You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Multiple gene polymorphisms can improve prediction of non-vertebral fracture in postmenopausal women. Clinical risk factors (CRFs), with or without bone mineral density (BMD), are used to determine the risk of osteoporotic fracture (OF), which has a heritable component. This study investigated whether genetic profiling can additionally improve the ability to predict OF. Using 1,229 unrelated Korean postmenopausal women, 39 single-nucleotide polymorphisms (SNPs) in 30 human genomic loci were tested for association with osteoporosis-related traits, such as BMD, osteoporosis, vertebral fracture (VF), non-vertebral fracture (NVF), and any fracture. To estimate the effects of genetic profiling, the genetic risk score (GRS) was calculated using five prediction models: (model I) GRSs only; (model II) BMD only; (model III) CRFs only; (model IV) CRFs and BMD; and (model V) CRFs, BMD and GRS. A total of 21 SNPs within 19 genes associated with one or more osteoporosis-related traits and were included for GRS calculation. GRS associated with BMD before and after adjustment for CRFs (P = < 0.001 to 0.018). GRS associated with NVF before and after adjustment for CRFs and BMD (P = 0.017 to 0.045), and with any fracture after adjustment for CRFs and femur neck BMD (P = 0.049). In terms of predicting NVF, the area under the receptor-operator characteristics curve (AUC) for model I was 0.55, which was lower than the AUCs of models II (0.60), III (0.64), and IV (0.65). Adding GRS to model IV (in model V) increased the AUC to 0.67, and improved the accuracy of NVF classification by 11.5% (P = 0.014). In terms of predicting any fracture, the AUC of model V (0.68) was similar to that of model IV (0.68), and model V did not significantly improve the accuracy of any fracture classification (P = 0.39). Thus, genetic profiling may enhance the accuracy of NVF predictions and help to delineate the intervention threshold.
{"Chemical": []}