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Book ; Online ; E-Book: Key concepts in traditional Chinese medicine II

Li, Zhaoguo / Wu, Qing / Xing, Yurui

(Palgrave pivot)

2021

Title variant	Key concepts in traditional Chinese medicine 2 ; Key concepts in traditional Chinese medicine two
Author's details	Li Zhaoguo, Wu Qing, Xing Yurui
Series title	Palgrave pivot
Keywords	Medicine, Chinese
Subject code	610.951
Language	English
Size	1 online resource (136 pages)
Publisher	Palgrave Macmillan
Publishing place	Gateway East, Singapore
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	981-16-2398-8 ; 981-16-2397-X ; 978-981-16-2398-1 ; 978-981-16-2397-4
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Genome-wide polygenic risk score for rheumatoid arthritis prediction in postmenopausal women.

Xu, Yingke / Wu, Qing

The journal of gene medicine

2024 Volume 26, Issue 1, Page(s) e3659

Abstract: Background: Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The ... ...

Abstract	Background: Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women. Methods: The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios. Results: High PRS was significantly associated with increased RA risk in individuals aged 60-70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%-42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%-64.9%) for those in the top 5% of the PRS distribution. Conclusions: The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.
MeSH term(s)	Humans ; Female ; Aged ; Risk Factors ; Genetic Risk Score ; Genome-Wide Association Study ; Postmenopause/genetics ; Genetic Predisposition to Disease ; Arthritis, Rheumatoid/diagnosis ; Arthritis, Rheumatoid/epidemiology ; Arthritis, Rheumatoid/genetics
Language	English
Publishing date	2024-01-28
Publishing country	England
Document type	Journal Article
ZDB-ID	1458024-x
ISSN	1521-2254 ; 1099-498X
ISSN (online)	1521-2254
ISSN	1099-498X
DOI	10.1002/jgm.3659
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Article ; Online: Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

Wu, Qing / Dai, Jingyuan

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research

2024 Volume 39, Issue 4, Page(s) 462–472

Abstract: This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), ... ...

Abstract	This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome and 10-year fracture risk prediction. GRS and FRAX clinical risk factors (CRFs) were used as predictors. Death as a competing risk was accounted for in ML models for time-to-fracture data. ML models were subsequently fine-tuned through Bayesian optimization, which displayed marked superiority over traditional grid search. Evaluation of the models' performance considered an array of metrics such as accuracy, weighted F1 Score, the area under the precision-recall curve (PRAUC), and the area under the receiver operating characteristic curve (AUC) for binary fracture predictions, and the C-index, Brier score, and dynamic mean AUC over a 10-year follow-up period for fracture risk predictions. We found that GRS-integrated XGBoost with Bayesian optimization is the most effective model, with an accuracy of 91.2% (95% CI: 90.4-92.0%) and an AUC of 0.739 (95% CI: 0.731-0.746) in MOF binary predictions. For 10-year fracture risk modeling, the XGBoost model attained a C-index of 0.795 (95% CI: 0.783-0.806) and a mean dynamic AUC of 0.799 (95% CI: 0.788-0.809). Compared to FRAX, the XGBoost model exhibited a categorical net reclassification improvement (NRI) of 22.6% (P = .004). A sensitivity analysis, which included BMD but lacked GRS, reaffirmed these findings. Furthermore, portability tests in diverse non-European groups, including Asians and African Americans, underscored the model's robustness and adaptability. This study accentuates the potential of combining genetic insights and optimized ML in strengthening fracture predictions, heralding new preventive strategies for postmenopausal women.
MeSH term(s)	Humans ; Female ; Bayes Theorem ; Osteoporotic Fractures/genetics ; Osteoporotic Fractures/epidemiology ; Machine Learning ; Aged ; Postmenopause/genetics ; Risk Factors ; Polymorphism, Single Nucleotide ; Middle Aged ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Risk Assessment ; Genetic Risk Score
Language	English
Publishing date	2024-03-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	632783-7
ISSN	1523-4681 ; 0884-0431
ISSN (online)	1523-4681
ISSN	0884-0431
DOI	10.1093/jbmr/zjae025
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Article ; Online: Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

Xiao, Xiangxue / Wu, Qing

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA

2024

Abstract: The novel metaPGS, integrating multiple fracture-related genetic traits, surpasses traditional polygenic scores in predicting fracture risk. Demonstrating a robust association with incident fractures, this metaPGS offers significant potential for ... ...

Abstract	The novel metaPGS, integrating multiple fracture-related genetic traits, surpasses traditional polygenic scores in predicting fracture risk. Demonstrating a robust association with incident fractures, this metaPGS offers significant potential for enhancing clinical fracture risk assessment and tailoring prevention strategies. Introduction: Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. Methods: We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. Conclusions: The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.
Language	English
Publishing date	2024-05-07
Publishing country	England
Document type	Journal Article
ZDB-ID	1064892-6
ISSN	1433-2965 ; 0937-941X
ISSN (online)	1433-2965
ISSN	0937-941X
DOI	10.1007/s00198-024-07105-5
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Article ; Online: Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1.

Jung, Jongyun / Wu, Qing

PloS one

2024 Volume 19, Issue 4, Page(s) e0300535

Abstract: Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone ... ...

Abstract	Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.
MeSH term(s)	Humans ; Bone and Bones/metabolism ; Bone Density/genetics ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide ; Spectrin/genetics ; Spectrin/metabolism
Chemical Substances	Spectrin (12634-43-4) ; SPTBN1 protein, human ; PKDCC protein, human (EC 2.7.10.2) ; EPDR1 protein, human
Language	English
Publishing date	2024-04-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0300535
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Article: Revealing the Organ-Specific Expression of

Jung, Jongyun / Wu, Qing

bioRxiv : the preprint server for biology

2023

Abstract: Despite the recent technological advances in single-cell RNA sequencing, it is still unknown how three marker genes ( ...

Abstract	Despite the recent technological advances in single-cell RNA sequencing, it is still unknown how three marker genes (
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.06.01.543198
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Article ; Online: The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures.

Xiao, Xiangxue / Wu, Qing

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA

2023 Volume 34, Issue 4, Page(s) 681–692

Abstract: The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1 ... ...

Abstract	The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS into clinical diagnosis is anticipated to increase the population-level screening benefits. Purpose: This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. Methods: Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment. Results: For each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. Out of four studied PGSs, [Formula: see text] (HR: 1.03; 95%CI 1.01-1.05, p = 0.001) had the weakest and the [Formula: see text] (HR: 1.17; 95%CI 1.15-1.19, p < 0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] improved the reclassification of fracture by 1.2% (95% CI, 1.0 to 1.3%), 0.2% (95% CI, 0.1 to 0.3%), 1.4% (95% CI, 1.3 to 1.5%), and 2.2% (95% CI, 2.1 to 2.4%), respectively. Conclusions: Our findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.7-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.
MeSH term(s)	Humans ; Osteoporotic Fractures/etiology ; Osteoporotic Fractures/genetics ; Bone Density/genetics ; Risk Assessment ; Risk Factors ; Femur Neck
Language	English
Publishing date	2023-01-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1064892-6
ISSN	1433-2965 ; 0937-941X
ISSN (online)	1433-2965
ISSN	0937-941X
DOI	10.1007/s00198-022-06654-x
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Article ; Online: Validation of a genome-wide polygenic score in improving fracture risk assessment beyond the FRAX tool in the Women's Health Initiative study.

Xiao, Xiangxue / Wu, Qing

PloS one

2023 Volume 18, Issue 6, Page(s) e0286689

Abstract: Background: Previous study has established two polygenic scores (PGSs) related to femoral neck bone mineral density (BMD) (PGS_FNBMDldpred) and total body BMD (PGS_TBBMDldpred) that are associated with fracture risk. However, these findings have not yet ...

Abstract	Background: Previous study has established two polygenic scores (PGSs) related to femoral neck bone mineral density (BMD) (PGS_FNBMDldpred) and total body BMD (PGS_TBBMDldpred) that are associated with fracture risk. However, these findings have not yet been externally validated in an independent cohort. Objectives: This study aimed to validate the predictive performance of the two established PGSs and to investigate whether adding PGSs to the Fracture Risk Assessment Tool (FRAX) improves the predictive ability of FRAX in identifying women at high risk of major osteoporotic fracture (MOF) and hip fractures (HF). Methods: The study used the Women's Health Initiative (WHI) cohort of 9,000 postmenopausal women of European ancestry. Cox Proportional Hazard Models were used to assess the association between each PGS and MOF/HF risk. Four models were formulated to investigate the effect of adding PGSs to the FRAX risk factors: (1) Base model: FRAX risk factors; (2) Base model + PGS_FNBMDldpred; (3) Base model + PGS_TBBMDldpred; (4) Base model + metaPGS. The reclassification ability of models with PGS was further assessed using the Net Reclassification Improvement (NRI) and the Integrated discrimination improvement (IDI). Results: The study found that the PGSs were not significantly associated with MOF or HF after adjusting for FRAX risk factors. The FRAX base model showed moderate discrimination of MOF and HF, with a C-index of 0.623 (95% CI, 0.609 to 0.641) and 0.702 (95% CI, 0.609 to 0.718), respectively. Adding PGSs to the base FRAX model did not improve the ability to discriminate MOF or HF. Reclassification analysis showed that compared to the model without PGS, the model with PGS_TBBMDldpred (1.2%, p = 0.04) and metaPGS (1.7%, p = 0.05) improve the reclassification of HF, but not MOF. Conclusions: The findings suggested that incorporating genetic information into the FRAX tool has minimal improvement in predicting HF risk for elderly Caucasian women. These results highlight the need for further research to identify other factors that may contribute to fracture risk in elderly Caucasian women.
MeSH term(s)	Humans ; Female ; Aged ; Bone Density/genetics ; Risk Assessment/methods ; Osteoporotic Fractures/epidemiology ; Osteoporotic Fractures/genetics ; Hip Fractures/epidemiology ; Hip Fractures/genetics ; Hip Fractures/complications ; Women's Health ; Risk Factors ; Absorptiometry, Photon/methods
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0286689
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Article: Shared Genetic Architecture between Muscle and Bone: Identification and Functional Implications of

Jung, Jongyun / Wu, Qing

bioRxiv : the preprint server for biology

2023

Abstract	Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes,
Language	English
Publishing date	2023-05-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.14.540743
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Article ; Online: Awareness, understanding, and interest in personalized medicine: A cross-sectional survey study of college students.

Xu, Yingke / Wu, Qing

PloS one

2023 Volume 18, Issue 1, Page(s) e0280832

Abstract: Introduction: Personalized Medicine (PM) holds great potential in healthcare. A few existing surveys have investigated awareness, understanding, and interest regarding PM in the general public; however, studies investigating college students' opinions ... ...

Abstract	Introduction: Personalized Medicine (PM) holds great potential in healthcare. A few existing surveys have investigated awareness, understanding, and interest regarding PM in the general public; however, studies investigating college students' opinions about PM are lacking. This study aimed to evaluate the college student's awareness, understanding, and interest in PM, and their opinion was also analyzed by their gender and major. Methods: The study samples were undergraduate students enrolled at the University of Nevada, Las Vegas (UNLV). A web-based survey with 42 questions was emailed to all UNLV undergraduate students. Overall survey results were analyzed by gender and each student's major. A chi-square test evaluated the significant association between responses to questions with regard to gender or major. Results: Among the participants, 1225 students completed the survey. This survey found that most college students had a neutral attitude to PM and were not entirely familiar with this field. For example, most students (57.6%) had a "neutral" attitude toward PM. In addition, 77.6% of students never received any personal genetic testing. More than 80% of students thought "interests" was the most important factor in using PM, and 50% of respondents chose "somewhat likely" to the recommendation about PM from the doctor. Also of importance was the finding that a significant association between the most important factor of using PM and gender was observed (p = 0.04), and the associations between a student's major affected his or her reaction to PM, how well informed she or he was about PM, his or her attitude toward a doctor's recommendation about using PM were all significant (all participant's p<0.004). Conclusion: UNLV undergraduate students had a neutral attitude to PM and were not entirely familiar with this field.
MeSH term(s)	Humans ; Male ; Female ; Cross-Sectional Studies ; Precision Medicine ; Students ; Surveys and Questionnaires ; Health Knowledge, Attitudes, Practice
Language	English
Publishing date	2023-01-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0280832
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