Type 2 Diabetes, the Epidemic: Trends in Prevalence and Incidence, 2004-2020

Jetty A Overbeek,1,2 Giel Nijpels,2 Karin MA Swart,1,2 Marieke T Blom,2,3 Petra JM Elders,2,3 Ron MC Herings1,4 1PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands; 2Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of General Practice, Amsterdam, Netherlands; 3Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, Netherlands; 4Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam, NetherlandsCorrespondence: Jetty A Overbeek, Email [email protected]/Hypothesis: Only a few studies reported the incidence of type 2 diabetes (T2D). Understanding recent trends in diabetes is vital for planning future diabetes care. This study updated national trends in the prevalence and incidence of type 2 diabetes (T2D) in the Netherlands from 2004– 2020.Methods: The DIAbetes, MANagement and Treatment (DIAMANT) cohort was used. A cross-sectional design with yearly measurements for the study period was used. The prevalence was calculated by dividing the total number of people with T2D by the total number of all residents. The incidence was calculated by dividing new cases of T2D by the resident population at risk during the calendar year of interest.Results: Among men, the prevalence of T2D in the Netherlands increased from 2.3% in 2004 to 6.3% in 2020. Women’s prevalence increased from 2.3% in 2004 to 5.3% in 2020. During 2005– 2009, the incidence rate for both men and women was relatively stable Between 2010 and 2020, the incidence rate fell about 1.5 per 1000 in both men and women.Conclusion: From 2004– 2020, the prevalence of T2D in the Netherlands more than doubled, with a decreasing incidence from 2010 onwards.Plain Language Summary: Research in contextWhat is already known about this subject?Many studies have reported the increasing prevalence of type 2 diabetes (T2D). However, only a few studies reported the incidence.In a recent systematic review of all these studies, the incidence fell in over a third of the most high-income populations and increased in a minority of populations. Data from the Netherlands were included, but they date back to 1996.Understanding recent trends in diabetes, the prevalence and incidence are vital for planning future diabetes care.What is the key question?To update national trends in the prevalence and incidence of T2D in the Netherlands for 2004-2020.What are the new findings?During 2004-2020, the prevalence of T2D in the Netherlands more than doubled, with a decreasing incidence from 2010 onwards.How might this impact on clinical practice in the foreseeable future?It demonstrates the effectiveness of preventive strategies, public health education and awareness campaigns contributing to this trend.Keywords: type 2 diabetes, prevalence, incidence, epidemiolog

Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database.

The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/time-intensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process

Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database.

The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/time-intensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process

The DIAbetes MANagement and Treatment (DIAMANT) Cohort

Jetty A Overbeek,1– 3 Karin MA Swart,1– 3 Emma YM van der Pal,2,3 Marieke T Blom,2,3 Joline WJ Beulens,3– 5 Giel Nijpels,2,3 Petra JM Elders,2,3 Ron MC Herings1,3,4 1Department Research, PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands; 2Department of General Practice, Amsterdam UMC - Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands; 3Health Behaviors & Chronic Diseases, Amsterdam Public Health, Amsterdam, Netherlands; 4Department of Epidemiology and Data Science, Amsterdam UMC - Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands; 5Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, NetherlandsCorrespondence: Jetty A Overbeek, Department Research, PHARMO Institute for Drug Outcomes Research, Van Deventerlaan 30-40, Utrecht, 3528 AE, Netherlands, Tel +3130 7440 800, Email [email protected]: The increasing number of people with diabetes and the unclear long-term safety and effectiveness of newer and older blood-glucose-lowering treatments emphasize the need for more pharmaco-epidemiological studies in this field. A prospective, regularly updated cohort of people with diabetes would provide quick and up-to-date information regarding prevalence, treatment, safety and effectiveness. The current aim was to describe the design of the DIAbetes MANagement and Treatment (DIAMANT) cohort.Methods: The DIAMANT cohort is a population-based, dynamic, prospective cohort of persons with diabetes. It contains real-world data (RWD) from general practitioners (GP), including diagnoses, symptoms, examinations, communication to/from specialists and medication. Diabetes is defined as a recorded diabetes diagnosis or a prescription of drugs used in diabetes. The cohort is part of the national infrastructure of “Stichting Informatievoorziening voor Zorg en Onderzoek” (STIZON) and is linked to other data sources.Results: Currently, the cohort enables access to information of 89,883 patients in 2004 to 344,914 in 2020 (6% T1D, 84% T2D and 10% unclassified type of diabetes), with 193,931 participants still registered as being present in the GP practice (active) in 2020. The frequency of follow-up of persons with diabetes is practice dependent. The Dutch guidelines advise 2– 4 contacts per year with a more extensive yearly check-up. The DIAMANT cohort is updated several times a year. Anonymised data from the DIAMANT cohort are available to researchers.Discussion: The DIAMANT cohort provides the opportunity to gain RWD insights into the treatment and outcomes among people with diabetes in daily general practice. The data can be enriched by established linkages to other data sources (eg, hospital data, the Perinatal Registry, the Cancer Registry). The DIAMANT cohort serves as a start of a national infrastructure to study, manage and provide personalised care in order to ultimately improve care and outcomes for people with diabetes.Keywords: diabetes, type 2 diabetes, epidemiology, follow-up, prospective cohort, real-world dat

Homocysteine and the methylenetetrahydrofolate reductase 677C -> T polymorphism in relation to muscle mass and strength, physical performance and postural sway

BACKGROUND/OBJECTIVES: Elevated plasma homocysteine has been linked to reduced mobility and muscle functioning in the elderly. The relation of methylenetetrahydrofolate reductase (MTHFR) 677C -> T polymorphism with these associations has not yet been studied. This study aimed to investigate (1) the association of plasma homocysteine and the MTHFR 677C -> T polymorphism with muscle mass, handgrip strength, physical performance and postural sway; (2) the interaction between plasma homocysteine and the MTHFR 677C -> T polymorphism. SUBJECTS/METHODS: Baseline data from the B-PROOF study (n = 2919, mean age = 74.1 +/- 6.5) were used. Muscle mass was measured using dual X-ray absorptiometry, handgrip strength with a handheld dynamometer, and physical performance with walking-, chair stand- and balance tests. Postural sway was assessed on a force platform. The data were analyzed using regression analyses with plasma homocysteine levels in quartiles. RESULTS: There was a significant inverse association between plasma homocysteine and handgrip strength (quartile 4: regression coefficient B = -1.14, 95% confidence interval (CI) = -1.96; -0.32) and physical performance score (quartile 3: B = -0.53, 95% CI = -0.95; -0.10 and quartile 4: -0.94; 95% CI = -1.40; -0.48) in women only, independent of serum vitamin B12 and folic acid. No association was observed between the MTHFR 677C -> T polymorphism and the outcomes. High plasma homocysteine in the CONCLUSIONS: Elevated plasma homocysteine concentrations are associated with reduced physical performance and muscle strength in older women. There is an urgent need for randomized controlled trials to examine whether lowering homocysteine levels might delay physical decline