Bioinformatics and machine learning were used to validate glutamine metabolism-related genes and immunotherapy in osteoporosis patients

BACKGROUND

Osteoporosis (OP), often referred to as the "silent disease of the twenty-first century," poses a significant public health concern due to its severity, chronic nature, and progressive course, predominantly affecting postmenopausal women and elderly individuals. The pathogenesis and progression of this disease have been associated with dysregulation in tumor metabolic pathways. Notably, the metabolic utilization of glutamine has emerged as a critical player in cancer biology. While metabolic reprogramming has been extensively studied in various malignancies and linked to clinical outcomes, its comprehensive investigation within the context of OP remains lacking.

METHODS

This study aimed to identify and validate potential glutamine metabolism genes (GlnMgs) associated with OP through comprehensive bioinformatics analysis. The identification of GlnMgs was achieved by integrating the weighted gene co-expression network analysis and a set of 28 candidate GlnMgs. Subsequently, the putative biological functions and pathways associated with GlnMgs were elucidated using gene set variation analysis. The LASSO method was employed to identify key hub genes, and the diagnostic efficacy of five selected GlnMgs in OP detection was assessed. Additionally, the relationship between hub GlnMgs and clinical characteristics was investigated. Finally, the expression levels of the five GlnMgs were validated using independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).

RESULTS

Five GlnMgs, namely IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N, were identified in this study. To gain insights into their biological functions, particular emphasis was placed on synaptic transmission GABAergic, inward rectifier potassium channel activity, and the cytoplasmic side of the lysosomal membrane. Furthermore, the diagnostic potential of these five GlnMgs in distinguishing individuals with OP yielded promising results, indicating their efficacy as discriminative markers for OP.

CONCLUSIONS

This study discovered five GlnMgs that are linked to OP. They shed light on potential new biomarkers for OP and tracking its progression.

Does Lumbar Interbody Fusion Modality Affect the Occurrence of Complications in an Osteoporotic Spine Under Whole-Body Vibration? A Finite Element Study

OBJECTIVE

To evaluate the effects of 3 lumbar interbody fusion techniques on the occurrence of complications in an osteoporotic spine under whole-body vibration.

METHODS

A previously developed and validated nonlinear finite element model of L1-S1was modified to develop anterior lumbar interbody fusion (ALIF), posterior lumbar interbody fusion (PLIF), and transforaminal lumbar interbody fusion (TLIF) models with osteoporosis. In each model, the lower surface of the sacrum was absolutely fixed, a follower load of 400N was applied through the axis of the lumbar spine, and an axial sinusoidal vertical load of ±40N (5 Hz) was imposed on the superior surface of L1, to perform a transient dynamic analysis. The maximal values of intradiscal pressure, shear stress on annulus substance, disc bulge, facet joint stress, and screw and rod stress, along with their dynamic response curves, were collected.

RESULTS

Among these 3 models, the TLIF model generated the greatest screw and rod stress, and the PLIF model generated the greatest cage-bone interface stress. At the L3-L4 level, compared with the other 2 models, the maximal values and dynamic response curves of intradiscal pressure, shear stress of annulus ground substance, and disc bulge were all lower in the ALIF model. However, the facet contact stress at the adjacent segment in the ALIF model was higher than that in the other 2 models.

CONCLUSIONS

In an osteoporotic spine under whole-body vibration, TLIF has the highest risk of screw and rod breakage, PLIF has the highest risk of cage subsidence, and ALIF has the lowest risk of upper adjacent disc degeneration, but the highest risk of adjacent facet joint degeneration.

Gold-nanosphere mitigates osteoporosis through regulating TMAO metabolism in a gut microbiota-dependent manner

Osteoporosis (OP) is a metabolic bone disease characterized by decreased bone mass and increased bone fragility. The imbalance of bone homeostasis modulated by osteoclasts and osteoblasts is the most crucial pathological change in osteoporosis. As a novel treatment strategy, nanomedicine has been applied in drug delivery and targeted therapy due to its high efficiency, precision, and fewer side effects. Gold nanospheres (GNS), as a common kind of gold nanoparticles (GNPs), possess significant antimicrobial and anti-inflammatory activity, which have been applied for the treatment of eye diseases and rheumatoid arthritis. However, the effect of GNS on osteoporosis remains elusive. In this study, we found that GNS significantly prevented ovariectomy (OVX)-induced osteoporosis in a gut microbiota-dependent manner. 16S rDNA gene sequencing demonstrated GNS markedly altered the gut microbial diversity and flora composition. In addition, GNS reduced the abundance of TMAO-related metabolites in OVX mice. Low TMAO levels might alleviate the bone loss phenomenon by reducing the inflammation response. Therefore, we investigated the alteration of cytokine profiles in OVX mice. GNS inhibited the release of pro-osteoclastogenic or proinflammatory cytokines including tumor necrosis factor α (TNF-α), interleukin (IL)-6, and granulocyte colony-stimulating factor (G-CSF) in the serum. In conclusion, GNS suppressed estrogen deficiency-induced bone loss by regulating the destroyed homeostasis of gut microbiota so as to reduce its relevant TMAO metabolism and restrain the release of proinflammatory cytokines. These results demonstrated the protective effects of GNS on osteoporosis as a gut microbiota modulator and offered novel insights into the regulation of the "gut-bone" axis.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors

BACKGROUND

Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk.

METHODS

Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods.

RESULTS

Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74.

CONCLUSION

These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.

The role of gut microbiota metabolite trimethylamine N-oxide in functional impairment of bone marrow mesenchymal stem cells in osteoporosis disease

Background

Osteoporosis (OP) is a prevalent metabolic bone disease characterized by bone loss and structural deterioration, which increases the risk of fracture especially in older people. Recent research has shown that gut microbes play an important role in OP. Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has been implicated in the pathogenesis of diseases, including Alzheimer’s and cerebrovascular disease. This study aimed to examine the effect of TMAO in OP.

Methods

In this study, we firstly investigated the relationship between TMAO and OP. Serum samples were collected from patients with OP (n=10), and healthy participants (n=10), and the TMAO level in the serum was detected by ELISA assay. Then, bone marrow mesenchymal stem cells (BMSCs) were treated with TMAO, and we observed its effect on adipogenic and osteogenic differentiation, cell proliferation, reactive oxygen species (ROS) release, and inflammatory cytokine[interleukin (IL)-1β, IL-6 and tumor necrosis factor-alpha (TNF-α)] levels. Finally, we illustrated the underlying mechanism through which TMAO influenced BMSCs functions.

Results

Compared to the healthy group, highly significant TMAO levels were observed in the serum of the OP patients. When studied in vitro, TMAO treatment significantly promoted BMSCs adipogenesis and attenuated osteogenesis, increased ROS release and pro-inflammatory cytokine IL-1β, IL-6 and TNF-α production, and inhibited cell proliferation. Furthermore, we found that activation of the nuclear factor-κB (NF-κB) signaling pathway was necessary for TMAO to induce pro-inflammatory cytokine production, ROS release, and adipogenic and osteogenic differentiation in BMSCs.

Conclusions

Elevated TMAO levels have a strong negative correlation with the degree of bone mineral density (BMD) in OP. TMAO regulates BMSCs cell function by activating the NF-κB signaling pathway, which affects the balance of bone metabolism, leading to acceleration of bone loss and further progression of OP.

Strategies for optimising musculoskeletal health in the 21st century

We live in a world with an ever-increasing ageing population. Studying healthy ageing and reducing the socioeconomic impact of age-related diseases is a key research priority for the industrialised and developing countries, along with a better mechanistic understanding of the physiology and pathophysiology of ageing that occurs in a number of age-related musculoskeletal disorders. Arthritis and musculoskeletal disorders constitute a major cause of disability and morbidity globally and result in enormous costs for our health and social-care systems.By gaining a better understanding of healthy musculoskeletal ageing and the risk factors associated with premature ageing and senescence, we can provide better care and develop new and better-targeted therapies for common musculoskeletal disorders. This review is the outcome of a two-day multidisciplinary, international workshop sponsored by the Institute of Advanced Studies entitled "Musculoskeletal Health in the 21st Century" and held at the University of Surrey from 30th June-1st July 2015.The aim of this narrative review is to summarise current knowledge of musculoskeletal health, ageing and disease and highlight strategies for prevention and reducing the impact of common musculoskeletal diseases.