There is evidence for a genetic contribution to bone mineral density (BMD×). Different loci affecting BMD have been identified by diverse linkage and genome-wide association studies. We studied the heritability of and the correlations among six densitometric phenotypes and four bone mass/fracture phenotypes. For this purpose, we used a family-based study of the genetics of osteoporosis, the Genetic Analysis of Osteoporosis Project. The primary aim of our study was to examine the roles of genetic and environmental factors in determining osteoporosis-related phenotypes. The project consisted of 11 extended families from Spain. All of them were selected through a proband with osteoporosis. BMD was measured using dual-energy X-ray absorptiometry. The proportion of variance of BMD attributable to significant covariates ranged from 25% (for femoral neck BMD) to 48% (for whole-body total BMD). The vast majority of the densitometric phenotypes had highly significant heritability, ranging from 0.252 (whole-body total BMD) to 0.537 (trochanteric BMD) after correcting for covariate effects. All of the densitometric phenotypes showed high and significant genetic correlations (from -0.772 to -1.000) with a low bone mass/osteopenia condition (Affected 3). Our findings provide additional evidence on the heritability of BMD and a strong genetic correlation between BMD and bone mass/fracture phenotypes in a Spanish population. Our results emphasize the importance of detecting genetic risk factors and the benefit of early diagnosis and especially therapeutic and preventive strategies.
Coronavirus disease 2019 (COVID-19) has rapidly expanded worldwide. Currently, there are no biomarkers to predict respiratory worsening in patients with mild to moderate COVID-19 pneumonia. Small studies explored the use of Krebs von de Lungen-6 circulating serum levels (sKL-6) as a prognostic biomarker of the worsening of COVID-19 pneumonia. We aimed at a large study to determine the prognostic value of sKL-6 in predicting evolving trends in COVID-19. We prospectively analyzed the characteristics of 836 patients with COVID-19 with mild lung disease on admission. sKL-6 was obtained in all patients at least at baseline and compared among patients with or without respiratory worsening. The receiver operating characteristic curve was used to find the optimal cutoff level. A total of 159 (19%) patients developed respiratory worsening during hospitalization. Baseline sKL-6 levels were not higher in patients who had respiratory worsening (median {IQR} 315.5 {209–469} vs. 306 {214–423} U/ml p = 0.38). The last sKL-6 and the change between baseline and last sKL-6 were higher in the respiratory worsening group (p = 0.02 and p < 0.0001, respectively). The best sKL-6 cutoff point for respiratory worsening was 497 U/ml (area under the curve 0.52; 23% sensitivity and 85% specificity). sKL-6 was not found to be an independent predictor of respiratory worsening. A conditional inference tree (CTREE) was not useful to discriminate patients at risk of worsening. We found that sKL-6 had a low sensibility to predict respiratory worsening in patients with mild-moderate COVID-19 pneumonia and may not be of use to assess the risk of present respiratory worsening in inpatients with COVID-19 pneumonia.
Purpose To examine the response to anti-osteoporotic treatment, considered as incident fragility fractures after a minimum follow-up of 1 year, according to sex, age, and number of comorbidities of the patients. Methods For this retrospective observational study, data from baseline and follow-up visits on the number of comorbidities, prescribed anti-osteoporotic treatment and vertebral, humerus or hip fractures in 993 patients from the OSTEOMED registry were analyzed using logistic regression and an artificial network model. Results Logistic regression showed that the probability of reducing fractures for each anti-osteoporotic treatment considered was independent of sex, age, and the number of comorbidities, increasing significantly only in males taking vitamin D (OR = 7.918), patients without comorbidities taking vitamin D (OR = 4.197) and patients with ≥ 3 comorbidities taking calcium (OR = 9.412). Logistic regression correctly classified 96% of patients (Hosmer–Lemeshow = 0.492) compared with the artificial neural network model, which correctly classified 95% of patients (AUC = 0.6). Conclusion In general, sex, age and the number of comorbidities did not influence the likelihood that a given anti-osteoporotic treatment improved the risk of incident fragility fractures after 1 year, but this appeared to increase when patients had been treated with risedronate, strontium or teriparatide. The two models used classified patients similarly, but predicted differently in terms of the probability of improvement, with logistic regression being the better fit.
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