Gut Microbiome Composition Remains Stable in Individuals with Diabetes-Related Early to Late Stage Chronic Kidney Disease

Alterations of the gut microbiota in patients with diabetic nephropathy and its association with the renin-angiotensin system

Objective

Type 2 Diabetes Mellitus (T2DM) is a global health concern, with complications such as diabetic nephropathy (DN) affecting 16.6% of patients and contributing to end-stage renal failure. Emerging research suggests that gut microbial communities may influence DN progression, potentially through mechanisms involving the renin-angiotensin system (RAS). This study aimed to evaluate changes in specific microbial genera in individuals with T2DM, both with and without DN, and to explore their associations with renal function markers and RAS activation.

Methods

A total of 120 participants were categorized into three groups: healthy controls, T2DM without DN, and T2DM with DN. Microbial abundances of genera including Escherichia, Prevotella, Bifidobacterium, Lactobacillus, Roseburia, Bacteroides, Faecalibacterium, and Akkermansia were quantified using qPCR targeting the bacterial 16 S rRNA gene. Gene expression levels of RAS-associated markers (ACE, AGT1R, AT2R, and Ang II) and inflammation-related genes (TNF-α, TLR4) were analyzed in peripheral blood mononuclear cells via qPCR.

Results

The study identified significant alterations in microbial composition. Genera such as Faecalibacterium, Akkermansia, Roseburia (butyrate producers), and Bifidobacterium (a potential probiotic) were markedly reduced in T2DM and DN groups compared to controls. Increased mRNA expression of RAS-related genes, including ACE, AGT1R, and Ang II, was observed in these groups. We also foun correlations between altered microbial genera, RAS gene expression, and clinical markers of renal dysfunction.

Conclusion

The findings suggest that specific microbial genera may influence the pathogenesis of DN through RAS activation and inflammatory pathways. These insights highlight potential therapeutic targets for mitigating DN progression in T2DM patients.

Predicting diabetic kidney disease with serum metabolomics and gut microbiota

This study aims to identify biomarkers for reliably predicting diabetic kidney disease (DKD), systematically characterize serum metabolites and gut microbiota in DKD patients, and investigate the correlation between differential serum metabolites and gut microbiota. From September 2021 to January 2023, 90 subjects were recruited: 30 with DKD, 30 with type 2 diabetes mellitus (T2DM), and 30 normal controls (NCs). Serum metabolites, including 180 different metabolites, were analyzed using untargeted metabolomics UPLC-MS/MS, and gut microbiota were assessed via 16S rRNA sequencing. Differential metabolites were identified through univariate (t-test or Mann-Whitney U-test, P < 0.05) and multivariate analyses (OPLS-DA model, VIP > 1, P < 0.05), followed by selection using the least absolute shrinkage and selection operator (LASSO). The selected overlapping serum metabolites, along with DKD-associated differential gut microbiota, were used to develop a logistic regression prediction model for DKD based on six markers. In the DKD group compared to the DM and NC groups, 39 and 60 differential serum metabolites were identified, respectively (VIP > 1, P < 0.01). Among these, 36 serum metabolites, including alpha-Hydroxyisobutyric acid, were significantly elevated in DKD patients compared to those with DM. Of these, 28 metabolites showed a negative correlation with estimated glomerular filtration rate (eGFR), while 29 showed a positive correlation with urine albumin creatinine ratio (UACR). Patients with DKD were further categorized into subgroups (DKD middle and DKD early) based on eGFR (eGFR < 90 ml/min/1.73m2, eGFR ≥ 90 ml/min/1.73m2), revealing 23 differential metabolites. Dysbiosis of the gut microbiota was evident in DKD patients, with lower relative abundances of g_Prevotella and g_Faecalibacterium compared to the DM and NC groups. Subgroup analysis indicated decreased relative abundances of g_Prevotella and g_Faecalibacterium in the DKD middle group, along with a decrease in g_Klebsiella compared to the DKD early group, which correlated positively with DKD patients' eGFR. There were 11 common metabolites among the three groups of differential metabolites. Among these, three serum metabolites-imidazolepropionic acid, adipoylcarnitine, and 1-methylhistidine-were identified as predictive serum metabolic markers. Disease prediction models (logistic regression models) were constructed based on these three metabolites combined with three genera of bacteria. These models demonstrated strong discriminatory power for diagnosing patients with DKD compared to patients with DM (area under the receiver operating characteristic curve (AUROC) = 0.939 and precision-recall curve (AUPR) = 0.940). The models also effectively discriminated between patients with DKD and NCs (0.976, 0.973). This study revealed distinctive serum metabolites and gut microbiota in patients with DKD. It demonstrated the potential utility of three specific serum metabolites and three genera of bacteria in diagnosing patients with DKD and assessing their renal dysfunction.

Compositional characteristics of the gut microbiome in patients with uremia

ABSTRACT

During acute or chronic uremia, the cumulative harmful effects of uremic toxins result in numerous health problems and, ultimately, mortality. Previous research has identified that uremic retention solutes originate from the gut microbiome, indicating that uremia may be closely associated with gut microbiome dysbiosis. To deepen our understanding of the compositional characteristics of the gut microbiome in patients with uremia and thereby promote precision medicine in the treatment of uremia, we conducted a study of the compositional characteristics of the gut microbiome in 20 patients with uremia. The gut microbiome diversity of uremic patients and the control group showed certain differences. Nonmetric multidimensional scaling analysis showed that the beta diversity of the gut microbiome of uremic patients was significantly different from that of the healthy control individuals, with a distinct clustering effect in the uremic patient group, and it also showed a similarly distinct clustering effect in the healthy control group. The Chao1 index and Sobs index were significantly lower in the uremic patient group than in the healthy control group ( P < 0.05). By analyzing the composition and abundance distribution of the gut microbiome in the uremic patient group and healthy control group, we found that the relative abundance of the gut microbiome constituents Fusobacteriota , Enterobacteriaceae, Oscillospirales, Ruminococcaceae, and Lachnospiraceae was significantly increased in the intestines of uremic patients. We also detected the rare taxa Erysipelotrichaceae, which was present only in the uremic patient group. Predictive functional analysis suggested that an increased abundance of Ruminococcaceae and Lachnospirales, which are associated with indoxyl sulfate and phenylacetyl glutamine, and an increased abundance of Oscillospirales, which is associated with pyruvate metabolism, in uremic patients may strongly influence the gut environment according to renal function, resulting in dysbiosis associated with uremic toxin production. Rare taxa such as Erysipelotrichaceae have been suggested to be detrimental to intestinal disease. Further research into these gut microbiomes may provide new ideas for the prevention and treatment of uremia with the gut microbiome.

The Traditional Chinese Medicine in Treating Diabetic Nephropathy: A Bibliometric Analysis

Background

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus, causing a serious economic burden worldwide. Traditional Chinese Medicine (TCM) is playing an increasingly important role in the treatment of DN. The purpose of this study was to comprehend the main themes and developments in relevant research throughout the last 11 years.

Methods

We looked for publications in the Web of Science Core Collection database (WOS) and the Chinese National Knowledge Infrastructure (CNKI) between 2013 and 2024. CiteSpace 6.3.R1 software was used to analyze the co-authorship of countries/regions, organizations, and co-occurrence of keywords. In addition, burst detection was applied to predict research hotspots and future trends.

Results

A total of 530 articles were included. The overall trend of published articles is increasing. China is the country with the highest number of publications and the highest impact. The research institutions are relatively scattered, with Beijing University of Traditional Chinese Medicine having the highest number of publications. The cooperation among institutions is mainly between universities of traditional Chinese medicine and their affiliated hospitals, and the cross-regional cooperation is not common. The research hotspots are the efficacy of TCM on DN and its mechanism of action, the exploration of TCM and formulas related to the treatment of DN, and the identification and typing of DN in TCM. The research frontiers lie in the control of oxidative stress and the effects of TCM on gut microbiota. In addition, the use of network pharmacology to explore the targets of Chinese herbal formulas for the treatment of this disease has also become popular.

Conclusion

TCM provides more possibilities for the treatment of DN. Researchers can refer to the research hotspots and trends in this paper for future research direction, on the one hand, they can focus on the study of the clinical efficacy of TCM and its improvement of renal function, on the other hand, they can also start from the pharmacological mechanism of TCM for the treatment of DN. Among them, improving oxidative stress in human body and regulating gut microbiota are the directions that can be studied.

Salivary microbiome in kidney diseases: A narrative review

Many research has been conducted since the microbiota's discovery that have focused on the role it plays in health and disease. Microbiota can be divided into categories like intestinal, oral, respiratory, and skin microbiota based on the specific localized areas. To maintain homeostasis and control immunological response, the microbial populations live in symbiosis with the host. On the other hand, dysbiosis of the microbiota can cause diseases including kidney diseases and the deregulation of body functioning. We discuss the current understanding of how various kidney diseases are caused by the salivary microbiome (SM) in this overview. First, we review the studies on the salivary microbiota in diverse clinical situations. The importance of the SM in diabetic kidney disease, chronic kidney disease, membranous nephropathy, and IgA nephropathy is next highlighted. We conclude that the characteristics of the SM of patients with various kidney diseases have revealed the potential of salivary microbial markers as noninvasive tool for the detection of various kidney diseases.

Astragalus membranaceus and Salvia miltiorrhiza ameliorate diabetic kidney disease via the "gut-kidney axis"

BACKGROUND

The combination of Astragalus membranaceus and Salvia miltiorrhiza (AS) is an effective prescription for treating diabetic kidney disease (DKD) in traditional Chinese medicine. Its efficacy in treating DKD has been confirmed, but the potential regulatory mechanism has not yet been fully clarified.

PURPOSE

To explore the mechanism by which AS regulates the "gut-metabolism-transcription" coexpression network under the action of the "gut-kidney axis" to ameliorate DKD.

METHODS

SD rats were used to establish the DKD model by injecting STZ. After AS intervention, the structure and function of the kidney and colon were observed. We sequenced the gut microbiota utilizing 16S rDNA, identified serum differential metabolites using LC‒MS/MS, and observed renal mRNA expression by RNA seq. The "gut-metabolism-transcription" coexpression network was further constructed, and the target bacteria, target metabolites, and target genes of AS were ultimately screened and validated.

RESULTS

AS improved renal pathology and functional damage and increased the abundance of Akkermansia, Akkermansia_muciniphila, Lactobacillus and Lactobacillus_murinus. Fourteen target metabolites of AS were identified, which were mainly concentrated in 19 KEGG pathways, including sphingolipid metabolism and glycerophospholipid metabolism. Sixty-three target mRNAs of AS were identified. The top 20 pathways were closely related to glycolipid metabolism, and 14 differential mRNAs were expressed in these pathways. Correlation analysis showed that Akkermansia, Akkermansia muciniphila, Lactobacillus and Lactobacillus murinus were closely associated with sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, ascorbate and aldarate metabolism and galactose metabolism. Moreover, the target metabolites and target mRNAs of AS were also enriched in five identical pathways of sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, ascorbate and aldarate metabolism and galactose metabolism, including 8 different metabolites, such as sphingosine, and 5 different genes, such as Kng1. The 8 metabolites had high AUC prediction values, and the validation of the 5 genes was consistent with the sequencing results.

CONCLUSION

Our research showed that AS can improve DKD via the "gut-kidney axis". Akkermansia muciniphila and Lactobacillus murinus were the main driving bacteria, and five pathways related to glycolipid metabolism, especially sphingolipid metabolism and glycerophospholipid metabolism, may be important follow-up reactions and regulatory mechanisms.

The Intestinal Microbiota Composition in Early and Late Stages of Diabetic Kidney Disease

Many studies have suggested that gut microbiota dysbiosis may be one of the pathogenesis factors of diabetes mellitus (DM), while it is not clear whether it is involved in the development of diabetic kidney diseases (DKD). The objective of this study was to determine bacterial taxa biomarkers during the progression of DKD by investigating bacterial compositional changes in early and late DKD. 16S rRNA gene sequencing was performed on fecal samples, including the diabetes mellitus (DM), DNa (early DKD), and DNb (late DKD) groups. Taxonomic annotation of microbial composition was performed. Samples were sequenced on the Illumina NovaSeq platform. At the genus level, we found counts of Fusobacterium, Parabacteroides, and Ruminococcus_gnavus were significantly elevated both in the DNa group (P = 0.0001, 0.0007, and 0.0174, respectively) and the DNb group (P < 0.0001, 0.0012, and 0.0003, respectively) compared with those in the DM group. Only the level of Agathobacter was significantly decreased in the DNa group than the DM group and in the DNb group than the DNa group. Counts of Prevotella_9, Roseburia were significantly decreased in the DNa group compared with those in the DM group (P = 0.001 and 0.006, respectively) and in the DNb group compared with those in the DM group (P < 0.0001 and 0.003, respectively). Levels of Agathobacter, Prevotella_9, Lachnospira, and Roseburia were positively correlated with an estimated glomerular filtration rate (eGFR), but negatively correlated with microalbuminuria (MAU), 24 h urinary protein quantity (24hUP), and serum creatinine (Scr). Moreover, the areas under the curve (AUCs) of Agathobacter and Fusobacteria were 83.33% and 80.77%, respectively, for the DM and DNa cohorts, respectively. Notably, the largest AUC for DNa and DNb cohorts was also that of Agathobacter at 83.60%. Gut microbiota dysbiosis was found in the early and late stages of DKD, especially in the early stage. Agathobacter may be the most promising intestinal bacteria biomarker that can help distinguish different stages of DKD. IMPORTANCE It is not clear as to whether gut microbiota dysbiosis is involved in the progression of DKD. This study may be the first to explore gut microbiota compositional changes in diabetes, early-DKD, and late DKD. We identify different gut microbial characteristics during different stages of DKD. Gut microbiota dysbiosis is found in the early and late stages of DKD. Agathobacter may be the most promising intestinal bacteria biomarker that can help distinguish different stages of DKD, although further studies are warranted to illustrate these mechanisms.

Gut Microbiome and Microbiome-Derived Metabolites in Patients with End-Stage Kidney Disease

The composition of the gut microbiome is altered in patients with chronic kidney disease (CKD). Dysbiosis leads to decreased levels of stool organic acids (OAs) and systemic inflammation, followed by accumulation of uremic toxins (UTs) and the development of end-stage kidney disease (ESKD). We assessed the relationship between the microbiome and UT levels or the development of ESKD by comparing patients undergoing hemodialysis (HD) and those with normal renal function (NRF). This cross-sectional study recruited 41 patients undergoing HD and 38 sex- and age-matched patients with NRF, and gut microbiome, levels of plasma UTs, inflammatory markers, and stool OAs were compared. The indices of beta-diversity differed significantly between patients with NRF and those undergoing HD, and between patients undergoing HD with and without type 2 diabetes. The levels of stool total OA, inflammatory markers, and UTs differed significantly between the patients with NRF and those undergoing HD. The combined main effects of type 2 diabetes and kidney function status were accumulation of indoxyl sulfate and p-cresyl sulfate. The relative abundances of Negativicutes and Megamonas were associated with development of ESKD and with the levels of UTs, even after adjustment for factors associated with the progression of ESKD. The present study indicates that the gut environment differs between patients with NRF and those undergoing HD and between patients undergoing HD with and without type 2 diabetes. Moreover, ESKD patients with diabetes accumulate more UTs derived from the gut microbiome, which might be associated with cardio-renal diseases and poor prognosis.

Gut Microbiota in Chronic Kidney Disease: From Composition to Modulation towards Better Outcomes-A Systematic Review

BACKGROUND

A bidirectional kidney-gut axis was described in patients with chronic kidney disease (CKD). On the one hand, gut dysbiosis could promote CKD progression, but on the other hand, studies reported specific gut microbiota alterations linked to CKD. Therefore, we aimed to systematically review the literature on gut microbiota composition in CKD patients, including those with advanced CKD stages and end-stage kidney disease (ESKD), possibilities to shift gut microbiota, and its impact on clinical outcomes.

MATERIALS AND METHODS

We performed a literature search in MEDLINE, Embase, Scopus, and Cochrane databases to find eligible studies using pre-specified keywords. Additionally, key inclusion and exclusion criteria were pre-defined to guide the eligibility assessment.

RESULTS

We retrieved 69 eligible studies which met all inclusion criteria and were analyzed in the present systematic review. Microbiota diversity was decreased in CKD patients as compared to healthy individuals. Ruminococcus and Roseburia had good power to discriminate between CKD patients and healthy controls (AUC = 0.771 and AUC = 0.803, respectively). Roseburia abundance was consistently decreased in CKD patients, especially in those with ESKD (p < 0.001). A model based on 25 microbiota dissimilarities had an excellent predictive power for diabetic nephropathy (AUC = 0.972). Several microbiota patterns were observed in deceased ESKD patients as compared to the survivor group (increased Lactobacillus, Yersinia, and decreased Bacteroides and Phascolarctobacterium levels). Additionally, gut dysbiosis was associated with peritonitis and enhanced inflammatory activity. In addition, some studies documented a beneficial effect on gut flora composition attributed to synbiotic and probiotic therapies. Large randomized clinical trials are required to investigate the impact of different microbiota modulation strategies on gut microflora composition and subsequent clinical outcomes.

CONCLUSIONS

Patients with CKD had an altered gut microbiome profile, even at early disease stages. Different abundance at genera and species levels could be used in clinical models to discriminate between healthy individuals and patients with CKD. ESKD patients with an increased mortality risk could be identified through gut microbiota analysis. Modulation therapy studies are warranted.

Gut microbiome studies in CKD: opportunities, pitfalls and therapeutic potential

Interest in gut microbiome dysbiosis and its potential association with the development and progression of chronic kidney disease (CKD) has increased substantially in the past 6 years. In parallel, the microbiome field has matured considerably as the importance of host-related and environmental factors is increasingly recognized. Past research output in the context of CKD insufficiently considered the myriad confounding factors that are characteristic of the disease. Gut microbiota-derived metabolites remain an interesting therapeutic target to decrease uraemic (cardio)toxicity. However, future studies on the effect of dietary and biotic interventions will require harmonization of relevant readouts to enable an in-depth understanding of the underlying beneficial mechanisms. High-quality standards throughout the entire microbiome analysis workflow are also of utmost importance to obtain reliable and reproducible results. Importantly, investigating the relative composition and abundance of gut bacteria, and their potential association with plasma uraemic toxins levels is not sufficient. As in other fields, the time has come to move towards in-depth quantitative and functional exploration of the patient's gut microbiome by relying on confounder-controlled quantitative microbial profiling, shotgun metagenomics and in vitro simulations of microorganism-microorganism and host-microorganism interactions. This step is crucial to enable the rational selection and monitoring of dietary and biotic intervention strategies that can be deployed as a personalized intervention in CKD.

Changes in gut microbial community upon chronic kidney disease

With the increasing incidence and mortality of chronic kidney disease (CKD), targeted therapies for CKD have been explored constantly. The important role of gut microbiota on CKD has been emphasized increasingly, it is necessary to analyze the metabolic mechanism of CKD patients from the perspective of gut microbiota. In this study, bioinformatics was used to analyze the changes of gut microbiota between CKD and healthy control (HC) groups using 315 samples from NCBI database. Diversity analysis showed significant changes in evenness compared to the HC group. PCoA analysis revealed significant differences between the two groups at phylum level. In addition, the F/B ratio was higher in CKD group than in HC group, suggesting the disorder of gut microbiota, imbalance of energy absorption and the occurrence of metabolic syndrome in CKD group. The study found that compared with HC group, the abundance of bacteria associated with impaired kidney was increased in CKD group, such as Ralstonia and Porphyromonas, which were negatively associated with eGFR. PICRUSt2 was used to predict related functions and found that different pathways between the two groups were mainly related to metabolism, involving the metabolism of exogenous and endogenous substances, as well as Glycerophospholipid metabolism, which provided evidence for exploring the relationship between gut microbiota and lipid metabolism. Therefore, in subsequent studies, special attention should be paid to these bacteria and metabolic pathway, and animal experiments and metabolomics studies should be conducted explore the association between bacterial community and CKD, as well as the therapeutic effects of these microbial populations on CKD.

Gut Mycobiome in Patients With Chronic Kidney Disease Was Altered and Associated With Immunological Profiles

Objectives

Mounting evidence suggests that bacterial dysbiosis and immunity disorder are associated with patients with chronic kidney disease (CKD), but the mycobiome is beginning to gain recognition as a fundamental part of our microbiome. We aim to characterize the profile of the mycobiome in the gut of CKD patients and its correlation to serum immunological profiles.

Methods and materials

Ninety-two CKD patients and sex-age-body mass index (BMI)-matched healthy controls (HCs) were recruited. Fresh samples were collected using sterile containers. ITS transcribed spacer ribosomal RNA gene sequencing was performed on the samples. An immunoturbidimetric test was used to assess the serum levels of immunological features.

Results

The CKD cohort displayed a different microbial community from that in the HC cohort according to principal coordinate analysis (PCoA). (P=0.001). The comparison of the two cohorts showed that the CKD cohort had significantly higher gut microbial richness and diversity (P<0.05). The CKD cohort had lower abundances of Candida, Bjerkandera, Rhodotorula, and Ganoderma compared to the HC cohort, while it had higher Saccharomyces (P<0.05). However, the microbial community alteration was inconsistent with the severity of kidney damage in patients, as only patients in CKD stage 1~3 had differed microbial community concerning for HCs based on PCoA (P<0.05). The serum concentration of the kappa light chain in CKD patients was positively associated with Saccharomyces, whereas the it was negatively associated with Ganoderma (P<0.05).

Conclusions

Not only was gut mycobiome dysbiosis observed in CKD patients, but the dysbiosis was also associated with the immunological disorder. These findings suggest that therapeutic strategies targeting gut mycobiome might be effective.

The Role of Gut Microbiota and Microbiota-Related Serum Metabolites in the Progression of Diabetic Kidney Disease

Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.

Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR ≥ 15 ml/min/1.73 m²) (n = 22), and the DKD ESRD group (eGFR < 15 ml/min/1.73 m²) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.

Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway [hippuric acid (HA), L-(−)-3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid] and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(−)-3-phenyllactic acid and IAA].

Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD.

Healthcare Data for Achieving a More Personalized Treatment of Chronic Kidney Disease

The current concept of healthcare incites a more personalized treatment of diseases. To this aim, biomarkers are needed to improve decision-making facing chronic kidney disease (CKD) patients. Prognostic markers provided by real-world (observational) evidence are proposed in this Special Issue entitled "Biomarkers in Chronic Kidney Disease", with the intention to identify high-risk patients. These markers do not target measurable parameters in patients but clinical endpoints that may be in turn transformed to benefits under the effect of future interventions.