Field cricket genome reveals the footprint of recent, abrupt adaptation in the wild.

Evolutionary adaptation is generally thought to occur through incremental mutational steps, but large mutational leaps can occur during its early stages. These are challenging to study in nature due to the difficulty of observing new genetic variants as they arise and spread, but characterizing their genomic dynamics is important for understanding factors favoring rapid adaptation. Here, we report genomic consequences of recent, adaptive song loss in a Hawaiian population of field crickets (Teleogryllus oceanicus). A discrete genetic variant, flatwing, appeared and spread approximately 15 years ago. Flatwing erases sound-producing veins on male wings. These silent flatwing males are protected from a lethal, eavesdropping parasitoid fly. We sequenced, assembled and annotated the cricket genome, produced a linkage map, and identified a flatwing quantitative trait locus covering a large region of the X chromosome. Gene expression profiling showed that flatwing is associated with extensive genome-wide effects on embryonic gene expression. We found that flatwing male crickets express feminized chemical pheromones. This male feminizing effect, on a different sexual signaling modality, is genetically associated with the flatwing genotype. Our findings suggest that the early stages of evolutionary adaptation to extreme pressures can be accompanied by greater genomic and phenotypic disruption than previously appreciated, and highlight how abrupt adaptation might involve suites of traits that arise through pleiotropy or genomic hitchhiking

Ethnic variations in asthma hospital admission, readmission and death:a retrospective, national cohort study of 4.62 million people in Scotland

Acknowledgements We thank our SHELS collaborators at NHS Information Services Division and at National Records Scotland and the SHELS Phase 3 Steering Group. We acknowledge input to this report from Jenny Holmes, SHELS Study assistant. AS was supported by The Commonwealth Fund, a private independent foundation based in New York City. The views presented here are those of the authors and not necessarily those of The Commonwealth Fund, its directors, officers, or staff. AS is Director of the Asthma UK Centre for Applied Research; he also acknowledges the support of the Farr Institute. Contributors from the Scottish Health and Ethnicity Linkage Study research team: these contributors served on the Steering Group and some on other important subgroups of SHELS, and therefore gave general direction that helped this analysis. Chris Povey was a co-applicant and the originator of the idea of linking the census data to the data held by ISD and he performed most of the linkage work, including developing linkage methods. Prof Jamie Pearce (co-applicant) advised especially on socioeconomic adjustment. Duncan Buchanan (co-applicant) chaired the analysis subgroup. Ganka Mueller (part study), Alex Stannard (part study) and Kirsty MacLachlan advised particularly in relation to National Records of Scotland contributions. These important contributions did not meet ICMJE authorship requirements. Funding Chief Scientist’s Office of the Scottish Government, British Lung Foundation and NHS Health Scotland.Peer reviewedPublisher PD

Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study

AbstractInfection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a ‘pre-vaccination’ cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and ‘vaccinated’ and ‘unvaccinated’ cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.</jats:p

Associations between weight change and biomarkers of cardiometabolic risk in South Asians:secondary analyses of the PODOSA trial

Background/Objectives: The association of weight changes with cardiometabolic biomarkers in South Asians has been sparsely studied. Subjects/Methods: We measured cardiometabolic biomarkers at baseline and after 3 years in the Prevention of Diabetes and Obesity in South Asians Trial. We investigated the effect of a lifestyle intervention on biomarkers in the randomized groups. In addition, treating the population as a single cohort, we estimated the association between change in weight and change in biomarkers. Results: Complete data were available at baseline and after 3 years in 151 participants. At 3 years, there was an adjusted mean reduction of 1·44 kg (95% confidence interval (95% CI): 0.18–2.71) in weight and 1.59 cm (95% CI: 0.08–3.09) in waist circumference in the intervention arm as compared with the control arm. There was no clear evidence of difference between the intervention and control arms in change of mean value of any biomarker. As a single cohort, every 1 kg weight reduction during follow-up was associated with a reduction in triglycerides (−1.3%, P=0.048), alanine aminotransferase (−2.5%, P=0.032), gamma-glutamyl transferase (−2.2%, P=0.040), leptin (−6.5%, P&lt;0.0001), insulin (−3.7%, P=0.0005), fasting glucose (−0.8%, P=0.0071), 2-h glucose (−2.3%, P=0.0002) and Homeostatic Model Assessment of insulin resistance (HOMA-IR: −4.5%, P=0.0002). There was no evidence of associations with other lipid measures, tissue plasminogen activator, markers of inflammation or blood pressure. Conclusions: We demonstrate that modest weight decrease in SAs is associated with improvements in markers of total and ectopic fat as well as insulin resistance and glycaemia in South Asians at risk of diabetes. Future trials with more intensive weight change are needed to extend these findings

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

The development and validation of a multivariable prognostic model to predict foot ulceration in diabetes using a systematic review and individual patient data meta-analyses

Aims: Diabetes guidelines recommend screening for the risk of foot ulceration but vary substantially in the underlying evidence base. Our purpose was to derive and validate a prognostic model of independent risk factors for foot ulceration in diabetes using all available individual patient data from cohort studies conducted worldwide. Methods: We conducted a systematic review and meta‐analysis of individual patient data from 10 cohort studies of risk factors in the prediction of foot ulceration in diabetes. Predictors were selected for plausibility, availability and low heterogeneity. Logistic regression produced adjusted odds ratios (ORs) for foot ulceration by ulceration history, monofilament insensitivity, any absent pedal pulse, age, sex and diabetes duration. Results: The 10 studies contained data from 16 385 participants. A history of foot ulceration produced the largest OR [6.59 (95% CI 2.49 to 17.45)], insensitivity to a 10 g monofilament [3.18 (95% CI 2.65 to 3.82)] and any absent pedal pulse [1.97 (95% CI 1.62 to 2.39)] were consistently, independently predictive. Combining three predictors produced sensitivities between 90.0% (95% CI 69.9% to 97.2%) and 95.3% (95% CI 84.5% to 98.7%); the corresponding specificities were between 12.1% (95% CI 8.2% to 17.3%) and 63.9% (95% CI 61.1% to 66.6%). Conclusions: This prognostic model of only three risk factors, a history of foot ulceration, an inability to feel a 10 g monofilament and the absence of any pedal pulse, compares favourably with more complex approaches to foot risk assessment recommended in clinical diabetes guidelines

Understanding multimorbidity trajectories in Scotland using sequence analysis.

Understanding how multiple conditions develop over time is of growing interest, but there is currently limited methodological development on the topic, especially in understanding how multimorbidity (the co-existence of at least two chronic conditions) develops longitudinally and in which order diseases occur. We aim to describe how a longitudinal method, sequence analysis, can be used to understand the sequencing of common chronic diseases that lead to multimorbidity and the socio-demographic factors and health outcomes associated with typical disease trajectories. We use the Scottish Longitudinal Study (SLS) linking the Scottish census 2001 to disease registries, hospitalisation and mortality records. SLS participants aged 40-74 years at baseline were followed over a 10-year period (2001-2011) for the onset of three commonly occurring diseases: diabetes, cardiovascular disease (CVD), and cancer. We focused on participants who transitioned to at least two of these conditions over the follow-up period (N = 6300). We use sequence analysis with optimal matching and hierarchical cluster analysis to understand the process of disease sequencing and to distinguish typical multimorbidity trajectories. Socio-demographic differences between specific disease trajectories were evaluated using multinomial logistic regression. Poisson and Cox regressions were used to assess differences in hospitalisation and mortality outcomes between typical trajectories. Individuals who transitioned to multimorbidity over 10 years were more likely to be older and living in more deprived areas than the rest of the population. We found seven typical trajectories: later fast transition to multimorbidity, CVD start with slow transition to multimorbidity, cancer start with slow transition to multimorbidity, diabetes start with slow transition to multimorbidity, fast transition to both diabetes and CVD, fast transition to multimorbidity and death, fast transition to both cancer and CVD. Those who quickly transitioned to multimorbidity and death were the most vulnerable, typically older, less educated, and more likely to live in more deprived areas. They also experienced higher number of hospitalisations and overnight stays while still alive. Sequence analysis can strengthen our understanding of typical disease trajectories when considering a few key diseases. This may have implications for more active clinical review of patients beginning quick transition trajectories

Did the weight loss in the Prevention of diabetes and Obesity in South Asians (PODOSA) trial differ by sex? An exploratory analysis

No abstract available