Predictors of undiagnosed prevalent type 2 diabetes – The Danish General Suburban Population Study

Heltberg, Andreas; Andersen, John; Sandholdt, Håkon; Siersma, Volkert; Kragstrup, Jakob; Ellervik, Christina

doi:10.1016/j.pcd.2017.08.005

Cited by 17 publications

(26 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in comparable reports on the risk assessment of diabetes, the area under the curves was 0.67 to 0.882. [5][6][7]16] A similar trend was seen in our study. The highest area under the curve was 0.77 for pre-diabetes risk assessment, while the highest area under the curve for diabetes risk assessment was 0.93.…”

Section: Discussionsupporting

confidence: 91%

“…[22] As far as we know, all previous diabetes risk assessment tools were based on information obtained from questionnaires, such as family history and lifestyle habits. [4][5][6][7]16] Because of this, our results could not be easily compared with previous reports. A previous study showed that combining the results of blood tests and questionnaire information improved the prediction accuracy of diabetes risk assessment.…”

Section: Limitationsmentioning

confidence: 82%

“…[1][2][3] We used the diabetes risk assessed from noninvasive questionnaire information, such as physique and age, as a first screening test to select subjects to have their plasma glucose measured. [4][5][6][7] However, we believe that the classification abilities of noninvasive diabetes screening tools are lacking in accuracy and reproducibility, having area under the curve values based on receiver operating characteristic curves between 0.617 to 0.85. A 75-g oral glucose tolerance test (OGTT) provides information regarding the diagnosis of diabetes and the pathological condition called glucose metabolism disorder.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study (Preprint)

Maeta¹,

Nishiyama²,

Fujibayashi³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Limitationsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study (Preprint)

Maeta¹,

Nishiyama²,

Fujibayashi³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The main weakness of the study is the self-reported disease progression. Had OGTT been performed systematically as part of the follow-up, the incidence of type 2 diabetes might have been higher in both cohorts, as the prevalence of undiagnosed disease is reported to be as high as 20% in the general population [11]. It is very unlikely that the participants would have mixed up the type of diabetes that they have, as the diagnosis, treatment and follow-up of type 1 diabetes takes place in a specialised hospital setting while type 2 diabetes care occurs at the health-centre level.…”

Section: Discussionmentioning

confidence: 99%

Type 1 and type 2 diabetes after gestational diabetes: a 23 year cohort study

et al. 2020

View full text Add to dashboard Cite

Aims/hypothesis The aim of this work was to examine the progression to type 1 and type 2 diabetes after gestational diabetes mellitus (GDM) in a 23 year follow-up study. Methods We carried out a cohort study of 391 women with GDM diagnosed by an OGTT or the use of insulin treatment during pregnancy, and 391 age- and parity-matched control participants, who delivered in 1984–1994 at the Oulu University Hospital, Finland. Diagnostic cut-off levels for glucose were as follows: fasting, ≥4.8 mmol/l; 1 h, ≥10.0 mmol/l; and 2 h, ≥8.7 mmol/l. Two follow-up questionnaires were sent (in 1995–1996 and 2012–2013) to assess the progression to type 1 and type 2 diabetes. Mean follow-up time was 23.1 (range 18.7–28.8) years. Results Type 1 diabetes developed (5.7%) during the first 7 years after GDM pregnancy and was predictable at a 2 h OGTT value of 11.9 mmol/l during pregnancy (receiver operating characteristic analysis: AUC 0.91, sensitivity 76.5%, specificity 96.0%). Type 2 diabetes increased linearly to 50.4% by the end of the follow-up period and was moderately predictable with fasting glucose (AUC 0.69, sensitivity 63.5%, specificity 68.2%) at a level of 5.1 mmol/l (identical to the fasting glucose cut-off recommended by the International Association of Diabetes and Pregnancy Study Groups [IADPSG) and WHO]). Conclusions/interpretation All women with GDM should be intensively monitored for a decade, after which the risk for type 1 diabetes is minimal. However, the incidence of type 2 diabetes remains linear, and therefore individualised lifelong follow-up is recommended.

show abstract

“… 13 However, correlates specific to undiagnosed diabetes in Germany have not been systematically investigated so far. Correlates and predictors of undiagnosed diabetes have been previously investigated in several other high-income countries such as the USA, 14–16 the UK, 17 18 Denmark, 19 Ireland, 20 21 South Korea 22 as well as in some low-income countries. 23–27 Results of previous studies from high-income countries vary, which may be explained, at least in part, by differences in study design, characteristics of the study population as well as health system factors.…”

Section: Introductionmentioning

confidence: 99%

Factors associated with undiagnosed type 2 diabetes in Germany: results from German Health Interview and Examination Survey for Adults 2008–2011

Baumert

Paprott

et al. 2020

BMJ Open Diab Res Care

View full text Add to dashboard Cite

IntroductionTo identify characteristics of people with undiagnosed type 2 diabetes (T2D) among adults in Germany.Research design and methodsThe study population comprised participants aged 40–79 years of the German Health Interview and Examination Survey for Adults 2008–2011. Glycemic status was categorized as undiagnosed T2D (glycated hemoglobin A1c (HbA1c) ≥48 mmol/mol (6.5%), n=135), diagnosed T2D (n=518) and normoglycemia (HbA1c<48 mmol/mol (6.5%), n=4451). Multinomial logistic regression models including glycemic status as the outcome variable and sociodemographic characteristics, living alone, diabetes risk factors and healthcare services utilization as independent variables were used to identify factors associated with undiagnosed T2D compared with normoglycemia and diagnosed T2D. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported as measure of association between the outcome and independent variables.ResultsThe prevalence of undiagnosed T2D was 2.9% (95% CI 2.2% to 3.9%) at an overall prevalence of 12.3% (11.0% to 13.6%) of persons with undiagnosed or diagnosed T2D. In multivariable analyses, factors associated with undiagnosed as well as diagnosed T2D in comparison to normoglycemia were older age (OR 1.04, 95% CI 1.01 to 1.06, per year, for undiagnosed T2D; OR 1.08, 1.07 to 1.10 for diagnosed T2D), male sex (3.33, 2.18 to 5.07; 1.91, 1.43 to 2.56), obesity (3.47, 2.17 to 5.56; 2.68, 2.04 to 3.52), hypertension (1.66, 1.09 to 2.53; 2.04, 1.42 to 2.95) and parental history of diabetes (2.04, 1.24 to 3.35; 3.16, 2.30 to 4.34). Variables independently associated with undiagnosed T2D but not diagnosed T2D included living alone (2.20; 1.36 to 3.56) and not seeing a doctor within the past year (2.57; 1.34 to 4.93). People with undiagnosed T2D were further younger and more likely to be male sex and reside in the western part of Germany than people with diagnosed T2D.ConclusionApart from major known risk factors of diabetes, characteristics specific to undiagnosed diabetes among adults in Germany will serve to inform the national education and communication strategy on diabetes mellitus in Germany.

show abstract

Predictors of undiagnosed prevalent type 2 diabetes – The Danish General Suburban Population Study

Cited by 17 publications

References 44 publications

Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study (Preprint)

Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study (Preprint)

Type 1 and type 2 diabetes after gestational diabetes: a 23 year cohort study

Factors associated with undiagnosed type 2 diabetes in Germany: results from German Health Interview and Examination Survey for Adults 2008–2011

Contact Info

Product

Resources

About