Single Primer Enrichment Technology (SPET) for High-Throughput Genotyping in Tomato and Eggplant Germplasm

[EN] Single primer enrichment technology (SPET) is a new, robust, and customizable solution for targeted genotyping. Unlike genotyping by sequencing (GBS), and like DNA chips, SPET is a targeted genotyping technology, relying on the sequencing of a region flanking a primer. Its reliance on single primers, rather than on primer pairs, greatly simplifies panel design, and allows higher levels of multiplexing than PCR-based genotyping. Thanks to the sequencing of the regions surrounding the target SNP, SPET allows the discovery of thousands of closely linked, novel SNPs. In order to assess the potential of SPET for high-throughput genotyping in plants, a panel comprising 5k target SNPs, designed both on coding regions and introns/UTRs, was developed for tomato and eggplant. Genotyping of two panels composed of 400 tomato and 422 eggplant accessions, comprising both domesticated material and wild relatives, generated a total of 12,002 and 30,731 high confidence SNPs, respectively, which comprised both target and novel SNPs in an approximate ratio of 1:1.6, and 1:5.5 in tomato and eggplant, respectively. The vast majority of the markers was transferrable to related species that diverged up to 3.4 million years ago (Solanum pennellii for tomato and S. macrocarpon for eggplant). Maximum Likelihood phylogenetic trees and PCA outputs obtained from the whole dataset highlighted genetic relationships among accessions and species which were congruent with what was previously reported in literature. Better discrimination among domesticated accessions was achieved by using the target SNPs, while better discrimination among wild species was achieved using the whole SNP dataset. Our results reveal that SPET genotyping is a robust, high-throughput technology for genetic fingerprinting, with a high degree of cross-transferability between crops and their cultivated and wild relatives, and allows identification of duplicates and mislabeled accessions in genebanks.This work has been funded by the European Union's Horizon 2020 Research and Innovation Programme under the grant agreement number 677379 (G2P-SOL project: Linking genetic resources, genomes, and phenotypes of solanaceous crops).Barchi, L.; Acquadro, A.; Alonso-Martín, D.; Aprea, G.; Bassolino, L.; Demurtas, O.; Ferrante, P.... (2019). Single Primer Enrichment Technology (SPET) for High-Throughput Genotyping in Tomato and Eggplant Germplasm. Frontiers in Plant Science. 10:1-17. https://doi.org/10.3389/fpls.2019.01005S11710Acquadro, A., Barchi, L., Gramazio, P., Portis, E., Vilanova, S., Comino, C., … Lanteri, S. (2017). Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes. PLOS ONE, 12(7), e0180774. doi:10.1371/journal.pone.0180774Anderson, J. A., Churchill, G. A., Autrique, J. E., Tanksley, S. D., & Sorrells, M. E. (1993). Optimizing parental selection for genetic linkage maps. Genome, 36(1), 181-186. doi:10.1139/g93-024Barchi, L., Pietrella, M., Venturini, L., Minio, A., Toppino, L., Acquadro, A., … Rotino, G. L. (2019). A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution. Scientific Reports, 9(1). doi:10.1038/s41598-019-47985-wBeddows, I., Reddy, A., Kloesges, T., & Rose, L. E. (2017). Population Genomics in Wild Tomatoes—The Interplay of Divergence and Admixture. Genome Biology and Evolution, 9(11), 3023-3038. doi:10.1093/gbe/evx224Blanca, J., Montero-Pau, J., Sauvage, C., Bauchet, G., Illa, E., Díez, M. J., … Cañizares, J. (2015). Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genomics, 16(1). doi:10.1186/s12864-015-1444-1Caicedo, A. L., & Schaal, B. A. (2004). Population structure and phylogeography of Solanum pimpinellifolium inferred from a nuclear gene. Molecular Ecology, 13(7), 1871-1882. doi:10.1111/j.1365-294x.2004.02191.xCastle, J. C. (2011). SNPs Occur in Regions with Less Genomic Sequence Conservation. PLoS ONE, 6(6), e20660. doi:10.1371/journal.pone.0020660Cericola, F., Portis, E., Toppino, L., Barchi, L., Acciarri, N., Ciriaci, T., … Lanteri, S. (2013). The Population Structure and Diversity of Eggplant from Asia and the Mediterranean Basin. PLoS ONE, 8(9), e73702. doi:10.1371/journal.pone.0073702Chen, K.-Y., Cong, B., Wing, R., Vrebalov, J., & Tanksley, S. D. (2007). Changes in Regulation of a Transcription Factor Lead to Autogamy in Cultivated Tomatoes. Science, 318(5850), 643-645. doi:10.1126/science.1148428Chen, K.-Y., & Tanksley, S. D. (2004). High-Resolution Mapping and Functional Analysis ofse2.1. Genetics, 168(3), 1563-1573. doi:10.1534/genetics.103.022558Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., … DePristo, M. A. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156-2158. doi:10.1093/bioinformatics/btr330Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M., & Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499-510. doi:10.1038/nrg3012Del Fabbro, C., Scalabrin, S., Morgante, M., & Giorgi, F. M. (2013). An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis. PLoS ONE, 8(12), e85024. doi:10.1371/journal.pone.0085024DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., … Daly, M. J. (2011). A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491-498. doi:10.1038/ng.806Dodsworth, S., Chase, M. W., Särkinen, T., Knapp, S., & Leitch, A. R. (2015). Using genomic repeats for phylogenomics: a case study in wild tomatoes (SolanumsectionLycopersicon: Solanaceae). Biological Journal of the Linnean Society, 117(1), 96-105. doi:10.1111/bij.12612Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., & Mitchell, S. E. (2011). A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS ONE, 6(5), e19379. doi:10.1371/journal.pone.0019379Flint-Garcia, S. A. (2013). Genetics and Consequences of Crop Domestication. Journal of Agricultural and Food Chemistry, 61(35), 8267-8276. doi:10.1021/jf305511dGramazio, P., Prohens, J., Borràs, D., Plazas, M., Herraiz, F. J., & Vilanova, S. (2017). Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants. Euphytica, 213(12). doi:10.1007/s10681-017-2057-3Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q., & Vinh, L. S. (2017). UFBoot2: Improving the Ultrafast Bootstrap Approximation. Molecular Biology and Evolution, 35(2), 518-522. doi:10.1093/molbev/msx281Hoheisel, J. D. (2006). Microarray technology: beyond transcript profiling and genotype analysis. Nature Reviews Genetics, 7(3), 200-210. doi:10.1038/nrg1809Huerta-Cepas, J., Serra, F., & Bork, P. (2016). ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. Molecular Biology and Evolution, 33(6), 1635-1638. doi:10.1093/molbev/msw046Isshiki, S., Iwata, N., & Khan, M. M. R. (2008). ISSR variations in eggplant (Solanum melongena L.) and related Solanum species. Scientia Horticulturae, 117(3), 186-190. doi:10.1016/j.scienta.2008.04.003Kamenetzky, L., Asís, R., Bassi, S., de Godoy, F., Bermúdez, L., Fernie, A. R., … Carrari, F. (2010). Genomic Analysis of Wild Tomato Introgressions Determining Metabolism- and Yield-Associated Traits. Plant Physiology, 152(4), 1772-1786. doi:10.1104/pp.109.150532Kouassi, B., Prohens, J., Gramazio, P., Kouassi, A. B., Vilanova, S., Galán-Ávila, A., … Plazas, M. (2016). Development of backcross generations and new interspecific hybrid combinations for introgression breeding in eggplant ( Solanum melongena ). Scientia Horticulturae, 213, 199-207. doi:10.1016/j.scienta.2016.10.039Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., … Homer, N. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. doi:10.1093/bioinformatics/btp352Lin, T., Zhu, G., Zhang, J., Xu, X., Yu, Q., Zheng, Z., … Huang, S. (2014). Genomic analyses provide insights into the history of tomato breeding. Nature Genetics, 46(11), 1220-1226. doi:10.1038/ng.3117Lynch, V. J., & Wagner, G. P. (2010). DID EGG-LAYING BOAS BREAK DOLLO’S LAW? PHYLOGENETIC EVIDENCE FOR REVERSAL TO OVIPARITY IN SAND BOAS (ERYX: BOIDAE). Evolution, 64(1), 207-216. doi:10.1111/j.1558-5646.2009.00790.xMammadov, J., Aggarwal, R., Buyyarapu, R., & Kumpatla, S. (2012). SNP Markers and Their Impact on Plant Breeding. International Journal of Plant Genomics, 2012, 1-11. doi:10.1155/2012/728398Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1), 10. doi:10.14806/ej.17.1.200Mason, A. S., Zhang, J., Tollenaere, R., Vasquez Teuber, P., Dalton-Morgan, J., Hu, L., … Batley, J. (2015). High-throughput genotyping for species identification and diversity assessment in germplasm collections. Molecular Ecology Resources, 15(5), 1091-1101. doi:10.1111/1755-0998.12379Meyer, R. S., Karol, K. G., Little, D. P., Nee, M. H., & Litt, A. (2012). Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Molecular Phylogenetics and Evolution, 63(3), 685-701. doi:10.1016/j.ympev.2012.02.006Miz, R. B., Mentz, L. A., & Souza-Chies, T. T. (2007). Overview of the phylogenetic relationships of some southern Brazilian species from section Torva and related sections of «spiny Solanum» (Solanum subgenus Leptostemonum, Solanaceae). Genetica, 132(2), 143-158. doi:10.1007/s10709-007-9156-3Nairismägi, M.-L., Tan, J., Lim, J. Q., Nagarajan, S., Ng, C. C. Y., Rajasegaran, V., … Ong, C. K. (2016). JAK-STAT and G-protein-coupled receptor signaling pathways are frequently altered in epitheliotropic intestinal T-cell lymphoma. Leukemia, 30(6), 1311-1319. doi:10.1038/leu.2016.13Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., & Minh, B. Q. (2014). IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution, 32(1), 268-274. doi:10.1093/molbev/msu300Pailles, Y., Ho, S., Pires, I. S., Tester, M., Negrão, S., & Schmöckel, S. M. (2017). Genetic Diversity and Population Structure of Two Tomato Species from the Galapagos Islands. Frontiers in Plant Science, 8. doi:10.3389/fpls.2017.00138Herraiz, F. J., Blanca, J., Ziarsolo, P., Gramazio, P., Plazas, M., Anderson, G. J., … Vilanova, S. (2016). The first de novo transcriptome of pepino (Solanum muricatum): assembly, comprehensive analysis and comparison with the closely related species S. caripense, potato and tomato. BMC Genomics, 17(1). doi:10.1186/s12864-016-2656-8Plazas, M., Andújar, I., Vilanova, S., Gramazio, P., Herraiz, F. J., & Prohens, J. (2014). Conventional and phenomics characterization provides insight into the diversity and relationships of hypervariable scarlet (Solanum aethiopicum L.) and gboma (S. macrocarpon L.) eggplant complexes. Frontiers in Plant Science, 5. doi:10.3389/fpls.2014.00318Plazas, M., Vilanova, S., Gramazio, P., Rodríguez-Burruezo, A., Fita, A., Herraiz, F. J., … Prohens, J. (2016). Interspecific Hybridization between Eggplant and Wild Relatives from Different Genepools. Journal of the American Society for Horticultural Science, 141(1), 34-44. doi:10.21273/jashs.141.1.34Poplin, R., Ruano-Rubio, V., DePristo, M. A., Fennell, T. J., Carneiro, M. O., Van der Auwera, G. A., … Banks, E. (2017). Scaling accurate genetic variant discovery to tens of thousands of samples. doi:10.1101/201178Razali, R., Bougouffa, S., Morton, M. J. L., Lightfoot, D. J., Alam, I., Essack, M., … Negrão, S. (2018). The Genome Sequence of the Wild Tomato Solanum pimpinellifolium Provides Insights Into Salinity Tolerance. Frontiers in Plant Science, 9. doi:10.3389/fpls.2018.01402Robinson, D. F., & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Biosciences, 53(1-2), 131-147. doi:10.1016/0025-5564(81)90043-2Rodriguez, F., Wu, F., Ané, C., Tanksley, S., & Spooner, D. M. (2009). Do potatoes and tomatoes have a single evolutionary history, and what proportion of the genome supports this history? BMC Evolutionary Biology, 9(1), 191. doi:10.1186/1471-2148-9-191Sakata, Y., & Lester, R. N. (1997). Euphytica, 97(3), 295-301. doi:10.1023/a:1003000612441Sakata, Y., Nishio, T., & Matthews, P. J. (1991). Chloroplast DNA analysis of eggplant (Solanum melongena) and related species for their taxonomic affinity. Euphytica, 55(1), 21-26. doi:10.1007/bf00022555Särkinen, T., Bohs, L., Olmstead, R. G., & Knapp, S. (2013). A phylogenetic framework for evolutionary study of the nightshades (Solanaceae): a dated 1000-tip tree. BMC Evolutionary Biology, 13(1), 214. doi:10.1186/1471-2148-13-214Scaglione, D., Pinosio, S., Marroni, F., Di Centa, E., Fornasiero, A., Magris, G., … Morgante, M. (2019). Single primer enrichment technology as a tool for massive genotyping: a benchmark on black poplar and maize. Annals of Botany, 124(4), 543-551. doi:10.1093/aob/mcz054Scheben, A., Batley, J., & Edwards, D. (2017). Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnology Journal, 15(2), 149-161. doi:10.1111/pbi.12645Scolnick, J. A., Dimon, M., Wang, I.-C., Huelga, S. C., & Amorese, D. A. (2015). An Efficient Method for Identifying Gene Fusions by Targeted RNA Sequencing from Fresh Frozen and FFPE Samples. PLOS ONE, 10(7), e0128916. doi:10.1371/journal.pone.0128916Semagn, K., Babu, R., Hearne, S., & Olsen, M. (2013). Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Molecular Breeding, 33(1), 1-14. doi:10.1007/s11032-013-9917-xSim, S.-C., Van Deynze, A., Stoffel, K., Douches, D. S., Zarka, D., Ganal, M. W., … Francis, D. M. (2012). High-Density SNP Genotyping of Tomato (Solanum lycopersicum L.) Reveals Patterns of Genetic Variation Due to Breeding. PLoS ONE, 7(9), e45520. doi:10.1371/journal.pone.0045520Syfert, M. M., Castañeda-Álvarez, N. P., Khoury, C. K., Särkinen, T., Sosa, C. C., Achicanoy, H. A., … Knapp, S. (2016). Crop wild relatives of the brinjal eggplant (Solanum melongena): Poorly represented in genebanks and many species at risk of extinction. American Journal of Botany, 103(4), 635-651. doi:10.3732/ajb.1500539(2012). The tomato genome sequence provides insights into fleshy fruit evolution. Nature, 485(7400), 635-641. doi:10.1038/nature11119Thomson, R. C., & Shaffer, H. B. (2010). Sparse Supermatrices for Phylogenetic Inference: Taxonomy, Alignment, Rogue Taxa, and the Phylogeny of Living Turtles. Systematic Biology, 59(1), 42-58. doi:10.1093/sysbio/syp075Tranchida-Lombardo, V., Mercati, F., Avino, M., Punzo, P., Fiore, M. C., Poma, I., … Grillo, S. (2018). Genetic diversity in a collection of Italian long storage tomato landraces as revealed by SNP markers array. Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology, 153(2), 288-297. doi:10.1080/11263504.2018.1478900Vilanova, S., Manzur, J. P., & Prohens, J. (2011). Development and characterization of genomic simple sequence repeat markers in eggplant and their application to the study of diversity and relationships in a collection of different cultivar types and origins. Molecular Breeding, 30(2), 647-660. doi:10.1007/s11032-011-9650-2Vorontsova, M. S., Stern, S., Bohs, L., & Knapp, S. (2013). African spinySolanum(subgenusLeptostemonum, Solanaceae): a thorny phylogenetic tangle. Botanical Journal of the Linnean Society, 173(2), 176-193. doi:10.1111/boj.12053Weese, T. L., & Bohs, L. (2010). Eggplant origins: Out of Africa, into the Orient. TAXON, 59(1), 49-56. doi:10.1002/tax.591006Tan, M. H., Gan, H. M., Schultz, M. B., & Austin, C. M. (2015). MitoPhAST, a new automated mitogenomic phylogeny tool in the post-genomic era with a case study of 89 decapod mitogenomes including eight new freshwater crayfish mitogenomes. Molecular Phylogenetics and Evolution, 85, 180-188. doi:10.1016/j.ympev.2015.02.009Wiens, J. J., & Morrill, M. C. (2011). Missing Data in Phylogenetic Analysis: Reconciling Results from Simulations and Empirical Data. Systematic Biology, 60(5), 719-731. doi:10.1093/sysbio/syr025Williams, C. E., & Clair, D. A. S. (1993). Phenetic relationships and levels of variability detected by restriction fragment length polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon esculentum. Genome, 36(3), 619-630. doi:10.1139/g93-083Zheng, X., Levine, D., Shen, J., Gogarten, S. M., Laurie, C., & Weir, B. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326-3328. doi:10.1093/bioinformatics/bts60

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries(1,2). However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world(3) and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health(4,5). However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol-which is a marker of cardiovascular riskchanged from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million-4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.Peer reviewe

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants.

BACKGROUND: Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. METHODS: We used data from 1990 to 2019 on people aged 30-79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. FINDINGS: The number of people aged 30-79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306-359) million women and 317 (292-344) million men in 1990 to 626 (584-668) million women and 652 (604-698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55-62) of women and 49% (46-52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43-51) of women and 38% (35-41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20-27) for women and 18% (16-21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. INTERPRETATION: Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings. FUNDING: WHO

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Background Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods We used data from 1990 to 2019 on people aged 30-79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings The number of people aged 30-79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306-359) million women and 317 (292-344) million men in 1990 to 626 (584-668) million women and 652 (604-698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55-62) of women and 49% (46-52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43-51) of women and 38% (35-41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20-27) for women and 18% (16-21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings. Copyright (C) 2021 World Health Organization; licensee Elsevier

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Background Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods We used data from 1990 to 2019 on people aged 30–79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings The number of people aged 30–79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306–359) million women and 317 (292–344) million men in 1990 to 626 (584–668) million women and 652 (604–698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55–62) of women and 49% (46–52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43–51) of women and 38% (35–41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20–27) for women and 18% (16–21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Background Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods We used data from 1990 to 2019 on people aged 30–79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings The number of people aged 30–79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306–359) million women and 317 (292–344) million men in 1990 to 626 (584–668) million women and 652 (604–698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55–62) of women and 49% (46–52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43–51) of women and 38% (35–41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20–27) for women and 18% (16–21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings