In total, 286 subjects were evaluated and reviewed in the nephrology outpatient clinic of a university hospital. Subjects previously diagnosed with HTN, DM, CKD or a combination of them were included. To be included in the study, subjects must not have had any cardiovascular events in the previous six months, kidney function must have been stable in the previous three months and there must have been no changes in antidiabetic, antihypertensive or lipid-lowering therapy during this time. Subjects with immunological diseases requiring immunosuppressive therapy, those diagnosed with malignancies, HTN of endocrine origin or aortic aneurysm, and those with atrial fibrillation that made it difficult to capture the pulse wave by tonometry were excluded.

Data on cardiovascular disease (CVD) requiring hospital admission and peripheral arteriopathy diagnosed by symptoms and/or an ankle-brachial index less than 0.9 were collected from the medical records. Smoking habits were investigated, considering both active smokers and those who had stopped smoking to be smokers, as opposed to those who had never smoked (non-smokers).

Haematological and biochemical parameters were measured in blood, including complete blood count, creatinine, lipids, uric acid, blood glucose, glycated haemoglobin (in DM), calcium-phosphorus metabolism with parathyroid hormone (PTH), and vitamin D (in CKD). Estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease, epidemiology (CKD-EPI) formula. In all subjects, the albumin-creatinine ratio was determined in the first morning urine.

In total, 38 healthy, non-smoking subjects with no history of CVD or other diseases and normal renal function were used as controls for arterial function parameters. In these subjects only renal function lab test parameters were determined.

CKD was defined as a urine albumin/creatinine ratio greater than 30 mg/g and/or eGFR less than 60 mL/min/1.73 m2. Since there may be a higher risk of cardiovascular morbidity and mortality in subjects with albuminuria values in the normal range, a subgroup of albuminuria (normal-high) was added and categorised as follows: normal 0−10 mg/g; high-normal: 11−29 mg/g; moderate increase: 30−299 mg/g; major increase: more than 300 mg/g. The subjects were distributed into five groups: control, HTN, DM, CKD and CKD plus DM.

All subjects gave informed consent and the study met all ethical requirements of the study site.

After resting for 15 min, the average of three brachial blood pressure (BrBP) measurements was taken, using an Omron M3 oscillometric device (Omron electrónica Iberia, S.A.U., Madrid). Central blood pressure (cBP) and cfPWV were studied by applanation tonometry using a SphygmoCor device (AtCor Medical, Sydney, Australia). The central aortic pressure wave and its different components (the cSBP, the central diastolic blood pressure [cDBP] and central pulse pressure [cPP]) were obtained from the pulse wave obtained by tonometry on the radial artery and using a generalised transfer function. In the generated aortic pressure wave, the junction point of the anterograde incident wave and the reflected wave (inflection point) was identified. Augmentation pressure (AP) was the maximum cSBP minus the pressure at the inflection point. The AI was defined as the AP divided by the cPP and is expressed as a percentage. Given the influence of heart rate (HR) on the AI, the SphygmoCor device standardises the AI to a HR of 75 L/min (AI75). To determine the carotid-femoral pulse wave velocity (representative of aortic or central stiffness), the pulse wave was obtained sequentially over the common carotid artery and the femoral artery, calculating the travel time between the two points from the difference between the R wave of the simultaneous electrocardiographic recording and the start of the pulse wave at the respective arterial sites. The crPWV, which is representative of peripheral arterial stiffness, was determined using the same technique, but on the radial artery. The paASG was calculated from the crPWV/cfPWV ratio.

The subendocardial viability ratio or Buckberg index was also derived from the aortic pulse wave, which results from the diastolic pressure-time integral/systolic pressure-time integral, and which represents a resting measure of myocardial oxygen supply and consumption.13 In 44% of subjects with CKD and in 42% of the group with CKD + DM, the degree of abdominal aorta calcification was studied by lateral X-ray of the lumbar spine, determining the Kauppila index.14

Qualitative variables are expressed as relative frequencies. Continuous variables are expressed as mean ± standard deviation (x ± SD) when the distribution was normal, and as median (interquartile range [IQR]) for non-normal distribution. Some of the variables with a highly skewed distribution, such as the urine albumin/creatinine ratio, were converted to their natural logarithms. The Shapiro-Wilk and Kolmogorov-Smirnov tests were used to establish the normality of the distribution of the variables, depending on the sample size.

Qualitative variables between groups were compared using the χ2 test, while quantitative variables were compared using one-way analysis of variance (ANOVA) with adjustment for covariates (ANCOVA) that could affect the results. Two-way ANOVA was used to analyse the possible effect of two qualitative variables and their possible interaction on the dependent variable of interest. To maximise possible differences in kidney function between groups, and when the requirement of equality of covariance matrices was met, multivariate analysis of variance (MANOVA) was used, introducing eGFR and albuminuria as dependent variables.

The relationship between variables was studied using the Pearson or Spearman correlation coefficient, depending on their distribution. The independent relationship between variables was investigated using multiple linear regression by selecting potential covariates that demonstrated significant correlation. Binary logistic regression was used to assess the independent effects and predictive value of arterial stiffness parameters on kidney function (eGFR and albuminuria). For greater precision in the predictive capacity of arterial stiffness markers on kidney function and their possible interaction with DM, multinomial logistic regression was used employing four possible classes of kidney function as categorical dependent variables: normal (normal albuminuria and eGFR); albuminuria (urine albumin/creatinine ratio greater than 30 mg/g); reduction in eGFR (eGFR less than 60 mL/min/1.73 m2); and reduced eGFR and albuminuria (eGFR less than 60 L/min/1.73 m2 and urine albumin/creatinine ratio greater than 30 mg/g), and the four categories of albuminuria (normal, high-normal, moderate and major). In these analyses, normal kidney function (eGFR ≥60 mL/min/1.73 m2 and albuminuria ≤30 mg/g) and normal albuminuria (≤10 mg/g), were used as reference categories, respectively.

In the statistical results, a p-value <0.05 was considered significant. All statistical analyses were performed using the IBM SPSS (Statistical Package for the Social Sciences) statistical program, version 25 for Windows.

Arterial stiffness is the expression of the effect that the interaction between haemodynamic and metabolic factors has on the arterial wall. Consequently, structural alterations occur in the vascular wall, resulting in an increase in the ratio between collagen and elastin, among other effects. There are many underlying mechanisms for increased arterial stiffness: molecular alterations induced by mechanotransduction between the extracellular matrix and vascular smooth muscle cells, phenotypic changes in these cells, endothelial dysfunction, oxidative stress, calcification of the vascular middle layer, metabolic abnormalities, inflammatory phenomena, perivascular adipose tissue, sex hormones and genetic and epigenetic factors.15–17 All of these mechanisms participate to varying degrees in the increase in arterial stiffness observed in the presence of different vascular risk factors, such as age, hypertension, DM and CKD.18

Similar to the normal ageing process, in the presence of these vascular risk factors, EVA occurs resulting in an increase in arterial stiffness.19

More than 90% of the subjects in our study were hypertensive. Hypertension is associated with increased arterial stiffness and, in turn, its increase leads to higher blood pressure.20 In addition to the structural modifications of the arterial wall caused by hypertension that induce a long-term increase in arterial stiffness, when blood pressure is raised acutely, the load supported by the vascular wall is transferred from the elastic fibres to the collagen fibres, increasing arterial stiffness. As has been shown in our study and others,4 there is a significant correlation (with quadratic function morphology) between age and cfPWV. Other factors such as sex and obesity can modify arterial stiffness.20,21 For all these reasons, it is necessary to consider these variables when analysing cfPWV.

In our study, cfPWV values in all subject groups were higher than in controls. However, after adjustment for age, blood pressure, sex and BMI, the significant difference between the control group and the group with hypertension disappeared. Despite the adjustment, subjects with DM, CKD and CKD + DM presented cfPWV values higher than those of the other groups. The largest contribution to the variance of the cfPWV was the group to which they belonged (27.7%), age (22.9%) and MAP (20.3%).

The cause of a similar marginal cfPWV value (after adjustment) in the control and HTN groups is unclear. It is possible that the criteria used to classify the groups, excluding from group 2 (HTN) those with albuminuria and decreased eGFR, generated a selection of subjects with better vascular health. It has been proven that, despite the presence of vascular risk factors, there are subjects with a vascular age (measured by cfPWV) lower than their chronological age (supernormal vascular ageing [SUPERNOVA]), with fewer vascular events. In the prospective study that validated the clinical significance of the concept of supernormal vascular age, 59.7% in the SUPERNOVA group had HTN.22 In our study, 27.5% of subjects in the HTN group had cfPWV values lower than the cut-off point of the 10th percentile of the European Society of Cardiology reference values for cfPWV.23 In this subgroup of our study, the cfPWV value, adjusted for age, sex and MAP, was 5.8 (1) m/s, and none had a history of CVD.

Another fact that could contribute to a lower cfPWV in all patient groups is treatment with RAS inhibitors: these drugs have the capacity to reduce arterial stiffness beyond their antihypertensive effects.24

As described in other studies, the presence of DM and CKD is associated with metabolic and inflammatory alterations, oxidative stress and vascular calcification, to name but a few, which induce an increase in arterial stiffness measured by cfPWV.25 In our research, groups 3 (DM) and 4 (CKD) had higher cfPWV values than the HTN group, although this difference was at the limit of statistical significance (p = 0.07) in the group that only had DM. The smaller number of subjects in this group may have limited significance. The duration of DM, which in this group was only two years, is an important determinant of aortic stiffness in DM. The role of DM in vascular damage and in the increase in arterial stiffness determined by cfPWV is reinforced by the fact that cfPWV was higher in the group with DN compared to subjects with non-diabetic glomerular nephropathy, despite a higher albuminuria value in the latter.

The higher cfPWV observed in the group with CKD + DM compared to the group with CKD can be attributed to the additive effect of metabolic and inflammatory alterations and oxidative stress that occur when the two processes simultaneously occur, and to the greater number and intensity of abdominal aortic calcifications that were observed in the group with CKD + DM. In subjects with CKD and DM, abdominal aortic calcification has been shown to be associated with elevated cfPWV values.26

Our study yielded similar results in arterial stiffness in the different groups using another arterial stiffness parameter; paASG. Applying this arterial stiffness index, the DM group had significantly lower values than the control and HTN groups. The difference in the cfPWV value between these groups was, however, at the limit of significance. During ageing or in the presence of vascular risk factors, aortic stiffness (measured by cfPWV) increases; however, the stiffness of the peripheral muscular arteries (measured by crPWV) hardly changes. Consequently, the paASG decreases progressively. Our study identified a significant correlation between age and cfPWV, a correlation that was not evident in the case of crPWV. One advantage of using the paASG is its independence from the MAP value.6 In fact, in our study, 20% of the variance of the cfPWV was dependent on the MAP. This percentage fell to 1% for paASG.

cPP is considered an indirect indicator of CAS. The loss of arterial elasticity reduces its distensibility and increases the speed of pulse wave transmission, producing an early return of the reflex wave that adds to the anterograde wave generated by ventricular systole, which translates into an increase in cSBP and, consequently, in cPP. Our study found that, after a multivariate adjustment, all subjects had a higher cPP than the control group, which was significant when CKD and DM both presented. Despite this, the percentage of subjects with a cPP greater than 50 mmHg, a value that has been shown to be predictive of cardiovascular events,27 was significantly higher in all subject groups. Other studies have shown that, in subjects with HTN and in patients with DM, increased cPP was associated with greater impedance of the proximal aorta and greater aortic stiffness12,28 and, in subjects with CKD,29 a direct correlation between cPP and PTH values, as we also observed in our research.

When arterial stiffness increases, the reflex wave propagates more quickly, joining the anterograde wave in systole with the consequent increase in the AI, which has been considered a marker of the reflex wave and, therefore, of arterial stiffness.30 In our research, the AI75 was significantly higher in all patients than in controls. Unlike the other markers of aortic stiffness, there were no differences in AI75 between the groups with HTN, DM and CKD, although cfPWV was higher in the groups with CKD, a finding also observed in other studies.10 Other research in subjects with DM verified the coexistence of an elevated cfPWV with AI75 and reflex wave values similar to or lower than the control group.28,31 An increase in aortic stiffness with a smaller increase or decrease in the reflex wave could suggest a greater penetration of pulsatile energy into the cerebral and renal microcirculation. However, this is inconsistent with the significant negative correlation between AI75 and paASG observed in our research. AI75 may not be a good marker of aortic stiffness and reflex wave. The limitations of AI75 as a measure of the magnitude of the reflex wave derive from the fact that it is influenced not only by the velocity of the pulse wave but also by other cardiac factors.32–34

It can be deduced from our findings that aortic stiffness increases in the presence of vascular risk factors such as HTN, DM and CKD. This increase is especially notable in subjects with CKD and in subjects with both CKD and DM. cfPWV and paASG are the most consistent markers for analysing CAS when the vascular risk factors studied here coexist.

The relevance of an increase in aortic stiffness will be determined by its possible impact on target organ damage that occurs in the presence of vascular risk factors, damage that may contribute to cardiovascular morbidity and mortality.

In addition to its impact on renal function, increased aortic stiffness also has cardiac effects. An increase in cfPWV favours the arrival of the reflex wave in the ascending aorta during systole, increasing the cSBP with the consequent cardiac overload and, on the other hand, decreases the aortic diastolic blood pressure, compromising coronary perfusion, which occurs mainly in diastole. Experimental studies have shown that a stiff proximal aorta is associated with a reduction in coronary perfusion, especially at the subendocardial level.46 In our study, we observed a direct correlation between the Buckberg index, a non-invasive estimate of oxygen delivery and subendocardial perfusion, with eGFR, and an inverse correlation with albuminuria, as well as an inverse correlation with aortic stiffness parameters (except for a direct relationship with paASG). However, in multiple regression, after multivariate adjustment, the only aortic stiffness marker independently and inversely associated with Buckberg index magnitude was cfPWV.

It has been shown that, both in the general population and in high vascular risk populations, eGFR and albuminuria, independently of other traditional vascular risk factors, predict cardiovascular morbidity and mortality.47 The association observed in our study of aortic stiffness with eGFR, albuminuria and subendocardial perfusion raises the possibility that increased aortic stiffness may be the link between kidney dysfunction and cardiovascular events.