Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults
The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings. In this cross-sectional study, we pooled individual-level data...
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| Published in | The Lancet (British edition) Vol. 398; no. 10296; pp. 238 - 248 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
17.07.2021
Elsevier B.V Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings.
In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5–22·9 kg/m2], upper-normal [23·0–24·9 kg/m2], overweight [25·0–29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region.
Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6–27·8), of obesity was 21·0% (19·6–22·5), and of diabetes was 9·3% (8·4–10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5–22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35–44 years and in men aged 25–34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean.
The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines.
Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program. |
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| AbstractList | The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings.
In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA
]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA
of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5-22·9 kg/m
], upper-normal [23·0-24·9 kg/m
], overweight [25·0-29·9 kg/m
], or obese [≥30·0 kg/m
]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region.
Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6-27·8), of obesity was 21·0% (19·6-22·5), and of diabetes was 9·3% (8·4-10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m
or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5-22·9 kg/m
. Diabetes risk also increased steeply in individuals aged 35-44 years and in men aged 25-34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m
among men in east, south, and southeast Asia to 28·3 kg/m
among women in the Middle East and north Africa and in Latin America and the Caribbean.
The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines.
Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program. The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings. In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5–22·9 kg/m2], upper-normal [23·0–24·9 kg/m2], overweight [25·0–29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region. Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6–27·8), of obesity was 21·0% (19·6–22·5), and of diabetes was 9·3% (8·4–10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5–22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35–44 years and in men aged 25–34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean. The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines. Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program. The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings.BACKGROUNDThe prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings.In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5-22·9 kg/m2], upper-normal [23·0-24·9 kg/m2], overweight [25·0-29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region.METHODSIn this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5-22·9 kg/m2], upper-normal [23·0-24·9 kg/m2], overweight [25·0-29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region.Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6-27·8), of obesity was 21·0% (19·6-22·5), and of diabetes was 9·3% (8·4-10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5-22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35-44 years and in men aged 25-34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean.FINDINGSOur pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6-27·8), of obesity was 21·0% (19·6-22·5), and of diabetes was 9·3% (8·4-10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5-22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35-44 years and in men aged 25-34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean.The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines.INTERPRETATIONThe association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines.Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program.FUNDINGHarvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program. Summary Background The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings. Methods In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5–22·9 kg/m2], upper-normal [23·0–24·9 kg/m2], overweight [25·0–29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region. Findings Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6–27·8), of obesity was 21·0% (19·6–22·5), and of diabetes was 9·3% (8·4–10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5–22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35–44 years and in men aged 25–34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean. Interpretation The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines. Funding Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program. |
| Audience | Academic |
| Author | Atun, Rifat Theilmann, Michaela Sturua, Lela Kagaruki, Gibson B Manne-Goehler, Jennifer Dorobantu, Maria Farzadfar, Farshad Wesseh, Chea Stanford Arboleda, William Andres Lopez Davies, Justine I Houehanou, Corine Gurung, Mongal Singh Brian, Garry Crooks, Sarah Essien, Utibe R Marcus, Maja E Bovet, Pascal Silver, Bahendeka K Ali, Mohammed K Mayige, Mary T Bärnighausen, Till W Bicaba, Brice Wilfried Seiglie, Jacqueline A Houinato, Dismand Moghaddam, Sahar Saeedi Meigs, James B Jorgensen, Jutta M Adelin Andall-Brereton, Glennis Mwangi, Joseph Kibachio Teufel, Felix McClure, Roy Wong Mwalim, Omar Norov, Bolormaa Guwatudde, David Wexler, Deborah J Labadarios, Demetre De Neve, Jan-Walter Stokes, Andrew C Martins, Joao S Karki, Khem B Ebert, Cara Agoudavi, Kokou Vollmer, Sebastian Geldsetzer, Pascal Aryal, Krishna K |
| Author_xml | – sequence: 1 givenname: Felix surname: Teufel fullname: Teufel, Felix organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 2 givenname: Jacqueline A surname: Seiglie fullname: Seiglie, Jacqueline A organization: Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 3 givenname: Pascal surname: Geldsetzer fullname: Geldsetzer, Pascal organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 4 givenname: Michaela surname: Theilmann fullname: Theilmann, Michaela organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 5 givenname: Maja E surname: Marcus fullname: Marcus, Maja E organization: Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany – sequence: 6 givenname: Cara surname: Ebert fullname: Ebert, Cara organization: RWI—Leibniz Institute for Economic Research, Essen (Berlin Office), Germany – sequence: 7 givenname: William Andres Lopez surname: Arboleda fullname: Arboleda, William Andres Lopez organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 8 givenname: Kokou surname: Agoudavi fullname: Agoudavi, Kokou organization: Togo Ministry of Health, Lome, Togo – sequence: 9 givenname: Glennis surname: Andall-Brereton fullname: Andall-Brereton, Glennis organization: Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago – sequence: 10 givenname: Krishna K surname: Aryal fullname: Aryal, Krishna K organization: Nepal Health Sector Programme 3, Monitoring Evaluation and Operational Research Project, Abt Associates, Kathmandu, Nepal – sequence: 11 givenname: Brice Wilfried surname: Bicaba fullname: Bicaba, Brice Wilfried organization: Institut National de Santé Publique, Ministère de la santé, Ouagadougou, Burkina Faso – sequence: 12 givenname: Garry surname: Brian fullname: Brian, Garry organization: The Fred Hollows Foundation New Zealand, Auckland, New Zealand – sequence: 13 givenname: Pascal surname: Bovet fullname: Bovet, Pascal organization: Ministry of Health, Victoria, Seychelles – sequence: 14 givenname: Maria surname: Dorobantu fullname: Dorobantu, Maria organization: University of Medicine and Pharmacy Carol Davila, Bucharest, Romania – sequence: 15 givenname: Mongal Singh surname: Gurung fullname: Gurung, Mongal Singh organization: Health Research and Epidemiology Unit, Ministry of Health, Thimphu, Bhutan – sequence: 16 givenname: David surname: Guwatudde fullname: Guwatudde, David organization: Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda – sequence: 17 givenname: Corine surname: Houehanou fullname: Houehanou, Corine organization: Laboratory of Epidemiology of Chronic and Neurological Diseases, Faculty of Health Sciences, University of Abomey-Calavi, Cotonou, Benin – sequence: 18 givenname: Dismand surname: Houinato fullname: Houinato, Dismand organization: Laboratory of Epidemiology of Chronic and Neurological Diseases, Faculty of Health Sciences, University of Abomey-Calavi, Cotonou, Benin – sequence: 19 givenname: Jutta M Adelin surname: Jorgensen fullname: Jorgensen, Jutta M Adelin organization: Department of Public Health, University of Copenhagen, Copenhagen, Denmark – sequence: 20 givenname: Gibson B surname: Kagaruki fullname: Kagaruki, Gibson B organization: National Institute for Medical Research, Dar es Salaam, Tanzania – sequence: 21 givenname: Khem B surname: Karki fullname: Karki, Khem B organization: Department of Community Medicine and Public Health, Institute of Medicine, Tribhuvan University, Kathmandu, Nepal – sequence: 22 givenname: Demetre surname: Labadarios fullname: Labadarios, Demetre organization: Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa – sequence: 23 givenname: Joao S surname: Martins fullname: Martins, Joao S organization: Faculty of Medicine and Health Sciences, Universidade Nacional Timor Lorosae, Rua Jacinto Candido, Dili, Timor-Leste – sequence: 24 givenname: Mary T surname: Mayige fullname: Mayige, Mary T organization: National Institute for Medical Research, Dar es Salaam, Tanzania – sequence: 25 givenname: Roy Wong surname: McClure fullname: McClure, Roy Wong organization: Epidemiology Office and Surveillance, Caja Costarricense de Seguro Social, San Jose, Costa Rica – sequence: 26 givenname: Joseph Kibachio surname: Mwangi fullname: Mwangi, Joseph Kibachio organization: Division of Non-Communicable Diseases, Ministry of Health, Nairobi, Kenya – sequence: 27 givenname: Omar surname: Mwalim fullname: Mwalim, Omar organization: Zanzibar Ministry of Health, Mnazi Mmoja, Zanzibar, Tanzania – sequence: 28 givenname: Bolormaa surname: Norov fullname: Norov, Bolormaa organization: National Center for Public Health, Ulaanbaatar, Mongolia – sequence: 29 givenname: Sarah surname: Crooks fullname: Crooks, Sarah organization: Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago – sequence: 30 givenname: Farshad surname: Farzadfar fullname: Farzadfar, Farshad organization: Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 31 givenname: Sahar Saeedi surname: Moghaddam fullname: Moghaddam, Sahar Saeedi organization: Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 32 givenname: Bahendeka K surname: Silver fullname: Silver, Bahendeka K organization: St Francis Hospital, Nsambya, Kampala, Uganda – sequence: 33 givenname: Lela surname: Sturua fullname: Sturua, Lela organization: Non-Communicable Diseases Department, National Center for Disease Control and Public Health, Tbilisi, Georgia – sequence: 34 givenname: Chea Stanford surname: Wesseh fullname: Wesseh, Chea Stanford organization: Liberia Ministry of Health, Monrovia, Liberia – sequence: 35 givenname: Andrew C surname: Stokes fullname: Stokes, Andrew C organization: Department of Global Health, Boston University School of Public Health, Boston, MA, USA – sequence: 36 givenname: Utibe R surname: Essien fullname: Essien, Utibe R organization: Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA – sequence: 37 givenname: Jan-Walter surname: De Neve fullname: De Neve, Jan-Walter organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 38 givenname: Rifat surname: Atun fullname: Atun, Rifat organization: Department of Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA – sequence: 39 givenname: Justine I surname: Davies fullname: Davies, Justine I organization: Department of Global Health, Centre for Global Surgery, Stellenbosch University, Stellenbosch, South Africa – sequence: 40 givenname: Sebastian surname: Vollmer fullname: Vollmer, Sebastian organization: Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany – sequence: 41 givenname: Till W surname: Bärnighausen fullname: Bärnighausen, Till W organization: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany – sequence: 42 givenname: Mohammed K surname: Ali fullname: Ali, Mohammed K organization: Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA – sequence: 43 givenname: James B surname: Meigs fullname: Meigs, James B organization: Department of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 44 givenname: Deborah J surname: Wexler fullname: Wexler, Deborah J organization: Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 45 givenname: Jennifer surname: Manne-Goehler fullname: Manne-Goehler, Jennifer email: jmanne@partners.org organization: Medical Practice Evaluation Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34274065$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1111/j.1467-789X.2011.00952.x 10.1136/bmjopen-2015-010836 10.1186/1758-5996-6-50 10.1371/journal.pmed.1002751 10.1093/ije/dyx078 10.2105/AJPH.2015.302962 10.1373/clinchem.2005.051979 10.1016/j.metabol.2018.09.005 10.2337/dc06-2544 10.1186/s13098-020-00601-8 10.1056/NEJMoa055643 10.1016/S0140-6736(03)15268-3 10.1016/j.gheart.2015.12.010 10.2337/dc13-2096 10.1001/jamacardio.2018.0022 10.1016/S0140-6736(16)30054-X 10.1016/S0140-6736(14)60460-8 10.1016/S0140-6736(16)00618-8 10.1111/obr.12102 10.1038/s41574-019-0310-7 10.1093/ajcn/80.5.1129 10.2337/dc19-1782 10.4103/0256-4947.57165 |
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| References | Manne-Goehler, Geldsetzer, Agoudavi (bib18) 2019; 16 (bib19) 2020 Stauffer, Viswanathan, Jean, Kinabo, Bovet (bib31) 2016; 40 Murphy, Asiki, Nsubuga (bib16) 2014; 37 (bib25) 2018 Di Cesare, Bentham, Stevens (bib3) 2016; 387 (bib21) 2016; 387 Khan, Ning, Wilkins (bib11) 2018; 3 Almajwal, Al-Baghli, Batterham, Williams, Al-Turki, Al-Ghamdi (bib15) 2009; 29 Seiglie, Marcus, Ebert (bib17) 2020; 43 D'Orazio, Burnett, Fogh-Andersen (bib22) 2005; 51 Ganz, Wintfeld, Li, Alas, Langer, Hammer (bib9) 2014; 6 (bib1) 2021 (bib26) 2003 (bib14) 2004; 363 (bib23) 2019 Ashwell, Gunn, Gibson (bib29) 2012; 13 Ross, Neeland, Yamashita (bib30) 2020; 16 (bib24) 2017 Aucott, Philip, Avenell, Afolabi, Sattar, Wild (bib32) 2016; 6 Chooi, Ding, Magkos (bib2) 2019; 92 (bib7) 2019 (bib28) 2020 Wildman, Gu, Reynolds, Duan, He (bib12) 2004; 80 Popkin, Slining (bib4) 2013; 14 Riley, Guthold, Cowan (bib20) 2016; 106 Patel, Shivashankar, Ali (bib13) 2016; 11 Bitew, Alemu, Ayele, Tenaw, Alebel, Worku (bib27) 2020; 12 Adams, Schatzkin, Harris (bib10) 2006; 355 (bib5) 2017; 46 Ng, Fleming, Robinson (bib6) 2014; 384 Narayan, Boyle, Thompson, Gregg, Williamson (bib8) 2007; 30 Khan (10.1016/S0140-6736(21)00844-8_bib11) 2018; 3 (10.1016/S0140-6736(21)00844-8_bib14) 2004; 363 Wildman (10.1016/S0140-6736(21)00844-8_bib12) 2004; 80 (10.1016/S0140-6736(21)00844-8_bib23) 2019 Almajwal (10.1016/S0140-6736(21)00844-8_bib15) 2009; 29 Ross (10.1016/S0140-6736(21)00844-8_bib30) 2020; 16 Ashwell (10.1016/S0140-6736(21)00844-8_bib29) 2012; 13 Narayan (10.1016/S0140-6736(21)00844-8_bib8) 2007; 30 Adams (10.1016/S0140-6736(21)00844-8_bib10) 2006; 355 Ng (10.1016/S0140-6736(21)00844-8_bib6) 2014; 384 (10.1016/S0140-6736(21)00844-8_bib5) 2017; 46 Seiglie (10.1016/S0140-6736(21)00844-8_bib17) 2020; 43 (10.1016/S0140-6736(21)00844-8_bib7) 2019 Chooi (10.1016/S0140-6736(21)00844-8_bib2) 2019; 92 D'Orazio (10.1016/S0140-6736(21)00844-8_bib22) 2005; 51 Stauffer (10.1016/S0140-6736(21)00844-8_bib31) 2016; 40 Riley (10.1016/S0140-6736(21)00844-8_bib20) 2016; 106 Ganz (10.1016/S0140-6736(21)00844-8_bib9) 2014; 6 Patel (10.1016/S0140-6736(21)00844-8_bib13) 2016; 11 Murphy (10.1016/S0140-6736(21)00844-8_bib16) 2014; 37 Di Cesare (10.1016/S0140-6736(21)00844-8_bib3) 2016; 387 Popkin (10.1016/S0140-6736(21)00844-8_bib4) 2013; 14 (10.1016/S0140-6736(21)00844-8_bib21) 2016; 387 Manne-Goehler (10.1016/S0140-6736(21)00844-8_bib18) 2019; 16 Bitew (10.1016/S0140-6736(21)00844-8_bib27) 2020; 12 Aucott (10.1016/S0140-6736(21)00844-8_bib32) 2016; 6 34274054 - Lancet. 2021 Jul 17;398(10296):190-192 |
| References_xml | – volume: 384 start-page: 766 year: 2014 end-page: 781 ident: bib6 article-title: Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013 publication-title: Lancet – year: 2018 ident: bib25 article-title: Best practices for quality anthropometric data collection at the DHS program – year: 2019 ident: bib23 article-title: Classification of diabetes mellitus – volume: 355 start-page: 763 year: 2006 end-page: 778 ident: bib10 article-title: Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old publication-title: N Engl J Med – volume: 106 start-page: 74 year: 2016 end-page: 78 ident: bib20 article-title: The world health organization STEPwise approach to noncommunicable disease risk-factor surveillance: methods, challenges, and opportunities publication-title: Am J Public Health – volume: 43 start-page: 767 year: 2020 end-page: 775 ident: bib17 article-title: Diabetes prevalence and its relationship with education, wealth, and BMI in 29 low- and middle-income countries publication-title: Diabetes Care – volume: 40 start-page: 133 year: 2016 end-page: 139 ident: bib31 article-title: Comparison between capillary glucose measured with a Contour glucometer and plasma glucose in a population survey publication-title: J Lab Med – volume: 13 start-page: 275 year: 2012 end-page: 286 ident: bib29 article-title: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis publication-title: Obes Rev Off J Int Assoc Study Obes – year: 2019 ident: bib7 article-title: IDF Diabetes Atlas – volume: 387 start-page: 1513 year: 2016 end-page: 1530 ident: bib21 article-title: Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants publication-title: Lancet – year: 2017 ident: bib24 article-title: The WHO STEPwise approach to noncommunicable disease risk factor surveillance – volume: 14 start-page: 11 year: 2013 end-page: 20 ident: bib4 article-title: New dynamics in global obesity facing low- and middle-income countries publication-title: Obes Rev – year: 2021 ident: bib1 article-title: Obesity and overweight – volume: 11 start-page: 89 year: 2016 end-page: 96 ident: bib13 article-title: Is the “south Asian phenotype” unique to south Asians? Comparing cardiometabolic risk factors in the CARRS and NHANES studies publication-title: Glob Heart – volume: 92 start-page: 6 year: 2019 end-page: 10 ident: bib2 article-title: The epidemiology of obesity publication-title: Metabolism – year: 2003 ident: bib26 article-title: Diet, nutrition and the prevention of chronic diseases: report of the joint WHO/FAO expert consultation – volume: 30 start-page: 1562 year: 2007 end-page: 1566 ident: bib8 article-title: Effect of BMI on lifetime risk for diabetes in the U.S publication-title: Diabetes Care – volume: 363 start-page: 157 year: 2004 end-page: 163 ident: bib14 article-title: Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies publication-title: Lancet – volume: 6 year: 2016 ident: bib32 article-title: Patterns of weight change after the diagnosis of type 2 diabetes in Scotland and their relationship with glycaemic control, mortality and cardiovascular outcomes: a retrospective cohort study publication-title: BMJ Open – volume: 16 year: 2019 ident: bib18 article-title: Health system performance for people with diabetes in 28 low- and middle-income countries: a cross-sectional study of nationally representative surveys publication-title: PLoS Med – volume: 51 start-page: 1573 year: 2005 end-page: 1576 ident: bib22 article-title: Approved IFCC recommendation on reporting results for blood glucose (abbreviated) publication-title: Clin Chem – volume: 3 start-page: 280 year: 2018 end-page: 287 ident: bib11 article-title: Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity publication-title: JAMA Cardiol – volume: 80 start-page: 1129 year: 2004 end-page: 1136 ident: bib12 article-title: Appropriate body mass index and waist circumference cutoffs for categorization of overweight and central adiposity among Chinese adults publication-title: Am J Clin Nutr – year: 2020 ident: bib19 article-title: World Bank Country and Lending Groups – volume: 6 start-page: 50 year: 2014 ident: bib9 article-title: The association of body mass index with the risk of type 2 diabetes: a case-control study nested in an electronic health records system in the United States publication-title: Diabetol Metab Syndr – volume: 16 start-page: 177 year: 2020 end-page: 189 ident: bib30 article-title: Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on Visceral Obesity publication-title: Nat Rev Endocrinol – volume: 29 start-page: 437 year: 2009 end-page: 445 ident: bib15 article-title: Performance of body mass index in predicting diabetes and hypertension in the Eastern Province of Saudi Arabia publication-title: Ann Saudi Med – volume: 387 start-page: 1377 year: 2016 end-page: 1396 ident: bib3 article-title: Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants publication-title: Lancet – volume: 46 start-page: 1421 year: 2017 end-page: 1432 ident: bib5 article-title: Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies publication-title: Int J Epidemiol – volume: 12 start-page: 93 year: 2020 ident: bib27 article-title: Metabolic syndrome among children and adolescents in low and middle income countries: a systematic review and meta-analysis publication-title: Diabetol Metab Syndr – volume: 37 start-page: e64 year: 2014 end-page: e65 ident: bib16 article-title: The use of anthropometric measures for cardiometabolic risk identification in a rural African population publication-title: Diabetes Care – year: 2020 ident: bib28 article-title: 2·2 diabetes type 2 management – volume: 13 start-page: 275 year: 2012 ident: 10.1016/S0140-6736(21)00844-8_bib29 article-title: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis publication-title: Obes Rev Off J Int Assoc Study Obes doi: 10.1111/j.1467-789X.2011.00952.x – volume: 6 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib32 article-title: Patterns of weight change after the diagnosis of type 2 diabetes in Scotland and their relationship with glycaemic control, mortality and cardiovascular outcomes: a retrospective cohort study publication-title: BMJ Open doi: 10.1136/bmjopen-2015-010836 – volume: 40 start-page: 133 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib31 article-title: Comparison between capillary glucose measured with a Contour glucometer and plasma glucose in a population survey publication-title: J Lab Med – year: 2019 ident: 10.1016/S0140-6736(21)00844-8_bib7 – volume: 6 start-page: 50 year: 2014 ident: 10.1016/S0140-6736(21)00844-8_bib9 article-title: The association of body mass index with the risk of type 2 diabetes: a case-control study nested in an electronic health records system in the United States publication-title: Diabetol Metab Syndr doi: 10.1186/1758-5996-6-50 – volume: 16 year: 2019 ident: 10.1016/S0140-6736(21)00844-8_bib18 article-title: Health system performance for people with diabetes in 28 low- and middle-income countries: a cross-sectional study of nationally representative surveys publication-title: PLoS Med doi: 10.1371/journal.pmed.1002751 – volume: 46 start-page: 1421 year: 2017 ident: 10.1016/S0140-6736(21)00844-8_bib5 article-title: Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies publication-title: Int J Epidemiol doi: 10.1093/ije/dyx078 – volume: 106 start-page: 74 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib20 article-title: The world health organization STEPwise approach to noncommunicable disease risk-factor surveillance: methods, challenges, and opportunities publication-title: Am J Public Health doi: 10.2105/AJPH.2015.302962 – volume: 51 start-page: 1573 year: 2005 ident: 10.1016/S0140-6736(21)00844-8_bib22 article-title: Approved IFCC recommendation on reporting results for blood glucose (abbreviated) publication-title: Clin Chem doi: 10.1373/clinchem.2005.051979 – volume: 92 start-page: 6 year: 2019 ident: 10.1016/S0140-6736(21)00844-8_bib2 article-title: The epidemiology of obesity publication-title: Metabolism doi: 10.1016/j.metabol.2018.09.005 – volume: 30 start-page: 1562 year: 2007 ident: 10.1016/S0140-6736(21)00844-8_bib8 article-title: Effect of BMI on lifetime risk for diabetes in the U.S publication-title: Diabetes Care doi: 10.2337/dc06-2544 – volume: 12 start-page: 93 year: 2020 ident: 10.1016/S0140-6736(21)00844-8_bib27 article-title: Metabolic syndrome among children and adolescents in low and middle income countries: a systematic review and meta-analysis publication-title: Diabetol Metab Syndr doi: 10.1186/s13098-020-00601-8 – year: 2019 ident: 10.1016/S0140-6736(21)00844-8_bib23 – volume: 355 start-page: 763 year: 2006 ident: 10.1016/S0140-6736(21)00844-8_bib10 article-title: Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old publication-title: N Engl J Med doi: 10.1056/NEJMoa055643 – volume: 363 start-page: 157 year: 2004 ident: 10.1016/S0140-6736(21)00844-8_bib14 article-title: Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies publication-title: Lancet doi: 10.1016/S0140-6736(03)15268-3 – volume: 11 start-page: 89 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib13 article-title: Is the “south Asian phenotype” unique to south Asians? Comparing cardiometabolic risk factors in the CARRS and NHANES studies publication-title: Glob Heart doi: 10.1016/j.gheart.2015.12.010 – volume: 37 start-page: e64 year: 2014 ident: 10.1016/S0140-6736(21)00844-8_bib16 article-title: The use of anthropometric measures for cardiometabolic risk identification in a rural African population publication-title: Diabetes Care doi: 10.2337/dc13-2096 – volume: 3 start-page: 280 year: 2018 ident: 10.1016/S0140-6736(21)00844-8_bib11 article-title: Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2018.0022 – volume: 387 start-page: 1377 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib3 article-title: Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants publication-title: Lancet doi: 10.1016/S0140-6736(16)30054-X – volume: 384 start-page: 766 year: 2014 ident: 10.1016/S0140-6736(21)00844-8_bib6 article-title: Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013 publication-title: Lancet doi: 10.1016/S0140-6736(14)60460-8 – volume: 387 start-page: 1513 year: 2016 ident: 10.1016/S0140-6736(21)00844-8_bib21 article-title: Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants publication-title: Lancet doi: 10.1016/S0140-6736(16)00618-8 – volume: 14 start-page: 11 year: 2013 ident: 10.1016/S0140-6736(21)00844-8_bib4 article-title: New dynamics in global obesity facing low- and middle-income countries publication-title: Obes Rev doi: 10.1111/obr.12102 – volume: 16 start-page: 177 year: 2020 ident: 10.1016/S0140-6736(21)00844-8_bib30 article-title: Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on Visceral Obesity publication-title: Nat Rev Endocrinol doi: 10.1038/s41574-019-0310-7 – volume: 80 start-page: 1129 year: 2004 ident: 10.1016/S0140-6736(21)00844-8_bib12 article-title: Appropriate body mass index and waist circumference cutoffs for categorization of overweight and central adiposity among Chinese adults publication-title: Am J Clin Nutr doi: 10.1093/ajcn/80.5.1129 – volume: 43 start-page: 767 year: 2020 ident: 10.1016/S0140-6736(21)00844-8_bib17 article-title: Diabetes prevalence and its relationship with education, wealth, and BMI in 29 low- and middle-income countries publication-title: Diabetes Care doi: 10.2337/dc19-1782 – volume: 29 start-page: 437 year: 2009 ident: 10.1016/S0140-6736(21)00844-8_bib15 article-title: Performance of body mass index in predicting diabetes and hypertension in the Eastern Province of Saudi Arabia publication-title: Ann Saudi Med doi: 10.4103/0256-4947.57165 – reference: 34274054 - Lancet. 2021 Jul 17;398(10296):190-192 |
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| SubjectTerms | Adult Biomarkers Body Mass Index Body weight Cross-Sectional Studies Datasets Developing Countries - statistics & numerical data Diabetes Diabetes mellitus Diabetes Mellitus - diagnosis Diabetes Mellitus - epidemiology Empirical analysis Fasting Female Global Health Glucose Glycated Hemoglobin - analysis Health risks Health Surveys Hemoglobin Humans Income Infectious diseases Low income groups Male Men Metabolism Middle Aged Obesity Obesity - epidemiology Overweight Plasma Polls & surveys Population Poverty Prevalence Public health Screening Sex Thresholds Trends Women |
| Title | Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults |
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