Bone Mineral Density In Menopause Patients: A Focus On Inflammation
Overview
This study aimed to explore the relationship between changes in bone mineral density (BMD) and circulating inflammatory markers in postmenopausal women. Conducted retrospectively, the study focused on women admitted to the orthopedic department at Suzhou Benq Medical Center between June 2022 and December 2023, adhering to specific inclusion and exclusion criteria. Data on initial blood test results and bone mineral density measurements were collected at the time of admission. Key parameters analyzed included white blood cell (WBC) count, C-reactive protein (CRP), interleukin-6 (IL-6), and procalcitonin (PCT). The systemic immune-inflammation index (SII) was also calculated using neutrophil, lymphocyte, and platelet counts. Statistical analysis was performed using SPSS and GraphPad software to examine the correlation between bone mineral density and these inflammatory markers.
Participants were divided into three groups based on their bone mineral density results: 60 individuals in the osteoporosis (OP) group, 127 in the osteopenia group, and 37 in the normal bone mineral density group. Principal component analysis highlighted WBC, SII, and postmenopausal osteoporosis (PMOP) as significant features. Correlation analysis showed a significant relationship between WBC (p = 0.021), IL-6 (p = 0.044), SII (p = 0.034), and PMOP. One-way ANOVA indicated significant differences in IL-6 (p = 0.0179), SII (p = 0.0210), and PCT (p = 0.0200) across the three groups. Additionally, ROC curve analysis revealed that SII (area under the curve = 0.716) has predictive value for PMOP.
In conclusion, this study found that certain inflammatory markers measured through routine blood tests can serve as predictive indicators for postmenopausal osteoporosis.
Introduction
Postmenopausal osteoporosis (PMOP) is a chronic disorder affecting bone metabolism, characterized by bone loss, changes in bone microstructure, and an increased risk of fragility fractures following menopause. A key factor contributing to PMOP is the decline in estrogen levels after menopause. Estrogen typically promotes the apoptosis of osteoclasts and supports osteogenic differentiation in mesenchymal stem cells; hence, its reduction disrupts normal bone metabolism.
With the aging population on the rise, PMOP has become a significant public health concern, posing substantial health risks and economic challenges. Early diagnosis and treatment are crucial in managing PMOP, leading to ongoing research focused on identifying efficient and effective screening methods.
Recent research has highlighted the relationship between inflammation and PMOP, revealing a strong link between systemic immunity, inflammation, and bone metabolism. Inflammatory conditions are known to negatively impact bone health, with the RANK-RANKL-OPG pathway—a key regulator of bone metabolism—being influenced by inflammatory factors. Additionally, studies have found that an elevated neutrophil-to-lymphocyte ratio is associated with reduced bone mineral density (BMD).
Some bacteria in the human body produce metabolites, such as short-chain fatty acids (SCFAs), during their metabolic processes. These metabolites, which have anti-inflammatory properties, may directly influence various types of bone cells. The emerging field of “osteoimmunology” further explores the interaction between the immune system and bone metabolism, underscoring the role of immune cells and related factors in regulating bone health.
A complete blood count (CBC) is a widely available clinical tool that includes several inflammatory markers, such as white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). The systemic immune-inflammation index (SII), calculated from neutrophil, lymphocyte, and platelet counts (SII = Neutrophil Count × Platelet Count / Lymphocyte Count), could potentially be used to predict PMOP.
The aim of this research is to analyze the relationship between inflammatory markers in CBC and PMOP, to assess the predictive value of these markers for early detection. If CBC proves to be a useful initial screening tool for PMOP, it could have significant implications for the early prevention and management of the condition.
Method
This study retrospectively analyzed 224 postmenopausal women who visited the orthopedic department at Suzhou Benq Medical Center between June 2022 and December 2023. Patients were divided into three groups based on their bone mineral density (BMD), measured using dual-energy X-ray absorptiometry (DXA). The groups were defined by their lowest T-scores: those with a T-score ≤ -2.5 were placed in the OP group, those with T-scores between -2.5 and -1.0 were classified as the osteopenia (ON) group, and those with T-scores > -1.0 were categorized as the normal (NO) group.
Inclusion Criteria
To be included in the study, participants had to meet the following criteria: (1) they were over 50 years old with confirmed menopausal status, and (2) they were diagnosed with postmenopausal osteoporosis (OP) based on the World Health Organization (WHO) criteria.
Exclusion Criteria
Exclusion criteria were extensive, including: (1) patients with primary or acquired immunodeficiency disorders, hematological conditions, psychiatric disorders, severe infections, or those who had received allogeneic blood transfusions within the last three months; (2) patients with malignancies, major organ failures, or those who had undergone organ transplants; (3) those who had used immunomodulatory drugs, glucocorticoids, or other hormone replacement therapies within the last year; (4) patients with conditions impacting bone metabolism or immune regulation; (5) those on medications affecting bone metabolism; and (6) individuals admitted due to significant trauma, such as car accidents or falls from a height.
Statistical Analysis
The data were analyzed using GraphPad Prism 9.3 and SPSS 26.0. Continuous variables were expressed as mean ± standard deviation (mean ± SD), and categorical variables were presented numerically. Principal component analysis (PCA) was employed to evaluate the influence of various factors on postmenopausal osteoporosis (PMOP), selecting principal components with eigenvalues greater than 5. Pearson correlation analysis was used to explore data correlations. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to determine the predictive value of the criteria, using the area under the curve (AUC) and Youden index.
Result
The study enrolled 224 participants with an average age of 72.42 ± 11.61 years. Based on bone mineral density (BMD) results, participants were categorized into three groups: the osteoporosis (OP) group with 60 individuals (mean age 76.24 ± 9.7 years), the osteopenia (ON) group with 127 individuals (mean age 72.42 ± 11.46 years), and the normal (NO) group with 37 individuals (mean age 67.41 ± 12.48 years).
Principal component analysis (PCA) was conducted to evaluate the influence of various inflammatory markers, including white blood cell count (WBC), interleukin-6 (IL-6), systemic immune-inflammation index (SII), procalcitonin (PCT), and C-reactive protein (CRP). Two principal components (PC1 and PC2) were identified with eigenvalues greater than 5, collectively explaining 72.34% of the variance (PC1: 48.71%, PC2: 23.64%). PCA highlighted that WBC and SII were significant contributors, showing clear cluster differentiation.
To investigate the relationship between inflammatory markers and postmenopausal osteoporosis (PMOP), correlations between WBC, IL-6, SII, PCT, CRP, and the lowest bone mineral density T-score were analyzed. Significant correlations were found for WBC (p = 0.021), IL-6 (p = 0.044), and SII (p = 0.031) with PMOP, while PCT (p = 0.943) and CRP (p = 0.328) did not show significant associations.
Further analysis using one-way ANOVA explored the variations of these inflammatory markers across the three groups, revealing significant differences for IL-6 (p = 0.0179), SII (p = 0.0210), and PCT (p = 0.0200), but not for WBC (p = 0.0526) and CRP (p = 0.2035). ROC curve analysis was used to evaluate the predictive accuracy of WBC, IL-6, and SII for PMOP. SII demonstrated moderate predictive accuracy with an AUC of 0.716, a maximum Youden’s index of 0.408, a sensitivity of 81.7%, specificity of 59.1%, and a cutoff value of 8.05. SII also showed a significant correlation with PMOP (R = −0.142, p = 0.034), confirming its potential as a predictive marker. In contrast, WBC and IL-6 had AUCs below 0.7, indicating lower predictive value.
Conclusion
This study primarily focused on examining the relationship between inflammatory markers found in peripheral blood and postmenopausal osteoporosis (PMOP), evaluating their trends, predictive value, and threshold levels. The findings demonstrate a correlation between elevated inflammatory markers and lower bone mineral density (BMD), with the Systemic Immune-Inflammation Index (SII) showing strong predictive value for PMOP. The study concludes that as SII increases, the risk of PMOP also rises, with a suggested threshold of 8.05 for predicting PMOP.
Given the rising incidence of PMOP due to an aging population, there is an urgent need for more accessible and cost-effective early screening methods. While dual-energy X-ray absorptiometry (DXA) is the current gold standard for diagnosing PMOP, its high cost and limited availability make it less suitable for early detection. This research highlights the potential of routine blood test markers, particularly SII, as a viable alternative for early PMOP screening.
The study also explores the role of immune status in osteoporosis. It is noted that inflammation and oxidative stress are closely linked with decreased bone mineral density, particularly in postmenopausal women. Pearson correlation analysis confirmed negative correlations between PMOP and markers such as WBC, IL-6, and SII, reinforcing the idea that higher levels of inflammation are associated with increased risk of PMOP.
The study employed principal component analysis (PCA) to identify significant inflammatory indicators, revealing that WBC and SII have notable predictive value. Additionally, one-way ANOVA indicated that markers like IL-6, SII, and PCT show significant trends, increasing as bone mineral density decreases. ROC curve analysis further validated the predictive value of SII, underscoring its clinical relevance.
Despite its findings, the study acknowledges several limitations, including its cross-sectional design, which precludes establishing causality, and the limited sample size, which may affect the generalizability of the results. Additionally, the study did not standardize bone mineral density measurements to a specific site, which may have introduced variability.
In conclusion, the research supports the potential of SII as a predictive marker for PMOP, suggesting that further large-scale, longitudinal studies are needed to confirm its effectiveness as a screening tool and to explore the broader implications of inflammatory markers in bone health. The study also highlights the importance of evaluating anti-inflammatory interventions to prevent bone loss in postmenopausal women.
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