was read the article
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"apellidos" => "Grinyó" ] 12 => array:2 [ "nombre" => "Nuria" "apellidos" => "Lloberas" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S0211699522002004" "doi" => "10.1016/j.nefro.2022.12.005" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0211699522002004?idApp=UINPBA000064" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2013251424000774?idApp=UINPBA000064" "url" => "/20132514/0000004400000002/v2_202410292119/S2013251424000774/v2_202410292119/en/main.assets" ] "itemAnterior" => array:17 [ "pii" => "S2013251424000865" "issn" => "20132514" "doi" => "10.1016/j.nefroe.2024.04.003" "estado" => "S300" "fechaPublicacion" => "2024-03-01" "aid" => "1201" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Nefrologia (English Version). 2024;44:180-93" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Dysregulation of ferroptosis may participate in the mitigating effect of CoCl<span class="elsevierStyleInf">2</span> on contrast-induced nephropathy" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "180" "paginaFinal" => "193" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "La desregulación de ferroptosis puede participar en el efecto mitigador de CoCl<span class="elsevierStyleInf">2</span> en la nefropatía inducida por contraste" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0045" "etiqueta" => "Fig. 7" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr7.jpeg" "Alto" => 2240 "Ancho" => 4010 "Tamanyo" => 843831 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Potential mechanism of Hp's involvement in CIN treatment using CoCl<span class="elsevierStyleInf">2</span>. Co: cobalt; DMT1: divalent metal transporter 1; GPX4: glutathione peroxidase 4; GSSG: glutathione disulfide; Hb: hemoglobin; Hp: haptoglobin; HO: heme oxygenase; PLOH: phospholipid alcohols; PLOOH: phospholipid hydroperoxides; TF: transferrin; TfR1: transferrin receptor 1; ZIP: zinc-regulated transporter/iron-regulated transporter-like protein. Solid line: <span class="elsevierStyleItalic">in vivo</span>; dotted line: <span class="elsevierStyleItalic">in vitro</span>.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Huilin Li, Shuang Liu, Dan Zhang, Xue Zong, Gengru Jiang, Chun Zhu" "autores" => array:6 [ 0 => array:2 [ "nombre" => "Huilin" "apellidos" => "Li" ] 1 => array:2 [ "nombre" => "Shuang" "apellidos" => "Liu" ] 2 => array:2 [ "nombre" => "Dan" "apellidos" => "Zhang" ] 3 => array:2 [ "nombre" => "Xue" "apellidos" => "Zong" ] 4 => array:2 [ "nombre" => "Gengru" "apellidos" => "Jiang" ] 5 => array:2 [ "nombre" => "Chun" "apellidos" => "Zhu" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2013251424000865?idApp=UINPBA000064" "url" => "/20132514/0000004400000002/v2_202410292119/S2013251424000865/v2_202410292119/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original 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array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] 2 => array:3 [ "nombre" => "Luciano" "apellidos" => "Pereira" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 3 => array:3 [ "nombre" => "Joana" "apellidos" => "Campos" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "Nádia" "apellidos" => "Silva" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 5 => array:3 [ "nombre" => "Inês Soares" "apellidos" => "Alencastre" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 6 => array:3 [ "nombre" => "Manuel" "apellidos" => "Pestana" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 7 => array:3 [ "nombre" => "Benedita" "apellidos" => "Sampaio-Maia" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] ] "afiliaciones" => array:5 [ 0 => array:3 [ "entidad" => "Nephrology & Infectious Diseases R&D Group, i3S – Instituto de Investigação e Inovação em Saúde, INEB – Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Nephrology Department, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Universitat Autònoma de Barcelona, Barcelona, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Nephrology Department, Centro Hospitalar Universitário de São João, Porto, Portugal" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Faculdade de Medicina Dentária, Universidade do Porto, Porto, Portugal" "etiqueta" => "e" "identificador" => "aff0025" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "<span class="elsevierStyleItalic">Corresponding author</span>." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "El microbioma urogenital en pacientes con enfermedad renal crónica en diálisis peritoneal" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1787 "Ancho" => 1810 "Tamanyo" => 148406 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Principal coordinate analysis of urinary, fecal and blood samples collected from patients on peritoneal dialysis. Distinct microbiomes are observed.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Chronic kidney disease (CKD) is a worldwide health problem carrying a high socioeconomic burden with elevated morbidity and mortality.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">1</span></a> CKD may progress to different stages resulting in a decrease of the ability of the kidneys to excrete liquids and toxic products in the urine. When the estimated glomerular filtration rate (eGFR) is lower than 15<span class="elsevierStyleHsp" style=""></span>mL/min per 1.73<span class="elsevierStyleHsp" style=""></span>m<span class="elsevierStyleSup">2</span>, patients with CKD require renal replacement therapy including kidney transplantation, hemodialysis, and peritoneal dialysis (PD).<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">2</span></a> In PD the peritoneum acts as a high vascularized dialysis membrane and allows the removal of water and solutes from the internal milieu. In comparison to hemodialysis, PD might enable a better quality of life for the patients, and can also preserve the residual renal function of the patients for a longer period.<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">3</span></a> Therefore, CKD-PD patients may maintain residual diuresis for longer periods and at higher values than hemodialysis patients. Still, PD-related infections, specially peritonitis and catheter exit-site infections, represent the major problem of PD units, as these infections can be responsible for catheter loss, transfer to hemodialysis, prolonged hospitalization, and, in more severe cases, death of these patients.<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">4</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Gut microbiome has been shown to be altered in CKD-PD patients.<a class="elsevierStyleCrossRefs" href="#bib0260"><span class="elsevierStyleSup">5–7</span></a> These alterations, also known as gut dysbiosis, contribute to the chronic inflammatory state of these patients. Particularly in the gut, CKD-PD patients present a significantly decreased bacterial diversity and a gut microbiome dominated by urease containing indole- and p-cresyl-forming bacteria.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">8</span></a> Inversely, taxa linked to the production of probiotic butyrate, short-chain fatty acids (SCFAs), and carbohydrate fermentation appear to be markedly reduced.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">9</span></a> A reduction in SCFAs production may be linked to longer dialysis duration, higher peritoneal glucose exposure, and loss of residual renal function. A recent study also correlated PD-protein-energy wasting with a decrease in butyric acid-producing bacteria, namely <span class="elsevierStyleItalic">Roseburia</span> and <span class="elsevierStyleItalic">Phascolarctobacterium</span>, and with an increase of bacteria associated to intestinal permeability, inflammation, and nutritional imbalance, such as <span class="elsevierStyleItalic">Escherichia</span>.<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">10</span></a><span class="elsevierStyleItalic">Dorea</span>, <span class="elsevierStyleItalic">Clostridium</span>, and SMB53 were reported to be less abundant in CKD-PD patients with peritonitis, suggesting that these bacteria may have anti-inflammatory roles.<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">11</span></a> A recent study also proposed a decrease in the ratio Bacillota/Bacteroidota (formerly Firmicutes/Bacteroidetes) as a potential biomarker of <span class="elsevierStyleItalic">Escherichia coli</span> peritonitis in these patients.<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">12</span></a> Beyond the gut, blood and peritoneum microbiomes were also recently described to be compromised in CKD-PD patients.<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">7,13,14</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Urine has been considered throughout the history a sterile fluid. However there is evidence showing that the urinary tract of healthy individuals without urinary infection is dominated by different kinds of viable microbes and the distribution pattern of these microorganisms may affect the urinary tract health.<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">15–17</span></a> Urinary microbiome, or urobiome, is an emerging topic in health and disease, specifically in urinary tract disorders.<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">15,17,18</span></a> Such differences could be observed in both bladder and voided urine.<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">17</span></a> Urinary dysbiosis has been associated with urinary tract infections and incontinence,<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">19</span></a> overactive bladder syndrome,<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">20,21</span></a> neuropathic bladder,<a class="elsevierStyleCrossRefs" href="#bib0315"><span class="elsevierStyleSup">16,22</span></a> chronic prostatitis,<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">23</span></a> nephrolithiasis,<a class="elsevierStyleCrossRefs" href="#bib0355"><span class="elsevierStyleSup">24–26</span></a> and urinary tract neoplasms,<a class="elsevierStyleCrossRef" href="#bib0370"><span class="elsevierStyleSup">27</span></a> and other systemic factors such as diabetes, dyslipidemia, older age, gender, and recent antibiotic intake.<a class="elsevierStyleCrossRefs" href="#bib0375"><span class="elsevierStyleSup">28,29</span></a> For example, patients with kidney stone disease and hypertension had a unique microbiome profile and such changes may reflect disease progression and may be useful to monitor response to treatments.<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">24</span></a> Also, some studies have observed a different urine microbiome composition in CKD patients when comparing with individuals with normal kidney function<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">18</span></a> and a reduction of diversity associated to a decrease in renal function.<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> Besides, some studies on the urinary microbiome of CKD-kidney transplant recipients (KTR) revealed an overall decrease in diversity and emergence of opportunistic pathogens promoting antibiotic resistance.<a class="elsevierStyleCrossRefs" href="#bib0390"><span class="elsevierStyleSup">31,32</span></a> Moreover, alteration in the urinary microbiome of KTR have been related to chronic allograft dysfunction and increased susceptibility to infection.<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">20</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">There are still no studies describing the urogenital microbiome (by testing voided urine) in CKD-PD patients, possibly due to the loss of residual renal function and, consequently, their diuresis. Therefore, this study aimed to evaluate the urogenital microbiome of CKD-PD patients’ population (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>46) by throughput sequencing and compare it with their gut and circulating (blood) microbiomes. Also, we aimed to analyze the patients’ inflammatory markers.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Patient selection and sample collection</span><p id="par0025" class="elsevierStylePara elsevierViewall">This cross-sectional observational study included 46 CKD patients undergoing PD at Centro Hospitalar Universitário de São João in Porto, Portugal. We invited all the prevalent patients from the PD unit that had a medical appointment at the hospital's outpatient clinic during the four months of recruitment period (September 2018 to January 2019), except those with exclusion criteria. The exclusion criteria for this study included age under 18 years old, inability to give informed consent, history of infection in the last three months, and antibiotic intake in the last three months. Clinical and demographic information was collected for each participant, regarding gender, age, CKD etiology, diabetes mellitus, dyslipidemia, obesity (defined as body mass index of 30<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span> and higher), and cardiovascular disease (peripheral vascular disease, ischemic cardiopathy, or cerebrovascular disease). The modality of PD (automated or manual), pharmacological treatment, and history of infection were also registered.</p><p id="par0030" class="elsevierStylePara elsevierViewall">Blood samples were collected in the PD unit, as previously described.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">7</span></a> At the same day, the self-collected stool and mid-stream urine specimens were brought refrigerated by the patient within 4<span class="elsevierStyleHsp" style=""></span>h or 48<span class="elsevierStyleHsp" style=""></span>h after self-collection, for urine and stool samples, respectively. When stool was collected more than 4<span class="elsevierStyleHsp" style=""></span>h before out-patients consultation, they were frozen. After aliquoted, the whole blood, urine, and stool samples were immediately frozen and stored at −80<span class="elsevierStyleHsp" style=""></span>°C until microbiome analysis.</p><p id="par0035" class="elsevierStylePara elsevierViewall">Routine clinical analyses were performed testing proteinuria, and serum levels of urea, albumin, hemoglobin, cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, phosphorus (P), calcium (Ca), calcium phosphate product, ferritin, B-type natriuretic peptide (BNP), parathyroid hormone (PTH), sedimentation velocity (SV), C-reactive protein (CRP), creatinine clearance (C<span class="elsevierStyleInf">creat</span>), residual renal function, and <span class="elsevierStyleItalic">K</span><span class="elsevierStyleInf">t</span>/<span class="elsevierStyleItalic">V</span> (urea). <span class="elsevierStyleItalic">K</span><span class="elsevierStyleInf">t</span>/<span class="elsevierStyleItalic">V</span> (urea) is a parameter that measures adequacy to PD using urea weekly clearance normalized by urea estimated distribution volume. Tumor necrosis factor α (TNF-α), IL-1, IL-6, IL-10 were determined in plasma by Luminex Multiplex Assay (Millipore). ELISA kits were used to evaluate lipopolysaccharide-binding protein (LPS-BP, Cloud-clone Corp®), Toll-like receptor 4 (TLR4, Cloud-clone Corp®), and soluble CD14 (sCD14, Quantikine® ELISA).</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Sample processing for microbiome analysis</span><p id="par0040" class="elsevierStylePara elsevierViewall">Genomic DNA was isolated in a strictly controlled environment at Vaiomer SAS (Labège, France). Total DNA was extracted from whole blood (100<span class="elsevierStyleHsp" style=""></span>μL) and urine using a protocol carefully designed to minimize risks of contamination.<a class="elsevierStyleCrossRef" href="#bib0400"><span class="elsevierStyleSup">33</span></a> Extraction of DNA from feces was done using QIAamp Fast DNA Stool Mini Kit (Qiagen). Negative controls (molecular grade water added in an empty tube, the same used for sample storage and PD solution) were used simultaneously with the tested samples. PCR amplification was performed using universal primers targeting the V3–V4 region of the bacterial 16S rRNA gene (340F-781R) to encompass 476-base pair amplicon. The usage of 2<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>300 paired-end MiSeq kit V3 was followed. Sample multiplexing and sequencing library generation were conducted, as previously described.<a class="elsevierStyleCrossRef" href="#bib0400"><span class="elsevierStyleSup">33</span></a> qPCR was used to quantify the DNA concentration in the pool employing a 7900HT Fast Real-Time PCR System (Life Technologies) and KAPA Library Quantification Kits for Illumina Platform (Kapa Biosystems, Inc.). The final pool with a concentration between 5 and 20<span class="elsevierStyleHsp" style=""></span>nM (after dilution) was used for sequencing in a MiSeq Illumina device, as previously suggested.<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">13</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">16S rRNA gene sequencing analysis</span><p id="par0045" class="elsevierStylePara elsevierViewall">The targeted gene regions were initially analyzed using the FROGS bioinformatics pipeline established by Vaiomer SAS (Labège, France).<a class="elsevierStyleCrossRef" href="#bib0405"><span class="elsevierStyleSup">34</span></a> Some filters were applied as previously suggested<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">13</span></a>: (1) amplicons with a length <350 nt or a length >480 nt were removed; (2) amplicons without the two PCR primers were removed (10% of mismatches were authorized); (3) amplicons with at least one ambiguous nucleotides (‘N’) were removed; (4) operational taxonomic units (OTUs) identified as chimera (with search v1.9.5) in all samples in which they were presented were removed; (5) OTU with an abundance lower than 0.005% of the whole dataset abundance were removed, and (6) OTU with a strong similarity (coverage and identity ≥80%) with the phiX (library used as a control for Illumina sequencing runs) were removed. OTU were obtained via single-linkage clustering and the taxonomic assignment performed by Blast+ v2.2.30+ with the RDP v11.4 databank.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Statistics and data analysis</span><p id="par0050" class="elsevierStylePara elsevierViewall">Primer v7 (PRIMER-e, Auckland, New Zealand) was used for the calculation of diversity indices, as well as, non-metric multidimensional scaling (NMDS) and principal coordinate analyses (PCO), and other multivariate analyses, mainly analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA), used to test the significance of β-diversity. The percentage of each OTU per sample was used for these analyses, followed by squared root transformed data, resemblance matrices of similarity data types using Bray–Curtis similarities, adding dummy value and testing 4999 permutations. The reads in each sample were initially converted into percentage values according to the total number of sequences in the sample to eliminate the effect of the final number of reads.<a class="elsevierStyleCrossRef" href="#bib0410"><span class="elsevierStyleSup">35</span></a> Post hoc analyses were done in STAMP 2.1.3<a class="elsevierStyleCrossRef" href="#bib0415"><span class="elsevierStyleSup">36</span></a> and the statistical tests used for multiple groups were one-way analysis of variance (ANOVA), Tukey–Kramer (0.95) and Eta-squared for effect size, while two groups were analyzed using Welch's <span class="elsevierStyleItalic">t</span>-test (two-sided, Welch's inverted for confidence interval method).</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Results</span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Urobiome characterization in CKD-PD patients</span><p id="par0055" class="elsevierStylePara elsevierViewall">The detailed characterization of patients on peritoneal dialysis included in this study is shown in <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1 and 2</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">The bacterial microbiomes evaluated in this study resulted in a total of 1127 OTUs obtained from the analysis of urine, gut, and blood samples from 46 CKD-PD patients. A set of 583 OTUs were found in urine samples, while gut samples showed 542 OTUs, and blood samples 514 OTUs. Regarding the Shannon diversity, urine showed the lower diversity in comparison to the gut and blood diversity: the median values was 2.29 for urine (ranging from 1.19 to 4.14), 3.41 for gut (2.39–4.09), and 2.66 for blood (1.79–3.09), with significance difference among all groups of samples (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). Regarding to other diversity indexes, urine samples showed a richness of 11.3 (range 2.0–44.7) and an evenness of 0.5 (range 0.1–0.8). The taxonomic profiles among the three habitats were distinct (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A). Urine samples were dominated by Bacillota, similarly to gut samples, but also comprised Actinomycetota (formerly Actinobacteria), and Pseudomonadota (formerly Proteobacteria). At family level, Streptococcaceae, Enterobacteriaceae, Lactobacillaceae, Family XI, and Bifidobacteriaceae were the most frequent groups in urine samples (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>B). The microbiome of the tested samples was significantly different comparing urine, gut, and blood (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001, <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">The taxonomic profiles allowed to organize the urogenital samples in multiple subtypes, comprising samples dominated by <span class="elsevierStyleItalic">Lactobacillus</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Streptococcus</span>, <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Prevotella</span>, <span class="elsevierStyleItalic">Escherichia-Shigella</span>, or others (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>). The value of these subtypes is complex, but it is not related to active urinary infections (because patients with active infections were excluded). Nevertheless, all urogenital samples of <span class="elsevierStyleItalic">Lactobacillus</span> subtype were linked to female patients, while the <span class="elsevierStyleItalic">Staphylococcus</span> and <span class="elsevierStyleItalic">Anaerococcus</span> dominated samples were exclusively found in male patients.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Comparison of OTUs</span><p id="par0070" class="elsevierStylePara elsevierViewall">In this study <span class="elsevierStyleItalic">Gardnerella</span> OTUs were exclusively found in urogenital samples (in 20 out of 46 samples). <span class="elsevierStyleItalic">Dermabacter</span> and <span class="elsevierStyleItalic">Atopobium</span> OTUs were rarely observed in gut and blood samples (in 1 and 2 samples, respectively), but more frequently found in urine (6 samples were positive for <span class="elsevierStyleItalic">Dermabacter</span> and 13 samples for <span class="elsevierStyleItalic">Atopobium</span>). There were other taxonomic groups only found in urogenital samples (see <a class="elsevierStyleCrossRef" href="#sec0100">supplementary Table S1</a>). When urine OTUs were compared with OTUs in gut and blood samples, some similarities were also observed. There were three OTUs (identified as <span class="elsevierStyleItalic">Pseudomona</span>s and <span class="elsevierStyleItalic">Stenotrophomonas</span>) common for all patients included in this study, and two OTUs (identified as <span class="elsevierStyleItalic">Pelomona</span>s and <span class="elsevierStyleItalic">Escherichia-Shigella</span>) presented in more than 90% of the patients. Interestingly, there were some OTUs found in multiple samples from a single patient (e.g. urine and blood). A set of 54 OTUs were common to multiple samples from a single patient, being 39 of these OTUs (72%) found in urogenital and fecal samples. Only 9 OTUs (17%) were simultaneously observed in urogenital and blood samples (17%), and 6 OTUs (11%) were observed in fecal and blood samples. Similarities were also high when the analysis was extended to OTUs present in more than one patient, suggesting a high interplay between taxa in urogenital and gut microbiomes.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Association between taxonomic groups and demographic and clinical factors</span><p id="par0075" class="elsevierStylePara elsevierViewall">Demographic and clinical variables of CKD-PD patients were studied to find correlations with the urogenital microbiome (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>), being the largest significance found for gender (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001), particularly of <span class="elsevierStyleItalic">Lactobacillus</span> linked to females and other families, such as <span class="elsevierStyleItalic">Staphylococcus</span> and <span class="elsevierStyleItalic">Anaerococcus</span>, to males (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a> shows the taxonomic groups significantly affected by the separation of urogenital samples in two groups according to different clinical variables. Patients with more than 3.5<span class="elsevierStyleHsp" style=""></span>μg/mL soluble CD14 levels showed increased levels of <span class="elsevierStyleItalic">Lactobacilllus</span>, <span class="elsevierStyleItalic">Dermabacter</span>, and <span class="elsevierStyleItalic">Gardnerella</span> in urine microbiome (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.03) when compared with patients with equal or lower levels of sCD14 (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). Lower relative abundance of <span class="elsevierStyleItalic">Atopobium</span>, <span class="elsevierStyleItalic">Dermabacter</span>, and <span class="elsevierStyleItalic">Gardnerella</span> were found in diabetic CKD-PD patients (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.055; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.04 by removing two outgroup samples as shown in <a class="elsevierStyleCrossRef" href="#sec0100">supplementary Fig. S1</a>). Residual diuresis lower than or equal to 1500<span class="elsevierStyleHsp" style=""></span>mL/24<span class="elsevierStyleHsp" style=""></span>h, proteinuria, and C<span class="elsevierStyleInf">creat</span> lower than 50<span class="elsevierStyleHsp" style=""></span>L/week were also associated to differences in the PD urobiome, while longer periods on PD (>5 years) and albumin did not significantly affect the urobiome (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>); multiple taxa were associated to these microbiome differences (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a> and <a class="elsevierStyleCrossRef" href="#sec0100">supplementary Fig. S1</a>). Curiously, patients with residual diuresis lower than or equal to 1500<span class="elsevierStyleHsp" style=""></span>mL/24<span class="elsevierStyleHsp" style=""></span>h also showed a lower Shannon diversity (median value of 1.66) than patients with normal values (median value of 2.41).</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">History of peritonitis was also associated with differences in the urogenital microbiome, being <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, and <span class="elsevierStyleItalic">Corynebacterium</span> decreased in such patients (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). Previous peritonitis was reported in 11 patients, being caused by <span class="elsevierStyleItalic">Staphylococcus epidermidis</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Streptococcus salivarius</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Rhizobium radiobacter</span>, <span class="elsevierStyleItalic">Klebsiella oxytoca</span>, <span class="elsevierStyleItalic">Streptococcus mitis</span>, <span class="elsevierStyleItalic">Pantoea</span> spp., <span class="elsevierStyleItalic">Serratia marcescens</span>, while no agents were identified in 4 cases. Also, there were 4 cases of reported urinary infections (occurring more than three months before samples collection) among these patients, being caused by <span class="elsevierStyleItalic">E. coli</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Streptococcus haemolyticus</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>1) and one agent was unidentified. Neither the history of peritonitis, nor previous urinary infection was associated to particular subtypes described above and in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>.</p><p id="par0085" class="elsevierStylePara elsevierViewall">In our work, we also measured markers of intestinal translocation (endotoxins, LPS-binding protein, TLR4, and sCD14), inflammatory parameters (C-reactive protein, ferritin, sedimentation velocity, IL-1β, IL-6, TNF-α, and the anti-inflammatory IL-10), and other routine laboratory parameters (such as urea, proteinuria, albumin, hemoglobin, cholesterol and its different fractions, triglycerides, calcium, parathormone, BNP), but no statistically significant differences were found (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). No difference was observed on the urogenital microbiome of patients depending on their PD modality (automated or manual).</p></span></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Discussion</span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Urobiome relevance in CKD-PD and other CKD patients</span><p id="par0090" class="elsevierStylePara elsevierViewall">The existence of an urobiome has been recognized since 2010<a class="elsevierStyleCrossRef" href="#bib0420"><span class="elsevierStyleSup">37</span></a> and multiple studies have described its diversity and complexity.<a class="elsevierStyleCrossRefs" href="#bib0315"><span class="elsevierStyleSup">16,17,28,30,38</span></a> The urinary microbiome has not been previously described in CKD-PD patients; these patients preserve residual renal function. In the present study, the urogenital microbiome of CKD-PD patients showed a lower biodiversity (Shannon index) compared to gut and blood microbiomes, as described for other non-PD-CKD patients.<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">25</span></a> The gut and blood microbiome profiles were recently published for CKD-PD patients.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">7</span></a> We found that the urogenital microbiome of our patients was distinct from the gut and blood microbiome and presented some exclusive taxa such as <span class="elsevierStyleItalic">Gardnerella</span>.</p><p id="par0095" class="elsevierStylePara elsevierViewall">In our population, the urobiome was dominated by Bacillota, Actinomycetota and Pseudomonadota, specifically by the families Streptococcaceae, Enterobacteriaceae, Lactobacillaceae, and Bifidobacteriaceae, as previously described for CKD patients stages 3–5 that were not on dialysis.<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> The urine subtypes described in this set of samples agreed with the previously described subtypes,<a class="elsevierStyleCrossRefs" href="#bib0385"><span class="elsevierStyleSup">30,39</span></a> being dominated by the genera <span class="elsevierStyleItalic">Lactobacillus</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Streptococcus</span>, <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Prevotella</span>, and <span class="elsevierStyleItalic">Escherichia-Shigella</span>, suggesting that such subtypes may be transversal to distinct groups of CKD patients<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> and may have a role in CKD pathophysiology. Interestingly, in the study of Kramer et al.<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> the urobiome diversity decreased as kidney function worsened and similar observation was seen in this study for CKD-PD patients with lower values of residual diuresis.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Demographic and clinical factors and urobiome</span><p id="par0100" class="elsevierStylePara elsevierViewall">It has been recognized the impact of the urinary microbiome on recurrent urinary infections among women.<a class="elsevierStyleCrossRef" href="#bib0435"><span class="elsevierStyleSup">40</span></a> Patients with history of infections and antibiotic intake in the last three months were excluded, so no conclusions can be drawn about that in this study. Nevertheless, previous history of peritonitis was described as an important factor associated to changes in the urobiome in this study, being <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, and <span class="elsevierStyleItalic">Corynebacterium</span> decreased in these patients. It is possible that peritonitis promotes a microbial translocation to the bladder, and this may alter the patient urobiome persistently even after successful treatment. Catheter exit-site infections were not associated to urogenital microbiome differences in our PD patients.</p><p id="par0105" class="elsevierStylePara elsevierViewall">Additionally, some clinical conditions, such as diabetes, dyslipidemia, older age, or gender,<a class="elsevierStyleCrossRef" href="#bib0375"><span class="elsevierStyleSup">28</span></a> have been associated with a decrease in the urogenital microbiome richness and other fluctuations in the urogenital microbiome. In line with previous reports, we found that gender and diabetes were associated to differences in CKD-PD urobiome, while age did not alter this microbiome. Regarding the gender, <span class="elsevierStyleItalic">Lactobacilllus</span> was previously described in the urobiome of healthy and CKD women,<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> corroborating our findings. Moreover, we found <span class="elsevierStyleItalic">Staphylococcus</span> increased in the urobiome of CKD-PD men, also as previously described.<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">Residual diuresis, proteinuria, and creatinine clearance (C<span class="elsevierStyleInf">creat</span>) were associated to changes in the urobiome of our CKD-PD. We observed higher levels of <span class="elsevierStyleItalic">Corynebacterium</span> and <span class="elsevierStyleItalic">Staphylococcus</span> in patients with residual diuresis ≤1500<span class="elsevierStyleHsp" style=""></span>mL/24<span class="elsevierStyleHsp" style=""></span>h. The genera <span class="elsevierStyleItalic">Staphylococcus</span> and <span class="elsevierStyleItalic">Corynebacterium</span>, among others, have been shown to be increased in patients with urinary tract infection.<a class="elsevierStyleCrossRef" href="#bib0440"><span class="elsevierStyleSup">41</span></a> Interestingly, we observed that urobiome diversity decreased with lower values of residual diuresis. Kramer et al.,<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">30</span></a> in a previous study in CKD patients not on dialysis, also showed that urobiome diversity decreased as kidney function worsened. The reduction in the residual diuresis is an indicator of loss of residual renal function and a risk factor for poor outcomes and prognosis in PD patients.<a class="elsevierStyleCrossRef" href="#bib0445"><span class="elsevierStyleSup">42</span></a> So, the changes in the urobiome that accompany the loss of residual renal function in CKD-PD patients should be further explored. Some species of <span class="elsevierStyleItalic">Corynebacterium</span> have a potent ability to metabolize urea and some studies relate an increase of this bacterium to urinary calculi.<a class="elsevierStyleCrossRefs" href="#bib0365"><span class="elsevierStyleSup">26,43</span></a> We also observed that proteinuria and C<span class="elsevierStyleInf">creat</span> lower than 50<span class="elsevierStyleHsp" style=""></span>L/week were associated to different taxonomic groups in the CKD-PD urobiome. Still, such findings result from a low number of samples analyzed and should be further confirmed.</p><p id="par0115" class="elsevierStylePara elsevierViewall">The levels of inflammatory parameters and intermediate size molecules clearance may be related to specific taxonomic groups frequently present in urine,<a class="elsevierStyleCrossRefs" href="#bib0350"><span class="elsevierStyleSup">23,40</span></a> but confirmatory studies supporting these associations are still lacking. Interestingly, in our study CKD-PD patients with higher sCD4 levels presented an increase in <span class="elsevierStyleItalic">Lactobacillus</span>, <span class="elsevierStyleItalic">Dermabacter</span>, and <span class="elsevierStyleItalic">Gardnerella</span>. CD14 is a human monocyte differentiation antigen that acts as a pattern recognition receptor by binding to pathogen-associated molecular patterns such as lipopolysaccharides (LPS), working as toll-like receptor (TLR) co-receptor for the detection of infections.<a class="elsevierStyleCrossRef" href="#bib0455"><span class="elsevierStyleSup">44</span></a> Particularly in CKD, higher levels of sCD14 have been associated to vascular calcification, cardiovascular disease and have been proposed as a predictor of all-cause mortality.<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">7,45</span></a> sCD14 role in CKD-associated dysbiosis should be explored in future studies.</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Origin of urogenital microorganisms</span><p id="par0120" class="elsevierStylePara elsevierViewall">In this study, the depletion of OTUs of <span class="elsevierStyleItalic">Atopobium</span> or <span class="elsevierStyleItalic">Dermabacter</span>, and <span class="elsevierStyleItalic">Gardnerella</span> in our CKD-PD patients was significantly associated with history of diabetes, history of peritonitis, and lower levels of sCD14. <span class="elsevierStyleItalic">Gardnerella</span> has been identified in the urobiome of patients with history of high blood pressure<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">24</span></a> and has also been described in the vagina microbiome of healthy women and women with bacterial vaginosis.<a class="elsevierStyleCrossRefs" href="#bib0430"><span class="elsevierStyleSup">39,46</span></a> The increase of <span class="elsevierStyleItalic">Gardnerella</span> in the vagina and the urogenital microbiomes has been associated to disease. In our CKD-PD patients, the reduction of <span class="elsevierStyleItalic">Gardnerella</span> was associated to diabetes and history of peritonitis, but the specific role of this bacterium is still unclear in these patients. <span class="elsevierStyleItalic">Gardnerella</span> has been shown to be depleted in the urobiome of diabetic patients compared to healthy controls and was not found in a diabetes plus dyslipidemia cohort comparing with other cohorts (diabetes only, diabetes plus hypertension, and diabetes plus hypertension and dyslipidemia).<a class="elsevierStyleCrossRef" href="#bib0470"><span class="elsevierStyleSup">47</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">CKD-PD patients may present a different quantitative and qualitative microbial profile in gut, blood, and urine when compared to healthy individuals. Changes in the gut and blood microbiomes of CKD-PD patients have been previously reported.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">7</span></a> But there are no previous studies describing the urobiome of CKD-PD patients. In this study, we have described for the first time the urobiome of CKD-PD patients. Results showed shared OTUs between the gut, the blood, and the urine microbiomes. These similarities can be influenced by the uremic environment that promotes the disruption of intestinal tight junctions, and the translocation of gut taxonomic groups and toxins into the blood and other body fluids.<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">15</span></a> Nonetheless, this is a unicentric, cross-sectional study. For this reason, future studies should explore unrecognized mobility pathways of the human microbiome through different body habitats, and its role in infections and systemic inflammation in CKD-PD patients.</p></span></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Conclusion</span><p id="par0130" class="elsevierStylePara elsevierViewall">CKD-PD patients presented a unique urogenital microbiome with lower biodiversity than gut and blood microbiomes. History of peritonitis and relevant clinical biomarkers of CKD were associated to variations in the urobiome. Depletion of certain <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Corynebacterium</span>, <span class="elsevierStyleItalic">Lactobacillus</span>, or <span class="elsevierStyleItalic">Dermabacter</span> populations were observed in CKD-PD patients with history of diabetes, history of peritonitis and altered levels of sCD14. Further, multicentric studies will allow the better understanding of the mobility pathways of the human microbiome and the clarification of the impact of urobiome on CKD-PD patients outcomes, namely on the risk of peritonitis, loss of residual renal function, and recurrent urinary tract infections.</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Ethical approval</span><p id="par0135" class="elsevierStylePara elsevierViewall">The study was approved by the local Ethics Committee (approval reference 200/18) in accordance with the 1964 Helsinki declaration and its later amendments. All participants agreed to participate in the study after receiving detailed information on the study objectives and protocol, and the written informed consent was obtained from all patients.</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Data availability</span><p id="par0140" class="elsevierStylePara elsevierViewall">The data supporting reported results can be found at NCBI under BioProject: PRJNA819486.</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Funding</span><p id="par0145" class="elsevierStylePara elsevierViewall">R.A. was supported by <span class="elsevierStyleGrantSponsor" id="gs1">Individual Call to Scientific Employment Stimulus – Second Edition</span> (grant number <span class="elsevierStyleGrantNumber" refid="gs1">CEECIND/01070/2018</span>).</p></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Competing interests</span><p id="par0150" class="elsevierStylePara elsevierViewall">All authors declare no conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:14 [ 0 => array:3 [ "identificador" => "xres2284153" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1900326" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2284152" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción y objetivos" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Pacientes y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1900325" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Patient selection and sample collection" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Sample processing for microbiome analysis" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "16S rRNA gene sequencing analysis" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Statistics and data analysis" ] ] ] 6 => array:3 [ "identificador" => "sec0035" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0040" "titulo" => "Urobiome characterization in CKD-PD patients" ] 1 => array:2 [ "identificador" => "sec0045" "titulo" => "Comparison of OTUs" ] 2 => array:2 [ "identificador" => "sec0050" "titulo" => "Association between taxonomic groups and demographic and clinical factors" ] ] ] 7 => array:3 [ "identificador" => "sec0055" "titulo" => "Discussion" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0060" "titulo" => "Urobiome relevance in CKD-PD and other CKD patients" ] 1 => array:2 [ "identificador" => "sec0065" "titulo" => "Demographic and clinical factors and urobiome" ] 2 => array:2 [ "identificador" => "sec0070" "titulo" => "Origin of urogenital microorganisms" ] ] ] 8 => array:2 [ "identificador" => "sec0075" "titulo" => "Conclusion" ] 9 => array:2 [ "identificador" => "sec0080" "titulo" => "Ethical approval" ] 10 => array:2 [ "identificador" => "sec0085" "titulo" => "Data availability" ] 11 => array:2 [ "identificador" => "sec0090" "titulo" => "Funding" ] 12 => array:2 [ "identificador" => "sec0095" "titulo" => "Competing interests" ] 13 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-03-10" "fechaAceptado" => "2023-08-27" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1900326" "palabras" => array:7 [ 0 => "Urogenital microbiome" 1 => "Urobiome" 2 => "Human microbiome" 3 => "Gut microbiome" 4 => "Urinary tract infection" 5 => "Peritoneal dialysis" 6 => "Chronic kidney disease" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1900325" "palabras" => array:7 [ 0 => "Microbioma urogenital" 1 => "Urobioma" 2 => "Microbioma humano" 3 => "Microbioma intestinal" 4 => "Infección del tracto urinario" 5 => "Diálisis peritoneal" 6 => "Enfermedad renal crónica" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction and objectives</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Diabetes, dyslipidemia, older age, gender, urinary tract infections, and recent antibiotic intake have been associated with a decrease in the urobiome richness and other fluctuations in this microbiome. Gut and blood microbiome have been reported to be altered in patients with chronic kidney disease (CKD), and specifically in peritoneal dialysis (PD) patients. Still, there are currently no studies describing the urogenital microbiome in CKD-PD patients. In this study we characterized the urobiome profile in 46 PD patients and analyzed its clinical and inflammatory parameters.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Mid-stream urine, fecal and blood samples were collected from 46 patients undergoing PD at Centro Hospitalar Universitário de São João (CHUSJ) in Porto, Portugal. Exclusion criteria were age under 18 years old, inability to give informed consent, history of infection in the last three months, and antibiotic intake in the last three months. The microbiome communities were analyzed by amplification and sequencing of the V3–V4 region of the bacterial 16S rRNA gene. Correlations with the patients’ clinical data and inflammatory profile were performed.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">CKD-PD patients presented a unique urobiome profile dominated by Bacillota, Actinomycetota and Pseudomonadota and characterized by a lower Shannon diversity than fecal and blood microbiome. The taxonomic profiles of urogenital samples were organized in multiple subtypes dominated by populations of <span class="elsevierStyleItalic">Lactobacillus</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Streptococcus</span>, <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Prevotella</span>, <span class="elsevierStyleItalic">Escherichia-Shigella</span>, being similar to other non-PD-CKD patients. Gender, sCD14, residual diuresis and history of peritonitis were significantly associated to variations in the urobiome. Although not reaching statistical significance, diabetes and the time on PD also showed association with particular taxonomic groups. Depletion of <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Corynebacterium</span>, <span class="elsevierStyleItalic">Lactobacillus</span> or <span class="elsevierStyleItalic">Dermabacter</span> populations correlated with CKD-PD patients with history of diabetes, history of peritonitis and altered levels of sCD14.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Our results highlight urogenital microbiome as a potential partner and/or marker in the overall health state of CKD-PD patients.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducción y objetivos</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Diabetes, dislipemia, edad avanzada, género, infecciones del tracto urinario y toma reciente de antibióticos, entre otras, se han asociado a una disminución en la riqueza del urobioma y a otras fluctuaciones de dicho microbioma.Recientemente, se han descrito alteraciones en losmicrobiomas intestinal y en sangreen pacientes con enfermedad renal crónica (ERC) y, específicamente, en pacientes en diálisis peritoneal (DP).A pesar de ello, aún no existen estudios que describan el microbioma urogenital en pacientes en DP. En el presente trabajo, caracterizamos el urobioma en 46 pacientes en DP.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Pacientes y métodos</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Se recogieron muestras de orina (micción espontánea), heces y sangre de 46 pacientes en DP del Centro HospitalarUniversitário de São João en Oporto, Portugal. Los criterios de exclusión fueron edad menor a 18 años, incapacidad para entenderel consentimiento informado, e historia de infección y toma de antibióticos en los últimos 3 meses. Las comunidades microbiológicas fueron analizadas por amplificación y secuenciación de las regiones V3-V4 del 16S rRNA bacteriano. Se realizaron correlaciones con los datos clínicos y el perfil inflamatorio de los pacientes.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Los pacientes en DP presentaron un urobioma único dominado por Bacillota, Actinomycetota yPseudomonadota, y caracterizado por una menor diversidad de Shannon que los microbiomas en sangre e intestinal. Los perfiles taxonómicos de las muestras urogenitales se organizaron en múltiples subtipos dominados por poblaciones de <span class="elsevierStyleItalic">Lactobacillus, Staphylococcus, Streptococcus, Gardnerella, Prevotella, Escherichia-Shigella</span>, siendo similar al descrito para otros pacientes con ERC no en DP.Género, factor sCD14, diuresis residual yantecedentes de peritonitis se asociaron de forma significativa a cambios en el urobioma. Diabetes y tiempo en DP también se asociaron con grupos taxonómicos específicos, aunque no de forma estadísticamente significativa. La deplección de <span class="elsevierStyleItalic">Gardnerella, Staphylococcus, Corynebacterium, Lactobacillus</span> o <span class="elsevierStyleItalic">Dermabacter</span>se correlacionó con antecedentes de diabetes, historia de peritonitis y niveles alterados de sCD14.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Nuestros resultados subrayan la importancia del microbioma urogenital como potencial biomarcadordel estado de salud de nuestros pacientes en DP.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción y objetivos" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Pacientes y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "<p id="par0160" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>" "etiqueta" => "Appendix B" "titulo" => "Supplementary data" "identificador" => "sec0105" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 837 "Ancho" => 2500 "Tamanyo" => 159405 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">The taxonomic profiles of urine, gut and blood samples obtained from patients on peritoneal dialysis: (A) phylum and (B) family levels.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1787 "Ancho" => 1810 "Tamanyo" => 148406 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Principal coordinate analysis of urinary, fecal and blood samples collected from patients on peritoneal dialysis. Distinct microbiomes are observed.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1492 "Ancho" => 2500 "Tamanyo" => 384283 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Sample subtypes observed among the urobiome of patients on peritoneal dialysis. Each subtype is dominated by a specific taxonomic group (genus), namely <span class="elsevierStyleItalic">Lactobacillus</span>, <span class="elsevierStyleItalic">Staphylococcus</span>, <span class="elsevierStyleItalic">Streptococcus</span>, <span class="elsevierStyleItalic">Gardnerella</span>, <span class="elsevierStyleItalic">Prevotella</span>, <span class="elsevierStyleItalic">Escherichia-Shigella,</span> or other subtypes (no specific genus dominates this subset of samples).</p>" ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 2594 "Ancho" => 4010 "Tamanyo" => 621844 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Post hoc analyses describing prevalence differences of multiple taxonomic groups (and <span class="elsevierStyleItalic">p</span> values associated to each taxa) considering the clinical and demographic factors associated to urobiome; all the remaining taxa not displayed in the figures are similar in both groups of patients – the figure displays only the differences. Gender, sCD14, residual diuresis, history of peritonitis, proteinuria and creatinine clearance were significantly associated to urobiome profiles (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05; see <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> with values for the analysis of similarities (ANOSIM) and confirmed by permutational multivariate analysis of variance (PERMANOVA). Post hoc analyses were done in STAMP 2.1.3.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">CKD-PD \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Age, years</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.9<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>10.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Sex, % male</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65.2% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">CKD etiology, %</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Diabetic nephropathy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23.9% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Hepatorenal polycystic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>IgA nephropathy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Urologic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Others \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Unknown \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">PD duration, months</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>29.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">PD type, %</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Automated PD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Continuous ambulatory PD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Creatinine clearance, L/week</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">115.5<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>56.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Residual renal function, mL/min</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">K</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">t</span></span><span class="elsevierStyleItalic">/V (urea)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.2<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Residual diuresis, cm</span><span class="elsevierStyleSup"><span class="elsevierStyleItalic">3</span></span><span class="elsevierStyleItalic">/24</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">h</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">115.5<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>56.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">History of peritonitis (last 24 months)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11<a class="elsevierStyleCrossRef" href="#tblfn0005">*</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">History of urinary infection (last 24 months)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">#</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3704367.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Cases caused by <span class="elsevierStyleItalic">Staphylococcus epidermidis</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Streptococcus salivarius</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Rhizobium radiobacter</span>, <span class="elsevierStyleItalic">Klebsiella oxytoca</span>, <span class="elsevierStyleItalic">Streptococcus mitis</span>, <span class="elsevierStyleItalic">Pantoea</span> spp., <span class="elsevierStyleItalic">Serratia marcescens</span>, and unidentified agent (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2). Patients with infections in the last 3 months were excluded from this study and are not included here.</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "#" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Cases caused by <span class="elsevierStyleItalic">Escherichia coli</span> (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2), <span class="elsevierStyleItalic">Streptococcus haemolyticus</span>, and unidentified agent. Patients with infections in the last 3 months were excluded from this study and are not included here.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Characterization of chronic kidney disease patients on peritoneal dialysis (CKD-PD).</p>" ] ] 5 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Specificity \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Female \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (33%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.10<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>10.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">More than 65 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (20%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.600 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 51 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (30%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.700 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Time in peritoneal dialysis, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.68<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.45 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">More than 5 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (11%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.090 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 1 year \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (26%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.170 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Type of peritoneal dialysis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Automated \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (48%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.460 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">History of peritonitis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Presence \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (24%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Iron supplementation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">On \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 (76%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.600 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypouricemic agents \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">On \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (39%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.350 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Phosphate binders \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">On \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39 (85%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.150 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Presence \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (30%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.055<a class="elsevierStyleCrossRef" href="#tblfn0015">*</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Obesity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI higher than 30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (11%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.230 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cardiovascular disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Presence \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (22%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.230 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neoplasm \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Present or previous \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (20%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.390 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Adragao index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No calcification \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (26%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.660 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Charlson index \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (26%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.380 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Creatinine clearance, L/week \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">114.09<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>56.21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 50<span class="elsevierStyleHsp" style=""></span>L/week \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.021 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Urea, mg/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">126.37<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>37.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 150<span class="elsevierStyleHsp" style=""></span>mg/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (22%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.930 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Urea depuration, <span class="elsevierStyleItalic">K</span><span class="elsevierStyleInf">t</span>/<span class="elsevierStyleItalic">V</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.22<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.52 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 1.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (13%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.470 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Renal clearance total, mL/min \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.62<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>3.94 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 10<span class="elsevierStyleHsp" style=""></span>mL/min \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (39%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.150 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Residual diuresis, cm<span class="elsevierStyleSup">3</span>/24<span class="elsevierStyleHsp" style=""></span>h \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1301.04<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>830.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 1500<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>/24<span class="elsevierStyleHsp" style=""></span>h \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19 (41%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.029 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Proteinuria \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.01<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Presence \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39 (85%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.020 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Albumin, g/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">37.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>3.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 35<span class="elsevierStyleHsp" style=""></span>g/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (24%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.063 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cholesterol, mg/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">171.48<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>55.86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 200<span class="elsevierStyleHsp" style=""></span>mg/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (20%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.360 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">C-reactive protein, mg/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.25<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>8.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 10<span class="elsevierStyleHsp" style=""></span>mg/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (13%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.560 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ferritin, ng/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">361.75<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>218.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 600<span class="elsevierStyleHsp" style=""></span>ng/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 (13%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.220 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hemoglobulin, g/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.5<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>61.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 12<span class="elsevierStyleHsp" style=""></span>g/dL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (33%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.690 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Erythrocyte sedimentation rate \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.28<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>25.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Higher than 81<span class="elsevierStyleHsp" style=""></span>mm \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (30%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.320 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CD14, μg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.13<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 3.5<span class="elsevierStyleHsp" style=""></span>μg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (20%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.030 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-10, pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.98<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>14.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 11.1<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17 (37%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.700 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-6, pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.82<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>6.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Equal or higher than 1.3<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (39%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.600 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-1β, 1<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.32<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 1.0<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (48%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.850 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TNF-α, pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.47<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 6.0<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.180 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TLR4, pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">632.85<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>442.02 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Less than 451<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19 (41%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.440 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LBP, pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.94<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>17.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Equal or higher than 41<span class="elsevierStyleHsp" style=""></span>pg/mL \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (39%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.620 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3704368.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0015"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.04 if two outgroup samples are removed.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Demographic and clinical characterization of PD-CKD patients and the association of these variables with urinary microbiome. Results expressed in mean and standard deviation (mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD) for continuous variables and number of patients (<span class="elsevierStyleItalic">n</span>) and prevalence (%) for categoric variables; for statistical analysis, continuous variables were transformed in categoric variables.</p>" ] ] 6 => array:5 [ "identificador" => "upi0005" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc1.pdf" "ficheroTamanyo" => 1009996 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:47 [ 0 => array:3 [ "identificador" => "bib0240" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:1 [ "titulo" => "System USRD: USRDS Annual Data Report: epidemiology of kidney disease in the United States" ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2021" "editorial" => "National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases" "editorialLocalizacion" => "Bethesda" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0245" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Eligibility for pharmacological therapies in heart failure with reduced ejection fraction: implications of the new Chronic Kidney Disease Epidemiology Collaboration creatinine equation for estimating glomerular filtration rate" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Butt" 1 => "C. Adamson" 2 => "K. Docherty" 3 => "M. Vaduganathan" 4 => "S. Solomon" 5 => "I. Anand" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Eur J Heart Fail" "fecha" => "2022" "volumen" => "24" "paginaInicial" => "861" "paginaFinal" => "866" ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0250" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Handbook of dialysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "J. Daugirdas" 1 => "P. Blake" 2 => "T. Ing" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2006" "editorial" => "Wolters Kluwer/Lippincott Williams & Wilkins" "editorialLocalizacion" => "Philadelphia" ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0255" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "ISPD peritonitis guideline recommendations: 2022 update on prevention and treatment" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "P. Li" 1 => "K. Chow" 2 => "Y. Cho" 3 => "S. Fan" 4 => "A. Figueiredo" 5 => "T. Harris" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1177/08968608221080586" "Revista" => array:6 [ "tituloSerie" => "Perit Dial Int" "fecha" => "2022" "volumen" => "42" "paginaInicial" => "110" "paginaFinal" => "153" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/35264029" "web" => "Medline" ] ] ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0260" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The microbiome in chronic kidney disease patients undergoing hemodialysis and peritoneal dialysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "L. Simões-Silva" 1 => "R. Araujo" 2 => "M. Pestana" 3 => "I. Soares-Silva" 4 => "B. Sampaio-Maia" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.phrs.2018.02.011" "Revista" => array:6 [ "tituloSerie" => "Pharmacol Res" "fecha" => "2018" "volumen" => "130" "paginaInicial" => "143" "paginaFinal" => "151" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/29444477" "web" => "Medline" ] ] ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0265" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The leaky gut and altered microbiome in chronic kidney disease" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "W. Lau" 1 => "N. Vaziri" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "J Ren Nutr" "fecha" => "2017" "volumen" => "27" "paginaInicial" => "458" "paginaFinal" => "461" ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0270" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Vascular calcification and the gut and blood microbiome in chronic kidney disease patients on peritoneal dialysis: a pilot study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Merino-Ribas" 1 => "R. Araujo" 2 => "L. Pereira" 3 => "J. Campos" 4 => "L. Barreiros" 5 => "M. Segundo" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:4 [ "tituloSerie" => "Biomolecules" "fecha" => "2022" "volumen" => "12" "paginaInicial" => "867" ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0275" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Intestinal microbiota in pediatric patients with end stage renal disease: a Midwest Pediatric Nephrology Consortium study" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. Crespo-Salgado" 1 => "V. Vehaskari" 2 => "T. Stewart" 3 => "M. Ferris" 4 => "Q. Zhang" 5 => "G. 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