Reprint of: High prey-predator size ratios and unselective feeding in copepods: A seasonal comparison of five species with contrasting feeding modes

There has been an upsurge of interest in trait-based approaches to zooplankton, modelling the seasonal changes in the feeding modes of zooplankton in relation to phytoplankton traits such as size or motility. We examined this link at two English Channel plankton monitoring sites south of Plymouth (L4 and E1). At L4 there was a general transition from diatoms in spring to motile microplankton in summer and autumn, but this was not mirrored in the succession of copepod feeding traits; for example the ambushing Oithona similis dominated during the spring diatom bloom. At nearby E1 we measured seasonality of food and grazers, finding strong variation between 2014 and 2015 but overall low mesozooplankton biomass (median 4.5 mg C m−3). We also made a seasonal grazing study of five copepods with contrasting feeding modes (Calanus helgolandicus, Centropages typicus, Acartia clausi, Pseudocalanus elongatus and Oithona similis), counting the larger prey items from the natural seston. All species of copepod fed on all food types and differences between their diets were only subtle; the overriding driver of diet was the composition of the prey field. Even the smaller copepods fed on copepod nauplii at significant rates, supporting previous suggestions of the importance of intra-guild predation. All copepods, including O. similis, were capable of tackling extremely long (>500 µm) diatom chains at clearance rates comparable to those on ciliates. Maximum observed prey:predator length ratios ranged from 0.12 (C. helgolandicus) up to 0.52 (O. similis). Unselective feeding behaviour and the ability to remove highly elongated cells have implications for how copepod feeding is represented in ecological and biogeochemical models

Genetic determinants of variable metabolism have little impact on the clinical use of leading antipsychotics in the CATIE study

To evaluate systematically in real clinical settings whether functional genetic variations in drug metabolizing enzymes influence optimized doses, efficacy, and safety of antipsychotic medications

Practice change in chronic conditions care: an appraisal of theories

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background Management of chronic conditions can be complex and burdensome for patients and complex and costly for health systems. Outcomes could be improved and costs reduced if proven clinical interventions were better implemented, but the complexity of chronic care services appears to make clinical change particularly challenging. Explicit use of theories may improve the success of clinical change in this area of care provision. Whilst theories to support implementation of practice change are apparent in the broad healthcare arena, the most applicable theories for the complexities of practice change in chronic care have not yet been identified. Methods We developed criteria to review the usefulness of change implementation theories for informing chronic care management and applied them to an existing list of theories used more widely in healthcare. Results Criteria related to the following characteristics of chronic care: breadth of the field; multi-disciplinarity; micro, meso and macro program levels; need for field-specific research on implementation requirements; and need for measurement. Six theories met the criteria to the greatest extent: the Consolidate Framework for Implementation Research; Normalization Process Theory and its extension General Theory of Implementation; two versions of the Promoting Action on Research Implementation in Health Services framework and Sticky Knowledge. None fully met all criteria. Involvement of several care provision organizations and groups, involvement of patients and carers, and policy level change are not well covered by most theories. However, adaptation may be possible to include multiple groups including patients and carers, and separate theories may be needed on policy change. Ways of qualitatively assessing theory constructs are available but quantitative measures are currently partial and under development for all theories. Conclusions Theoretical bases are available to structure clinical change research in chronic condition care. Theories will however need to be adapted and supplemented to account for the particular features of care in this field, particularly in relation to involvement of multiple organizations and groups, including patients, and in relation to policy influence. Quantitative measurement of theory constructs may present difficulties

Talking about depression: a qualitative study of barriers to managing depression in people with long term conditions in primary care

<p>Abstract</p> <p>Background</p> <p>The risk of depression is increased in people with long term conditions (LTCs) and is associated with poorer patient outcomes for both the depressive illness and the LTC, but often remains undetected and poorly managed. The aim of this study was to identify and explore barriers to detecting and managing depression in primary care in people with two exemplar LTCs: diabetes and coronary heart disease (CHD).</p> <p>Methods</p> <p>Qualitative in-depth interviews were conducted with 19 healthcare professionals drawn predominately from primary care, along with 7 service users and 3 carers (n = 29). One focus group was then held with a set of 6 healthcare professionals and a set of 7 service users and 1 carer (n = 14). Interviews and the focus group were digitally recorded, transcribed verbatim, and analysed independently. The two data sets were then inspected for commonalities using a constant comparative method, leading to a final thematic framework used in this paper.</p> <p>Results</p> <p>Barriers to detecting and managing depression in people with LTCs in primary care exist: i) when practitioners in partnership with patients conceptualise depression as a common and understandable response to the losses associated with LTCs - depression in the presence of LTCs is normalised, militating against its recognition and treatment; ii) where highly performanced managed consultations under the terms of the Quality and Outcomes Framework encourage reductionist approaches to case-finding in people with CHD and diabetes, and iii) where there is uncertainty among practitioners about how to negotiate labels for depression in people with LTCs in ways that might facilitate shared understanding and future management.</p> <p>Conclusion</p> <p>Depression was often normalised in the presence of LTCs, obviating rather than facilitating further assessment and management. Furthermore, structural constraints imposed by the QOF encouraged reductionist approaches to case-finding for depression in consultations for CHD and diabetes. Future work might focus on how interventions that draw on the principles of the chronic care model, such as collaborative care, could support primary care practitioners to better recognise and manage depression in patients with LTCs.</p

Assessment of Psychosocial and Neonatal Risk Factors for Trajectories of Behavioral Dysregulation Among Young Children From 18 to 72 Months of Age

Importance Emotional and behavioral dysregulation during early childhood are associated with severe psychiatric, behavioral, and cognitive disorders through adulthood. Identifying the earliest antecedents of persisting emotional and behavioral dysregulation can inform risk detection practices and targeted interventions to promote adaptive developmental trajectories among at-risk children. Objective To characterize children’s emotional and behavioral regulation trajectories and examine risk factors associated with persisting dysregulation across early childhood. Design, Setting, and Participants This cohort study examined data from 20 United States cohorts participating in Environmental influences on Child Health Outcomes, which included 3934 mother-child pairs (singleton births) from 1990 to 2019. Statistical analysis was performed from January to August 2022. Exposures Standardized self-reports and medical data ascertained maternal, child, and environmental characteristics, including prenatal substance exposures, preterm birth, and multiple psychosocial adversities. Main Outcomes and Measures Child Behavior Checklist caregiver reports at 18 to 72 months of age, with Dysregulation Profile (CBCL-DP = sum of anxiety/depression, attention, and aggression). Results The sample included 3934 mother-child pairs studied at 18 to 72 months. Among the mothers, 718 (18.7%) were Hispanic, 275 (7.2%) were non-Hispanic Asian, 1220 (31.8%) were non-Hispanic Black, 1412 (36.9%) were non-Hispanic White; 3501 (89.7%) were at least 21 years of age at delivery. Among the children, 2093 (53.2%) were male, 1178 of 2143 with Psychosocial Adversity Index [PAI] data (55.0%) experienced multiple psychosocial adversities, 1148 (29.2%) were exposed prenatally to at least 1 psychoactive substance, and 3066 (80.2%) were term-born (≥37 weeks’ gestation). Growth mixture modeling characterized a 3-class CBCL-DP trajectory model: high and increasing (2.3% [n = 89]), borderline and stable (12.3% [n = 479]), and low and decreasing (85.6% [n = 3366]). Children in high and borderline dysregulation trajectories had more prevalent maternal psychological challenges (29.4%-50.0%). Multinomial logistic regression analyses indicated that children born preterm were more likely to be in the high dysregulation trajectory (adjusted odds ratio [aOR], 2.76; 95% CI, 2.08-3.65; P &lt; .001) or borderline dysregulation trajectory (aOR, 1.36; 95% CI, 1.06-1.76; P = .02) vs low dysregulation trajectory. High vs low dysregulation trajectories were less prevalent for girls compared with boys (aOR, 0.60; 95% CI, 0.36-1.01; P = .05) and children with lower PAI (aOR, 1.94; 95% CI, 1.51-2.49; P &lt; .001). Combined increases in PAI and prenatal substance exposures were associated with increased odds of high vs borderline dysregulation (aOR, 1.28; 95% CI, 1.08-1.53; P = .006) and decreased odds of low vs high dysregulation (aOR, 0.77; 95% CI, 0.64-0.92; P = .005). Conclusions and Relevance In this cohort study of behavioral dysregulation trajectories, associations were found with early risk factors. These findings may inform screening and diagnostic practices for addressing observed precursors of persisting dysregulation as they emerge among at-risk children

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue

Neighborhood Food Access in Early Life and Trajectories of Child Body Mass Index and Obesity

Importance: Limited access to healthy foods, resulting from residence in neighborhoods with low food access, is a public health concern. The contribution of this exposure in early life to child obesity remains uncertain. Objective: To examine associations of neighborhood food access during pregnancy or early childhood with child body mass index (BMI) and obesity risk. Design, Setting, and Participants: Data from cohorts participating in the US nationwide Environmental Influences on Child Health Outcomes consortium between January 1, 1994, and March 31, 2023, were used. Participant inclusion required a geocoded residential address in pregnancy (mean 32.4 gestational weeks) or early childhood (mean 4.3 years) and information on child BMI. Exposures: Residence in low-income, low-food access neighborhoods, defined as low-income neighborhoods where the nearest supermarket is more than 0.5 miles for urban areas or more than 10 miles for rural areas. Main Outcomes and Measures: BMI z score, obesity (age- and sex-specific BMI ≥95th percentile), and severe obesity (age- and sex-specific BMI ≥120% of the 95th percentile) from age 0 to 15 years. Results: Of 28 359 children (55 cohorts; 14 657 [51.7%] male and 13 702 [48.3%] female; 590 [2.2%] American Indian, Alaska Native, Native Hawaiian, or Other Pacific Islander; 1430 [5.4%] Asian; 4034 [15.3%] Black; 17 730 [67.2%] White; and 2592 [9.8%] other [unspecified] or more than 1 race; 5754 [20.9%] Hispanic and 21 838 [79.1%] non-Hispanic) with neighborhood food access data, 23.2% resided in low-income, low-food access neighborhoods in pregnancy and 24.4% in early childhood. After adjusting for individual sociodemographic characteristics, residence in low-income, low-food access (vs non-low-income, low-food access) neighborhoods in pregnancy was associated with higher BMI z scores at ages 5 years (β, 0.07; 95% CI, 0.03-0.11), 10 years (β, 0.11; 95% CI, 0.06-0.17), and 15 years (β, 0.16; 95% CI, 0.07-0.24); higher obesity risk at 5 years (risk ratio [RR], 1.37; 95% CI, 1.21-1.55), 10 years (RR, 1.71; 95% CI, 1.37-2.12), and 15 years (RR, 2.08; 95% CI, 1.53-2.83); and higher severe obesity risk at 5 years (RR, 1.21; 95% CI, 0.95-1.53), 10 years (RR, 1.54; 95% CI, 1.20-1.99), and 15 years (RR, 1.92; 95% CI, 1.32-2.80). Findings were similar for residence in low-income, low-food access neighborhoods in early childhood. These associations were robust to alternative definitions of low income and low food access and additional adjustment for prenatal characteristics associated with child obesity. Conclusions: Residence in low-income, low-food access neighborhoods in early life was associated with higher subsequent child BMI and higher risk of obesity and severe obesity. We encourage future studies to examine whether investments in neighborhood resources to improve food access in early life would prevent child obesity

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).