Plasma endocannabinoid levels in lean, overweight, and obese humans: relationships to intestinal permeability markers, inflammation, and incretin secretion

First published February 13, 2018Intestinal production of endocannabinoid and oleoylethanolamide (OEA) is impaired in high-fat diet/obese rodents, leading to reduced satiety. Such diets also alter the intestinal microbiome in association with enhanced intestinal permeability and inflammation, however little is known of these effects in humans. This study aimed to: (i) evaluate effects of lipid on plasma anandamide (AEA), 2-arachidonyl-sn-glycerol (2-AG) and OEA in humans, and (ii) examine relationships with intestinal permeability, inflammation markers and incretin hormone secretion.20 lean, 18 overweight and 19 obese participants underwent intraduodenal Intralipid® infusion (2 kcal/min) with collection of endoscopic duodenal biopsies and blood. Plasma AEA, 2-AG, and OEA (HPLC/tandem mass spectrometry), tumour necrosis factor-α (TNF-α), glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) (multiplex), and duodenal expression of occludin, zona-occludin-1 (ZO-1), intestinal-alkaline-phosphatase (IAP), and toll-like receptor-4 (TLR4) (RT-PCR), were assessed.Fasting plasma AEA was increased in obese, compared with lean and overweight (P<0.05), with no effect of BMI group or ID lipid infusion on plasma 2-AG or OEA. Duodenal expression of IAP and ZO-1 was reduced in obese, compared with lean (P<0.05), and these levels related negatively to plasma AEA (P<0.05). The iAUC for AEA was positively related to iAUC GIP (r=0.384, P=0.005).Obese individuals have increased plasma AEA and decreased duodenal expression of ZO-1 and IAP, in comparison to lean and overweight. The relationships between plasma AEA with duodenal ZO-1 and IAP, and GIP, suggest that altered endocannabinoid signalling may contribute to changes in intestinal permeability, inflammation and incretin release in human obesity.Tanya J. Little, Nada Cvijanovic, Nicholas V. DiPatrizio, Donovan A. Argueta, Christopher K. Rayner, X Christine Feinle-Bisset, and Richard L. Youn

Regional variation in the role of humidity on city-level heat-related mortality.

The rising humid heat is regarded as a severe threat to human survivability, but the proper integration of humid heat into heat-health alerts is still being explored. Using state-of-the-art epidemiological and climatological datasets, we examined the association between multiple heat stress indicators (HSIs) and daily human mortality in 739 cities worldwide. Notable differences were observed in the long-term trends and timing of heat events detected by HSIs. Air temperature (Tair) predicts heat-related mortality well in cities with a robust negative Tair-relative humidity correlation (CT-RH). However, in cities with near-zero or weak positive CT-RH, HSIs considering humidity provide enhanced predictive power compared to Tair. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with Tair in many cities. Our findings provide important insights into specific regions where humans are vulnerable to humid heat and can facilitate the further enhancement of heat-health alert systems

Impact of population aging on future temperature-related mortality at different global warming levels

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%-0.4% at 1.5-3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. K.C. was supported by the Yale Planetary Solutions Project seed grant. A.G., A.S., and S.R. were supported by the European Union’s Horizon 2020 Project Exhaustion grant (820655). A.G. was also supported by the Medical Research Council UK grant (MR/V034162/1). J.M. received funding from the Fundação para a Ciência e a Tecnlogia Grant (SFRH/BPD/115112/2016). A.T. was supported by the MCIN/AEI/10.13039/501100011033 grant (CEX2018-000794-S). A.U. and J.K. were supported by the Czech Science Foundation (22-24920S). F.S. was supported by the Italian Ministry of University and Research (MUR), Department of Excellence project 2023-2027 ReDS ‘Rethinking Data Science’ - Department of Statistics, Computer Science and Applications - University of Florence. MNM. was supported by the European Commission (H2020-MSCA-IF-2020) under REA grant agreement no. 101022870. A.V.C. acknowledges the support of the Swiss National Foundation (TMSGI3_211626). V.H. received funding from the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie Grant Agreement No.: 101032087).Peer reviewe

Impact of population aging on future temperature-related mortality at different global warming levels.

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%-0.4% at 1.5-3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population

Regional variation in the role of humidity on city-level heat-related mortality

The rising humid heat is regarded as a severe threat to human survivability, but the proper integration of humid heat into heat-health alerts is still being explored. Using state-of-the-art epidemiological and climatological datasets, we examined the association between multiple heat stress indicators (HSIs) and daily human mortality in 739 cities worldwide. Notable differences were observed in the long-term trends and timing of heat events detected by HSIs. Air temperature (Tair) predicts heat-related mortality well in cities with a robust negative Tair-relative humidity correlation (CT-RH). However, in cities with near-zero or weak positive CT-RH, HSIs considering humidity provide enhanced predictive power compared to Tair. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with Tair in many cities. Our findings provide important insights into specific regions where humans are vulnerable to humid heat and can facilitate the further enhancement of heat-health alert systems. © The Author(s) 2024.Q.G., M.H., and T.O. were supported by the Environment Research and Technology Development Fund (JPMEERF23S21120) of the Environmental Restoration and Conservation Agency provided by the Ministry of the Environment of Japan. Q.G. was supported by the Musha Shugyo international travel grants from the School of Engineering, The University of Tokyo. T.O. was supported by the Japan Society for the Promotion of Science (KAKENHI: 21H05002), and the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan (JPMEERF23S21100). M.N.M. was supported by the European Commission (H2020-MSCA-IF-2020) under REA grant agreement no. 101022870. A.G. was supported by the Medical Research Council-UK (Grant ID: MR/V034162/1) and European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). J.K. was supported by the Czech Science Foundation, project 23-06749S. A.M.V.-C. supported by the Swiss National Science Foundation (TMSGI3_211626). V.H. was supported by the European Union’s Horizon 2020 research and innovation program (H2020-MSCA-IF-2020, Grant No.: 101032087). Y.S. was supported by Brain Pool Plus program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (NRF-2021H1D3A2A03097768), and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2023R1A2C1004754)

Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordThe decline of Arctic sea ice is an integral part of anthropogenic climate change. Sea-ice loss is already having a significant impact on Arctic communities and ecosystems. Its role as a cause of climate changes outside of the Arctic has also attracted much scientific interest. Evidence is mounting that Arctic sea-ice loss can affect weather and climate throughout the Northern Hemisphere. The remote impacts of Arctic sea-ice loss can only be properly represented using models that simulate interactions among the ocean, sea ice, land and atmosphere. A synthesis of six such experiments with different models shows consistent hemispheric-wide atmospheric warming, strongest in the mid-to-high-latitude lower troposphere; an intensification of the wintertime Aleutian Low and, in most cases, the Siberian High; a weakening of the Icelandic Low; and a reduction in strength and southward shift of the mid-latitude westerly winds in winter. The atmospheric circulation response seems to be sensitive to the magnitude and geographic pattern of sea-ice loss and, in some cases, to the background climate state. However, it is unclear whether current-generation climate models respond too weakly to sea-ice change. We advocate for coordinated experiments that use different models and observational constraints to quantify the climate response to Arctic sea-ice loss.J.A.S. and R.B. were funded by the Natural Environment Research Council (NE/P006760/1). C.D. acknowledges the National Science Foundation (NSF), which sponsors the National Center for Atmospheric Research. D.M.S. was supported by the Met Office Hadley Centre Climate Programme (GA01101) and the APPLICATE project, which is funded by the European Union’s Horizon 2020 programme. X.Z. was supported by the NSF (ARC#1023592). P.J.K. and K.E.M. were supported by the Canadian Sea Ice and Snow Evolution Network, which is funded by the Natural Science and Engineering Research Council of Canada. T.O. was funded by Environment and Climate Change Canada (GCXE17S038). L.S. was supported by the National Oceanic and Atmospheric Administration’s Climate Program Office

Seasonality of mortality under climate change: a multicountry projection study.

BACKGROUND: Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones. METHODS: In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones. FINDINGS: The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario. INTERPRETATION: A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates. FUNDING: The Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan

Institutional investors and corporate governance

We provide a comprehensive overview of the role of institutional investors in corporate governance with three main components. First, we establish new stylized facts documenting the evolution and importance of institutional ownership. Second, we provide a detailed characterization of key aspects of the legal and regulatory setting within which institutional investors govern portfolio firms. Third, we synthesize the evolving response of the recent theoretical and empirical academic literature in finance to the emergence of institutional investors in corporate governance. We highlight how the defining aspect of institutional investors – the fact that they are financial intermediaries – differentiates them in their governance role from standard principal blockholders. Further, not all institutional investors are identical, and we pay close attention to heterogeneity amongst institutional investors as blockholders

Seasonality of mortality under climate change: a multicountry projection study

Data sharing: All data used in our study were obtained from the MCC Collaborative Research Network under a data-sharing agreement and cannot be made publicly available. Researchers can refer to collaborators of the Network, who are listed as coauthors of this Article (primary contact: Antonio Gasparrini, [email protected]), for information on accessing the data for each country. The R code is available on request, and a reproducible example is publicly available on the personal GitHub website of the first author (https://github.com/LinaMadaniyazi).For more on the MCC see https://mccstudy.lshtm.ac.uk/Supplementary Material is available online at: https://www.sciencedirect.com/science/article/pii/S2542519623002693#sec1 .Background: Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones. Methods: In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones. Findings: The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario. Interpretation: A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates.This study was primarily supported by the Environment Research and Technology Development Fund (grant number JPMEERF20231007) of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan. MH was supported by the Japan Science and Technology Agency as part of the Strategic International Collaborative Research Program (grant number JPMJSC20E4). AG was supported by the UK Medical Research Council (grant number MR/V034162/1) and the EU's Horizon 2020 research project Exhaustion (grant number 820655). AU and JK were supported by the Czech Science Foundation (project 22–24920S). JJKJ was supported by the Academy of Finland (grant number 310372; Global Health Risks Related to Atmospheric Composition and Weather Consortium). FS was supported by the Italian Ministry of University and Research, Department of Excellence project 2023–2027, Rethinking Data Science—Department of Statistics, Computer Science and Applications—University of Florence

Impact of population aging on future temperature-related mortality at different global warming levels

Data availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data were collected within the MCC Collaborative Research Network under a data sharing agreement and cannot be made publicly available.Code availability: A sample of the analysis code is available from https://github.com/CHENlab-Yale/MCC_ProjAging_Temp .Supplementary information is available online at: https://link-springer-com.ezproxytest.brunel.ac.uk/article/10.1038/s41467-024-45901-z#Sec15 .Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. K.C. was supported by the Yale Planetary Solutions Project seed grant. A.G., A.S., and S.R. were supported by the European Union’s Horizon 2020 Project Exhaustion grant (820655). A.G. was also supported by the Medical Research Council UK grant (MR/V034162/1). J.M. received funding from the Fundação para a Ciência e a Tecnlogia Grant (SFRH/BPD/115112/2016). A.T. was supported by the MCIN/AEI/10.13039/501100011033 grant (CEX2018-000794-S). A.U. and J.K. were supported by the Czech Science Foundation (22-24920S). F.S. was supported by the Italian Ministry of University and Research (MUR), Department of Excellence project 2023-2027 ReDS ‘Rethinking Data Science’ - Department of Statistics, Computer Science and Applications - University of Florence. MNM. was supported by the European Commission (H2020-MSCA-IF-2020) under REA grant agreement no. 101022870. A.V.C. acknowledges the support of the Swiss National Foundation (TMSGI3_211626). V.H. received funding from the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie Grant Agreement No.: 101032087)