Background Differences in death rate and cardiovascular disease (CVD) between Black and White patients with chronic kidney disease is attributed to sociocultural factors, comorbidities, genetics, and inflammation. Methods and Results We examined the interaction of race, plasma IL-6 (interleukin-6), and TMPRSS6 genotype as determinants of CVD and mortality in 3031 Chronic Renal Insufficiency Cohort study participants. The primary outcomes were all-cause mortality and a composite of incident myocardial infarction, peripheral artery disease, stroke, and heart failure. During the median follow-up of 10 years, Black patients with chronic kidney disease experienced a significantly higher mortality (34% versus 26%) and CVD composite (41% versus 28%) compared with White patients. After adjustment, TMPRSS6 genotype did not associate with the outcomes. The adjusted hazard ratio for mortality (4.11 [2.48-6.80], P<0.001) and CVD composite (2.52 [1.96-3.24], P<0.001) were higher for the highest versus lowest IL-6 quintile. The adjusted hazards for death per 1-quintile increase in IL-6 in White and Black individuals were 1.53 (1.42-1.64) versus 1.29 (1.20-1.38) (P<0.001), respectively. For CVD composite they were 1.61 (1.50-1.74) versus 1.30 (1.22-1.39) (P<0.001), respectively. In Cox proportional hazard models that included IL-6, there was no longer a racial disparity for death (1.01 [0.87-1.16], P=0.92), but significant unexplained mediation remained for CVD (1.24 [1.07-1.43]; P=0.004). Path models that included IL-6, diabetes, and urine albumin to creatinine ratio were able to identify variables responsible for racial disparity in mortality and CVD. Conclusions Racial differences in mortality and CVD among patients with chronic kidney disease could be explained by good-fitting path models that include selected mediator variables including diabetes and plasma IL-6.

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.

Hyperkalemia is a common and serious electrolyte abnormality in advanced CKD patients.1Bianchi S. Aucella F. De Nicola L. Genovesi S. et al.Management of hyperkalemia in patients with kidney disease: a position paper endorsed by the Italian Society of Nephrology.J Nephrol. 2019; 32: 499-516Crossref PubMed Scopus (40) Google Scholar Adaptive increase in colonic potassium (K+) excretion in patients with end-stage renal disease (ESRD) partially compensates for the loss of K+ excretion by the kidney.2Panese S. Mártin R.S. Virginillo M. et al.Mechanism of enhanced transcellular potassium-secretion in man with chronic renal failure.Kidney Int. 1987; 31: 1377-1382Abstract Full Text PDF PubMed Scopus (22) Google Scholar Patiromer is a nonabsorbed polymer that binds K+ throughout the gastrointestinal (GI) tract and leads to lowering serum K+ levels.3Weir M.R. Bakris G.L. Bushinsky D.A. et al.Patiromer in patients with kidney disease and hyperkalemia receiving RAAS inhibitors.N Engl J Med. 2015; 372: 211-221Crossref PubMed Scopus (429) Google Scholar Interestingly, K+ is essential for the growth, colonization, and virulence of some bacteria.4Price-Whelan A. Poon C.K. Benson M.A. et al.Transcriptional profiling of Staphylococcus aureus during growth in 2 M NaCl leads to clarification of physiological roles for Kdp and Ktr K+ uptake systems.mBio. 2013; 4Crossref PubMed Scopus (52) Google Scholar,5MacLEOD R.A. Snell E.E. The effect of related ions on the potassium requirement of lactic acid bacteria.J Biol Chem. 1948; 176: 39-52Abstract Full Text PDF PubMed Google Scholar Thus, ambient K+ could have a significant effect on gut microbiota and their metabolic function. Previously, we have examined the effect to K+ binder patiromer treatment on serum and stool electrolytes in hemodialysis (HD) patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar To our knowledge, there have been no previous reports regarding the impact of K+ binder–induced changes in intestinal K+ content on the gut microbiota and microbiota-related metabolites in HD patients. In this clinical trial, we investigated the effect of lowering serum K+ by patiromer on the gut microbial community composition, their functional capacity, and host-cometabolism in HD patients.ResultsClinical ParametersThis is an ancillary to a parent study that examined the effect to patiromer treatment on serum and stool electrolytes in HD patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Characteristics of the study participants (20 controls, 21 HD patients) are shown in Table 1. Blood urea nitrogen, creatinine, and K+ were significantly elevated in HD patients. Dialysis prescription was unchanged during the study and dialysate K+ was 2 mEq/l throughout the study period. The dietary intake estimated by food frequency questionnaire showed that there was no significant change in the dietary intake of K+, protein, carbohydrate, or fat intake during the study phases (Table S1). Patiromer treatment resulted in a significant decrease in serum K+, which was accompanied increase in stool K+ (Figure 1b and 1c , Tables S2 and S3). Circulating level of soluble CD14 (Scd14) was lower in control subjects without kidney disease compared to HD patients. None of the inflammatory markers were altered by patiromer treatment (Table S4). The information about dialysis and medication was summarized in Table S5.Table 1Subject characteristics at baselinePatient variableControl (n = 20)ESRD/dialysis (n = 21)P valueSex female, n (%)11 (55)14 (52)0.99Age60 ± 1057 ± 110.22Race, n (%)0.12 Asian3 (15)0 (0) Black11 (55)20 (74) Caucasian5 (25)4 (15) Other1 (5)3 (11)BMI31.4 ± 7.232.7 ± 6.70.53DM, n (%)8 (40)11 (41)0.99HIV or Hep, n (%)0 (0)6 (22)0.03Glucose131 ± 82123 ± 550.68BUN12 ± 561 ± 16<0.0001Creatinine0.8 ± 0.210.2 ± 2.0<0.0001Sodium142 ± 3138 ± 30.0013Potassium4.3 ± 0.45.6 ± 0.6<0.0001Chloride103 ± 396 ± 4<0.0001CO224.8 ± 2.620.2 ± 2.9<0.0001Ca9.5 ± 0.48.9 ± 0.70.004SBP140 ± 20145 ± 230.58DBP79 ± 1077 ± 110.72BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; DM, diabetes mellitus; ESRD, end-stage renal disease; Hep, hepatitis; SBP, systolic blood pressure.Unless otherwise noted, values are mean ± SD. We compared hemodialysis patients’ laboratory parameters at baseline (week 2) with controls. Values in bold indicate statistical significance. Open table in a new tab Association of Gut Microbiota With K+Alpha diversity estimated by Shannon index was negatively correlated with K+ in stool and positively correlated with K+ in serum (Figure 2a). A total of 18 sensitive bacterial species were identified, with 10 and 9 significantly correlated with stool and serum K+, respectively (Figure 2b). Nine microbial species positively associated with stool K+, including Streptococcus gordonii, Alistipes senegalensis, Alistipes finegoldii, Clostridium scindens, Ruminococcus obeum, Parabacteroides merdae, and Lachnospiraceae bacterium 2-1-58FAA, 3-1-46FAA, and 1-1-57FAA (Figure 2c). Escherichia coli was negatively associated with stool K+ but positively correlated with serum K+ (Figure 2d). Three additional microbial species were positively associated with serum K+, including Parabacteroides unclassified, Pyramidobacter piscolens, and Bacteroides vulgatus. Five microbial species were negatively correlated with serum K+, including Bacteroides caccae, Streptococcus salivarius, Streptococcus parasanguinis, Eggerthella unclassified, and Granulicatella unclassified (Figure 2c).Figure 2Association of gut microbiota with serum and stool potassium. (a) Correlation between Shannon index and K+ in stool (left) and serum (right). (b) Venn diagram of microbial species associated with stool and serum K+. (c) Correlation network of 18 sensitive microbial species. Green: microbial species correlated with serum K+; blue: microbial species correlated with stool K+; and red: microbial species correlated with both serum and stool K+. (d) Correlation between Escherichia coli and K+ in stool (left) and serum (right).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Association of Microbial Pathways With K+A total of 132 microbial pathways were significantly correlated with stool K+, and 75 were correlated with serum K+ (Figure 3a). Notably, 48 microbial pathways were correlated with K+ in both serum and stool (Figure 3a). Among 48 microbial pathways, 17 microbial pathways were positively associated with stool K+ and negatively associated with serum K+. They belonged to 6 functional categories (Figure 3b and d). The rest 31 microbial pathways were negatively associated with stool K+ and positively associated with serum K+, which belonged to 11 functional categories (Figure 3c and e).Figure 3Association of microbial pathways with serum and stool potassium. (a) Venn diagram of microbial pathways associated with stool and serum K+. (b) Classification of microbial pathway positively associated with K+ in stool and negatively associated with K+ in serum. (c) Classification of microbial pathway negatively associated with K+ in stool and positively associated with K+ in serum. (d) Microbial pathway positively associated with K+ in stool and negatively associated with K+ in serum. (e) Microbial pathway negatively associated with K+ in stool and positively associated with K+ in serum.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Association of Plasma Metabolome With Serum K+A total of 66 plasma metabolites were significantly associated with serum K+, chemical enrichment analysis of which revealed that 5 compound clusters were enriched (Figure 4a). Pathway enrichment analysis showed that 2 pathways were enriched, aminoacyl-Trna biosynthesis and ascorbate and aldarate metabolism (Figure 4b). Among 66 plasma metabolites, 27 were negatively correlated with serum K+ (Figure 4c). Meanwhile, 39 of 66 plasma metabolites were positively correlated with serum K+ (Figure 4d).Figure 4Association of plasma metabolites with serum potassium. (a) Chemical enrichment analysis of plasma metabolites correlated with serum K+. (b) Pathway enrichment analysis of plasma metabolites correlated with serum K+. (c) Plasma metabolites negatively correlated with serum K+. (d) Plasma metabolites positively correlated with serum K+. ∗ in heatmap: significantly altered between patients at week 2 and control subjects. Text in red: significantly increased metabolites in patients at week 14 compared to week 2. Text in blue: significantly decreased metabolites in patients at week 14 compared to week 2. Text with ∗ on top right: significantly altered metabolites in patients at week 20 compared to week 14.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Association of Stool Metabolome With Stool K+A total of 26 stool metabolites were associated with stool K+; chemical enrichment analysis showed that 1 compound cluster was enriched (Figure 5a and b). One of 26 metabolites, pipecolinic acid, was significantly associated with both serum and stool K+ (Figure 5a). Among 26 stool metabolites, 23 were positively correlated with stool K+; meanwhile, 3 were negatively correlated with stool K+ (Figure 5c).Figure 5Association of stool metabolites with stool potassium. (a) Venn diagram of metabolites associated with stool and serum K+. (b) Chemical enrichment analysis of stool metabolites correlated with stool K+. (c) Stool metabolites correlated with stool K+. Text with underscore: negative correlation. Text without underscore: positive correlation. ∗ in heatmap: significantly altered between patients at week 2 and control subjects. Text in Red: significantly increased metabolites in patients at week 14 compared to week 2. Text with ∗ on top right: significantly altered metabolites in patients at week 20 compared to week 14.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Integrative AnalysisFurther, we examined whether any microbial pathway and its related metabolites were correlated with serum and stool K+. Plasma level of methionine was negatively correlated with serum K+, as well as microbial pathways such as l-methionine biosynthesis I, l-methionine biosynthesis III, l-homoserine and l-methionine biosynthesis, superpathway of l-methionine biosynthesis transsulfuration, and superpathway of S-adenosyl-l-methionine biosynthesis, which were negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6a and b). In addition, plasma level of serine was also negatively correlated with serum K+, as well as the microbial pathway, superpathway of l-serine and glycine biosynthesis I, which was negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6c and d).Figure 6Methionine and serine biosynthesis. (a) Five microbial methionine biosynthesis pathways were negatively associated with serum K+ and positively associated with stool K+. (b) Plasma level of methionine was negatively associated with serum K+. (c) Superpathway of l-serine and glycine biosynthesis was negatively correlated with serum K+ and positively correlated with stool K+. (d) Plasma level of serine was negatively correlated with serum K+.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Comparison Host-Cometabolism in Controls Versus HD PatientsWe compared the microbiome and metabolome profile in HD patients to control subjects. Shannon index was increased in HD patients compared with controls (Figure S1A). However, the 18 K+-sensitive microbial species were not significantly different in HD patients at baseline compared with control subjects. Among the 48 K+-associated microbial pathways identified in HD patients, Trna processing was significantly decreased in HD patients at week 2 compared with controls. Among the 27 plasma metabolites negatively associated with serum K+ in HD patients, 16 were significantly different between HD patients at week 2 and control subjects (Figure 4c). We noted that 21 of the 39 plasma metabolites positively associated with serum K+ in HD patients were significantly different control subjects (Figure 4d). Among 26 stool metabolites that were associated with stool K+ in HD patients, 8 were significantly different between the HD patients at week 2 and control subjects, with half increased and half decreased (Figure 5c).Impact of K+ on the Growth of E coli and Clostridium scindens In VitroMaximum density was lower in the culture supplemented with 3.5 and 6.0 mEq/l K+ compared with control group (0 mEq/l K+) in 2 ollie coli strains, ollie coli 11775, and ollie coli 25922 (Figure 7a and b). Maximum density of C scindens was higher in the culture supplemented with 6 mEq/l K+ compared with culture supplemented with 3.5 mEq/l K+ and the control group without K+ supplementation (Figure 7c).Figure 7In vitro culture. (a) Escherichia coli 11775. (b) E coli 25922. (c) Clostridium scindens.View Large Image Figure ViewerDownload Hi-res image Download (PPT)DiscussionThis study has several strengths, including robust study design with rigorous patient selection criteria; controlling for diet, medication use, and dialysis treatment; sequential measurements of K+ in stool and serum; and integrated microbiome-metabolome analysis. Some of the potential limitations include the control subjects were studied only at 1 time point and the confounding effect of patiromer treatment on the findings could not be totally excluded. There was no controlling for phosphate binder use nor a reporting of either of phosphate binders or the recipient of iron dosing, both of which have the potential to change the gut microbiome. The quality of nutrients can affect gut microbiota variability; however, we have no information on fiber intake and the ratio of animal-vegetal protein intake. Patients using prebiotic or probiotic agents were excluded in this patient cohort. But in real life, this is a rather common occurrence so that the results of this study could not apply to most patients. Hyperkalemia is a common and serious electrolyte abnormality in advanced CKD patients.1Bianchi S. Aucella F. De Nicola L. Genovesi S. et al.Management of hyperkalemia in patients with kidney disease: a position paper endorsed by the Italian Society of Nephrology.J Nephrol. 2019; 32: 499-516Crossref PubMed Scopus (40) Google Scholar Adaptive increase in colonic potassium (K+) excretion in patients with end-stage renal disease (ESRD) partially compensates for the loss of K+ excretion by the kidney.2Panese S. Mártin R.S. Virginillo M. et al.Mechanism of enhanced transcellular potassium-secretion in man with chronic renal failure.Kidney Int. 1987; 31: 1377-1382Abstract Full Text PDF PubMed Scopus (22) Google Scholar Patiromer is a nonabsorbed polymer that binds K+ throughout the gastrointestinal (GI) tract and leads to lowering serum K+ levels.3Weir M.R. Bakris G.L. Bushinsky D.A. et al.Patiromer in patients with kidney disease and hyperkalemia receiving RAAS inhibitors.N Engl J Med. 2015; 372: 211-221Crossref PubMed Scopus (429) Google Scholar Interestingly, K+ is essential for the growth, colonization, and virulence of some bacteria.4Price-Whelan A. Poon C.K. Benson M.A. et al.Transcriptional profiling of Staphylococcus aureus during growth in 2 M NaCl leads to clarification of physiological roles for Kdp and Ktr K+ uptake systems.mBio. 2013; 4Crossref PubMed Scopus (52) Google Scholar,5MacLEOD R.A. Snell E.E. The effect of related ions on the potassium requirement of lactic acid bacteria.J Biol Chem. 1948; 176: 39-52Abstract Full Text PDF PubMed Google Scholar Thus, ambient K+ could have a significant effect on gut microbiota and their metabolic function. Previously, we have examined the effect to K+ binder patiromer treatment on serum and stool electrolytes in hemodialysis (HD) patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar To our knowledge, there have been no previous reports regarding the impact of K+ binder–induced changes in intestinal K+ content on the gut microbiota and microbiota-related metabolites in HD patients. In this clinical trial, we investigated the effect of lowering serum K+ by patiromer on the gut microbial community composition, their functional capacity, and host-cometabolism in HD patients. ResultsClinical ParametersThis is an ancillary to a parent study that examined the effect to patiromer treatment on serum and stool electrolytes in HD patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Characteristics of the study participants (20 controls, 21 HD patients) are shown in Table 1. Blood urea nitrogen, creatinine, and K+ were significantly elevated in HD patients. Dialysis prescription was unchanged during the study and dialysate K+ was 2 mEq/l throughout the study period. The dietary intake estimated by food frequency questionnaire showed that there was no significant change in the dietary intake of K+, protein, carbohydrate, or fat intake during the study phases (Table S1). Patiromer treatment resulted in a significant decrease in serum K+, which was accompanied increase in stool K+ (Figure 1b and 1c , Tables S2 and S3). Circulating level of soluble CD14 (Scd14) was lower in control subjects without kidney disease compared to HD patients. None of the inflammatory markers were altered by patiromer treatment (Table S4). The information about dialysis and medication was summarized in Table S5.Table 1Subject characteristics at baselinePatient variableControl (n = 20)ESRD/dialysis (n = 21)P valueSex female, n (%)11 (55)14 (52)0.99Age60 ± 1057 ± 110.22Race, n (%)0.12 Asian3 (15)0 (0) Black11 (55)20 (74) Caucasian5 (25)4 (15) Other1 (5)3 (11)BMI31.4 ± 7.232.7 ± 6.70.53DM, n (%)8 (40)11 (41)0.99HIV or Hep, n (%)0 (0)6 (22)0.03Glucose131 ± 82123 ± 550.68BUN12 ± 561 ± 16<0.0001Creatinine0.8 ± 0.210.2 ± 2.0<0.0001Sodium142 ± 3138 ± 30.0013Potassium4.3 ± 0.45.6 ± 0.6<0.0001Chloride103 ± 396 ± 4<0.0001CO224.8 ± 2.620.2 ± 2.9<0.0001Ca9.5 ± 0.48.9 ± 0.70.004SBP140 ± 20145 ± 230.58DBP79 ± 1077 ± 110.72BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; DM, diabetes mellitus; ESRD, end-stage renal disease; Hep, hepatitis; SBP, systolic blood pressure.Unless otherwise noted, values are mean ± SD. We compared hemodialysis patients’ laboratory parameters at baseline (week 2) with controls. Values in bold indicate statistical significance. Open table in a new tab Association of Gut Microbiota With K+Alpha diversity estimated by Shannon index was negatively correlated with K+ in stool and positively correlated with K+ in serum (Figure 2a). A total of 18 sensitive bacterial species were identified, with 10 and 9 significantly correlated with stool and serum K+, respectively (Figure 2b). Nine microbial species positively associated with stool K+, including Streptococcus gordonii, Alistipes senegalensis, Alistipes finegoldii, Clostridium scindens, Ruminococcus obeum, Parabacteroides merdae, and Lachnospiraceae bacterium 2-1-58FAA, 3-1-46FAA, and 1-1-57FAA (Figure 2c). Escherichia coli was negatively associated with stool K+ but positively correlated with serum K+ (Figure 2d). Three additional microbial species were positively associated with serum K+, including Parabacteroides unclassified, Pyramidobacter piscolens, and Bacteroides vulgatus. Five microbial species were negatively correlated with serum K+, including Bacteroides caccae, Streptococcus salivarius, Streptococcus parasanguinis, Eggerthella unclassified, and Granulicatella unclassified (Figure 2c).Association of Microbial Pathways With K+A total of 132 microbial pathways were significantly correlated with stool K+, and 75 were correlated with serum K+ (Figure 3a). Notably, 48 microbial pathways were correlated with K+ in both serum and stool (Figure 3a). Among 48 microbial pathways, 17 microbial pathways were positively associated with stool K+ and negatively associated with serum K+. They belonged to 6 functional categories (Figure 3b and d). The rest 31 microbial pathways were negatively associated with stool K+ and positively associated with serum K+, which belonged to 11 functional categories (Figure 3c and e).Figure 3Association of microbial pathways with serum and stool potassium. (a) Venn diagram of microbial pathways associated with stool and serum K+. (b) Classification of microbial pathway positively associated with K+ in stool and negatively associated with K+ in serum. (c) Classification of microbial pathway negatively associated with K+ in stool and positively associated with K+ in serum. (d) Microbial pathway positively associated with K+ in stool and negatively associated with K+ in serum. (e) Microbial pathway negatively associated with K+ in stool and positively associated with K+ in serum.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Association of Plasma Metabolome With Serum K+A total of 66 plasma metabolites were significantly associated with serum K+, chemical enrichment analysis of which revealed that 5 compound clusters were enriched (Figure 4a). Pathway enrichment analysis showed that 2 pathways were enriched, aminoacyl-Trna biosynthesis and ascorbate and aldarate metabolism (Figure 4b). Among 66 plasma metabolites, 27 were negatively correlated with serum K+ (Figure 4c). Meanwhile, 39 of 66 plasma metabolites were positively correlated with serum K+ (Figure 4d).Figure 4Association of plasma metabolites with serum potassium. (a) Chemical enrichment analysis of plasma metabolites correlated with serum K+. (b) Pathway enrichment analysis of plasma metabolites correlated with serum K+. (c) Plasma metabolites negatively correlated with serum K+. (d) Plasma metabolites positively correlated with serum K+. ∗ in heatmap: significantly altered between patients at week 2 and control subjects. Text in red: significantly increased metabolites in patients at week 14 compared to week 2. Text in blue: significantly decreased metabolites in patients at week 14 compared to week 2. Text with ∗ on top right: significantly altered metabolites in patients at week 20 compared to week 14.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Association of Stool Metabolome With Stool K+A total of 26 stool metabolites were associated with stool K+; chemical enrichment analysis showed that 1 compound cluster was enriched (Figure 5a and b). One of 26 metabolites, pipecolinic acid, was significantly associated with both serum and stool K+ (Figure 5a). Among 26 stool metabolites, 23 were positively correlated with stool K+; meanwhile, 3 were negatively correlated with stool K+ (Figure 5c).Figure 5Association of stool metabolites with stool potassium. (a) Venn diagram of metabolites associated with stool and serum K+. (b) Chemical enrichment analysis of stool metabolites correlated with stool K+. (c) Stool metabolites correlated with stool K+. Text with underscore: negative correlation. Text without underscore: positive correlation. ∗ in heatmap: significantly altered between patients at week 2 and control subjects. Text in Red: significantly increased metabolites in patients at week 14 compared to week 2. Text with ∗ on top right: significantly altered metabolites in patients at week 20 compared to week 14.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Integrative AnalysisFurther, we examined whether any microbial pathway and its related metabolites were correlated with serum and stool K+. Plasma level of methionine was negatively correlated with serum K+, as well as microbial pathways such as l-methionine biosynthesis I, l-methionine biosynthesis III, l-homoserine and l-methionine biosynthesis, superpathway of l-methionine biosynthesis transsulfuration, and superpathway of S-adenosyl-l-methionine biosynthesis, which were negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6a and b). In addition, plasma level of serine was also negatively correlated with serum K+, as well as the microbial pathway, superpathway of l-serine and glycine biosynthesis I, which was negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6c and d).Figure 6Methionine and serine biosynthesis. (a) Five microbial methionine biosynthesis pathways were negatively associated with serum K+ and positively associated with stool K+. (b) Plasma level of methionine was negatively associated with serum K+. (c) Superpathway of l-serine and glycine biosynthesis was negatively correlated with serum K+ and positively correlated with stool K+. (d) Plasma level of serine was negatively correlated with serum K+.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Comparison Host-Cometabolism in Controls Versus HD PatientsWe compared the microbiome and metabolome profile in HD patients to control subjects. Shannon index was increased in HD patients compared with controls (Figure S1A). However, the 18 K+-sensitive microbial species were not significantly different in HD patients at baseline compared with control subjects. Among the 48 K+-associated microbial pathways identified in HD patients, Trna processing was significantly decreased in HD patients at week 2 compared with controls. Among the 27 plasma metabolites negatively associated with serum K+ in HD patients, 16 were significantly different between HD patients at week 2 and control subjects (Figure 4c). We noted that 21 of the 39 plasma metabolites positively associated with serum K+ in HD patients were significantly different control subjects (Figure 4d). Among 26 stool metabolites that were associated with stool K+ in HD patients, 8 were significantly different between the HD patients at week 2 and control subjects, with half increased and half decreased (Figure 5c).Impact of K+ on the Growth of E coli and Clostridium scindens In VitroMaximum density was lower in the culture supplemented with 3.5 and 6.0 mEq/l K+ compared with control group (0 mEq/l K+) in 2 ollie coli strains, ollie coli 11775, and ollie coli 25922 (Figure 7a and b). Maximum density of C scindens was higher in the culture supplemented with 6 mEq/l K+ compared with culture supplemented with 3.5 mEq/l K+ and the control group without K+ supplementation (Figure 7c).Figure 7In vitro culture. (a) Escherichia coli 11775. (b) E coli 25922. (c) Clostridium scindens.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Clinical ParametersThis is an ancillary to a parent study that examined the effect to patiromer treatment on serum and stool electrolytes in HD patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Characteristics of the study participants (20 controls, 21 HD patients) are shown in Table 1. Blood urea nitrogen, creatinine, and K+ were significantly elevated in HD patients. Dialysis prescription was unchanged during the study and dialysate K+ was 2 mEq/l throughout the study period. The dietary intake estimated by food frequency questionnaire showed that there was no significant change in the dietary intake of K+, protein, carbohydrate, or fat intake during the study phases (Table S1). Patiromer treatment resulted in a significant decrease in serum K+, which was accompanied increase in stool K+ (Figure 1b and 1c , Tables S2 and S3). Circulating level of soluble CD14 (Scd14) was lower in control subjects without kidney disease compared to HD patients. None of the inflammatory markers were altered by patiromer treatment (Table S4). The information about dialysis and medication was summarized in Table S5.Table 1Subject characteristics at baselinePatient variableControl (n = 20)ESRD/dialysis (n = 21)P valueSex female, n (%)11 (55)14 (52)0.99Age60 ± 1057 ± 110.22Race, n (%)0.12 Asian3 (15)0 (0) Black11 (55)20 (74) Caucasian5 (25)4 (15) Other1 (5)3 (11)BMI31.4 ± 7.232.7 ± 6.70.53DM, n (%)8 (40)11 (41)0.99HIV or Hep, n (%)0 (0)6 (22)0.03Glucose131 ± 82123 ± 550.68BUN12 ± 561 ± 16<0.0001Creatinine0.8 ± 0.210.2 ± 2.0<0.0001Sodium142 ± 3138 ± 30.0013Potassium4.3 ± 0.45.6 ± 0.6<0.0001Chloride103 ± 396 ± 4<0.0001CO224.8 ± 2.620.2 ± 2.9<0.0001Ca9.5 ± 0.48.9 ± 0.70.004SBP140 ± 20145 ± 230.58DBP79 ± 1077 ± 110.72BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; DM, diabetes mellitus; ESRD, end-stage renal disease; Hep, hepatitis; SBP, systolic blood pressure.Unless otherwise noted, values are mean ± SD. We compared hemodialysis patients’ laboratory parameters at baseline (week 2) with controls. Values in bold indicate statistical significance. Open table in a new tab This is an ancillary to a parent study that examined the effect to patiromer treatment on serum and stool electrolytes in HD patients.6Amdur R.L. Paul R. Barrows E.D. et al.The potassium regulator patiromer affects serum and stool electrolytes in patients receiving hemodialysis.Kidney Int. 2020; 98: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Characteristics of the study participants (20 controls, 21 HD patients) are shown in Table 1. Blood urea nitrogen, creatinine, and K+ were significantly elevated in HD patients. Dialysis prescription was unchanged during the study and dialysate K+ was 2 mEq/l throughout the study period. The dietary intake estimated by food frequency questionnaire showed that there was no significant change in the dietary intake of K+, protein, carbohydrate, or fat intake during the study phases (Table S1). Patiromer treatment resulted in a significant decrease in serum K+, which was accompanied increase in stool K+ (Figure 1b and 1c , Tables S2 and S3). Circulating level of soluble CD14 (Scd14) was lower in control subjects without kidney disease compared to HD patients. None of the inflammatory markers were altered by patiromer treatment (Table S4). The information about dialysis and medication was summarized in Table S5. BMI, body mass index; BUN, blood urea nitrogen; DBP, diastolic blood pressure; DM, diabetes mellitus; ESRD, end-stage renal disease; Hep, hepatitis; SBP, systolic blood pressure. Unless otherwise noted, values are mean ± SD. We compared hemodialysis patients’ laboratory parameters at baseline (week 2) with controls. Values in bold indicate statistical significance. Association of Gut Microbiota With K+Alpha diversity estimated by Shannon index was negatively correlated with K+ in stool and positively correlated with K+ in serum (Figure 2a). A total of 18 sensitive bacterial species were identified, with 10 and 9 significantly correlated with stool and serum K+, respectively (Figure 2b). Nine microbial species positively associated with stool K+, including Streptococcus gordonii, Alistipes senegalensis, Alistipes finegoldii, Clostridium scindens, Ruminococcus obeum, Parabacteroides merdae, and Lachnospiraceae bacterium 2-1-58FAA, 3-1-46FAA, and 1-1-57FAA (Figure 2c). Escherichia coli was negatively associated with stool K+ but positively correlated with serum K+ (Figure 2d). Three additional microbial species were positively associated with serum K+, including Parabacteroides unclassified, Pyramidobacter piscolens, and Bacteroides vulgatus. Five microbial species were negatively correlated with serum K+, including Bacteroides caccae, Streptococcus salivarius, Streptococcus parasanguinis, Eggerthella unclassified, and Granulicatella unclassified (Figure 2c). Alpha diversity estimated by Shannon index was negatively correlated with K+ in stool and positively correlated with K+ in serum (Figure 2a). A total of 18 sensitive bacterial species were identified, with 10 and 9 significantly correlated with stool and serum K+, respectively (Figure 2b). Nine microbial species positively associated with stool K+, including Streptococcus gordonii, Alistipes senegalensis, Alistipes finegoldii, Clostridium scindens, Ruminococcus obeum, Parabacteroides merdae, and Lachnospiraceae bacterium 2-1-58FAA, 3-1-46FAA, and 1-1-57FAA (Figure 2c). Escherichia coli was negatively associated with stool K+ but positively correlated with serum K+ (Figure 2d). Three additional microbial species were positively associated with serum K+, including Parabacteroides unclassified, Pyramidobacter piscolens, and Bacteroides vulgatus. Five microbial species were negatively correlated with serum K+, including Bacteroides caccae, Streptococcus salivarius, Streptococcus parasanguinis, Eggerthella unclassified, and Granulicatella unclassified (Figure 2c). Association of Microbial Pathways With K+A total of 132 microbial pathways were significantly correlated with stool K+, and 75 were correlated with serum K+ (Figure 3a). Notably, 48 microbial pathways were correlated with K+ in both serum and stool (Figure 3a). Among 48 microbial pathways, 17 microbial pathways were positively associated with stool K+ and negatively associated with serum K+. They belonged to 6 functional categories (Figure 3b and d). The rest 31 microbial pathways were negatively associated with stool K+ and positively associated with serum K+, which belonged to 11 functional categories (Figure 3c and e). A total of 132 microbial pathways were significantly correlated with stool K+, and 75 were correlated with serum K+ (Figure 3a). Notably, 48 microbial pathways were correlated with K+ in both serum and stool (Figure 3a). Among 48 microbial pathways, 17 microbial pathways were positively associated with stool K+ and negatively associated with serum K+. They belonged to 6 functional categories (Figure 3b and d). The rest 31 microbial pathways were negatively associated with stool K+ and positively associated with serum K+, which belonged to 11 functional categories (Figure 3c and e). Association of Plasma Metabolome With Serum K+A total of 66 plasma metabolites were significantly associated with serum K+, chemical enrichment analysis of which revealed that 5 compound clusters were enriched (Figure 4a). Pathway enrichment analysis showed that 2 pathways were enriched, aminoacyl-Trna biosynthesis and ascorbate and aldarate metabolism (Figure 4b). Among 66 plasma metabolites, 27 were negatively correlated with serum K+ (Figure 4c). Meanwhile, 39 of 66 plasma metabolites were positively correlated with serum K+ (Figure 4d). A total of 66 plasma metabolites were significantly associated with serum K+, chemical enrichment analysis of which revealed that 5 compound clusters were enriched (Figure 4a). Pathway enrichment analysis showed that 2 pathways were enriched, aminoacyl-Trna biosynthesis and ascorbate and aldarate metabolism (Figure 4b). Among 66 plasma metabolites, 27 were negatively correlated with serum K+ (Figure 4c). Meanwhile, 39 of 66 plasma metabolites were positively correlated with serum K+ (Figure 4d). Association of Stool Metabolome With Stool K+A total of 26 stool metabolites were associated with stool K+; chemical enrichment analysis showed that 1 compound cluster was enriched (Figure 5a and b). One of 26 metabolites, pipecolinic acid, was significantly associated with both serum and stool K+ (Figure 5a). Among 26 stool metabolites, 23 were positively correlated with stool K+; meanwhile, 3 were negatively correlated with stool K+ (Figure 5c). A total of 26 stool metabolites were associated with stool K+; chemical enrichment analysis showed that 1 compound cluster was enriched (Figure 5a and b). One of 26 metabolites, pipecolinic acid, was significantly associated with both serum and stool K+ (Figure 5a). Among 26 stool metabolites, 23 were positively correlated with stool K+; meanwhile, 3 were negatively correlated with stool K+ (Figure 5c). Integrative AnalysisFurther, we examined whether any microbial pathway and its related metabolites were correlated with serum and stool K+. Plasma level of methionine was negatively correlated with serum K+, as well as microbial pathways such as l-methionine biosynthesis I, l-methionine biosynthesis III, l-homoserine and l-methionine biosynthesis, superpathway of l-methionine biosynthesis transsulfuration, and superpathway of S-adenosyl-l-methionine biosynthesis, which were negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6a and b). In addition, plasma level of serine was also negatively correlated with serum K+, as well as the microbial pathway, superpathway of l-serine and glycine biosynthesis I, which was negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6c and d). Further, we examined whether any microbial pathway and its related metabolites were correlated with serum and stool K+. Plasma level of methionine was negatively correlated with serum K+, as well as microbial pathways such as l-methionine biosynthesis I, l-methionine biosynthesis III, l-homoserine and l-methionine biosynthesis, superpathway of l-methionine biosynthesis transsulfuration, and superpathway of S-adenosyl-l-methionine biosynthesis, which were negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6a and b). In addition, plasma level of serine was also negatively correlated with serum K+, as well as the microbial pathway, superpathway of l-serine and glycine biosynthesis I, which was negatively correlated with serum K+ and positively correlated with stool K+ (Figure 6c and d). Comparison Host-Cometabolism in Controls Versus HD PatientsWe compared the microbiome and metabolome profile in HD patients to control subjects. Shannon index was increased in HD patients compared with controls (Figure S1A). However, the 18 K+-sensitive microbial species were not significantly different in HD patients at baseline compared with control subjects. Among the 48 K+-associated microbial pathways identified in HD patients, Trna processing was significantly decreased in HD patients at week 2 compared with controls. Among the 27 plasma metabolites negatively associated with serum K+ in HD patients, 16 were significantly different between HD patients at week 2 and control subjects (Figure 4c). We noted that 21 of the 39 plasma metabolites positively associated with serum K+ in HD patients were significantly different control subjects (Figure 4d). Among 26 stool metabolites that were associated with stool K+ in HD patients, 8 were significantly different between the HD patients at week 2 and control subjects, with half increased and half decreased (Figure 5c). We compared the microbiome and metabolome profile in HD patients to control subjects. Shannon index was increased in HD patients compared with controls (Figure S1A). However, the 18 K+-sensitive microbial species were not significantly different in HD patients at baseline compared with control subjects. Among the 48 K+-associated microbial pathways identified in HD patients, Trna processing was significantly decreased in HD patients at week 2 compared with controls. Among the 27 plasma metabolites negatively associated with serum K+ in HD patients, 16 were significantly different between HD patients at week 2 and control subjects (Figure 4c). We noted that 21 of the 39 plasma metabolites positively associated with serum K+ in HD patients were significantly different control subjects (Figure 4d). Among 26 stool metabolites that were associated with stool K+ in HD patients, 8 were significantly different between the HD patients at week 2 and control subjects, with half increased and half decreased (Figure 5c). Impact of K+ on the Growth of E coli and Clostridium scindens In VitroMaximum density was lower in the culture supplemented with 3.5 and 6.0 mEq/l K+ compared with control group (0 mEq/l K+) in 2 ollie coli strains, ollie coli 11775, and ollie coli 25922 (Figure 7a and b). Maximum density of C scindens was higher in the culture supplemented with 6 mEq/l K+ compared with culture supplemented with 3.5 mEq/l K+ and the control group without K+ supplementation (Figure 7c). Maximum density was lower in the culture supplemented with 3.5 and 6.0 mEq/l K+ compared with control group (0 mEq/l K+) in 2 ollie coli strains, ollie coli 11775, and ollie coli 25922 (Figure 7a and b). Maximum density of C scindens was higher in the culture supplemented with 6 mEq/l K+ compared with culture supplemented with 3.5 mEq/l K+ and the control group without K+ supplementation (Figure 7c). DiscussionThis study has several strengths, including robust study design with rigorous patient selection criteria; controlling for diet, medication use, and dialysis treatment; sequential measurements of K+ in stool and serum; and integrated microbiome-metabolome analysis. Some of the potential limitations include the control subjects were studied only at 1 time point and the confounding effect of patiromer treatment on the findings could not be totally excluded. There was no controlling for phosphate binder use nor a reporting of either of phosphate binders or the recipient of iron dosing, both of which have the potential to change the gut microbiome. The quality of nutrients can affect gut microbiota variability; however, we have no information on fiber intake and the ratio of animal-vegetal protein intake. Patients using prebiotic or probiotic agents were excluded in this patient cohort. But in real life, this is a rather common occurrence so that the results of this study could not apply to most patients. This study has several strengths, including robust study design with rigorous patient selection criteria; controlling for diet, medication use, and dialysis treatment; sequential measurements of K+ in stool and serum; and integrated microbiome-metabolome analysis. Some of the potential limitations include the control subjects were studied only at 1 time point and the confounding effect of patiromer treatment on the findings could not be totally excluded. There was no controlling for phosphate binder use nor a reporting of either of phosphate binders or the recipient of iron dosing, both of which have the potential to change the gut microbiome. The quality of nutrients can affect gut microbiota variability; however, we have no information on fiber intake and the ratio of animal-vegetal protein intake. Patients using prebiotic or probiotic agents were excluded in this patient cohort. But in real life, this is a rather common occurrence so that the results of this study could not apply to most patients. This study is supported in part by Relypsa Inc , Redwood, California, and intramural funding from the George Washington University School of Medicine . DSR is supported by NIH grants 1U01DK099924-01 and 1U01DK099914-01 . All the other authors declared no competing interests. Supplementary Material Download .pdf (.81 MB) Help with pdf files Supplementary File (PDF)Table S1. Dietary pattern during the study period.Table S2. Serum electrolytes.Table S3. Stool electrolytes.Table S4. Cytokine levels.Table S5. Dialysis and medication information.Figure S1. Shannon diversity of control subjects and patients at different time points.Supplementary MethodsSTROBE statement. Download .pdf (.81 MB) Help with pdf files Supplementary File (PDF) Table S1. Dietary pattern during the study period. Table S2. Serum electrolytes. Table S3. Stool electrolytes. Table S4. Cytokine levels. Table S5. Dialysis and medication information. Figure S1. Shannon diversity of control subjects and patients at different time points. Supplementary Methods STROBE statement.

Hypertension is considered as the most common risk factor for cardiovascular disease. Inflammatory processes link hypertension and cardiovascular disease, and participate in their pathophysiology. In recent years, there has been an increase in research focused on unraveling the role of inflammation and immune activation in development and maintenance of hypertension. Although inflammation is known to be associated with hypertension, whether inflammation is a cause or effect of hypertension remains to be elucidated. This review describes the recent studies that link inflammation and hypertension and demonstrate the involvement of oxidative stress and endothelial dysfunction-two of the key processes in the development of hypertension. Etiology of hypertension, including novel immune cell subtypes, cytokines, toll-like receptors, inflammasomes, and gut microbiome, found to be associated with inflammation and hypertension are summarized and discussed. Most recent findings in this field are presented with special emphasis on potential of anti-inflammatory drugs and statins for treatment of hypertension.

A mechanistic link between trimethylamine N-oxide (TMAO) and atherogenesis has been reported. TMAO is generated enzymatically in the liver by the oxidation of trimethylamine (TMA), which is produced from dietary choline, carnitine and betaine by gut bacteria. It is known that certain members of methanogenic archaea (MA) could use methylated amines such as trimethylamine as growth substrates in culture. Therefore, we investigated the efficacy of gut colonization with MA on lowering plasma TMAO concentrations. Initially, we screened for the colonization potential and TMAO lowering efficacy of five MA species in C57BL/6 mice fed with high choline/TMA supplemented diet, and found out that all five species could colonize and lover plasma TMAO levels, although with different efficacies. The top performing MA, Methanobrevibacter smithii, Methanosarcina mazei, and Methanomicrococcus blatticola, were transplanted into Apoe−/− mice fed with high choline/TMA supplemented diet. Similar to C57BL/6 mice, following initial provision of the MA, there was progressive attrition of MA within fecal microbial communities post-transplantation during the initial 3 weeks of the study. In general, plasma TMAO concentrations decreased significantly in proportion to the level of MA colonization. In a subsequent experiment, use of antibiotics and repeated transplantation of Apoe−/− mice with M. smithii, led to high engraftment levels during the 9 weeks of the study, resulting in a sustained and significantly lower average plasma TMAO concentrations (18.2 ± 19.6 μM) compared to that in mock-transplanted control mice (120.8 ± 13.0 μM, p < 0.001). Compared to control Apoe−/− mice, M. smithii-colonized mice also had a 44% decrease in aortic plaque area (8,570 μm [95% CI 19587–151821] vs. 15,369 μm [95% CI [70058–237321], p = 0.34), and 52% reduction in the fat content in the atherosclerotic plaques (14,283 μm [95% CI 4,957–23,608] vs. 29,870 μm [95% CI 18,074–41,666], p = 0.10), although these differences did not reach significance. Gut colonization with M. smithii leads to a significant reduction in plasma TMAO levels, with a tendency for attenuation of atherosclerosis burden in Apoe−/− mice. The anti-atherogenic potential of MA should be further tested in adequately powered experiments.

Cardiovascular disease (CVD) remains a major cause of high morbidity and mortality in patients with chronic kidney disease (CKD). Numerous CVD risk factors in CKD patients have been described, but these do not fully explain the high pervasiveness of CVD or increased mortality rates in CKD patients. In CKD the loss of urinary excretory function results in the retention of various substances referred to as “uremic retention solutes”. Many of these molecules have been found to exert toxicity on virtually all organ systems of the human body, leading to the clinical syndrome of uremia. In recent years, an increasing body of evidence has been accumulated that suggests that uremic toxins may contribute to an increased cardiovascular disease (CVD) burden associated with CKD. This review examined the evidence from several clinical and experimental studies showing an association between uremic toxins and CVD. Special emphasis is addressed on emerging data linking gut microbiota with the production of uremic toxins and the development of CKD and CVD. The biological toxicity of some uremic toxins on the myocardium and the vasculature and their possible contribution to cardiovascular injury in uremia are also discussed. Finally, various therapeutic interventions that have been applied to effectively reduce uremic toxins in patients with CKD, including dietary modifications, use of prebiotics and/or probiotics, an oral intestinal sorbent that adsorbs uremic toxins and precursors, and innovative dialysis therapies targeting the protein-bound uremic toxins are also highlighted. Future studies are needed to determine whether these novel therapies to reduce or remove uremic toxins will reduce CVD and related cardiovascular events in the long-term in patients with chronic renal failure.

HomeJournal of the American Heart AssociationVol. 5, No. 4Janus Face of Coronary Artery Disease and Chronic Kidney Disease Open AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citations ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toOpen AccessEditorialPDF/EPUBJanus Face of Coronary Artery Disease and Chronic Kidney Disease Ian R. Barrows, MD and Dominic S. Raj, MD Ian R. BarrowsIan R. Barrows George Washington University School of Medicine, Washington, DC Search for more papers by this author and Dominic S. RajDominic S. Raj Division of Renal Diseases and Hypertension, George Washington University School of Medicine, Washington, DC Search for more papers by this author Originally published23 Apr 2016https://doi.org/10.1161/JAHA.116.003596Journal of the American Heart Association. 2016;5:e003596Janus is the ancient Roman god of beginning and transition. He is depicted as having 2 faces that can look back to the past and also into the future. Lindner and colleagues first reported that hemodialysis patients have accelerated atherosclerosis and cautioned that cardiovascular mortality could be the major impediment to the long‐term survival of these patients.1 Indeed, it is now well recognized that patients with decreased glomerular filtration rate (GFR) are at a greater risk for incident myocardial infarction and death from coronary artery disease (CAD) compared with the general population. Emerging literature also suggest that underlying macrovascular disease could also contribute to progression of chronic kidney disease (CKD). Findings from the Atherosclerosis Risk in Communities (ARC) and the Cardiovascular Health Studies (CHS) showed those with a history of cardiovascular disease (CVD) are associated with a faster decline in estimated GFR (eGFR) compared with patients without such history.2 A retrospective cohort study from Canada reported that an interim cardiovascular event was associated with a 4‐ to 5‐fold higher relative risk of subsequent end‐stage renal disease (ESRD).3 The Reasons for Geographical and Racial Difference in Stroke (REGARDS) cohort study showed that patients with CAD have a high prevalence of CKD and these individuals are largely unaware of their kidney disease.4 Thus, there is a bidirectional relationship between CKD and cardiovascular disease (CVD) and the magnitude of the problem is underappreciated.Growing opulence and urbanization has led to globalization of the epidemic of type 2 diabetes mellitus (T2DM). It is estimated that the number of people with diabetes will reach 300 million by 2025. Diabetes is the most common cause of ESRD in the United States and across the world, accounting for about 45% of new patients initiated on renal replacement therapy. Patients with T2DM have a higher risk of CV mortality than nondiabetic populations.5 It is well recognized that the presence of CKD greatly amplifies the CVD risk associated with T2DM.In this issue of JAHA, Sabe and associates report that in diabetic patients with CKD and anemia, history of CAD is associated with progression to ESRD, in the Trial to Reduce Cardiovascular Events with Aranesp Therapy (TREAT) study participants.6 The TREAT study randomized 4038 patients with diabetes, CKD, and anemia to darbepoetin α or placebo. The study results indicated that the routine use of erythropoietin‐stimulating agents in anemic patients with diabetes and CKD not on dialysis does not reduce renal and cardiovascular events. However, study participants with elevated levels of baseline troponin T and N‐terminal pro‐brain natriuretic peptide were independently associated with a higher risk of ESRD, suggesting that underlying CVD is a risk factor for CKD progression.6In the present study, Sabe et al noted that those with CAD were less likely to have proteinuria, but the eGFR was not significantly different between those with and without CAD, which could not be explained by use of angiotensin‐converting enzyme inhibitor or angiotensin receptor blockers.7 Traditionally, diabetic nephropathy is described as a chronic progressive disorder that is characterized by microalbuminuria, followed by macroalbuminuria, and hypertension leading to progressive loss of eGFR resulting in ESRD. Recently, several epidemiological studies have shown that frequently T2DM patients with reduced eGFR have no proteinuria.8 The mechanism underlying the progressive GFR decline in nonalbuminuric diabetic nephropathy is not known, but ischemic vascular disease, cholesterol microemboli, interstitial fibrosis, and premature senescence of the diabetic kidney have been proposed as potential causes. It is possible that subjects with CAD also have intrarenal vascular diseases. However, in a study of patients with T2DM and CKD, intrarenal vascular resistance, studied by renal duplex scan, was not different in those with and without proteinuria.9 However, Doppler ultrasound may not be sensitive enough to detect intrarenal atherosclerotic changes.Findings from observational studies have shown that those surviving an episode of acute kidney injury (AKI) are at risk for CKD. Patients with CAD have high risk for AKI because of advanced age, comorbidities, heart failure, radiocontrast exposure, medication use, and coronary artery bypass surgery. James et al observed a graded increase in risk of ESRD, which varied by the severity of AKI, among patients who underwent coronary angiography and developed AKI.10 In a nationwide cohort of patients who underwent coronary artery bypass grafting, even a small postoperative increase in serum creatinine was associated with an increase in the long‐term risk of ESRD.11 In this study, Sabe et al tested this possibility by including a time‐varying covariate for coronary revascularization in the model and did not find any significant impact on CKD progression, but this secondary analysis of TREAT cannot completely exclude the possibility that AKI could have contributed to the risk of ESRD in this population.7Atherosclerosis is an indolent, fibroproliferative disease fueled by chronic inflammation. Immune cells dominate the atherosclerotic lesion and exhibit evidence of activation. Findings from the Chronic Renal Insufficiency Cohort study showed that plasma levels of pro‐inflammatory cytokines and positive acute‐phase proteins were higher in subjects with lower levels of kidney function.12 We showed that specific inflammatory biomarkers are associated with cardiac geometry and risk of atrial fibrillation in patients with CKD.13, 14 Potential causes of inflammation in CKD include chronic subclinical infections, volume overload, increased oxidative stress, sympathetic overactivity, poor nutrition, and vitamin D deficiency, which are also risk factors for CVD. Inflammation plays a critical role in the progression of CKD. A variety of cytokines, chemokines, and growth factors act in concert to create an imbalance in matrix formation and degradation, which leads to overall accumulation of extracellular matrix and eventually glomerulosclerosis and interstitial fibrosis. Future studies should examine whether attenuation of inflammation has a salutary effect on the progression of CKD and CVD.Sabe et al also found that CAD was independently associated with death in TREAT study participants.7 Study of prevalent cohorts of Medicare enrollees from 1996 to 2000 showed that those with CKD are 5 to 10 times more likely to die before reaching ESRD than the non‐CKD group.15 In studies examining the risk of ESRD, death before ESRD prevents ESRD from occurring and is a competing event. The standard Cox model, which fails to adjust for competing risk of death, can overestimate the absolute risk. The present study does not account for death as a competing event in the analysis, which the authors acknowledge in the discussion.The investigators noted that CAD was associated with death among TREAT study participants only when history of heart failure (HF) was excluded from the model. HF is an important and rapidly growing healthcare problem. It is estimated that about 50% of HF patients die at 4 years and 40% of admitted patients with HF are dead or readmitted within 1 year.16 Epidemiological studies indicate that CKD is present in 35% to 70% of HF patients and is a strong and independent predictor of death. The prevalence of reduced kidney function in patients with HF increases with age, HF severity, and presence of hypertension and diabetes. Other risk factors include anemia, low serum albumin, and uses of renin–angiotensin system inhibitors, aldosterone antagonists, and diuretics. It is possible that the mortality in patients with CAD is mediated through HF.To summarize, this study highlights the interaction and interdependency of progression of CAD and CVD, and also presents heightened risk for death in patients with CKD. The cause for this close relationship between loss of kidney function and accelerated atherosclerosis could be attributable to a set of distinct as well as shared risk factors (Figure). Framingham score has poor accuracy in predicting incident CHD in patients with CKD, but intensive search for silent heart disease in CKD and incipient CKD in patients with CAD is not practical. It is important to include CKD patients in studies examining CVD to further understand the risk of progression and prognosis of these intertwined disease processes. Laboratory‐based and translational research will enable us to better understand the key factors and molecular pathways mediating the cross‐link between CKD and CVD, and thus lead to target specific interventions.Download PowerPointFigure 1. Microvascular and macrovascular diseases in patients with diabetes lead to loss of kidney function through proteinuric and nonproteinuric kidney disease. There is a bidirectional relationship between loss of glomerular filtration rate and progression of atherosclerosis. In addition to CKD‐specific risk factors, these patients have an increased burden of traditional and novel risk factors for CVD, which accelerates the progression of atherosclerosis. The association between coronary artery disease and progression of CKD could be related to acute KI (due to exposure to radiocontrast and coronary artery bypass surgery) and heart failure. CKD indicates chronic kidney disease; CVD, cardiovascular disease; GFR, glomerular filtration rate; KI, kidney injury.Sources of FundingRaj is supported by National Institutes of Health grants 1R01DK073665‐01A1, 1U01DK099924‐01, and 1U01DK099914‐01.DisclosuresNone.Footnotes*Correspondence to: Dominic S. Raj, MD, Division of Renal Diseases and Hypertension, The George Washington University School of Medicine, 2150 Pennsylvania Ave NW, Washington, DC 20037. E‐mail: [email protected]gwu.eduThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.References1 Lindner A, Charra B, Sherrard DJ, Scribner BH. Accelerated atherosclerosis in prolonged maintenance hemodialysis. N Engl J Med. 1974; 290:697–701.CrossrefMedlineGoogle Scholar2 Elsayed EF, Tighiouart H, Griffith J, Kurth T, Levey AS, Salem D, Sarnak MJ, Weiner DE. Cardiovascular disease and subsequent kidney disease. Arch Intern Med. 2007; 167:1130–1136.CrossrefMedlineGoogle Scholar3 Sud M, Tangri N, Pintilie M, Levey AS, Naimark D. Risk of end‐stage renal disease and death after cardiovascular events in chronic kidney disease. Circulation. 2014; 130:458–465.LinkGoogle Scholar4 McClellan WM, Newsome BB, McClure LA, Cushman M, Howard G, Audhya P, Abramson JL, Warnock DG. Chronic kidney disease is often unrecognized among patients with coronary heart disease: the REGARDS Cohort Study. Am J Nephrol. 2009; 29:10–17.CrossrefMedlineGoogle Scholar5 Bruno G, Merletti F, Bargero G, Novelli G, Melis D, Soddu A, Perotto M, Pagano G, Cavallo‐Perin P. Estimated glomerular filtration rate, albuminuria and mortality in type 2 diabetes: the Casale Monferrato study. Diabetologia. 2007; 50:941–948.CrossrefMedlineGoogle Scholar6 Pfeffer MA, Burdmann EA, Chen CY, Cooper ME, de Zeeuw D, Eckardt KU, Feyzi JM, Ivanovich P, Kewalramani R, Levey AS, Lewis EF, McGill JB, McMurray JJ, Parfrey P, Parving HH, Remuzzi G, Singh AK, Solomon SD, Toto R. A trial of darbepoetin alfa in type 2 diabetes and chronic kidney disease. N Engl J Med. 2009; 361:2019–2032.CrossrefMedlineGoogle Scholar7 Sabe M, Claggett B, Burdmann E, Desai A, Ivanovich P, Lewalramani R, Lewis E, McMurray J, Olson K, Parfrey P, Solomon S, Pfeffer M. Coronary artery disease is a predictor of progression to dialysis in patients with chronic kidney disease, type 2 diabetes, and anemia: an analysis of the Trial to Reduce Cardiovascular Events With Aranesp Therapy (TREAT). J Am Heart Assoc. 2016; 5:e002850. doi: 10.1161/JAHA.115.002850.LinkGoogle Scholar8 Porrini E, Ruggenenti P, Mogensen CE, Barlovic DP, Praga M, Cruzado JM, Hojs R, Abbate M, de Vries AP. Non‐proteinuric pathways in loss of renal function in patients with type 2 diabetes. Lancet Diabetes Endocrinol. 2015; 3:382–391.CrossrefMedlineGoogle Scholar9 MacIsaac RJ, Panagiotopoulos S, McNeil KJ, Smith TJ, Tsalamandris C, Hao H, Matthews PG, Thomas MC, Power DA, Jerums G. Is nonalbuminuric renal insufficiency in type 2 diabetes related to an increase in intrarenal vascular disease?Diabetes Care. 2006; 29:1560–1566.CrossrefMedlineGoogle Scholar10 James MT, Ghali WA, Knudtson ML, Ravani P, Tonelli M, Faris P, Pannu N, Manns BJ, Klarenbach SW, Hemmelgarn BR. Associations between acute kidney injury and cardiovascular and renal outcomes after coronary angiography. Circulation. 2011; 123:409–416.LinkGoogle Scholar11 Ryden L, Sartipy U, Evans M, Holzmann MJ. Acute kidney injury after coronary artery bypass grafting and long‐term risk of end‐stage renal disease. Circulation. 2014; 130:2005–2011.LinkGoogle Scholar12 Gupta J, Mitra N, Kanetsky PA, Devaney J, Wing MR, Reilly M, Shah VO, Balakrishnan VS, Guzman NJ, Girndt M, Periera BG, Feldman HI, Kusek JW, Joffe MM, Raj DS. Association between albuminuria, kidney function, and inflammatory biomarker profile. Clin J Am Soc Nephrol. 2012; 7:1938–1946.CrossrefMedlineGoogle Scholar13 Amdur RL, Mukherjee M, Go A, Barrows IR, Ramezani A, Shoji J, Reilly MP, Gnanaraj J, Deo R, Roas S, Keane M, Master S, Teal V, Soliman EZ, Yang P, Feldman H, Kusek JW, Tracy CM, Raj DS. Interleukin‐6 is a risk factor for atrial fibrillation in chronic kidney disease: findings from the CRIC Study. PLoS One. 2016; 11:e0148189.CrossrefMedlineGoogle Scholar14 Gupta J, Dominic EA, Fink JC, Ojo AO, Barrows IR, Reilly MP, Townsend RR, Joffe MM, Rosas SE, Wolman M, Patel SS, Keane MG, Feldman HI, Kusek JW, Raj DS. Association between inflammation and cardiac geometry in chronic kidney disease: findings from the CRIC Study. PLoS One. 2015; 10:e0124772.CrossrefMedlineGoogle Scholar15 Collins AJ, Li S, Gilbertson DT, Liu J, Chen SC, Herzog CA. Chronic kidney disease and cardiovascular disease in the Medicare population. Kidney Int Suppl. 2003; 87:S24–S31.CrossrefGoogle Scholar16 Dickstein K, Cohen‐Solal A, Filippatos G, McMurray JJ, Ponikowski P, Poole‐Wilson PA, Stromberg A, van Veldhuisen DJ, Atar D, Hoes AW, Keren A, Mebazaa A, Nieminen M, Priori SG, Swedberg K. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2008 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association of the ESC (HFA) and endorsed by the European Society of Intensive Care Medicine (ESICM). Eur Heart J. 2008; 29:2388–2442.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Wuwu Z, Tjandra D, Sumangkut R and Langi F (2021) Effect of Venoplasty on Arteriovenous Fistula Dysfunction on Quick of Blood Values of Hemodialysis Patients, Journal of Indonesian Society for Vascular and Endovascular Surgery, 10.36864/jinasvs.2021.1.004, 2:1, (4-9), Online publication date: 21-Jan-2021. Wan E, Chin W, Yu E, Wong I, Chan E, Li S, Cheung N, Wang Y and Lam C (2020) The Impact of Cardiovascular Disease and Chronic Kidney Disease on Life Expectancy and Direct Medical Cost in a 10-Year Diabetes Cohort Study, Diabetes Care, 10.2337/dc19-2137, 43:8, (1750-1758), Online publication date: 1-Aug-2020. Wan E, Yu E, Chin W, Fong D, Choi E, Tang E and Lam C (2019) Burden of CKD and Cardiovascular Disease on Life Expectancy and Health Service Utilization: a Cohort Study of Hong Kong Chinese Hypertensive Patients, Journal of the American Society of Nephrology, 10.1681/ASN.2018101037, 30:10, (1991-1999), Online publication date: 1-Oct-2019. Losito A, Nunzi E, Pittavini L, Zampi I and Zampi E (2017) Cardiovascular morbidity and long term mortality associated with in hospital small increases of serum creatinine, Journal of Nephrology, 10.1007/s40620-017-0413-y, 31:1, (71-77), Online publication date: 1-Feb-2018. April 3, 2016Vol 5, Issue 4Article InformationMetrics © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.https://doi.org/10.1161/JAHA.116.003596PMID: 27108249 Originally publishedApril 23, 2016 Keywordscoronary artery diseasecardiovascular diseaseheart failureatherosclerosisdiabetes (kidney)EditorialsPDF download SubjectsAtherosclerosisCoronary Artery DiseaseHeart FailureRisk FactorsVascular Disease

Atrial fibrillation (AF) is the most common sustained arrhythmia in patients with chronic kidney disease (CKD). In this study, we examined the association between inflammation and AF in 3,762 adults with CKD, enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study. AF was determined at baseline by self-report and electrocardiogram (ECG). Plasma concentrations of interleukin(IL)-1, IL-1 Receptor antagonist, IL-6, tumor necrosis factor (TNF)-α, transforming growth factor-β, high sensitivity C-Reactive protein, and fibrinogen, measured at baseline. At baseline, 642 subjects had history of AF, but only 44 had AF in ECG recording. During a mean follow-up of 3.7 years, 108 subjects developed new-onset AF. There was no significant association between inflammatory biomarkers and past history of AF. After adjustment for demographic characteristics, comorbid conditions, laboratory values, echocardiographic variables, and medication use, plasma IL-6 level was significantly associated with presence of AF at baseline (Odds ratio [OR], 1.61; 95% confidence interval [CI], 1.21 to 2.14; P = 0.001) and new-onset AF (OR, 1.25; 95% CI, 1.02 to 1.53; P = 0.03). To summarize, plasma IL-6 level is an independent and consistent predictor of AF in patients with CKD.

In 1907, Elie Metchnikoff hypothesized that "autointoxication" by "putrefactive" bacteria accelerated aging and caused disease. Emerging science from the Human Microbiome Project and the Metagenomics of Human Intestinal Tract projects has brought in a paradigm shift in our perception about the gut microbiome.1 The human microbiome has coevolved with the host and established a symbiotic relationship, which has expanded our metabolic and biosynthetic capabilities well beyond what is coded in our genomes. Numbers of signaling molecules, receptors, and effectors from the microbiome that regulate host functions are being constantly unraveled.2 Short–chain fatty acids (SCFAs) are organic fatty acids with one to six carbons, which are products of bacterial fermentation of complex polysaccharides in the colon. The most abundant SCFAs are acetate, propionate, and bytyrate. SCFAs are shown to have physiologic functions and beneficial effects on the human host, but they are essentially waste products to the microbes, which are required to balance redox in the anaerobic environment of the colon.3 These molecules are partly metabolized by colonic epithelial cells, and a proportion enters the portal and peripheral circulation, where they exert their systemic effects through the G protein–coupled receptors, such as GPR41 and GPR43. An observation that has intrigued researchers is that germfree mice have increased susceptibility to ischemia and reperfusion injury (IRI), which is reversed by colonization with commensal bacteria.4 The mechanism by which the gut microbiome confers protection against IRI is the focus of the study by Andrade-Oliveira et al.,5 which appears in this issue of JASN. In this exciting study, Andrade-Oliveira et al.5 have expanded the role of SCFAs beyond their well known role as nutrient for colonic epithelium and regulators of intracellular pH, ion transport, and cell proliferation to explain the gut-kidney connection in IRI. In a series of well designed in vivo and in vitro experiments, Andrade-Oliveira et al.5 show that treatment with SCFAs reduces IRI–induced kidney injury. Among the SCFAs, acetate treatment offered the best protection. Andrade-Oliveira et al.5 believe that the key mechanism that confers protection against AKI is reduction in inflammation mediated by an epigenetic mechanism. Andrade-Oliveira et al.5 also noticed an increase in autophagy, a reduction in apoptosis, and an improvement in mitochondrial biogenesis in response to treatment with SCFA. Furthermore, treatment with acetate-producing bacteria protected the mice kidneys from IRI.5 This study clearly shows that SCFA protects against IRI through convergence of multiple mechanisms, but it also provokes a number of questions.5 Considering the complexity of the communication between microbiome, cells, genes, and the ecosystem, it is often challenging to clearly define the role of individual components, which is the case in this study. Inflammation plays a critical role in induction, maintenance, and resolution of AKI. Innate pattern recognition receptors, including Toll-like receptors (TLRs) and the inflammasome, trigger inflammation in response to tissue injury and pathogens.6 The composition of the microbiome influences the balance between immune regulatory (Treg) and proinflammatory (TH17) T cells. For instance, segmented filamentous bacterium residing in the terminal ileum in mice recruits CD4+ T helper cells that produce IL-17 and IL-22 (Th17 cells) in the lamina propria. Another commensal bacteria in the gut, Bacteroides fragilis, induces accumulation of Foxp3+ Treg cells. This effect was dependent on the expression of a capsular polysaccharide known as polysaccharide A by the bacteria.7 Smith et al.8 showed that feeding germfree mice with SCFAs, acetate, propionate, and butyrate increased the abundance of Foxp3+ Treg cells in the large intestine in a GPR43-dependent manner. Immune cells express the SCFA receptors GPR41 and GPR43.9 SCFAs may modulate the magnitude and direction of the immune responses by influencing the differentiation and proliferation of T cells and reducing proinflammatory cytokine expression initiated by TLR signaling.8 In the study by Andrade-Oliveira et al.,5 acetate treatment reduced inflammatory cell infiltration and expression of TLR-4 and its endogenous ligand, Biglycan. However, among the SCFAs, acetate is not the most potent activator of these receptors.9 Immune response is a highly coordinated multistep process that involves sequential epigenetic changes. Transition from euchromatin to transcriptionally silent heterochromatin is mediated by histone deacetylases (HDACs). Butyrate plays a role in modulating immune responses of intestinal macrophages by inhibiting HDAC, leading to a decreased production of proinflammatory mediators, such as NO, IL-6, and IL-12.10 HDAC also plays an important role in cell survival and cell proliferation. Recent studies have shown that a significant proportion of surviving, proliferating renal tubular epithelial cells undergo G2/M arrest after injury, which delays recovery from AKI. Hypermethylation of renin-angiotensin system protein activator like-1, which encodes renin-angiotensin system oncoprotein, perpetuates fibroblast activation and fibrogenesis in the kidney, and thus, it may lead to progressive loss of kidney function. In vitro studies have shown that butyrate regulates expression of genes that arrest growth and induces cellular differentiation.11 Furthermore, in the HT-29 carcinoma cell line, butyrate inhibited proliferation and increased apoptosis but had no effect on the normal epithelial cell line,12 suggesting that the action of SCFAs may depend on the state of activation of the target cells. Future studies should consider examining the effect of different SCFAs at different stages of IRI injury. The hallmark of IRI is profound depletion of intracellular ATP content. In fact, adenine nucleotides infusion enhanced recovery from AKI after an ischemic insult. Mitochondria are the principal generators of cellular ATP. Two mechanistically distinct forms of programmed cell deaths (autophagy and apoptosis) may be induced by cellular stress. Mitochondria regulate the transition between apoptosis and autophagy, with low-intensity stress favoring autophagy and high intensity of cellular stress leading to apoptosis. Autophagy is an evolutionarily conserved cell survival mechanism that recycles cellular constituents to sustain bioenergetics. Jiang et al.13 showed that hypoxia induces autophagy in cultured renal proximal tubular cells. Blocking autophagy by 3-methyladenine or knockdown of autophagic genes (Becline-1 and ATG5) sensitized the cells to apoptosis. Providing butyrate or colonizing with butyrate-producing bacteria (Butyrivibrio fibrisolvens) improved oxidative phosphorylation and ATP synthesis and prevented autophagy.14 It is important to remember that autophagy is the lesser of two evils. The decrease in apoptosis and increase in autophagy observed in the study by Andrade-Oliveira et al.5 may be caused by improved mitochondrial energetics with acetate treatment. Although the increase in mitochondrial DNA content reported in the study provides some clue, future studies should be designed to examine mitochondrial function by assessing mitochondrial membrane potential, ATP content, and mitochondrial dynamics.5 The 2013 World Kidney Day Steering Committee focused on AKI: directing awareness to its effect and calling for campaigns to promote early detection, prevention, and implementation of evidence-based therapies. The study by Andrade-Oliveira et al.5 is very important in that it shows that SCFA confers kidney protection against IRI through multiple potentially biologically interrelated mechanisms. When analyzed critically, it seems that the common factor that underpins the kidney-protective effect of SCFA is through energy conservation in the oxygen-deprived kidney and by improvement of mitochondrial energetics. SCFAs provide about 10% of the daily caloric requirement in humans. They affect lipid, glucose, and cholesterol metabolism in various tissues. It is important to remember that mounting an immune response is an energy-consuming process, which may compete for meager energy resources in the setting of AKI. It is tempting to hypothesize that, other than providing an energy source to the kidney, SCFAs may be reducing the energy consumption by reducing inflammation, promoting apoptosis, and thereby, diverting the much-desired energy toward cellular regeneration. Although the pioneering study by Andrade-Oliveira et al.5 is an important step in our comprehension of the effect of SCFAs on the ischemic kidney, it has left a number of questions to be addressed by future researchers. (1) Is the kidney-protective effect confined to acetate alone or applicable to other SCFAs as well? Convincing evidence indicates that propionate and butyrate are more effective than acetate in reducing inflammation and improving mitochondrial energetics, but then, why is acetate most effective in kidney IRI? (2) Do the signals from the ischemic kidney influence the generation of the amount and type of SCFAs produced by the gut microbiome? (3) Is there a mechanism by which the body traffics the SCFAs from the gut to the kidneys in IRI? (4) Does the kidney express specific receptors in response to IRI, or is it is solely through receptors present in the invading inflammatory cells? (5) Is there a role for the vascular effects of SCFAs mediated through Olfr78 and Gpr41 in protection against the IRI?15 Answering these questions will improve our understanding and propel the SCFAs to prime time in the management of AKI. In this state of knowledge, it is important that we do not overemphasize the importance of a single metabolite without fully understanding how the modulations of these biologic effects interact to confer protection against a complex disease process, such as AKI. Although the results from the study are encouraging, caution must be exercised when in vitro and animal studies are extrapolated to human disease. Disclosures None. D.S.R. was supported by National Institutes of Health Grants 1R01-DK073665-01A1, 1U01-DK099924-01, and 1U01-DK099914-01.

In 1907, Elie Metchnikoff hypothesized that “autointoxication” by “putrefactive” bacteria accelerated aging and caused disease. Emerging science from the Human Microbiome Project and the Metagenomics of Human Intestinal Tract projects has brought in a paradigm shift in our perception about

Background Left ventricular hypertrophy (LVH) and myocardial contractile dysfunction are independent predictors of mortality in patients with chronic kidney disease (CKD). The association between inflammatory biomarkers and cardiac geometry has not yet been studied in a large cohort of CKD patients with a wide range of kidney function. Methods Plasma levels of interleukin (IL)-1β, IL-1 receptor antagonist (IL-1RA), IL-6, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β, high-sensitivity C-Reactive protein (hs-CRP), fibrinogen and serum albumin were measured in 3,939 Chronic Renal Insufficiency Cohort study participants. Echocardiography was performed according to the recommendations of the American Society of Echocardiography and interpreted at a centralized core laboratory. Results LVH, systolic dysfunction and diastolic dysfunction were present in 52.3%, 11.8% and 76.3% of the study subjects, respectively. In logistic regression analysis adjusted for age, sex, race/ethnicity, diabetic status, current smoking status, systolic blood pressure, urinary albumin- creatinine ratio and estimated glomerular filtration rate, hs-CRP (OR 1.26 [95% CI 1.16, 1.37], p<0.001), IL-1RA (1.23 [1.13, 1.34], p<0.0001), IL-6 (1.25 [1.14, 1.36], p<0.001) and TNF-α (1.14 [1.04, 1.25], p = 0.004) were associated with LVH. The odds for systolic dysfunction were greater for subjects with elevated levels of hs-CRP (1.32 [1.18, 1.48], p<0.001) and IL-6 (1.34 [1.21, 1.49], p<0.001). Only hs-CRP was associated with diastolic dysfunction (1.14 [1.04, 1.26], p = 0.005). Conclusion In patients with CKD, elevated plasma levels of hs-CRP and IL-6 are associated with LVH and systolic dysfunction.

BACKGROUND AND OBJECTIVES CD14 plays a key role in the innate immunity as pattern-recognition receptor of endotoxin. Higher levels of soluble CD14 (sCD14) are associated with overall mortality in hemodialysis patients. The influence of kidney function on plasma sCD14 levels and its relationship with adverse outcomes in patients with CKD not yet on dialysis is unknown. This study examines the associations between plasma levels of sCD14 and endotoxin with adverse outcomes in patients with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We measured plasma levels of sCD14 and endotoxin in 495 Leuven Mild-to-Moderate CKD Study participants. Mild-to-moderate CKD was defined as presence of kidney damage or eGFR<60 ml/min per 1.73 m(2) for ≥3 months, with exclusion of patients on RRT. Study participants were enrolled between November 2005 and September 2006. RESULTS Plasma sCD14 was negatively associated with eGFR (ρ=-0.34, P<0.001). During a median follow-up of 54 (interquartile range, 23-58) months, 53 patients died. Plasma sCD14 was predictive of mortality, even after adjustment for renal function, Framingham risk factors, markers of mineral bone metabolism, and nutritional and inflammatory parameters (hazard ratio [HR] per SD higher of 1.90; 95% confidence interval [95% CI],1.32 to 2.74; P<0.001). After adjustment for the same risk factors, plasma sCD14 was also a predictor of cardiovascular disease (HR, 1.30; 95% CI, 1.00 to 1.69; P=0.05). Although plasma sCD14 was associated with progression of CKD, defined as reaching ESRD or doubling of serum creatinine in models adjusted for CKD-specific risk factors (HR, 1.24; 95% CI, 1.01 to 1.52; P=0.04), significance was lost when adjusted for proteinuria (HR, 1.19; 95% CI, 0.96 to 1.48; P=0.11). There was neither correlation between plasma endotoxin and sCD14 (ρ=-0.06, P=0.20) nor was endotoxin independently associated with adverse outcome during follow-up. CONCLUSIONS Plasma sCD14 is elevated in patients with decreased kidney function and associated with mortality and cardiovascular disease in patients with CKD not yet on dialysis.

Muscle wasting is highly prevalent among patients with CKD. The cellular mechanisms of muscle atrophy have been identified, yet definitive treatment to prevent or reverse this complication is not fully in sight. Despite proven efficacy in persons without kidney disease, resistance exercise training