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Ebdrup B.H.,Center for Neuropsychiatric Schizophrenia Research | Knop F.K.,Glostrup | Madsen A.,Copenhagen University | Mortensen H.B.,Copenhagen University | And 4 more authors.
Journal of Clinical Psychiatry | Year: 2014

Objective: Treatment with antipsychotic drugs is widely associated with metabolic side effects such as weight gain and disturbed glucose metabolism, but the pathophysiologic mechanisms are unclear. Method: Fifty nondiabetic (fasting plasma glucose ≤ 7.0 mmol/L), antipsychotic-treated male patients (ICD-10 diagnosis code F20, F21, F22, F25, F28, or F60; mean±SD age = 33.0±6.7 years; body mass index [BMI; kg/ m2] = 26.0±4.7; waist circumference = 95.9±13.3 cm; glycated hemoglobin A1c [HbA1c] = 5.7%±0.3%) and 93 age- and waist circumference-matched healthy male controls (age = 33±7.3 years; BMI = 26.1±3.9; waist circumference = 94.6±11.9 cm; HbA1c = 5.7%±0.3%) participated in this cross-sectional study. Blood was sampled in the fasting state and 90 minutes after ingestion of a standardized liquid meal (2,268 kJ). The primary outcomes were glucometabolic hormones and cardiovascular risk markers. Data were collected between March 2008 and February 2010. Results: Compared to healthy controls, patients were characterized by elevated fasting levels of proinsulin, C-peptide, and glucose-dependent insulinotropic polypeptide (GIP) (P < .05) and higher postprandial levels of insulin, proinsulin, C-peptide, and GIP (P ≤ .02). Also, patients exhibited elevated plasma levels of C-reactive protein and signs of dyslipidemia. Fasting plasma levels of insulin, glucagon, glucagon-like peptide-1 (GLP-1), ghrelin, leptin, adiponectin, tumor necrosis factor-α, plasminogen activator inhibitor-1, and interleukin-6 and postprandial levels of glucagon, GLP-1, ghrelin, leptin, and adiponectin did not differ between groups. Conclusions: Presenting with an insulin resistant-like pattern, including beta cell hypersecretion and elevated GIP levels, nondiabetic antipsychotic-treated patients display emerging signs of dysmetabolism and a compromised cardiovascular risk profile. The appetite-regulating hormones GLP-1 and ghrelin appear not to be influenced by antipsychotic treatment. Our findings provide new clinical insight into the pathophysiology associated with metabolic side effects of antipsychotic treatment and put emphasis on the importance of implementing metabolic screening into psychiatric practice. © Copyright 2014 Physicians Postgraduate Press, Inc. Source

News Article
Site: http://www.nature.com/nature/current_issue/

No statistical methods were used to predetermine sample size. Patient recruitment, enrolment and processing. Patients with T2D were either recruited from the Inter99 study population24 or from the out-patient clinic at Steno Diabetes Center, Gentofte, Denmark. Patients with known T2D were included if the patient had clinically defined T2D on the day of examination according to the WHO definition25. Inclusion criteria were fasting serum C-peptide above 200 pmol l−1 and negative testing for serum glutamic acid decarboxylase (GAD) 65 antibodies (to exclude T1D, latent autoimmune diabetes in adults), no secondary forms of diabetes like chronic pancreatitis diabetes or syndromic diabetes, no antibiotic treatment 2 months before inclusion, and no known gastro-intestinal diseases, no previous bariatric surgery or medication known to affect the immune system. All patients with T1D were recruited from the out-patient clinic at Steno Diabetes Center, Gentofte, Denmark (n = 31). Inclusion criteria were dependence on insulin treatment from time of diagnosis, fasting serum C-peptide below 200 pmol l−1, glycated haemoglobin (HbA1c) above 8.0% (64 mmol l−1) to ensure current hyperglycaemia, T1D duration and dependence on insulin treatment > 5 years, no antibiotic treatment at least 2 months before inclusion, and no known gastrointestinal diseases. All study participants were of North European ethnicity. The study participants were examined on 2 days that were approximately 14 days apart. On the first day, study participants were examined after an over-night fast. Height was measured without shoes to the nearest 0.5 cm, and weight was measured without shoes and wearing light clothes to the nearest 0.1 kg. Hip and waist circumference was measured using a non-expandable measuring tape to the nearest 0.5 cm. Waist circumference was measured midway between the lower rib margin and the iliac crest. Hip circumference was measured as the largest circumference between the waist and the thighs. Blood pressure was assessed while the participant was lying in an up-right position after at least 5 min of rest using a cuff of appropriate size (A&D, UA-787 plus digital or A&D, UA-779). Blood pressure was measured at least twice and the average of the measurements was calculated. On the second day of examination, all participants provided a stool sample which was immediately frozen after home collection and stored at −80 °C. Information on medication status was obtained by questionnaire and interview on the first day of examination. Of the 75 T2D patients, 10 patients (13%) received no hyperglycaemic medications and 58 patients (77%) received the biguanide metformin; of these 75 TD2 patients, 28 patients (37%) received metformin as the only anti-hyperglycaemic medication, 10 patients (13%) received sulfonylurea alone or in combination with metformin, 14 patients (19%) received a combination of oral antidiabetic drugs and insulin treatment and 4 patients (5%) were on insulin treatment only. Eleven patients (15%) received dipeptidyl peptidase-4 (DPP4) inhibitors or glucagon-like peptide-1 (GLP1), all of them in combination with metformin. Patients were reported as receiving anti-hypertensive treatment if at least one of the following drugs was reported: spironolactone, thiazides, loop diuretics, beta blockers, calcium channel blockers, moxonidine or drugs affecting the renin–angiotensin system (n = 55 for T2D (73%) and n = 23 (74%) for T1D). Patients receiving statins, fibrates and/or ezetimibe were reported as receiving lipid-lowering medication (n = 56 for T2D (75%; all on statin treatment), and n = 24 for T1D (77%; 74% on statin treatment)). All T1D patients were on insulin treatment as their only blood glucose lowering treatment. All biochemical analyses were performed on blood samples drawn in the morning after an over-night fast of at least 10 h. Plasma glucose was analysed by a glucose oxidase method (Granutest, Merck) with a detection limit of 0.11 mmol l−1 and intra- and interassay coefficients of variation (CV) of <0.8% and <1.4%, respectively. HbA1c was measured on G7 HPLC Analyzer (Tosoh) by ion-exchange high-performance liquid chromatography. Serum C-peptide was measured using a time-resolved fluoroimmunoassay with the AutoDELFIA C-peptide kit (PerkinElmer, Wallac), with a detection limit of 5 pmol l−1 and intra- and interassay CV of <4.7% and <6.4%, respectively. Serum insulin (excluding des and intact proinsulin) was measured using the AutoDELFIA insulin kit (PerkinElmer, Wallac) with a detection limit of 3 pmol l−1 and with intra- and interassay CV of <3.2% and <4.5%, respectively. Plasma cholesterol, plasma high-density lipoprotein cholesterol and plasma triglycerides were all measured on Vitros 5600 using reflect-spectrophotometrics. Plasma low-density lipoprotein cholesterol was calculated using Friedewald’s equation. Blood leukocytes and white blood cell differential count were measured on Sysmex XS 1000i using flow cytometrics. Plasma metformin was determined by high performance liquid chromatography followed by tandem mass spectrometry. Briefly, the proteins were precipitated with acetonitrile containing the deuterated internal standard, metformin-d6, hydrochloride and the supernatant diluted by acetonitrile. The analysis was performed on a Waters Acquity UPLC I-class system connected to a Xevo TQ-S tandem mass spectrometer in electrospray positive ionization mode. Separation was achieved on a Waters XBridgeT BEH Amide 2.5-μm column and gradient elution with 100 mM ammonium formate (pH 3.2), and with acetonitrile. The multiple reaction monitoring transitions used for metformin and metformin-d6 were 130.2 > 71.0 and 136.2 > 60.0. Calibrators were prepared by spiking drug-free serum with metformin to a concentration of 2,000 ng ml−1. B12 was measured using Vitros Immunodiagnostic Products. GAD65 was measured on serum samples by a sandwich ELISA (RSR ltd.). Inter- and intra-assay CV were < 16.6% and < 6.7% respectively, and with a detection limit of 0.57 Uml−1. Stool samples were obtained at the homes of each participant and samples were immediately frozen by storing them in their home freezer. Frozen samples were delivered to Steno Diabetes Center using insulating polystyrene foam containers, and then they were stored at −80 °C until analysis. The time span from sampling to delivery at the Steno Diabetes Center was intended to be as short as possible and no more than 48 h. A frozen aliquot (200 mg) of each faecal sample was suspended in 250 μl of guanidine thiocyanate, 0.1 M Tris, pH 7.5, and 40 μl of 10% N-lauroylsarcosine. Microbial DNA extraction was then performed as previously described12. The DNA concentration and its molecular size were estimated using nanodrop (Thermo Scientific) and agarose gel electrophoresis. Already available Danish metagenomic samples were those reported in ref. 26 and references therein (excluding 14 samples removed due to average read length below 40 nucleotides, and with 5 Chinese and 21 Swedish samples with less than the rarefaction threshold of 7 million reads in total excluded from functional profile or diversity analyses), with newly sequenced samples deposited in the European Bioinformatics Institute Sequence Read Archive under accession ERP004605. All information on Swedish samples was retrieved from previously published data4. In addition to published data on Chinese individuals3, we retrieved information on metformin treatment in a subset of 71 Chinese T2D patients. One-hundred and twelve samples from ref. 3 lacked metformin treatment metadata and were therefore discarded, except for measuring differences between the country data sets disregarding treatment or diabetic status. Characteristics of all study participants included in the present protocol are given in Supplementary Table 1. Additional Danish T2D patients were recruited at the Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen throughout 2014 as a part of the ongoing MicrobDiab study (http://metabol.ku.dk/research-project-sites/microbdiab/). T2D patients were included in the study if the time of T2D diagnosis was less than 5 years ago, they were between 35 and 75 years of age, Caucasian and they had not received antibiotics within the past 4 months of inclusion. In total, 30 T2D patients (21 male and 9 female) were identified. Faecal samples were collected at the home of the patients, followed by immediate freezing of samples in home freezers, and transport of samples to the hospital stored on dry ice. The samples were stored at −80 °C until DNA extraction. Information of medication was obtained from questionnaires. In total, 21 (70%) of the T2D patients received metformin. All individuals in both the Danish MetaHIT study and the Danish validation study gave written informed consent before participation in the studies. Both studies were approved by the Ethical Committees of the Capital Region of Denmark (MetaHIT study: HC-2008-017; validation study: H-3-2013-102). Both studies were conducted in accordance with the principles of the Declaration of Helsinki. Illumina shotgun sequencing was applied to DNA extracted from 620 faecal samples originating from the MetaHIT project (Supplementary Table 1). Raw sequencing data were processed using the MOCAT (version 1.1) software package27. Reads were trimmed (option read_trim_filter) using a quality and length cut-off of 20 and 30 bp, respectively. Trimmed reads were subsequently screened against a custom database of Illumina adapters (option screen_fastafile) and the human genome version 19 using a 90% identity cut-off (option screen). The resulting high-quality reads were assembled (option assembly) and assemblies revised (option assembly revision). Genes were predicted on scaftigs with a minimum length of 500 bp (option gene_prediction). Predicted protein-coding genes with a minimum length of 100 bp were clustered at 95% sequence identity using Cd-hit (version 4.6.1)28 with parameters set to: -c 0.95, -G 0 -aS 0.9, -g 1, -r 1. The representative genes of the resulting clusters were ‘padded’ (that is, extended up to 100 bp at each end of the sequence using the sequence information available from the assembled scaftigs), resulting in the final reference gene catalogue used in this study. The reference gene catalogue was functionally annotated using SmashCommunity29 (version 1.6) after aligning the amino acid sequence of each gene to the KEGG30 (version 62) and eggNOG31 (version 3) databases. Raw insert (sequenced fragments of DNA represented by single or paired-end reads) count profiles were generated using MOCAT27 by mapping high-quality reads from each metagenome to the reference gene catalogue (option screen) using an alignment length and identity cut-off of 45% and 95%, respectively. For each gene, the number of inserts that matched the protein-coding region was counted. Counts of inserts that mapped with the same alignment score to multiple genes were distributed equally among them. Taxonomic abundances were computed at the level of metagenomic operational taxonomic units (mOTUs)32, normalized to the length of the concatenated marker genes for each mOTU to yield the abundances used for the study, and subsequently binned at broader taxonomic levels (genus, family, class, etc.). For all metagenome-derived measures except the mOTU taxonomic assignments, read counts were ‘rarefied’ in order to avoid any artefacts of sample size on low-abundance genes. Rarefied matrices were obtained as follows. Data matrices were rarefied to 7 million reads per sample. This threshold was chosen to include most samples, but 5 Chinese and 21 Swedish samples were excluded due to having less than 7 million reads per sample. Rarefactions were performed using a C++ program developed for the Tara project33. In total we performed 30 repetitions, and in each of these we measured the richness, evenness, chao1 and Shannon diversity metrics within a rarefaction. The median value of these was taken as the respective diversity measurement for each sample. The first of 30 rarefactions of each sample were used to create a rarefied gene abundance matrix and KEGG orthologue abundance profiles were calculated by summing the rarefied abundance of genes annotated to the respective KEGG orthologue gene. Clustering of the catalogue genes by co-abundance, as described in ref. 34, defined 10,754 co-abundance gene groups (CAGs) with very high correlations (Pearson correlation coefficient > 0.9). The 925 largest of these, with more than 700 genes, were termed metagenomic species (MGS). The abundance profiles of the CAGs and MGSs were determined as the medium gene abundance (downsized to 7 million reads per sample) throughout the samples. Furthermore, the CAGs and MGS were taxonomically annotated by sequence similarity to known reference genomes. To avoid drawing false conclusions about gut microbial functions from high abundance of single genes remotely homologous to members of a functional pathway, we used an approach that required presence of multiple pathway members. Functional pathway abundance was calculated from gene catalogue KEGG orthologue annotation and MGS abundances per sample. Thus KEGG orthologues present in each MGS were used to determine for that CAG/MGS which functional modules were represented within its genetic repertoire. This required that >90% of KEGG orthologues necessary for the completion of a reaction pathway should be present, when also taking alternative enzymatic pathways into account. The module abundance within a sample was calculated from CAG abundance in each respective sample, summing over all CAGs which had the module present. Rarefied median coverages of CAG/MGS were used, so no further normalization of the module abundance matrix was required. Abundance of genetic potential falling under the same higher-order functional levels was calculated by summing up all abundances of the lower-level functional modules within each sample. Existing functional annotation databases cover gut metabolic pathways relatively poorly. To account for this, a number of additional bacterial gene functional modules were curated and annotated, extending the KEGG system; these are referred to in result tables as GMMs (gut microbial modules) and were previously described in ref. 12. 16S amplicons from frozen samples were sequenced 300 bp and 200 bp paired-end reads using an Illumina miSeq machine. We used the LotuS35 pipeline in short amplicon mode with default quality filtering, clustering and denoising operational taxonomic units (OTUs) with UPARSE36, removing chimaeric OTUs against the RDP reference database (http://drive5.com/uchime/rdp_gold.fa) with uchime37, merging reads with FLASH38 and assigning a taxonomy against the SILVA 119 rRNA database39, and further refined by BLAST searches against the NCBI rRNA database40 to identify Intestinibacter OTUs, using the following LotuS command line options: ‘-p miSeq -refDB SLV -doBlast blast -amplicon_type SSU -tax_group bacteria -derepMin 2 -CL 2 -thr 14’. Microbial taxa where mean abundance over all samples was less than 30 reads, or that were present in less than 3 samples, were excluded from univariate and classifier analyses. All abundances were normalized by total sample sum. For module tables, no feature filters were used except requiring the module to be present in at least 20 samples. Filtered data tables were made available online (http://vm-lux.embl.de/~forslund/t2d/). Univariate testing for differential abundances of each taxonomic unit between two or more groups was tested using Mann–Whitney-U or Kruskal–Wallis tests, respectively, corrected for multiple testing using the Benjamini–Hochberg false discovery rate control procedure (Q values)41. Post-hoc statistical testing for significant differences between all combinations of two groups was conducted only for taxa with abundances significantly different at P < 0.2. Wilcoxon rank-sum tests were calculated for all possible group combinations and corrected for multiple testing again using the Benjamini–Hochberg false discovery rate, as implemented in R. When controlling for potential confounders such as source study, we used blocked ‘independence_test’ function calls with options ‘ytrafo = rank, teststat=scalar’ for blocked WRST and ‘ytrafo = rank, teststat=quad’ for blocked Kruskal–Wallis test, as implemented in the COIN software package42 for R. Similarly, we applied these independence tests in the framework of post-hoc testing as described above. Analysis of correlations between taxonomic or functional features, community diversity indices and sample metadata variables were conducted using Spearman correlation tests as implemented in R, and corrected for multiple tests using the Benjamini–Hochberg false discovery rate control procedure. To control for confounders such as source study in univariate correlation analyses, blocked Spearman tests as implemented in COIN (settings ‘independence_test’, options ytrafo = rank, xtrafo = rank, distribution = asymptotic) were used. In some analyses, taxa were corrected for the influence of a continuous confounder variable such as microbial community richness; in these cases, the residual of a linear model between normalized log-transformed taxa abundances and overall sample gene richness was used to correct for the confounding variable. Power analysis was conducted by randomly subsampling to a given sample number, repeated 5 times to achieve robust results. All ordinations (NMDS, dbRDA) and subsequent statistical analyses were calculated using the R package vegan43 using Canberra distances on normalized taxa abundance matrices, then visualized using the ggplot2 R package44. Community differences were calculated using a permutation test on the respective NMDS reduced feature space, as implemented in vegan. Furthermore, we calculated intergroup differences for the microbiota using PERMANOVA45 as implemented in vegan. This test compares the intragroup distances to the intergroup distances in a permutation scheme and from this calculates a P value. For all PERMANOVA tests, we used 2 × 105 randomizations and a normalized genus-level mOTU abundance matrix, using Canberra intersample distances. PERMANOVA post-hoc P values were corrected for multiple testing using the Benjamini–Hochberg false discovery rate control procedure. Analysis of variance broken down by cohort, treatment and disease status was conducted by fitting these distances to a linear model of sample metadata distances, as further described in Supplementary Discussion 3.2. To create classifiers for separating samples from different subsets, an L1 restricted LASSO using the R glmnet package46 was carried out to test for an optimal value of lambda (number of features to be used in the final predictor) in a fivefold cross-validated and internally fourfold cross-validated LASSO run on all data. After this, the previously determined value of lambda was manually controlled for number of features used against the root mean square error of the classifier. In a fivefold cross-validation, an independent LASSO classifier was trained on 4/5 of the data using the previously determined value of lambda, and response values were predicted on 1/5 of the data. LASSO models with a Poisson response type were used in all cases. Binary classifications between T2D and ND control samples were performed with an R reimplementation of the robust recursive feature elimination support vector machine (rRFE-SVM)47 procedure. The SVM was performed in an outer cross-validation scheme on 4/5 of the data. Of these, 90% were randomly selected 200 times in each cross-validation for the RFE, to create a feature ranking from an average over these runs. Classifier performance was validated on the remaining 1/5 of samples using the pre-established feature ranking. In case of several cohorts, the area under the receiver operating characteristic curve (ROC-AUC) scores were measured for each cohort separately. The MGS technology has previously been described34 and is available online (http://git.dworzynski.eu/mgs-canopy-algorithm/wiki/Home). The mOTU resource has been made publically available (http://www.bork.embl.de/software/mOTU/) and was analysed using MOCAT27 which is also publically available (http://vm-lux.embl.de/~kultima/MOCAT/). The 16S pipeline LotuS35 is freely available online (http://psbweb05.psb.ugent.be/lotus). The novel gene catalogue has been deposited online (http://vm-lux.embl.de/~kultima/share/gene_catalogs/620mhT2D/), as have the raw amplicon sequences (http://vm-lux.embl.de/~forslund/t2d/). Statistical analysis and data visualization was conducted using freely available R libraries: vegan, COIN and ggplot2 and is described in more details elsewhere48, 49. Data matrices and R source code for replicating the central tests conducted on the data have been deposited online (http://vm-lux.embl.de/~forslund/t2d/). A subset of the Danish study participants answered a validated food frequency questionnaire in order to obtain information on the habitual dietary habits. A complete data set was obtained for 66% of the nondiabetic individuals and 88% of T2D patients. When evaluating the dietary data, the consumed quantity was determined by multiplying portion size by the corresponding consumption frequency reported. Standard portion sizes for women and men, separately, were used in this calculation50, 51. All food items in the questionnaire were linked to food items in the Danish Food Composition Databank52. Estimation of daily intake of macro- and micronutrients for each participant was based on calculations in the software program FoodCalc version 1.353.

Persson F.,Steno Diabetes Center | Rossing P.,University of Aarhus | Rossing P.,Center for Basic Metabolic Research | Parving H.-H.,University of Aarhus | Parving H.-H.,Copenhagen University
Journal of Hypertension | Year: 2013

OBJECTIVE: Urinary levels of renin-angiotensin-aldosterone system (RAAS) components may reflect renal RAAS activity and/or the renal efficacy of RAAS inhibition. Our aim was to determine whether urinary angiotensinogen and renin are circulating RAAS-independent markers during RAAS blockade. METHODS: Urinary and plasma levels of angiotensinogen, renin, and albumin were measured in 22 patients with type 2 diabetes, hypertension, and albuminuria, during 2-month treatment periods with placebo, aliskiren, irbesartan, or their combination in random order in a crossover study. RESULTS: Aliskiren and irbesartan both increased plasma renin 3-4-fold, and above 10-fold when combined. Irbesartan decreased plasma angiotensinogen by approximately 25%, and no changes in plasma angiotensinogen were observed during the combination. Urine contained aliskiren at micromolar levels, blocking urinary renin by above 90%. Both blockers reduced urinary angiotensinogen, significant for irbesartan only. Combination blockade reduced urinary angiotensinogen even further. Reductions in urinary angiotensinogen paralleled albuminuria changes, and the urine/plasma concentration ratio of angiotensinogen was identical to that of albumin under all conditions. In contrast, urinary renin did not follow albumin, and remained unaltered after all treatments. Yet, the urine/plasma concentration ratio of renin was more than 100-fold higher than that of angiotensinogen and albumin, and approximately 4-fold reduced by single RAAS blockade, and more than 10-fold by dual RAAS blockade. CONCLUSIONS: Aliskiren filters into urine and influences urinary renin measurements. The urine/plasma renin ratio, but not urinary renin alone, may reflect the renal efficacy of RAAS blockade. Urinary angiotensinogen is a marker of filtration barrier damage rather than intrarenal RAAS activity. © 2013 Wolters Kluwer Health / Lippincott Williams & Wilkins. Source

Bak M.J.,Center for Basic Metabolic Research | Bak M.J.,Copenhagen University | Albrechtsen N.W.,Center for Basic Metabolic Research | Pedersen J.,Center for Basic Metabolic Research | And 7 more authors.
European Journal of Endocrinology | Year: 2014

Aim: To determine the specificity and sensitivity of assays carried out using commercially available kits for glucagon and/or oxyntomodulin measurements. Methods: Ten different assay kits used for the measurement of either glucagon or oxyntomodulin concentrations were obtained. Solutions of synthetic glucagon (proglucagon (PG) residues 33-61), oxyntomodulin (PG residues 33-69) and glicentin (PG residues 1-69) were prepared and peptide concentrations were verified by quantitative amino acid analysis and a processing-independent in-house RIA. Peptides were added to the matrix (assay buffer) supplied with the kits (concentration range: 1.25-300 pmol/l) and to human plasma and recoveries were determined. Assays yielding meaningful results were analysed for precision and sensitivity by repeated analysis and ability to discriminate low concentrations. Results and conclusion: Three assays were specific for glucagon (carried out using the Millipore (Billerica, MA, USA), Bio-Rad (Sundbyberg, Sweden), and ALPCO (Salem, NH, USA) and Yanaihara Institute (Shizuoka, Japan) kits), but none was specific for oxyntomodulin. The assay carried out using the Phoenix (Burlingame, CA, USA) glucagon kit measured the concentrations of all three peptides (total glucagon) equally. Sensitivity and precision were generally poor; the assay carried out using the Millipore RIA kit performed best with a sensitivity around 10 pmol/l. Assays carried out using the BlueGene (Shanghai, China), USCN LIFE (Wuhan, China) (oxyntomodulin andglucagon),MyBioSource (San Diego, CA, USA)and Phoenix oxyntomodulin kits yielded inconsistent results. © 2014 European Society of Endocrinology Printed in Great Britain. Source

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