Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings

Magliano, Dianna J.; Chen, Lei; Islam, Rakibul M; Carstensen, Bendix; Gregg, Edward W.; Pavkov, Meda E.; Andes, Linda J.; Balicer, Ran D.; Baviera, Marta; Dam, Elise Boersma-van; Booth, Gillian L.; Chan, Juliana C. N.; Chua, Yi Xian; Fosse-Edorh, Sandrine; Fuentes, Sonsoles; Gulseth, Hanne Løvdal; Gurevičius, Romualdas; Ha, Kyoung Hwa; Hird, Thomas R; Jermendy, György; Khalangot, Mykola; Kim, Dae Jung; Kiss, Zoltán; Kravchenko, Victor; Leventer‐Roberts, Maya; Lin, Chun Yi; Luk, Andrea; Mata-Cases, Manel; Mauricio, Dı́dac; Nichols, Gregory A.; Nielen, M.; Pang, Deanette; Paul, Sanjoy K.; Pelletier, Catherine; Pildava, Santa; Porath, Avi; Read, Stephanie H.; Roncaglioni, Maria Carla; Ruiz, Paz Lopez‐Doriga; Shestakova, Marina Vladimirovna; Викулова, Ольга Константиновна; Wang, Kang Ling; Wild, Sarah H.; Yekutiel, Naama; Shaw, Jonathan E.

doi:10.1016/s2213-8587(20)30402-2

Cited by 113 publications

(74 citation statements)

References 27 publications

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“…However, prevalence is affected by both risk of developing diabetes and survival of those with existing diabetes, and therefore diabetes incidence is a better metric to understand the trends in population risk of diabetes. A recent population-based analysis indicates that type 2 diabetes incidence rates in Australia have been declining relatively quickly since 2010 [ 27 ], and this may be at least in part due to improvements in carbohydrate-containing food choices and the associated decrease in population dietary GI and GL.…”

Section: Resultsmentioning

confidence: 99%

Dietary Glycaemic Index Labelling: A Global Perspective

Barclay¹,

Augustin

Brighenti

et al. 2021

Nutrients

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The glycaemic index (GI) is a food metric that ranks the acute impact of available (digestible) carbohydrates on blood glucose. At present, few countries regulate the inclusion of GI on food labels even though the information may assist consumers to manage blood glucose levels. Australia and New Zealand regulate GI claims as nutrition content claims and also recognize the GI Foundation’s certified Low GI trademark as an endorsement. The GI Foundation of South Africa endorses foods with low, medium and high GI symbols. In Asia, Singapore’s Healthier Choice Symbol has specific provisions for low GI claims. Low GI claims are also permitted on food labels in India. In China, there are no national regulations specific to GI; however, voluntary claims are permitted. In the USA, GI claims are not specifically regulated but are permitted, as they are deemed to fall under general food-labelling provisions. In Canada and the European Union, GI claims are not legal under current food law. Inconsistences in food regulation around the world undermine consumer and health professional confidence and call for harmonization. Global provisions for GI claims/endorsements in food standard codes would be in the best interests of people with diabetes and those at risk.

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Section: Resultsmentioning

confidence: 99%

Dietary Glycaemic Index Labelling: A Global Perspective

Barclay¹,

Augustin

Brighenti

et al. 2021

Nutrients

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“…Furthermore, to account for potential confounding effect of age and gender, the estimates of yearly incidence ratios were adjusted by including gender and age in the Poisson model, and the test procedure above described was performed on the new model. Gender was included by dummy coding, age was considered as numerical variable, and a potential non-linear effect was assessed by restricted cubic splines with 9 knots [10]. The model results were reported in terms of incidence ratios, with a respective 95% confidence interval (CI).…”

Section: Implications Of All the Available Evidencementioning

confidence: 99%

Type 1 diabetes onset in Lombardy region, Italy, during the COVID-19 pandemic: The double-wave occurrence

Mameli¹,

Scaramuzza²,

Macedoni³

et al. 2021

EClinicalMedicine

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“…Type 2 diabetes mellitus is one of the most common metabolic conditions, with an increasing prevalence attributable to aging, sedentary lifestyles, environmental changes and better disease management. [1][2][3] Patients with this condition are at an increased risk of premature death and other complications. 4 5 Existing risk models have been developed, such as QDiabetes for predicting new onset diabetes, 6 and CORE, 7 BRAVO 8 and Michigan 9 models for predicting disease progression, complications and mortality.…”

Section: Introductionmentioning

confidence: 99%

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

Lee¹,

Zhou

Leung

et al. 2021

BMJ Open Diab Res Care

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IntroductionPatients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.Research design and methodsThis study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.ResultsA total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106–142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.ConclusionsA multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

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Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings

Cited by 113 publications

References 27 publications

Dietary Glycaemic Index Labelling: A Global Perspective

Dietary Glycaemic Index Labelling: A Global Perspective

Type 1 diabetes onset in Lombardy region, Italy, during the COVID-19 pandemic: The double-wave occurrence

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

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