The Signal in the Extreme: A Systematic Outlier Framework Identifies Discrete Immunometabolic Subtypes in Human and Cellular Models
Julio Jesús Garcia-Coste, Karla Aidee Aguayo-Cerón, Judith Espinosa-Raya, Alexis Alejandro García-Rivero, Carina López-Leyva, Rocío Alejandra Gutiérrez-Rojas, Cruz Vargas-De-León, Rodrigo Romero-Nava
TL;DR
This study shows that analyzing outliers in metabolic-inflammation data reveals hidden subtypes that traditional methods miss, offering new insights into disease mechanisms.
Contribution
A novel framework for systematically analyzing outliers to identify discrete immunometabolic subtypes in complex diseases.
Findings
An outlier subgroup in the clinical cohort showed hyperactivation of Th1/Th17 pathways and hypertriglyceridemia.
Outlier samples in the cellular model exhibited IL-6 overproduction and IL-10 suppression.
Multivariate analysis confirmed distinct spatial segregation of the identified immunometabolic profiles.
Abstract
Background: Conventional omics analysis often treats outliers as noise, yet they may harbor critical biological insights. Objetive: This study proposes a paradigm shift: actively investigating outliers to discover biologically relevant subtypes within metabolic–inflammatory syndromes. Methods: We applied a comprehensive analytical framework for outlier detection based on a multi-algorithm consensus (IQR, MAD, Isolation Forest) to a clinical cohort of diabetic neuropathy (n = 93) and an in vitro 3T3-L1 adipocyte model (n = 39). The identified outliers were characterized using robust PCA, co-expression networks, unsupervised clustering, and Random Forest predictive modeling. Results: In the clinical cohort, an outlier subgroup (47.3%) exhibited an extreme immune–metabolic phenotype characterized by hyperactivation of Th1/Th17 pathways (elevated T-bet and IL-17; p < 0.001),…
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Taxonomy
TopicsAdipokines, Inflammation, and Metabolic Diseases · Bioinformatics and Genomic Networks · Atherosclerosis and Cardiovascular Diseases