IR fingerprinting can accurately identify healthy individuals and detect common medical conditions from a single measurement, according to a study using the largest population ever profiled with the technique.
Investigating the feasibility of infrared (IR) molecular fingerprinting as a potential health-screening tool, scientists from Ludwig-Maximilians-Universität München and the Max Planck Institute of Quantum Optics applied Fourier transform infrared (FTIR) spectroscopy and machine learning to profile more than 5,100 blood plasma samples from a naturally heterogeneous patient population.
The approach could one day boost the efficiency and speed of medical diagnostic routines.
Optical molecular fingerprinting
A point-of-care device that tests for numerous medical conditions at once has long been desirable for high-throughput health diagnostics.
Mihaela Žigman and her colleagues in Germany aimed to devise a new minimally-invasive screening and risk-assessment tool that would contribute to advancing health diagnostics.
They chose to explore a concept based on FTIR spectroscopy, which produces an IR spectrum with absorption peaks corresponding to the vibrational frequencies of molecular fragments. The IR absorption spectrum can be thought of as a molecular fingerprint that is characteristic of a sample’s overall molecular composition.
When tested on unseen datasets, IR fingerprinting effectively distinguished between dyslipidemia, hypertension, prediabetes, type 2 diabetes and healthy states.
The largest population ever profiled with IR fingerprinting
The researchers measured 5,184 blood plasma samples from 3,169 individuals – the largest population ever profiled with IR fingerprinting, to the team's knowledge. The samples came from the Cooperative Health Research in the Region of Augsburg longitudinal cohort, a regional research platform of independent population-based health surveys and subsequent follow-up examinations of individuals in Germany.
The IR fingerprint of each participant was linked to their medical information to build a multi-label machine-learning classifier capable of simultaneously detecting and distinguishing between conditions of interest.
When tested on unseen datasets, IR fingerprinting effectively distinguished between dyslipidemia, hypertension, prediabetes, type 2 diabetes and healthy states. It could also characterise participants with multiple conditions and forecast the development of metabolic syndrome years before onset.
In the future, the team aims to test the concept on larger and more diverse populations, including people from different genetic and lifestyle backgrounds.
The research was recently published in Cell Reports Medicine.
Credit for main image: phipatbig/Shutterstock
