Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics

Abstract

Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men Study (MrOS) comprises men ≥65 in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multi-dimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS). We followed the participants for a mean of 4.6 years for changes in femoral neck bone mineral density (BMD) and for incident hip fracture. Change in BMD was determined from mixed effects regression models taking age and weight into account. Participants were categorized into three groups: BMD maintenance (no decline; estimated change ≥ 0 g/cm2, n=453); expected loss (estimated change 0 to 1 SD below the estimated mean change, −0.034 g/cm2 for femoral neck, n=1184); and accelerated loss (estimated change ≥ 1 SD below mean change, n=237). Differential abundance values of 3,946 peptides were summarized by meta-analysis to determine differential abundance of each of 339 corresponding proteins for accelerated BMD loss vs maintenance. Using this meta-analytic standardized fold change at cutoffs of ≥ 1.1 or ≤ 0.9 (p < 0.10), 20 proteins were associated with accelerated BMD loss. Associations of those 20 proteins with incident hip fracture were tested using Cox proportional hazards models with age and BMI adjustment in 2473 men. Five proteins were associated with incident hip fracture (HR between 1.29 and 1.41 per SD increase in estimated protein abundance). Some proteins have been previously associated with fracture risk (e.g., CD14 and SHBG), while others have roles in cellular senescence and aging (B2MG and TIMP1) and complement activation and innate immunity (CO7, CO9, CFAD). These findings may inform development of biomarkers for future research in bone biology and fracture prediction.

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

“…To best ensure the linkages, we employed a conservative confidence scoring system in identifying proteins from peptides. Our methods have been extensively described (Nielson et al., ). The number of identified peptides that could not be unambiguously rolled up to proteins (and were thus excluded from consideration for SRM validation) was very small (~2%).…”

“…For more details of the sample processing and LC‐IMS‐MS analysis, see Nielson et al. (). The detected LC‐IMS‐MS features were identified by mapping their mass, elution time, and drift time to an accurate mass and elution time (AMT) tag database using VIPER software tools and linked to proteins.…”

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

“…To implement saturation correction on a very complex sample, depleted human serum datasets acquired from a previous proteomics study [29] were also evaluated. Serum datasets are particularly useful for exploring the benefits of our saturation correction method since they have an extended dynamic range and numerous species with saturated isotopic profiles.…”

“…The tryptically digested bovine serum albumin (BSA) and yeast were purchased from Promega (Madison, WI, USA) and the porcine brain total lipid extract (BTLE) was acquired from Avanti Polar Lipids (Alabaster, AL, USA). Human serum datasets from the study detailed in (Nielson et al [29]) were also used for the complex proteomic analyses. In this study, the serum was prepared by unthawing 150 µL and depleting it of the 14 high abundance proteins using IgY14 immunoaffinity depletion columns (Sigma-Aldrich).…”

“…Each BSA/yeast mixture was analyzed with the 100 min LC-IMS–MS platform. The most complex proteomic sample was the human serum which was analyzed with the 60 min LC-IMS–MS platform [29] and had numerous species that co-eluted. Finally, to investigate how the algorithm worked in lipidomic studies, the BTLE sample was analyzed using the 34-min lipid LC method coupled to the IMS-MS platform.…”