# CMEO: a metadata-centric ontology for clinical studies exploration and harmonization assessment

**Authors:** Komal Gilani, Wei Wei, Christof Peters, Marlo Verket, Hans-Peter Brunner-La Rocca, Enrico Nicolis, Martina Colombo, Katharina Marx-Schütt, Visara Urovi, Michel Dumontier

PMC · DOI: 10.1186/s12911-025-03272-5 · 2025-12-06

## TL;DR

CMEO is a new ontology that helps integrate clinical data by harmonizing metadata across studies, enabling privacy-conscious research.

## Contribution

CMEO introduces a metadata-centric ontology for harmonizing clinical study metadata and enabling semantic querying.

## Key findings

- CMEO enables semantic querying and comparison of clinical study metadata.
- Demonstrated utility with five studies, including heart failure and diabetes.
- Supports FAIR-compliant integration and governance-constrained reuse of clinical data.

## Abstract

The integration of clinical research data across various institutions faces hurdles due to differing definitions, inconsistent terminologies, and inadequate support for interoperable metadata. While biomedical ontologies offer valuable tools for structuring clinical data, they have not yet been fully utilized for creating comprehensive metadata descriptors, such as variable semantics, statistical summaries, and governance elements essential for data discovery and alignment. We present the Clinical Metadata Exploration Ontology (CMEO) that builds upon well-established ontologies to provide a cohesive representation of study designs, data elements, exploratory statistics, and data reuse permissions. CMEO facilitates semantic querying for study exploration and comparison of data elements across studies, particularly when individual-level data cannot be shared. We demonstrate its utility using metadata from five studies: four heart-failure studies and one wearable-based type 1 diabetes study. After serializing, we executed SPARQL queries that operationalized study-level discovery, variable alignment across studies, and governance-constrained reuse. This FAIR-compliant, metadata-driven integration across heterogeneous sources enables scalable, privacy-conscious research and underpins federated clinical data exploration.

The online version contains supplementary material available at 10.1186/s12911-025-03272-5.

## Linked entities

- **Diseases:** heart failure (MONDO:0005252), type 1 diabetes (MONDO:0005147)

## Full-text entities

- **Diseases:** type 1 diabetes (MESH:D003922), heart-failure (MESH:D006333)

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12798102/full.md

---
Source: https://tomesphere.com/paper/PMC12798102