Monte, R. E. C. | bioRxiv Science

bioRxiv2026

The metabolic profile of Extracellular Vesicles identifies and separates patients with Sarcoidosis and Anti-Synthetase Syndrome

Sarcoidosis is a multisystem disorder that primarily affects the lungs and is characterizedby granulomatous inflammation. However, much of the underlying disease mechanisms remain poorly understood. Extracellular vesicles (EVs) are small membrane-bound particles released by all cells and carry various cargos including metabolites. They are involved in intercellular communication that can be dysregulated in diseases.This study characterizes the metabolic cargo of EVs isolated from bronchoalveolar lavage fluid (BALF), using liquid chromatography-mass spectrometry (LC-MS)-based metabolomic analysis, in patients with sarcoidosis (n=37), compared to healthy controls (n=10). Additionally, the sarcoidosis signature was compared to another pulmonary disorder, anti-synthetase syndrome (ASyS, n=10). Arachidonic acid (AA) results were verified by ELISA. A total of 1202 metabolites were detected, with 111 annotated ones further analyzed. EVs from sarcoidosis patients showed distinct metabolomic profiles compared to both ASyS patients and healthy controls, with 38 annotated metabolites differentially expressed in any of the groups. In both annotated and non-annotated data, sarcoidosis patients clustered separately from ASyS patients and healthy individuals. Furthermore, sarcoidosis patients clustered in 3 subgroups, whereof one was similar to ASyS patients and one stood out as showing higher cell counts in BALF. Higher AA levels were found in sarcoidosis patient EVs by LC-MS, and AA results were verified by ELISA. Our data show that BALF EV metabolites are disease-dependent and support the notion thatsarcoidosis patients should be further subgrouped for better diagnosis and treatment.

immunology↗

bioRxiv2026

Detecting and Subtyping Ketoacidosis from Metabolomic Patterns in Forensic Casework

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Postmortem omics samples paired with machine learning offers an independent tool to address this challenge. However, such data, especially related to real forensic cases, are rare. In Sweden, high-resolution mass spectrometry data routinely collected in forensic toxicology, can be leveraged for metabolomic analysis. Here, we integrate postmortem metabolomics and machine learning models to detect and subtype ketoacidosis-related deaths using real forensic cases in Sweden. From femoral blood samples of 109 alcoholic ketoacidosis cases, 220 diabetic ketoacidosis cases, 140 hypothermia cases, and 1,229 controls (hanging cases), we developed and tested three machine learning models, which achieved over 90% accuracy in ketoacidosis detection and over 80% in subtyping. Validation with independent cohorts (21 starvation cases, 29 alcoholic controls, and 40 diabetic controls) confirmed robustness with over 80% of starvation cases classified as ketoacidosis-related. Feature clustering highlighted metabolites such as cortisol to be important for subtyping. In summary, our findings demonstrate that combining machine learning with postmortem metabolomics enables accurate detection and subtyping of ketoacidosis-related deaths, which is useful for forensic casework.

systems biology↗