Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders

Trajanoska, Katerina; Rivadeneira, Fernando

doi:10.3389/fendo.2020.556610

Cited by 6 publications

(5 citation statements)

References 199 publications

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“…Family history of hip fractures . It has been suggested that the degree of bone remodeling and geometry may depend on genetic factors [ 44 ]. The occurrence of hip fractures in the immediate family is included among the clinical factors in the FRAX form [ 3 ].…”

Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Lipnicka

Studniarski

Irzmański

2023

Life

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Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient’s potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

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Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Lipnicka

Studniarski

Irzmański

2023

Life

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“…According to [22], the degree of bone remodeling may depend on genetic factors. It is estimated that genetic factors influence the occurrence of low-energy fractures in 25-35%.…”

Section: History Of Hip Fractures In Parents (Fam/hip)mentioning

confidence: 99%

Determining the hierarchy of risk factors for low-energy fractures in patients of an Osteoporosis Treatment Clinic

Lis-Studniarska,

Studniarski,

Zakrzewska

et al. 2024

Ann Agric Environ Med.

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Introduction andObjective. The medical records were examined of 222 patients of the Osteoporosis Treatment Clinic at the Central Clinical Hospital of the Medical University of Łódź, Poland. The influence was analyzed of 27 clinical risk factors on the occurrence of low-energetic fractures in this population. The aim of the research was to find possible dependencies between different risk factors, and the actual fractures that were recorded in the database. Materials and Method. For each risk factor and for each category (e.g., patients with diabetes and patients without diabetes), the percentage was computed of patients who had incidents osteoporotic fractures, and the percentage of those without fractures. Student's t-test and Pearson's chi-squared test were used to find statistically significant risk factors. Results. Statistically significant risk factors were found: age, chronic kidney disease, T-scores of the femoral neck and T-score of the lumbar spine, serum phosphate levels, FRAX-BMD, FRAX-BMI, and the type of diet. Conclusions. Some observations concerning the influence of individual risk factors on the occurrence of fractures are consistent with those presented in the literature. However, it was also noticed that the patients with hyperthyroidism, rheumatic diseases, diabetes, cancer or gastrointestinal diseases, had a smaller percentage of fractures than the patients who did not have these diseases. This may be explained by the small number of those having these diseases, or by the fact that they had already received appropriate treatment.

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“…It might also reflect the idea that clinical variability, observed in many monogenic diseases, can be explained by variants in modifier genes ( 19 ). The discovery of such genetic variants opens an application window into personalized medicine ( 20 ).…”

Section: Causality Of Genetic Mutations Associated With Rare Skeletal Diseases Requires Proofmentioning

confidence: 99%

Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

Rauner¹,

Foessl

Formosa

et al. 2021

Front. Endocrinol.

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The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits (“endophenotypes”), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

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Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders

Cited by 6 publications

References 199 publications

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Determining the hierarchy of risk factors for low-energy fractures in patients of an Osteoporosis Treatment Clinic

Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

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