Polygenic genetic risk score for blood lipids and its application

Abstract

The present invention provides a blood lipid polygenic genetic risk score (Polygenic risk score, PRS) and its application. Specifically, the present invention provides the application of a reagent for detecting individual information in the preparation of a detection device for assessing the risk of dyslipidemia, wherein the individual information includes multiple single nucleotide polymorphism sites associated with total cholesterol (TC), multiple single nucleotide polymorphism sites associated with triglycerides (TG), multiple single nucleotide polymorphism sites associated with low-density lipoprotein cholesterol (LDL-C) and / or multiple single nucleotide polymorphism sites associated with high-density lipoprotein cholesterol (HDL-C). The blood lipid polygenic genetic risk score of the present invention has high accuracy and sensitivity and can well identify people at high risk of dyslipidemia.

Description

Technical Field

[0001] The present invention relates to a blood lipid polygenic genetic risk score and its application. Background Art

[0002] Blood lipids are the primary cause of atherosclerosis. Low-density lipoprotein cholesterol (LDL-C) is the primary form of atherogenic cholesterol. Furthermore, triglycerides (TG) also play a significant role in promoting cardiovascular disease through TG-rich lipoproteins. Therefore, lipid control is the foundation of primary prevention of cardiovascular disease.

[0003] Currently, blood lipid control focuses more on individuals with high blood lipid levels. However, once an individual develops abnormal blood lipids, it is difficult to reverse this adverse condition and the subsequent adverse cardiovascular effects. Therefore, it is crucial to identify potential high-risk individuals and carry out targeted zero-level prevention as early as possible. Summary of the Invention

[0004] One object of the present invention is to provide a single nucleotide polymorphism site associated with dyslipidemia risk and an evaluation system.

[0005] In their research, the inventors developed a PRS score for blood lipids, clarifying the relationship between the PRS and blood lipid levels, the average annual change in blood lipids, and the risk of developing dyslipidemia. This provides evidence for the use of the PRS for early screening and assessment of individuals at high risk for blood lipids. Furthermore, the inventors also identified the dynamic trend of the average annual change in blood lipids with age, which helps identify critical age windows for rapid changes in blood lipids, thereby determining potential critical periods for prevention and enabling early intervention while individuals' blood lipid levels are still within normal levels.

[0006] Specifically, in one aspect, the present invention provides the use of a reagent for detecting individual information in preparing a detection device for assessing the risk of dyslipidemia, wherein the individual information includes one or more sets of the following single nucleotide polymorphism site information:

[0007] Group I: rs1077834, rs10889353, rs11136341, rs1129555, rs11557092, rs1169288, rs117711462, rs12027135, rs12042319, rs12453914, rs1260326, rs12740374, rs12927205, rs13277801, rs13306194, rs1367117, rs1495741, rs151193009, rs1532085, rs17122278, rs17358402, rs174546, rs174547, rs1800588, rs1883025, rs2000999, rs200990725, rs2043085, rs2066714, rs2081687, rs2230808, rs2297991, rs247616, rs2575876, rs2642442, rs312949, rs3846663, rs4377290, rs439401, rs4883201, rs4939883, rs507666, rs579459, rs58542926, rs651821, rs6871667, rs6882076, rs7185272, rs7258950, rs72654473, rs7306523, rs737337, rs7525649, rs7616006, rs769449, rs7770628, rs7965082, rs9357121, rs9376090, rs9390698;

[0008] Group II: rs10096633, rs1037814, rs10889353, rs12042319, rs1260326, rs130071, rs13306194, rs1495741, rs1532085, rs157582, rs16990971, rs17145738, rs174546, rs174547, rs1800234, rs1800588, rs180327, rs1832007, rs2043085, rs2068888, rs2075260, rs2075291, rs2081687, rs2144300, rs3129853, rs35332062, rs439401, rs4719841, rs58542926, rs651821, rs6818397, rs6831256, rs6882076, rs6905288, rs72654473, rs738409, rs7499892, rs769449, rs7897379, rs995000;

[0009] Group III: rs11125936, rs11136341, rs1129555, rs11557092, rs1169288, rs117711462, rs12027135, rs12453914, rs12740374, rs12927205, rs13277801, rs13306194, rs1367117, rs151193009, rs17135399, rs17358402, rs191835914, rs2000999, rs200990725, rs2081687, rs2328223, rs2642442, rs312949, rs3846663, rs4302748, rs507666, rs579459, rs58542926, rs6065311, rs6871667, rs6882076, rs7185272, rs7258950, rs7306523, rs737337, rs7499892, rs7525649, rs769449, rs7770628, rs7901016, rs7965082, rs9357121, rs9390698, rs9534262;

[0010] Group IV: rs10096633, rs10773003, rs1077834, rs11869286, rs12718465, rs12801636, rs12970066, rs13702, rs148910227, rs1532085, rs1689800, rs17145738, rs174546, rs17 4547, rs17695224, rs1800588, rs180327, rs181359, rs181360, rs1883025, rs2000813 , rs2043085, rs2066714, rs2068888, rs2075291, rs2156552, rs2230808, rs2245019, rs 2292318, rs2296172, rs2297991, rs2303790, rs247616, rs2575876, rs2925979, rs297 2143, rs326214, rs3785100, rs4129767, rs4142995, rs4148008, rs439401, rs4883263, rs4917014, rs4939883, rs499974, rs634501, rs651821, rs671, rs6905288, rs702485, rs7134594, rs7208487, rs737337, rs7499892, rs769449, rs838880, rs884366, rs9593.

[0011] In another aspect, the present invention further provides a method for assessing dyslipidemia risk, comprising: detecting individual information; and assessing the individual's dyslipidemia risk based on the detection results. The individual information includes information on one or more single nucleotide polymorphism sites in Groups I, II, III, and IV.

[0012] According to a specific embodiment of the present invention, in the present invention, the individual is from the East Asian population.

[0013] According to a specific embodiment of the present invention, in the present invention, the risk of dyslipidemia includes at least one of the following situations:

[0014] blood lipid levels;

[0015] Average annual change in blood lipid indicators; and / or

[0016] The risk of developing abnormal blood lipid indicators.

[0017] According to a specific embodiment of the present invention, in the present invention, the blood lipid indicators include: total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and / or high-density lipoprotein cholesterol (HDL-C).

[0018] According to a specific embodiment of the present invention, in the present invention, the risk of abnormal blood lipid indicators includes: the risk of high TC blood, the risk of high TG blood, the risk of high LDL-C blood and / or the risk of low HDL-C blood.

[0019] According to a specific embodiment of the present invention, in the present invention, the group I single nucleotide polymorphism site information is used to assess the TC level, the average annual change in TC and / or the risk of developing hyperTCemia.

[0020] According to a specific embodiment of the present invention, in the present invention, the group II single nucleotide polymorphism site information is used to assess the TG level, the average annual change in TG and / or the risk of developing hypertriglyceridemia.

[0021] According to a specific embodiment of the present invention, in the present invention, the Group III single nucleotide polymorphism site information is used to assess the LDL-C level, the average annual change in LDL-C and / or the risk of developing hyperLDL-Cemia.

[0022] According to a specific embodiment of the present invention, in the present invention, the Group IV single nucleotide polymorphism site information is used to assess the HDL-C level, the average annual change in HDL-C and / or the risk of low HDL-C blood disease.

[0023] According to some specific embodiments of the present invention, the present invention is to assess the risk of individual dyslipidemia by detecting all single nucleotide polymorphism site information of group I, group II, group III and group IV. That is, the individual information includes the following single nucleotide polymorphism site information: rs10096633, rs1037814, rs10773003, rs1077834, rs10889353, rs11125936, rs11136341, rs1129555, rs11557092, rs1169288, rs117711462, rs11869286, rs12027135, rs12042319, rs12453914, rs1260326, rs12718465, rs12740374, 927205, rs12970066, rs130071, rs13277801, rs13306194, rs1367117, rs13702, rs148910227, rs1495741, rs151193009, rs1532085, rs1575 82. rs1689800, rs16990971, rs17122278, rs17135399, rs17145738, rs17358402, rs174546, rs174547, rs17695224, rs1800234, rs1800588, rs180327, rs181359, rs181360, rs1832007, rs1883025, rs191835914, rs2000813, rs2000999, rs200990725, rs2043085, rs2066714, rs206 8888, rs2075260, rs2075291, rs2081687, rs2144300, rs2156552, rs2230808, rs2245019, rs2292318, rs2296172, rs2297991, rs2303790, rs 2328223, rs247616, rs2575876, rs2642442, rs2925979, rs2972143, rs312949, rs3129853, rs326214, rs35332062, rs3785100, rs3846663, r s4129767, rs4142995, rs4148008, rs4302748, rs4377290, rs439401, rs4719841, rs4883201, rs4883263, rs4917014, rs4939883, rs499974,rs507666, rs579459, rs58542926, rs6065311, rs634501, rs651821, rs671, rs6818397, rs6831256, rs687 1667, rs6882076, rs6905288, rs702485, rs7134594, rs7185272, rs7208487, rs7258950, rs72654473, rs73 06523, rs737337, rs738409, rs7499892, rs7525649, rs7616006, rs769449, rs7770628, rs7897379, rs790 1016, rs7965082, rs838880, rs884366, rs9357121, rs9376090, rs9390698, rs9534262, rs9593, rs995000. ,

[0024] In the present invention, any feasible detection method in the art can be used to detect the individual information. For example, multiplex PCR targeted amplicon sequencing technology can be used to genotype an in vitro DNA sample from an individual to obtain genotype information for each single nucleotide polymorphism site. In the present invention, the reagents for detecting individual information include the reagent materials used in the detection method.

[0025] According to a specific embodiment of the present invention, in the present invention, a blood lipid polygenic risk score (Polygenic risk score, PRS) is obtained according to the information of each single nucleotide polymorphism site in accordance with the following calculation method:

[0026]

[0027] Where i represents the SNP site, m represents the total number of SNP sites, β represents the effect of the SNP site on blood lipid traits, j represents the genotype of the SNP site, 0, 1, and 2 represent no mutation, heterozygous mutation, and homozygous mutation. The PRS value is the sum of the effect values of all blood lipid-related sites.

[0028] According to a specific embodiment of the present invention, in the present invention, the effect value of each SNP is shown in Table 2.

[0029] According to a specific embodiment of the present invention, in the present invention, the higher the PRS score of the blood lipid index, the higher the level of the blood lipid index.

[0030] According to a specific embodiment of the present invention, in the present invention, the higher the PRS score of the blood lipid index is, the greater the average annual change of the blood lipid index is.

[0031] According to a specific embodiment of the present invention, in the present invention, the higher the PRS score of the blood lipid index, the higher the risk of developing abnormal blood lipid index.

[0032] On the other hand, the present invention also provides a dyslipidemia risk assessment device, which includes a detection unit and a data analysis unit, wherein:

[0033] The detection unit is used to detect information from the individual to be tested and obtain a detection result; wherein the individual information is the individual information mentioned above;

[0034] The data analysis unit is used to analyze and process the detection results of the detection unit.

[0035] According to a specific embodiment of the present invention, the dyslipidemia risk assessment device of the present invention, wherein the data analysis unit, when analyzing and processing the detection results of the detection unit, includes: assigning the detection results of the single nucleotide polymorphism site to a weight coefficient to calculate the blood lipid index PRS score of the individual to be tested.

[0036] According to a specific embodiment of the present invention, the dyslipidemia risk assessment device of the present invention, wherein the data analysis unit further comprises:

[0037] The output module is used to receive the PRS score information of blood lipid indicators and output it as the dyslipidemia risk diagnosis classification result.

[0038] In another aspect, the present invention further provides a computer storage medium storing computer program instructions, which, when executed, achieve the following: obtaining an individual dyslipidemia risk assessment result based on individual information to be tested, wherein the individual information is as described above.

[0039] In another aspect, the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it is configured to obtain an individual dyslipidemia risk assessment result based on information about the individual to be tested, wherein the individual information is as described above.

[0040] In a specific embodiment of the present invention, the present invention conducted a study and analysis based on a longitudinal cohort with repeated measurements of a large population. The cohort was from the China Atherosclerotic Cardiovascular Disease Risk Prediction Study (China-PAR), which was first established in 1998 and followed up until 2020, including four repeated blood lipid measurement data. This study involved 47,691 participants. After excluding those who failed to follow up or had major chronic diseases, and those who did not have blood lipid measurements at baseline and any subsequent follow-up surveys, 37,317 people remained for analysis. The present invention constructed a polygenic genetic risk score for four blood lipid indicators suitable for East Asian populations based on 126 blood lipid-related gene variants, and evaluated its relationship with blood lipid levels, average annual changes, and the risk of dyslipidemia. The results showed that with the increase of PRS, the levels of total cholesterol (TC), TG, LDL-C, and high-density lipoprotein cholesterol (HDL-C) all showed a significant upward trend. For example, compared with the general population (TC-PRS 40%-49%), the TC levels of the population with TC-PRS scores <10%, 10%-19%, 20%-29%, and 30%-39% decreased by an average of -15.5 (95% CI: -16.8, -14.2) mg / dL, -7.90 (95% CI: -9.21, -6.59) mg / dL, -5.99 (95% CI: -7.28, -4.71) mg / dL, and -2.70 (95% CI: -3.43, -3.91) mg / dL, respectively. The mean annual changes in TC, TG, LDL-C, and HDL-C were significantly higher in those with scores in the 60%-69% (95% CI: -4.00, -1.39) mg / dL range, while those with scores in the 70%-79% range (95% CI: 80%-89%) and 90% range (95% CI: ≥90%) range (1.62 (95% CI: 0.30, 2.93) mg / dL, 4.44 (95% CI: 3.14, 5.74) mg / dL, 6.43 (95% CI: 5.11, 7.76) mg / dL, and 11.00 (95% CI: 9.65, 12.35) mg / dL range, respectively. Similar patterns were observed in both sexes. With increasing polygenic genetic risk, the mean annual changes in TC, TG, LDL-C, and HDL-C also showed a significant upward trend.For example, compared with the general population (TC-PRS 40%-49%), the mean annual change in TC in the population with TC-PRS <10%, 10%-19%, and 20%-29% decreased by -1.01 (95% CI: -1.19, -0.83) mg / dL, -0.57 (95% CI: -0.74, -0.40) mg / dL, and -0.47 (95% CI: -0.64, -0.30) mg / dL, respectively, whereas the mean annual change in TC in the population with TC-PRS 70%-79%, 80%-89%, and ≥90% increased by 0.28 (95% CI: 0.11, 0.46) mg / dL, 0.34 (95% CI: 0.16, 0.51) mg / dL, and 0.67 (95% CI: 0.49, 0.85) mg / dL, respectively. The blood lipid levels of the group with lower polygenic genetic risk tended to change to lower levels, while the blood lipid levels of the group with higher polygenic risk developed to higher levels at a faster rate. For example, in the group with PRS <10%, the average annual changes in TC, TG, LDL-C, and HDL-C were -0.39 (95% CI: -0.52, -0.26) mg / dL, -1.82 (95% CI: -2.10, -1.53) mg / dL, and -0.33 (95% CI: -0. The mean annual changes in serum lipids were 1.44 (95% CI: -0.23) mg / dL, -0.19 (95% CI: -0.24, -0.14) mg / dL, and -0.19 (95% CI: -0.24, -0.14) mg / dL, respectively. Those with a score in the 90th percentile or higher had significantly higher annual changes of 1.29 (95% CI: 1.16, 1.42) mg / dL, 4.92 (95% CI: 4.37, 5.47) mg / dL, 0.80 (95% CI: 0.69, 0.92) mg / dL, and 0.58 (95% CI: 0.53, 0.63) mg / dL, respectively. Age was also significantly associated with the mean annual change in serum lipids, but this association differed significantly between men and women. In men, the mean annual change in TC, TG, and LDL-C decreased with age, while in women, an inverted V-shaped relationship was observed, with the greatest annual change occurring in the 40-49 age group. In women, the mean annual change in HDL-C increased significantly after age 60. In people over 40 years old, the average annual changes in TC, TG, and LDL-C in women were significantly higher than in men. In addition, the average annual changes in the four blood lipid indicators were positively correlated with genetic risk, regardless of gender or age group. As the risk of polygenic genetic risk increases, the risk of high TC, high TG, and high LDL-C gradually increases, while the risk of low HDL-C gradually decreases. For example, compared with the general population (people with TC-PRS in the 40%-49% range), the risk of high TC in people with scores <10%, 10%-19%, and 20%-29% decreased by 27%, 30%, and 24%, respectively, while the risk of high TC in people with scores in the 80%-89% range and ≥90% increased by 25% and 46%, respectively.The results were similar in men and women. The results of this study have important clinical implications.

[0041] In summary, the present study found that the lipid profile ratio (PRS) was positively correlated with lipid levels, lipid variability, and the risk of dyslipidemia. Furthermore, sex differences were identified in the relationship between average annual lipid change and age. With increasing age, average annual lipid change in men showed a gradually decreasing trend, while in women, the relationship was inverted V-shaped. The current study constructed a PRS using 126 significant genetic variants associated with lipid traits in an East Asian population. Using a large sample of repeated lipid measurements, the PRS was comprehensively evaluated for its association with lipid levels, lipid variability, and the risk of dyslipidemia. The results suggest that polygenic genetic risk can predict lipid levels, lipid variability, and the risk of dyslipidemia. Individuals with a lower polygenic genetic risk had lower lipid levels, smaller average annual changes, and a lower risk of dyslipidemia, while those with a higher genetic risk had higher lipid levels, greater average annual changes, and a higher risk of dyslipidemia. Therefore, greater attention should be paid to populations at higher genetic risk (particularly those with a genetic risk ≥80%) to maximize the potential health benefits of lipid management. Considering the extremely low treatment and control rates of dyslipidemia in China, it is recommended to use polygenic risk scores to encourage high-risk groups to strictly adhere to healthy lifestyle changes.

[0042] Overall, the present study found that blood lipid levels, annual average change, and the risk of dyslipidemia were significantly correlated with polygenic genetic risk, clarifying the relationship between annual average change in blood lipids and age and genetic risk in Chinese adults. This suggests that prevention strategies for blood lipids should focus on individuals at high genetic risk and those in a critical age window. It is recommended that in blood lipid prevention and treatment, genetic risk should be utilized and critical age windows should be considered to optimize precise blood lipid prevention strategies and reduce the subsequent burden of cardiovascular disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figures 1A to 1D Shown are the distributions of the polygenic genetic risk scores for blood lipids.

[0044] Figures 2A to 2D Shows the changing trend of blood lipid levels with polygenic genetic risk.

[0045] Figures 3A to 3D Shows the changing trend of the average annual change in blood lipids with polygenic genetic risk.

[0046] Figures 4A to 4D Shows the average annual change in blood lipids in different age groups.

[0047] 5A to 5D Shows the average annual change in blood lipids in male populations according to age and genetic score groups.

[0048] 6A to 6DShows the average annual change in blood lipids in different age and genetic score groups among women.

[0049] 7A to 7D It shows the changing trend of blood lipid levels in people who do not take lipid-lowering drugs along with polygenic genetic risk.

[0050] Figures 8A to 8D It shows the changing trend of the average annual change in blood lipids in people who do not take lipid-lowering drugs with the multi-gene genetic risk. DETAILED DESCRIPTION

[0051] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the following detailed description is now given in conjunction with specific embodiments and the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In the embodiments, all raw materials and reagents are commercially available, and the experimental methods without specifying specific conditions are conventional methods and conventional conditions well known in the art, or according to the conditions recommended by the instrument manufacturer.

[0052] Example 1

[0053] Study population

[0054] The study population was drawn from three subcohorts of the China Atherosclerotic Cardiovascular Risk Prediction Study (China-PAR), including the China Multicenter Collaborative Study of Cardiovascular Disease Epidemiology (ChinaMUCA), the China Multicenter Collaborative Study of Cardiovascular Health (InterASIA), and the China Community Intervention Study of Metabolic Syndrome and the China Family Health Study (CIMIC). ChinaMUCA, InterASIA, and CIMIC were established in 1998, 2000-2001, and 2007-2008, respectively. InterASIA and ChinaMUCA-1998 underwent their first follow-up in 2007-2008, and all three subcohorts underwent second and third follow-up in 2012-2015 and 2018-2020, respectively, according to unified criteria.

[0055] A total of 47,691 subjects aged 18 years and older with available genotype data were included in the three subcohorts, of whom 46,508 completed follow-up examinations. Further exclusions included 1,803 subjects with major chronic diseases (myocardial infarction, stroke, heart failure, renal failure, and cancer), 726 subjects without lipid profiles at baseline, and 6,662 subjects without lipid profiles at any of the three follow-up visits. A total of 37,317 Chinese subjects (all of East Asian descent) were included in the analysis.

[0056] All studies were approved by the Ethics Review Committee of Fuwai Hospital (Beijing, China). Each participant signed an informed consent form before data collection.

[0057] Data Collection

[0058] The baseline and three follow-up surveys were conducted using a standard survey protocol. Qualified investigators used standard questionnaires to collect information on the subjects' demographic characteristics, lifestyle, and medical history. Participants also underwent physical examinations (weight, height, blood pressure, etc.) and provided fasting blood samples for testing blood lipid levels, including TC, TG, and HDL-C. LDL-C levels were calculated using the Friedewald formula.

[0059] The baseline characteristics of the subjects are shown in Table 1. Among the 37,317 subjects, 15,664 (41.98%) were male, with a mean age of 51.37 years. Compared with women, men were more likely to reside in urban areas, had higher rates of smoking and drinking, and had higher education levels and dietary scores, more optimal physical activity, and lower body mass index, TC, TG, LDL-C, and HDL-C levels.

[0060] Table 1. Baseline characteristics of the study subjects

[0061] male female P-value Number of cases 15 664 21 653 Age, years old 51.57±10.87 51.22±10.78 .002 North, number of cases (%) 7921(50.57) 10 822(49.98) .26 City, number of cases (%) 3564(22.75) 4003(18.49) <.001 High school education and above, number of cases (%) 3733(23.96) 3090(14.37) <.001 Smoking, number of cases (%) 10 112(64.64) 651(3.02) <.001 Drinking, number of cases (%) 6532(41.75) 919(4.25) <.001 Ideal physical activity, number of cases (%) 10291(66.43) 12 875(60.89) <.001 Dietary score (≥2), number of cases (%) 11 461(74.18) 14 209(66.67) <.001 <![CDATA[BMI,kg / m 2 ]]> 23.56±3.44 24.15±3.75 <.001 TC, mg / dL 178.55±35.99 182.27±36.35 <.001 ln(TG) 4.79±0.56 4.80±0.54 .01 LDL-C, mg / dL 101.16±31.33 102.72±31.61 <.001 HDL-C, mg / dL 50.11±13.83 52.08±12.72 <.001

[0062] Genetic variant site selection and genotyping

[0063] Three large genome-wide association studies in East Asian populations showed genome-wide significant associations with lipid levels (P < 5 × 10 -8 ) genetic variation. In the present invention, through the pruning process (r 2 <0.6) were removed from the pool of variants associated with high linkage disequilibrium for each lipid trait. This left 126 SNPs for the construction of polygenic genetic risk scores for four lipid traits (TC-PRS, LDL-C-PRS, HDL-C-PRS, and TG-PRS). Of these, 60 SNPs were associated with TC, 44 with LDL-C, 59 with HDL-C, and 40 with TG. The present invention used multiplex PCR-targeted amplicon sequencing to genotype DNA samples from 47,691 subjects. Multiple primers were designed for each variant, and high-throughput sequencing of the target region was performed using the Illumina HiSeq XTen sequencer. The average detection rate reached 99.9% and the sequencing depth reached 990-fold. Strict quality control was performed throughout the genotyping process. Repeated quality control samples were placed on each sequencing plate, and a subset of samples were tested using the Fludigm platform to assess reproducibility. Information on all genetic variant loci is provided in Table 2.

[0064] Table 2. Genetic variants selected for constructing polygenic risk scores

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] Construction of genetic risk scores

[0071] In the present invention, the effect values of each genetic variation site for calculating the PRS of the four blood lipid trait indices are shown in Table 2. The number of each variant allele (0, 1, or 2) of each individual is weighted according to the effect value of its corresponding allele in each blood lipid index and summed to calculate the PRS of each blood lipid index separately. The calculation formula is as follows:

[0072]

[0073] Where i represents the SNP site, m represents the total number of SNP sites, β represents the effect of the SNP site on blood lipid traits, j represents the genotype of the SNP site, 0, 1, and 2 represent no mutation, heterozygous mutation, and homozygous mutation. The PRS value is the sum of the effect values of all blood lipid-related sites.

[0074] The present invention calculated the PRS of TC, TG, LDL-C and HDL-C for each research subject and found that the PRS of the four blood lipid indicators were normally distributed ( Figures 1A to 1D ). These four indicators were divided into 10 groups from small to large using deciles. The higher the group, the higher the genetic risk of the corresponding blood lipid index (Table 3).

[0075] Table 3. Lipid polygenic risk groups and cut-off values

[0076]

[0077] Changes in blood lipids

[0078] Changes in blood lipids are reflected by the average annual change in blood lipids, calculated as the difference between blood lipid levels measured in any two consecutive surveys divided by the time interval. A positive value indicates an increase in blood lipid levels, while a negative value indicates a decrease.

[0079] Dyslipidemia

[0080] According to the "Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults (2016 Revised Edition)", dyslipidemia includes hyperTC, hypertriglyceridemia, hyperLDL-C and hypoHDL-C. HyperTC, hypertriglyceridemia and hyperLDL-C are defined as TC ≥ 240 mg / dL, TG ≥ 200 mg / dL and LDL-C ≥ 160 mg / dL, respectively. HypoHDL-C is defined as HDL-C < 40 mg / dL.

[0081] Statistical analysis

[0082] The present invention used generalized estimating equations to assess the relationship between PRS and lipid levels and annual mean change in lipids (linear regression model), as well as dyslipidemia (logistic regression model). The analysis took into account a repeated measures design and estimated its confidence interval using empirical standard errors. Potential confounders were selected based on previous literature. Univariate analysis further identified (P < 0.05) the covariates ultimately included in the multivariate analysis, including region (north / south), area (urban / rural), subcohort, sex, age, education level, lipid levels, smoking, alcohol consumption, body mass index, physical activity, dietary score, and survey year. In addition, the median PRS within each PRS group was used as a continuous variable and included in generalized estimating equations to assess the linear trend of the association between lipid levels, annual mean change in lipids, and dyslipidemia. To exclude the influence of lipid-lowering treatment on the study results, participants who received lipid-lowering treatment during the study period were further excluded and a sensitivity analysis was performed.

[0083] Relationship between PRS and blood lipid levels

[0084] This study evaluated the relationship between polygenic genetic risk scores and four lipid profiles. The results showed that as polygenic genetic risk increased, TC, TG, LDL-C, and HDL-C levels all showed a significant upward trend (trend P values < 0.001) ( Figures 2A to 2DFor example, compared with the group with TC-PRS of 40%-49%, the group with TC-PRS of <10%, 10%-19%, 20%-29%, and 30%-39% had an average decrease in TC level of -15.5 (95% CI: -16.8, -14.2) mg / dL, -7.90 (95% CI: -9.21, -6.59) mg / dL, -5.99 (95% CI: -7.28, -4.71) mg / dL, and -2.70 (95% CI: -4.00, The mean TC level increased by 1.62 (95% CI: 0.30, 2.93) mg / dL, 4.44 (95% CI: 3.14, 5.74) mg / dL, 6.43 (95% CI: 5.11, 7.76) mg / dL, and 11.00 (95% CI: 9.65, 12.35) mg / dL, respectively, in the 60%-69%, 70%-79%, 80%-89%, and ≥90% TC-PRS groups (Table 4). This pattern was also observed for TG, LDL-C, and HDL-C, and was present in both men and women.

[0085] Table 4. Changes in blood lipids in different genetic risk groups

[0086]

[0087]

[0088] Relationship between PRS and annual average change of blood lipids

[0089] With the increase of polygenic genetic risk, the annual average changes of TC, TG, LDL-C and HDL-C also showed a significant increasing trend (trend P values < 0.001) ( Figures 3A to 3D ). For example, taking the population with a TC-PRS of 40%-49% as the reference, the annual mean change in TC in the population with a TC-PRS of <10%, 10%-19%, and 20%-29% decreased by -1.01 (95% CI: -1.19, -0.83) mg / dL, -0.57 (95% CI: -0.74, -0.40) mg / dL, and -0.47 (95% CI: -0.64, -0.30) mg / dL, respectively. However, the annual mean change in TC in the population with a TC-PRS of 70%-79%, 80%-89%, and ≥90% increased by 0.28 (95% CI: 0.11, 0.46) mg / dL, 0.34 (95% CI: 0.16, 0.51) mg / dL, and 0.67 (95% CI: 0.49, 0.85) mg / dL, respectively (Table 5). The situation of other blood lipid indicators is similar, with similar trends in men and women.

[0090] In addition, this study also found that the blood lipid levels of the group with lower polygenic genetic risk tended to change to lower levels, while the blood lipid levels of the group with higher polygenic risk developed to higher levels at a greater rate. For example, in the group with PRS <10%, the average annual changes in TC, TG, LDL-C and HDL-C were -0.39 (95% CI: -0.52, -0.26) mg / dL, -1.82 (95% CI: -2.10, -1.53) mg / dL, -0.33 (95% CI: -1. I: -0.44, -0.23) mg / dL and -0.19 (95% CI: -0.24, -0.14) mg / dL, while those with a score of ≥90% had an average annual change of 1.29 (95% CI: 1.16, 1.42) mg / dL, 4.92 (95% CI: 4.37, 5.47) mg / dL, 0.80 (95% CI: 0.69, 0.92) mg / dL and 0.58 (95% CI: 0.53, 0.63) mg / dL.

[0091] Table 5. Changes in the average annual changes in blood lipids in different genetic risk groups

[0092]

[0093] The relationship between age and the average annual change in blood lipids: The present invention evaluates the relationship between age and the average annual change in blood lipids ( Figures 4A to 4D ), and found that their correlations differed significantly between men and women. In men, after multivariate adjustment, the average annual changes in TC, TG, and LDL-C gradually decreased with age, while the average annual change in HDL-C gradually increased. However, in women, the average annual changes in TC, TG, and LDL-C showed an inverted V-shaped relationship with age, with the highest average annual changes occurring in the 40-49 age group. The average annual change in HDL-C in women increased significantly after the age of 60. In people over 40 years old, the average annual changes in TC, TG, and LDL-C in women were significantly higher than those in men.

[0094] Relationship between age and PRS and the average annual change in blood lipids: In both sexes and in different age groups, the average annual change in the four blood lipid indicators was positively correlated with genetic risk ( 5A to 5D and 6A to 6DUsing a PRS <20% as the low genetic risk group, a PRS 20%-79% as the medium genetic risk group, and a PRS ≥80% as the high genetic risk group, the results showed that within the low genetic risk group, lipid levels tended to gradually decrease or remain stable at each age group. For example, among men at low genetic risk, the mean annual changes in LDL-C in the age groups <40, 40-49, 50-59, and ≥60 years were -0.35 (95% CI: -0.70, 0.01), -0.36 (95% CI: -0.59, -0.12), -0.21 (95% CI: -0.41, -0.01), and -0.45 (95% CI: -0.66, -0.24) mg / dL, respectively. However, those at high genetic risk had the greatest mean annual changes in lipids across all age groups. The association between mean annual change in TG and genetic risk was moderated by age (interaction P values for both men and women were less than 0.001). In men, the difference in mean annual change in TG between the low and high genetic risk groups decreased dramatically from 8.47 (95% CI: 5.81, 11.13) mg / dL in those <40 years to 3.27 (95% CI: 2.37, 4.16) mg / dL in those ≥60 years, and from 7.00 (95% CI: 5.96, 8.05) mg / dL in those 40-49 years to 3.73 (95% CI: 2.70, 4.75) in those ≥60 years.

[0095] Relationship between PRS and dyslipidemia

[0096] PRS was associated with the risk of dyslipidemia. With the increase of polygenic genetic risk, the risk of high TC, high TG, and high LDL-C gradually increased, while the risk of low HDL-C gradually decreased (trend P values were all < 0.001) (Table 6). Compared with the population with TC-PRS of 40%-49%, the risk of high TCemia in the population with scores of <10%, 10%-19% and 20%-29% decreased by 27%, 30% and 24%, respectively, while the risk of high TCemia in the population with scores of 80%-89% and ≥90% increased by 25% and 46%, respectively; compared with the population with TG-PRS of 40%-49%, the risk of high TGemia in the population with scores of <10% and 10%-19% decreased by 39% and 30%, respectively, while the risk of high TGemia in the population with scores of 50%-59%, 60%-69%, 70%-79%, 80%-89% and ≥90% increased by 15%, 15%, 49%, 57% and 120%, respectively; compared with the population with LDL-C-PRS of Compared with those with a HDL-C PRS of 40%-49%, those with scores of <10% and 10%-19% had a 20% and 18% lower risk of high LDL-C, respectively, while those with scores of 60%-69%, 70%-79%, 80%-89%, and ≥90% had a 25% higher risk of high LDL-C, 32% higher risk, 45% higher risk, and 55% higher risk, respectively. Compared with those with a HDL-C PRS of 40%-49%, those with scores of <10%, 10%-19%, 20%-29%, and 30%-39% had a 76% higher risk of low HDL-C, 40% higher risk, 19% higher risk, and 17% higher risk, respectively, while those with scores of 70%-79%, 80%-89%, and ≥90% had a 12% lower risk, 16% lower risk, and 32% lower risk, respectively. This trend was similar in both sexes.

[0097] Table 6. Relationship between polygenic genetic risk and the incidence of dyslipidemia

[0098]

[0099] In order to avoid the influence of blood lipid treatment on the results, the present invention further eliminated patients who received lipid-lowering treatment and conducted sensitivity analysis, and the results did not change significantly ( 7A to 7D 、 Figures 8A to 8D and Table 7 ).

[0100] Table 7. Relationship between PRS and the incidence of dyslipidemia in people not taking lipid-lowering drugs

[0101]

[0102] Example 2

[0103] Practical application case: The test results of a Chinese Han male individual are analyzed and processed: the test results of each SNP are compared with Table 2 to find the genetic contribution of the corresponding effect allele at each site, and the formula The calculated genetic risk scores for TC, TG, LDL-C, and HDL-C were 4.1426, 2.4300, 4.0433, and 3.0939, respectively. Table 3 shows that the TC, TG, LDL-C, and HDL-C groups for this individual were ≥90%, 70%-79%, ≥90%, and 10%-19%, respectively. Tables 4-6 for the male group show that compared with the general population (PRS at 40%-49%), their TC, TG, and LDL-C levels were increased by 10.85 (95% CI: 8.79, 12.90) mg / dL, 23.55 (95% CI: 17.42, 29.67) mg / dL, and 8.67 (95% CI: 6.87, 10.47) mg / dL, respectively, with an average annual change of The serum HDL-C level decreased by -2.71 (95% CI: -3.42, -1.99) mg / dL, the average annual change decreased by -0.24 (95% CI: -0.35, -0.14) mg / dL, and the risk of low HDL-C increased by 39% (16%, 66%). Therefore, it is strongly recommended that they pay attention to their diet and lifestyle, have regular physical examinations, closely monitor changes in blood lipids, and provide timely symptomatic treatment once the disease occurs.

Claims

1. Use of a reagent for detecting individual information in the preparation of a detection device for assessing the risk of dyslipidemia, wherein: The individual information includes one or more sets of the following single nucleotide polymorphism site information: Group I: rs1077834, rs10889353, rs11136341, rs1129555, rs11557092, rs1169288, rs11771146 2. rs12027135, rs12042319, rs12453914, rs1260326, rs12740374, rs12927205, rs13277801 , rs13306194, rs1367117, rs1495741, rs151193009, rs1532085, rs17122278, rs17358402, rs174546, rs174547, rs1800588, rs1883025, rs2000999, rs200990725, rs2043085, rs20667 14. rs2081687, rs2230808, rs2297991, rs247616, rs2575876, rs2642442, rs312949, rs384 6663, rs4377290, rs439401, rs4883201, rs4939883, rs507666, rs579459, rs58542926, rs65 1821, rs6871667, rs6882076, rs7185272, rs7258950, rs72654473, rs7306523, rs737337, rs 7525649, rs7616006, rs769449, rs7770628, rs7965082, rs9357121, rs9376090, rs9390698; Group II: rs10096633, rs1037814, rs10889353, rs12042319, rs1260326, rs130071, rs13306194, rs1495741, rs1532085, rs157582, rs16990971, rs17145738, rs174546, rs174547, rs1800234, rs1800588, rs180327, rs1832007, rs2043085, rs2068888, rs2075260, rs2075291, rs2081687, rs2144300, rs3129853, rs35332062, rs439401, rs4719841, rs58542926, rs651821, rs6818397, rs6831256, rs6882076, rs6905288, rs72654473, rs738409, rs7499892, rs769449, rs7897379, rs995000; Group III: rs11125936, rs11136341, rs1129555, rs11557092, rs1169288, rs117711462, rs12027135, rs12453914, rs12740374, rs12927205, rs13277801, rs13306194, rs1367117, rs151193009, rs17135399, rs17358402, rs191835914, rs2000999, rs200990725, rs2081687, rs2328223, rs2642442, rs312949, rs3846663, rs4302748, rs507666, rs579459, rs58542926, rs6065311, rs6871667, rs6882076, rs7185272, rs7258950, rs7306523, rs737337, rs7499892, rs7525649, rs769449, rs7770628, rs7901016, rs7965082, rs9357121, rs9390698, rs9534262; Group IV: rs10096633, rs10773003, rs1077834, rs11869286, rs12718465, rs12801636, rs12970066, rs13702, rs148910227, rs1532085, rs1689800, rs17145738, rs174546, rs17 4547, rs17695224, rs1800588, rs180327, rs181359, rs181360, rs1883025, rs2000813 , rs2043085, rs2066714, rs2068888, rs2075291, rs2156552, rs2230808, rs2245019, rs 2292318, rs2296172, rs2297991, rs2303790, rs247616, rs2575876, rs2925979, rs297 2143, rs326214, rs3785100, rs4129767, rs4142995, rs4148008, rs439401, rs4883263, rs4917014, rs4939883, rs499974, rs634501, rs651821, rs671, rs6905288, rs702485, rs7134594, rs7208487, rs737337, rs7499892, rs769449, rs838880, rs884366, rs9593; The risk of dyslipidemia includes at least one of the following: blood lipid levels; Average annual changes in blood lipid indicators; and / or Risk of developing abnormal blood lipid indicators; The blood lipid indexes include: total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and / or high-density lipoprotein cholesterol (HDL-C); The risk of abnormal blood lipid indicators includes: the risk of high TC blood, the risk of high TG blood, the risk of high LDL-C blood and / or the risk of low HDL-C blood; Among them, the single nucleotide polymorphism site information of group I is used to evaluate the TC level, the average annual change of TC and / or the risk of high TC blood disease; the single nucleotide polymorphism site information of group II is used to evaluate the TG level, the average annual change of TG and / or the risk of high TG blood disease; the single nucleotide polymorphism site information of group III is used to evaluate the LDL-C level, the average annual change of LDL-C and / or the risk of high LDL-C blood disease; the single nucleotide polymorphism site information of group IV is used to evaluate the HDL-C level, the average annual change of HDL-C and / or the risk of low HDL-C blood disease; Among them, the polygenic risk score (PRS) of blood lipids was obtained according to the information of each single nucleotide polymorphism site in accordance with the following calculation method: Where i represents the SNP site, m represents the total number of SNP sites, β represents the effect of the SNP site on blood lipid traits, j represents the genotype of the SNP site, 0, 1, and 2 represent no mutation, heterozygous mutation, and homozygous mutation, and the PRS value is the sum of the effect values of all blood lipid-related sites; The effect size of each SNP is shown in Table 2 ; The higher the PRS score of blood lipid index, the higher the blood lipid index level; The higher the PRS score of blood lipid index, the greater the average annual change of blood lipid index; and / or The higher the PRS score of blood lipid indicators, the higher the risk of developing abnormal blood lipid indicators.

2. The use according to claim 1, wherein The individual is from an East Asian population.

3. The use according to claim 1, wherein: The individual information includes the following single nucleotide polymorphism site information: rs10096633, rs1037814, rs10773003, rs1077834, rs10889353, rs11125936, rs11136341, rs1129555, rs11557092, rs1169288, rs117711462, rs11869286, rs12027135, rs12042319, rs12453914, rs1260326, rs12718465, rs12740374, rs12801636, rs12927205, rs12970066 , rs130071, rs13277801, rs13306194, rs1367117, rs13702, rs148910227, rs1495741, rs151193009, rs1532085, rs157582, rs1689800, rs16990971, rs17122278, rs17135399, rs17145738, rs17358402, rs174546, rs174547, rs17695224, rs1800234, rs1800588, rs180327, rs181359, rs181360, rs183 2007, rs1883025, rs191835914, rs2000813, rs2000999, rs200990725, rs2043085, rs2066714, rs2068888, rs2075260, rs2075291, rs2081687, rs214 4300, rs2156552, rs2230808, rs2245019, rs2292318, rs2296172, rs2297991, rs2303790, rs2328223, rs247616, rs2575876, rs2642442, rs2925979, r s2972143, rs312949, rs3129853, rs326214, rs35332062, rs3785100, rs3846663, rs4129767, rs4142995, rs4148008, rs4302748, rs4377290, rs4394 01. rs4719841, rs4883201, rs4883263, rs4917014, rs4939883, rs499974, rs507666, rs579459, rs58542926, rs6065311, rs634501, rs651821, rs671,<h2 style=";text-align:left;direction:ltr">rs6818397, rs6831256, rs6871667, rs6882076, rs6905288, rs702485, rs7134594, rs7185272, rs7208487, rs7258950, rs72654473, rs7306523, rs737337, rs738409, rs749989 2, rs7525649, rs7616006, rs769449, rs7770628, rs7897379, rs7901016, rs7965082, rs838880, rs884366, rs9357121, rs9376090, rs9390698, rs9534262, rs9593, rs995000.

4. A dyslipidemia risk assessment device, comprising a detection unit and a data analysis unit, wherein: The detection unit is used to detect information from the individual to be tested and obtain a detection result; wherein the individual information is the individual information described in claim 1; The data analysis unit is used to analyze and process the detection results of the detection unit; wherein the blood lipid polygenic genetic risk score described in claim 1 is obtained.

5. The dyslipidemia risk assessment device according to claim 4, wherein: When the data analysis unit analyzes and processes the detection result of the detection unit, it includes: matching the detection result of the single nucleotide polymorphism site with a weight coefficient to calculate the blood lipid index PRS score of the individual to be tested.

6. The dyslipidemia risk assessment device according to claim 5, wherein: The data analysis unit further includes: The output module is used to receive the PRS score information of blood lipid indicators and output it as the dyslipidemia risk diagnosis classification result.

7. A computer storage medium storing computer program instructions, wherein when executed, the computer program instructions are configured to: obtain an individual dyslipidemia risk assessment result based on information of the individual to be tested; in, The individual information and dyslipidemia risk assessment results are the same as the individual information and dyslipidemia risk assessment results described in claim 1.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the following steps are achieved: obtaining an individual dyslipidemia risk assessment result based on the individual information to be tested; The individual information and dyslipidemia risk assessment results are the same as the individual information and dyslipidemia risk assessment results described in claim 1.

Images