A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density

J Therkildsen*, P D Rohde, L Nissen, J Thygesen, E-M Hauge, B L Langdahl, M Boettcher, M Nyegaard, S Winther

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

Summary: The role of integrating genomic scores (GSs) needs to be assessed. Adding a GS to recommended stratification tools does not improve the prediction of very low bone mineral density. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Purpose: We aimed to investigate whether adding a genomic score (GS) to recommended stratification tools improves the discrimination of participants with very low bone mineral density (BMD). Methods: BMD was measured in three thoracic vertebrae using CT. All participants provided information on standard osteoporosis risk factors. GSs and FRAX scores were calculated. Participants were grouped according to mean BMD into very low (<80 mg/cm 3), low (80–120 mg/cm 3), and normal (>120 mg/cm 3) and according to the Bone Health and Osteoporosis Foundation recommendations for BMD testing into an “indication for BMD testing” and “no indication for BMD testing” group. Different models were assessed using the area under the receiver operating characteristics curves (AUC) and reclassification analyses. Results: In the total cohort (n=1421), the AUC for the GS was 0.57 (95% CI 0.52–0.61) corresponding to AUCs for osteoporosis risk factors. In participants without indication for BMD testing, the AUC was 0.60 (95% CI 0.52–0.69) above or equal to AUCs for osteoporosis risk factors. Adding the GS to a clinical risk factor (CRF) model resulted in AUCs not statistically significant from the CRF model. Using probability cutoff values of 6, 12, and 24%, we found no improved reclassification or risk discrimination using the CRF-GS model compared to the CRF model. Conclusion: Our results suggest adding a GS to a CRF model does not improve prediction. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Clinical risk factors combined showed superior discrimination to individual risk factors and the GS, underlining the value of combined CRFs in routine clinics as a stratification tool.

OriginalsprogEngelsk
TidsskriftOsteoporosis International
Vol/bind34
Nummer11
Sider (fra-til)1893-1906
Antal sider14
ISSN0937-941X
DOI
StatusUdgivet - nov. 2023

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