MD Marcella Rietschel
email: marcella.rietschel@zi-mannheim.de

Manuel Mattheisen

Franziska Degenhardt

Thomas W Mühleisen

Peter Kirsch

Christine Esslinger

Stefan Herms

Ditte Demontis

Michael Steffens

Jana Strohmaier

Britta Haenisch

René Breuer

Piotr M Czerski

Ina Giegling

Eric Strengman

Christine Schmael

Ole Mors

Preben Bo Mortensen

David M Hougaard

Torben Ørntoft

Paweł Kapelski

Lutz Priebe

F Buket Basmanav

Andreas J Forstner

Sandra Meier

Susanne Moebus

Michael Alexander

Rainald Mössner

H.-Erich Wichmann

Stefan Schreiber

Fernando Rivandeneira

Albert Hofman

Andre G Uitterlinden

Thomas F Wienker

Johannes Schumacher

Joanna Hauser

Wolfgang Maier

Rita M Cantor

Susanne Erk

Thomas G Schulze 25

Sgene-Plus Consortium

Nick Craddock

Michael J Owen

Michael C O'donovan

Anders D Børglum

Dan Rujescu

Henrik Walter

Andreas Meyer-Lindenberg

Markus M Nöthen

Roel A Ophoff

PhD Sven Cichon
email: sven.cichon@uni-bonn.de.

Association between genetic variation in a region on chromosome 11 and schizophrenia in large samples from Europe Running Title: Variation in AMBRA1 conferring risk of schizophrenia

Keywords: genome-wide association study, GWAS, schizophrenia, common variation, imaging genetics

Recent molecular studies have implicated common alleles of small to moderate effect and rare alleles with larger effect sizes in the genetic architecture of schizophrenia (SCZ). It is expected that the reliable detection of risk variants with very small effect sizes can only be achieved through the recruitment of very large samples of patients and controls (i.e. tens of thousands) , or large, potentially more homogeneous samples that have been recruited from confined geographical areas using identical diagnostic criteria. Applying the latter strategy, we performed a genome-wide association study (GWAS) of 1,169 clinically well-characterized and ethnically homogeneous SCZ patients from a confined area of Western Europe (464 from Germany, 705 from The Netherlands) and 3,714 ethnically matched controls (1,272 and 2,442, respectively).

In a subsequent follow-up study of our top GWAS results, we included an additional 2,569 SCZ patients and 4,088 controls (from Germany, The Netherlands, and Denmark). Genetic variation in a region on chromosome 11 that contains the candidate genes AMBRA1, DGKZ, CHRM4, and MDK was significantly associated with SCZ in the combined sample (n= 11,540; P= 3.89 x 10 -9 , OR= 1.25). This finding was replicated in 23,206 independent samples of European ancestry (P= 0.0029, OR= 1.11). In a subsequent imaging genetics study, healthy carriers of the risk allele exhibited altered activation in the cingulate cortex during a cognitive control task. The area of interest is a critical interface between emotion-regulation and cognition that is structurally and functionally abnormal in SCZ and bipolar disorder.

Introduction

Schizophrenia (SCZ) is a severe psychiatric disorder characterized by fundamental and characteristic distortions of thought and perception. It has a life-time prevalence of around 0.5-1%. Formal genetic studies have shown that genetic factors contribute substantially to the disease risk. Meta-analyses of pooled data from 12 twin studies have estimated that susceptibility to SCZ has a heritability of approximately 80% [START_REF] Sullivan | Schizophrenia as a complex trait: evidence from a metaanalysis of twin studies[END_REF] .

Recent molecular studies have implicated common alleles of small to moderate effect and rare alleles with larger effect sizes in the genetic architecture of SCZ 2 . Genomewide association studies (GWAS) using Single Nucleotide Polymorphism (SNP) array technologies have been applied to detect common risk alleles, and a total of 12 GWAS of SCZ have been published to date (Table S3). Common risk alleles in the major histocompatibility (MHC) region on chromosome 6 have so far shown the most statistically significant evidence of association [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF][START_REF] International | Common polygenic variation contributes to risk of schizophrenia and bipolar disorder[END_REF][START_REF] Shi | Common variants on chromosome 6p22.1 are associated with schizophrenia[END_REF] . Interestingly, the MHC region, which is involved in the immune response, has long been postulated to harbor variants conferring a risk for SCZ since there is evidence for linkage in this region [START_REF] Lewis | Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia[END_REF] and research has suggested the involvement of infection in disease development [START_REF] Brown | Prenatal infection and schizophrenia: a review of epidemiologic and translational studies[END_REF] .

Genome-wide significance has also been reported for risk alleles at TCF4 3 , NGRN [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF] , and ZNF804A [START_REF] O'donovan | Identification of loci associated with schizophrenia by genome-wide association and followup[END_REF] .

All GWAS of SCZ performed to date have indicated that the strongest common genetic risk factors have odds ratios that are no greater than 1.15-1.20. In fact, recent molecular genetic evidence points to a substantial polygenic component to the risk of SCZ that involves a large number of common risk alleles of very small effect [START_REF] International | Common polygenic variation contributes to risk of schizophrenia and bipolar disorder[END_REF] . It is likely that the reliable detection of risk variants with very small effects can only be achieved through the study of very large samples of patients and controls (i.e.tens of thousands) or large, potentially more homogeneous samples that have been recruited from a confined geographical area using the same diagnostic criteria. Using the latter strategy, we performed a GWAS of clinically well-characterized and ethnically homogeneous SCZ patients recruited from a confined area of Western Europe (Germany, The Netherlands, and Denmark). Our results suggest that genetic variation in a region on chromosome 11, which contains the candidate genes AMBRA1, DGKZ, CHRM4, and MDK, is implicated in the etiology of SCZ.

Patients and Methods

The following section provides details of sample recruitment and quality control for the GWAS dataset. Information concerning specific aspects of the GWAS dataset, and data from the follow-up samples is provided in the SI Patients and Methods.

Sample Ascertainment and Selection for the GWAS sample

All participating individuals provided written informed consent. The study protocols were approved by the respective institutional review boards or ethics committees.

German Sample (Bonn -Mannheim).

The German SCZ patients used in the GWAS step (n= 487) were recruited from consecutive hospital admissions and were all of German descent. Lifetime best estimate diagnoses were assigned according to DSM-IV criteria on the basis of multiple sources of information including structured interviews with the SCID [START_REF] First | Structured clinical interview for DSM-IV axis I disorders[END_REF] or SADS-L 10 , the OPCRIT [START_REF] Mcguffin | A polydiagnostic application of operational criteria in studies of psychotic illness. Development and reliability of the OPCRIT system[END_REF] (i) Schleswig-Holstein (Northern Germany); (ii) Essen, Bochum, and Mühlheim (Ruhr area); and (iii) Augsburg (Southern Germany), respectively. Ethnicity was assigned to patients and controls on the basis of self-reported ancestry.

Dutch Sample (Utrecht and Rotterdam

). Inpatients and outpatients (n= 804) were recruited from various psychiatric hospitals and institutions throughout the Netherlands. Detailed medical and psychiatric histories were collected, and this process included use of the Comprehensive Assessment of Symptoms and History (CASH) [START_REF] Andreasen | The Comprehensive Assessment of Symptoms and History (CASH). An instrument for assessing diagnosis and psychopathology[END_REF] , an instrument for assessing diagnosis and psychopathology. Only those patients with a DSM-IV diagnosis of SCZ were finally included as cases. The controls from Utrecht (n= 704) were volunteers with no history of psychiatric disorder. The Rotterdam control individuals (n= 2302) were drawn from a large population-based project on the genetics of complex traits and diseases which is financed by the Dutch government through the Netherlands Scientific Organization -Large Investments (NWO Groot; 175.010.2005.011). This prospective population-based cohort study of chronic disabling conditions in Dutch individuals aged 55 years and above is described elsewhere [START_REF] Hofman | The Rotterdam Study: objectives and design update[END_REF][START_REF] Hofman | Determinants of disease and disability in the elderly: the Rotterdam Elderly Study[END_REF] . All patients and controls had at least 3 grandparents of Dutch ancestry.

Sample ascertainment and Selection for the Imaging Genetics Study sample

Subjects (n= 122) were drawn from an ongoing large-scale multicenter imaging genetics study [START_REF] Esslinger | Neural mechanisms of a genome-wide supported psychosis variant[END_REF]

Genotyping and Quality Control for the GWAS and the Imaging Genetics Study

Ethylenediaminetetraacetic acid anti-coagulated venous blood samples were collected from all participating individuals (GWAS and imaging genetics study). Lymphocyte DNA was isolated by salting-out [START_REF] Miller | A simple salting out precedure for extracting DNA from Human nucleated cells[END_REF] with saturated sodium chloride solution or by a Chemagic Magnetic Separation Module I (Chemagen, Baesweiler, Germany) used according to the manufacturer's recommendations. The GWAS dataset was assembled from 7 sub-datasets which were genotyped by: (i) Illumina's customer service, San We developed a protocol of filters for the stringent quality control (QC) of wholegenome and sub-whole genome datasets. This accounted for call rates (CR), heterozygosity, cross-contamination, population stratification, relatedness, nonrandom-missingness, Hardy-Weinberg equilibrium (HWE), minor allele frequency (MAF), and others. With the exception of the cluster plot investigations for the follow-up SNPs, each QC filter for the GWAS, the imaging genetics study, and the follow-up datasets was performed using the PLINK toolset [START_REF] Purcell | PLINK: a tool set for whole-genome association and population-based linkage analyses[END_REF] . The samples from the Rotterdam study (controls for the GWAS and the replication step) underwent QC together with the other GWAS samples.

The QC protocol was applied to a total of 1,291 patients and 4,367 controls for the GWAS step. As a result, 375 individuals including 122 patients and 253 controls (representing 6.70%, 9.73%, and 5.80%, of the sample) as well as 86,037 SNPs (15.32% of the HumanHap550v3 BeadArray content) were excluded prior to the association analysis. The final GWAS data set was comprised of 464,030 autosomal (including PAR1+2) and 11,397 X-chromosomal SNPs, genotyped in 1,169 SCZ patients and 3,714 controls. An additional 400 controls were added to the Dutch follow-up dataset described above.

The QC protocol was also applied to the 122 healthy German individuals who participated in the imaging genetics study. As a result, one individual and 39,356 SNPs (6.64% of the Human610-quad BeadArray content of evaluable 592,532 SNPs) were excluded prior to the association analyses. The mean age of the remaining 121 individuals (43.4 % males) was 33.1 (SD 10.3). For the analysis of the imaging genetics data, only information for rs11819869, which was present in the post QC dataset, was extracted and analyzed.

A detailed description of the QC filters is provided in the SI Patients and Methods.

Statistical analyses for GWAS and Follow-Up

All association analyses were performed using PLINK [START_REF] Purcell | PLINK: a tool set for whole-genome association and population-based linkage analyses[END_REF] (v1.07). In the single-marker analysis, all autosomal (including PAR1 and PAR2) and gonosomal SNPs that passed QC checks were tested for association with SCZ using the Cochran-Mantel-Haenszel test (CMH) with 2 x 2 x K stratified tables {disease x SNP disease | cluster} with K= 2 (assigning German and Dutch individuals to one cluster each) for the GWAS step, K= 4 (one cluster for the German, Dutch, and Danish samples, respectively) for Replication 1, and K= 6 for the combined analysis. Following analysis of the initial GWAS, a set of 60 SNPs was chosen for the follow-up step using a top-down approach (Replication 1; more information on the selection of SNPs is provided in the SI Patients and Methods). The CMH was also applied to the replication sample as described above. In the replication analysis, the test statistic was one-tailed if the OR was in the same direction as in the GWAS. SNPs that exceeded the threshold for nominal significance (P= 0.05) were identified. Finally, for all SNPs included in the replication step, a combined analysis of individuals from the GWAS and the replication step was performed using the CMH (K= 6; test statistic always two-tailed).

The Breslow-Day test was used to investigate the homogeneity of the odds ratios for the replicated SNPs. The meta-analysis for the 15 independent samples of European ancestry (Replication 2) was performed using PLINK [START_REF] Purcell | PLINK: a tool set for whole-genome association and population-based linkage analyses[END_REF] (v1.07) and considering a fixed effect model. There was no evidence of heterogeneity (Cochrane's Q). In cases where genome-wide data was available, the two-tailed P-value of the TREND test was corrected by the genomic inflation factor for the specific sample as calculated from the genome-wide data.

A note on possible stratification

We have assumed that our sample is characterized by a high degree of ethnic (and presumably genetic) homogeneity. Nonetheless, several steps were taken to minimize population stratification. Patients and controls were only included when a minimum of 3 grandparents originated from the respective country. Outliers were identified using 2 methods: 1) multidimensional scaling (MDS), and 2) outlier detection diagnostics as implemented in PLINK [START_REF] Purcell | PLINK: a tool set for whole-genome association and population-based linkage analyses[END_REF] . This was performed for the Dutch and German samples individually, and then in the combined sample (Figure S6

Imaging Genetics Study

The participants of the imaging genetics study completed a battery of six cognitive tasks that have been used and validated previously in imaging genetics. Specifically, the following tasks were performed: (1) an associative learning task [START_REF] Erk | Altered neural function in carriers of a bipolar disorder variant with genome-wide support[END_REF] , in which subjects learn and are then tested on face-job associations, (2) an n-back working memory task [START_REF] Callicott | Functional magnetic resonance imaging brain mapping in psychiatry: methodological issues illustrated in a study of working memory in schizophrenia[END_REF] , in which subjects are presented with a sequence of numbers and press a button corresponding either to the number currently seen (control condition, '0back) or the number seen two presentations previously ('2-back'), (3) a theory of mind task [START_REF] Walter | Effects of a genome-wide supported psychosis risk variant on neural activation during a theory-of-mind task[END_REF] , in which subjects have to make inferences about the state of mind of a human based on a series of cartoons, (4) a flanker task [START_REF] Meyer-Lindenberg | Neural mechanisms of genetic risk for impulsivity and violence in humans[END_REF] in which subjects have to perform or withhold a button press depending on a set of either congruent or incongruent stimuli, requiring cognitive control, (5) an implicit emotion recognition task [START_REF] Hariri | The amygdala response to emotional stimuli: a comparison of faces and scenes[END_REF] , in which subjects match pictures of angry and fearful faces, (6) a monetary reward task [START_REF] Kirsch | Anticipation of reward in a nonaversive differential conditioning paradigm and the brain reward system: an eventrelated fMRI study[END_REF] , in which subjects receive or do not receive monetary rewards according to their performance in a reaction time task and a 5 minutes of rest period. Further information, including details of the imaging parameters, is provided in the SI Patients and Methods.

For analysis of the influence of rs11819869, contrast images of incongruent > congruent trials were subjected to a two sample t-test with age, gender, and scanning site as covariates of no interest. To avoid small cell sizes, CT heterozygotes and TT homozygotes were combined and compared with the CC homozygous group.

In view of our exploratory approach, we used a significant threshold of P< 0.0083 (equivalent to P< 0.05 with Bonferroni correction for 6 tasks), correcting for multiple comparisons across all voxels of the brain using family-wise error (FWE), based on Gaussian Random Fields theory. Further information is provided in the SI Patients and Methods.

Results

We performed a GWAS of 1,169 clinically well-characterized and ethnically homogeneous SCZ patients who had been recruited from a confined area of Western Europe (n= 464 from Germany, n= 705 from The Netherlands) and 3,714 ethnically matched controls (n= 1,272 and n= 2,442, respectively; Table 1). A total of 475,427 SNPs (Illumina's HumanHap550v3 BeadArray) were tested using the Cochran Mantel Haenszel test (CMH, for 2x2xK stratified tables, Methods section). No marker exceeded the widely acknowledged genome-wide significance threshold of 5 x 10 -8 , or a Bonferroni-corrected significance threshold of 1.1 x 10 -7 . The best result (P GWAS = 4.50 x 10 -7 ) was for the SNP rs11154491, which is located in an intron of the Rho GTPase activating protein 18 gene on chromosome 6 (ARGHAP18, Figure S2). An overview of the GWAS results is provided in Figure 1a.

Under the assumptions that the most significantly associated SNPs included true SCZ susceptibility factors and that the failure to reach genome-wide significance in the GWAS step was a result of insufficient power, we performed a subsequent follow-up analysis (Replication 1). A total of 43 SNPs were selected using a top down P-value approach in 2,569 additional patients and 4,088 controls from Western Europe (Germany/Munich: 913 patients/1,668 controls; Bonn/Mannheim: 600/1,146; The Netherlands: 178/400; Denmark: 878/874; Table 1). Nine of the 43 SNPs (21%) showed nominal significance in the combined replication samples, and all with the same alleles as in the GWAS (Table 2 and Table S1a/b). The strongest evidence for association was found for 4 highly correlated SNPs (rs7112229, rs11819869, rs7130141, and rs12575668). These 4 SNPs are located in intronic regions of the activating molecule in beclin-1-regulated gene (AMBRA1) on chromosome 11. The best SNP (rs11819869) showed P REPLI = 5.04 x 10 -5 (P CORR = 0.0022). One further SNP (rs4309482) withstood Bonferroni correction (P CORR = 0.013) for the number of SNPs tested in the replication step. This SNP is located in an intergenic region of chromosome 18 between the coiled-coil domain containing 68 gene (CCDC68) and the transcription factor 4 gene (TCF4). Our top GWAS SNP (rs11154491), which is located in ARGHAP18 on chromosome 6q22.33, was not replicated (P REPLI = 0.38).

In an analysis of the combined samples (GWAS + follow-up; n= 3,738 patients/ 7,802 controls; CMH, K= 6), variation in the chromosome 11 region surpassed the threshold for genome-wide significance (rs11819869, P COMB = 3.89 x 10 -9 , OR= 1.25, Figure 1b). A Breslow-Day test was performed across all of the analyzed samples for all SNPs that had withstood Bonferroni correction in the replication step. This revealed no significant differences in odds ratios (P > 0.05) between the investigated samples.

In support of this, subtraction of any one individual replication sample failed to alter the effect sizes to any substantial degree (Table S1b).

We then attempted to replicate our genome-wide significant result for rs11819869 in 15 independent samples of European ancestry (Replication 2; 4,734 patients and 18,472 controls). We observed significant association for the T risk allele of rs11819869, as had been observed in the GWAS study (P META = 0.0029, OR= 1.11;

Table 3). We then applied a functional magnetic resonance imaging (fMRI) test battery for imaging genetics in a total of 121 healthy subjects (53 males, 68 females) of German descent with no family history of affective disorder or SCZ. This demonstrated a significant effect of the rs11819869 genotype on medial prefrontal activation during a flanker task which contrasted incongruent and congruent stimuli configurations (P< 0.05, family wise error corrected for multiple testing, Cluster size: K= 9, Maximum activation found at [-3,36,-18], T(116)= 5.522, P CORR < 0.008 (see Patients and Methods for details of the task and the statistics). Carriers of the risk allele showed increased activation during incongruent conditions, whereas homozygote carriers of the C-allele showed decreased activation (Figure 2b).

Discussion

The SNP rs11819869 is located in the gene AMBRA1, in a region of strong LD that spans approximately 360 kb (r 2 > 0.8 based on HapMap Phase 2 CEU data [START_REF]The International HapMap Consortium. A haplotype map of the human genome[END_REF] , Figure S3). Other RefSeq genes in the LD region are KIAA0652/ATG13, CHRM4, DGKZ, MDK, HARBI1, ARGAPH1, and ZNF408. In our initial GWAS, 11 of the 25 most significant SNPs were located in this region. Although this region also showed evidence for association in the GWAS study of the SGENE consortium 3 , it was not among the top findings selected for follow-up [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF] .

AMBRA1 plays a major role in the development of the nervous system and has been reported to be a member of the Autophagy Interaction Network (AIN) [START_REF] Behrends | Network organization of the human autophagy system[END_REF] . In mouse models, AMBRA1 functional deficiency results in severe neural tube defects that are associated with impaired autophagy, accumulation of ubiquitinated proteins, unbalanced cell proliferation, and excessive apoptotic cell death [START_REF] Fimia | Ambra1 regulates autophagy and development of the nervous system[END_REF][START_REF] Cecconi | A novel role for autophagy in neurodevelopment[END_REF] . The gene is highly expressed in the most ventral part of the undifferentiated neural tube during embryogenesis [START_REF] Cecconi | A novel role for autophagy in neurodevelopment[END_REF] . Another gene in the chromosome 11 region of strong LD (KIAA0652/ATG13) is also a member of the AIN [START_REF] Behrends | Network organization of the human autophagy system[END_REF] and acts as a regulator of autophagy [START_REF] Behrends | Network organization of the human autophagy system[END_REF] . Together with the association findings for genetic variation in TCF4 and NRGN [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF] , these reports and our findings may be regarded as supportive evidence for the role of brain-development-genes in the etiology of SCZ.

Although AMBRA1 is an interesting functional candidate gene and includes the top associated SNP, (Figure 1b, Figure S3) it is difficult to pinpoint the potential susceptibility gene on the basis of the genetic data alone due to the presence of strong LD in the associated region. Another very interesting gene that is covered by LD in this region is the muscarinic acetylcholine receptor M4 gene (CHRM4).

Neuropsychopharmacological and neuroimaging studies have produced strong evidence that muscarinic cholinergic receptors are involved in SCZ [START_REF] Scarr | Muscarinic Receptors in Psychiatric Disorders -Can We Mimic[END_REF] . Studies of muscarinic receptor knockout (KO) mice have suggested that CHRM4 has an impact on the homeostatic control of cholinergic activity and dopaminergic neurotransmission in mesolimbic and hippocampal brain regions [START_REF] Raedler | Cholinergic mechanisms in schizophrenia: Current concepts[END_REF] . Interestingly, the muscarinic agonist Xanomeline, which is selective for the CHRM1 and CHRM4 subtypes, exhibited functional dopamine antagonism and resulted in antipsychoticlike effects in rodent models predictive of antipsychotic behaviors [START_REF] Raedler | Cholinergic mechanisms in schizophrenia: Current concepts[END_REF] . Furthermore, a recent study proposed allosteric modulation of the muscarinic M 4 receptor as a potential approach to the treatment of SCZ [START_REF] Chan | Allosteric modulation of the muscarinic M4 receptor as an approach to treating schizophrenia[END_REF] . Further information concerning the impact of CHRM4 on the pharmaceutical treatment of SCZ is provided in the SI.

Another gene of interest in the LD region of chromosome 11 (Figure 1b, Figure S3) is the diacylglycerol kinase zeta gene (DGKZ). There are a total of 10 diacylglycerol kinase enzymes (DGKs) (DGKalpha, DGKbeta, DGKgamma, DGKdelta, DGKepsilon, DGKzeta, DGKeta, DGKtheta, DGKiota and DGKkappa) and these metabolize 1,2,diacylglycerol (DAG) to phosphatidic acid (PA). Diacylglycerol kinases are central to a wide range of signal transduction pathways of potential relevance to neuropsychiatric disorders [START_REF] Moskvina | Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk[END_REF] . The diacylglycerol kinase eta (DGKH), a key protein in the lithium-sensitive phosphatidyl inositol pathway, has recently been implicated in the etiology of bipolar disorder [START_REF] Baum | A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder[END_REF] . A further member of this protein family, DGKbeta, has been reported to promote dendritic outgrowth and spine maturation in developing hippocampal neurons [START_REF] Hozumi | Diacylglycerol kinase beta promotes dendritic outgrowth and spine maturation in developing hippocompal neurons[END_REF] . In addition, gene-wide evidence for association with SCZ has been reported for the gene that encodes for DGKiota (DGKI) [START_REF] Chan | Allosteric modulation of the muscarinic M4 receptor as an approach to treating schizophrenia[END_REF] . One independent study 3 found evidence for association between SCZ and a DGK1 variant. In view of these findings, DGKZ may represent a further promising candidate gene for SCZ in the LD region of chromosome 11.

Last but not least, Midkine (MDK) is an interesting candidate gene in the region of our top finding. It has been shown that Mdk(-/-) mice exhibited a delayed hippocampal development with impaired working memory and increased anxiety [START_REF] Nakamura | Disruption of the midkine gene (Mdk) resulted in altered expression of a calcium binding protein in the hippocampus of infant mice and their abnormal behaviour[END_REF] . In addition, midkine was found to accumulate in senile plaques in the hippocampus of patients with Alzheimer's disease [START_REF] Yasuhara | Midkine, a novel neurpotropic factor, is present in senile plaques of Alzheimer disease[END_REF] . Ohgake et al. (2009) reported that Mdk(-/-) mice showed a significantly disrupted Prepulse inhibition (PPI) test when compared to Mdk(+/+) mice [START_REF] Ohgake | Dopaminergic hypofunctions and preulse inhibition deficits in mice lacking midkine[END_REF] . This is interesting, because PPI has previously been shown to be a valuable tool for evaluating models or model organisms relevant to schizophrenia [START_REF] Swerdlow | Realistic expectations of prepulse inhibition in translational models for schizophrenia research[END_REF] . The PPI reduction was compensated when Mdk(-/-) mice were pre-treated with either haloperidol or clozapine (both known to reverse PPI deficits) [START_REF] Ohgake | Dopaminergic hypofunctions and preulse inhibition deficits in mice lacking midkine[END_REF] . In view of their findings, Ohgake et al. (2009) hypnotized, that Mdk(-/-) mice might act as a putative animal model of schizophrenia [START_REF] Ohgake | Dopaminergic hypofunctions and preulse inhibition deficits in mice lacking midkine[END_REF] .

Since none of the 11 top SNPs, which are all located in introns of AMBRA1, have any known disease causing function, the SNAP tool [START_REF] Johnson | SNAP: A web-based tool for identification and annotation of proxy SNPs using HapMap[END_REF] was used to test whether our top SNP (rs11819869) was in strong LD (r 2 > 0.8, based on HapMap 27 CEU Phase 2 and 3 data) with SNPs that are functional variants. We found that a DGKZ splice-site variant (rs2046768) was in strong LD with our top SNP (r 2 = 0.935 for HapMap 10 CEU data of Phase 2 release 22, Figure S3). Since this SNP was not present in our post QC dataset, it was necessary to impute the information for the GWAS dataset using the strategy described in the SI Patients and Methods (rs2046768, P IMPUT= 8.79 x 10 -5 ). Although the result for the functional variant itself lags behind the GWAS result for the top SNP (rs11819869, P GWAS = 4.71 x 10 -6 ), we cannot excluded the possibility that this is the functionally relevant effect that was picked up by our top SNP in AMBRA1.

Irrespective of which specific gene in the region is tagged by the identified SNPs, the impact of the risk allele on brain function and SCZ can be tested using imaging genetics. This approach has high specificity when applied to risk genes for SCZ [START_REF] Meyer-Lindenberg | False positives in imaging genetics[END_REF] .

The stringent statistics statistical approach, which combines Bonferroni correction over tasks with family-wise error correction over brain regions tested, provides strong protection against false positive findings 42 . The present results therefore indicate a regionally specific impact on the function of the subgenual cingulate during a cognitive control task. This represents a critical interface between emotion regulation and cognition that is structurally [START_REF] Koo | A crosssectional and longitudinal magnetic resonance imaging study of cingulate gyrus gray matter volume abnormalities in first-episode schizophrenia and first-episode affective psychosis[END_REF] and functionally [START_REF] Rasetti | Evidence that altered amygdala activity in schizophrenia is related to clinical state and not genetic risk[END_REF] abnormal in SCZ and bipolar disorder. The present findings therefore provide evidence that the identified risk allele is functional in a neural system of relevance to the disorder.

Our second best result was for rs4309482 (P COMB = 9.68 x 10 -7 , OR=0.87, Figure 1c), which is located near CCDC68 and TCF4 on chromosome 18. This SNP, unlike the SNPs in the chromosome 11 region, was included in the follow-up of the SGENE study and achieved a combined P-value of 7.1 x 10 -5 (Supplementary Table 2 in Stefansson et al. [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF] ). However, since a substantial proportion of our combined case sample (and a smaller proportion of our control sample) was included in that study, our result for this SNP cannot be regarded as an independent replication.

In summary, the present study has identified a susceptibility region for SCZ on chromosome 11 which contains four excellent functional candidate genes: AMBRA1, DGKZ, CHRM4, and MDK. In addition, we found evidence that the identified risk allele is functional in a neural system that is of relevance to the disorder. The aim of future studies will be to determine which of the genes in the region contribute to the risk for SCZ. CHRM4 is of particular interest as it may lead to novel therapeutic interventions for SCZ. OR, odds ratio referring to minor allele; CMH was one-tailed for the replication analysis if OR was in the same direction as in the GWAS and two-tailed for the meta-analysis; K, CMH's cluster variable;

Figure Legend

The table is sorted according to the results of the follow-up analysis (Replication 1). CMH, Cochran-Mantel-Haenszel test, was one-tailed for Replication 1, and two-tailed for GWAS and the combined analysis of GWAS plus Replication 1; TREND, Cochran-Armitage test, was one-tailed when OR was in the same direction as in the GWAS for Replication 1 subsamples and two-tailed for GWAS subsamples; LOGISTIC, Logistic regression (additive effect) was one-tailed when OR was in the same direction as in the GWAS for Replication 2 subsamples; META, meta-analysis (random effects model) was two-tailed; OR, odds ratio referring to the minor allele (MA), italic when OR was in the opposite direction to GWAS (; MAF, MA frequency; Pat, patients; Con, controls.

  , medical records, and family history. Best estimate diagnoses were assigned by at least 2 experienced psychiatrists/ psychologists. The controls were drawn from 3 population-based epidemiological studies: (A) PopGen 12 (n= 490), (B) KORA 13 (n= 488), and (C) the Heinz Nixdorf Recall (HNR, n= 383) study 14 (Risk Factors, Evaluation of Coronary Calcification, and Lifestyle). The recruitment areas for PopGen, KORA, and HNR were located in:

  Diego, CA, USA (all PopGen controls, all German SCZ patients); (ii) the Department of Genomics, Life & Brain Center, University of Bonn (all HNR controls); (iii) the Helmholtz Zentrum München, Germany (all KORA controls); (iv) The Southern California Genotyping Consortium (SCGC) at UCLA, Los Angeles, USA (all Dutch SCZ patients and n= 704 Dutch controls); and (v) the genotyping facility of the ErasmusMC Biomics core facility, Rotterdam, The Netherlands (n= 2,302 Dutch controls, including the controls for the follow-up sample). All genome-wide genotyping for the GWAS was performed on HumanHap550v3 BeadArrays using the Infinium II assay (Illumina, San Diego, CA, USA). The imaging genetics sample (n= 122) was genotyped by the Department of Genomics, Life & Brain Center, University of Bonn. The genome-wide genotyping was performed on Human610-quad beadchips using the Infinium HD assay (Illumina, San Diego, CA, USA). For a discussion on the possible confounding of case/control status with plate and genotyping procedures please see the SI Patients and Methods.

  , further information is provided in the SI Patients and Methods). Tests were applied to detect differences in allele frequencies and missingness patterns between the single Dutch and German control samples (Rotterdam, Utrecht, Kiel, Munich, and Essen). Finally, association analyses were performed using a Cochran-Mantel-Haenszel test and a logistic regression with covariates derived from MDS analyses (first six dimensions) to minimize potential stratification effects (Figure S5, more information is provided in the SI Patients and Methods).

Figure 1 :

 1 Figure 1: Association results for the GWAS and the 2 best supported genes from the follow-up study (replication 1). (a) Manhattan plot. Regional plots depicting (b) AMBRA1/CHRM4/DGKZ/MDK region and (c) CCDC68/TCF4 region. The bestassociated marker from the GWAS (enlarged red diamond) is centered in a genomic window of 1 Mb (hg18, RefSeq genes); its P-value from the combined analysis (P COMB ) is shown (enlarged blue diamond). The LD strength (r 2 ) between the sentinel SNP from the GWAS and its flanking markers is demonstrated by the red (high) to white (low) colored bar. The recombination rate (cM/Mb; second y-axis) is plotted in blue, according to HapMap 27 CEU.

Figure 2 :

 2 Figure 2: Effect of the rs11819869 genotype on brain activation during a flanker task (contrast incongruent trials > congruent trials). (a) The only cluster showing a significant group difference between carriers of at least one T allele and homozygote C allele carriers after correction for multiple testing. (P< 0.05, family wise error corrected for the entire brain and the number of different tasks tested). (b) Mean contrast estimates (+/-standard errors) for the significant cluster, reflecting an increased activation during incongruent trials for the T-allele carriers and during congruent trials for participants homozygous for the C allele.

  (Esslinger et al. plus 7 subsequently scanned subjects) that is being conducted at two sites in Mannheim and Bonn, Germany. All participants were

	healthy German volunteers with parents and grandparents of European origin. None
	of the participants had any self-reported lifetime or family history of SCZ or affective
	disorder. All subjects provided written informed consent. The study was approved by
	the local ethics committees of the Universities of Heidelberg and Bonn.

Table 2 :

 2 Nine SNPs replicated in the follow-up study (Replication 1) with the same alleles as in the GWAS

	SNP data

Alleles, major/minor allele where minor allele refers to dbSNP build129 and was determined in patients and controls in each analysis; CMH, Cochran-Mantel-Haenszel test; MAF, minor allele frequency;

Table 3 :

 3 Association results for rs11819869 at all stages of the analysis: GWAS, Replication 1, and Replication 2.

	SNP	Sample	% Males	Test, P	OR	MA	MAF
		(Patients / Controls)	Pat	Con				Pat	Con
	rs11819869	GWAS (1169 / 3714)			CMH (K= 2), 4.71 x 10 -6	1.32
		Germany The Netherlands	50.1 76.5	51.0 50.1	TREND, 0.008 TREND, 1.85 x 10 -4	1.31 1.34	T T	0.19 0.20	0.15 0.16
		Replication 1 (2569 / 4088)			CMH (K= 4), 5.04 x 10 -5	1.20
		Germany (Bonn -Mannheim)	62.0	49.4	TREND, 0.012	1.24	T	0.18	0.15
		The Netherlands	69.7	41.3	TREND, 0.033	1.36	T	0.19	0.15
		Germany (Munich)	62.7	48.6	TREND, 0.038	1.15	T	0.17	0.15
		Denmark (Aarhus)	54.8	54.3	TREND, 0.016	1.20	T	0.20	0.17
		GWAS + Replication 1 (3738, 7802)			CMH (K= 6), 3.89 x 10 -9	1.25
		Replication 2 (4734/ 18472)			META (n= 15), 0.0029	1.11
		Denmark (Copenhagen)	58.6	58.1	LOGISTIC, 0.133	1.12	T	0.18	0.17
		England	76.3	54.5	LOGISTIC, 0.172	1.29	T	0.20	0.16
		Finland (Helsinki)	59.3	65.3	LOGISTIC, 0.256	1.26	T	0.13	0.11
		Finland (Kuusamo)	62.6	52.0	LOGISTIC, 0.423	0.73	T	0.08	0.11
		Iceland	64.5	49.9	LOGISTIC, 0.101	1.11	T	0.19	0.17
		Wales/ UK (Cardiff)	67.8	49.2	LOGISTIC, 0.052	1.16	T	0.19	0.17
		Italy	57.1	56.2	LOGISTIC, 0.784	0.91	T	0.18	0.19
		Scotland	72.2	58.1	LOGISTIC, 0.211	1.09	T	0.18	0.16
		Denmark (Aarhus)	53.7	35.7	LOGISTIC, 0.698	0.94	T	0.16	0.17
		Poland	46.9	51.6	LOGISTIC, 0.098	1.19	T	0.18	0.16
		Belgium	68.3	43.7	LOGISTIC, 0.317	1.10	T	0.17	0.16
		Hungary	43.6	41.6	LOGISTIC, 0.354	1.07	T	0.20	0.19
		Russia	27.8	38.0	LOGISTIC, 0.172	1.13	T	0.17	0.16
		Sweden	62.7	62.0	LOGISTIC, 0.461	1.02	T	0.14	0.14
		Norway	59.2	50.4	LOGISTIC, 0.112	1.24	T	0.18	0.15

ACKNOWLEDGMENTS

We are grateful to all of the patients who contributed to this study. We also thank the probands from the community-based cohorts of PopGen, KORA, the Heinz Nixdorf Recall (HNR) study, the Rotterdam Elderly Study, as well as the Munich controls. This study was supported by the German Federal Ministry of Education and Research (BMBF), within the context of the National Genome Research Network plus (NGFNplus) and the MooDS-Net (grant 01GS08144 to S.C. and M.M.N., grant 01GS08147 to M.R.). M.M.N. also received support from the Alfried Krupp von Bohlen und Halbach-Stiftung. Further funding came from the European Union Seventh Framework Programme [FP7/2007-2011] under grant agreement n° 242257 (ADAMS). The Heinz Nixdorf Recall cohort was established with the support of the Heinz Nixdorf Foundation (Dr. G. Schmidt, Chairman). This study was also supported by The Polish Ministry of Science and Higher Education (grant N N402 244035 to P.M.C.), The Danish Council for Strategic Research (grant no. 2101-07-0059), H. Lundbeck A/S, the Faculty of Health Sciences, Aarhus University, and the Stanley Medical Research Institute. Genotyping of the Dutch samples from Utrecht was sponsored by NIMH funding, R01 MH078075.

SGENE-plus consortium

(Only those persons responsible for the samples of Replication 2 are listed) Hreinn Stefansson 1 , Stacy Steinberg 1 , Omar Gustafsson 1 , Engilbert Sigurdsson 2 , Hannes Petursson 2 , Augustine Kong 1 , Kari Stefansson 1 , Olli P.H. Pietiläinen 3,4 , Annamari Tuulio-Henriksson 5 , Tiina Paunio 6 , Jouko Lonnqvist 5 , Jaana Suvisaari 5 , Leena Peltonen 3,4 , Mirella Ruggeri 7 , Sarah Tosato 7 , Muriel Walshe 8 , Robin Murray 8 , David A. Collier 8 , David St Clair 9 , Thomas Hansen 10 , Andres Ingason 10 , Klaus D. Jakobsen 10 , Linh Duong 10 , Thomas Werge 10 , Ingrid Melle 11 , Ole A. Andreassen 12 , Srdjan Djurovic 12 , István Bitter 13 , János M. Réthelyi 13 , Lilia Abramova 14 , Vasily Kaleda 14 , Vera Golimbet 14 , Erik G. Jönsson 15 , Lars Terenius 15 , Ingrid Agartz 15 , Ruud van Winkel 16,17 , Gunter Kenis 16 , Marc De Hert 17 , Jan Veldink 18 , Carsten Wiuf 19 , Michael Didriksen 20 1 deCODE genetics,Sturlugata 8,IS-101,Reykjavik,Iceland. 2 Department of Psychiatry, National University Hospital, Hringbraut, IS-101 Reykjavik, Iceland. 3 Institute for Molecular Medicine Finland FIMM and Department of Medical Genetics, University of Helsinki, FI-00014 Helsinki Finland. 4 Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK. 5

Department of Mental Health and

Alcohol Research, National Public Health Institute, Mannerheimintie 166, FI-00300 Helsinki, Finland. 6 Health Genomics Unit, National Institute for Health and Welfare THL,FI-00270 Helsinki, Finland. 7 Section of Psychiatry and Clinical Psychology, University of Verona, 37134 Verona, Italy. 8 Institute of Psychiatry, King's College, London SE5 8AF, UK. 9 Department of Mental Health, University of Aberdeen, Royal Cornhill Hospital, Aberdeen AB25 2ZD, UK. 10 Institute of Biological Psychiatry, Mental Health Centre Sct. Hans Copenhagen University Hospital, DK-4000 Roskilde, Denmark. 11

Departments of Medical Genetics, Ulleval University Hospital, Institute of Psychiatry, University of

Oslo, Kirkeveien 166, N-0407 Oslo, Norway. 12 Dept. of Psychiatry, Ullevål University Hospital and Institute of Psychiatry, University of Oslo, Kirkeveien 166, N-0407 Oslo, Norway. 13 Semmelweis University, Department of Psychiatry and Psychotherapy, Budapest, Hungary. 14 Mental Health Research Center, Russian Academy of Medical Sciences, Zagorodnoe sh. 2/2, 117152 Moscow, Russia. 15 Department of Clinical Neuroscience, HUBIN project, Karolinska Institutet and Hospital, R5:00, SE-171 76 Stockholm, Sweden. 16 Department of Psychiatry and Psychology, School of Mental Health and Neuroscience, European Graduate School of Neuroscience (EURON), South Limburg Mental Health Research and Teaching Network (SEARCH), Maastricht University Medical Centre, 6200 MD Maastricht, The Netherlands. 17 University Psychiatric Centre Catholic University Leuven, Leuvensesteenweg 517, B-3070 Kortenberg, Belgium. 18 Rudolf Magnus Institute of Neuroscience, Dept of Neurology, UMC Utrecht, 3584 CG Utrecht, The Netherlands. 19 Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus C, Denmark. 20 Discovery Biology Research, H. Lundbeck A/S, Ottiliavej 9, DK-2500 Valby, Denmark.

Supplementary information is available at Molecular Psychiatry's website.

Genetic Risk and Outcome in Psychosis (GROUP investigators)

René S. Kahn 1 , Don H. Linszen 2 , Jim van Os [START_REF] Williams | New findings from genetic association studies of schizophrenia[END_REF] Academic Medical Centre University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands. [START_REF] Stefansson | Common variants conferring risk of schizophrenia[END_REF] Maastricht University Medical Centre, South Limburg Mental Health Research and Teaching Network, Maastricht, The Netherlands. [START_REF] International | Common polygenic variation contributes to risk of schizophrenia and bipolar disorder[END_REF] University Medical Center Groningen, Department of Psychiatry, University of Groningen, Groningen, The Netherlands.

Conflict of interest

The authors declare no conflict of interest.


O Rodriguez Colomo

Álvarez Lerma

M I González Pérez

J-M Sirvent

García Simón

Oscar Rodríguez Colomo
email: orcolomo@gmail.com

Impact of administration of vancomycin or linezolid to critically ill patients with impaired renal function

Keywords: Linezolid, Vancomycin, Renal failure, ICU

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Impact of administration of vancomycin or linezolid to critically ill patients with impaired renal function

Introduction

Multiple epidemiological studies, including those conducted at intensive care units (ICUs), show a significant increase in infections caused by Gram-positive cocci (GPCs), including their multiresistant strains (MR-GPCs) [START_REF] Vincent | International study of the prevalence and outcomes of infection in intensive care units[END_REF](2)[START_REF] Rosenthal | International Nosocomial Infection Control Consortium report, data summary for 2002-2007, issued[END_REF][START_REF] Picazo | Vigilancia de resistencias a los antimicrobianos: estudio VIRA 2006[END_REF][START_REF] Jones | Emerging resistance among bacterial pathogens in the intensive care unit-a European and North American Surveillance study (2000-2002)[END_REF][START_REF] Lerma | Grupo de Estudio Nacional de Vigilancia de Infección Nosocomial en UCI. Staphylococcus aureus nosocomial infections in critically ill patients admitted in intensive care units[END_REF]. In Spain, according to data from the National Study of Nosocomial Infection Surveillance (ENVIN), the presence of methicillin-resistant (MRSA) Staphylococcus aureus (SA) in ICU-acquired infections has gradually increased, and SA accounted in 2006 for 4.6% of all isolates from ICU-acquired infections (2). This proportion decreased in 2008 to 2.22%, or 25% of all SAs identified (2). On the other hand, resistance of coagulase-negative staphylococci (CNS) to methicillin continues to be higher than 80%. This epidemiological situation has led to an increased use of antibiotics active against these organisms, such as vancomycin (VAN) [START_REF] Insausti | Consumo de antibióticos en medicina intensiva[END_REF][START_REF] Malacarne | Antibiotic usage in intensive care units: a pharmaco-epidemiological multicentre study[END_REF] and linezolid (LZD). Tigecycline, a drug of the glycylcycline class, and daptomycin, a macrocyclic lipopeptide, have recently been added to this group of drugs. VAN and LZD are the drugs most commonly used for critically ill patients, despite the fact that use of VAN is greatly controversial [START_REF] Colomo | Study Group on the use of active antibiotics against multiresistant gram positive cocci in critical patients. Use of antibiotics for the treatment of multiresistant gram positive cocci infections in critical patients[END_REF][START_REF] Rybak | Nephrotoxicity of vancomycin, alone and with an aminoglycoside[END_REF][START_REF] Goetz | Nephrotoxicity of vancomycin and aminoglycoside therapy separately and in combination[END_REF][START_REF] Ziglam | Limitations of presently available glycopeptides in the treatment of Grampositive infection[END_REF][START_REF] Iwamoto | Clinical efficacy of therapeutic drug monitoring in patients receiving vancomycin[END_REF][START_REF] Jeffres | A retrospective analysis of possible renal toxicity associated with vancomycin in patients with health care associated methicillin-resistant Staphylococcus aureus pneumonia[END_REF][START_REF] Lodise | Larger vancomycin doses ( at least four grames per day) are associated with an increased incidence of nephrotoxicity[END_REF]. In addition to tolerability and renal toxicity problems, a high treatment failure rate in severe infections has been reported for VAN, probably related to its poor tissue penetration in the lung, bone tissue, central nervous system (CNS), and inflammatory fluids [START_REF] Pou | Changes in vancomycin pharmacokinetics during treatment[END_REF][START_REF] Albanèse | Cerebrospinal fluid penetration and pharmacokinetics of vancomycin administered by continuous infusion to mechanically ventilated patients in an intensive care unit[END_REF][START_REF] Georges | Pulmonary disposition of vancomycin in critically ill patients[END_REF][START_REF] Lamer | Analysis of vancomycin entry into pulmonary lining fluid by bronchoalveolar lavage in critically ill patients[END_REF][START_REF] Daschner | Pharmacokinetics of vancomycin in serum and tissue of patients undergoing open-heart surgery[END_REF]. This requires use of higher doses, with the resultant increase in adverse events, one of the most significant of which is renal function impairment [START_REF] Bingen | Optimal vancomycin serum level in Staphylococcus aureus infections?[END_REF][START_REF] Wang | Incresed vancomycin MICs for Staphylococcus aureus clinical isolated from a university hospital during a 5-year period[END_REF][START_REF] Sakoulas | Relationship of MIC and Bactericidal Activity to Efficacy of Vancomycin for Treatment of Methicillin-Resistant Staphylococcus aureus Bacteremia[END_REF][START_REF] Soriano | Influence of vancomycin minimum inhibitory concentration on the treatment of methicillinresistant Staphylococcus aureus bacteremia[END_REF][START_REF] Hidayat | High-dose vancomycin therapy for methicillin-resistant Staphylococcus aureus infections: efficacy and toxicity[END_REF][START_REF] Rybak | Therapeutic monitoring of vancomycin in adult patients: A consensus review of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists[END_REF]. Despite these limitations, this glycopeptide continues to be widely used at Spanish ICUs. The possibility of monitoring VAN plasma levels and adjusting its dosage based on creatinine clearance has probably promoted use of the drug. However, the clinical impact of this decision is not known, and this study was therefore intended to assess the temporal changes in renal function in patients with biological criteria of renal failure (RF) treated with VAN versus LZD for documented or suspected infections by MR-GPCs.

Materials and methods

A multicenter, retrospective, observational study was conducted in 17 Spanish ICUs. Data from patients admitted to the participating ICUs in 2006 were retrospectively collected.

All patients over 18 years of age admitted to an ICU who were administered VAN or LZD (initially or as rescue treatment), alone or combined with other antimicrobial drugs, for proven or suspected infections by MR-GPCs in any site and who had renal impairment and were not subject to hemodialysis or any other renal replacement therapy (RRT) before or at the start of treatment were enrolled into the study. RF was defined as serum creatinine (Cr) levels higher than 1.5 mg/dL and/or Cr clearance (ClCr) < 60 ml/min, as estimated using the Cockcroft-Gault equation [START_REF] Cockcroft | Prediction of creatinine clearance from serum creatinine[END_REF].

Exclusion criteria included age under 18 years, use of VAN or LZD as prophylaxis, use of any RRT before or at treatment start, absence of renal function monitoring procedures, use of VAN or LZD for less than 3 days, and use of teicoplanin as initial treatment.

A comparative analysis was made of the groups of patients treated with VAN (Group A) and with LZD (Group B). Data collected included demographic characteristics, comorbidities, immunocompetence, ICU and hospital stay, mortality, reason for admission, infection site and microorganisms involved, type of treatment (empirical or specific), and reasons for treatment discontinuation. Infection severity was assessed both at admission and treatment start (APACHE II) [START_REF] Knaus | APACHE II: a severity of disease classification system[END_REF], and infection was classified as sepsis, severe sepsis, and septic shock based on Roger C. Bone criteria [START_REF] Bone | American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis[END_REF]. Cr and glomerular filtration rate (GFR) values, dosage used, and concomitant nephrotoxic medication (amikacin, tobramycin, gentamicin, colistin, acyclovir, tacrolimus, cyclosporine, and foscarnet) were also recorded during treatment. In VANtreated patients, administration form (continuous or intermittent) and plasma levels of the drug (when measured) were recorded. Finally, RRTs performed during treatment were recorded, as well as their adverse effects and therapeutic result. An effectiveness analysis was performed on the complete intentionto-treat (ITT) sample and on the clinically evaluable population (CEP), defined as patients with a documented diagnosis of GPC infection.

Immunosuppressed patients were defined as those administered corticosteroid therapy (methylprednisolone > 40 mg/day for longer than 15 days) and/or treated with other immunosuppressants.

SA and CNS resistant to methicillin and/or VAN and VAN-resistant enterococci were considered as MR-GPCs. Clinical cure was defined as resolution of infection with no presence of signs or symptoms associated to the original infection, and microbiological eradication as complete eradication of the initial microorganisms. Treatment for at least three days was required to assess treatment failure.

To assess the impact of antibiotic therapy on renal function, patients not requiring any RRT during or within one week of therapy were analyzed. Patients who required RRT were separately analyzed. Plasma Cr levels and GFR were compared at both treatment start and end, while percent change in Cr and GFR was compared during treatment. This percentage was obtained using the following formula: Cr at treatment end -Cr at treatment start / Cr at treatment start. Percent change in GFR was estimated using the same formula.

Statistical analysis: To assess differences in variables analyzed in both study arms, distribution of tested parameters was compared between the treatment groups using a Chi-square test for homogeneity of proportions in qualitative variables, and a Student's t test for independent quantitative variables. A value of p <0.05 was considered statistically significant. The nominal significance level was not adjusted for multiplicity.

Results

A total of 147 patients treated with VAN (68, Group A) or LZD (79, Group B) for documented or suspected infections by MR-GPCs were enrolled into the study. Mean age of patients analyzed was 64 years (SD 13.98), and 70.7% were males. Mean APACHE II on admission was 21.71 (SD 7.36). Of all patients, 67.1% came from other hospital departments and 64.6% were medical patients. Sepsis was the most common reason for ICU admission (33.8%). ICU mortality was high, and hospital mortality was 50.3%. Table 1 shows the demographic characteristics, severity, admission diagnosis, and concomitant diseases of patients by treatment received. There were no significant differences between both groups. Concomitant diseases included chronic obstructive pulmonary disease and diabetes mellitus, which was more common in Group B (p.007).

Table 2 breaks down infections by type, location, and systemic response. Nosocomial infections were most common, accounting for 73% of all infections, with no differences between both treatment groups.

Infections were quite serious, as shown by a mean APACHE II score of 21.88 points (SD 17.29) at the start of treatment. Septic shock occurred in 67.3% of all patients and in a greater percentage of patients given LZD (p.013), while a higher proportion of sepsis with no organ failure was found in VAN-treated patients (p.002).

One or more Gram-positive bacteria were isolated in 97 patients (65.9%). SA (40.2%) and CNS (32.9%) were the most commonly found microorganisms (Table 3). Polymicrobial infections associated to Gramnegative bacilli were found in 24.7% of patients, with no difference between the two treatment groups analyzed (Table 3). VAN Table 5 shows the changes in plasma Cr levels and GFR from the start to the end of treatment as measured in the 129 patients (85.8%) who required no extrarenal clearance techniques during or at the end of treatment. GFR improved during antibiotic therapy in both groups, with a resultant decrease in Cr levels, but a greater improvement was seen in LZD-treated patients (p.05 and p.02 respectively) (Figure 1). Clinical and microbiological effectiveness was compared by ITT and in the CEP (Table 6). Clinical cure rates were 49.7% and 67.0% respectively. They were higher in Group B in both cases, but the difference was not statistically significant. Microbiological eradication rates were 48.3% and 61.9%. Higher rates were found in Group B, but there were more indeterminate results in Group A.

Twelve (8.2%) adverse effects related to the antibiotics tested were recorded. Nine (13.2%) patients treated with VAN had an increased renal impairment which was attributed to drug administration, as compared to none of the patients treated with LZD (p.001). Thrombocytopenia occurred in 3 patients (3.8%) treated with LZD and in no patient in the VAN group (p.15).

Table 7 shows the reasons for treatment discontinuation. The main reason for stopping treatment was clinical cure (49.7% of patients), which occurred in significantly more Group B patients (55.7% vs. 42.6%, p.04). Treatment was discontinued for adverse effects in 5 patients, all from Group A (7.4% vs. 0.0%, p.02).

Discussion

This observational, retrospective study to analyze the impact of use of VAN and LZD in patients with biological criteria of RF showed a better evolution of renal function in critically ill patients with documented or suspected infection by MR-GPCs when treated with LZD as compared to VAN. Although an overall improvement in renal function occurred in both groups, VAN-treated patients had a significantly lower improvement, and 13.2% of them experienced a worsening in renal function attributable to VAN which required drug discontinuation in half of them. It is currently recommended that VAN trough plasma levels ranging from 15 and 20 µg/ml are achieved, and even higher levels if infection involves the CNS [START_REF] Hidayat | High-dose vancomycin therapy for methicillin-resistant Staphylococcus aureus infections: efficacy and toxicity[END_REF][START_REF] Rybak | Therapeutic monitoring of vancomycin in adult patients: A consensus review of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists[END_REF]. This requires use of doses up to 60 mg/kg/day, which involves a greater risk of renal toxicity. These recommendations have been supported by the results of recent studies showing that minimal inhibitory concentration (MIC) of VAN against SA has increased since the 90s. There are studies reporting an incidence of strains with MIC > 1 µg/ml higher than 50%, which is associated to a decreased therapeutic effectiveness [START_REF] Bingen | Optimal vancomycin serum level in Staphylococcus aureus infections?[END_REF][START_REF] Wang | Incresed vancomycin MICs for Staphylococcus aureus clinical isolated from a university hospital during a 5-year period[END_REF][START_REF] Sakoulas | Relationship of MIC and Bactericidal Activity to Efficacy of Vancomycin for Treatment of Methicillin-Resistant Staphylococcus aureus Bacteremia[END_REF][START_REF] Soriano | Influence of vancomycin minimum inhibitory concentration on the treatment of methicillinresistant Staphylococcus aureus bacteremia[END_REF]. In this setting, if VAN administration is decided, MICs of VAN against the pathogens involved would have to be known and VAN plasma levels would have to be monitored, mainly in critically ill patients with severe sepsis, in whom changes in hemodynamics and distribution volume are also common. In our study, reporting use of VAN in some Spanish ICUs in 2006, such levels were measured in few cases (in 66.7% and 34% of patients on continuous and intermittent administration respectively), and mean values obtained were in the upper range of recommended concentrations (21.16 µg/ml and 29.26 µg/ml respectively). These data support the convenience of monitoring VAN levels in these patients when the drug is used, and also the need to consider alternative treatment particularly in severe infections and when the MIC of this antibiotic against SA is unknown or higher than 1 µg/ml. While the proportion of patients on hemodialysis was similar in both groups, CVVHDF was used in 15.2% of patients treated with LZD as compared to 2.9% of patients treated with VAN (p.01). It should be noted that patients treated with LZD had a greater renal function impairment at baseline, as shown by a lower ClCr at treatment start (37.57 vs. 42.24, p.04). On the other hand, among Group B patients undergoing RRTs, 35.7% had chronic renal failure, as compared to 25% in Group A; although the difference was not statistically significant. Finally, it is very likely that the greater proportion of patients with septic shock in the group treated with LZD (75.9% vs. 57.4% LZD vs. VAN, p .013) and the fact that 11 out of the 14 patients in this group receiving RRT were in such hemodynamic situation (78.6%) influenced the higher rate of CVVHDF which, as known, is a procedure used to remove inflammatory mediators in the systemic inflammatory response syndrome of sepsis.

On the other hand, another aspect worthy of mentioning is the percentage of cases of thrombocytopenia related with the antibiotic treatment (3.8%), considering that it was higher than what would have been expected in patients treated with LZD, taken into account the duration of the treatment [11.27 days (SD 8.98)]. This result could also have been conditioned by a more serious presentation of the infection in this patient group.

ICU and hospital mortality of patients enrolled into this study was high. This was related to the severity of the condition of these patients, as shown by both mean APACHE II (21.88) and impact on organs of infections occurring as severe sepsis/septic shock in 83.6% of cases. It should be noted that while a greater systemic impact was seen in patients treated with LZD (75.9% vs. 57.4% patients in septic shock), there were no differences in mortality.

In the effectiveness analysis by IT and in the CEP, a higher cure rate was seen in LZD-treated patients, but the difference was not statistically significant, probably because of the small size of the sample used to analyze this variable. Analysis of microbiological effectiveness did not provide significant conclusions. Although microbiological eradication rates were significantly higher in Group B, a significantly greater proportion of indeterminate results was found in Group A. On the other hand, an analysis of the reasons for drug discontinuation showed superiority of LZD over VAN. Among LZD patients, there were more cases where treatment was discontinued due to clinical cure (55.7% vs. 42.6%, p.04) and less patients with drug discontinuation for adverse effects (7.4% vs. 0%, p.02). Both data show the higher effectiveness of LZD as compared to VAN for treating this group of patients with impaired renal function.

Limitations of this study include retrospective data collection, with its inherent limitations. Despite this important consideration, retrospective recruitment was consecutively performed, with a balanced number of patients in both treatment groups included at each hospital. Sample size was adequate for analyzing the primary objective of the study, and although the group treated with LZD included patients who were more seriously ill and, had lower ClCr values, and a greater proportion of patients with diabetes mellitus, this does not invalidate results because renal function evolved better in this group.

Based on the results of this study, it is reasonable to conclude that use of VAN should be avoided in critically ill patients with acute renal failure. If VAN is used, it is essential to monitor its plasma levels and to ascertain the MICs for the pathogens involved in infection.

  O. Rodriguez Colomo, F. Álvarez Lerma, M.I. González Pérez, J-M. Sirvent, M. García Simón To cite this version: O. Rodriguez Colomo, F. Álvarez Lerma, M.I. González Pérez, J-M. Sirvent, M. García Simón. Impact of administration of vancomycin or linezolid to critically ill patients with impaired renal function. European Journal of Clinical Microbiology and Infectious Diseases, 2011, 30 (5), pp.635-643. 10.1007/s10096-010-1133-6. hal-00659046

  Concomitant nephrotoxic drugs were administered to 27.9% of patients, with no differences between both groups[17/68 (25%) vs. 24/79 (30.4%), p. 29]. Aminoglycosides were most common in both groups [11/17 (63.4%) vs. 15/24 (62.5%) p. 70].

	27 (SD 8.98) vs. 7.44 days
	(SD 4.17) p.002]. VAN was continuously administered to 15 patients (22.1%). Steady state plasma levels
	of VAN were measured in 10 (66.7%) of these patients [mean: 21.16 µg/ml (SD 6.56)]. In 18 (34%) of
	the 53 patients on intermittent administration, trough levels were measured [mean: 29.26 µg/ml (SD
	13.47)].
	At treatment start, Group B patients had a greater renal function impairment than Group A patients, as
	shown by higher mean Cr levels (2.30 vs. 2.11) and significantly lower ClCr values (37.57 vs. 42.24,
	p.04). During treatment, RRTs were used in 18/147 patients (12.2%). Continuous venovenous
	hemodiafiltration (CVVHDF) was the RRT used in 14 of these patients. Renal function support measures
	were used more frequently in Group B [14/79 (17.7%)] as compared to Group A [4/68 (5.9%)], and
	CVVHDF was indicated in most cases (12/14; 85.7%). Table 4 details demographic characteristics,

and LZD were administered in most cases empirically (76.2%) and combined with other antimicrobials, most commonly broad-spectrum antibiotics (87.1%). Combinations with piperacillin/tazobactam and carbapenems were most common. No differences were found between the groups compared. The duration of the treatment was longer in Group B [11.severity, organic impact of infections, mortality, and renal function changes over time in these patients. Chronic renal failure was diagnosed in 33.3% (6/18) of the patients, and was more prevalent in the Group B (35.7% vs. 25%), even though the difference was not statistically significant. Mean APACHE II score was 24.22, 77.8% were in septic shock, and mortality was 77.8%. There were no significant differences between both groups of antibiotic therapy.

TABLE 1 . Demographic and clinical characteristics and concomitant diseases of patients by treatment group.

 1

		Total n = 147	VAN n = 68	LZD n = 79	P
			Group A	Group B
	Mean age (SD)	64.07 (13.98)	65.06 (14.64)	63.22 (13.41)	.42
	Male, n (%)	104 (70.7)	45 (66.2)	49 (74.7)	.17
	Mean APACHE II (SD)	21.71 (7.36)	21.83 (7.20)	21.62 (7.54)	.86
	Death at ICU, n (%)	57 (38.8)	27 (39.7)	30 (38.0)	.48
	Death at hospital, n (%)	74 (50.3)	38 (55.9)	36 (45.6)	.14
	Patient source, n (%)
	Community	44 (30.1)	22 (32.4)	22 (28.2)
	Hospital	98 (67.1)	43 (63.2)	55 (70.5)	.40
	Long-term facility	4 (2.7)	3 (4.4)	1 (0.7)
	Patient type, n (%)
	Medical	95 (64.6)	47 (69.1)	48 (60.8)
	Surgical	38 (25.9)	13 (19.1)	25 (31.6)
	Trauma	10 (6.8)	6 (8.8)	4 (5.1)
	Coronary	4 (2.7)	2 (2.9)	2 (2.5)
	Admission diagnosis, n (%)
	Cardiovascular disease	15 (10.3)	9 (13.2)	6 (7.8)
	Sepsis	49 (33.8)	24 (35.3)	25 (32.5)
	Trauma	8 (5.5)	5 (7.4)	3 (3.9)
	Respiratory failure	11 (7.6)	3 (4.4)	8 (10.4)
	Pneumonia	19 (13.1)	9 (13.2)	10 (13.0)	.95
	Pancreatitis	5 (3.4)	1 (1.5)	4 (5.2)
	Peritonitis	8 (5.5)	2 (2.9)	6 (7.8)
	Postoperative	5 (3.4)	1 (1.5)	4 (5.2)
	CNS disease	12 (8.3)	8 (11.8)	4 (5.2)
	Other	13 (9.0)	6 (8.8)	7 (9.1)
	Concomitant diseases, n (%)
	Diabetes	34 (23.1)	9 (13.2)	25 (31.6)	.007
	Immunodeficiency	42 (28.6)	21 (30.9)	21 (26.6)	.34
	Chronic renal failure	21 (14.3)	8 (11.8)	13 (16.5)	.28
	COPD	34 (23.1)	19 (27.9)	15 (19.0)	.13
	Severe heart failure	15 (10.2)	10 (14.7)	5 (6.3)	.08
	Chronic liver disease	16 (10.9)	10 (14.7)	6 (7.6)	.13
	Alcoholism	20 (13.6)	10 (14.7)	10 (12.7)	.45
	CNS: central nervous system; COPD: chronic obstructive pulmonary disease

TABLE 2 . Infection type, location, and severity by treatment group

 2

	Infection type, n (%)	Total	VAN n = 68	LZD n = 79	P
		n = 147	Group A	Group B
	Community-acquired	39 (26.5)	19 (27.9)	20 (25.3)
	Hospital-acquired (outside ICU)	55 (37.4)	25 (36.8)	30 (38.0)	.93
	ICU-acquired	53 (36.1)	24 (35.3)	29 (36.7)
	Location, n (%)
	VAP	18 (12.2)	9 (13.2)	9 (11.4)
	Primary bacteremia	17 (11.6)	8 (11.8)	9 (11.4)
	Secondary bacteremia	16 (10.9)	9 (13.2)	7 (8.9)
	Vascular catheter infection	14 (9.5)	8 (11.8)	6 (7.6)
	Non-VAP	34 (23.1)	13 (19.1)	21 (26.6)	.67
	Non-CAUTI	3 (2.0)	2 (2.9)	1 (1.3)
	SWI	20 (13.6)	10 (14.7)	10 (12.7)
	SSTI	7 (4.8)	4 (5.9)	3 (3.8)
	Other locations	18 (12.2)	5 (7.4)	13 (16.5)
	at treatment start
	Mean APACHE II (SD)*	21.88 (7.29)	22.54 (7.87)	21.31 (6.75)	.31
	Sepsis n (%)	24 (16.3)	18 (26.5)	6 (7.6)	.002
	Severe sepsis, n (%)	24 (16.3)	11 (16.2)	13 (16.5)	.57
	Septic shock, n (%)	99 (67.3)	39 (57.4)	60 (75.9)	.013
	VAP: Ventilator-associated pneumonia
	Non-VAP: Non-ventilator-associated pneumonia
	Non-CAUTI: Non-catheter-associated urinary tract infection
	SSTI: Skin and soft tissue infection
	SWI: Surgical wound infection

TABLE 3 . Microorganisms identified in infections included in the study

 3

	Microorganisms	Total n = 97	VAN n = 44	LZD n = 53
	Gram-positive, n (%)	97/147 (65.9)	44/97 (45,4)	53/97 (54,6)
	Staphylococcus aureus, MS 17/97 (17.5)	7/44 (15,9)	10/53 (18,9)
	Staphylococcus aureus, MR 22/97 (22.7)	6/44 (13,6)	16/53 (30,2)
	Coagulase-negative
	Staphylococcus

TABLE 4 . General characteristics and change in renal function of patients undergoing RRT during antibiotic therapy Demographic characteristics and infection severity and presentation

 4 -RRT: Renal replacement therapy -Creat: Creatinine ClCr: Creatinine clearance -CRF: Chronic renal failure

		Total n = 18	VAN n = 4	LZD n = 14	p
	Mean age (SD)	65.28 (10.76)	69.00 (5.77)	64.21 (11.76)	.45
	Male, n (%)	14 (77.8)	3 (75)	11 (78.6)	.88
	Mean APACHE II (SD)	24.22 (7.53)	27.25 (9.91)	23.35 (6.92)	.37
	Severe sepsis, n (%)	3 (16.7)	1 (25)	2 (14.3)	.61
	Septic shock, n (%)	14 (77.8)	3 (75)	11 (78.6)	.88
	Death at ICU, n (%)	14 (77.8)	3 (75)	11 (78.6)	.88
	Concomitant diseases, n (%)
	Diabetes (%)	8 (44.4)	3 (75)	5 (35.7)	.16
	CRF (%)	6 (33.3)	1 (25)	5 (35.7)	.68
	COPD (%)	5 (27.8)	1 (25)	4 (28.6)	.13
	Change in renal function over time	VAN n= 4*	LZD n= 14*	p
	Creatinine at treatment start		3.52 (1.60)	3.36 (1.49)	.85
	Creatinine at treatment end		2.09 (0.79)	2.03 (1.40)	.94
	Percent change in creatinine		-13.68 (79.57)	-39.15 (30.11)	.57
	ClCr at treatment start		30.04 (20.93)	28.23 (12.65)	.83
	ClCr at treatment end		42.61 (14.98)	60.92 (47.99)	.47
	Percent change in ClCr		122.17 (194.66)	117.27 (137.22)	.95
	*Mean (SD)

TABLE 5 . Analysis of change in renal failure in patients undergoing no RRT during or at the end of antibiotic therapy

 5

	n = 129	VAN n= 64*	LZD n= 65*	p
	Creatinine at treatment start	2.02 (1.91)	2.08 (0.91)	.82
	Creatinine at treatment end	1.65 (1.34)	1.40 (0.84)	.20
	Percent change in creatinine	-9.48 (51.92)	-27.94 (41.11)	.02
	ClCr at treatment start	43.01 (13.44)	39.58 (13.34)	.14
	ClCr at treatment end	63.57 (41.91)	75.80 (56.69)	.16
	Percent change in ClCr	55.06 (112.91)	95.96 (127.40)	.05
	*Mean (SD) -RRT: Renal replacement therapy -ClCr: Creatinine clearance

TABLE 6 . Effectiveness Clinical effectiveness in the intention-to-treat population

 6

	n (%)	Total n = 147	VAN n = 68	LZD n = 79	p
			Group A	Group B
	Cure	73 (49.7)	29 (42.6)	44 (55.7)	.11
	Failure	33 (22.4)	20 (29.4)	13 (16.5)
	Indeterminate	41 (27.9)	19 (27.9)	22 (27.8)
	Microbiological effectiveness in the intention-to-treat population
	Eradication	71 (48.3)	26 (38.2)	45 (57.9)	.04
	Persistence	12 (8.2)	5 (7.4)	7 (8.9)
	Indeterminate	64 (43.5)	37 (54.4)	27 (34.2)
	Clinical effectiveness in the clinically evaluable population*
	n (%)	Total n = 97	VAN n = 44	LZD n = 53	p
	Cure	65 (67.0)	26 (59.1)	39 (73.6)	.23
	Failure	25 (25.8)	15 (34.1)	10 (18.9)
	Indeterminate	7 (7.2)	3 (6.8)	4 (7.5)

Microbiological effectiveness in the clinically evaluable population*

	Eradication	60 (61.9)	23 (52.3)	37 (69.8)	.02
	Persistence	8 (8.2)	2 (4.5)	6 (11.3)
	Indeterminate	29 (29.9)	19 (43.2)	10 (18.9)
	*Documented diagnosis of GPC infection

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