JP7750481B2 - Visceral fat area detection method - Google Patents

Description

The present invention relates to a method for detecting visceral fat area using a visceral fat area marker.

Metabolic syndrome is a condition in which visceral fat accumulation is combined with conditions such as high blood pressure, hyperglycemia, and lipid metabolism disorders, leading to an increased risk of myocardial infarction and stroke. Visceral fat accumulation is a required diagnostic criterion for metabolic syndrome. In Japan, in abdominal (umbilical fossa) CT (computed tomography) examinations, a visceral fat area of 100 cm2 or ^more is used as the diagnostic criterion for visceral obesity for both men and women.

In addition to CT, waist circumference, waist-hip ratio, abdominal ultrasound, MRI (magnetic resonance imaging), etc. are used to measure visceral fat accumulation (Non-Patent Document 1). However, diagnostic imaging using CT or MRI requires large-scale equipment and is difficult to perform casually due to the risk of radiation exposure and cost. For this reason, health checkups use a waist circumference equivalent to a visceral fat area of 100 ^cm2 or more, 85 cm or more for men and 90 cm or more for women. However, it is difficult to accurately measure abdominal circumference by oneself, and a more convenient way to understand the state of visceral fat accumulation is desired.

In recent years, technologies have been developed to examine the current and future physiological state of the human body by analyzing nucleic acids such as DNA and RNA in biological samples. Biologically derived nucleic acids can be extracted from bodily fluids such as blood, secretions, and tissues. Furthermore, it has recently been reported that RNA contained in skin surface lipids (SSL) can be used as a sample for biological analysis (Patent Document 1).

International Publication No. 2018/008319

Ningen Dock (2011) 26: 539-546

The present invention provides a marker for detecting visceral fat area and a method for detecting visceral fat area using the marker.

The present inventors collected SSL from individuals with a visceral fat area of less than 100 ^cm2 and individuals with a visceral fat area of 100 ^cm2 or more, and comprehensively analyzed the expression state of RNA contained in the SSL as sequence information. As a result, they found that the expression level of a specific gene significantly correlates with the state of visceral fat accumulation, and that visceral fat area can be detected using this as an indicator.

That is, the present invention relates to the following 1) to 3).
1) A method for detecting visceral fat area of a subject, comprising a step of measuring the expression level of at least one gene or its expression product selected from a group of 11 genes, namely CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA and R3HDM4, in a biological sample collected from the subject.
2) A test kit for detecting visceral fat area used in the method of 1), which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene.
3) A marker for detecting visceral fat area, consisting of at least one gene or its expression product selected from the group of 11 genes: CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA, and R3HDM4.

The present invention makes it possible to detect visceral fat area easily and non-invasively.

All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety.

In the present invention, the terms "nucleic acid" and "polynucleotide" refer to DNA or RNA. DNA includes cDNA, genomic DNA, and synthetic DNA, and "RNA" includes total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.

In the present invention, the term "gene" includes double-stranded DNA including human genomic DNA, single-stranded DNA (positive strand) including cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof, and refers to DNA in which some biological information is contained in the sequence information of the bases that make up the DNA.
Furthermore, the "gene" in the present invention includes not only "genes" represented by a specific base sequence, but also their homologues (i.e., homologs or orthologs), variants such as genetic polymorphisms, and derivatives.
Here, the names of the genes disclosed in this specification follow the official symbols listed in NCBI ([www.ncbi.nlm.nih.gov/]).

In the present invention, the term "expression product" of a gene encompasses both transcription products and translation products of the gene. A "transcription product" is RNA produced by transcription from a gene (DNA), and a "translation product" refers to a protein encoded by the gene that is translated and synthesized based on the RNA.

In the present invention, "visceral fat area" refers to the area of adipose tissue distributed around internal organs (stomach and liver) among body fat. Specifically, it refers to the area of visceral fat when the body is sliced at the navel using a CT scan or impedance method, and more specifically, it refers to the visceral fat area measured at the navel using a visceral fat meter using the impedance method, such as the Panasonic EW-F90.

In the present invention, "detection" of visceral fat area can also be expressed in other terms such as examination, measurement, judgment, or evaluation support. Note that the terms "detection," "examination," "measurement," "judgment," or "evaluation" of visceral fat area in the present invention do not include diagnosis by a doctor.

As will be described in the Examples below, RNA expression analysis was performed on a group including individuals with a visceral fat area of less than 100 ^cm2 and individuals with a visceral fat area of 100 ^cm2 or more using the following steps 1) to 3). As a result, it was found that the expression of 11 genes shown in Table 1 below in SSL is positively correlated with visceral fat area.
1) Data on the expression level of SSL-derived RNA (read count value) is obtained.
2) The lead count value is converted to an RPM value corrected for differences in the total number of lead counts between sample subjects, and the Spearman's rank correlation coefficient is calculated for the combination of the logarithm to base 2 obtained by adding an integer 1 to this value ( _log2 (RPM+1) value) and the natural logarithm obtained by adding an integer 1 to the visceral fat area value (ln(measurement value+1) value)).
3) Genes with large Spearman's rank correlation coefficient (ρ) (top genes) are extracted and selected.

The 11 genes listed in Table 1, CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA, and R3HDM4, have not previously been reported to be associated with visceral fat.

Therefore, the genes selected from the 11 gene groups or their expression products can be used as markers for detecting visceral fat area, and the visceral fat area of a subject can be detected based on the expression levels of the genes selected from the 11 gene groups or their expression products.
Although each of the 11 genes can be used independently as a marker for detecting visceral fat area, preferably two or more, more preferably five or more, and even more preferably all 11 of the 11 genes are used. Of the 11 genes, preferably one or more selected from CPPED1, FMNL1, XPO6, EFHD2, and RPTN, more preferably CPPED1, FMNL1, and XPO6, are used.
Detecting the visceral fat area allows the state of visceral fat accumulation to be understood, which can be useful for detecting the risk of visceral fat obesity, and for providing guidance and treatment to those with visceral fat obesity.

The above-mentioned genes that can serve as markers for detecting visceral fat area (hereinafter also referred to as "target genes") also include genes that have a base sequence substantially identical to the base sequence of the DNA that constitutes the gene, as long as they can serve as biomarkers for detecting visceral fat area. Here, "substantially identical" means, for example, that when searched using the homology calculation algorithm NCBI BLAST under the following conditions: expectation value = 10; gaps allowed; filtering = ON; match score = 1; mismatch score = -3, the base sequence has 90% or more, preferably 95% or more, more preferably 98% or more, and even more preferably 99% or more identity with the base sequence of the DNA that constitutes the gene.

The visceral fat area detection method of the present invention includes a step of measuring the expression level of a target gene, in one embodiment at least one gene or its expression product selected from a group of 11 genes: CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA, and R3HDM4, in a biological sample collected from a subject.

The subject in the present invention may be, for example, a human or non-human mammal, and may be one who desires or requires detection of visceral fat area. Examples include individuals who are constitutionally prone to gaining weight, individuals with metabolic syndrome, or individuals who are slightly overweight, specifically individuals with a waist diameter (around the navel) of 90 cm or more for women and 85 cm or more for men, or individuals with a BMI (body mass index) of 25 or more. The subject is preferably a human.

The biological sample used in the present invention may be any cell, tissue, or biological material that can be collected non-invasively and in which the expression of the gene of the present invention changes. Specific examples include body fluids such as skin, urine, saliva, sweat, stratum corneum, skin surface lipids (SSL), and tissue exudates, as well as feces and hair. Preferably, the sample is skin or skin surface lipids (SSL), and more preferably skin surface lipids (SSL).

Here, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, and is sometimes called sebum. Generally, SSL mainly contains secretions from exocrine glands such as sebaceous glands in the skin, and is present on the skin surface in the form of a thin layer that covers the skin surface. SSL contains RNA expressed in skin cells. (See Patent Document 1, cited above.) In addition, in the present invention, unless otherwise specified, "skin" is a general term for the area including the stratum corneum, epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands.

Any method commonly used for recovering or removing SSL from skin can be used to collect SSL from a subject's skin. Preferably, the SSL absorbent material, SSL adhesive material, or device for scraping SSL from skin, described below, can be used. The SSL absorbent material or SSL adhesive material can be any material that has affinity for SSL, including polypropylene, pulp, etc. More specific examples of procedures for collecting SSL from skin include absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, adhering SSL to a glass plate or tape, or scraping SSL off with a spatula, scraper, or the like. To improve SSL adsorption, an SSL absorbent material pre-soaked with a highly lipid-soluble solvent may be used. However, since the presence of highly water-soluble solvents or moisture inhibits SSL adsorption, it is preferable for the SSL absorbent material to contain a low content of highly water-soluble solvents or moisture. The SSL absorbent material is preferably used in a dry state.
The area of the skin from which SSL is collected is not particularly limited, and may be any area of the body such as the head, face, neck, trunk, limbs, etc., with areas that secrete a lot of sebum, such as the skin of the face, being preferred.

RNA-containing SSL collected from a subject may be stored for a certain period of time. To minimize degradation of the RNA contained in the collected SSL, it is preferable to store the collected SSL under low-temperature conditions as soon as possible after collection. The temperature conditions for storing the RNA-containing SSL in the present invention are sufficient as long as they are below 0°C, and are preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C, even more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and even more preferably -20±5°C. The storage period for the RNA-containing SSL under low-temperature conditions is not particularly limited, but is preferably 12 months or less, for example, 6 hours to 12 months, more preferably 6 months or less, for example, 1 day to 6 months, even more preferably 3 months or less, for example, 3 days to 3 months.

In the present invention, objects for measuring the expression level of a target gene or its expression product include cDNA artificially synthesized from RNA, DNA encoding that RNA, a protein encoded by that RNA, a molecule that interacts with that protein, a molecule that interacts with that RNA, or a molecule that interacts with that DNA. Here, molecules that interact with RNA, DNA, or protein include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, and phosphorylations, alkylations, sugar adducts, etc. of these, as well as complexes of any of the above. Furthermore, the expression level comprehensively refers to the expression amount and activity of the gene or expression product.

In a preferred embodiment of the method of the present invention, SSL is used as the biological sample. In this case, the expression level of RNA contained in the SSL is analyzed, specifically, the RNA is converted into cDNA by reverse transcription, and then the cDNA or its amplification product is measured.
For extraction of RNA from SSL, methods that are commonly used for extracting or purifying RNA from biological samples, such as the phenol/chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or methods using columns such as TRIzol (registered trademark), RNeasy (registered trademark), or QIAzol (registered trademark), methods using special silica-coated magnetic particles, methods using Solid Phase Reversible Immobilization magnetic particles, or extraction using commercially available RNA extraction reagents such as ISOGEN, can be used.

For the reverse transcription, a primer targeting the specific RNA to be analyzed may be used, but for more comprehensive nucleic acid preservation and analysis, it is preferable to use a random primer. A general reverse transcriptase or reverse transcription reagent kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcription reagent kit is used, such as M-MLV Reverse Transcriptase and its variants, or a commercially available reverse transcriptase or reverse transcription reagent kit, such as the PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.) or the SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific). SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both manufactured by Thermo Scientific), etc. are preferably used.
The temperature of the extension reaction in the reverse transcription is preferably adjusted to 42°C ± 1°C, more preferably 42°C ± 0.5°C, and even more preferably 42°C ± 0.25°C, while the reaction time is preferably adjusted to 60 minutes or more, more preferably 80 to 120 minutes.

When targeting RNA, cDNA, or DNA, the method for measuring expression levels can be selected from nucleic acid amplification methods such as PCR using DNA that hybridizes to these as primers, real-time RT-PCR, multiplex PCR, SmartAmp, and LAMP, hybridization methods (DNA chips, DNA microarrays, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.) that use nucleic acids that hybridize to these as probes, methods for determining base sequences (sequencing), or combinations of these.

In PCR, a primer pair targeting a specific DNA to be analyzed may be used to amplify only that specific DNA, or multiple primer pairs may be used to simultaneously amplify multiple specific DNAs. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method in which multiple gene regions are simultaneously amplified by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (e.g., Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan, Inc., etc.).
The temperatures for the annealing and extension reactions in the PCR depend on the primers used and cannot be generalized; however, when using the multiplex PCR kit described above, the temperatures are preferably 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even more preferably 62°C ± 0.25°C. Therefore, in the PCR, the annealing and extension reactions are preferably carried out in one step. The time for the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, etc., but is preferably 14 to 18 minutes. The conditions for the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but are preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the temperatures and times described above can be carried out using a thermal cycler commonly used for PCR.

The purification of the reaction products obtained by the PCR is preferably carried out by size separation of the reaction products. Size separation allows the target PCR reaction products to be separated from primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be carried out using, for example, a size separation column, a size separation chip, or magnetic beads that can be used for size separation. Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

The purified PCR reaction products may be further processed as necessary for subsequent quantitative analysis. For example, for DNA sequencing, the purified PCR reaction products may be prepared in an appropriate buffer solution, the PCR primer regions contained in the PCR-amplified DNA may be cleaved, or an adapter sequence may be added to the amplified DNA. For example, the purified PCR reaction products may be prepared in a buffer solution, and the amplified DNA may be subjected to PCR primer sequence removal and adapter ligation. The resulting reaction products may then be amplified as necessary to prepare a library for quantitative analysis. These operations can be performed, for example, using the 5x VILO RT Reaction Mix included with the SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan, Inc.), and the 5x Ion AmpliSeq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) and the Ion AmpliSeq Transcriptome Human Gene Expression Core Panel, following the protocols included with each kit.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using Northern blot hybridization, for example, probe DNA is first labeled with a radioisotope, fluorescent substance, etc., and the resulting labeled DNA is then hybridized with RNA derived from a biological sample that has been transferred to a nylon membrane or the like using standard methods. The resulting double strand of labeled DNA and RNA is then measured by detecting the signal derived from the label.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using RT-PCR, for example, cDNA is first prepared from RNA derived from a biological sample using standard methods, and then a pair of primers (a positive strand that binds to the cDNA (-strand) and a reverse strand that binds to the + strand) prepared so that the target gene of the present invention can be amplified using this as a template are hybridized to it. PCR is then performed using standard methods, and the resulting amplified double-stranded DNA is detected. The amplified double-stranded DNA can be detected by performing the PCR using primers that have been labeled in advance with RI, a fluorescent substance, or the like, to detect the labeled double-stranded DNA produced.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using a DNA microarray, for example, an array having at least one type of nucleic acid (cDNA or DNA) derived from the target gene of the present invention immobilized on a support is used, labeled cDNA or cRNA prepared from mRNA is bound to the microarray, and the label on the microarray is detected, thereby measuring the expression level of mRNA.
The nucleic acid immobilized on the array may be any nucleic acid that hybridizes specifically (i.e., substantially only to the nucleic acid of interest) under stringent conditions. For example, it may be a nucleic acid having the entire sequence of the target gene of the present invention, or a nucleic acid consisting of a partial sequence. Here, a "partial sequence" may be a nucleic acid consisting of at least 15 to 25 bases. Typical stringent conditions include washing conditions of approximately 1×SSC, 0.1% SDS, and 37°C. More stringent hybridization conditions include washing conditions of approximately 0.5×SSC, 0.1% SDS, and 42°C. Even more stringent hybridization conditions include washing conditions of approximately 0.1×SSC, 0.1% SDS, and 65°C. Hybridization conditions are described in, for example, J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition, Cold Spring Harbor Laboratory Press (2001).

When measuring the expression level of a target gene or a nucleic acid derived therefrom by sequencing, analysis can be performed using, for example, a next-generation sequencer (e.g., Ion S5/XL system, Life Technologies Japan, Inc.). RNA expression can be quantified based on the number of reads (read count) generated by sequencing.

The probes or primers used in the above measurements, i.e., primers for specifically recognizing and amplifying the target gene of the present invention or a nucleic acid derived therefrom, or probes for specifically detecting said RNA or a nucleic acid derived therefrom, fall into this category, and can be designed based on the nucleotide sequence constituting the target gene. Here, "specifically recognize" means that the detected substance or product can be determined to be the gene or a nucleic acid derived therefrom, such that, for example, in Northern blotting, substantially only the target gene of the present invention or a nucleic acid derived therefrom can be detected, or, for example, in RT-PCR, substantially only the nucleic acid is amplified.
Specifically, DNA consisting of the base sequence constituting the target gene of the present invention or an oligonucleotide containing a certain number of nucleotides complementary to its complementary strand can be used. Here, "complementary strand" refers to one strand of double-stranded DNA consisting of A:T (U in the case of RNA) and G:C base pairs relative to the other strand. Furthermore, "complementary" does not necessarily mean a perfectly complementary sequence over a certain number of consecutive nucleotides, but rather means that the base sequence has an identity of preferably 80% or more, more preferably 90% or more, and even more preferably 95% or more.
The identity of the nucleotide sequences can be determined by an algorithm such as the BLAST algorithm.
When used as a primer, such an oligonucleotide may be capable of specific annealing and chain elongation, and typically has a chain length of, for example, 10 bases or more, preferably 15 bases or more, more preferably 20 bases or more, and for example, 100 bases or less, preferably 50 bases or less, more preferably 35 bases or less. When used as a probe, it may be capable of specific hybridization, and may have at least a partial or complete sequence of DNA (or its complementary strand) consisting of the base sequence constituting the target gene of the present invention, and a chain length of, for example, 10 bases or more, preferably 15 bases or more, and for example, 100 bases or less, preferably 50 bases or less, more preferably 25 bases or less is used.
Here, "oligonucleotide" can be DNA or RNA, and can be either synthetic or natural. Furthermore, the probe used for hybridization is usually labeled.

Furthermore, when measuring the translation product (protein) of the target gene of the present invention, a molecule that interacts with the protein, a molecule that interacts with RNA, or a molecule that interacts with DNA, methods such as protein chip analysis, immunoassays (e.g., ELISA, etc.), mass spectrometry (e.g., LC-MS/MS, MALDI-TOF/MS), the one-hybrid method (PNAS 100, 12271-12276 (2003)), and the two-hybrid method (Biol. Reprod. 58, 302-311 (1998)) can be used, and can be appropriately selected depending on the subject.
For example, when a protein is used as the measurement target, the measurement is carried out by contacting a biological sample with an antibody that specifically recognizes the expression product of the present invention, specifically an antibody that recognizes a structural characteristic site (epitope) that can distinguish the expression product protein from other proteins, detecting the polypeptide or protein in the sample that binds to the antibody, and measuring its level. For example, in the Western blot method, the above-mentioned antibody is used as the primary antibody, and then an antibody that binds to the primary antibody labeled with a radioisotope, fluorescent substance, enzyme, or the like is used as the secondary antibody to label the primary antibody, and the signal derived from this label is measured using a radiation measuring instrument, fluorescence detector, or the like.
The antibody against the translation product may be a polyclonal antibody or a monoclonal antibody. These antibodies can be produced according to known methods. Specifically, polyclonal antibodies can be obtained according to standard methods by immunizing a non-human animal such as a rabbit with a protein expressed in E. coli or the like and purified according to standard methods, or by synthesizing a partial polypeptide of the protein according to standard methods, and then extracting the antibody from the serum of the immunized animal.
On the other hand, monoclonal antibodies can be obtained from hybridoma cells prepared by immunizing a non-human animal such as a mouse with a protein expressed and purified in Escherichia coli or a partial polypeptide of the protein according to a conventional method, and fusing the resulting spleen cells with myeloma cells. Monoclonal antibodies can also be produced using phage display (Griffiths, AD; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February 1998, pp. 102-108(7)).

Thus, the expression level of the target gene of the present invention or its expression product in a biological sample collected from a subject is measured, and the visceral fat area of the subject is detected based on the expression level.
Specifically, an optimal prediction model for predicting visceral fat area from the expression level is constructed by performing machine learning using the expression level of the target gene or its expression product in a population with a sufficient number of parameters as an explanatory variable and the visceral fat area obtained from the population as a response variable. Next, based on the constructed prediction model, a predicted value of the visceral fat area of a subject for whom visceral fat area is to be detected can be calculated from the expression level of the target gene or its expression product in the subject.
The population used to construct a prediction model is preferably a population in which the attributes (gender, race, age, etc.) of the subjects are consistent.
As described above, the expression level is preferably determined by using the following indicators: the read count value, which is data on expression level; the RPM value obtained by correcting the read count value for differences in the total number of reads between samples; the RPM value converted to a logarithmic value with base 2 (Log ₂ RPM value) or the logarithmic value with base 2 added by an integer 1 (log ₂ (RPM+1) value); or the count value corrected using DESeq2 (Love MI et al. Genome Biol. 2014) (Normalized count value) or the logarithmic value with base 2 added by an integer 1 (log ₂ (Normalized count+1) value). Furthermore, the quantitative value of RNA-seq may be a value calculated by, for example, fragments per kilobase of exon per million reads mapped (FPKM), reads per kilobase of exon per million reads mapped (RPKM), or transcripts per million (TPM). It may also be a signal value obtained by a microarray method, and its corrected value. Furthermore, when analyzing only a specific target gene by RT-PCR or the like, a method in which the expression level of the target gene is converted into a relative expression level based on the expression level of a housekeeping gene, or a method in which the absolute copy number is quantified (absolute quantification) using a plasmid containing the target gene region, is preferred. It may also be a copy number obtained by a digital PCR method.
On the other hand, the visceral fat area can be calculated by adding the integer 1 to the actual measurement value (ln(measurement value+1) value).
Furthermore, from the viewpoint of improving accuracy, in constructing a prediction model, age information may be added as an explanatory variable in addition to the expression level of the target gene of the present invention or its expression product.

Algorithms used to build predictive models can include well-known algorithms, such as those used in machine learning. Examples of machine learning algorithms include random forest, support vector machine with a linear kernel (SVM linear), support vector machine with an rbf kernel (SVM rbf), neural network, generalized linear model, regularized linear discriminant analysis, regularized logistic regression, and lasso (Least Absolute Shrinkage and Selection Operator) regression. Verification data is input into the constructed prediction model to calculate predicted values, and the model whose predicted values best match the actual measured values, for example the model with the highest accuracy, can be selected as the optimal prediction model. Furthermore, the detection rate (Recall), accuracy (Precision), and their harmonic mean, the F-value, can be calculated from the predicted and actual measured values, and the model with the highest F-value can be selected as the optimal prediction model. Furthermore, the root mean square error (RMSE) between the predicted and actual measured values can be used as an accuracy evaluation index for the prediction model, and the model with the smallest RMSE can be selected as the optimal prediction model.

Furthermore, in the present invention, the expression level of a target gene or its expression product can be compared with a predetermined reference value to detect health risks such as metabolic syndrome in a subject. Specifically, this is done by comparing the expression level of a target gene or its expression product with a predetermined reference value.

Here, the "reference value" can be determined in advance based on the relationship between visceral fat area and the expression level of the target gene or its expression product of the present invention. For example, a population can be divided into groups based on the state of visceral fat accumulation, such as "visceral fat area < 100 ^cm2 " or "100 ^cm2 ≦ visceral fat area," and values determined based on statistical values such as the average value and standard deviation of the expression level of the target gene or its expression product in each group can be used as the reference value for determining whether or not a subject belongs to each group.
When multiple types of genes are used as target genes, it is preferable to determine the reference value for each gene or its expression product.
It is preferable that the group be a group in which the attributes (gender, race, age, etc.) of the subjects to be studied are consistent.

The method for determining the reference value is not particularly limited and can be determined according to known techniques. For example, it can be obtained from an ROC (Receiver Operating Characteristic Curve) curve created using a discriminant (prediction model). In an ROC curve, the vertical axis plots the probability of a positive result in a positive patient (sensitivity), and the horizontal axis plots the value obtained by subtracting the probability of a negative result in a negative patient (specificity) from 1 (false positive rate). With regard to the "true positive (sensitivity)" and "false positive (1 - specificity)" shown on the ROC curve, the value (Youden index) at which "true positive (sensitivity)" - "false positive (1 - specificity)" is maximized can be used as the reference value.

The test kit for detecting visceral fat area of the present invention contains a test reagent for measuring the expression level of the target gene of the present invention or its expression product in a biological sample isolated from a patient.Specific examples include reagents for nucleic acid amplification and hybridization containing oligonucleotides (e.g., PCR primers) that specifically bind (hybridize) to the target gene of the present invention or a nucleic acid derived therefrom, or reagents for immunological measurement containing antibodies that recognize the expression product (protein) of the target gene of the present invention.The oligonucleotides, antibodies, etc. included in the kit can be obtained by known methods as described above.
In addition to the above-mentioned antibodies and nucleic acids, the test kit may also include labeling reagents, buffer solutions, color-developing substrates, secondary antibodies, blocking agents, equipment necessary for the test, control reagents used as positive controls and negative controls, and tools for collecting biological samples (e.g., oil blotting films for collecting SSL).

With respect to the above-described embodiment, the present invention further discloses the following aspects:

<1> A method for detecting visceral fat area in a subject, comprising the step of measuring the expression level of at least one gene or its expression product selected from a group of 11 genes: CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA, and R3HDM4, in a biological sample collected from the subject.

<2> The detection method according to <1>, wherein the expression levels of preferably two or more, more preferably five or more, and even more preferably all 11 genes or their expression products are measured among the 11 genes.
<3> The detection method according to <1>, wherein the expression level of one or more genes or expression products thereof selected from the 11 genes is measured, preferably CPPED1, FMNL1, XPO6, EFHD2 and RPTN, more preferably CPPED1, FMNL1 and XPO6.
<4> The detection method according to any one of <1> to <3>, wherein the object of measurement of the expression level of the gene or its expression product is preferably cDNA artificially synthesized from RNA, DNA encoding the RNA, a protein encoded by the RNA, a molecule that interacts with the protein, a molecule that interacts with the RNA, or a molecule that interacts with the DNA.
<5> The detection method according to any one of <1> to <4>, wherein the expression level of the gene or its expression product is preferably the expression level of mRNA.
<6> The detection method according to any one of <1> to <5>, wherein the biological sample is preferably a body fluid such as skin, urine, saliva, sweat, stratum corneum, skin surface lipids (SSL), tissue exudate, or feces or hair of the subject, more preferably skin or skin surface lipids (SSL), and even more preferably skin surface lipids (SSL).
<7> The detection method according to any one of <1> to <6>, which comprises detecting the visceral fat area of the subject based on the expression level of the gene or its expression product.
<8> detecting a visceral fat area using a prediction model based on the expression level of the gene or its expression product,
The detection method according to any one of <1> to <7>, wherein the prediction model is constructed using a measured value of the expression level of the gene or its expression product, or the measured value of the expression level and the subject's age as explanatory variables, and a visceral fat area as a response variable.
<9> A test kit for detecting visceral fat area used in the detection method according to any one of <1> to <8>, which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom, or an antibody that recognizes an expression product of the gene.
<10> A marker for detecting visceral fat area, consisting of at least one gene or its expression product selected from the group of 11 genes: CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA and R3HDM4.

Example 1: Detection of visceral fat area using RNA extracted from SSL 1) Subjects and measurement of visceral fat area A study was conducted on 376 women aged 20 to 80. The subjects' visceral fat area was measured using a visceral fat meter (EW-FA90, Panasonic) using abdominal bioelectrical impedance analysis, with the subjects standing and wearing a belt wrapped horizontally around their abdomen at navel height. The mean ± standard deviation of visceral fat area for this group was 64.2 ± 35.9 ^cm2 , with a median of 59 ^cm2 . 316 subjects had a visceral fat area of less than 100 ^cm2 , and 60 subjects had a visceral fat area of 100 ^cm2 or greater.

2) SSL Collection Sebum was collected from the entire face of each subject using an oil blotting film (5 x 8 cm, made of polypropylene, 3M Company), and the oil blotting film was then transferred to a vial and stored at -80°C until use in RNA extraction.

3) RNA Preparation and Sequencing The oil blotting film from 2) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Using the extracted RNA, reverse transcription was performed at 42°C for 90 minutes using the SuperScript VILO cDNA Synthesis kit (Life Technologies Japan, Inc.) to synthesize cDNA. The random primers included in the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the obtained cDNA by multiplex PCR. Multiplex PCR was performed using the Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the following conditions: 99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) x 20 cycles → 4°C, hold. The resulting PCR products were purified using Ampure XP (Beckman Coulter, Inc.), followed by buffer reconstitution, primer digestion, adapter ligation, purification, and amplification to prepare a library. The prepared library was loaded onto an Ion 540 Chip and sequenced using the Ion S5/XL system (Life Technologies Japan, Inc.).

4) Data Analysis (1) Data Used Data The expression level data (read count values) of SSL-derived RNA measured in 3) above were obtained and converted to RPM values corrected for differences in the total number of reads between sample subjects. To construct the machine learning model, the RPM values, which follow a negative binomial distribution, were approximated to a normal distribution by using the base 2 logarithm (log ₂ (RPM + 1) value) obtained by adding an integer 1 to the RPM value. Note that only 2,227 genes for which non-missing expression level data was obtained for 90% or more of the sample subjects out of the expression level data for all sample subjects were used in the following analysis.
In addition, since the visceral fat area value approximates a normal distribution, the natural logarithm (ln(measured value+1) value) obtained by adding an integer 1 to the measured value of the visceral fat area was used.

(2) Dataset Division Of the dataset of 376 subjects, the RNA profile data of 304 subjects was used as training data for model construction, and the RNA profile data of the remaining 72 subjects was used as test data for evaluating the accuracy of the model.

(3) Selection of Feature Genes Spearman's rank correlation coefficient was calculated for combinations of visceral fat area values (ln(measured value+1) values) of 304 individuals and all gene RNA expression data (log ₂ (RPM+1) values).
As a result, genes with large Spearman's rank correlation coefficients (ρ) were extracted and the 11 genes shown in Table 2 were selected. None of these 11 genes have been reported to be associated with visceral fat accumulation.

(4) Model Construction (a) Prediction by Feature Genes A prediction model was constructed using the training data, which was the expression level data (log ₂ (RPM + 1) value) of the feature genes selected from SSL-derived RNA, as the explanatory variable, and the visceral fat area (ln(measured value + 1) value) as the objective variable. The prediction model was trained using six algorithms: generalized linear model, neural network, lasso regression, random forest, linear kernel support vector machine (SVM linear), and rbf kernel support vector machine (SVM rbf), using 10-fold cross-validation. For each algorithm, the training data was input into the trained model, the root mean square error (RMSE) was calculated, and the model with the smallest RMSE was adopted.
The feature gene expression levels ( _log2 (RPM+1) values) of the test data were input into the selected model to calculate the predicted value of visceral fat area (ln(measured value+1) value). Finally, Spearman's rank correlation coefficient was calculated from the predicted value and the actual measured value.

(b) Prediction by Feature Genes + Age A prediction model was constructed using the training data, which were the expression level data ( _log2 (RPM + 1) value) of the feature genes selected from SSL-derived RNA and the subject's age, as explanatory variables, and the visceral fat area (ln(measured value + 1) value) as the objective variable. Thereafter, a prediction model was constructed in the same manner as in (a) above, and finally, the Spearman's rank correlation coefficient was calculated from the predicted value and the actual measured value.

5) Results In a prediction model (support vector machine with RBF kernel) using 11 types of feature genes, the rank correlation coefficient for the test data was 0.335 (p<0.01), demonstrating that it was possible to predict visceral fat area. In the case of a prediction model (support vector machine with RBF kernel) using 11 types of feature genes and age, the rank correlation coefficient was 0.443 (p<0.001), demonstrating improved prediction accuracy.

Claims (5)

A method for detecting visceral fat area of a subject, comprising measuring the expression levels of all genes or their expression products of a group of 11 genes, CPPED1, FMNL1, XPO6, EFHD2, RPTN, GNB2, ABTB1, CTDSP2, SHISA5, HECA and R3HDM4 , in a biological sample collected from the subject, and detecting the visceral fat area of the subject based on the expression levels of the genes or their expression products . The detection method according to claim 1, wherein the expression level of the gene or its expression product is the expression level of mRNA. The detection method according to claim 1 or 2, wherein the biological sample is lipids on the skin surface of a subject. detecting a visceral fat area using a prediction model based on the expression level of the gene or its expression product,
The detection method according to any one of claims 1 to 3 , wherein the prediction model is constructed using a measured value of the expression level of the gene or its expression product, or the measured value of the expression level and the subject's age as explanatory variables, and a visceral fat area as a response variable. A test kit for detecting visceral fat area, which is used in the detection method according to any one of claims 1 to 4 , comprising an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom, or an antibody that recognizes an expression product of the gene.