scholarly journals Associations between Osteocalcin, Calciotropic Hormones, and Energy Metabolism in a Cohort of Chinese Postmenopausal Women: Peking Vertebral Fracture Study

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruizhi Jiajue ◽  
Shuying Liu ◽  
Yu Pei ◽  
Xuan Qi ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vertebral Fracture ◽  
Energy Metabolism ◽  
Regression Model ◽  
Postmenopausal Women ◽  
Logistic Regression Model ◽  
Lower Serum ◽  
Cholesterol Levels ◽  
Chinese Postmenopausal Women ◽  
Fracture Study

Objective. The endocrine function of bone in energy metabolism may be mediated by the osteocalcin (OC). We examined the association between OC and energy metabolism among Chinese postmenopausal women. Design and Setting. A cross-sectional cohort study enrolling 1635 participants was conducted using data from the Peking Vertebral Fracture study. Partial correlation analysis was performed to explore the correlation of OC, parathyroid hormone (PTH), or 25-hydroxyvitamin D (25(OH)D) with glycemic and lipid metabolic parameters. A logistic regression model was used to investigate the association of OC, PTH, or 25(OH)D with the prevalence of diabetes and dyslipidemia. Results. Serum levels of OC, PTH, and 25(OH)D were all positively correlated with serum cholesterol levels, whereas only OC was negatively associated with serum glucose level. In the logistic regression model, both OC and PTH were negatively associated with the prevalence of diabetes (odds ratio [OR], 95% confidence interval [95% CI]: 0.967, 0.948–0.986 for OC and 0.986, 0.978–0.994 for PTH). No significant association was found between 25(OH)D and diabetes. Both OC and 25(OH)D, rather than PTH, were associated with abnormalities of high cholesterol levels, such as hypercholesterolemia and high LDL-C levels. Further classifying the population based on the median value of OC and PTH, low OC and low PTH subgroup had the highest OR, 95% CI for diabetes (1.873, 1.287–2.737) and the lowest OR, 95% CI for hypercholesterolemia (0.472, 0.324–0.688) and for high LDL-C (0.538, 0.376–0.771). Conclusion. Among Chinese postmenopausal women, a lower serum level of OC was associated with a higher prevalence of diabetes and lower serum cholesterol levels, and a low PTH concentration could magnify these associations.

scholarly journals Association of dyslipidaemia with osteoporosis in postmenopausal women

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199955
Author(s):  
Lingnan Zhang ◽  
Qilong Liu ◽  
Xianshang Zeng ◽  
Wenshan Gao ◽  
Yanan Niu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Model ◽  
Postmenopausal Women ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Control Group ◽  
Binary Logistic Regression Model

Objective To assess the association of dyslipidaemia with osteoporosis in postmenopausal women. Methods Data from 160 postmenopausal women with newly diagnosed osteoporosis (osteoporosis group) and 156 healthy controls (control group) were retrospectively reviewed from 2016 to 2020. The primary outcomes were laboratory values assessed by a multivariate binary logistic regression model. Results Factors that greatly increased the risk of being in the osteoporosis group included high low-density lipoprotein (LDL) and low high-density lipoprotein (HDL) levels. The osteoporosis group had lower HDL and higher LDL levels than the control group. A multivariate binary logistic regression model showed that lower HDL and higher LDL levels were the only variables that were significantly associated with osteoporosis (odds ratio 1.86, 95% confidence interval: 3.66–4.25 and odds ratio 1.47, 95% confidence interval: 1.25–2.74, respectively). Conclusion Low HDL and high LDL levels may be associated with the occurrence of osteoporosis in postmenopausal women.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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