Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures

Abstract

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.

Keywords: LXR/RXR; hip fracture; meta-analysis; osteoporosis; proteomics.

Figure 1

Correlation between protein levels and QQ plot for CPAS analyses. (A) Correlation between protein levels in CHS (x-axis) and HUNT (y-axis), with log scaled axes. Each point corresponds to the mean relative fluorescent unit (RFU) of one of the 4979 aptamers in all individuals. R is the Pearson correlation coefficient. (B) QQ plot of observed Cox regression P-values (y-axis) vs expected null model P-values (x-axis) for each cohort, and for the meta-analysis. Negative log P-values are shown. The red line shows expected result if no associations exist. The green dotted line indicates the Bonferroni threshold (P = 1 × 10⁻⁵). CHS, Cardiovascular Health Study; CPAS, Circulating Proteome Association Study; HUNT, Trøndelag Health Study; QQ, quantile–quantile.

Figure 2

Proteins associated with incident hip fracture risk. (A) Volcano plot of the results from the Circulating Proteome Association Study (CPAS) meta-analysis, with hazard ratios on the x-axis and negative log P-values on the y-axis. The red line marks the Bonferroni significance level P = 1 × 10⁻⁵. Aptamers above the significance line in blue were associated with a reduced hip fracture risk, and those above the significance line in red were associated with an increased hip fracture risk. (B) The proteins corresponding to the 23 aptamers most significantly associated with incident hip fracture. HR, hazard ratio, error bars show 95% CIs. Results are shown for the CHS cohort (orange), the HUNT cohort (blue), and combined cohorts (black). CHS, Cardiovascular Health Study; HUNT, Trøndelag Health Study.

Figure 3

Ingenuity pathway analysis (IPA). The input for the IPA was a list of 155 top hit aptamers, passing a false discovery rate threshold for significance of 0.10, and their direction of associations with incident hip fractures (Supplementary Table S8). The IPA software calculates one-sided P-values using a right-tailed Fisher’s exact test to estimate the probability that the overlap between the top hits identified from association analyses and proteins that the software has classified within a particular canonical pathway are due to random chance. P-values given in the figure are adjusted using a Benjamini–Hochberg multiple testing correction. A z-score is additionally calculated to estimate the direction of association between top hits and canonical pathways based on the known directional effect of each molecule on the pathway.