The future burden of type 2 diabetes in Belgium: a microsimulation model

OBJECTIVE

To forecast the annual burden of type 2 diabetes and related socio-demographic disparities in Belgium until 2030.

METHODS

This study utilized a discrete-event transition microsimulation model. A synthetic population was created using 2018 national register data of the Belgian population aged 0-80 years, along with the national representative prevalence of diabetes risk factors obtained from the latest (2018) Belgian Health Interview and Examination Surveys using Multiple Imputation by Chained Equations (MICE) as inputs to the Simulation of Synthetic Complex Data (simPop) model. Mortality information was obtained from the Belgian vital statistics and used to calculate annual death probabilities. From 2018 to 2030, synthetic individuals transitioned annually from health to death, with or without developing type 2 diabetes, as predicted by the Finnish Diabetes Risk Score, and risk factors were updated via strata-specific transition probabilities.

RESULTS

A total of 6722 [95% UI 3421, 11,583] new cases of type 2 diabetes per 100,000 inhabitants are expected between 2018 and 2030 in Belgium, representing a 32.8% and 19.3% increase in T2D prevalence rate and DALYs rate, respectively. While T2D burden remained highest for lower-education subgroups across all three Belgian regions, the highest increases in incidence and prevalence rates by 2030 are observed for women in general, and particularly among Flemish women reporting higher-education levels with a 114.5% and 44.6% increase in prevalence and DALYs rates, respectively. Existing age- and education-related inequalities will remain apparent in 2030 across all three regions.

CONCLUSIONS

The projected increase in the burden of T2D in Belgium highlights the urgent need for primary and secondary preventive strategies. While emphasis should be placed on the lower-education groups, it is also crucial to reinforce strategies for people of higher socioeconomic status as the burden of T2D is expected to increase significantly in this population segment.

Detecting gene-gene interactions from GWAS using diffusion kernel principal components

Genes and gene products do not function in isolation but as components of complex networks of macromolecules through physical or biochemical interactions. Dependencies of gene mutations on genetic background (i.e., epistasis) are believed to play a role in understanding molecular underpinnings of complex diseases such as inflammatory bowel disease (IBD). However, the process of identifying such interactions is complex due to for instance the curse of high dimensionality, dependencies in the data and non-linearity. Here, we propose a novel approach for robust and computationally efficient epistasis detection. We do so by first reducing dimensionality, per gene via diffusion kernel principal components (kpc). Subsequently, kpc gene summaries are used for downstream analysis including the construction of a gene-based epistasis network. We show that our approach is not only able to recover known IBD associated genes but also additional genes of interest linked to this difficult gastrointestinal disease.

Microsimulation Modelling in Food Policy: A Scoping Review of Methodological Aspects

Food policies for the prevention and management of diet-related non-communicable diseases (NCDs) have been increasingly relying on microsimulation models (MSMs) to assess effectiveness. Given the increased uptake of MSMs, this review aims to provide an overview of the characteristics of MSMs that link diets with NCDs. A comprehensive review was conducted in PubMed and Web of Knowledge. Inclusion criteria were: (i) findings from a MSM, (ii) diets, foods or nutrients as main exposure of interest, (iii) NCDs, such as overweight/obesity, type 2 diabetes, coronary heart disease, stroke or cancer as disease outcome for impact assessment. This review included information from 33 studies using MSM in analyzing diet and diverse food policies on NCDs. Hereby, most models employed stochastic, discrete-time, dynamic microsimulation techniques to calculate anticipated (cost-)effectiveness of strategies based on food pricing, food reformulation or dietary (lifestyle) interventions. Currently available models differ in the methodology used for quantifying the effect of the dietary changes on disease, and in the method for modelling disease incidence and mortality. However, all studies provided evidence that the models were sufficiently capturing the close-to-reality situation by justifying their choice of model parameters and validating externally their modelled disease incidence and mortality with observed or predicted event data. With the increasing use of various MSMs, between-model comparisons, facilitated by open access models and good reporting practices, would be important for judging model's accuracy, leading to continued improvement in the methodologies for developing and applying MSMs, and subsequently a better understanding of the results by policymakers.

A STATEMENT OF SIGNIFICANCE

Given the advancement in the application of microsimulation modelling in evaluating food policies and measuring diet-related disease burdens, the present scoping review serves as an exercise to inform future modelling, hereby highlighting the need for transparency in model development, application and dissemination to advance and safeguard accuracy and relevance in modelling efforts.