54 research outputs found
Neuroendocrine and immune responses undertake different fates following tryptophan or methionine dietary treatment: tales from a teleost model
Methionine and tryptophan appear to be fundamental in specific cellular pathways involved in the immune response mechanisms, including stimulation of T-regulatory cells by tryptophan metabolites or pro-inflammatory effects upon methionine supplementation. Thus, the aim of this study was to evaluate the immunomodulatory effect of these amino acids on the inflammatory and neuroendocrine responses in juveniles of European seabass, Dicentrarchus labrax. To achieve this, goal fish were fed for 14 days methionine and tryptophan-supplemented diets (MET and TRP, respectively, 2× dietary requirement level) or a control diet meeting the amino acids requirement levels (CTRL). Fish were sampled for immune status assessment and the remaining fish were challenged with intraperitoneally injected inactivated Photobacterium damselae subsp. piscicida and sampled either 4 or 24 h post-injection. Respiratory burst activity, brain monoamines, plasma cortisol, and immune-related gene expression showed distinct and sometimes opposite patterns regarding the effects of dietary amino acids. While neuroendocrine intermediates were not affected by any dietary treatment at the end of the feeding trial, both supplemented diets led to increased levels of plasma cortisol after the inflammatory insult, while brain monoamine content was higher in TRP-fed fish. Peripheral blood respiratory burst was higher in TRP-fed fish injected with the bacteria inoculum but only compared to those fed MET. However, no changes were detected in total antioxidant capacity. Complement factor 3 was upregulated in MET-fed fish but methionine seemed to poorly affect other genes expression patterns. In contrast, fish fed MET showed increased immune cells numbers both before and after immune challenge, suggesting a strong enhancing effect of methionine on immune cells proliferation. Differently, tryptophan effects on inflammatory transcripts suggested an inhibitory mode of action. This, together with a high production of brain monoamine and cortisol levels, suggests that tryptophan might mediate regulatory mechanisms of neuroendocrine and immune systems cooperation. Overall, more studies are needed to ascertain the role of methionine and tryptophan in modulating (stimulate or regulate) fish immune and neuroendocrine responses
Neuroendocrine and Immune Responses Undertake Different Fates following Tryptophan or Methionine Dietary Treatment: Tales from a Teleost Model
Methionine and tryptophan appear to be fundamental in specific cellular pathways involved in the immune response mechanisms, including stimulation of T-regulatory cells by tryptophan metabolites or pro-inflammatory effects upon methionine supplementation. Thus, the aim of this study was to evaluate the immunomodulatory effect of these amino acids on the inflammatory and neuroendocrine responses in juveniles of European seabass, Dicentrarchus labrax. To achieve this, goal fish were fed for 14 days methionine and tryptophan-supplemented diets (MET and TRP, respectively, 2× dietary requirement level) or a control diet meeting the amino acids requirement levels (CTRL). Fish were sampled for immune status assessment and the remaining fish were challenged with intraperitoneally injected inactivated Photobacterium damselae subsp. piscicida and sampled either 4 or 24 h post-injection. Respiratory burst activity, brain monoamines, plasma cortisol, and immune-related gene expression showed distinct and sometimes opposite patterns regarding the effects of dietary amino acids. While neuroendocrine intermediates were not affected by any dietary treatment at the end of the feeding trial, both supplemented diets led to increased levels of plasma cortisol after the inflammatory insult, while brain monoamine content was higher in TRP-fed fish. Peripheral blood respiratory burst was higher in TRP-fed fish injected with the bacteria inoculum but only compared to those fed MET. However, no changes were detected in total antioxidant capacity. Complement factor 3 was upregulated in MET-fed fish but methionine seemed to poorly affect other genes expression patterns. In contrast, fish fed MET showed increased immune cells numbers both before and after immune challenge, suggesting a strong enhancing effect of methionine on immune cells proliferation. Differently, tryptophan effects on inflammatory transcripts suggested an inhibitory mode of action. This, together with a high production of brain monoamine and cortisol levels, suggests that tryptophan might mediate regulatory mechanisms of neuroendocrine and immune systems cooperation. Overall, more studies are needed to ascertain the role of methionine and tryptophan in modulating (stimulate or regulate) fish immune and neuroendocrine responses
Long -term feeding with high plant protein based diets in gilthead seabream (Sparus aurata, L.) leads to changes in the inflammatory and immune related gene expression at intestinal level
[EN] Background: In order to ensure sustainability of aquaculture production of carnivourous fish species such as the gilthead seabream (Sparus aurata, L.), the impact of the inclusion of alternative protein sources to fishmeal, including plants, has been assessed. With the aim of evaluating long-term effects of vegetable diets on growth and intestinal status of the on-growing gilthead seabream (initial weight = 129 g), three experimental diets were tested: a strict plant protein-based diet (VM), a fishmeal based diet (FM) and a plant protein-based diet with 15% of marine ingredients (squid and krill meal) alternative to fishmeal (VM+). Intestines were sampled after 154 days. Besides studying growth parameters and survival, the gene expression related to inflammatory response, immune system, epithelia integrity and digestive process was analysed in the foregut and hindgut sections, as well as different histological parameters in the foregut.
Results: There were no differences in growth performance (p = 0.2703) and feed utilization (p = 0.1536), although a greater fish mortality was recorded in the VM group (p = 0.0141). In addition, this group reported a lower expression in genes related to pro-inflammatory response, as Interleukine-1 beta (il1 beta, p = 0.0415), Interleukine-6 (il6, p = 0.0347) and cyclooxigenase-2 (cox2, p = 0.0014), immune-related genes as immunoglobulin M (igm, p = 0.0002) or bacterial defence genes as alkaline phosphatase (alp, p = 0.0069). In contrast, the VM+ group yielded similar survival rate to FM (p = 0.0141) and the gene expression patterns indicated a greater induction of the inflammatory and immune markers (il1 beta, cox2 and igm). However, major histological changes in gut were not detected.
Conclusions: Using plants as the unique source of protein on a long term basis, replacing fishmeal in aqua feeds for gilthead seabream, may have been the reason of a decrease in the level of different pro-inflammatory mediators (il1 beta, il6 and cox2) and immune-related molecules (igm and alp), which reflects a possible lack of local immune response at the intestinal mucosa, explaining the higher mortality observed. Krill and squid meal inclusion in vegetable diets, even at low concentrations, provided an improvement in nutrition and survival parameters compared to strictly plant protein based diets as VM, maybe explained by the maintenance of an effective immune response throughout the assay.The research has been partially funded by Vicerrectorat d'Investigacio, Innovacio i Transferencia of the Universitat Politecnica de Valencia, which belongs to the project Aquaculture feed without fishmeal (SP20120603). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.Estruch-Cucarella, G.; Collado, MC.; Monge-Ortiz, R.; Tomas-Vidal, A.; Jover Cerdá, M.; Peñaranda, D.; Perez Martinez, G.... (2018). Long -term feeding with high plant protein based diets in gilthead seabream (Sparus aurata, L.) leads to changes in the inflammatory and immune related gene expression at intestinal level. BMC Veterinary Research. 14. https://doi.org/10.1186/s12917-018-1626-6S14Hardy RW. Utilization of plant proteins in fish diets: effects of global demand and supplies of fishmeal. Aquac Res. 2010;41:770–6.Martínez-Llorens S, Moñino AV, Vidal AT, Salvador VJM, Pla Torres M, Jover Cerdá M, et al. Soybean meal as a protein source in gilthead sea bream (Sparus aurata L.) diets: effects on growth and nutrient utilization. Aquac Res. 2007;38(1):82–90.Tacon AGJ, Metian M. Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: trends and future prospects. Aquaculture. 2008;285:146–58.Bonaldo A, Roem AJ, Fagioli P, Pecchini A, Cipollini I, Gatta PP. Influence of dietary levels of soybean meal on the performance and gut histology of gilthead sea bream (Sparus aurata L.) and European sea bass (Dicentrarchus labrax L.). Aquac Res. 2008;39(9):970–8.Kissil G, Lupatsch I. Successful replacement of fishmeal by plant proteins in diets for the gilthead seabream, Sparus Aurata L. Isr J Aquac – Bamidgeh. 2004;56(3):188–99.Monge-Ortíz R, Martínez-Llorens S, Márquez L, Moyano FJ, Jover-Cerdá M, Tomás-Vidal A. Potential use of high levels of vegetal proteins in diets for market-sized gilthead sea bream (Sparus aurata). Arch Anim Nutr. 2016;70(2):155–72.Santigosa E, Sánchez J, Médale F, Kaushik S, Pérez-Sánchez J, Gallardo MA. Modifications of digestive enzymes in trout (Oncorhynchus mykiss) and sea bream (Sparus aurata) in response to dietary fish meal replacement by plant protein sources. Aquaculture. 2008;282:68–74.Santigosa E, García-Meilán I, Valentin JM, Pérez-Sánchez J, Médale F, Kaushik S, et al. Modifications of intestinal nutrient absorption in response to dietary fish meal replacement by plant protein sources in sea bream (Sparus aurata) and rainbow trout (Onchorynchus mykiss). Aquaculture. 2011;317:146–54.Sitjá-Bobadilla A, Peña-Llopis S, Gómez-Requeni P, Médale F, Kaushik S, Pérez-Sánchez J. Effect of fish meal replacement by plant protein sources on non-specific defence mechanisms and oxidative stress in gilthead sea bream (Sparus aurata). Aquaculture. 2005;249:387–400.Martínez-Llorens S, Baeza-Ariño R, Nogales-Mérida S, Jover-Cerdá M, Tomás-Vidal A. Carob seed germ meal as a partial substitute in gilthead sea bream (Sparus aurata) diets: amino acid retention, digestibility, gut and liver histology. Aquaculture. 2012;338-341:124–33.Baeza-Ariño R, Martínez-Llorens S, Nogales-Mérida S, Jover-Cerda M, Tomás-Vidal A. Study of liver and gut alterations in sea bream, Sparus aurata L., fed a mixture of vegetable protein concentrates. Aquac Res. 2014;47(2):460–71.Estruch G, Collado MC, Peñaranda DS, Tomás Vidal A, Jover Cerdá M, Pérez Martínez G, et al. Impact of fishmeal replacement in diets for gilthead sea bream (Sparus aurata) on the gastrointestinal microbiota determined by pyrosequencing the 16S rRNA gene. PLoS One. 2015;10(8):e0136389. https://doi.org/10.1371/journal.pone.0136389 .Fekete SG, Kellems RO. Interrelationship of feeding with immunity and parasitic infection: a review. Vet Med. 2007;52(4):131–43.Kiron V. Fish immune system and its nutritional modulation for preventive health care. Anim Feed Sci Technol. 2012;173(1–2):111–33.Minghetti M, Drieschner C, Bramaz N, Schug H, Schirmer K. A fish intestinal epithelial barrier model established from the rainbow trout (Oncorhynchus mykiss) cell line, RTgutGC. Cell Biol Toxicol. 2017;33:539–55.Cerezuela R, Meseguer J, Esteban MÁ. Effects of dietary inulin, Bacillus subtilis and microalgae on intestinal gene expression in gilthead seabream (Sparus aurata L.). Fish Shellfish Immunol. 2013;34(3):843–8.Couto A, Kortner TM, Penn M, Bakke AM, Krogdahl O-TA, et al. Effects of dietary soy saponins and phytosterols on gilthead sea bream (Sparus aurata) during the on-growing period. Anim Feed Sci Technol. 2014;198:203–14.Estensoro I, Calduch-Giner JA, Kaushik S, Pérez-Sánchez J, Sitjá-Bobadilla A. Modulation of the IgM gene expression and IgM immunoreactive cell distribution by the nutritional background in gilthead sea bream (Sparus aurata) challenged with Enteromyxum leei (Myxozoa). Fish Shellfish Immunol. 2012;33(2):401–10.Pérez-Sánchez J, Estensoro I, Redondo MJ, Calduch-Giner JA, Kaushik S, Sitjà-Bobadilla A. Mucins as diagnostic and prognostic biomarkers in a fish-parasite model: transcriptional and functional analysis. PLoS One. 2013;8(6):e65457.Reyes-Becerril M, Guardiola F, Rojas M, Ascencio-Valle F, Esteban MÁ. Dietary administration of microalgae Navicula sp. affects immune status and gene expression of gilthead seabream (Sparus aurata). Fish Shellfish Immunol. 2013;35(3):883–9.Pérez-Sánchez J, Benedito-Palos L, Estensoro I, Petropoulos Y, Calduch-Giner JA, Browdy CL, et al. Effects of dietary NEXT ENHANCE ® 150 on growth performance and expression of immune and intestinal integrity related genes in gilthead sea bream (Sparus aurata L.). Fish Shellfish Immunol. 2015;44:117–28.Estensoro I, Ballester-Lozano G, Benedito-Palos L, Grammes F, Martos-Sitcha JA, Mydland L-T, et al. Dietary butyrate helps to restore the intestinal status of a marine teleost (Sparus aurata) fed extreme diets low in fish meal and fish oil. PLoS One. 2016;11(11):1–21.Torrecillas S, Caballero MJ, Mompel D, Montero D, Zamorano MJ, Robaina L, et al. Disease resistance and response against Vibrio anguillarum intestinal infection in European seabass (Dicentrarchus labrax) fed low fish meal and fish oil diets. Fish Shellfish Immunol. 2017;67:302–11.Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C T method. Nat Protoc. 2008;3(6):1101–8.Omnes MH, Silva FCP, Moriceau J, Aguirre P, Kaushik S, Gatesoupe F-J. Influence of lupin and rapeseed meals on the integrity of digestive tract and organs in gilthead seabream (Sparus aurata L.) and goldfish (Carassius auratus L.) juveniles. Aquac Nutr. 2015;21:223–33.Francis G, Makkar HPS, Becker K. Antinutritional factors present in plant-derived alternate fish feed ingredients and their effects in fish. Aquaculture. 2001;199:197–227.Gatlin DM III, Barrows FT, Brown P, Dabrowski K, Gaylord TG, Hardy RW, et al. Expanding the utilization of sustainable plant products in aquafeeds: a review. Aquac Res. 2007;38:551–79.Kader MA, Bulbul M, Koshio S, Ishikawa M, Yokoyama S, Nguyen BT, et al. Effect of complete replacement of fishmeal by dehulled soybean meal with crude attractants supplementation in diets for red sea bream, Pagrus major. Aquaculture. 2012;350-353:109–16.Gómez-Requeni P, Mingarro M, Calduch-Giner JA, Médale F, Martin SAM, Houlihan DF, et al. Protein growth performance, amino acid utilisation and somatotropic axis responsiveness to fish meal replacement by plant protein sources in gilthead sea bream (Sparus aurata). Aquaculture. 2004;232(1–4):493–510.Kader MA, Koshio S, Ishikawa M, Yokoyama S, Bulbul M. Supplemental effects of some crude ingredients in improving nutritive values of low fishmeal diets for red sea bream, Pagrus major. Aquaculture. 2010;308(3–4):136–44.Mai K, Li H, Ai Q, Duan Q, Xu W, Zhang C, et al. Effects of dietary squid viscera meal on growth and cadmium accumulation in tissues of Japanese seabass, Lateolabrax japonicus (Cuvier 1828). Aquac Res. 2006;37(11):1063–9.Peres H, Oliva-Teles A. The optimum dietary essential amino acid profile for gilthead seabream (Sparus aurata) juveniles. Aquaculture. 2009;296(1–2):81–6.Cho CY, Slinger SJ, Bayley HS. Bioenergetics of salmonid fishes: energy intake, expenditure and productivity. Comp Biochem Physiol Part B. 1982;73(1):25–41.Venou B, Alexis MN, Fountoulaki E, Haralabous J. Effects of extrusion and inclusion level of soybean meal on diet digestibility , performance and nutrient utilization of gilthead sea bream ( Sparus aurata ). Aquaculture. 2006;261:343–56.Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26:509–15.Terova G, Robaina L, Izquierdo M, Cattaneo A, Molinari S, Bernardini G, et al. PepT1 mRNA expression levels in sea bream (Sparus aurata) fed different plant protein sources. Springerplus. 2013;2:17.Bates JM, Akerlund J, Mittge E, Guillemin K. Intestinal alkaline phosphatase detoxifies lipopolysaccharide and prevents inflammation in zebrafish in response to the gut microbiota. Cell Host Microbe. 2007;2(6):371–82.Adamidou S, Nengas I, Henry M, Grigorakis K, Rigos G, Nikolopoulou D, et al. Growth, feed utilization, health and organoleptic characteristics of European seabass (Dicentrarchus labrax) fed extruded diets including low and high levels of three different legumes. Aquaculture. 2009;293(3–4):263–71.Daprà F, Gai F, Costanzo MT, Maricchiolo G, Micale V, Sicuro B, et al. Rice protein-concentrate meal as a potential dietary ingredient in practical diets for blackspot seabream Pagellus bogaraveo: a histological and enzymatic investigation. J Fish Biol. 2009;74(4):773–89.Overland M, Sorensen M, Storebakken T, Penn M, Krogdahl A, Skrede A. Pea protein concentrate substituting fish meal or soybean meal in diets for Atlantic salmon (Salmo salar)-effect on growth performance, nutrient digestibility, carcass composition, gut health, and physical feed quality. Aquaculture. 2009;288(3–4):305–11.Penn MH, Bendiksen EA, Campbell P, Krogdahl AS. High level of dietary pea protein concentrate induces enteropathy in Atlantic salmon (Salmo salar L.). Aquaculture. 2011;310(3–4):267–73.Hedrera MI, Galdames JA, Jimenez-Reyes MF, Reyes AE, Avendaño-Herrera R, Romero J, et al. Soybean meal induces intestinal inflammation in zebrafish larvae. PLoS One. 2013;8(7):1–10.Kokou F, Sarropoulou E, Cotou E, Rigos G, Henry M, Alexis M. Effects of fish meal replacement by a soybean protein on growth, histology, selected immune and oxidative status markers of Gilthead Sea bream, Sparus aurata. J World Aquac Soc. 2015;46(2):115–28.Kokou F, Sarropoulou E, Cotou E, Kentouri M, Alexis M, Rigos G. Effects of graded dietary levels of soy protein concentrate supplemented with methionine and phosphate on the immune and antioxidant responses of gilthead sea bream (Sparus aurata L.). Fish Shellfish Immunol. 2017;64:111–21.Calduch-Giner JA, Sitjà-Bobadilla A, Davey GC, Cairns MT, Kaushik S, Pérez-Sánchez J. Dietary vegetable oils do not alter the intestine transcriptome of gilthead sea bream (Sparus aurata), but modulate the transcriptomic response to infection with Enteromyxum leei. BMC Genomics. 2012;13(1):470.Piazzon MC, Galindo-Villegas J, Pereiro P, Estensoro I, Calduch-Giner JA, Gómez-Casado E, et al. Differential modulation of IgT and IgM upon parasitic, bacterial, viral, and dietary challenges in a perciform fish. Front Immunol. 2016;7. Article 637. https://doi.org/10.3389/fimmu.2016.00637 .Salinas I, Zhang Y, Sunyer JO. Mucosal immunoglobulins and B cells of teleost fish. Dev Comp Immunol. 2011;35(12):1346–65.Krogdahl A, Bakke-McKellep AM, Roed KH, Baeverfjord G. Feeding Atlantic salmon Salmo salar L. soybean products: effects on disease resistance (furunculosis), and lysozyme and IgM levels in the intestinal mucosa. Aquac Nutr. 2000;6:77–84.Chasiotis H, Effendi JC, Kelly SP. Occludin expression in goldfish held in ion-poor water. J Comp Physiol B Biochem Syst Environ Physiol. 2009;179(2):145–54.Chen KT, Malo MS, Beasley-Topliffe LK, Poelstra K, Millan JL, Mostafa G, et al. A role for intestinal alkaline phosphatase in the maintenance of local gut immunity. Dig Dis Sci. 2011;56(4):1020–7.Vaishnava S, Hooper LV. Alkaline phosphatase: keeping the peace at the gut epithelial surface. Cell Host Microbe. 2007;2(6):365–7.Tort L. Stress and immune modulation in fish. Dev Comp Immunol [internet]. Elsevier Ltd. 2011;35(12):1366–75.Martin SAM, Król E. Nutrigenomics and immune function in fish: new insights from omics technologies. Dev Comp Immunol. 2017;75:86–98.Burrells C, Williams PD, Southgate PJ, Crampton VO. Immunological , physiological and pathological responses of rainbow trout (Oncorhynchus mykiss) to increasing dietary concentrations of soybean proteins. Vet Immunol Immunopathol. 1999;72:277–88.Sahlmann C, Sutherland BJG, Kortner TM, Koop BF, Krogdahl Å, Bakke AM. Early response of gene expression in the distal intestine of Atlantic salmon (Salmo salar L.) during the development of soybean meal induced enteritis. Fish Shellfish Immunol. 2013;34(2):599–609.Esteban MÁ, Cuesta A, Ortuño J, Meseguer J. Immunomodulatory effects of dietary intake of chitin on gilthead seabream ( Sparus aurata L .) innate immune system. Fish Shellfish Immunol. 2001;11:303–15.Storebakken T, Kvien IS, Shearer KD, Grisdale-Helland B, Helland SJ. Estimation of gastrointestinal evacuation rate in Atlantic salmon (Salmo salar) using inert markers and collection of faeces by sieving: evacuation of diets with fish meal, soybean meal or bacterial meal. Aquaculture. 1999;172(3–4):291–9.Olsen RE, Myklebust R, Ringø E, Mayhew TM. The influences of dietary linseed oil and saturated fatty acids on caecal enterocytes in Arctic char (Salvelinus alpinus L.): a quantitative ultrastructural study. Fish Physiol Biochem. 2000;22(3):207–16.Heikkinen J, Vielma J, Kemiläinen O, Tiirola M, Eskelinen P, Kiuru T, et al. Effects of soybean meal based diet on growth performance, gut histopathology and intestinal microbiota of juvenile rainbow trout (Oncorhynchus mykiss). Aquaculture. 2006;261(1):259–68.Krogdahl A, Bakke-McKellep AM, Baeverfjord G. Effects of graded levels of standard soybean meal on intestinal structure, mucosal enzyme activities, and pancreatic response in Atlantic salmon (Salmo salar L.). Aquac Nutr. 2003;9:361–71.Cerezuela R, Fumanal M, Tapia-Paniagua ST, Meseguer J, Moriñigo MA, Esteban MA. Changes in intestinal morphology and microbiota caused by dietary administration of inulin and Bacillus subtilis in gilthead sea bream (Sparus aurata L.) specimens. Fish Shellfish Immunol. 2013;34(5):1063–70.Cerezuela R, Fumanal M, Tapia-Paniagua ST, Meseguer J, Moriñigo MÁ, Esteban MÁ. Histological alterations and microbial ecology of the intestine in gilthead seabream (Sparus aurata L.) fed dietary probiotics and microalgae. Cell Tissue Res. 2012;350(3):477–89.Deplancke B, Gaskins HR. Microbial modulation of innate defense: goblet cells and the intestinal mucus layer. Am J Clin Nutr. 2001;73(suppl):1131S–41S.Kokou F, Rigos G, Henry M, Kentouri M, Alexis M. Growth performance, feed utilization and non-specific immune response of gilthead sea bream (Sparus aurata L.) fed graded levels of a bioprocessed soybean meal. Aquaculture. 2012;364-365:74–81
Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment
Background High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular
diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four
cardiometabolic risk factors for all countries and regions from 1980 to 2010.
Methods We used data for exposure to risk factors by country, age group, and sex from pooled analyses of populationbased health surveys. We obtained relative risks for the eff ects of risk factors on cause-specifi c mortality from metaanalyses
of large prospective studies. We calculated the population attributable fractions for- each risk factor alone,
and for the combination of all risk factors, accounting for multicausality and for mediation of the eff ects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specifi c population attributable fractions by the number of disease-specifi c deaths. We obtained cause-specifi c mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the fi nal estimates.
Findings In 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After
accounting for multicausality, 63% (10\ub78 million deaths, 95% CI 10\ub71\u201311\ub75) of deaths from these diseases in 2010 were attributable to the combined eff ect of these four metabolic risk factors, compared with 67% (7\ub71 million deaths,
6\ub76\u20137\ub76) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country
level, age-standardised death rates from these diseases attributable to the combined eff ects of these four risk factors
surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France,
Japan, the Netherlands, Singapore, South Korea, and Spain.
Interpretation The salient features of the cardiometabolic disease and risk factor epidemic at the beginning of
the 21st century are high blood pressure and an increasing eff ect of obesity and diabetes. The mortality burden
of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering
cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the globalresponse to non-communicable diseases
Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment
Background: High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010. Methods: We used data for exposure to risk factors by country, age group, and sex from pooled analyses of population-based health surveys. We obtained relative risks for the effects of risk factors on cause-specific mortality from meta-analyses of large prospective studies. We calculated the population attributable fractions for each risk factor alone, and for the combination of all risk factors, accounting for multicausality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific population attributable fractions by the number of disease-specific deaths. We obtained cause-specific mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the final estimates. Findings: In 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After accounting for multicausality, 63% (10·8 million deaths, 95% CI 10·1-11·5) of deaths from these diseases in 2010 were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7·1 million deaths, 6·6-7·6) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country level, age-standardised death rates from these diseases attributable to the combined effects of these four risk factors surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France, Japan, the Netherlands, Singapore, South Korea, and Spain. Interpretation: The salient features of the cardiometabolic disease and risk factor epidemic at the beginning of the 21st century are high blood pressure and an increasing effect of obesity and diabetes. The mortality burden of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the global response to non-communicable diseases. Funding: UK Medical Research Council, US National Institutes of Health. © 2014 Elsevier Ltd
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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
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Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
Background
Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.
Methods
22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.
Findings
Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.
Interpretation
Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.
Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19
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