Timing as a sexually selected trait: the right mate at the right moment

Sexual selection favours the expression of traits in one sex that attract members of the opposite sex for mating. The nature of sexually selected traits such as vocalization, colour and ornamentation, their fitness benefits as well as their costs have received ample attention in field and laboratory studies. However, sexually selected traits may not always be expressed: coloration and ornaments often follow a seasonal pattern and behaviours may be displayed only at specific times of the day. Despite the widely recognized differences in the daily and seasonal timing of traits and their consequences for reproductive success, the actions of sexual selection on the temporal organization of traits has received only scant attention. Drawing on selected examples from bird and mammal studies, here we summarize the current evidence for the daily and seasonal timing of traits. We highlight that molecular advances in chronobiology have opened exciting new opportunities for identifying the genetic targets that sexual selection may act on to shape the timing of trait expression. Furthermore, known genetic links between daily and seasonal timing mechanisms lead to the hypothesis that selection on one timescale may simultaneously also affect the other. We emphasize that studies on the timing of sexual displays of both males and females from wild populations will be invaluable for understanding the nature of sexual selection and its potential to act on differences within and between the sexes in timing. Molecular approaches will be important for pinpointing genetic components of biological rhythms that are targeted by sexual selection, and to clarify whether these represent core or peripheral components of endogenous clocks. Finally, we call for a renewed integration of the fields of evolution, behavioural ecology and chronobiology to tackle the exciting question of how sexual selection contributes to the evolution of biological clocks.This article is part of the themed issue 'Wild clocks: integrating chronobiology and ecology to understand timekeeping in free-living animals'

Increased glucocorticoid concentrations in early life cause mitochondrial inefficiency and short telomeres

Telomeres are DNA structures that protect chromosome ends. However, telomeres shorten during cell replication and at critically low lengths can reduce cell replicative potential, induce cell senescence and decrease fitness. Stress exposure, which elevates glucocorticoid hormone concentrations, can exacerbate telomere attrition. This phenomenon has been attributed to increased oxidative stress generated by glucocorticoids ('oxidative stress hypothesis'). We recently suggested that glucocorticoids could increase telomere attrition during stressful periods by reducing the resources available for telomere maintenance through changes in the metabolic machinery ('metabolic telomere attrition hypothesis'). Here, we tested whether experimental increases in glucocorticoid levels affected telomere length and mitochondrial function in wild great tit (Parus major) nestlings during the energy-demanding early growth period. We monitored resulting corticosterone (Cort) concentrations in plasma and red blood cells, telomere lengths and mitochondrial metabolism (metabolic rate, proton leak, oxidative phosphorylation, maximal mitochondrial capacity and mitochondrial inefficiency). We assessed oxidative damage caused by reactive oxygen species (ROS) metabolites as well as the total non-enzymatic antioxidant protection in plasma. Compared with control nestlings, Cort-nestlings had higher baseline corticosterone, shorter telomeres and higher mitochondrial metabolic rate. Importantly, Cort-nestlings showed increased mitochondrial proton leak, leading to a decreased ATP production efficiency. Treatment groups did not differ in oxidative damage or antioxidants. Hence, glucocorticoid-induced telomere attrition is associated with changes in mitochondrial metabolism, but not with ROS production. These findings support the hypothesis that shortening of telomere length during stressful periods is mediated by glucocorticoids through metabolic rearrangements

Juvenile neuropsychiatric systemic lupus erythematosus: identification of novel central neuroinflammation biomarkers

International audienceIntroduction Juvenile systemic lupus erythematosus (j-SLE) is a rare chronic autoimmune disease affecting multiple organs. Ranging from minor features, such as headache or mild cognitive impairment, to serious and life-threatening presentations, j-neuropsychiatric SLE (j-NPSLE) is a therapeutic challenge. Thus, the diagnosis of NPSLE remains difficult, especially in pediatrics, with no specific biomarker of the disease yet validated. Objectives To identify central nervous system (CNS) disease biomarkers of j-NPSLE. Methods A 5-year retrospective tertiary reference monocentric j-SLE study. A combination of standardized diagnostic criteria and multidisciplinary pediatric clinical expertise was combined to attribute NP involvement in the context of j-SLE. Neopterin and interferon-alpha (IFN-α) protein levels in cerebrospinal fluid (CSF) were assessed, together with routine biological and radiological investigations. Results Among 51 patients with j-SLE included, 39% presented with j-NPSLE. J-NPSLE was diagnosed at onset of j-SLE in 65% of patients. No specific routine biological or radiological marker of j-NPSLE was identified. However, CSF neopterin levels were significantly higher in active j-NPSLE with CNS involvement than in j-SLE alone ( p = 0.0008). Neopterin and IFN-α protein levels in CSF were significantly higher at diagnosis of j-NPSLE with CNS involvement than after resolution of NP features (respectively p = 0.0015 and p = 0.0010) upon immunosuppressive treatment in all patients tested ( n = 10). Both biomarkers correlated strongly with each other ( R s = 0.832, p < 0.0001, n = 23 paired samples). Conclusion CSF IFN-α and neopterin constitute promising biomarkers useful in the diagnosis and monitoring of activity in j-NPSLE

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Inferring Whole-Organism Metabolic Rate From Red Blood Cells in Birds

Metabolic rate is a key ecological variable that quantifies the energy expenditure needed to fuel almost all biological processes in an organism. Metabolic rates are typically measured at the whole-organism level (woMR) with protocols that can elicit stress responses due to handling and confinement, potentially biasing resulting data. Improved, non-stressful methodology would be especially valuable for measures of field metabolic rate, which quantifies the energy expenditure of free-living individuals. Recently, techniques to measure cellular metabolic rate (cMR) in mitochondria of blood cells have become available, suggesting that blood-based cMR can be a proxy of organismal aerobic performance. Aerobic metabolism actually takes place in the mitochondria. Quantifying cMR from blood samples offers several advantages such as direct estimates of metabolism and minimized disturbance of individuals. To our knowledge, the hypothesis that blood-based cMR correlates with woMR has not yet been directly tested. We measured cMR in red blood cells of captive great tits (Parus major), first during their morning activity period and second after subjecting them to a 2.5 h day-time respirometry protocol to quantify woMR. We predicted cMR to decrease as individuals transitioned from an active to a resting state. In the two blood samples we also assessed circulating corticosterone concentrations to determine the perceived disturbance of individuals. From respirometry traces we extracted initial and final woMR measures to test for a predicted positive correlation with cMR measures, while accounting for corticosterone concentrations. Indeed, cMR declined from the first to the second measurement. Furthermore, woMR and cMR were positively related in individuals that had relatively low corticosterone concentrations and displayed little locomotor activity throughout respirometry. By contrast, woMR and cMR covaried negatively in birds that increased corticosterone concentrations and activity levels substantially. Our results show that red blood cell cMR represents a proxy for woMR when birds do not display signs of stress, i.e., either before increases in hormonal or behavioral parameters have occurred or after they have abated. This method represents a valuable tool for obtaining metabolic data repeatedly and in free-living individuals. Our findings also highlight the importance of accounting for individual stress responses when measuring metabolic rate at any level.publishe

Quantifying Glucocorticoid Plasticity Using Reaction Norm Approaches : There Still is So Much to Discover!

Hormones are highly responsive internal signals that help organisms adjust their phenotype to fluctuations in environmental and internal conditions. Our knowledge of the causes and consequences of variation in circulating hormone concentrations has improved greatly in the past. However, this knowledge often comes from population-level studies, which generally tend to make the flawed assumption that all individuals respond in the same way to environmental changes. Here, we advocate that we can vastly expand our understanding of the ecology and evolution of hormonal traits once we acknowledge the existence of individual differences by quantifying hormonal plasticity at the individual level, where selection acts. In this review, we use glucocorticoid (GC) hormones as examples of highly plastic endocrine traits that interact intimately with energy metabolism but also with other organismal traits like behavior and physiology. First, we highlight the insights gained by repeatedly assessing an individual's GC concentrations along a gradient of environmental or internal conditions using a “reaction norm approach.” This study design should be followed by a hierarchical statistical partitioning of the total endocrine variance into the among-individual component (individual differences in average hormone concentrations, i.e., in the intercept of the reaction norm) and the residual (within-individual) component. The latter is ideally further partitioned by estimating more precisely hormonal plasticity (i.e., the slope of the reaction norm), which allows to test whether individuals differ in the degree of hormonal change along the gradient. Second, we critically review the published evidence for GC variation, focusing mostly on among- and within-individual levels, finding only a good handful of studies that used repeated-measures designs and random regression statistics to investigate GC plasticity. These studies indicate that individuals can differ in both the intercept and the slope of their GC reaction norm to a known gradient. Third, we suggest rewarding avenues for future work on hormonal reaction norms, for example to uncover potential costs and trade-offs associated with GC plasticity, to test whether GC plasticity varies when an individual's reaction norm is repeatedly assessed along the same gradient, whether reaction norms in GCs covary with those in other traits like behavior and fitness (generating multivariate plasticity), or to quantify GC reaction norms along multiple external and internal gradients that act simultaneously (leading to multidimensional plasticity). Throughout this review, we emphasize the power that reaction norm approaches offer for resolving unanswered questions in ecological and evolutionary endocrinology.publishe