Relationship Between Renal Resistive Index and Retinal Vascular Density in Individuals with Hypertension

Academic Editor: Ramón C. Hermida Received: 31 December 2024 Revised: 19 January 2025 Accepted: 24 January 2025 Published: 28 January 2025 Citation: Carollo, C.; Vadalà, M.; Sorce, A.; Sinatra, N.; Orlando, E.; Cirafici, E.; Bennici, M.; Polosa, R.; Bonfiglio, V.M.E.; Mulè, G.; et al. Relationship Between Renal Resistive Index and Retinal Vascular Density in Individuals with Hypertension. Biomedicines 2025,13, 312. https:// doi.org/10.3390/biomedicines 13020312 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Relationship Between Renal Resistive Index and Retinal Vascular Density in Individuals with Hypertension Caterina Carollo 1,* , Maria Vadalà2, Alessandra Sorce 1, Nicola Sinatra 3, Emanuele Orlando 4, Emanuele Cirafici 1, Miriam Bennici 1, Riccardo Polosa 5, Vincenza Maria Elena Bonfiglio 2, Giuseppe Mulè1 and Giulio Geraci 5 1 Unit of Nephrology and Dialysis, Hypertension Excellence Centre, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90133 Palermo, Italy; [email protected] (E.C.); [email protected] (G.M.) 2Biomedicine, Neuroscience and Advance Diagnostic (BIND) Department, University of Palermo, 90133 Palermo, Italy; [email protected] (M.V.) 3UOSD Nefrologia e Dialisi, Ospedale Paolo Borsellino, 91025 Marsala, Italy; [email protected] 4Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90133 Palermo, Italy; [email protected] 5Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; [email protected] (G.G.) *Correspondence: caterina.car[email protected] Abstract: Background/Objectives: Considering the physiological analogies between the eye and the kidney, this study aimed to investigate the potential relationship between retinal vascular density, assessed using Optical Coherence Tomography Angiography (OCT-A), and the renal resistive index (RRI) in patients with arterial hypertension. Methods: A total of 82 hypertensive patients (mean age 48 ± 13) were enrolled in the study. Participants underwent routine biochemical evaluations, office-based blood pressure measurement, 24 h ambulatory blood pressure monitoring, OCT-A imaging, and renal Doppler ultrasound examinations. Results: The mean RRI in the study population was 0.616 ± 0.06. Participants were divided into two groups based on the 75th percentile threshold of the RRI distribution (0.66, 95% CI 0.64–0.68). The group with RRI > 75th percentile, which appeared to have a higher number of smokers, exhibited significantly higher mean triglyceride and urinary albumin excretion (UAE) levels and a significantly reduced estimated glomerular filtration rate (eGFR) as compared to the group with RRI < 75th percentile. Among the hemodynamic parameters, 24 h pulse pressure (PP), daytime and nighttime PP, and nighttime systolic blood pressure (SBP) were significantly higher in the group with RRI > 75th percentile. Regarding retinal vascular density indices, the only significant difference was observed in the deep foveal vascular plexus, which displayed a reduced density in the group with RRI > 75th percentile. Logistic regression analysis revealed that RRI > 75th percentile was independently associated with increased nighttime mean pulse pressure (OR = 1.13, 95% CI : 1.049–1.221, p= 0.0014) and reduced deep foveal vascular density (OR = − 0.5026, 95% CI: 1.0493–1.2211, p= 0.0044). Conclusions: Our findings demonstrate that ocular microvascular alterations are associated with RRI, a marker with a well-established prognostic value for renal disease progression and systemic macrovascular dysfunction. These results further substantiate the close relationship between renal and ocular microcirculation. Keywords: hypertension; microcirculation; angio-OCT; renal resistive index Biomedicines 2025,13, 312 https://doi.org/10.3390/biomedicines13020312 Biomedicines 2025,13, 312 2 of 13 1. Introduction Systemic hypertension significantly impacts both the structure and function of the microvascular system [ 1 ]. The development of microvascular damage, particularly microvascular rarefaction, is believed to be a crucial pathological feature of hypertension. Changes in the structure and function of the microvasculature, in addition to being linked to the development of hypertension by influencing flow resistance and tissue perfusion, underlie much of the organ damage associated with arterial hypertension and appear to be crucial to its pathogenesis and progression [2,3]. Since the eye and kidney share common developmental, structural, and pathogenic pathways, changes in eye microcirculation could therefore be correlated with intrarenal hemodynamic damage, which has been associated with endothelial dysfunction, subclinical organ damage, and adverse cardiovascular outcomes, and appears to be a good indicator of systemic morphofunctional arterial impairment, particularly in hypertensive individuals with or without normal renal function [2,4–7]. The ocular microcirculatory system is readily accessible for clinical and morphological assessment, allowing for repeated and non-invasive examination [ 8 , 9 ]. This provides a unique opportunity to observe the vascular network when impacted by systemic conditions like hypertension, diabetes mellitus, and chronic kidney disease (CKD). The integration of optical coherence tomography angiography (OCTA) into clinical practice has introduced a dependable method for examining retinal and choroidal circulation from a morphological perspective. There is a substantial body of evidence that the arterial stiffness predicts future cardiovascular and total mortality risk in various patient populations, and clinical studies have associated its elevation with the development of macrovascular and microvascular damage [10–12]. The renal resistive index (RRI) is a sonographic measure of the intrarenal arteries, calculated as (peak systolic velocity—end-diastolic velocity)/peak systolic velocity. RRI measurement is relatively simple, and Doppler ultrasonography is a non-invasive, costeffective, and rapid imaging technique that provides real-time visualization of blood flow within the renal vessels. The RRI is a well-established prognostic marker for both renal disease progression and systemic macrovascular alterations [ 13 – 15 ]. Despite the evidence of strong prognostic potential, there is no universal optimal cut-off value for the RRI. Most of the dedicated studies report a range between 0.5 and 0.7 [16,17]. Since both renal and ocular microcirculation are influenced by similar mechanisms of hemodynamic and endothelial dysfunction, we aimed to investigate whether changes in the renal resistive index could be correlated with alterations in ocular microcirculation. Such a relationship could provide valuable insights into the systemic nature of vascular diseases, where both the kidney and the eye may reflect shared pathophysiological mechanisms, offering potential for the early diagnosis and monitoring of cardiovascular and renal conditions. 2. Materials and Methods The population for this study was selected from hypertensive patients attending the ESH Hypertension Excellence Centre Outpatient clinic of our Nephrology and Hypertension Unit. Enrolment was conducted in accordance with the following exclusion criteria: •Age < 20 years or >70 years; •Known diabetes or fasting glucose levels > 126 mg/dL; •Pregnancy; Biomedicines 2025,13, 312 3 of 13 • Systemic or ocular diseases (e.g., glaucoma, uveitis, high myopia, macular degeneration) or a history of ocular surgeries potentially causing retinal or choroidal damage; • Nephroparenchymal, renovascular, malignant, or endocrine hypertension, or obstructive sleep apnea syndrome; • Hereditary or non-hereditary kidney diseases, nephritic syndrome, or overt proteinuria/hematuria; • Estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m 2 or renal replacement therapy (transplant or dialysis); • Rapid decline in renal function, defined as a >25% reduction in eGFR or a >1.5-fold increase in serum creatinine levels from baseline [18]; • Poor-quality ultrasound imaging or abnormal renal morphology, as previously described [6]; • History or clinical evidence of heart failure (NYHA class II–IV), coronary artery disease, or cerebrovascular disease; • Major non-cardiovascular conditions (e.g., liver cirrhosis, chronic obstructive pulmonary disease, or a history of malignancies); • Conditions interfering with reliable blood pressure (BP) measurements using the oscillometric technique, such as atrial fibrillation, frequent ectopic beats, or second/thirddegree atrioventricular blocks. Patients with an arm circumference exceeding 32 cm were not excluded. Instead, appropriately sized cuffs were used to ensure accurate BP measurements. The study protocol adhered to the principles of the Declaration of Helsinki, and written informed consent was obtained from all participants. 2.1. Study Design A total of 82 patients with arterial hypertension (mean age 48.78 ± 12.61 years; 79% male) were enrolled and underwent the following assessments: •Routine biochemical evaluations; • 24 h ambulatory brachial blood pressure monitoring (ABPM) using an oscillometric BP Lab Vasotens device; •Optical Coherence Tomography Angiography (OCT-A); •Renal Doppler ultrasound. 2.1.1. Blood Pressure Measurement Office-based blood pressure (BP) was determined as the average of three consecutive measurements taken at two-minute intervals using an electronic oscillometric device (WatchBP Office, Microlife AG, Widnau, Switzerland) after five minutes of seated rest. For the 24 h ambulatory blood pressure monitoring (ABPM), an oscillometric device (BP Lab Vasotens) was used, adhering to current European Society of Hypertension (ESH) guidelines for proper recording [ 19 ]. Measurements were automatically taken at 15 min intervals during the daytime and at 20 min intervals during nighttime. The cuff was placed around the non-dominant arm, and patients were instructed to keep the arm still and avoid any movement during the readings. Oscillations within the cuff were recorded during gradual deflation to measure the BP. The 24 h pulse pressure (24 h PP) is defined as the difference between 24 h systolic blood pressure (SBP) and 24 h diastolic blood pressure (DBP). Daytime and nighttime pulse pressure are calculated using the same method within their respective time periods. Biomedicines 2025,13, 312 4 of 13 2.1.2. Biochemical Parameters Routine biochemical parameters were determined using the standard techniques with an automated analyzer (Boehringer Mannheim for Hitachi system 911, Mannheim, Germany). Glomerular filtration rate (GFR) was estimated using the CKD-EPI equation. 2.1.3. Ophthalmological Evaluation A comprehensive ophthalmological examination was performed on all the patients, including corrected visual acuity measurement using the Early Treatment Diabetic Retinopathy Study (ETDRS) charts [ 20 ]. Intraocular pressure (IOP) was assessed with a Goldmann applanation tonometer. Anterior and posterior segment evaluations were conducted using a slit lamp under pharmacologically induced mydriasis with 1% phenylephrine drops. Retinal imaging was performed using a swept-source optical coherence tomography (SS-OCT) device (Triton; Topcon Inc., Itabashi, Japan). All scans were conducted by a single operator between 10:00 A.M. and 12:00 P.M. The right eye was examined first, followed by a standardized scanning protocol. Poor-quality scans were repeated or discarded. Since no significant differences were observed between the two eyes, only one eye per subject was selected for analysis using a random number generator. If the selected eye’s scan quality was deemed insufficient, the contralateral eye was analyzed. 2.1.4. Retinal Imaging Protocols The following OCT scan protocols were used for each eye: •3D 7 ×7H Scan; •Macular Radial 6.0 Scan; •Angio-OCT 4.5 Scan, Retinal thickness (from the internal limiting membrane to the inner surface of the retinal pigment epithelium) and choroidal thickness (from the outer surface of the retinal pigment epithelium to the sclera) were automatically calculated using the OCT mapping software. Measurements were presented as mean ± standard deviation across the nine regions defined by the ETDRS study grid. The ETDRS grid divides the macula and choroid into nine fields. The grid, centered on the fovea, consists of three concentric rings with diameters of 1 mm, 3 mm, and 6 mm. The innermost and outermost rings are further divided into temporal, nasal, inferior, and superior quadrants, enabling detailed topographic analysis. 2.1.5. Quantitative Analysis OCT angiograms centered on the fovea (4.5 × 4.5 mm; 320 × 320 pixels) were analyzed to evaluate the superficial vascular plexus and the deep vascular plexus. The superficial vascular plexus is located within the ganglion cell layer, while the intermediate and deep vascular plexuses are positioned above and below the inner nuclear layer, collectively referred to as the deep capillary complex. The perimeter of the foveal avascular zone (FAZ) was manually delineated by a single operator on all the images of the superficial plexus. Using the OCT software (DRI Triton version 1.04E—1.36.2, Topcon Inc., Tokyo, Japan), the FAZ area was automatically calculated. To minimize the statistical errors associated with subjective measurements, the final data used in the study were derived from the average of two independent measurements. Image processing and measurements of retinal vascular density were performed using Image J software, version 1.49 (National Institutes of Health, Bethesda, MD, USA). Retinal vascular network images were generated using an automatic thresholding algorithm. Biomedicines 2025,13, 312 5 of 13 Vascular density was defined as the percentage of the area occupied by blood vessels, with vessels identified as pixels exceeding the defined threshold value. The calculations were performed on the following two regions of interest (ROIs): the foveal and parafoveal regions. The foveal ROI was defined as a central circle with a diameter of 120 pixels (1.2 mm), while the parafoveal ROI was defined as an annulus 91 pixels wide surrounding the foveal region. 2.1.6. Ultrasound Evaluation Intrarenal duplex ultrasonography was performed on all patients by a single trained operator blinded to the clinical data. The measurements were obtained using a GE Logiq P5-PRO device with a 4 MHz transducer and a Doppler frequency of 2.5 MHz. Patients were in a supine position, and the Doppler signal was obtained from the interlobar arteries by positioning the sample volume at the cortico-medullary junction. The Renal Resistive Index (RRI) was calculated using the following formula: RRI = Peak Systolic Velocity −End-Diastolic Velocity/Peak Systolic Velocity Values were averaged from six measurements (three per kidney) after conducting hypothesis testing and finding no statistically significant difference in the RRI values between the two kidneys. Doppler angles were maintained at <60 ◦ , ensuring no renal compression or Valsalva maneuver, which could artificially elevate RRI. 2.1.7. Renal Function Parameters In patients with urinalysis showing proteinuria, even in trace amounts, or microalbuminuria detected via semiquantitative dipstick evaluation, a 24 h urinary albumin excretion assay was requested. Albuminuria was measured using a turbidimetric method and expressed in mg/day. Serum creatinine levels were determined using a standardized enzymatic method (Creatinine Plus, Roche Diagnostics). The glomerular filtration rate (GFR) was estimated using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. The study population was stratified into two groups based on the intrarenal parenchymal renal resistive index (RRI) values above and below the 75th percentile of the RRI distribution; the threshold value was set at 0.66. 2.2. Statistical Analysis Statistical analysis was conducted using Medcalc version 15 and IBM-SPSS version 26 software packages. The distribution of continuous variables was evaluated for normality using the Kolmogorov–Smirnov test, which revealed a normal distribution for all variables except urinary albumin excretion and triglyceride levels, which exhibited a positively skewed distribution. These non-normally distributed variables were reported as medians and interquartile ranges and were log-transformed prior to further statistical analysis. Normally distributed continuous variables were presented as means and standard deviations. Categorical variables were expressed as percentages. Differences between groups were assessed using the independent t-test for continuous variables and the chi-squared test or, when appropriate, Fisher’s exact test for categorical variables. Potential confounders were adjusted using the analysis of covariance (ANCOVA). To examine the relationship between retinal vascular densities and the other variables, simple linear regression analysis and Pearson’s correlation coefficients were employed. To assess the independent contribution of retinal vascular densities, multiple linear regression models were built, with each retinal vascular density variable as the dependent variable and Biomedicines 2025,13, 312 6 of 13 the parameters that demonstrated significant associations with both the renal resistive index (RRI) and the retinal vascular densities in univariate analyses as independent variables. Additionally, stepwise logistic regression analysis was conducted, using RRI> or <the 75th percentile as the dependent variable, with the retinal vascular densities, the blood pressure values, and the other clinical parameters as explanatory variables. The null hypothesis was rejected for all two-tailed tests with p-values < 0.05. 3. Results The mean RRI in the entire study population was 0.616 ±0.06. Table 1presents the main demographic, anthropometric, and clinical characteristics of both the entire study population and the two groups in which the patients were categorized based on them being above or below the 75th percentile of the distribution of RRI (0.66, 95% CI 0.64–0.68). Table 1. Main demographic, anthropometric, clinical, and biochemical characteristics of the study population. Total (n= 92) RRI < 75 pct RRI > 75 pct p Age, y 48 ±13 48 ±12 52 ±14 0.24 Male sex, n(%) 65 (79) 47 (78) 18 (83) 0.85 BMI (kg/m2)28 ±4.6 27.5 ±4.6 29.4 ±4.5 0.10 Waist circumference (cm) 96 ±13 96 ±13 99 ±12 0.10 Current smokers, n(%) 20 (22.2) 11 (17.4) 9 (38.9) 0.054 eGFR (mL/min/1.73 m2)87 ±20 90 ±19 76 ±24 0.018 Urinary albumin excretion (mg/day) 76 (31–252) 36 (30–86) 387 (126–646) 0.009 Hemoglobin (g/dL) 14.6 ±1.4 14.7 ±1.3 14.2 ±1.6 0.12 Total Cholesterol (mg/dL) 194 ±29 196 ±29 186 ±26 0.14 HDL-Cholesterol (mg/dL) 48 ±12 49 ±12 45 ±9 0.11 Tryglicerides (mg/dL) 118 (75–154) 106 (89–131) 122 (80–165) 0.02 Fasting Blood glucose (mg/dL) 96 ±17 96 ±18 97 ±14 0.80 Table 2illustrates the distribution of pharmacologically treated patients, including the various antihypertensive drugs and other medications targeting the cardiovascular system. Table 2. Percentage of Pharmacologically Treated Patients. Total RRI < 75 pct RRI > 75 pct p Pharmacologically treated hypertensive subjects 67.1 69.4 58.8 0.60 Antihypertensive drugs Angiotensin-converting enzyme (ACE) inhibitors, % 29.1 27.4 35.3 0.57 Sartans, % 37.9 40.3 29.4 0.46 Calcium channel blockers, % 35.4 38.7 23.5 0.28 Alpha-2 Adrenergic Receptor Agonists, % 7.6 3.2 23.5 0.005 A-Blockers, % 27.8 29 23.6 0.57 α β-blockers, % 12.7 12.9 11.8 0.58 β-blockers, % 12.7 14.5 5.8 0.63 Diuretics % 35.4 33.9 41.2 0.71 Other cardiovascular agents Statins, % 11.4 9.7 17.6 0.71 Antiplatelet agents, % 25.3 29 11.8 0.57 Allopurinol, % 6.3 4.8 11.8 0.83 Biomedicines 2025,13, 312 7 of 13 While the percentage of subjects treated for hypertension did not differ significantly between the two groups, a higher prevalence of patients receiving centrally acting antiadrenergic agents was observed in the group with RRI > 75th percentile. Among the hemodynamic parameters, 24 h pulse pressure and daytime and nighttime pulse pressure were significantly higher in the group with RRI > 75th percentile (Table 3). Table 3. Hemodynamic parameters of whole population and two subgroups. Total RRI < 75 pct RRI > 75 pct p Office-based SBP (mmHg) 137 ±12 137 ±13 136 ±11 0.94 Office-based DBP (mmHg) 86 ±9 87 ±9 85 ±9 0.83 Office-based PP (mmHg) 51 ±9 50 ±9 52 ±12 0.83 Heart Rate (bpm) 73 ±11 74 ±12 72 ±11 0.87 Mean 24-h SBP (mmHg) 130 ±13 133 ±13 138 ±13 0.71 Mean 24-h DBP (mmHg) 82 ±9 83 ±9 82 ±9 0.92 24-h PP (mmHg) 47 ±10 45 ±10 52 ±9 0.013 Daytime SBP (mmHg) 134 ±13 129 ±13 134 ±13 0.70 Daytime DBP (mmHg) 85 ±9 86 ±10 85 ±9 0.92 Daytime PP (mmHg) 48 ±11 47 ±11 54 ±9 0.038 Nighttime SBP (mmHg) 119 ±14 117 ±13 126 ±15 0.03 Nighttime DBP (mmHg) 75 ±10 75 ±10 75 ±10 1 Nighttime PP (mmHg) 44 ±10 43 ±9 51 ±10 0.001 Abbreviations—SBP: systolic blood pressure; DBP: diastolic blood pressure; PP: pulse pressure. Regarding the retinal vascular density parameters, the only significant difference between the two groups was observed at the level of the deep foveal vascular plexus, which showed lower density in the group with the higher RRI (Table 4). Table 4. Retinal vascular density parameters of whole population and two subgroups. Total RRI < 75 pct RRI > 75 pct p p * Parafoveal Vascular Plexus Density (%)—Superficial Layer 37.3 ±0.87 37.4 ±0.84 36.9 ±0.89 0.053 0.052 Parafoveal Vascular Plexus Density (%)—Deep Layer 38.3 ±1.08 38.3 ±1.06 38.3 ±1.19 0.956 0.876 Foveal Vascular Density (%) –Superficial Layer 34.5 ±1.88 34.7 ±1.69 33.7 ±2.34 0.147 0.110 Foveal Vascular Density (%) –Deep Layer 32.9 ±1.95 33.26 ±1.72 31.47 ±2.11 0.01 0.001 p* after ANCOVA correction for eGFR, log-transformed triglycerides, mean nocturnal pulse pressure, and smoking). This difference remained statistically significant even after correction through the analysis of covariance (ANCOVA) for eGFR, mean nighttime pulse pressure, triglyceridemia, and smoking (p= 0.01 and p< 0.001, respectively). Table 5presents the statistically significant correlations between RRI and the various parameters, including demographic factors, markers of renal damage, glomerular filtration rate, nighttime pulse pressure, and deep foveal vascular density. Biomedicines 2025,13, 312 8 of 13 Table 5. Correlations between RRI and other parameters. Age eGFR Nighttime PP UAE Deep Foveal Plexus Vascular Density (RRI) r= 0.235 −0.288 0.3027 0.555 * −0.524 p= 0.0336 0.0087 0.0057 <0.001 <0.001 * The data related to UAE* refer to the patients who tested positive in the semi-quantitative evaluation of this parameter and subsequently underwent quantification through a 24-h urine collection. The inverse relationship between the RRI and the deep foveal density appeared particularly strong (Figure 1). Biomedicines 2025, 13, x FOR PEER REVIEW 8 of 14 Table 5. Correlations between RRI and other parameters. Age eGFR Nighttime PP UAE Deep Foveal Plexus Vascular Density (RRI) r= 0.235 −0.288 0.3027 0.555 * −0.524 p= 0.0336 0.0087 0.0057 <0.001 <0.001 * The data related to UAE* refer to the patients who tested positive in the semi-quantitative evaluation of this parameter and subsequently underwent quantification through a 24-hour urine collection. The inverse relationship between the RRI and the deep foveal density appeared particularly strong (Figure 1). Figure 1. Correlation between RRI and Deep Foveal Vascular Density. (Blue Line represents the linear regression trendline, showing the negative correlation between the two variables. The slope indicates the strength and direction of the relationship; dotted lines represents the confidence intervals for the regression line; orange dots represents re individual data points. In the group of patients in which the semiquantitative analysis of microalbuminuria was positive, comprising 18 subjects, a significant inverse correlation was observed between the logarithm of urinary albumin excretion and the superficial parafoveal vascular density (r = −0.555; p < 0.001). The small size of this subgroup does not allow us to perform statistical corrections to assess whether this relationship is independent of potential confounding factors. The association between renal resistive index and deep foveal vascular density was tested in multivariate models where, alternately, RRI and deep foveal vascular density were considered as dependent variables (see Tables 6 and 7). In both cases, the relationships between these two variables remained largely significant. 0.4 0.5 0.6 0.7 0.8 0.9 24 26 28 30 32 34 36 38 40 Foveal Vascular Density –Deep Layer (%) RRI r = − 0.524 P < 0.0001 N = 82 Figure 1. Correlation between RRI and Deep Foveal Vascular Density. (Blue Line represents the linear regression trendline, showing the negative correlation between the two variables. The slope indicates the strength and direction of the relationship; dotted lines represents the confidence intervals for the regression line; orange dots represents re individual data points. In the group of patients in which the semiquantitative analysis of microalbuminuria was positive, comprising 18 subjects, a significant inverse correlation was observed between the logarithm of urinary albumin excretion and the superficial parafoveal vascular density ( r=−0.555 ;p< 0.001). The small size of this subgroup does not allow us to perform statistical corrections to assess whether this relationship is independent of potential confounding factors. The association between renal resistive index and deep foveal vascular density was tested in multivariate models where, alternately, RRI and deep foveal vascular density were considered as dependent variables (see Tables 6and 7). In both cases, the relationships between these two variables remained largely significant. Biomedicines 2025,13, 312 9 of 13 Table 6. Multiple linear regression analysis. Dependent Variable: RRI B * SE R Partial p Foveal Vascular Density (%) –Deep Layer −0.0158 0.0029 −0.549 <0.0001 Mean nighttime PP (mmHg) 0.0020 0.0006 0.3063 0.0007 Constant 1.0468 * B unstandardized regression coefficient. SE standard error; R correlation coefficient. Other variables that did not reach statistical significance include eGFR, LogT, smoking, centrally acting anti-adrenergic drugs. Table 7. Multiple linear regression analysis. Dependent Variable: Foveal Vascular Density (%) –Deep Layer B * SE R Partial p RRI −18.98 3.43 −0.549 <0.0001 Mean nighttime PP (mmHg) 0.048 0.0027 0.2615 0.025 Constant 42.38 * B unstandardized regression coefficient. SE standard error; R correlation coefficient. Other variables that did not reach statistical significance include eGFR, LogT, smoking, centrally acting anti-adrenergic drugs. Moreover, in multiple logistic regression analysis, an increase in mean nighttime pulse pressure (OR = 1.1319, CI 1.049–1.221) and a reduction in deep foveal vascular density ( OR = 0.5026 ) (Table 8) are independently associated with a higher likelihood of an RRI above the 75th percentile. Table 8. Multiple logistic regression analysis. Dependent Variable: RRI> or <75 pct Odds Ratio 95% CI p Covariates Foveal Vascular Density (%) –DeepLayer −0.5026 0.3129–0.8073 0.0044 Mean nighttime PP (mmHg) 1.1319 1.0493–1.2211 0.0014 Constant 15.3587 Other variables that did not reach statistical significance include eGFR, LogT, smoking, centrally acting antiadrenergic drugs. 4. Discussion It is widely recognized that damage to small vessels exerts a comparable effect on morbidity and mortality, particularly due to the impairment of cerebral and renal microcirculations, which are especially vulnerable to fluctuations in systemic pulsatile blood flow. In this context, albuminuria, although not universally associated with microvascular damage, is regarded as a biomarker of microvascular dysfunction and serves as an independent predictor of both morbidity and mortality [21]. The assessment of macrovascular and microvascular circulation is essential for the timely and accurate diagnosis of vascular abnormalities, playing a critical role in the primary and secondary prevention of cardiovascular diseases, as well as in determining the most effective therapeutic strategies, particularly in patients with hypertension or chronic kidney disease. In the context of cardiovascular risk, an increased RRI, as an indicator of enhanced microvascular tone, is associated with the degree of renal impairment caused by elevated