Helicobacter pylori versus the Host: Remodeling of the Bacterial Outer Membrane Is Required for Survival in the Gastric Mucosa

Modification of bacterial surface structures, such as the lipid A portion of lipopolysaccharide (LPS), is used by many pathogenic bacteria to help evade the host innate immune response. Helicobacter pylori, a gram-negative bacterium capable of chronic colonization of the human stomach, modifies its lipid A by removal of phosphate groups from the 1- and 4′-positions of the lipid A backbone. In this study, we identify the enzyme responsible for dephosphorylation of the lipid A 4′-phosphate group in H. pylori, Jhp1487 (LpxF). To ascertain the role these modifications play in the pathogenesis of H. pylori, we created mutants in lpxE (1-phosphatase), lpxF (4′-phosphatase) and a double lpxE/F mutant. Analysis of lipid A isolated from lpxE and lpxF mutants revealed lipid A species with a 1 or 4′-phosphate group, respectively while the double lpxE/F mutant revealed a bis-phosphorylated lipid A. Mutants lacking lpxE, lpxF, or lpxE/F show a 16, 360 and 1020 fold increase in sensitivity to the cationic antimicrobial peptide polymyxin B, respectively. Moreover, a similar loss of resistance is seen against a variety of CAMPs found in the human body including LL37, β-defensin 2, and P-113. Using a fluorescent derivative of polymyxin we demonstrate that, unlike wild type bacteria, polymyxin readily associates with the lpxE/F mutant. Presumably, the increase in the negative charge of H. pylori LPS allows for binding of the peptide to the bacterial surface. Interestingly, the action of LpxE and LpxF was shown to decrease recognition of Helicobacter LPS by the innate immune receptor, Toll-like Receptor 4. Furthermore, lpxE/F mutants were unable to colonize the gastric mucosa of C57BL/6J and C57BL/6J tlr4 -/- mice when compared to wild type H. pylori. Our results demonstrate that dephosphorylation of the lipid A domain of H. pylori LPS by LpxE and LpxF is key to its ability to colonize a mammalian host

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

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

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

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management

Population Genetic Analyses of Helicobacter pylori Isolates from Gambian Adults and Children

The gastric pathogen Helicobacter pylori is one of the most genetically diverse of bacterial species. Much of its diversity stems from frequent mutation and recombination, preferential transmission within families and local communities, and selection during persistent gastric mucosal infection. MLST of seven housekeeping genes had identified multiple distinct H. pylori populations, including three from Africa: hpNEAfrica, hpAfrica1 and hpAfrica2, which consists of three subpopulations (hspWAfrica, hspCAfrica and hspSAfrica). Most detailed H. pylori population analyses have used strains from non-African countries, despite Africa's high importance in the emergence and evolution of humans and their pathogens. Our concatenated sequences from seven H. pylori housekeeping genes from 44 Gambian patients (MLST) identified 42 distinct sequence types (or haplotypes), and no clustering with age or disease. STRUCTURE analysis of the sequence data indicated that Gambian H. pylori strains belong to the hspWAfrica subpopulation of hpAfrica1, in accord with Gambia's West African location. Despite Gambia's history of invasion and colonisation by Europeans and North Africans during the last millennium, no traces of Ancestral Europe1 (AE1) population carried by those people were found. Instead, admixture of 17% from Ancestral Europe2 (AE2) was detected in Gambian strains; this population predominates in Nilo-Saharan speakers of North-East Africa, and might have been derived from admixture of hpNEAfrica strains these people carried when they migrated across the Sahara during the Holocene humid period 6,000-9,000 years ago. Alternatively, shared AE2 ancestry might have resulted from shared ancestral polymorphisms already present in the common ancestor of sister populations hpAfrica1 and hpNEAfrica

Placebo effect in chronic inflammatory demyelinating polyneuropathy: The PATH study and a systematic review

none148siThe Polyneuropathy And Treatment with Hizentra (PATH) study required subjects with chronic inflammatory demyelinating polyneuropathy (CIDP) to show dependency on immunoglobulin G (IgG) and then be restabilized on IgG before being randomized to placebo or one of two doses of subcutaneous immunoglobulin (SCIG). Nineteen of the 51 subjects (37%) randomized to placebo did not relapse over the next 24 weeks. This article explores the reasons for this effect. A post-hoc analysis of the PATH placebo group was undertaken. A literature search identified other placebo-controlled CIDP trials for review and comparison. In PATH, subjects randomized to placebo who did not relapse were significantly older, had more severe disease, and took longer to deteriorate in the IgG dependency period compared with those who relapsed. Published trials in CIDP, whose primary endpoint was stability or deterioration, had a mean non-deterioration (placebo effect) of 43%, while trials with a primary endpoint of improvement had a placebo response of only 11%. Placebo is an important variable in the design of CIDP trials. Trials designed to show clinical improvement will have a significantly lower effect of this phenomenon than those designed to show stability or deterioration.nonenoneLewis R.A.; Cornblath D.R.; Hartung H.-P.; Sobue G.; Lawo J.-P.; Mielke O.; Durn B.L.; Bril V.; Merkies I.S.J.; Bassett P.; Cleasby A.; van Schaik I.N.; Sabet A.; George K.; Roberts L.; Carne R.; Blum S.; Henderson R.; Van Damme P.; Demeestere J.; Larue S.; D'Amour C.; Kunc P.; Valis M.; Sussova J.; Kalous T.; Talab R.; Bednar M.; Toomsoo T.; Rubanovits I.; Gross-Paju K.; Sorro U.; Saarela M.; Auranen M.; Pouget J.; Attarian S.; Le Masson G.; Wielanek A.; Desnuelle C.; Delmon E.; Clavelou P.; Aufauvre D.; Schmidt J.; Zschuentzsch J.; Sommer C.; Kramer D.; Hoffmann O.; Goerlitz C.; Haas J.; Chatzopoulos M.; Yoon R.; Gold R.; Berlit P.; Jaspert-Grehl A.; Liebetanz D.; Kutschenko A.; Stangel M.; Trebst C.; Baum P.; Bergh F.; Klehmet J.; Meisel A.; Klostermann F.; Oechtering J.; Lehmann H.; Schroeter M.; Hagenacker T.; Mueller D.; Sperfeld A.; Bethke F.; Drory I.V.; Algom A.; Yarnitsky D.; Murinson B.; Di Muzio A.; Ciccocioppo F.; Sorbi S.; Mata S.; Schenone A.; Grandis M.; Lauria G.; Cazzato D.; Antonini G.; Morino S.; Cocito D.; Zibetti M.; Yokota T.; Ohkubo T.; Kanda T.; Kawai M.; Kaida K.; Onoue H.; Kuwabara S.; Mori M.; Iijima M.; Ohyama K.; Baba M.; Tomiyama M.; Nishiyama K.; Akutsu T.; Yokoyama K.; Kanai K.; van Schaik I.N.; Eftimov F.; Notermans N.C.; Visser N.; Faber C.; Hoeijmakers J.; Rejdak K.; Chyrchel-Paszkiewicz U.; Casanovas Pons C.; Antonia M.; Gamez J.; Salvado M.; Infante C.M.; Benitez S.; Lunn M.; Morrow J.; Gosal D.; Lavin T.; Melamed I.; Testori A.; Ajroud-Driss S.; Menichella D.; Simpson E.; Lai E.C.-H.; Dimachkie M.; Barohn R.J.; Beydoun S.; Johl H.; Lange D.; Shtilbans A.; Muley S.; Ladha S.; Freimer M.; Kissel J.; Latov N.; Chin R.; Ubogu E.; Mumfrey S.; Rao T.; MacDonald P.; Sharma K.; Gonzalez G.; Allen J.; Walk D.; Hobson-Webb L.; Gable K.Lewis, R. A.; Cornblath, D. R.; Hartung, H. -P.; Sobue, G.; Lawo, J. -P.; Mielke, O.; Durn, B. L.; Bril, V.; Merkies, I. S. J.; Bassett, P.; Cleasby, A.; van Schaik, I. N.; Sabet, A.; George, K.; Roberts, L.; Carne, R.; Blum, S.; Henderson, R.; Van Damme, P.; Demeestere, J.; Larue, S.; D'Amour, C.; Kunc, P.; Valis, M.; Sussova, J.; Kalous, T.; Talab, R.; Bednar, M.; Toomsoo, T.; Rubanovits, I.; Gross-Paju, K.; Sorro, U.; Saarela, M.; Auranen, M.; Pouget, J.; Attarian, S.; Le Masson, G.; Wielanek, A.; Desnuelle, C.; Delmon, E.; Clavelou, P.; Aufauvre, D.; Schmidt, J.; Zschuentzsch, J.; Sommer, C.; Kramer, D.; Hoffmann, O.; Goerlitz, C.; Haas, J.; Chatzopoulos, M.; Yoon, R.; Gold, R.; Berlit, P.; Jaspert-Grehl, A.; Liebetanz, D.; Kutschenko, A.; Stangel, M.; Trebst, C.; Baum, P.; Bergh, F.; Klehmet, J.; Meisel, A.; Klostermann, F.; Oechtering, J.; Lehmann, H.; Schroeter, M.; Hagenacker, T.; Mueller, D.; Sperfeld, A.; Bethke, F.; Drory, I. V.; Algom, A.; Yarnitsky, D.; Murinson, B.; Di Muzio, A.; Ciccocioppo, F.; Sorbi, S.; Mata, S.; Schenone, A.; Grandis, M.; Lauria, G.; Cazzato, D.; Antonini, G.; Morino, S.; Cocito, D.; Zibetti, M.; Yokota, T.; Ohkubo, T.; Kanda, T.; Kawai, M.; Kaida, K.; Onoue, H.; Kuwabara, S.; Mori, M.; Iijima, M.; Ohyama, K.; Baba, M.; Tomiyama, M.; Nishiyama, K.; Akutsu, T.; Yokoyama, K.; Kanai, K.; van Schaik, I. N.; Eftimov, F.; Notermans, N. C.; Visser, N.; Faber, C.; Hoeijmakers, J.; Rejdak, K.; Chyrchel-Paszkiewicz, U.; Casanovas Pons, C.; Antonia, M.; Gamez, J.; Salvado, M.; Infante, C. M.; Benitez, S.; Lunn, M.; Morrow, J.; Gosal, D.; Lavin, T.; Melamed, I.; Testori, A.; Ajroud-Driss, S.; Menichella, D.; Simpson, E.; Lai, E. C. -H.; Dimachkie, M.; Barohn, R. J.; Beydoun, S.; Johl, H.; Lange, D.; Shtilbans, A.; Muley, S.; Ladha, S.; Freimer, M.; Kissel, J.; Latov, N.; Chin, R.; Ubogu, E.; Mumfrey, S.; Rao, T.; Macdonald, P.; Sharma, K.; Gonzalez, G.; Allen, J.; Walk, D.; Hobson-Webb, L.; Gable, K

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

BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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