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ObesityObesityObesity is a chronic, complex metabolic disorder characterized by excessive accumulation of body fat that impacts health and increases the risk of numerous comorbidities (Panuganti KK et al. (2024)). Some of these comorbidities include type 2 diabetes, hypertension, cardiovascular diseases, certain cancers, osteoarthritis, and metabolic associated steatotic liver disease (MASLD, previously known as NAFLD) (Blüher M. (2019)). Risk factors for obesity include sedentary lifestyle, high-calorie diet, genetic predisposition, and socioeconomic factors. It primarily affects adipose tissue, leading to adipocyte hypertrophy and hyperplasia. Obesity can also impact other organ systems, including the endocrine, cardiovascular, musculoskeletal, and digestive systems (Lim Y et al. (2024)). Common signs and symptoms include increased body mass index (BMI) ≥30 kg/m², excessive body fat (especially visceral fat), shortness of breath, joint pain, fatigue, and sleep disturbances (Brod M et al. (2017)). Thus, clinical markers used to assess obesity include BMI, waist circumference, waist-to-hip ratio, and body fat percentage (Ghesmaty Sangachin M et al. (2018)). BMI is also used to classify patients with this disorder into Class I (BMI 30-34.9), Class II (BMI 35-39.9), and Class III (BMI ≥40, severe obesity). Globally, obesity has reached epidemic proportions, affecting approximately 1 in 8 people worldwide in 2022. According to the World Health Organization (WHO), the prevalence of obesity has more than tripled in adults and quadrupled in adolescents since 1990 (WHO - obesity and overweight). 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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The Human Protein Atlas
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