Association of serum chemerin with calcium, alkaline phosphatase and bone mineral density in postmenopausal females

Bridging the Gap: A narrative review of osteoporosis disability, adipokines, and the role of AI in postmenopausal women

Osteoporosis is a global health concern characterized by reduced bone density and compromised bone quality, resulting in an increased risk of fractures, particularly in postmenopausal women. The assessment of bone mineral density (BMD) plays a pivotal role in diagnosing osteoporosis, as it accounts for approximately 70% of overall bone strength. The World Health Organization (WHO) has endorsed BMD measurement as a reliable method for diagnosing this condition. In Pakistan, the incidence of bone fractures is on the rise, largely attributable to an aging population and a range of contributing factors. Understanding the global and local prevalence of osteoporosis, its impact on morbidity and mortality, and the contributing factors is vital for developing effective preventive and therapeutic strategies. The role of adipokines, including chemerin, vaspin, and omentin-1, in bone metabolism is an emerging area of investigation. These adipokines play diverse roles in physiology, ranging from inflammation and metabolic regulation to cardiovascular health. Understanding their potential impact on bone health is a topic of ongoing research. The intricate relationship between bone density, bone quality, and overall bone strength is central to understanding the diagnosis and management of osteoporosis. Current innovation in machine learning and predictive model can bring revolution in the field of bone health and osteoporosis. Early identification of people with osteoporosis or risk of fracture through machine learning can prevent disability and improve the quality of life.

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

INTRODUCTION

The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications.

MATERIALS AND METHODS

A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).

RESULTS

A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.

CONCLUSION

The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

Application of bone alkaline phosphatase and 25-oxhydryl-vitamin D in diagnosis and prediction of osteoporotic vertebral compression fractures

BACKGROUND

Osteoporosis is a bone metabolic disease that usually causes fracture. The improvement of the clinical diagnostic efficiency of osteoporosis is of great significance for the prevention of fracture. The predictive and diagnostic values of bone alkaline phosphatase (B-ALP) and 25-oxhydryl-vitamin D (25-OH-VD) for osteoporotic vertebral compression fractures (OVCFs) were evaluated.

METHODS

110 OVCFs patients undergoing percutaneous vertebroplasty were included as subjects and their spinal computed tomography (CT) images were collected. After that, deep convolutional neural network model was employed for intelligent fracture recognition. Next, the patients were randomly enrolled into Ctrl group (65 cases receiving postoperative routine treatment) and VD2 group (65 cases injected with vitamin D2 into muscle after the surgery). In addition, 100 healthy people who participated in physical examination were included in Normal group. The differences in Oswestry dysfunction indexes (ODI), imaging parameters, B-ALP and 25-OH-VD expressions, and quality of life (QOL) scores of patients among the three groups were compared. The values of B-ALP and 25-OH-VD in predicting and diagnosing OVCFs and their correlation with bone density were analyzed.

RESULTS

It was demonstrated that computer intelligent medical image technique was more efficient in fracture CT recognition than artificial recognition. In contrast to those among patients in Normal group, B-ALP rose while 25-OH-VD declined among patients in Ctrl and VD2 groups (P < 0.05). Versus those among patients in Ctrl group, ODI, Cobb angle, and B-ALP reduced, while bone density, the height ratio of the injured vertebrae, 25-OH-VD, and QOL score increased among patients in VD2 group after the treatment (P < 0.05). The critical values, accuracy, and areas under the curve (AUC) of the diagnosis of OVCFs by B-ALP and 25-OH-VD amounted to 87.8 μg/L versus 30.3 nmol/L, 86.7% versus 83.3%, and 0.86 versus 0.82, respectively. B-ALP was apparently negatively correlated with bone density (r =  - 0.602, P < 0.05), while 25-OH-VD was remarkably positively correlated with bone density (r = 0.576, P < 0.05).

CONCLUSION

To sum up, deep learning-based computer CT image intelligent detection technique could improve the diagnostic efficacy of fracture. B-ALP rose while 25-OH-VD declined among patients with OVCFs and OVCFs could be predicted and diagnosed based on B-ALP and 25-OH-VD. Postoperative intramuscular injection of VD2 could effectively improve the therapeutic effect on patients with OVCFs and QOL.

The correlation between serum total alkaline phosphatase and bone mineral density in young adults

Background

Elevated total alkaline phosphatase (T-ALP) levels are usually indicative of enhanced osteoblastic activity and bone conversion status and are thus considered as a key factor needed for fresh bone mineralization and synthesis. To date, there is no consistent conclusion on the association between the serum T-ALP levels and bone mineral density (BMD). Therefore, the present study focused on exploring the association of serum T-ALP with lumbar BMD among young adults.

Methods

The present cross-sectional study included 6,331 subjects included in the National Health and Nutrition Examination Survey (NHANES) during 2011–2016. The participants aged 20–40 years included 3,349 males and 2,982 females. Serum T-ALP was our main variable, lumbar BMD was our outcome variable, and additional variables were the possible impact modifiers. The relations were analysed by the trend study, weighted multiple linear regression models, smooth curve fitting, and stratified analyses.

Results

In a completely corrected multiple regression model, a negative association between serum T-ALP and lumbar BMD was discovered (β = -0.0007, 95% CI: –0.0009– –0.0005, P < 0.000001). After converting the continuous variable serum T-ALP into the categorical one, the significant negative association was still observed (P < 0.001), and in the subgroup and smooth curve fitting analyses, this negative correlation remained significant, too.

Conclusions

Our study results indicated that serum T-ALP was negatively associated with lumbar BMD among young adults. Serum T-ALP measurement in the near future might become an effective biomarker to diagnose and treat osteoporosis on time.

Association of serum levels of Visfatin, Intelectin-1, RARRES2 and their genetic variants with bone mineral density in postmenopausal females

BACKGROUND

Adipokines are engaged in bone physiology and regulate bone mineral density (BMD) by playing protective or cynical role in bone metabolism. The study is designed to measure and compare BMD, adipokines (retinoic acid receptor responder protein-2 RARRES2, visfatin and Intelectin-1) and their genetic variants in postmenopausal osteoporotic, osteopenic and non-osteoporotic females.

METHODS

This comparative study included postmenopausal non-osteoporotic (n=72), osteopenic (n=72) and osteoporotic (n=100) females with two years of amenorrhea and age between 50 to 70 years. Gold standard DXA was used to measure BMD. Hardy-Weinberg equilibrium was established. Kruskal-Wallis test for comparisons, logistic and multivariate regression analysis were used to rule out the predictors of BMD.

RESULTS

On comparing the three groups, significant differences were observed in serum RARRES2 (p <0.001) and serum visfatin (p=0.050). The significant positive predictor of BMD at lumbar spine and total hip was serum visfatin. BMD at right and left femoral neck was predicted negatively by serum chemerin while BMD at left femoral neck was also predicted positively by serum calcium levels. There was significant difference in BMD at right femoral neck (p = 0.033) between rs7806429 genotypes. The odds of having low BMD increases with increasing serum levels of chemerin and decreasing serum levels of visfatin and calcium.

CONCLUSION

The adipokines RARRES2 and visfatin are associated with BMD. RARRES2 is an independent negative and visfatin is positive predictor of BMD in postmenopausal females. BMD at right femoral neck was significantly low in RARRES2 rs7806429 TC heterozygotes.