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The nitrogen (N) isotope composition (δ15N) of cold-water corals is a promising proxy for reconstructing past ocean N cycling, as a strong correlation was found between the δ15N of the organic nitrogen preserved in coral skeletons and the δ15N of particulate organic matter exported from the surface ocean. However, a large offset of 8 ‰–9 ‰ between the δ15N recorded by the coral and that of exported particulate organic matter remains unexplained. The 8 ‰–9 ‰ offset may signal a higher trophic level of coral dietary sources, an unusually large trophic isotope effect or a biosynthetic δ15N offset between the coral's soft tissue and skeletal organic matter, or some combinations of these factors. To understand the origin of the offset and further validate the proxy, we investigated the trophic ecology of the asymbiotic scleractinian cold-water coral Balanophyllia elegans, both in a laboratory setting and in its natural habitat. A long-term incubation experiment of B. elegans fed on an isotopically controlled diet yielded a canonical trophic isotope effect of 3.0 ± 0.1 ‰ between coral soft tissue and the Artemia prey. The trophic isotope effect was not detectably influenced by sustained food limitation. A long N turnover of coral soft tissue, expressed as an e-folding time, of 291 ± 15 d in the well-fed incubations indicates that coral skeleton δ15N is not likely to track subannual (e.g., seasonal) variability in diet δ15N. Specimens of B. elegans from the subtidal zone near San Juan Channel (WA, USA) revealed a modest difference of 1.2 ± 0.6 ‰ between soft tissue and skeletal δ15N. The δ15N of the coral soft tissue was 12.0 ± 0.6 ‰, which was ∼6 ‰ higher than that of suspended organic material that was comprised dominantly of phytoplankton – suggesting that phytoplankton is not the primary component of B. elegans' diet. An analysis of size-fractionated net tow material suggests that B. elegans fed predominantly on a size class of zooplankton ≥500 µm, implicating a two-level trophic transfer between phytoplankton material and coral tissue. These results point to a feeding strategy that may result in an influence of the regional food web structure on the cold-water coral δ15N. This factor should be taken into consideration when applying the proxy to paleo-oceanographic studies of ocean N cycling.

Background: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene-drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed., Methods: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug-gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug-gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug-gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants., Findings: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54-0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61-0·79]; p <0·0001)., Interpretation: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe., Funding: European Union Horizon 2020., Competing Interests: Declaration of interests MP received partnership funding from the UK Medical Research Council (MRC) Clinical Pharmacology Training Scheme (cofunded by MRC, Roche, Union Chimique Belge [UCB] Pharma, Eli Lilly, and Novartis); a PhD studentship jointly funded by the UK Engineering and Physical Sciences Research Council and AstraZeneca; unrestricted educational grant support for the UK Pharmacogenetics and Stratified Medicine Network from Bristol Myers Squibb; and human leucocyte antigen genotyping panel with MC Diagnostics but does not benefit financially from this, outside of the submitted work. JCS received speaker honoraria from Novartis for lectures on CYP2C9 pharmacogenetics and siponimod metabolism, outside of the submitted work. MS was partly supported by the Robert Bosch Stiftung and German Research Foundation (DFG) under Germany's Excellence Strategy (EXC 2180—390900677); and outside of the submitted work received support from Green Cross WellBeing, Gilead Sciences, Robert Bosch, CORAT Therapeutics, and Agena Bioscience. ES was partly supported by the Robert Bosch Stiftung and the German Research Foundation (DFG) under Germany's Excellence Strategy (EXC 2180—390900677). RT was partly supported by the Robert Bosch Stiftung. MK received research funding from Bayer and Roche, educational grants from Novartis and Servier, and consultancy fees from Pharmetheus, outside of the submitted work. SJ received consultancy fees from Pharmetheus, outside of the submitted work. All other authors declare no competing interests., (Crown Copyright © 2023 Published by Elsevier Ltd. All rights reserved.)

Most of the prescribing and dispensing of medicines happens in primary care. Pharmacogenomics (PGx) is the study and clinical application of the role of genetic variation on drug response. Mounting evidence suggests PGx can improve the safety and/or efficacy of several medications commonly prescribed in primary care. However, implementation of PGx has generally been limited to a relatively few academic hospital centres, with little adoption in primary care. Despite this, many primary healthcare providers are optimistic about the role of PGx in their future practice. The increasing prevalence of direct-to-consumer genetic testing and primary care PGx studies herald the plausible gradual introduction of PGx into primary care and highlight the changes needed for optimal translation. In this article, the potential utility of PGx in primary care will be explored and on-going barriers to implementation discussed. The evidence base of several drug-gene pairs relevant to primary care will be outlined with a focus on antidepressants, codeine and tramadol, statins, clopidogrel, warfarin, metoprolol and allopurinol. This review is intended to provide both a general introduction to PGx with a more in-depth overview of elements relevant to primary care.