The Unique Association Between Estimated Pulse Wave Velocity And The Prevalence Of Diabetic Kidney Disease: A Cross-Sectional Study

doi:10.21203/rs.3.rs-5115269/v1

Download PDF

Research Article

The Unique Association Between Estimated Pulse Wave Velocity And The Prevalence Of Diabetic Kidney Disease: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-5115269/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective

Arteriosclerosis is the pathological basis for Diabetic Kidney Disease (DKD). The estimated Pulse Wave Velocity (ePWV) is used to assess arteriosclerosis, and is considered a potential clinical surrogate for pulse wave velocity. There are no studies on ePWV in relation to DKD. Our research represents the first embark to explore the relationship between ePWV and DKD.

Methods

In this cross-sectional analysis, we collected ePWV data from a cohort of 4,296 hospitalized Chinese patients. Multivariable-adjusted logistic regression models and restricted cubic spline (RCS) analysis were employed to examine the relationship between eGDR and the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m².

Results

After adjusting for confounding factors, each unit increase in ePWV was associated with a 23%, 21% and 25% increase in the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m² in T2DM participants, respectively. A J-shaped relationship was observed between ePWV and the prevalence of DKD and eGFR < 60 mL/min per 1.73 m², and a linear association between ePWV and the prevalence of UACR ≥ 30 mg/g.

Conclusion

ePWV is independently positively correlated with the prevalence of DKD. Integrating ePWV into routine clinical evaluations enable timely interventions and personalized management approaches.

Estimated Pulse Wave Velocity

Type 2 Diabetes Mellitus

Diabetic Kidney Disease

Urinary Albumin-to-creatinine Ratio

Estimated Glomerular Filtration Rate

Diabetes mellitus (DM) has emerged as a significant public health concern in the 21st century. Projections suggest that by 2045, there will be 783.2 million diabetic patients globally. In China alone, the diabetic population has reached 116 million, making it the highest in the world, with type 2 diabetes mellitus(T2DM) accounting for 90% of cases[1]. Among the various complications of T2DM, Diabetic kidney disease (DKD) stands out as one of the most prevalent microvascular complications. It has become a leading cause of end-stage renal disease worldwide, significantly elevating the risk of premature mortality in affected individuals, with an incidence rate of 30% − 40% among T2DM patients[2]. Hence, effectively predicting and mitigating renal function impairment in the early stages of T2DM constitutes a critical strategy in preventing the onset and progression of DKD.

Arteriosclerosis serves as the pathological cornerstone of systemic vascular ailments, particularly in cases involving T2DM, wherein its implications are intimately intertwined with the onset and progression of DKD[3–5]. Elevated arterial stiffness contributes to heightened blood flow and pulse pressure, disseminating to organs characterized by high blood flow and microvascular resistance, such as the kidneys[6]. This phenomenon can inflict damage upon the glomerulus, consequently precipitating a decline in renal function.Research indicated a compelling association between the presence of arteriosclerosis and the commencement of renal replacement therapy among DKD patients[7]. Furthermore, within the DKD patient cohort, augmented arteriosclerosis scores correlated with deteriorating renal function, consequently abbreviating the time interval progressing to end-stage renal disease[8].

Carotid-femoral pulse wave velocity (CfPWV) stands as the gold standard in clinically assessing arteriosclerosis. However, its practical application is hindered by various factors such as equipment limitations, costly testing, and complex operation [9]. Recently, estimated pulse wave velocity (ePWV) was unveiled that utilizes chronological age and blood pressure as input values to estimate the pulse wave propagation velocity. This innovation offers not only convenient results but also demonstrates robust agreement with CfPWV outcomes[10]. ePWV has exhibited promising potential in predicting cardiovascular disease events after adjustments based on CfPWV[11]. Furthermore, ePWV is associated with both diabetes and kidney disease [12–13], including its potential to predict newly diagnosed diabetes and serve as a valuable indicator for assessing mortality risk in patients with acute kidney injury.

Building upon this foundation, the present study represents the first endeavor to investigate the relationship between ePWV and DKD in a cohort of 4296 Chinese T2DM inpatients. The primary objective is to elucidate whether ePWV levels are correlated with the prevalence of DKD.

Study design and participants

The study enrolled a total of 6,306 individuals aged 18 and older diagnosed with T2DM, who were admitted to Guang’anmen Hospital between February 2017 and February 2022. Those with incomplete clinical data, totaling 2,010 participants, were excluded from the analysis. As a result, 4,296 participants were ultimately included in the study. This retrospective study was approved by the Medical Ethics Committee of Guang'anmen Hospital, which is affiliated with the China Academy of Chinese Medical Sciences (Approval No. 2023-187-KY). The study was carried out in strict compliance with the ethical standards set forth in the Declaration of Helsinki.

Measurements

Demographic details, blood pressure records, and clinical indices for inpatients were systematically extracted from electronic medical records by uniformly trained personnel. The ePWV was calculated using a formula first introduced by Greve et al. and further elaborated by the Arterial Stiffness Collaboration [14–15]. The calculation is as follows: ePWV = 9.587—0.402 × age + 4.560 × 10^− 2 × age² —2.621 × 10^− 5 × age² × Mean Blood Pressure (MBP) + 3.176 × 10^− 3 × age × MBP— 1.832 × 10^− 2 × MBP, with age measured in years and MBP derived as diastolic blood pressure (DBP) + 0.4 × (systolic blood pressure (SBP) - DBP). The estimated glomerular filtration rate (eGFR) was assessed using the Chronic Kidney Disease Epidemiology Collaboration equation, specifically designed for the Asian demographic. DKD was identified based on a Urine albumin to creatinine ratio (UACR) of ≥ 30 mg/g and/or an eGFR of < 60 mL/min per 1.73 m², adhering to the guidelines established by the American Diabetes Association[16].

Statistical analysis

The analysis was conducted using R (v4.4.1) and GraphPad Prism (v8.0.2), with statistical significance defined as a two-sided P-value of < 0.05. Continuous variables were presented as means ± standard deviations or medians with interquartile ranges, while categorical variables were reported as counts (percentages). ePWV was categorized into quartiles, and for trend analysis, the median value of each quartile was treated as a continuous variable in the model. Multivariable-adjusted logistic regression models were employed to examine the relationship between ePWV and the prevalence of DKD, UACR ≥ 30 mg/g and eGFR < 60 mL/min per 1.73 m², with odds ratios (OR) and 95% confidence intervals (CIs) provided. Restricted cubic spline (RCS) analysis was utilized to explore the dose-response relationship between ePWV and the prevalence of DKD, UACR ≥ 30 mg/g and eGFR < 60 mL/min per 1.73 m². To explore potential effect moderators, patients were stratified into subgroups based on age (< 60 or ≥ 60 years), sex (male or female), duration of diabetes (< 10 or ≥ 10 years), and presence of hypertension (yes or no).

Table 1 displays the general and sociodemographic characteristics of the participants in the study. The analysis ultimately encompassed 4,296 individuals with diabetes. Out of these, 2,821 (65.6%) were identified as Non-DKD, whereas 1,475 (34.4%) were diagnosed with DKD. Compared to the Non-DKD group, individuals with DKD showed significantly higher levels across multiple parameters, including age, male population, diabetes duration, SBP, HbA1c, total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), UACR, and ePWV. While high-density lipoprotein (HDL) and eGFR were notably lower in the DKD group compared to their Non-DKD counterparts.

Table 1

General and sociodemographic characteristics of the participants by DKD
Characteristics	Non-DKD	DKD	Total	P _value
N	2821	1475	4296
Age, years	59.0 [65.0–51.0]	61.0 [69.0–53.0]	59.0 [66.0–52.0]	< 0.001
Men, %	1525 (54.1%)	873 (59.2%)	2398 (55.8%)	0.001
Duration of diabetes, years	14.0 [19.0–8.00]	15.0 [21.0–9.00]	14.0 [20.0–8.00]	< 0.001
SBP, mmHg	136 [145−126]	139 [148−128]	137 [147−126]	< 0.001
DBP, mmHg	80.0 [87.0–73.0]	80.0 [88.0–73.0]	80.0 [87.0–73.0]	0.644
HbA1c, %	8.40 [9.80–7.30]	8.80 [10.2–7.50]	8.60 [10.0−7.30]	< 0.001
TC, mmol/L	4.62 [5.43–3.87]	4.73 [5.71–3.90]	4.65 [5.50–3.88]	0.002
TG, mmol/L	1.45 [2.06–1.05]	1.77 [2.56–1.22]	1.54 [2.23–1.11]	< 0.001
HDL, mmol/L	1.12 [1.31–0.960]	1.09 [1.28–0.940]	1.11 [1.30–0.950]	< 0.001
LDL, mmol/L	2.93 [3.54–2.37]	3.01 [3.69–2.38]	2.96 [3.60–2.37]	0.011
UACR, mg/g	8.63 [13.7–5.60]	102 [378−42.6]	13.1 [45.9–6.82]	< 0.001
eGFR, mL/min per 1.73 m²	104 [114−93.9]	88.4 [107−58.3]	101 [112−85.5]	< 0.001
ePWV, m/s	10.6 [11.8–9.35]	11.0 [12.5–9.68]	10.7 [12.0−9.48]	< 0.001

DKD Diabetic kidney disease, SBP Systolic blood pressure, DBP Diastolic blood pressure, TC Total cholesterol, TG Triglycerides, HDL High-density lipoprotein, LDL Low-density lipoprotein, UACR Urine albumin to creatinine ratio, eGFR Estimated glomerular infiltration rate, ePWV Estimated pulse-wave velocity

Table 2 delineates the characteristics of participants, organized by quartiles of ePWV. Participants within the highest ePWV quartile, relative to those in the lowest quartile, were generally older, had a higher prevalence of being female and DKD, and showed increased levels of diabetes duration, SBP, DBP, HDL, and UACR. Moreover, in the highest ePWV quartile compared to the lowest, lower levels of HbA1c, TC, TG, LDL, and eGFR were observed.

Table 2

General and sociodemographic characteristics of the participants by ePWV quartiles
Characteristics	Quartile 0(≤ 9.48)	Quartile1(> 9.48, ≤ 10.69)	Quartile2(> 10.69,≤12.03)	Quartile 3(> 12.03)	P _{for trend}
N	1075(25%)	1069(24.8%)	1079(25.1%)	1073(24.9%)
Age, years	45.0 [51.0–38.0]	56.0 [60.0–53.0]	63.0 [66.0–59.0]	71.0 [76.0–67.0]	< 0.001
Men, %	753(70.0%)	634(59.3%)	559(51.8%)	452(42.1%)	< 0.001
Duration of diabetes, years	11.0 [17.0–6.00]	14.0 [19.0–8.00]	15.0 [21.0–9.00]	16.0 [22.0–10.0]	< 0.001
SBP, mmHg	126 [136 − 119]	133 [141 − 125]	140 [148 − 131]	147 [159 − 138]	< 0.001
DBP, mmHg	79.0 [85.0–72.0]	80.0 [86.0–73.0]	81.0 [87.0–74.0]	82.0 [89.0–75.0]	< 0.001
HbA1c, %	8.90 [10.4–7.50]	8.60 [9.90–7.30]	8.40 [9.90–7.30]	8.40 [9.80–7.20]	< 0.001
TC, mmol/L	4.90 [5.72–4.16]	4.72 [5.54–3.99]	4.57 [5.41–3.80]	4.37 [5.31–3.69]	< 0.001
TG, mmol/L	1.81 [2.74–1.20]	1.53 [2.23–1.14]	1.47 [2.05–1.06]	1.44 [1.96–1.05]	< 0.001
HDL, mmol/L	1.06 [1.24–0.930]	1.12 [1.31–0.960]	1.12 [1.30–0.960]	1.14 [1.34–0.970]	< 0.001
LDL, mmol/L	3.17 [3.73–2.64]	3.01 [3.61–2.43]	2.91 [3.53–2.30]	2.76 [3.45–2.18]	< 0.001
UACR, mg/g	10.5 [29.8–6.13]	11.6 [38.2–6.36]	13.0 [46.7–6.86]	18.7 [68.8–8.66]	< 0.001
eGFR, mL/min per 1.73 m²	115 [126 − 103]	105 [113 − 94.0]	98.3 [106 − 85.7]	88.6 [97.8–71.7]	< 0.001
DKD, %	316(29.4%)	324(30.3%)	360(33.8%)	470(43.8%)	< 0.001

We devised two models to evaluate the independent impact of ePWV on the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m². As illustrated in Table 3, a higher ePWV was linked to a greater prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m², even when adjusting for a range of confounding factors, including sex, duration of diabetes, HbA1c, TC, TG, HDL, and LDL. When comparing individuals in the highest ePWV quartile to those in the first quartile, the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m² was significantly higher by 133%, 125%, and 134% respectively after adjusting for these confounding factors. Each unit increase in ePWV was associated with a 23%, 21% and 25% increase in the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m² in T2DM participants, respectively, after adjusting for the same confounding factors.

	ePWV, m/s				P _{for trend}	Total	P _value
	Quartile 0	Quartile1	Quartile2	Quartile 3	P _{for trend}	Total	P _value
The prevalence of DKD
Model 0 OR(95% CIs)	Ref.	1.04 (0.87–1.26)	1.23 (1.02–1.47)	1.87 (1.57–2.24)	< 0.001	1.17(1.13, 1.21)	< 0.001
Model 1 OR(95% CIs)	Ref.	1.19 (0.98–1.45)	1.47 (1.21–1.78)	2.33 (1.92–2.84)	< 0.001	1.23(1.19, 1.28)	< 0.001
The prevalence of UACR ≥ 30 mg/g
Model 0 OR(95% CIs)	Ref.	1.16 (0.96–1.41)	1.37 (1.13–1.65)	1.82 (1.51–2.19)	< 0.001	1.15(1.12, 1.19)	< 0.001
Model 1 OR(95% CIs)	Ref.	1.34 (1.09–1.64)	1.64 (1.34-2.00)	2.25 (1.84–2.76)	< 0.001	1.21(1.16, 1.26)	< 0.001
The prevalence of eGFR < 60 mL/min per 1.73 m²
Model 0 OR(95% CIs)	Ref.	0.92 (0.67–1.26)	0.90 (0.65–1.23)	2.09 (1.58–2.75)	< 0.001	1.22(1.16, 1.28)	< 0.001
Model 1 OR(95% CIs)	Ref.	0.96 (0.70–1.34)	0.98 (0.70–1.37)	2.34 (1.73–3.16)	< 0.001	1.25(1.18, 1.32)	< 0.001

DKD Diabetic kidney disease, UACR Urine albumin to creatinine ratio, eGFR Estimated glomerular infiltration rate, ePWV Estimated pulse-wave velocity

Model 0 The model was not adjusted

Model 1 The model was adjusted for sex, duration of diabetes, HbA1c, TC, TG, HDL, and LDL

RCS curves were utilized to evaluate the dose-response relationship between ePWV and the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m². As illustrated in Fig. 1, a J-shaped relationship was observed between ePWV and the prevalence of DKD and eGFR < 60 mL/min per 1.73 m², even after adjusting for sex, duration of diabetes, HbA1c, TC, TG, HDL, and LDL. Additionally, the analysis revealed a linear association between ePWV and the prevalence of UACR ≥ 30 mg/g, after controlling for the same confounding factors.

Model 0 The model was not adjusted

Model 1 The model was adjusted for sex, duration of diabetes, HbA1c, TC, TG, HDL, and LDL

As illustrated in Fig. 2, we stratified the analyses by age (< 60 or ≥ 60 years), sex (male or female), duration of diabetes (< 10 or ≥ 10 years), and presence of hypertension (yes or no) to examine whether these potential confounders influenced the associations between ePWV and the prevalence of DKD, and to evaluate any interactions. The results of these stratified analyses demonstrated that the associations between ePWV and the prevalence of DKD were generally consistent across all sub-populations. However, significant interactions were observed between ePWV and age for DKD in participants and statistically significant associations were only observed in participants older than 60 years.

Based on retrospective data from Guan’anmen hospital between 2017 and 2022, we included 4,296 patients with T2DM and revealed for the first time an independent positive correlation between ePWV and the prevalence of DKD in this population. This association was confirmed after adjusting for multiple confounding factors. Our findings address a gap in research concerning ePWV and its role in the microvascular complications of T2DM. ePWV may aid in identifying DKD patients within the T2DM population and could become a potential therapeutic target. Additionally, we observed a significant interaction with age in the relationship between ePWV and DKD prevalence, with the correlation being significant only in individuals aged 60 years or older.

Although CfPWV is regarded as the gold standard for diagnosing arterial stiffness, its measurement is highly complex. Participants must refrain from caffeine intake for at least 3 hours before the test and maintain a supine position for 10 minutes. Additionally, the measurement tools, which often involve sophisticated physical control systems, are relatively expensive and require a high level of technical expertise from the operator[17]. These factors have limited its widespread use in clinical practice. In 2010, the concept of ePWV (estimated pulse wave velocity) was introduced as a new metric for assessing arterial stiffness, calculated using age and mean blood pressure[18]. Compared to CfPWV, ePWV offers greater convenience and speed, making it more patient-friendly. Research has shown that ePWV closely correlates with cfPWV and exhibits high sensitivity and specificity for detecting aortic stiffness[19–20]. As a result, ePWV shows promise as a viable alternative to pulse wave velocity measurements in clinical settings.

The clinical predictive value of ePWV has been confirmed across various diseases. A prospective cohort study involving 5,325 diabetic patients underscored the importance of ePWV, revealing that each 1 m/s increase in ePWV was associated with a 53–102% increase in mortality risk for diabetic individuals, suggesting that ePWV could be an effective tool for assessing mortality risk in diabetic patients[21]. Additionally, in non-diabetic populations, ePWV has been associated with eGFR and UACR. Specifically, eGFR is negatively correlated with ePWV, while UACR is positively correlated, aligning with our findings.This suggests a relationship between ePWV and kidney function, with age-related arterial stiffness potentially being a major mechanism leading to impaired kidney function.[21]. We found that each unit increase in ePWV was associated with a 23%, 21% and 25% increase in the prevalence of DKD, UACR ≥ 30 mg/g, and eGFR < 60 mL/min per 1.73 m² in T2DM participants, respectively, after adjusting for various confounding factors. A J-shaped relationship was observed between ePWV and the prevalence of DKD and eGFR < 60 mL/min per 1.73 m², and a linear association between ePWV and the prevalence of UACR ≥ 30 mg/g, which were consistent with previous research findings.

Based on existing literature and our research findings, we hypothesize that the correlation between ePWV and DKD is influenced by the impact of arterial stiffness on the development of DKD. Chronic hyperglycemia in patients with type 2 diabetes leads to long-term low-grade inflammation. As inflammatory signaling pathways become increasingly activated, oxidative stress and the accumulation of advanced glycation end products (AGEs) rise. These factors drive the continuous proliferation and expansion of vascular endothelial cells, resulting in the production of harmful substances. Concurrently, the enzyme systems within endothelial cells become activated, causing damage to the vascular intima. This leads to reduced vessel wall elasticity and increased vascular stiffness, ultimately causing vascular damage and arterial sclerosis. As arterial sclerosis progresses, it affects the small arteries of the glomeruli in the kidneys, leading to glomerular changes characterized by hypertrophy and hyperfiltration. This impairs the filtering capacity of the glomeruli, which is reflected in a decrease in eGFR. Additionally, damage to the glomerular filtration barrier increases its permeability, allowing large molecules such as albumin to escape, resulting in an elevated UACR[22–24]. This mechanism aligns with our findings. The decline in eGFR and the occurrence of proteinuria together promote the development of DKD[25]. As a chronic condition, arterial sclerosis is well-documented in its association with kidney damage, such as increasing cardiovascular or all-cause mortality risk in dialysis patients, or being linked to early declines in kidney function[26–30].

These findings have significant implications for clinicians managing diabetic patients, emphasizing the value of ePWV as a marker for the early detection of DKD. Incorporating ePWV into clinical evaluations could improve risk stratification and pave the way for personalized treatment strategies and preventive measures, thereby enhancing our approach to managing DKD. Our study, a large-scale investigation involving inpatients, ensures a substantial sample size while rigorously controlling participant information. We have made efforts to adjust for confounding factors to produce more reliable results. However, our study has limitations. First, as a cross-sectional study, it cannot infer causality. Although a link between ePWV and DKD has been identified, further research in larger, diverse cohorts is needed to understand the impact of ePWV on DKD development. Second, the absence of body mass index (BMI) data may limit a comprehensive understanding of the ePWV-DKD relationship. Future research should include BMI-focused stratified analyses. Third, our study was conducted with Chinese adults in a single-center setting, which may limit the generalizability of the findings to other racial backgrounds. Future studies should incorporate multi-ethnic populations to broaden the applicability of the results.

After adjusting for potential confounding factors, our analysis identified a significant association between higher ePWV levels and the increased prevalence of DKD, UACR ≥ 30 mg/g and eGFR < 60 mL/min per 1.73 m². These insights underscore the potential benefits of monitoring ePWV levels—a simple, cost-effective, and broadly accessible approach—for the early detection of DKD.

Ethical approval and consent to participate

This research was authorized by the Ethics Committee of Guang’anmen Hospital, affiliated with the China Academy of Chinese Medical Sciences (Approval No. 2023-187-KY). Every procedure executed in this study adhered rigorously to the ethical guidelines outlined in the 1964 Declaration of Helsinki, along with its subsequent amendments, incorporating the revisions introduced in 2008.

Availability of data and material

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interest

All the authors declare that they have no conflict of interest.

Funding

This project was funded by Beijing Municipal Natural Science Foundation (No.7242255), the Traditional Chinese Medicine Evidence-Based Capacity Building Project(No.60104), the High Level Chinese Medical Hospital Promotion Project (No. HLCMHPP2023084), and the Technology Innovation Project of Major Key Projects at the China Academy of Chinese Medical Sciences (No.C12021A01617).

Authors' contributions

Shuwu Wei and Xinyu Pan conceived the study, participated in its design and coordination, analyzed the data and drafted the manuscript. Yao Xiao recruited patients and collected data. Junping Wei participated in its design and coordination, and was responsible for project administration and visualization. All authors read and approved the final version of manuscript.

Acknowledgments

The authors thank all the participants in the study and colleagues in the nursing group in their department for blood sampling.

Magliano DJ, Boyko E, J D A.. IDF DIABETES ATLAS[M]. Brussels:International Diabetes Federation; 2021.
Koye DN, Magliano DJ, Nelson RG, Pavkov ME. The Global Epidemiology of Diabetes and Kidney Disease. Adv Chronic Kidney Dis. 2018;25(2):121–32.
Georgianos PI, Sarafidis PA, Liakopoulos V. Arterial stiffness: a novel risk factor for kidney injury progression? Am J Hypertens. 2015;28(8):958–65.
Sun D, Li S, Chen S, et al. TRIM25 inhibition attenuates inflammation, senescence, and oxidative stress in microvascular endothelial cells induced by hyperglycemia. Graefes Arch Clin Exp Ophthalmol. 2024;262(1):81–91.
Sawada N, Arany Z. Metabolic Regulation of Angiogenesis in Diabetes and Aging. Physiol (Bethesda). 2017;32(4):290–307.
Oliveira AC, Cunha PMGM, Vitorino PVO, et al. Vascular aging and arterial stiffness. Arq Bras Cardiol. 2022;119(4):604–15.
Stefan G, Stancu S, Zugravu A, et al. Histologic predictors of renal outcome in diabetic nephropathy: Beyond renal pathology society classification. Med (Baltim). 2019;98(27):e16333.
Zhang Y, Jiang Q, Xie J, et al. Modified arteriosclerosis score predicts the outcomes of diabetic kidney disease. BMC Nephrol. 2021;22(1):281.
Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH guidelines for the management of arterial hypertension. Eur Heart J. 2018;39:3021–104.
Greve SV, Blicher MK, Kruger R, et al. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity. J Hypertens. 2016;34(7):1279–89.
Heffernan KS, Stoner L, London AS, et al. Estimated pulse wave velocity as a measure of vascular aging. PLoS ONE. 2023;18(1):e0280896.
Cui X, Hu Y, Li D, et al. Association between estimated pulse wave velocity and in-hospital mortality of patients with acute kidney injury: a retrospective cohort analysis of the MIMIC-IV database. Ren Fail. 2024;46(1):2313172.
Bao W, Chen C, Chen C, et al. Association between estimated pulse wave velocity and risk of diabetes: A large sample size cohort study. Nutr Metab Cardiovasc Dis. 2023;33(9):1716–24.
Greve SV, Blicher MK, Kruger R, et al. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity. J Hypertens. 2016;34:1279–89.
Reference Values for. Arterial Stiffness' Collaboration Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors: 'establishing normal and reference values'. Eur Heart J. 2010;31:2338–50.
American Diabetes Association. 11. Microvascular Complications and Foot Care: Standards of Medical Care in Diabetes-2019. Diabetes Care. 2019;42(Suppl 1):S124-S138.
Townsend RR, et al. Recommendations for Improving and Standardizing Vascular Research on Arterial Stiffness: A Scientific Statement From the American Heart Association. Hypertension. 2015;66:698–722.
(2010) Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors: ’establishing normal and reference values’. Eur Heart J 31: 2338–50.
Wu LD, Chu P, Kong CH, et al. Estimated pulse wave velocity is associated with all-cause mortality and cardiovascular mortality among adults with diabetes. Front Cardiovasc Med. 2023;10:1157163.
Heffernan KS, Stoner L, London AS, et al. Estimated pulse wave velocity as a measure of vascular aging. PLoS ONE. 2023;18(1):e0280896.
Liu C, Pan H, Kong F, et al. Association of arterial stiffness with all-cause and cause-specific mortality in the diabetic population: A national cohort study. Front Endocrinol (Lausanne). 2023;14:1145914.
Pérez-Morales RE, Del PM, Valdivielso JM et al. Inflamm Diabet Kidney Disease[J] Nephron 2019,143(1):12–6.
Sanchez-Alamo B, Shabaka A, Cachofeiro V, et al. Serum interleukin-6 levels predict kidney disease progression in diabetic nephropathy[J]. Clin Nephrol. 2022;97(1):1–9.
Ahmad AA, Draves SO, Rosca M. Mitochondria in Diabetic Kidney Disease. Cells. 2021;10(11):2945.
Esper RJ, Vilariño JO, Machado RA, et al. Endothelial dysfunction in normal and abnormal glucose metabolism. Adv Cardiol. 2008;45:17–43.
Hill GS. Hypertensive nephrosclerosis. Curr Opin Nephrol Hypertens. 2008;17(3):266–70.
Wu Y, Hou J, Li J, et al. Correlation between Carotid Intima-Media Thickness and Early-Stage Chronic Kidney Disease: Results from Asymptomatic Polyvascular Abnormalities in Community Study. J Stroke Cerebrovasc Dis. 2016;25(2):259–65.
Tanaka M, Abe Y, Furukado S, et al. Chronic kidney disease and carotid atherosclerosis. J Stroke Cerebrovasc Dis. 2012;21(1):47–51.
Verbeke F, Van Biesen W, Honkanen E, et al. Prognostic value of aortic stiffness and calcification for cardiovascular events and mortality in dialysis patients: outcome of the calcification outcome in renal disease (CORD) study. Clin J Am Soc Nephrol. 2011;6(1):153–9.
Ozkok A, Akpinar TS, Tufan F, et al. Cystatin C is better than albuminuria as a predictor of pulse wave velocity in hypertensive patients. Clin Exp Hypertens. 2014;36(4):222–6.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
25 Sep, 2024
Editor assigned by journal
23 Sep, 2024
Submission checks completed at journal
23 Sep, 2024
First submitted to journal
19 Sep, 2024

You are reading this latest preprint version

The Unique Association Between Estimated Pulse Wave Velocity And The Prevalence Of Diabetic Kidney Disease: A Cross-Sectional Study

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Study design and participants

Measurements

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1