# Label-Free Detection of Molecular Signatures in Heart Failure with Preserved Ejection Fraction Using Raman Micro-Spectroscopy

## TL;DR

This study explores using Raman micro-spectroscopy to detect molecular signs of heart failure with preserved ejection fraction, offering a fast and label-free diagnostic method.

## Contribution

The study introduces a novel spectroscopic data analysis strategy for HFpEF diagnosis with high classification accuracy.

## Key findings

- Raman micro-spectroscopy achieved 92% classification accuracy in distinguishing HFpEF from control tissues.
- The method uses protein-to-tryptophan ratios in cardiac tissue for reliable disease classification.
- The approach minimizes preprocessing and enables organ-specific therapeutic response analysis.

## Abstract

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous syndrome characterized by delayed diagnosis and limited therapeutic options, contributing to poor clinical outcomes. In the present study, we investigated the applicability of Raman micro-spectroscopy (RmS) as a label-free, rapid, and cost-effective approach for identifying molecular signatures associated with HFpEF and enabling reliable disease classification. RmS was applied to evaluate disease-related biochemical alterations in cardiac and renal tissues obtained from a clinically relevant HFpEF model (ZSF1 rat). Furthermore, the effects of three pharmacological interventions were analyzed and classified (five experimental groups—36 animals in total), highlighting organ-specific therapeutic responses. We developed a spectroscopic data analysis strategy in which second-derivative Raman spectral features serve as quantitative inputs to a supervised classification model, enabling micro-spectroscopic discrimination of HFpEF versus control tissues and achieving a classification accuracy of 92% (sensitivity 93% and specificity 91%) based on the protein-to-tryptophan ratio in cardiac tissue, while minimizing the need for extensive data preprocessing. The spectroscopic markers used in this study were derived from prior multivariate discovery analyses and are evaluated here within a validation and translational classification framework. Collectively, these findings support the integration of RmS into molecular and translational research settings and suggest its potential utility for improving HFpEF diagnosis and treatment monitoring.