Research note: Urban street tree density and antidepressant prescription rates—A cross-sectional study in London, UK

This is the author’s version of a work that was accepted for publication in Landscape and Urban Planning. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at doi:10.1016/j.landurbplan.2014.12.005.Abstract: Growing evidence suggests an association between access to urban greenspace and mental health and wellbeing. Street trees may be an important facet of everyday exposure to nature in urban environments, but there is little evidence regarding their role in influencing population mental health. In this brief report, we raise the issue of street trees in the nature-health nexus, and use secondary data sources to examine the association between the density of street trees (trees/km street) in London boroughs and rates of antidepressant prescribing. After adjustment for potential confounders, and allowing for unmeasured area-effects using Bayesian mixed effects models, we find an inverse association, with a decrease of 1.18 prescriptions per thousand population per unit increase in trees per km of street (95% credible interval 0.00, 2.45). This study suggests that street trees may be a positive urban asset to decrease the risk of negative mental health outcomes.European Regional Development Fund Programme 2007 to 2013 and European Social Fund Convergence Programme for Cornwall and the Isles of Scill

Multi-omic prediction of incident type 2 diabetes.

AIMS/HYPOTHESIS: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. METHODS: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c. RESULTS: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period. CONCLUSIONS/INTERPRETATION: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions

Trust or verification? Accepting Vulnerability in the making of the INF Treaty

This chapter uses the Intermediate-Range Nuclear Forces (INF) Treaty as a case study to explore the relationship between trust and verification. It argues that the acceptance of verification measures has to be considered an act of trust, since it implies the acceptance of one's vulnerability as a result of an altered perception of the trustworthiness of one's opponent. More specifically, the chapter illustrates how Gorbachev's notion of trustworthiness toward the United States changed through the influence of his inner circle, his understanding of the dynamics of a security dilemma fed by mutual fear and mistrust, his trusting actions toward the development of a common security on an international level, and his personal relationship with Ronald Reagan

Phylogenetics and Mitogenome Organisation in Black Corals (Anthozoa: Hexacorallia: Antipatharia): An Order-Wide Survey Inferred From Complete Mitochondrial Genomes

Black corals (Anthozoa: Antipatharia) are an ecologically and culturally important group of deep-sea cnidarians. However, as the majority of species inhabit depths >50 m, they are relatively understudied. The inaccessibility of well-preserved tissue for species of interest has limited the scope of molecular analysis, and as a result only a small number of antipatharian mitochondrial genomes have been published. Using next generation sequencing, 18 complete and five partial antipatharian mitochondrial genomes were assembled, increasing the number of complete mitochondrial genomes to 22. This includes species from six antipatharian families, four of which were previously unrepresented, enabling the first family-level, full mitochondrial gene analysis over the whole order. The circular mitogenomes ranged in size from 17,681 to 21,669 bp with the large range in size due to the addition of an intron in COX1 in some species and size variation of intergenic regions. All mitogenomes contained the genes standard to all hexacoral mitogenomes (13 protein coding genes, two rRNAs and two tRNAs). The only difference in gene content is the presence of the COX1 intron in five families. The most variable mitochondrial gene is ND4 which may have implications for future barcoding studies. Phylogenetic analysis confirms that Leiopathidae is sister to all other families. Families Antipathidae, Cladopathidae and Schizopathidae are polyphyletic, supporting previous studies that call for a taxonomic revision

What accounts for ‘England’s green and pleasant land’? A panel data analysis of mental health and land cover types in rural England

This is the author’s version of a work that was accepted for publication in Landscape and Urban Planning. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at doi:10.1016/j.landurbplan.2015.05.008.Exposure to green space is associated with a variety of positive health states. Research to date has focused primarily on ‘generic’ green space in urban areas, where green space is relatively scarce and where it is dominated by playing fields and parks. The current research adds to our understanding with an examination of relationships between different types of green space and mental health in rural areas in England (approximate rural population = 4 million). The aggregate land cover classes of Land Cover Map 2007 were linked to rural residential areas (Lower-level Super Output Areas) and then linked to rural participants (n = 2020) in the 18-year longitudinal British Household Panel Survey. Random effects regression of mental health (as measured by GHQ12 scores) against land cover enabled effects to be simultaneously estimated from both mean between-individual differences and from within-individual differences over time. The nine natural land cover classes (Broadleaved woodland; Coniferous woodland; Arable; Improved grassland; Semi-natural grassland; Mountain, heath and bog; Saltwater; Freshwater; Coastal) were not significantly associated with differences in mental health between individuals. However, significant relationships were observed between some types of land cover and within-individual change in mental health amongst individuals who relocated during the 18 annual waves of the panel. These findings indicate the presence of important health related ecosystem services from different land cover types that have not previously been investigated and which help more effective spatial planning and land use management.Economic and Social Research CouncilNational Institute for Health Research Health Protection Research Unit (NIHR HPRU

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study.

BACKGROUND: Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. METHODS: We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. FINDINGS: Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. INTERPRETATION: We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. FUNDING: Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust

Beyond greenspace: an ecological study of population general health and indicators of natural environment type and quality.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Many studies suggest that exposure to natural environments ('greenspace') enhances human health and wellbeing. Benefits potentially arise via several mechanisms including stress reduction, opportunity and motivation for physical activity, and reduced air pollution exposure. However, the evidence is mixed and sometimes inconclusive. One explanation may be that "greenspace" is typically treated as a homogenous environment type. However, recent research has revealed that different types and qualities of natural environments may influence health and wellbeing to different extents. METHODS: This ecological study explores this issue further using data on land cover type, bird species richness, water quality and protected or designated status to create small-area environmental indicators across Great Britain. Associations between these indicators and age/sex standardised prevalence of both good and bad health from the 2011 Census were assessed using linear regression models. Models were adjusted for indicators of socio-economic deprivation and rurality, and also investigated effect modification by these contextual characteristics. RESULTS: Positive associations were observed between good health prevalence and the density of the greenspace types, "broadleaf woodland", "arable and horticulture", "improved grassland", "saltwater" and "coastal", after adjusting for potential confounders. Inverse associations with bad health prevalence were observed for the same greenspace types, with the exception of "saltwater". Land cover diversity and density of protected/designated areas were also associated with good and bad health in the predicted manner. Bird species richness (an indicator of local biodiversity) was only associated with good health prevalence. Surface water quality, an indicator of general local environmental condition, was associated with good and bad health prevalence contrary to the manner expected, with poorer water quality associated with better population health. Effect modification by income deprivation and urban/rural status was observed for several of the indicators. CONCLUSIONS: The findings indicate that the type, quality and context of 'greenspace' should be considered in the assessment of relationships between greenspace and human health and wellbeing. Opportunities exist to further integrate approaches from ecosystem services and public health perspectives to maximise opportunities to inform policies for health and environmental improvement and protection.Economic and Social Research CouncilEuropean Regional Development Fund Programme 2007 to 2013 and European Social Fund Convergence Programme for Cornwall and the Isles of Scill

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan® v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries

The responsibility to protect and the use of force: remaking the procrustean bed?

The emergence of the Responsibility to Protect (R2P) owed much to the need to enhance the UN’s ability to act forcibly in the face of the most extreme cases of gross human suffering. Too often in the past such responses were emasculated or thwarted by the necessity to successfully navigate the UN Charter’s prescriptions over the use of force, by the unwillingness of member states to provide military forces, or by a combination of the two. In accepting that certain types of inhuman activity can lead to the legitimate use of force within the UN Charter framework, the adoption of R2P appeared to resolve at least some of these problems, and as such it offered hope to those wishing to see the UN adopt a more assertive response to the grossest of human rights abuses. But, using stalemate over Syria as its backdrop, this article demonstrates the dubiousness of the claim that such a normative development can ever trump the hard edged political and strategic factors which determine when states will accept and/or participate in the use of force, and it suggests a radical solution to the dangers inherent in R2P’s intimate association with military intervention