Genomewide Association Study of Fracture Nonunion Using Electronic Health Records

Genetic Influence of Fracture Nonunion (FNU): A Systematic Review

Purpose

Nonunion of fractures occurs in about 15% of all fractures causing repeated surgical interference and prolonged morbidity. We performed this systematic review to assess genes and polymorphisms influencing fractures' nonunion (FNU).

Methods

We searched between 2000 and July 2022 in PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, Genome Wide Association Studies (GWAS) Catalog, and the Science Citation Index, with the keywords nonunion of fractures, genetic influence, and GWAS. The exclusion criteria were review articles and correspondence. The data were retrieved to determine the number of studies, genes, and polymorphisms and the total number of subjects screened.

Results

A total of 79 studies were reported on nonunion of fractures and genetic influence. After the inclusion and exclusion criteria, ten studies with 4402 patients' data were analyzed. Nine studies were case-controlled, and 1 GWAS. It was identified that patients with polymorphisms in the genes ANXA3, BMP2, CALY, CYR61, FGFR1, IL1β, NOG, NOS2, PDGF gene, and TACR1 are prone to develop a nonunion of fractures.

Conclusion

We believe that for patients who develop an early nonunion of fractures, a genetic study should be conducted for single nucleotide polymorphism (SNP) and genes so that alternative and more aggressive treatment can be performed to heal fractures without prolonged morbidity.

Deciphering the chromatin spatial organization landscapes during BMMSC differentiation

The differentiation imbalance in bone marrow mesenchymal stem cells (BMMSCs) is critical for the development of bone density diseases as the population ages. BMMSCs are precursor cells for osteoblasts and adipocytes; however, the chromatin organization landscapes during BMMSC differentiation remain elusive. In this study, we systematically delineate the four-dimensional (4D) genome and dynamic epigenetic atlas of BMMSCs by RNA sequencing (RNA-seq), assay for transposase-accessible chromatin sequencing (ATAC-seq), and high-throughput chromosome conformation capture (Hi-C). The structure analyses reveal 17.5% common and 28.5%-30% specific loops among BMMSCs, osteoblasts, and adipocytes. The subsequent correlation of genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) data with multi-omics analysis reveal 274 genes and 3634 single nucleotide polymorphisms (SNPs) associated with bone degeneration and osteoporosis (OP). We hypothesize that SNP mutations affect transcription factor (TF) binding sites, thereby affecting changes in gene expression. Furthermore, 26 motifs, 260 TFs, and 291 SNPs are identified to affect the eQTL. Among these genes, DAAM2, TIMP2, and TMEM241 were found to be essential for diseases such as bone degeneration and OP and may serve as potential drug targets.

[Artificial intelligence and novel approaches for treatment of non-union in bone : From established standard methods in medicine up to novel fields of research]

Methods of artificial intelligence (AI) have found applications in many fields of medicine within the last few years. Some disciplines already use these methods regularly within their clinical routine. However, the fields of application are wide and there are still many opportunities to apply these new AI concepts. This review article gives an insight into the history of AI and defines the special terms and fields, such as machine learning (ML), neural networks and deep learning. The classical steps in developing AI models are demonstrated here, as well as the iteration of data rectification and preparation, the training of a model and subsequent validation before transfer into a clinical setting are explained. Currently, musculoskeletal disciplines implement methods of ML and also neural networks, e.g. for identification of fractures or for classifications. Also, predictive models based on risk factor analysis for prevention of complications are being initiated. As non-union in bone is a rare but very complex disease with dramatic socioeconomic impact for the healthcare system, many open questions arise which could be better understood by using methods of AI in the future. New fields of research applying AI models range from predictive models and cost analysis to personalized treatment strategies.

Biological and molecular profile of fracture non-union tissue: A systematic review and an update on current insights

Fracture non‐union represents a common complication, seen in 5%–10% of all acute fractures. Despite the enhancement in scientific understanding and treatment methods, rates of fracture non‐union remain largely unchanged over the years. This systematic review investigates the biological, molecular and genetic profiles of both (i) non‐union tissue and (ii) non–union‐related tissues, and the genetic predisposition to fracture non‐union. This is crucially important as it could facilitate earlier identification and targeted treatment of high‐risk patients, along with improving our understanding on pathophysiology of fracture non‐union. Since this is an update on our previous systematic review, we searched the literature indexed in PubMed Medline; Ovid Medline; Embase; Scopus; Google Scholar; and the Cochrane Library using Medical Subject Heading (MeSH) or Title/Abstract words (non‐union(s), non‐union(s), human, tissue, bone morphogenic protein(s) (BMPs) and MSCs) from August 2014 (date of our previous publication) to 2 October 2021 for non‐union tissue studies, whereas no date restrictions imposed on non–union‐related tissue studies. Inclusion criteria of this systematic review are human studies investigating the characteristics and properties of non‐union tissue and non–union‐related tissues, available in full‐text English language. Limitations of this systematic review are exclusion of animal studies, the heterogeneity in the definition of non‐union and timing of tissue harvest seen in the included studies, and the search term MSC which may result in the exclusion of studies using historical terms such as ‘osteoprogenitors’ and ‘skeletal stem cells’. A total of 24 studies (non‐union tissue: n = 10; non–union‐related tissues: n = 14) met the inclusion criteria. Soft tissue interposition, bony sclerosis of fracture ends and complete obliteration of medullary canal are commonest macroscopic appearances of non‐unions. Non‐union tissue colour and surrounding fluid are two important characteristics that could be used clinically to distinguish between septic and aseptic non‐unions. Atrophic non‐unions had a predominance of endochondral bone formation and lower cellular density, when compared against hypertrophic non‐unions. Vascular tissues were present in both atrophic and hypertrophic non‐unions, with no difference in vessel density between the two. Studies have found non‐union tissue to contain biologically active MSCs with potential for osteoblastic, chondrogenic and adipogenic differentiation. Proliferative capacity of non‐union tissue MSCs was comparable to that of bone marrow MSCs. Rates of cell senescence of non‐union tissue remain inconclusive and require further investigation. There was a lower BMP expression in non‐union site and absent in the extracellular matrix, with no difference observed between atrophic and hypertrophic non‐unions. The reduced BMP‐7 gene expression and elevated levels of its inhibitors (Chordin, Noggin and Gremlin) could potentially explain impaired bone healing observed in non‐union MSCs. Expression of Dkk‐1 in osteogenic medium was higher in non‐union MSCs. Numerous genetic polymorphisms associated with fracture non‐union have been identified, with some involving the BMP and MMP pathways. Further research is required on determining the sensitivity and specificity of molecular and genetic profiling of relevant tissues as a potential screening biomarker for fracture non‐unions.

Predicting fracture healing with blood biomarkers: the potential to assess patient risk of fracture nonunion

Fracture non-union is a significant orthopaedic problem affecting a substantial number of patients yearly. Treatment of nonunions is devastating to patients and costly to the healthcare system. Unfortunately, the diagnosis of non-union is typically made in a reactionary fashion by an orthopaedic surgeon based on clinical assessment and radiographic features several months into treatment. For this reason, investigators have been trying to develop prediction algorithms; however, these have relied on population-based approaches and lack the predictive capability necessary to make individual treatment decisions. There is also a growing body of literature focussed on identifying blood biomarkers that are associated with non-union. This review describes the research that has been done in this area. Further studies of patient-centered, precision medicine approaches will likely improve fracture non-union diagnostic/prognostic capabilities.

The Life of a Fracture: Biologic Progression, Healing Gone Awry, and Evaluation of Union

New knowledge about the molecular biology of fracture-healing provides opportunities for intervention and reduction of risk for specific phases that are affected by disease and medications. Modifiable and nonmodifiable risk factors can prolong healing, and the informed clinician should optimize each patient to provide the best chance for union. Techniques to monitor progression of fracture-healing have not changed substantially over time; new objective modalities are needed.

Translating Discoveries in Attention-Deficit/Hyperactivity Disorder Genomics to an Outpatient Child and Adolescent Psychiatric Cohort

OBJECTIVE

Genomic discoveries should be investigated in generalizable child psychiatric samples in order to justify and inform studies that will evaluate their use for specific clinical purposes. In youth consecutively referred for neuropsychiatric evaluation, we examined 1) the convergent and discriminant validity of attention-deficit/hyperactivity disorder (ADHD) polygenic risk scores (PRSs) in relation to DSM-based ADHD phenotypes; 2) the association of ADHD PRSs with phenotypes beyond ADHD that share its liability and have implications for outcome; and 3) the extent to which youth with high ADHD PRSs manifest a distinctive clinical profile.

METHOD

Participants were 433 youth, ages 7-18 years, from the Longitudinal Study of Genetic Influences on Cognition. We used logistic/linear regression and mixed effects models to examine associations with ADHD-related polygenic variation from the largest ADHD genome-wide association study to date. We replicated key findings in 5,140 adult patients from a local health system biobank.

RESULTS

Among referred youth, ADHD PRSs were associated with ADHD diagnoses, cross-diagnostic ADHD symptoms and academic impairment (odds ratios ∼1.4; R² values ∼2%-3%), as well as cross-diagnostic variation in aggression and working memory. In adults, ADHD PRSs were associated with ADHD and phenotypes beyond the condition that have public health implications. Finally, youth with a high ADHD polygenic burden showed a more severe clinical profile than youth with a low burden (β coefficients ∼.2).

CONCLUSION

Among child and adolescent outpatients, ADHD polygenic risk was associated with ADHD and related phenotypes as well as clinical severity. These results extend the scientific foundation for studies of ADHD polygenic risk in the clinical setting and highlight directions for further research.

Verification of hub genes in the expression profile of aortic dissection

Background

To assess the mRNA expression profile and explore the hub mRNAs and potential molecular mechanisms in the pathogenesis of human thoracic aortic dissection (TAD).

Methodology

mRNA microarray expression signatures of TAD tissues (n = 6) and non-TAD tissues (NT; n = 6) were analyzed by an Arraystar human mRNA microarray. Real-time PCR (qRT-PCR) was used to validate the results of the mRNA microarray. Bioinformatic tools, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, were utilized. Protein-protein interaction (PPI) networks were constructed based on data from the STRING database. Molecular Complex Detection (MCODE) and cytoHubba analyses were used to predict the strongest hub gene and pathway.

Results

The top 10 hub genes were CDK1, CDC20, CCNB2, CCNB1, MAD2L1, AURKA, C3AR1, NCAPG, CXCL12 and ASPM, which were identified from the PPI network. Module analysis revealed that TAD was associated with the cell cycle, oocyte meiosis, the p53 signaling pathway, and progesterone-mediated oocyte maturation. The qRT-PCR results showed that the expression of all hub genes was significantly increased in TAD samples (p < 0.05). Immunostaining of Ki-67 and CDK1 showed a high proliferation state and high expression in TAD, respectively.

Conclusions

CDK1 could be used as a potential diagnostic biomarker and therapeutic target of TAD.