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


Supplemental Digital Content is available in the text. Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability.We performed a genome-wide association study in the Million Veteran Program testing ≈18 million DNA sequence variants with AAA (7642 cases and 172 172 controls) in veterans of European ancestry with independent replication in up to 4972 cases and 99 858 controls. We then used mendelian randomization to examine the causal effects of blood pressure on AAA. We examined the association of AAA risk variants with aneurysms in the lower extremity, cerebral, and iliac arterial beds, and derived a genome-wide polygenic risk score (PRS) to identify a subset of the population at greater risk for disease.P=1.6×10−6), as opposed to systolic blood pressure , likely has a causal relationship with AAA development. We observed that 19 of 24 AAA risk variants associate with aneurysms in at least 1 other vascular territory. A 29-variant PRS was strongly associated with AAA , independent of family history and smoking risk factors . Using this PRS, we identified a subset of the population with AAA prevalence greater than that observed in screening trials informing current guidelines.Through a genome-wide association study, we identified 14 novel loci, bringing the total number of known significant AAA loci to 24. In our mendelian randomization analysis, we demonstrate that a genetic increase of 10 mm Hg in diastolic blood pressure (odds ratio, 1.43 ; We identify novel AAA genetic associations with therapeutic implications and identify a subset of the population at significantly increased genetic risk of AAA independent of family history. Our data suggest that extending current screening guidelines to include testing to identify those with high polygenic AAA risk, once the cost of genotyping becomes comparable with that of screening ultrasound, would significantly increase the yield of current screening at reasonable cost. A genome-wide association study of abdominal aortic aneurysm (AAA) further defines the role of common genetic variation in disease susceptibility.Mendelian randomization analysis supports a causal role for smoking liability and diastolic blood pressure in the pathogenesis of AAA.A polygenic risk score based on 29 genome-wide AAA risk variants strongly associates with AAA independent of family history and 6 additional clinical risk factors.LPA and PCSK9.These results highlight novel AAA genetic associations with therapeutic implications, including Diastolic blood pressure, as opposed to systolic blood pressure, is likely of greater significance in the pathogenesis of AAA.Polygenic risk score analysis can identify a subset of the population at significantly increased genetic risk of AAA independent of family history; extending current screening guidelines to include testing for those with high polygenic AAA risk deserves further investigation, particularly once genotyping becomes standard of care in healthcare systems and can be performed at nominal cost.1 and genetic factors,2 and heritability has been estimated to be as high as 70%.3 Genome-wide association studies (GWAS) have revealed only 10 loci reaching genome-wide significance,7 leaving a significant portion of AAA heritability unknown. Previously published discovery efforts have been limited by small sample sizes, with the largest previous GWAS analyzing ≈5000 AAA cases.7Abdominal aortic aneurysm (AAA) is a complex disease affected by both environmental9 The Million Veteran Program (MVP) was established in 2011 to study how genes affect health in the Veterans Health Administration. Recent efforts have demonstrated that a Veterans Health Administration–based biobank provides the ability to aid genetic discovery for understudied vascular diseases with a higher prevalence among US veterans.11 Leveraging the MVP resource, we sought to perform a genetic discovery analysis for AAA; explore the spectrum of phenotypic consequences associated with AAA risk variants; examine the genetic relationship between smoking and blood pressure and AAA; test the effects of AAA risk variants on aneurysms in other territories; and develop and evaluate a genome-wide polygenic risk score (PRS) for AAA to identify a subset of the population at higher risk for disease.Large-scale biobanks combining genetic data with electronic health record (EHR)–derived phenotypes have continued to evolve over the past decade.The full summary-level association data from the MVP AAA discovery analysis from this article are available through dbGaP .Figure I in the Data Supplement). In the MVP, individuals ages 18 to >100 years have been recruited from 63 Veterans Affairs medical centers across the United States. After performing quality control, we identified 227 817 European participants for AAA discovery analysis from MVP release 2.1. After applying our phenotypic algorithm, we evaluated 7642 AAA cases and 172 172 controls free from clinical evidence of disease. For variants with suggestive associations (P<10−5), we sought replication of our findings with data from either of 2 independent datasets. We first attempted replication of lead AAA variants using summary statistics from the 2016 AAA meta-analysis consisting of 4972 AAA cases and 99 858 controls7 (stage 2a).We conducted a discovery genetic association analysis using DNA samples and phenotypic data from the MVP  and the eMERGE Network . Network datasets consisted of up to 2835 AAA cases and 107 469 controls in total (stage 2b). HUNT is a population-based health survey conducted in the county of Nord-Trøndelag, Norway, since 1984. Individuals were included at 3 different time points over ≈20 years . The eMERGE Network is a collaboration of genetic biobanks with phenotypic data linked to EHRs. The study design and sample collection of eMERGE Network cohorts has been described previously.12 Deidentified samples linked to EHRs were supplied from multiple investigators from different institutions with approval across the consortium for analysis. Additional details of genetic data and quality control are available in the Data Supplement.If association statistics for promising DNA sequence variants from stage 1 were not available for replication, we sought replication of these variants from a combined meta-analysis of 2 studies: HUNT –9 or ICD-10 codes in a participant’s EHR: 441.3, 441.4, I71.3, or I71.4. Controls were defined as possessing no occurrences of the aforementioned ICD codes, as well as no occurrences of the ICD-9 codes 440 through 448 or ICD-10 codes I71 through I75, I77 through I79, or K55. This identified 7642 AAA cases and 172 172 controls of European ancestry for genetic discovery, and an independent set of 1656 AAA cases and 44 908 controls of European ancestry and 718 AAA cases and 46 380 controls of African ancestry for our PRS analysis. In HUNT, all patients diagnosed with AAA from 1995 through 2014 in Nord-Trøndelag were identified from hospital and outpatient registries from the 3 local hospitals: St Olav’s Hospital, Levanger Hospital, and Namsos Hospital. All diagnoses were verified manually using patient chart data. The unique personal identification number was used to link data on AAA diagnoses and death with exposure data from HUNT. This identified 674 AAA cases and 66 358 controls for downstream analysis. In the eMERGE Network, BioMe, and the Penn Medicine Biobank, AAA was defined in a manner identical to the definition proposed by Denny et al.14 In the Mayo Clinic Vascular Disease Biorepository, which consists of patients referred for noninvasive vascular evaluation including abdominal ultrasound, AAA cases were defined as having an infrarenal abdominal aortic diameter ≥3 cm or a history of open or endovascular AAA repair. Controls were not known to have AAA and had no ICD-9 diagnosis codes for AAA.From the participants passing quality control in MVP, individuals were defined as having AAA or being a disease-free control using a previously adjudicated14 and tested using logistic regression. Diseases were required to have a prevalence of >0.13% (≈300 cases) to be included in the Phenome-Wide Association Study (PheWAS) analysis. We tested a total of 1249 distinct diseases, symptoms, and injuries in MVP using the R package PheWAS.15Of the genotyped veterans passing quality control, we identified 23 172 451 distinct, prevalent ICD diagnosis codes available for analysis. We focused on the entire group of 227 817 White participants passing quality control, in which the mean age was 64.3±13.1 years, and 93.3% (212 465) were male. ICD-9 or ICD-10 diagnosis codes were collapsed to clinical disease groups and corresponding controls using the groupings proposed by Denny et al16 to calculate the genetic correlation between AAA and 10 known risk factors for disease and aneurysm rupture. MVP association summary statistics for AAA were uploaded to LD Hub (http://ldsc.broadinstitute.org/ldhub/) and genetic correlation analyses performed between AAA, 4 smoking categories , and 2 blood pressure traits (systolic blood pressure [SBP] and diastolic blood pressure [DBP]) from UK Biobank and previously published GLGC plasma lipid 17 and coronary artery disease statistics from the CARDIoGRAMplusC4D  GWAS.18To better understand how common genetic variation influences risk for AAA and its risk factors, we used linkage disequilibrium score regressionP<5×10−8) with an R2<0.001. All clumping was performed using the TwoSampleMR package of R.19 Genetic instruments for the smoking initiation, smoking heaviness, and smoking cessation exposures were derived from publicly available GSCAN (GWAS & Sequencing Consortium of Alcohol and Nicotine Use) data from up to 1 232 091 participants20; instruments of the SBP and DBP traits were derived from the International Consortium of Blood Pressure and UK Biobank Discovery GWAS21 analysis in up to 757 601 individuals. Inverse variance–weighted MR was used for the primary analysis, with weighted median22 MR performed as sensitivity analysis, allowing for up to 50% of the weight of each instrument to be drawn from invalid instruments while controlling type I error. Diagnostic leave-one-out, single-variant, funnel-plot, and MR-Egger23 analyses were performed to evaluate for evidence of heterogeneity and horizontal pleiotropy. In addition, we performed the MR-PRESSO test 24 to identify evidence of horizontal pleiotropy, which consists of 3 parts: (1) the global test for horizontal pleiotropy; (2) the outlier corrected causal estimate, which corrects for the detected horizontal pleiotropy; and (3) the distortion test, which tests whether the causal estimate is significantly different after outlier adjustment.Mendelian randomization (MR) analyses for 3 smoking phenotypes  and 2 blood pressure phenotypes (SBP and DBP) with AAA development were performed. Genetic instruments were selected as DNA sequence variants that associated with the exposure at genome-wide significance . Individuals are then additively scored in a weighted fashion based on the number of risk alleles they carry for each variant in the PRS.P<0.01, P<1×10−4, P<1×10−6) and with an R2>0.0001 based on the linkage disequilibrium reference. From our initial set of summary statistics, the algorithm selected only 1 associated variant from each clump below our prespecified P value threshold. The final output from this procedure generated 3 scores of 29, 301, and 3699 independent (R2<0.0001) AAA-associated variants, representing the strongest disease-associated variant for each linkage disequilibrium–based clump across the genome.To generate our scores, we used summary statistics from the MVP release 2.1 European ancestry GWAS analysis  and a linkage disequilibrium panel of 20 000 randomly selected European samples from UK Biobank. To increase the number of independent variants included in our score, we performed a pruning and thresholding analysis using the linkage disequilibrium–driven clumping procedure in PLINK version 1.90b (clump). In brief, this algorithm formed “clumps” around variants with AAA association  prevalent AAA data from MVP release 3.0 using 1656 AAA cases and 44 908 controls of European ancestry and 718 AAA cases and 46 380 controls of African ancestry independent from the individuals in the MVP discovery GWAS and specifically designated for PRS analysis; (2) a set of 195 AAA cases and 9348 controls from the BioMe Biobank Program; and (3) a set of 396 AAA cases and 9835 controls from the Penn Medicine Biobank. We tested the association of the continuous PRSAAA with prevalent AAA in each cohort and identified the prevalence of AAA in those individuals with the highest 5% of polygenic risk. We calculated the prevalence of AAA in each PRS quintile and in those individuals with the highest 5% of polygenic risk among men aged 50 or older, because individuals in their 6th decade of life or older are significantly more likely to have AAA.25 Last, we tested the association of our continuous PRSAAA while including 6 traditional AAA risk factors in the association model: smoking, hypertension, low-density lipoprotein cholesterol with statin adjustment, high-density lipoprotein cholesterol, triglycerides, and coronary artery disease as a marker of atherosclerosis burden.We first tested the associated AAA risk for each of the PRSIn our primary discovery analysis, genotyped and imputed DNA sequence variants in individuals of European ancestry were tested for association with AAA using logistic regression adjusting for age, sex, and 5 principal components of ancestry assuming an additive model using the SNPTESTv2.5.3 statistical software program.P<10−5), we sought replication of our findings from either an in silico query from the 2016 AAA meta-analysis7 (stage 2a) or the HUNT/eMERGE Network datasets (stage 2b). In HUNT, the GWAS was performed using a logistic mixed model implemented in SAIGE .26 Covariates in the model were sex, history of cardiovascular event as defined from HUNT questionnaires, history of hypertension, history of diabetes mellitus, myocardial infarction, angina pectoris, and smoking status (ever/never). In the eMERGE Network, association testing was performed using the PLATO software27 adjusted for year of birth, sex, 6 principal components, and sample collection site. Analyses were restricted to eMERGE Network locations with adult participants and those contributing cases to the analysis. Discovery and replication results were combined using an inverse variance–weighted fixed-effects method as implemented in the METAL software program.28 We did not attempt replication of any variant in either stage and required P<0.05 with concordant direction of effect for successful replication. We then combined statistical evidence across MVP and the replication studies and set a significance threshold of P<5×10−8 (genome-wide significance). Novel loci were defined as being >500 000 base pairs away from a known AAA genome-wide associated lead variant. Linkage disequilibrium information from the 1000 Genomes Project29 was used to determine independent variants when a locus extended beyond 500 000 base pairs. All logistic regression values of P were 2-sided.For variants with suggestive AAA associations (P<1.7×10−6 (P<0.05/[1249 diseases × 24 variants]). In our genetic correlation analyses, traits were declared to be significant if they demonstrated a correlation P<0.005 (P<0.05/10 AAA risk factors).In the PheWAS analysis, DNA sequence variants were tested using logistic regression adjusting for age, sex, and 5 principal components. Variants were declared to be significantly associated with the disease if they met P<0.01 (0.05/5 traits) for statistical significance.In our MR analyses, a random-effects inverse variance–weighted method was used as the main analysis, with sensitivity analyses performed for the statistically significant associations as described previously. We set a 2-sided 2 tests of independence were used to examine whether the occurrence of AAA with aneurysms in other vascular territories was more frequent than would be expected by chance. In the association analysis of AAA risk variants with aneurysms in other vascular territories, we performed logistic regression adjusting for age, sex, and 5 principal components of ancestry assuming an additive model using the R statistical software program30 (R version 3.3). In our sensitivity analysis, risk variants were tested using the same logistic model with individuals with AAA excluded. Given the known prior association with AAA, we used a nominally significant P<0.05 to declare significance. All P values were 2-sided.In addition, χAAA relative to the rest of the population and generated CIs using R (version 3.3). All P values were 2-sided.In our PRS analysis, logistic regression models were used to estimate odds ratios (ORs) and 95% CIs for the associations of each of the continuous PRSs (1 SD unit), first adjusting only for age, sex, and 5 principal components. Family history and smoking status were then included in the model as appropriate. We then performed logistic regression adjusting for age, sex, 5 principal components, statins, and 6 clinical risk factors for AAA in the association model. We calculated prevalence of AAA for the 5% of individuals with the highest PRSFigures II and III in the Data Supplement). We replicated all 10 previously described genome-wide AAA loci with association P<10−5 (Table Table I in the Data Supplement). The 9p21 variant rs4007642 was the top association result .The MVP discovery analysis comprised 7642 patients with AAA and 172 172 controls of European ancestry. Their baseline characteristics are presented in Table P<10−5 in the MVP discovery analysis and were taken forward for replication. Of those, 28 were available for independent testing in the 2016 AAA meta-analysis, and the remaining were tested in the combined HUNT and eMERGE Network datasets. After independent replication, 14 novel loci exceeded genome-wide significance . The remaining variants either did not meet the prespecified P<0.05 with consistent direction of effect required for independent replication or did not surpass the genome-wide significance threshold after meta-analysis (Table III in the Data Supplement).A total of 70 lead variants at candidate novel loci demonstrated association P<1.7×10−6) PheWAS associations across the 24 genetic variants. Thirteen of the loci replicated known associations with atherosclerosis in at least 1 arterial territory . We also found that several of the DNA sequence variants associated with a range of known risk factors for AAA (Table IV in the Data Supplement). For example, we found 7 PheWAS associations with hyperlipidemia and hypertriglyceridemia. These loci have previously associated with either low-density lipoprotein cholesterol 17 or triglycerides ,17 both of which have been causally implicated in AAA development.31 The AAA risk allele for rs7255 near the lipid droplet-associated hydrogenase (LDAH) gene was associated with a decreased risk of hypercholesterolemia, suggesting that a mechanism other than increased serum cholesterol likely links the locus to aneurysmal degeneration. In CHRNA3 (encoding cholinergic receptor nicotinic α-3), rs55958997 demonstrated associations with chronic obstructive pulmonary disease and ventilator dependence. This DNA sequence variant is strongly correlated (R2=0.83) with variants shown to predict nicotine dependence and a reduced likelihood for cigarette smoking cessation32 and appears to confer AAA risk entirely through its effect on smokers .Understanding the full spectrum of phenotypic consequences of a given DNA sequence variant can help identify the mechanism by which a variant or gene leads to disease. Using a median of 65 distinct ICD-9 or ICD-10 EHR-derived diagnosis codes per participant, we tested each of the 24 AAA lead risk variants  across 1249 disease phenotypes. In total, we identified 232 statistically significant . We found that 9 of the 10 risk factor traits were significantly correlated with AAA after Bonferroni correction (P<0.005), and all risk factors showed a consistent direction of effects between the epidemiologically posited association and the genetic correlation. Current smoking and coronary artery disease (a surrogate for atherosclerosis) demonstrated the strongest positive correlation (Table V in the Data Supplement). These results suggest a common genetic architecture for AAA and its individual risk factors.We next sought to more broadly understand the relationship of traditional AAA risk factors with disease at the genome-wide level. Analysis of shared heritability provides a mechanism to better understand the relationship of common variant risk across phenotypes.31 and here we performed MR analyses for 3 smoking phenotypes—smoking initiation, smoking heaviness, and smoking cessation—with AAA development. Genetic instruments of 84, 20, and 8 variants for the smoking initiation, smoking heaviness, and smoking cessation exposures, respectively, were derived from publicly available GSCAN data20 (Table VI in the Data Supplement). Using the inverse variance–weighted method, we observed that a genetically increased risk of ever being a regular smoker  and a genetic increase in smoking heaviness  were both associated with increased risk of AAA , although this result did not survive Bonferroni correction. Our results remained robust to multiple sensitivity analyses, including the weighted median22 and leave-one-out analyses, as well as MR-PRESSO24 and MR-Egger23 tests for evidence of horizontal pleiotropy .In observational studies, lipids, smoking, and hypertension are modifiable, independent risk factors for AAA. We have previously examined the causal relationship between lipids and AAA,21 (Table VIII in the Data Supplement). We observed that a genetic 10 mm Hg increase in DBP was associated with an increased risk of AAA . We did not detect a significant association between a genetic 10 mm Hg increase in SBP and AAA risk .We then examined the effects of 2 blood pressure traits, SBP and DBP, with AAA through MR. We generated genetic instruments of 390 and 396 variants for the SBP and DBP traits, respectively, from the combined International Consortium for Blood Pressure and UK Biobank discovery GWAS analysis2P<1×10−300) as well as AAA and lower extremity aneurysms (χ2P<1×10−300) than AAA and cerebral aneurysms (χ2P=6×10−12). A total of 66% of patients with AAA were also noted to have an iliac aneurysm, 44% a lower extremity aneurysm, and 9.7% a cerebral aneurysm (Table X in the Data Supplement). We then extended our analysis to examine the underlying genetics of these diseases. For each of the 24 lead AAA risk variants, we tested their association with cerebral, iliac artery, and lower extremity aneurysms. We observed that 4 AAA risk variants demonstrated at least nominal association (P<0.05) with cerebral aneurysms, 7 with lower extremity aneurysms, and 18 with iliac aneurysms . In a sensitivity analysis, we examined the association of the AAA risk variants with isolated iliac, lower extremity, and cerebral aneurysms by excluding individuals with a diagnosis of AAA from the analysis. We noted a greater attenuation of association for the AAA risk variants with iliac artery and lower extremity aneurysms than for cerebral aneurysms, likely driven by the greater overlap of AAA diagnosis for aneurysms of the iliac and lower extremity vessels than in the cerebral territory (Table XII in the Data Supplement).Next, we examined the strength of the relationship between AAA and aneurysms in other vascular territories. In total, we identified 659 cerebral, 520 lower extremity, and 844 iliac aneurysm cases among MVP participants of European ancestry. Whereas a significant relationship was observed for AAA and all 3 aneurysm types, we observed a stronger association among AAA and iliac aneurysms . We first tested the associated AAA risk for each of the PRSAAA using ascertained AAA case–control cohort data from the Mayo Clinic Vascular Disease Biorepository34 consisting of 1022 AAA cases and 7750 controls. Demographic and clinical characteristics of this European ancestry cohort are depicted in Table XIV in the Data Supplement. We observed that the 29 variant score  performed best based on its effect estimate and P value of association, with a 1-SD increase in PRSAAA associated with a 26% increased risk of AAA  would identify a population at increased risk of AAA who may be candidates for screening. We generated 3 distinct AAA PRSs using a pruning and thresholding method from European MVP release 2.1 AAA summary statistics (Table XV in the Data Supplement). We observed that the 29-variant PRSAAA was associated with AAA in all of the cohorts, though the effect estimate was diminished most in individuals of African ancestry . The prevalence estimates observed in individuals of European ancestry were greater than those found in individuals of African ancestry . Prevalence results stratified by PRS quintile are depicted in Table XVII in the Data Supplement.We sought validation of our PRS findings using AAA data from 3 additional population-based datasets: (1) prevalent AAA data from MVP release 3.0, which comprised 1656 AAA cases and 44 908 controls of European ancestry and 718 AAA cases and 46 380 controls of African ancestry that were entirely independent from the individuals in the MVP discovery GWAS; (2) a set of 195 AAA cases and 9348 controls from the BioMe Biobank Program; and (3) a set of 396 AAA cases and 9835 controls from the Penn Medicine Biobank  demonstrated that increased DBP was associated with incident AAA37 and estimates for lower extremity and iliac aneurysms among those with AAA approach 14%38 and 40%,39 respectively. In our population-based genetic analysis, we observed a much greater amount of disease overlap between AAA and iliac or lower extremity aneurysms than previously reported, with the shared genetic risk associations reflecting this distribution. Our findings support the concept that the biology underlying AAA, iliac, and lower extremity aneurysms are likely closely related, whereas cerebral aneurysms appear to be a physiologic entity divergent in genetic origin. Whereas the final common pathway of arterial wall degeneration is common to all 4 diseases, these results suggest that therapies targeting AAA are more likely to be efficacious for preventing aneurysmal disease in the lower extremity and iliac vascular beds.Second, our results highlight biological similarities and differences among aneurysms of the aortic, cerebral, lower extremity, and iliac systems. Previous analyses demonstrated that cerebral aneurysms may be observed in up to 10% of patients with AAA,40 The US Preventative Service Task Force recently released updated guidelines41 regarding the utility of screening asymptomatic patients on the basis of pooled data from 4 randomized control trials.45 Across these studies, a net reduction in aneurysm-related mortality and incidence of rupture was observed by screening men >65 years of age, leading to the recommendation to screen individuals aged 65 to 75 with a history of smoking with a single visceral ultrasound. This recommendation was based on the assumption that 6% to 7% of individuals in this cohort will have an identified AAA.25 Here, through the use of a 29-variant PRSAAA, we identified a subset of the European ancestry population who had high genetic risk and an AAA prevalence similar to (5.9% across the top 5% PRSAAA) or greater than (8.6% among men over 50 in the top 5% PRSAAA) that observed in screening trials informing current guidelines. Furthermore, a family history of AAA, often touted as an equivalent means of assessing genetic risk, only slightly attenuated the PRS association. Results from our study suggest that PRSs represent an orthogonal source of disease risk not captured by standard AAA risk factors, and testing individuals with a high PRSAAA through a noninvasive abdominal duplex study may increase the yield of AAA screening, particularly once genotyping becomes standard of care in healthcare systems and can be performed at nominal cost.Last, our data identify a role for polygenic risk prediction in the targeting of asymptomatic individuals for AAA screening. AAA remains underdiagnosed in the general population. A 2013 report examining Medicare data demonstrated that up to 40% of individuals with AAA were diagnosed late in their disease course, and late diagnosis was associated with an increased likelihood of rupture at the time of repair.These results also underscore an important issue with respect to applicability of polygenic scoring across ancestry groups. To date, the overwhelming majority of PRSs have been developed using summary statistics from patients with European ancestry. Whereas the 29-variant score constructed here associated with AAA in individuals with African ancestry, the attenuated effect estimate could limit its predictive accuracy. As the field of genetics moves forward, it is critical that large-scale association studies include individuals of diverse genetic backgrounds to limit the potential for ethnic disparities in precision medicine.AAA are at increased risk for AAA, the PRS mechanism of action represents a combination of many causal risk factors, rather than a single pathway that leads to disease. However, assessment of individual risk can aid in identifying those at highest risk for AAA and more likely to obtain benefit from screening, regardless of mechanism. Third, the Veterans Health Administration healthcare system contains a much higher proportion of men than the general population, and our ability to detect female-specific associations was more limited. However, AAA is a disease process that overwhelmingly affects men,25 and our results should therefore still be generalizable to patients with AAA.Our study should be interpreted within the context of its limitations. First, our AAA phenotype is based on EHR data and may result in misclassification of case status. Such misclassification should, however, reduce statistical power for discovery and on average bias results toward the null. Second, although those with the highest PRSWe identified 14 novel genomic loci associated with AAA risk, explored the phenotypic consequences of blood pressure on AAA, identified AAA risk variants associated with aneurysms in other vascular beds, and generated a PRS for AAA that may improve the current screening paradigm once genotyping costs are further reduced in the future. Our data provide mechanistic insights into the genetic architecture of AAA that can inform clinical care.This work was supported by funding from the Department of Veterans Affairs Office of Research and Development, Million Veteran Program Grant MVP000; Department of Veterans Affairs awards I01-01BX03340 (Drs Cho and Wilson), I01-BX003362 (Drs Chang and Tsao), and IK2-CX001780 (Dr Damrauer); Veterans Affairs Informatics and Computing Infrastructure (VINCI) grant VA HSR RES 130457 ; and National Institutes of Health grant K08HL140203 (Dr Natarajan). Phase III of the eMERGE Network  was initiated and funded by the National Human Genome Research Institute through the following grants: U01HG8657 (Kaiser Washington/University of Washington), U01HG8685 , U01HG8672 , U01HG8666 , U01HG6379 (Mayo Clinic), U01HG8679 (Geisinger Clinic), U01HG8680 , U01HG8684 , U01HG8673 (Northwestern University), U01HG8701 , U01HG8676 , and U01HG8664 (Baylor College of Medicine). Phase I and II of the eMERGE Network was initiated and funded by the National Human Genome Research Institute through the following grants: U01HG006828 , U01HG006830 , U01HG006389 , U01HG006382 (Geisinger Clinic), U01HG006375 , U01HG006379 (Mayo Clinic), U01HG006380 (Icahn School of Medicine at Mount Sinai), U01HG006388 (Northwestern University), U01HG006378 , U01HG006385 , U01HG004438 , and U01HG004424 .Dr Klarin has received consulting fees from Regeneron Pharmaceuticals unrelated to the work in this article. Dr DuVall reports grants and nonfinancial support from the Department of Veterans Affairs during the conduct of the study; and grants from AbbVie Inc, Amgen Inc, Anolinx LLC, Astellas Pharma Inc, AstraZeneca Pharmaceuticals LP, Boehringer Ingelheim International GmbH, Celgene Corporation, Eli Lilly and Company, Genentech Inc, Genomic Health Inc, Gilead Sciences Inc, GlaxoSmithKline PLC, Innocrin Pharmaceuticals Inc, Janssen Pharmaceuticals Inc, Kantar Health, Myriad Genetics Laboratories Inc, Novartis International AG, and Parexel International Corporation outside the submitted work. Dr Damrauer receives research support to his institution from RenalytixAI and is a paid consultant for Calico Labs. Dr Kathiresan is a founder of Maze Therapeutics, Verve Therapeutics, and San Therapeutics; holds equity in Catabasis and San Therapeutics; is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, NovoNordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Huag Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; and reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119).Supplemental MethodsData Supplement Figures I–IXData Supplement Tables I–XVIIData Supplement Excel File IReferences 46–54


Marine mammals display several physiological adaptations to their marine environment. Higher myoglobin concentrations in their muscles compared to terrestrial mammals allow them to increase their onboard oxygen stores, enhancing the time available to dive. Most previous studies have calculated cetaceans’ onboard oxygen stores by assuming the myoglobin concentration of a single muscle to be representative of all the muscles in the body. In this study, we analyzed this assumption by comparing it to a more precise method that weighs all body muscles and measures myoglobin concentration in different functional groups.−1 muscle), enhancing their onboard oxygen (O2) stores and their aerobic dive limit. Although myoglobin is not homogeneously distributed, cetacean muscle O2 stores have been often determined by measuring Mb concentration from a single muscle sample (longissimus dorsi) and multiplying that value by the animal’s locomotor muscle or total muscle mass. This study serves to determine the accuracy of previous cetacean muscle O2 stores calculations. For that, body muscles from three delphinid species: Delphinus delphis, Stenella coeruleoalba, and Stenella frontalis, were dissected and weighed. Mb concentration was calculated from six muscles/muscle groups , each representative of different functional groups . Results demonstrated that the Mb concentration was heterogeneously distributed, being significantly higher in locomotor muscles. Locomotor muscles were the major contributors to total muscle O2 stores (mean 92.8%) due to their high Mb concentration and large muscle masses. Compared to this method, previous studies assuming homogenous Mb concentration distribution likely underestimated total muscle O2 stores by 10% when only considering locomotor muscles and overestimated them by 13% when total muscle mass was considered.Compared with terrestrial mammals, marine mammals possess increased muscle myoglobin concentrations (Mb concentration, g Mb · 100g Most previous studies have calculated total Mb from a single biopsy or muscle sample extrapolated to the locomotor muscle mass  or the entire muscle mass, e.g., [Myoglobin (Mb) is an Ole cells . Myogloble cells . In maris, e.g., ,15,16,17s, e.g., ,19,20,21s, e.g., ,23,24 in2 stored in the muscles is crucial for a proper determination of ADL. The goal of this study was to investigate the effect of the heterogeneity of skeletal muscle Mb concentration on calculations of locomotor muscle, and total muscle, oxygen stores in three species of similar size, closely related pelagic delphinids [Delphinus delphis (common dolphin), Stenella coeruleoalba (striped dolphin), and Stenella frontalis (spotted dolphin). Kroeger and colleagues recently investigated the morphological features of a primary locomotor muscle, the longissimus dorsi, of these three species, including muscle fiber-type and size, index of mitochondrial volume density, and Mb concentration [rectus abdominis), pectoral fin movement , feeding (sternohyoideus), and respiration . The Mb concentration values from each of these representative muscles were then used to compare multiple methods for calculating onboard oxygen stores.An accurate determination of Olphinids ,26: Delpntration . Thus, tDelphinus delphis (n = 4), Stenella coeruleoalba (n = 3), and Stenella frontalis (n = 4)  from September 2013 to September 2014, and on the Canary Islands (Spain) from October 2014 to March 2017. Eleven individuals from three species were used:  (n = 4) . Carcasscutaneous trunci muscle, which was not separated from the blubber, were dissected based upon the methods of McLellan et al. [During necropsy, all body muscles, excluding those of the head and the n et al. . They wen et al. ,31,32,33mastohumeralis muscle , the sternohyoideus muscle (suction feeding), and the dorsal scalenus muscle (respiration). In the case of locomotor muscles powering swimming, several muscles were sampled given that more and larger muscles are involved: the longissimus dorsi in the epaxial muscle group (locomotor upstroke), the hypaxial muscle group (locomotor downstroke), and the rectus abdominis muscle (assistance in the tail fluke downstroke)  . Within movement . Howeversternohyoideus to inhalation, during a respiratory event [This muscle grouping scheme was used to recognize that cetacean muscles can perform multiple functions as in all mammals. For example, the primary downstroke muscles also contribute to exhalation, and the ry event .longissimus dorsi at the level of the axilla, middle (mid-distance between axilla and anus), and anus; the hypaxial group at the middle and anus levels. The rest of the muscles were sampled at the mid-belly. Muscle samples were wrapped in plastic wrap, placed in sealed bags and frozen at −20 °C until analysis.The longest muscles were also sampled at multiple locations: within the epaxial group, the g for 50 min at 4 °C (Beckman J2-MI Centrifuge). Five ml of supernatant were transferred to a test tube and bubbled with carbon monoxide  at room temperature for 8 min. Approximately 0.02 g of sodium dithionite were added to the sample to ensure a complete reduction of Mb. The sample was bubbled again for 2 min with CO. The absorbance was measured at 538 and 568 nm . Three muscle samples were analyzed for each sampling location, and three spectrophotometric measurements were obtained for each of the replicates from an individual.Muscle Mb concentration was measured in two laboratories: UNC Wilmington in North Carolina, USA, and the University of Las Palmas de Gran Canaria in the Canary Islands, Spain, using methods adapted from Reynafarje by Noren and Williams and Etnier et al. ,34,35. F−1 muscle) was calculated using the equation developed by Reynafarje [To compare our results with the literature, Mb concentration  were divided by 0.025 g muscle· ml−1 (0.5 g muscle were dissolved in 20 mL of solvent (buffer volume + % water in muscle) and then transformed to obtain g Mb · 100g-1 muscle. Mb concentration values of CET 834, calculated following Reynafarje (1963) and using the calibration curve, were compared to test both approaches.Mb concentration values for one animal (CET 834) were also calculated using a calibration curve to test if calculated Mb concentration values may vary when using Reynafarje’s equation with different spectrophotometers. Horse Mb standard  was used to construct the calibration curve . Values 2 storage. Since the goal of this study was to investigate how Mb concentration heterogeneity affects calculations of locomotor muscle and total muscle oxygen stores, total muscle myoglobin was calculated in three different ways:(1)Longissimus dorsi) muscle position was multiplied by the animal’s total locomotor muscle mass Homogeneous myoglobin distribution was assumed across all the skeletal muscles. Mb concentration at the middle epaxial muscle was multiplied by the animal’s total muscle mass (TMM) (Equation (2)). This method will be referred to as the “total muscle method.”(3)Heterogeneous myoglobin distribution was assumed across the skeletal muscles. The Mb for each functional group was calculated by multiplying the Mb concentration of the representative muscle by the mass of all muscles in each functional muscle group (FMG) following Equation (3). In those muscles where Mb concentration was determined in different locations, the mean Mb concentration among or between locations was calculated. This method will be referred to as the “functional muscle group method.”Total Mb within each muscle was calculated to determine locomotor and total muscle OTotal muscle Mb was then calculated as the summation of Mb values within the different functional muscle groups (Equation (4)).2 stored within the muscle was obtained by multiplying the total amount of Mb, calculated by the three methods, by the O2 binding capacity of Mb  (Equation (5)) [Total Oion (5)) ,37.(5)TD. delphis, which acted as the reference specie), th dolphin and Whether the Mb concentration varied between species and muscle groups was assessed using a mixed model  and the P-values . From thBecause CET 829 was the only juvenile in the sample, and Mb concentration changes across development ,39, the To compare the Mb concentration between the locomotor and non-locomotor muscles, the difference between the means of effects was considered:i,j the muscle mass of the jth muscle and ei,j is the variability within the dolphin, log stands for natural logarithm. The goodness of fit was summarized as the marginal R2 (represents the variance explained by the fixed effects) and the conditional R2 . Muscle mass was logarithmically transformed to achieve a better fit.To test if there was a relationship between Mb concentration and muscle mass, the following mixed model was used:2 stored determination, we used the intraclass correlation coefficient (ICC) and represented the data graphically using scatterplots. The ICC between two numerical variables measures the degree of coincidence between the variable values.To compare the results from this functional muscle method with the more commonly used total and locomotor muscle methods for muscle Op < 0.05. Mb concentration variation between species and muscle groups and locations was assessed using a mixed model (see methods 2.4.1), whereas the rest of the results are raw data. Dot and box plots were made using SPSS 17 .For statistical analysis, the R package, version 3.3.1 , was useThe results shown below for Mb concentration variation between species and muscle groups and locations were assessed using a mixed model see , whereas−1 muscle (95% CI = 2.54–2.71) higher than in the non-locomotor muscles. Moreover, Mb concentration in locomotor muscles was significantly higher than in non-locomotor muscles . Heterogeneity in Mb concentration was observed among and within the various muscle groups studied , meaning that locomotor muscles had higher mass and higher Mb concentrations than non-locomotor muscles.Mb concentration and muscle mass showed a strong positive relationship  among the three species included in the study. The highest Mb concentration was observed in S. coeruleoalba, followed by D. delphis and S. frontalis . Among the different locomotor muscle groups, following the classification proposed in this study, the epaxial upstroke group stored the largest amount of O2 , followed by the hypaxial (mean 26%) and the rectus abdominis downstroke groups (mean 12.8%)  . Interin2 storage of cetaceans compared with the functional muscle group method by an average of 6.2%, as the O2 stored in non-locomotor muscles is not taken into account. In contrast, the total muscle mass method overestimated total muscle O2 storage when compared to the functional muscle group method by 13.0% since it relied upon a single Mb concentration value taken at the middle of the epaxial muscle, which is one of the highest measured in any muscle .As shown in previous studies ,22,23,24−1 muscle higher than non-locomotor), which can be interpreted within the context of the functional roles of these muscle groups. Locomotor muscles generate the thrust to produce the tail fluke movement and are likely more metabolically active during a dive than non-locomotor muscles [Locomotor muscles contained significantly higher Mb concentration than non-locomotor muscles in the three species studied ; or just the opposite, large-body mass species showing low Mb concentration values , e.g., [Total body mass was not explored as a factor in the mixed model to estimate Mb concentration because the marine mammal literature indicates that Mb concentration is more related to the species and its ecophysiology  than to the body mass itself. Data supporting this position are different Mb concentration values for species with similar body masses; small species possessing higher Mb concentration values than larger species , e.g., ,47,48,492 stores in the body and estimate ADL. Thus, we suggest using standard calibration curves, instead of Reynafarje’s equation, to allow a more reliable comparison of Mb concentration across laboratories and studies.Different Mb concentration values for the same muscle group within an individual were obtained using the Reynafarje equation and the calibration curve methods. Proper determination of total Mb in the body is essential to accurately estimate muscle O2 stores than the non-locomotor muscles due to their larger muscle mass and higher Mb concentration. These results are similar to those of Dolar et al. [2 stores in the locomotor muscles  (82–86%) in Fraser’s dolphin (Lagenodelphis hosei), the spinner dolphin (Stenella longirostris), and the pygmy killer whale (Feresa attenuata). Large O2 stores within the locomotor muscle are essential because, as primary swimming muscles, they require a source of O2 to maintain aerobic muscle metabolism [2 stored in the various functional groups among individuals from the same species may be explained due to interindividual differences. In the case of indivuals from the species S. frontalis, CET 822 showed the highest differences compared to the other three. This individual was a male compared to the other three that were females. Future studies, with a higher sample size, may be needed to determine if males of this species have more locomotor musculature than females.The locomotor muscle groups had higher calculated Or et al. , who destabolism ,15,22,342 stores compared to the functional muscle group method due to the omission of non-locomotor muscle mass, which had a mean muscle O2 stored percentage value of 7.7 ± 2.3% among the three species. In contrast, the total muscle mass method overestimated muscle O2 stores. This method extrapolates Mb values from the middle epaxial location, which has the highest Mb concentration, to all muscles.The locomotor muscle mass method underestimated dolphin adult muscle O2 stores in different muscles  of the cetacean species mentioned in 2 stores when assuming homogeneous Mb concentration distribution using middle epaxial values. The differences in the mean overestimation values between both studies (7.6% vs. 13% of our study) may be due to both the choice of muscles and species-specific patterns of muscle Mb concentration heterogeneity.Dolar et al.  determin2 stores. Nevertheless, in both methods, the locomotor and/or entire skeletal muscle mass should be weighed appropriately. Data of the relative muscle mass in the body for different species and age classes are scarce for marine mammals, e.g., [The functional muscle group method presented in this study is labor-intensive, as it requires the measurement of Mb concentration in multiple muscles and dissecting and weighing most of the body muscles. Comparison with previous methods demonstrated a high intraclass correlation , meanings, e.g., ,51,52,53s, e.g., ,39. Thus2 storage site due to their high Mb concentration and large muscle mass. This study demonstrates that using the locomotor muscle mass method yields quantitative data similar to those of the functional muscle group method, which is more time-consuming and technically challenging. Because locomotor muscles represent the overwhelming majority of the body’s skeletal muscle mass, this method provides accurate comparative insights into cetaceans’ diving and swimming capabilities whenever the locomotor muscles are properly dissected and weighed.As previous studies have demonstrated, Mb concentration was heterogeneously distributed between and within cetacean muscles investigated in this study. Locomotor muscles constituted the primary muscle O


Objective: This study assessed the prevalence of MRSA, ESBL and VRE in students from four dental schools in Europe.Methods: The hand, tongue and nostrils of the students who treated patients (study group) and who did not treat patients (control group) were sampled. After incubation in TSB and subculturing in the presence of 4 µg/ml oxacillin, positive cultures were identified for Staphylococcus aureus by Mannitol salt agar and agglutination tests. The presence of MRSA was confirmed by specific PCR on the species and on the SSCmec genes. ESBL and VRE were isolated using specific CHROMagar and confirmed using antibiotic sensitivity tests.Results: Of the 879 students who participated in this study  a total of 50 students (5.7%) tested positive for a multi-drug resistant bacterium (MDRB); 13 (1.5%) students tested positive for MRSA, 26 (3.0%) for ESBL and 12 (1.4%) for VRE. No statistically significant differences were found between the students who treated patients compared to the control group for any of the MDRB and study centres, excluding MRSA carriage in the Italian student population. The use of antibiotics the year before sampling, was positively associated with the presence of an MDRB .Conclusion: The risk for MDRB carriage and sequential transmission of MDRB for dental health care students and their patients were acceptably low. Transmission of pathogens is to be expected during a dental treatment . TransmiStaphylococcus aureus (MRSA), Extended Spectrum Beta-lactamase producing Enterobacteriaceae (ESBL) and Vancomycin Resistant Enterococci (VRE) [An important theme within infection control is the transmission of MDRB with a large health burden . Therefoci (VRE) . The occci (VRE) .S. aureus is a commensal bacterium which resides in the nose, throat and oral cavity. MRSA has been isolated from a variety of oral infections [fections . In somefections 9, 10.Previous research reported that the prevalence of MDRB carriers, within dental workers, is not higher than the prevalence in a normal adult population . This isThis cross-sectional study is conducted to investigate whether dental students, who perform clinical procedures, are at risk of infection from MRSA, ESBL-bacteria and VRE. It is hypothesized that transmission occurs during patient treatment, especially in countries where the carriage prevalence of MDRB in the population is relatively high . TherefoA multi-centre cross-sectional study was conducted at four dental institutes in Europe: Sahlgrenska Academy , Aristotle University of Thessaloniki (Greece), Sapienza University  and the Academic Centre for Dentistry Amsterdam . From each dental school a group of dental students was invited to participate., Patient treatment status was recorded to determine whether the students performed patient treatment (study group) or only pre-clinical work (control group).nd year of the Faculty of Life Sciences (Vrije Universiteit Amsterdam) and of the Saxion University of Applied Sciences, Deventer were included as a control group for the Dutch dental student population.As students at the ACTA already have clinical patient contact in the first year, students of the 2th WMA Declaration of Helsinki  and the Medical Research Involving Human Subjects Act (WMO), approximating Good Clinical Practice (CPMP/ICH/135/95) guidelines, and the study was approved by the Medical Ethical Committee (MEC) of the VU Medical Centre, Amsterdam, the Netherlands (2014.429).This study is reported in accordance with the STROBE guidelines for reporting observational studies . The stuAs no previous valid information regarding the MDRB carriage rate was available, a non-probability sampling method was chosen. Within each dental school a convenience sample of clinical (STP+) and pre-/non-clinical (STP-) dental students was selected. Only students from the participating universities and over 18 years were eligible to participate in this study. Students were asked to participate in the study by the researchers, and not by teaching staff to ensure voluntary participation. Potential participants received a letter with information about the study and the study procedures. After adequate time to consider participation and to ask questions, written informed consent was obtained. Samples were coded, but not linked to the participants’ identity to assure that they would not face a study-delay when testing positive for MRSA . In compliance to the guidelines of the MEC, participants were able to be informed about their MRSA carriage by providing their sample number.Each participant received a questionnaire to collect demographic data. Questions on antibiotic use, hospital visits, patient treatment status and living in the vicinity of a livestock were asked to assess possible cofounders. After completion of the survey form, the students were carefully instructed on how to take the clinical sample. Three sites, each with a separate sterile cotton swab , were sampled: (1) the interdigital folds between the ring- and little-finger on their dominant hand, (2) both anterior nares of their nose and (3) the dorsum of their tongue. Samples were immediately transported to a microbiology laboratory, and cultured in 500 µl Tryptic Soy Broth . Cultures were stored at −80°C and stored until further analyses after the addition of 500 µl 60% (v/v) glycerol . All culturing on solid media and in TSB were performed under aerobic conditions at 37°C for either 24 or 16 hours, respectively.S. aureus and subcultured in TSB. To confirm antibiotic resistance, 10 µl of each overnight culture was added to the wells of microtiter plates containing 90 µl of TSB with various concentrations of oxacillin  and incubated subsequently. Isolates positive for growth on MSA and in TSB with 4 μg/ml oxacillin were selected and subcultured in TSB. S. aureus identity was confirmed by a positive coagulase test . To assess the final identity of MRSA, genomic DNA was isolated from each positive isolate using the GeneJet kit . Isolated DNA was stored at −20°C, until further use. Possible MRSA strains, being positive for the presence of the SSCmec genes (MecA and MecC), were confirmed by PCR [To assess the presence of MRSA in the stored samples, an aliquot of each sample was plated onto Mannitol-Salt Agar (MSA) and incubated. Single colonies with a yellow halo were identified as possible d by PCR . The MRSd by PCR .TM or VRE CHROMagarTM plates, according to the manufacturer’s instructions . Colonies resembling the proper morphology (according to instructions) were picked and streaked onto Blood Agar plates  to check their purity. Each isolate was Gram’s stained and was subsequently subcultured in TSB. Each ESBL positive culture was spread onto Tryptic Soy Agar (BD) and incubated with an E-test® ESBL  to confirm resistance towards cefepime (MIC ≥ 1.0 μg/ml) in the presence and absence of clavulanic acid [The presence of ESBL and VRE was established by plating aliquots of the frozen samples onto either ESBL CHROMagarnic acid . VRE posThe main study parameters were the presence of MSRA, ESBL, and VRE in students, associated with their contact with patients or other possible sources for multi-resistant bacteria. For each university, carriage rates of MDRB were assessed in both clinical and non-clinical students. The 95% confidence intervals (CI) were assessed using the Wilson method, with continuity correction, in order to account for relatively small sample sizes. To account for different sample sizes the overall weighted carriage rate was assessed, using the inverse of the variance as weight. The crude odds ratios (OR) for MRSA, ESBL, VRE, and any MDRB carriage in STP+ relatively to STP- were assessed. The overall weighted crude ORs were also assessed using the inverse of the variance weighting method. Additionally, the adjusted OR for MDRB carriage, attributable to clinical activity, was assessed using multiple logistic regression analyses, with a correction for study centres. Differences between students who treated patients and students who did not treat patients were tested using a Fisher exact test. Alpha was set at 0.05. Correction for multiple comparisons was performed using the Bonferroni correction; alpha was set on 0.025.A total of 879 students participated in this study; 454 students who treated patients (STP+) and 425 students who did not treat patients (STP-). In Thessaloniki, 196 students took part in the study (98 STP+), in Rome, 157 students (90 STP+), in The Netherlands 340 students (163 STP+) and in Gothenburg, 186 students (103 STP+). The demographic characteristics were different between the two groups; 64% of the STP+ were females compared to 73% females in the STP- group. The mean age of the STP+ (24.3 years ± 4.4) was not significantly higher compared to the STP- (20.1 years ± 3.0).A total of 50 students (5.7%) tested positive for an MDRB; 13 (1.5%) students tested positive for MRSA, 26 (3.0%) for ESBL, and 12 (1.4%) for VRE. One Swedish student tested positive for both ESBL and VRE, all other students tested positive for only one MDRB. The difference in prevalence between STP+ and STP- did not result in statistically significant differences for any of the MDRB and study centres, excluding MRSA carriage in the Italian student population which was significantly lower in STP+ than in STP- . Eight s2 = 23.3, df = 3, p = 0.00004). Post hoc analyses revealed that Dutch students had significantly less MDRB cases as compared to Italian students , Swedish students had significantly less MDRB cases as compared to Italian  and Greek  students.The prevalence of MDRB was statistically significantly different between countries . The frequency of hand hygiene performance, of the students who treat patients, is suggested to be the basis for this difference.Since Thessaloniki and Rome have a relatively high carriage rate of MDRB in the general population , the lowCarriage rates for MRSA, in dentistry, differ considerably between countries (0–20%) ,21–25. TThe current study included considerably larger numbers of participants as compared to previous studies, providing an important contribution to a reliable overview of risks of transmission between people in the dental practice. It therewith fulfills the call from Yoo, et al ,28, for However, multinational and multi-institu-tional studies are required to clarify the true MRSA carriage rate among DHCPs because most of the stud-ies were performed at a single institutio


We report a new configuration for enhancing the performance of scanning electrochemical microscopy (SECM) via heating of the substrate electrode. A flattened Pt microwire was employed as the substrate electrode. The substrate was heated by an alternating current (AC), resulting in an increased mass transfer between the wire surface and the bulk solution. The electrochemical response of the Pt wire during heating was investigated by means of cyclic voltammetry (CV). The open circuit potential (OCP) of the wire was recorded over time, while varied heating currents were applied to investigate the time needed for establishing steady-state conditions. Diffusion layer studies were carried out by performing probe approach curves (PACs) for various measuring modes of SECM. Finally, imaging studies of a heated substrate electrode surface, applying feedback, substrate generation/tip collection (SG/TC), and the competition mode of SECM, were performed and compared with room temperature results. Thermoelectrochemistry is a discipline that adds the temperature parameter to the three classical parameters of electrochemistry, known as voltage, current, and time. In a heated electrolyte solution, the resulting increased mass transport yields a higher rate of analyte conversion at the electrode surface. In such an environment, special attention is required to maintain a constant temperature around the reference electrode to prevent a shift of its potential. However, this can be neglected if heating is limited to the area closest to the working electrode, also known as non-isothermal heating. In recent research, non-isothermal heating was mostly achieved either by directly heating the electrode  or by irFor the electrochemical characterization of surfaces, scanning electrochemical microscopy (SECM) has become an established technique ,21,22,23Since heating the electrolyte results in an increased convection rate, the non-isothermal approach increases mass transport exclusively near the heated electrode. Boika et al. have made use of this effect by employing heated UMEs as probes in SECM. They refer to this as “hot tip SECM” ,32. OtheIn this work, we present a novel hot-wire electrochemistry setup, featuring flattened microwires suitable for SECM studies under well-defined substrate temperature conditions. The steady-state voltammetric behavior under heat application is shown. For several measuring modes of SECM, approach curves of heated wire substrates have been studied, and imaging experiments have been carried out.The effect of heating the wire electrode on cyclic voltammograms (CVs) was studies in 5 mM ferro/ferricyanide by applying different magnitudes of alternating current (AC). In the following section, the substrate electrode was studied in a physiologically relevant temperature region. For the investigation of temperature changes directly at the substrate, the OCP dependent on the applied heating AC was measured, which is in direct correlation with the temperature. The influence of different temperatures of the flattened wire substrate on the approach response of probes in different SECM operational modes was investigated in a solution of ferrocenemethanol (FcMeOH). The effect of heating of a flattened wire on corresponding SECM images studies in different operational modes is shown in 3  was used as a mediator solution. All other experiments were carried out in an aqueous solution containing 5 mM of both K3(Fe(CN)6) and K4(Fe(CN)6), as well as 1 M KNO3. All solutions were prepared using ultrapure water with a resistivity higher than 18 MΩ cm−1 .For SECM imaging, an aqueous solution of 1.5 mM ferrocenemethanol  and 0.2 M KNOThe setup used for all experiments consisted of a commercial SECM , with an electrochemical cell made of polytetrafluoroethylene and a wire heating device, which was used to apply a 100 kHz AC with varying amplitude to Pt wires. Heating intensity could be adjusted by an arbitrary percentage value corresponding to a certain voltage amplitude applied. The heating device was laboratory-built, and mainly consisted of a radio frequency generator and a radio frequency transformer, based on the constructions proposed by Gründler . The worFor the fabrication of flattened microwires, a 3 cm piece of Pt wire with a diameter of 25 µm  was placed between two glass slides, and force was applied using a hammer. To prevent breaking of the glass, the glass slides were placed between polymethylmethacrylate plates. When a flattened wire with a diameter of 70 µm b was obtExperiments were performed in an electrochemical cell containing redox mediator solution, with a Pt wire as counter electrode and an Ag/AgCl 3M KCl reference electrode immersed into the solution. All potentials mentioned herein refer to this reference system.Voltammetric studies of the substrate were conducted in a solution of ferro/ferricyanide. The heating current was varied to determine the limit where experiments could be performed. Three consecutive CVs were recorded for each AC current investigated. In addition, chrono-OCP measurements were performed while the heating was varied between 0 and 188 mA, to test the possibility of undergoing quick temperature changes. Substrates were cleaned with isopropanol and water before measurements. Temperature calibration was done in an electrochemical cell connected to Peltier elements . An external current was applied to the Peltier elements to establish different temperatures, and three consecutive CVs were recorded. The corresponding temperatures, measured with a thermometer, could then be related to the respective half-wave potentials. The temperature calibration is included in the PACs were carried out using a UME with an electrode diameter of 25 µm and an RG value of 4. This value represents the ratio between the total probe tip diameter and the diameter of the electroactive area. Fabrication of probes was done according to a procedure described elsewhere . The cenSECM images in feedback, SG/TC, and competition modes were recorded with and without heating. An area of 135 × 500 µm covering the wire was imaged at a tip-to-wire distance of 7 µm. Measurements were performed with 1.5 mM FcMeOH as redox mediator, with an E (Probe) and E (wire) of 0.5 V and OCP, respectively (feedback mode); and 0.0 V and 0.5 V, respectively (SG/TC mode); as well as 0.5 V for both tip and wire (competition mode). The pixel size was set to 10 µm with a probe velocity of 100 µm/s. Heating was applied 10 min prior to imaging, to ensure steady temperatures during measurements.A new hot-wire electrochemistry setup suitable for SECM studies was developed and characterized. In contrast to cylindrical microwires used in conventional hot-wire electrochemistry, flattened microwires were implemented in a planar glass substrate, enabling a straightforward operation in SECM measurements.The new setup was successfully characterized via CV and OCP measurements, as well as both PACs and images of the microwire substrate recorded in feedback, SG/TC, and competition modes of SECM. CVs revealed that mass transport between wires and bulk solution drastically increased with a higher wire temperature. In this context, the present hot-wire SECM configuration can be seen as a new option of hydrodynamic SECM. Fast changes of temperature could be carried out, shown by OCP monitoring. Finally, SECM images of the heated microwire exhibited higher contrast in comparison to images obtained at room temperature. The concept presented in this manuscript paves the way for a wide range of studies of temperature-dependent processes by means of SECM.