Method, device, medium and program product for predicting osteoporosis risk of postmenopausal diabetic population

Abstract

The invention provides a method, equipment, medium and program product for predicting the osteoporosis risk of postmenopausal diabetes people, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring one or more predictive factors of an age, a menopausal age, a diabetes disease course, PINP, CTX and an FF score of a subject; the subject is a postmenopausal diabetes patient; calculating a risk score based on the predictive factor, and outputting an auxiliary predictive result of the risk probability of osteoporosis of the subject according to the risk score; when the risk score is greater than a first threshold value, outputting an auxiliary prediction result that the probability of occurrence of osteoporosis risk of the subject is large; and when the risk score is smaller than a first threshold value, outputting an auxiliary prediction result with a small probability of occurrence of osteoporosis risk of the subject. The osteoporosis risk of postmenopausal diabetes people is predicted specifically aiming at the postmenopausal diabetes people.

Description

Technical Field

[0001] The present invention relates to the field of intelligent healthcare, and more particularly, to a method, device, medium, and program product for predicting the osteoporosis risk in postmenopausal diabetic patients. Background Art

[0002] Osteoporosis is a common metabolic bone disease, and its assessment and diagnosis methods are constantly evolving. Using Dual-Energy X-ray Absorptiometry (DXA) to measure Bone Mineral Density (BMD) is the gold standard for assessing osteoporosis. However, postmenopausal T2DM patients have their own characteristics, and relying solely on BMD for assessment has certain limitations. The relationship between bone density and fracture risk in diabetic patients is not completely consistent. Due to the impact of blood glucose on bone metabolism in diabetic patients, even when BMD is normal, they may still face a relatively high fracture risk. For postmenopausal T2DM patients, a single BMD test has obvious lag for them.

[0003] Bone turnover markers (BTMs) are dynamic indicators of bone metabolism. Although they cannot be used as the diagnostic criteria for osteoporosis, they can reflect the dynamic process of bone remodeling and help clinicians more comprehensively understand the bone metabolism status of patients. PINP and CTX are common bone metabolism indicators. In addition, other BTMs such as total alkaline phosphatase (ALP), osteocalcin (OC), tartrate-resistant acid phosphatase 5b (TRACP-5b), etc. are widely used in clinical practice. However, these indicators are easily affected by other diseases, such as primary hyperparathyroidism, malabsorption, Paget's disease, chronic renal osteopathy, bone metastasis of malignant tumors, multiple myeloma, etc., drugs such as alendronate sodium, denosumab, etc., and diet, such as food intake can significantly reduce the serum CTX content. Postmenopausal T2DM patients have various hormonal and blood glucose metabolic changes, which affect the levels of BTMs. Therefore, using BTMs to assess osteoporosis also has certain limitations.

[0004] In summary, due to the dual impact of hormones and blood glucose on bone metabolism, postmenopausal T2DM patients with osteoporosis have their own characteristics. The traditional methods for assessing osteoporosis have lag, and there is an urgent need for a new assessment method for early diagnosis of postmenopausal T2DM patients, facilitating early intervention, preventing the occurrence of serious complications such as fractures, and improving the quality of life of patients. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a method, device, medium and program product for predicting the osteoporosis risk in postmenopausal diabetic patients; the method of the present invention specifically targets postmenopausal diabetic patients and predicts their risk of having osteoporosis.

[0006] The first aspect of the present application discloses a method for predicting the osteoporosis risk in postmenopausal diabetic patients, characterized in that the method comprises: S101, obtaining one or several prediction factors including age, menopause age, diabetes duration, PINP (N-terminal propeptide of type I procollagen), CTX (C-terminal cross-linked peptide), FF (fat fraction) score from the subject; the subject is a postmenopausal diabetic patient; S102, calculating a risk score based on the prediction factors, and outputting an auxiliary prediction result of the probability of the subject having an osteoporosis risk according to the risk score; when the risk score is greater than the first threshold, output an auxiliary prediction result that the subject has a high probability of having an osteoporosis risk; when the risk score is less than the first threshold, output an auxiliary prediction result that the subject has a low probability of having an osteoporosis risk.

[0007] In some embodiments, the FF lumbar fat fraction is obtained through the IDEAL-IQ sequence.

[0008] In some embodiments, the risk score is obtained through an osteoporosis risk prediction model, and the construction method of the osteoporosis risk prediction model comprises: Obtaining the basic data of the training set samples and the group to which a single sample belongs, including the osteopenia group, the normal bone mass group and the osteoporosis group; the classification criteria for the groups include: according to the bone mineral density value T measured by dual-energy X-ray of the spine or hip joint, when T < -2.5, it is the osteoporosis group, and when -1 ≤ T ≤ -2.5, it is the osteopenia group; when T > -1, it is the normal bone mass group; Comparing the differential characteristics among the osteoporosis group, the osteopenia group and the normal bone mass group; Processing the differential characteristics according to a multi-factor regression model and constructing an osteoporosis risk prediction model according to different categories.

[0009] In some embodiments, the inclusion criteria for the training set samples include: no age limit, women who have been naturally postmenopausal for more than 1 year; meeting the diagnostic criteria for type 2 diabetes; BMI between 18.5 - 24; obtaining the lumbar fat fraction (Fat Fraction, FF) based on IDEAL-IQ images; performing PINP (N-terminal propeptide of type I procollagen) and CTX (C-terminal cross-linked peptide) tests.

[0010] In some embodiments, the exclusion criteria for the training set samples include: non-natural menopause; suffering from diseases affecting bone metabolism; patients taking drugs affecting bone metabolism; type 1 diabetes, special types of diabetes.

[0011] In some embodiments, the non-natural menopause includes patients after hysterectomy; Optionally, the diseases affecting bone metabolism include any one or more of the following: Cushing's syndrome, hyperthyroidism, primary hyperparathyroidism, rheumatoid arthritis, leukemia, multiple myeloma, subtotal gastrectomy, chronic hepatitis, congenital bone metabolism disorders and other endocrine diseases; Optionally, the drugs affecting bone metabolism include any one or more of the following: glucocorticoids, estrogens, calcitonin, bisphosphonates, anti-epileptic drugs, thyroxine, diuretics, anticoagulants.

[0012] In some embodiments, the multi-factor regression model includes: logistic regression, Cox regression.

[0013] The second aspect of the present application discloses a computer device, the device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the above method.

[0014] The third aspect of the present application discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0015] The fourth aspect of the present application discloses a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0016] The present application has the following beneficial effects: The present application innovatively discloses a method for predicting the osteoporosis risk in postmenopausal diabetic populations. This method has its own characteristics for osteoporosis in postmenopausal T2DM patients. Traditional methods for assessing osteoporosis have the defect of lag, and specifically introduces prediction factors including age, menopause age, diabetes duration, PINP (N-terminal propeptide of type I collagen), CTX (C-terminal cross-linked peptide), and FF (fat fraction) score to achieve the prediction effect of osteoporosis in postmenopausal T2DM patients. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of the method provided in the first aspect of the embodiments of the present invention; Figure 2 It is a schematic diagram of the system for predicting the osteoporosis risk of postmenopausal diabetic patients provided in the second aspect of the embodiments of the present invention; Figure 3 It is a schematic diagram of the computer device provided in the embodiments of the present invention; Figure 4 It is a schematic diagram of the architecture of the exemplary computing device provided in the embodiments of the present invention; Figure 5 It is a schematic diagram of the storage medium provided in the embodiments of the present invention; Figure 6 It is a nomogram for predicting the fracture risk of T2DM osteoporosis patients constructed based on age, menopause age, diabetes duration, PINP, and FF score provided in the embodiments of the present invention. Detailed implementation manners

[0019] To enable those skilled in the art of the present technology to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] In some processes described in the specification, claims, and the above accompanying drawings of the present invention, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0022] Figure 1 It is a schematic flowchart of a method for predicting the osteoporosis risk of postmenopausal diabetic patients provided by an embodiment of the present invention. Specifically, the method includes the following steps: S101, obtaining one or several predictor factors including age, menopause age, diabetes duration, PINP (N-terminal propeptide of type I collagen precursor), CTX (C-terminal cross-linked peptide), and FF (fat fraction) score from the test subject; the test subject is a postmenopausal diabetic patient; In some embodiments, the term "subject" or "test subject" or "test sample" used herein refers to any animal (e.g., mammal), including but not limited to humans, non-human primates, rodents, etc., which will be the recipient of a specific treatment. Generally, the terms "subject" and "patient" can be used interchangeably herein when referring to human subjects. Preferably, the subject is a human.

[0023] In some embodiments, the FF lumbar fat fraction is obtained through the IDEAL-IQ sequence (this imaging technique is based on routine lumbar examinations and can non-invasively measure the fat content in vertebral bone marrow with high sensitivity. It may become a new method for achieving precise water-fat separation and evaluating fat content).

[0024] BTMs refer to intermediate metabolites or enzymes produced during bone metabolism, which play important roles in the processes of bone formation and bone resorption. PINP and CTX are important BTMs. PINP and CTX are independent risk factors for osteoporosis in postmenopausal type 2 diabetic patients.

[0025] S102, calculating a risk score based on the predictor factors and outputting an auxiliary prediction result of the probability of the test subject having an osteoporosis risk according to the risk score; when the risk score is greater than the first threshold, output an auxiliary prediction result with a high probability of the test subject having an osteoporosis risk; when the risk score is less than the first threshold, output an auxiliary prediction result with a low probability of the test subject having an osteoporosis risk.

[0026] In some embodiments, the risk score is obtained through an osteoporosis risk prediction model, and the construction method of the osteoporosis risk prediction model: Obtaining the basic data of the training set samples and the group to which each single sample belongs, including the osteopenia group, normal bone mass group, and osteoporosis group; the classification criteria for the groups include: according to the bone mineral density value T measured by dual-energy X-ray of the spine or hip joint, when T < -2.5, it is the osteoporosis group, when -1 ≤ T ≤ -2.5, it is the osteopenia group; when T > -1, it is the normal bone mass group; Comparing the differential characteristics among the osteoporosis group, osteopenia group, and normal bone mass group; Process the differential features according to the multi-factor regression model, and construct an osteoporosis risk prediction model according to different categories.

[0027] In some embodiments, the inclusion criteria for the training set samples include: women of any age who have been naturally menopausal for more than 1 year; meeting the diagnostic criteria for type 2 diabetes; BMI ranging from 18.5 to 24; lumbar fat fraction (Fat Fraction, FF) obtained based on IDEAL-IQ images; performing PINP (N-terminal propeptide of type I procollagen) and CTX (C-terminal cross-linked peptide) tests.

[0028] In some embodiments, the exclusion criteria for the training set samples include: non-naturally menopausal women; those suffering from diseases affecting bone metabolism; patients taking drugs affecting bone metabolism; type 1 diabetes and special types of diabetes. Only type 2 diabetes is studied. The reasons for excluding type 1 diabetes and special types of diabetes are as follows: 1. The association between T1DM and osteoporosis is more directly due to low BMD caused by insulin deficiency, while the osteoporosis risk in T2DM is more mediated by bone quality decline and metabolic disorders, and different intervention strategies need to be adopted for different mechanisms. Therefore, separate studies are required. 2. Multiple studies have consistently shown that BMD in T1DM patients is significantly reduced, which may be directly related to absolute insulin deficiency. Insulin not only regulates blood glucose but also affects bone formation by stimulating osteoblast differentiation. Therefore, the bone formation ability in T1DM patients is more significantly impaired. T2DM: Different from T1DM, the BMD in T2DM patients is usually normal or even slightly higher, but the fracture risk is still increased. This contradictory phenomenon may be related to bone quality decline (such as bone microstructure damage and abnormal collagen cross-linking). Since the magnetic resonance sequence studied in this research is more preeminent than other screening methods, that is, other examinations have a lag, the research on type 1 diabetes is of little significance. In addition, the pathogenesis of other diabetes such as gestational diabetes is also different, and the impact on osteoporosis is also different. We only selected type 2 diabetes. 3. The incidence of type 2 diabetes is high, and there are many patients, so the research results are more conducive to guiding clinical applications.

[0029] In some embodiments, the non-naturally menopausal women include patients after hysterectomy; the main reason for excluding non-naturally menopausal women is to study this group of type 2 diabetes patients with natural menopause. First of all, natural menopause faces hormone decline, and the hormones of patients after hysterectomy are normal, which is a confounding factor. The pathogenesis of each type of diabetes is different. Type 1 diabetes is insulin-dependent, and other diabetes includes gestational diabetes, etc.

[0030] Optionally, the diseases affecting bone metabolism include any one or more of the following: Cushing's syndrome, hyperthyroidism, primary hyperparathyroidism, rheumatoid arthritis, leukemia, multiple myeloma, subtotal gastrectomy, chronic hepatitis, congenital bone metabolism disorders, and other endocrine diseases; Optionally, the drugs affecting bone metabolism include any one or more of the following: glucocorticoids, estrogen, calcitonin, bisphosphonates, anti-epileptic drugs, thyroxine, diuretics, anticoagulants.

[0031] In some embodiments, the multi-factor regression model includes: logistic regression, Cox regression.

[0032] In some embodiments, the auxiliary prediction result includes but is not limited to the form of a paper or electronic report. This result is only obtained by the intelligent machine based on the relevant data of the subject and is only for reference by medical staff, not as the final diagnosis result of the subject.

[0033] In some embodiments, the first threshold is obtained by training with a training set sample. It can be a specific threshold or an interval range, and the specific form is not specifically limited in this embodiment.

[0034] Figure 3 It is a schematic diagram of a computer device provided by an embodiment of the present invention, as Figure 3 shown, the device 2000 may include: one or more processors 2010, and one or more memories 2020; wherein, computer-readable code is stored in the memory, and when the computer-readable code is run by the one or more processors, the method described above can be executed.

[0035] The processor in this embodiment may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, operations, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and may be of the X86 architecture or the ARM architecture.

[0036] In general, the various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller or other computing device, or some combination thereof.

[0037] For example, the method or apparatus according to an embodiment of the present disclosure may also be implemented by means of Figure 4 the architecture of the computing device 3000 shown. As Figure 4 shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, input / output components 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for the processing and / or communication of the method provided by the present disclosure, as well as the program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 the architecture shown is only exemplary, and when implementing different devices, one or more components shown in the Figure 4 computing device may be omitted according to actual needs.

[0038] An embodiment of the present invention also provides a computer-readable storage medium, such as Figure 5As shown, it is a schematic diagram of a storage medium 4000 provided by an embodiment of the present invention. Computer-readable instructions 4010 are stored on the computer storage medium 4020. When the computer-readable instructions 4010 are run by a processor, the methods according to the embodiments of the present disclosure described with reference to the above drawings can be executed. The computer-readable storage medium in the embodiments of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memories for the methods described herein are intended to include but not be limited to these and any other suitable types of memories. It should be noted that the memories for the methods described herein are intended to include but not be limited to these and any other suitable types of memories.

[0039] The embodiments of the present disclosure also provide a computer program product or system, including a computer program, which implements the steps of the above method when executed by a processor.

[0040] In some embodiments, this embodiment also discloses a system for predicting the osteoporosis risk in postmenopausal diabetic patients, as Figure 2 shown, the system includes: A prediction factor acquisition module 201, configured to obtain one or several prediction factors including age, menopause age, diabetes duration, PINP, CTX, FF score, etc. for the subject; the subject is a postmenopausal diabetic patient; An auxiliary prediction result output module 202, configured to calculate a risk score based on the prediction factors and output an auxiliary prediction result of the probability of osteoporosis risk for the subject according to the risk score; when the risk score is greater than a first threshold, output an auxiliary prediction result with a high probability of osteoporosis risk for the subject; when the risk score is less than the first threshold, output an auxiliary prediction result with a low probability of osteoporosis risk for the subject.

[0041] In some embodiments, this embodiment also discloses a method for predicting the fracture risk in osteoporosis patients. Specifically, the method includes the following steps: Obtain predictors for the subjects including age, menopause age, diabetes duration, PINP, CTX, and FF score; the subjects are T2DM osteoporotic patients; Calculate a risk score based on the predictors and output an auxiliary prediction result of the probability of fracture occurrence for the subject according to the risk score; when the risk score is greater than the second threshold, output an auxiliary prediction result indicating a high probability of fracture occurrence for the subject; when the risk score is less than the second threshold, output an auxiliary prediction result indicating a low probability of fracture occurrence for the subject.

[0042] In some embodiments, the method further includes: for patients with age > 60 years, menopause age < 47 years at early menopause, diabetes duration > 10 years, CTX > 500 pg / mL and FF > 75%, recommend bone density combined with TBS detection every 6 months for treatment, and initiate bisphosphonate combined with SGLT2 inhibitor treatment if necessary.

[0043] Establish a nomogram with factors such as age, menopause age, diabetes duration, PINP, CTX, and FF score (as Figure 6 shown in the following table). Through the nomogram model, we can visually observe the scores associated with each independent risk factor. In the nomogram, the scale value of each prediction index corresponds to the score on the scoring scale, and all the prediction variables are added together to obtain a total score, which reflects the probability of the patient having an adverse prognosis. For example: For a postmenopausal female diabetic osteoporosis patient, the examination results show: age 60 years (38 points), menopause age 46 years (55 points), diabetes duration 10 years (32 points), PINP 80 (35 points), CTX 650 (35 points), FF 85 (25 points). The total score of this patient is 38 + 55 + 32 + 35 + 35 + 25 = 220, and the risk of fracture is 0.8.

[0044]

[0045] After modeling, the area under the curve (AUC) and C-index of the training set are 0.972 (95% CI: 0.947~0.997), while the AUC and C-index of the validation set are 0.906 (95% CI: 0.923~1.000), which indicates that the model has good discrimination ability, as shown in the figure. The specific results of the ROC analysis show that the AUC is 0.972 (95% CI: 0.947~0.997), the sensitivity is 0.900, the specificity is 0.907, and the optimal cut-off value is 0.308. After verification, the prediction effect of this model is good. Specific embodiments:

[0047] 2.1 Research subjects 2.1.1 Sample Estimation: The guidelines of the British Medical Journal recommend that for a binary outcome prediction model, the error range of the estimated proportion of the overall result should be ≤ 0.05. When the binary outcome occurs in half of the individuals, a sample size of at least 385 people is required to make the confidence interval of the overall result proportion be 0.45 to 0.55, so the error near the true value of 0.5 is at most 0.05. To achieve the same error range, for result proportions of 0.1 and 0.2, at least 139 to 286 participants are required respectively. In this study, 272 patients were selected as the research subjects, within the range of the sample estimation results.

[0048] 2.1.2 Inclusion and Exclusion Criteria: A total of 272 postmenopausal T2DM patients who visited the Second Affiliated Hospital of Shandong First Medical University from November 2022 to April 2024 were collected as the research subjects. This study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Shandong First Medical University.

[0049] Inclusion Criteria: ① Women of any age with natural menopause for more than 1 year.

[0050] ② Meeting the diagnostic criteria for type 2 diabetes (T2DM). According to the Guidelines for the Prevention and Treatment of Type 2 Diabetes in China (2020 Edition), the diagnostic criteria for T2DM are as follows: Patients have typical diabetes-related symptoms (polydipsia, polyuria, polyphagia, weight loss). If a patient has no typical diabetes symptoms, a retest is required. On the basis of the patient having typical symptoms or on the basis of a retest, meeting any one of the following four indicators can confirm the diagnosis. Fasting Blood Glucose (FPG) ≥ 7.0 mmol / L (126 mg / dL), where fasting means not eating for at least 8 hours. Postprandial Blood Glucose at 2 Hours (OGTT2h) ≥ 11.1 mmol / L (200 mg / dL), which is measured by the Oral Glucose Tolerance Test (OGTT). Random blood glucose ≥ 11.1 mmol / L (200 mg / dL), and random blood glucose refers to the blood glucose of the patient at any time without considering the time of the last meal. Glycated Hemoglobin (HbA1c) ≥ 6.5% (48 mmol / mol). Note: The blood glucose in the diagnostic criteria is venous plasma glucose, not capillary blood glucose.

[0051] ③ Body Mass Index (BMI) range: 18.5 - 24 (There is still debate about the impact of weight on osteoporosis, so the BMI range is restricted).

[0052] ④ All the research subjects underwent magnetic resonance imaging scans to obtain IDEAL-IQ images, and the lumbar fat fraction (FatFraction, FF) was measured.

[0053] ⑤ All the research subjects had complete clinical data and underwent tests for procollagen type I N-terminal propeptide (PINP) and C-terminal cross-linked telopeptide of type I collagen (CTX).

[0054] ⑥ The diagnosis of osteoporosis (OP) conforms to the relevant diagnosis and treatment guidelines. Dual-energy X-ray absorptiometry (DXA) measurement of bone mineral density in the spine or hip joint is the gold standard for diagnosing OP. Detection site: lumbar spine (L1-4). The results are expressed as T-scores. A T-score less than -2.5 indicates osteoporosis; -1 ≤ T-score ≤ -2.5 indicates osteopenia; a T-score greater than -1 indicates normal bone mass.

[0055] Exclusion criteria: ① Patients who are not naturally postmenopausal, such as those after hysterectomy; ② Patients with diseases affecting bone metabolism, such as Cushing's syndrome, hyperthyroidism, primary hyperparathyroidism, rheumatoid arthritis, leukemia, multiple myeloma, subtotal gastrectomy, chronic hepatitis, congenital bone metabolism disorders, and other endocrine diseases; ③ Patients taking drugs that affect bone metabolism, including glucocorticoids, estrogens, calcitonin, bisphosphonates, antiepileptic drugs, thyroid hormones, diuretics, anticoagulants, etc.; ④ Type 1 diabetes and special types of diabetes; ⑤ Hepatic and renal insufficiency or other severe chronic diseases; ⑥ Cancer patients; ⑦ Those who do not cooperate with the examination due to personal or other factors.

[0056] 2.1.3 Research process 2.1.3.1 Data collection (1)Collect the clinical data of the patients: including age, menopause time, diabetes duration, height, weight, body mass index, daily exercise duration, smoking history, drinking history, and comorbidities (hypertension, coronary heart disease, cerebral infarction). Improve the test indicators such as biochemistry, blood lipids, glycated hemoglobin, fasting insulin, oral glucose tolerance test, calculate the insulin resistance index (IR) (fasting insulin * fasting blood glucose / 22.5), biochemistry and urinary microalbumin / creatinine, urinary renal function panel, 24-hour urinary protein quantification, etc.

[0057] (2)DXA scan: Scanning instrument: Dual-energy X-ray absorptiometer; Manufacturer: HOLLOGIC (USA); Measurement site: Lumbar spine (vertebrae L1-L4); Scanning parameters: Scanning direction: Anterior-posterior view of the spine, Current range: 2.5 - 3.0 mA, Voltage range: 100 - 140 kV, Scanning width: 11.4 cm, Scanning length: 20.4 m; Measurement steps: Calibrate the instrument: Calibrate the instrument according to the manufacturer's standards before each measurement. Perform the scan: Perform a BMD scan of the L1 to L4 vertebrae in the anterior-posterior view. According to the diagnostic criteria: 272 patients were divided into three groups: osteopenia group (n = 57), normal bone mass group (n = 57), osteoporosis group (n = 158) (3)MRI scan: Instrument: GE 3.0T Discovery MR 750 magnetic resonance imaging system.

[0058] Conventional lumbar spine scan: Preparation before scanning, ensure that the patient empties the bladder before scanning to reduce the impact on image quality. Explain the scanning process to the patient to ensure their understanding and relaxation. Remove all metal objects, jewelry, and items that may affect imaging. Scanning parameter settings: T1-weighted sequence: TR (repetition time): 400 - 600 ms, TE (echo time): 10 - 20 ms, Matrix: 256x256 or 512x512, FOV (field of view): 20 - 30 cm, Slice thickness: 3 - 5 mm. T2-weighted sequence: TR: 3000 - 5000 ms, TE: 80 - 120 ms, Matrix: 256x256 or 512x512, FOV: 20 - 30 cm, Slice thickness: 3 - 5 mm. Use longer TR and TE to enhance the fluid signal. Scanning steps: Patient positioning: Place the patient flat on the scanning bed, ensure that the lumbar region is located at the center of the magnetic resonance machine. Localization scan: Perform a localization scan to determine the correct scanning position, ensure that the scanning range covers the lumbar region from L1 to L5. Perform the scan: Perform T1 and T2-weighted scans according to the set parameters. Monitor the scanning process to ensure good image quality. Post-processing: After the scan is completed, perform image reconstruction and post-processing, including possible image contrast and analysis.

[0059] Lumbar spine IDEAL-IQ sequence scan: After the routine scan, perform the lumbar spine IDEAL-IQ sequence scan. Select the positioning plane: Use the midline plane of lumbar vertebra L3 as the scan center, ensuring that the scan includes L3 and its upper and lower vertebrae (L2 and L4). Scan parameter settings: Slice thickness 3 mm, TE (echo time): Select an appropriate TE value. The TE you mentioned may be between 1.2 ms and 7.38 ms. Typically, a shorter TE is selected to increase the strength of the water signal. TR (repetition time): 9 ms, inversion angle: 15°, number of excitations: 1, scan time: approximately 15 seconds.

[0060] Lumbar spine sagittal and transverse images obtained from the IDEAL-IQ sequence are processed, and regions of interest (ROIs) are delineated. Vertebral ROI delineation method: In the midsagittal plane of the vertebral fat fraction map (i.e., the plane with the largest vertebral display), the ROI for L1 to L5 vertebrae should be delineated within the cancellous bone region at the center of the vertebral body, avoiding surrounding cortical bone and areas of bone hyperplasia. The size and position of the ROI should be adjusted based on the size and shape of the vertebral body. The signal intensity of fat and water within the ROI is calculated automatically or manually using the software to determine the FF value. The FF value for the paraspinal muscles is the average of the bilateral FF values from two measurements.

[0061] Quality Control: Reconstructed images were regularly reviewed to ensure the absence of significant artifacts and inhomogeneities. Regions of interest (ROIs) were drawn at the same location and size to ensure data consistency. ROI selection criteria were regularly reviewed to ensure consistency across scans. FF values were measured three times by three senior physicians, and the average of their measurements was used.

[0062] (3) PINP and CTX detection: Instruments: Reagents: PINP detection kit, CTX detection kit; Serum-related markers were measured using ELISA kits. After centrifugation of the blood sample, PINP and CTX were assayed according to the kit instructions. Equilibrate the reagents and dilute with concentrated wash buffer for later use. Add the sample, seal the plate, incubate, wash, and pat dry. Add the enzyme, incubate, and wash. Add the colorimetric reagent for color development, terminate the reaction with stop solution, and read the OD value at a wavelength of 450 nm.

[0063] 2.1.3.2 Research Methods: 272 patients were divided into three groups according to the diagnostic criteria for osteoporosis: the osteopenia group (n = 57), the normal bone mass group (n = 57), and the osteoporosis group (n = 158). The clinical data and imaging data of the three groups of patients were compared. The different clinical data and imaging data were analyzed for correlation with FF. The Receiver Operating Characteristic (ROC) curve was used to judge the diagnostic value of FF for type 2 diabetic osteoporosis in postmenopausal women.

[0064] 2.1.4 Statistical methods: SPSS 28.0 statistical software was used. Measurement data were expressed as mean ± standard deviation (±S), and count data were expressed as %. One-way analysis of variance was used for measurement data. When the variances were homogeneous, pairwise comparisons were performed using the least significant difference method. When the variances were heterogeneous, pairwise comparisons were performed using the Bonfemroni method. Count data were analyzed using the χ2 test and the exact probability calculation method. Pearson correlation analysis was used to evaluate the correlation between various indicators, and partial correlation analysis was used after adjusting for confounding factors. The ROC curve was used to judge the risk assessment value and threshold definition of FF for type 2 diabetic osteoporosis in postmenopausal women. P < 0.05 was considered statistically significant.

[0065] 3 Results 3.1 Comparison of the general conditions of the three groups of patients: There were 272 people in this study. Among them, 57 patients had osteopenia, accounting for 20.96%, 57 patients had normal bone mass, accounting for 20.96%, and 158 patients had osteoporosis, accounting for 58.09%. There were differences in age, BMI, fasting blood glucose, glycated hemoglobin, T, PINP, CTX, and FF among the three groups of patients, and the differences were statistically significant (F = 5.126, 43.859, 7.780, 4.074, 108.107, 84.047, 121.635, 38.331, P < 0.05), while there were no differences in the menopause time, diabetes duration, vitamin D, calcium, and phosphorus (F = 0.207, 0.942, 0.146, 1.972, 1.210, P > 0.05), as shown in Table 1.

[0066] Table 1 Comparison of the general conditions of the three groups of patients

[0067]

[0068] 3.2 Correlation analysis of FF and other indicators: Correlation between FF value and age, BMI, T, PINP, and CTX: There was no correlation between the FF value and age (r = -0.059, P = 0.334), a negative correlation with BMI (r = -0.511, P < 0.001), a negative correlation with T (r = -0.464, P < 0.001), a positive correlation with CTX (r = 0.366, P < 0.001), and a positive correlation with PINP (r = 0.163, P = 0.007). See Table 2.

[0069] Table 2 Correlation analysis of FF and other indicators

[0070]

[0071] 3.3 Analysis of single - factor influencing factors of osteoporosis

[0072] There were no differences between the two groups of patients in terms of menopause duration, diabetes duration, vitamin D, calcium, phosphorus, etc. However, there were differences in age, fasting blood glucose, glycated hemoglobin, BMI, T, PINP, CTX, and FF. Specifically, in terms of age, the age of patients in the osteoporosis group was significantly higher than that in the non - osteoporosis group (t = 3.969, P < 0.05). The fasting blood glucose and glycated hemoglobin of patients in the osteoporosis group were significantly higher than those in the non - osteoporosis group (t = 3.272, 5.625, P < 0.05). The BMI of patients in the osteoporosis group was significantly lower than that in the non - osteoporosis group (t = 5.397, P < 0.05). The T value of patients in the osteoporosis group was significantly lower than that in the non - osteoporosis group (t = 19.120, P < 0.05). The PINP of patients in the osteoporosis group was significantly higher than that in the non - osteoporosis group (t = 12.204, P < 0.05). The CTX of patients in the osteoporosis group was significantly higher than that in the non - osteoporosis group (t = 15.402, P < 0.05). The FF of patients in the osteoporosis group was significantly higher than that in the non - osteoporosis group (t = 12.036, P < 0.05). Single - factor analysis showed that there were differences between the two groups of patients in menopause duration, diabetes duration, vitamin D, calcium, phosphorus, age, fasting blood glucose, glycated hemoglobin, BMI, T, PINP, CTX, and FF.

[0073] Table 3 Analysis of single - factor influencing factors of osteoporosis

[0074]

[0075] 3.4 Multivariate regression analysis of osteoporosis

[0076] Age, fasting blood glucose, glycated hemoglobin, BMI, T, PINP, CTX, and FF, which showed differences in univariate analysis, were included in the multivariate regression analysis. The results showed that age, fasting blood glucose, BMI, T, PINP, CTX, and FF were independent risk factors for osteoporosis in postmenopausal women with diabetes (OR (95%CI): 1.170 (1.017 - 1.345), 1.885 (1.034 - 3.435), 0.891 (0.811 - 0.972), 0.247 (0.077 - 0.794), 1.176 (1.065 - 1.298), 1.012 (1.004 - 1.020), 1.293 (1.138 - 1.469), P < 0.05). Multivariate analysis showed that age, menopause age, diabetes duration, PINP, CTX, and FF score factors were independent risk factors affecting fractures.

[0077] Table 4 Multivariate regression analysis of osteoporosis

[0078]

[0079] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] In general, the various example embodiments of the present disclosure may be implemented in hardware or in dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, a microprocessor, or other computing device. When aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general hardware or a controller or other computing device, or some combination thereof.

[0081] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.

[0085] The example embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art should understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for predicting the osteoporosis risk in postmenopausal diabetic patients, characterized in that, The method includes: S101. Obtain one or several predictive factors of the subject, including age, menopause age, diabetes duration, PINP, CTX, FF score, etc.; the subject is a postmenopausal diabetic patient. S102. Calculate a risk score based on the predictive factors, and output an auxiliary prediction result of the risk probability of osteoporosis occurrence in the subject according to the risk score; when the risk score is greater than the first threshold, output an auxiliary prediction result with a high risk probability of osteoporosis occurrence in the subject; when the risk score is less than the first threshold, output an auxiliary prediction result with a low risk probability of osteoporosis occurrence in the subject.

2. The method for predicting the osteoporosis risk in postmenopausal diabetic patients according to claim 1, wherein The FF lumbar fat fraction is obtained through the IDEAL-IQ sequence.

3. The method for predicting the osteoporosis risk of postmenopausal diabetic patients according to claim 1, characterized in that, The risk score is obtained through an osteoporosis risk prediction model. The construction method of the osteoporosis risk prediction model includes: Obtain the basic data of the training set samples and the group to which each single sample belongs, including the osteopenia group, normal bone mass group, and osteoporosis group; the classification criteria for the groups include: according to the bone mineral density value T measured by dual-energy X-ray of the spine or hip joint, when T < -2.5, it is the osteoporosis group, and when -1 ≤ T ≤ -2.5, it is the osteopenia group; when T > -1, it is the normal bone mass group. Compare the differential characteristics among the osteoporosis group, osteopenia group, and normal bone mass group. Process the differential characteristics according to the multivariate regression model, and construct an osteoporosis risk prediction model according to different categories.

4. The method for predicting the osteoporosis risk in postmenopausal diabetic patients according to claim 3, wherein, The inclusion criteria for the training set samples include: no age limit, women with natural menopause for more than 1 year; meeting the diagnostic criteria for type 2 diabetes; BMI between 18.5 - 24; obtaining the lumbar fat fraction (Fat Fraction, FF) based on IDEAL-IQ images; performing PINP and CTX tests.

5. The method for predicting the osteoporosis risk in postmenopausal diabetic patients according to claim 3, wherein The exclusion criteria for the training set samples include: those with non-natural menopause; those suffering from diseases affecting bone metabolism; patients taking drugs affecting bone metabolism; type 1 diabetes, special type diabetes.

6. The method for predicting the osteoporosis risk in postmenopausal diabetic patients according to claim 1, characterized in that, The non-natural menopause includes patients after hysterectomy. Optionally, the diseases affecting bone metabolism include any one or several of the following: Cushing's syndrome, hyperthyroidism, primary hyperparathyroidism, rheumatoid arthritis, leukemia, multiple myeloma, subtotal gastrectomy, chronic hepatitis, congenital bone metabolism disorders, and other endocrine diseases. Optionally, the drugs affecting bone metabolism include any one or several of the following: glucocorticoids, estrogens, calcitonin, diphosphonates, antiepileptic drugs, thyroxine, diuretics, anticoagulants.

7. The method for predicting the osteoporosis risk in postmenopausal diabetic patients according to claim 1, wherein The multivariate regression model includes: logistic regression, Cox regression.

8. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 - 7.

9. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 - 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 - 7.

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