Nutrients in the nexus

Synthetic nitrogen (N) fertilizer has enabled modern agriculture to greatly improve human nutrition during the twentieth century, but it has also created unintended human health and environmental pollution challenges for the twentyfirst century. Averaged globally, about half of the fertilizer-N applied to farms is removed with the crops, while the other half remains in the soil or is lost from farmers’ fields, resulting in water and air pollution. As human population continues to grow and food security improves in the developing world, the dual development goals of producing more nutritious food with low pollution will require both technological and socioeconomic innovations in agriculture. Two case studies presented here, one in sub-Saharan Africa and the other in Midwestern United States, demonstrate how management of nutrients, water, and energy is inextricably linked in both small-scale and large-scale food production, and that science-based solutions to improve the efficiency of nutrient use can optimize food production while minimizing pollution. To achieve the needed large increases in nutrient use efficiency, however, technological developments must be accompanied by policies that recognize the complex economic and social factors affecting farmer decision-making and national policy priorities. Farmers need access to affordable nutrient supplies and support information, and the costs of improving efficiencies and avoiding pollution may need to be shared by society through innovative policies. Success will require interdisciplinary partnerships across public and private sectors, including farmers, private sector crop advisors, commodity supply chains, government agencies, university research and extension, and consumers

Nutrients in the nexus

Synthetic nitrogen (N) fertilizer has enabled modern agriculture to greatly improve human nutrition during the twentieth century, but it has also created unintended human health and environmental pollution challenges for the twentyfirst century. Averaged globally, about half of the fertilizer-N applied to farms is removed with the crops, while the other half remains in the soil or is lost from farmers’ fields, resulting in water and air pollution. As human population continues to grow and food security improves in the developing world, the dual development goals of producing more nutritious food with low pollution will require both technological and socioeconomic innovations in agriculture. Two case studies presented here, one in sub-Saharan Africa and the other in Midwestern United States, demonstrate how management of nutrients, water, and energy is inextricably linked in both small-scale and large-scale food production, and that science-based solutions to improve the efficiency of nutrient use can optimize food production while minimizing pollution. To achieve the needed large increases in nutrient use efficiency, however, technological developments must be accompanied by policies that recognize the complex economic and social factors affecting farmer decision-making and national policy priorities. Farmers need access to affordable nutrient supplies and support information, and the costs of improving efficiencies and avoiding pollution may need to be shared by society through innovative policies. Success will require interdisciplinary partnerships across public and private sectors, including farmers, private sector crop advisors, commodity supply chains, government agencies, university research and extension, and consumers

Genome-wide analysis in UK Biobank identifies four loci associated with mood instability and genetic correlation with MDD, anxiety disorder and schizophrenia

Mood instability is a core clinical feature of affective and psychotic disorders. In keeping with the Research Domain Criteria approach, it may be a useful construct for identifying biology that cuts across psychiatric categories. We aimed to investigate the biological validity of a simple measure of mood instability and evaluate its genetic relationship with several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia, attention deficit hyperactivity disorder (ADHD), anxiety disorder and post-traumatic stress disorder (PTSD). We conducted a genome-wide association study (GWAS) of mood instability in 53,525 cases and 60,443 controls from UK Biobank, identifying four independently associated loci (on chromosomes 8, 9, 14 and 18), and a common single-nucleotide polymorphism (SNP)-based heritability estimate of ~8%. We found a strong genetic correlation between mood instability and MDD (r g = 0.60, SE = 0.07, p = 8.95 × 10−17) and a small but significant genetic correlation with both schizophrenia (r g = 0.11, SE = 0.04, p = 0.01) and anxiety disorders (r g = 0.28, SE = 0.14, p = 0.04), although no genetic correlation with BD, ADHD or PTSD was observed. Several genes at the associated loci may have a role in mood instability, including the DCC netrin 1 receptor (DCC) gene, eukaryotic translation initiation factor 2B subunit beta (eIF2B2), placental growth factor (PGF) and protein tyrosine phosphatase, receptor type D (PTPRD). Strengths of this study include the very large sample size, but our measure of mood instability may be limited by the use of a single question. Overall, this work suggests a polygenic basis for mood instability. This simple measure can be obtained in very large samples; our findings suggest that doing so may offer the opportunity to illuminate the fundamental biology of mood regulation

Multiple reassortment events in the evolutionary history of H1N1 influenza A virus since 1918

The H1N1 subtype of influenza A virus has caused substantial morbidity and mortality in humans, first documented in the global pandemic of 1918 and continuing to the present day. Despite this disease burden, the evolutionary history of the A/H1N1 virus is not well understood, particularly whether there is a virological basis for several notable epidemics of unusual severity in the 1940s and 1950s. Using a data set of 71 representative complete genome sequences sampled between 1918 and 2006, we show that segmental reassortment has played an important role in the genomic evolution of A/H1N1 since 1918. Specifically, we demonstrate that an A/H1N1 isolate from the 1947 epidemic acquired novel PB2 and HA genes through intra-subtype reassortment, which may explain the abrupt antigenic evolution of this virus. Similarly, the 1951 influenza epidemic may also have been associated with reassortant A/H1N1 viruses. Intra-subtype reassortment therefore appears to be a more important process in the evolution and epidemiology of H1N1 influenza A virus than previously realized

Comparison of conventional lipoprotein tests and apolipoproteins in the prediction of cardiovascular disease: data from UK Biobank

Background: Total cholesterol and high-density lipoprotein cholesterol (HDL-C) measurements are central to cardiovascular disease (CVD) risk assessment, but there is continuing debate around the utility of other lipids for risk prediction. Methods: Participants from UK Biobank without baseline CVD and not taking statins, with relevant lipid measurements (n=346 686), were included in the primary analysis. An incident fatal or nonfatal CVD event occurred in 6216 participants (1656 fatal) over a median of 8.9 years. Associations of nonfasting lipid measurements (total cholesterol, HDL-C, non–HDL-C, direct and calculated low-density lipoprotein cholesterol [LDL-C], and apolipoproteins [Apo] A1 and B) with CVD were compared using Cox models adjusting for classical risk factors, and predictive utility was determined by the C-index and net reclassification index. Prediction was also tested in 68 649 participants taking a statin with or without baseline CVD (3515 CVD events). Results: ApoB, LDL-C, and non–HDL-C were highly correlated (r>0.90), while HDL-C was strongly correlated with ApoA1 (r=0.92). After adjustment for classical risk factors, 1 SD increase in ApoB, direct LDL-C, and non–HDL-C had similar associations with composite fatal/nonfatal CVD events (hazard ratio, 1.23, 1.20, 1.21, respectively). Associations for 1 SD increase in HDL-C and ApoA1 were also similar (hazard ratios, 0.81 [both]). Adding either total cholesterol and HDL-C, or ApoB and ApoA, to a CVD risk prediction model (C-index, 0.7378) yielded similar improvement in discrimination (C-index change, 0.0084; 95% CI, 0.0065, 0.0104, and 0.0089; 95% CI, 0.0069, 0.0109, respectively). Once total and HDL-C were in the model, no further substantive improvement was achieved with the addition of ApoB (C-index change, 0.0004; 95% CI, 0.0000, 0.0008) or any measure of LDL-C. Results for predictive utility were similar for a fatal CVD outcome, and in a discordance analysis. In participants taking a statin, classical risk factors (C-index, 0.7118) were improved by non–HDL-C (C-index change, 0.0030; 95% CI, 0.0012, 0.0048) or ApoB (C-index change, 0.0030; 95% CI, 0.0011, 0.0048). However, adding ApoB or LDL-C to a model already containing non–HDL-C did not further improve discrimination. Conclusions: Measurement of total cholesterol and HDL-C in the nonfasted state is sufficient to capture the lipid-associated risk in CVD prediction, with no meaningful improvement from addition of apolipoproteins, direct or calculated LDL-C

Glycated hemoglobin, prediabetes and the links to cardiovascular disease: data from UK Biobank

OBJECTIVE: HbA1c levels are increasingly measured in screening for diabetes; we investigated whether HbA1c may simultaneously improve cardiovascular disease (CVD) risk assessment, using QRISK3, American College of Cardiology/American Heart Association (ACC/AHA), and Systematic COronary Risk Evaluation (SCORE) scoring systems. RESEARCH DESIGN AND METHODS: UK Biobank participants without baseline CVD or known diabetes (n = 357,833) were included. Associations of HbA1c with CVD was assessed using Cox models adjusting for classical risk factors. Predictive utility was determined by the C-index and net reclassification index (NRI). A separate analysis was conducted in 16,596 participants with known baseline diabetes. RESULTS: Incident fatal or nonfatal CVD, as defined in the QRISK3 prediction model, occurred in 12,877 participants over 8.9 years. Of participants, 3.3% (n = 11,665) had prediabetes (42.0–47.9 mmol/mol [6.0–6.4%]) and 0.7% (n = 2,573) had undiagnosed diabetes (≥48.0 mmol/mol [≥6.5%]). In unadjusted models, compared with the reference group (<42.0 mmol/mol [<6.0%]), those with prediabetes and undiagnosed diabetes were at higher CVD risk: hazard ratio (HR) 1.83 (95% CI 1.69–1.97) and 2.26 (95% CI 1.96–2.60), respectively. After adjustment for classical risk factors, these attenuated to HR 1.11 (95% CI 1.03–1.20) and 1.20 (1.04–1.38), respectively. Adding HbA1c to the QRISK3 CVD risk prediction model (C-index 0.7392) yielded a small improvement in discrimination (C-index increase of 0.0004 [95% CI 0.0001–0.0007]). The NRI showed no improvement. Results were similar for models based on the ACC/AHA and SCORE risk models. CONCLUSIONS: The near twofold higher unadjusted risk for CVD in people with prediabetes is driven mainly by abnormal levels of conventional CVD risk factors. While HbA1c adds minimally to cardiovascular risk prediction, those with prediabetes should have their conventional cardiovascular risk factors appropriately measured and managed

Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk taker?” Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders

The genomic basis of mood instability:identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health