314 research outputs found

    Osteoporosis in Men

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    Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study

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    Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mortality. Being a silent disease before fracture, osteoporosis often remains undiagnosed and untreated. Areal bone mineral density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the gold-standard method for osteoporosis diagnosis and hence also for future fracture prediction (prognostic). However, the required special equipment is not broadly available everywhere, in particular not to patients in developing countries. We propose a deep learning classification model (FORM) that can directly predict hip fracture risk from either plain radiographs (X-ray) or 2D projection images of computed tomography (CT) data. Our method is fully automated and therefore well suited for opportunistic screening settings, identifying high risk patients in a broader population without additional screening. FORM was trained and evaluated on X-rays and CT projections from the Osteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or 2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that FORM can correctly predict the 10-year hip fracture risk with a validation AUC of 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional information like age, BMI, fall history and health background across a 5-fold cross validation on the X-ray and CT cohort, respectively. Our approach significantly (p < 0.01) outperforms previous methods like Cox Proportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21 respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD based predictions. We are confident that FORM can contribute on improving osteoporosis diagnosis at an early stage.Comment: Accepted at MICCAI 2022 Workshop (PRIME

    Epidemiology of rib fractures in older men: Osteoporotic Fractures in Men (MrOS) prospective cohort study

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    Objective To study the causes and consequences of radiologically confirmed rib fractures (seldom considered in the context of osteoporosis) in community dwelling older men

    Fibroblast growth factor 23, mineral metabolism and mortality among elderly men (Swedish MrOs)

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    Background: Fibroblast growth factor 23 (FGF23) is the earliest marker of disturbed mineral metabolism as renal function decreases. Its serum levels are associated with mortality in dialysis patients, persons with chronic kidney disease (CKD) and prevalent cardiovascular disease (CVD), and it is associated with atherosclerosis, endothelial dysfunction and left ventricular hypertrophy in the general population. The primary aim of this study is to examine the association between FGF23 and mortality, in relation to renal function in the community. A secondary aim is to examine the association between FGF23 and CVD related death. Methods: The population-based cohort of MrOS Sweden included 3014 men (age 69-81 years). At inclusion intact FGF23, intact parathyroid hormone (PTH), 25 hydroxyl vitamin D (25D), calcium and phosphate were measured. Mortality data were collected after an average of 4.5 years follow-up. 352 deaths occurred, 132 of CVD. Association between FGF23 and mortality was analyzed in quartiles of FGF23. Kaplan-Meier curves and Log-rank test were used to examine time to events. Cox proportional hazards regression was used to examine the association between FGF23, in quartiles and as a continuous variable, with mortality. The associations were also analyzed in the sub-cohort with estimated glomerular filtration rate (eGFR) above 60 ml/min/1.73 m(2). Results: There was no association between FGF23 and all-cause mortality, Hazard ratio (HR) 95% confidence interval (CI): 1.02 (0.89-1.17). For CVD death the HR (95% CI) was 1.26 (0.99 - 1.59)/(1-SD) increase in log(10) FGF23 after adjustment for eGFR, and other confounders. In the sub-cohort with eGFR > 60 ml/min/1.73 m(2) the HR (95% CI) for CVD death was 55% (13-111)/(1-SD) increase in log(10) FGF23. Conclusions: FGF23 is not associated with mortality of all-cause in elderly community living men, but there is a weak association with CVD death, even after adjustment for eGFR and the other confounders. The association with CVD death is noticeable only in the sub-cohort with preserved renal function

    Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information

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    Janssen S, McDonald D, Gonzalez A, et al. Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information. mSystems. 2018;3(3):e00021-18

    Protein co-expression network analysis (ProCoNA)

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    Abstract Background Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. Results We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Conclusions Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery
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