A relationship between osteoporosis (OP) and vascular calcification (VC) is now proposed. There are common mechanisms underlying the regulation of them. Fibroblast growth factor- (FGF-) 23 and Klotho are hormones associated with the metabolic axis of osteovascular metabolism. Most recently, it was suggested that the FGF23-klotho axis is associated with increasing incidence of fractures and is potentially involved in the progression of the aortic-brachial stiffness ratio. Herein, we discussed the potential role of the FGF23/Klotho axis in the pathophysiology of OP and VC. We want to provide an update review in order to allow a better understanding of the potential role of the FGF23/Klotho axis in comorbidity of OP and VC. We believe that a better understanding of the relationship between both entities can help in proposing new therapeutic targets for reducing the increasing prevalence of OP and VC in the aging population.

Osteoporotic fracture, a major complication which is known as the outcome postmenopausal osteoporosis, seriously threatens the health of postmenopausal women. At present, the traditional osteoporotic fracture prediction methods are characterized by inconvenient application and time-consuming statistical results, while predictive serum biomarkers can make up for this shortcoming. Accurate and advanced risk prediction of osteoporotic fracture is meaningful to early prevention and intervention, effectively avoiding the risk of this disease and the secondary fracture in the surgical treatment. In this study, based on the BEYOND cohort, a 2-year follow-up study was conducted after subjects participated to survey if OF occurred. Independent sample t -test and Mann–Whitney U -test were used to analyze the differences of bone metabolism biomarkers between the OF and non-OF group. Cox proportional hazard model was used to screen the potential biomarkers might be used to predict OF risk. ROC curves and AUCs were used to analyze the predictive accuracy, and the Delong’s test was used to compare the differences between the AUCs. 15 postmenopausal women with low bone mass and OF were found, and other 60 subjects without OF were matched with 1 : 4, age, and BMI classification as control group. The serum IL-6 ( OR = 1.139 , 95 % CI = 1.058 − 1.226 ) and leptin ( OR = 0.921 , 95 % CI = 0.848 − 1.000 ) were found as OF risk predictive biomarkers for postmenopausal women with low bone mass with high accuracy ( IL − 6 = 0.871 ) ( leptin = 0.813 ) and accuracy enhanced when they were combined ( AUC = 0.898 ). The results of Delong’s test showed that the difference of AUC between leptin and IL-6&Leptin was meaningful ( P = 0.024 ) but meaningless between IL-6 and leptin ( P = 0.436 ), IL-6 and IL-6&Leptin ( P = 0.606 ). To sum up, IL-6 and leptin are the predictive biomarkers of OF for postmenopausal women with low bone mass. The IL-6 can improve the prediction accuracy of leptin ( P = 0.024 ), but not vice versa ( P = 0.606 ). Trial Information. Registered on the Chinese Clinical Trial Registry already. (Registration Number: ChiCTR-SOC-17013090).

Hypoxia is a characteristic feature of numerous diseases, including metabolic bone disease, solid tumors, cardiovascular diseases, neurodegeneration and inflammation. It is also a risk factor for a poor prognosis in various diseases. Hypoxia-inducible factor-1α (HIF-1α) is activated by hypoxia to regulate a series of pathophysiological pathways, which is of utmost significance for maintaining body homeostasis. The present review highlights the role of the HIF-1α in oxygen, bone and iron homeostasis, and alludes on the biological complexity and dual functions of HIF-1α regulation. In addition, the pathophysiological significance of HIF-1α in bone formation, bone absorption, angiogenesis, erythropoiesis, oxidative stress, energy metabolism, iron death, etc., is discussed An accurate understanding of all these processes may aid in the identification of possible therapeutic targets that may then be used in the treatment of related diseases. However, further studies are required to unravel the extensive complexity of HIF-1α regulation and to develop more precise treatment strategies. Contents1. Introduction 2. Oxygen homeostasis 3. Bone homeostasis 4. Iron homeostasis 5.