Association between renal hyperfiltration, arterial stiffness and subendocardial viability ratio in prediabetic subjects: a cross-sectional study.

doi:10.21203/rs.3.rs-27648/v1

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Original investigation

Association between renal hyperfiltration, arterial stiffness and subendocardial viability ratio in prediabetic subjects: a cross-sectional study.

https://doi.org/10.21203/rs.3.rs-27648/v1

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posted 28 May, 2020

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Background: Glomerular hyperfiltration is a well-recognized early renal alteration in subjects with diabetes mellitus and a strong and independent predictor of cardiovascular events in these patients. Prediabetes has been associated with increased glomerular filtration rate (GFR), however, the association between prediabetes, glomerular hyperfiltration and early markers of cardiovascular damage has not been investigated. The aim of this study was to investigate the association between renal hyperfiltration (RHF) and early markers of cardiovascular disease in subjects with prediabetes.

Methods: Arterial stiffness [Augmentation Pressure (Aug), Augmentation Index (AugI)], subendocardial viability ratio (SEVR), pulse wave velocity (PWV), intima-media thickness (IMT), glycated hemoglobin (HbA_1c), oral glucose tolerance test and estimated GFR (eGFR) were evaluated in 230 subjects with prediabetes. The eGFR was assessed using the Chronic Kidney Disease Epidemiology Collaboration formula (CKD-EPI). Hyperfiltration was defined as an eGFR above the 75^th percentile.

The subjects were divided into two groups according to the presence or absence of RHF: 169 subjects with prediabetes without RHF and 61 subjects with prediabetes with RHF.

Results: Subjects with RHF showed higher Aug, AugI and lower SEVR compared with prediabetic subjects with lower eGFR (14.1±7.2 vs 10.8±6.2, 32.9±12.7 vs 27.6±11.7, 153.5±27.8 vs 162±30.2, P<0.05). No differences were found in PWV and IMT values between the two groups. We then performed multiple regression analysis to test the relationship between Aug, SEVR and several cardiovascular risk factors. In multiple regression analysis Aug was associated with age, systolic blood pressure (SBP), homeostatic model assessment for insulin resistance (HOMA-IR) and eGFR; the major determinants of SEVR were eGFR, HOMA-IR and SBP.

Conclusions: Our data show that subjects with prediabetes and RHF exhibited an increased Aug, AugI and a reduced SEVR. Longitudinal studies are needed to explore whether hyperfiltration increases the possibility of diabetic nephropathy and cardiovascular disease in individuals with prediabetes.

Endocrinology & Metabolism

Cardiac & Cardiovascular Systems

Renal Hyperfiltration

Prediabetes

Cardiovascular Risk

Arterial Stiffness

Prediabetes, a common disorder of glucose homeostasis, is considered a state associated with increased risk of cardiovascular disease [1, 2]; indeed, events portending vascular complications are under way prior to the formal diagnosis of diabetes, and a number of studies have confirmed that prediabetes is associated with more advanced vascular damage compared with normoglycemia [3].

Diabetic kidney disease (DKD) represents the leading cause of end-stage renal disease in the world, being responsible for 40% of the cases requiring renal replacement therapy [4]. Recently, there has been increased interest in renal hyperfiltration (RHF), which is a supraphysiological increase in glomerular filtration rate (GFR) and is well recognized as an early renal alteration in subjects with diabetes mellitus; moreover, RHF has been reported to be associated with increased cardiovascular risk and higher mortality in several categories of patients including people with diabetes [5, 6]. Several hypotheses may explain the relationship between hyperglycemia and RHF, including a crucial role of obesity and elevated plasma glucose levels per se, but one of the most corroborated theories is the “tubular hypothesis” [7]. This basically states that hyperglycemia leads to increased glucose and sodium chloride reabsorption at the proximal tubule of the nephron, mainly through the sodium–glucose cotransporters 2. The reduced amount of salts to the macula densa inhibits tubuloglomerular feedback that, together with the lower tubular back pressure, favors RHF.

Prediabetes has also been associated with RHF: Melsom et al. demonstrated that prediabetes was an independent risk factor for the development of RHF in a 6-year follow-up study conducted on 1261 patients using plasma iohexol clearance, which is the gold standard technique for the evaluation of GFR [8]. Moreover, other studies have shown that hyperglycemia and insulin resistance, both key features of prediabetic status, are both associated with RHF. Rodriguez-Poncelas et al. demonstrated in a study population including 9238 European subjects with prediabetes a positive correlation between fasting plasma glucose and estimated GFR, (eGFR) [9]. Another study reported that increased insulin resistance is associated with intrarenal hemodynamic abnormalities in subjects with impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT) [10].

Although prediabetes and RHF are both associated with increased cardiovascular risk, there is still no evidence in the literature concerning the role of RHF in enhancing cardiovascular damage in subjects with prediabetes. Previous data showed that subjects with prediabetes are a heterogeneous population of patients with different levels of cardiometabolic risk. Thus, identifying subjects with glomerular hyperfiltration among those with prediabetes might be helpful and clinically relevant to implement preventive and therapeutic strategies [11, 12].

The aim of our study was to investigate the association between renal hyperfiltration (RHF) and early markers of cardiovascular disease in subjects with prediabetes.

This cross-sectional study was conducted in the Department of Clinical and experimental Medicine, University of Catania, Italy. The aim was to investigate the association between RHF and early markers of cardiovascular disease in subjects with prediabetes.

Study Population: 435 subjects with no previous history of diabetes and cardiovascular disease attending our University Hospital for cardiometabolic risk evaluation were consecutively screened based on inclusion/exclusion criteria. The inclusion criteria were the following: age range between 35 and 65 years; body mass index (BMI) between 18.2–40 kg/m².

The exclusion criteria were: a previous history of diabetes, previous history of overt cardiovascular events (stroke, ischemic heart disease, chronic obstructive peripheral arteriopathy, or heart failure), chronic kidney disease (eGFR < 60 ml/min/1.73 m²), anemia, or hemoglobinopathies, use of medications known to affect glucose metabolism, positivity for antibodies to hepatitis C virus or hepatitis B surface antigen, clinical evidence of advanced liver disease, chronic inflammatory disease or other chronic diseases and/or recent history of acute illness, malignant disease, and drug or alcohol abuse.

All subjects underwent a complete evaluation of glycemic status including fasting glucose, oral glucose tolerance test (OGTT) and glycated hemoglobin A_1c (HbA_1c). Only subjects with prediabetes participated in the study.

75-g OGTT was performed with basal, 1-h and 2-h sampling for plasma and insulin as previously described [13, 14]. Glucose tolerance status was defined on the basis of fasting glycemia, OGTT and HbA_1c according to American Diabetes Association recommendations [15]. Prediabetes was defined as a fasting glycemia between 100–125 mg/dL and/or a 120-min after OGTT glycemia between 140–199 mg/dL and/or a HbA_1c between 5.7–6.4%. Homeostatic model assessment for insulin resistance (HOMA-IR) and Matsuda index were calculated as previously described [16]. Body weight and height were measured, and BMI was calculated as weight (kg)/[height (m)]². Blood pressure (BP) was measured with a calibrated sphygmomanometer after the subject had rested in the supine position for 10 min. Venous blood samples were drawn from the antecubital vein on the morning after an overnight fast. Baseline venous blood samples were obtained for the measurement of plasma glucose, total cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, and high sensitivity C-reactive protein (hs-CRP). Low density lipoprotein (LDL) cholesterol concentrations were estimated using the Friedewald formula

Biochemical Analyses

Plasma glucose, serum creatinine, total cholesterol, triglycerides, HDL cholesterol, and hs-CRP were measured using available enzymatic methods.

HbA_1c was measured via high performance liquid chromatography using a National Glycohemoglobin Standardization Program and standardized to the Diabetes Control and Complications Trial assay reference [17]. Chromatography was performed using a certified automated analyzer (HPLC; HLC-723G7 hemoglobin HPLC analyzer; Tosoh Corp.) (normal range 4.25–5.9% [23–41 mmol/mol]).

Pulse wave analysis

Arterial stiffness evaluation was performed with the patients in fasting status and the explicitly expressed recommendation to avoid smoking and coffee intake in the morning of the procedure. All measurements were performed from the right radial artery by applanation tonometry using a Millar tonometer (SPC-301; Millar instruments, Houston, TX.) as previously described [18]. Data were collected directly into a desk-top computer and processed with the SphygmoCor CvMS (Atcor Medical, Sidney, Australia), which allows continuous on-line recording of the radial artery pressure waveform. The integral system software was used to calculate an average radial artery waveform, and generate the corresponding ascending aortic pressure waveform.

The aortic waveform was subjected to further analysis for calculation of aortic pressure augmentation pressure (Aug), the augmentation Index (AugI, calculated by dividing augmentation by pulse pressure) and Buckberg’s subendocardial viability ratio (SEVR, area of diastole divided by area of systole during one cardiac cycle in the aorta). Pulse pressure is the difference between systolic and diastolic blood pressures.

Pulse wave velocity

The SphygmoCor CvMS (AtCor Medical, Sydney, Australia) system was used for the determination of the pulse wave velocity (PWV) as previously described [19]. This system uses a tonometer and two different pressure waves obtained at the common carotid artery (proximal recording site) and at the femoral artery (distal recording site). The distance between the recording sites and suprasternal notch was measured using a tape measure. An electrocardiogram was used to determine the start of the pulse wave. The PWV was determined as the difference in travel time of the pulse wave between the two different recording sites and the heart, divided by the travel distance of the pulse waveform. The PWV was calculated on the mean of 10 consecutive pressure waveforms to cover a complete respiratory cycle.

Carotid Ultrasound examination

Ultrasound scans were performed using a high-resolution B-mode ultrasound system (MyLab 50 Xvision; Esaote Biomedica SpA, Florence, Italy) equipped with a 7.5-MHz linear array transducer as previously described [20]. To exclude interobserver variability, a single physician who was blinded to the clinical and laboratory characteristics of the patients performed all ultrasound examinations. The subjects were examined in the supine position. Longitudinal scans were performed, and measurements were conducted at a total of six plaque-free sites 1 cm proximal to the carotid bulb. The obtained values were averaged and are presented as the mean of the intima media thickness (IMT) of the common carotid artery. Plaques, defined as a clearly isolated focal thickening of the intima-media layer with a thickness of 1.4 mm, were not observed in any individuals. All measurements were obtained in diastole, assessed as the phase in which the lumen diameter is at its smallest and the IMT is at its largest.

Statistical analyses

The sample size was calculated based on Aug using a level of significance (α) set to 5% and power (1- β) to 80%. We based the power calculation on previous studies examining Aug among patients with early alteration of glucose homeostasis and controls. The estimated sample size was 60 patients per group. Statistical comparisons of clinical and biomedical parameters were performed using Stat View 6.0 for Windows. Data are given as means ± SD or median (IQR). Each variable’s distributional characteristics including normality were assessed by the Kolmogorov-Smirnov test. Statistical analysis consisted of ANOVA followed by Bonferroni post-hoc test for continued variables and χ² test for non-continuous variables. All analyses were adjusted for age and sex. A P value less than 0.05 was considered statistically significant. When necessary, numerical variables were logarithmically transformed to reduce skewness, and values were expressed as median and interquartile range.

In order to identify variables independently associated with variations of Aug and SEVR, we performed three multivariate regression models: the first model included cardiovascular risk factors (age, BMI, sex, smoking status, systolic and diastolic BP, total cholesterol, HDL cholesterol, triglycerides, LDL cholesterol, and uric acid); the second model included variables related to glycemic status (fasting glucose, HbA_1c, 1-h glucose and 2-h glucose, and HOMA-IR). Subsequently, variables reaching significance were inserted into a multiple regression model including eGFR. The variance inflation factor was used to check for the problem of multi-collinearity among the predictor variables in multiple regression analysis.

The local ethics committee approved the study. Informed consent was obtained from each participant.

In total, 419 individuals, referred to our outpatient clinic, were evaluated. Of these, 230 subjects satisfied prediabetes diagnostic criteria and were included in the study (Fig. 1).

Prediabetic subjects were stratified according to the presence or absence of RHF (61 prediabetic subjects with RHF and 169 prediabetic subjects without RHF). The eGFR was assessed using the CKD-EPI formula. Hyperfiltration was defined as an eGFR above the 75th percentile.

The clinical and biochemical characteristics of the study subjects are presented in Table 1. Subjects with prediabetes and RHF exhibited similar characteristics compared with the subjects with a lower eGFR. The two groups differed in triglycerides and HDL cholesterol serum levels. Furthermore, prediabetic subjects with RHF exhibited a significantly lower Matsuda Index [3.9(2.8–7.4) vs 4.5(3–9), P = 0.01] and a higher HOMA-IR without statistical significance [1.87(1.3–2.8) vs 1.75(1.1–2.2), P = 0.14)

A simple regression analysis was performed to test the relationship between eGFR and different clinical variables in prediabetic subjects. eGFR was associated with HDL cholesterol (r = 0.17, P < 0.05) triglycerides (r = 0.12, P < 0.05), HOMA-IR (r = 0.14, P < 0.05), fasting glucose (r = 0.22, P < 0.05), 1-h glycemia (0.18, P < 0.05) and uric acid (r = 0.14, P < 0.05).

Table 1

– Clinical and metabolic characteristics of the prediabetic population according to presence or absence of renal hyperfiltration.
	Overall (n = 230)	Prediabetes without RHF (n = 169)	Prediabetes with RHF (n = 61)	P Value
Age (year)	50.4 ± 11.4	50.2 ± 9.6	51.1 ± 11.1	0.56
BMI (Kg/m²)	30.2 ± 5.3	29.9 ± 4.8	30.7 ± 5.1	0.28
Systolic BP (mmHg)	121.8 ± 13.8	122.1 ± 14.1	120.4 ± 13	0.42
Diastolic BP (mmHg)	74.7 ± 10.8	74.9 ± 10.6	74.2 ± 11.6	0.60
Total cholesterol (mg/dL)	203.2 ± 39	202.9 ± 38.4	205.4 ± 43.6	0.68
HDL cholesterol (mg/dL)	47.4 ± 13.4	46.2 ± 13.1	52.1 ± 13.3*	0.02
Triglycerides (mg/dL)	101.5(75–143)	106(82-145.5)	82(68.5–136)*	0.009
LDL cholesterol (mg/dL)	121.5 ± 33.9	132.8 ± 34.7	132.1 ± 38.3	0.9
eGFR (ml/min)	108.2 ± 16.3	100.7 ± 11.3	129.7 ± 6.3*	0.00
HOMA-IR	1.8(1.2–2.6)	1.75(1.1–2.2)	1.87(1.3–2.8)	0.14
Matsuda Index	4.2(2.9–6.2)	4.5(3–9)	3.9(2.8–7.4)	0.03
Fasting glucose (mg/dL)	93.1 ± 11.2	94.1 ± 11.9	90.1 ± 8.8	0.63
1-h glucose (mg/dL)	162.3 ± 43.1	163.6 ± 44.3	157.6 ± 40.3	0.31
2-h glucose (mg/dL)	128 ± 33.5	129.1 ± 32.4	125.5 ± 36.6	0.16
HbA_1c (%)	5.9 ± 0.28	5.9 ± 0.29	5.9 ± 0.22	0.43
Hs-CRP(mg/dL)	0.22(.1-0.44)	0.33(0.1–0.43)	0.35(0.09–0.44)	0.70
Uric acid (mg/dL)	5.1 ± 1.4	5.3 ± 1.5	4.7 ± 0.9	0.06
Active smokers (%)	38%	39%	32%	0.59
Hypertension (%)	30%	30%	30%	0.98
Statin therapy (%)	24%	23%	25%	0.65
Sex (M%)	47%	45%	27%*	0.001
ACEIs or ARBs therapy (%)	13%	14%	12%	0.54
Data are presented as mean ± SD or median(IQR); p values refer to results after analysis with adjustment for age and sex; RHF, renal hyperfiltration; BMI, body mass index; HbA_1c, glycated haemoglobin; BP, Blood pressure; eGFR, estimated glomerular filtration rate; HOMA-IR, homeostasis model assessment-insulin resistance; hs-CRP, high sensitivity C-reactive protein; ACEIs, angiotensin converting enzyme inhibitors; ARBs angiotensin receptor blockers. Smoking was quantified (number of cigarettes and years smoked) and smoking status was classified in active and nonsmokers. Hypertension was defined as systolic blood pressure ≥ 135 mmHg or diastolic blood pressure ≥ 85 mmHg or taking any hypertension medications.

Prediabetes, Renal Hyperfiltration and Cardiovascular risk

Subjects with prediabetes and RHF exhibited increased Aug and AugI (14.1 ± 7.2 vs 10.8 ± 6.2 mmHg, P < 0.05 and 32.9 ± 12.7 vs 27.6 ± 27.1%, P < 0.05) and SEVR was reduced (153.5 ± 27.3 vs 162.1 ± 30.2, P < 0.05) compared with subjects with prediabetes without RHF. No differences were found in PWV and IMT values between the two groups (Table 2).

Table 2

Early markers of cardiovascular damage according to presence or absence of renal hyperfiltration.
	Overall (n = 230)	Prediabetes without RHF (n = 169)	Prediabetes with RHF (n = 61)	P value
Aug (mmHg)	11.7 ± 6.6	10.8 ± 6.2	14.1 ± 7.2	0.001
AugI (%)	29 ± 12.2	27.6 ± 11.7	32.9 ± 12.7	0.004
PWV (cm/sec)	7.7 ± 1.6	7.7 ± 1.6*	7.6 ± 1.4	0.66
IMT (mm)	0.73(0.66–0.82)	0.73(0.66–0.82)*	0.73(0.65–0.83)	0.52
SEVR (%)	159.5 ± 29.9	162.5 ± 30.2	153.5 ± 27.8	0.008
Data are presented as mean ± SD; p values refer to results after analysis with adjustment for age and sex: RHF, renal hyperfiltration; Aug, Augmentation pressure: AugI, Augmentation index; PWV pulse wave velocity; IMT, intima-media thickness; SEVR, subendocardial viability ratio.

We then performed multiple regression analysis to test the relationship between Aug, SEVR and several cardiovascular risk factors. Three multivariate regression models were performed: the first model included cardiovascular risk factors (age, BMI, sex, smoking status, systolic and diastolic BP, total cholesterol, HDL cholesterol, triglycerides, LDL cholesterol, and uric acid); the second model included variables related to glycemic status (fasting glucose, HbA_1c, 1-h and 2-h glucose, HOMA-IR). Subsequently, variables reaching significance were inserted into a multiple regression model including eGFR. The first model exhibited a significant correlation among Aug, age (P = 0.001) and systolic BP (P = 0.001). The second model exhibited a significant correlation between Aug, age (P = 0.001), systolic BP (P = 0.01) and HOMA-IR (P = 0.02). Finally, the third model showed a significant correlation among Aug, age (P = 0.001), systolic BP (P = 0.01), HOMA-IR (P = 0.01) and eGFR (P = 0.003) (Table 3).

SEVR was associated with systolic blood pressure (P = 0.04) and uric acid (P = 0.03) in the first model (P < 0.05). The second model showed a significant association between SEVR, systolic BP (P = 0.03) and HOMA-IR (P = 0.02). Finally, the third model showed a significant correlation among SEVR, systolic BP (P = 0.003), HOMA-IR (P = 0.02) and eGFR (P = 0.001) (Table 3).

Table 3

Multiple regression analysis evaluating Aug and SEVR as dependent variables.
	Coefficient β	P value
Aug
Model 1*
Age	0.5	0.001
Systolic BP	0.18	0.001
HDL	0.14	0.01
Model 2**
Age	0.55	0.001
Systolic BP	0.15	0.01
HOMA-IR	0.14	0.02
Model 3***
Age	0.53	0.001
Systolic BP	0.21	0.01
HOMA-IR	0.17	0.01
eGFR	0.2	0.0003
SEVR
Model 1*
Systolic BP	-0.31	0.04
Uric acid	-0.26	0.03
Model 2**
Systolic BP	-0.15	0.03
HOMA-IR	-0.17	0.02
Model 3***
Systolic BP	-0.17	0.003
HOMA-IR	-0.16	0.02
eGFR	-0.23	0.001
*Model 1 was adjusted for age, sex, BMI, smoking status, systolic BP, diastolic BP, LDL cholesterol, HDL cholesterol, uric acid.
**Model 2 was adjusted for fasting glucose, 60 min glucose, 120 min glucose, HbA_1c, HOMA-IR, and Matsuda Index
***Model 3 was adjusted for eGFR

Prediabetes is a condition at increased cardiovascular risk and the identification of individuals with higher risk is critical from the clinical point of view [21]. In this study, we investigated the association between renal hyperfiltration (RHF) and early markers of cardiovascular disease in subjects with prediabetes.

The main finding of this study is that prediabetic subjects with RHF exhibited higher Aug and AugI compared with prediabetic subjects without RHF. This is not the first study indicating RHF as an early sign of cardiovascular disease: RHF has been reported to be associated with other conditions at high cardiovascular risk such as diabetes, hypertension and obesity [22–24]; however, to the best of our knowledge, this is the first study to investigate the association of RHF with early markers of cardiovascular damage in subjects with prediabetes. The clinical relevance of RHF in determining cardiovascular risk has been underlined by several studies: Park et al. reported 37% higher risk for all-cause mortality and 66% higher risk of cardiovascular mortality in individuals with RHF in an apparently healthy population after adjustment for body mass index and known risk factors, including smoking [25]. Another study reported that hyperfiltration (eGFR > 105 mL/min per 1.73 m2) was associated with a significant increase risk of death after adjustment for age and exclusion of patients with diagnosed diabetes [6]. Finally, in a study including only patients with clinical cardiovascular disease, van der Sande et al. showed an increased risk of all-cause mortality in the highest quartile of eGFR/kidney length ratio, which was presumed to represent hyperfiltration [26].

In our study, we found a significant increase in Aug and AugI in subjects with RHF compared with subjects without RHF; in contrast, no difference was observed between PWV and IMT in the same groups. This discordance is not surprising: indeed, wave reflection indices and aortic stiffness do not always change in parallel; other studies reported that Aug, AugI and PWV were differentially affected in subjects with an alteration of glucose homeostasis [3, 27, 28]. AugI is primarily determined by the magnitude and timing of the reflected pressure waves, which depend on the tone and elasticity of the small muscular arteries at the major sites of pressure wave reflection; PWV is a measure of elastic- type large artery stiffness and is inversely related to aortic distensibility and compliance [29]. Therefore, arterial stiffness may be changed independently of PWV due to alterations in vascular smooth muscle tone that do not affect the elastic aorta. Increases in oxidative stress and reduced endothelial nitric oxide availability may impact the peripheral arteries more than the aorta.

Furthermore, we found a lower insulin sensitivity (expressed as Matsuda Index) in prediabetic subjects with RHF and demonstrated a significant association between high eGFR and insulin resistance (expressed as HOMA index); as mentioned above, this is not the first study addressing this topic; recently, van Bommel E et al. investigated the relationship between insulin sensitivity and renal hemodynamics measured with gold standard methodologies (hyperinsulinemic-euglycemic clamp and urinary inulin) in adults with type 2 diabetes and preserved renal function [30]. The association between insulin resistance and cardiovascular disease has been previously reported [31], however, these results suggest that insulin resistance may be associated with hemodynamic renal dysfunction, including hyperfiltration, and raise the possibility that it may represent one factor linking renal function and cardiovascular risk. It is interesting to underline that treatments known to increase insulin sensitivity, such as rosiglitazone or bariatric surgery, were found to reduce glomerular hyperfiltration and renal outcomes in patients with early type 2 diabetes [32].

In this study, SEVR, a non-invasive parameter of coronary perfusion, was reduced in prediabetic subjects with RHF compared with subjects with prediabetes and lower eGFR. Few studies investigated the relationship between SEVR and renal hemodynamic parameters. Di Micco L et al. analyzed the relationship between SEVR and cardiovascular mortality in patients with chronic kidney disease (CKD); they reported that a reduction of SEVR values impacts cardiovascular mortality in CKD patients [33]. Another study explored the hypothesis that arterial stiffness indexes, which predict cardiovascular disease, might have potential for risk assessment; in this study, SEVR, but not Aug, was independently and negatively related with measures of renal function such as presence of macroalbuminuria and degree of albuminuria indicating a potential use of SEVR for early detection of individuals at risk of cardiovascular and renal complications of type 1 diabetes [34].

There were several limitations of this study. First, this was a cross-sectional study, and a longitudinal causal relationship cannot be established between changes in cardiovascular markers and eGFR. Moreover, as mentioned above, there is not a universal definition of renal hyperfiltration. Therefore, we decided to adopt a relative definition such as a eGFR above the 75th percentile in our population, making, perhaps, the term of “highest GFR quartile group” more appropriate instead of RHF. The data regarding albuminuria in our population are not available for most subjects. As is already known, albuminuria represents a cardiovascular risk factor. Nevertheless, we conducted an analysis in a small subgroup of subjects and no statistically significant difference emerged as far as cardiovascular risk markers are concerned. Finally, only Caucasian patients were included, it is a single-center study, and no follow-up has been performed; all these study characteristics limit the generalizability of the results.

Our data show that subjects with prediabetes and RHF exhibited an increased Aug, AugI and a reduced SEVR. Longitudinal studies are needed to explore whether hyperfiltration increases the odds of diabetic nephropathy and cardiovascular disease in individuals with prediabetes. According to these data, subjects with prediabetes and RHF should be treated earlier in cardiovascular risk prevention to delay the progression from chronic kidney disease to renal dysfunction.

AugI

Augmentation Index

Aug

Augmentation pressure

Blood pressure

BMI

Body mass index

CKD

Chronic kidney disease

DKD

Diabetic kidney disease

eGFR

estimated Glomerular filtration rate

HbA_1c

Glycated hemoglobin A_1c

HDL

High density lipoprotein cholesterol

Hs-CRP

High sensitivity C-reactive protein

HOMA-IR

Homeostatic model assessment for insulin resistance

IFG

Impaired fasting glucose

IGT

Impaired glucose tolerance

IMT

Intima media thickness

LDL

Low density lipoprotein cholesterol

OGTT

Oral glucose tolerance test

PWV

Pulse wave velocity

RHF

Renal hyperfiltration

SEVR

Subendocardial viability ratio

Ethics approval and consent to participate

The study was approved by ‘Catania 2’ Ethical Committee and informed consent was obtained from each participant.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by 2016/2018 Department Research Plan of University of Catania, Italy, Department of Clinical and Experimental Medicine (project #A).

Authors’ contribution

A.D. contributed to study design, researched data, contributed to discussion, and wrote the manuscript. S.M. contributed to study design, researched data and reviewed and edited the manuscript. R.S. contributed to discussion, and reviewed and edited the manuscript. Z.L. researched data and reviewed and edited the manuscript. V.F. reviewed and edited the manuscript. F.U. researched data and reviewed and edited the manuscript. A.F. researched data and reviewed and edited the manuscript. S.D. researched data and reviewed and edited the manuscript. A.S. researched data and reviewed and edited the manuscript. S.P. contributed to study design and discussion and reviewed and edited the manuscript. P.C contributed to study design and discussion and reviewed and edited the manuscript. F.P. researched data, contributed to discussion, and reviewed and edited the manuscript. R.A. designed the study, researched data, contributed to discussion, and reviewed and edited the manuscript.

Acknowledgements

The authors wish to thank the Scientific Bureau of the University of Catania for language support.

Author’s information

F.P. is the current president of the Italian diabetes Society.

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Association between renal hyperfiltration, arterial stiffness and subendocardial viability ratio in prediabetic subjects: a cross-sectional study.

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Abstract

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Background

Methods

Results

Prediabetes, Renal Hyperfiltration and Cardiovascular risk

Discussion

Conclusions

Abbreviations

Declarations

References

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