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Type 2 diabetes burden among migrants in Europe: unravelling the causal pathways.

ABSTRACT: European populations are ethnically and culturally diverse due to international migration. Evidence indicates large ethnic inequalities in the prevalence of type 2 diabetes. This review discusses the burden of type 2 diabetes and its related complications, and the potential explanatory mechanisms among migrants in Europe. The current available data suggest that the rate of type 2 diabetes is higher in all migrant groups and that they develop this disease at an earlier age than the host European populations. The level of diabetes awareness among migrant populations is high, but glycaemic control remains suboptimal compared with Europeans. The culturally adapted lifestyle modification intervention trials to prevent type 2 diabetes mainly focus on South Asian adults in Europe. Diabetes-related microvascular and macrovascular complications remain a major burden among migrant populations in Europe. Earlier studies found higher mortality rates among migrants, but recent studies seem to suggest a shifting trend in favour of first-generation migrants. However, the extent of the burden of type 2 diabetes varies across migrant groups and European countries. Despite the higher burden of type 2 diabetes among migrants, the key underlying factors are not well understood mainly due to limited investment in basic science research and development of prospective cohort studies. We hypothesise that the underlying risk factors for the high burden of type 2 diabetes and its related complications in migrants are multifaceted and include pre-migration factors, post-migration factors and genetic predispositions. Given the multi-ethnic nature of the current European population, there is a clear need for investment in research among migrant populations to gain insight into factors driving the high burden of type 2 diabetes and related complications to facilitate prevention and treatment efforts in Europe.

SUBMITTER: Agyemang C

PROVIDER: S-EPMC8563673 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionWith population aging rampant globally, Europe faces unique challenges and achievements in chronic disease prevention. Despite this, comprehensive studies examining the diabetes burden remain absent. We investigated the burden of type 1 and type 2 diabetes, alongside high fasting plasma glucose (HFPG), in Europe from 1990-2019, to provide evidence for global diabetes strategies.MethodsDisease burden estimates due to type 1 and type 2 diabetes and HFPG were extracted from the GBD 2019 across Eastern, Central, and Western Europe. We analyzed trends from 1990 to 2019 by Joinpoint regression, examined correlations between diabetes burden and Socio-demographic indices (SDI), healthcare access quality (HAQ), and prevalence using linear regression models. The Population Attributable Fraction (PAF) was used to described diabetes risks.ResultsIn Europe, diabetes accounted for 596 age-standardized disability-adjusted life years (DALYs) per 100,000 people in 2019, lower than globally. The disease burden from type 1 and type 2 diabetes was markedly higher in males and escalated with increasing age. Most DALYs were due to type 2 diabetes, showing regional inconsistency, highest in Central Europe. From 1990-2019, age-standardized DALYs attributable to type 2 diabetes rose faster in Eastern and Central Europe, slower in Western Europe. HFPG led to 2794 crude DALYs per 100,000 people in 2019. Type 1 and type 2 diabetes burdens correlated positively with diabetes prevalence and negatively with SDI and HAQ. High BMI (PAF 60.1%) and dietary risks (PAF 34.6%) were significant risk factors.ConclusionEurope's diabetes burden was lower than the global average, but substantial from type 2 diabetes, reflecting regional heterogeneity. Altered DALYs composition suggested increased YLDs. Addressing the heavy burden of high fasting plasma glucose and the increasing burden of both types diabetes necessitate region-specific interventions to reduce type 2 diabetes risk, improve healthcare systems, and offer cost-effective care.

Project description:BackgroundEvery year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment.ResultsThe current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999.ConclusionsThe nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.

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Type 2 diabetes burden among migrants in Europe: unravelling the causal pathways.

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