Browsing by Author "Deng, Hong-Wen"

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    Quantification of aminobutyric acids and their clinical applications as biomarkers for osteoporosis
    (Nature Research, 2020-01-22) Wang, Zhiying; Bian, Liangqiao; Mo, Chenglin; Shen, Hui; Zhao, Lan Juan; Su, Kuan-Jui; Kukula, Maciej; Lee, Jauh Tzuoh; Armstrong, Daniel W.; Recker, Robert; Lappe, Joan; Bonewald, Lynda F.; Deng, Hong-Wen; Brotto, Marco; Anatomy and Cell Biology, School of Medicine
    Osteoporosis is a highly prevalent chronic aging-related disease that frequently is only detected after fracture. We hypothesized that aminobutyric acids could serve as biomarkers for osteoporosis. We developed a quick, accurate, and sensitive screening method for aminobutyric acid isomers and enantiomers yielding correlations with bone mineral density (BMD) and osteoporotic fracture. In serum, γ-aminobutyric acid (GABA) and (R)-3-aminoisobutyric acid (D-BAIBA) have positive associations with physical activity in young lean women. D-BAIBA positively associated with hip BMD in older individuals without osteoporosis/osteopenia. Lower levels of GABA were observed in 60-80 year old women with osteoporotic fractures. Single nucleotide polymorphisms in seven genes related to these metabolites associated with BMD and osteoporosis. In peripheral blood monocytes, dihydropyrimidine dehydrogenase, an enzyme essential to D-BAIBA generation, exhibited positive association with physical activity and hip BMD. Along with their signaling roles, BAIBA and GABA might serve as biomarkers for diagnosis and treatments of osteoporosis.
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    Social and Behavior Factors of Alzheimer’s Disease and Related Dementias: A National Study in the U.S.
    (Elsevier, 2024) Ciciora, David; Vásquez, Elizabeth; Valachovic, Edward; Hou, Lifang; Zheng, Yinan; Xu, Hua; Jiang, Xiaoqian; Huang, Kun; Gabriel, Kelley Pettee; Deng, Hong-Wen; Gallant, Mary P.; Zhang, Kai; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Introduction: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated. Methods: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by their relative contribution to the model performance. Stratified ranking was also performed based on a county's level of disadvantage. Shapley Additive exPlanations (SHAP) provided marginal contributions for each variable. Results: The top three ranked predictors at the county level were insufficient sleep, consuming less than one serving of fruits/vegetables per day among adults, and having less than a high school diploma. In both disadvantaged and non-disadvantaged counties, demographic variables such as sex and race were important in predicting ADRD. Lifestyle factors ranked highly in non-disadvantaged counties compared to more environmental factors in disadvantaged counties. Conclusions: This ranked list of factors can provide a guided approach to ADRD primary prevention strategies in the U.S., as the effects of sleep, diet, and education on ADRD can be further developed. While sleep, diet, and education are important nationally, differing prevention strategies could be employed based on a county's level of disadvantage.