Management of osteoporosis in postmenopausal women: the 2021 position statement of The North American Menopause Society

Race and Age Impact Osteoporosis Screening Rates in Women Prior to Hip Fracture

Bone mineral density screening and clinical risk factors are important to stratify individuals for increased risk of fracture. In a population with no history of fractures or baseline bone density measurement, black women were less likely to be screened than white counterparts prior to hip fracture.

PURPOSE

To evaluate overall BMD (bone mineral density) screening rates within two years of hip fracture and to identify any disparities for osteoporosis screening or treatment in a female cohort who were eligible for screening under insurance and national recommendations.

METHODS

Data were obtained from 1,109 female patients listed in the Research Action for Health Network (REACHnet) database, which consists of multiple health partner systems in Louisiana and Texas. Patients < 65 years old or with a history of hip fracture or osteoporosis diagnosis, screening or treatment more than 2 years before hip fracture were removed.

RESULTS

Only 223 (20.1%) females were screened within the two years prior to hip fracture. Additionally, only 23 (10%) of the screened patients received treatment, despite 187 (86.6%) patients being diagnosed with osteoporosis or osteopenia. Screening rates reached a maximum of 27.9% in the 75-80 age group, while the 90 + age group had the lowest screening rates of 12%. We found a quadratic relationship between age and screening rates, indicating that the screening rate increases in age until age 72 and then decreases starkly. After adjusting for potential confounders, we found that black patients had significantly decreased screening rates compared to white patients (adjusted OR = .454, 95% CI = .227-.908, p value = .026) which held in general and for patient ages 65-97.

CONCLUSION

Despite national recommendations, overall BMD screening rates among women prior to hip fracture are low. If individuals are not initially screened when eligible, they are less likely to ever be screened prior to fracture. Clinicians should address racial disparities by recommending more screening to otherwise healthy black patients above the age of 65. Lastly, treatment rates need to increase among those diagnosed with osteoporosis since all patients went on to hip fracture.

The relationship between uric acid and total femur bone mineral density in hypertensive and non-hypertensive populations

OBJECTIVE

This study aimed to explore the association between uric acid (UA) and total femur bone mineral density (BMD) in hypertensive and non-hypertensive groups.

METHODS

We conducted a cross-sectional study of 13,108 participants in the NHANES database, including 4,679 hypertensive and 8,429 non-hypertensive subjects. A weighted multiple linear regression analysis was conducted to explore the association between UA and total femur BMD.

RESULTS

In the hypertensive group, the relationship between UA and total femur BMD was positive [β, 3.02 (95% CI, -0.44 to 6.48), p = 0.0962). In the non-hypertensive group, the association was significantly positive [β, 5.64 (95% CI, 2.06-9.22), p = 0.0038]. In gender-stratified analysis, UA was analyzed as a continuous variable and a categorical variable (quartile). The significantly positive association was present in both the hypertensive male group [β, 5.10 (95% CI, 0.98-9.21), p for trend = 0.0042] and non-hypertensive male group [β, 10.63 (95% CI, 6.32-14.94), p for trend = 0.0001]. A smooth curve fitting showed that in the hypertensive male group, the relationship between UA and total femur BMD was an inverted U-shaped curve. In the hypertensive female group, the relationship was basically negative. In the non-hypertensive population, the relationship between UA and total femur BMD was an inverted U curve in both men and women.

CONCLUSION

In the hypertensive male group, the association between UA and total femur BMD was an inverted U-shaped curve. As to women, the relationship was basically negative. In the non-hypertensive group, the association between UA and total femur BMD was an inverted U-shaped curve in different genders.

Skeletal consequences of heart failure

Heart failure (HF) is a prevalent clinical syndrome that causes significant physical limitations. Osteoporosis is also an important cause of loss of functionality, and it mainly affects women. There are several reports linking HF and osteoporosis, and both share risk factors. Most of the data available so far point to bone fragility as a consequence of HF, and several mechanisms have been identified to explain this relationship. Among the proposed pathophysiological mechanisms are the hyperactivation of the renin-angiotensin-aldosterone system and the increase in parathyroid hormone, functional limitation, production of inflammatory mediators and the use of drugs for HF. The role of osteoprotegerin has gained attention owing to its cardiovascular and skeletal effects, its observed deficiency during the postmenopausal period along with its compensatory increases in HF and severe osteoporosis. The objective of this review was to perform a literature search for the main evidence on skeletal impairment in HF, with emphasis on women. As for epidemiological studies, we selected data from 3 meta-analyses and 20 individual observational studies, which together showed the interrelationship between the two clinical conditions in terms of both decreased bone density and increased fracture risk. In conclusion, HF and osteoporosis are interrelated conditions mediated by complex pathophysiological mechanisms which may be more relevant for postmenopausal women, considered to be a vulnerable population for both cardiovascular diseases and bone fragility.

Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis

OBJECTIVE

Postmenopausal osteoporosis (PMOP) is one of the most commonly occurring conditions worldwide and is characterized by estrogen deficiency as well as persistent calcium loss with age. The aim of our study was to identify significant ferroptosis-associated biomarkers for PMOP.

METHODS AND MATERIALS

We obtained our training dataset from the Gene Expression Omnibus (GEO) database using GSE56815 expression profiling data. Meanwhile, we extracted ferroptosis-associated genes for further analysis. Differentially expressed ferroptosis-associated genes (DEFAGs) between OP patients and normal controls were selected using the "limma" package. We established a ferroptosis-associated gene signature using training models, specifically, random forest (RF) and support vector machine (SVM) models. It was further validated in another dataset (GSE56814) which also showed a high AUC: 0.98, indicating high diagnostic value. Using consensus clustering, the OP patient subtypes were identified. A ferroptosis associated gene (FAG)-Scoring scheme was developed by PCA. The important candidate genes associated with OP were also compared between different ferrclusters and geneclusters.

RESULTS

There were significant DEFAGs acquired, of which five (HMOX1, HAMP, LPIN1, MAP3K5, FLT3) were selected for establishing a ferroptosis-associated gene signature. Analyzed from the ROC curve, our established RF model had a higher AUC value than the SVM model (RF model AUC:1.00). Considering these results, the established RF model was chosen to be the most appropriate training model. Later, based on the expression levels of the five DEFAGs, a clinical application nomogram was established. The OP patients were divided into two subtypes (ferrcluster A, B and genecluster A, B, respectively) according to the consensus clustering method based on DEFAGs and differentially expressed genes (DEGs). Ferrcluster B and genecluster B had higher ferroptosis score than ferrcluster A and genecluster A, respectively. The expression of COL1A1 gene was significantly higher in ferrcluster B and gencluster B compared with ferrcluster A and gencluster A, respectively, while there is no statistical difference in term of VDR gene, COL1A2 genes, and PTH gene expressions between ferrcluster A and B, together with gencluster A and B.

CONCLUSIONS

On the basis of five explanatory variables (HMOX1, HAMP, LPIN1, MAP3K5 and FLT3), we developed a diagnostic ferroptosis-associated gene signature and identified two differently categorized OP subtypes that may potentially be applied for the early diagnosis and individualized treatment of PMOP. The ER gene, VDR gene, IL-6 gene, COL1A1 and COL1A2 genes, and PTH gene are important candidate gene of OP, however, more studies are still anticipated to further elucidate the relationship between these genes and ferroptosis in OP.