Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021
Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific,...
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| Vydané v: | The Lancet (British edition) Ročník 402; číslo 10397; s. 203 - 234 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier
15.07.2023
Elsevier Ltd Elsevier Limited |
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| ISSN: | 1474-547X, 0140-6736, 1474-547X |
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| Abstract | Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050.
Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively.
In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.
Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.
Bill & Melinda Gates Foundation. |
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| AbstractList | Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6 center dot 1% (5 center dot 8-6 center dot 5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9 center dot 3% [8 center dot 7-9 center dot 9]) and, at the regional level, in Oceania (12 center dot 3% [11 center dot 5-13 center dot 0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76 center dot 1% (73 center dot 1-79 center dot 5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96 center dot 0% (95 center dot 1-96 center dot 8) of diabetes cases and 95 center dot 4% (94 center dot 9-95 center dot 9) of diabetes DALYs worldwide. In 2021, 52 center dot 2% (25 center dot 5-71 center dot 8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24 center dot 3% (18 center dot 5-30 center dot 4) worldwide between 1990 and 2021. By 2050, more than 1 center dot 31 billion (1 center dot 22-1 center dot 39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16 center dot 8% (16 center dot 1-17 center dot 6) in north Africa and the Middle East and 11 center dot 3% (10 center dot 8-11 center dot 9) in Latin America and Caribbean. By 2050, 89 (43 center dot 6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050.BACKGROUNDDiabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050.Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively.METHODSEstimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively.In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.FINDINGSIn 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.INTERPRETATIONDiabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.Bill & Melinda Gates Foundation.FUNDINGBill & Melinda Gates Foundation. Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Bill & Melinda Gates Foundation. Summary Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding Bill & Melinda Gates Foundation. |
| Author | Iso, Hiroyasu Alemi, Sharifullah Simpson, Colin R Joukar, Farahnaz Naghavi, Mohsen Liu, Chaojie Gudayu, Temesgen Worku Kadashetti, Vidya Rancic, Nemanja Rawal, Lal Butt, Zahid A Kazemian, Sina Jamalpoor, Zahra Deng, Xinlei Soleimani, Hamidreza Rawaf, Salman Shaikh, Masood Ali Shiri, Rahman Abdulah, Deldar Morad Das, Saswati Qadir, Mirza Muhammad Fahd Boloor, Archith Ullah, Sana Saad, Aly M A Ilic, Irena M Olufadewa, Isaac Iyinoluwa Zastrozhin, Mikhail Sergeevich Shuval, Kerem Momtazmanesh, Sara Eshetu, Habitu Birhan AL-Ahdal, Tareq Mohammed Ali Pourali, Ghazaleh Ghamari, Seyyed-Hadi Solikhah, Solikhah Saheb Sharif-Askari, Narjes Singh, Surjit Falahi, Shahab Mohammadi, Mohsen Rashedi, Vahid Waheed, Yasir Morovatdar, Negar Hasan, S M Mahmudul Krishan, Kewal Mojiri-forushani, Hoda Adekanmbi, Victor Dhama, Kuldeep Islam, Sheikh Mohammed Shariful Lee, Munjae Bhatti, Jasvinder Singh Ribeiro, Daniela Dabo, Bashir Ahmed, Ayman Wu, YiFan Cousin, Ewerton Okonji, Osaretin Christabel Westerman, Ronny Prates, Elton Junio Sady Gebrehiwot, Mesfin Valizadeh, |
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givenname: Fataneh surname: Ghadirian fullname: Ghadirian, Fataneh – sequence: 285 givenname: Bishal surname: Gyawali fullname: Gyawali, Bishal – sequence: 303 givenname: Johannes surname: Haubold fullname: Haubold, Johannes – sequence: 307 givenname: Kamran surname: Hessami fullname: Hessami, Kamran – sequence: 324 givenname: Leeberk Raja surname: Inbaraj fullname: Inbaraj, Leeberk Raja – sequence: 337 givenname: Abdollah surname: Jafarzadeh fullname: Jafarzadeh, Abdollah – sequence: 339 givenname: Rajesh surname: Jain fullname: Jain, Rajesh – sequence: 349 givenname: Wonjeong surname: Jeong fullname: Jeong, Wonjeong – sequence: 352 givenname: Jost B surname: Jonas fullname: Jonas, Jost B – sequence: 354 givenname: Abel surname: Joseph fullname: Joseph, Abel – sequence: 355 givenname: Charity Ehimwenma surname: Joshua fullname: Joshua, Charity Ehimwenma – sequence: 359 givenname: Ali surname: Kabir fullname: Kabir, Ali – sequence: 369 givenname: Navjot surname: Kaur fullname: Kaur, Navjot – sequence: 375 givenname: Moien AB surname: Khan fullname: Khan, Moien AB – sequence: 427 givenname: Xuefeng surname: Liu fullname: Liu, Xuefeng – sequence: 428 givenname: Chaojie surname: Liu fullname: Liu, Chaojie – sequence: 431 givenname: Stefan surname: Lorkowski fullname: Lorkowski, Stefan – sequence: 443 givenname: Konstantinos Christos surname: Makris fullname: Makris, Konstantinos Christos – sequence: 446 givenname: Iram surname: Malik fullname: Malik, Iram – sequence: 455 givenname: Roy Rillera surname: Marzo fullname: Marzo, Roy Rillera – sequence: 457 givenname: Sahar surname: Masoudi fullname: Masoudi, Sahar – sequence: 468 givenname: GK surname: Mini fullname: Mini, GK – sequence: 474 givenname: Kebede Haile surname: Misgina fullname: Misgina, Kebede Haile – sequence: 492 givenname: Maryam surname: Moradi fullname: Moradi, Maryam – sequence: 515 givenname: Javaid surname: Nauman fullname: Nauman, Javaid – sequence: 525 givenname: Robina Khan surname: Niazi fullname: Niazi, Robina Khan – sequence: 526 givenname: Yeshambel T surname: Nigatu fullname: Nigatu, Yeshambel T – sequence: 529 givenname: Lawrence Achilles surname: Nnyanzi fullname: Nnyanzi, Lawrence Achilles – sequence: 532 givenname: Ogochukwu Janet surname: Nzoputam fullname: Nzoputam, Ogochukwu Janet – sequence: 534 givenname: Bogdan surname: Oancea fullname: Oancea, Bogdan – sequence: 541 givenname: Patrick Godwin surname: Okwute fullname: Okwute, Patrick Godwin – sequence: 542 givenname: Isaac Iyinoluwa surname: Olufadewa fullname: Olufadewa, Isaac Iyinoluwa – sequence: 545 givenname: Alberto surname: Ortiz fullname: Ortiz, Alberto – sequence: 551 givenname: Raffaele surname: Palladino fullname: Palladino, Raffaele – sequence: 561 givenname: Maja surname: Pasovic fullname: Pasovic, Maja – sequence: 564 givenname: Uttam surname: Paudel fullname: Paudel, Uttam – sequence: 566 givenname: Marcos surname: Pereira fullname: Pereira, Marcos – sequence: 570 givenname: Fanny Emily surname: Petermann-Rocha fullname: Petermann-Rocha, Fanny Emily – sequence: 590 givenname: Vahid surname: Rahmanian fullname: Rahmanian, Vahid – sequence: 591 givenname: Setyaningrum surname: Rahmawaty fullname: Rahmawaty, Setyaningrum – sequence: 606 givenname: Zubair Ahmed surname: Ratan fullname: Ratan, Zubair Ahmed – sequence: 607 givenname: Salman surname: Rawaf fullname: Rawaf, Salman – sequence: 611 givenname: Kannan RR surname: Rengasamy fullname: Rengasamy, Kannan RR – sequence: 617 givenname: Hossein surname: Rezazadeh fullname: Rezazadeh, Hossein – sequence: 619 givenname: Yohanes Andy surname: Rias fullname: Rias, Yohanes Andy – sequence: 628 givenname: Godfrey M surname: Rwegerera fullname: Rwegerera, Godfrey M – sequence: 637 givenname: Sare surname: Safi fullname: Safi, Sare – sequence: 644 givenname: Harihar surname: Sahoo fullname: Sahoo, Harihar – sequence: 678 givenname: Sadaf surname: Sharfaei fullname: Sharfaei, Sadaf – sequence: 692 givenname: Surjit surname: Singh fullname: Singh, Surjit – sequence: 703 givenname: Muhammad surname: Suleman fullname: Suleman, Muhammad – sequence: 713 givenname: Jacques JL Lukenze surname: Tamuzi fullname: Tamuzi, Jacques JL Lukenze – sequence: 714 givenname: Ker-Kan surname: Tan fullname: Tan, Ker-Kan – sequence: 716 givenname: Birhan Tsegaw surname: Taye fullname: Taye, Birhan Tsegaw – sequence: 718 givenname: Mohamad-Hani surname: Temsah fullname: Temsah, Mohamad-Hani – sequence: 719 givenname: Riki surname: Tesler fullname: Tesler, Riki – sequence: 728 givenname: Marcos Roberto surname: Tovani-Palone fullname: Tovani-Palone, Marcos Roberto – sequence: 744 givenname: Shoban Babu surname: Varthya fullname: Varthya, Shoban Babu – sequence: 770 givenname: Metin surname: Yesiltepe fullname: Yesiltepe, Metin – sequence: 772 givenname: Hunachew Kibret surname: Yohannis fullname: Yohannis, Hunachew Kibret – sequence: 773 givenname: Naohiro surname: Yonemoto fullname: Yonemoto, Naohiro – sequence: 779 givenname: Armin surname: Zarrintan fullname: Zarrintan, Armin – sequence: 781 givenname: Naod Gebrekrstos surname: Zeru fullname: Zeru, Naod Gebrekrstos – sequence: 782 givenname: Zhi-Jiang surname: Zhang fullname: Zhang, Zhi-Jiang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37356446$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:du-47221$$DView record from Swedish Publication Index https://gup.ub.gu.se/publication/335543$$DView record from Swedish Publication Index (Göteborgs universitet) http://kipublications.ki.se/Default.aspx?queryparsed=id:153970094$$DView record from Swedish Publication Index (Karolinska Institutet) |
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| ContentType | Journal Article |
| Contributor | Alemi, Sharifullah Ansari, Golnoosh Arifin, Hidayat Addo, Isaac Yeboah Asmelash, Daniel Athari, Seyyed Shamsadin Alvis-Guzman, Nelson Ali, Hassam Abbasi-Kangevari, Mohsen Askari, Elaheh Agyemang-Duah, Williams Arulappan, Judie Areda, Demelash Ahmadi, Keivan Smith, Amanda E Ansari-Moghaddam, Alireza Abdulah, Deldar Morad Adnani, Qorinah Estiningtyas Sakilah Ansar, Adnan Abate, Melsew Dagne Andrei, Catalina Liliana Akinyemi, Rufus Olusola Abd-Rabu, Rami Aguilera Arriagada, Constanza Elizabeth Aryan, Zahra Ahmed, Luai A Lindstedt, Paulina A Abbasian, Mohammadreza Al Hamad, Hanadi Aboagye, Richard Gyan Alcalde-Rabanal, Jacqueline Elizabeth Armocida, Benedetta Abolhassani, Hassan Ameyaw, Edward Kwabena Astell-Burt, Thomas Anjana, Ranjit Mohan Ahmadi, Ali Ärnlöv, Johan Ajami, Marjan Asghari-Jafarabadi, Mohammad Adekanmbi, Victor Ong, Kanyin Liane McLaughlin, Susan A Afolabi, Rotimi Felix Awaisu, Ahmed Aali, Amirali Arumugam, Ashokan Ahmed, Ayman Al-Aly, Ziyad Azadnajafabad, Sina Aruleba, Raphael Taiwo Aghdam, Zahra Babaei Alalwan, Tariq A Abat |
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| Copyright | 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved. 2023. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. This work is published under https://creativecommons.org/licenses/by/3.0/ (theLicense”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license 2023 |
| Copyright_xml | – notice: 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license – notice: Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved. – notice: 2023. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. This work is published under https://creativecommons.org/licenses/by/3.0/ (theLicense”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license 2023 |
| CorporateAuthor | GBD 2021 Diabetes Collaborators |
| CorporateAuthor_xml | – name: GBD 2021 Diabetes Collaborators |
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| DOI | 10.1016/S0140-6736(23)01301-6 |
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| Title | Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021 |
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