Cellular immune response to Mycobacterium tuberculosis-speciWc antigen culture Wltrate protein-10 in south India

The Mycobacterium tuberculosis (M. tuberculosis)- speciWc culture Wltrate protein-10 (CFP-10) is highly recognized by M. tuberculosis infected subjects. In the present study, the proliferative response and IFN-� secretion was found for C-terminal peptides of the protein (Cfp651–70, Cfp761–80, Cfp871–90, and Cfp981–100). The alleles HLA DRB1 *04 and HLA DRB1 *10 recognized the C-terminal peptides Cfp7, Cfp8, and Cfp9 in HHC. Cfp6 was predominantly recognized by the alleles HLA DRB1 *03 and HLA DRB1 *15 by PTB. The minimal nonameric epitopes from the C-terminal region were CFP-1056–64 and CFP-1076–84. These two peptides deserve attention for inclusion in a vaccine against tuberculosis in this region

Immune response to Mycobacterium tuberculosis specific antigen ESAT-6 among south Indians

The 6-kDa early secreted antigenic target (ESAT-6) is a T-cell antigen recognized by individuals infected with Mycobacterium tuberculosis. The aim of the study was to identify ‘‘protective epitopes’’ of ESAT-6 protein in the south Indian population. Proliferative and Interferon gamma (IFN-g) responses to ESAT-6 peptides were studied by flow cytometry and Enzyme linked immunosorbent assay (ELISA). Healthy household contacts (HHC) recognized Esp1 (10/17) and Esp6 (9/17) peptides. Among pulmonary tuberculosis patients (PTB), Esp1 (3/11) and Esp6 (5/11) were recognized. Maximal response (7/10) was found for Esp1 and Esp8 in treated patients (TR). Median values for the responding subjects gave the following results: Esp1 (76 pg/ml), Esp6 (64 pg/ml), induced IFN-g production in HHC; PTB gave low IFN-g responses for the peptides. TR responded to the peptides Esp1 (141 pg/ml), Esp8 (102 pg/ml). The proliferation of CD4 cells was similar in both PTB and TR for all peptides; but HHC showed an increase for Esp1 (p < 0.05) and Esp6 (p < 0.01). Esp1 (amino acids aa 1–20) and Esp6 (aa 51–70) were the immunogenic peptides recognized by the alleles HLA DRB1*04 and HLA DRB1*10 among HHC. But the association of the alleles with ESAT-6 peptide presentation needs to be confirmed in a large cohort of subjects. We speculate that ESAT-6 can be used along with other immune-eliciting proteins for vaccine design strategies in south Indian population

Variants in KCNQ1 increase type II diabetes susceptibility in South Asians: A study of 3,310 subjects from India and the US

<p>Abstract</p> <p>Background</p> <p>Polymorphisms in intron 15 of potassium voltage-gated channel, KQT-like subfamily member 1 (<it>KCNQ1</it>) gene have been associated with type II diabetes (T2D) in Japanese genome-wide association studies (GWAS). More recently a meta-analysis of European GWAS has detected a new independent signal associated with T2D in intron 11 of the <it>KCNQ1 </it>gene. The purpose of this investigation is to examine the role of these variants with T2D in populations of Asian Indian descent from India and the US.</p> <p>Methods</p> <p>We examined the association between four variants in the <it>KCNQ1 </it>gene with T2D and related quantitative traits in a total of 3,310 Asian Indian participants from two different cohorts comprising 2,431 individuals of the Punjabi case-control cohort from the Sikh Diabetes Study and 879 migrant Asian Indians living in the US.</p> <p>Results</p> <p>Our data confirmed the association of a new signal at the <it>KCNQ1 </it>locus (rs231362) with T2D showing an allelic odds ratio (OR) of 1.24 95%CI [1.08-1.43], p = 0.002 in the Punjabi cohort. A moderate association with T2D was also seen for rs2237895 in the Punjabi (OR 1.14; p = 0.036) and combined cohorts (meta-analysis OR 1.14; p = 0.018). Three-site haplotype analysis of rs231362, rs2237892, rs2237895 exhibited considerably stronger evidence of association of the GCC haplotype with T2D showing OR of 1.24 95%CI [1.00-1.53], p = 0.001, permutation p = 8 × 10^-4in combined cohorts. The 'C' risk allele carriers of rs2237895 had significantly reduced measures of HOMA-B in the US cohort (p = 0.008) as well as in combined cohort in meta-analysis (p = 0.009).</p> <p>Conclusions</p> <p>Our investigation has confirmed that the variation within the <it>KCNQ1 </it>locus confers a significant risk to T2D among Asian Indians. Haplotype analysis further suggested that the T2D risk associated with <it>KCNQ1 </it>SNPs may be derived from 'G' allele of rs231362 and 'C' allele of rs2237895 and this appears to be mediated through β cell function.</p

Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk

<p>Abstract</p> <p>Background</p> <p>Recent genome-wide association (GWA) studies have identified several unsuspected genes associated with type 2 diabetes (T2D) with previously unknown functions. In this investigation, we have examined the role of 9 most significant SNPs reported in GWA studies: [peroxisome proliferator-activated receptor gamma 2 (<it>PPARG2</it>; rs 1801282); insulin-like growth factor two binding protein 2 (<it>IGF2BP2</it>; rs 4402960); cyclin-dependent kinase 5, a regulatory subunit-associated protein1-like 1 (<it>CDK5</it>; rs7754840); a zinc transporter and member of solute carrier family 30 (<it>SLC30A8</it>; rs13266634); a variant found near cyclin-dependent kinase inhibitor 2A (<it>CDKN2A</it>; rs10811661); hematopoietically expressed homeobox (<it>HHEX</it>; rs 1111875); transcription factor-7-like 2 (<it>TCF7L2</it>; rs 10885409); potassium inwardly rectifying channel subfamily J member 11(<it>KCNJ11</it>; rs 5219); and fat mass obesity-associated gene (<it>FTO</it>; rs 9939609)].</p> <p>Methods</p> <p>We genotyped these SNPs in a case-control sample of 918 individuals consisting of 532 T2D cases and 386 normal glucose tolerant (NGT) subjects of an Asian Sikh community from North India. We tested the association between T2D and each SNP using unconditional logistic regression before and after adjusting for age, gender, and other covariates. We also examined the impact of these variants on body mass index (BMI), waist to hip ratio (WHR), fasting insulin, and glucose and lipid levels using multiple linear regression analysis.</p> <p>Results</p> <p>Four of the nine SNPs revealed a significant association with T2D; <it>PPARG2 </it>(Pro12Ala) [odds ratio (OR) 0.12; 95% confidence interval (CI) (0.03–0.52); p = 0.005], <it>IGF2BP2 </it>[OR 1.37; 95% CI (1.04–1.82); p = 0.027], <it>TCF7L2 </it>[OR 1.64; 95% CI (1.20–2.24); p = 0.001] and <it>FTO </it>[OR 1.46; 95% CI (1.11–1.93); p = 0.007] after adjusting for age, sex and BMI. Multiple linear regression analysis revealed significant association of two of nine investigated loci with diabetes-related quantitative traits. The 'C' (risk) allele of <it>CDK5 </it>(rs 7754840) was significantly associated with decreased HDL-cholesterol levels in both NGT (p = 0.005) and combined (NGT and T2D) (0.005) groups. The less common 'C' (risk) allele of <it>TCF7L2 </it>(rs 10885409) was associated with increased LDL-cholesterol (p = 0.010) in NGT and total and LDL-cholesterol levels (p = 0.008; p = 0.003, respectively) in combined cohort.</p> <p>Conclusion</p> <p>To our knowledge, this is first study reporting the role of some recently emerged loci with T2D in a high risk population of Asian Indian origin. Further investigations are warranted to understand the pathway-based functional implications of these important loci in T2D pathophysiology in different ethnicities.</p

A Bidirectional Mendelian Randomization Study to evaluate the causal role of reduced blood vitamin D levels with type 2 diabetes risk in South Asians and Europeans.

Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D

Genome-Wide Linkage Scan to Identify Loci Associated with Type 2 Diabetes and Blood Lipid Phenotypes in the Sikh Diabetes Study

In this investigation, we have carried out an autosomal genome-wide linkage analysis to map genes associated with type 2 diabetes (T2D) and five quantitative traits of blood lipids including total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, very low-density lipoprotein (VLDL) cholesterol, and triglycerides in a unique family-based cohort from the Sikh Diabetes Study (SDS). A total of 870 individuals (526 male/344 female) from 321 families were successfully genotyped using 398 polymorphic microsatellite markers with an average spacing of 9.26 cM on the autosomes. Results of non-parametric multipoint linkage analysis using Sall statistics (implemented in Merlin) did not reveal any chromosomal region to be significantly associated with T2D in this Sikh cohort. However, linkage analysis for lipid traits using QTL-ALL analysis revealed promising linkage signals with p≤0.005 for total cholesterol, LDL cholesterol, and HDL cholesterol at chromosomes 5p15, 9q21, 10p11, 10q21, and 22q13. The most significant signal (p = 0.0011) occurred at 10q21.2 for HDL cholesterol. We also observed linkage signals for total cholesterol at 22q13.32 (p = 0.0016) and 5p15.33 (p = 0.0031) and for LDL cholesterol at 10p11.23 (p = 0.0045). Interestingly, some of linkage regions identified in this Sikh population coincide with plausible candidate genes reported in recent genome-wide association and meta-analysis studies for lipid traits. Our study provides the first evidence of linkage for loci associated with quantitative lipid traits at four chromosomal regions in this Asian Indian population from Punjab. More detailed examination of these regions with more informative genotyping, sequencing, and functional studies should lead to rapid detection of novel targets of therapeutic importance

Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India.

We performed a genome-wide association study (GWAS) and a multistage meta-analysis of type 2 diabetes (T2D) in Punjabi Sikhs from India. Our discovery GWAS in 1,616 individuals (842 case subjects) was followed by in silico replication of the top 513 independent single nucleotide polymorphisms (SNPs) (P < 10⁻³) in Punjabi Sikhs (n = 2,819; 801 case subjects). We further replicated 66 SNPs (P < 10⁻⁴) through genotyping in a Punjabi Sikh sample (n = 2,894; 1,711 case subjects). On combined meta-analysis in Sikh populations (n = 7,329; 3,354 case subjects), we identified a novel locus in association with T2D at 13q12 represented by a directly genotyped intronic SNP (rs9552911, P = 1.82 × 10⁻⁸) in the SGCG gene. Next, we undertook in silico replication (stage 2b) of the top 513 signals (P < 10⁻³) in 29,157 non-Sikh South Asians (10,971 case subjects) and de novo genotyping of up to 31 top signals (P < 10⁻⁴) in 10,817 South Asians (5,157 case subjects) (stage 3b). In combined South Asian meta-analysis, we observed six suggestive associations (P < 10⁻⁵ to < 10⁻⁷), including SNPs at HMG1L1/CTCFL, PLXNA4, SCAP, and chr5p11. Further evaluation of 31 top SNPs in 33,707 East Asians (16,746 case subjects) (stage 3c) and 47,117 Europeans (8,130 case subjects) (stage 3d), and joint meta-analysis of 128,127 individuals (44,358 case subjects) from 27 multiethnic studies, did not reveal any additional loci nor was there any evidence of replication for the new variant. Our findings provide new evidence on the presence of a population-specific signal in relation to T2D, which may provide additional insights into T2D pathogenesis

Association of the PHACTR1/EDN1 genetic locus with spontaneous coronary artery dissection

Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. Objectives: This study sought to test the association between the rs9349379 genotype and SCAD. Methods: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. Results: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. Conclusions: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD

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

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