Scalp metabolite-based biomarker for diagnosing alopecia and method for diagnosing alopecia using same

Abstract

An embodiment of the present invention relates to: a marker composition for diagnosing alopecia, comprising a scalp-derived metabolite; a composition for diagnosing alopecia, comprising an agent for measuring an expression level of the metabolite; and use thereof. The present invention has the advantage of precisely distinguishing between alopecia groups and non-alopecia groups by analyzing scalp metabolites using metabolomic analysis techniques.

Description

Scalp metabolome-based biomarkers for hair loss diagnosis and methods for diagnosing hair loss using them.

[0001] One example of the present invention relates to a marker composition for diagnosing hair loss, comprising a scalp-derived metabolite; a composition for diagnosing hair loss, comprising a preparation for measuring the expression level of the metabolite; and uses thereof.

[0002] Hair loss is a growing problem in modern society. It's recognized as a condition that goes beyond a mere cosmetic issue and seriously impacts an individual's quality of life. While not directly life-threatening, hair loss significantly impacts a person's self-confidence and social life. As hair loss increases, particularly among young people, the number of cases experiencing psychological stress and interpersonal difficulties is increasing. Pediatric alopecia areata can negatively impact a child's interpersonal relationships, social development, and academic achievement, significantly reducing their quality of life.

[0003] As the number of people with hair loss steadily increases, so does the demand for treatment. According to data from the Health Insurance Review & Assessment Service, the number of patients receiving treatment for hair loss increased by approximately 11% over four years, from approximately 225,000 in 2018 to approximately 250,000 in 2022. Young hair loss patients in their 20s and 30s account for 40% of the total, suggesting a complex mix of factors, including Westernized eating habits, stress, and environmental factors.

[0004] The hair loss treatment market is experiencing high growth of 20-30% annually. The domestic hair loss treatment market is estimated to be worth over 4 trillion won annually, and the hair loss treatment market is also showing steady growth. According to pharmaceutical market research firm UBIST, the domestic hair loss treatment market is expected to grow from 99 billion won in 2021 to 103.6 billion won in 2022 and 102.4 billion won in 2023.

[0005] In this context, the importance of accurately diagnosing hair loss is increasingly highlighted. Hair loss has various causes and types, each requiring appropriate treatment. However, current diagnostic methods primarily rely on visual observation or hair density measurements, which limits the detection of early-stage hair loss or the precise identification of its cause. Therefore, the development of more objective and accurate hair loss diagnostic methods is urgent. This could lead to effective prevention and treatment through early diagnosis, ultimately contributing significantly to reducing the personal and societal burden of hair loss.

[0006] The purpose of the present invention is to provide a marker composition for hair loss diagnosis, which comprises a scalp-derived metabolite.

[0007] In addition, the present invention aims to provide a composition for diagnosing hair loss, which comprises a preparation for measuring the expression level of the metabolite.

[0008] In addition, the present invention aims to provide a hair loss diagnosis kit comprising the above composition.

[0009] In addition, the present invention aims to provide a method for providing information for hair loss diagnosis, which comprises a step of measuring the level of the metabolite in a scalp sample derived from a subject.

[0010] To achieve the above purpose, the present invention provides a marker composition for hair loss diagnosis, which comprises a scalp-derived metabolite.

[0011] In one embodiment of the present invention, the metabolite may be L-norleucine or L-isoleucine.

[0012] In another embodiment of the present invention, the metabolite may further include at least one selected from the group consisting of L-threonine, 1,2-dihydroxy-3-(1-naphthoxy)-propane, and L-5-oxoproline.

[0013]

[0014] In addition, the present invention provides a composition for diagnosing hair loss, comprising a preparation for measuring the expression level of the metabolite.

[0015]

[0016] In addition, the present invention provides a hair loss diagnosis kit comprising the composition.

[0017]

[0018] In addition, the present invention provides a method for providing information for hair loss diagnosis, comprising a step of measuring the level of the metabolite in a scalp sample derived from a subject.

[0019] In one embodiment of the present invention, a step of determining hair loss when the level of the metabolite is higher than that of the normal group may be further included.

[0020] In another embodiment of the present invention, the metabolite may be extracted from a scalp sample using at least one selected from the group consisting of methanol, acetonitrile, isopropanol, ethyl acetate, dichloromethane, hexane, and chloroform.

[0021] In another embodiment of the present invention, the metabolite level may be measured by one or more methods selected from the group consisting of Gas Chromatography-Mass Spectrometry (GS-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), Nuclear Magnetic Resonance (NMR), High-Performance Liquid Chromatography (HPLC), Capillary Electrophoresis-Mass Spectrometry (CE-MS), Fourier Transform Infrared (FT-IR) spectroscopy, Raman spectroscopy, and Supercritical Fluid Chromatography (SFC) analysis.

[0022] The present invention has the advantage of being able to precisely distinguish between a hair loss group and a non-hair loss group by analyzing the scalp metabolites using a metabolomic analysis technique using GC-MS. In addition, L-norleucine, L-isoleucine, L-threonine, 1,2-dihydroxy-3-(1-naphthoxy)-propane or

[0023] According to the L-5-oxoproline metabolite marker, hair loss groups can be predicted with high accuracy. These results suggest that the present invention is useful in increasing the accuracy of hair loss diagnosis and providing objective diagnostic criteria.

[0024] Figure 1 illustrates the GC-MS-based untargeted metabolite analysis workflow.

[0025] Figure 2 illustrates the BASP classification system for classifying hair loss and non-hair loss groups.

[0026] Figure 3 shows the results of a principal component analysis of the scalp metabolome. The three graphs divide the groups by age, hair loss status, and BASP classification.

[0027] Figures 4a and 4b illustrate heatmap clustering results for scalp metabolites. Figure 4a shows the overall metabolite distribution, while Figure 4b shows eight major metabolites that exhibited significant differences within the clusters.

[0028] Figure 5 shows data showing the relative abundance of five metabolites that show significance and high contribution between the hair loss and non-hair loss groups.

[0029] Figure 6 shows the results of univariate ROC curve analysis for candidate biomarker exploration.

[0030] Figure 7 shows the annotation results for candidate biomarker substance number 127 (158.1045mz / 16.14min).

[0031] Hereinafter, the present invention will be described in detail.

[0032] The present invention provides a marker composition for hair loss diagnosis, which comprises a scalp-derived metabolite.

[0033] As used herein, the term "scalp-derived metabolites" refers to intermediate or final products of in vivo chemical reactions extracted from the scalp. In the present invention, metabolites extracted from the scalp were analyzed using a GC-MS-based non-targeted metabolomic analysis method, and a marker composition for hair loss diagnosis was developed based on this analysis.

[0034] As used herein, the term "hair loss" means the absence of hair in an area where hair should normally be present, and includes a phenomenon in which the number of hairs decreases compared to the normal state. The hair loss includes both non-scarring alopecia and scarring alopecia depending on whether hair follicles are destroyed. In one embodiment, the hair loss may be selected from the group consisting of androgenetic alopecia, alopecia areata, telogen effluvium, traumatic alopecia, trichotillomania, pressure alopecia, anagen effluvium, pityriasis versicolor, alopecia syphilitica, alopecia seborrheica, symptomatic alopecia, and congenital alopecia.

[0035] As used herein, the term "diagnosis" refers to the process of determining the presence or absence of a specific disease or condition. Diagnosing hair loss involves observing the condition of the scalp and hair, and, if necessary, conducting additional tests to determine the cause, type, and severity of hair loss.

[0036] The term “biomarker” used in this specification is an indicator that can detect changes in the body, and is a substance that can confirm the normal or pathological state of a living organism, or whether there is a change therein, and may include organic biomolecules such as polypeptides, nucleic acids, lipids, glycolipids, glycoproteins, sugars (monosaccharides, disaccharides, oligosaccharides, etc.), and can be used to diagnose hair loss as in the present invention.

[0037] In one embodiment of the present invention, the metabolite may be L-norleucine or L-isoleucine.

[0038] At this time, L-norleucine acts as a glutamine analogue and inhibits glutamine metabolism, and L-Isoleucine is an essential amino acid required for protein synthesis.

[0039] In another embodiment of the present invention, the metabolite may further include at least one selected from the group consisting of L-threonine, 1,2-dihydroxy-3-(1-naphthoxy)-propane, and L-5-oxoproline.

[0040] At this time, L-threonine is an essential amino acid required for protein synthesis, 1,2-dihydroxy-3-(1-naphthoxy)-propane is an intermediate metabolite produced during the hepatic metabolism of propranolol, and L-5-oxoproline is an intermediate metabolite of the glutathione metabolic cycle.

[0041]

[0042] In addition, the present invention provides a composition for diagnosing hair loss, comprising a preparation for measuring the expression level of the metabolite.

[0043] The term “preparation” as used herein means a preparation for quantitatively detecting the metabolite from a biological sample, and the preparation is not particularly limited.

[0044] Additionally, the agent may be a primer, probe, aptamer, small molecule compound, protein, ligand or antibody capable of complementary binding that interacts with the metabolite to generate a signal (e.g., a fluorescent, luminescent or radioactive signal).

[0045] The above kit may further include a composition for diagnosing alopecia, which comprises as an active ingredient a preparation capable of measuring the level of a metabolite, and a quantitative device.

[0046]

[0047] In addition, the present invention provides a hair loss diagnosis kit comprising the composition.

[0048] The kit may comprise a formulation for measuring the level of the scalp-derived metabolite, as well as one or more other compositions, solutions or devices suitable for the analytical method.

[0049]

[0050] In addition, the present invention provides a method for providing information for hair loss diagnosis, comprising a step of measuring the level of the metabolite in a scalp sample derived from a subject.

[0051] In one embodiment of the present invention, a step of determining hair loss when the level of the metabolite is higher than that of the normal group may be further included.

[0052] In another embodiment of the present invention, the metabolite may be extracted from a scalp sample using at least one selected from the group consisting of methanol, acetonitrile, isopropanol, ethyl acetate, dichloromethane, hexane, and chloroform.

[0053] In another embodiment of the present invention, the metabolite level may be measured by one or more methods selected from the group consisting of GC-MS (Gas Chromatography-Mass Spectrometry), LC-MS (Liquid Chromatography-Mass Spectrometry), NMR (Nuclear Magnetic Resonance), HPLC (High-Performance Liquid Chromatography), CE-MS (Capillary Electrophoresis-Mass Spectrometry), FT-IR (Fourier Transform Infrared) spectroscopy, Raman spectroscopy, and supercritical fluid chromatography (SFC) analysis.

[0054] As used herein, the term "Gas Chromatography-Mass Spectrometry (GC-MS)" is a technique used to analyze volatile compounds, combining two techniques to separate and identify sample components. Gas chromatography (GC) separates various volatile compounds in a mixture using differences in boiling points, and mass spectrometry (MS) measures the mass-to-charge ratio of each separated component to identify the type of compound. GC-MS has the advantage of providing high sensitivity and resolution, allowing for accurate analysis of even trace metabolites in complex mixtures.

[0055]

[0056] To facilitate understanding of the present invention, the following examples will be described in more detail. However, these examples are intended only to exemplify the content of the present invention and are not intended to limit the scope of the present invention. These examples are provided to more fully explain the present invention to those with average knowledge in the technical field to which the invention pertains.

[0057]

[0058] Example

[0059] Example 1. Method for Exploring Scalp-Derived Metabolites Based on Non-Target Metabolome Analysis

[0060] GC-MS-based untargeted metabolite analysis proceeds largely through three main steps (Figure 1). First, in the metabolite extraction step, metabolites are extracted using 80% methanol (MeOH), concentrated fivefold, and then silylated using MOX / MSTFA. Second, in the data processing step, data are acquired via GC-MS and preprocessed, including mass detection, peak deconvolution, annotation, and baseline elimination. This is followed by internal standard normalization, filtering of constant or outlier values, and imputation of missing values. Finally, in the data analysis step, data scaling and transformation are performed through log transformation and mean centering, and the results are finally derived through ordination analysis and statistical analysis.

[0061] This analytical method can measure amino acids, sugars, nitrogenous bases, organic acids, etc., and representative compounds are shown in Table 1.

[0062] [Table 1] Types of substances that can be measured using non-target metabolite analysis methods

[0063]

[0064] Example 2. Comparative analysis of metabolites in hair loss and non-hair loss groups.

[0065] Using the above analytical method, we performed non-targeted metabolomic analysis on 24 scalp samples (11 non-hair loss group, 13 hair loss group). The scalp samples were classified using the BASP classification system shown in Figure 2 (Lee et al. Journal of the American Academy of Dermatology, Volume 57, Issue 1, 37-46.). Non-hair loss and hair loss were classified according to the BASP criteria, with L, 0, and 1 being non-hair loss, and 2 and 3 being hair loss. Specific information on the 24 scalp samples is shown in Table 2.

[0066] [Table 2] Information on hair loss and non-hair loss groups

[0067]

[0068] * Non-hair loss group: L, M1 based on Basic type

[0069] ** Hair loss group: M2, M3, C2, C3 based on basic type, V2, V3, F2, F3 based on specific type

[0070]

[0071] 2-1. Scalp metabolome principal component analysis (PCA)

[0072] The above samples were used to analyze scalp metabolites using principal component analysis (PCA) (Fig. 3). PCA analyzed metabolites based on three different criteria: age-based analysis, hair loss-based analysis, and BASP-based analysis.

[0073] The left graph in Figure 3 shows the distribution by age group, divided into three groups: under 40 (purple), 40-49 (yellow), and 50-59 (blue). PC1 and PC2 account for 14.71% and 12.47% of the variance, respectively, and the distributions show a certain degree of overlap across age groups.

[0074] The middle graph in Figure 3 shows the distribution by hair loss status, divided into a hair loss group (red) and a non-hair loss group (gray). While the distributions between the two groups overlap somewhat, they show distinct patterns over a significant area.

[0075] The graph on the right side of Figure 3 shows the distribution by hair loss grade (Grade 0-3). As the grade increases, the trend shifts toward the upper right, with a relatively clear distinction observed between Grades 0 and 1 and Grade 2.

[0076] In summary, the total explanatory power of PC1 and PC2 was 27.18% (14.71% + 12.47%), indicating that these principal components account for some of the variance in metabolite profiles. Furthermore, while no age-related differences were observed, a clustering trend was observed according to the hair loss (n=13) and non-hair loss (n=11) groups.

[0077]

[0078] 2-2. Scalp Metabolite Heatmap Clustering Analysis

[0079] The scalp metabolite heatmap clustering was analyzed using the above sample (Fig. 4).

[0080] Figure 4a shows the overall distribution of metabolites in the panel, and the vertical axis represents each sample (A01-A13, N01-N11, NC1-3). (A represents the alopecia group, and N represents the non-alopecia group.) The color intensity indicates the amount of metabolites and ranges from blue (-3) to red (+3). The samples were divided into the alopecia group (Alopecia), the non-alopecia group (Non-alopecia), and the control group (NC). The analysis results confirmed the observation of two major clusters, distinguished by yellow lines.

[0081] Figure 4b shows eight specific major metabolites that showed significant differences in the major clusters identified by the yellow lines in Figure 4a (L-Threonine (73.0825 m / z, 20.07 min), L-Isoleucine (158.1104 m / z, 16.86 min), L-5-Oxoproline (156.0594 m / z, 23.94 min), Glycine (73.0382 m / z, 8.65 min and 73.0813 m / z, 26.97 min), Octadecanoic acid trimethylsilyl ester (341.2998 m / z, 41.73 min), 1,2-Dihydroxy-3-(1-naphthoxy)-propane (144.1014 m / z, 14.22 min), 58 Substance (221.0970 m / z, 6.86 min), Substance 127 (158.1045 m / z, 16.14 min).

[0082] These are classified into amino acids and derivatives, DNA components, fatty acids, and other organic acids. Significant differences were observed, particularly in the amino acid series, including L-Threonine and L-Isoleucine. Statistical significance is indicated by the p-value, with a single asterisk (*) indicating p<0.1, and a double asterisk (**) indicating p<0.05.

[0083] In summary, heatmap analysis identified two clusters showing differences in metabolite abundance between the non-hair loss group and the hair loss group, and eight substances among the clusters showing significant differences between the groups were identified.

[0084]

[0085] Example 3. Exploration of hair loss biomarker metabolites

[0086] The above eight metabolites showed significance (p<0.1) between the hair loss group and the non-hair loss group, and five metabolites showing high contribution in PCA analysis were finally identified.

[0087] Figure 5 shows the results of the analysis of five metabolites that showed significant and high contribution between the alopecia group, non-alopecia group, and control group (NC). The metabolites analyzed were L-(-)-Threonine (151), substance 127 (158.1045mz), 1,2-Dihydroxy-3-(1-naphthoxy)-propane (105), L-Isoleucine (138), and L-5-Oxoproline (158).

[0088] For L-(-)-Threonine, substance 127 (158.1045mz), and 1,2-Dihydroxy-3-(1-naphthoxy)-propane, the highest abundance was observed in the hair loss group, while relatively low levels were detected in the non-hair loss group, and they were not detected (ND) in the control group (NC). In particular, substance 127 (158.1045mz) showed the greatest difference in abundance in the hair loss group.

[0089] L-Isoleucine and L-5-Oxoproline were detected only in the alopecia group and were not detected in either the non-alopecia group or the control group (ND). These results suggest that these metabolites have value as potential biomarkers for hair loss diagnosis. All measurements are expressed in abundances (au × 10000), and the error range for each measurement is indicated by the error bar.

[0090] In addition, candidate biomarkers were explored through univariate ROC curve analysis among a total of 94 metabolites analyzed through GC-MS. Among these, L-isoleucine and substance 127 (158.1045 m / z) showed the best diagnostic effect with an area under the curve (AUC) value of 0.8 or higher. Specifically, L-isoleucine showed high diagnostic value with an AUC value of 0.885 (0.769-1), and substance 127 (158.1045 mz) showed good diagnostic value with an AUC value of 0.825 (0.664-0.958).

[0091] Additional MS spectral analysis was performed to annotate substance 127 (158.1045 mz / 16.14 min) that was not annotated in the existing data, and the results are shown in Fig. 7. When the m / z and intensity information representing the fragment ion pattern were confirmed using the GC Mass Spectrum Spectra Search function of the HMDB (The Human Metabolome Database, https: / hmdb.ca / ) homepage, this substance was predicted to be L-norleucine.

[0092] The foregoing description of the present invention is provided for illustrative purposes only. Those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

1. A marker composition for hair loss diagnosis, comprising a scalp-derived metabolite.

2. A composition according to claim 1, characterized in that the metabolite is L-norleucine or L-isoleucine.

3. A composition according to claim 2, characterized in that the metabolite further comprises at least one selected from the group consisting of L-threonine, 1,2-dihydroxy-3-(1-naphthoxy)-propane, and L-5-oxoproline.

4. A composition according to claim 1, wherein the hair loss is selected from the group consisting of androgenetic alopecia, alopecia areata, telogen effluvium, traumatic alopecia, trichotillomania, pressure alopecia, anagen effluvium, pityriasis alopecia, syphilitic alopecia, seborrheic alopecia, symptomatic alopecia, and congenital alopecia.

5. A composition for diagnosing hair loss, comprising a preparation for measuring the expression level of a metabolite of any one of claims 1 to 4.

6. A hair loss diagnostic kit comprising the composition of claim 5.

7. A method for providing information for diagnosing hair loss, comprising a step of measuring the level of a metabolite of any one of claims 1 to 4 in a scalp sample derived from a subject.

8. An information providing method according to claim 7, characterized in that it further includes a step of determining hair loss when the level of the metabolite is higher than that of the normal group.

9. In claim 7, the information providing method is characterized in that the metabolite is extracted from a scalp sample with at least one selected from the group consisting of methanol, acetonitrile, isopropanol, ethyl acetate, dichloromethane, hexane, and chloroform.

10. A method for providing information, characterized in that, in claim 7, the metabolite level is measured by at least one method selected from the group consisting of GS-MS (Gas Chromatography-Mass Spectrometry), LC-MS (Liquid Chromatography-Mass Spectrometry), NMR (Nuclear Magnetic Resonance), HPLC (High-Performance Liquid Chromatography), CE-MS (Capillary Electrophoresis-Mass Spectrometry), FT-IR (Fourier Transform Infrared) spectroscopy, Raman spectroscopy, and supercritical fluid chromatography (SFC) analysis.

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