Climate warming leads to decline in frequencies of melanic individuals in subarctic leaf beetle populations

Intraspecific diversity buffers populations from deleterious impacts of environmental change. Nevertheless, the consequences of climate warming for phenotypic and genetic diversity within populations and species remain poorly understood. The goal of our study was to explore among-year variations in the phenotypic structure of populations and their relationships with climate variability and population dynamics. We analysed multiyear (1992-2018) data on colour morph frequencies within populations of the leaf beetle, Chrysomela lapponica, from multiple sites in the Kola Peninsula (northwestern Russia). We observed a strong decline in the proportion of dark (melanic) morphs among overwintered beetles during the study period; this decline was consistent across all study sites. Using model selection procedures, we explained declines in the dark morph of overwintered beetles by increases in minimum spring (May-June) daily temperatures. Other climatic characteristics, pollution load, and beetle population density were unrelated to variation in colour morph frequencies. Among newly emerged beetles (August), dark morph frequencies also decreased with an increase in average spring temperatures, but were unrelated to mean temperatures during the larval development period (July). These results suggest that the two-fold decline in dark morph frequencies during the past 26 years has been driven by the 2.5 degrees C increase in spring temperatures, most likely because dark males lose the mating advantages over light males that they obtain during cold springs. The continued loss of dark morphs and related decrease in within-population diversity may render leaf beetle populations more vulnerable to future environmental changes, in particular to those expressed in extreme weather fluctuations. Our study demonstrates that declines in within-population diversity are already underway in subarctic areas, and that these declines are likely driven by climate warming

Climate shapes the spatiotemporal variation in color morph diversity and composition across the distribution range of Chrysomela lapponica leaf beetle

Color polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments. We explored the color morph diversity and composition in a Chrysomela lapponica leaf beetle across its entire distribution range to test the hypothesis that environmental and climatic variables shape spatiotemporal variation in the phenotypic structure of a polymorphic species. We obtained information on 13 617 specimens of this beetle from museums, private collections, and websites. These specimens (collected from 1830-2020) originated from 959 localities spanning 33 degrees latitude, 178 degrees longitude, and 4200 m altitude. We classified the beetles into five color morphs and searched for environmental factors that could explain the variation in the level of polymorphism (quantified by the Shannon diversity index) and in the relative frequencies of individual color morphs. The highest level of polymorphism was found at high latitudes and altitudes. The color morphs differed in their climatic requirements; composition of colour morphs was independent of the geographic distance that separated populations but changed with collection year, longitude, mean July temperature and between-year temperature fluctuations. The proportion of melanic beetles, in line with the thermal melanism hypothesis, increased with increasing latitude and altitude and decreased with increasing climate seasonality. Melanic morph frequencies also declined during the past century, but only at high latitudes and altitudes where recent climate warming was especially strong. The observed patterns suggest that color polymorphism is especially advantageous for populations inhabiting unpredictable environments, presumably due to the different climatic requirements of coexisting color morphs

Climate shapes the spatiotemporal variation in color morph diversity and composition across the distribution range of Chrysomela lapponica leaf beetle

Color polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments. We explored the color morph diversity and composition in a Chrysomela lapponica leaf beetle across its entire distribution range to test the hypothesis that environmental and climatic variables shape spatiotemporal variation in the phenotypic structure of a polymorphic species. We obtained information on 13 617 specimens of this beetle from museums, private collections, and websites. These specimens (collected from 1830-2020) originated from 959 localities spanning 33 degrees latitude, 178 degrees longitude, and 4200 m altitude. We classified the beetles into five color morphs and searched for environmental factors that could explain the variation in the level of polymorphism (quantified by the Shannon diversity index) and in the relative frequencies of individual color morphs. The highest level of polymorphism was found at high latitudes and altitudes. The color morphs differed in their climatic requirements; composition of colour morphs was independent of the geographic distance that separated populations but changed with collection year, longitude, mean July temperature and between-year temperature fluctuations. The proportion of melanic beetles, in line with the thermal melanism hypothesis, increased with increasing latitude and altitude and decreased with increasing climate seasonality. Melanic morph frequencies also declined during the past century, but only at high latitudes and altitudes where recent climate warming was especially strong. The observed patterns suggest that color polymorphism is especially advantageous for populations inhabiting unpredictable environments, presumably due to the different climatic requirements of coexisting color morphs

Climate shapes the spatiotemporal variation in color morph diversity and composition across the distribution range of Chrysomela lapponica leaf beetle

Color polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments. We explored the color morph diversity and composition in a Chrysomela lapponica leaf beetle across its entire distribution range to test the hypothesis that environmental and climatic variables shape spatiotemporal variation in the phenotypic structure of a polymorphic species. We obtained information on 13 617 specimens of this beetle from museums, private collections, and websites. These specimens (collected from 1830-2020) originated from 959 localities spanning 33 degrees latitude, 178 degrees longitude, and 4200 m altitude. We classified the beetles into five color morphs and searched for environmental factors that could explain the variation in the level of polymorphism (quantified by the Shannon diversity index) and in the relative frequencies of individual color morphs. The highest level of polymorphism was found at high latitudes and altitudes. The color morphs differed in their climatic requirements; composition of colour morphs was independent of the geographic distance that separated populations but changed with collection year, longitude, mean July temperature and between-year temperature fluctuations. The proportion of melanic beetles, in line with the thermal melanism hypothesis, increased with increasing latitude and altitude and decreased with increasing climate seasonality. Melanic morph frequencies also declined during the past century, but only at high latitudes and altitudes where recent climate warming was especially strong. The observed patterns suggest that color polymorphism is especially advantageous for populations inhabiting unpredictable environments, presumably due to the different climatic requirements of coexisting color morphs

Climate shapes the spatiotemporal variation in color morph diversity and composition across the distribution range of Chrysomela lapponica leaf beetle

Abstract Color polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments. We explored the color morph diversity and composition in a Chrysomela lapponica leaf beetle across its entire distribution range to test the hypothesis that environmental and climatic variables shape spatiotemporal variation in the phenotypic structure of a polymorphic species. We obtained information on 13 617 specimens of this beetle from museums, private collections, and websites. These specimens (collected from 1830–2020) originated from 959 localities spanning 33° latitude, 178° longitude, and 4200 m altitude. We classified the beetles into five color morphs and searched for environmental factors that could explain the variation in the level of polymorphism (quantified by the Shannon diversity index) and in the relative frequencies of individual color morphs. The highest level of polymorphism was found at high latitudes and altitudes. The color morphs differed in their climatic requirements; composition of colour morphs was independent of the geographic distance that separated populations but changed with collection year, longitude, mean July temperature and between-year temperature fluctuations. The proportion of melanic beetles, in line with the thermal melanism hypothesis, increased with increasing latitude and altitude and decreased with increasing climate seasonality. Melanic morph frequencies also declined during the past century, but only at high latitudes and altitudes where recent climate warming was especially strong. The observed patterns suggest that color polymorphism is especially advantageous for populations inhabiting unpredictable environments, presumably due to the different climatic requirements of coexisting color morphs

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes

Liver injury in hospitalized patients with COVID-19: An International observational cohort study

Background: Using a large dataset, we evaluated prevalence and severity of alterations in liver enzymes in COVID-19 and association with patient-centred outcomes.MethodsWe included hospitalized patients with confirmed or suspected SARS-CoV-2 infection from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) database. Key exposure was baseline liver enzymes (AST, ALT, bilirubin). Patients were assigned Liver Injury Classification score based on 3 components of enzymes at admission: Normal; Stage I) Liver injury: any component between 1-3x upper limit of normal (ULN); Stage II) Severe liver injury: any component & GE;3x ULN. Outcomes were hospital mortality, utilization of selected resources, complications, and durations of hospital and ICU stay. Analyses used logistic regression with associations expressed as adjusted odds ratios (OR) with 95% confidence intervals (CI).ResultsOf 17,531 included patients, 46.2% (8099) and 8.2% (1430) of patients had stage 1 and 2 liver injury respectively. Compared to normal, stages 1 and 2 were associated with higher odds of mortality (OR 1.53 [1.37-1.71]; OR 2.50 [2.10-2.96]), ICU admission (OR 1.63 [1.48-1.79]; OR 1.90 [1.62-2.23]), and invasive mechanical ventilation (OR 1.43 [1.27-1.70]; OR 1.95 (1.55-2.45). Stages 1 and 2 were also associated with higher odds of developing sepsis (OR 1.38 [1.27-1.50]; OR 1.46 [1.25-1.70]), acute kidney injury (OR 1.13 [1.00-1.27]; OR 1.59 [1.32-1.91]), and acute respiratory distress syndrome (OR 1.38 [1.22-1.55]; OR 1.80 [1.49-2.17]).ConclusionsLiver enzyme abnormalities are common among COVID-19 patients and associated with worse outcomes

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients