One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation

Abstract

Although iron is an essential element for vital body functions, iron overload (IO) is accompanied by significant cellular damage due to its accumulation within organs. Thus, early diagnosis and accurate identification of the affected organs are critical for preventing irreversible damage and improving patient survival rates. Diagnosing tissue iron deposits relieves invasive biopsies with atomic absorption spectrometry (reserved for specific cases) or noninvasive but costly and time-consuming imaging techniques like computerized tomography and magnetic resonance, which provide limited analytical data and are unsuitable for routine screening. As an alternative, Fourier transform infrared spectroscopy combined with machine learning has emerged as a promising approach for supporting medical decision-making. In this study, we developed a minimally invasive method to identify IO and quantify iron levels in blood and tissues (heart, liver, spleen, and kidney) without biopsies. PLS-DA classification models and PLS regression models were constructed based on samples categorized into a control group (n = 10) and three iron-administered groups at 250 mg kg^–1 (n = 14), 500 mg·kg^–1 (n = 13), and 1000 mg·kg^–1 (n = 15). Iron levels were measured in blood samples and tissue biopsies (spleen, heart, liver, and kidney). The binary classification models (control vs iron-administered) and multiclass models (control, 250, 500, and 1000 mg·kg^–1) demonstrated satisfactory performance into train and validation groups. PLS regression models for quantifying iron concentrations in blood and tissues exhibited excellent linearity and low associated errors across both calibration and test groups. Permutation tests confirmed that all models found a real class structure in the data, were not random, and were built using true chemical information. The chemical insights from the spectra may reflect adaptations associated with iron-induced dysregulation. Alterations in biomolecules could reflect systemic stress responses and may result from free radicals generated by the iron-induced Fenton reaction. Moreover, key spectral regions revealed functional interrelationships, particularly between spleen and liver, and heart and kidneys. In summary, the findings support the potential of this innovative for future research to identify IO and quantify iron levels in human blood and different human tissues using only a single drop of blood without tissue biopsies.