Orotracheal tube and face mask ventilation was performed on room air. Chest compressions were performed during the experimental procedure. Arterial blood gases were performed prior to euthanasia (baseline), at 3 min and at 6 min of CPR. PaO2 and PaCO2 were compared for each time point and each group. There was no difference in PaO2 or PaCO2 between groups at baseline. At 6 min all groups had a significantly higher PaCO2 (P ≤ 0.005) and the facemask and compression only groups had a significantly lower PaO2 (P < 0.02) when compared to the orotracheal tube group. There was no difference between the PaO2 of the mouth-to-nose group compared to the orotracheal tube group at 3 or 6 min. Gastric distension, regurgitation, gas leakage around the mouth, and ineffective breaths were all noted in both the face mask and mouth-to-nose group. The results of this study supports that orotracheal intubation is the preferred technique for ventilation during CPR in dogs. When orotracheal intubation is not possible, face mask ventilation or mouth-to-nose ventilation would be reasonable alternatives. When oxygen supplementation is available, face mask ventilation is likely to be superior. Appropriate training for both face mask and mouth-to-nose ventilation techniques is recommended.The efficacy of ventilation of dogs during cardiopulmonary resuscitation (CPR) with a tight fitting face mask or mouth-to-nose rescue breathing has not been evaluated. Twenty-four purpose bred research dogs: Dogs were randomized to be ventilated by cuffed orotracheal tube, tight fitting face mask, mouth-to-nose breathing or compressions only during CPR ( Artificial ventilation during resuscitation for in-hospital cardiopulmonary arrest or respiratory arrest in dogs and cats is routinely performed by manual ventilation via a cuffed orotracheal tube. When orotracheal intubation is not feasible the veterinary CPR guidelines, known as RECOVER, state “it is reasonable to recommend mouth-to-snout rescue breathing for dogs and cats with respiratory arrest or with cardiopulmonary arrest in a 30:2 ratio with chest compressions when endotracheal intubation is not available . Bag-masIn apneic anesthetized human patients, manual ventilation via a face mask has been shown to provide adequate ventilation and oxygenation , 3. Mask2 could be maintained, although no measure of oxygenation was performed in this study  in place. After complete evacuation of the abdomen, the animals were positioned in right lateral recumbency and transitioned from isoflurane in oxygen to total intravenous anesthesia with a propofol constant rate infusion  as needed to maintain a surgical plane of anesthesia. The dogs were disconnected from the anesthetic machine and attached to a bag valve resuscitator  and manually ventilated on room air, targeting an ETCO2 of 30 mmHg and an SpO2 of 92–94%. After 30 min of manual ventilation, baseline arterial blood samples were taken using purpose made heparinized syringes  for blood gas analysis . The arterial blood gas values were not temperature corrected.At the end of the initial study the dogs remained orotracheally intubated, under general anesthesia with isoflurane in 100% oxygen and an intravenous infusion of morphine (0.1 mg/kg/h), ketamine (10 mcg/kg/min) and lidocaine (30 mcg/kg/min). Electrocardiograph, direct arterial blood pressure, body temperature, ETCO2 of 30–40 mmHg and a PaO2 of 70–75 mmHg. If after 30 min the baseline values were not within these ranges, manual ventilation was continued as described and arterial blood samples were evaluated every 15 min until the appropriate baseline values were achieved.At baseline, the target arterial blood gas values were a PaCOEuthanasia was performed via injection of pentobarbital 120 mg/kg IV. The propofol and morphine, ketamine and lidocaine infusions were stopped at this time. Death was confirmed by absence of heart sounds on thoracic auscultation and loss of a pulsatile waveform on the direct arterial blood pressure monitor. Sixty seconds following confirmation of death, basic life support was initiated.The method of ventilation used was randomized to one of four techniques (see below). For ventilation techniques that did not require orotracheal intubation, the dogs were extubated during the 60 s following confirmation of death. External chest compressions were performed at ~100 compressions per minute with hands placed over the heart, for a period of 6 min. Chest compressions were performed in 2 min cycles and the person performing chest compressions was changed for each cycle with less than a 10 s interruption in chest compressions. No other CPR interventions were performed. Arterial blood samples for blood gas  analysis were evaluated at 3 and 6 min following initiation of chest compressions.At the end of the 6 min study period the dogs were evaluated including thoracic auscultation and direct arterial blood pressure waveform analysis to confirm death.N = 6 for each group). The orotracheal group were ventilated at a respiratory rate of 10 breaths per minute with a tidal volume to generate an adequate chest rise and an inspiratory time of 1 s. For the tight fitting face mask and mouth-to-nose techniques, a 30:2 compression to breath ratio was used and a single investigator (KH) performed all mouth-to-nose breathing.Twenty-four dogs were randomized to one of four manual ventilation techniques; bag valve resuscitator , Vital Signs Inc., GE Healthcare company, NJ, USA) with orotracheal intubation on room air, bag valve resuscitator with tight fitting face mask  on room air, mouth-to-nose breathing or no ventilation (chest compression only) CPR .Data was analyzed for normality with a Shapiro–Wilk normality test and normally distributed data are reported as mean ± standard deviation. PaO2 and PaCO2 values is presented in Table A total of 26 hound type, female dogs were randomized for the study and the results from 24 dogs with a mean body weight of 27 ± 4.9 kg are reported. The experiment in two dogs had to be repeated because arterial blood samples could not be obtained in one dog in the face mask group and no ventilations were effective in one dog in the mouth-to-nose group. A summary of PaO2 between groups at baseline , which will lower PaO2 values compared to those obtained at sea level. A previous study in pigs ventilated via orotracheal intubation on room air had similar results to this study with a reported PaO2 of 69 ± 15 mmHg after 5 min of cardiopulmonary arrest  than with laryngeal mask airway ventilation (3.5%) during CPR  is common in human CPR and is well-described in the literature, but to the author's knowledge it has not been previously evaluated in dogs. In this study, a clear plastic face mask with a rubber diaphragm was used to give a tight fitting seal around the muzzle. The oxygenation provided using this technique was not different than that of the compression only group. This may be due to the difficulties in consistently producing effective breaths. The operator had to hold the mask tightly against the face and occlude the lips to prevent air leakage. The head and neck needed to be maintained in extension, as often small changes in position resulted in an ineffective breath and a second operator to help keep the neck and head appropriately positioned was frequently needed. The oxygenation achieved with this technique in this study was poorer than expected. In a clinical setting, face mask ventilation would allow ventilation with an enriched oxygen source which may increase the PaOring CPR .2. The gas delivered by mouth for rescue breaths has a higher PCO2 and lower PO2 than room air. Studies have reported the fraction of oxygen in expired gas to be in the range of 15.9–17.9%  ease of use; (2) efficacy in maintaining oxygenation and ventilation; (3) frequency of complications . Using tKH, MR, and AB designed the study and performed the experimental procedures. SE analyzed the data and KH wrote the manuscript. All authors contributed to read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer LG and the handling Editor declared their shared affiliation


Background: Genome-wide association studies have identified genetic variants associated with coronary artery disease (CAD) in adults – the leading cause of death worldwide. It often occurs later in life, but variants may impact CAD-relevant phenotypes early and throughout the life-course. Cohorts with longitudinal and genetic data on thousands of individuals are letting us explore the antecedents of this adult disease.Methods: 148 metabolites, with a focus on the lipidome, measured using nuclear magnetic resonance (1H-NMR) spectroscopy, and genotype data were available from 5,907 individuals at ages 7, 15, and 17 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Linear regression was used to assess the association between the metabolites and an adult-derived genetic risk score (GRS) of CAD comprising 146 variants. Individual variant-metabolite associations were also examined.Results: The CAD-GRS associated with 118 of 148 metabolites , the strongest associations being with low-density lipoprotein (LDL) and atherogenic non-LDL subgroups. Nine of 146 variants in the GRS associated with one or more metabolites (FDR < 0.05). Seven of these are within lipid loci: rs11591147PCSK9, rs12149545HERPUD1-CETP, rs17091891LPL, rs515135APOB, rs602633CELSR2-PSRC1, rs651821APOA5, rs7412APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both including aggregate cholesterol measures. The other two variants identified were rs112635299SERPINA1 and rs2519093ABO. Conclusions: Genetic variants that influence CAD risk in adults are associated with large perturbations in metabolite levels in individuals as young as seven. The variants identified are mostly within lipid-related loci and the metabolites they associated with are primarily linked to lipoproteins. Along with further research, this knowledge could allow for preventative measures, such as increased monitoring of at-risk individuals and perhaps treatment earlier in life, to be taken years before any symptoms of the disease arise. Many of the risk factors have long been established to be modifiable exposures such as low-density lipoprotein (LDL) cholesterol levels, smoking and hypertension2. In the developed world, the average age of developing Angina Pectoris, often the first clinical sign of CAD, is typically over 603. However, there is evidence that “fatty streaks”, the precursors to atherosclerosis and thus CAD, form in almost all adolescents from developed countries4. Furthermore, there is evidence that the development of atherosclerotic plaques in coronary arteries is prolonged over the life course5. Thus, it is unsurprising that risk factors for CAD, including obesity and serum low-density lipoprotein levels, have been associated with an increased rate of plaque formation in children6. Also, a recent study suggested that higher BMI early in life was causally associated with adverse cardiovascular health7. These observations strongly suggest that at least some CAD risk factors may be contributing to disease development within children and there is potential for early life intervention, even if it involves nothing other than heightened clinical surveillance (not screening) by measured genetic burden.Coronary artery disease (CAD) is the leading cause of adult death worldwide and is a gross contributor to global morbidity9. Over 100 genetic variants have been identified as being reliably associated with an increased risk of CAD in adults9. These variants, which are likely to be exerting their influence through a diverse collection of mechanisms, are common and exert relatively small effects on disease outcome singularly, but together these variants explain over 10% of CAD heritability9.Genome-wide association studies (GWAS) have been conducted to explore common forms of heritable contributions to this complex diseaseFTO locus and BMI has shown that risk alleles don’t always have fixed effects on outcomes throughout life10. This may also be the case for other traits, like CAD. This has clinical importance because at risk individuals may gain from treatment or monitoring at various time-points across their life course. There are also implications for applied epidemiology using genetics. Currently, it is often assumed the effect of a genetic variant is fixed across the life course, but whilst the nature of the code itself may be static, the penetrance may be variable, one possible source of this variation comes from gene-environment interactions.It is unclear what effect these variants are having on CAD-relevant phenotypes at an earlier age (i.e. latent disease) or the longitudinal nature of the associations. Elsewhere, work analysing variation near the1H-NMR) spectroscopy offers a cost effective, high throughput technology to analyse multiple metabolic measures from a single sample, providing quantitative information on 149 metabolites13. The platform focuses largely on lipoproteins and fatty acids and provides the opportunity to examine individual components of lipoproteins in addition to aggregate measures. With such detailed measures of both genotypes and phenotypes, studies have already begun to successfully associate genotypic and metabolic profiles to disease phenotype, such as type 2 diabetes14.Proton nuclear magnetic resonance (15) to begin to appraise the causal relationship between metabolites and CAD in adults16. This technique, along with new methods to quantify metabolites are starting to build evidence for the causal associations between metabolites and CAD that are beyond the well-known LDL-C and CAD relationship.Furthermore, single nucleotide polymorphisms (SNPs) have been used as instrumental variables .Avon Longitudinal Study of Parents and Children (ALSPAC). ALSPAC recruited pregnant women in the Bristol and Avon area, United Kingdom, with an expected delivery date between April 1991 and December 1992. Over 14,000 pregnancies have been followed up (both children and parents) throughout the life-course. Full details of the cohort has been published previously17. This study focuses on the children of these pregnancies. EDTA plasma samples were collected for metabolite extraction at ages 7, 15 and 17. Individuals at ages 15 and 17 were fasted prior to sample collection, but individuals at age 7 were not. Samples were aliquoted at 200μl or 500μl and stored below -70°C. Of the 7,176 participants available, 1,269 were removed due to incomplete data, leaving 5,907 for the analysis. Data at the three ages were combined in order to maximise the power of the study . This was achieved by taking an individual’s metabolite data at the earliest time point possible. Full details of their characteristics are inThe study used a single cohort: theALSPAC website. Written informed consent was obtained from both the parent/guardian and, after the age of 16, children provided written assent. Please note that the study website contains details of all the data that is available through a fully searchabledata dictionary.Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and from the UK National Health Service Local Research Ethics Committees. Full references of committee approval can be found on theChildren were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform  by the Wellcome Trust Sanger Institute  and the Laboratory Corporation of America . Participants were excluded due to having at least one of: incorrectly recorded sex, minimal or excessive heterozygosity, disproportionate levels of individual missingness (>3%), evidence of cryptic relatedness or non-European ancestry. SNPs with a minor allele frequency (MAF) <1%, a genotype missingness >1% and a call rate <95% were removed and only SNPs that passed an exact test of Hardy-Weinberg equilibrium (P<5x10-7) were included.ShapeIT (v2.r644), which utilises relatedness during phasing. A phased version of the 1000 genomes reference panel  was obtained from theImpute2 reference data repository. Imputation was performed usingImpute V2.2.2 against the reference panel , using all 2186 reference haplotypes (including non-Europeans).For imputation, genotypes of ALSPAC mothers and children were combined. Haplotypes were estimated using-8)9. 146 of these variants were present in the genotype data after quality control (see above) and were included in the genetic risk score. The effect size of each variant in relation to CAD was used to weight the variants – specifically the natural log of the odds ratio (OR) was used. These weightings were multiplied by the variant dosage and a CAD-GRS was produced for each individual by summing all the weighted variant values. All the loci are outlined inA GWAS meta-analysis conducted using data from UK biobank and CARDIoGRAMplusC4D identified 148 variants associated with CAD at genome-wide significance  sample changer. Finally, the data are measured using a Bruker AVANCE III spectrometer. Metabolite data contains known risk factors for CAD, such as LDL-cholesterol, but also many other metabolites, as well as multiple lipoprotein subclasses. Due to the unreliability of the signal, pyruvate was removed from the analyses, leaving 148 metabolites. All abbreviations of metabolites used can be found inNMR analyses of the metabolic measures was carried out at the University of Eastern Finland quantifying 149 metabolites from serum samples of the participants. The process has been described elsewhere19, which is required for atherosclerosis.To examine the association between the GRS and different classes of lipoproteins, lipoproteins were split into six groups based on their size and density. The groups are labelled LDL, atherogenic non-LDL, large very low-density lipoproteins (VLDL), small high-density lipoproteins (HDL), large HDL, and very large HDL . Groups HMGCR locus, rs17238484 and rs12916, were chosen as proxies for statin use, as has been done previously20. As these were separate from the GRS, the variants were not weighted by their association with CAD and their impact on metabolite concentrations was assessed separately to all the other variants.We sought to gauge whether a lipid lowering therapy may impact the metabolome similarly in young individuals and adults, and thus potentially reduce risk of CAD in later life. Two additional variants, external to the GRS, within the21.Metabolites were rank normalised prior to analyses to approximate normal distributions and to remove the impact of outliers. Linear regression models were used to estimate the association between metabolites in adolescence and genotype. Separately, metabolite concentrations were fitted against the CAD-GRS and each of the individual variants. Age was the only covariate in the models. An FDR-corrected P value < 0.05 was calculated using the Benjamini and Hochberg method22. Specifically, distances among metabolites was estimated by 1. subtracting the absolute Pearson's correlation coefficient from one, 2. performing hierarchal clustering on a matrix of those distances with the hclust function and the method "complete", 3. followed by a tree cutting step at the height of 0.2 with the function cutree. The functions hclust and cutree are both available in the 'stats' package22.The metabolites measured here do not necessarily represent independent phenotypes, as many are the product of the same biological event or pathway. As such, to estimate the number of independent metabolites or features present in our dataset we performed a hierarchical clustering and tree cutting analysis on the metabolite abundance data, in R22 (version 3.2.2).All analyses were conducted in R5,907 individuals aged 7, 15 and 17 had NMR-measured metabolite data and genotype data . Many meWhen grouping lipoproteins based on their size and density we found a large overlap between the biological groupings and the clusters, with the lipoproteins within each group mostly mapping to a single cluster. This is with the exception of atherogenic non-LDL particles, where the metabolites overlap largely with clusters containing LDL and VLDL particles.A GRS produced from 146 CAD-associated variants, distribution shown in-14) and the median effect size on LDL and atherogenic non-LDL are larger than those observed for the other four groups , but are not different to each other (P = 0.86).When considering our lipoprotein biological groupings, it was observed that the GRS associated most strongly with atherogenic non-LDL particles and LDL . FurtherPCSK9, rs12149545HERPUD1-CETP, rs17091891LPL, rs515135APOB, rs602633CELSR2-PSRC1, rs651821APOA5, rs7412APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both, including aggregate cholesterol measures LDL-C, VLDL-C and IDL-C. rs2519093ABO associated with three VLDL cholesterol measures and rs112635299SERPINA1 associated with glycoprotein acetyl and phenylalanine concentrations. Full tables of results for these nine variants found inTo explore the variants driving the association between the GRS and various metabolites, all of the metabolites were regressed against each variant individually . In totaHMGCR and the 148 metabolites was investigated. Neither of the SNPs associated with any metabolites at FDR < 0.05. At P < 0.05, rs17238484 and rs12916 associated with 17 and 42 metabolites respectively. Mostly, the presence of the effect allele (G and T respectively) associated with a decrease in metabolite levels within the lipoprotein subclasses LDL and atherogenic non-LDL particles.  and NMR LDL measures (23 measures) in individuals aged around seven were made. The NMR measures explained 80% of the variance of the composite LDL measure. The effect estimates from the association between all the SNPs and the NMR measures explained 93% of the variance of the effect estimates for the clinically measured LDL and all the SNPs.In these analyses, we combined data at ages 7, 15 and 17. There were 4,685, 858 and 364 individuals from each age group respectively. To understand if grouping the individuals in this way impacted results we conducted sensitivity analyses using only individuals from each age group. We observed no strong evidence for a difference between the median metabolite levels at different age groups , and there was minimal evidence for a difference between the association of the CAD-GRS and metabolites between age groups . The extent of these differences for each metabolite is displayed in9, associated with 118 metabolites in a sample of 5,907 individuals aged 7, 15 and 17. These metabolites were mostly lipoproteins, with stronger associations occurring in LDL and atherogenic non-LDL particles subtypes. Nine of the variants were associated with one or more metabolites. When these variants were removed from a CAD-GRS, the association between the residual GRS and the metabolites attenuated to the null, strongly suggesting these nine variants were driving CAD-related metabolomic differences in young individuals.In this study a GRS of CAD, made from 146 variants identified in a previous GWAS24. Therefore, it is potentially unsurprising thatThe association between circulating metabolite levels and CAD has been demonstrated many times, especially with lipoproteins23.In vivo experiments suggest that not all lipoprotein particles can cross the intima19. Interestingly, the CAD-GRS associated most strongly with LDL and atherogenic non-LDL particles, both of which are hypothesised to be small enough to cross the intima. Furthermore, there is good evidence from randomised controlled trials and Mendelian randomization studies that lowering LDL-C  reduces risk of CAD25. Thus, our results suggest genetic variants associated with CAD can drive an increase in metabolites that have evidence for causally influencing the disease.The accumulation of lipoprotein particles, particularly LDL, within the intima has long been observed in atherosclerotic plaques26. All nine genetic variants identified in our study had good evidence for association with the same or similar metabolites in the adult GWAS. Interestingly, the GWAS identified five additional variants that were present in our study but had little evidence for association with metabolites. As only five more genetic variants were identified, it suggests many variants associated with CAD are acting through pathways independent of the metabolites measured here. In total, in the adult GWAS, the five SNPs associated with 89 metabolites. Of these 89 associations the direction of effect was the same for all but one within children and the 95% confidence intervals overlapped for 80 of the associations. Therefore, the discrepancy between the studies seems to be primarily due to power differences . The other discrepancies could be due to chance differences, or the effect of CAD-associated genetic variants on metabolites may vary temporally. Thus, more work is required to elucidate if it could be preferential to target some pathways within critical windows of time.Only 9 of the 146 variants associated with CAD, showed good evidence they associated with NMR measured metabolites in young individuals in this study. A recent GWAS of metabolites that featured 112 of our 149 metabolites was conducted in adults (mean age = 44.6)et al. showed that alleles rs17238484-G and rs12916-T (HMGCR) associate with a decrease in LDL-C20. Here we observed weak evidence that these variants associate with LDL and intermediate-density lipoprotein (IDL) subtypes in young individuals. Along with the association between thePCSK9 variant and metabolites, it suggests that treatments attempting to target metabolites to reduce risk of CAD or prevent other adverse CAD-related outcomes, may have similar influences within young individuals, even if the effect is reduced. These results agree with the current treatment of familial hypercholesterolemia, whereby statins are administered at young ages27. Unfortunately, there may be negative side effects of administering statins to younger individuals, with evidence linking statins to increases in risk of both type 2 diabetes and myopathy. Nevertheless, the consequences, negative and positive, of administering these agents early in life to “seemingly healthy” individuals need to be examined. There is the hypothetical potential that administering treatment early in life could delay onset of disease for at risk individuals.We assessed whether statin use might have a similar effect in young individuals as in adults in reducing LDL-C levels, to explore whether early-life drug-intervention may be a possibility for some individuals. A previous study by SwerdlowEven though it is unlikely clinicians will prescribe pharmaceutical agents for CAD to very young people, the variants identified in this study could be used to select those who would benefit from a less dangerous lipoprotein lowering treatment. If no treatments became available, the identification of high-risk individuals could still be used to monitor them so that intervention could begin before symptoms start to arise. Furthermore, notification of those at risk could increase caution amongst parents and individuals over environmental exposures such as diet, physical activity and smoking.The study combined metabolite data from young people aged 7, 15 and 17. Even though age was used as a covariate in the main models, sensitivity analysis revealed a potential difference in CAD-GRS associations with metabolites at different ages.28, but the study should be replicated using only fasting or only non-fasting data.These data also combine metabolite data that was collected after fasting and non-fasting. There is evidence that fasting and non-fasting metabolite data are similarRank-normalisation of the metabolite data removes the influence of outliers on the data but prevents true quantification of association between genotype and metabolite concentrations, i.e. with the addition of one risk allele the level of metabolite X increases by Y.21 was used to correct for multiple tests, rather than a more stringent family-wise error rate correction method such as Bonferroni correction. Furthermore, the study investigated how the GRS of CAD influenced lipoproteins grouped based on previous biological knowledge.There is redundancy in the metabolite data, as many of the metabolites are highly correlated. This leads to an increase in false negatives when correcting for multiple testing. To reduce this, the Benjamini and Hochberg (FDR) methodA CAD-GRS associated with differential abundance of 118 metabolites in young individuals. The majority of these metabolites were lipoproteins and fatty acids, and it associated most strongly with lipoproteins that are hypothesised to causally influence CAD development. We believe these results warrant further research into whether identification of high-risk individuals, identified by their genetic profile, can benefit from increased monitoring and early life intervention, either by pharmaceutical agents or by behavioural changes.ALSPAC data access is through a system of managed open access. The steps below highlight how to apply for access to the data included in this data note and all other ALSPAC data. The datasets presented in this article are linked to ALSPAC project number B2714, please quote this project number during your application. The ALSPAC variable codes highlighted in the dataset descriptions can be used to specify required variables.1. ALSPAC access policy  which describes the process of accessing the data and samples in detail, and outlines the costs associated with doing so.Please read the2. research proposals database, which lists all research projects that have been approved since April 2011.You may also find it useful to browse our fully searchable3. submit your research proposal for consideration by the ALSPAC Executive Committee. You will receive a response within 10 working days to advise you whether your proposal has been approved.Pleasealspac-data@bristol.ac.uk.If you have any questions about accessing data, please emailThe ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access.https://github.com/thomasbattram/CAD_analysisAll code for the analysis is freely available on GitHub:http://doi.org/10.5281/zenodo.141026329Archived code at time of publication:Licence: MITstudy website.Written informed consent was obtained from both the parent/guardian and, after the age of 16, children provided written assent. Children were invited to give assent where appropriate. Study members have the right to withdraw their consent for elements of the study or from the study entirely at any time. Full details of the ALSPAC consent procedures are available of the The investigators have explored the genetic risk score versus metabolites mainly lipoproteins. This is done in a group of children and adolescents of which there is a scarcity in the literature. The results are worthy of publication and will serve as part of the increasing number of studies, we hope will be performed in this age group in the future. The paper is well done but the conclusions are perhaps overstated. The conclusion that early life intervention for high risk individuals identified by their genetic profile could help prevent onset of the disease is an overstatement for this study. Why? The investigators correlate the genetic risk score with the lipoproteins which is a good segregate for atherosclerosis but is not atherosclerosis per say. The phenotype of atherosclerosis is not assessed. Secondly, there is no intervention in this study to indicate lowering of lipoprotein or the disease. The reviewer would also like to see the raw genetic risk score and how they vary across this population. This should be included in the manuscript not in the supplemental. Secondly, if the data is available, the plasma lipoprotein level should be included. This is of interest for a variety of reasons and is too important to be included in a supplemental. The reviewer also hopes that in the future, at least, the investigators will test the genetic risk score based on non-lipid related genetic risk variants.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for these positive remarks Dr Roberts. We have addressed your specific comments below. “The conclusion that early life intervention for high risk individuals identified by their genetic profile could help prevent onset of the disease is an overstatement for this study. Why? The investigators correlate the genetic risk score with the lipoproteins which is a good segregate for atherosclerosis but is not atherosclerosis per say. The phenotype of atherosclerosis is not assessed. Secondly, there is no intervention in this study to indicate lowering of lipoprotein or the disease.”Thank you for this suggestion. We have now replaced the overstated conclusion with something more reserved. New text:CONCLUSION) “We believe this warrants further research into whether identification of high-risk individuals, identified by their genetic profile, can benefit from increased monitoring and early life intervention, either by pharmaceutical agents or by behavioural changes” “ To assess whether the NMR measurements were representative of what is routinely measured in the clinic, a comparison between clinically measured LDL (a composite of NMR measures) and NMR LDL measures (23 measures) in individuals aged around seven were made. The NMR measures explained 80% of the variance of the composite LDL measure. The effect estimates from the association between all the SNPs and the NMR measures explained 93% of the variance of the effect estimates for the clinically measured LDL and all the SNPs.?In the HMGCoR section – is the FDR calculation correct ? 42 / 149 at p<0.05 sounds like an enrichment! Also clarify directions when talking about “inverse associations” – especially in the context of the SNPs.In the discussion you say you found little evidence of association for five SNPs found in adults, and that that could indicate non lipid pathways but could it also be relative lack of power? What were the sample sizes in the GWAS discovery? Presumably much bigger?In the discussion about giving kids statins could mention the specific dangers – myopathy and T2D.  Minor points:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for these positive remarks Professor Frayling. We have addressed your specific comments below. “I found the penultimate sentence of the results hard to parse.”We’ve now adapted the final sentences of the results to read: RESULTS) “.” “The final sentence of the conclusions gets a little too speculative in my opinion – suggest take out or tone down – e.g. “further research needed to find out if useful, given marker in young children.”Thank you for this suggestion. We have now replaced the overstated conclusion with something more reserved. New text:CONCLUSION) “We believe this warrants further research into whether identification of high-risk individuals, identified by their genetic profile, can benefit from increased monitoring and early life intervention, either by pharmaceutical agents or by behavioural changes” Currently, it is often assumed the effect of a genetic variant is fixed across the life course, but whilst the nature of the code itself may be static, the penetrance may be variable, one possible source of this variation comes from gene-environment interactions.?”Thank you for raising this point. Within the top 10, there is only 1 LDL subgroup lipoprotein, but 6 atherogenic non-LDL particles, which we hypothesised to be damaging with regards to cardiovascular health. Thus, we are not too surprised with the result, although did think it deserved a comment as you mentioned:DISCUSSION) Of the nine metabolites that associated most strongly with the GRS, none of them were part of the LDL subgroup, however six were part of the atherogenic non-LDL subgroup previously hypothesised to be dangerous (Supplementary Table 4). associated with a decrease in metabolite levels within the lipoprotein subclasses LDL and atherogenic non-LDL particles.DISCUSSION) (HMGCR) associate with a decrease in LDL-CA previous study by Swerdlow et al. showed that alleles rs17238484-G and rs12916-T . In the discussion about giving kids statins could mention the specific dangers – myopathy and T2D. Thank you for the suggestion, it has been incorporated into the discussion:DISCUSSION) (Unfortunately, there may be negative side effects of administering statins to younger individuals, with evidence linking statins to increases in risk of both type 2 diabetes and myopathy.


We developed an unbiased framework to study the association of several mutations in predicting resistance to hypomethylating agents (HMAs) in patients with myelodysplastic syndromes (MDS), analogous to consumer and commercial recommender systems in which customers who bought products A and B are likely to buy C: patients who have a mutation in gene A and gene B are likely to respond or not respond to HMAs.We screened a cohort of 433 patients with MDS who received HMAs for the presence of common myeloid mutations in 29 genes that were obtained before the patients started therapy. The association between mutations and response was evaluated by the Apriori market basket analysis algorithm. Rules with the highest confidence (confidence that the association exists) and the highest lift (strength of the association) were chosen. We validated our biomarkers in samples from patients enrolled in the S1117 trial.Among 433 patients, 193 (45%) received azacitidine, 176 (40%) received decitabine, and 64 (15%) received HMA alone or in combination. The median age was 70 years , and 28% were female. The median number of mutations per sample was three , and 176 patients (41%) had three or more mutations per sample. Association rules identified several genomic combinations as being highly associated with no response. These molecular signatures were present in 30% of patients with three or more mutations/sample with an accuracy rate of 87% in the training cohort and 93% in the validation cohort.Genomic biomarkers can identify, with high accuracy, approximately one third of patients with MDS who will not respond to HMAs. This study highlights the importance of machine learning technologies such as the recommender system algorithm in translating genomic data into useful clinical tools. Although recurrent somatic mutations have been described in several genes in MDS and have implications for disease biology and OS,9 the impact of these mutations on response to HMAs remains controversial, with studies evaluating the impact including a small number of patients or a small number of gene sets.14 For example, some studies have shown that TP53 mutations may predict a higher response of limited duration to HMAs, whereas others have shown no impact of TP53 mutations on response.14 Similarly, studies have shown that mutations along methylation pathways, such as TET2, may predict higher responses to HMAs, but only in patients with a variant allelic frequency of 10% or more,11 whereas a combination of genes such as ASXL1 mutations with wild-type TET2 may predict resistance to HMAs.11 These approaches do not take into account the genomic heterogeneity or hierarchy of MDS or the association of these mutations with each other. Because identifying a single gene or comutated genes is unlikely to yield an understanding of how these mutations define disease biology or phenotype, an unbiased approach is needed to study the relationship of these abnormalities to each other and to MDS biology.Because clinical variables and patient characteristics have not consistently predicted response to hypomethylating agents (HMAs), molecular data represent a biologic opportunity15 of AZA alone and in combination.In this study, we used unbiased, machine learning approaches (a recommender system similar to that used by Netflix or Amazon.com) to assess the impact of molecular data on resistance to HMAs in a large cohort of patients treated with HMAs at different academic institutions, and we validated our results in a population treated in a contemporary prospective clinical trial of HMA therapy11 All patients consented to blood or bone marrow samples at each institution under institutional review board–approved protocols in accordance with each institution policy and the Declaration of Helsinki. More information regarding the patient cohort, response criteria, and validation cohort is included in the Data Supplement.For the training cohort, patients treated with either AZA- or DAC-based regimens were included in this study: 230 patients were treated at Cleveland Clinic (between 2002 and 2012); 203 were from other academic institutions  or were part of the DACO-020 clinical trial .A panel of 29 genes that are commonly mutated in MDS and myeloid malignancies was evaluated (Data Supplement). For samples obtained from Cleveland Clinic, genomic DNA was extracted from peripheral blood or bone marrow mononuclear cells before treatment. More information regarding sequencing method is included in the Data Supplement.P values of < .1 from univariate analyses. Details regarding the recommender system algorithm are included in the Data Supplement.Variables were compared using the Wilcoxon rank sum test and Fisher’s exact test for continuous and categorical variables, respectively. OS was calculated from the date of diagnosis to the date of last follow-up or death (whichever came first), and survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. Univariate analyses were conducted to evaluate the impact of single mutations on response. A multivariate analysis using logistic regression was conducted and included variables with 16 included 234 patients (54%) with very good or good risk, 66 (15%) with intermediate risk, 33 (8%) with poor risk, 66 (15%) with very poor risk, and 34 (8%) not documented . cumented . A totalcumented . The 200ASXL1 (31%), TET2 (22%), SRSF2 (23%), RUNX1 (15%), DNMT3A (14%), and SF3B1 . Patterns of mutation association were also similar to those in previous reports (ASXL1 mutations were commonly commutated with TET2 in 42 patients (10%), and with SRSF2 38 (9%), RUNX1 35 (8%), U2AF1 24 (6%), and DNMT3a 21  in the training cohort had a mutation in at least one gene. The median number of mutations per patient was three , and a total of 176 patients (41%) had three or more mutations/sample. The most frequently mutated genes were 3B1 12%; . The fre reports . ASXL1 ma 21 5%; .v 2%, P = .02; The overall response rate to HMAs was 43%, with 109 patients (25%) achieving complete remission (CR), 16 (4%) partial remission (PR), 59 (14%) hematologic improvement (HI), 142 (33%) stable disease, and 107 (24%) progressive disease. In general, clinical characteristics such as age, cytopenias, and treatment regimens did not affect response, with the exception of the median blast percentage in the bone marrow, which was higher among responders compared with nonresponders .v no response]), we used strict criteria to identify rules with the highest support (how many times the rules appeared in the data set), high confidence , and higher lift  in the training cohort. On the basis of these criteria, we found eight rules that predicted resistance to HMAs , the median OS for the entire group was 19.5 months . The median OS for HMA responders was 29.5 months  compared with 18.9 months  for nonresponders , respectively , TET2 (n = 26), SRSF2 (n = 23), TP53 (n = 22), RUNX1 (n = 21), and U2AF1 (n=19). The median number of mutations per sample was two . Thirty-nine patients (35%) had three or more mutations/sample. Genomic biomarkers of resistance to AZA were present in 14 of 39 samples (35%) with three or more mutations; 13 of 14 of these patients (93%) did not respond to therapy.One hundred three of 113 (91%) in the validation cohort had at least one mutation, the most common being Predicting response or resistance to our currently available standard HMA therapy in MDS remains a significant clinical challenge. Identifying patients up front who may not respond to HMAs can potentially improve outcome, decrease unnecessary adverse effects, and save money, especially when current recommendations are for a minimum of 6 months of treatment before deeming it a failure. Although it is tempting to identify an isolated molecular abnormality or a pair of mutations that can predict HMA resistance, this approach does not allow for the complexity and evolution of the genomic landscape in MDS.In this study, we developed an unbiased framework using a machine learning, recommender system algorithm to identify highly sensitive genomic associations (molecular signatures or genomic biomarkers) that can predict resistance to HMAs with high accuracy. The recommender system algorithm allowed us to identify complex genomic associations that were associated with resistance to HMAs without pregrouping mutations. These associations were validated in an independent cohort in samples from patients enrolled in a randomized phase II/III clinical trial. Although our biomarkers were identified in only 25% of patients, their presence predicted resistance in almost all patients who had these mutations. By definition, a biomarker can be present in a small subset of patients, but when present can predict, with high accuracy and reliability, response or resistance to a therapy. More importantly, our biomarkers also correlated with worse survival, suggesting higher-risk features of disease resistance and progression. Detecting these biomarkers in 29% of patients suggests that other biologic mechanisms  or clinical characteristics may contribute more to HMA response and failure than does genomics. Indeed, several studies have shown that genomic clonal architecture does not change at the time of response to HMAs in serial samples obtained from patients during therapy. Our findings confirm that genomic associations may lead to different gene expressions and/or epigenetic changes that contribute to the response or resistance and thus, identifying one or two genes that can predict response may not be sufficient to build reliable and predictable models.15Although we included patients who received HMAs in combination with other investigational agents, these combinations did not affect the response or resistance rate or OS; thus, their impact on the output of our recommender system algorithms is negligible. Furthermore, neither IPSS nor IPSS-R predicted response or resistance to HMAs in our study in accordance with prior reports.TP53 mutations may predict response to HMAs, whereas others did not confirm that finding.17 In a small study of 84 patients with acute myeloid leukemia (AML) and MDS treated with a 10-day DAC course, a small subset of patients with TP53 mutations had a higher response rate to DAC compared with TP53 wild-type patients. Furthermore, the median OS for TP53 mutated patients who received DAC and underwent an allogeneic stem-cell transplantation was similar to that of patients with wild-type TP5317. Contrary to this finding, in a study of 71 patients with AML, there was no difference in overall response rate and survival among patients who received 5 days of DAC compared with those who had a 10-day schedule. More importantly, TP53 status did not affect their response.18 Similarly, prior studies have shown that TET2 mutations with variant allele frequency greater than 10% may predict response to HMAs, especially in patients with wild-type ASXL1 mutations, but this finding was not validated in our study.11 The discrepancy in the results of these studies could be related to sample size, the number of genes tested, and the statistical approach that was used to analyze the data. It is also possible that genomic changes in themselves are not the drivers of response to HMAs but rather, changes in the gene expression and methylation profile that are derived from the combination of these mutations. In a study of whole-genome sequencing, RNA sequencing, and methylation profile of samples from patients with chronic myelomonocytic leukemia, a serial sequencing demonstrates that the response to hypomethylating agents is associated with changes in DNA methylation and gene expression, without any decrease in the mutation allele burden or prevention of new genetic alteration occurrence.18Prior studies have attempted to use genomic data to predict response or resistance to HMAs. The results among these studies have been controversial. For example, some studies have shown that 19 Alternatively, patients with higher-risk disease and a high blast percentage may consider intensive, AML-type chemotherapy before allogeneic stem-cell transplantation, as opposed to an HMA that is predicted to do little to affect the disease in the absence of other biomarkers that could predict resistance to chemotherapy, such as complex karyotype cytogenetics, and the presence of TP53 mutations and the absence of targetable mutations such as IDH1 and IDH2. Because the optimal timing for patients with MDS with lower-risk disease can be challenging and because these genomic biomarkers predicted poor outcome even in patients with lower-risk disease. These biomarkers could be used as a justification to proceed with allogeneic stem-cell transplantation early in the disease course, especially if the patient has a lower blast percentage. In addition, identifying, with high accuracy, patients who may or may not respond to therapy can prevent prolonged exposure to ineffective therapy and can lead to lower cost without decreasing value or changing patient outcomes. Translationally, these genomic biomarkers can also be used to model HMA resistance in the laboratory and to study the mechanisms of resistance in cell cultures and animal models. Introducing these biomarkers into normal hematopoietic cells using CRISPR/cas9 may offer an opportunity to model and understand HMA resistance to develop novel drugs to overcome this resistance.This study includes several areas of innovation. On the clinical side, these genomic biomarkers can be used to tailor therapy. For example, if a patient with MDS with higher-risk disease carries one of these biomarkers, he or she should be encouraged to enroll in a clinical trial with a novel therapy or to proceed with an allogeneic stem-cell transplantation, if eligible, directly, without the use of HMAs, because the response to such therapy is predicted to be low. Although all patients with MDS should be encouraged to enroll in a clinical trial with novel therapies, having biomarkers that accurately predict resistance may ease the conversation with some patients who are hesitant to try newer approaches and prefer Food and Drug Administration–approved therapies.20 Nevertheless, some limitations to our approach exist. These limitations include the presence of these genomic abnormalities in only approximately one quarter of patients and the lack of identification of rules that predict response to HMAs. It is possible that the response to HMAs is derived mainly from epigenetic changes and is not dependent on the genomic changes that we studied here.Our study highlights the importance of machine learning algorithms such as the recommender system in translating genomic data into useful clinical tools that can be used by physicians in the clinic.In summary, our study identified genomic abnormalities that predict response or resistance to HMAs in patients with MDS, and we validated our results in an independent patient cohort treated in a randomized clinical trial. Identification of biomarkers that can provide personalized treatment approaches that can predict response or resistance to cancer therapy remains an important clinical challenge, and future drug development should focus on identifying the subgroup of patients who may benefit the most from any given cancer therapy. Such an approach can aid physicians and their patients in selecting the best available therapy to obtain the best outcome.


With significant numbers of individuals in the criminal justice system having mental health problems, court-based diversion programmes and liaison services have been established to address this problem.To examine the effectiveness of the New South Wales  court diversion programme in reducing re-offending among those diagnosed with psychosis by comparing the treatment order group with a comparison group who received a punitive sanction.Those with psychoses were identified from New South Wales Ministry of Health records between 2001 and 2012 and linked to offending records. Cox regression models were used to identify factors associated with re-offending.v. 27%). Drug-related offences were more likely to be punished with a punitive sanction than a treatment order (12% v. 2%).A total of 7743 individuals were identified as diagnosed with a psychotic disorder prior to their court finalisation date for their first principal offence. Overall, 26% of the cohort received a treatment order and 74% received a punitive sanction. The re-offending rate in the treatment order group was 12% lower than the punitive sanction group. ‘Acts intended to cause injury’ was the most common type of the first principal offence for the treatment order group compared with the punitive sanction group , receiving a treatment order by the court rather than a punitive sanction was associated with reduced risk for subsequent offending. We further examined actual mental health treatment received and found that receiving no treatment following the first offence was associated with an increased risk of re-offending and, so, highlighting the importance of treatment for those with serious mental illness in the criminal justice system. We examined factors associated with the incidence of re-offending by psychosis type, offence, sociodemographic factors and mental health service contact.,Diagnostic information was retrieved from the NSW Ministry of Health's Admitted Patient Data Collection (APDC) and the Emergency Department Data Collection (EDDC). The APDC covered the period 1 July 2001 to 31 December 2012, and the EDDC 1 January 2005 to 31 December 2012. Both the APDC and EDDC contain details of both primary and secondary diagnoses of health records, which were coded using ICD-9 and ICD-10,Individuals with psychosis were either diverted from the courts into treatment (treatment order group) and management under Section 32 and Section 33 of the Mental Health (Forensic Provisions) Act between 2001 and 2012 or received a punitive sanction  and were identified from the NSW Bureau of Crime Statistics and Research's Re-offending Database (RoD). Other data extracted from the RoD included: gender, Aboriginality status, date of birth, offence date, date of the finalisation of contact with the court, date of death, offence type and principal penalty. Minor traffic infringements were excluded from the offence data.Mental health treatment contacts were identified from the APDC, EDDC and Mental Health Ambulatory Data Collection after the court finalisation date for the first offence and before the second offence date or the end of the study period on 31 December 2015 for those who did not commit a second offence.ChoiceMaker software. Identifying information such as name, address, date of birth and gender for each data-set is included in the Master Linkage Key. No health data are used in the linkage process. Once the linkages were finalised, the CHeReL created a project person number for each person identified in the linkage, and assigned this project person number to the RoD, APDC, EDDC, RBDM death records. These data sources were merged by using the project person number.Record linkage between the health (APDC and EDDC), RoD and RBDM data sources was conducted by the NSW Ministry of Health's Centre for Health Record Linkage (CHeReL) using probabilistic record linkage methods and Those with a pre-existing diagnosis of psychosis prior to their court finalisation date for the first offence were identified and formed the basis for the cohort examined in the analysis. ICD-9 and ICD-10 primary and secondary diagnosis codes for psychosis were obtained from the APDC and EDDC.,The following ICD-9 and ICD-10 codes were used to define psychosis: schizophrenia and related psychoses ; affective psychosis , and substance-related psychosis .Socio-Economic Indexes for Areas (SEIFA) is a measure derived from the statistical local area, which is a geographic area based on local government area. One SEIFA measure is the Index of Relative Socio-economic Advantage and Disadvantage, which is based on national census data using income, education, employment, occupation and housing. Areas are ranked with the lowest indicating the most disadvantage and highest the most advantage for an area. For the purpose of the current analysis, we used the SEIFA score based on the area of residence at the time of the most recent diagnosis, and categorised the ten categories into two representing ‘disadvantaged’ (SEIFA score 1–5) and ‘advantaged’ (SEIFA score 6–10).Criminal charges were classified as either violent or non-violent based on the Australian and New Zealand Standard Offence Classification system.The outcomes of interest were: (a) time to the second offence following the first principal offence in the data-set by sanction type (i.e. treatment order versus punitive sanction) for men and women, offence type (violent and non-violent) and diagnostic group; and (b) the predictors of second offence.2 tests were used to examine the difference between the treatment order and punitive sanction groups among men and women, and Aboriginal and non-Aboriginal groups.Characteristics of the study population at the time of diagnosis were summarised using descriptive statistics frequencies and proportions for the categorical variables and median and interquartile range (IQR) for the continuous variables. χ,The main outcome variable was second offence following the first principal offence. For this  analysis, time to second offence was calculated as time (years) from the court finalisation date for the first principal offence record in the data to the second offence. Univariable and multivariable Cox regression models with random effect were used to identify significant predictors of the second offence after accounting for potential heterogeneity because of socioeconomic status (SEIFA).per se, we identified those codes in the Mental Health Ambulatory Data Collection system likely to involve a clinical component rather than those representing administrative tasks.Treatment received by those in both the treatment order (diverted) and comparison groups between the finalisation date for the first offence and second offence date (or end of the study for those who did not re-offend) was determined by examining all contacts with community mental health services , emergency department contacts associated with psychosis (EDDC) and hospital admissions associated with psychosis (APDC). As community mental health contacts do not always involve a clinical interaction The current study considered the following factors as potential predictors for second offence: court outcome (Section 32/Section 33 treatment order/type of punitive sanction), psychosis type , age at the first principal offence , Aboriginal status , marital status (married versus other), country of birth , contact with mental health services between the court finalisation date for the first principal offence and second offence (no versus yes) and type of first principal offence (violent versus non-violent). Crude re-offending incidence rates were presented using Kaplan–Meier curves for the treatment order and punitive sanction groups by gender, psychosis type and offence.Approvals for the project were independently received from the following: NSW Population & Health Services Research Ethics Committee (HREC/15/CIPHS/17); Justice Health and Forensic Mental Health Network (G324/14); Corrective Services NSW (D15/138715); and the NSW Aboriginal Health and Medical Research Council (1089/15).n = 1469), 85% received a Section 32 order and 15% received a Section 33 order.A total of 7743 individuals were identified who had been diagnosed with a psychotic disorder before the court finalisation date for their first principal offence. Of these 1996 (26%) received a treatment order and diverted from criminal justice system under Section 32 or Section 33 , and 5747 (74%) individuals received a punitive sanction  for the first principal offence. Of the 1996 individuals who received a treatment order, and for whom we had information on the type of order (P = 0.471). However, a significantly lower proportion of Aboriginal people received a treatment order compared with non-Aboriginal people   individuals with psychosis appearing in the NSW courts where the SCCLS was in operation, received a treatment order. The SCCLS currently operates in 22 courts across NSW growing from 7 courts in 2001.< 0.001) .Table 1v. 65%) and substance-related psychosis more common in the comparison (punitive) group (25% v. 7%) (The treatment order and punitive sanction groups were similar with regard to their age at the time of first offence: 34 years (IQR 27–44) and 34 years (IQR 27–42), respectively. However, schizophrenia and related psychoses prior to the court finalisation date was more common in the treatment order than the punitive sanction group (81% % v. 7%) . There wv. 27%) (v. 38%) (v. 2%). Few homicides and sexual offences occurred in the cohort, which is consistent with more serious matters being heard in higher courts.Overall, the most common first principal offence was ‘Acts intended to cause injury’, which was significantly higher in the treatment order group compared with the punitive sanction group (48% v. 27%) . Further(v. 38%) . Those cv. 62%). Similarly, in both men and women those who committed non-violent offences were less likely to receive a treatment order   .v. 7%) (The most common non-violent offence types in the diversion group were offences against justice/government procedures (23%), property and environmental damage (22%) and public order (15%), whereas among the punitive sanction group the most common non-violent offence types were drugs (19%), theft (19%) and offences against government procedures (17%). The most common type of violent offence committed for the first principal offence in both the treatment order and punitive sanction groups were acts intended to cause injury . Theft was more frequent in women compared with men in the treatment order group (11% v. 7%) .v. 50%). The median number of years between the finalisation date for the first principal offence and the second offence or the end study date for the diversion group was 5.1 years (IQR 2.2–8.4) and 4.0 year (IQR 1.1–7.6) for the punitive sanction group. These findings are further supported by survival analysis that indicated that there was a significant difference in the time to re-offence from the first recorded offence in the data-set between those men and women who received a treatment order and those who receive a punitive sanction (P < 0.001 log-Rank test) (Re-offending in the treatment order group was 12% lower than the punitive sanction group (38% nk test) . Re-offeCrude incidence rates for re-offending were also calculated by diagnostic groups. Re-offending rates ranged from 5.3 per 100 person-years (95% CI 4.2–6.6) for those with affective psychosis in the treatment order group to 7.8 per 100 person-years (95% CI 6.9–8.9) for those with affective psychosis in the punitive sanction group. For individuals with schizophrenia and related psychoses re-offending rates ranged from 7.0 per 100 person-years (95% CI 6.5–7.6) in the treatment order group to 10.6 per 100 person-years (95% CI 10.1–11.1) in the punitive sanction group. Moreover, re-offending rates for individuals with substance-related psychosis ranged from 8.1 per 100 person-years (95% CI 6.4–10.4) in the treatment order group to 12.3 per 100 person-years (95% CI 11.4–13.2) in the punitive sanction group.P = 0.006), and most effective for those diagnosed with affective psychosis (P < 0.001) (Treatment orders were least effective in terms of reducing re-offending in those with a diagnosis of substance-related psychosis (< 0.001) . Those w< 0.001) .Fig. 2KP < 0.001)  = 0.68, 95% CI 0.62–0.74, < 0.001) . Individ< 0.001) . In addiP < 0.001). In addition, the results showed that those of Aboriginal heritage had an 85% higher risk of re-offending compared with the non-Aboriginal group   .v. 80%) . The proportion of those with a record of contact with mental health services following the court finalisation date for the first offence was higher in the treatment order group versus the punitive sanction group (91% v. 80%) .Table 5Overall, regardless of group (i.e. treatment order or punitive) those who had no contact with mental health services had an increased risk of re-offending, and the more treatment received was associated with a reduced risk of re-offending. Those in either the treatment order or punitive sanction groups who received no discernible treatment had the highest risk of re-offending .This study represents the most comprehensive analysis of a court diversion programme for those with a mental illness in Australia, and one of the few studies to examine court diversion for those with serious mental illness (psychosis) who had contact with the NSW public service and mental health treatment services. Our findings indicate that diversion into treatment is associated with a significantly lower rate of subsequent re-offending in men and women, regardless of offence type (violent or non-violent). Those with psychosis who received a treatment order had a 12% lower re-offending rate than those in the comparison group who received a punitive sanction such as a fine, community order or good behaviour bond. Overall, the effectiveness of receiving a treatment order increased – re-offending was 32% lower hazard than in the punitive sanction group – when adjusted for age, marital status, country of birth and psychosis type.Between 2009 and 2011 the number of treatment orders was 50% lower in the Aboriginal group compared with the non-Aboriginal group. This may indicate that diversion programmes need to be culturally sensitive to Aboriginal people and further research is needed to examine this issue.The study also provides potential insights into magistrates' sentencing patterns for different offender groups. Overall, almost three-quarters of those with a prior diagnosis of psychosis were given punitive sanctions rather than a treatment order. This suggests caution on the part of magistrates to divert those before the courts into mental health treatment. Diversion into treatment under Section 32 or Section 33 of the Mental Health Act is discretionary. Magistrates are not obliged to deal with the person under Section 32 or Section 33, even if expert evidence is available to support the presence of a mental illness/condition or developmental disability.Once the magistrate has determined that an individual has a mental condition/illness or developmental disability they are required to consider whether diversion is ‘more appropriate’, with regard to factors such as the seriousness of the offence, the degree to which the condition contributed to the offence, the treatment and management plan and the likelihood of the person adhering to the treatment and management plan. Magistrates must also balance legal issues such as the need for punishment and deterrence, with the public interest in diverting the particular individual away from the criminal justice system and into treatment and management. In addition, magistrates have the option of dealing with the matter by way of a conditional bond that could include the expectation to pursue mental health treatment and in some cases be indistinguishable from a treatment order.This relatively low diversion rate 26%,  for thosIn addition, it is likely that a number of those who received a treatment order were followed up by private mental health practitioners rather than a public hospital or community mental health service. Private mental health practitioner records and general practitioner records are not part of the current linkage as these are held by the Commonwealth government as part of the Medicare system. Thus, it cannot be assumed that all of the 9% who were diverted failed to comply and this requires further investigation. These findings suggest that magistrates' discretionary application of diversion by treatment order warrants further investigation, particularly in light of our finding that engagement in treatment has a positive impact in terms of reducing subsequent offending.Those who had a diagnosis of psychosis closer in time to the court finalisation date were more likely to receive a treatment order than those with a more remote diagnosis; this may relate to the former group being more mentally unwell and this being apparent to the magistrate and lawyers. The median lag between diagnosis and court finalisation date was 114 days in the diversion group versus 258 days in the punitive sanction group. It is possible that closer proximity between the diagnosis of psychosis and the court appearance may impress on the magistrate an apparent association between mental illness and offending resulting in a treatment order rather than a punitive sanction. However, this could also reflect the effectiveness of having an on-site court liaison service available to the court to provide a more immediate opinion and increase confidence in a forensic mental health opinion given the pressure on magistrates to finalise matters and clear court backlogs. It is possible that a treatment order is a means of disposing of cases fairly quickly. Recently, the NSW government invested AU$39 million dollars to appoint new judges to clear court backlogs in NSW.v. 2%). This pattern is further reinforced by looking at the diagnosis whereby those with a prior diagnosis of substance-related psychosis were more likely to receive a punitive sanction and not a treatment order (25% v. 7%). Of the 701 offenders appearing before the courts with a drug-related offence, only 33 (5%) received a treatment order.The data suggest that magistrates are less likely to divert those with drug-related offences; these individuals were more likely to receive a punitive sanction than a treatment order (12% v. 38% non-violent). It is possible that magistrates divert individuals with psychosis with violent-related offences because they regard the violent behaviour as being driven by the psychosis and thus mitigating the level of responsibility. In contrast, in the drug-induced psychosis group, the violence is likely attributed to the drugs, not mental illness. It could be that magistrates perceive a nexus between illness and violence and have greater confidence in the effectiveness of treatment and management in diminishing the risk to the community in those with a non-drug-related psychosis as opposed to those with a drug-related psychosis. A further justification for this pattern may be that treatment orders appeared to be the least effective approach in terms of reducing re-offending in those with a diagnosis of psychosis because of psychoactive substance use.This contrasts with violence-related offences whereby magistrates were more likely to divert this group into treatment and management ; there may be social bias in the courts and community.Limitations of the current study include the extent to which the application of Section 32 or Section 33 orders might influence discretion by police in relation to charging individuals for subsequent incidents, but it is possible that greater latitude might be offered such individuals for their behaviour (relative to those who were punished rather than received treatment orders) to account for some of the differences in subsequent rates of court-determined re-offending. Furthermore, because of the observational nature of the study, we cannot exclude the possibility that punitive sanctions in this population have an unwanted effect of increasing, rather than decreasing, the risk of re-offending and that this might account for some of the differences in rates of re-offending by court outcomes that we observed.We did not have access to general practitioner records of treatment for the cohort and this may have resulted in an incorrect assumption that some of those individuals identifying as receiving no treatment and management may, in fact, have received treatment and management. We had no detailed information on the type and quality of treatment and management received for those individuals who had a record of contact with a mental health service. However, we were able to disaggregate administrative contacts from actual clinical contact and this is one of the strengths of the current study. Future research could focus on the impact of specific treatment modalities to enable better elucidation of what works for this population in terms of reducing offending. Similarly, linking data such as ours to Medicare records held by the Commonwealth government would allow the impact of general practitioner contact and medication to be considered. As is the case in many longitudinal data-linkage studies, we did not have access to complete offence histories for the cohort and it is likely that some individuals had offence records that pre-dated the court outcome data that we had access to.Notwithstanding these considerations, overall our findings suggest that changes to current practices by the SCCLS and community mental health services might improve outcomes for those with psychosis appearing before the courts. Assuming that treatment and management is effective in reducing subsequent offending in those with psychosis, it may be worthwhile resourcing the community forensic mental health services to ensure that treatment and management is received by those with Section 32 treatment orders (i.e. self-initiated treatment). Interventions and incentives may need to be developed and explored to increase adherence.Magistrates' behaviour may reflect their long-term observations and knowledge of different offender groups' behaviour in complying with treatment orders, such that they perceive those with drugs-related diagnoses as being less likely to respond to treatment. We were unable to conduct a separate analysis of treatment and management success or failure among those who had committed drug-related offences as only 5% of these individuals received a treatment order.In conclusion, this study contributes to the existing literature on court diversion schemes for offenders who are mentally ill by demonstrating that offenders with a diagnosis of psychosis prior to their court appearance for the first principal offence were less likely to commit a second offence if they received a treatment order rather than a punitive sanction, such as a fine or good behaviour bond. It appears that those presented before the courts with either a drug-related offence or diagnoses of substance-related psychosis were less likely to receive a treatment order.


Antidepressants such as amitryptiline and fluoxetine are on the list of modern essential medicines of the World Health Organization. However, there are growing concerns regarding the ecological impact of these pharmaceuticals, leading to a great need to improve current wastewater treatment procedures. In this contribution, we will report on the use of molecularly imprinted polymers (MIPs) for the extraction of antidepressants in water samples. MIPs were developed for fluoxetine and duloxetine, antidepressants belonging to the class of selective serotonin reuptake inhibitors (SSRIs). The binding capacity of these microparticles was evaluated using ultraviolet–visible (UV–Vis) spectroscopy. A new high-performance liquid chromatography (HPLC) procedure coupled to UV detection was developed, which enabled the study of mixtures of fluoxetine and duloxetine with other nitrogen-containing compounds. These results indicate that it is possible to selectively extract SSRIs from complex samples. Therefore, these versatile polymers are a promising analytical tool for the clean-up of water samples, which will benefit aquatic life and reduce the ecological impact of pharmaceuticals. Prescriptions of antidepressants are at an all-time high; in the last decade, the number of antidepressants that have been dispensed by pharmacies in the United Kingdom have increased by more than 100% . SelectiWith their increased use and availability, the environmental impact of these antidepressants becomes a greater concern. Pharmaceuticals are widely found in aquatic systems, which is due to a combination of low removal efficiency in sewage treatment plants (STPs) or a lack of available STPs . The preLiquid chromatography–mass spectrometry (LC–MS) ,14, gas The reports on MIPs for fluoxetine detection in literature are sparse. Nezhadali et al.  reportedFor environmental purposes, it is preferable to detect a range of SSRIs instead of focusing solely on fluoxetine. We demonstrate that when imprinting fluoxetine as the template, we can develop materials that are suitable sorbents for fluoxetine but also other SSRIs, such as duloxetine . We moniS)-duloxetine hydrochloride salt (99%) were both purchased from TCI Chemicals Ltd. . Clomipramine hydrochloride salt (98%), initiator azobisisobutyronitrile (AIBN) and caffeine were purchased from Sigma-Aldrich . Methacrylic acid (MAA), ethylene glycol dimethacrylate (EGDMA), 2-(hydroxyethyl)methacrylate (HEMA), acrylamide (AA), itaconic acid (IA), acrylonitrile, 2-vinylpyridine (2-VP), toluene, methanol, and dimethylsulfoxide (DMSO) were purchased from Acros . Prior to polymerization, the stabilizers in all monomers and crosslinker monomers were removed by passing the solutions over a column packed with alumina (Sigma-Aldrich). Solutions of HCl and NaOH (0.1 mM) in water were used to adjust the pH values of these solutions. All solutions were prepared with deionized water or phosphate-buffered saline (PBS) solutions. Phosphate-buffered saline solutions were prepared using Dulbecco tablets obtained from Oxoid Limited . (±)-Fluoxetine hydrochloride salt (99%), and , using a 50 mL stainless steel grinding jar and five stainless steel grinding balls of 1 cm diameter each. Three Soxhlet extractions were performed sequentially on the grounded polymer, with different solvent mixtures: (i) methanol/deionized water (50:50); (ii) acetonitrile/glacial acetic acid (50:50); and (iii) methanol/deionized water (50:50). The extraction was performed until no traces of the template were observed in the filtrate using UV–Vis spectroscopy. Thermogravimetric analysis (TGA) with a TG4000 from Perkin Elmer  was performed to assess the stability of the polymer after extraction of the template. The particles were dried under vacuum for 16 h, reground, and sieved to obtain only particles with sizes smaller than <25 µm. Nonimprinted polymers (NIP-1–7) were prepared in the same manner but without the addition of the template during the polymerization. MIP-8 was also prepared using the same protocol and IA as monomer, but using a 1:1 mixture of fluoxetine and duloxetine as a template instead of just fluoxetine. To compare the specificity of the polymers towards fluoxetine, imprint factors (IFs) were determined by fitting the data with an allometric fit for concentrations of 0.05 and 0.25 mM. (±)-Fluoxetine hydrochloride (0.16 mmol) was dissolved in DMSO (7 mL) together with a functional monomer (0.32 mmol). The functional monomers that were used for the synthesis of fluoxetine MIPs (MIP-1-7) are described in λmax = 264 nm) of the solutions with an Agilent 8453 spectrophotometer . For each experiment, MIP or NIP powder (10 mg) was added to several solutions of antidepressants in PBS (5 mL) with different known concentrations. The resulting suspensions were placed on a STUART SSM1 orbital shaker  at 125 rpm for 90 min and passed through 0.45 µm filters prior to UV–Vis analysis. Optical batch rebinding experiments were evaluated by measuring absorbance values (fC) by dividing the absorbance over the gradient of the calibration curve. Subsequently, the amount bound to template (bC) was calculated by subtracting the initial concentration added to the solution (iC) by the free template concentration of the filtered solution (fC). The moles of template bound per gram of polymer (bS) was obtained by dividing bC × V, where V is the volume in liters, over the amount of polymer added in the rebinding experiment (10 mg). The IF was determined by dividing the bS for a given MIP by the bS for the corresponding NIP.Various fluoxetine concentrations, ranging from 0 to 1.5 mM in PBS, were used to construct dose-response curves for MIP-1–7. These curves were then employed to determine the free concentration of the template in the filtered solutions (Cf) of 0.05 and 0.2 mM. This was done by fitting the MIP data with an allometric fit (y = bax) which is in line with the heterogeneity of the binding sites. The data generated with the NIP, in general, followed a linear fit line, which is to be expected for nonspecific binding. The amount of analyte bound at Cf was determined according to the fit for both MIPs and NIPs, with an IF calculated at a given concentration. As a measure of specificity and in order to compare the different MIP compositions, the IF was determined at a free concentration of the template  and the free (fC) concentration were analyzed for binding. Data analysis was carried out using ChemStation for LC software . Measurements were performed in triplicate in order to determine the standard deviation of the signal. Two sets of experimental parameters were used, one for the analysis of the individual antidepressants and one for the mixture of them. Both the initial (v/v) mixture of acetonitrile and orthophosphoric acid solution (0.1% H3PO4 (aq), pH 2.1), with the injection volume set at 10 µL. For the mixed samples, a longer column  was used along with sodium acetate buffer (pH 4.5) in the mobile phase to facilitate discrimination between the various drugs. The mobile phase was composed of the buffer along with acetonitrile and methanol (6.5:3.2:0.3), which was run at a flow rate of 1.1 mL/min using an injection volume of 20 µL.For individual analysis, a Hypersil ODS-2 C18 HPLC column  was used with a flow rate of 1.1 mL/min. The mobile phase was a 75:25  containing fluoxetine concentrations ranging from 0 to 1 mM using UV–Vis spectrophotometry as the read-out technique. Binding isotherms were constructed by using both results obtained by UV–Vis and HPLC analysis. The binding of fluoxetine to the MIP and NIP layers is provided in MIP-1–3, synthesized with methacrylic-based monomers  behaves the same as fluoxetine (provided with a secondary amine) suggests that binding may not only be caused by H bonding but also by other noncovalent interactions. The binding of duloxetine at lower initial concentrations is similar to both these two compounds, but the overall binding capacity of ≈600 µmol/g is slightly lower. The reason for this could be that these compounds share a similar pharmacophore, and the main interaction of the functional monomer is with the nitrogen. Caffeine is substantially different in structure from the other compounds, and thus shows minimal binding to MIP-2. This is clear from iC = 0.75 mM). MIP-2 was used in these experiments, with fluoxetine as the template. Since HPLC is able to separate out this mixture, it can be determined whether binding to the polymers is a competitive process and what level of selectivity can be achieved. The binding time was kept at 1 h but the amount of the polymer was reduced from 10 to 5 mg due to the higher injection volume used in these measurements. This prevented saturation of the signal, and measured peak areas would fall within the calibration range. The retention time for all compounds is provided in The viability of the method to separate mixtures from compounds of the same drug family was first evaluated by measuring a mixture with 1:1 ratio with each of the other compounds  as well as structurally different drugs (caffeine) which allows competition for binding to occur. These mixtures are more representative for real samples, which will be composed of a variety of compounds present in different ratios. The differences in binding at a concentration of 0.25 mM between MIP-2 and NIP-2 for fluoxetine in the presence of other compounds are listed in As expected, there is no difference in binding for MIP and NIP towards caffeine, as its structure is significantly different from fluoxetine. Fluoxetine had the highest specific binding compared to the other compounds, which shows that there is still an increased preference for the imprinted molecule even when there are substantial similarities in structure between the competing species. However, the imprint factors are significant lower compared to The presence of multiple templates on the MIPs’ performance was explored using fluoxetine and duloxetine. MIP-8 was prepared using IA as monomer and an equimolar mixture of fluoxetine and duloxetine as template. Batch rebinding experiments were performed on the resulting polymer MIP-8  as previously outlined, with a concentration range from 0 to 2.5 mM for fluoxetine and duloxetine each (only fluoxetine was studied for NIP-8) in PBS. The results analyzed by HPLC are shown in Both targets were successfully bound by the polymer, with increased levels seen at each concentration relative to the NIP. This demonstrates that both molecules can be imprinted simultaneously without inhibiting the polymer’s ability to rebind. Interestingly, more of the fluoxetine compared to the duloxetine was bound at lower concentrations, indicating the developed polymers had a preference for one target molecule. At higher concentrations, where binding sites are increasingly occupied, the binding of both target molecules converged. This indicates there is no significant difference between binding capacities of these polymers for the two target molecules, but there is a difference in the affinity of binding sites for fluoxetine and duloxetine. A larger library of these materials should be prepared in order to gain further insight in the properties of these dual-imprinted polymers. Imprinted polymers were successfully synthesized and optimized for fluoxetine, with various functional monomers investigated. The resulting material showed strong binding of the target molecule as well as compounds within the same drug family. Analysis using HPLC was able to show preferential binding of the target to the MIP for solutions containing multiple SSRIs although evidence of competition was present. These results indicate that high selectivity for SSRIs can be obtained using low-cost, easily synthesized imprinted polymers. Experimental parameters, such as concentration ratios and absorption time, can be adjusted in the future to tune the MIP’s selectivity further.