Dataset Information

The association of calcium intake with osteoporotic vertebral fractures in a large Chinese cohort.

ABSTRACT: The effect of calcium on prevention of osteoporosis and related fracture which are aging issues is unclear. The aim of this study is to explore the association of calcium intake with vertebral fracture. This study enrolled 3,457 participants from China Action on Spine and Hip Status (CASH) study from 2013 and 2017. Dietary calcium intake was collected using validated food frequency questionnaires (FFQ). Vertebral fracture of CT images was defined as the primary outcome. The mean calcium intake of men and women were 522.75mg/day and 507.21mg/day, respectively. 6% reduction in the odds of fracture risk was observed per 100 unit increase of calcium intake from food among females (OR, 0.94; 95% CI, 0.89-0.99), but results among males were not significant. We divided calcium intake into quintiles when modelling its associations with fracture risk, negative associations of fracture risk with calcium intake were found among females. In a population with low usual calcium intake, higher dietary calcium intake was associated with fewer vertebral fracture in women and that no such association was seen in men.

SUBMITTER: Wang L

PROVIDER: S-EPMC7138559 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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The association of calcium intake with osteoporotic vertebral fractures in a large Chinese cohort.

Wang Ling L Yin Lu L Cheng Xiaoguang X Li Kai K Wang Yuebo Y Zhang Yong Y Duanmu Yang-Yang YY Liu Xiaoyun X Deng Guijuan G Wang Yang Y Veronese Nicola N Li Wei W Tian Wei W

Aging 20200328 6

The effect of calcium on prevention of osteoporosis and related fracture which are aging issues is unclear. The aim of this study is to explore the association of calcium intake with vertebral fracture. This study enrolled 3,457 participants from China Action on Spine and Hip Status (CASH) study from 2013 and 2017. Dietary calcium intake was collected using validated food frequency questionnaires (FFQ). Vertebral fracture of CT images was defined as the primary outcome. The mean calcium intake o ...[more]

PMID: 32221046

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Project description:PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.ConclusionThe DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

Dataset Information

The association of calcium intake with osteoporotic vertebral fractures in a large Chinese cohort.

Publications

The association of calcium intake with osteoporotic vertebral fractures in a large Chinese cohort.

Similar Datasets

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