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Cited by 1,015Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and ManagementDiabetes Management and Research

Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants

Open access
Zhou, Bin, Rayner, Archie W, Gregg, Edward W, Sheffer, Kate E, Carrillo-Larco, Rodrigo M, Bennett, James E, Shaw, Jonathan E, Paciorek, Christopher J +941 more·2024·The Lancet
DOI 10.1016/s0140-6736(24)02317-1PMC PMC7616842
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Abstract

BACKGROUND: Diabetes can be detected at the primary health-care level, and effective treatments lower the risk of complications. There are insufficient data on the coverage of treatment for diabetes and how it has changed. We estimated trends from 1990 to 2022 in diabetes prevalence and treatment for 200 countries and territories. METHODS: of 6·5% or higher, or taking medication for diabetes. We defined diabetes treatment as the proportion of people with diabetes who were taking medication for diabetes. We analysed the data in a Bayesian hierarchical meta-regression model to estimate diabetes prevalence and treatment. FINDINGS: In 2022, an estimated 828 million (95% credible interval [CrI] 757-908) adults (those aged 18 years and older) had diabetes, an increase of 630 million (554-713) from 1990. From 1990 to 2022, the age-standardised prevalence of diabetes increased in 131 countries for women and in 155 countries for men with a posterior probability of more than 0·80. The largest increases were in low-income and middle-income countries in southeast Asia (eg, Malaysia), south Asia (eg, Pakistan), the Middle East and north Africa (eg, Egypt), and Latin America and the Caribbean (eg, Jamaica, Trinidad and Tobago, and Costa Rica). Age-standardised prevalence neither increased nor decreased with a posterior probability of more than 0·80 in some countries in western and central Europe, sub-Saharan Africa, east Asia and the Pacific, Canada, and some Pacific island nations where prevalence was already high in 1990; it decreased with a posterior probability of more than 0·80 in women in Japan, Spain, and France, and in men in Nauru. The lowest prevalence in the world in 2022 was in western Europe and east Africa for both sexes, and in Japan and Canada for women, and the highest prevalence in the world in 2022 was in countries in Polynesia and Micronesia, some countries in the Caribbean and the Middle East and north Africa, as well as Pakistan and Malaysia. In 2022, 445 million (95% CrI 401-496) adults aged 30 years or older with diabetes did not receive treatment (59% of adults aged 30 years or older with diabetes), 3·5 times the number in 1990. From 1990 to 2022, diabetes treatment coverage increased in 118 countries for women and 98 countries for men with a posterior probability of more than 0·80. The largest improvement in treatment coverage was in some countries from central and western Europe and Latin America (Mexico, Colombia, Chile, and Costa Rica), Canada, South Korea, Russia, Seychelles, and Jordan. There was no increase in treatment coverage in most countries in sub-Saharan Africa; the Caribbean; Pacific island nations; and south, southeast, and central Asia. In 2022, age-standardised treatment coverage was lowest in countries in sub-Saharan Africa and south Asia, and treatment coverage was less than 10% in some African countries. Treatment coverage was 55% or higher in South Korea, many high-income western countries, and some countries in central and eastern Europe (eg, Poland, Czechia, and Russia), Latin America (eg, Costa Rica, Chile, and Mexico), and the Middle East and north Africa (eg, Jordan, Qatar, and Kuwait). INTERPRETATION: In most countries, especially in low-income and middle-income countries, diabetes treatment has not increased at all or has not increased sufficiently in comparison with the rise in prevalence. The burden of diabetes and untreated diabetes is increasingly borne by low-income and middle-income countries. The expansion of health insurance and primary health care should be accompanied with diabetes programmes that realign and resource health services to enhance the early detection and effective treatment of diabetes. FUNDING: UK Medical Research Council, UK Research and Innovation (Research England), and US Centers for Disease Control and Prevention.

AI snapshot

Written from the paper’s full text.

TL;DR

This pooled analysis of 1108 population-representative studies estimates that in 2022, 828 million adults had diabetes and 445 million adults aged 30 years or older with diabetes were untreated. Although diabetes prevalence rose in many low-income and middle-income countries, treatment coverage increased mainly in high-income and some middle-income countries, widening global inequities.

Key findings

  • In 2022, an estimated 828 million (95% CrI 757–908) adults aged 18 years or older had diabetes, an increase of 630 million (554–713) from 1990; 420 million (372–473) were women and 408 million (356–467) were men. India (212 million, 175–250) and China (148 million, 103–202) had the largest numbers.
  • Age-standardised diabetes prevalence increased from 1990 to 2022 in 131 countries for women and 155 countries for men with posterior probability >0·80; the largest increases were 15–22 percentage points in countries from southeast Asia, south Asia, the Middle East and north Africa, and Latin America and the Caribbean. Prevalence decreased with posterior probability >0·80 in women in Japan, Spain, and France, and in men in Nauru by 7 percentage points.
  • In 2022, 445 million (95% CrI 401–496) adults aged 30 years or older with diabetes did not receive treatment (59% of adults aged 30 years or older with diabetes), 3·5 times the number in 1990 (129 million, 112–147). 30% of untreated diabetes cases (133 million, 109–160) were in India, followed by China (78 million, 51–112).
  • Treatment coverage increased from 1990 to 2022 in 118 countries for women and 98 countries for men with posterior probability >0·80; largest improvements were 25–37 percentage points in countries including Mexico, Colombia, Chile, Costa Rica, Brazil, the Netherlands, Finland, Croatia, Canada, South Korea, Russia, Seychelles, and Jordan. In 2022, treatment coverage was 5–10% in a few sub-Saharan African countries and 55% or higher in South Korea, many high-income western countries, and some countries in central and eastern Europe, Latin America, and the Middle East and north Africa; the highest was 86% (74–95) for women and 77% (60–89) for men in Belgium.

Methods

  • Pooled analysis of 1108 population-representative studies with 141 million participants aged 18 years or older, conducted from 1980 to 2022, with measurements of fasting glucose and/or HbA1c and information on diabetes treatment; 833 (75%) studies had treatment data.
  • Diabetes was defined as fasting plasma glucose ≥7·0 mmol/L, HbA1c ≥6·5%, or taking medication for diabetes; treatment was defined as the proportion of people with diabetes taking oral hypoglycaemic drugs or insulin. Prevalence was estimated for women and men aged 18 years or older, and treatment for those aged 30 years or older.
  • A Bayesian hierarchical meta-regression model estimated trends by sex, age, country, and year for 200 countries and territories; estimates were age-standardised to the WHO standard population, and posterior probabilities were used to identify probable increases or decreases. The change in untreated diabetes was decomposed into contributions from population size and age structure, prevalence, and treatment coverage.

Limitations

  • The paper states that some countries had fewer data and 25 had no data, so estimates for those countries were informed more strongly by data from other countries through geographical hierarchy, increasing uncertainty; survey participation rates vary and can be low, especially in Europe; unobserved differences in assays and measurement methods might persist; 2-h postprandial glucose was not included, so true prevalence could be higher; HbA1c can be affected by anaemia, haemoglobin variants, and other non-glycaemic factors; isolated elevated HbA1c or FPG was predicted in studies missing one marker; type 1 and type 2 diabetes were not separated in adults; most surveys used a single visit rather than a confirmatory test; COVID-19 might have affected surveys and outcomes; treatment details such as insulin and specific medicines were insufficiently collected; and children and adolescents were not included.
  • Evident from the text: treatment coverage is estimated only for adults aged 30 years or older, not all adults with diabetes, and is based on current medication use rather than adherence, clinical control, or medicine type.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(24)02317-1

Cites

69 of 72 references verified3 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

32 of 49 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Genetic and phenotypic differences due to foetal and childhood nutrition and growth also influence worldwide variations in diabetes, especially when accompanied by rapid weight gain, as in the high diabetes prevalence in south Asia.

    “Genetic and phenotypic differences due to foetal and childhood nutrition and growth also influence worldwide variations, especially when accompanied with rapid weight gain—for example the high diabetes prevalence in south Asia.56,57”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [56] Cole, 2020, Genetics of diabetes mellitus and diabetes complications, Nat Rev Nephrol source

      The Introduction describes genetic and environmental components of diabetes, and the Diabetes section discusses genetic studies across populations (page unavailable). Neither passage addresses foetal or childhood nutrition and growth, rapid weight gain, or their proposed role in South Asia’s diabetes prevalence; targeted searches for those terms found no supporting passage in our copy. (LLM, conf=0.73)

      Experimental — accuracy not yet measured
    • cites [57] Bhargava, 2004, Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood, N Engl J Med source

      The Discussion (page not provided) links thinness in infancy and accelerated BMI gain after age two to impaired glucose tolerance or diabetes in a Delhi cohort, and proposes that this trajectory may underlie India's diabetes epidemic. It does not establish genetic differences or explain worldwide variations in diabetes prevalence; its proposed causal role for BMI gain is explicitly tentative. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation

    HbA1c can be affected by non-glycaemic factors, including anaemia due to iron deficiency or malaria, specific haemoglobin variants (eg, HbS and HbF), other haemoglobinopathies, polycythaemia due to living at high altitude, liver and kidney diseases, HIV, and drugs such as antiretroviral therapy for HIV.

    “HbA1c can be affected by some non-glycaemic factors, including anaemia due to iron deficiency or malaria, specific haemoglobin variants (eg, HbS and HbF), other haemoglobinopathies, polycythaemia due to living in high altitude, liver and kidney diseases, HIV, and specific drugs such as antiretroviral therapy for HIV.”
    Quote in the paperSubstantial claim1 of 8 sources checked
    • cites [6] Cosentino, 2020, 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD, Eur Heart J source
    • cites [7]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8] 2017, SEMDSA 2017 guidelines for the management of type 2 diabetes mellitus, J Endocrinol Metab Diabetes South Africa

      Not checked in the free run.

    • cites [9] Makkar, 2022, RSSDI clinical practice recommendations for the management of type 2 diabetes mellitus 2022, Int J Diabetes Dev Ctries

      Not checked in the free run.

    • cites [21] ElSayed, 2024, 2. Diagnosis and classification of diabetes: standards of care in diabetes—2024, Diabetes Care source

      The Recommendations (page not provided) say that anemia, hemoglobinopathies, some hemoglobin variants, and HIV treatment can affect A1C or make it unsuitable for diagnosis. People With HIV (page not provided) also says A1C may underestimate glycemia in people with HIV. The copy does not substantiate several specific examples in the claim—malaria, iron deficiency, HbF, high-altitude polycythaemia, or liver disease—and its discussion of antiretroviral drugs chiefly concerns their effects on glycemia, not a direct non-glycaemic effect on A1C. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [22]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [24] 2021, Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition), Zhonghua Neifenmi Daixie Zazhi

      Not checked in the free run.

  • Findingown data

    The number of adults with diabetes increased by 630 million from 1990 to 2022.

    “an increase of 630 million (554–713) from 1990”
    • Also stated in: § Results · plain
      “a substantial increase of 630 million (554–713) from 1990”
    Quote in the paperSubstantial claim
  • Findingown data

    Current variations in treatment were largely related to the extent of diabetes under-diagnosis.

    “We also found that the current variations in treatment were largely related to the extent of diabetes under-diagnosis, which means that improving case detection is a prerequisite to increasing treatment coverage.”
    Quote in the paperSubstantial claim
  • Findingown data

    Treatment coverage varies substantially globally.

    “Our results show that treatment coverage varies substantially globally, and is low in most low-income and middle-income countries.”
    Quote in the paperSubstantial claim
  • May combine two claims. 19
  • Classified as probably not a substantial claim. 11

  • May combine two claims.

    Premisecitation

    An important driver of the rise in diabetes, and of its variation across countries, is obesity.

    “An important driver of the rise in diabetes, and its variation across countries, is obesity.3”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [3] 2024, Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults, Lancet source

      The Results (page not provided) describe rising obesity and differences in obesity trends across countries, but do not link those trends to diabetes. A search for “diabetes” found it only in reference titles; the Discussion (page not provided) mentions disease risk generally, without addressing whether obesity drives the rise in diabetes or its variation across countries. (LLM, conf=0.75)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Yogurt and possibly some other forms of dairy, whole grains, and green leafy vegetables reduce the risk of diabetes.

    “For example, yogurt and possibly some other forms of dairy, whole grains, and green leafy vegetables reduce the risk of diabetes,42–44”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [42] Chen, 2014, Dairy consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis, BMC Med source

      The Conclusions report that higher yogurt intake is associated with lower type 2 diabetes risk, but say that trials are needed to test whether yogurt causes that reduction. They also report no appreciable association for other dairy foods overall. This dairy-focused source does not address the sentence’s whole-grain or green-leafy-vegetable examples, which may be covered by its other citations. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [43] Hu, 2020, Intake of whole grain foods and risk of type 2 diabetes: results from three prospective cohort studies, BMJ source

      The Abstract Conclusion reports that higher whole-grain consumption was significantly associated with a lower risk of type 2 diabetes. That supports the whole-grain portion of the sentence, which is the portion this paper addresses; it does not establish that whole grains causally reduce risk. The Discussion (Strengths and weaknesses; page unavailable) notes that residual confounding cannot be excluded. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [44] Li, 2014, Fruit and vegetable intake and risk of type 2 diabetes mellitus: meta-analysis of prospective cohort studies, BMJ Open source

      The Results section (‘GLV intake and risk of T2D’) reports that higher green leafy vegetable intake was associated with lower type 2 diabetes risk, supporting the portion of the multi-citation sentence relevant to this paper. The meta-analysis covers prospective observational studies, so it does not establish the causal claim that green leafy vegetables reduce risk. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Many clinical guidelines recommend using a second confirmatory test for diabetes diagnosis and before initiating treatment, although there is variation in this guidance.

    “Many clinical guidelines recommend using a second confirmatory test for diabetes diagnosis and initiating treatment,6,21–23 although there is variation in this guidance.”
    Quote in the paperNot a substantial claim1 of 4 sources checked
    • cites [6] Cosentino, 2020, 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD, Eur Heart J source
    • cites [21] ElSayed, 2024, 2. Diagnosis and classification of diabetes: standards of care in diabetes—2024, Diabetes Care source

      The Recommendations and Confirming the Diagnosis sections (page not provided) require two abnormal results to diagnose diabetes unless there is unequivocal hyperglycemia. They do not state that treatment must await a confirmatory test. The paper is one guideline, so it does not establish how many clinical guidelines give this advice. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [22]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In addition to obesity, the consumption of specific foods might influence the risk of diabetes.

    “In addition to obesity, the consumption of specific foods might influence the risk of diabetes.41”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [41] Ley, 2014, Prevention and management of type 2 diabetes: dietary components and nutritional strategies, Lancet source

  • May combine two claims.

    Premisecitation

    For people who already have diabetes, lifestyle modification and pharmacological treatment, as well as blood pressure and cholesterol control, can delay progression to complications and reduce mortality, or even help with remission, especially if started early.

    “For people who already have diabetes, lifestyle modification and pharmacological treatment, as well as blood pressure and cholesterol control, can delay progression to complications and reduce mortality, or even help with remission, especially if started early.1,2,58–62”
    Quote in the paperNot a substantial claim5 of 7 sources checked
    • cites [1] ElSayed, 2024, 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2024, Diabetes Care source
    • cites [2] Adler, 2024, Post-trial monitoring of a randomised controlled trial of intensive glycaemic control in type 2 diabetes extended from 10 years to 24 years (UKPDS 91), Lancet source
    • cites [58] Gaede, 2003, Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes, N Engl J Med source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [59] Fox, 2015, Update on prevention of cardiovascular disease in adults with type 2 diabetes mellitus in light of recent evidence: a scientific statement from the American Heart Association and the American Diabetes Association, Circulation source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [60] Wu, 2024, 1-year weight change after diabetes diagnosis and long-term incidence and sustainability of remission of type 2 diabetes in real-world settings in Hong Kong: an observational cohort study, PLoS Med source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [61] Ali, 2024, Effect of a multicomponent quality improvement strategy on sustained achievement of diabetes care goals and macrovascular and microvascular complications in South Asia at 6.5 years follow-up: post hoc analyses of the CARRS randomized clinical trial, PLoS Med source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [62] Valabhji, 2024, Early findings from the NHS Type 2 Diabetes Path to Remission Programme: a prospective evaluation of real-world implementation, Lancet Diabetes Endocrinol source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured

  • May combine two claims.

    Study design and conductcitation

    The equations used to predict isolated elevated HbA1c (or FPG) from the other marker performed well in cross-validation, but their use increases the uncertainty of the estimates.

    “Although these equations performed well in cross-validation,25 their use increases the uncertainty of our estimates.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [25] 2023, Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c, Nat Med source

  • May combine two claims.

    Premisecitation

    Diet and lifestyle modification and medicines in people at high risk of diabetes (including those with prediabetes) can prevent or delay diabetes onset and reduce the risk of some complications.

    “Alongside these population-based measures, diet and lifestyle modification and medicines in people at a high risk of diabetes (including those with prediabetes) can prevent or delay diabetes onset and reduce the risk of some complications.48–55”
    Quote in the paperNot a substantial claim6 of 8 sources checked
    • cites [48] 2015, Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study, Lancet Diabetes Endocrinol source

      The Incidence of Diabetes results report that lifestyle intervention and metformin reduced diabetes incidence in this high-risk cohort. The Microvascular Outcomes results and conclusion report no reduction in aggregate microvascular complications for either intervention in the total cohort; a significant reduction was found with lifestyle intervention in women, but not with metformin. The claim's unqualified suggestion that both approaches reduce complications therefore goes beyond this paper's findings. Page numbers were not available in our copy. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [49] Saaristo, 2010, Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D), Diabetes Care source

      The Conclusions report that lifestyle changes can prevent or postpone type 2 diabetes in high-risk people and describe a shorter-lived preventive effect of drug treatment. The Results report improvements in blood pressure and lipid levels with weight loss, but do not establish a reduced risk of diabetes complications; risk-factor changes are not complication outcomes. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [50] Ramachandran, 2006, The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1), Diabetologia source

      The Results report lower diabetes incidence with lifestyle modification and metformin than with control treatment in Asian Indian subjects with impaired glucose tolerance. The paper does not establish that these interventions reduce complications: its primary outcome was diabetes onset, and the Adverse events section reports cardiovascular events without showing a reduction. (LLM, conf=0.89)

      Experimental — accuracy not yet measured
    • cites [51] Chakkalakal, 2024, Test and treat for prediabetes: a review of the health effects of prediabetes and the role of screening and prevention, Annu Rev Public Health source

      The passages labeled “acknowledgments” (page not supplied) report that lifestyle modification and medicines reduce diabetes incidence in people with prediabetes. They also report fewer microvascular complications among lifestyle-intervention participants who returned to normal glucose levels, but explicitly say data are limited on whether the interventions themselves reduce complication risk. That part of the claim is therefore overstated. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [52] Echouffo-Tcheugui, 2023, Diagnosis and management of prediabetes: a review, JAMA source
    • cites [53] Haw, 2017, Long-term sustainability of diabetes prevention approaches: a systematic review and meta-analysis of randomized clinical trials, JAMA Intern Med source

      The Discussion (page not provided) reports that lifestyle modification and certain medications prevent progression to diabetes in people at risk. The paper evaluates diabetes incidence, but the passages do not show that these interventions reduce the risk of complications; that part of the attributed claim is unsupported. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [54] Galaviz, 2022, Interventions for reversing prediabetes: a systematic review and meta-analysis, Am J Prev Med source
    • cites [55] Jonas, 2021, Screening for prediabetes and type 2 diabetes: updated evidence report and systematic review for the US Preventive Services Task Force, JAMA source

      Key Question 7a reports that lifestyle interventions and several medicines reduced diabetes incidence in people with prediabetes. The Benefits of Interventions for Type 2 Diabetes and Prediabetes section reports lower cardiovascular-related mortality after long-term follow-up in one lifestyle-intervention trial, but says most trials were too short to assess effects on health outcomes. It does not establish that medicines reduce the risk of complications in this group. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Diabetes was either already high or increased more in some regions where obesity was or became prevalent, such as Pacific and Caribbean island nations and the Middle East and north Africa, compared with high-income countries in Europe and east Asia and the Pacific, where obesity and diabetes did not rise or rose by a relatively small amount.

    “Diabetes was either already high or increased more in some of the regions where obesity was or became prevalent, such as Pacific and Caribbean island nations and the Middle East and north Africa, compared with high-income countries in Europe and east Asia and the Pacific, where obesity and diabetes did not rise or rose by a relatively small amount.3”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [3] 2024, Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults, Lancet source

      The Results and Discussion (page not provided) report that obesity was prevalent or rose substantially in Pacific and Caribbean island nations and the Middle East and north Africa, with some of these countries exceeding industrialised high-income countries in obesity prevalence. But the paper does not report regional diabetes levels or trends, so it cannot support the claimed comparison in diabetes. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    In countries with universal health insurance and good access to primary care, people at high risk of diabetes might be identified early and advised to use a combination of diet and lifestyle modifications and medicines to prevent or delay diabetes onset.

    “In countries with universal health insurance and good access to primary care, people at a high risk of diabetes might also be identified early and advised to use a combination of diet and lifestyle modifications and medicines to prevent or delay diabetes onset.48–55”
    Quote in the paperNot a substantial claim6 of 8 sources checked
    • cites [48] 2015, Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study, Lancet Diabetes Endocrinol source

      The Introduction and conclusion report that lifestyle intervention and metformin reduced or delayed diabetes in a high-risk population. The Methods describe them as separate original treatment arms, not a tested combination. The paper does not establish that universal health insurance or primary-care access enables early identification and advice; page numbers were not supplied in our copy. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [49] Saaristo, 2010, Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D), Diabetes Care source

      The Research Design and Methods section (page unavailable) describes identifying high-risk people in Finnish primary care and counseling them on diet and other lifestyle changes. The paper does not describe advising them to combine those changes with medicines to prevent diabetes, nor does it establish that the approach applies specifically to countries with universal health insurance. Its discussion contrasts the lasting effects of lifestyle intervention with the rapidly vanishing preventive effect of drug treatment. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
    • cites [50] Ramachandran, 2006, The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1), Diabetologia source

      Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [51] Chakkalakal, 2024, Test and treat for prediabetes: a review of the health effects of prediabetes and the role of screening and prevention, Annu Rev Public Health source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [52] Echouffo-Tcheugui, 2023, Diagnosis and management of prediabetes: a review, JAMA source
    • cites [53] Haw, 2017, Long-term sustainability of diabetes prevention approaches: a systematic review and meta-analysis of randomized clinical trials, JAMA Intern Med source

      Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [54] Galaviz, 2022, Interventions for reversing prediabetes: a systematic review and meta-analysis, Am J Prev Med source
    • cites [55] Jonas, 2021, Screening for prediabetes and type 2 diabetes: updated evidence report and systematic review for the US Preventive Services Task Force, JAMA source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Refined carbohydrates, including in sugar-sweetened beverages, increase the risk of diabetes.

    “whereas refined carbohydrates, including in sugar-sweetened beverages, increase this risk.41,45–47”
    Quote in the paperNot a substantial claim4 of 4 sources checked
    • cites [41] Ley, 2014, Prevention and management of type 2 diabetes: dietary components and nutritional strategies, Lancet source
    • cites [45] Imamura, 2015, Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction, BMJ source

      The Discussion > Conclusions section (page not provided) reports an association between sugar-sweetened beverage consumption and incident type 2 diabetes, but says causality has not been established. The paper does not establish that refined carbohydrates generally increase diabetes risk, so the claim extends beyond the beverages studied and states the relationship more definitively than the source does. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [46] Bhavadharini, 2020, White rice intake and incident diabetes: a study of 132,373 participants in 21 countries, Diabetes Care source

      The Results and Conclusions report an association between higher white-rice intake and incident diabetes, particularly in South Asia. White rice is one refined carbohydrate, but the paper does not establish that refined carbohydrates generally increase diabetes risk or address sugar-sweetened beverages. The claim also states a causal effect more strongly than the reported association. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [47] Mohan, 2009, Dietary carbohydrates, glycaemic load, food groups and newly detected type 2 diabetes among urban Asian Indian population in Chennai, India (Chennai Urban Rural Epidemiology Study 59), Br J Nutr source

      The Results report a positive association between refined-grain intake and newly detected type 2 diabetes in the Chennai population. They do not establish that refined carbohydrates cause diabetes, and the paper reports no association for added sugar, which it says was consumed mainly in tea and coffee (Results; Discussion, page unavailable). It does not report a diabetes-risk finding for sugar-sweetened beverages as a category. (LLM, conf=0.89)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    In 2022, India and China accounted for the largest number of adults with diabetes, followed by the USA, Pakistan, Indonesia, and Brazil.

    “India (212 million, 175–250) and China (148 million, 103–202) accounted for the largest proportion of this number, followed by the USA, Pakistan, Indonesia, and Brazil.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In 2022, of adults with diabetes, 420 million were women and 408 million were men.

    “Of these, 420 million (372–473) were women and 408 million (356–467) were men.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Most health surveys are based on one visit to participants, in which a glycaemic marker is measured.

    “Most health surveys are based on one visit to participants, in which a glycaemic marker is measured.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Diabetes prevalence based on data collected in multiple visits might be lower, and treatment coverage might be higher, than that based on one measurement.

    “Nonetheless, diabetes prevalence based on data collected in multiple visits might be lower, and treatment coverage might be higher, than that based on one measurement.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Most (85-95%) cases of diabetes in adults are type 2.

    “However, most (85–95%) cases of diabetes in adults are type 2.40”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] Tuomilehto, 2013, The emerging global epidemic of type 1 diabetes, Curr Diab Rep source

  • May combine two claims.

    Interpretationreasoning

    Some of the factors that affect HbA1c (malaria-induced and iron deficiency anaemia, haemoglobinopathies such as sickle cell disease and thalassemia, and antiretroviral therapy) are more prevalent in parts of Asia and Africa than elsewhere and might have affected HbA1c or its measurement.

    “Some of these factors, including malaria-induced and iron deficiency anaemia, haemoglobinopathies such as sickle cell disease and thalassemia, and antiretroviral therapy, are more prevalent in parts of Asia and Africa than other parts of the world, and might have affected HbA1c or its measurement.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Most survey data did not separate type 1 and type 2 diabetes in adults, because distinguishing between these disorders can be challenging in adults.

    “Most survey data did not separate type 1 and type 2 diabetes in adults because distinguishing between these disorders can be challenging in adults.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The risk of erroneous results from a single measurement is not a relevant issue in population prevalence studies, and hence population surveillance uses a single measurement.

    “This is not a relevant issue in prevalence studies in a population, and hence population surveillance uses a single measurement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Some countries had fewer data and 25 had no data, and the estimates for countries with fewer or no data were informed more strongly by data from other countries through geographical hierarchy, which increased the uncertainty of their estimates.

    “Some countries had fewer data and 25 had no data; the estimates for countries with fewer or no data were informed to a stronger degree by data from other countries through geographical hierarchy, which increased the uncertainty of their estimates.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    The COVID-19 pandemic might have impeded the rolling out of some health surveys or affected participation.

    “The COVID-19 pandemic might have impeded the rolling out of some health surveys or affected participation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The analysis accounted for whether a study measured fasting glucose in plasma or whole blood, but other unobserved differences due to differing methods, such as assays for measuring FPG and HbA1c, might persist.

    “Although we accounted for whether a study had measured fasting glucose in plasma or whole blood, other unobserved differences might persist due to differing methods—for example, in assays used for measuring FPG and HbA1c.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    The COVID-19 pandemic might also have affected diabetes prevalence and especially treatment coverage.

    “The pandemic might also have affected diabetes prevalence and especially treatment coverage.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Survey sample weights adjust for non-response, but this adjustment might be imperfect, especially as response rates decrease.

    “Survey sample weights adjust for non-response but this adjustment might be imperfect, especially as response rates decrease.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Health survey participation rates vary across countries, can change over time, and have become particularly low in Europe.
23 of 23

  • The paper relies on this without support.

    High-risk prevention using diet, lifestyle modification and medicines is less widely used in low-resourced health systems with limited attention to, or resources for, diabetes screening.

    “This approach is less widely used in low-resourced health systems with limited attention to, or resources for, diabetes screening.”

  • The paper relies on this without support.

    Health survey participation rates vary across countries, can change over time, and have become particularly low in Europe.

    “Health survey participation rate varies across countries and can change over time, and has become particularly low in Europe.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The magnitude of diabetes and its sequelae including diabetic retinopathy require large-scale screening to enable early intervention.

    “The magnitude of diabetes and its sequelae including DR require large-scale screening to enable early intervention, as described in the recent Lancet Global Health Commission on Global Eye Health and contemporary modelling of diabetes prevalence worldwide1,4.”

    From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [4]

    The Results report an estimated 828 million adults with diabetes in 2022. The Discussion calls for expanded diabetes screening and early treatment to reduce complications, but it does not specifically call for large-scale diabetic retinopathy screening; that part of the citing sentence is not supported by this source. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Type 2 diabetes accounts for 90% to 95% of all cases of diabetes globally.

    “Type 2 diabetes accounts for 90% to 95% of all cases of diabetes globally”

    From Diagnosis and Treatment of Type 2 Diabetes in Adults (2025) · cites this paper as [1]

    The Discussion (page unavailable) says that most diabetes cases in adults are type 2, but gives a range of 85–95%, not 90–95%. It does not extend that proportion to all diabetes cases across all ages globally. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The number of people with diabetes worldwide was estimated to be between 589 million and 828 million during 2022 to 2024.

    “The number of people with diabetes worldwide was estimated to be between 589 million and 828 million during 2022 to 2024,1,2”

    From Diagnosis and Treatment of Type 2 Diabetes in Adults (2025) · cites this paper as [1]

    The Results (page not provided) estimate that 828 million adults worldwide had diabetes in 2022. The cited paper does not give a 589 million estimate or an estimate for 2024, so it supports only the 2022 figure, not the stated range across 2022–2024. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Type 2 diabetes affects up to 14% of the global population.

    “Type 2 diabetes affects up to 14% of the global population and is associated with preventable long-term complications such as cardiovascular disease, kidney failure, vision loss and increased mortality.”

    From Diagnosis and Treatment of Type 2 Diabetes in Adults (2025) · cites this paper as [1]

    The Results (page not provided) report approximately 14% age-standardised diabetes prevalence worldwide in 2022, separately for adult women and men. The Discussion (page not provided) says the surveys generally did not distinguish diabetes types and that 85–95% of adult cases are type 2. The paper therefore does not establish that type 2 diabetes affects 14% of the global population. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. 2017, SEMDSA 2017 guidelines for the management of type 2 diabetes mellitus, J Endocrinol Metab Diabetes South Africa, 22, S1
  2. 2021, Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition), Zhonghua Neifenmi Daixie Zazhi, 37, 311

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Zhou, B., Rayner, A. W., Gregg, E. W., Sheffer, K. E., Carrillo-Larco, R. M., Bennett, J. E., Shaw, J. E., Paciorek, C. J., Singleton, R. K., Barradas Pires, A., Stevens, G. A., Danaei, G., Lhoste, V. P., Phelps, N. H., Heap, R. A., Jain, L., D'Ailhaud De Brisis, Y., Galeazzi, A., Kengne, A. P., Mishra, A., Ikeda, N., Lin, H.-H., Aguilar-Salinas, C. A., Anjana, R. M., Ben Romdhane, H., Davletov, K., Ganapathy, S., Heidemann, C., Khader, Y. S., Khang, Y.-H., Laxmaiah, A., Mbanya, J. C. N., Mohan, V., Narayan, K. V., Pavkov, M. E., Sobngwi, E., Wade, A. N., Younger-Coleman, N. O., Zdrojewski, T., Abarca-Gómez, L., Abbasi-Kangevari, M., Abdul Rahim, H. F., Abu-Rmeileh, N. M., Adambekov, S., Adams, R. J., Aekplakorn, W., Afzal, S., Agdeppa, I. A., Aghazadeh-Attari, J., Agyemang, C., Ahmad, N. A., Ahmadi, A., Ahmadi, N., Ahmadi, N., Ahmed, S. H., Ahrens, W., Ajlouni, K., Al-Hamli, S. F., Al-Hinai, H., Al-Lawati, J. A., Al Asfoor, D., Alarouj, M., AlBuhairan, F., AlDhukair, S., Ali, M. M., Ali, M. K., Alieva, A. V., Alinezhad, F., Alkandari, A., Alkerwi, A., Aly, E., Amarapurkar, D. N., Andersen, L. B., Anderssen, S. A., Andrade, D. S., Ansari-Moghaddam, A., Aounallah-Skhiri, H., Araújo, J., Aris, T., Arku, R. E., Arlappa, N., Aryal, K. K., Aspelund, T., Assah, F. K., Assembekov, B., Au Yeung, S. L., Auvinen, J., Avdičová, M., Azad, K., Azevedo, A., Azimi-Nezhad, M., Azizi, F., Bacopoulou, F., Balakrishna, N., Balanova, Y., Bamoshmoosh, M., Banach, M., Bandosz, P., Banegas, J. R., Barbagallo, C. M., Barcelo, A., Baretić, M., Barrera, L., Barreto, M., Basit, A., Batieha, A. M., Batista, A. P., Baur, L. A., Belavendra, A., Benedek, T., Benet, M., Benzeval, M., Berkinbayev, S., Bernabe-Ortiz, A., Berrios Carrasola, X., Bettiol, H., Beybey, A. F., Bhargava, S. K., Bi, Y., Bika Lele, E. C., Bikbov, M. M., Bista, B., Bjerregaard, P., Bjertness, E., Bjertness, M. B., Björkelund, C., Bloch, K. V., Blokstra, A., Bobak, M., Boehm, B. O., Boggia, J. G., Boissonnet, C. P., Bojesen, S. E., Bonaccio, M., Bonilla-Vargas, A., Borghs, H., Botomba, S., Bovet, P., Brajkovich, I., Brenner, H., Brewster, L. M., Brian, G. R., Briceño, Y., Brito, M., Bueno, G., Bugge, A., Buntinx, F., Cabrera de León, A., Caixeta, R. B., Can, G., Cândido, A. P. C., Capanzana, M. V., Čapková, N., Capuano, E., Capuano, R., Capuano, V., Cardoso, V. C., Carlsson, A. C., Casanueva, F. F., Censi, L., Cervantes–Loaiza, M., Chadjigeorgiou, C. A., Chamnan, P., Chamukuttan, S., Chan, Q., Charchar, F. J., Chaturvedi, N., Chen, C.-J., Chen, H., Chen, L.-S., Cheng, C.-Y., Cheraghian, B., Chetrit, A., Chiou, S.-T., Chirlaque, M.-D., Chudek, J., Cifkova, R., Cirillo, M., Claessens, F., Clarke, J., Cohen, E., Concin, H., Cooper, C., Cosmin, C. R., Costanzo, S., Cowan, M. J., Cowell, C., Crampin, A. C., Crujeiras, A. B., Cruz, J. J., Cureau, F. 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A., Mediene Benchekor, S., Mehlig, K., Mehrparvar, A. H., Melgarejo, J. D., Méndez, F., Menezes, A. M. B., Mereke, A., Meshram, I. I., Meto, D. T., Michels, N., Minderico, C. S., Mini, G., Miquel, J. F., Miranda, J. J., Mirjalili, M. R., Mirkopoulou, D., Modesti, P. A., Moghaddam, S. S., Mohammad, K., Mohammadi, M. R., Mohammadi, Z., Mohammadifard, N., Mohammadpourhodki, R., Mohd Yusoff, M. F., Mohebbi, I., Møller, N. C., Molnár, D., Momenan, A., Montenegro Mendoza, R. A., Moosazadeh, M., Moradpour, F., Morejon, A., Moreno, L. A., Morgan, K., Morin, S. N., Moschonis, G., Moslem, A., Mosquera, M., Mossakowska, M., Mostafa, A., Mostafavi, S.-A., Mota, E., Mota, J., Mota, M., Motlagh, M. E., Motta, J., Msyamboza, K. P., Mu, T. T., Muiesan, M. L., Munroe, P. B., Mursu, J., Musa, K. I., Mustafa, N., Muyer, M. T. M., Nabipour, I., Nagel, G., Naidu, B. M., Najafi, F., Námešná, J., Nang, E. E. K., Nangia, V. B., Naseri, T., Navarro-Ramírez, A. 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R., Safi, S., Safiri, S., Saghi, M. H., Saidi, O., Sakata, S., Saki, N., Šalaj, S., Salanave, B., Salonen, J. T., Salvetti, M., Sánchez-Abanto, J., Santos, D. A., Santos, L. C., Santos, M. P., Santos, R., Santos, T. R., Saramies, J. L., Sardinha, L. B., Sarrafzadegan, N., Sato, Y., Saum, K.-U., Savin, S., Sawada, N., Sbaraini, M., Scazufca, M., Schaan, B. D., Schargrodsky, H., Scheidt-Nave, C., Schipf, S., Schmidt, A. F., Schmidt, B., Schmidt, C. O., Schnohr, P., Schooling, C. M., Schöttker, B., Schramm, S., Sebert, S., Sedaghattalab, M., Sein, A. A., Sen, A., Sepanlou, S. G., Servais, J., Sewpaul, R., Shalnova, S., Shamshirgaran, S. M., Shanthirani, C. S., Sharafkhah, M., Sharma, S. K., Sharman, A., Shayanrad, A., Shayesteh, A. A., Shibuya, K., Shimizu-Furusawa, H., Shiri, R., Shoranov, M., Shrestha, N., Si-Ramlee, K., Siani, A., Siedner, M. J., Silva, D. A. S., Sim, X., Simon, M., Simons, J., Simons, L. A., Sjöström, M., Slowikowska-Hilczer, J., Slusarczyk, P., Smeeth, L., Söderberg, S., Soemantri, A., Solfrizzi, V., Somi, M. H., Soumaré, A., Sousa-Poza, A., Sousa-Uva, M., Sparrenberger, K., Staessen, J. A., Stang, A., Stavreski, B., Steene-Johannessen, J., Stehle, P., Stein, A. D., Stessman, J., Stokwiszewski, J., Stronks, K., Suarez-Ortegón, M. F., Suebsamran, P., Suka, M., Sun, C.-A., Sun, J., Sundström, J., Suriyawongpaisal, P., Sylva, R. C., Tai, E. S., Takuro, F., Tamosiunas, A., Tan, E. J., Tanabayev, B., Tandon, N., Tarawneh, M. R., Tarqui-Mamani, C. B., Taylor, A., Tello, T., Tham, Y. C., Thankappan, K., Theobald, H., Theodoridis, X., Thomas, N., Thrift, A. G., Timmermans, E. J., Tolonen, H. K., Tolstrup, J. S., Tomaszewski, M., Topbas, M., Tornaritis, M. J., Torrent, M., Torres-Collado, L., Touloumi, G., Traissac, P., Triantafyllou, A., Trinh, O. T., Tsao, Y.-H., Tsiampalis, T., Tsugane, S., Tuitele, J., Tuliakova, A. M., Tulloch-Reid, M. K., Tuomainen, T.-P., Turley, M. L., Tzala, E., Tzourio, C., Ueda, P., Ugel, E., Ukoli, F. A., Ulmer, H., Uusitalo, H. M., Valdivia, G., Valvi, D., van Dam, R. M., van den Born, B.-J., Van der Heyden, J., Van Minh, H., van Rossem, L., Van Schoor, N. M., van Valkengoed, I. G., Vanderschueren, D., Vanuzzo, D., Varbo, A., Varona-Pérez, P., Vasan, S. K., Vega, T., Veidebaum, T., Velasquez-Melendez, G., Verdot, C., Veronesi, G., Verstraeten, R., Victora, C. G., Viet, L., Villarroel, L., Vioque, J., Virtanen, J. K., Viswanathan, B., Vollenweider, P., Voutilainen, A., Vrijheid, M., Walton, J., Wan Bebakar, W. M., Wan Mohamud, W. N., Wang, C., Wang, H., Wang, N., Wang, Q., Wang, W., Wang, Y. X., Wang, Y.-R., Wang, Y.-W., Wannamethee, S. G., Webster-Kerr, K., Wedderkopp, N., Wei, W., Westbury, L. D., Whincup, P. H., Widhalm, K., Widyahening, I. S., Więcek, A., Wijemunige, N., Wilks, R. J., Willeit, K., Willeit, P., Wilsgaard, T., Wojtyniak, B., Wong-McClure, R. A., Wong, A., Wong, E. B., Woodward, M., Wu, C.-C., Wu, F. 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Affiliations
  • Center for Excellence in Molecular Cell Science (CN)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • Imperial College London (GB)
  • Hôpital Paul-Brousse (FR)
Corresponding author
Ezzati, Majid
ORCID
Venue
The Lancet
Publisher
Elsevier BV
Published
2024-11
Volume
404
Issue
10467
Pages
2077-2093
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
DOI
10.1016/s0140-6736(24)02317-1
PMC
PMC7616842
OpenAlex
W4404311881
Semantic Scholar
17e4cce70292f8e89698fa26ed35b9e08b6c1245
Cited by
1,013
References
70
Citations by year
2026: 492 · 2025: 517 · 2024: 4
FWCI
333.54
Type
journal article
Language
en
Update notices
erratum (2025-04-05)
Open access
Licence
Locations
  • The Lancet (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion, cc-by)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, cc-by)
  • PubMed Central (acceptedVersion, cc-by)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • St George's Online Research Archive (St George's University of London) (acceptedVersion)
  • Open Access at Essex (University of Essex) (acceptedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
  • ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion, cc-by)
  • OAR@UM (University of Malta) (submittedVersion, other-oa)
  • Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
  • HAL (submittedVersion, cc-by)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
  • Digital Access to Libraries (submittedVersion, other-oa)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Florence Research (University of Florence) (submittedVersion, cc-by-nc-nd)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
  • Institutional Repositories DataBase (IRDB) (submittedVersion)
  • IrInSubria (University of Insubria) (submittedVersion, cc-by)
  • Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
  • Lirias (submittedVersion, cc-by)
  • Lithuanian University of Health Sciences (submittedVersion, cc-by)
  • Research Explorer (The University of Manchester) (submittedVersion, cc-by)
  • Repositório Científico do Instituto Politécnico de Lisboa (Instituto Politécnico de Lisboa) (submittedVersion, cc-by-nc-nd)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
  • University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
  • Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
  • UTUPub (University of Turku) (submittedVersion)
  • Zaguan (Universidad de Zaragoza) (submittedVersion, cc-by)
Funders
  • UK Research and Innovation
  • Centers for Disease Control and Prevention
  • UKRI Medical Research Council (MR/V034057/1)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords
diabetes prevalence, diabetes medication use, low- and middle-income countries, regional variation in prevalence, underdiagnosis of diabetes, diabetes epidemiology
MeSH terms
Adolescent, Adult, Aged, Bayes Theorem, Blood Glucose, Diabetes Mellitus, Female, Glycated Hemoglobin, Humans, Hypoglycemic Agents, Male, Middle Aged, Global Health, Prevalence, Sex Distribution, Young Adult

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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