49 research outputs found

    Risk factors associated with acute kidney injury in a cohort of 20,575 arthroplasty patients

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    <p><b>Background and purpose — Patients developing postoperative acute kidney injury (AKI) are at risk of higher morbidity and mortality. In arthroplasty patients, many pre- and perioperative factors are associated with AKI but some of the risk factors are unclear. We report the incidence of postoperative AKI, the conditions associated with it, and survival rates in AKI patients.</b></p> <p><b>Patients and methods — We obtained data from 20,575 consecutive hip or knee arthroplasties. Postoperative AKI, occurring within 7 days after the operation, was defined using the risk, injury, failure, loss, and end-stage (RIFLE) criteria. We analyzed independent risk factors for AKI using binary logistic regression. In addition, we reviewed the records of AKI patients and performed a survival analysis.</b></p> <p><b>Results — The AKI incidence was 3.3 per 1,000 operations. We found preoperative estimated glomerular filtration rate, ASA classification, body mass index, and duration of operation to be independent risk factors for AKI. Infections, paralytic ileus, and cardiac causes were the predominant underlying conditions, whereas half of all AKI cases occurred without any clear underlying condition. Survival rates were lower in AKI patients.</b></p> <p><b>Interpretation — Supporting earlier results, existing renal insufficiency and patient-related characteristics were found to be associated with an increased risk of postoperative AKI. Furthermore, duration of operation was identified as an independent risk factor. We suggest careful renal monitoring postoperatively for patients with these risk factors.</b></p

    Characteristics of the study population.

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    *<p>Median values and interquartile range (IQR), Mann-Whitney test for between-group differences.</p>†<p>Chi-squared test for differences between groups.</p><p>Abbreviations: BMI,body mass index; cf-DNA, cell-free DNA; COCs, combined oral contraceptives; CRP, C-reactive protein; N.S., not significant.</p

    The UGT1A1 interaction network generated using the Ingenuity Pathway Analysis.

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    <p>The molecular relationships connecting UGT1A1 to DNA metabolism synthesis, fragmentation and quantity of DNA are indicated with Fx symbols, and the associated Canonical Pathways are indicated with CP symbols. Abbreviations: AHR, aryl hydrocarbon receptor; HMGB1, high-mobility group box protein 1; IL1B, interleukin-1 beta; MYC, v-myc myelocytomatosis viral oncogene homolog; NR1I3, nuclear receptor subfamily 1, group I, member 3; PPARGC1A, peroxisome proliferator –activated receptor gamma coactivator 1 alpha; RELA, v-rel reticuloendotheliosis viral oncogene homolog A (avian); TLR4, Toll-like receptor 4.</p

    The top SNPs in each of the 9 sub-blocks in the <i>UGT1A</i> gene region (see Figure 2).

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    <p>The four SNPs that are associated most significantly with serum cf-DNA levels are in sub-block 8.</p>†<p>For each SNP, the ancestral allele was modelled, and the β coefficient represents the change in cf-DNA level (µg/ml) with one additional copy of the ancestral allele.</p>*<p>Adjusted for age, sex, BMI and genetic East-West stratification in the Finnish population.</p><p>Abbreviations: SNP, single nucleotide polymorphism; Chr, chromosome; bp, base pairs; MAF, minor allele frequency; S.E., standard error.</p

    Connecting genetic variation, gene expression, metabolites, and atherosclerosis for <i>SERPINA1</i> and <i>AQP9</i>.

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    <p>(a) Boxplots show SNPs associated with metabolic networks are also <i>cis</i> eQTLs for <i>SERPINA1</i> (human blood and liver) and <i>AQP9</i> (human liver). Boxplots consist of median log2-normalised expression for each genotype with first and third quartiles designated by box edges. Whiskers extend to +/−1.5 times interquartile range. (b) Human blood expression of <i>SERPINA1</i> and <i>AQP9</i> was associated with metabolites derived from the same metabolic networks as their corresponding genetic variants. Edge widths are proportional to the strength of association (<i>P</i> value). (c) Liver expression of <i>AQP9</i> (but not <i>SERPINA1</i>) in mice on a hyperlipidemic <i>APOE</i><sup>−/−</sup> background showed significant positive association with aortic lesion area. (d) Boxplots for log2-normalised expression of <i>SERPINA1</i> and <i>AQP9</i> in healthy human arterial tissue versus that for atherosclerotic plaques.</p

    Loci detected using joint multivariate association analysis.

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    *<p>Complete metabolic network loadings, indicating the relative contributions of single metabolites to the overall association, are given in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002907#pgen.1002907.s004" target="_blank">Figure S4</a>.</p>**<p>Multiple test corrected significance threshold for a metabolic network association was <i>P</i><4.5×10<sup>−9</sup>.</p>#<p>Indicates that the top metabolite was also detected by univariate test.</p

    Serum metabolic networks.

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    <p>A Pearson correlation matrix of serum metabolites across both YFS and NFBC66 cohorts was hierarchically clustered and the resulting heatmap and dendrogram are presented here with red indicating high positive correlation, blue high negative correlation, and white no correlation. Clusters of tightly correlated metabolites, metabolic networks, are labeled 1–11.</p

    Association of cortical and trabecular vBMD with top cortical and trabecular vBMD SNPs.

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    <p>Models adjusted for sex (ALSPAC and YFS), age, height, weight (ln). Betas in standard deviations and standard errors are presented. vBMD = volumetric bone mineral density; EAF = effect allele frequency; n<sup>GEFOS</sup> and P<sup>GEFOS</sup> is the number of subjects and p values as given from the GEFOS consortium (<a href="http://www.gefos.org/?q=content/data-release" target="_blank">http://www.gefos.org/?q=content/data-release</a>; <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003247#pgen.1003247-Estrada1" target="_blank">[2]</a>). Dir = Direction of the effect in the GEFOS publication using the same effect allele as in the present pQCT meta-analysis.</p
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