Global biogeographical patterns of plankton in response to climate change

226 p.La evidencia científica es abrumadora: el cambio climático está ocurriendo y gran parte delcambio es un resultado directo de la actividad humana. Las observaciones demuestran quedurante los últimos 40 años el 84% del total del calentamiento de la tierra ha ido a calentarlos océanos, alterando así los sistemas naturales que habitan en él. Se han observadoincrementos en la temperatura superficial del agua (0.11°C década-1) entre 1971-2010,incrementos en la velocidad del viento y frecuencias de tormentas, cambios en la circulaciónoceánica, estructura vertical y aporte de nutrientes, acidificación de los océanos, así como unincremento del nivel del mar de en torno a unos 15 cm durante el último siglo.Los impactos del cambio climático afectan a todo el ecosistema pelágico, desde el planctonhasta niveles tróficos superiores. Estos impactos generan una serie de respuestas en lasespecies como por ejemplo cambios latitudinales en los rangos de su distribución, cambios enlos ciclos estacionales, así como cambios en la abundancia y estructura de las comunidades.Particularmente en el plancton, se ha demostrado que los cambios en la biogeografía son delos más grandes y rápidos observados hasta ahora. Por ello, el cambio climático puede darlugar a extinciones o invasiones locales en las especies, que repercuten en los patrones debiodiversidad y causan desajustes tróficos. En esta serie de respuestas, la dispersión de lasespecies, con el simple hecho de desplazarse desde un hábitat a otro, es un proceso clave;determina el ritmo de extensión de una población y permite el flujo genético entre ellas, locual es fundamental en los procesos de adaptación a condiciones climáticas cambiantes. Deeste modo, la resiliencia o vulnerabilidad de las comunidades marinas frente al cambioclimático dependerá de la capacidad de adaptación o dispersión de cada especie así como desu grado de conectividad.Comprender, predecir y gestionar las respuestas de la biodiversidad al cambio climático exigeuna consideración completa de los patrones biogeográficos de las especies, que estándefinidos por su nicho ecológico y características de dispersión, y cómo estas característicaspueden cambiar debido al cambio climático. Los estudios sobre el zooplancton, losprincipales productores secundarios del océano y el principal grupo objetivo de esta tesis,pueden mejorar el conocimiento existente sobre cómo los ecosistemas marinos estánrespondiendo al cambio climático. El zooplancton es particularmente sensible a los cambiosambientales a corto plazo, ya que tanto su dinámica poblacional como sus procesosfisiológicos están altamente ligados a la temperatura. Debido a esta sensibilidad, lascomunidades de zooplancton pueden utilizarse como indicadores para evaluar la salud de losecosistemas marinos, y la variación de sus patrones de distribución puede proporcionarinformación valiosa sobre los cambios físicos que ocurren en los océanos globales.Nuestro estudio tiene como objetivo analizar los patrones biogeográficos del plancton aescala global y su respuesta frente al cambio climático. Los estudios biogeográficos requierenun profundo conocimiento del nicho ecológico de las especies, definido aquí como el rango detolerancia de cada especie a un set de variables ambientales que limitan su distribución. Estatesis pretende estudiar los patrones macro-ecológicos del plancton en varias escalas: desdelos genes hasta las comunidades, desde zonas costeras hasta el océano global, desdetendencias históricas hasta proyecciones futuras. Para tal fin, hemos aplicado técnicas ymodelización estadística en bases de datos globales.Aztitecnali

Large-scale ocean connectivity and planktonic body size

Villarino, Ernesto ... et al.-- 13 pages, 5 figures, 5 tables, supplementary material https://dx.doi.org/10.1038/s41467-017-02535-8Global patterns of planktonic diversity are mainly determined by the dispersal of propagules with ocean currents. However, the role that abundance and body size play in determining spatial patterns of diversity remains unclear. Here we analyse spatial community structure - β-diversity - for several planktonic and nektonic organisms from prokaryotes to small mesopelagic fishes collected during the Malaspina 2010 Expedition. β-diversity was compared to surface ocean transit times derived from a global circulation model, revealing a significant negative relationship that is stronger than environmental differences. Estimated dispersal scales for different groups show a negative correlation with body size, where less abundant large-bodied communities have significantly shorter dispersal scales and larger species spatial turnover rates than more abundant small-bodied plankton. Our results confirm that the dispersal scale of planktonic and micro-nektonic organisms is determined by local abundance, which scales with body size, ultimately setting global spatial patterns of diversityThis research was funded by the project Malaspina 2010 Circumnavigation Expedition (Consolider-Ingenio 2010, CSD2008-00077) and cofounded by the Basque Government (Department Deputy of Agriculture, Fishing and Food Policy). [...] E.V. was supported by a PhD Scholarship granted by the Iñaki Goenaga−Technology Centres FoundationPeer Reviewe

Lessons from the first generation of marine ecological forecast products

publishedVersio

Global beta diversity patterns of microbial communities in the surface and deep ocean

This is contribution 1112 from AZTI Marine Research Division.-- 14 pages, 4 figures, 3 tables, supporting information https://doi.org/10.1111/geb.13572.-- Data Availability Statement: DNA sequences for surface prokaryotes are publicly available at the European Nucleotide Archive [http://www.ebi.ac.uk/ena; accession number PRJEB25224 (16S rRNA genes)], for deep prokaryotes at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra) under accession ID SRP031469, and for surface and deep picoeukaryotes at the European Nucleotide Archive with accession number PRJEB23771 (http://www.ebi.ac.uk/ena). Environmental data used in this study are available from https://github.com/ramalok/malaspina.surface.metabacoding, Giner et al. (2020) and Salazar et al. (2015). The code to analyze the data and produce the figures of this research is available from the corresponding author upon request.-- This is the pre-peer reviewed version of the following article: Ernesto Villarino, James R. Watson, Guillem Chust ,A. John Woodill, Benjamin Klempay, Bror Jonsson, Josep M. Gasol, Ramiro Logares, Ramon Massana, Caterina R. Giner, Guillem Salazar, X. Anton Alvarez-Salgado, Teresa S. Catala, Carlos M. Duarte, Susana Agusti, Francisco Mauro, Xabier Irigoien, Andrew D. Barton; Global beta diversity patterns of microbial communities in the surface and deep ocean; Global Ecology and Biogeography 31(11): 2323-2336 (2022), which has been published in final form at https://doi.org/10.1111/geb.13572. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsAim: Dispersal and environmental gradients shape marine microbial communities, yet the relative importance of these factors across taxa with distinct sizes and dispersal capacity in different ocean layers is unknown. Here, we report a comparative analysis of surface and deep ocean microbial beta diversity and examine how these patterns are tied to oceanic distance and environmental gradients. Location: Tropical and subtropical oceans (30°N–40°S). Time period: 2010-2011. Major taxa studied: Prokaryotes and picoeukaryotes (eukaryotes between 0.2 and 3 μm). Methods: Beta diversity was calculated from metabarcoding data on prokaryotic and picoeukaryotic microbes collected during the Malaspina expedition across the tropical and subtropical oceans. Mantel correlations were used to determine the relative contribution of environment and oceanic distance driving community beta diversity. Results: Mean community similarity across all sites for prokaryotes was 38.9% in the surface and 51.4% in the deep ocean, compared to mean similarity of 25.8 and 12.1% in the surface and deep ocean, respectively, for picoeukaryotes. Higher dispersal rates and smaller body sizes of prokaryotes relative to picoeukaryotes likely contributed to the significantly higher community similarity for prokaryotes compared with picoeukaryotes. The ecological mechanisms determining the biogeography of microbes varied across depth. In the surface ocean, the environmental differences in space were a more important factor driving microbial distribution compared with the oceanic distance, defined as the shortest path between two sites avoiding land. In the deep ocean, picoeukaryote communities were slightly more structured by the oceanic distance, while prokaryotes were shaped by the combined action of oceanic distance and environmental filtering. Main conclusions: Horizontal gradients in microbial community assembly differed across ocean depths, as did mechanisms shaping them. In the deep ocean, the oceanic distance and environment played significant roles driving microbial spatial distribution, while in the surface the influence of the environment was stronger than oceanic distanceData collection was funded by the Malaspina 2010 Circumnavigation Expedition project (Consolider-Ingenio 2010, CSD2008-00077) and cofunded by the Basque Government (Department Deputy of Agriculture, Fishing and Food Policy). We acknowledge funding from the Spanish Government through the “Severo Ochoa Center of Excelence” accreditation CEX2019-000928-S. [...] We also acknowledge H2020 Mission Atlantic project (Ref. Grant Agreement Number 862428). EV was supported by an international exchange post-doc scholarship to Scripps Institution of Oceanography and Oregon State University granted by the Education Department of the Basque GovernmentPeer reviewe

Wax worm saliva and the enzymes therein are the key to polyethylene degradation by Galleria mellonella

11 p.-6 fig.Plastic degradation by biological systems with re-utilization of the by-products can be the future solution to the global threat of plastic waste accumulation. We report that the saliva of Galleria mellonella larvae (wax worms) is capable of oxidizing and depolymerizing polyethylene (PE), one of the most produced and sturdy polyolefin-derived plastics. This effect is achieved after a few hours’ exposure at room temperature and physiological conditions (neutral pH). The wax worm saliva can indeed overcome the bottleneck step in PE biodegradation, that is the initial oxidation step. Within the saliva, we identified two enzymes that can reproduce the same effect. This is the first report of enzymes with this capability, opening up the way to new ground-breaking solutions for plastic waste management through bio-recycling/up-cycling.Roechling Stiftung to FB Consejo Superior de Investigaciones Científicas (CSIC) to FB NATO Science for Peace and Security Programme (Grant SPS G5536) to TT Junta de Castilla y León, Consejería de Educación y Cultura y Fondo Social Europeo (Grant BU263P18) to TT Ministerio de Ciencia e Innovación (Grant PID2019-111215RB-100) to TT The Generalitat de Catalunya (2017 SGR 1192) to MS Ministerio de Ciencia e Innovación (Grant BFU2017-89143-P) to EA-PN

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

Background The nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter‑annual climate variability. Methods We used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud‑based computing platform for subsequent modelling. To determine the biodiversity threats from high inter‑annual climate variability, we employed the spatial–temporal satellite‑derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery. Results SOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions. Conclusions In the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.EEA Santa CruzFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gaitan, Juan José. Universidad Nacional de Luján. Buenos Aires; Argentina.Fil: Gaitan, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Nosetto, Marcelo Daniel. Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales; Argentina.Fil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Villagra, Pablo Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Villagra, Pablo Eugenio. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Balducci, Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Yuto; Argentina.Fil: Pinazo, Martín Alcides. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Eclesia, Roxana Paola. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Von Wallis, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Villarino, Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Villarino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alaggia, Francisco Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Alaggia, Francisco Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gonzalez-Polo, Marina. Universidad Nacional del Comahue; Argentina.Fil: Gonzalez-Polo, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. INIBIOMA; Argentina.Fil: Manrique, Silvana M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional. CCT Salta‑Jujuy; Argentina.Fil: Meglioli, Pablo A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Meglioli, Pablo A. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Rodríguez‑Souilla, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Mónaco, Martín H. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Medina, Ariel. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Gasparri, Ignacio. Universidad Nacional de Tucumán. Instituto de Ecología Regional; Argentina.Fil: Gasparri, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alvarez Arnesi, Eugenio. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Alvarez Arnesi, Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barral, María Paula. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Barral, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Von Müller, Axel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel Argentina.Fil: Pahr, Norberto Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Uribe Echevarría, Josefina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimilí; Argentina.Fil: Fernandez, Pedro Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Fernandez, Pedro Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Morsucci, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Morsucci, Marina. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Lopez, Dardo Ruben. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Lopez, Dardo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cellini, Juan Manuel. Universidad Nacional de la Plata (UNLP). Facultad de Ciencias Naturales y Museo. Laboratorio de Investigaciones en Maderas; Argentina.Fil: Alvarez, Leandro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Alvarez, Leandro M. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Barberis, Ignacio Martín. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barberis, Ignacio Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Colomb, Hernán Pablo. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Colomb, Hernán. Administración de Parques Nacionales (APN). Parque Nacional Los Alerces; Argentina.Fil: La Manna, Ludmila. Universidad Nacional de la Patagonia San Juan Bosco. Centro de Estudios Ambientales Integrados (CEAI); Argentina.Fil: La Manna, Ludmila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Barbaro, Sebastian Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina.Fil: Blundo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Blundo, Cecilia. Universidad Nacional de Tucumán. Tucumán; Argentina.Fil: Sirimarco, Marina Ximena. Universidad Nacional de Mar del Plata. Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP); Argentina.Fil: Sirimarco, Marina Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cavallero, Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Zalazar, Gualberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Zalazar, Gualberto. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina

Experimental study of differentially rotating supersonic plasma flows produced by aluminium wire array Z-pinches

A novel approach to cylindrical wire array z-pinches has been developed in order to create a rotating plasma flow analogous to astrophysical accretion discs. The method involves subjecting the wire array to a cusp magnetic field (B_r) to create converging off axis ablation streams to form a rotating flow. The rotation is sustained by the ram pressure of the ablation streams in a quasi-equilibrium state for approximately 150 ns. This corresponds to one full rotation of the plasma about the axis. The rotating plasma is supersonic with Mach number ~2 and a radially constant rotation velocity between 60 and 75 km/s; the angular velocity therefore has an r^-1 dependence and the flow is differential. A Thomson scattering diagnostic is used to measure the electron and ion temperatures as Te ~30 eV and Ti >55 eV and the ionisation of the plasma (Z) between 6 and 8. These parameters are used to calculate the Reynolds number (10^5 to 10^6) and magnetic Reynolds numbers (20 to 100) which are large enough for viscous and resistive effects to be negligible on the large scale of the flow. These are of sufficient magnitude for the experiment to be scalable to astrophysical accretion discs. Further more the Reynolds number for the experiment is large enough for shear instabilities to manifest in the plasma. Some evidence for this can be seen in XUV images and Thomson spectra which indicate the development of perturbations and vorticity within the flow. Predictions for the growth rate of the Kelvin Helmholtz instability, 12 to 40 ns, agree reasonably well with the observed perturbation growth of ~30 ns. It is also possible that shear instabilities are driving hydrodynamic turbulence. Turbulent heating of the plasma could explain the approximately 500 eV increase in the ion temperature observed from some Thomson spectra. Further work is required however to prove the existence of shear flows and turbulence within the experiments.Open Acces

Cross-basin and cross-taxa patterns of marine community tropicalization and deborealization in warming European seas.

Ocean warming and acidification, decreases in dissolved oxygen concentrations, and changes in primary production are causing an unprecedented global redistribution of marine life. The identification of underlying ecological processes underpinning marine species turnover, particularly the prevalence of increases of warm-water species or declines of cold-water species, has been recently debated in the context of ocean warming. Here, we track changes in the mean thermal affinity of marine communities across European seas by calculating the Community Temperature Index for 65 biodiversity time series collected over four decades and containing 1,817 species from different communities (zooplankton, coastal benthos, pelagic and demersal invertebrates and fish). We show that most communities and sites have clearly responded to ongoing ocean warming via abundance increases of warm-water species (tropicalization, 54%) and decreases of cold-water species (deborealization, 18%). Tropicalization dominated Atlantic sites compared to semi-enclosed basins such as the Mediterranean and Baltic Seas, probably due to physical barrier constraints to connectivity and species colonization. Semi-enclosed basins appeared to be particularly vulnerable to ocean warming, experiencing the fastest rates of warming and biodiversity loss through deborealization

Canagliflozin and Cardiovascular and Renal Outcomes in Type 2 Diabetes Mellitus and Chronic Kidney Disease in Primary and Secondary Cardiovascular Prevention Groups

Background: Canagliflozin reduces the risk of kidney failure in patients with type 2 diabetes mellitus and chronic kidney disease, but effects on specific cardiovascular outcomes are uncertain, as are effects in people without previous cardiovascular disease (primary prevention). Methods: In CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation), 4401 participants with type 2 diabetes mellitus and chronic kidney disease were randomly assigned to canagliflozin or placebo on a background of optimized standard of care. Results: Primary prevention participants (n=2181, 49.6%) were younger (61 versus 65 years), were more often female (37% versus 31%), and had shorter duration of diabetes mellitus (15 years versus 16 years) compared with secondary prevention participants (n=2220, 50.4%). Canagliflozin reduced the risk of major cardiovascular events overall (hazard ratio [HR], 0.80 [95% CI, 0.67-0.95]; P=0.01), with consistent reductions in both the primary (HR, 0.68 [95% CI, 0.49-0.94]) and secondary (HR, 0.85 [95% CI, 0.69-1.06]) prevention groups (P for interaction=0.25). Effects were also similar for the components of the composite including cardiovascular death (HR, 0.78 [95% CI, 0.61-1.00]), nonfatal myocardial infarction (HR, 0.81 [95% CI, 0.59-1.10]), and nonfatal stroke (HR, 0.80 [95% CI, 0.56-1.15]). The risk of the primary composite renal outcome and the composite of cardiovascular death or hospitalization for heart failure were also consistently reduced in both the primary and secondary prevention groups (P for interaction >0.5 for each outcome). Conclusions: Canagliflozin significantly reduced major cardiovascular events and kidney failure in patients with type 2 diabetes mellitus and chronic kidney disease, including in participants who did not have previous cardiovascular disease

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years