Vitamin B6 as a novel risk biomarker of fractured ankles

Role of vitamins beyond vitamin D3 in bone health and osteoporosis (Review)

The objective of the present review was to summarize the molecular mechanisms associated with the effects of the vitamins A, C, E and K, and group B vitamins on bone and their potential roles in the development of osteoporosis. Epidemiological findings have demonstrated an association between vitamin deficiency and a higher risk of developing osteoporosis; vitamins are positively related to bone health upon their intake at the physiological range. Excessive vitamin intake can also adversely affect bone formation, as clearly demonstrated for vitamin A. Vitamins E (tocopherols and tocotrienols), K2 (menaquinones 4 and 7) and C have also been shown to promote osteoblast development through bone morphogenetic protein (BMP)/Smad and Wnt/β‑catenin signaling, as well as the TGFβ/Smad pathway (α‑tocopherol). Vitamin A metabolite (all‑trans retinoic acid) exerts both inhibitory and stimulatory effects on BMP‑ and Wnt/β‑catenin‑mediated osteogenesis at the nanomolar and micromolar range, respectively. Certain vitamins significantly reduce receptor activator of nuclear factor kappa‑B ligand (RANKL) production and RANKL/RANK signaling, while increasing the level of osteoprotegerin (OPG), thus reducing the RANKL/OPG ratio and exerting anti‑osteoclastogenic effects. Ascorbic acid can both promote and inhibit RANKL signaling, being essential for osteoclastogenesis. Vitamin K2 has also been shown to prevent vascular calcification by activating matrix Gla protein through its carboxylation. Therefore, the maintenance of a physiological intake of vitamins should be considered as a nutritional strategy for the prevention of osteoporosis.

A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort

BACKGROUND

Osteoporosis develops in the elderly due to decreased bone mineral density (BMD), potentially increasing bone fracture risk. However, the BMD is not regularly measured in a clinical setting. This study aimed to develop a good prediction model for the osteoporosis risk using a machine learning (ML) approach in adults over 40 years in the Ansan/Anseong cohort and the association of predicted osteoporosis risk with a fracture in the Health Examinees (HEXA) cohort.

METHODS

The 109 demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8,842 participants were manually selected in an Ansan/Anseong cohort and included in the ML algorithm. The polygenic risk score (PRS) of osteoporosis was generated with a genome-wide association study and added for the genetic impact of osteoporosis. Osteoporosis was defined with < -2.5 T scores of the tibia or radius compared to people in their 20s-30s. They were divided randomly into the training (n = 7,074) and test (n = 1,768) sets-Pearson's correlation between the predicted osteoporosis risk and fracture in the HEXA cohort.

RESULTS

XGBoost, deep neural network, and random forest generated the prediction model with a high area under the curve (AUC, 0.86) of the receiver operating characteristic (ROC) with 10, 15, and 20 features; the prediction model by XGBoost had the highest AUC of ROC, high accuracy and k-fold values (> 0.85) in 15 features among seven ML approaches. The model included the genetic factor, genders, number of children and breastfed children, age, residence area, education, seasons to measure, height, smoking status, hormone replacement therapy, serum albumin, hip circumferences, vitamin B6 intake, and body weight. The prediction models for women alone were similar to those for both genders, with lower accuracy. When the prediction model was applied to the HEXA study, the correlation between the fracture incidence and predicted osteoporosis risk was significant but weak (r = 0.173, P < 0.001).

CONCLUSION

The prediction model for osteoporosis risk generated by XGBoost can be applied to estimate osteoporosis risk. The biomarkers can be considered for enhancing the prevention, detection, and early therapy of osteoporosis risk in Asians.

Chondroitin sulfate alleviates osteoporosis caused by calcium deficiency by regulating lipid metabolism

The use of non-drug intervention for calcium deficiency has attracted attention in recent years. Although calcium carbonate is the preferred raw material for calcium supplementation, there are few reports on the mechanism of the combined action of chondroitin sulfate and calcium to alleviate osteoporosis from the perspective of gut microbiota and metabolomics. In this study, a rat model of osteoporosis was established by feeding a low-calcium diet. The intestinal microbiota abundance, fecal and plasma metabolite expression levels of rats fed a basal diet, a low-calcium diet, a low-calcium diet plus calcium carbonate, and a low-calcium diet plus chondroitin sulfate were compared. The results showed that compared with the low calcium group, the calcium content and bone mineral density of femur were significantly increased in the calcium carbonate and chondroitin sulfate groups. 16 S rRNA sequencing and metabolomics analysis showed that chondroitin sulfate intervention could reduce short-chain fatty acid synthesis of intestinal flora, slow down inflammatory response, inhibit osteoclast differentiation, promote calcium absorption and antioxidant mechanism, and alleviate osteoporosis in low-calcium feeding rats. Correlation analysis showed that the selected intestinal flora was significantly correlated with metabolites enriched in feces and plasma. This study provides scientific evidence of the potential impact of chondroitin sulfate as a dietary supplement for patients with osteoporosis.