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The Signal in the Extreme: A Systematic Outlier Framework Identifies Discrete Immunometabolic Subtypes in Human and
Julio Jesús Garcia-Coste1, Karla Aidee Aguayo-Cerón1, Judith Espinosa-Raya2
1Laboratorio de Investigación en Genética de Enfermedades Metabólicas, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico.
Medical Sciences (Basel, Switzerland)
|March 27, 2026
Summary
Investigating outliers in omics data reveals distinct disease subtypes. Extreme values in metabolic-inflammatory syndromes identified unique immune-metabolic phenotypes and pro-inflammatory behaviors, offering new therapeutic targets.
Area of Science:
- Metabolomics and Immunology
- Systems Biology
- Computational Biology
Background:
- Omics analysis often discards outliers as noise, potentially missing crucial biological information.
- Metabolic-inflammatory syndromes are complex, with heterogeneity often overlooked by standard analyses.
Purpose of the Study:
- To shift the paradigm by actively investigating outliers to identify novel biological subtypes.
- To explore the utility of outlier detection in uncovering clinical heterogeneity within metabolic-inflammatory syndromes.
Main Methods:
- Applied a multi-algorithm consensus framework (IQR, MAD, Isolation Forest) for outlier detection.
- Characterized outliers using robust PCA, co-expression networks, unsupervised clustering, and Random Forest modeling.
- Utilized both a clinical cohort of diabetic neuropathy and an in vitro adipocyte model.
Main Results:
- Identified outlier subgroups with distinct immune-metabolic phenotypes in both clinical and cellular models.
- Clinical outliers showed hyperactivated Th1/Th17 pathways, hypertriglyceridemia, and specific network hubs.
- Cellular outliers exhibited autonomous pro-inflammatory behavior with IL-6 overproduction and IL-10 suppression.
Conclusions:
- Systematic outlier investigation reveals pathophysiological subtypes missed by mean-focused analyses.
- Extreme values contain significant biological signals, crucial for understanding disease complexity.
- This framework provides a generalizable method for discovering clinical heterogeneity and therapeutic targets.
