Calcium preparation sensitive subtype prediction method and system based on methylation sites

Abstract

The invention discloses a method and a system for predicting the sensitivity of a patient to a calcium preparation based on 28 methylation sites. According to the invention, a risk scoring formula Marker of the sensitive subtype of the calcium preparation is constructed according to the 28 methylation markers. Researches show that the method can accurately and conveniently predict the calcium preparation sensitive subtype of the to-be-detected object and guide a medication scheme by performing methylation site sequencing on the to-be-detected object and calculating the Marker value, and has an application prospect.

Description

Technical Field

[0001] This invention relates to the fields of molecular biology and medical diagnostics, specifically to a method and system for predicting a patient's sensitive subtype to calcium preparations using DNA methylation sites, which can be used for clinical intervention. Background Technology

[0002] Calcium supplementation is a fundamental prevention and treatment strategy for osteoporosis and fragility fractures. Current international and domestic guidelines widely recommend calcium supplementation to prevent osteoporosis and fragility fractures in clinical treatment of osteoporosis; however, the effectiveness of calcium supplementation remains controversial in the academic community. A meta-analysis of 33 randomized controlled clinical trials (a total of 51,145 participants) showed that calcium supplementation did not reduce the risk of hip fractures compared to placebo or no intervention, and was not significantly associated with the incidence of vertebral fractures, non-vertebral fractures, or overall fractures. Another systematic study involving 12 prospective cohorts (170,991 women and 68,606 men) and 5 clinical trials (6,740 participants) also indicated that calcium intake was not significantly associated with the risk of hip fractures. Other studies suggest that calcium supplementation alone can effectively improve bone mineral density and significantly reduce the incidence of fragility fractures. The contradictory findings in the above studies suggest that the efficacy of calcium supplements may be influenced by unidentified individual factors, leading to high heterogeneity among different patients.

[0003] Currently, a prospective multi-omics atlas of osteoporosis in China has been established. This study uses deep hidden space fusion (DLSF) to integrate and analyze multi-omics data, establishing the M3S multimodal molecular subtyping based on molecular phenotypes (such as differences in bone mineral density (BMD) index and changes in the bone turnover marker BTM) and clinical relevance (such as calcium supplementation response). This subtyping divides the population into two subtypes: CIS1 (calcium-sensitive) and CIS2 (calcium-insensitive). CIS1 is more sensitive to calcium supplements, showing significant improvement in BMD during 2 years of follow-up, while CIS2 has a weaker response, possibly due to calcium homeostasis disturbances (such as hypermethylated genes and microbe-metabolite interactions). Furthermore, 4-year follow-up shows a lower fracture risk in CIS1. An independent Jinshan cohort (166 participants) validated the M3S model's AUC for predicting osteoporosis at 0.819 (related research findings on the M3S model have been published). The above research results show that the M3S model can preliminarily identify calcium-sensitive subtypes. However, this model relies on multi-omics data, is complex to operate, and lacks a simple single-modality prediction method and clinical validation. Therefore, this invention develops a simple prediction model based on screening methylation sites to improve clinical applicability, and validates it in conjunction with clinical intervention data.

[0004] Therefore, there is an urgent need in the field for a simple method and system that can predict patients’ sensitivity to calcium preparations, in order to solve the problems of complex prediction models and difficulties in clinical translation, thereby providing guidance for the clinical treatment of the corresponding diseases. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting a patient's sensitivity to calcium supplements based on methylation biomarkers.

[0006] In a first aspect, the present invention provides the use of a methylation marker and a detection reagent thereof for preparing a diagnostic reagent or kit, said diagnostic reagent or kit being used for: (i) typing a subject for calcium-sensitive subtypes; and / or (ii) determining whether a subject is suitable for treatment with calcium preparations;

[0007] The methylation markers include:

[0008] Group A includes: (A1) cg05952841; (A2) cg16540842; (A3) cg17501300; (A4) cg07262842; (A5) cg09825153; (A6) cg19283928; (A7) cg16114258; (A8) cg10593135; (A9) cg05046829; (A10) cg16655388; (A11) cg13509608; (A12) cg08839306; (A13) cg10871345; and

[0009] Group B, including: (B1)cg23088318; (B2)cg15508935; (B3)cg00638797; (B4)cg15320980; (B5)cg18243574; (B6)cg18249968; (B7)cg09656629; (B8)cg0 0431894; (B9) cg25960393; (B10) cg00329101; (B11) cg09057517; (B12 )cg11070274; (B13)cg21813369; (B14)cg22026953; (B15)cg18008345.

[0010] In another preferred embodiment, group A includes:

[0011]

[0012]

[0013] In another preferred embodiment, group B includes:

[0014] serial number name Location chain B1 cg23088318 chr15:24848838-24848839 positive links B2 cg15508935 chr13:61184576-61184577 anti-link B3 cg00638797 chr15:52155596-52155597 anti-link B4 cg15320980 chr8:632908-632909 anti-link B5 cg18243574 chr16:78022507-78022508 anti-link B6 cg18249968 chr21:34873287-34873288 anti-link B7 cg09656629 chr6:162151058-162151059 anti-link B8 cg00431894 chr4:188949858-188949859 positive links B9 cg25960393 chr8:9249049-9249050 anti-link B10 cg00329101 chr3:14297421-14297422 positive links B11 cg09057517 chr6:27602711-27602712 positive links B12 cg11070274 chr8:9249100-9249101 positive links B13 cg21813369 chr4:70133645-70133646 anti-link B14 cg22026953 chr6:87054239-87054240 anti-link B15 cg18008345 chr19:1096085-1096086 positive links .

[0015] In another preferred embodiment, the object includes a human or a mammal.

[0016] In another preferred embodiment, the subjects include healthy subjects, patients with osteopenia / osteoporosis / fragility fractures, and patients suspected of having osteopenia / osteoporosis / fragility fractures.

[0017] In another preferred embodiment, the detection is performed on an ex vivo sample.

[0018] In another preferred embodiment, the ex vivo sample includes a blood sample or a body fluid sample.

[0019] In another preferred embodiment, the ex vivo sample is a DNA sample.

[0020] In another preferred embodiment, the detection reagent is selected from the group consisting of sequencing libraries, primers, antibodies, probes, nucleic acid chips, or combinations thereof.

[0021] In another preferred embodiment, the detection reagent is a specific primer for a methylation marker.

[0022] In another preferred embodiment, the detection reagent comprises a nucleotide sequence as shown in SEQ ID NO:1-54.

[0023] In another preferred embodiment, the diagnosis is selected from the group consisting of: early diagnosis, auxiliary diagnosis, or a combination thereof.

[0024] In another preferred embodiment, the diagnostic reagent includes: antibodies, primers, probes, or nucleic acid chips.

[0025] In another preferred embodiment, the diagnostic reagent further includes a pharmaceutically acceptable carrier, diluent, or excipient.

[0026] In another preferred embodiment, the kit also includes a label or instructions.

[0027] In another preferred embodiment, the typing includes calcium-sensitive type and calcium-insensitive type.

[0028] In another preferred embodiment, the results of the diagnostic reagent or kit are processed and input into a calcium-sensitive subtype prediction model to classify the subject as a calcium-sensitive subtype; and / or determine whether a subject is suitable for treatment with calcium preparations.

[0029] A second aspect of the present invention provides a method for constructing a predictive model of calcium preparation sensitive subtypes based on methylation markers, comprising the steps of:

[0030] (S1) Provides multi-omics data and bone mineral density data for patients with osteoporosis; the multi-omics data includes methylome data, metabolome data and gut microbiome data;

[0031] (S2) Use correlation analysis to screen methylome data related to the metabolomics data and the gut microbiomes data to obtain a set of methylation biomarkers A; merge the set of methylation biomarkers A with the set of methylation biomarkers B in the M3S model and remove duplicates to obtain a set of methylation biomarkers.

[0032] (S3) Use EWAS data to verify the association between the set of methylation biomarkers and calcium-sensitive subtypes; use the association as the training target, and use feature selection method to select methylation biomarkers from the set of methylation biomarkers for training the additive regression model;

[0033] (S4) When the additive regression model reaches the predetermined performance, the training is terminated and the calcium preparation sensitive subtype prediction model (Marker) is obtained.

[0034] In another preferred embodiment, the metabolomics data include: L-threonine, suberic acid, X9E tetradecenoic acid, and X5Z dodecenoic acid.

[0035] In another preferred embodiment, the gut microbiome data includes: *Streptococcus salivarius*, *Streptococcus parasanguinis*, *Streptococcus vestibularis*, *Veillonella parvula*, *Veillonella atypica*, *Streptococcus anginosus group*, *Streptococcus thermophilus*, *Klebsiella variicola*, *Klebsiella pneumoniae*, *Klebsiella quasipneumoniae*, and *Intestinibacter bartletti*.

[0036] In another preferred embodiment, step (S2) further includes using correlation analysis to screen methylomics data related to bone mineral density data.

[0037] In another preferred embodiment, the bone mineral density data includes hip bone mineral density Z-values, femoral neck bone mineral density Z-values, and lumbar bone mineral density Z-values.

[0038] In another preferred embodiment, the correlation analysis is the Spearman correlation coefficient.

[0039] In another preferred embodiment, the feature selection method is incremental feature selection.

[0040] In another preferred embodiment, the set of methylation markers includes:

[0041] Group A includes: (A1) cg05952841; (A2) cg16540842; (A3) cg17501300; (A4) cg07262842; (A5) cg09825153; (A6) cg19283928; (A7) cg16114258; (A8) cg10593135; (A9) cg05046829; (A10) cg16655388; (A11) cg13509608; (A12) cg08839306; (A13) cg10871345; and

[0042] Group B, including: (B1)cg23088318; (B2)cg15508935; (B3)cg00638797; (B4)cg15320980; (B5)cg18243574; (B6)cg18249968; (B7)cg09656629; (B8)cg0 0431894; (B9) cg25960393; (B10) cg00329101; (B11) cg09057517; (B12 )cg11070274; (B13)cg21813369; (B14)cg22026953; (B15)cg18008345.

[0043] In another preferred example, the Marker is calculated as follows:

[0044] Marker=-cg05952841-cg16540842-cg17501300-cg07262842+cg09825153-cg19283928-cg16114258-cg10593135-cg05046829.

[0045] A third aspect of the present invention provides a prediction system for calcium preparation sensitivity subtypes, the prediction system comprising:

[0046] An input module, configured to input data, the data including methylation level data of the test object;

[0047] The prediction and diagnosis module includes:

[0048] A prediction unit, configured as a prediction model for a calcium preparation sensitivity subtype, the prediction model predicts the calcium preparation sensitivity subtype based on the methylation level data, and outputs the prediction result; the prediction model is constructed using the method of claim 2.

[0049] The output module outputs the results of the prediction and diagnosis module.

[0050] In another preferred embodiment, alignment data is obtained by aligning the methylation sequencing data of the test object to a reference genome, and the methylation level data is obtained based on the alignment data.

[0051] In another preferred embodiment, the prediction and diagnosis module further includes:

[0052] A diagnostic module is configured to generate a treatment plan based on the prediction results of the prediction unit.

[0053] A fourth aspect of the present invention provides a reagent kit, the reagent comprising:

[0054] (I) Specific primers for methylation markers, including:

[0055] (a1) Primer pairs with nucleotide sequences as shown in SEQ ID NO:1-2;

[0056] (a2) Primer pairs with nucleotide sequences as shown in SEQ ID NO:3-4;

[0057] (a3) Primer pairs with nucleotide sequences as shown in SEQ ID NO:5-6;

[0058] (a4) Primer pairs with nucleotide sequences as shown in SEQ ID NO:7-8;

[0059] (a5) Primer pairs with nucleotide sequences as shown in SEQ ID NO:9-10;

[0060] (a6) Primer pairs with nucleotide sequences as shown in SEQ ID NO:11-12;

[0061] (a7) Primer pairs with nucleotide sequences as shown in SEQ ID NO:13-14;

[0062] (a8) Primer pairs with nucleotide sequences as shown in SEQ ID NO:15-16;

[0063] (a9) Primer pairs with nucleotide sequences as shown in SEQ ID NO:17-18; and

[0064] (II) Pharmaceutically acceptable carriers, diluents or excipients.

[0065] In another preferred embodiment, the specific primers for the methylation marker further include:

[0066] (a10) Primer pairs with nucleotide sequences as shown in SEQ ID NO:19-20;

[0067] (a11) Primer pairs with nucleotide sequences as shown in SEQ ID NO:21-22;

[0068] (a12) Primer pairs with nucleotide sequences as shown in SEQ ID NO:23-24;

[0069] (a13) Primer pairs with nucleotide sequences as shown in SEQ ID NO:25-26;

[0070] (b1) Primer pairs with nucleotide sequences as shown in SEQ ID NO:27-28;

[0071] (b2) Primer pairs with nucleotide sequences as shown in SEQ ID NO:29-30;

[0072] (b3) Primer pairs with nucleotide sequences as shown in SEQ ID NO:31-32;

[0073] (b4) Primer pairs with nucleotide sequences as shown in SEQ ID NO:33-34;

[0074] (b5) Primer pairs with nucleotide sequences as shown in SEQ ID NO:35-36;

[0075] (b6) Primer pairs with nucleotide sequences as shown in SEQ ID NO:37-38;

[0076] (b7) Primer pairs with nucleotide sequences as shown in SEQ ID NO:39-40;

[0077] (b8) Primer pairs with nucleotide sequences as shown in SEQ ID NO:41-42;

[0078] (b9) Primer pairs with nucleotide sequences as shown in SEQ ID NO:43-44;

[0079] (b10) Primer pairs with nucleotide sequences as shown in SEQ ID NO:45-46;

[0080] (b11) Primer pairs with nucleotide sequences as shown in SEQ ID NO:47-48;

[0081] (b13) Primer pairs with nucleotide sequences as shown in SEQ ID NO:49-50;

[0082] (b14) Primer pairs with nucleotide sequences as shown in SEQ ID NO:51-52; and

[0083] (b15) Primer pairs of nucleotide sequences as shown in SEQ ID NO:53-54.

[0084] In another preferred embodiment, the specific primers for the methylation marker are stored in the same or different containers.

[0085] In another preferred embodiment, the kit also includes a label or instructions.

[0086] In another preferred embodiment, the label or instructions specify that the kit is used for methylation sequencing of a subject; and / or for calcium-sensitive subtype typing of a subject; and / or for determining whether a subject is suitable for treatment with calcium preparations.

[0087] A fifth aspect of the present invention provides an electronic device including a processor and a memory, the memory having a plurality of executable instructions, the processor being configured to read the instructions and execute a step in a method for calcium-sensitive subtype typing of a test subject; and / or determining whether a subject is suitable for treatment with calcium preparations, the method comprising the steps of:

[0088] (Z1) Provides methylation sequencing data of the object to be tested, and preprocesses the methylation sequencing data to obtain alignment data;

[0089] (Z2) Obtain the methylation level of the test object based on the comparison data;

[0090] (Z3) Calculate the Marker value based on the methylation level and compare the Marker value with the reference / standard value; wherein, if the Marker value of the test subject is higher than the reference / standard value, it indicates that the test subject is calcium sensitive and / or the test subject is suitable for treatment with calcium preparations; if the Marker value of the test subject is lower than the reference / standard value, it indicates that the test subject is calcium insensitive and / or the test subject is not suitable for treatment with calcium preparations.

[0091] In another preferred embodiment, in step (Z1), the preprocessing includes the following steps:

[0092] (z1.1) Perform quality control on the methylation sequencing data to obtain quality-controlled sequencing data;

[0093] (z1.2) Align the sequencing data to the reference genome to obtain alignment data.

[0094] In another preferred embodiment, the methylation sequencing data is obtained by performing methylation sequencing on a sample taken from the object to be tested.

[0095] In another preferred embodiment, the reference genome is the human reference genome hg19.

[0096] In another preferred embodiment, the methylation sequencing is multiplex methylation sequencing.

[0097] In another preferred embodiment, the sample includes a blood sample or a body fluid sample.

[0098] In another preferred embodiment, the sample is a DNA sample.

[0099] In another preferred embodiment, the reference / standard value is -400.55.

[0100] In another preferred embodiment, the calcium preparation comprises calcium tablets.

[0101] A sixth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when read and executed by a processor, implement steps in a method for typing a test subject for calcium-sensitive subtypes and / or determining whether a subject is suitable for treatment with calcium preparations, the method comprising the steps of:

[0102] (Z1) Provides methylation sequencing data of the object to be tested, and preprocesses the methylation sequencing data to obtain alignment data;

[0103] (Z2) Obtain the methylation level of the test object based on the comparison data;

[0104] (Z3) Calculate the Marker value based on the methylation level and compare the Marker value with the reference / standard value; wherein, if the Marker value of the test subject is higher than the reference / standard value, it indicates that the test subject is calcium sensitive and / or the test subject is suitable for treatment with calcium preparations; if the Marker value of the test subject is lower than the reference / standard value, it indicates that the test subject is calcium insensitive and / or the test subject is not suitable for treatment with calcium preparations.

[0105] A seventh aspect of the present invention provides a computer program product comprising computer-executable instructions, which, when executed by a processor, implement a method for classifying a test subject as a calcium-sensitive subtype and / or determining whether a subject is suitable for treatment with calcium preparations, the method comprising the steps of:

[0106] (Z1) Provides methylation sequencing data of the object to be tested, and preprocesses the methylation sequencing data to obtain alignment data;

[0107] (Z2) Obtain the methylation level of the test object based on the comparison data;

[0108] (Z3) Calculate the Marker value based on the methylation level and compare the Marker value with the reference / standard value; wherein, if the Marker value of the test subject is higher than the reference / standard value, it indicates that the test subject is calcium sensitive and / or the test subject is suitable for treatment with calcium preparations; if the Marker value of the test subject is lower than the reference / standard value, it indicates that the test subject is calcium insensitive and / or the test subject is not suitable for treatment with calcium preparations.

[0109] An eighth aspect of the present invention provides a method for calcium-sensitive subtype typing of a test subject; and / or for determining whether a subject is suitable for treatment with calcium preparations, the method comprising the steps of:

[0110] (Z1) Provides a sample of the object to be tested;

[0111] (Z2) Perform methylation sequencing on the sample to obtain methylation sequencing data;

[0112] (Z3) The methylation sequencing data is preprocessed to obtain alignment data;

[0113] (Z4) Obtain the methylation level of the test object based on the comparison data;

[0114] (Z5) Calculate the Marker value based on the methylation level and compare the Marker value with the reference / standard value; wherein, if the Marker value of the test subject is higher than the reference / standard value, it indicates that the test subject is calcium sensitive and / or the test subject is suitable for treatment with calcium preparations; if the Marker value of the test subject is lower than the reference / standard value, it indicates that the test subject is calcium insensitive and / or the test subject is not suitable for treatment with calcium preparations.

[0115] In another preferred embodiment, the subjects to be tested include healthy subjects, patients with osteopenia / osteoporosis / fragility fractures, and patients suspected of having osteopenia / osteoporosis / fragility fractures.

[0116] In another preferred embodiment, the sample includes a blood sample or a body fluid sample.

[0117] In another preferred embodiment, the sample is a DNA sample.

[0118] In another preferred embodiment, the reference / standard value is -400.55.

[0119] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0120] Figure 1 A heatmap showing the correlation between methylation sites and M3S microbial and metabolic biomarkers is displayed. Red indicates a positive correlation, and blue indicates a negative correlation. Detailed Implementation

[0121] Through extensive and in-depth research, the inventors have, for the first time, proposed a method and system for predicting patient sensitivity to calcium supplements based on 28 methylation sites. Using Spearman correlation coefficients, 14 methylation sites significantly correlated with microbial and metabolic biomarkers in existing M3S models were identified. These sites were then merged with 15 methylation sites from the M3S model and duplicated, resulting in 28 methylation sites. Based on these 28 methylation sites, a risk scoring formula (Marker) was constructed. Multiplex PCR methylation sequencing was used to sequence these 28 methylation sites in the test subjects, and Marker values ​​were calculated to achieve calcium supplement sensitivity typing / prediction and guide medication regimens. This invention is based on this foundation.

[0122] In clinical practice, MultiplexBS is used to detect loci in new patients, and marker values ​​are calculated. If the marker value is greater than the threshold - 400.55, it is predicted to be type I, and calcium supplementation is recommended; otherwise, alternative treatments are considered.

[0123] It should be understood that the specific methods and experimental conditions of the invention described below in varying degrees of detail are intended to provide a substantive understanding of the invention. Definitions of certain terms used in this specification are provided below. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0124] the term

[0125] As used herein, the terms “containing” or “including (comprise)” can be open-ended, semi-closed, or closed-ended. In other words, the terms also include “consistently made of” or “made of”.

[0126] As used herein, the term “and / or” refers to and covers any and all possible combinations of one or more of the related listed items.

[0127] As used in this article, the term "significant" means that, in a hypothesis test, the observed effect (such as the difference between the experimental and control groups) is unlikely to be caused solely by random error. A hypothesis test includes: the null hypothesis (H0), which assumes that the observed effect does not exist (such as no difference between the experimental and control groups); the p-value, which is the probability of observing the current or more extreme effect when H0 is true; and the significance threshold (α). The significance threshold is typically used to determine whether a hypothesis test is significant. Generally, the significance threshold is 0.05. If the p-value ≤ α, then H0 is rejected, meaning the observed effect exists, and the result is called "significant."

[0128] The system of this invention can be a computer system. The computer system is equipped with at least one processor and a memory. The processor calls a sequence of computer-executable instructions stored in the memory to implement the process defined in this invention. Although the flowchart describes the operation steps in a specific logical order, in actual execution, the steps can be processed in parallel, their order adjusted, or partially omitted in some cases. As long as such adjustments do not deviate from the core features of the technical solution described in this invention and achieve the same technical effect, they all fall within the protection scope of this invention. This flexibility in execution order is determined by the programmable nature of computer instructions.

[0129] As used herein, the term "osteoporosis" refers to a systemic bone disease caused by various factors, mainly characterized by decreased bone density and bone mass, damage to bone microstructure, increased bone fragility, and a state prone to fractures. Currently, the diagnostic criteria for osteoporosis include diagnostic criteria based on bone density measurement and diagnostic criteria based on fragility fractures. Diagnosis based on bone density: DXA bone density is currently the common diagnostic basis for osteoporosis. For postmenopausal women and men aged 50 and above, a T-value ≤ -2.5 of the axial skeleton (lumbar vertebrae 1-4, femoral neck, or total hip) or the distal 1 / 3 of the radius measured by DXA bone density is recommended as the diagnostic criteria for osteoporosis. Diagnosis based on fragility fractures: Hip or vertebral fragility fractures can be clinically diagnosed as osteoporosis without relying on bone density measurement; fragility fractures of the proximal humerus, pelvis, or distal forearm, combined with bone density measurement showing osteopenia (-2.5 < T-value < -1.0), can be diagnosed as osteoporosis. "Fragility fractures" and "osteopenia" can be pathological manifestations of osteoporosis or pathological manifestations caused by other diseases.

[0130] As used herein, the terms "subject", "subject to be tested", and "subject" can be used interchangeably. Preferably, the subject is a human. Preferably, the subject includes healthy subjects, osteopenia / osteoporosis / fragility fracture patients, and suspected osteopenia / osteoporosis / fragility fracture patients. The "suspected osteopenia / osteoporosis / fragility fracture patients" refer to patients in the early stage of the disease, who usually have no obvious symptoms but already have a state of bone mass loss. The subject can be administered a calcium preparation; the subject can be a subject who actually does not need to supplement calcium, such as a healthy subject, or a calcium-insensitive subject.

[0131] Calcium preparation-sensitive subtype

[0132] In the present invention, patients can be classified into type I patients and type II patients by using the methods and systems of the present invention.

[0133] As used herein, the terms "type I", "calcium-sensitive", and "calcium-sensitivity type I subtype" can be used interchangeably, referring to a class of subjects who are sensitive to calcium preparations or can produce significant therapeutic effects when treated with calcium preparations. Correspondingly, the terms "type II" and "calcium-insensitive" can be used interchangeably, referring to a class of subjects who are not sensitive to calcium preparations, that is, have a certain drug resistance. Treating such subjects with calcium preparations cannot produce effective therapeutic effects.

[0134] The methylation markers of the present invention and their uses

[0135] This invention provides the use of methylation markers and their detection reagents for preparing a diagnostic reagent or kit, said diagnostic reagent or kit being used for: (i) typing a subject for calcium-sensitive subtypes; and / or (ii) determining whether a subject is suitable for treatment with calcium preparations.

[0136] As used herein, the terms "methylation marker" and "methylation site" are used interchangeably. The methylation levels of these methylation markers differ between calcium-sensitive and calcium-insensitive patients, thus allowing for patient classification and treatment guidance based on these methylation markers. The methylation markers include:

[0137] Group A, based on the methylation sequencing data provided by the cohort, methylation biomarkers obtained through correlation analysis with various metabolite and gut microbiota data were screened, including: (A1) cg05952841; (A2) cg16540842; (A3) cg17501300; (A4) cg07262842; (A5) cg09825153; (A6) cg19283928; (A7) cg16114258; (A8) cg10593135; (A9) cg05046829; (A10) cg16655388; (A11) cg13509608; (A12) cg08839306; (A13) cg10871345; and

[0138] Group B contains the following methylation markers included in the existing model M2S: (B1) cg23088318; (B2) cg15508935; (B3) cg00638797; (B4) cg15320980; (B5) cg18243574; (B6) cg18249968; (B7) cg09656629; (B8)cg00431894; (B9)cg25960393; (B10)cg00329101; (B11)cg09057517; ( B12)cg11070274; (B13)cg21813369; (B14)cg22026953; (B15)cg18008345.

[0139] Methods for detecting methylation markers at known sites are well known to those skilled in the art, such as in this invention, where the primers described in this invention are used to detect the methylation markers described in this invention.

[0140] The prediction method of the present invention

[0141] The present invention also provides a method for (ii) classifying a subject to calcium-sensitive subtypes; and / or determining whether a subject is suitable for treatment with calcium preparations, comprising the steps of:

[0142] (Z1) Provides a sample of the object to be tested;

[0143] (Z2) Perform methylation sequencing on the sample to obtain methylation sequencing data;

[0144] (Z3) The methylation sequencing data is preprocessed to obtain alignment data;

[0145] (Z4) Obtain the methylation level of the test object based on the comparison data;

[0146] (Z5) Calculate the Marker value based on the methylation level and compare the Marker value with the reference / standard value; wherein, if the Marker value of the test subject is higher than the reference / standard value, it indicates that the test subject is calcium sensitive and / or the test subject is suitable for treatment with calcium preparations; if the Marker value of the test subject is lower than the reference / standard value, it indicates that the test subject is calcium insensitive and / or the test subject is not suitable for treatment with calcium preparations.

[0147] As used in this article, “predicting calcium-sensitive subtypes” and “calcium-sensitive subtype typing” can be used interchangeably, referring to determining whether the subject to be tested is calcium-sensitive or calcium-insensitive.

[0148] sample

[0149] As used herein, the term "sample" refers to material specifically associated with a subject from which specific information relating to the subject can be determined, calculated, or inferred. A sample may consist wholly or partially of biological material from the subject. A sample may also be material that has been in contact with the subject in a manner that allows testing of the sample to provide information relating to the subject. A sample may also be material (a first material) that has been in contact with other materials (a second material), wherein the other material may not be from the subject, but the first material can be detected by the second material to determine information relating to the subject; for example, a sample may be a cleaning solution for a probe or collection tube. A sample may be a source of biological material other than that in contact with the subject, as long as those skilled in the art can still determine information relating to the subject from the sample.

[0150] Reagent test kit

[0151] This invention provides a kit comprising specific primer sequences for various methylation markers of this invention. It is understood that the methylation level of methylation sites can be determined by sequencing using these specific primers. Preferably, the specific primer sequences for the methylation markers comprise the nucleotide sequences shown in SEQ ID NO:1-18. Further, the specific primer sequences for the methylation markers also include the nucleotide sequences shown in SEQ ID NO:19-54.

[0152] Preferably, the kit of the present invention further includes a label or instructions for use of one or more components contained therein, and / or a label or instructions for use in combination with one or more additional components that may be available elsewhere or are required.

[0153] Preferably, the kit of the present invention further includes a label or instruction manual indicating that the kit is used to predict calcium-sensitive subtypes. The label or instruction manual indicates the following: detecting methylation levels at methylation markers in the subject; distinguishing between calcium-sensitive and calcium-insensitive subjects; and / or determining whether the subject is suitable for treatment with calcium supplements.

[0154] Preferably, the label or instructions also indicate methods for distinguishing whether the subject is calcium-sensitive or calcium-insensitive; and / or for determining whether the subject is suitable for treatment with calcium preparations.

[0155] Preferably, the kit further comprises one or more buffer solutions that can be used to dissolve any of the one or more components contained therein, and / or to provide suitable reaction conditions for one or more of the components. Such buffer solutions may include one or more of the following: PBS, HEPES, Tris, MOPS, Na₂CO₃, NaHCO₃, NaB, or combinations thereof. Preferably, the reaction conditions include a suitable pH, such as an alkaline pH. In some embodiments, the pH is between 7 and 10.

[0156] Preferably, any one or more of the reagent kit components can be stored in a suitable container or at a suitable temperature, such as 4 degrees Celsius.

[0157] The main advantages of this invention include:

[0158] (1) The present invention constructs a risk scoring formula based on 28 methylation sites. It can predict the calcium-sensitive subtype of the test object by only performing methylation detection on the test object and calculating the risk score, thus overcoming the dependence on multimodal data in the existing M3S model.

[0159] (2) The present invention uses multiplex PCR methylation sequencing in methylation detection, which is low in cost and simple and quick to operate.

[0160] (3) The prediction model of the present invention has been validated on clinical data. The model has a high accuracy in typing, with an AUC of about 0.9 and a classification precision of 0.8.

[0161] The present invention will be further illustrated below with reference to specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. To provide a thorough understanding of the invention, numerous specific details will be included in the following description. The invention may also be practiced without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description.

[0162] Data source:

[0163] The Chinese Community Prospective Cohort for Osteoporosis (CCCO) recruited residents from the Lujiazui and Jinshan communities in Shanghai from September 2019 to November 2023, with a total of 366 participants (including 91 participants with normal bone mass, 158 participants with osteopenia, and 117 participants with osteoporosis). All participants signed informed consent forms. Participants used dual-energy X-ray absorptiometry (DXA, Hologic Discovery CI) to measure bone mineral density (BMD) in the lumbar spine (L1-L4) and left hip joint, and calculated T-scores to diagnose osteoporosis (T ≤ -2.5 for osteoporosis, -1.0 to -2.5 for osteopenia, and ≥ -1.0 for normal bone mass). Multi-omics data included methylomics (Infinium 850K EPIC chip, 713,195 CpGs, processed with ChAMP), metabolomics (UPLC-MS / MS quantification of metabolites), and gut microbiomics (NovaSeq shallow shotgun sequencing, processed with bioBakery).

[0164] Example 1: Screening of methylation sites

[0165] This embodiment involves using correlation screening to identify methylation sites in an M3S model that are significantly associated with microbial and metabolic biomarkers. The specific steps are as follows:

[0166] Based on the publicly available M3S model (containing the following 15 methylation sites: cg23088318, cg15508935, cg00638797, cg15320980, cg18243574, cg18249968, cg09656629, cg00431894, cg25960393, cg00329101, cg09057517, cg11070274, cg21813369, cg22026953, cg18008345), using cohort multi-omics data, through Spear Spearman correlation analysis (P<0.05) identified 14 methylation sites significantly associated with microbial and metabolic biomarkers: cg05952841, cg16655388, cg16540842, cg21813369, cg17501300, cg13509608, cg07262842, cg09825153, cg19283928, cg16114258, cg08839306, cg10593135, cg05046829, and cg10871345. (See...) Figure 1 Among them, microbial and metabolic biomarkers include:

[0167] Eleven microorganisms: Streptococcus salivarius, Streptococcus parasanguinis, Streptococcus vestibularis, Veillonella parvula, Veillonella atypica, Streptococcus anginosus group, Streptococcus thermophilus, Klebsiella variicola, Klebsiella pneumoniae, Klebsiella quasipneumoniae, and Intestinibacter bartletti;

[0168] Four metabolites: L-threonine, suberic acid, X9Etetradecenoic acid, and X5Zdodecenoic acid.

[0169] The results showed that cg21813369 was a repeating site among the 15 methylation sites in the M3S model. Therefore, these 14 sites together with the 15 sites in the M3S model constitute a set of 28 sites.

[0170] Example 2: Constructing a Risk Scoring Formula

[0171] This embodiment involves constructing a risk scoring model based on a set of methylation biomarkers using a regression model.

[0172] Risk scoring formula construction: Based on the aforementioned set of 28 loci, the EWAS database was used to validate the association between loci and phenotype. The subtypes (ST) were distinguished by AUC values ​​at individual CpG loci. An additive regression model was generated by incrementally adding loci using the feature increment method (each locus coefficient was +1 or -1). With ST as the target, when the AUC in the increment first exceeded 0.9, 9 loci were selected to construct the risk scoring formula (the larger the marker value, the closer the subtype is to the calcium-sensitive type I subtype).

[0173] Marker=-cg05952841-cg16540842-cg17501300-cg07262842+cg09825153-cg19283928-cg16114258-cg10593135-cg05046829.

[0174] This formula achieves high efficiency when the number of loci is small: the AUC for distinguishing subtypes is about 0.9, the classification accuracy is about 0.8, and the significance P<0.05 when grouped in a 1:3 ratio.

[0175] Example 3: Verification of methylation sites

[0176] This embodiment involves constructing sequencing primers for 28 methylation sites to detect the methylation level of the aforementioned sites.

[0177] To detect the above-mentioned sites, this invention employs multiplex PCR methylation sequencing (MultiplexBS) technology, which includes extracting genomic DNA from patients, treating the genomic DNA with bisulfite, designing BSP primers to amplify the target fragment, and performing high-throughput sequencing to determine the methylation level of 28 CpG sites.

[0178] This method can simultaneously detect the aforementioned 28 methylation sites. For these sites, this invention provides specific BSP primer sequences, as shown in Table 1:

[0179] Table 1. BSP primer sequence list for 28 methylation sites

[0180]

[0181]

[0182] These primer sequences are specifically designed for multiplex PCR amplification at the aforementioned sites, ensuring efficient detection of methylation levels.

[0183] During prediction, the subjects were first methylated and sequenced using the aforementioned primers to obtain raw sequencing files. The raw sequencing files were then subjected to quality control. Specifically, the quality control included preliminary quality control using FastP v0.19.4 to filter adapters, contaminating sequences, and low-quality bases. This quality control process yielded clean and reliable raw data.

[0184] Bismark v0.22.1 was used to align the quality-controlled data to the human reference genome hg19 version to obtain the sequence status corresponding to methylation in the sample genome. Then, MethylKit v1.8.1 was used to calculate the sequencing coverage depth and methylation level (Beta value) at each CpG site.

[0185] Example 4: Clinical Validation and New Predictive Model

[0186] This embodiment involves using the predictive model of the present invention to predict the outcomes of 67 subjects outside the cohort, obtaining a Marker score for each subject, and classifying the subjects based on the score. The accuracy of the predictive model of the present invention was verified through drug administration experiments and follow-up studies.

[0187] Sixty-seven subjects were included and given calcium supplements (1000 mg daily) for six months. Changes in bone mineral density were observed. The MultiplexBS method described above was used to detect 28 methylation sites in the subjects. For each sample, a marker value was calculated using the risk scoring formula. Based on the score, the top 25% of marker values ​​were defined as Type I, and the rest as Type II (approximately maintaining a 1:3 ratio; Type I group: calcium-sensitive n=16; Type II group: calcium-insensitive n=51). The marker threshold was -400.55. Bone mineral density, marker values, and classifications of the 67 subjects before and after the intervention are shown in Table 2.

[0188] The results after six months of intervention showed that the type I group had significant improvement in bone mineral density in the lower lumbar spine and femur, while the type II group had no significant change, confirming the accuracy of the calcium supplement sensitivity subtype prediction model based on methylation markers in predicting calcium supplement sensitivity.

[0189] Table 2. BMD values ​​and classifications of 67 subjects before and after intervention.

[0190]

[0191]

[0192] Baseline characteristics: The two groups were comparable overall. The age of the type I group was 68.25 ± 7.90 years, and that of the type II group was 65.49 ± 7.43 years (P = 0.241); BMI was similar (type I group 24.53 ± 2.83, type II group 23.51 ± 3.09, P = 0.19); there was a significant difference in gender distribution (type I group 68.8% female, type II group 92.2%, P = 0.048); there was no significant difference in baseline BMD (see Table 3). (The Wilcoxon rank-sum test was used for continuous variables, and the chi-square test was used for categorical variables.)

[0193] Table 3: Comparison of baseline characteristics between the two groups of patients

[0194]

[0195] BMD after 6 months: After 6 months of intervention, L3 BMD (Type I group 0.95±0.21, Type II group 0.83±0.12, P=0.021), L4 BMD (Type I group 0.96±0.20, Type II group 0.83±0.14, P=0.019), and femoral BMD (Type I group 0.84±0.12, Type II group 0.75±0.11, P=0.003) in the Type I group were significantly higher than those in the Type II group (see Table 4).

[0196] Table 4. Comparison of BMD values ​​between the two groups of patients after 6 months

[0197]

[0198] Differences in BMD changes: The changes in L3 BMD (Type I 0.05±0.07, Type II 0.00±0.05, P=0.028) and L4 BMD (Type I 0.06±0.07, Type II 0.00±0.09, P=0.010) during the intervention period were significantly higher in the Type I group than in the Type II group (see Table 5).

[0199] Table 5. Comparison of BMD changes between the two groups of patients

[0200]

[0201]

[0202] The results show that type I patients are significantly better than type II patients in terms of bone mineral density improvement, which verifies the effectiveness of the model.

[0203] All documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference. Furthermore, it should be understood that after reading the foregoing teachings of this invention, those skilled in the art can make various alterations or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. Use of a methylation marker and a detection reagent thereof, characterized in that, A diagnostic reagent or kit for: (i) subtyping a subject into a calcium formulation sensitive subtype; and / or (ii) determining whether a subject is suitable for treatment with a calcium formulation; wherein the methylation markers comprise: A group comprising: (A1) cg05952841; (A2) cg16540842; (A3) cg17501300; (A4) cg07262842; (A5) cg09825153; (A6) cg19283928; (A7) cg16114258; (A8) cg10593135; (A9) cg05046829; (A10) cg16655388; (A11) cg13509608; (A12) cg08839306; (A13) cg10871345; and B group comprising: (B1) cg23088318; (B2) cg15508935; (B3) cg00638797; (B4) cg15320980; (B5) cg18243574; (B6) cg18249968; (B7) cg09656629; (B8) cg00431894; (B9) cg25960393; (B10) cg00329101; (B11) cg09057517; (B12) cg11070274; (B13) cg21813369; (B14) cg22026953; (B15) cg18008345. comprising the steps of: (S1) providing multi-omics data and bone density data of osteoporosis patients; the multi-omics data comprising methylation omics data, metabolomics data and gut microbiome data; (S2) screening methylation omics data related to the metabolomics data and the gut microbiome data using correlation analysis to obtain a methylation marker set A; merging the methylation marker set A with a methylation marker set B in the M3S model and removing duplicates to obtain a methylation marker set; (S3) verifying the association of the methylation marker set with calcium formulation sensitive subtypes using EWAS data; (S4) using feature selection method to select methylation markers from the methylation marker set for training of additive regression model with the association as the training target; (S5) when the additive regression model reaches a predetermined performance, terminating the training to obtain a calcium formulation sensitive subtype prediction model (Marker). the metabolomics data comprising: L-threonine acid, suberic acid, X9E tetradecenoic acid, and X5Z dodecenoic acid; ​ ​ 2. A method of constructing a methylation marker-based prediction model for calcium formulation sensitivity subtypes, characterized by, ​ ​ ​ ​ ​ ​ 3. The method of claim 2, wherein, ​ The gut microbiome data comprises: Streptococcus salivarius, Streptococcus parasanguinis, Streptococcus vestibularis, Veillonella parvula, Veillonella atypica, Streptococcus anginosus group, Streptococcus thermophilus, Klebsiella variicola, Klebsiella pneumoniae, Klebsiella quasipneumoniae, and Intestinibacter bartletti.

4. The method of claim 2, wherein, The Marker is calculated as follows: Marker = -cg05952841 - cg16540842 - cg17501300 - cg07262842 + cg09825153 - cg19283928 - cg16114258 - cg10593135 - cg05046829.

5. A system for predicting a calcium formulation sensitivity subtype, characterized by, The prediction system comprises: an input module configured to input data, the data comprising methylation level data of a subject to be tested; a prediction and diagnosis module comprising: a prediction unit configured as a prediction model of a calcium formulation sensitivity subtype, the prediction model predicting a calcium formulation sensitivity subtype according to the methylation level data, thereby outputting a prediction result; the prediction model being constructed by the method of claim 2; an output module outputting the results of the prediction and diagnosis module.

6. A kit characterized in that, The reagent comprises: (I) specific primers of methylation markers, comprising: (a1) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 1-2; (a2) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 3-4; (a3) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 5-6; (a4) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 7-8; (a5) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 9-10; (a6) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 11-12; (a7) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 13-14; (a8) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 15-16; (a9) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 17-18; and (a9) a primer pair of the nucleotide sequence as shown in SEQ ID NO: 17-18; and (II) a pharmaceutically acceptable carrier, diluent or excipient.

7. The kit of claim 6, wherein Specific primers of the methylation markers further include: (a10) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 19-20; (a11) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 21-22; (a12) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 23-24; (a13) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 25-26; (b1) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 27-28; (b2) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 29-30; (b3) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 31-32; (b4) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 33-34; (b5) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 35-36; (b6) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 37-38; (b7) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 39-40; (b8) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 41-42; (b9) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 43-44; (b10) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 45-46; (b11) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 47-48; (b13) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 49-50; (b14) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 51-52; and (b15) a primer pair of nucleotide sequences as shown in SEQ ID NOs: 53-54.

8. An electronic device comprising a processor and a memory, characterized in that The memory has a plurality of executable instructions, and the processor is configured to read the instructions and perform steps in a method for subtyping a calcium preparation sensitive subtype of a subject and / or determining whether a subject is suitable for calcium preparation treatment, the method comprising steps of: (Z1) providing methylation sequencing data of a subject to be tested, preprocessing the methylation sequencing data to obtain alignment data; (Z2) obtaining the methylation level of the subject to be tested according to the alignment data; (Z3) calculating a Marker value according to the methylation level, comparing the Marker value with a reference value / standard value; wherein if the Marker value of the subject to be tested is higher than the reference value / standard value, it indicates that the subject to be tested is calcium sensitive, and / or the subject to be tested is suitable for calcium preparation treatment; if the Marker value of the subject to be tested is lower than the reference value / standard value, it indicates that the subject to be tested is calcium non-sensitive, and / or the subject to be tested is not suitable for calcium preparation treatment.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, which are read and executed by the processor to implement a method for subtyping a test subject into a calcium preparation sensitive subtype, and / or determining whether a subject is suitable for calcium preparation treatment, the method comprising the steps of: (Z1) providing methylation sequencing data of the test subject, preprocessing the methylation sequencing data to obtain alignment data; (Z2) obtaining methylation level of the test subject according to the alignment data; (Z3) calculating Marker value according to the methylation level, and comparing the Marker value with a reference value / standard value; wherein if the Marker value of the test subject is higher than the reference value / standard value, it indicates that the test subject is calcium sensitive, and / or the test subject is suitable for calcium preparation treatment; if the Marker value of the test subject is lower than the reference value / standard value, it indicates that the test subject is calcium non-sensitive, and / or the test subject is not suitable for calcium preparation treatment.

10. A computer program product comprising computer executable instructions, characterised in that, The computer executable instructions are executed by the processor to implement a method for subtyping a test subject into a calcium preparation sensitive subtype, and / or determining whether a subject is suitable for calcium preparation treatment, the method comprising the steps of: (Z1) providing methylation sequencing data of the test subject, preprocessing the methylation sequencing data to obtain alignment data; (Z2) obtaining methylation level of the test subject according to the alignment data; (Z3) calculating Marker value according to the methylation level, and comparing the Marker value with a reference value / standard value; wherein if the Marker value of the test subject is higher than the reference value / standard value, it indicates that the test subject is calcium sensitive, and / or the test subject is suitable for calcium preparation treatment; if the Marker value of the test subject is lower than the reference value / standard value, it indicates that the test subject is calcium non-sensitive, and / or the test subject is not suitable for calcium preparation treatment.

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