| Title: | Dissection of MASLD heterogeneity using latent class analysis of multi-OMICs data |
| Lead Institution: | CONICET |
| Principal investigator: |
Application 485675
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
Metabolic steatotic liver disease (MASLD) is a complex chronic disease characterised by considerable phenotypic heterogeneity. This clinical and histological heterogeneity poses a challenge for both clinical prognosis and clinical trials. The primary research question is whether the heterogeneous clinical presentation of MASLD can be explained by the underlying molecular heterogeneity, which is responsible for the different patterns of disease manifestation and the existence of overlapping sub-phenotypes. We aim to apply a Latent Class Analysis (LCA) to clinical and biochemical data to obtain phenotypic clusters to which individuals can be objectively assigned. These classes will be further associated with metabolomic and genome-wide data collected from UKBB individuals diagnosed with MASLD or hepatic steatosis, fibrosis, or end-stage liver disease to dissect this heterogeneity and gain insight into the underlying biological processes. Further, we will investigate circulating proteomics across cluster-derived subtypes using the collected proteomics data from a subset of individuals of the UKBB. We will complement these analyses by exploring cell state heterogeneity using single-cell transcriptomics and single-cell proteomics using various publicly available resources.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21276 | Proteomic resolution of the MASLD cardiometabolic spectrum identifies sex-driven endotypes and predicts systemic mortality. | Luis Diambra (+2) | 2026 | Gut |
Enabling scientific discoveries that improve human health