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OBJECTIVE: Mepolizumab proved to be an efficacious treatment for eosinophilic granulomatosis with polyangiitis (EGPA) at a dose of 300 mg every 4 weeks in the randomized, controlled MIRRA trial. In a few recently reported studies, successful real-life experiences with the approved dose for treating severe eosinophilic asthma (100 mg every 4 weeks) were observed. We undertook this study to assess the effectiveness and safety of mepolizumab 100 mg every 4 weeks and 300 mg every 4 weeks in a large European EGPA cohort. METHODS: We included all patients with EGPA treated with mepolizumab at the recruiting centers in 2015-2020. Treatment response was evaluated from 3 months to 24 months after initiation of mepolizumab. Complete response to treatment was defined as no disease activity (Birmingham Vasculitis Activity Score [BVAS] = 0) and a prednisolone or prednisone dose (or equivalent) of ���4 mg/day. Respiratory outcomes included asthma and ear, nose, and throat (ENT) exacerbations. RESULTS: Two hundred three patients, of whom 191 received a stable dose of mepolizumab (158 received 100 mg every 4 weeks and 33 received 300 mg every 4 weeks) were included. Twenty-five patients (12.3%) had a complete response to treatment at 3 months. Complete response rates increased to 30.4% and 35.7% at 12 months and 24 months, respectively, and rates were comparable between mepolizumab 100 mg every 4 weeks and 300 mg every 4 weeks. Mepolizumab led to a significant reduction in BVAS score, prednisone dose, and eosinophil counts from 3 months to 24 months, with no significant differences observed between 100 mg every 4 weeks and 300 mg every 4 weeks. Eighty-two patients (40.4%) experienced asthma exacerbations (57 of 158 [36%] who received 100 mg every 4 weeks; 17 of 33 [52%] who received 300 mg every 4 weeks), and 31 patients (15.3%) experienced ENT exacerbations. Forty-four patients (21.7%) experienced adverse events (AEs), most of which were nonserious AEs (38 of 44). CONCLUSION: Mepolizumab at both 100 mg every 4 weeks and 300 mg every 4 weeks is effective for the treatment of EGPA. The 2 doses should be compared in the setting of a controlled trial.

Telomere dynamics are linked with both cellular and organismal senescence, and life history, individual quality and health. Telomere dynamics, particularly telomere length, have therefore garnered much research interest in evolutionary biology. To examine the evolution of telomere length, it is important to quantify its heritability, the proportion of total variation explained by additive genetic effects. Many studies have quantified telomere length heritability, but estimates are varied, and no general conclusion has been drawn. Additionally, it is unclear whether biological and methodological factors influence telomere length heritability estimates. We present the first meta-analysis of telomere length heritability, using 104 estimates from 43 studies over 18 vertebrate species. We calculated an overall mean heritability and examined how estimates varied by study, phylogeny, species-specific ecology, environmental setting, age at sampling, laboratory methods, statistical methods, sex and repeated measurements. Overall heritability was moderate (44.9%, 95% CI: 25.2–64.7%), and there was considerable heterogeneity in heritability estimates, in particular among studies and estimates. Laboratory method influenced heritability estimates, with in-gel hybridization TRF yielding higher heritabilities than qPCR and Southern blot TRF. There was also an effect from statistical method, with twin-based and SNP-based estimates lower than correlation-based or pedigree-based estimates. Our results highlight an overall heritable basis of telomere length, and we recommend future research on a wider range of taxa, and the use of variance-partitioning methods with relatedness or SNP data over correlation methods to minimize heritability estimation bias.

The longitudinal study of populations is a core tool for understanding ecological and evolutionary processes. Long‐term studies typically collect samples repeatedly over individual lifetimes and across generations. These samples are then analysed in batches (e.g. qPCR plates) and clusters (i.e. group of batches) over time in the laboratory. However, these analyses are constrained by cross‐classified data structures introduced biologically or through experimental design. The separation of biological variation from the confounding among‐batch and among‐cluster variation is crucial, yet often ignored.\ud \ud The commonly used approaches to structuring samples for analysis, sequential and randomization, generate bias due to the non‐independence between time of collection and the batch and cluster they are analysed in. We propose a new sample structuring strategy, called slicing, designed to separate confounding among‐batch and among‐cluster variation from biological variation. Through simulations, we tested the statistical power and precision to detect within‐individual, between‐individual, year and cohort effects of this novel approach.\ud \ud Our slicing approach, whereby recently and previously collected samples are sequentially analysed in clusters together, enables the statistical separation of collection time and cluster effects by bridging clusters together, for which we provide a case study. Our simulations show, with reasonable slicing width and angle, similar precision and similar or greater statistical power to detect year, cohort, within‐ and between‐individual effects when samples are sliced across batches, compared with strategies that aggregate longitudinal samples or use randomized allocation.\ud \ud While the best approach to analysing long‐term datasets depends on the structure of the data and questions of interest, it is vital to account for confounding among‐cluster and batch variation. Our slicing approach is simple to apply and creates the necessary statistical independence of batch and cluster from environmental or biological variables of interest. Crucially, it allows sequential analysis of samples and flexible inclusion of current data in later analyses without completely confounding the analysis. Our approach maximizes the scientific value of every sample, as each will optimally contribute to unbiased statistical inference from the data. Slicing thereby maximizes the power of growing biobanks to address important ecological, epidemiological and evolutionary questions.

Extra-pair copulation (EPC) is widespread in socially monogamous species, but its evolutionary benefits remain controversial. Indirect genetic benefit hypotheses postulate that females engage in EPC to produce higher quality extra-pair offspring (EPO) than within-pair offspring (WPO). In contrast, the sexual conflict hypothesis posits that EPC is beneficial to males but not to females. Thus, under the sexual conflict hypothesis, EPO are predicted to be no fitter than WPO. We tested these two hypotheses in a 12-year dataset with complete life-history and pedigree information from an isolated island population of house sparrows (Passer domesticus). We compared fitness components of EPO and two types of WPO: (1) WPO from genetically polyandrous “unfaithful” mothers, and (2) WPO from genetically monogamous mothers. We found that all three groups of offspring had similar probabilities of hatching and nestling survival. Unexpectedly, EPO had the lowest probability of recruiting into the breeding population and the lowest lifetime reproductive output. Our results indicate that EPO incurred indirect genetic costs, rather than benefits, which is contrary to indirect benefit models. Importantly, the indirect costs we observed are also underappreciated in current sexual conflict models. Our results call for improved theoretical frameworks that incorporate indirect costs by extending current sexual conflict models.