High Circulating Sphingosine 1-Phosphate is a Risk Factor for Osteoporotic Fracture Independent of Fracture Risk Assessment Tool

The Fracture Risk Assessment Tool Probability and Trabecular Bone Score Mediate the Relationship between Sphingosine 1-phosphate Levels and Fracture Risk

BACKGROUND

The sphingosine 1-phosphate (S1P) concentration is a potential biomarker of osteoporotic fracture and is associated with both the fracture risk assessment tool (FRAX) probability and trabecular bone score (TBS), which are well-known predictors of fracture. We sought to estimate the effect of the S1P concentration on fracture risk using the FRAX probability and TBS as mediators.

METHODS

Plasma S1P concentrations, FRAX variables, and TBSs were measured in 66 postmenopausal women with fractures and 273 postmenopausal women without fractures. Associations between S1P concentration, FRAX probability, TBS, and fracture risk were analyzed using correlation, logistic regression, and mediation analyses.

RESULTS

Subjects in the highest S1P concentration tertile had a higher fracture risk (odds ratio [OR], 5.09; 95% confidence interval [CI], 2.22-11.67) than those in the lowest S1P concentration tertile before adjustment. Subjects in the highest FRAX probability tertile had a higher fracture risk (OR, 14.59; 95% CI, 5.01-42.53) than those in the lowest FRAX probability tertile before adjustment. Subjects in the lowest TBS tertile had a higher fracture risk (OR, 4.76; 95% CI, 2.28-9.93) than those in the highest TBS tertile before adjustment. After adjustment for FRAX probability and TBS, the highest S1P concentration tertile was still associated with a higher fracture risk (OR, 3.13; 95% CI, 1.28-7.66). The FRAX probability and TBS accounted for 32.6% and 21.7%, respectively, of the relationship between the S1P concentration and fracture risk.

CONCLUSIONS

The relationship between the circulating S1P concentration and fracture risk was partly mediated by the FRAX probability, bone microarchitecture, and other factors.

The role of sphingosine-1-phosphate in bone remodeling and osteoporosis

Osteoporosis is a systemic bone disease that affects more than 200 million people worldwide and is caused by the disruption of the equilibrium between osteoclastic bone resorption and osteoblastic bone formation. Sphingosine-1-phosphate (S1P) is a natural, bioactive sphingolipid that has been shown to play a major role in cardiovascular and immunological pathologies by regulating biological and cellular processes, including migration, differentiation, proliferation and survival. Recent studies also suggest a central role for S1P in bone diseases, including osteoporosis; however, the effects of S1P, particularly in bone metabolism, remain to be further elucidated. In this review, we summarize the available literature on the role of S1P in bone metabolism with a focus on osteoporosis. On the cellular level, S1P acts as an osteoclast-osteoblast coupling factor to promote osteoblast proliferation and bone formation. Moreover, the recruitment of osteoclast precursors to resorption sites is regulated by the interplay of S1P gradients and S1P receptor expression. From a clinical perspective, increasing evidence suggests that systemically elevated S1P blood levels may serve as an independent risk factor for osteoporosis-related fractures. Taken together, S1P signaling is a potential therapeutic target and may serve as a novel biomarker in patients with systemic bone disease.

Management of patients at very high risk of osteoporotic fractures through sequential treatments

Osteoporosis care has evolved markedly over the last 50 years, such that there are now an established clinical definition, validated methods of fracture risk assessment and a range of effective pharmacological agents. Currently, bone-forming (anabolic) agents, in many countries, are used in those patients who have continued to lose bone mineral density (BMD), patients with multiple subsequent fractures or those who have fractured despite treatment with antiresorptive agents. However, head-to-head data suggest that anabolic agents have greater rapidity and efficacy for fracture risk reduction than do antiresorptive therapies. The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) convened an expert working group to discuss the tools available to identify patients at high risk of fracture, review the evidence for the use of anabolic agents as the initial intervention in patients at highest risk of fracture and consider the sequence of therapy following their use. This position paper sets out the findings of the group and the consequent recommendations. The key conclusion is that the current evidence base supports an "anabolic first" approach in patients found to be at very high risk of fracture, followed by maintenance therapy using an antiresorptive agent, and with the subsequent need for antiosteoporosis therapy addressed over a lifetime horizon.

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning-based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual-energy X-ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1089 chest radiographs, 2010 and 2017). Using a well-performed deep learning model, we trained the OsPor-screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T-score ≤ -2.5) in a supervised learning manner. The OsPor-screen model was assessed in the internal and external test sets. We performed substudies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor-screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient-weighted class activation maps (Grad-CAMs). The OsPor-screen model showed promising performances. Osteoporosis screening with the OsPor-screen model achieved an AUC of 0.91 (95% confidence interval [CI], 0.90-0.92) and an AUC of 0.88 (95% CI, 0.85-0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad-CAMs is unclear, these results suggest that a deep learning-based model using chest radiographs could have the potential to be used for opportunistic automated screening of patients with osteoporosis in clinical settings. © 2021 American Society for Bone and Mineral Research (ASBMR).