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Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).  相似文献   
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Background

Resistin is an immunometabolic mediator that is elevated in several inflammatory disorders. A ligand for Toll-like receptor 4, resistin modulates the recruitment and activation of myeloid cells, notably neutrophils. Neutrophils are major drivers of cystic fibrosis (CF) lung disease, in part due to the release of human neutrophil elastase- and myeloperoxidase-rich primary granules, leading to tissue damage. Here we assessed the relationship of resistin to CF lung disease.

Methods

Resistin levels were measured in plasma and sputum from three retrospective CF cohorts spanning a wide range of disease. We also assessed the ability of neutrophils to secrete resistin upon activation in vitro. Finally, we constructed a multivariate model assessing the relationship between resistin levels and lung function.

Results

Plasma resistin levels were only marginally higher in CF than in healthy control subjects. By contrast, sputum resistin levels were very high in CF, reaching 50–100 fold higher levels than in plasma. Among CF patients, higher plasma resistin levels were associated with allergic bronchopulmonary aspergillosis, and higher sputum resistin levels were associated with CF-related diabetes. Mechanistically, in vitro release of neutrophil primary granules was concomitant with resistin secretion. Overall, sputum resistin levels were negatively correlated with CF lung function, independently of other variables (age, sex, and genotype).

Conclusions

Our data establish relationships between resistin levels in the plasma and sputum of CF patients that correlate with disease status, and identify resistin as a novel mechanistic link between neutrophilic inflammation and lung disease in CF.  相似文献   
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