After eight years, 13.5% of subjects had CKD. Three amino acid metabolites were positively associated with new-onset CKD: citrulline , kynurenine , and phenylalanine . The kynurenine:tryptophan ratio was also associated with CKD . The addition of multiple metabolites significantly improved the CKD prediction by C statistics , and the net reclassification improvement was 0.84 (95% CI: 0.72–0.96). Elevated hs-C reactive protein (CRP) was associated with new-onset CKD ; however, this association disappeared following adjustment with the kynurenine:tryptophan ratio. The levels of citrulline and kynurenine and their ratio to tryptophan in CKD patients with proteinuria were worse than those with one or neither characteristic. Together, the results of this study demonstrate that amino acid metabolites are associated with CKD eight years after initial metabolite assessment. These results could improve the identification of subjects at high risk of CKD who have modified amino acid metabolism.The discovery of metabolomics-based biomarkers has been a focus of recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic kidney disease (CKD) in the general population using a cross-sectional study design. At baseline, 6.5% of subjects had CKD. Pearson correlation analysis showed that 28 metabolites were significantly associated with estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were associated with CKD ( More than 11% of the global population suffers from chronic kidney disease (CKD) . Early-sCKD biomarker research has focused on metabolomics-based discovery. The kidney directly impacts circulating metabolite levels, including their uptake by glomerular filtration, tubular secretion, and catabolism and the release of several amino acids and other metabolites ,6,7. RecTo our knowledge, the association between metabolites and CKD incidence in Korea has not been intensively investigated. Therefore, we identified the plasma metabolites related to the estimated glomerular filtration rate (eGFR) and CKD using data from the Korean Genome and Epidemiology Study (KoGES). We further identified selected metabolites as CKD biomarker candidates according to their association with CKD eight years after the initial study.n = 2580) were included. Among these, one steroid user was excluded (n = 1), as were subjects with missing creatinine data at baseline (n = 337). Thus, we analyzed a total of 2579 subjects in the cross-sectional study. To examine the CKD incidence after eight years, we excluded 168 CKD patients at baseline and subjects with missing creatinine data at follow-up (n = 680). A total of 1741 subjects were therefore analyzed (This study used data drawn from the second follow-up of the Ansan–Ansung population cohort study (2005–2006) in the Korean Genome Epidemiology Study . Subjectanalyzed . The stu2.Kidney function was evaluated based on eGFR using the CKD-Epidemiology Collaboration equation, which considered race, age, sex, and serum creatinine concentration :GFR = 14C (HbA1C), and high-sensitivity C-reactive protein (hs-CRP) were obtained from the KoGES database. Proteinuria was defined as the presence of protein > grade 1 using the urine dipstick test.General characteristics  and anthropometrics  of the subjects were investigated. Data on systolic and diastolic blood pressure, blood parameters related to kidney status , glucose, hemoglobin A1Serum metabolite data were obtained from the KoGES database, and assays were conducted according to the manufacturer’s instructions . Brieflyp < 0.00037). Multivariable logistic regression analysis was performed to estimate OR values for CKD prevalence and incidence in association with the screened metabolites. To examine the predictive ability of multiple amino acid metabolites  for CKD, we performed C statistics-based model discrimination. To ensure that the model could correctly reclassify risk groups, we assessed net reclassification improvement, and to test the ability of the model to evaluate increased average sensitivity, we assessed integrated discrimination improvement. Differences in metabolite levels among the four groups (patients with CKD and/or proteinuria) were assessed using a general linear model adjusted for age, sex, BMI, smoking, drinking, systolic blood pressure, HbA1C, and hs-CRP. Statistical analyses were performed using the SAS ver. 9.4 software . Statistical significance was assessed at a level of p < 0.05.Comparisons of subject characteristics between groups were assessed using the chi-squared test for categorical variables or the Wilcoxon rank sum test for continuous variables. In subsequent analyses, variables with non-normal distributions  were log-transformed prior to analysis. To screen for eGFR-related metabolites, correlations between metabolite levels and eGFR were assessed using Pearson’s partial correlation coefficients, with Bonferroni correction (p < 0.05). The mean systolic blood pressure was higher in subjects with CKD than in those without, after the data were adjusted for age, sex, drinking, smoking, and the prevalence of hypertension and diabetes mellitus (p < 0.05).Among the candidate metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were positively associated with CKD . Kynurenp < 0.05). Mean body mass index, HbA1C, and hs-CRP values were significantly higher in the CKD group than in the control group after adjusting for age, sex, drinking, smoking, and the prevalence of hypertension and diabetes (p < 0.05)  .p < 0.0001)  . MultiplWe also confirmed that the association between baseline hs-CRP level and CKD after eight years was dependent on metabolites . Baselinp < 0.05)  . Levels This is the first prospective study to investigate the association between plasma metabolites and CKD in the Korean population. Using data from an eight-year follow-up cohort study, we found 22 metabolites  related to CKD prevalence as well as baseline eGFR. Among these metabolites, citrulline, kynurenine, and the kynurenine:tryptophan ratio were found to be associated with a risk of developing CKD.2). The KORA study, which had a larger sample size, also performed metabolomics analysis using a 150p kit similar to that used in this study, and determined that acylcarnitines were strongly associated with decreased eGFR [Early prediction of reduced kidney function would assist prevention of CKD and its complications. Thus, it is important to find biomarkers that can detect decreased kidney function early within the general population. In the present study, we found 28 metabolites cross-sectionally correlated with eGFR, compared with previous studies  9,10,1919,20 andsed eGFR . Long-chsed eGFR . Similarsed eGFR ,24.To determine whether the cross-sectional association between these metabolites and eGFR is a result of decreased kidney function or whether they play a biological role in CKD pathogenesis, it is important to assess these relationships over time. In the present study, we assessed the association between 22 baseline metabolites and CKD incidence after eight years in subjects without CKD at baseline, and found that only two amino acid metabolites, citrulline and kynurenine, affected the development of CKD. Citrulline is generated from ornithine and carbamoyl phosphate in the urea cycle  and fromOne of the metabolites examined in this study showed significant association with CKD; kynurenine was reported in a previous longitudinal study as an indicator for predicting CKD . Kynurenp < 0.0001). Previous studies have reported that elevation of most inflammatory markers including hs-CRP, TNF-αR2, WBC count, and IL6 can predict the risk of developing CKD [Because IDO is potently induced by proinflammatory cytokines ,34, trypping CKD ,36,37. Aping CKD . These fPhenylalanine, an indispensable amino acid, is converted to tyrosine, a semi-essential amino acid, which is synthesized only by the phenylalanine 4-hydroxylase enzyme ,40. In cn = 12), we observed that proteinuria alone was more closely related to acylcarnitines and phosphatidylcholine than to amino acid metabolites (Since several studies have reported that proteinuria is an independent marker of risk for adverse CKD outcomes ,46, we fabolites . These rThis study had some common limitations. GFR was estimated by a single creatinine measurement, although eGFR is the gold standard diagnostic method for CKD. We also performed a nonglobal analysis, which limited the number of metabolites that could be evaluated. Additionally, we did not adjust for protein or amino acid intake or urine albumin in the multivariable logistic regression model, because these data were not available. Further research is needed to explore the global metabolites related to kidney function, confirmation of CKD status, and direct enzyme activities integrated with amino acid metabolites. Nevertheless, this study had a large sample size and applied long-term follow-up of the association between metabolites and CKD in the Korean population.In conclusion, we observed that high concentrations of baseline amino acids and their ratios were associated with CKD incidence in an eight-year follow-up study. Multiple amino acid metabolites showed higher predictive ability than single metabolites. We determined that amino acid levels and their ratios differed according to inflammation or proteinuria status. Thus, amino acids and their ratios, as underlying drivers of CKD pathology, can be used as biomarkers of CKD due to their modified metabolism. Absolute quantification of amino acid metabolites and their regulatory enzyme activities is needed to design CKD treatment strategies.


Escherichia.Shigella genus. Interestingly, only Parabacteroides was found to significantly differentiate patients that were not suffering from lifestyle diseases and those who suffer from post-graft complications. Furthermore, network analysis showed that the occurrence of lifestyle diseases was significantly linked with a higher number of negative interactions of Sutterella and Succinivibrio genera within patients. This study characterizes gut microbiome fluctuation in stable kidney transplant patients after a long post-allograft period. Analysis of fecal microbiota could be useful for nephrologists as a new clinical tool that can improve kidney allograft monitoring and outcomes.Gaining long-term graft function and patient life quality remain critical challenges following kidney transplantation. Advances in immunology, gnotobiotics, and culture-independent molecular techniques have provided growing insights into the complex relationship of the microbiome and the host. However, little is known about the over time-shift of the gut microbiota in the context of kidney transplantation and its impact on both graft and health stability. Here we aimed to characterize the structure of gut microbiota within stable kidney graft recipients. We enrolled forty kidney transplant patients after at least three months of transplantation and compared them to eighteen healthy controls. The overall microbial community structure of the kidney transplanted group was clearly different from control subjects. We found lower relative abundances of Actinobacteria, Bacteroidetes, and Verrucomicrobia within the patient group and a higher abundance of Proteobacteria compared to the control group. Both richness and Shannon diversity indexes were significantly lower in the kidney graft recipients than in healthy controls. Post-graft period was positively correlated with the relative abundance of the Proteobacteria phylum, especially  Increasing kidney disease and subsequent chronic kidney disease (CKD) is related to the ageing society and high morbidity due to lifestyle diseases such as diabetes, atherosclerosis, and hypertension . During Both human and mouse studies reported that the gut microbial community is associated with complications in kidney allograft recipients, including overall survival, infections, and graft rejection ,9. GrowiFaecalibacterium prausnitzii in feces early after kidney transplantation is associated with tacrolimus dosing requirements in kidney transplant recipients [Additionally, the microbiota has been found to modulate drug pharmacokinetics and accordingly, therapeutic response. Of interest, deep sequencing identified that the abundance of cipients . Furthercipients . HoweverWith the approval of the Tunisian Military University hospital’s ethics committee, we enrolled forty renal transplant recipients for a fecal specimen collection and clinical data study. The subjects provided the fecal specimens within one day of production, and the samples were frozen at −80 °C. Similarly, stool samples were collected from (n = 18) healthy subjects and written informed consent was obtained for each enrollee.In order to investigate the variability in fecal gut microbiota over time, we divided the study cohort into three subgroups according to the graft stability state: short post-graft period , medium-length post-graft period , and long post-transplant period (“LG” from 10 to 22 years n = 9). These three subgroups were compared to the healthy control samples (n = 18). We conducted further analysis of the fecal specimens based on the health status of the participants and whether or not they suffer from any lifestyle diseases that were named associated diseases (ADs). In the present study, n = 24 patients suffered from one or multiple complications from the following list: obesity, diabetes, high blood pressure, and dyslipidemia. However, n = 16 were kidney graft recipients that did not suffer from other associated diseases. Age and gender factors were also investigated but not reported in the present study for non-significance. This could be due to the small number of subjects covering a broad range of ages. The study was conducted according to the principles expressed in the Declaration of Helsinki, and all research procedures were approved on 5 March 2018 by the Bioethics Committee of the Military University Hospital of Tunis (No. 05032018).g for 20 min. The precipitate was washed twice with 125 µL of a 70% vol/vol ethanol solution and centrifuged at 14,000× g for 10 min. The dried precipitates were suspended in 15 µL of sterile deionized water, and the concentration was measured with a Q5000 micro-volume UV–Vis spectrophotometer .Total genomic DNA was isolated from the fecal specimens using an InnuPREP DNA kit  according to the manufacturer’s instructions. Three replicates of each sample were extracted. The quality and quantity of DNA samples were tested using a Q5000 micro-volume UV–Vis spectrophotometer . DNA samples were stored in Eppendorf tubes at −20 °C until PCR amplification and amplicon sequencing analysis. PCR was performed with a KAPA Taq PCR kit  and the previously extracted DNA as a template. The variable V3–V4 region of the bacterial 16S rRNA gene was amplified with the primer pair U341F-MiSeq 5-CCTACG GGR SGC AGC AG-3 and 805R-MiSeq 5-GA CTACHV GGG TAT CTA ATC C-3 . Each 25The resulting PCR amplicons were diluted up to 10 ng/µL and then used as templates within the second-step PCR for further amplification, and to include the indexes (barcodes) as well as the Illumina adaptors. The combinatorial use of index primers resulted in unique samples that were pooled and sequenced by Macrogen using a 2 × 300 bp pair-end kit on a MiSeq platform.In more detail, the amplification reaction was performed using the KAPA HiFi HotStart PCR kit in a final volume of 50 µL. Each reaction contained 10 µL of KAPA HiFi Fidelity buffer (5×), 1.5 µL of dNTPs solution (10 mM each), 5 µL of the forward indexing primer (10 µM), 5 µL of the reverse indexing primer (10 µM), 1 µL of KAPA HiFi Hot Start DNA polymerase (1 U/µL), 2 µL from the diluted PCR product (10 ng/µL), and 25.5 µL of sterile deionized water. The PCR amplifications were performed with a 3-min incubation at 95 °C followed by eight cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and a final 5-min terminator reaction at 72 °C. The resulting amplicons from indexing PCR were purified using the NucleoMag NGS Clean-up and Size Selection kit  according to the manufacturer’s recommendations. Amplicons from different samples were quantified with a Quawell Q5000 micro-volume UV–Vis spectrophotometer and merged in equimolar ratios (8 nM). Raw sequencing reads were de-multiplexed and converted to FASTQ, and the Illumina adapters were trimmed using Illumina standard algorithms. Paired-end reads were assembled, trimmed by length, and further corrected for error and quality using the usearch -fastq_mergepairs option. All subsequent analyses were conducted in usearch version v.10 . BrieflyRelative abundance plots were created based on the OTU table along with the taxonomy data previously obtained with the aim to visualize the most dominant OTUs in each sample group at phylum and genus levels. Alpha diversity was then computed based on the MicrobioSeq package to analyze within-group diversity . SignifiPrincipal coordinate analysis (PCA) was performed on the resulting distance matrix to search for similarities between control and KT groups. To estimate the variation of gut bacteria among period subgroups, short, medium, and long period , and associated disease (AD) and no AD subgroups compared to healthy controls, a canonical analysis of principal coordinates (CAP) using PERMANOVA+ , was perp-values were multiple-testing corrected using the Benjamini–Hochberg method. Nodes in each network visualization correspond to microbial OTUs and edges to the microbial associations. The size of each node is proportional to the degree of interactions. The Bar chart graphs were performed using the ggplot2. 3.3.2  package in R version 4.0.3  to indicate the number of positive and negative interaction for each bacterial member of the gut microbiota. Furthermore, we aimed to investigate possible interactions between microorganisms. These interactions were analyzed and visualized through the co-occurrence analyses and association network. This may correspond to microorganisms performing similar or complementary functions and/or sharing similar preferred environmental conditions, but not necessarily having physical interactions ,23. Co-oFinally, serial group comparison analysis was performed to compare variables among the study groups of samples and to detect differences in composition and abundance using the non-parametric Kruskal–Wallis rank sum test along with the Wilcoxon rank sum test .Prevotella 9, in the Bacteroidetes phylum, as well as Faecalibacterium were the bacterial groups with the highest abundance of this cohort. Kidney transplant individuals were divided into 12 women and 28 men aged between 17 and 67 years. Demographic and alpha diversity data of the kidney transplant cohort are further listed in p = 0.0035, Wilcoxon rank-sum test)  (p = 0.001), as shown by PCoA analysis  patients was significantly different compared to healthy subjects. Alpha diversity (within-sample diversity) analysis showed significant difference in both microbial diversity and richness between study groups. In fact, the median Shannon diversity index was lower in the KT fecal specimens than in the control group  . Meanwhium test) . Principanalysis A. At the phylum level, Bacteroidetes and Firmicutes were the most dominant members of the gut microbiota present in the fecal specimens in control and KT patients . Additionally, bacterial profiles showed the presence of Actinobacteria and Verrucomicrobia phyla with respectively higher relative abundances among the control individuals  than in the patient group . Interestingly, Proteobacteria were found to be three times more abundant within the KT group (13.18% ± 1.40% vs. 4.94% ± 0.69%) . Ruminococcaceae.UCG.002, Clostridium sensu strico 1, Subdoligranulum, Dialister, Parabacteroides, Alistipes, and Prevotella 9 was significantly lower and the relative abundance of Escherichia/Shigella, Roseburia, Succinivibrio, Faecalibacterium, and Bacteroides was higher in the KT fecal specimens than in the fecal specimens from the control group  was performed at the genus level. The relative abundance of ol group . A bar col group A.Faecalibacterium, other members of the Ruminococcaceae family, as well as Alistipes, Bacteroides, and Prevotella 9   . Bacteroides and Prevotella 9 had the highest number of connections  with the rest of the community, while in the microbiota of kidney transplanted group, the genera Bacteroides, Prevotella 2, and Succinivibrio had the highest number of connections  within the community . However, there was no significant difference in Shannon diversity index between the patient’s subgroups when compared pairwise (p > 0.05). Similarly, according to the median richness index, a significant difference was found between the control and all three patient subgroups . Moreover, a significant difference was also found between “SG” and “MG” (p = 0.025), as well as between “MG” and “LG” (p = 0.04)  B. Beta diversity performing canonical analysis of principal coordinates (CAP), calculated the variation of bacterial profiles between KT and control groups. Contrarily to PCoA, CAP visualization showed a clear separation between the control subjects and KT patients over time B. RelatiAsteroleplasma, Roseburia, Faecalibacterium, and Bacteroides was notably higher in fecal specimens of patients after the short transplant period (“SG”), while the relative abundance of Rikenellaceae RC9 gut group, Dialister, Parabacteroides, Sutterella, Escherichia/Shigella, and Succinivibrio was higher in patients after the long period of renal graft (“LG”). Among the twelve more abundant bacteria at the genus level described in Alistipes and Prevotella 9 appeared to be more abundant in the control group compared to the KT subgroups over time . The Sutterella genus was also found to be more abundant after the long graft period compared with SG and MG, but this difference was not statistically significant ; in control individuals, it only represented 0.26% ± 0.08% of the fecal bacterial genera. Additionally, a significantly higher relative abundance of Escherichia/Shigella and Bacteroides was observed between “SG” and “MG”   .Alpha diversity and beta-diversity analysis were performed to investigate differences in whole gut microbial diversity in patients with and without associated diseases (ADs) after kidney transplantation compared to healthy controls C. Relative abundances analysis showed that the phylum Bacteroidetes had a lower relative abundance in the patient groups with associated diseases (AD subgroup) compared to patients not suffering from other diseases and to healthy controls . Asteroleplasma, Parabacteroides, Alistipes, Roseburia, Escherichia.Shigella, Faecalibacterium, and Bacteroides in the samples from the no AD subgroup, including individuals not suffering from associated diseases. While the genera of Sutterella and Succinivibrio were highly abundant in the subgroup of patients with post graft complications (AD), Clostridium sensu stricto 1, Dialister, Alistipes, and Prevotella 9 were more abundant among control individuals  and to the control group. . Serial group comparison was calculated using the Wilcoxon rank sum test  with the rest of the community, while in the microbiota of the AD group, the genera Bacteroides, Prevotella 2, and Sutterella had the highest number of connections  within the community  . Herein,In the present study, the median Shannon diversity index was lower in the KT group compared to the control group. In line with our finding, several studies showed that low diversity was associated with renal graft . Of noteThe evaluation of the gut microbiota’s structure in kidney transplant recipients and in CKD patients will pave the way to new tools to restore a microbial balance that is more conducive to health and will help identify new non-invasive approaches to diagnose and treat renal complications and help confer a better quality of life for this population.Some clinical trials have been performed to characterize the gut microbiota after kidney transplantation ,11,27,29Taur et al. (2012) previously reported that immunosuppressive therapies  in mice modify innate antimicrobial defenses and disturb gut microbiota . FurtherWe also observed significant differences in the composition of gut microbiota over time after kidney transplant. Our data showed a three-fold increase in the relative abundance of Proteobacteria within the KT group, reaching 26% after a long graft period. A study by Lee et al. (2014), reported a significant increase in the mean fecal abundance of Proteobacteria within one month after kidney transplant, in line with our results . As for Bacteroides and Faecalibacterium were highly abundant after kidney graft compared to healthy controls. Bacteria in the Faecalibacterium genus degrade glucose, fructose, fructooligosaccharides, and complex molecules such as pectin [Faecalibacterium and Prevotella 9 in KT recipients after long graft period. Most of these microorganisms are anaerobic or facultative anaerobic probiotics and produce short-chain fatty acids (SCFAs). Emerging evidence shows that SCFAs seem to be the link that connects gut microbiota and host homeostasis [Faecalibacterium, Clostridium, Coprococcus, and Prevotella, were reduced in an end-stage renal disease (ESRD) group [At the genus level, we found that s pectin . This mieostasis . In facteostasis . In lineD) group . AnotherD) group . This evEscherichia coli, Sutterella, and Dialister showed a significant increase after the renal graft. This increase was found to be gradual over time for Escherichia.Shigella, namely from 3.11% within “SG” to 4.67% after 1-year post-graft and to 6.16% within patients in the “LG”. Furthermore, in the study by Jiang et al. (2017), it was shown that the genera Escherichia.Shigella were enriched in ESRD patients after six months of kidney graft, which is consistent with our results [E. coli is the most frequent bacterial pathogen that causes pneumonia [Sutterella species have been frequently associated with human diseases such as autism, Down’s syndrome, inflammatory bowel disease (IBD) [Dialister, this is a non-fermentative, anaerobic, Gram-negative rod that has pathogenic potential in dental root canals and various body sites, including the lung and brain [Dialister in kidney transplantation is not yet clear. Mainly, these results show a shift in the level of colonization of some bacterial phyla over time, such us Firmicutes, Bacteroidetes, and Actinobacteria after a short period of transplantation, and Proteobacteria after a long time. It also reveals that at some point after surgery, new genera may appear, such as Dialister and Sutterella. All these changes could have a clinical impact on the health of patients after kidney transplantation.In addition, the relative abundances of  results . E. colineumonia , urinaryneumonia , and necneumonia  after rese (IBD) , and kidse (IBD) . Its patse (IBD) . As for nd brain . Howeverp = 0.008). Interestingly, serial group comparison by running a Kruskal–Wallis rank sum test along with a Wilcoxon rank sum showed a lower abundance of Parabacteroides within recipients suffering from associated diseases (AD group) when compared to patients with no AD. Species of Bacteroides and Parabacteroides represent opportunistic pathogens in infectious diseases, and they are able to develop antimicrobial drug resistance [Parabacteroides and diabetes [Parabacteroides distasonis and obesity [Several data support that the dysbiosis in bacterial structure and diversity are even more significant when post-operative complications occur ,27. In tsistance . Consistdiabetes ,55. Furt obesity .E. coli and opportunistic pathogens [Dysbiosis can also be a result of the maintained immunosuppressive therapy after kidney graft. For instance, it is reported that immunosuppression-driven dysbiosis causes the overgrowth of athogens . Moreoveathogens . AltogetSuccinivibrio and Sutterella genera had a higher number of mutual exclusion interactions in kidney transplant recipients suffering from lifestyle diseases than those who were more stable. The increased abundance of the Sutterella genus has been observed to be involved in the pathogenesis of autism [Succinivibrio genus was described to cause bacteremia [Escherichia coli [Other complications related to post graft dysbiosis are shown through the network analysis and bacterial co-occurrence visualization among members of the gut microbiota. Our interaction network analysis revealed that within the microbiota of stool contents, the occurrence of lifestyle diseases resulted in an increased number of positive interactions of Actinobacteria and Firmicutes genera with other members of the community. Moreover, the present findings indicated a higher inhibition rate of the Proteobacteria phylum over the rest of the community members. Mainly, f autism , while tcteremia . The pothia coli . Moreovehia coli , as wellhia coli . An incrhia coli . These dWe can hypothesize that the differences in the number of OTUs as well as the members and potential of interactions between the AD patient and the no AD patient could represent specific markers that could enable us to distinguish whether the dysbiosis is due to the kidney graft itself or it is associated to different pathogenesis mechanisms like metabolic diseases.Altogether, these reports offer a specific connotation between alterations in gut microbiota and the immune response that determines the allograft outcome in the kidney ,33. MoreThe differential gut microbiota has the potential to guide non-invasive diagnosis and targeted interventions. Given that many factors are associated with the diagnosis of gut microbiota, the biomarker is not a single bacterium but a set of bacteria. The present study found associated phylotypes by analyzing data from the high-throughput sequencing of the16S rRNA gene, but the reliability of these bacteria as biomarkers needs to be explored further. Hence, large-scale prospective studies with large sample panels, especially pre-transplant samples acting as their own controls, should be conducted with the aim of developing more reliable microbiome biomarkers.Our study shows specific kidney transplant-related effects of the fecal microbiome on graft stability and patient’s health status when compared to healthy subjects. Such findings suggest that the analysis of the gut microbial community could represent a new tool to better evaluate the effects of drugs currently employed in organ transplantations. Our results show that alterations in the composition of gut microbiota are significantly correlated with the clinical conditions of renal transplant recipients, and future studies of these correlations may provide evidence for predicting the clinical outcomes of RT recipients. A better understanding of the relationship between gut microbiota and the complications related to allogeneic transplantation may convert gut microbiota into a diagnostic and therapeutic target in the future.


To evaluate the effects of intravenous maropitant on arterial blood pressure in healthy dogs while awake and under general anesthesia.Experimental crossover study.Eight healthy adult Beagle dogs.-1) intravenously under the following conditions: 1) awake with non-invasive blood pressure monitoring (AwNIBP), 2) awake with invasive blood pressure monitoring (AwIBP), 3) premedication with acepromazine (0.005 mg kg-1) and butorphanol (0.2 mg kg-1) intramuscularly followed by propofol induction and isoflurane anesthesia (GaAB), and 4) premedication with dexmedetomidine (0.005 mg kg-1) and butorphanol (0.2 mg kg-1) intramuscularly followed by propofol induction and isoflurane anesthesia (GaDB). Heart rate (HR), systolic (SAP), diastolic (DAP), and mean blood pressures (MAP) were recorded before injection of maropitant (baseline), during the first 60 seconds of injection, during the second 60 seconds of injection, at the completion of injection and every 2 minutes post injection for 18 minutes. The data were compared over time using a Generalized Linear Model with mixed effects and then with simple effect comparison with Bonferroni adjustments (p <0.05).All dogs received maropitant (1 mg kgp < 0.01) and in MAP and DAP in the AwIBP and GaAB (p < 0.001) groups during injection. A significant decrease in SAP (p < 0.05), DAP (p < 0.05), and MAP (p < 0.05) occurred at 16 minutes post injection in GaDB group. There was also a significant increase in HR in the AwIBP group (p < 0.01) during injection. Clinically significant hypotension occurred in the GaAB group with a mean MAP at 54 ± 6 mmHg during injection.There were significant decreases from baseline in SAP in the GaAB group (Intravenous maropitant administration significantly decreases arterial blood pressure during inhalant anesthesia. Patients premedicated with acepromazine prior to isoflurane anesthesia may develop clinically significant hypotension. Maropitant is a neurokinin NK-1) receptor antagonist that inhibits binding of substance P (SP) in the chemoreceptor trigger zone (CTZ) and the vomiting center (VC), thereby inhibiting emesis in dogs and cats  receptor.Maropitant is effective in decreasing opioid and alpha-2 agonist induced vomiting and nausea when administered subcutaneously or orally prior to premedication, thereby decreasing patient discomfort and risk of peri-anesthetic aspiration pneumonia . Dogs reBoscan et al and Alvillar et al observed a transient decrease in mean arterial blood pressure for approximately 10 minutes when maropitant was administered IV in healthy dogs under general anesthesia [Studies by esthesia ,16. Thesα1-adrenergic receptor antagonist that causes a dose dependent decrease in mean arterial blood pressure due to vasodilation [α2-adrenergic receptor agonist that increases systemic arterial blood pressure by α2- adrenergic receptor-mediated constriction of the vascular smooth muscles of arterial vessels [The primary goal of this study was to document the effect of IV administration of maropitant on arterial blood pressure in a controlled setting with healthy awake and anesthetized dogs. The effects of maropitant were evaluated with different premedication drugs  which are commonly used in clinical veterinary anesthesia . Acepromdilation . Dexmede vessels . Butorph vessels . We hypoThis study was approved by the Iowa State University Institutional Animal Care and Use Committee (protocol number 2-18-8702-K). This study was carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, and all efforts were made to minimize patient distress and suffering.Eight research spayed female beagles from Iowa State University Laboratory Animal Resource (ISU LAR) were studied in a crossover design. All dogs were 2 years of age and the mean body weight was 8.7 ± 0.8 kg. The dogs were assigned an ASA (American Society of Anesthesiologists) physical status of 1  based on physical examination. Dogs were fasted for 12 hours prior to anesthesia but had free access to water up until the time of the study. The invasive blood pressure transducer was calibrated against a mercury manometer prior to the start of each experiment. The dogs underwent 4 treatment protocols each preceded by a 72 hour washout period: 1) awake with indirect (oscillometric) blood pressure monitoring, 2) awake with direct blood pressure monitoring, 3) general anesthesia with direct blood pressure monitoring after premedication with acepromazine/butorphanol, and 4) general anesthesia with direct blood pressure monitoring after premedication with dexmedetomidine/butorphanol. General anesthesia was induced with propofol and followed by isoflurane inhalant anesthesia. Each dog served as its own control.® 9402). Dogs were placed in right lateral recumbency and allowed 5 minutes acclimation time. Lidocaine (2.5 mg)  subcutaneous (SC) was injected at the cephalic catheter site with an insulin syringe  to prevent discomfort associated with IV catheter placement. A 20-gauge IV catheter  was placed in the cephalic vein. Maropitant (1 mg kg-1)  IV was administered over 2 minutes using hand injection with a timer. The administration time was determined according to the manufacturer package insert, which states that maropitant should be administered intravenously over 1–2 minutes. The blood pressure cuff was sized at 40% of limb circumference and placed on the pelvic limb proximal to the tarsus. Heart rate (HR) and oscillometric pressure readings of systolic (SAP), diastolic (DAP), and mean arterial pressure (MAP) were obtained prior to injection (baseline), at completion of injection (Tc), and every 10 minutes for 20 minutes after injection. Three readings were obtained at each recording time point and averaged.In the awake protocol with non-invasive blood pressure monitoring (AwNIBP), blood pressure was measured with an oscillometric blood pressure monitor . A 20-gauge IV catheter was placed in the cephalic vein. Then, a 22-gauge catheter was placed in the dorsal pedal artery for invasive blood pressure monitoring . The dorsal pedal artery arterial catheter was attached to a low-compliance pressure tubing that connected to an electronic pressure transducer positioned and zeroed at the level of the sternal manubrium. Maropitant (1 mg kg-1 IV) was administered over 2 minutes using hand injection with a timer. Heart rate, SAP, DAP and MAP were recorded before injection (baseline), at the start of injection (T0), during the first 60 seconds of injection (Ta), during the second 60 seconds of injection (Tb), at completion of injection (Tc) and every 2 minutes post injection (T-2) for 18 minutes (T-18).In the awake group with invasive blood pressure (AwIBP), dogs were placed in right lateral recumbency and allowed 5 minutes acclimation time. Lidocaine was injected SC at the cephalic (2.5 mg) and dorsal pedal (2.5 mg) catheter sites with an insulin syringe (U-100 Insulin Syringes with Ultra-Fine-1)  and butorphanol (0.2 mg kg-1)  (GaAB group) or dexmedetomidine (0.005 mg kg-1)  and butorphanol (0.2 mg kg-1)  (GaDB group) intramuscularly (IM) in the lumbar epaxial muscles. The premedication drug doses were chosen based on doses commonly used at this institution for clinical patients undergoing anesthesia. Fifteen minutes after premedication, a 20-gauge IV cephalic catheter was placed after local block with lidocaine (2.5 mg SC) over the catheter site with an insulin syringe . Dogs were induced with propofol  2–4 mg kg-1 IV at a rate of 1.0 mg kg-1 min-1 until intubation could be achieved. The dogs were intubated when there was an absence of palpebral reflex, the eye was rotated ventrally, and a lack of jaw tone was evident. Anesthesia was maintained with isoflurane  and 100% oxygen; the vaporizer was set at 1.0–1.3% with an oxygen flow rate of 1L min-1. The dogs were placed in right lateral recumbency. Lactated Ringer’s  was administered at 5 ml kg-1 hr-1 IV. After induction, lidocaine (2.5 mg) was injected SC at dorsal pedal catheter site with an insulin syringe . Arterial catheter was placed in the dorsal pedal artery using a 22-gauge catheter for invasive blood pressure monitoring. Electrocardiogram, pulse oximetry, body temperature, invasive blood pressure , end-tidal carbon dioxide, and fraction of expired isoflurane were monitored continuously . The dorsal pedal arterial catheter was attached to a low-compliance pressure tubing that connected to electronic pressure transducer positioned and zeroed at the level of the sternal manubrium. Anesthetic depth was assessed as adequate when there was an absence of palpebral reflex, loss of purposeful movement in response to noxious stimulation (toe pinch) with end-tidal isoflurane concentration (EtISO) of 1.0 ± 0.2%. Hypotension was defined as MAP less than 60 mmHg [-1 fluid bolus of Lactated Ringer’s was administered [-1 IV) was administered in conjunction with the fluid bolus. When MAP was maintained above 60 mmHg for at least 5 minutes, maropitant (1 mg kg-1) IV was administered over 2 minutes by hand injection using a timer. Heart rate, SAP, DAP, and MAP were recorded before maropitant injection (baseline), at the start of injection (T0), during the first 60 seconds of injection (Ta), during the second 60 seconds of injection (Tb), at completion of the injection (Tc) and every 2 minutes post injection (T-2) for 18 minutes (T-18). Ephedrine (0.1 mg kg-1 IV) was the planned rescue protocol for dogs in which MAP was < 50 mmHg for ≥ 5 minutes duration after maropitant injection. All research beagles were returned to ISU LAR at the completion of the study. Recording of the blood pressure and heart rate values was performed by a non-blinded observer.Dogs were premedicated with acepromazine . The fixed effects were Method and Time; the random effects were beagles. All time points were compared to baseline and adjusted using a Bonferroni correction. Differences were considered statistically significant when p ≤ 0.05. The results were expressed as mean ± SD. The sample size was determined using a paired t-test with type I error of 5%, power of 80%, with an effect size of 1.0 based on a previous maropitant study by Boscan et al [All variables were compared between groups and from baseline within each group. A Generalized Linear Model with mixed effects was fitted using SASan et al .Blood pressure and heart rate changes associated with maropitant administration in the awake and anesthetized groups are summarized in p <0.0001) and between GaAB, GaDB, and AwIBP (p<0.0001).There was significant difference in SAP, DAP, and MAP over time (a) thru 2 minutes post injection (T-2), and again at 6 minutes post injection (T-6) thru 18 minutes (T-18) compared to baseline. A significant decrease in DAP was also observed from the first 60 seconds of injection (Ta) to 2 minutes post injection (T-2), at 8 minutes post injection (T-8), and 12 minutes post injection (T-12) thru 18 minutes post injection (T-18) compared to baseline.In the awake group with invasive blood pressure monitoring (AwIBP), there was a significant decrease in MAP from the first 60 seconds of injection (Tb) and at the completion (Tc) of injection compared to baseline. A significant decrease in MAP was observed during injection of maropitant (Ta–Tc). This group also had a significant decrease in DAP during injection (Ta–Tc) and at 8 minutes (T-8), 12 minutes (T-12), and 16 (T-16) thru 18 minutes (T-18) post injection compared to baseline. In the anesthetized dogs premedicated with dexmedetomidine (GaDB), a significant decrease in SAP, MAP and DAP compared to baseline was only observed at T-16. A significant decrease in SAP and MAP was also observed at T-18 in the GaDB group.In the anesthetized dogs, the group premedicated with acepromazine (GaAB) showed a significant decrease in SAP during the second minute of injection (Tb). Prior to injection of maropitant, five of the eight dogs required a fluid bolus and two of the eight dogs also required treatment with glycopyrrolate IV to achieve MAP > 60 mmHg in GaAB group. None of the other groups required treatment of hypotension prior to maropitant administration. No dogs required ephedrine administration during the study.Clinically significant hypotension (defined as MAP less than 60 mmHg) ,21 was ob) and at the completion of injection (Tc) compared to baseline, and significant decrease in heart rate from T-14 to T-18 compared to baseline. There was a significant difference in heart rate between the awake dogs (AwIBP), and the anesthetized dogs ) over time. The dogs receiving dexmedetomidine (GaDB) had significantly lower heart rates compared to the awake dogs (AwIBP) and dogs receiving acepromazine (GaAB) (p <0.0001) during all time points. Dogs in the awake group (AwIBP) had significantly lower heart rates compared to dogs receiving acepromazine (GaAB) (p <0.05) at T-8, T-14, T-16, T-18.The awake dogs (AwIBP) were the only group that experienced a significant change in heart rate over time compared to baseline. This group had a significant increase in heart rate during the second minute of injection  in cats receiving an IV bolus of maropitant with morphine and acepromazine as premedication [-1) which resulted in transient hypotension with a return to normotension without requiring rescue blood pressure treatment with ephedrine. Intravenous administration of maropitant may have a dose dependent response on blood pressure and/or potential species differences in the severity of blood pressure effects.Clinically significant arterial hypotension was observed without a significant compensatory increase in heart rate during maropitant injection in the acepromazine group. Conversely, the awake group had a significant increase in HR during the period of lower MAP and DAP associated with maropitant injection. Inhibition of sympathetic activity, adrenergic neurotransmission, and baroreceptor reflex sensitivity by the inhalant and phenothiazine, are possible factors that contributed to a depressed compensatory response to hypotension during maropitant injection . Persistdication . Our stu2-receptor agonist that produces a biphasic hemodynamic response. Activation of subtype α2B-receptorsin the vascular smooth muscle leads to vasoconstriction and decreased heart rate (phase 1). As dexmedetomidine plasma concentration decreases, vasodilation occurs due to α2A-receptor activation in vascular endothelium and central effects from decreased norepinephrine release [2-agonist subsiding [The dexmedetomidine group did not experience a significant decrease in blood pressure associated with maropitant injection. Dexmedetomidine is an α release ,35. In dubsiding ,37. The The mechanism of blood pressure reduction during maropitant administration remains unclear, but may be associated with the formulation of injectable maropitant which contains maropitant, sulfobutylether-beta-cyclodextrin, and metacresol . Sulfobu®, Dechra®) is available which does not contain metacresol. This formulation is marketed in Europe for veterinary use and is currently not available in the United States. In the European formulation, the excipient contains benzyl alcohol, betadex sulfobutyl ether sodium, citric acid anhydrous and sodium hydroxide [The preservative, metacresol, is a common excipient in insulin formulations for humans. Currently, there is no literature that documents the blood pressure effects of metacresol injection in veterinary medicine. Reported side effects of metacresol in human medicine include localized allergic skin reactions, l cytotoxicity to fibroblast cell, human adipocyte, and monocytic cells –43. Furtydroxide . There iNeurokinin receptors are associated with blood pressure regulation ,46. NeurIn human clinical trials, both aprepitant and fosaprepitant, which are NK-1 antagonists used for oral/intranasal and IV administration, respectively, are reported to be associated with a decrease in blood pressure or the occurrence of hypotension –53. MaroArterial blood pressure is regulated by the autonomic nervous system, renin angiotensin system, and the arginine vasopressin pressor systems in dogs ,55. The The non-invasive blood pressure measurement method was unable to detect the effect of blood pressure changes over time. Non-invasive blood pressure measurement in awake patients requires a minimum of three to five consecutive values and the values also need to have less than 20% variability to be considered accurate ,58. Our -1 acepromazine is a standard dose when administered concurrently with an opioid analgesic drug such as butorphanol. The choice of drug doses for dexmedetomidine and butorphanol falls within published dosing ranges [-1 of acepromazine for preanesthetic medication or tranquilization [Results of this study only hold true for the drug doses that were used in this study. Drug doses were chosen based on doses used for premedication on canine anesthesia patients at this institution. At our institution, 0.005 mg kgg ranges . The aceg ranges . A publilization . PublishThe goal of subcutaneous lidocaine injection prior to intravenous and intraarterial catheter placement was to decrease discomfort and pain associated with catheterization. Pain from local anesthetic injections primarily occurs with needle insertion and infiltration of the drug . To miniOne limitation of this study was that a control group with dogs anesthetized with only isoflurane without premedication was not included. Previous studies have already suggested that a decrease in blood pressure was associated with IV maropitant injection in dogs anesthetized with inhalant anesthesia without premedication ,14. Not An additional limitation was that cardiac output was not measured precluding differentiation of cardiac output changes versus systemic vascular resistance as responsible for the blood pressure changes associated with maropitant administration. We also did not measure invasive blood pressure and non-invasive blood pressure concurrently in each group. This was due to the inability to collect all five necessary consecutive readings within two minutes due to the natural limitations of the oscillometric blood pressure device used in the study. The oscillometric device we used will take more than 2 minutes to cycle for five blood pressure readings. We did not investigate the excipients of the maropitant formulation. We cannot isolate the effects of maropitant from that of the carrier formulation. However, current literature in humans suggests that blood pressure changes occur with different formulations of NK-1 antagonists, indicating that the blood pressure effects observed are most likely associated with the NK-1 antagonist themselves and not the excipient. Lastly, healthy research beagles with intact cardiovascular responses were used in this study, therefore, results may not be extrapolated to critically ill patients or patients with cardiovascular compromise.Intravenous administration of maropitant is associated with a transient decrease in blood pressure in awake dogs with a concomitant increase in HR. Clinically significant hypotension occurred in dogs premedicated with acepromazine, induced with propofol, and maintained on isoflurane. Dogs premedicated with dexmedetomidine did not experienced a significant decrease in blood pressure associated with IV maropitant administration during isoflurane anesthesia. Maropitant should be administered slowly when given IV and blood pressure and heart rate should be monitored during and after administration, especially in anesthetized dogs receiving acepromazine premedication.


The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment  validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe. Sarcopenia is a disease of multifactorial origin. The main factors are malnutrition, neuromuscular, and mitochondrial dysfunction as well as hormonal changes. The disease leads to a loss of muscle mass in older adults. In Mexico, there are almost 12 million people who suffer from sarcopenia and do not know it, with a prevalence of 48.5% in women and 27.4% in men . As Machine Learning (ML) is a technique consisting of a dataset that identifies relationships between features and algorithm outputs, it was applied in this study. By using algorithms, it is possible to develop techniques that allow the computer to “learn” to classify features, creating algorithms capable of generalizing data from unstructured information as samples ,5. MachiSupervised learning in ML is a set of techniques that allow for making predictions based on behavior or features analyzed from known data (input). A supervised learning algorithm analyzes the input data  and produces an output which is the variable being predicted ,7. By seA wide variety of algorithms are used in Machine Learning, some of which are extremely popular like Nearest Neighbors, Linear Support Vector Machines (SVM), Radial Basis Function (RBF) SVM, Gaussian process RBF, Decision Tree, Random Forest, AdaBoost, Gaussian Naive Bayes, etc. However, there is no predefined validated model that works efficiently and effectively on all databases. One or several algorithms must be selected to create a model and later validate it to ensure optimal functions, according to the type of data and the output variable being predicted ,10. The objective of this article is to classify the variables that determine the level of moderate or severe sarcopenia, depending on the case, with the use of machine learning algorithms, which will allow us to know the prevalence of sarcopenia and the status of chronic patients at the Hospital General de Tijuana. The patients that attend the hospital have only scarce resources.This article is based on a study of the level of sarcopenia in older patients at the Tijuana General Hospital, specifically from the geriatrics section. In 2017, the older adult population in Tijuana was 85,259, of which 65% were attended at the Hospital . This puThe necessary sample size was estimated by taking into account the expected prevalence in studies made via bioimpedance analysis in older adults, 17% , assuminThe patients were from the following areas of Baja California: Tijuana, Ensenada, Tecate, Mexicali, and Rosarito. Patients who had been diagnosed with moderate to severe sarcopenia and who were willing to take part in the study signed an informed consent. No physically or psychologically dependent patients were included in the study. Older adults are sedentary, that is, they do not perform any physical activity. The diagnosis of sarcopenia was made by geriatricians at the Tijuana General Hospital. The research included patients’ medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), malnutrition  validated test), as well as biochemical and socio-demographic data. Information on 166 patients with 99 variables for each one was compiled to create the database. The study timeline used to obtain information was at the beginning of the study (first event), 3 months later (second event), and 6 months later (third event) see . According to clinical practice manuals like ResOhma, variables which provide post diagnostic information were eliminated to create machine learning models ,14,15. DThe dataset for the first event was initially split into 90% for the training group and 10% for the test group, preserving the distribution of the classes of elements. Taking the size of the dataset into account, a stratified 5-fold cross-validation that preserves the balance of classes in the different folds was used instead of creating a validation group from the training group.Each dataset was evaluated on different Machine Learning models for classification, evaluating the following metrics for each one: Accuracy, F1, and Precision see . Once the characteristics were selected, the training was performed and the 10 models for each of the datasets were cross-validated at each information collection event. In other words, at the first, second, and third events they were examined to determine if they knew the patients’ evolution. In general, it can be seen that the data were equal or even better results were obtained for dataSET 1 (which contains the four variables which were considered most important) than when taking variables which were listed as less important in the ranking elaborated. This refers to the first and second event. The reason for this is that the models trained on a lower number of variables are of good quality and are not exposed to overtraining due to an excess of information.The RBF SVM classifier obtained good results for all the metrics, regardless of the dataset used in both the first and second event. The Decision Tree classifier obtained better results using dataSET 1 and dataSET 3 in the first event. However, the best results were obtained using dataSET 1 in the second event. DataSETs 1, 2, and 4 obtained the best results in the first event with the Random Forest classifier. However, better results were obtained with 2 and 4 in the second event. The Linear SVM classifier obtained better results for dataSETs 2 and 3 and, on the other hand, it did not give optimum results in the second event. The Quadratic Discriminant Classifier (QDA) obtained better results for dataSET 1.Due to the above, we can therefore suggest that using the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables to train the Decision Tree classifier helps in assessing patients diagnosed with sarcopenia. In order to model and assess the dynamics over time for a patient with the determining variables in dataSET 1, a model based on a hidden Markov model was developed. The model is shown in M state specifically represents the patients with a diagnosis of moderate sarcopenia at time t, whereas the S state represents the patients diagnosed with severe sarcopenia at said time t. A patient in the M state at a certain time t  has some probability of the condition worsening to state S at the following point in time t + 1. On the other hand, they also have a certain probability of remaining in state M. However, patients with severe sarcopenia do not have the possibility of returning to state M at some time in the future [In the Markov chains-based model, the gray states represent the unobservable states and the blue states represent the observable states. In addition, the solid lines represent transition probabilities over time and the dotted lines represent emission probabilities. The e future ,19.M or S at time period t, and it is not possible to directly infer their current condition, it is possible to observe the X vector (characteristic vector) instead. For this section, we chose to include the characteristics with the greatest weight on the classifiers in X. Due to patients being in state Q: the set of states, Q = {s1, s2}, where s1 = M and s2 = S;A = {aij}: state transition matrixoaij = P (qt + 1 = sj | qt = si);B = {bj(k)}: State emission probability jobj(k) = P (k en t | qt = sj);oπi = P (q0 = si).π = {πi}: initial distributionA Markov hidden model may formally represent the five-tuple , where:Due to an observation sequence O = {o1, o2,.., oT} and a model λ = {Q, A, B, π}, it is possible to compute the probability that said sequence comes from the model raised. In other words, P(O | λ), using the advanced regression algorithm. Taking this point into account, it is possible to generate an observation sequence for each classifier and use a Markov hidden model to verify whether it aligns with our priorities.The distribution of the determining variables by the Decision Tree classifier is as follows: The Sodium variable was modeled as a mix between a normal distribution and dirac delta. In this case, the parameters to be estimated are those that correspond to the normal distribution and those that correspond to the "vanishing” values. A frequency histogram is shown below for the best fit curve of the previously described characteristics over the complete dataset .By considering each cardiovascular disease as a Bernoulli trial , the binAs this is a variable with two possible outputs, it was considered a Bernoulli distribution .A negative binomial distribution was chosen as it was the one that minimized KL divergence .A beta distribution was chosen as it was the one that minimized the KL divergence .M to S state is always null. This is due to the fact that no patient’s condition became worse during the time recorded. It is therefore not possible to assign adequate priors to the phenomenon.When analyzing different paths on the model, it was observed that the sequences that contained state changes between times were especially penalized by the model raised. Further analysis of the chain clearly showed the reason: the transition probability from the There is research that studies the CT scan using automatic learning techniques, which evaluate the muscle volume of adults with sarcopenia, and the results are between 0.80 and 0.87 of precision ,22,23,24In our study on the diagnostic detection of sarcopenia over a six-month period, 99 variables from 166 patients were analyzed, obtaining an accuracy of 0.825, precision of 0.867, and an F1 score of 0.895, which is the harmonic mean of accuracy and recovery, using the Decision Tree Classifier. This compared with the study of  makes a Our study suggests that the Age, Systolic blood pressure, MNA, Number of chronic diseases, and Sodium variables are determinants when evaluating patients with moderate and severe sarcopenia. Therefore, these variables can complement the assessment of sarcopenia based on ResOhms, MMI, hand grip strength, calf circumference, and walking speed, which are the standard variables for the assessment of this pathology.A limitation of this study is that it was conducted over one year ago. This stage of the project presents results from data obtained in nine months. As for future lines of research, data are being collected for the last period of the study to complete the follow-up year. The aim is to generate a predictive model to prevent the deterioration of the patient diagnosed with sarcopenia, that is to say, to know which variables are decisive for a patient to remain at a moderate or severe level, or to change from severe to moderate.


Elderly patients aged ≥65 years represent a growing population in the perioperative field, particularly orthopedic and vascular surgery. The higher degree of age‐related or comorbid‐dependent vascular alterations renders these patients at risk for hemodynamic complications and likely denote a possible limitation for modern, non‐invasive arterial pressure monitoring devices. The aim was to compare vascular unloading technique‐derived to invasive measurements of systolic (SAP), diastolic (DAP), and mean arterial pressure (MAP) in elderly perioperative patients.This prospective observational study included patients aged ≥65 years scheduled for orthopedic and patients ≥50 years with peripheral artery disease Fontaine stage ≥ II scheduled for vascular surgery, respectively. Invasive radial artery and non‐invasive finger‐cuff (Nexfin system) arterial pressures were recorded before and after induction of general anesthesia and during surgery. Correlation, Bland‐Altman, and concordance analyses were performed. Measurements of arterial pressure were also compared during intraoperative hypotension (MAP <70 mm Hg) and hypertension (MAP >105 mm Hg).Sixty patients with orthopedic  age 77 (5) years) and vascular surgery  were enrolled. Seven hundred data pairs of all patients were analysed and pooled bias and percentage error were: SAP: 14.43 mm Hg, 43.79%; DAP: −2.40 mm Hg, 53.78% and MAP: 1.73 mm Hg, 45.05%. Concordance rates were 84.01% for SAP, 77.87% for DAP, and 86.47% for MAP. Predefined criteria for interchangeability of absolute and trending values could neither be reached in the overall nor in the subgroup analyses orthopedic vs vascular surgery. During hypertension, percentage error was found to be lowest for all pressure values, still not reaching predefined criteria.Arterial pressure monitoring with the vascular unloading technique did not reach criteria of interchangeability for absolute and trending values. Nevertheless, the putatively beneficial use of noninvasive arterial pressure measurements should be further evaluated in the elderly perioperative patient. Written informed consent was obtained from all patients prior to study participation. All study procedures were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.This was a prospective observational cohort study conducted from October 2015 to June 2017 at the Department of Anaesthesiology and Intensive Care Medicine, University Medical Center, Schleswig‐Holstein, Campus Kiel, Kiel, Germany. Inclusion criteria were defined as patients with an age ≥65 years scheduled for orthopedic surgery and ≥50 years with preexisting peripheral artery disease Fontaine stage ≥ II scheduled for (elective or urgent) vascular surgery, respectively. Further inclusion criteria were an American Society of Anesthesiologists (ASA) physical status classification II‐IV and written informed consent for study participation. Exclusion criteria were defined as need for vasopressor support prior to surgery, ASA physical status classification V, cognitive or linguistic barriers and emergency surgery. Types of orthopedic  and vascular surgery  were not further restricted in order to cover a broad spectrum of possible different hemodynamic changes associated with surgical‐ and anesthesia‐related hemodynamic changes and carotid baroreflex sensitivity. The study protocol was approved by the local ethics committee of the Christian‐Albrechts‐University Kiel, Germany (file number: A 135/14) and retrospectively registered with the trial registration number NCT03178097 at 2.2The standard anesthesia monitoring was established as follows: pulse oximetry, electrocardiography, and noninvasive blood pressure measurement taken by a cuff at the upper limb. The arterial line  was placed under local anesthesia in Seldinger technique in the radial artery on the same side as the finger cuff, in accordance to previous study protocols.2.3The study protocol is illustrated in Figure 2.4α = 5%, the minimum required sample size for the effect size of 5/8 = 0.625 was calculated to be at least 44 . AAMI recommends a minimum sample size of 85 patients, although comparisons of continuous, finger‐cuff arterial pressure measurements with an invasive reference method are excluded by this standard.SD of bias/[invasive arterial pressure/2] to quantify the relative differences between both measurement techniques as an additional statistical estimate with acceptable cut‐off values at 14.7% for SAP, 17.5% for DAP, and 18.7% for MAP as reported by Ilies et al.P value of <.05 was considered as statistically significant. Statistical analysis was performed using SPSS Statistics 21 for Windows .The recommended Association for the Advancement of Medical Instrumentation (AAMI) criterion of a mean difference ≤5 mm Hg and associated SD of ≤8 mm Hg3Sixty patients were included in the study with 25 participants undergoing orthopedic and 35 participants vascular surgery. Table 3.1r = .542, P < .001; DAP: r = .407, P < .001; MAP: r = .538, P < .001. According to the Bland‐Altman analysis, Nexfin underestimated SAP and MAP with a bias (SD) of 14.43 (28.24) mm Hg and 1.73 (18.76) mm Hg, respectively and overestimated DAP with a bias of −2.40 (16.49) mm Hg. Thus, the AAMI criterion for interchangeability was not met. For SAP, PE was 43.79%, for DAP 53.78% and for MAP 45.05% exceeding the predefined cut‐off values.Table Table 3.2Table During hypertension, SAP and MAP correlated significantly, whereas no significant correlation could be found for DAP between the Nexfin‐ and invasive reference system. Nexfin consistently overestimated all arterial pressure values during hypertension. Percentage error was found to be lowest during hypertension as opposed to the PE of the whole data sample  but still higher than the predefined cut‐off values. During hypotension, no significant correlation could be shown for any of the three arterial pressure parameters. The bias was 7.86 mm Hg for SAP, −9.44 mm Hg for DAP, and −6.40 mm Hg for MAP. PE was highest during hypotension for all arterial pressure parameters in comparison to the total cohort: 55.57% vs 43.79% for SAP, 76.08% vs 53.78% for DAP, and 60.81% vs 45.05% for MAP. Figure 4In this prospective observational cohort study arterial pressure measurement by the vascular unloading technology (Nexfin system) was compared to the invasive radial arterial line reference method in 60 elderly patients undergoing orthopedic and vascular surgery. We found that the accuracy of the vascular unloading technology during the preoperative and intraoperative phase was not sufficient. The lowest PE of all blood pressure recordings was calculated for SAP during periods of arterial hypertension while the PE was highest for DAP during arterial hypotension, still not reaching predefined cut‐off values. Furthermore, the vascular unloading method did not show an acceptable blood pressure trending capability in our perioperative setting.A number of studies exist comparing the Nexfin technology with invasive arterial pressure measurements in different clinical settings and patient categories, including cardiac,However, systematic evidence is scarce regarding whether the noninvasive vascular‐unloading method provides valid continuous arterial pressure measurements in elderly perioperative patients who exhibit a higher risk of age‐related or comorbid‐dependent vascular disorders. A recent meta‐analysis including 28 studies with 919 patients revealed that the pooled bias (SD) of continuous noninvasive compared with invasive arterial pressure measurements was −1.6 (12.2) mm Hg for SAP, 5.3 (8.3) mm Hg for DAP, and 3.2 (8.4) mm Hg for MAP which would not satisfy the standards of the AAMI guidelines.Our study has some limitations to be addressed. Our study sample size of 60 participants deviates from the current recommendation of the AAMI, the European Society of Hypertension (ESH), and the International Organization for Standardization (ISO) collaboration that at least 85 participants are required for studies validating non‐invasive sphygmomanometers.5In conclusion, noninvasive arterial pressure measurement using the vascular unloading technique (Nexfin finger‐cuff technology) in the perioperative phase of elderly or vascular comorbid patients, respectively was not interchangeable with the gold standard of invasive arterial line measurement. Our findings underline further demand of larger clinical trials to better evaluate the useability of noninvasive measurement devices in this growing proportion of perioperative patients.No external funding was received for this study.All authors have provided information on potential conflicts of interests directly or indirectly related to the work submitted and filled out the ICMJE disclosure forms. All authors declare that they have no conflict of interest.Conceptualization: Gunnar Elke, Jochen RennerData Curation: Phil Klose, Ulf Lorenzen, Gunnar Elke, Jochen RennerFormal Analysis: Phil Klose, Ulf Lorenzen, Christoph Borzikowsky, Matthias Gruenewald, Gunnar Elke, Jochen RennerFunding Acquisition: Not applicableInvestigation: Phil Klose, Ulf Lorenzen, Rouven Berndt, Moritz Hill, Matthias Gruenewald, Gunnar Elke, Jochen RennerMethodology: Phil Klose, Christoph Borzikowsky, Matthias Gruenewald, Gunnar Elke, Jochen RennerProject Administration: Phil Klose, Gunnar Elke, Jochen RennerResources: Phil Klose, Ulf Lorenzen, Rouven Berndt, Christoph Borzikowsky, Moritz Hill, Matthias Gruenewald, Gunnar Elke, Jochen RennerSoftware: Not applicableSupervision: Gunnar Elke, Jochen RennerValidation: Phil Klose, Ulf Lorenzen, Christoph Borzikowsky, Matthias Gruenewald, Gunnar Elke, Jochen RennerVisualization: Phil Klose, Ulf Lorenzen, Gunnar Elke, Jochen RennerWriting ‐ Original Draft Preparation: Phil Klose, Ulf Lorenzen, Gunnar Elke, Jochen RennerWriting ‐ Review and Editing: Phil Klose, Ulf Lorenzen, Rouven Berndt, Christoph Borzikowsky, Moritz Hill, Matthias Gruenewald, Gunnar Elke, Jochen Renner All authors have read and approved the final version of the manuscript. The corresponding author Gunnar Elke confirms that he had full access to all of the data in the study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.The corresponding author Gunnar Elke confirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.


In chronic kidney disease (CKD), patients’ adherence to prescriptions for diet and for medications might depend on the degree to which they have hope that they will enjoy life, and that hope could vary with the stage of CKD. The aims of this study were to quantify both the association of CKD stage with health-related hope (HR-Hope), and the association of that hope with psychological and physiological manifestations of adherence.This was a cross-sectional study involving 461 adult CKD patients, some of whom were receiving dialysis. The main exposure was HR-Hope, measured using a recently-developed 18-item scale. The outcomes were perceived burden of fluid restriction and of diet restriction, measured using the KDQOL, and physiological manifestations of adherence .General linear models and generalized ordered logit models were fit.Participants at non-dialysis stage 4 and those at stage 5 had lower HR-Hope scores than did those at stage 2 or 3 (combined). Those at non-dialysis stage 5 had the lowest scores. HR-Hope scores of participants at stage 5D were similar to those of participants at stage 4, but they were lower than the scores of participants at stage 2 or 3 (combined). Higher HR-Hope scores were associated with lower perceived burdens of fluid restriction and of diet restriction . Higher HR-Hope scores were associated with lower systolic BP (adjusted mean difference in systolic BP per ten-point difference in HR-Hope scores was − 1.87 mmHg). In contrast, HR-Hope scores were not associated with diastolic BP, serum phosphorus levels, or serum potassium levels.Among CKD patients, HR-Hope is associated with disease stage, with psychological burden, and with some physiological manifestations of adherence.Supplementary information accompanies this paper at 10.1186/s12882-020-02120-0. In patients with chronic kidney disease (CKD), mortality and cardiovascular morbidity can be reduced. Specifically, they can be reduced if increases in blood pressure, retention of fluid, and excessive accumulation of salt, potassium, and phosphorus are prevented. Those goals can be achieved at least to some extent by adherence – both adherence to prescriptions for medication and adherence to prescribed restrictions on the intake of fluids and solid foods , 2. UnfoHope can be regarded as a goal-oriented way of thinking that enables people both to find routes toward their goals and also to sustain their motivation to pursue those goals , 7. MoreOne analytical cross-sectional study of patients at CKD stage 5D showed that high scores on Snyder’s hope scale were associated with a lower burden of kidney disease as measured using the Kidney Disease Quality of Life (KDQOL) instrument . That stTo quantify the association of CKD stage with health-related hope, and the association of that hope with psychological and physiological manifestations of adherence to prescribed medications and diet, we used a recently developed scale to measure health-related hope (HR-Hope)  and we aThis was a multicenter cross-sectional study. The protocol was approved by the institutional review boards of St Marianna University (number 3209) and Fukushima Medical University (number 2417).All of the participants were adults with CKD who were being treated by nephrologists. Some did not require dialysis, while others had CKD stage 5D and were receiving hemodialysis or peritoneal dialysis. Those with advanced cancer who were likely to die within 1 year were not included. Also not included were those with a psychiatric condition that could impair their ability to understand or respond to verbal or written instructions . The settings were one university hospital and four general hospitals. A total of 461 patients were registered between February 2016 and September 2017.Patients were asked to respond to the questionnaire after they gave informed consent. The questionnaire included the HR-Hope scale, and selected items from the Japanese version of the KDQOL instrument.The HR-Hope scale measures hope among people with chronic diseases Table S and S2 . The iteWe examined how HR-Hope was associated with the psychological burden of CKD and with physiological manifestations of adherence that are applicable across all stages of kidney disease , primary renal disease, comorbidities , performance status (PS), and medication . Presence of family was asked with the item “Do you have any family?” and the patients chose yes/no. Working status was asked using an item from KDQOL , 16, in Medical data were collected from medical records by trained staff. Data on socio-demographic factors, primary renal disease, dialysis duration , comorbidities, medications, BP, and serum phosphorus and potassium were extracted from medical records written at the time when the participants received the questionnaires during their hospital visits. Among those receiving hemodialysis, the data on BP and on serum phosphorus and potassium were those collected routinely, before the first dialysis session of each week .All statistical analyses were done using Stata/SE version 15 . Socio-demographic characteristics, comorbidities, laboratory data, and medication data were summarized as means and standard deviations for continuous variables and percentages for categorical variables. The distributions of degree of burdens of fluid or diet restriction by quartile-defined categories of HR-Hope score were graphed. Trends across those categories of HR-Hope score were analyzed using a non-parametric trend test for degree of burdens of fluid or diet restriction. Associations of HR-Hope with the perceived burdens of fluid and diet restriction were analyzed using generalized ordered logit models , with anP values < 0.05 were taken as indicators of statistical significance.The analytic models of the associations of HR-Hope with systolic and diastolic BP were general linear models, with and without adjustment for key socio-demographic variables (adjusted model 1), and for the variables in adjusted model 1, primary renal disease, comorbidities, and the number of classes of prescribed antihypertensives (adjusted model 2). The analytic models of the association of HR-Hope with serum phosphorus were general linear models, with and without adjustment for key socio-demographic variables (adjusted model 1), and for the variables in adjusted model 1, primary renal disease, comorbidities, and the number of prescribed phosphate binders (adjusted model 2). The analytic models of the association of HR-Hope with serum potassium were general linear models, with and without adjustment for key socio-demographic variables (adjusted model 1), and for the variables in adjusted model 1, primary renal disease, comorbidities, and prescription of potassium binders (adjusted model 2). The analytic models of the association of categories of CKD with HR-Hope were general linear models, with and without adjustment for key socio-demographic variables (adjusted model 1), and for the variables in adjusted model 1 together with primary renal disease and comorbidities (adjusted model 2). Within each analysis, all problems of missing data on covariates were addressed by multiple imputation . Five imCharacteristics of the 461 participants are shown in Table p < 0.001 and p = 0.001, respectively). Those associations were unchanged after adjustment for likely confounders  (Table SHR-Hope scores were not associated with the level of phosphorus or potassium in serum (phosphorus: Table Sd) for the differences between HR-Hope scores among participants at stage 4 and at stage 5 versus those at stages 2 and 3 (combined), were 0.38 for stage 4 and 0.91 for stage 5 [HR-Hope scores were lower in participants at CKD stages 4 and 5 than in participants at CKD stages 2 and 3  suggests that hopeful patients may more effectively adopt some self-management behaviors, resulting in better BP management. Specifically, that association may have resulted from less salt intake or better adherence to prescriptions for antihypertensive agents, as hopeful patients might be more likely to develop strategies for dealing with their disease and for preventing it from worsening, both of which are measured using the HR-Hope scale  found no association of hope with physical functioning or with symptoms and problems related to kidney disease . Second,Several limitations of this study also warrant mention. First, because the study was cross-sectional, we cannot infer causal relationships from the associations of HR-Hope with the stage of kidney disease, or with objective manifestations of adherence. Patients with high systolic BP might struggle with the responsibility of taking multiple antihypertensive agents and following dietary instructions, and thus they might find fluid restriction and dietary restriction to be very burdensome, which might cause them to have little or no hope. Second, we were not able to quantify adherence itself by measuring, for example, intake of salt, phosphorus, and potassium, or by counting the numbers of prescribed doses of medications that patients did and did not take. Nonetheless, it may be argued that the objective indices we used are at least as important clinically as adherence itself. Third, HR-Hope may be affected by spiritual and religious factors. This could be important in some countries, but we believe that any effects it might have had on the differences we found in this study are negligible, as all of the participants were Japanese, among whom few regularly engage in religious activities. Fourth, data on some potential predictors of dietary adherence, including detailed measures of socioeconomic status, were not collected. However, the analyses included adjustments for working status, which served as a proxy for socioeconomic status. In addition, patients’ medical costs related to dialysis treatment were almost entirely covered by Japan’s health insurance system. Thus, the associations of HR-Hope with adherence to prescribed treatment are not likely to have been confounded by socioeconomic status. Fifth, while the internal-consistency reliability of the HR-Hope scores is high, it was not possible to quantify test-retest reliability or to examine whether HR-Hope varied between dialysis days and non-dialysis days. Further examination of the HR-Hope scale’s psychometric properties is warranted.Throughout the trajectory of CKD, HR-Hope changed with disease stage. It was lowest at non-dialysis stage 5, followed by stages 4 and 5D. Health-related hope was associated with the patient-perceived burden of fluid and dietary restriction, and also with some objective manifestations of adherence to prescriptions for diet and medications. Therefore, nephrologists and dieticians should consider interventions for increasing HR-Hope to promote self-care and improve CKD patients’ adherence to those prescriptions.Additional file 1: Table S1. English version of the 18-item Health-Related Hope scale.Additional file 2: Table S2. Japanese version of the 18-item Health-Related Hope scale.Additional file 3: Figure S1. Conceptual framework used in regression analyses.Additional file 4: Table S3. The associations between HR-Hope and blood pressures.Additional file 5: Table S4. The associations between HR-Hope and serum phosphorus levels.Additional file 6: Table S5. The associations between HR-Hope and serum potassium levels.


This study aims to assess the prevalence and factors associated with uncontrolled hypertension among adult hypertensive patients.hospital-based cross-sectional study was conducted. Systematic random sampling technique was used to select 396 hypertensive patients. Respondents were interviewed and their medical charts were reviewed using pretested structured questionnaire. Bivariable logistic regression was employed to examine the crude associations between the outcome variable and determinant variables. This was followed by multivariable logistic regression analysis using those variables with P-value ≤ 0.25 in the bivariable analysis.of the total 396 hypertensive patients the prevalence of uncontrolled hypertension was found to be 48.6%. One fourth (26.1%), 231(59.1%), 289(73.9%), and 151(38.6%) hypertensive respondents were non adherent to anti-hypertensive medication, physical exercise, low salt diet, and weight management respectively. Age ≥50 years old , non-adherence to anti-hypertensive medication, , non-adherence to physical exercise , non-adherence to low-salt diet , and non-adherence to weight management  were significantly associated with uncontrolled hypertension.the prevalence of uncontrolled hypertension was high. Older hypertensive patients, non-adherent to their medications, physical inactivity, non-adherent to low salt diet and non-adherent to weight management were more likely to have uncontrolled hypertension. Therefore, more effort should be dedicated to those identified modifiable risk factors to maximize blood pressure control. Uncontrolled hypertension is a major public health challenge among hypertensive patients both in high and low-income countries -4. GlobaStudy area and period: the study was conducted at Mekelle public hospitals of the Tigray region, Northern Ethiopia. Mekelle is found at 783 Km away to the north from the capital city of Addis Ababa. In Mekelle city, there are two general public hospitals , one comprehensive specialized hospital Ayder comprehensive specialized hospital that provides service for more than 9 million populations in its catchment areas. The study was conducted from March to May 2019 at Mekelle public hospitals.Study design: a hospital-based cross-sectional study was conducted.Study population: the study population was all adult hypertensive patients who were under treatment follow-up at Mekelle public hospitals during the data collection period.Inclusion and exclusion criteria: all adult (≥ 18 years old) hypertensive patients who were on anti-hypertensive treatment follow up for at least 6 months duration at the time of data collection were included in this study. However, unconscious hypertensive patients and pregnant mothers were excluded from this study.Sample size determination: the sample size was calculated using a single population proportion formula by considering the following assumptions:2x 0.571(1-0.571)) / (0.05)2 = 377. By adding 5% of none response rate the final sample size was 396.n = minimum sample size, P= estimated proportion of uncontrolled hypertension (57.1%) , d = theSampling technique and procedure: from the two month report of 842 hypertensive patients who had attended follow up at Mekelle city public hospitals, the sample size was proportionally allocated to the three public hospitals. The first study subjects were randomly selected using the lottery method from each hypertension follow up unit of the three public hospitals. Finally, 396 hypertensive patients were selected using a systematic random sampling technique (K=2).Data collection tools and instrument: a structured questionnaire was used to collect data from the selected study participants. Weight and height were measured to determine anthropometric data using the Seca weighing scale and stadiometer respectively. The tool contains four sections  characteristics. The first part (socio-demographic) and the third  part of the tool were developed based on a review of different literatures Whereas Adherence to self-care activities  were measured using the H-SCALE [ H-SCALE .Data collection procedure: five BSc nurses as data collectors and one senior BSc nurse as supervisor were recruited. The data were collected through face-to-face interviews and document reviews. Weight and height were measured with participants standing without shoes and wearing light clothing. Participants were standing upright with the head in the Frankfort plane for height measurement. Bodyweight (kg) was measured using an electronic scale to the nearest 10g, and standing height was measured using a wall stadiometer to the nearest 0.1cm. BMI was calculated as body weight (kg)/height (m2). The subjects were then classified into four WHO BMI cut-offs points. Underweight < 18.50, normal range 18.5-24.9, overweight 25-30, and obese ≥ 30s.Study variables: the dependent variable is uncontrolled hypertension. The independent variables were socio-demographic characteristic , knowledge, behavioral characteristics  and clinical characteristics .Adherent to low salt diet: twelve items were assessed practices related to eating a healthy diet, avoiding salt while cooking and eating, and avoiding foods high in salt content. Scores of 6 or better were considered adherent [adherent .Diabetic mellitus: was defined as self-reported diabetes or the use of hypoglycemic agents or both.Hypertension: was defined as those who had a documented diagnosis of hypertension (i.e. BP ≥ 140/90 mmHg) or those on anti-hypertensive agents.Uncontrolled hypertension: is BP ≥ 140/90 mmHg using digital sphygmomanometer for adult hypertensive clients without diabetes mellitus and chronic kidney disease for at least three consecutive follow-up measurements and blood pressure ≥ 130/80 mmHg using digital sphygmomanometer for adult hypertensive clients with diabetes mellitus and chronic kidney disease for at least three consecutive follow-up measurements.Physical activity: was assessed by 2 items. How many of the past 7 days did you do at least 30 minutes total physical activity? and how many of the past 7 days did you do a specific exercise activity  other than what you do around the house or as part of your work? Responses were summed . Participants who scored ≥ 8 were coded as adhering to physical activity recommendations [ndations .Weight management: ten items were used to assess activities undertaken in the past 30 days to manage weight through dietary practices and physical exercise. Response categories ranged from strongly disagree to strongly agree. Participants who agree or strongly agree with all 10 items (score ≥ 40) were considered to be following good weight management practices [ractices .Data quality assurance: to assure the data quality training was given for the data collectors and supervisor. The weight measured by the digital scale was checked that it was at zero before each measurement. The questionnaire was translated into the local language (Tigrigna) and back to English. Five percent of the questionnaire was pre-tested before data collection to check logical sequence and consistency with desired objectives at Adigrat general hospital. After completing the pre-test they were asked about the clarity and relevance of each item and then based on comments and responses collected during the pre-test, adjustments were made to the questionnaire. The supervisor and principal investigator supervised the correct implementation of the data collection procedure, checked the completeness and logical consistency of the study tool daily. Besides this, the principal investigator carefully checked the entered data and thoroughly cleaned it before the start of the analysis.Data processing and analysis: the data were checked for completeness, the response was coded and entered into Epi-data manager version 4.4.3.1 for windows and exported to SPSS version 23 for analysis. Descriptive statistics were computed and the result was summarized and presented by texts, tables, percentage, and frequency. Mean and the standard deviation were used for normally distributed data. Analysis using bivariable logistic regression model was made to see the association between the explanatory variables and the outcome variable. This was followed by multivariable logistic regression analysis using those variables with P-value ≤ 0.25 in the bivariable analysis and statistical significance was declared at P < 0.05. The Magnitude of the association was measured by using the adjusted odds ratio at a 95% confidence interval. Hosmer - Lemeshow test (0.741) was used to check the fitness model. Multi-Collinearity was checked using the variance inflation factor (< 1.16) and tolerance test (> 0.86).Socio-demographic characteristics: in the study, 391 hypertensive respondents had participated with a response rate of 98.7%. The mean age of the study participants was 52.5 years (± 12.6) years which range from 24-89 years. Two hundred nineteen (56%) of the respondents were females, 215 (55%) were currently married, and two third (66.2%) of the respondents were orthodox Christian followers. One fourth (25.8%) were Colleague/university, and 316(80.8%) of the respondents were urban dwellers , 231(59.1%), 289(73.9%), and 151(38.6%) were non adherent to anti-hypertensive medication, physical exercise, low salt diet, and weight management respectively. About three fourth (76%) of the participants were adherent to alcohol abstinence  years with a minimum of 1 year and a maximum of 25 years. Of all respondents, 75(19.2%) had comorbid disease. From those who had comorbid disease, 64(85.3%), and 10(13.3%) had DM and CVD respectively. Among the respondents, 52(13.3%) of the participants reported as they had family history of hypertension, and 318(81.3%) of the study subjects had been taking one type of drugs per day ( per day .Uncontrolled hypertension and associated factors: the magnitude of uncontrolled hypertension was found to 48.6 % (95%CI: 43.5-53.2%). In the bivariate logistic regression analysis, uncontrolled hypertension was significantly associated with nine variables. After adjustment for potential confounders older age, non-adherence to antihypertensive medication, non-adherence to physical exercise, non-adherence to dietary management and non-adherence to weight management were significantly and positively associated with uncontrolled hypertension among hypertensive patients. The odds of having uncontrolled hypertension was 2.3 times higher among the age of ≥ 50 years old compared to the age of < 50 years old . Patients who were non-adherent to their prescribed antihypertensive drugs were two times  more likely to have uncontrolled hypertension as compared to those who were adherent to their antihypertensive drugs. Patients who didn’t adhere to physical exercise were 1.8 times more likely to have uncontrolled hypertension compared to those who adhered to physical exercise . Hypertensive patients who were non-adherent to Low-salt diet were two times more likely to develop uncontrolled hypertension compared to counterparts . Similarly, the odds of having uncontrolled hypertension were two times higher among non-adherent to weight management respondents compared to adherent respondents   .This study revealed that 48.6% 95% CI: 43.5-53.2%) of hypertensive patients had uncontrolled hypertension. This finding is in line with studies done Lebanon 51.1%) , Malaysi.5-53.2% .1% , MalThe prevalence of uncontrolled hypertension among adult hypertensive patients was high. This study revealed that nearly one out of two hypertensive patients had uncontrolled hypertension. Older age, non-adherence to antihypertensive medication, non-adherence to physical exercise, non-adherence to low salt diet and non-adherence to weight management were significantly associated with uncontrolled hypertension. Therefore, more effort should be dedicated to those identified modifiable risk factors to maximize blood pressure control.Uncontrolled hypertension has become the commonest cause of cardiovascular, renal, and cerebrovascular morbidities and mortalities on the continent;The prevalence of uncontrolled hypertension among adult hypertensive patients in Africa is a major public health problem;The annual death of Ethiopia population due to non-communicable disease such as uncontrolled hypertension is still high (39%).The prevalence of uncontrolled hypertension among adult hypertensive patients was considerably high;Older age, non-adherence to antihypertensive medication, physical inactivity, non-adherence to low salt diet and non-adherence to weight management were significantly associated with uncontrolled hypertension;Health care personnel should be concerned to those identified modifiable risk factors to maximize blood pressure control.