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

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

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

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.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

Trends in cardiometabolic risk factors in the Americas between 1980 and 2014: a pooled analysis of population-based surveys

Background: Describing the prevalence and trends of cardiometabolic risk factors that are associated with noncommunicable diseases (NCDs) is crucial for monitoring progress, planning prevention, and providing evidence to support policy efforts. We aimed to analyse the transition in body-mass index (BMI), obesity, blood pressure, raised blood pressure, and diabetes in the Americas, between 1980 and 2014. Methods: We did a pooled analysis of population-based studies with data on anthropometric measurements, biomarkers for diabetes, and blood pressure from adults aged 18 years or older. A Bayesian model was used to estimate trends in BMI, raised blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg), and diabetes (fasting plasma glucose ≥7•0 mmol/L, history of diabetes, or diabetes treatment) from 1980 to 2014, in 37 countries and six subregions of the Americas. Findings: 389 population-based surveys from the Americas were available. Comparing prevalence estimates from 2014 with those of 1980, in the non-English speaking Caribbean subregion, the prevalence of obesity increased from 3•9% (95% CI 2•2–6•3) in 1980, to 18•6% (14•3–23•3) in 2014, in men; and from 12•2% (8•2–17•0) in 1980, to 30•5% (25•7–35•5) in 2014, in women. The English-speaking Caribbean subregion had the largest increase in the prevalence of diabetes, from 5•2% (2•1–10•4) in men and 6•4% (2•6–10•4) in women in 1980, to 11•1% (6•4–17•3) in men and 13•6% (8•2–21•0) in women in 2014). Conversely, the prevalence of raised blood pressure has decreased in all subregions; the largest decrease was found in North America from 27•6% (22•3–33•2) in men and 19•9% (15•8–24•4) in women in 1980, to 15•5% (11•1–20•9) in men and 10•7% (7•7–14•5) in women in 2014. Interpretation: Despite the generally high prevalence of cardiometabolic risk factors across the Americas, estimates also showed a high level of heterogeneity in the transition between countries. The increasing prevalence of obesity and diabetes observed over time requires appropriate measures to deal with these public health challenges. Our results support a diversification of health interventions across subregions and countries.Fil: Miranda, J. Jaime. Universidad Peruana Cayetano Heredia; PerúFil: Carrillo-Larco, Rodrigo M.. Imperial College London; Reino UnidoFil: Ferreccio, Catterina. Pontificia Universidad Católica de Chile; ChileFil: Hambleton, Ian R.. The University Of The West Indies; BarbadosFil: Lotufo, Paulo A.. Universidade de Sao Paulo; BrasilFil: Nieto-Martinez, Ramfis. Miami Veterans Affairs Healthcare System; Estados UnidosFil: Zhou, Bin. Imperial College London; Reino UnidoFil: Bentham, James. University Of Kent; Reino UnidoFil: Bixby, Honor. Imperial College London; Reino UnidoFil: Hajifathalian, Kaveh. Cleveland Clinic; Estados UnidosFil: Lu, Yuan. University of Yale; Estados UnidosFil: Taddei, Cristina. Imperial College London; Reino UnidoFil: Abarca-Gomez, Leandra. Caja Costarricense de Seguro Social; Costa RicaFil: Acosta-Cazares, Benjamin. Instituto Mexicano del Seguro Social; MéxicoFil: Aguilar-Salinas, Carlos A.. (Instituto Nacional de Ciencias Médicas y Nutrición; MéxicoFil: Andrade, Dolores S.. Universidad de Cuenca; EcuadorFil: Assunção, Maria Cecilia F.. Universidade Federal de Pelotas; BrasilFil: Barcelo, Alberto. Pan American Health Organization; Estados UnidosFil: Barros, Aluisio J.D.. Universidade Federal de Pelotas; BrasilFil: Barros, Mauro V.G.. Universidade de Pernambuco; BrasilFil: Bata, Iqbal. Dalhousie University Halifax; CanadáFil: Batista, Rosangela L.. Universidade Federal Do Maranhao; BrasilFil: Benet, Mikhail. Cafam University Foundation; ColombiaFil: Bernabe-Ortiz, Antonio. Universidad Peruana Cayetano Heredia; PerúFil: Bettiol, Heloisa. Universidade de Sao Paulo; BrasilFil: Boggia, Jose G.. Universidad de la Republica; UruguayFil: Boissonnet, Carlos P.. Centro de Educación Médica e Investigaciones Clínicas; ArgentinaFil: Brewster, Lizzy M.. University of Amsterdam; Países BajosFil: Cameron, Christine. Canadian Fitness and Lifestyle Research Institute; CanadáFil: Cândido, Ana Paula C.. Universidade Federal de Juiz de Fora; BrasilFil: Cardoso, Viviane C.. Universidade de Sao Paulo; BrasilFil: Chan, Queenie. Imperial College London; Reino UnidoFil: Christofaro, Diego G.. Universidade Estadual Paulista; BrasilFil: Confortin, Susana C.. Universidade Federal de Santa Catarina; BrasilFil: Craig, Cora L.. Canadian Fitness and Lifestyle Research Institute; CanadáFil: d'Orsi, Eleonora. Universidade Federal de Santa Catarina; BrasilFil: Delisle, Hélène. University of Montreal; CanadáFil: De Oliveira, Paula Duarte. Universidade Federal de Pelotas; BrasilFil: Dias-da-Costa, Juvenal Soares. Universidade do Vale do Rio Dos Sinos; BrasilFil: Diaz, Alberto Alejandro. Universidad Nacional del Centro de la Provincia de Buenos Aires. Escuela Superior de Ciencias de la Salud. Instituto de Investigación en Ciencias de la Salud; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Provincia de Buenos Aires. Municipalidad de Tandil. Hospital Municipal Ramón Santamarina; ArgentinaFil: Donoso, Silvana P.. Universidad de Cuenca; EcuadorFil: Elliott, Paul. Imperial College London; Reino UnidoFil: Escobedo-de La Peña, Jorge. Instituto Mexicano del Seguro Social; MéxicoFil: Ferguson, Trevor S.. The University of The West Indies; JamaicaFil: Fernandes, Romulo A.. Universidade Estadual Paulista; BrasilFil: Ferrante, Daniel. Ministerio de Salud; ArgentinaFil: Flores, Eric Monterubio. Instituto Nacional de Salud Pública; MéxicoFil: Francis, Damian K.. The University of The West Indies; JamaicaFil: Do Carmo Franco, Maria. Universidade Federal de Sao Paulo; BrasilFil: Fuchs, Flavio D.. Hospital de Clinicas de Porto Alegre; BrasilFil: Fuchs, Sandra C.. Universidade Federal do Rio Grande do Sul; BrasilFil: Goltzman, David. Université McGill; CanadáFil: Gonçalves, Helen. Universidade Federal de Pelotas; BrasilFil: Gonzalez-Rivas, Juan P.. The Andes Clinic Of Cardio-Metabolic Studies; VenezuelaFil: Gorbea, Mariano Bonet. Instituto Nacional de Higiene, Epidemiología y Microbiología; CubaFil: Gregor, Ronald D.. Dalhousie University Halifax; CanadáFil: Guerrero, Ramiro. Universidad Icesi; ColombiaFil: Guimaraes, Andre L.. Universidade Estadual de Montes Claros; BrasilFil: Gulliford, Martin C.. King’s College London; Reino UnidoFil: Gutierrez, Laura. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Hernandez Cadena, Leticia. Instituto Nacional de Salud Pública; MéxicoFil: Herrera, Víctor M.. (Universidad Autónoma de Bucaramanga; ColombiaFil: Hopman, Wilma M.. Kingston General Hospital; CanadáFil: Horimoto, Andrea RVR. Instituto do Coração; BrasilFil: Hormiga, Claudia M.. Fundación Oftalmológica de Santander; ColombiaFil: Horta, Bernardo L.. Universidade Federal de Pelotas; BrasilFil: Howitt, Christina. The University of the West Indies; BarbadosFil: Irazola, Wilma E.. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Jiménez-Acosta, Santa Magaly. Instituto Nacional de Higiene, Epidemiología y Microbiología; CubaFil: Joffres, Michel. Simon Fraser University; CanadáFil: Kolsteren, Patricia. (Institute of Tropical Medicine; BélgicaFil: Landrove, Orlando. Ministerio de Salud Pública; CubaFil: Li, Yanping. Harvard TH Chan School of Public Health; Estados UnidosFil: Lilly, Christa L.. West Virginia University; Estados UnidosFil: Lima-Costa, M. Fernanda. Fundação Oswaldo Cruz; BrasilFil: Louzada Strufaldi, Maria Wany. Universidade Federal de Sao Paulo; BrasilFil: Machado-Coelho, George L. L.. Universidade Federal de Ouro Preto; BrasilFil: Makdisse, Marcia. Hospital Israelita Albert Einstein; BrasilFil: Margozzini, Paula. Pontificia Universidad Católica de Chile; ChileFil: Pruner Marques, Larissa. Universidade Federal de Santa Catarina; BrasilFil: Martorell, Reynaldo. Emory University; Estados UnidosFil: Mascarenhas, Luis. Universidade Federal do Paraná; BrasilFil: Matijasevich, Alicia. Universidade Federal de Sao Paulo; BrasilFil: Mc Donald Posso, Anselmo J.. Gorgas Memorial Institute of Health Studies; PanamáFil: McFarlane, Shelly R.. The University of the West Indies; JamaicaFil: McLean, Scott B.. (Statistics Canada; CanadáFil: Menezes, Ana Maria B.. Universidade Federal de Pelotas; BrasilFil: Miquel, Juan Francisco. Pontificia Universidad Católica de Chile; ChileFil: Mohanna, Salim. Universidad Peruana Cayetano Heredia; PerúFil: Monterrubio, Eric A.. Instituto Nacional de Salud Pública; MéxicoFil: Moreira, Leila B.. Universidade Federal do Rio Grande do Sul; BrasilFil: Morejon, Alain. Universidad de Ciencias Médicas; CubaFil: Motta, Jorge. Gorgas Memorial Institute of Public Health; PanamáFil: Neal, William A.. West Virginia University; Estados UnidosFil: Nervi, Flavio. Pontificia Universidad Católica de Chile; ChileFil: Noboa, Oscar A.. Universidad de la República; UruguayFil: Ochoa-Avilés, Angélica M.. Universidad de Cuenca; EcuadorFil: Olinto, Maria Teresa Anselmo. Universidad de Vale do Rio dos Sinos; BrasilFil: Oliveira, Isabel O.. Universidade Federal de Pelotas; BrasilFil: Ono, Lariane M.. Universidade Federal de Santa Catarina; BrasilFil: Ordunez, Pedro. Pan American Health Organization; Estados UnidosFil: Ortiz, Ana P.. Universidad de Puerto Rico; Puerto RicoFil: Otero, Johanna A.. Fundación Oftalmológica de Santander; ColombiaFil: Palloni, Alberto. University of Wisconsin-Madison; Estados UnidosFil: Viana Peixoto, Sergio. Fundação Oswaldo Cruz; BrasilFil: Pereira, Alexandre C.. Instituto do Coração; BrasilFil: Pérez, Cynthia M.. Universidad de Puerto Rico; Puerto RicoFil: Rangel Reina, Daniel A.. Gorgas Memorial Institute of Health Studies; PanamáFil: Ribeiro, Robespierre. Secretaria de Estado de Saúde de Minas Gerais; BrasilFil: Ritti-Dias, Raphael M.. Universidade Nove de Julho; BrasilFil: Rivera, Juan A.. Instituto Nacional de Salud Pública; MéxicoFil: Robitaille, Cynthia. Public Health Agency of Canada; CanadáFil: Rodríguez-Villamizar, Laura A.. Universidad Industrial de Santander; ColombiaFil: Rojas-Martinez, Rosalba. Instituto Nacional de Salud Pública; MéxicoFil: Roy, Joel G. R.. Statistics Canada; CanadáFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Ruiz-Betancourt, Blanca Sandra. Instituto Mexicano del Seguro Social; MéxicoFil: Salazar Martinez, Eduardo. Instituto Nacional de Salud Pública; MéxicoFil: Sánchez-Abanto, José. Instituto Nacional de Salud; PerúFil: Santos , Ina S.. Universidade Federal de Pelotas; BrasilFil: dos Santos, Renata Nunes. Universidade Federal de Sao Paulo; BrasilFil: Scazufca, Marcia. Universidade Federal de Sao Paulo; BrasilFil: Schargrodsky, Herman. Hospital Italiano; ArgentinaFil: Silva, Antonio M.. Universidade Federal do Maranhao; BrasilFil: Santos Silva, Diego Augusto. Universidade Federal de Santa Catarina; BrasilFil: Stein, Aryeh D.. Emory University; Estados UnidosFil: Suárez-Medina, Ramón. Instituto Nacional de Higiene, Epidemiología y Microbiología; CubaFil: Tarqui-Mamani, Carolina B.. Instituto Nacional de Salud; PerúFil: Tulloch-Reid, Marshall K.. The University of the West Indies; JamaicaFil: Ueda, Peter. Harvard TH Chan School of Public Health; Estados UnidosFil: Ugel, Eunice E.. Universidad Centro-Occidental Lisandro Alvarado; VenezuelaFil: Valdivia, Gonzalo. Pontificia Universidad Católica de Chile; ChileFil: Varona, Patricia. Instituto Nacional de Higiene, Epidemiología y Microbiología; CubaFil: Velasquez-Melendez, Gustavo. Universidade Federal de Minas Gerais; BrasilFil: Verstraeten, Roosmarijn. Institute of Tropical Medicine; BélgicaFil: Victora, Cesar G.. Universidade Federal de Pelotas; BrasilFil: Wanderley Jr, Rildo S.. Universidade Federal de Pernambuco; BrasilFil: Wang, Ming-Dong. Public Health Agency of Canada; CanadáFil: Wilks, Rainford J.. The University of the West Indies; JamaicaFil: Wong-McClure, Roy A.. Caja Costarricense de Seguro Social; Costa RicaFil: Younger-Coleman, Novie O.. The University of the West Indies; JamaicaFil: Zuñiga Cisneros, Julio. Gorgas Memorial Institute of Public Health; PanamáFil: Danaei, Goodarz. Harvard TH Chan School of Public Health; Estados UnidosFil: Stevens, Gretchen A.. World Health Organization; SuizaFil: Riley, Leanne M.. World Health Organization; SuizaFil: Ezzati, Majid. (Imperial College London; Reino UnidoFil: Di Cesare, Mariachiara. Middlesex University; Reino Unid

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

International audienceAbstract Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29–39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

: Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

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