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Type 2 diabetesType 2 diabetesType 2 diabetes is a chronic metabolic disorder characterized by hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It primarily affects the pancreas, liver, muscle, and adipose tissue, leading to impaired glucose homeostasis (Ahlqvist E et al. (2018)). Common signs and symptoms include polyuria, polydipsia, polyphagia, fatigue, blurred vision, slow-healing wounds, and recurrent infections. However, many individuals with type 2 diabetes may be asymptomatic in the early stages (American Diabetes Association. (2021)). Risk factors include obesity, physical inactivity, age (>45 years), family history of diabetes, ethnicity (higher risk in African Americans, Hispanics, Native Americans, and Asian Americans), history of gestational diabetes, and presence of prediabetes (Zheng Y et al. (2018)). Type 2 diabetes is the most common form of diabetes, accounting for 90-95% of all cases worldwide. Its prevalence has been rising rapidly, with an estimated 462 million individuals affected globally in 2019 (Khan MAB et al. (2020)). Subtypes include classical type 2 diabetes, latent autoimmune diabetes in adults (LADA), and maturity-onset diabetes of the young (MODY) (Ahlqvist E et al. (2018)). Clinical markers used for diagnosis and monitoring include fasting plasma glucose, oral glucose tolerance test, HbA1c, and in some cases, C-peptide and autoantibody testing (American Diabetes Association. (2021)). Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses. The analyses are reported for three comparisons: 1) disease vs. all other diseases, 2) disease vs. diseases from the same class, and 3) disease vs. healthy samples. Disease vs All other
Disease vs Class
Disease vs Healthy
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
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