal microvascular injury. EASIX integrates three dimensions: LDH, as an indicator of tissue injury, serum creatinine reflecting organ function/metabolic burden, and platelet counts representing inflammation/coagulation. Its association with microalbuminuria can be explained by endothelial dysfunction as a common pathophysiological hub.³² Elevated LDH indicates cellular hypoxia, a key driver of CKD progression that directly damages endothelial cells and increases vascular permeability³³. Serum creatinine reflects not only GFR, but also metabolic stress from uremic toxins, which induce endothelial dysfunction.³⁴ Lower platelet indicates inflammation-driven consumption, and the chronic microinflammatory state in CKD impairs glomerular microcirculation.³⁵ Our finding that fibrinogen was an independent risk factor for microalbuminuria further supports this pathway.³⁶ Compared with specific endothelial markers, eg, von Willebrand factor, EASIX is non-specific but offers unique advantages, it is derived entirely from routine blood tests without additional costs, making it suitable for large-scale primary screening. In a previous study, EASIX was reported to be an independent predictor of overall survival among patients with steroid-refractory graft-versus-host disease.¹⁶ In another cohort investigation, age-adjusted EASIX was significantly associated with the mortality of intensive care unit and demonstrated better discriminatory ability than sequential organ failure assessment (SOFA) score for predicting the mortality of intensive care unit.¹⁷ Furthermore, EASIX serves as an independent and clinically meaningful predictor of major adverse cardiovascular events in patients with chronic coronary syndrome and prediabetes.¹⁹ Our study additionally observed a correlation between EASIX and microalbuminuria, further illustrating the association of EASIX with endothelial-related adverse events.

We identified a U-shaped relationship between EASIX and microalbuminuria risk with an inflection point at 0.625. This finding is consistent with previous studies that reported non-linear associations between EASIX and adverse outcomes in other clinical settings, such as critically ill patients with atrial fibrillation³⁷ and patients with T-cell lymphoma.³⁸ However, these studies focused on acute or hematologic conditions and did not identify a specific threshold. In contrast, our study extends these findings to a chronic metabolic disease population. To our knowledge, this is the first study to quantify a specific inflection point of EASIX for microalbuminuria risk. The U-shaped curve suggests that when EASIX is below 0.625, endothelial function remains compensatory. Once EASIX rises above this cutoff, endothelial self-repair is exhausted, which was associated with barrier dysfunction.³⁹

In the patients with EASIX > 0.625, the positive association with microalbuminuria remained stable across subgroups of age, sex, BMI, hypertension, and diabetes. This consistency strongly supports the generalizability of our findings. The stability in both hypertensive and diabetic subgroups, despite their distinct endothelial injury mechanisms rooted in mechanical stress versus metabolic dysregulation, suggests that EASIX reflects a common downstream “endothelial stress state”. The stable association in the elderly (≥65 years) further indicates that EASIX captures “endothelial vulnerability” from aging and comorbidity.⁴⁰ EASIX > 0.625 may represent a subgroup with a higher burden of endothelial stress. In patients with hypertension or diabetes, an EASIX exceeding this threshold may warrant increased vigilance and monitoring. However, given the cross-sectional design, these findings should not be interpreted as direct clinical guidance. EASIX is derived from routine blood tests, which may facilitate its use in primary care settings; however, its screening performance warrants evaluation in future prospective studies. It should therefore be positioned as an adjunctive tool, not a replacement for UACR or eGFR. In addition, dynamic EASIX monitoring may serve as a convenient indicator for assessing endothelial-targeted interventions. Future prospective studies are needed to evaluate whether baseline EASIX predicts long-term outcomes such as rapid eGFR decline, cardiovascular events, or end-stage renal disease.

There are some strengths in our study. Firstly, this study introduces EASIX from acute endothelial injury fields into early CKD screening in chronic diseases. Secondly, we quantified a U-shaped relationship of EASIX with microalbuminuria with a specific inflection point of EASIX, providing a clinically actionable threshold. Thirdly, rigorous subgroup analyses demonstrated robustness across diverse populations. However, several limitations must be acknowledged. Firstly, the cross-sectional design precludes causal inference, and prospective validation is needed. Secondly, as a single-center study in southern China, the generalizability may be limited. Thirdly, LDH, serum creatinine, and platelet are non-specific and susceptible to non-renal factors. Fourthly, microalbuminuria was based on single UACR measurement without confirmation. Fifthly, although EASIX was positioned as a potential screening tool, we did not directly compare its performance with established screening tools. Future studies should evaluate the incremental value of EASIX over existing screening strategies in prospective cohorts.

Conclusion

This study provides evidence that EASIX, a composite marker derived from routine blood tests, is independently associated with microalbuminuria in patients with chronic metabolic diseases. A U-shaped non-linear dose-response relationship was observed between EASIX and microalbuminuria, with an inflection point at 0.625 above which microalbuminuria risk increased significantly. This association was consistent across the subgroups of age, sex, BMI, hypertension, and diabetes. Given the cross-sectional design, these findings should be interpreted as hypothesis-generating. Future longitudinal studies are needed to evaluate whether EASIX can predict the development or progression of early renal microvascular injury.

Funding Statement

This study was supported by Joint Funds for the Innovation of Science and Technology, Fujian Province (2025Y9174).

Abbreviations

EASIX, endothelial activation and stress index; UACR, urinary albumin-to-creatinine ratio; RCS, restricted cubic splines; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; VLDL-C,very low-density lipoprotein cholesterol; BMI, body mass index; CKD, chronic kidney disease; KDIGO, Kidney Disease: Improving Global Outcomes; LDH, lactate dehydrogenase; CAR-T, Chimeric Antigen Receptor T-Cell Immunotherapy; COVID-19, coronavirus disease 2019; NSAIDs, nonsteroidal anti-inflammatory drugs; Q, quartile; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; OGTT, oral glucose tolerance test; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; CCB, calcium channel blockers; SGLT2i, sodium-glucose cotransporter 2 inhibitor.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author (Tingjun Wang) on reasonable request.

Ethics Approval

The study protocol adhered to the Helsinki declaration and all participants signed the informed consent. The protocol received approval from the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval No. [2020]306).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas: taking part in drafting, revising or critically reviewing the article; giving final approval of the version to be published. All authors have agreed on the journal to which the article has been submitted and agreed to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.