Review and Practical Excursus of the Propensity Score: Low Protein Diet Compared to Mediterranean Diet in Patients With Chronic Kidney Disease

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Vincenzo Calabrese

Vincenzo Calabrese1, Guido Gembillo1, Martina Buda2, Valeria Cernaro1, Elisa Longhitano1, Antonello Pontoriero3, Matteo Polizzi4, Daniela Metro1, Domenico Santoro1

1 Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, A.O.U. ‘G. Martino’, University of Messina, Messina, Italy
2 General and Oncologic Surgery Unit, Faculty of Medicine, University of Messina, G. Martino University Hospital, Messina, Italy
3 Unit of Nephrology, Department of medicine ASP 5 Messina, P.O. Milazzo
4 University of Messina, Messina, Italy

Corresponding author:
Vincenzo Calabrese
Tel: 328736905
E-mail: v.calabrese@outlook.it

 

Abstract

Although Randomized clinical trials (RCT) represent the gold standard to compare two or more treatments, the impact of observational studies cannot be ignored. Obviously, these latter are performed on unbalanced sample, and differences among the compared groups could be detected. These differences could have an impact on the estimated association between our allocation and our outcome. To avoid it, some methods should be applied in the analysis of observational cohort.
Propensity score (PS) can be considered as a value which sums up and balances the known variables. It aims to adjust or balance the probability of receiving a specific allocation group, and could be used to match, stratify, weight, and perform a covariate adjustment. PS is calculated with a logistic regression, using allocation groups as the outcome. Thanks to PS, we compute the probability of being allocated to one group and we can match patients obtaining two balanced groups. It avoids computing analysis in unbalanced groups.
We compared low protein diet (LPD) and the Mediterranean diet in CKD patients and analysed them using the PS methods. Nutritional therapy is fundamental for the prevention, progression and treatment of Chronic Kidney Disease (CKD) and its complications. An individualized, stepwise approach is essential to guarantee high adherence to nutritional patterns and to reach therapeutic goals. The best dietary regimen is still a matter of discussion.  In our example, unbalanced analysis showed a significant renal function preservation in LPD, but this correlation was denied after the PS analysis.
In conclusion, although unmatched analysis showed differences between the two diets, after propensity analysis no differences were detected. If RCT cannot be performed, balancing the PS score allows to balance the sample and avoids biased results.

Keywords: Chronic Kidney Disease, Low Protein Diet, Matching, Mediterranean Diet, Nutritional Therapy, Propensity Score, Randomized Clinical Trials

Introduction

Clinical investigations are mainly categorized in observational and interventional studies, the latter including randomized controlled trials (RCT) [1]. Comparative effectiveness studies belong to the family of observational studies and aim to compare two active treatments to identify which one is more efficient in improving the time course of a disease or reducing the risk of a given condition in real life (i.e., in a context different from an RCT) [2]. From this perspective, this type of study design differs from RCTs because the latter specifically contemplate ‘no intervention’ (i.e., the placebo arm).

Treatments are candidates to be investigated by a study of comparative effectiveness only when the same treatment was proved to be effective versus a control in an RCT. The main reason why these studies are considered with caution by the scientific community is the lack of randomization, which implies that the results of these studies are prone to a peculiar type of bias called ‘confounding by indication’ [3]. In a given treatment-outcome pathway, a confounder is a variable that is associated with the treatment (i.e., it differs between the study arms). It is not an effect of the treatment, does not lie in the causal pathway between the treatment and the outcome, and represents a risk factor for the outcome. In real life, a confounder can increase, reduce, or definitely obscure the true effect of treatment on an outcome. Despite these challenges, observational studies of effectiveness offer opportunities to examine questions impossible to be investigated by RCTs [4]. First, they can be used to examine the effectiveness of medication that has already obtained marketing authorization and for which funding for further trials may be limited. Second, they can allow the examination of effectiveness for rare treatment indications. Third, a large observational study can be more representative of a clinical population and less prone to selection bias than a trial.

In observational studies of effectiveness, common methods used to adjust to confounding are multiple regression models [5], the use of instrumental variables [6], and the propensity score (PS) [7]. Briefly, multiple regression analyses are performed by including in the model all variables that meet the criteria to be considered as confounders. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the study outcome. As an example, we can consider an observational multicenter study that evaluates how different treatments can affect a clinical outcome. The facility allocation can be considered as the result of a ‘natural experiment’ by simulating a randomization. In this manuscript, we describe an efficient statistical technique used by researchers to mitigate the problem of confounding in observational studies of effectiveness.

 

Propensity score

The propensity score (PS) was described in 1983. This method allows adjusting or balancing for the probability to receive a specific allocation group, an estimation of the likelihood of being in one or in another group in relation to a set of covariates [8]. PS could be used to match, stratify, weight, and perform a covariate adjustment. If the outcome is a binary variable, matching has less bias than stratification or covariate adjustment, as in a time-dependent outcome both matching and Inverse Probability of Treatment Weighting (IPTW) are less biased than stratification or covariate adjustment. PS is calculated with a logistic regression, using allocation groups as outcome. Thanks to this method, we can compute the probability of confounder variables to be allocated in one group. Since PS has no limits of variables, it can be used in small samples and for rare diseases [9], unlike multivariate regression.

Matching

Matching with PS methods allows us to compare one or more patients with the same allocation probability, so it follows that matched patients have similar features, decreasing bias. This method consists of matching cases of two or more groups on the basis of similar predicted PS, thus allowing the comparison of groups with an equal distribution of confounders (covariate balance) [10, 11]. Imaging having two groups of patients, at first, we need to compute PS, corresponding to the probability of receiving allocation in group A, for each one of them [12]. By doing this, a binomial logistic regression is performed to select, among the study variables, those associated with the allocation variable. Patients with the same PS value are thus compared. Minimizing the differences between patients, and comparing homogeneous groups, confounding is reduced.

Stratification

The stratification by PS follows the matching methods. Strata will be created between subjects with similar PS of treatments. The Stratification method removes about 90% of bias due to covariate imbalance [13].

Formally, stratification by PS can be resumed as follows:

  • choosing variables included in the PS model among personal data, comorbidities, laboratory data, and variables clinically related to outcome
  • estimating PS value for each subject, with logistic regression using allocation as the dependent variable
  • calculating the Cumulative Distribution Function for each subject, able to define the distribution also in a discrete and binomial variable
  • ranking population based on PS value, dividing the whole sample into quartiles, tertiles, deciles, etc., based on PS values
  • assessing balance for each of the K (K is the indicator of the treatment group), analyzing the baseline features
  • retaining the PS value ordering that creates strata with the best covariate balance and conducting a stratified outcome analysis to estimate ATE or ATT [14].

The number of strata can be evaluated based on the number of covariates (2×covariates – 1) with groups of more than ten subjects [15]. In a large observational study, Cernaro V. et al. [16], on behalf of the Workgroup of the Sicilian Registry of Nephrology, analyzed the impact of convective dialysis on mortality and cardiovascular mortality. They performed Cox Regression analysis with incremental multivariate models but, although the independent impact of convective dialysis on mortality, many other variables were related to the outcome.

Thus, as highlighted in their methods section, PS stratification was computed to perform a sensitivity analysis [17]. PS was computed through a multivariate logistic regression model including age, gender, ethnicity, arterial hypertension, diabetes mellitus, and cardiac diseases. Then, the whole sample was divided into quartiles (based on PS value) and survival analyses computed in the whole sample were repeated. These latter results confirmed the independent impact of the treatment, but in subsamples that are theoretically more homogenous because PS value was computed on the bases of the possible confounding.

Inverse Probability of Treatment Weighting (IPTW) Estimation

IPTW analyses aimed to create a weighted sample in which the distribution of each confounding variable was the same between the compared groups [18]. Patients will be allocated the reciprocal of the PS value: each patient of the treated group receives the weight of 1/PS and each patient of the untreated group receives the weight of 1/(1-PS). A treated patient with a low PS value enters in the analysis with a high weighting because he is considered likely an untreated patient in terms of comorbidities, so a valid comparison can be made between the two [19]. Practically, in the analysis, each patient is evaluated as many times as their IPTW is.  A treated patient with a PS of 0.1 will weigh 1/0.1=10 and will be considered in the analysis ten times. Similarly, a treated patient with high PS, for example 0.8, will weigh 1/0.8=1.25 and it will be considered in the analysis 1.25 times. Moreover, IPTW was at the basis of the Marginal Structured Models, a multistep estimation procedure designed to control confounding variables at different time points in longitudinal studies [20]. IPTW method is not robust against the outliers.

Covariate adjustment

This method uses the PS values as a covariate in a linear regression analysis. Even if there is no significant association between the covariates used to compute PS value and the outcome, the use of PS value as a covariate allows us to approximate the effect of each of the aforementioned covariates [21].

 

Practical example

To explain these methods, we will use a dataset containing 75 non-randomized patients with CKD stage III-IV.

All the remaining patients gave written consent to data processing for research purposes in respect of privacy. Ethical approval was not necessary according to National Code on Clinical Trials declaration and according to Italian ministerial rules of September 6, 2002 n°6, because our observation derives from a real-life retrospective study.

Patients were followed up for one year.  40 patients followed an LPD, defined by a protein intake of 0.6 g/kg/day (Group A), and 35 patients were subjected to the Mediterranean diet (Group B). The allocation, according to the real-life observation design, was based on dietician suggestions, patient’ habits, and adherence abilities, which were evaluated during the baseline visit.  Supplementary Table 1 and Table 2 summarized the details about the quantity and the nature of both diet regimens. Laboratory data were collected at the baseline visit (T0) and the annual follow-up (T1), as follows: serum urea (mg/dl), serum creatinine(mg/dl), serum phosphorous (mg/dl), serum sodium (mmol/l), serum potassium (mmol/l), white blood cells (WBC) (cc/mmc).

The groups had significant differences in BMI (28.7 [25.0, 34.7] vs 26.4 [24.0, 28.0], p=0.02), age (68 ± 9 vs 74 + 13, p=0.04), and basal creatinine clearance (33 [25, 44] vs 27 [21, 36], p=0.03). Baseline features were summed up in Table 1.

Variable Whole sample Group A (n= 40) Group B (n= 35) p
Age (years) 71 ± 11 68 ± 9 74 + 13 0.04
Sex (M/F) 45/55 40/60 49/51 0.32
BMI (kg/mq) 27.4 [24.2 – 30] 28.7 [25.0 – 34.7] 26.4 [24.0 – 28.0] 0.02
Clearance (ml/min) 31 [23 – 41] 33 [25 – 44] 27 [21- 36] 0.03
Serum Urea (mg/dl) 73 [64 -102] 75 [65 -99] 73 [60 -121] 0.84
Serum creatinine (mg/dl) 1.8 [1.5 – 2.5] 1.7 [1.4 – 2.4] 2.0 [1.6 – 2.7] 0.03
Serum sodium (mmol/L) 141 ±3.3 4.7 [4.5 – 5.0] 4.4 [4.9 – 5.2] 0.40
Serum Potassium (mmol/l) 4.74 ± 0.58 4.72 ± 0.53 4.76 ± 0.64 0.68
Serum phosphorous (mg/dl) 3.8 [3.6 – 4.1] 3.7 [3.5 – 4.0] 3.8 [3.7 – 4.3] 0.35
WBC (cc/mmc) 7744 ± 1824 7575 ± 1947 7932 ±1683 0.46
Delta_Clearance -3.50/ 0.00/ 4.00 -0.25/ 1.00/ 7.25 -5.50/-2.00/ 2.00 0.001
Table 1. Baseline features of whole sample and into the two groups. Body mass index (BMI); White blood cells (WBC).

An unadjusted model with GLM for repeated measures showed a significant effect on creatinine clearance of the Mediterranean diet compared to LPD, with an estimate marginal mean of -9.98 ml/min [95% CI], 15.6/, 4.3]. Adjusted model for age, BMI and sex (Table 2) appeared to confirm this significance in the between-group mean in the joint mean difference (‒9.34, 95%CI ‒15.44/ ‒3.24) (Table 2).

Variable F p ᶇ2
Mediterranean diet vs low protein diet ‒9.34 0.003 0.119
Sex (Male vs female) 2.71 0.104 0.038
Age (years) 0.08 0.780 0.001
BMI (kg/m2) 0.04 0.947 0.000
Table 2. Between-group mean in the joint mean differences: Adjusted GLM model for repeated measures. Body mass index (BMI).

Due to the non-randomized study design and the unbalanced groups, we decided to implement the analysis with the PS matching. We computed PS value using the treatment as dependent variable of the logistic regression, and graphically evaluated it (Figure 1). The PS values were not equally distributed between the two groups. Carrying on with the matching, choosing a caliper of 0.2, 20 patients from group A were paired with 20 patients from group B (Table 3). Unmatched patients are excluded from the analysis, reducing sample’s size. This reduction of the patients admitted in the analysis is one of the major limitations of the matching.

Analyzing the standardized means of the baseline features before and after the matching, a better balance between the two groups could be shown (Figures 2a and 2b).

GLM for repeated measures performed in the matched sample did not show significant differences between the two groups (2.737, 95%CI –4.328/9.803). Also using the covariate adjustment, that uses the whole sample, the not significant relationship between the two treatments and the clearance progression was confirmed in the GLM for repeated measures including treatment and ps-value (-3.314, 95%CI -8.524/1.897).

Figure 1. Propensity score distribution before the matching.
Figure 1. Propensity score distribution before the matching.
Group A Group B PS value group A PS value group B
1 48 0.5728 0.5990
2 56 0.5029 0.4885
3 43 0.7979 0.8133
4 53 0.2244 0.2236
5 41 0.8256 0.8244
6 65 0.2370 0.2436
8 49 0.7872 0.7496
9 47 0.7313 0.7068
10 52 0.2709 0.2670
11 66 0.5662 0.5339
12 68 0.6588 0.6768
14 71 0.6731 0.6888
15 75 0.1971 0.2084
16 39 0.6640 0.6990
18 63 0.3849 0.3833
19 55 0.4595 0.6256
21 67 0.6014 0.6256
26 45 0.4350 0.4386
27 42 0.2674 0.2947
31 60 0.4544 0.4280
Table 3. Groups composition based on Propensity Score Matching.
Figure 2a. Balance of the covariate before and after the Matching.
Figure 2a. Balance of the covariate before and after the Matching.
Figure 2b. Propensity score distribution after the Matching.
Figure 2b. Propensity score distribution after the Matching.

 

Usefulness of propensity score

A few RCTs were conducted on ERSD patients due to high costs and their difficult organization. In these cases, a well-structured comparative effectiveness study could be done to generate hypothesis or to add results to existing RCT. For Example, Chan KE et al. conducted a large observational study including more than 10000 patients, the study’s population and structure were modeled on 4D study’s methods, using the same eligibility criteria, endpoints, and similar timeline. To reduce bias caused by known and unknown variables, patients were initially matched in statin-group and control-group based on similar lipid profiles and years of dialytic treatment. Subsequently, a logistic model was performed to compute the probability of receiving the therapy, also all Cox analyses were weighted using the IPTW methods. Differently from the unmatched baseline analysis, the baseline characteristic computed after propensity scoring showed two well-balanced groups. At the outcome analysis, all HRs computed in this observational study were compared with the HRs showed in the 4D Study, and no significant differences were found between these two studies (Figure 1). Furthermore, RCTs are often smaller than observational studies, due to the stronger inclusion criteria and the higher costs than observational design. As shown in Figure 1, PS methods computed in a big sample, allowed to find a smaller confidence interval compared to 4D RCT, without significant differences in anyone outcome.

Through these comparisons, although RCTs were the lowest-biased studies, we can speculate about the effective validity of observational comparative studies using PS methods to reduce biases.

 

Limitation of propensity score methods

PS is applicable when the treatment assignment is neglectable, with unknown and unmeasured confounders. Furthermore, PS value > 0 is necessary. According to G. et Lepeyre-Mestre M. [22], propensity score methods is not very able to reduce selection bias, information bias and instrumental bias. Despite PS reducing inhomogeneity between groups, some unconsidered variables can exist, hence residual bias should be taken into account in the interpretation of results and in the critical appraisal of the study [23].

Leisman D.E. et al., resumed ten “Pearls and Pitfalls” about the use of matching method [24]. They highlighted problems regarding the reduction of sample size: the number of cases does not represent the whole sample because every unpaired subject is excluded from the analysis.  This can impair the external validity of the study, reducing its applicability. Consequently, the power of the study should be computed on the balanced sample, excluding the unmatched patients. Indeed, the analysis reflects the matched sample, losing information about the excluded cases. However, no patients were excluded by the analysis using the covariate adjustment and the IPTW. We highlighted that, similarly to our sample, no significant differences between matching and covariate adjustment were found. However, can be useful performing more PS methods, to compare the results. Furthermore, machine learning methods can be used to compute PS, and they reduce the variability of the PS.

Last but not least, a limitation of these methods is the inability to detect interaction variables. In correlated subgroup effects, these variables could indeed invalidate the PS model and should be excluded from it [25].

 

Discussion

Our analysis seemed to show a slow CKD progression in patients treated with LPD compared to patients treated with Mediterranean diet. However, the unbalanced covariate distribution between the two groups must be highlighted. Conversely to classic analysis, our result showed no difference between the two groups in matched sample, where the two groups were well balanced.

Healthy dietary habits are essential to contrast the progression of chronic diseases such as CKD and the risk factors related to its development. A tailored diet that follows patients’ eating habits can enhance compliance with nutritional therapy, improving the conservative management of CKD patients.

In patients with renal impairment, optimal eating is crucial, representing a high-impact modifiable lifestyle factor for the primary prevention of CKD progression [26], and it avoids the dysregulation of fluid status, pH, electrolytes [27, 29], chronic metabolic acidosis [30], all factors that should be corrected by an adequate dietary regimen and balanced supplementation of the missing nutrients.

Nutritional therapy can be useful to slow CKD progression and delay ESRD with a consistent improvement of the patient’s quality of life [31]. LPD should be started from GFR <30 ml/min, with a protein intake below 0.8 mg/kg/die, and it shown slower CKD progression and reduction of the mortality [32]. Rhee et al. (2018) [33] in their meta-analysis of randomized controlled trials (RCTs) found that the risk of progression to ESRD was significantly lower in patients with LPD regimens than those with higher‐protein diets, with serum bicarbonate augmentation. Notwithstanding its restrictions, LPD does not seem to impair the quality of life of CKD patients. The study of Piccoli et al. (2020) [34] on 422 CKD patients with stages III-V demonstrated that moderately protein-restricted diets (0.6 g/kg/day) guaranteed good compliance to therapy, with a median dietary satisfaction of 4 on a 1-5 scale with a minimal dropout.

The Mediterranean diet is a nutritious regimen first proposed by Keys in the mid-1980s that has been demonstrated to exert a favourable action on inflammation, CKD, cardiovascular health, and overall mortality [35, 37]. Different studies demonstrated a tight link between CKD prevention and Mediterranean diet regimen [38, 39]. How the Mediterranean diet exerts kidney protection is still under debate, and the anti-inflammatory and antioxidant effects were suggested [40, 41]. Moreover, tighter adherence to a healthy plant-based diet was associated with a slower eGFR decrease [42].

Asghari et al. (2017) [43] showed, in a six-year follow-up study, that adherence to the Mediterranean diet is associated with a reduced risk of 50% of incident CKD. These results are in line with the ones from the Northern Manhattan Study. In this cohort of patients, the patients with relatively preserved renal function and high adherence to the Mediterranean diet experienced an approximate 50% decreased odds for incidence of eGFR<60 ml/min/1.73m2.

The effectiveness of LPD compared to the Mediterranean diet is still a matter of debate. Mediterranean diet is characterized by free fat, abundant vegetables, legumes, fresh fruits, cereals, moderate wine consumption, low milk and milk products, low meat/animal products, and frequent fish. Moreover, both the Mediterranean diet and LPD are effective in the modulation of gut microbiota, reducing protein-bound uremic toxins levels, especially in patients suffering from moderate to advanced CKD.

Davis et al. (2015) [44] tried to define nutrient content and range of servings for the Mediterranean diet, analysing the variations in the quantity of this diet components in recent literature. The Mediterranean diet’s positive effects are not only limited to metabolic influence, but the conviviality, culinary and physical activity exerts a beneficial effect on mental health, ameliorating body homeostasis and reactivity to the chronic disease [45].

A diet regimen feasible in different settings is essential for adherence to nutritional therapy. Different dietetic strategies have been investigated over the years, but which is the best nutritional regimen remains controversial. Kim et al. analysed the data of 4343 incident CKD patients, during a median follow-up of 24 years and showed that higher adherence to a balanced diet was linked to a lower risk of CKD progression.

In conclusion, although our previous analysis showed differences between the two diets, after propensity match no differences were detected, as well as after the covariate adjustment methods. In the study of Hu et al. (2021) [46] adherence to healthy nutritional patterns was associated with lower risk for renal impairment progression and all-cause mortality in CKD patients. Thus, based on our results and according to the literature, the Mediterranean diet should be a good choice for patients who are not compliant with a low-protein diet, without a significant increase of CKD progression risk [47].

 

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Urea and impairment of the Gut-Kidney axis in Chronic Kidney Disease

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Biagio Raffaele Di Iorio1, Stefania Marzocco2, Luca Nardone1, Marilisa Sirico1, Emanuele De Simone3, Gabriella Di Natale1, Lucia Di Micco1

1 UOC di Nefrologia, “A. Landolfi Hospital”, 83029 Solofra (Avellino), Italy
2 Department of Pharmacy, School of Pharmacy, University of Salerno, 84084 Fisciano (Salerno), Italy
3 UOC di Nefrologia, AORN “S. Giuseppe Moscati”, 83100 Avellino, Italy

Corrispondente:
Dott. Biagio Raffaele Di Iorio,
UOC di Nefrologia, Ospedale “A. Landolfi",
83029 Solofra (Avellino), Italia
e-mail: br.diiorio@gmail.com

 

Abstract

Gut microbiota can be considered a real organ coordinating health and wellness of our body. It is made of more than 100 trillions of microorganisms, thus about 3 times higher than the number of human body cells and more than 150 times than human genes containing 1000 different microbe species.
It has been described a symbiotic relationship between gut and kidney, confirmed by several observations. This is a bi-directional relation with a mutual influence, even when kidney disease occurs, and consequent alterations of intestinal microbiota and production of uremic toxins, that in turn worsens kidney disease and its progression.
Our review analyzes the components of gut-kidney axis and relative clinical consequences.

KEYWORDS: urea, microbioma, indoxyl sulphate, p-cresol sulphate, Mediterranean Diet, VLPD, Short Chain Fat Acid, prebiotics, probiotics

Introduction

Already in 1963 Giordano and in 1964 Giovannetti (1) realized that urea was not only a marker of reduced renal function, but a real uremic toxin. They understood how to reduce serum urea in subjects with chronic kidney disease without inducing malnutrition but improving symptoms related to hyperazotemia (1). Later, urea was put aside as marker of renal function, then was used, through Kt/V (2), to measure dialysis efficiency and protein intake (3).

Nowadays, we know that chronic kidney disease is characterized by a progressive increase of waste products with different molecular weights that may alter cellular functions (here comes the name “uremic toxins”). Moreover, it is well known the importance and the contribution to cardiovascular disease in chronic kidney disease (4). Very recently, urea has been reconsidered as uremic toxin (5). In fact, it is now known the relation between urea and cyanate production and its role in CKD through post-translational modification of proteins (6), and the action exerted by urea degradation products on the intestine and the integrity of intestinal barrier (7).

Intestinal microbiota is a real organ that coordinates in a flexible way health and wellness of our body (8): it is made of more than 100 trillions of microorganisms, thus about 3 times higher than the number of human body cells and more than 150 times than human genes containing 1000 different microbe species. Definitively, it has the same metabolic potential of liver (8). Intestinal microbiota may be considered as an external milieu with an estimated weight of about 2 kilos, lasting the entire life and interacting with all body organs, with regulation actions on immune system, but also with a potential in production of toxins (9–15). In fact, in the entire intestinal tract there is A physiological balance between saccharolytic and proteolytic fermentation and an alteration of this balance may cause detrimental effects in chronic kidney disease or in dialysis patients (16).

Nowadays, there is great interest on this topic with more than 25000 papers published in PubMed (Figure 1), more than 10000 of them only in the last two years.

 

Urea and Intestine

Chronic kidney disease patient is notoriously a subject with micro-inflammation and oxidative stress, that may be caused by several factors (17). A recent evidence shows that high levels of urea contribute to deteriorate these aspects, themselves damaging permeability of intestinal barrier (5). In fact, urea easily spread in the intestinal fluid where it is degraded by bacterial urease enzymes, then it is hydrolyzed in ammonium hydroxyde that increases fecal pH with a consequent alteration of intestinal cellular junctions (5, 18). In addition, high levels of urea cause a change of intestinal microbiota promoting proteolysis (19) with production and absorption of uremic toxins, such as indoxyl sulfate and p-cresol sulfate (20).

Very recently, Andersen and coll showed in mouse experiments that disbiosis caused by chronic kidney disease-related inflammation produced a diffusion through intercellular spaces of intestinal bacteria to the liver, with an increased release of endotoxins (21). In fact, the authors showed increased levels of serum pentraxin-2/serum amyloid-P (the equivalent of human Pentraxin-1/C-reactive protein) in mouse with disbiosis (21).

A recent systematic review form Vanholder and coll evidenced that indoxyl sulfate damaged endothelium, hepatocytes, muscle cells, myocytes, renal proximal tubular cells, intestinal cells, so as p-cresol sulfate damaged leucocytes, adipocytes, renal proximal tubular cells, intestinal cells and myocytes. As a consequence of high values of indoxyl sulfate and p-cresol sulfate, relevant pathophysiologic changes occurred such as production of reactive oxygen species, interaction between leukocytes and endothelium, increase of cellular proliferation and aging, myocytes hypertrophy, cardiac fibrosis, inflammation, oxidative stress, cytokines production, inflammatory genes expression, RAAS activation, renal tubular damage, insulin-resistance, lipogenesis reduction and lipolysis activation (22).

Intestinal alterations in chronic kidney disease may be a consequence of an erroneous nutritional therapy with the use of a low content of fibers due to the fear of hyperkalemia. Hence, it is necessary to delete some ambiguities and misleading beliefs: dietetic administration of vegetal fibers and fruit do not cause serious hyperkalemia because the simultaneous consumption of alkali (with vegetables and fruit) induces intracellular potassium shift (22); in fact, hyperkalemia in chronic kidney disease patients with an efficient urine output especially occurs when RAAS inhibitors and aldosterone antagonists are prescribed (23). Furthermore, a lower intake of fibers causes a reduction of short-chain fatty acids (SCFAs) into intestinal lumen, as propionic and butyric acids. SCFAs derive from bacterial fermentation and are normally present at high concentrations into the intestine. These metabolites represent a junction between microbiota and immune system (24–25). After internalization into enterocytes to be used as energy fuel, they increase expression of antimicrobial peptides secreted on the external surface of intestinal cells (26, 27), and modulate immune mediators production, as IL-18, a cytokine fundamental in reparation and conservation of cellular integrity, and other cytokines and chemokines (26, 27). Moreover, SCFAs regulate differentiation, recruitment and activation of immune system cells like neutrophils, macrophages and lymphocytes T (26, 27).

The use of a nutritional therapy with a very low protein content and a high quantity of vegetables and fruit with the supplementation of essential amino acids and ketoanalogues of non essential aminoacids ensures the lowering of urea levels with an appropriate amount of fibers, promoting the building of a physiological intestinal microbioma (23, 28–30).

 

Microbiote and Kidney

Symbiotic gut microbiota has the important function of preserve the intestinal barrier through mucus, antimicrobial peptides and IgA production that contribute to maintain microbiota into the intestinal lumen and far from epithelial intestinal cells. Intestinal immune system is very tolerant with symbiotes, so that epithelial cells are able to recognize microbes through recognition receptors like Toll-Like Receptor 4 (31). Intestinal response to inflammation and infections is very complex and depends from collaborations with symbiotic bacteria and from regulatory mechanisms including T-helper cells1 and 2, MYD88 that induces down-regulation of IL-1 receptor-associated kinase 1 (IRAK1) with the consequent activation of NK-kB cascade and production of antimicrobial proteins and pro-inflammatory cytokines (32).

The exposure of intestinal cells to lipopolysaccharides induce them to the secretion of TGF-b, B-cell-activating factor of TNF family (BAFF), and a ligand inducing proliferation (33). As a consequence, microbiota immune cells (dendritic CD103 cells, T-cells secreting IL-10 and TGF-b) activate their tolerance responses and stimulate specific intestinal IgA (30).

A subclinical endotoxemia is a potential cause of inflammation in chronic kidney disease. An altered immune response and production of pro-inflammatory cytokines at the intestine level may accelerate progression of renal disease and cardiovascular complications (31, 34, 35, 36, 37).

 

 Microbiote and Aging

Aging is a physiological senescence process of body functions with age; several studies evidenced a tight relation between age and microbiota (38). Already during intrauterine period intestine is sterile and it has been observed a different bacterial colonization depending on vaginal or cesarean delivery, with a higher prevalence of Clostridium difficilis after cesarean delivery compared with a higher prevalence of Bifidobacteria, Proponiumbacteria and other symbiontes after vaginal delivery (38, 39, 40, 41). This different colonization has remarkable influence on intestinal microbioma building in the adult age (42), and on the onset of metabolic disorders like type 2 diabetes, obesity, atherosclerosis, gastrointestinal inflammatory diseases (43, 44). Finally, the transition from the adult age to the elderly induces a rapid change of microbiota with a reduction of Firmicutes and an increase of Bacteroidetes (45, 46).

Throughout life, diet considerably influences intestinal microbioma composition inducing a shift from physiological saccharolytic bacteria to proteolytic bacteria (15, 16).

The influence of gut and microbioma composition on chronic kidney disease has already been issued from the scientific literature (31, 37, 47). On the other side, the aspect not previously explored is whether low-protein diet may influence the quality and composition of intestinal microbioma. Moreover, aging seems to preponderantly alter microbioma senescence so as kidney deterioration (48, 49, 50).

 

Mediterranean Diet and Disbiosis

Healthy effects of Mediterranean diet on cardiovascular complications (51, 55) and neoplasia incidence (56, 57) are known all over the world. Mediterranean diet is characterized by the assumption of fresh products, and great amount of vegetables and fruit with use of legumes, nuts, olive oil, fish and a moderate consumption of red wine.

Knowledge of a relationship between microbiota and chronic kidney disease has led nephrologists to study with even greater interest the relation between Mediterranean diet and chronic kidney disease. Mediterranean diet efficiently contributes to intestinal microbioma building; in fact, it promotes intestinal development of saccharolytic bacteria with a competitive reduction of proteolytic bacteria that instead induce p-cresol and indoxyl sulfate production (58). Several researchers confirmed these data and believe that a physiological microbioma may delay progression of renal disease (59, 60) and mortality in chronic kidney disease patients (61, 62).

 

 Short Chain Fat Acid (SCFA) and Disbiosis

We have previously described the role of intestinal microbioma in the production of SCFAs from fibers metabolism and in the immune-regulatory action at the intestine level (24–27). SCFAs are produced in the colon and distal small intestine by anaerobic bacteria following fermentation of complex carbohydrates. The major compounds are acetic acid, butyric acid and propionic acid, with positive effects on microbioma and intestinal mucosa. It is known that they exert anti-inflammatory, anti-cancer, antibacterial and antidiabetic effects. Lower values with a consequent dysbiotic gut contribute to the pathogenesis of different diseases such colitis, type 2 diabetes, rheumatoid disease and multiple sclerosis (63). Synthesis SCFAs may also be administered orally (63). Supplementation of SCFA has been shown to have anti-inflammatory actions both in intestinal epithelial cells (64) and in the cardiovascular system (64). They also positively influence auto-immune diseases (64–70).

 

 Urea, Disbiosis and Outcomes

In chronic kidney disease and in dialysis patients there is an accumulation of uremic toxins with an intermediate molecular weight such as phenylacetylglutamine, hippurate, indoxyl sulfate and p-cresol sulfate (22). These are protein-bound solutes with a scarce dialytic clearance (36% for indoxyl sulfate and 31% for p-cresol sulfate, respectively). Several studies described the toxicity of indoxyl sulfate and p-cresol sulfate on kidney disease progression and cardiovascular system (4, 22, 71–80). Our group was one of the first to evidence the efficacy of a very-low protein-diet supplemented with ketoanalogues and essential amino-acids and with a high fibers content in reducing of 35% indoxyl sulfate levels (81); this effect was studied later (82). Preliminary data from MEDIKA study (“Renal Effects of Mediterranean Diet and Very Low-protein Diet With Ketoacids (VLPD) on Physiological Intestinal Mibrobiota in CKD” registered in ClinicalTrial.gov with the number NCT02302287, ongoing) show that in 30 patients a very-low protein diet significantly reduces indoxyl sulfate levels of 72% (from 0.46±0.12 to 0.13±0.05 mcg/mL, p=0.002) and p-cresol sulfate levels of 51% (p<0001) (Figure 2) (83). In detail, VLPD is a vegetarian diet with no animal proteins and 0,3 g/kg of body weight/day of vegetable proteins with supplementation of essential amino-acids and ketoanalogues of non essential amino-acids, and a caloric intake of 30-35 kcal/kg of body weight/day (84).

Moreover, nowadays it has been put back at the top the idea that urea is a toxin and it must be therefore treated. Regarding intestinal microbiota, disbiosis in chronic kidney disease is determined also by high levels of urea that causes enterocolites (not clinically relevant) through ammonium hydroxide formation from urea decomposition and ammonium ion hydroxilation due to intestinal bacterial urease. On the other side, high levels of urea also causes an exaggerated production of cyanate with consequent protein carbamylation and atherosclerotic effects (5). Therefore, urea, an old and forgotten molecule, must be reconsidered now as a real uremic toxin.

VLPD is able to efficiently reduce urea levels in patients with chronic kidney disease, leading its values into the normal range in many patients despite a reduced residual renal function lower than 15 ml/min (29, 84, 85). A prospective randomized cross-over controlled trial showed that urea reduction in 60 subjects induced a significant cyanate lowering (86); also correlation between urea and homocitrulline was significant (y = 10.2 x + 6.97, r = 0.72; p<0.001) (Figure 4 shows that VLPD was more efficient than Mediterranean diet in reducing urea and therefore cyanate levels (86). Urea is directly involved in the pathogenesis of cardiovascular diseases in chronic kidney disease patients through the generation of isocyanic acid and protein carbamylation, both processes with a high atherosclerotic impact (5, 87–89).

Moreover, VLPD allows a better control of metabolic acidosis in chronic kidney disease because it ensures bicarbonate administration with vegetable proteins (90).

 

Microbioma and Pre/Pro-Biotics

Probiotics (microorganisms belonging to human microbioma) and prebiotics (digestible but not fermentable oligosaccharides) are useful to restore intestinal microbiota and promote health of the host also in chronic kidney disease subjects (91). Prebiotics have synergistic action with probiotics and administered together (symbiotics) may have an important role in restoring the physiological intestinal microbiota and delaying chronic kidney disease progression (92, 93).

Scientific literature described the relationship between use of symbiotics and reduction of indoxyl sulfate and p-cresol sulphate (94 – 96). This effect is evident also in patients already in hemodialysis treatment (97). In fact, the use of SCFAs to restore physiological levels of anaerobic bacteria (Sutterellaceae, Lactobaillaceae and Bacteroidaceae) and not of aerobic bacteria such as Finucutes Proteobacteriae and Actinobacteriae, that are at higher concentration in the intestine of chronic kidney disease patients, allows the reduction of uremic toxins like indoxyl sulfate and p-cresol sulfate (98).

Very recently, Soleimani has shown, (with a prospective randomized, double-blind, placebo-control clinical trial) that supplementation for 12 weeks of pro-biotics produced in 60 diabetic hemodialysis patients the reduction of 22 mg/dl for glucose, 0.4% for HbA1c, 6.4 mcU/ml for serum insulin, 1933 ng/ml for C-reactive protein compared to the placebo group, and then, in conclusion, improvement in diabetic parameters (99).

Moreover, use of natural symbiotics like beta glucan contained in whole-grain pasta induces increase of SCFAs such as 2-methyl-propanoic, acetic, butyric, and propionic acids and significant reduction of indoxyl sulfate and p-cresol sulfate (100, 101).

 

Conclusions

Researchers investigated a lot in the last years the interrelation between kidney and gut , the so-called gut-kidney axis, and between intestinal microbioma or disbiosis and kidney damage.

It is indisputable that inflammation and intestinal disbiosis influence chronic kidney disease progression and, conversely, chronic kidney disease influences microbiota changes towards a proteolyitic flora instead of a saccarolytic one with production and absorption of uremic toxins such as indoxyl sulfate and p-cresol sulfate. Therefore, taking into consideration the mutual influence between kidney and intestine, the care of intestinal disbiosis must be a therapeutic target in chronic kidney disease (102).

Nowadays, the role of these uremic toxins in promoting negative cardiovascular outcomes in chronic kidney disease patients it is well known in the scientific community. Also, it is well recognized the relationship between indoxyl sulfate and cardio-renal syndrome through mechanisms favoring oxidative stress, production of reactive oxygen species, nicotinamide-adenine-dinucleotide-phosphate activity, and reduction of glutathione levels (103, 104). Similarly, the role of intestinal microbiota on nitrose cycle, that induces reduction of nitrogen oxide in chronic kidney disease through reduction of intestinal species that are able to transform ammonium in nitrate was already explored (105).

Use of a proper nutrition, as Mediterranean diet is, may induce the development of intestinal bacteria flora able to control the intestinal cellular immune system, contrast the formation of uremic toxins in the intestine, and favor production of SCFAs.

Moreover, chronic kidney disease influences microbiota characteristics especially through high levels of urea, that is reconsidered a real uremic toxin that need therefore to be treated (5). Only the use of a low-protein content nutrition, such as VLPD with supplement of essential amino-acids and ketoanalogues, allows an efficient control of urea plasma levels. In fact, administration of ketoanalogues, permits recycling urea nitrogen to transform the ketoanalogous in the corresponding no-essential amino acid, maintaining a proper dose of energy intake, In conclusion, we think that the utilization of nutritional therapy to reduce urea levels and restore physiological microbioma in chronic kidney disease is mandatory.

 

 

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