ORIGINAL
The effect of arteriosclerosis on new-onset renal damage in diabetic patients
2023 Volume 70 Issue 2 Pages 173-183
Details
2023 Volume 70 Issue 2 Pages 173-183
The aim of this study was to investigate the effect of arteriosclerosis on new-onset renal damage in a Chinese community population with diabetes. Patients with diabetes who had attended at least one physical examination after the Brachial-ankle pulse wave velocity (BaPWV) test from 2010 to 2018 were selected as subjects. A total of 4,462 patients were included in the study cohort. BaPWV levels <1,400 cm/s, 1,400–1,799 cm/s, and ≥1,800 cm/s were applied to divide the subjects into a normal arterial stiffness group, borderline atherosclerosis group and atherosclerosis group. Renal damage was defined by isolated proteinuria, isolated eGFR <60 mL/min/1.73 m2, proteinuria and eGFR <60 mL/min/1.73 m2. A Cox proportional risk model was used to analyze the effect of different groups on new-onset renal damage. After a median follow-up of 2.85 (1.88–4.90) years, Cox proportional risk models showed that after adjusting for risk factors, compared with the normal group, the HR and 95% CI of the risk of new-onset renal damage were 1.29 (95% CI: 0.95–1.76) and 1.59 (95% CI: 1.14–2.22) in the borderline atherosclerosis group and the atherosclerosis group, respectively. Atherosclerosis is a risk factor for new-onset renal damage, especially new-onset proteinuria, in diabetic patients.
WITH RAPID SOCIOECONOMIC DEVELOPMENT and rapid changes in people’s lifestyles, the prevalence of diabetes is increasing annually. A study [1] showed that 463 million people worldwide had diabetes as of 2019, and this number is predicted to reach 700 million by 2045. Diabetes mellitus is a chronic metabolic disease that can cause multi-tissue and organ complications, such as ocular, renal, neurological, and cardiovascular complications. Research [2] has reported that approximately 20 to 40% of diabetic patients have chronic kidney disease (CKD). CKD is characterized by persistent albuminuria and/or a progressive decrease in the estimated glomerular filtration rate (eGFR), two manifestations of renal damage [3]. It is a major cause of end-stage renal disease (ESRD) and is associated with a higher risk of cardiovascular events and death [4, 5]. Therefore, early identification of risk factors associated with renal damage in diabetic patients and implementation of effective preventive measures are essential.
A cross-sectional study [6] confirmed that atherosclerosis is associated with renal damage in diabetic patients. However, the effect of atherosclerosis on the decrease in the new-onset glomerular filtration rate and proteinuria has been inconsistent. A study [7] has shown that atherosclerosis may contribute to the occurrence of proteinuria and decreased eGFR in diabetic patients. However, studies [8, 9] have shown that arteriosclerosis is only associated with new-onset proteinuria and not significantly associated with eGFR decline. The past studies were hospital based and enrolled a relatively small number of patients. Therefore, we conducted a large study in China to investigate the association between atherosclerosis and renal damage in community diabetic patients.
Brachial-ankle pulse wave velocity (BaPWV) is an index of arterial stiffness is a quick, inexpensive, noninvasive, and reproducible method. Although aortic (carotid-femoral) PWV is currently the gold standard for measuring arterial stiffness [10], BaPWV correlates well with central aortic PWV [11] (correlation coefficient = 0.73) [12] and has been widely accepted for measuring arterial stiffness in clinical practice [13]. To determine whether atherosclerosis is associated with new-onset renal damage in diabetic patients, we investigated the effect of atherosclerosis on new-onset renal damage in diabetic patients in a Chinese community based on the population of the Kailuan study (trial registration number ChiCTR-TNC-11001489), using BaPWV as an index.
The Kailuan Study is a large ongoing prospective cohort study based on the investigation and intervention of risk factors for cardiovascular diseases in a functional community population. Participants consisted of current and retired workers in the service of the Kailuan Coal Mine Group Corporation who resided in the Kailuan community. From 2006–2007, they received questionnaires and the first survey in Kailuan General Hospital and 10 affiliated hospitals. The following information was collected every two years: fasting blood glucose, blood creatinine and proteinuria. Since the third survey in 2010, participants who consented to join nested studies on vascular health have undergone the BaPWV test to assess the health status of the arterial wall.
All the participants were from the Kailuan Study. Patients with diabetes underwent physical examination during 2010–2018 and completed BaPWV detection during the same period. The concurrent physical examination after BaPWV detection was taken as the starting point of follow-up, and the new onset of eGFR <60 mL/min/1.73 m2 and/or proteinuria in diabetic patients was used as the end event. If no endpoints occurred in the study, the end time of follow-up was the time of the last health examination attended by the patients. The inclusion criteria were as follows: (1) diabetes patients who participated in physical examination and completed the corresponding BaPWV test; (2) those who attended at least one physical examination after completing the BaPWV test; and (3) those who agreed to participate in the study and gave their written informed consent. The exclusion criteria were as follows: (1) those with BaPWV outliers and (2) those diagnosed with proteinuria or eGFR <60 mL/min/1.73 m2 at the same physical examination as the BaPWV test. This study followed the Declaration of Helsinki and was approved by the Ethics Committee of Kailuan General Hospital.
Relevant definitions and diagnostic criteriaDiabetes was defined as either fasting blood glucose (FBG) ≥7.0 mmol/L, self-report of a physician diagnosis, or self-reported use of anti-diabetic medication [14]. Renal impairment includes the following three types: isolated proteinuria, reduced eGFR, proteinuria and reduced eGFR [15, 16]. Reduced eGFR was defifined as <60 mL/min/1.73 m2, as in stage 3 of the CKD classification system, representing a loss of at least half the normal glomerular kidney function [15]. Proteinuria positivity was defined as trace or more with dipstick analysis either at baseline or at the follow-up visit. A number from 1 to 5 was used to describe each urine protein level from “none, ” “trace,” “1+,” “2+,” and “≥3+” to reflect the severity of proteinuria. Change in eGFR was defined as the difference between the eGFR at the last physical examination during the follow-up period minus the baseline eGFR. Previous studies showed the risk stratification point for BaPWV [17, 18]. We categorized the participants into three groups according to their BaPWV levels: normal arterial stiffness (<1,400 cm/s); borderline arterial stiffness (1,400–1,800 cm/s); and elevated arterial stiffness (≥1,800 cm/s) [19]. Current smoking was defined as smoking at least one cigarette/day on average in a recent year. Alcohol consumption status was defined according to the average alcohol consumption in the past year, as detailed elsewhere [20]. Physical exercise was defined as exercising ≥3 times per week for ≥30 minutes each time.
Questionnaires and anthropometryData on birth date, sex, smoking status, alcohol use, and past medical history (diabetes, duration of diabetes and antihypertensive drugs, etc.) were collected using a self-report questionnaire. Measurements of height, body mass and blood pressure were performed according to the published literature of our group [21].
Laboratory testsFor all the participants, blood samples were collected in the morning from the median cubital vein after fasting overnight and were reserved in vacuum tubes containing ethylenediaminetetraacetic acid. The levels of FBG, serum creatinine (Scr), total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), and high-sensitivity C-reactive protein (hs-CRP) were detected by an autoanalyzer (Hitachi 747, Hitachi, Tokyo, Japan). Scr was determined enzymatically (interassay coefficient of variation <10%; Mind Bioengineering Co. Ltd, Shanghai, China). Operations were carried out strictly according to the reagent instructions by professional examiners. The methods for the determination of the remaining biochemical parameters have been described previously [22].
The eGFR was calculated by using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [23]:
eGFR (mL/min/1.73 m2) = 144 × (Scr/0.7)–0.329 × (0.993)Age (if female Scr ≤62 μmol/L)
eGFR (mL/min/1.73 m2) = 144 × (Scr/0.7)–1.209 × (0.993)Age (if female Scr >62 μmol/L)
eGFR (mL/min/1.73 m2) = 141 × (Scr/0.9)–0.411 × (0.993)Age (if male Scr ≤80 μmol/L)
eGFR (mL/min/1.73 m2) = 141 × (Scr/0.9)–1.209 × (0.993)Age (if male Scr >80 μmol/L).
A midstream morning urine sample was collected from each participant at the baseline examination (2006–2007) and at subsequent follow-ups, that is, first (2008–2009), second (2010–2011), third (2012–2013), fourth (2014–2015), fifth (2016–2017), and sixth (2018–2019). Proteinuria was detected by urine dipstick. A freshly obtained urine sample was first visually examined for 1 minute by a trained physician (H12-MA and DIRUI N-600). Using a color scale, the results of the urine strip test were semi quantified as five degrees (none, trace, 1+, 2+, and 3+). Women were assessed during the nonmenstrual period.
BaPWV was measured using a BP-203 RPEIII networked arterial stiffness detection device (Omron Health Medical Co., Ltd., China) by nurses trained with a standard protocol provided by the manufacturer. After not smoking and being seated for at least 5 minutes in a room with the temperature controlled between 22 and 25°C, participants in thin clothes were asked to lie down on the examination couch in a supine position and remain quiet during the measurement. The upper arms and ankles were wrapped in blood pressure (BP) cuffs. The lower edges of the arm cuffs were positioned 2–3 cm above the transverse striation of the cubital fossa, while the lower edges of the ankle cuffs were positioned 1–2 cm above the superior aspect of the medial malleolus. Electrodes of the electrocardiogram were placed on both wrists, and a microphone to detect heart sounds was placed on the left edge of the sternum. Measurement was repeated twice for each observation object, and the result of the second measurement was used as the final result. The maximum BaPWV on the left and right sides was used for analysis.
Statistical analysesWe used SAS (Version 94; SAS Institute, Cary, NC) for statistical analysis. For baseline descriptions, the mean ± standard deviation (SD) is used for normally distributed variables, and the median with interquartile range (25%, 75%) is used for variables with a skewed distribution. Number and percentage (%) were used to describe categorical variables. Normally distributed variables were compared using Student’s t test or one-way ANOVA, while skewed variables were compared using the Kruskal-Wallis test. Categorical variables were compared using the chi-square test. The cumulative incidence of endpoints was calculated using the Kaplan–Meier approach, and the difference in cumulative incidence between the two groups was compared with a log-rank test. The incidence density of new-onset renal damage was calculated by dividing the number of endpoints by the total person-years of follow-up (1,000 person-years). Classic Cox proportional hazards analysis was performed as a secondary analysis. In addition, we verified whether the Cox proportional hazards model with Schoenfeld residuals [24] satisfied the proportional hazard (PH) assumption before establishing the models. Renal damage was assessed as a composite endpoint (new-onset proteinuria and/or eGFR <60 mL/min/1.73 m2). Model 1 was adjusted for age and sex; model 2 was further adjusted for smoking status, alcohol consumption status, salt status, physical activity, and educational level; and model 3 was further adjusted for mean arterial blood pressure (MAP), FBG, TC, body mass index (BMI), UA, hs-CRP, eGFR, the duration of diabetes, use of antihypertensive drugs, use of hypoglycemic drugs, and use of hypolipemic drugs. Additionally, we performed generalized linear model (GLM) analysis for the association between BaPWV and changes in eGFR. We also performed subgroup analyses by baseline age (<50, 50–60, 60–70, and ≥70 years), sex, BMI (<28 and ≥28 kg/m2), eGFR (<90 and ≥90 mL/min/1.73 m2), hypertension or not, use of antihypertensive drugs or not, use of hypoglycemic drugs or not, and use of hypolipemic drugs. Sensitivity analyses were performed to verify the robustness of the study findings. We excluded participants with a follow-up time less than 2 years, a history of cancer, CVD (including fatal and nonfatal myocardial infarction, cerebral infarction, and cerebral hemorrhage), or hypertension. p < 0.05 was regarded as significant for 2-sided tests.
From 2010 to 2018, of the 9,744 participants who participated in health examination and completed BaPWV in the same period enrolled in the cohort, 4,462 were included in the analysis, excluding 15 participants with BaPWV outliers, 2,439 patients with renal damage at enrollment and 2,828 participants who did not attend health examination after BaPWV testing (including 217 cases who died and 836 cases who attended in 2018) (Fig. 1). The number of dropouts in each observation year is recorded in Supplementary Table 1. The mean BaPWV level was 1,282.72 ± 88.02 cm/s in 835 cases in the normal arterial stiffness group, 1,596.11 ± 113.76 cm/s in 2,080 cases in the borderline atherosclerosis group, and 2,122.61 ± 307.57 cm/s in 1,547 cases in the atherosclerosis group. Participants with higher BaPWV were older and tended to have higher systolic blood pressure (SBP), diastolic blood pressure(DBP), MAP, FBG, and duration of diabetes and had a higher prevalence of prior use of hypoglycemic drugs, antihypertensive drugs and lipid-lowering drugs. However, they were more likely to have lower eGFR, follow-up time and proportions of higher education level. The comparison between groups showed significant differences (p < 0.05; Table 1).
Flow chart with inclusion and exclusion criteria.
| All (n = 4,462) |
BaPWV <1,400 cm/s (n = 835) |
1,400 cm ≤ BaPWV <1,800 cm/s (n = 2,080) |
BaPWV ≥1,800 cm/s (n = 1,547) |
p | |
|---|---|---|---|---|---|
| BaPWV (cm/s) | 1,720.00 ± 373.39 | 1,282.72 ± 88.02 | 1,596.11 ± 113.76 | 2,122.61 ± 307.57 | <0.001 |
| Male, n (%) | 3,501 (78.46) | 630 (75.45) | 1,675 (80.53) | 1,196 (77.31) | 0.004 |
| Age (year) | 56.17 ± 10.64 | 48.68 ± 9.49 | 54.79 ± 9.63 | 61.81 ± 9.46 | <0.001 |
| BMI (kg/m2) | 25. 91 ± 3.32 | 25.94 ± 3.46 | 26.04 ± 3.34 | 25.70 ± 3.20 | 0.010 |
| SBP (mmHg) | 140.84 ± 19.01 | 128.68 ± 15.63 | 139.00 ± 16.75 | 149.88 ± 19.18 | <0.001 |
| DBP (mmHg) | 84.47 ± 10.60 | 80.79 ± 9.43 | 84.72 ± 10.14 | 86.12 ± 11.32 | <0.001 |
| MAP (mmHg) | 103.30 ± 11.85 | 96.75 ± 10.32 | 102.85 ± 10.95 | 107.42 ± 12.08 | <0.001 |
| FBG (mmol/L) | 8.22 ± 2.71 | 7.54 ± 2.55 | 8.18 ± 2.68 | 8.63 ± 2.75 | <0.001 |
| LDL C (mmol/L) | 2.91 ± 0.88 | 2.85 ± 0.84 | 2.94 ± 0.86 | 2.90 ± 0.93 | 0.038 |
| HDL C (mmol/L) | 1.45 ± 0.56 | 1.46 ± 0.65 | 1.44 ± 0.60 | 1.47 ± 0.45 | 0.429 |
| TG (mmol/L) | 1.62 (1.08–2.63) | 1.53 (1.01–2.39) | 1.65 (1.10–2.75) | 1.63 (1.09–2.60) | 0.001 |
| TC (mmol/L) | 5.07 ± 1.37 | 5.09 ± 1.14 | 5.10 ± 1.36 | 5.01 ± 1.50 | 0.113 |
| UA (umol/L) | 313.51 ± 86.90 | 309.63 ± 87.99 | 313.28 ± 85.86 | 315.75 ± 87.68 | 0.295 |
| CRP (mg/L) | 1.30 (0.50–3.00) | 1.20 (0.43–2.80) | 1.30 (0.52–2.90) | 1.30 (0.50–3.20) | 0.167 |
| eGFR (mL/min/1.73 m2) | 97.77 ± 17.80 | 103.51 ± 16.96 | 98.82 ± 18.33 | 93.26 ± 16.37 | <0.001 |
| Duration of diabetes (year) | 1.93 (1.19–8.26) | 1.63 (1.02–3.17) | 1.90 (1.14–7.28) | 2.48 (1.40–11.41) | <0.001 |
| Follow up (year) | 2.85 (1.88–4.90) | 3.49 (1.89–5.87) | 2.94 (1.90–4.98) | 2.58 (1.86–4.49) | <0.001 |
| Smoking status, n (%) | 1,698 (38.11) | 321 (38.49) | 834 (40.17) | 543 (35.12) | 0.008 |
| Alcohol drinking, n (%) | 2,357 (53.00) | 417 (50.00) | 1,114 (53.87) | 826 (53.46) | 0.152 |
| Physical activities, n (%) | 633 (14.29) | 102 (12.32) | 293 (14.34) | 238 (15.41) | <0.001 |
| Salt status, n (%) | 422 (9.50) | 87 (10.46) | 206 (9.95) | 129 (8.37) | 0.161 |
| Educational level, n (%) | 1,171 (26.73) | 325 (39.63) | 556 (27.20) | 290 (19.12) | <0.001 |
| Cancer history, n (%) | 45 (1.01) | 8 (0.96) | 16 (0.77) | 21 (1.36) | 0.212 |
| CVD history, n (%) | 171 (3.83) | 27 (3.23) | 65 (3.13) | 79 (5.11) | 0.005 |
| Hypoglycemic drugs, n (%) | 1,887 (42.29) | 247 (29.58) | 831 (39.95) | 809 (52.29) | <0.001 |
| Anti hypertensive drugs, n (%) | 1,566 (35.10) | 158 (18.92) | 662 (31.83) | 746 (48.22) | <0.001 |
| Lipid lowering drugs, n (%) | 905 (20.28) | 115 (13.77) | 376 (18.08) | 414 (26.76) | <0.001 |
BaPWV, brachial ankle pulse wave velocity; MAP, mean arterial blood pressure, defined as 1/3 SBP + 2/3 DBP; BMI, body mass index; SBP, Systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; TC, total cholesterol; LDL_C, low density lipoprotein cholesterol, HDL C, high density lipoprotein cholesterol; TG, triglyceride; UA, uric acid; CRP, C reactive protein; eGFR, estimated glomerular filtration rate); CVD cardiovascular disease.
The overall median follow-up time was 2.85 (interquartile range 1.88–4.90) years. There were 56 (6.71%), 221 (10.62%) and 245 (15.84%) cases of new-onset renal damage in the normal, borderline arteriosclerosis and atherosclerosis groups, respectively (Table 2). With increasing BaPWV levels, the cumulative incidence of the endpoint tended to increase (log-rank test, p < 0.05) (Fig. 2A). The incidence densities among the three groups were 16.70, 28.18 and 45.13/1,000 person-years, respectively.
| Events | Group | Case (n) | Incidence rate (per 1,000 person years) | Model 1 | Model 2 | Model 3 |
|---|---|---|---|---|---|---|
| OR (95% CI) | ||||||
| Isolated proteinuria | BaPWV <1,400 cm | 40 (4.79) | 11.93 | (ref) | (ref) | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 165 (7.93) | 21.04 | 1.64 (1.16–2.33) |
1.58 (1.11–2.25) |
1.41 (0.98–2.03) | |
| BaPWV ≥1,800 cm/s | 175 (11.31) | 32.23 | 2.40 (1.67–3.45) |
2.36 (1.63–3.41) |
1.81 (1.22–2.69) | |
| p for trend | — | — | <0.001 | <0.001 | <0.001 | |
| Isolated eGFR <60 mL/min/1.73 m2 | BaPWV <1,400 cm | 18 (2.16) | 5.36 | (ref) | (ref) | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 68 (3.27) | 8.67 | 1.14 (0.67–1.94) |
1.10 (0.65–1.87) |
1.07 (0.62–1.83) | |
| BaPWV ≥1,800 cm/s | 93 (6.01) | 17.13 | 1.50 (0.87–2.58) |
1.42 (0.82–2.47) |
1.31 (0.74–2.34) | |
| p for trend | — | — | <0.001 | <0.001 | <0.001 | |
| Renal damage | BaPWV <1,400 cm | 56 (6.71) | 16.70 | (ref) | (ref) | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 221 (10.63) | 28.18 | 1.46 (1.09–1.97) |
1.40 (1.04–1.89) |
1.29 (0.95–1.76) | |
| BaPWV ≥1,800 cm/s | 245 (15.84) | 45.13 | 2.02 (1.48–2.75) |
1.95 (1.43–2.67) |
1.59 (1.14–2.22) | |
| Per SD | — | — | 1.27 (1.17–1.39) |
1.25 (1.15–1.36) |
1.18 (1.07–1.29) | |
| p for trend | — | — | <0.001 | <0.001 | <0.001 | |
p for trend, p value for trend across the different level of BaPWV; Per SD, hazard ratio for per standard deviation (373.39 cm/s) change in BaPWV.
Model 1 was adjusted for sex, age;
Model 2 was further adjusted for smoking status (never smoker, past or current smoker), drinking status (never drink, past or current drink), salt status (light and common salt, heavy salt), physical activity (low or moderate, high), educational level (primary or middle, college);
Model 3 was further adjusted for MAP (mmHg), FBG (mmol/L), TC (mmol/L), body mass index (kg/m2), uric acid (umol/L), CRP (mg/L), eGFR (mL/min/1.73 m2), the duration of diabetes (year), using antihypertensive drugs (no, yes), using hypoglycemic drugs (no, yes), using hypolipemic drugs (no, yes).
Kaplan-Meier estimates of cumulative incidence of new-onset renal damage, isolated proteinuria and isolated eGFR <60 mL/min/1.73 m2 according to different BaPWV levels
A: Kaplan-Meier curve for the new-onset renal damage in the normal, borderline arteriosclerosis and atherosclerosis groups. B: Kaplan-Meier curve for the new-onset isolated proteinuria in the normal, borderline arteriosclerosis and atherosclerosis groups. C: Kaplan-Meier curve for the new-onset isolated eGFR <60 mL/min/1.73 m2 in the normal, borderline arteriosclerosis and atherosclerosis groups
When adjusted for age, sex, smoking status, alcohol consumption, salt status, physical activity, education level, MAP, FBG, TC, BMI, UA, CRP, eGFR, duration of diabetes, use of antihypertensive drugs, use of hypoglycemic drugs, antihypertensive drugs and use of lipid-lowering drugs, the hazard ratios (HRs) and 95% confidence intervals (CIs) of new-onset kidney damage in the borderline arteriosclerosis and atherosclerosis groups were 1.29 (95% CI: 0.95–1.76) and 1.59 (95% CI: 1.14–2.22), respectively, compared with participants in the normal groups. The HR and 95% CI for the risk of new kidney damage was 1.18 (95% CI: 1.07–1.29) for each standard deviation increase in BaPWV (373.39 cm/s) (Table 2). The HR values and 95% CIs for renal damage among the three groups of different BaPWV levels after excluding dropouts were consistent with the main model (Supplementary Table 2). We conducted GLM analysis to reveal the association between BaPWV and changes in eGFR when eGFR was treated as continuous variables. The β (95% CI) values of changes in eGFR in the borderline arteriosclerosis and atherosclerosis groups were –0.29 (–1.84–1.26) and –0.67 (–2.49–1.15), respectively, compared with participants in the normal groups (Table 3).
| β (95% CI) | Se | p | |
|---|---|---|---|
| BaPWV <1,400 cm | (ref) | — | — |
| 1,400 cm ≤ BaPWV <1,800 cm/s | –0.29 (–1.84–1.26) |
0.79 | 0.71 |
| BaPWV ≥1,800 cm/s | –0.67 (–2.49–1.15) |
0.93 | 0.47 |
Model was adjusted for age, sex, smoking status (never smoker, past or current smoker), drinking status (never drink, past or current drink), salt status (light and common salt, heavy salt), physical activity (low or moderate, high), educational level (primary or middle, college), MAP (mmHg), FBG (mmol/L), TC (mmol/L), body mass index (kg/m2), uric acid (umol/L), CRP (mg/L), eGFR (mL/min/1.73 m2), the duration of diabetes (year), using antihypertensive drugs (no, yes), using hypoglycemic drugs (no, yes), using hypolipemic drugs (no, yes).
New-onset isolated proteinuria was detected in 40 (4.79%), 165 (7.93%) and 175 (11.31%) cases in the normal, borderline arteriosclerosis and atherosclerosis groups, respectively. The number of isolated new-onset eGFR <60 mL/min/1.73 m2 was 18 (2.16%), 68 (3.27%) and 93 (6.01%), respectively. The cumulative incidence of both new-onset isolated proteinuria and isolated eGFR <60 mL/min/1.73 m2 tended to increase with increasing BaPWV levels (Fig. 2B and 2C). After adjusting for risk factors, the HR and 95% CI for the risk of new-onset isolated proteinuria were 1.41 (95% CI: 0.98–2.03) and 1.81 (95% CI: 1.22–2.69) in the borderline atherosclerosis and atherosclerosis groups, respectively, compared with the normal group. The HRs and 95% CIs for the risk of new-onset eGFR <60 mL/min/1.73 m2 were 1.07 (95% CI: 0.62–1.83) and 1.31 (95% CI: 0.74–2.34), respectively. Although the risk for decline in new-onset isolated eGFR did not reach a significant difference, they still showed a gradual increase with a trend test p for trend <0.001 (Table 2).
Subgroup analysisIn the age stratification, after adjusting for risk factors, among patients younger than 50 years, the HR and 95% CI for the risk of new-onset renal damage were 1.36 (95% CI: 0.83–2.23) and 1.93 (95% CI: 1.01–3.69) in the borderline atherosclerosis and atherosclerosis groups, respectively, compared with the normal group. Among patients aged 50–60 years, the HR and 95% CI were 1.39 (0.80–2.42) and 2.02 (1.13–3.62), respectively. Among patients 60–70 years, the HR and 95% CI were 1.11 (0.56–2.20) and 1.22 (0.61–2.42), respectively. Among patients older than 70 years, the HR and 95% CI were 0.62 (0.13–2.97) and 0.77 (0.19–3.49), respectively. (Table 4). The same trend was observed in females, obese people, those with an eGFR ≥90 mL/min/1.73 m2, those with hypertension and participants not using antihypertensive drugs, hypoglycemic drugs and hypolipemic drugs (Supplementary Table 3).
| Age | BaPWV | Case (n) | HR (95% CI) |
|---|---|---|---|
| <50 years | BaPWV <1,400 cm | 26/471 | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 57/611 | 1.36 (0.83–2.23) | |
| BaPWV ≥1,800 cm/s | 27/177 | 1.93 (1.01–3.69) | |
| 50–60 years | BaPWV <1,400 cm | 16/264 | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 75/800 | 1.39 (0.80–2.42) | |
| BaPWV ≥1,800 cm/s | 66/453 | 2.02 (1.13–3.62) | |
| 60–70 years | BaPWV <1,400 cm | 11/94 | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 72/568 | 1.11 (0.56–2.20) | |
| BaPWV ≥1,800 cm/s | 89/629 | 1.22 (0.61–2.42) | |
| ≥70 years | BaPWV <1,400 cm | 3/6 | (ref) |
| 1,400 cm ≤ BaPWV <1,800 cm/s | 17/101 | 0.62 (0.13–2.97) | |
| BaPWV ≥1,800 cm/s | 63/288 | 0.77 (0.19–3.49) |
Model was adjusted for sex, smoking status (never smoker, past or current smoker), drinking status (never drink, past or current drink), salt status (light and common salt, heavy salt), physical activity (low or moderate, high), educational level (primary or middle, college), MAP (mmHg), FBG (mmol/L), TC (mmol/L), body mass index (kg/m2), uric acid (umol/L), CRP (mg/L), eGFR (mL/min/1.73 m2), the duration of diabetes (year), using antihypertensive drugs (no, yes), using hypoglycemic drugs (no, yes), using hypolipemic drugs (no, yes).
In sensitivity analysis excluding individuals with a follow-up time less than 2 years, a history of cancer, and a history of CVD at baseline, increasing stiffness still predicted new-onset renal damage. In the same way, after excluding individuals with hypertension, the risk of new-onset kidney damage tended to increase with increasing BaPWV levels (p for trend <0.001), whereas no significant risk was observed in the borderline atherosclerosis and atherosclerosis groups, respectively, compared with the normal group (Table 5).
| BaPWV <1,400 cm | 1,400 cm ≤ BaPWV <1,800 cm/s | BaPWV ≥1,800 cm/s | p for trend | |
|---|---|---|---|---|
| Model 1 | ref | 1.13 (0.80–1.60) | 1.56 (1.07–2.28) | 0.005 |
| Model 2 | ref | 1.31 (0.96–1.79) | 1.59 (1.13–2.22) | <0.001 |
| Model 3 | ref | 1.25 (0.92–1.71) | 1.58 (1.13–2.22) | <0.001 |
| Model 4 | ref | 1.36 (0.83–2.22) | 1.58 (0.82–3.03) | <0.001 |
Model was adjusted for sex, age, smoking status (never smoker, past or current smoker), drinking status (never drink, past or current drink), salt status (light and common salt, heavy salt), physical activity (low or moderate, high), educational level (primary or middle, college), MAP (mmHg), FBG (mmol/L), TC (mmol/L), body mass index (kg/m2), uric acid (umol/L), CRP (mg/L), eGFR (mL/min/1.73 m2), the duration of diabetes (year), using antihypertensive drugs (no, yes), using hypoglycemic drugs (no, yes), using hypolipemic drugs (no, yes).
Model 1 was for then Participants without a follow up time less than 2 year.
Model 2 was for the participants without cancer.
Model 3 was for the participants without CVD.
Model 4 was for the participants without hypertension.
Our important finding is that atherosclerosis is a risk factor for new-onset renal damage in diabetic patients, especially an independent risk factor for new-onset proteinuria. The risk effect of arteriosclerosis on new-onset renal damage in diabetic patients may be age dependent.
We found that the cumulative incidence of new-onset renal damage and isolated proteinuria in the normal arterial stiffness group, borderline atherosclerosis group, and arteriosclerosis group increased gradually, from 6.71% and 4.79% in the normal group to 15.84% and 11.31% in the atherosclerosis group, respectively. Similar to our follow-up time and age, Lim et al. [8] also found a higher cumulative incidence of new renal damage in the atherosclerotic group than in the nonatherosclerotic group in diabetic patients, 13.6% and 4.9%, respectively. Although the follow-up time and cumulative incidence of Bouchi et al. [7] were different from ours, the incidence of renal damage in the arteriosclerosis group was higher than that in the nonarteriosclerosis group, which was consistent with ours.
It was observed for the first time that arteriosclerosis was a risk factor for new kidney damage in diabetic patients in the community population. Compared with the normal group, the relative risk of new kidney damage in the arteriosclerosis group increased by 59%. However, the risk of increased kidney damage in the borderline atherosclerotic group did not reach a significant difference (p for trend <0.01). The relative risk of new-onset kidney damage increased by 18% for each 1 SD increase in BaPWV, suggesting a possible dose–response relationship between atherosclerosis and increased risk of new-onset kidney damage in diabetes. Although no similar observations have been reported previously, Cardoso et al. [9] found an increased risk of new-onset renal damage or progression of renal damage (microalbuminuria to macroalbuminuria) in the atherosclerotic group in diabetic patients with an HR of 1.15 (95% CI: 0.99, 1.34). Both our results and those of Cardoso et al. [9] support the conclusion that atherosclerosis is a risk factor for new-onset renal damage in diabetic patients.
We found that in diabetic patients, the risk of new-onset isolated proteinuria was 1.81 times higher in the atherosclerotic group than in the normal group, confirming that atherosclerosis is a risk factor for new-onset isolated proteinuria and that its effect is independent of traditional risk factors. Both Japanese [7] and Brazilian [9] studies showed that atherosclerosis, as measured by carotid-femoral pulse wave velocity (cf-PWV), was a risk factor for new-onset proteinuria in diabetic patients (HR 1.26 and 1.34, 95% CI: 1.13–1.41 and 1.01–1.78, respectively). A study from Singapore [25] suggested that the progressive proteinuria group (normal urine protein progressing to microalbuminuria, microalbuminuria progressing to massive albuminuria or normal to massive proteinuria) had higher baseline cf-PWV than the nonprogressive group. Cf-PWV was a risk factor for proteinuria progression, with an HR of 1.24 (95% CI: 1.03–1.49). All the above studies concluded that arteriosclerosis is a risk factor for new-onset proteinuria in diabetic patients.
We also found that the risk of atherosclerosis for new-onset eGFR decline did not exhibit a significant difference in diabetic patients (HR 1.31 (95% CI: 0.74, 2.34)), but the p for trend was <0.001. Moreover, GLM analysis for the association between BaPWV and changes in eGFR did not exhibit a significant difference. In addition, a study [7] of 461 Japanese DM patients revealed that cf-PWV had an independent association with a faster decline in kidney function. In contrast, a study [8] of 186 South Korea DM patients, showed a negative correlation between PWV and annual change in eGFR, whereas no signifificant risk was observed for the decline in eGFR among individuals with a BaPWV of ≥1,800 cm/s (HR 2.76, 95% CI 0.15–52.07). However, such results should be interpreted with caution because of small sample size. In contrast, longitudinal analysis from the Framingham Heart Study [26] was unable to reveal the signifificant association of cf-PWV with a decline in eGFR. Brazilian [9] study also did not find an association between atherosclerosis and decreased eGFR in diabetic patients, HR 1.06 (95% CI: 0.85, 1.32). Regarding the strength of the association between eGFR and PWV, eGFR was cross-sectionally reported to be able to explain only 1% of PWV variability [27]. Differences of the study design and populations among these studies may lead to contradictory results. In patients with diabetes, the final conclusion may need to be clarified by larger studies.
In addition, age-stratified analysis showed that in diabetic patients, the risk of new-onset renal damage was increased 1.93-fold in the atherosclerotic group under 50 years and 2.02-fold in the atherosclerotic group 50–60 years compared to the normal group, suggesting that the increased risk of new-onset renal damage in patients with arteriosclerosis was age dependent. Although no studies have directly investigated the association between atherosclerosis at different ages and the risk of new-onset diabetic kidney damage, previous studies [28] have shown that cf-PWV is only associated with a decrease in eGFR in diabetic patients under 60 years of age. The risk of atherosclerosis to other target organs was also age dependent. Cardoso et al. [29] showed that arteriosclerosis significantly increased the risk of cardiovascular events only in diabetic patients younger than 65 years old. Both studies indirectly support our findings.
The pathogenesis of renal damage in diabetic patients due to atherosclerosis is complex. On the one hand, atherosclerosis reduces the pressure-buffering capacity of the vessel wall, while the degree of intrarenal small artery sclerosis and the blood flow pressure affect the perfusion of the kidney, thereby leading to endothelial dysfunction and microcirculatory ischemia, which results in renal damage [30]. On the other hand, persistent atherosclerosis can induce microvascular remodeling, which decreases the autoregulation of the glomerular and renal microcirculatory systems, resulting in mechanical force on the glomerular capillary wall, podocytes, and mesangial cells and ultimately triggering the fibrotic response [31, 32]. In addition, renal damage caused by atherosclerosis may be achieved by increasing blood pressure, since the effect of arteriosclerosis on renal damage is no longer significantly different after excluding hypertensive populations in sensitivity analysis [33]. Finally, in the setting of diabetes, inflammation associated with hyperglycemia and arterial stiffness may have contributed to the damage of the glomerular filtration barrier, resulting in the increased leakage of protein across the membrane and the development of albuminuria [34-36].
Our study had some limitations. First, this study only measured BaPWV levels at baseline, so it was not possible to assess the effect of changes in the degree of atherosclerosis on renal damage. This study used a single measurement of BaPWV. We did not use cf-PWV, which is considered the gold standard to assess the status of arterial stiffness. BaPWV also reflects structural and functional stiffness of the arterial wall and can be applied to assess arterial stiffness simply, reproducibly, and noninvasively [37]. Second, since proteinuria was determined by the dipstick method, the 24-hour urine creatinine excretion rate was not detected, which may lead to diagnostic errors of the type of kidney damage. Meanwhile, albuminuria was not quantitatively measured and that this approach may not properly refect the association. Third, it is an observational cohort, so pathophysiological mechanisms cannot be inferred but only speculated. Fourth, we only recorded whether it was used for drugs in questionnaires. ARBs, SGLT2 inhibitors, and GLP1 agonists are protective for renal function, which is not taken into account. However, in the subgroup analysis after excluding participants using antihypertensive drugs and hypoglycemic drugs, compared with the normal group, the relative risk of new kidney damage in the arteriosclerosis group increased by 71% and 106%, respectively, whereas no significant risk was observed in the participants using drugs. Fifth, HbAc1 was not included in this study. Since more than half of the subjects did not take diabetes medications and the duration of DM was very short, this study population was analyzed in a very mild diabetes group rather than a diabetes group. Finally, the study population had an unbalanced sex distributionin the Kailuan cohort study, considering that most of the participants were male coal miners. However, the sample size was large, and the cohort was relatively stable. The results of this study are still of great significance.
In conclusion, this Kailuan-based cohort study is the first to demonstrate that atherosclerosis is a risk factor for new-onset renal damage, especially new-onset proteinuria, in community-based diabetic patients and that the effect of atherosclerosis on new-onset eGFR decline needs to be demonstrated in larger studies in the future.
We thank the staff and participants of this study for their important contributions.
The study was supported by Medical Science Research Project of Hebei Province in 2020 (NO. 20201458) and Medical Science Research Project of Hebei Province in 2021 (NO. 20211157).
Lishu Gao and Ri Liu wrote the manuscript. Lishu Gao and Shuohua Chen researched the data. Ri Liu, Lihua Zhang and Xuan Qiu contributed to the discussion. Shouling Wu reviewed and edited the manuscript. Kuanzhi Liu contributed to the discussion and reviewed/edited the manuscript. All authors read and approved the final manuscript.
None.
The Kailuan Study (trial registration number ChiCTR-TNC-11001489).
