Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

PURPOSE OF THE REVIEW

Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).

RECENT FINDINGS

Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

Vitamin D levels, prediabetes risk and hemoglobin A1c levels in young non-diabetic Saudi women

AIMS/INTRODUCTION

Vitamin D levels are negatively correlated with prediabetes risk and hemoglobin A1c levels in individuals with prediabetes. The data are, however, scarce and inconsistent among different populations. We aimed to assess the association of vitamin D with prediabetes risk and hemoglobin A1c levels in young Saudi women with normoglycemia and prediabetes.

MATERIALS AND METHODS

We analyzed the data of individuals without diabetes (without diabetes history and hemoglobin A1c <6.4%) from the Princess Nourah bint Abdulrahman University's non-communicable diseases student registry. Demographic data, anthropometric and blood pressure measurements, and hemoglobin A1c and vitamin D results were retrieved and analyzed.

RESULTS

In total, 345 participants were included in the analysis. The prediabetes status showed no association with vitamin D levels, but it was significantly associated with the participants' weight and body mass index. Additionally, there was no correlation between the levels of vitamin D and hemoglobin A1c across the whole population, even after correction for body mass index. However, in the body mass index subgroups, when individuals with potentially harmful levels of vitamin D (>125 nmol/L) were excluded, a positive association was detected between vitamin D and hemoglobin A1c levels in the underweight individuals. Hemoglobin A1c values showed a positive correlation only with bodyweight and body mass index.

CONCLUSIONS

Vitamin D levels did not predict prediabetes status and showed no correlation with hemoglobin A1c levels in this population. Vitamin D levels' effect on the risk of prediabetes might be small compared with other well-established risk factors, such as obesity.

Metabolic health in normal-weight and obese individuals

Cardiovascular complications are commonly associated with obesity. However, a subgroup of obese individuals may not be at an increased risk for cardiovascular complications; these individuals are said to have metabolically healthy obesity (MHO). In contrast, metabolically unhealthy individuals are at high risk of cardiovascular disease (CVD), irrespective of BMI; thus, this group can include individuals within the normal weight category (BMI 18.5-24.9 kg/m²). This review provides a summary of prospective studies on MHO and metabolically unhealthy normal-weight (MUHNW) phenotypes. Notably, there is ongoing dispute surrounding the concept of MHO, including the lack of a uniform definition and the potentially transient nature of metabolic health status. This review highlights the relevance of alternative measures of body fatness, specifically measures of fat distribution, for determining MHO and MUHNW. It also highlights alternative approaches of risk stratification, which account for the continuum of risk in relation to CVD, which is observable for most risk factors. Moreover, studies evaluating the transition from metabolically healthy to unhealthy phenotypes and potential determinants for such conversions are discussed. Finally, the review proposes several strategies for the use of epidemiological research to further inform the current debate on metabolic health and its determination across different stages of body fatness.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.