Obesity susceptibility risk assessment method, device, equipment and storage medium

Abstract

The application discloses an obesity susceptibility risk assessment method, device, equipment and storage medium, and the method comprises the following steps: acquiring genotype detection results of a plurality of obesity-related gene sites of a subject, wherein the obesity-related gene sites comprise a gene site associated with a body mass index-related phenotype and a gene site associated with an abdominal obesity-related phenotype; determining a genotype score of each gene site according to a genetic effect model corresponding to each gene site based on the genotype detection results; performing weighted summation on the genotype scores of the gene sites according to effect value weight coefficients corresponding to the gene sites to obtain a polygenic risk score of the subject; and determining an obesity susceptibility risk grade of the subject based on a comparison result of the polygenic risk score and a preset risk threshold. The application obtains a plurality of gene sites related to two types of phenotypes, i.e., a body mass index and abdominal obesity, combines genetic effect model scoring and effect value weight coefficient weighted summation, and improves the accuracy of obesity susceptibility risk assessment.

Description

Technical Field

[0001] This invention relates to the field of health risk assessment technology, and in particular to a method, apparatus, device, and storage medium for assessing obesity susceptibility risk. Background Technology

[0002] Obesity has become a significant public health problem affecting the nation's health. Obesity is mostly a polygenic disease, caused by the combined effects of genetic, environmental, and dietary factors. Conducting genetic testing for obesity susceptibility gene polymorphisms can help achieve early intervention and precise prevention.

[0003] Since the genome-wide association study (GWAS) identified the first obesity susceptibility gene, research on the genetic susceptibility to obesity has deepened. The polygenic risk score (PRS), by integrating genome-wide genetic risk variants, has become an important tool for assessing the genetic susceptibility to complex diseases. However, due to racial specificity in genetic background, research findings from some populations cannot be directly applied to others, limiting the cross-population applicability of related assessment methods. Currently, the number of gene loci involved in related studies remains relatively limited, resulting in insufficient coverage. The influence of a single locus on polygenic obesity is not high, limiting the predictive power of the PRS. Furthermore, most existing obesity susceptibility risk assessment methods are based solely on a single body mass index-related phenotype, neglecting other important obesity characteristics such as abdominal obesity, making it difficult to comprehensively reflect the complex genetic structure of polygenic obesity. In summary, existing obesity susceptibility risk assessment methods suffer from technical deficiencies such as insufficient locus coverage and a single phenotypic dimension, leading to insufficient assessment accuracy.

[0004] Therefore, there is an urgent need for a method to assess obesity susceptibility risk that can improve the accuracy of obesity susceptibility risk assessment. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for assessing obesity susceptibility risk, aiming to solve the technical problem of insufficient accuracy in existing obesity susceptibility risk assessments.

[0006] To achieve the above objectives, the present invention provides a method for assessing obesity susceptibility risk, the method comprising the following steps: Obtain the genotype test results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes; Based on the genotype detection results, the genotype score of each gene locus is determined according to the genetic effect model corresponding to each gene locus. Based on the effect value weighting coefficients corresponding to each gene locus, the genotype scores of each gene locus are weighted and summed to obtain the polygenic risk score of the subject. Based on the comparison results between the polygenic risk score and the preset risk threshold, the obesity susceptibility risk level of the examinee is determined.

[0007] Optionally, the step of determining the genotype score of each gene locus based on the genotype detection results and according to the genetic effect model corresponding to each gene locus includes: Based on the effect direction corresponding to each gene locus, the allele type of each gene locus is determined; For gene loci whose allele type is a risk allele, the genotype score of the gene locus is determined based on the genotype detection results, according to the first complete dominance model, the first incomplete dominance model, or the first recessive model. For gene loci whose allele type is a protective allele, the genotype score of the gene locus is determined based on the genotype detection results, according to the second complete dominance model, the second incomplete dominance model, or the second recessive model.

[0008] Optionally, before the step of weighted summing of the genotype scores of each gene locus based on the effect value weighting coefficients corresponding to each gene locus to obtain the polygenic risk score of the examinee, the method further includes: Obtain the effect value and standard error corresponding to each of the gene loci; Based on the effect size and the standard error, a standardized statistic is obtained; The effect weight coefficients corresponding to each gene locus are determined based on the standardized statistics.

[0009] Optionally, the step of obtaining the genotype test results for multiple obesity-related gene loci of the examinee includes: Search the literature database to obtain research literature related to obesity susceptibility genes; Population genetic parameters, molecular epidemiological evidence, and functional genomics data for candidate gene loci were extracted from the aforementioned research literature. Based on the population genetic parameters, the molecular epidemiological evidence, and the functional genomics data, the candidate gene loci are screened in multiple stages to determine the set of target gene loci. Obtain the genotype detection results of the subject at each gene locus in the target gene locus set.

[0010] Optionally, the step of determining the obesity susceptibility risk level of the examinee based on the comparison result of the polygenic risk score and the preset risk threshold includes: Based on the genotype frequency distribution data of the target population at each of the aforementioned gene loci; A genetic risk scoring model for the target population is constructed based on the genotype scores of each gene locus and the genotype frequency distribution data. Based on the central tendency parameter and dispersion parameter of the gene risk scoring model, a preset risk threshold is determined; The polygenic risk score is compared with the preset risk threshold, and the obesity susceptibility risk level of the examinee is determined based on the comparison result.

[0011] Optionally, the step of comparing the polygenic risk score with the preset risk threshold and determining the subject's obesity susceptibility risk level based on the comparison result includes: Determine the theoretical maximum value of the polygenic risk score; Based on the theoretical maximum value, the polygenic risk score is converted into a standardized risk score through a proportional mapping function. The standardized risk score is compared with the preset risk threshold, and the obesity susceptibility risk level of the examinee is determined based on the comparison result.

[0012] Optionally, the step of determining the obesity susceptibility risk level of the examinee based on the comparison result of the polygenic risk score and the preset risk threshold includes: Based on the comparison results between the polygenic risk score and the preset risk threshold, the first risk level of the subject for the body mass index-related phenotype and the second risk level for the abdominal obesity-related phenotype are determined. The first risk level and the second risk level are used as the obesity susceptibility risk levels of the examinee; Accordingly, after the step of using the first risk level and the second risk level as the obesity susceptibility risk level of the examinee, the method further includes: Based on the subject's obesity susceptibility risk level, a comprehensive risk assessment report is generated, which includes risk level information, risk site analysis information, and intervention recommendation information.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes an obesity susceptibility risk assessment device, the device comprising: The detection acquisition module is used to acquire the genotype detection results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes. The scoring module is used to determine the genotype score of each gene locus based on the genotype detection results and according to the genetic effect model corresponding to each gene locus. The risk scoring module is used to perform a weighted summation of the genotype scores of each gene locus based on the effect value weight coefficients corresponding to each gene locus, so as to obtain the multigene risk score of the examinee. The rating determination module is used to determine the obesity susceptibility risk level of the examinee based on the comparison results between the polygenic risk score and the preset risk threshold.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes an obesity susceptibility risk assessment device, the device comprising: a memory, a processor, and an obesity susceptibility risk assessment program stored in the memory and executable on the processor, the obesity susceptibility risk assessment program being configured to implement the steps of the obesity susceptibility risk assessment method as described above.

[0015] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing an obesity susceptibility risk assessment program, which, when executed by a processor, implements the steps of the obesity susceptibility risk assessment method as described above.

[0016] This invention discloses a method for obtaining genotype detection results for multiple obesity-related gene loci in subjects. These loci include those associated with body mass index (BMI)-related phenotypes and those associated with abdominal obesity-related phenotypes. Based on the genotype detection results, a genotype score for each gene locus is determined according to a genetic effect model corresponding to each locus. The genotype scores for each gene locus are then weighted and summed according to the effect value weighting coefficients corresponding to each gene locus to obtain a multi-gene risk score for the subject. Based on a comparison of the multi-gene risk score with a preset risk threshold, the subject's obesity susceptibility risk level is determined. Because this invention obtains multiple gene loci related to two phenotypes—BMI and abdominal obesity—and combines genetic effect model scoring with weighted summation of effect value weighting coefficients, it achieves a multi-gene synergistic risk quantification assessment. Compared to existing technologies, this invention improves the accuracy of obesity susceptibility risk assessment. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the obesity susceptibility risk assessment method of the present invention; Figure 2 This is a schematic diagram of 29 gene loci associated with body mass index-related phenotypes in the obesity susceptibility risk assessment method of the present invention; Figure 3This is a schematic diagram of eight gene loci associated with abdominal obesity phenotypes in the obesity susceptibility risk assessment method of the present invention; Figure 4 This is a flowchart illustrating the second embodiment of the obesity susceptibility risk assessment method of the present invention; Figure 5 This is a flowchart illustrating the third embodiment of the obesity susceptibility risk assessment method of the present invention; Figure 6 This is a structural block diagram of the first embodiment of the obesity susceptibility risk assessment device of the present invention; Figure 7 This is a schematic diagram of the structure of an obesity susceptibility risk assessment device in the hardware operating environment involved in the embodiments of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0020] This invention provides a method for assessing obesity susceptibility risk, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the obesity susceptibility risk assessment method of the present invention.

[0021] In this embodiment, the obesity susceptibility risk assessment method includes steps S10 to S40: Step S10: Obtain the genotype test results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes.

[0022] It should be noted that the executing entity in this embodiment can be a computer server device with data processing, network communication, and program execution functions applied in a health risk assessment scenario, such as a server, tablet computer, or personal computer, or an electronic device capable of performing the above functions (such as an obesity susceptibility risk assessment device). The following uses a system including an obesity susceptibility risk assessment device (hereinafter referred to as the system) as an example to illustrate this embodiment and the following embodiments.

[0023] Understandably, the aforementioned examinees may refer to individuals who need to undergo obesity susceptibility risk assessment, such as people undergoing health checkups in China, overweight or obese individuals, individuals who need to lose weight, or athletes who need to undergo physical fitness assessment.

[0024] It should be explained that the above-mentioned genotype test results may refer to genetic information data reflecting the combination of alleles carried by the subject at a specific gene locus, such as the base sequence information of single nucleotide polymorphism sites (e.g., AA, AG, GG) obtained through gene chip typing technology or next-generation sequencing technology.

[0025] To overcome the shortcomings of existing obesity gene assessment loci being few in number and lacking suitability for the Chinese population, and to improve the accuracy of obesity risk assessment in the Chinese population, the aforementioned obesity-related gene loci can refer to specific single nucleotide polymorphism loci in the genome that have been proven by research to be statistically associated with the risk of obesity phenotype in the Chinese population. These loci are obtained based on the genetic characteristics of East Asian populations, excluding population-specific linkage disequilibrium interference, and can reflect the genetic background of the Chinese population.

[0026] It should be understood that the aforementioned body mass index-related phenotypes can refer to body measurements reflecting the degree of general obesity, such as body mass index (BMI). The aforementioned abdominal obesity-related phenotypes can refer to body measurements reflecting the degree of abnormal fat accumulation in the abdominal region, such as waist circumference or waist-to-hip ratio.

[0027] In its implementation, the system first acquires the subject's identification information when assessing obesity susceptibility risk. Based on this identification information, it retrieves the subject's original genotype detection data from its internal database. It's important to note that the set of gene loci corresponding to the genotype detection data stored within the system is a pre-selected set of obesity-related gene loci based on the genetic characteristics of the Chinese population. These loci are derived from obesity susceptibility gene studies targeting the Chinese or East Asian populations. Next, the system extracts genotype detection results for each gene locus in the pre-determined set of obesity-related gene loci from the retrieved original genotype detection data. The extracted genotype detection results specifically include two types of gene locus detection data: one type is gene locus detection data associated with body mass index-related phenotypes, and the other type is gene locus detection data associated with abdominal obesity-related phenotypes. After acquiring the genotype detection results for both types of gene loci, the system uses these results as input data for subsequent genetic effect model analysis.

[0028] To facilitate understanding, the following explanation uses examples, but does not limit the scope of this embodiment. For example, suppose the subject, Zhang San, is an adult male from mainland China who needs to undergo an obesity susceptibility risk assessment. The system first obtains Zhang San's identification number and retrieves his genotype report from the testing database based on this number. The genotype report covers a set of 37 obesity-related single nucleotide polymorphism (SNP) sites selected based on the genetic characteristics of the Chinese population. These include 29 body mass index-related sites (e.g., the rs6545814 site of the ADCY3 gene shows an AG result, and the rs9939609 site of the FTO gene shows a TT result) and 8 abdominal obesity-related sites (e.g., the rs984222 site of the TBX15 gene shows a CG result). The system extracts the genotype data for these 37 sites from the genotype report as the basis for subsequent risk assessment.

[0029] refer to Figure 2 , Figure 2 This diagram illustrates 29 gene loci associated with body mass index (BMI)-related phenotypes in the obesity susceptibility risk assessment method of this invention. The 29 gene loci associated with BMI-related phenotypes are: ADCY3 gene rs6545814, ADIPOQ gene rs266729, ADRB3 gene rs4994, BDNF gene rs11030104, CDKAL1 gene rs9356744, FTO gene rs1121980, FTO gene rs1421085, FTO gene rs1558902, FTO gene rs17817449, FTO gene rs8050136, FTO gene rs9939609, FTO gene rs9941349, GIPR gene rs11671664, GNPDA2 gene rs16858082, G... The following genes are associated with the following gene sequences: TF3A (rs7988412), LEP (rs7799039), MAP2K5 (rs4776970), MC4R (rs17782313), MC4R (rs591166), MC4R (rs6567160), NT5C2 (rs11191580), PPARG (rs1801282), SEC16B (rs10913469), SEC16B (rs543874), SEC16B (rs574367), SH2B1 (rs7498665), TMEM18 (rs12463617), VDR (rs2228570), and VDR (rs731236).

[0030] refer to Figure 3 , Figure 3This diagram illustrates eight gene loci associated with abdominal obesity phenotypes in the obesity susceptibility risk assessment method of this invention. The eight gene loci associated with abdominal obesity phenotypes are rs10518107 of ADAMTS3 gene, rs8030379 of ADAMTS13 gene, rs3809128 of CNPY2 gene, rs3791679 of EFEMP1 gene, rs2057291 of GNAS gene, rs1982963 of NID2 gene, rs4912314 of PPAP2B gene, and rs984222 of TBX15 gene.

[0031] To ensure the scientific validity and representativeness of the target gene locus set, the step of obtaining the genotype detection results of the subject for multiple obesity-related gene loci may include: searching a literature database to obtain research literature related to obesity susceptibility genes; extracting population genetic parameters, molecular epidemiological evidence, and functional genomics data of candidate gene loci from the research literature; performing multi-stage screening of the candidate gene loci based on the population genetic parameters, the molecular epidemiological evidence, and the functional genomics data to determine the target gene locus set; and obtaining the genotype detection results of the subject at each gene locus in the target gene locus set.

[0032] It should be noted that the aforementioned literature databases may refer to electronic resource platforms that store academic literature in the biomedical field, such as CNKI and PubMed.

[0033] It should be explained that the aforementioned population genetic parameters can refer to quantitative indicators that reflect the genetic variation characteristics of gene loci in a specific population, such as minimum allele frequency, chromosome location information, and gene annotation information. Among them, minimum allele frequency is used to measure the degree of polymorphism of the locus in the population, while chromosome location and gene annotation are used to determine the physical location of the locus in the genome and the functional gene to which it belongs.

[0034] The aforementioned molecular epidemiological evidence can refer to research data obtained from population genetic association studies that support a statistical association between gene loci and the obesity phenotype, such as the sample size, effect size (e.g., odds ratio or regression coefficient), confidence interval, and significance level of the association study.

[0035] The aforementioned functional genomics data can refer to information reflecting the biological function of a gene locus, such as the metabolic pathways involved by the gene at the locus, the potential impact of the locus on gene expression regulation, and whether the region where the locus is located belongs to a highly variable region of the genome (such as the HLA6p21.3 region).

[0036] It should be understood that the above-mentioned multi-stage screening can refer to the process of screening candidate gene loci step by step according to preset exclusion and inclusion criteria. For example, first exclude loci that do not meet the basic genetic conditions, then screen based on the level of evidence, and finally confirm based on functional relevance.

[0037] Accordingly, the aforementioned set of target gene loci can refer to the combination of gene loci that is finally determined after multi-stage screening and used for subsequent obesity susceptibility risk assessment. This combination includes two types of gene loci: those associated with body mass index-related phenotypes and those associated with abdominal obesity-related phenotypes.

[0038] In its implementation, the system first performs a search on a literature database based on a preset search strategy to obtain a collection of research literature related to obesity susceptibility genes. The search strategy employed by the system includes combining obesity-related keywords (such as obesity, overweight, body weight management) with population-specific terms (such as Chinese, Asian population) and genetic-related keywords (such as genetic susceptibility, single nucleotide polymorphism, susceptibility gene), and then performing a cross-database search on CNKI and PubMed databases.

[0039] Next, the system filters eligible research literature from the retrieved literature according to preset inclusion and exclusion criteria, and extracts population genetic parameters, molecular epidemiological evidence, and functional genomics data for candidate gene loci from the filtered literature. The population genetic parameters extracted by the system include chromosomal location information, gene annotation information, and the minimum allele frequency in East Asian populations for the candidate loci. The molecular epidemiological evidence extracted by the system includes the number of studies supporting the association between candidate loci and the obesity phenotype, the sample size of each study, the effect size, and the consistency of their directions. The functional genomics data extracted by the system includes whether the candidate locus belongs to a single-gene obesity-related variant (such as the MC4R gene mutation), whether it is located in a highly variable region of the genome (such as the HLA6p21.3 region), and whether the type of locus variation is a single nucleotide polymorphism.

[0040] Subsequently, the system performs multi-stage screening of candidate gene loci based on the three types of data mentioned above. During the screening process, the system first excludes single-gene obesity-related variant loci, avoids highly variable regions of the genome, and limits the variant type to single nucleotide polymorphisms. Next, the system retains common variant loci with a minimum allele frequency greater than or equal to 1% in East Asian populations, loci supported by at least two independent studies with consistent conclusions, and loci with cross-study consistency in the direction of risk effect. Through these multi-stage screenings, the system determines a target gene locus set, which includes gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes.

[0041] Subsequently, the system obtains the genotype detection results of the subject at each gene locus in the target gene locus set. Specifically, the acquisition method is as follows: based on the subject's biological sample (such as peripheral blood sample or oral mucosa sample), the base sequence of each target gene locus is detected through the genotyping technology platform to obtain the genotype data of each gene locus (such as wild-type homozygous, heterozygous or variant homozygous).

[0042] It should be understood that by combining the steps of "retrieving from literature databases → extracting population genetic parameters, molecular epidemiological evidence and functional genomics data → multi-stage screening", a systematic and standardized screening of obesity susceptibility gene loci has been achieved. This solves the problems of scattered locus selection and lack of unified screening standards in existing technologies, and ensures the scientific nature and representativeness of the target gene locus set.

[0043] Step S20: Based on the genotype detection results, determine the genotype score of each gene locus according to the genetic effect model corresponding to each gene locus.

[0044] It should be explained that the above-mentioned genetic effect model can refer to the mathematical rules describing the genetic relationship between different genotypes and the target phenotype at a specific gene locus. This model is usually based on the dominant and recessive characteristics of alleles and the direction of effect to set the assignment rules corresponding to different genotypes, such as the complete dominant model, the incomplete dominant model, and the recessive model.

[0045] Understandably, the genotype score mentioned above can refer to a quantitative value calculated based on the specific genotype (such as wild-type homozygous, heterozygous, or variant homozygous) carried by the subject at a gene locus, combined with the genetic effect model corresponding to that locus. This value is used to reflect the degree of contribution of that locus to obesity susceptibility.

[0046] Step S30: Based on the effect value weighting coefficients corresponding to each gene locus, the genotype scores of each gene locus are weighted and summed to obtain the multigene risk score of the subject.

[0047] It should be noted that the above effect value weighting coefficient can refer to the weighted value pre-calculated for each gene locus. This value is used to reflect the degree of influence of the corresponding gene locus in the multi-gene risk score. For example, the weighting coefficient is obtained after standardization of the effect values ​​(such as regression coefficient β or odds ratio OR) of each gene locus.

[0048] Understandably, the aforementioned polygenic risk score can refer to a comprehensive quantitative indicator that integrates the genotype scores of multiple gene loci into a single index through a weighted summation method. This index is used to reflect the cumulative effect of all obesity-related risk alleles carried by the examinee on obesity susceptibility.

[0049] In its implementation, after obtaining the genotype scores for each gene locus, the system reads the effect weight coefficients corresponding to each gene locus from a parameter database stored internally. The system pre-stores a set of effect weight coefficients corresponding one-to-one with the multiple obesity-related gene loci involved in the genotype detection results.

[0050] Next, for each gene locus, the system multiplies the genotype score of that locus by the corresponding effect weight coefficient to obtain a weighted score for that locus. The system performs this multiplication operation on all gene loci to obtain the weighted score for each locus.

[0051] Subsequently, the system sums the weighted scores of all gene loci, and uses the sum as the subject's polygenic risk score. After calculating the polygenic risk score, the system uses this score as input data for subsequent risk level determination steps.

[0052] To improve the objectivity and accuracy of the weighting of each point in the polygenic risk score, before the step of weighting and summing the genotype scores of each gene locus according to the effect value weighting coefficients corresponding to each gene locus to obtain the polygenic risk score of the subject, the method may further include: obtaining the effect value and standard error corresponding to each gene locus; obtaining a standardized statistic based on the effect value and the standard error; and determining the effect value weighting coefficients corresponding to each gene locus based on the standardized statistics.

[0053] It should be explained that the effect value mentioned above can refer to a statistical indicator obtained from genetic association studies, used to quantify the strength and direction of the association between a specific gene locus and a target phenotype, such as a regression coefficient (i.e., the beta coefficient) or an odds ratio (i.e., the odds ratio). When the effect value is the beta coefficient, its positive or negative sign reflects the direction of risk enhancement or reduction of the phenotype by the risk allele, and its absolute value reflects the strength of the effect; when the effect value is the odds ratio, a value greater than 1 indicates a risk-enhancing effect, and a value less than 1 indicates a protective effect.

[0054] The standard error mentioned above can refer to a statistic that measures the accuracy of effect size estimation. It is used to reflect the degree of sampling variation of the effect size as an estimate of the true population parameter. The smaller the standard error, the higher the accuracy of the effect size estimation.

[0055] Furthermore, the aforementioned standardized statistic can refer to a dimensionless statistic obtained by dividing the effect size by its standard error, such as the Z-score statistic. This statistic is used to eliminate the problem that effect sizes cannot be directly compared between different studies due to differences in sample size, scale units, and models.

[0056] Accordingly, the aforementioned effect value weighting coefficient can refer to the weighted value assigned to each gene locus in the calculation of multigene risk scores. This coefficient is determined by integrating standardized statistics from different studies and is used to reflect the relative contribution of each gene locus in the comprehensive score.

[0057] In its implementation, when calculating the effect weighting coefficients for each gene locus, the system first reads the effect value and standard error corresponding to each gene locus from its internal parameter database. The system pre-stores effect values ​​and their corresponding standard errors from multiple independent research studies for each obesity-related gene locus.

[0058] Next, for each gene locus, the system divides the effect value of that locus by its corresponding standard error to obtain the standardized statistic for that locus. Specifically, when the effect value is the beta coefficient, the system divides the beta coefficient by the standard error to obtain the Z-score; when the effect value is the OR value, the system first takes the natural logarithm of the OR value to obtain log(OR), and then divides log(OR) by the standard error to obtain the Z-score. The system performs the above division operation for each gene locus to obtain the standardized statistic for each gene locus.

[0059] Subsequently, the system determines the effect weighting coefficients for each gene locus based on the standardized statistics of each gene locus. When the same gene locus has standardized statistics in multiple independent studies, the system calculates the arithmetic mean of the standardized statistics of that locus in multiple studies and uses this arithmetic mean as the effect weighting coefficient for that gene locus. The system performs the above determination operation on all obesity-related gene loci one by one to obtain the effect weighting coefficients corresponding to each gene locus, and stores these effect weighting coefficients in the system's internal parameter database for use in subsequent weighted summation steps.

[0060] To facilitate understanding, the following example is provided for illustration, but it does not impose specific limitations on this embodiment. For instance, suppose the effect value reported for the FTO gene rs9939609 locus in study A is β=0.15, with a standard error of SE=0.05; and the effect value reported in study B is β=0.18, with a standard error of SE=0.06. The system reads the two sets of effect values ​​and standard errors for this locus and calculates the standardized statistics: the Z-score for study A is 0.15 divided by 0.05, which equals 3.00; the Z-score for study B is 0.18 divided by 0.06, which equals 3.00. The system calculates the arithmetic mean of the Z-scores for this locus in the two studies, i.e., (3.00+3.00) / 2=3.00, and stores 3.00 as the effect value weighting coefficient for the FTO gene rs9939609 locus in the system's internal parameter database. For another locus, the MC4R gene rs17782313, assuming only one study reported an OR of 1.25 and a standard error of SE of 0.10, the system first calculates log(1.25) = 0.223, then calculates 0.223 divided by 0.10, which equals 2.23. This 2.23 is then used as the effect weighting coefficient for this locus. The system performs the calculation and storage of effect weighting coefficients for all 37 loci sequentially.

[0061] It should be understood that by obtaining the effect value and its standard error at each point and calculating the standardized statistic to determine the weight coefficient, the problem of incomparable weights caused by inconsistent effect value dimensions and differences in study populations in different studies is solved. This achieves scientific integration of cross-study data and improves the objectivity and accuracy of weight allocation at each point in the multi-gene risk score.

[0062] Step S40: Based on the comparison results between the polygenic risk score and the preset risk threshold, determine the obesity susceptibility risk level of the examinee.

[0063] It is understood that the aforementioned preset risk threshold may refer to one or more pre-set critical values ​​used to classify different risk levels, such as cutoff values ​​determined based on the distribution characteristics of the multi-gene risk scores of the target population.

[0064] The aforementioned obesity susceptibility risk level can refer to the risk category divided according to the comparison results of the examinee's polygenic risk score and the preset risk threshold, such as low risk level, medium risk level and high risk level, which is used to characterize the examinee's genetic susceptibility to obesity.

[0065] To achieve a multidimensional assessment of obesity susceptibility, the step of determining the subject's obesity susceptibility risk level based on the comparison results of the polygenic risk score and the preset risk threshold may include: determining the subject's first risk level for a body mass index-related phenotype and a second risk level for an abdominal obesity-related phenotype based on the comparison results of the polygenic risk score and the preset risk threshold; and using the first risk level and the second risk level as the subject's obesity susceptibility risk level.

[0066] Understandably, the aforementioned first risk level can refer to the risk category determined based on the multigene risk score of the subject at the gene loci associated with the body mass index-related phenotype, for the single assessment dimension of body mass index-related phenotype, such as low risk, medium risk, or high risk.

[0067] The aforementioned second risk level can refer to the risk category determined based on the multigene risk score of the subject at the gene loci associated with the abdominal obesity-related phenotype, for the single assessment dimension of abdominal obesity-related phenotype, such as low risk, medium risk, or high risk.

[0068] Accordingly, the aforementioned obesity susceptibility risk level can refer to the risk level used to characterize the overall genetic susceptibility to obesity of the examinee after comprehensively considering two assessment dimensions: body mass index-related phenotype and abdominal obesity-related phenotype. This risk level includes information from two dimensions: the first risk level and the second risk level.

[0069] In its implementation, after obtaining the subject's polygenic risk score, the system first retrieves a first set of pre-defined risk thresholds for body mass index (BMI)-related phenotypes and a second set of pre-defined risk thresholds for abdominal obesity-related phenotypes from its internal parameter database. The system stores independent pre-defined risk thresholds for each phenotype because the polygenic risk scores for the two phenotypes have different distribution characteristics and numerical ranges. Next, the system compares the subject's polygenic risk score at the BMI-related phenotype-associated gene loci with the first risk threshold set. Based on the comparison result, it determines a risk level corresponding to the subject's score from multiple predefined risk levels, serving as the first risk level. Simultaneously, the system compares the subject's polygenic risk score at the abdominal obesity-related phenotype-associated gene loci with the second risk threshold set. Based on the comparison result, it determines a risk level corresponding to the subject's score from multiple predefined risk levels, serving as the second risk level. Finally, the system integrates the first and second risk levels into the subject's obesity susceptibility risk level, which includes risk level information from both dimensions.

[0070] To facilitate understanding, the following examples are provided for illustration, but they do not impose specific limitations on this embodiment. For instance, suppose subject Zhang San's polygenic risk score for the body mass index (BMI)-related phenotype is 34.4. The system's preset first risk threshold set for BMI-related phenotypes includes: low risk (score less than 28), medium risk (score greater than or equal to 28 and less than or equal to 37), and high risk (score greater than 37). The system compares 34.4 with this threshold set and determines that 34.4 falls between 28 and 37, thus classifying the first risk level as medium risk. Similarly, suppose Zhang San's polygenic risk score for the abdominal obesity-related phenotype is 68.75. The system's preset second risk threshold set for the abdominal obesity-related phenotype includes: low risk (score less than 55), medium risk (score greater than or equal to 55 and less than or equal to 70), and high risk (score greater than 70). The system compares 68.75 with this threshold set and determines that 68.75 falls between 55 and 70, thus classifying the second risk level as medium risk. Subsequently, the system uses both the first risk level "medium risk" and the second risk level "medium risk" as Zhang San's obesity susceptibility risk level. For example, if another subject, Li Si, has a body mass index (BMI) related phenotypic score of 53.32, which is greater than 37, then the first risk level is high risk; and his abdominal obesity related phenotypic score is 50, which is less than 55, then the second risk level is low risk. Therefore, Li Si's obesity susceptibility risk level is high risk at the first risk level and low risk at the second risk level.

[0071] It should be understood that by separately determining the risk levels of body mass index-related phenotypes and abdominal obesity-related phenotypes and outputting them comprehensively, a multi-dimensional assessment of obesity susceptibility is achieved, breaking through the limitations of single phenotype assessment. This enables the assessment results to comprehensively reflect the genetic risk characteristics of obesity in the examinee and adapt to the differentiated needs of different application scenarios.

[0072] To enhance the comprehensiveness and clinical guidance value of the assessment, after the step of using the first risk level and the second risk level as the obesity susceptibility risk level of the examinee, the assessment may further include: generating a comprehensive risk assessment report based on the obesity susceptibility risk level of the examinee, the comprehensive risk assessment report including risk level information, risk site analysis information, and intervention recommendation information.

[0073] Understandably, the aforementioned comprehensive risk assessment report can refer to a written or electronic document generated by the system based on the examinee's obesity susceptibility risk level, containing multi-dimensional assessment results. This report is used to provide examinees or health management personnel with intuitive risk assessment conclusions and references for follow-up actions.

[0074] It should be noted that the aforementioned risk level information may refer to the textual label used in the comprehensive risk assessment report to describe the overall obesity susceptibility risk category of the examinee, such as "low risk", "medium risk" or "high risk".

[0075] The aforementioned risk locus analysis information may refer to the textual content in the comprehensive risk assessment report that lists and explains the specific gene loci and their related genetic information that contribute significantly to the risk assessment results of the examinee, such as locus name, gene name, tested genotype and its risk implications.

[0076] The aforementioned intervention recommendations may refer to health management or lifestyle adjustment suggestions generated in the comprehensive risk assessment report based on the examinee's risk level and specific risk sites, tailored to the individual's actual situation, such as recommendations for dietary control, exercise program selection, or further clinical examinations.

[0077] It should be understood that by integrating the first risk level of the body mass index-related phenotype with the second risk level of the abdominal obesity-related phenotype into a comprehensive risk assessment report, and outputting risk site analysis information and intervention recommendations, a leap from single-phenotype assessment to multidimensional collaborative assessment of obesity risk has been achieved. This allows the assessment results to simultaneously cover both general obesity and abdominal obesity subtypes, improving the comprehensiveness of the assessment and its clinical guidance value.

[0078] This embodiment discloses obtaining genotype detection results for multiple obesity-related gene loci in a subject. These obesity-related gene loci include those associated with body mass index (BMI)-related phenotypes and those associated with abdominal obesity-related phenotypes. Based on the genotype detection results, a genotype score for each gene locus is determined according to a genetic effect model corresponding to each locus. The genotype scores for each gene locus are then weighted and summed according to the effect value weighting coefficients corresponding to each gene locus to obtain a multi-gene risk score for the subject. Based on a comparison of the multi-gene risk score with a preset risk threshold, the subject's obesity susceptibility risk level is determined. Because this embodiment achieves multi-gene synergistic risk quantification assessment by obtaining multiple gene loci related to two phenotypes—BMI and abdominal obesity—and combining genetic effect model scoring with weighted summation of effect value weighting coefficients, it improves the accuracy of obesity susceptibility risk assessment compared to existing technologies.

[0079] refer to Figure 4 , Figure 4 This is a flowchart illustrating the second embodiment of the obesity susceptibility risk assessment method of the present invention.

[0080] Based on the first embodiment described above, in this embodiment, step S20 includes steps S201 to S203: Step S201: Determine the allele type of each gene locus based on the effect direction corresponding to each gene locus.

[0081] It should be noted that the aforementioned effect direction can refer to the tendency of a specific allele to influence a target phenotype (such as a body mass index-related phenotype or an abdominal obesity-related phenotype), specifically including both risk-increasing and risk-reducing effects. A risk-increasing effect indicates that carrying the allele increases the risk of developing the obesity phenotype, while a risk-reducing effect indicates that carrying the allele decreases the risk of developing the obesity phenotype.

[0082] Accordingly, the aforementioned allele types can refer to the categories classified according to the direction of effect of different alleles at a gene locus, specifically including two categories: risk allele types and protective allele types. Risk allele types correspond to alleles with a risk-increasing effect, while protective allele types correspond to alleles with a risk-reducing effect.

[0083] In its implementation, after obtaining the genotype test results of the subjects, the system reads the effect direction information corresponding to each gene locus involved in the genotype test results from the genetic parameter database stored within the system. The system pre-stores effect direction identifiers determined based on literature research for each obesity-related gene locus; these identifiers indicate the tendency of a specific allele at that gene locus to influence the obesity phenotype. Next, the system determines the allele type of the gene locus based on the read effect direction. When the effect direction is a risk-increasing effect, the system identifies the corresponding allele as a risk allele type; when the effect direction is a risk-reducing effect, the system identifies the corresponding allele as a protective allele type. The system performs the above reading and determination operations for each of the multiple obesity-related gene loci until the allele types of all gene loci are determined. After determining the allele types of all gene loci, the system uses the determination results as input data for subsequent genetic effect model scoring operations.

[0084] Step S202: For the gene locus whose allele type is a risk allele, based on the genotype detection results, determine the genotype score of the gene locus according to the first complete dominant model, the first incomplete dominant model, or the first recessive model.

[0085] It should be explained that the aforementioned first complete dominance model can refer to a genetic effect scoring rule used for risk allele types. Under this model, heterozygous and homozygous genotypes carrying one or more risk alleles are assigned the same highest score, while wild-type homozygous genotypes are assigned the lowest score. For example, for a locus with a risk allele of A, genotypes AA (homozygous risk) and AG (heterozygous) are both assigned 2 points, while genotype GG (wild-type) is assigned 0 points.

[0086] The aforementioned first incomplete dominance model can refer to another genetic effect scoring rule used for risk allele types. Under this model, the score of the heterozygous genotype lies between that of the wild-type homozygous and risk homozygous genotypes, reflecting the additive effect of alleles. For example, genotype GG is assigned 0 points, AG is assigned 1 point, and AA is assigned 2 points.

[0087] The aforementioned first recessive model can refer to another genetic effect scoring rule used for risk allele types. Under this model, the highest score is assigned only when an individual carries a homozygous risk allele, while heterozygous genotypes and wild-type homozygous genotypes are assigned the lowest scores. For example, genotype GG is assigned 0 points, AG is assigned 0 points, and AA is assigned 2 points.

[0088] In its implementation, after determining the allele type of each gene locus, the system identifies gene loci whose allele type belongs to the risk allele category. For each gene locus with the risk allele type, the system retrieves the corresponding allele dominance / recessiveness relationship from its internally stored genetic parameter database. The system pre-stores a dominance / recessiveness classification identifier for each risk allele type gene locus, indicating which model—the first complete dominance model, the first incomplete dominance model, or the first recessive model—then applies to that locus.

[0089] Next, the system determines the target model corresponding to the gene locus based on the dominant-recessive relationship read. When the dominant-recessive relationship is complete, the system uses the first complete dominant model; when the dominant-recessive relationship is incomplete, the system uses the first incomplete dominant model; and when the dominant-recessive relationship is recessive, the system uses the first recessive model.

[0090] Subsequently, the system inputs the genotype detection results (e.g., wild-type homozygous, heterozygous, or risk allele homozygous) of the subject at the designated target model, and calculates the genotype score for that gene locus according to the model's scoring rules. Specifically, under the first complete dominant model, the system assigns 0 points to wild-type homozygous, and 2 points to both heterozygous and risk allele homozygous; under the first incomplete dominant model, the system assigns 0 points to wild-type homozygous, 1 point to heterozygous, and 2 points to risk allele homozygous; under the first recessive model, the system assigns 0 points to wild-type homozygous, 0 points to heterozygous, and 2 points to risk allele homozygous. The system performs the model determination and scoring calculation operations for each gene locus with a risk allele type, obtaining the genotype score for each risk allele locus.

[0091] It should be added that if the dominant-recessive relationship of the risk alleles is not clearly defined, the scores should be assigned according to the first incomplete dominance model.

[0092] Step S203: For gene loci whose allele type is a protective allele, based on the genotype detection results, determine the genotype score of the gene locus according to the second complete dominance model, the second incomplete dominance model, or the second recessive model.

[0093] It should be explained that the aforementioned second complete dominance model may refer to a genetic effect model used for protective allele types. Under this model, the homozygous and heterozygous protective alleles have the same protective effect, that is, the wild-type homozygous is given a higher score, while the heterozygous and protective allele homozygous are given lower scores.

[0094] The aforementioned second incomplete dominance model can refer to a genetic effect model used for the type of protective allele. In this model, the three genotypes are assigned different scores: the wild-type homozygous is assigned the highest score, the protective allele homozygous is assigned the lowest score, and the heterozygous is assigned the intermediate score, in order to reflect the dose-effect of the number of protective alleles on risk reduction.

[0095] The aforementioned second recessive model can refer to a genetic effect model used for protective allele types. Under this model, a protective effect is only shown when both alleles are protective alleles (i.e., homozygous protective alleles). Heterozygous and wild-type homozygous alleles have the same high effect size.

[0096] In its implementation, under the second complete dominant model, the system assigns 2 points to wild-type homozygotes and 0 points to both heterozygotes and homozygotes of the protective allele; under the second incomplete dominant model, the system assigns 2 points to wild-type homozygotes, 1 point to heterozygotes, and 0 points to homozygotes of the protective allele; under the second recessive model, the system assigns 2 points to wild-type homozygotes, 2 points to heterozygotes, and 0 points to homozygotes of the protective allele. The system performs the model determination and scoring calculation operations on each gene locus with the protective allele type to obtain the genotype score for each protective allele locus.

[0097] It should be added that if the dominant-recessive relationship of the protective alleles is not clearly defined, the score should be assigned according to the second incomplete dominance model.

[0098] This embodiment discloses a method for determining the allele type of each gene locus based on the effect direction corresponding to each gene locus. For gene loci with an allele type of risk allele, the genotype score of the gene locus is determined based on the genotype detection results using a first complete dominance model, a first incomplete dominance model, or a first recessive model. For gene loci with an allele type of protective allele, the genotype score of the gene locus is determined based on the genotype detection results using a second complete dominance model, a second incomplete dominance model, or a second recessive model. Because this embodiment distinguishes between risk alleles and protective alleles based on the risk effect direction of the alleles and uses three differentiated dominance and recessive models for scoring, compared to existing technologies, this embodiment overcomes the limitation of a single scoring rule being unable to adapt to different allele effect directions, thus improving the accuracy of genotype scoring in representing genetic effects.

[0099] refer to Figure 5 , Figure 5 This is a flowchart illustrating the third embodiment of the obesity susceptibility risk assessment method of the present invention.

[0100] Based on the above embodiments, in this embodiment, step S40 includes steps S401 to S404: Step S401: Based on the genotype frequency distribution data of the target population at each of the gene loci.

[0101] Step S402: Construct a gene risk scoring model for the target population based on the genotype scores of each gene locus and the genotype frequency distribution data.

[0102] Step S403: Determine the preset risk threshold based on the central tendency parameter and dispersion parameter of the gene risk scoring model.

[0103] Step S404: Compare the polygenic risk score with the preset risk threshold, and determine the obesity susceptibility risk level of the examinee based on the comparison result.

[0104] Understandably, the target population mentioned above can refer to a specific population group used to establish risk scoring reference standards, such as East Asian or Chinese populations, whose genetic background belongs to the same category as the examinee.

[0105] It should be noted that the above genotype frequency distribution data may refer to the proportion of different genotypes (e.g., wild-type homozygous, heterozygous, and variant homozygous) at each gene locus in the target population. This data can be obtained through population genetic databases (e.g., the gnomAD database).

[0106] It should be explained that the above-mentioned gene risk scoring model can refer to a mathematical model constructed using statistical methods based on the genotype frequency distribution and corresponding genotype scores of each gene locus in the target population, which is used to describe the probability distribution characteristics of the multi-gene risk score of the target population.

[0107] The aforementioned central tendency parameter can refer to a statistic used to describe the central location of the genetic risk score distribution of the target population, such as the arithmetic mean (i.e., the mean μ). The aforementioned dispersion parameter can refer to a statistic used to describe the dispersion of the genetic risk score distribution of the target population, such as the standard deviation σ.

[0108] Furthermore, the aforementioned preset risk threshold may refer to one or more critical values ​​determined based on the central tendency parameter and dispersion parameter of the target population gene risk scoring model, such as mean minus one standard deviation (μ-σ) and mean plus one standard deviation (μ+σ), used to divide the polygenic risk score of the examinee into different risk level intervals.

[0109] In its implementation, the system first retrieves genotype frequency distribution data for each gene locus of the target population from a population genetic parameter database stored within the system. The target population is an East Asian population, and the genotype frequency distribution data originates from a population genetic database (e.g., the gnomAD database). Next, the system obtains the genotype scores for each previously determined gene locus and performs joint analysis with the genotype frequency distribution data of the target population to construct a gene risk scoring model for the target population. Specifically, for each individual in the target population, the system calculates the theoretical genotype at each gene locus based on the genotype frequency distribution. Then, combining this with the determined genotype scores, it simulates the theoretical polygenic risk score for each individual using a weighted summation method. Through statistical fitting of the theoretical polygenic risk scores for all individuals in the target population, the system constructs a gene risk scoring model that conforms to a normal distribution.

[0110] Subsequently, the system extracts the central tendency parameter (mean μ) and dispersion parameter (standard deviation σ) from the constructed genetic risk scoring model. Based on the mean μ and standard deviation σ, the system determines preset risk thresholds; specifically, μ-σ and μ+σ are defined as two preset risk thresholds. After obtaining these preset risk thresholds, the system compares the subject's polygenic risk score with each of the two preset risk thresholds. Based on the comparison results, the system determines the subject's obesity susceptibility risk level: a polygenic risk score less than μ-σ is classified as low risk; a polygenic risk score greater than or equal to μ-σ and less than or equal to μ+σ is classified as medium risk; and a polygenic risk score greater than μ+σ is classified as high risk. The system outputs the determined risk level as the assessment result.

[0111] To achieve unified dimensions and comparability of risk scores across different assessment systems, and to overcome the incomparability across individuals and models caused by differences in the number of loci and weights in the original weighted scores, the step of comparing the polygenic risk score with the preset risk threshold and determining the subject's obesity susceptibility risk level based on the comparison result may include: determining the theoretical maximum value of the polygenic risk score; converting the polygenic risk score into a standardized risk score based on the theoretical maximum value using a proportional mapping function; and comparing the standardized risk score with the preset risk threshold and determining the subject's obesity susceptibility risk level based on the comparison result.

[0112] It should be understood that the above theoretical maximum value may refer to the theoretical value of the polygenic risk score corresponding to the subject carrying the highest risk genotype at all obesity-related gene loci. This value is the sum of the products of the upper limit of the genotype score at each gene locus and the corresponding effect value weighting coefficient.

[0113] Understandably, the aforementioned proportional mapping function can refer to a mathematical transformation relationship that maps the original polygenic risk score to a standardized score range. This function uses the theoretical maximum value as a benchmark reference value and adopts an inverse proportional mapping method to convert the original score into a standardized risk score that is easy to understand and compare across individuals. For example, one hundred times the ratio of the original score to the theoretical maximum value can be used as the calculation formula for the standardized risk score.

[0114] Accordingly, the aforementioned standardized risk score can refer to the score result presented in percentage form after the original polygenic risk score is transformed by a proportional mapping function. This score is used to characterize the relative position of the examinee in the entire theoretical risk space.

[0115] This embodiment discloses a method based on genotype frequency distribution data of a target population at various gene loci. A gene risk scoring model for the target population is constructed based on the genotype scores at each gene locus and the genotype frequency distribution data. A preset risk threshold is determined based on the central tendency and dispersion parameters of the gene risk scoring model. The multi-gene risk score is compared with the preset risk threshold, and the obesity susceptibility risk level of the examinee is determined based on the comparison result. Because this embodiment constructs a gene risk scoring model based on the genotype frequency distribution data of the target population and dynamically determines the risk threshold based on the model's central tendency and dispersion parameters, it achieves adaptive matching between the risk stratification standard and the genetic background of the target population. Compared with existing technologies, this embodiment overcomes the deficiency that fixed thresholds cannot adapt to the differences in genetic characteristics among different populations, thus improving the population adaptability of risk level determination.

[0116] Furthermore, embodiments of the present invention also propose a storage medium storing an obesity susceptibility risk assessment program, wherein when the obesity susceptibility risk assessment program is executed by a processor, it implements the steps of the obesity susceptibility risk assessment method described above.

[0117] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the obesity susceptibility risk assessment device of the present invention.

[0118] like Figure 6 As shown, the obesity susceptibility risk assessment device proposed in this embodiment of the invention includes: a detection acquisition module 501, a score determination module 502, a risk scoring module 503, and a level determination module 504.

[0119] The detection acquisition module 501 is used to acquire the genotype detection results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes.

[0120] The scoring determination module 502 is used to determine the genotype score of each gene locus based on the genotype detection results and according to the genetic effect model corresponding to each gene locus.

[0121] The risk scoring module 503 is used to perform a weighted summation of the genotype scores of each gene locus based on the effect value weight coefficients corresponding to each gene locus, so as to obtain the multigene risk score of the examinee.

[0122] The level determination module 504 is used to determine the obesity susceptibility risk level of the examinee based on the comparison result of the multigene risk score and the preset risk threshold.

[0123] The detection and acquisition module 501 is further configured to retrieve research literature related to obesity susceptibility genes based on a literature database; extract population genetic parameters, molecular epidemiological evidence, and functional genomics data of candidate gene loci from the research literature; perform multi-stage screening of the candidate gene loci based on the population genetic parameters, the molecular epidemiological evidence, and the functional genomics data to determine the target gene locus set; and obtain the genotype detection results of the subject at each gene locus in the target gene locus set.

[0124] The risk scoring module 503 is further configured to obtain the effect value and standard error corresponding to each of the gene loci; obtain standardized statistics based on the effect value and standard error; and determine the effect value weight coefficient corresponding to each of the gene loci based on the standardized statistics.

[0125] The level determination module 504 is further configured to determine, based on the comparison results of the polygenic risk score and the preset risk threshold, the first risk level of the subject for the body mass index-related phenotype and the second risk level for the abdominal obesity-related phenotype; and to use the first risk level and the second risk level as the subject's obesity susceptibility risk level.

[0126] The level determination module 504 is also used to generate a comprehensive risk assessment report based on the subject's obesity susceptibility risk level. The comprehensive risk assessment report includes risk level information, risk site analysis information, and intervention recommendation information.

[0127] This device embodiment discloses the acquisition of genotype detection results for multiple obesity-related gene loci in a subject. These obesity-related gene loci include those associated with body mass index (BMI)-related phenotypes and those associated with abdominal obesity-related phenotypes. Based on the genotype detection results, a genotype score for each gene locus is determined according to a genetic effect model corresponding to each locus. The genotype scores for each gene locus are then weighted and summed according to the effect value weighting coefficients corresponding to each gene locus to obtain a multi-gene risk score for the subject. Based on a comparison of the multi-gene risk score with a preset risk threshold, the subject's obesity susceptibility risk level is determined. Because this device embodiment acquires multiple gene loci related to two phenotypes—BMI and abdominal obesity—and combines genetic effect model scoring with weighted summation of effect value weighting coefficients, it achieves a multi-gene synergistic risk quantification assessment. Compared to existing technologies, this device embodiment improves the accuracy of obesity susceptibility risk assessment.

[0128] Based on the first embodiment of the obesity susceptibility risk assessment device of the present invention, a second embodiment of the obesity susceptibility risk assessment device of the present invention is proposed.

[0129] In this embodiment, the score determination module 502 is further configured to determine the allele type of each gene locus based on the effect direction corresponding to each gene locus; for gene loci whose allele type is a risk allele, the genotype score of the gene locus is determined based on the genotype detection results according to a first complete dominance model, a first incomplete dominance model, or a first recessive model; for gene loci whose allele type is a protective allele, the genotype score of the gene locus is determined based on the genotype detection results according to a second complete dominance model, a second incomplete dominance model, or a second recessive model.

[0130] Other embodiments or specific implementations of the obesity susceptibility risk assessment device of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0131] This application provides an obesity susceptibility risk assessment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the obesity susceptibility risk assessment method in the first embodiment described above.

[0132] The following is for reference. Figure 7The diagram illustrates a structural schematic suitable for implementing the obesity susceptibility risk assessment device in the embodiments of this application. The obesity susceptibility risk assessment device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The obesity susceptibility risk assessment device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0133] like Figure 7 As shown, the obesity susceptibility risk assessment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the obesity susceptibility risk assessment device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the obesity susceptibility risk assessment device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows obesity susceptibility risk assessment devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0134] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0135] The obesity susceptibility risk assessment device provided in this application, employing the obesity susceptibility risk assessment method described in the above embodiments, can solve the technical problem of insufficient accuracy in obesity susceptibility risk assessment in the prior art. Compared with the prior art, the beneficial effects of the obesity susceptibility risk assessment device provided in this application are the same as those of the obesity susceptibility risk assessment method provided in the above embodiments, and other technical features of this obesity susceptibility risk assessment device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0136] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0137] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0138] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0139] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0140] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0141] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for assessing obesity susceptibility risk, characterized in that, The method includes: Obtain the genotype test results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes; Based on the genotype detection results, the genotype score of each gene locus is determined according to the genetic effect model corresponding to each gene locus. Based on the effect value weighting coefficients corresponding to each gene locus, the genotype scores of each gene locus are weighted and summed to obtain the polygenic risk score of the subject. Based on the comparison results between the polygenic risk score and the preset risk threshold, the obesity susceptibility risk level of the examinee is determined.

2. The obesity susceptibility risk assessment method as described in claim 1, characterized in that, The step of determining the genotype score of each gene locus based on the genotype detection results and according to the genetic effect model corresponding to each gene locus includes: Based on the effect direction corresponding to each gene locus, the allele type of each gene locus is determined; For gene loci whose allele type is a risk allele, the genotype score of the gene locus is determined based on the genotype detection results, according to the first complete dominance model, the first incomplete dominance model, or the first recessive model. For gene loci whose allele type is a protective allele, the genotype score of the gene locus is determined based on the genotype detection results, according to the second complete dominance model, the second incomplete dominance model, or the second recessive model.

3. The obesity susceptibility risk assessment method as described in claim 1, characterized in that, Before the step of weighting and summing the genotype scores of each gene locus according to the effect value weighting coefficients corresponding to each gene locus to obtain the polygenic risk score of the examinee, the method further includes: Obtain the effect value and standard error corresponding to each of the gene loci; Based on the effect size and the standard error, a standardized statistic is obtained; The effect weight coefficients corresponding to each gene locus are determined based on the standardized statistics.

4. The obesity susceptibility risk assessment method as described in claim 1, characterized in that, The steps for obtaining genotypic test results for multiple obesity-related gene loci in the subject include: Search the literature database to obtain research literature related to obesity susceptibility genes; Population genetic parameters, molecular epidemiological evidence, and functional genomics data for candidate gene loci were extracted from the aforementioned research literature. Based on the population genetic parameters, the molecular epidemiological evidence, and the functional genomics data, the candidate gene loci are screened in multiple stages to determine the set of target gene loci. Obtain the genotype detection results of the subject at each gene locus in the target gene locus set.

5. The obesity susceptibility risk assessment method as described in claim 1, characterized in that, The step of determining the obesity susceptibility risk level of the examinee based on the comparison result of the polygenic risk score and the preset risk threshold includes: Based on the genotype frequency distribution data of the target population at each of the aforementioned gene loci; A genetic risk scoring model for the target population is constructed based on the genotype scores of each gene locus and the genotype frequency distribution data. Based on the central tendency parameter and dispersion parameter of the gene risk scoring model, a preset risk threshold is determined; The polygenic risk score is compared with the preset risk threshold, and the obesity susceptibility risk level of the examinee is determined based on the comparison result.

6. The obesity susceptibility risk assessment method as described in claim 5, characterized in that, The step of comparing the polygenic risk score with the preset risk threshold and determining the obesity susceptibility risk level of the examinee based on the comparison result includes: Determine the theoretical maximum value of the polygenic risk score; Based on the theoretical maximum value, the polygenic risk score is converted into a standardized risk score through a proportional mapping function. The standardized risk score is compared with the preset risk threshold, and the obesity susceptibility risk level of the examinee is determined based on the comparison result.

7. The method for assessing obesity susceptibility risk as described in any one of claims 1-6, characterized in that, The step of determining the obesity susceptibility risk level of the examinee based on the comparison result of the polygenic risk score and the preset risk threshold includes: Based on the comparison results between the polygenic risk score and the preset risk threshold, the first risk level of the subject for the body mass index-related phenotype and the second risk level for the abdominal obesity-related phenotype are determined. The first risk level and the second risk level are used as the obesity susceptibility risk levels of the examinee; Accordingly, after the step of using the first risk level and the second risk level as the obesity susceptibility risk level of the examinee, the method further includes: Based on the subject's obesity susceptibility risk level, a comprehensive risk assessment report is generated, which includes risk level information, risk site analysis information, and intervention recommendation information.

8. A device for assessing obesity susceptibility risk, characterized in that, The device includes: The detection acquisition module is used to acquire the genotype detection results of the subject for multiple obesity-related gene loci, including gene loci associated with body mass index-related phenotypes and gene loci associated with abdominal obesity-related phenotypes. The scoring module is used to determine the genotype score of each gene locus based on the genotype detection results and according to the genetic effect model corresponding to each gene locus. The risk scoring module is used to perform a weighted summation of the genotype scores of each gene locus based on the effect value weight coefficients corresponding to each gene locus, so as to obtain the multigene risk score of the examinee. The rating determination module is used to determine the obesity susceptibility risk level of the examinee based on the comparison results between the polygenic risk score and the preset risk threshold.

9. A device for assessing obesity susceptibility risk, characterized in that, The device includes: a memory, a processor, and an obesity susceptibility risk assessment program stored in the memory and executable on the processor, the obesity susceptibility risk assessment program being configured to implement the steps of the obesity susceptibility risk assessment method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores an obesity susceptibility risk assessment program, which, when executed by a processor, implements the steps of the obesity susceptibility risk assessment method as described in any one of claims 1 to 7.

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