A diagnostic method for postmenopausal osteoporosis based on oral salivary bacteria

Abstract

This invention relates to the field of medical data processing technology, specifically to a diagnostic method for postmenopausal osteoporosis (PMOP) based on oral salivary bacteria. The method includes: collecting saliva samples from the subject in the morning on an empty stomach and labeling the subject's menopausal years; obtaining the relative abundance of Lactobacillus and Streptococcus, the concentrations of propionic acid and acetic acid, and the pH value; determining the corrected bone mineral density (BMD) correlation value using a diagnostic model; and outputting suggestions based on the BMD correlation value. This invention utilizes the principle that the decline in estrogen levels in postmenopausal women leads to an imbalance in the salivary flora, with an increase in acid-producing bacteria leading to increased acetic acid secretion and a decrease in alkali-producing bacteria leading to decreased propionic acid secretion. These two factors synergistically exacerbate osteogenic inhibition and increased bone resorption. Furthermore, since most saliva is partially absorbed after swallowing, the long-term accumulation of propionic acid and acetic acid can interfere with bone metabolism through the circulatory system, thus providing a preliminary diagnosis of PMOP. By collecting only saliva samples, without blood collection or radiation, the procedure is simple, painless, and provides physicians with preliminary diagnostic suggestions.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and particularly relates to a method for diagnosing postmenopausal osteoporosis based on oral saliva bacteria. Background Art

[0002] Postmenopausal osteoporosis (PMOP) is a systemic skeletal disease that occurs in women after menopause due to a significant decrease in estrogen levels, resulting in reduced osteoblast activity, increased osteoclast activity, imbalance between bone resorption and bone formation, and ultimately leading to decreased bone mass, damaged bone microstructure, and increased fracture risk. According to global epidemiological data, the incidence of PMOP in postmenopausal women aged 45 - 65 years reaches 30% - 50%. Moreover, there are no typical clinical symptoms in the early stage of the disease. Once vertebral or hip fractures occur, the disability rate and mortality rate increase significantly, imposing a heavy burden on individuals, families, and the medical system.

[0003] Currently, the gold standard for PMOP diagnosis is dual - energy X - ray absorptiometry (DXA). By measuring the bone mineral density (BMD) of the lumbar spine and hip, the bone health status is defined by the T - value (osteoporosis when T ≤ - 2.5, osteopenia when - 2.5 < T < - 1.0, and normal when T ≥ - 1.0). At the same time, blood tests (such as bone metabolism markers like osteocalcin OC, tartrate - resistant acid phosphatase TRAP, and C - terminal cross - linked telopeptide of type I collagen CTX) are used as auxiliary diagnostic methods to reflect the dynamics of bone metabolism. However There are obvious limitations in the existing technologies: firstly, DXA equipment is expensive and the detection is radioactive, resulting in a low penetration rate in primary medical institutions. The proportion of women over 50 years old who undergo bone density testing is only 4.3%; secondly, bone metabolism biochemical indicators (such as osteocalcin OC and β - CTX) are easily interfered by diet and drugs, and their specificity is insufficient.

[0004] In recent years, the research on the correlation between oral microecology and systemic diseases has become a hot topic. As the core carrier of oral microecology, the bacterial community structure in saliva can indirectly reflect the systemic physiological and pathological states. However, up to now, the relevant research only stays at the qualitative analysis level of "there is a correlation between the bacterial flora abundance and PMOP", and a preliminary diagnosis method for PMOP based on saliva bacteria has not been established. There are still many limitations in the existing diagnostic technologies, and there is an urgent need to develop a non - invasive and low - cost method for diagnosing postmenopausal osteoporosis based on oral saliva bacteria. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a non - invasive and low - cost method for diagnosing postmenopausal osteoporosis based on oral saliva bacteria.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A diagnostic method for postmenopausal osteoporosis based on oral salivary bacteria includes: Subject selection: Postmenopausal women aged 45-65 years with ≥1 year of menopause were selected as the subjects of the study; Saliva collection: Saliva samples were collected from the test subjects in the morning on an empty stomach. Sterile saliva collection tubes were used to collect the saliva, and the test subjects' menopausal years Y were labeled. Sample processing: The saliva sample was centrifuged to remove cells and impurities. The supernatant was divided into two portions: one portion was used for abundance detection of Lactobacillus and Streptococcus, and the other portion was used for detection of the concentrations of the metabolites propionic acid and acetic acid, as well as pH value; the relative abundance of Lactobacillus was obtained. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value P; Diagnostic model: Establish saliva pH correction coefficient f(P), f(P) = 1 - Establish the salivary microbiota metabolic synergy index S, S = ( × + × )×( × + × )×f(P); Establish the corrected bone mineral density correlation value , =k×S+b×(1-α×Y;where, The normal pH value of saliva in healthy postmenopausal women; - α is the weighting coefficient; k and b are calibration coefficients; α is the menopausal age correction coefficient. Output suggestions, when When the value is ≥-1.0, it is considered to have no PMOP risk; when -2.5 < A value <-1.0 indicates a high risk of PMOP; retesting is recommended after 3 months. If the value is ≤-2.5, it is considered a suspected PMOP, and it is recommended that the subject undergo further DXA testing for confirmation.

[0007] Preferably, fasting, abstaining from water, and brushing teeth are required for 6-8 hours before collecting fasting saliva samples in the morning.

[0008] Preferably, the genus *Lactobacillus* includes *Lactobacillus salivarius* and *Lactobacillus casei*, and the genus *Streptococcus* includes *Streptococcus mutans* and *Streptococcus sanguinis*.

[0009] Preferably, abundance is detected by qPCR; propionic acid and acetic acid concentrations are detected by liquid chromatography; and pH value is detected by a pH meter.

[0010] Preferably, the pH value is the average value obtained by averaging multiple measurements when using a pH meter.

[0011] Preferably, the diagnostic model also includes a database; The database stores multiple healthy sample objects and multiple PMOP sample objects; The healthy sample consisted of healthy postmenopausal women aged 45-65 who had been menopausal for ≥1 year. The PMOP sample consisted of postmenopausal women aged 45-65 who had only PMOP and had been menopausal for ≥1 year. DXA detection was performed on all healthy samples and PMOP samples to obtain T-values; Saliva samples were collected and processed from all healthy subjects and PMOP subjects. The relative abundance of Lactobacillus spp. obtained from the sample processing was then analyzed. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value (P) and menopausal years (Y) were used as inputs to the diagnostic model, and the T value was used as the corrected bone mineral density correlation value. Output the results, train the diagnostic model, and obtain... - , k, b, α.

[0012] Preferably, The average pH value of saliva collected from all healthy subjects after sample processing.

[0013] Preferably, the training is performed using a Lasso regression or elastic network regression model. - , k, b, α.

[0014] Preferably, the training is calibrated using multiple linear regression analysis. - , k, b, α.

[0015] Preferably, a generalized additive model is used to correlate the corrected bone mineral density values. Conduct training.

[0016] The beneficial effects of this invention are as follows: Postmenopausal women experience a decline in estrogen levels, leading to an imbalance in salivary flora. An increase in acid-producing bacteria results in increased acetic acid secretion, while a decrease in alkali-producing bacteria leads to decreased propionic acid secretion. These two factors synergistically exacerbate osteogenic inhibition and hyperbacterial resorption. Humans typically secrete 1000-1500 mL of saliva per day. Most of this saliva is swallowed and absorbed. Although some acetic acid and propionic acid in saliva are broken down by stomach acid, a portion still enters the intestines and is absorbed. Long-term accumulation can interfere with bone metabolism through the circulatory system. Therefore, this invention focuses on postmenopausal women with a menopausal period of ≥1 year as the testing subjects to achieve a sufficient accumulation. Propionic acid, as an important member of short-chain fatty acids (SCFA), can regulate cells by inhibiting histone deacetylase (HDAC) and enhancing Notch... This signaling pathway promotes osteoblast proliferation and maturation while inhibiting osteoblast apoptosis and strengthening the bone formation process. Increased acetic acid secretion, on the other hand, inhibits osteoblast activity and bone formation. This application uses only saliva samples, eliminating the need for venous blood collection and radiation exposure. The operation is simple and painless. The detection targets are fixed flora and pH value. The equipment requirements are low, the cost is low, and the detection is faster, achieving "instant detection and instant diagnosis." This meets the efficiency requirements of large-scale preliminary screening and can timely screen high-risk groups in the early stage of PMOP (osteopenia stage), providing physicians with preliminary diagnostic suggestions. Further diagnostic suggestions can help detect PMOP in time and reduce fracture risk, demonstrating significant clinical application value and social benefits. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria, as described in a specific embodiment of the present invention. Detailed Implementation

[0018] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0019] Please refer to Figure 1 A diagnostic method for postmenopausal osteoporosis based on oral salivary bacteria, comprising: Subject selection: Postmenopausal women aged 45-65 years with ≥1 year of menopause were selected as the subjects of the study; Saliva collection: Saliva samples were collected from the test subjects in the morning on an empty stomach. Sterile saliva collection tubes were used to collect the saliva, and the test subjects' menopausal years Y were labeled. Sample processing involved centrifuging the saliva sample to remove cells and impurities. The supernatant was divided into two portions: one portion was used for abundance detection of *Lactobacillus* and *Streptococcus*, and the other portion was used for detecting the concentrations of metabolites propionic acid and acetic acid, as well as pH value. The relative abundance of *Lactobacillus* was then obtained. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value P; Diagnostic model, establish saliva pH correction coefficient f(P), f(P) = 1 - Establish the salivary microbiota metabolic synergy index S, S = ( × + × )×( × + × )×f(P); Establish the corrected bone mineral density correlation value , =k×S+b×(1-α×Y;where, The normal pH value of saliva in healthy postmenopausal women; - α is the weighting coefficient; k and b are calibration coefficients; α is the menopausal age correction coefficient. Output suggestions, when When the value is ≥-1.0, it is considered to have no PMOP risk; when -2.5 < A value <-1.0 indicates a high risk of PMOP; retesting is recommended after 3 months. If the value is ≤-2.5, it is considered a suspected PMOP, and it is recommended that the subject undergo further DXA testing for confirmation.

[0020] As described above, the decline in estrogen levels in postmenopausal women leads to an imbalance in salivary flora, with an increase in acid-producing bacteria resulting in increased acetic acid secretion and a decrease in alkali-producing bacteria leading to decreased propionic acid secretion. These two factors synergistically exacerbate osteogenic inhibition and hyperbacterial resorption. A person typically secretes 1000-1500 mL of saliva per day. Most of this saliva is swallowed and absorbed. While some acetic acid and propionic acid in saliva are broken down by stomach acid, a portion still enters the intestines and is absorbed. Long-term accumulation can interfere with bone metabolism through the circulatory system. Therefore, this study focuses on postmenopausal women with a postmenopausal period of ≥1 year to achieve a sufficient accumulation. Propionic acid, as an important member of short-chain fatty acids (SCFAs), can regulate cells by inhibiting histone deacetylase (HDAC) and enhancing Notch... This signaling pathway promotes osteoblast proliferation and maturation while inhibiting osteoblast apoptosis and strengthening the bone formation process. Increased acetic acid secretion, on the other hand, inhibits osteoblast activity and bone formation. This application uses only saliva samples, eliminating the need for venous blood collection and radiation exposure. The operation is simple and painless. The detection targets are fixed flora and pH value. The equipment requirements are low, the cost is low, and the detection is faster, achieving "instant detection and instant diagnosis." This meets the efficiency requirements of large-scale preliminary screening and can timely screen high-risk groups in the early stage of PMOP (osteopenia stage), providing physicians with preliminary diagnostic suggestions. Further diagnostic suggestions can help detect PMOP in time and reduce fracture risk, demonstrating significant clinical application value and social benefits.

[0021] Furthermore, fasting, abstaining from water, and brushing teeth are required for 6-8 hours before collecting fasting saliva samples in the morning.

[0022] As described above, the ideal time for sampling is generally between 7 and 9 a.m. Once you are awake, you should refrain from eating, drinking water, and brushing your teeth before sampling. This refrain ensures a natural oral environment, improving the accuracy of the sampling.

[0023] Furthermore, the genus *Lactobacillus* includes *Lactobacillus salivarius* and *Lactobacillus casei*, while the genus *Streptococcus* includes *Streptococcus mutans* and *Streptococcus sanguinis*.

[0024] Furthermore, abundance was detected using qPCR; propionic acid and acetic acid concentrations were detected using liquid chromatography; and pH was detected using a pH meter.

[0025] As can be seen from the above description, by selecting Lactobacillus salivarius and Lactobacillus casei, both of which are common acid-producing strains in the oral cavity and have a strong ability to produce acetic acid through metabolism; and Streptococcus mutans and Streptococcus sanguinis, both of which are dominant oral flora and can produce propionic acid through metabolism.

[0026] Furthermore, the pH value is the average value obtained by averaging multiple measurements using a pH meter.

[0027] As can be seen from the above description, taking multiple measurements and averaging them when using a pH meter can ensure the accuracy of the pH value.

[0028] Furthermore, the diagnostic model also includes a database; The database stores multiple healthy sample objects and multiple PMOP sample objects; The healthy sample consisted of healthy postmenopausal women aged 45-65 who had been menopausal for ≥1 year. The PMOP sample consisted of postmenopausal women aged 45-65 who had only PMOP and had been menopausal for ≥1 year. DXA detection was performed on all healthy samples and PMOP samples to obtain T-values; Saliva samples were collected and processed from all healthy subjects and PMOP subjects. The relative abundance of Lactobacillus spp. obtained from the sample processing was then analyzed. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value (P) and menopausal years (Y) were used as inputs to the diagnostic model, and the T value was used as the corrected bone mineral density correlation value. Output the results, train the diagnostic model, and obtain... - , k, b, α.

[0029] As can be seen from the above description, training the model with multiple healthy sample objects (healthy samples) and multiple PMOP sample objects (PMOP samples) can improve the accuracy of the diagnostic model's initial diagnosis.

[0030] Furthermore, The average pH value of saliva collected from all healthy subjects after sample processing.

[0031] Furthermore, training is performed using Lasso regression or elastic network regression models to obtain... - , k, b, α.

[0032] Furthermore, the training was calibrated using multiple linear regression analysis. - , k, b, α.

[0033] Furthermore, a generalized additive model was used to analyze the modified bone mineral density correlation values. Conduct training.

[0034] As can be seen from the above description, by using the generalized additive model GAM, since the rate of bone mineral density decline is fastest in women in the 5 years before menopause and the rate of decline slows down after 5 years, GAM can more accurately capture nonlinear relationships that the original linear regression cannot fit.

[0035] Example 1 A diagnostic method for postmenopausal osteoporosis based on oral salivary bacteria includes: Target population: Postmenopausal women aged 45-65 who have been menopausal for ≥1 year; Healthy sample subjects: Postmenopausal women aged 45-65 who are healthy and have been menopausal for ≥1 year; PMOP sample subjects: Postmenopausal women aged 45-65 years who only have PMOP and have been menopausal for ≥1 year; Saliva collection: Saliva samples were collected from the subjects in the morning on an empty stomach. The subjects were required to fast, abstain from water, and refrain from brushing their teeth for 6-8 hours prior to saliva collection. 5 mL of saliva was collected using a sterile saliva collection tube, and the subjects' menopausal age Y was noted. Sample processing: Centrifuge the saliva sample at 3000 rpm for 10 minutes to remove cells and impurities. Divide the supernatant into two portions: One 2 mL supernatant (with sterile DNA extraction reagent added to extract total bacterial DNA, no purification required, directly used for subsequent detection) was used to detect the abundance of Lactobacillus and Streptococcus. Lactobacillus includes Lactobacillus salivarius and Lactobacillus casei, both of which are common acid-producing strains in the oral cavity and have a strong ability to produce acetic acid. Streptococcus includes Streptococcus mutans and Streptococcus sanguinis, which can metabolize to produce propionic acid and can neutralize the acidic environment of the oral cavity. (Unit: %), Ct values ​​were detected by qPCR, and the relative abundance was calculated using the 2^(-ΔΔCt) method (ΔΔCt = (target bacterial Ct value - internal reference gene Ct value) - (Ct difference corresponding to healthy control sample). The healthy control sample is a mixed saliva sample from all healthy postmenopausal women (or a portion of them).

[0036] Another 3 mL supernatant (filtered through a 0.22 μm membrane to remove bacterial debris, kept at room temperature) was used to determine the concentrations of the metabolites propionic acid and acetic acid. The concentrations of propionic acid and acetic acid were determined by liquid chromatography (unit: μmol / L). The instrument used was a Shimadzu LC-2070. The detection conditions were as follows: a C18 short column (4.6 mm × 150 mm, to reduce detection time); a mobile phase of methanol-0.1% phosphoric acid aqueous solution (volume ratio 30:70); a flow rate of 1 mL / min; a detection wavelength of 210 nm; a column temperature of 30 °C; an injection volume of 20 μL; and a detection cycle of ≤30 minutes / sample. The concentrations were calculated using the standard curve method (standard curve regression equation R² ≥ 0.99, error ≤ 5%).

[0037] pH value detection was performed using a precision pH meter (accuracy 0.01). The electrode was inserted into the saliva supernatant, and the pH value (P) was read after stabilizing for 30 seconds. Each sample was tested 3 times, and the average value was taken as the final pH value (to avoid errors from a single test).

[0038] Obtain the relative abundance of Lactobacillus. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value P; Diagnostic model: Establish a saliva pH correction coefficient f(P), f(P) = 1 - (Formula 1) The average saliva pH value obtained after saliva collection and processing from all healthy sample subjects (or a subset of healthy sample subjects) can also be fixed at 6.8; the range is 0.8-1.0, used to correct for the interference of abnormal saliva pH value on bacterial metabolic activity (metabolite concentration). The closer the pH value is to the normal range, the closer the correction coefficient is to 1.0, and the higher the detection accuracy.

[0039] Establish the salivary microbiota metabolic synergy index S. S = ( × + × )×( × + × )×f(P) (Formula 2); Salivary microbiota metabolic synergy index S: ranging from 0.5 to 2.0, is positively correlated with bone mineral density (the higher the S, the more normal the bone mineral density; the lower the S, the higher the risk of osteoporosis).

[0040] The relative abundance of Lactobacillus (acid-producing bacteria) (unit: %) was calculated by qPCR detection, with a value range of 5%-30%. Excessive acid-producing bacteria can lead to increased acetic acid secretion and inhibit osteoblast activity, hence the lower weighting coefficient.

[0041] The relative abundance of Streptococcus (alkali-producing bacteria) (unit: %) was calculated by qPCR detection, with a value range of 40%-70%. Alkali-producing bacteria can neutralize the metabolites of acid-producing bacteria, maintain the stability of the oral microenvironment, and promote propionic acid secretion, hence the high weighting coefficient.

[0042] Propionic acid concentration (unit: μmol / L): a beneficial metabolite that enters the intestines through swallowing, is cumulatively absorbed, and then enters the bloodstream to promote osteoblast proliferation. The value range is 10-50 μmol / L.

[0043] Acetic acid concentration (unit: μmol / L): a harmful metabolite that enters the intestines through swallowing, is cumulatively absorbed, and enters the bloodstream to promote osteoclast activity and inhibit osteogenic formation. The value range is 20-80 μmol / L.

[0044] - The weighting coefficients can be obtained through multiple linear regression analysis of clinical samples (multiple healthy postmenopausal women (healthy sample subjects) and multiple PMOP patients (PMOP sample subjects), with T-values ​​obtained from DXA testing of clinical samples (gold standard)). Alternatively, they can be obtained through training a Lasso regression or elastic network regression model (during training...). , , , P and Y are used as inputs (layers), and T is used as the result (output layer); formulas 2 and 3 can be used as different intermediate layers. Fixed values ​​can also be selected; for ease of understanding, fixed values ​​are used as an example. - The values ​​are for illustrative purposes only and do not limit the specific parameters. The fixed values ​​are as follows: =3.2 (weight of acid-producing bacteria) =2.8 (weight of alkali-producing bacteria) =0.058 (propionic acid weight) =0.042 (acetic acid weight).

[0045] Establish modified bone mineral density correlation values , =k×S+b×(1-α×Y) (Formula 3); Final diagnostic indicators, with values ​​ranging from -3.5 to -0.5; k: Calibration coefficient, which can be obtained through linear regression analysis, model training, or a fixed value. For ease of understanding, a fixed value is chosen as an example (the value of k is only for illustrative purposes and its specific parameters are not limited), k=1.2; b: Calibration coefficient, as above, a fixed value is selected for explanation, and its specific parameters are not limited, b=-1.8; α: Menopausal age correction factor; a fixed value of 0.015 was chosen to reflect the effect of menopausal age on bone mineral density. The longer the menopausal age, the lower the estrogen level, the more significant the decrease in bone mineral density, and the greater the correction magnitude.

[0046] Y: Years since menopause (unit: years), confirmed by the subject's self-report and clinical records (e.g., 5 years since menopause, Y=5), with a range of 1-20 years (covering the high-incidence age group of postmenopausal PMOP).

[0047] (1-α×Y): A correction term for the duration of menopause, ranging from 0.7 to 0.97. The longer the duration of menopause, the smaller the correction term. The lower the value, the more consistent it is with the pathogenesis of PMOP (the longer the menopause period, the higher the risk of osteoporosis).

[0048] Corrected bone mineral density correlation value A generalized additive model can also be used to correlate the corrected bone mineral density values. If GAM is added during training, then Equation 2 will be obtained by training with other models. - Formula 3 uses GAM training alone to obtain k and b. The generalized additive model GAM is adopted because the rate of bone mineral density decline is fastest in women 5 years before menopause, and slows down after 5 years. GAM can more accurately capture nonlinear relationships that the original linear regression could not fit.

[0049] Output suggestions: The value is set with reference to the results of the gold standard T for easy understanding.

[0050] when When (T) ≥ -1.0, it is judged that there is no PMOP risk, and the corresponding gold standard T is within the normal range, which does not require special attention; When -2.5 < (T) < -1.0 indicates a high risk of PMOP. It is recommended to retest after 3 months. The corresponding gold standard T indicates an increased risk of osteoporosis and bone density changes should be monitored regularly. when When (T) ≤ -2.5, it is judged as suspected PMOP. It is recommended that the subject undergo further DXA testing (gold standard) for diagnosis. The corresponding gold standard T is osteoporosis, which requires timely attention and treatment.

[0051] Sample description: Sample 1: A 52-year-old woman who had experienced natural menopause for 3 years (Y=3), with no history of osteoporosis or periodontal disease, and whose lumbar spine bone mineral density T-score measured by the gold standard DXA was -0.7 (meeting the normal bone health standard), was used as a healthy control for verification.

[0052] Corresponding detection index: relative abundance of Lactobacillus 14% Relative abundance of Streptococcus 58% propionic acid concentration 33 μmol / L Acetic acid concentration 29 μmol / L Saliva pH value P = 6.76 f(P) = 1 - =1- ≈0.9941 S = ( × + × )×( × + × )×f(P)=(3.2×0.14+2.8×0.58)×(0.058×33 (0.042 × 29) × 0.9941 ≈ 1.433 =k×S+b×(1-α×Y)=1.2×1.433+( 1.8)×(1 (0.015 × 3) ≈ 0.0006 ( When the value is ≥-1.0, it is judged as having no PMOP risk, which also meets the gold standard T value = -0.7 (meeting the normal bone health standard).

[0053] Sample 2: A 59-year-old woman who had experienced natural menopause for 8 years (Y=8) had symptoms of lower back pain and no other underlying diseases. Her lumbar spine bone mineral density T-score, measured by the gold standard DXA, was -2.8 (meeting the diagnostic criteria for PMOP). She was used as a control sample for PMOP.

[0054] Corresponding detection index: relative abundance of Lactobacillus 27% Relative abundance of Streptococcus 42% propionic acid concentration 17 μmol / L Acetic acid concentration 69 μmol / L Saliva pH value P=6.12 f(P) = 1 - =1- =0.9 S = ( × + × )×( × + × )×f(P)=(3.2×0.27+2.8×0.42)×(0.058×17 (0.042 × 69) × 0.9 ≈ -3.51 =k×S+b×(1-α×Y)=1.2×-3.51+( 1.8)×(1 (0.015 × 8) ≈ -5.796 ( When the T value is ≤-2.5, it is judged as a suspected PMOP, which meets the gold standard T value = -2.8 (meets the diagnostic criteria for PMOP).

[0055] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A diagnostic method for postmenopausal osteoporosis based on oral salivary bacteria, characterized in that, include: Subject selection: Postmenopausal women aged 45-65 years with ≥1 year of menopause were selected as the subjects of the study; Saliva collection: Saliva samples were collected from the test subjects in the morning on an empty stomach. Sterile saliva collection tubes were used to collect the saliva, and the test subjects' menopausal years Y were labeled. Sample processing: The saliva sample was centrifuged to remove cells and impurities. The supernatant was divided into two portions: one portion was used for abundance detection of Lactobacillus and Streptococcus, and the other portion was used for detection of the concentrations of the metabolites propionic acid and acetic acid, as well as pH value; the relative abundance of Lactobacillus was obtained. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value P; Diagnostic model: Establish saliva pH correction coefficient f(P), f(P) = 1 - Establish the salivary microbiota metabolic synergy index S, S = ( × + × )×( × + × )×f(P); Establish the corrected bone mineral density correlation value , =k×S+b×(1-α×Y;where, The normal pH value of saliva in healthy postmenopausal women; - α is the weighting coefficient; k and b are calibration coefficients; α is the menopausal age correction coefficient. Output suggestion: When When the value is ≥-1.0, it is considered to have no PMOP risk; when -2.5 < A value <-1.0 indicates a high risk of PMOP; retesting is recommended after 3 months. If the value is ≤-2.5, it is considered a suspected PMOP, and it is recommended that the subject undergo further DXA testing for confirmation.

2. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 1, characterized in that, Fasting, abstaining from water, and brushing teeth are required for 6-8 hours before collecting fasting saliva samples in the morning.

3. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 1, characterized in that, The genus *Lactobacillus* includes *Lactobacillus salivarius* and *Lactobacillus casei*, while the genus *Streptococcus* includes *Streptococcus mutans* and *Streptococcus sanguinis*.

4. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 1, characterized in that, Abundance was detected by qPCR; propionic acid and acetic acid concentrations were detected by liquid chromatography; pH ​​was measured using a pH meter.

5. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 4, characterized in that, The pH value is the average of multiple measurements taken with a pH meter.

6. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 1, characterized in that, The diagnostic model also includes a database; The database stores multiple healthy sample objects and multiple PMOP sample objects; The healthy sample consisted of healthy postmenopausal women aged 45-65 who had been menopausal for ≥1 year. The PMOP sample consisted of postmenopausal women aged 45-65 who had only PMOP and had been menopausal for ≥1 year. DXA detection was performed on all healthy samples and PMOP samples to obtain T-values; Saliva samples were collected and processed from all healthy subjects and PMOP subjects. The relative abundance of Lactobacillus spp. obtained from the sample processing was then analyzed. Relative abundance of Streptococcus Propionic acid concentration acetic acid concentration pH value (P) and menopausal years (Y) were used as inputs to the diagnostic model, and the T value was used as the corrected bone mineral density correlation value. Output the results, train the diagnostic model, and obtain... - , k, b, α.

7. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 6, characterized in that, The average pH value of saliva collected and processed from all healthy subjects.

8. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 6, characterized in that, Training is performed using Lasso regression or elastic network regression models. - , k, b, α.

9. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 6, characterized in that, Training was calibrated using multiple linear regression analysis. - , k, b, α.

10. The method for diagnosing postmenopausal osteoporosis based on oral salivary bacteria according to claim 6, characterized in that, A generalized additive model was used to correlate the corrected bone mineral density values. Conduct training.

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