TY - JOUR
T1 - Metabolomics Insights into Osteoporosis Through Association With Bone Mineral Density
AU - Zhang, Xiaoyu
AU - Xu, Hanfei
AU - Li, Gloria H.Y.
AU - Long, Michelle T.
AU - Cheung, Ching Lung
AU - Vasan, Ramachandran S.
AU - Hsu, Yi Hsiang
AU - Kiel, Douglas P.
AU - Liu, Ching Ti
N1 - Funding Information:
This work was partly supported by NHLBI/NIH Framingham Heart Study contract numbers NO1-HC-25195 (RSV), HHSN268201500001I (RSV), and 75N92019D00031 (RSV), NIDDK/NIH R01 DK081572 (RSV), NIAMS/NIH R01 AR041398 (DPK, CTL), NIAM/NIH R01 AR072199 (YHH, HX, CTL), Doris Duke Charitable Foundation (XZ, MTL, CTL), and the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine (RSV).The replication study is supported by Early Career Scheme (27110416) funded by the Research Grants Council, HKSAR, China. Authors' roles: CTL conceived of the idea and supervised the work. XZ and CTL designed the model and framework. XZ and GHYL carried out the analysis. XZ prepared the original draft. All authors discussed the results, provided critical feedback, and helped shape the manuscript. Author Contributions: Xiaoyu Zhang: Formal analysis; investigation; methodology; project administration; visualization; writing-original draft; writing-review and editing. Hanfei Xu: Investigation; writing-review and editing. Gloria Li: Formal analysis; validation; writing-review and editing. Michelle Long: Funding acquisition; writing-review and editing. Ching-Lung Cheung: Funding acquisition; validation; writing-review and editing. Ramachandran Vasan: Data curation; writing-review and editing. Yi-Hsiang Hsu: Funding acquisition; investigation; writing-review and editing. Douglas Kiel: Data curation; funding acquisition; writing-review and editing. Ching-Ti Liu: Conceptualization; funding acquisition; investigation; methodology; project administration; supervision; writing-original draft; writing-review and editing.
Funding Information:
This work was partly supported by NHLBI/NIH Framingham Heart Study contract numbers NO1‐HC‐25195 (RSV), HHSN268201500001I (RSV), and 75N92019D00031 (RSV), NIDDK/NIH R01 DK081572 (RSV), NIAMS/NIH R01 AR041398 (DPK, CTL), NIAM/NIH R01 AR072199 (YHH, HX, CTL), Doris Duke Charitable Foundation (XZ, MTL, CTL), and the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine (RSV).The replication study is supported by Early Career Scheme (27110416) funded by the Research Grants Council, HKSAR, China.
Publisher Copyright:
© 2021 American Society for Bone and Mineral Research (ASBMR)
PY - 2021/4
Y1 - 2021/4
N2 - Osteoporosis, a disease characterized by low bone mineral density (BMD), increases the risk for fractures. Conventional risk factors alone do not completely explain measured BMD or osteoporotic fracture risk. Metabolomics may provide additional information. We aim to identify BMD-associated metabolomic markers that are predictive of fracture risk. We assessed 209 plasma metabolites by liquid chromatography with tandem mass spectrometry (LC–MS/MS) in 1552 Framingham Offspring Study participants, and measured femoral neck (FN) and lumbar spine (LS) BMD 2 to 10 years later using dual-energy X-ray absorptiometry. We assessed osteoporotic fractures up to 27-year follow-up after metabolomic profiling. We identified 27 metabolites associated with FN-BMD or LS-BMD by LASSO regression with internal validation. Incorporating selected metabolites significantly improved the prediction and the classification of osteoporotic fracture risk beyond conventional risk factors (area under the curve [AUC] = 0.74 for the model with identified metabolites and risk factors versus AUC = 0.70 with risk factors alone, p =.001; net reclassification index = 0.07, p =.03). We replicated significant improvement in fracture prediction by incorporating selected metabolites in 634 participants from the Hong Kong Osteoporosis Study (HKOS). The glycine, serine, and threonine metabolism pathway (including four identified metabolites: creatine, dimethylglycine, glycine, and serine) was significantly enriched (false discovery rate [FDR] p value =.028). Furthermore, three causally related metabolites (glycine, phosphatidylcholine [PC], and triacylglycerol [TAG]) were negatively associated with FN-BMD, whereas PC and TAG were negatively associated with LS-BMD through Mendelian randomization analysis. In summary, metabolites associated with BMD are helpful in osteoporotic fracture risk prediction. Potential causal mechanisms explaining the three metabolites on BMD are worthy of further experimental validation. Our findings may provide novel insights into the pathogenesis of osteoporosis.
AB - Osteoporosis, a disease characterized by low bone mineral density (BMD), increases the risk for fractures. Conventional risk factors alone do not completely explain measured BMD or osteoporotic fracture risk. Metabolomics may provide additional information. We aim to identify BMD-associated metabolomic markers that are predictive of fracture risk. We assessed 209 plasma metabolites by liquid chromatography with tandem mass spectrometry (LC–MS/MS) in 1552 Framingham Offspring Study participants, and measured femoral neck (FN) and lumbar spine (LS) BMD 2 to 10 years later using dual-energy X-ray absorptiometry. We assessed osteoporotic fractures up to 27-year follow-up after metabolomic profiling. We identified 27 metabolites associated with FN-BMD or LS-BMD by LASSO regression with internal validation. Incorporating selected metabolites significantly improved the prediction and the classification of osteoporotic fracture risk beyond conventional risk factors (area under the curve [AUC] = 0.74 for the model with identified metabolites and risk factors versus AUC = 0.70 with risk factors alone, p =.001; net reclassification index = 0.07, p =.03). We replicated significant improvement in fracture prediction by incorporating selected metabolites in 634 participants from the Hong Kong Osteoporosis Study (HKOS). The glycine, serine, and threonine metabolism pathway (including four identified metabolites: creatine, dimethylglycine, glycine, and serine) was significantly enriched (false discovery rate [FDR] p value =.028). Furthermore, three causally related metabolites (glycine, phosphatidylcholine [PC], and triacylglycerol [TAG]) were negatively associated with FN-BMD, whereas PC and TAG were negatively associated with LS-BMD through Mendelian randomization analysis. In summary, metabolites associated with BMD are helpful in osteoporotic fracture risk prediction. Potential causal mechanisms explaining the three metabolites on BMD are worthy of further experimental validation. Our findings may provide novel insights into the pathogenesis of osteoporosis.
KW - DXA
KW - FRACTURE RISK ASSESSMENT METABOLOMICS
KW - GENERAL POPULATION STUDIES
KW - OSTEOPOROSIS
UR - http://www.scopus.com/inward/record.url?scp=85100136113&partnerID=8YFLogxK
U2 - 10.1002/jbmr.4240
DO - 10.1002/jbmr.4240
M3 - Journal article
C2 - 33434288
AN - SCOPUS:85100136113
SN - 0884-0431
VL - 36
SP - 729
EP - 738
JO - Journal of Bone and Mineral Research
JF - Journal of Bone and Mineral Research
IS - 4
ER -