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

Ritonavir Form III: An unexpected discovery while searching for the late-appearing polymorph II from melts

Here we present the rich polymorph of ritonavir achieved by melt crystallization, including two previously known polymorphs (I and II) and a new polymorph, denoted Form III. This new polymorph was unexpectedly discovered to crystallize from melts as a major phase as we tried to search for the kinetically hindered polymorph II. Form II was found at very high temperature (0.90-0.93 melting point). The addition of 30% or 50% PEG 1000 highly tunes the nucleation of RIT and makes Form II the only phase when crystallizing at 0.89-0.90 melting point. The ability to reveal rich polymorph of RIT, especially the kenitically hindered stable Form II, highlights the urgen need to involve melt crystallization as a regular method in polymorphism screening in early stage of drug development

Polymorphism in Griseofulvin: New Story of an Old Drug with Polyethylene Glycol

Griseofulvin (GSF) is an antifungal drug that has been clinically used for six decades. Here, we present a rich polymorphism of GSF crystallizing from GSF dispersions with polyethylene glycol (PEG), including five true polymorphs (Forms I-V) and one inclusion complex (IC). Two new polymorphs were reported for the first time, denoted Forms IV and V. Single-crystal structures of new polymorphs and a GSF-PEG IC were determined by X-ray crystallography using single crystals cultivated by microdroplet melt crystallization. A comprehensive solid form landscape of GSF is established to describe phase conversions between polymorphs. Enhancement in molecular mobility by PEG is suggested to be the reason for the nucleation of two new polymorphs, while the small geographic radius of PEG is attributed to the formation of a GSF-PEG IC increasing the density and lowering the Gibbs free energy of the system. This work expands our understanding of the complicated crystallization behavior of GSF in dispersions with PEG and emphasizes the importance of polymorphism control during the manufacturing and storage of PEG-based solid dispersions to achieve reproducible and consistent pharmaceutical performance. The results also suggest that polymer addition is an alternative strategy that cannot be neglected in polymorphism screening

Processing Laue Microdiffraction Raster Scanning Patterns with Machine Learning Algorithms: A Case Study with a Fatigued Polycrystalline Sample

The massive amount of diffraction images collected in a raster scan of Laue microdiffraction calls for a fast treatment with little if any human intervention. The conventional method that has to index diffraction patterns one-by-one is laborious and can hardly give real-time feedback. In this work, a data mining protocol based on unsupervised machine learning algorithm was proposed to have a fast segmentation of the scanning grid from the diffraction patterns without indexation. The sole parameter that had to be set was the so-called “distance threshold” that determined the number of segments. A statistics-oriented criterion was proposed to set the “distance threshold”. The protocol was applied to the scanning images of a fatigued polycrystalline sample and identified several regions that deserved further study with, for instance, differential aperture X-ray microscopy. The proposed data mining protocol is promising to help economize the limited beamtime

Microstructure and Deformation of Over-Aged Al-Zn-Mg-Cu Alloy with Fine Grains during Multiple Stress Relaxation Tests

Strong obstacles can greatly impede the motion or transmission of dislocations, which can be reflected by strain rate sensitivity and activation volume. In this study, the strain rate sensitivity and activation volume of overaged Al-Zn-Mg-Cu alloys with a grain size of 3.1 μm fabricated by powder metallurgy were measured by two different methods: a stress relaxation test and strain rate jump test. It was found that the former method gave much higher strain rate sensitivity values. After reviewing the conventional theory of stress relaxation test, it is unreasonable that the activation volume at each cycle during the stress relaxation test is a constant. At a strain rate higher than 10−5 s−1, ∂lnε˙/∂τ*, which is proportional to the activation volume in the conventional theory of the stress relaxation test, increases significantly, and nearly linearly increases with the strain rate in its logarithmic form, while at a strain rate lower than 10−5 s−1, the value of ∂lnε˙/∂τ* is nearly a constant. The grain boundary sliding mechanism was incorporated into the plastic deformation during the stress relaxation test, and the strain rate sensitivity and activation volume obtained by stress relaxation after modification agree well with that obtained by the strain rate jump test

Nicotinamide: Seven New Polymorphic Structures Revealed by Melt Crystallization and Crystal Structure Prediction

Here, we reported nicotinamide (NIC), a long-known vitamin, was revealed in fact to be a highly polymorphic compound with nine solved single-crystal structures by performing melt crystallization. A CSP calculation successfully identified all six Z’ = 1 and 2 experimental structures. Melt crystallization has turned out to be an efficient tool for exploring polymorphic landscape, especially in regions inaccessbile by solution crystallization.</p

Dalbergioidin Ameliorates Doxorubicin-Induced Renal Fibrosis by Suppressing the TGF-β Signal Pathway

We investigated the effect of Dalbergioidin (DAL), a well-known natural product extracted from Uraria crinita, on doxorubicin- (DXR-) induced renal fibrosis in mice. The mice were pretreated for 7 days with DAL followed by a single injection of DXR (10 mg/kg) via the tail vein. Renal function was analyzed 5 weeks after DXR treatment. DXR caused nephrotoxicity. The symptoms of nephrotic syndrome were greatly improved after DAL treatment. The indices of renal fibrosis, the phosphorylation of Smad3, and the expression of alpha-smooth muscle actin (α-SMA), fibronectin, collagen III (Col III), E-cadherin, TGF-β, and Smad7 in response to DXR were all similarly modified by DAL. The present findings suggest that DAL improved the markers for kidney damage investigated in this model of DXR-induced experimental nephrotoxicity

Pectin-derived oligogalacturonic acids ameliorate high-fat diet-induced obesity in mice by regulating gut microbiota and inflammation

Pectic oligosaccharides have been proposed as a novel prebiotic to prevent obesity and associated with metabolic disorders by regulating gut microbiota, but their various probiotic functions in the gut depended on their complex and heterogeneous sugar composition. This study aimed to obtain specific oligogalacturonic acids (POAS) from pectin and investigate the effects of POAS on high-fat diet-induced obesity in mice. The results showed that POAS improved body weight gain, serum lipids and hepatic steatosis in obese mice. POAS notably enhanced intestinal barrier function, reduced inflammatory response and regulated the expression of key genes in lipid synthesis and metabolism. Besides, POAS also promoted short-chain fatty acids (SCFAs) production. Furthermore, POAS alleviated the gut microbiota dysbiosis caused by HFD with significantly increasing beneficial gut microbiota such as Akkermansia and Lactobacillus. Spearman's correlation analysis indicated that these bacteria were strongly correlated with obesity-related parameters. We conclude that POAS is a potential prebiotic to treat obesity and related complications

Lysophospholipid acylation modulates plasma membrane lipid organization and insulin sensitivity in skeletal muscle

Aberrant lipid metabolism promotes the development of skeletal muscle insulin resistance, but the exact identity of lipid-mediated mechanisms relevant to human obesity remains unclear. A comprehensive lipidomic analysis of primary myocytes from individuals who were insulin-sensitive and lean (LN) or insulin-resistant with obesity (OB) revealed several species of lysophospholipids (lyso-PLs) that were differentially abundant. These changes coincided with greater expression of lysophosphatidylcholine acyltransferase 3 (LPCAT3), an enzyme involved in phospholipid transacylation (Lands cycle). Strikingly, mice with skeletal muscle-specific knockout of LPCAT3 (LPCAT3-MKO) exhibited greater muscle lysophosphatidylcholine/phosphatidylcholine, concomitant with improved skeletal muscle insulin sensitivity. Conversely, skeletal muscle-specific overexpression of LPCAT3 (LPCAT3-MKI) promoted glucose intolerance. The absence of LPCAT3 reduced phospholipid packing of cellular membranes and increased plasma membrane lipid clustering, suggesting that LPCAT3 affects insulin receptor phosphorylation by modulating plasma membrane lipid organization. In conclusion, obesity accelerates the skeletal muscle Lands cycle, whose consequence might induce the disruption of plasma membrane organization that suppresses muscle insulin action