Medical sciences (Basel, Switzerland) · 2026
The Signal in the Extreme: A Systematic Outlier Framework Identifies Discrete Immunometabolic Subtypes in Human and Cellular Models.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Garcia-Coste Julio Jesús, Aguayo-Cerón Karla Aidee, Espinosa-Raya Judith, García-Rivero Alexis Alejandro, López-Leyva Carina, Gutiérrez-Rojas Rocío Alejandra, Vargas-De-León Cruz, Romero-Nava Rodrigo
Аннотация
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), hypertriglyceridemia, and network reconfiguration (TGFβ and STAT4 hubs). In the cellular model, outlier samples (12.8%) showed autonomous pro-inflammatory behavior characterized by IL-6 overproduction (p = 0.002) and IL-10 suppression.
CONCLUSIONS: Multivariate analysis confirmed spatial segregation of these profiles. Systematic outlier investigation revealed discrete pathophysiological subtypes invisible to mean-focused analyses, demonstrating that extreme values encapsulate potent biological signals. This framework offers a generalizable approach for uncovering clinical heterogeneity and identifying therapeutic targets in complex diseases.
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), hypertriglyceridemia, and network reconfiguration (TGFβ and STAT4 hubs). In the cellular model, outlier samples (12.8%) showed autonomous pro-inflammatory behavior characterized by IL-6 overproduction (p = 0.002) and IL-10 suppression.
CONCLUSIONS: Multivariate analysis confirmed spatial segregation of these profiles. Systematic outlier investigation revealed discrete pathophysiological subtypes invisible to mean-focused analyses, demonstrating that extreme values encapsulate potent biological signals. This framework offers a generalizable approach for uncovering clinical heterogeneity and identifying therapeutic targets in complex diseases.