Smoking intensity determination method, device, electronic device and non-volatile storage medium

Abstract

The present application discloses a method, device, electronic device, and non-volatile storage medium for determining smoking intensity. The method comprises: obtaining target deoxyribonucleic acid information of a subject to be tested, wherein the target deoxyribonucleic acid includes nuclear deoxyribonucleic acid and mitochondrial deoxyribonucleic acid; determining a methylation degree parameter of a target gene site in the target deoxyribonucleic acid, wherein the target gene site is a gene site associated with smoking status, including a first gene site in nuclear deoxyribonucleic acid and a second gene site in mitochondrial deoxyribonucleic acid; and determining the smoking intensity score of the subject to be tested based on the methylation degree parameter of the target gene site. The present application solves the technical problem of poor accuracy in smoking status assessment, which is caused by the fact that current assessments of smoking status are mostly based on subjective self-reports, subject to recall bias and concealment.

Description

Technical Field

[0001] The present application relates to the field of biomedicine technology, and in particular to a method, device, electronic device, and non-volatile storage medium for determining smoking intensity. Background Art

[0002] Currently, assessments of smoking status are mostly based on subjective self-reports, which are subject to recall bias and concealment. Self-reported smoking rates tend to be underestimated. In addition to self-reporting, related technologies can also use serum cotinine or exhaled breath carbon monoxide levels to assess smoking status. However, due to the short half-life of serum cotinine or exhaled breath carbon monoxide levels, these methods can only detect short-term exposure. In summary, current smoking intensity assessment methods in related technologies all have certain limitations, leading to technical issues such as poor accuracy in smoking status assessment.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, electronic device, and non-volatile storage medium for determining smoking intensity, to at least address the technical problem of poor accuracy in smoking status assessment, which is caused by the fact that current assessments of smoking status are mostly based on subjective self-reporting, subject to recall bias and concealment.

[0005] According to one aspect of an embodiment of the present application, a method for determining smoking intensity is provided, comprising: obtaining target deoxyribonucleic acid (DNA) information of a subject to be detected, wherein the target DNA comprises nuclear DNA and mitochondrial DNA; determining a methylation degree parameter of a target gene locus in the target DNA, wherein the target gene locus is a gene locus associated with smoking status, and comprises a first gene locus in the nuclear DNA and a second gene locus in the mitochondrial DNA; and determining a smoking intensity score for the subject to be detected based on the methylation degree parameter of the target gene locus, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

[0006] Optionally, the target gene loci are determined based on a target population data set, wherein the target population data set contains information data of multiple target subjects, the information data including: methylation degree parameters of each gene loci in the target deoxyribonucleic acid of the target subjects, the age, gender, body mass index of the target subjects, and the smoking status of the target subjects, wherein the smoking status includes: smoking, non-smoking, and quitting smoking; the step of determining the target gene loci based on the target population data set includes: using a least absolute shrinkage and selection operator algorithm to calculate a correlation parameter between the methylation degree parameter of each gene loci and the smoking status of the target subjects, wherein the correlation parameter is used to characterize the degree of correlation between the gene loci and the smoking status; and determining the target gene loci based on the correlation parameter.

[0007] Optionally, determining the smoking intensity score of the subject to be tested based on the methylation degree parameters of the target gene sites includes: correcting the methylation degree parameters of the first gene sites; determining the weight coefficients corresponding to the corrected methylation degree parameters of each first gene site and the methylation degree parameters of each second gene site; determining the smoking intensity score of the subject to be tested based on the corrected methylation degree parameters of the first gene sites and their corresponding weight coefficients, the methylation degree parameters of the second gene sites and their corresponding parameters, the corresponding age, gender, and body mass index to be tested, and the corresponding age weight, gender weight, and body mass index parameters.

[0008] Optionally, correcting the methylation degree parameter of the first gene site includes: determining a correction parameter corresponding to the methylation degree parameter of each first gene site, wherein the correction parameter includes: a first parameter and a second parameter, the first parameter being the average value of the methylation degree parameter of the first gene site of all target objects in the target population data set, and the second parameter being the standard deviation of the methylation degree parameter of the first gene site of all target objects in the target population data set; and correcting the methylation degree parameter of each first gene site based on the correction parameter.

[0009] Optionally, after determining the smoking intensity score of the subject to be detected, the method further includes: if the smoking intensity score is not greater than a first preset score threshold, determining that the smoking status of the subject to be detected is non-smoking; if the smoking intensity score is not greater than a second preset score threshold, determining that the smoking status of the subject to be detected is quit smoking, wherein the second preset score threshold is greater than the first preset score threshold; if the smoking intensity score is greater than the second preset score threshold, determining that the smoking status of the subject to be detected is smoking.

[0010] Optionally, determining the methylation degree parameter of the target gene site in the target deoxyribonucleic acid includes: obtaining the target deoxyribonucleic acid after bisulfite treatment, wherein the bisulfite is used to convert the unmethylated cytosine base in the target deoxyribonucleic acid into uracil base; performing polymerase chain reaction (PCR) amplification on the target deoxyribonucleic acid after bisulfite treatment, and sequencing the target gene site of the PCR-amplified target deoxyribonucleic acid to determine the proportion of cytosine bases in the target gene site to obtain the methylation degree parameter.

[0011] Optionally, the first gene locus includes: cg14021375, cg03561637, cg20736847, cg12810233, cg09167044 and cg04720886; the second gene locus includes: D-loop, ATP6, ATP8 and MT-COX1.

[0012] According to another aspect of an embodiment of the present application, a smoking intensity determination device is provided, comprising: a sample acquisition module for acquiring target deoxyribonucleic acid information of a subject to be detected, wherein the target deoxyribonucleic acid includes nuclear deoxyribonucleic acid and mitochondrial deoxyribonucleic acid; a parameter determination module for determining methylation degree parameters of target gene loci in the target deoxyribonucleic acid, wherein the target gene loci are gene loci associated with smoking status, including a first gene loci in nuclear deoxyribonucleic acid and a second gene loci in mitochondrial deoxyribonucleic acid; and a smoking intensity determination module for determining a smoking intensity score of the subject to be detected based on the methylation degree parameters of the target gene loci, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

[0013] According to another aspect of the embodiments of the present application, an electronic device is provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the smoking intensity determination method is executed when the program is run.

[0014] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided. The non-volatile storage medium includes a stored computer program, wherein a device where the non-volatile storage medium is located executes the smoking intensity determination method by running the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, which implements the steps of the method for determining smoking intensity when the computer program is executed by a processor.

[0016] In an embodiment of the present application, target DNA information of a subject to be tested is obtained, wherein the target DNA includes nuclear DNA and mitochondrial DNA; methylation degree parameters of target gene loci in the target DNA are determined, wherein the target gene loci are gene loci associated with smoking status, including a first gene locus in the nuclear DNA and a second gene locus in the mitochondrial DNA; and a smoking intensity score of the subject to be tested is determined based on the methylation degree parameters of the target gene loci, wherein the smoking intensity score is used to characterize the intensity of smoking exposure of the subject to be tested. By jointly assessing smoking exposure intensity based on nuclear deoxyribonucleic acid (DNA) methylation sites and mitochondrial DNA methylation sites, the purpose of accurately obtaining a predicted smoking status based on the subject's lifetime tobacco exposure level is achieved, thereby solving the technical problem of poor accuracy in smoking status assessment caused by the current assessment of smoking status, which is mostly based on subjective self-reporting and is subject to recall bias and concealment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 This is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for determining smoking intensity according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of a method flow for determining smoking intensity provided in an embodiment of the present application;

[0020] Figure 3 This is a schematic diagram of a receiver operating characteristic curve (ROC) result for distinguishing whether a subject is a smoker using multiple methods according to an embodiment of the present application;

[0021] Figure 4 Schematic diagram of a smoking intensity determination device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Current assessments of smoking status are mostly based on subjective self-reports, which are subject to recall bias and concealment. Self-reported smoking prevalence tends to be underestimated. Self-reported smoking status is not always available in clinical settings.

[0025] In addition to self-reporting, related technologies can also use serum cotinine or exhaled carbon monoxide levels to assess smoking status. However, the half-life of serum cotinine or exhaled carbon monoxide levels is short and can only detect short-term exposure. Cotinine levels are also susceptible to other factors, such as nicotine replacement therapies such as nicotine chewing gum or transdermal patches, and have certain limitations. In addition, related technologies also exist to construct smoking status identification algorithms based on gene expression markers, but gene expression levels are easily affected by factors other than tobacco exposure and cannot accurately characterize previous tobacco exposure levels. In addition, some related technologies may also be limited to certain special populations, such as the aforementioned bladder cancer patients.

[0026] To solve the above problems, the present invention provides a solution for identifying smoking habits based on DNA methylation data. The principle of the solution is as follows:

[0027] Epigenetic modification is a crucial regulatory mechanism by which the environment influences gene expression, reversibly regulating gene expression levels and the subsequent development and progression of diseases without altering the gene sequence. DNA methylation, one of the earliest discovered and most intensively studied epigenetic mechanisms, serves as a key to the dynamic regulation of spatiotemporal gene expression and is a classic indicator of the early health effects of environmental exposure. DNA methylation levels remain stable in the absence of environmental triggers and, even when altered by environmental triggers, retain a certain degree of "stickiness," demonstrating a degree of "biological memory." In the case of smoking, methylation levels at some CpG sites associated with smoking do not return to levels comparable to those of a non-smoker even 30 years after quitting. Therefore, using DNA methylation levels at specific smoking-related sites can accurately and objectively identify an individual's smoking status.

[0028] Compared to the related art, which only conducts smoking-related research based on nuclear DNA methylation without considering changes in mitochondrial DNA methylation, the present application scheme assesses smoking exposure intensity based on both nuclear DNA methylation sites and mitochondrial DNA methylation sites. Because mitochondria lack DNA repair mechanisms, their DNA methylation is an effective marker of smoking exposure. Therefore, compared with the related art scheme, the present application scheme can more accurately obtain a prediction of smoking status based on the subject's lifetime tobacco exposure level. The present application scheme is described in detail below.

[0029] It should be noted that the relevant information (including but not limited to the user's personal information, genetic information, etc.) and data (including but not limited to data used for display and analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0030] According to an embodiment of the present application, an embodiment of a method for determining smoking intensity is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG1 shows a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for determining smoking intensity. Figure 1As shown, the computer terminal 10 (or electronic device) may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0032] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0033] Memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the puff intensity determination method described in the embodiments of the present application. Processor 102 executes the software programs and modules stored in memory 104 to perform various functional applications and data processing, thereby implementing the puff intensity determination method described above. Memory 104 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory located remotely from processor 102, which can be connected to computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0034] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0035] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0036] Under the above operating environment, the embodiment of the present application provides a method for determining smoking intensity. Figure 2 FIG. 1 is a schematic diagram of a method for determining smoking intensity according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0037] Step S202, obtaining target DNA information of the object to be detected, wherein the target DNA includes: nuclear DNA and mitochondrial DNA;

[0038] Step S204, determining a methylation degree parameter of a target gene locus in the target DNA, wherein the target gene locus is a gene locus associated with smoking status, including: a first gene locus in the nuclear DNA and a second gene locus in the mitochondrial DNA;

[0039] Step S206 , determining the smoking intensity score of the subject to be detected based on the methylation degree parameter of the target gene site, wherein the smoking intensity score is used to represent the smoking exposure intensity of the subject to be detected.

[0040] Through the above steps, the smoking exposure intensity is assessed jointly based on the DNA methylation sites in the cell nucleus and the mitochondrial DNA methylation sites, thereby achieving the purpose of accurately obtaining the smoking status predicted based on the subject's lifetime tobacco exposure level, thereby solving the technical problem of poor accuracy in smoking status assessment caused by the fact that the current assessment of smoking status is mostly based on subjective self-reporting, which is subject to recall bias and concealment.

[0041] The following further introduces the method for determining the smoking intensity in steps S202 to S206 of the embodiment of the present application.

[0042] First, the steps for determining target gene loci related to smoking status are introduced. The specific steps are as follows.

[0043] In some embodiments of the present application, the target gene loci are determined based on a target population dataset, wherein the target population dataset contains information data of multiple target subjects, the information data including: methylation degree parameters of each gene loci in the target deoxyribonucleic acid of the target subjects, the age, gender, body mass index of the target subjects, and the smoking status of the target subjects, wherein the smoking status includes: smoking, non-smoking, and quitting smoking; the step of determining the target gene loci based on the target population dataset includes the following steps: using a least absolute shrinkage and selection operator algorithm to calculate a correlation parameter between the methylation degree parameter of each gene loci and the smoking status of the target subjects, wherein the correlation parameter is used to characterize the degree of correlation between the gene loci and the smoking status; and determining the target gene loci based on the correlation parameter.

[0044] In this example, the target population dataset is described using data from 600 people aged 45 and over in a certain region of China. This includes 150 smokers, 150 ex-smokers, and 300 non-smokers. This dataset contains basic information about these 600 target subjects, including their height, weight, age, gender, smoking habits, drinking habits, and history of chronic diseases. By measuring their height and weight, the target subjects' body mass index (BMI) can be calculated.

[0045] In addition, the target population dataset also includes the methylation degree parameters of each gene site in the target deoxyribonucleic acid of the target subject. The methylation degree parameters can be obtained through the following process: for the target subjects in the target population dataset, peripheral blood is collected to extract nuclear DNA and platelet mitochondrial DNA respectively. After bisulfite treatment, the unmethylated cytosine C base is converted into uracil U, and then converted into thymine T after PCR amplification, which is distinguished from the C base originally with methylation modification. Then, sequencing is performed to determine the proportion of C bases in the sample cell nuclear DNA methylation sites and platelet mitochondrial DNA methylation sites, which is their methylation degree, thereby obtaining the methylation degree parameters of each gene site in the target deoxyribonucleic acid of the target subject.

[0046] Based on the information data of each target object in the target population dataset, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm can be used to include more than 30,000 nuclear DNA methylation sites and more than 400 platelet mitochondrial DNA methylation sites to find the gene sites most correlated with smoking status (non-smoking / quitting smoking / smoking), while retaining gender, age, and BMI. Based on correlation parameters (such as Lambda value), the target gene sites that are highly correlated with smoking status and their corresponding weight parameters are ultimately determined. In this embodiment, 10 target gene sites are used as an example, including 6 nuclear DNA methylation sites (i.e., the first gene sites) and 4 mitochondrial DNA methylation sites (i.e., the second gene sites).

[0047] In some embodiments of the present application, the first gene locus includes at least one of the following: cg14021375, cg03561637, cg20736847, cg12810233, cg09167044 and cg04720886; the second gene locus includes at least one of the following: D-loop, ATP6, ATP8 and MT-COX1.

[0048] After determining the target gene locus, a smoking score model can be established using data such as the six nuclear DNA methylation sites (i.e., the first gene locus), the four mitochondrial DNA methylation sites (i.e., the second gene locus), gender, age, BMI, and their parameters of the target gene locus. This smoking score model can then be used to determine the smoking exposure intensity of the subject to be tested. This process is further described below.

[0049] First, it is necessary to obtain target DNA information from the subject to be tested, including nuclear DNA and mitochondrial DNA. Specifically, a kit can be used to extract nuclear DNA from the blood of the subject to be tested, and to separate platelets for mitochondrial DNA extraction. Afterwards, six nuclear DNA methylation sites and four platelet mitochondrial DNA methylation sites can be tested to determine the methylation degree parameters of the target gene sites. The specific steps are as follows.

[0050] In some embodiments of the present application, determining the methylation degree parameter of a target gene site in a target deoxyribonucleic acid includes the following steps: obtaining the target deoxyribonucleic acid after bisulfite treatment, wherein the bisulfite is used to convert unmethylated cytosine bases in the target deoxyribonucleic acid into uracil bases; performing polymerase chain reaction (PCR) amplification on the target deoxyribonucleic acid after bisulfite treatment, and sequencing the target gene site of the PCR-amplified target deoxyribonucleic acid to determine the proportion of cytosine bases in the target gene site to obtain the methylation degree parameter.

[0051] Specifically, peripheral blood can be collected to extract nuclear DNA and platelet mitochondrial DNA respectively. After bisulfite treatment, the unmethylated cytosine C base is converted into uracil U, and then becomes thymine T after PCR amplification, which is distinguished from the C base that originally has methylation modification. Then, sequencing is performed separately to determine the proportion of C bases in the sample cell nuclear DNA methylation sites and platelet mitochondrial DNA methylation sites, which is the methylation degree parameter.

[0052] After obtaining the methylation degree parameter of the target gene site, the established smoking score model can be used to determine the smoking intensity score of the subject to be tested based on the methylation degree parameter of the target gene site. The specific steps are as follows.

[0053] In some embodiments of the present application, determining the smoking intensity score of the subject to be tested based on the methylation degree parameters of the target gene loci includes the following steps: correcting the methylation degree parameters of the first gene loci; determining the weight coefficients corresponding to the corrected methylation degree parameters of each first gene loci and the methylation degree parameters of each second gene loci; and determining the smoking intensity score of the subject to be tested based on the corrected methylation degree parameters of the first gene loci and their corresponding weight coefficients, the methylation degree parameters of the second gene loci and their corresponding parameters, the corresponding age, gender, and body mass index of the subject to be tested, and the corresponding age weight, gender weight, and body mass index parameters.

[0054] In some embodiments of the present application, correcting the methylation degree parameter of the first gene site includes the following steps: determining the correction parameter corresponding to the methylation degree parameter of each first gene site, wherein the correction parameter includes: a first parameter and a second parameter, the first parameter being the average value of the methylation degree parameter of the first gene site of all target objects in the target population data set, and the second parameter being the standard deviation of the methylation degree parameter of the first gene site of all target objects in the target population data set; and correcting the methylation degree parameter of each first gene site based on the correction parameter.

[0055] Specifically, the above smoking score model is shown as follows:

[0056] Smoking score = [-(cg14021375-0.126) / 0.023×1.634+(cg03561637-a2) / b2×c2+(cg20736847-a3) / b3×c3+(cg12810233-a4) / b4×c4+(cg09167044-a5) / b5×c5+(cg04720886-a6) / b6×c6+(D-loop×c7+ATP6×c8+ATP8×c9+MT-COX1×4.80) / (b7+b8+b9+2.61)] / 10+β 性别 × Gender + β 年龄 ×(age-60)+β BMI ×(BMI-24.5).

[0057] Among them, cg14021375, cg03561637, cg20736847, cg12810233, cg09167044, and cg04720886 are the methylation levels of six nuclear DNA methylation sites (i.e., the methylation level parameters of the first gene site), and D-loop, ATP6, ATP8, and MT-COX1 are the methylation levels of four mitochondrial DNA methylation sites (i.e., the methylation level parameters of the second gene site). 26, a2-a6 are the average methylation levels of the six nuclear DNA methylation sites in the target population dataset (i.e., the first parameter mentioned above), 0.023, b2-b6 are the standard deviations of the methylation levels of the DNA methylation sites in the target population dataset (i.e., the second parameter mentioned above), for normalization, c1-c10 are the coefficients of the methylation levels of the 10 DNA methylation sites estimated by the algorithm (i.e., the weight coefficients mentioned above, where c1 is 1.634 and c10 is 4.80), β 性别 , β 年龄 and β BMI are gender weight, age weight, and body mass index parameters, respectively.

[0058] In this formula, b7-b9 and 2.61 are also the standard deviations of the methylation levels of mitochondrial DNA methylation sites in the target population dataset, which are included in the weight coefficients of c7-c10. For example, the weight parameter corresponding to the target gene site CpG No. 7 is c7 / (b7+b8+b9+2.61).

[0059] As an optional embodiment, after determining the smoking intensity score of the subject to be detected, the method further includes the following steps: if the smoking intensity score is not greater than a first preset score threshold, determining that the smoking status of the subject to be detected is non-smoking; if the smoking intensity score is not greater than a second preset score threshold, determining that the smoking status of the subject to be detected is quit smoking, wherein the second preset score threshold is greater than the first preset score threshold; if the smoking intensity score is greater than the second preset score threshold, determining that the smoking status of the subject to be detected is smoking.

[0060] In this embodiment, subjects with a smoking score no greater than 0.5 (i.e., the first preset score threshold) are determined to be non-smokers, subjects with a smoking score between 0.5 and 1.0 are determined to be quitters, and subjects with a smoking score greater than 1.0 (i.e., the second preset score threshold) are determined to be smokers.

[0061] The smoking score model can be used to identify the smoking status of any subject (targeted subject). The required parameters are the methylation levels of the aforementioned 10 DNA methylation sites (target gene sites), the subject's gender, age, and BMI. The following table shows an example of the various parameter information required for a subject to be tested:

[0062] parameter value CpG No.1:cg14021375 0.4352 CpG No.2: cg03561637 0.9527 CpG No.3:cg20736847 0.3486 CpG No.4:cg12810233 0.3657 CpG No.5: cg09167044 0.6357 CpG No.6: cg04720886 0.2486 CpG No.7: D-loop 0.7536 CpG No.8: ATP6 0.6425 CpG No.9: ATP8 0.1495 CpG No.10: MT-COX1 0.7361 Gender (male = 0, female = 1) 1 age 47 BMI 23.3

[0063] The above parameters are calculated by the smoking score model to obtain a smoking score = 0.3486. The smoking score can evaluate the subject's lifetime tobacco exposure level. A score = 0.3486 < 0.5 indicates a very low exposure level and can be judged as a non-smoker.

[0064] Based on 180 independent sample populations collected separately, the ability of three algorithms and a clinical smoking marker to judge whether the subjects are smokers was compared with non-smokers and ex-smokers as the control group. Score 1 is the algorithm proposed in the embodiment of this application, and the calculation method is consistent with the embodiment of this application; Score 2 is an algorithm based only on nuclear DNA methylation sites (CpGNo.1-6) (the coefficient remains unchanged), and the calculation method is consistent with the embodiment of this application; Score 3 is an algorithm based on the article published by the inventor in 2017 (http: / / link.springer.com / 10.1007 / s10654-017-0248-9), and Score 4 is a commonly used serum cotinine smoking exposure marker in clinical practice. The judgment results of the four judgment methods are as follows: Figure 3 shown.

[0065] Based on the AUC (Area Under the Curve) value, it can be seen that after incorporating both nuclear and mitochondrial DNA methylation sites, the algorithm achieved an AUC of 0.88, which is better than the score based only on some nuclear DNA methylation sites (AUC = 0.72) and far better than the algorithm published by the inventors in 2017 (AUC = 0.68). However, the discriminatory ability based on clinical serum cotinine levels was only 0.60. In summary, the applicant's algorithm outperforms existing algorithms and commonly used clinical markers.

[0066] In a clinical setting, the present application scheme can evaluate the smoking exposure level of the subject (the subject to be tested) based on the methylation level of nuclear DNA and mitochondrial DNA methylation sites related to smoking measured in the peripheral blood of the subject (the subject to be tested), as well as the gender, age and BMI, objectively predict the smoking status of the subject, and provide an objective reference for medical workers to consider medical behavior based on the smoking habits of the subject.

[0067] According to an embodiment of the present application, an embodiment of a device for determining smoking intensity is also provided. Figure 4 FIG. 1 is a schematic diagram of a smoking intensity determination device according to an embodiment of the present application. Figure 4 As shown, the device includes:

[0068] The sample acquisition module 40 is used to obtain information of target DNA of the object to be detected, wherein the target DNA includes: nuclear DNA and mitochondrial DNA;

[0069] a parameter determination module 42 for determining a methylation degree parameter of a target gene locus in a target DNA, wherein the target gene locus is a gene locus associated with smoking status, including: a first gene locus in nuclear DNA and a second gene locus in mitochondrial DNA;

[0070] The smoking intensity determination module 44 is used to determine the smoking intensity score of the subject to be detected based on the methylation degree parameter of the target gene site, wherein the smoking intensity score is used to represent the smoking exposure intensity of the subject to be detected.

[0071] Optionally, the target gene loci are determined based on a target population data set, wherein the target population data set contains information data of multiple target subjects, the information data including: methylation degree parameters of each gene loci in the target deoxyribonucleic acid of the target subjects, the age, gender, body mass index of the target subjects, and the smoking status of the target subjects, wherein the smoking status includes: smoking, non-smoking, and quitting smoking; the step of determining the target gene loci based on the target population data set includes: using a least absolute shrinkage and selection operator algorithm to calculate a correlation parameter between the methylation degree parameter of each gene loci and the smoking status of the target subjects, wherein the correlation parameter is used to characterize the degree of correlation between the gene loci and the smoking status; and determining the target gene loci based on the correlation parameter.

[0072] In this example, the target population dataset is described using data from 600 people aged 45 and over in a certain region of China. This includes 150 smokers, 150 ex-smokers, and 300 non-smokers. This dataset contains basic information about these 600 target subjects, including their height, weight, age, gender, smoking habits, drinking habits, and history of chronic diseases. By measuring their height and weight, the target subjects' body mass index (BMI) can be calculated.

[0073] Based on the information data of each target object in the target population dataset, the least absolute shrinkage and selection operator LASSO algorithm can be used to include more than 30,000 nuclear DNA methylation sites and more than 400 platelet mitochondrial DNA methylation sites to find the gene sites most correlated with smoking status (non-smoking / quitting smoking / smoking), while retaining gender, age and BMI. According to the correlation parameters (such as Lambda value), the target gene sites that are highly correlated with smoking status and their corresponding weight parameters are finally determined. In this embodiment, 10 target gene sites are used as an example, including 6 nuclear DNA methylation sites (i.e., the first gene sites) and 4 mitochondrial DNA methylation sites (i.e., the second gene sites).

[0074] Optionally, the first gene locus includes at least one of the following: cg14021375, cg03561637, cg20736847, cg12810233, cg09167044 and cg04720886; the second gene locus includes at least one of the following: D-loop, ATP6, ATP8 and MT-COX1.

[0075] Optionally, determining the methylation degree parameter of the target gene site in the target deoxyribonucleic acid includes: obtaining the target deoxyribonucleic acid after bisulfite treatment, wherein the bisulfite is used to convert the unmethylated cytosine base in the target deoxyribonucleic acid into uracil base; performing polymerase chain reaction (PCR) amplification on the target deoxyribonucleic acid after bisulfite treatment, and sequencing the target gene site of the PCR-amplified target deoxyribonucleic acid to determine the proportion of cytosine bases in the target gene site to obtain the methylation degree parameter.

[0076] Specifically, peripheral blood can be collected to extract nuclear DNA and platelet mitochondrial DNA respectively. After bisulfite treatment, the unmethylated cytosine C base is converted into uracil U, and then becomes thymine T after PCR amplification, which is distinguished from the C base that originally has methylation modification. Then, sequencing is performed separately to determine the proportion of C bases in the sample cell nuclear DNA methylation sites and platelet mitochondrial DNA methylation sites, which is the methylation degree parameter.

[0077] Optionally, determining the smoking intensity score of the subject to be tested based on the methylation degree parameters of the target gene sites includes: correcting the methylation degree parameters of the first gene sites; determining the weight coefficients corresponding to the corrected methylation degree parameters of each first gene site and the methylation degree parameters of each second gene site; determining the smoking intensity score of the subject to be tested based on the corrected methylation degree parameters of the first gene sites and their corresponding weight coefficients, the methylation degree parameters of the second gene sites and their corresponding parameters, the corresponding age, gender, and body mass index to be tested, and the corresponding age weight, gender weight, and body mass index parameters.

[0078] Optionally, correcting the methylation degree parameter of the first gene site includes: determining a correction parameter corresponding to the methylation degree parameter of each first gene site, wherein the correction parameter includes: a first parameter and a second parameter, the first parameter being the average value of the methylation degree parameter of the first gene site of all target objects in the target population data set, and the second parameter being the standard deviation of the methylation degree parameter of the first gene site of all target objects in the target population data set; and correcting the methylation degree parameter of each first gene site based on the correction parameter.

[0079] Optionally, after determining the smoking intensity score of the subject to be detected, the smoking intensity determination module 44 is further configured to: determine that the smoking status of the subject to be detected is non-smoking if the smoking intensity score is not greater than a first preset score threshold; determine that the smoking status of the subject to be detected is quit smoking if the smoking intensity score is not greater than a second preset score threshold, wherein the second preset score threshold is greater than the first preset score threshold; and determine that the smoking status of the subject to be detected is smoking if the smoking intensity score is greater than the second preset score threshold.

[0080] The present application scheme evaluates smoking exposure intensity based on both nuclear DNA methylation sites and mitochondrial DNA methylation sites, thereby achieving the goal of accurately obtaining a predicted smoking status based on the subject's lifetime tobacco exposure level, thereby solving the technical problem of poor accuracy in smoking status assessment, which is caused by the fact that the current assessment of smoking status is mostly based on subjective self-reporting, subject to recall bias and concealment.

[0081] In a clinical setting, the smoking exposure level of the subject (the subject to be tested) can be evaluated based on the methylation level of nuclear DNA and mitochondrial DNA methylation sites related to smoking measured in the peripheral blood of the subject (the subject to be tested), as well as the gender, age and BMI, to objectively predict the smoking status of the subject and provide an objective reference for medical workers to consider medical behavior targeting the smoking habits of the subject.

[0082] It should be noted that the various modules in the above-mentioned smoking intensity determination device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0083] It should be noted that the smoking intensity determination device provided in this embodiment can be used to perform Figure 2 Therefore, the relevant explanations and descriptions of the above-mentioned method for determining the smoking intensity are also applicable to the embodiments of the present application and will not be repeated here.

[0084] An embodiment of the present application further provides a non-volatile storage medium, the non-volatile storage medium including a stored computer program, wherein a device containing the non-volatile storage medium executes the following method for determining smoking intensity by running the computer program: obtaining target deoxyribonucleic acid of a subject to be detected, wherein the target deoxyribonucleic acid includes: nuclear deoxyribonucleic acid and mitochondrial deoxyribonucleic acid; determining a methylation degree parameter of a target gene locus in the target deoxyribonucleic acid, wherein the target gene locus is a gene locus associated with smoking status, including: a first gene locus in the nuclear deoxyribonucleic acid and a second gene locus in the mitochondrial deoxyribonucleic acid; and determining a smoking intensity score for the subject to be detected based on the methylation degree parameter of the target gene locus, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

[0085] The present application also provides a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the method for determining smoking intensity described in each embodiment of the present application: obtaining target deoxyribonucleic acid (DNA) of a subject to be detected, wherein the target DNA includes nuclear DNA and mitochondrial DNA; determining methylation degree parameters of target gene loci in the target DNA, wherein the target gene loci are gene loci associated with smoking status, including a first gene loci in nuclear DNA and a second gene loci in mitochondrial DNA; and determining a smoking intensity score for the subject to be detected based on the methylation degree parameters of the target gene loci, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

[0086] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0087] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0092] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining smoking intensity for non-diagnostic purposes, characterized in that: include: Acquiring target deoxyribonucleic acid information of the subject to be detected, wherein the target deoxyribonucleic acid includes: nuclear deoxyribonucleic acid extracted from peripheral blood and platelet mitochondrial deoxyribonucleic acid; Determining a methylation degree parameter of a target gene locus in the target deoxyribonucleic acid, wherein the target gene locus is a gene locus associated with smoking status and is composed of a first gene locus in the nuclear deoxyribonucleic acid and a second gene locus in the mitochondrial deoxyribonucleic acid, the first gene locus including: cg14021375, cg03561637, cg20736847, cg12810233, cg09167044, and cg04720886; the second gene locus including: D-loop, ATP6, ATP8, and MT-COX1; The smoking intensity score of the subject to be detected is determined based on the methylation degree parameter of the target gene site, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

2. The method for determining smoking intensity for non-diagnostic purposes according to claim 1, characterized in that: The target gene loci are determined based on a target population dataset, wherein the target population dataset contains information data of multiple target subjects, the information data including: methylation level parameters of each gene loci in the target DNA of the target subjects, the age, gender, body mass index of the target subjects, and the smoking status of the target subjects, wherein the smoking status includes: smoking, non-smoking, and quitting smoking. The step of determining the target gene loci based on the target population dataset includes: Using a least absolute shrinkage and selection operator algorithm, calculating a correlation parameter between the methylation degree parameter of each gene locus and the smoking status of the target subject, wherein the correlation parameter is used to characterize the degree of correlation between the gene locus and the smoking status; The target gene location is determined based on the correlation parameter.

3. The method for determining smoking intensity for non-diagnostic purposes according to claim 2, characterized in that: Determining the smoking intensity score of the subject to be detected based on the methylation degree parameter of the target gene site includes: Correcting the methylation degree parameter of the first gene locus; Determining a weight coefficient corresponding to the corrected methylation level parameter of each first gene locus and the methylation level parameter of each second gene locus; The smoking intensity score of the subject to be tested is determined based on the corrected methylation degree parameter of the first gene site and its corresponding weight coefficient, the methylation degree parameter of the second gene site and its corresponding parameter, the corresponding age, gender, body mass index of the subject to be tested, and the corresponding age weight, gender weight, and body mass index parameters.

4. The method for determining smoking intensity for non-diagnostic purposes according to claim 3, characterized in that: Correcting the methylation degree parameter of the first gene locus includes: Determining a correction parameter corresponding to the methylation degree parameter of each first gene locus, wherein the correction parameter includes: a first parameter and a second parameter, the first parameter being an average value of the methylation degree parameter of the first gene locus of all the target subjects in the target population dataset, and the second parameter being a standard deviation of the methylation degree parameter of the first gene locus of all the target subjects in the target population dataset; The methylation degree parameter of each of the first gene sites is corrected according to the correction parameter.

5. The method for determining smoking intensity for non-diagnostic purposes according to claim 3, wherein: After determining the smoking intensity score of the subject to be detected, the method further includes: If the smoking intensity score is not greater than a first preset score threshold, determining that the smoking status of the subject to be detected is non-smoking; If the smoking intensity score is not greater than a second preset score threshold, determining that the smoking status of the subject to be detected is quit smoking, wherein the second preset score threshold is greater than the first preset score threshold; When the smoking intensity score is greater than the second preset score threshold, it is determined that the smoking status of the subject to be detected is smoking.

6. The method for determining smoking intensity for non-diagnostic purposes according to claim 1, characterized in that: Determining the methylation degree parameters of the target gene site in the target deoxyribonucleic acid includes: Obtaining the target deoxyribonucleic acid after bisulfite treatment, wherein the bisulfite treatment is used to convert unmethylated cytosine bases in the target deoxyribonucleic acid into uracil bases; The target deoxyribonucleic acid after bisulfite treatment is amplified by polymerase chain reaction (PCR), and the target gene site of the target deoxyribonucleic acid after PCR amplification is sequenced to determine the proportion of cytosine bases in the target gene site to obtain the methylation degree parameter.

7. A device for determining smoking intensity, characterized in that: include: A sample acquisition module is used to obtain information about target deoxyribonucleic acid of the subject to be detected, wherein the target deoxyribonucleic acid includes: nuclear deoxyribonucleic acid and platelet mitochondrial deoxyribonucleic acid extracted from peripheral blood; a parameter determination module, configured to determine a methylation degree parameter of a target gene locus in the target deoxyribonucleic acid, wherein the target gene locus is a gene locus associated with smoking status and is composed of a first gene locus in the nuclear deoxyribonucleic acid and a second gene locus in the mitochondrial deoxyribonucleic acid, wherein the first gene locus includes: cg14021375, cg03561637, cg20736847, cg12810233, cg09167044, and cg04720886; and the second gene locus includes: D-loop, ATP6, ATP8, and MT-COX1; The smoking intensity determination module is used to determine the smoking intensity score of the subject to be detected based on the methylation degree parameter of the target gene site, wherein the smoking intensity score is used to characterize the smoking exposure intensity of the subject to be detected.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the method for determining smoking intensity according to any one of claims 1 to 6 is executed when the program is run.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the method for determining smoking intensity according to any one of claims 1 to 6 by running the computer program.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for determining smoking intensity according to any one of claims 1 to 6 are implemented.

Images