CC BY
0ANALYSIS OF THE METABOLIC PROFILE OF HUMAN RESPIRATION WAS CONDUCTED TO
IDENTIFY SPECIFIC BIOMARKER MOLECULES
O. A. NEBRITOVA1, I. L. FUFURIN1
1Bauman Moscow State Technical University, Physics Department
ABSTRACT
Various socially significant diseases can cause significant harm to both society and the state, leading to a high incidence rate, impaired quality of life, and hindered societal adaptation for individuals. The demand for effective treatments for chronic conditions like cardiovascular disease, diabetes, cancer, schizophrenia, and obesity necessitates a departure from the traditional universal approach. Instead, there is an increasing recognition of the importance of developing patient-centered diagnosis. Central to this paradigm shift is the understanding that each individual is genetically and biologically unique. Developing a patient-centered approach requires the systematic organization of clinical data by linking quantitative and/or qualitative analysis of biomarkers, or a combination of the two. This study showcases the latest findings pertaining to the identification of statistical relationships between metabolite profiles, other volatile organic compounds (VOCs), and human health.
Grant: the work was carried out within the framework of the implementation of the program of strategic academic leadership "Priority-2030", approved by the Decree of the Government of the Russian Federation from May 13, 2021. №729
Keywords: infrared spectroscopy, diabetes, biomarkers, exhaled breath, biomarker, quality of life INTRODUCTION
With the development of scientific, technical, and clinical knowledge, the definition of the term "biomarker" has evolved. The origins of the term "biomarker" date back to 1949 as the concept of a "biochemical marker" [[1]]. By 1957, the term had changed, appearing in literature as "biological marker" [[2]]. Starting in 1973, the term "biomarker" was first used to denote the presence or absence of biological material. In 2000, the Biomarker Definition Working Group, supported by the U.S. National Institutes of Health (NIH), defined a biomarker as a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions [[3]]. However, this definition does not take into account two main limitations. The first limitation is that sometimes biomarkers are measured using subjective parameters. The second is the exclusion of additional processes and reactions that fall outside the definition. In 2016, Prof. FitzGerald and colleagues redefined the concept of a biomarker as a functional variant or quantitative indicator of a biological process that predicts or reflects the development or predisposition to a disease or the response to therapy [[4]]. In the same year, the Food and Drug Administration (FDA), in collaboration with the U.S. National Institutes of Health, convened the Biomarker Working Group to clarify and simplify the definition of the term "biomarker."
This work conducted a review study of potential biomarker molecules that can be considered as potential biomarker molecules for the diagnosis and monitoring of cancer and diabetes mellitus with the aim of further developing a patient-oriented approach. The interest of the term «biomarker» showed in Fig.1.
Google Academy search result for the term "biomarker"
^ 800000 i 600000 600000
I 400000 £ 200000 © 53 0 1130 1600 2230 -8590 135000 260000 1
.c 2 3 s 1950 - 1960 - 1970 - 1980 - 1990 - 2000 - 2010 -
1960 1970 1980 1990 decade 2000 2010 2020
Figure 1 Dependence of the number of queries for the term "biomarker" from 1950 to 2024
in Google Academy.
MATERIALS AND METHODS
An analysis of literature in databases was carried out using the keywords "diabetes mellitus", "oncological diseases", "biomarker", "volatile organic compounds". Volatile organic compounds and biomarker molecules are two terms that are often used in the medical field, but they have different meanings and applications.
Volatile organic compounds are chemical compounds that evaporate easily under normal temperatures and pressure. They can be present in gaseous form in the atmosphere or released from biological systems, for example from urine, exhaled air, blood, under normal or pathological conditions. They are usually short and light hydrocarbons or their derivatives. Examples include acetone, benzene, formaldehyde, etc.
A biomarker is a specific characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to exposure or intervention [[5]]. Biomarkers can be molecules (eg, proteins, DNA, RNA), metabolites, or other chemical compounds that reflect physiological states or processes. These may include both VOCs and other molecules such as antibodies, hormones and proteins. May represent a wide range of chemical compounds, including amino acids, proteins, nucleic acids, lipids, etc. For use in clinical practice, a biomarker must have characteristics such as being detectable, a significant signal-to-noise ratio, and measurement with high reproducibility using histological methods or imaging techniques [[6]]. Thus, VOCs may be one category of biomarkers, but not all biomarkers are VOCs. The difference between them lies in their chemical nature, methods of application and the context in which they are used.
Target VOCs of exhaled breath for patients with oncologic diseases
Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body. The following cancers have been identified: lung cancer, liver cancer, colorectal cancer, stomach cancer, breast cancer and other. Mortality from cancer worldwide is at least 8.5 million people. for 2018 (Fig. 1). As of 2021, cancer is the second most deadly disease in Russia after cardiovascular diseases. Therefore, there is a need for early diagnosis of diseases. In figure 1 showed worldwide cancer deaths.
0,571 millions 6,72%
0,754 millions
[ПРОЦЕНТ]
0,774 millions
9,11%
0,788 millions
[ПРОЦЕНТ]
[ЗНАЧЕНИЕ]
millions [ПРОЦЕНТ]
1,69 millions [ПРОЦЕНТ]
1 Lungs
Liver
Coloerectal
Stomach
Breast
Others
Figure 2 Worldwide cancer deaths by 11th July 2018. (https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2018.html)
Research shows that specific VOCs may be associated with different types of cancer, such as lung, breast and stomach cancer. For example, the use of gas chromatography and mass spectrometry can detect specific VOCs in the exhaled breath of patients. VOCs can be useful in assessing the effectiveness of therapy, as their levels may vary depending on the stage of the disease and the response to care received. In table 1 shows some VOCs for various types of oncology.
Table 1. Depends of target VOCs for oncologic diseases
Target VOCs Methodology Sample (Patients/ Controls) Sensitivity/ Specificity (%) Statistical Approach
Oncologic Diseases: Lung cancer ( LC)
isoprene (81.5 ppb), acetone (458.7 ppb), methanol (118.5 ppb) PTR-MS/GC-MS 285/472 4 compounds: 52.0/100; 15 (or 21) compounds: 71.0 (80.0)/100 Kruskal-Wallis ANOVA
formaldehyde (7 ppb) PTR-MS 17/170 54.0/99.0 FQDM; p-values from Wilcoxon; ROC; MATLAB (classify.m)
isobutene, methanol, ethanol, acetone, pentane, isoprene, isopropanol, dimethylsulfide, carbon disulphide, benzene, toluene e-nose, GC-MS 14/45; 14/62 71.4/91.9 PCA, CDA, SVM
Oncologic Diseases: Breast Cancer (BC)
hexanal (3.75 ppbV), heptanal (3.22 ppbV), octanal (3.39 ppbV), nonanal (2.49 ppbV) GC-MS 22,17/24 72.7/91.7 Fisher DA; leave-one-out (LOO) DA; Kruskal-Wallis ANOVA, ROC, AUC
Oncologic Diseases: Colorectal cancer (CRC)
decanal; 1,3-dimethylbenzene; 1,2-pentadiene Cyclohexane; Methyl cyclohexane; 4-methyloctane GC-MS 37/41 86/83 PNN validated by the LOO method
Oncologic Diseases: Gastric cancer
6 discriminant VOCs sensors, GC-MS 37,32, -61 89/90 LDA; Wilcoxon/Kruskal-Wallis ANOVA
Type 1 diabetes mellitus is a chronic disease characterized by insulin deficiency due to loss of pancreatic beta cells. According to the International Diabetes Federation, 537 million people (~ 8%) in the world suffer from this disease.
Exhaled breath testing for VOCs may provide an alternative way to monitor glucose levels, which is especially important for patients who require frequent monitoring. Articles [7, 9-12] show an experimental setup based on an infrared quantum cascade laser for the analysis of gas mixtures, such as the exhaled air of healthy volunteers and patients diagnosed with type 1 diabetes.
Acetone is one of the most prominent ketones produced by human metabolism and is also a major component of human respiration. For example, acetone concentrations are particularly elevated in the breath of a patient with uncontrolled diabetes, providing a simple way to assess treatment success and a supportive approach to disease monitoring when combined with regular glucose testing. However, acetone should not be considered a marker for diabetes, but the formation of ketone bodies should be considered instead. The work [8] analyzed the infrared spectra of exhaled air from healthy volunteers (60 people) and patients suffering from type 1 diabetes (60 people), and showed changes in the concentrations of three main biomarker molecules (acetone, ethanol and isopropanol).
Table 2. Depends of target VOCs for diabetes mellitus
Target VOCs Methodology Sample (Patients/ Controls) Sensitivity/ Specificity (%) Statistical Approach
Dia betes mellitus
acetone (160-862 ppb) SIFT-MS 97.9/100 p-value; Non-parametric tests
acetone; isopropanol; toluene; tridecane and undecane SPME/GC-MS 4S/39 PCA, OPLS-DA; MVA, Wilcoxon
VOCs pattern recognition e-nose 117/10S S7.7/S6.9 k-NN voting rule to classify features extracted by PCA
CONCLUSIONS
The paper presents the results of systematizing the connections of some biomarkers with diseases such as cancer and type 1 diabetes. Volatile organic compounds are a promising class of biomarkers for the diagnosis and monitoring of both cancer and diabetes. The result of the work may be useful for the development of approaches for analyzing human breath for biomedical applications. Current research highlights the need for further clinical trials to standardize methods for their detection and evaluate their accuracy in different patient populations. The use of VOCs as a noninvasive diagnostic tool has the potential to improve patient care and quality of life.
The work was carried out as part of the implementation of the strategic academic leadership program "Priority 2030".
REFERENCES
[1] Mundkur BD. Evidence excluding mutations, polysomy, and polyploidy as possible causes of non-mendelian segregations in Saccharomyces. Ann Messouri Botanical Garden (1949) 36(3):23. doi: 10.2307/2394394
[2] Porter KA. Effect of homologous bone marrow injections in x-irradiated rabbits. Br J Exp Pathol (1957) 38(4):401-12
[3] Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther (2001) 69(3):89-95. doi: 10.1067/mcp.2001.113989
[4] FitzGerald GA. Measure for Measure: Biomarker standards and transparency. Sci Trans Med (2016) 8(343):343fs10. doi: 10.1126/scitranslmed.aaf8590
[5] Biomarker Working Group F-N. BEST (Biomarkers, Endpoints, and other Tools) Resource. In: Spring S, editor. BEST (Biomarkers, Endpoints, and other Tools) Resource. Silver Spring (MD): FDA-NIH (2016)
[6] Aronson JK, Ferner RE. Biomarkers-A General Review. Curr Protoc Pharmacol (2017) 76:9 23:1-9 17. doi: 10.1002/cpph.19
[7] Golyak, I. S., Anfimov, D. R., Demkin, P. P., Berezhanskiy, P. V, Nebritova, O. A., Morozov, A. N. and Fufurin, I. L., "A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases," Journal of Biophotonics (2024).
[8] Небритова О.А., Демкин П.П., Морозов А.Н. и др. Ацетон, этанол и изопропанол как совокупность биомаркеров в выдыхаемом воздухе пациентов с диабетом первого типа. Вестник МГТУ им. Н.Э. Баумана. Сер. Естественные науки, 2023, № 6 (111), с. 39--54.
[9] Fufurin I, Berezhanskiy P, Golyak I, Anfimov D, Kareva E, Scherbakova A, Demkin P, Nebritova O, Morozov A. Deep Learning for Type 1 Diabetes Mellitus Diagnosis Using Infrared Quantum Cascade Laser Spectroscopy. Materials (Basel). 2022 Apr 20;15(9):2984. doi: 10.3390/ma15092984. PMID: 35591319
[10] Fufurin, I. L., et al., "Numerical techniques for infrared spectra analysis of organic and inorganic volatile compounds for biomedical applications," Opt. Eng. 60(08) (2021).
[11] Golyak, I. S., Kareva, E. R., Fufurin, I. L., Anfimov, D. R., Scherbakova, A. V., Nebritova, A. O., Demkin, P. P. and Morozov, A. N., "Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath," Computer Optics 46(4) (2022).
[12] Ig.S. Golyak at al. "Application of machine learning for the diagnosis of some socially significant diseases from an exhaled human air by the infrared laser spectroscopy.", Optics and Spectroscopy 131(6), 780 (2023).
