About this Research Topic
Diabetes is a major worldwide public health problem, affecting over 463 million middle-aged adults. The most common type of diabetes is Type 2 diabetes mellitus (T2DM), which is diagnostically characterized by carbohydrate, lipid and protein metabolism dysregulation. Our understanding of T2DM development and progress is rising rapidly in the past decades.
Multiple pathophysiological disturbances, induced by environmental factors (such as obesity, unhealthy diet, and lack of physical activity) and inherited factors, contribute to impaired glucose homeostasis in T2DM.
Recent advances of high-throughput technologies enable to analyze and profile the multiple-layer biological systems, containing DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics), resulting in the generation of a huge amount of biological data, which could be integrated with multi-omics researches. It is well known that the combining of high-throughput data and multi-omics data is a promising strategy to obtain a full understanding of diabetes mechanisms in detail and more specifically, as well as the causal chain. In addition, this combination is probably essential to guide novel therapies. However, integrative analyses of such datasets are not straightforward and are particularly complicated by the high dimensionality of the data.
Therefore, more focus should be on developing strategies to address the challenges of various data integration, and the application of statistical methods in multi-omics studies to elucidate the mechanism of diabetes.
This Research Topic welcomes reviews, mini-reviews, and perspective articles as well as original research covering sub-topics including, but not limited to, the following:
1. Epidemiology of diabetes
2. Multi-omics (metabolomics, genetics, proteomics, microbiome) of diabetes, multi-omics interrogating methods
3. the use of a multi-omics-based framework that integrates metabolomics, genetics, proteomics, microbiome, and clinical information to unveil the molecular mechanisms of diabetes
4. Gene-environment interaction and diabetes,
5. Mendelian randomization analyses, genetic correlation of diabetes and related risk factors
Multiple pathophysiological disturbances, induced by environmental factors (such as obesity, unhealthy diet, and lack of physical activity) and inherited factors, contribute to impaired glucose homeostasis in T2DM.
Recent advances of high-throughput technologies enable to analyze and profile the multiple-layer biological systems, containing DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics), resulting in the generation of a huge amount of biological data, which could be integrated with multi-omics researches. It is well known that the combining of high-throughput data and multi-omics data is a promising strategy to obtain a full understanding of diabetes mechanisms in detail and more specifically, as well as the causal chain. In addition, this combination is probably essential to guide novel therapies. However, integrative analyses of such datasets are not straightforward and are particularly complicated by the high dimensionality of the data.
Therefore, more focus should be on developing strategies to address the challenges of various data integration, and the application of statistical methods in multi-omics studies to elucidate the mechanism of diabetes.
This Research Topic welcomes reviews, mini-reviews, and perspective articles as well as original research covering sub-topics including, but not limited to, the following:
1. Epidemiology of diabetes
2. Multi-omics (metabolomics, genetics, proteomics, microbiome) of diabetes, multi-omics interrogating methods
3. the use of a multi-omics-based framework that integrates metabolomics, genetics, proteomics, microbiome, and clinical information to unveil the molecular mechanisms of diabetes
4. Gene-environment interaction and diabetes,
5. Mendelian randomization analyses, genetic correlation of diabetes and related risk factors
Keywords: Systems epidemiology, Multi-omics, Integrative methods, Causal effect, Diabetes
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